id stringlengths 2 115 | author stringlengths 2 42 ⌀ | last_modified timestamp[us, tz=UTC] | downloads int64 0 8.87M | likes int64 0 3.84k | paperswithcode_id stringlengths 2 45 ⌀ | tags list | lastModified timestamp[us, tz=UTC] | createdAt stringlengths 24 24 | key stringclasses 1 value | created timestamp[us] | card stringlengths 1 1.01M | embedding list | library_name stringclasses 21 values | pipeline_tag stringclasses 27 values | mask_token null | card_data null | widget_data null | model_index null | config null | transformers_info null | spaces null | safetensors null | transformersInfo null | modelId stringlengths 5 111 ⌀ | embeddings list |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
range3/wiki40b-ja | range3 | 2023-02-04T05:44:21Z | 1,345 | 5 | null | [
"task_categories:text-generation",
"task_categories:fill-mask",
"language:ja",
"region:us"
] | 2023-02-04T05:44:21Z | 2023-02-04T04:54:17.000Z | 2023-02-04T04:54:17 | ---
task_categories:
- text-generation
- fill-mask
language:
- ja
---
# range3/wiki40b-ja
This dataset consists of three parquet files from the wiki40b dataset with only Japanese data extracted. It is generated by the following python code.
このデータセットは、wiki40bデータセットの日本語データのみを抽出した3つのparquetファイルで構成されます。以下のpythonコードによって生成しています。
```py
import datasets
dss = datasets.load_dataset(
"wiki40b",
"ja",
beam_runner="DirectRunner",
)
for split,ds in dss.items():
ds.to_parquet(f"wikipedia-ja-20230101/{split}.parquet")
``` | [
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darentang/sroie | darentang | 2021-12-09T15:11:29Z | 1,343 | 2 | null | [
"region:us"
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DFKI-SLT/cdcp | DFKI-SLT | 2023-11-09T15:54:23Z | 1,342 | 0 | null | [
"size_categories:n<1K",
"language:en",
"region:us"
] | 2023-11-09T15:54:23Z | 2023-06-26T10:07:42.000Z | 2023-06-26T10:07:42 | ---
language:
- en
size_categories:
- n<1K
---
# Dataset Card for "CDCP"
### Dataset Summary
CDCP (a.k.a. *Cornell eRulemaking Corpus*; [Park and Cardie, 2018](https://aclanthology.org/L18-1257.pdf)) consists of 731 user comments from an eRulemaking platform in the English language. There are five types of components (`Fact`, `Testimony`, `Reference`, `Value`, and `Policy`) and two types of supporting relations (`Reason` and `Evidence`) are annotated on the basis of the study by Park et al. (2015). The resulting dataset contains 4931 elementary unit and 1221 support relation annotations. (pp. 1623-1624)
### Supported Tasks and Leaderboards
- **Tasks:** Argument Mining, Link Prediction, Component Classification, Relation Classification
- **Leaderboards:** https://paperswithcode.com/dataset/cdcp
### Languages
The language in the dataset is English (AmE).
## Dataset Structure
### Data Instances
- **Size of downloaded dataset files:** 5.37 MB
```
{
'id': "00195",
'text': "State and local court rules sometimes make default judgments much more likely. For example, when a person who allegedly owes a debt is told to come to court on a work day, they may be forced to choose between a default judgment and their job. I urge the CFPB to find practices that involve scheduling hearings at inconvenient times unfair, deceptive, and abusive, or inconsistent with 1692i",
'proposition': {
"start": [0, 78, 242],
"end": [78, 242, 391],
"label": [4, 4, 1],
"url": ["", "", ""],
},
'relations': {"head": [0, 2], "tail": [1, 0], "label": [1, 1]},
}
```
### Data Fields
- `id`: the instance id of the text, a `string` feature
- `text`: the text (with URLs marked as `__URL__`), a `string` feature
- `proposition`: the annotation list of spans with labels and URL (if applicable), a `dictionary` feature
- `start`: the indices indicating the inclusive start of the spans, a `list` of `int` feature
- `end`: the indices indicating the exclusive end of the spans, a `list` of `int` feature
- `label`: the indices indicating the span type, a `list` of `int` feature (see [label list](https://huggingface.co/datasets/DFKI-SLT/cdcp/blob/main/cdcp.py#L40))
- `urls`: the URLs link with corresponding indices to each proposition, a `list` of `str` feature
- `relation`: the relation between labeled spans with relation labels, a `dictionary` feature
- `head`: the indices indicating the first element in a relation, a `list` of `int` feature
- `tail`: the indices indicating the second element in a relation, a `list` of `int` feature
- `label`: the indices indicating the relation type in a relation, a `list` of `int` feature (see [label list](https://huggingface.co/datasets/DFKI-SLT/cdcp/blob/main/cdcp.py#L41))
### Data Splits
| | train | test |
| ------------------------------------------------------------------------------------------------ | ---------------------------------------: | -------------------------------------: |
| No. of instances | 581 | 150 |
| No. of span labels<br/>- `Fact`<br/>- `Testimony`<br/>- `Reference`<br/>- `Value`<br/>- `Policy` | <br/>654<br/>873<br/>31<br/>1686<br/>662 | <br/>132<br/>244<br/>1<br/>496<br/>153 |
| No. of relation labels<br/>- `reason`<br/>- `evidence` | <br/>1055<br/>47 | <br/>298<br/>26 |
## Dataset Creation
### Curation Rationale
"eRulemaking is a means for government agencies to directly reach citizens to solicit their opinions and experiences regarding newly proposed rules. The effort, however, is partly hampered by citizens’ comments that lack reasoning and evidence, which are largely ignored since government agencies are unable to evaluate the validity and strength." (p. 1623)
"It will be a valuable resource for building argument mining systems that can not only extract arguments from unstructured text, but also identify ways in which a given argument can be improved with respect to its evaluability." (p. 1624)
### Source Data
eRulemaking comments (see [eRulemaking](https://www.gsa.gov/about-us/organization/federal-acquisition-service/technology-transformation-services/erulemaking))
#### Initial Data Collection and Normalization
"Annotated 731 user comments on Consumer Debt Collection Practices (CDCP) rule by the Consumer Financial Protection Bureau (CFPB) posted on www.regulationroom.org." (p. 1624)
#### Who are the source language producers?
General public participants, implying American citizens.
"According to a voluntary user survey that asked the commenters to self-identify themselves, about 64% of the comments came from consumers, 22% from debt collectors, and the remainder from others, such as consumer advocates and counsellor organizations." (p. 1624)
### Annotations
#### Annotation process
"The annotators annotated the elementary units and support relations defined in the argumentation model proposed by [Park et al. (2015)](https://dl.acm.org/doi/10.1145/2746090.2746118)."
"Each user comment was annotated by two annotators, who independently determined the types of elementary units and support relations among them using the GATE annotation tool (Cunningham et al., 2011). A third annotator manually resolved the conflicts to produce the final dataset."
"Inter-annotator agreement between 2 annotators is measured with Krippendorf’s α with respect to elementary unit type (α=64.8%) and support relations (α=44.1%); IDs of supported elementary units are treated as labels for the supporting elementary units."
(p. 1626)
#### Who are the annotators?
\[More Information Needed\]
### Personal and Sensitive Information
\[More Information Needed\]
## Considerations for Using the Data
### Social Impact of Dataset
"Immediate applications include automatically ranking arguments based on their evaluability for a (crude) identification of read-worthy comments and providing real-time feedback to writers, specifying which types of support for which propositions can be added to construct better-formed arguments." (p. 1624)
### Discussion of Biases
About 45% of the elementary units are `VALUE` type. A significant portion, roughly 75%, of support relation annotations are between adjacent elementary units. While commenters certainly tend to provide reasons immediately after the proposition to be supported, it is also easier for annotators to identify support relations in proximity. Thus, support relations in the wild may be not as skewed toward those between adjacent elementary units. (pp. 1626-1627)
### Other Known Limitations
\[More Information Needed\]
## Additional Information
### Dataset Curators
\[More Information Needed\]
### Licensing Information
\[More Information Needed\]
### Citation Information
```
@inproceedings{park2018corpus,
title={A corpus of erulemaking user comments for measuring evaluability of arguments},
author={Park, Joonsuk and Cardie, Claire},
booktitle={Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)},
year={2018}
}
```
### Contributions
Thanks to [@idalr](https://github.com/idalr) for adding this dataset. | [
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dart | null | 2022-11-18T19:57:00Z | 1,338 | 4 | dart | [
"task_categories:tabular-to-text",
"task_ids:rdf-to-text",
"annotations_creators:crowdsourced",
"annotations_creators:machine-generated",
"language_creators:crowdsourced",
"language_creators:machine-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:extended|wikitable_questions",
"source_datasets:extended|wikisql",
"source_datasets:extended|web_nlg",
"source_datasets:extended|cleaned_e2e",
"language:en",
"license:mit",
"arxiv:2007.02871",
"region:us"
] | 2022-11-18T19:57:00Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
- machine-generated
language_creators:
- crowdsourced
- machine-generated
language:
- en
license:
- mit
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- extended|wikitable_questions
- extended|wikisql
- extended|web_nlg
- extended|cleaned_e2e
task_categories:
- tabular-to-text
task_ids:
- rdf-to-text
paperswithcode_id: dart
pretty_name: DART
dataset_info:
features:
- name: tripleset
sequence:
sequence: string
- name: subtree_was_extended
dtype: bool
- name: annotations
sequence:
- name: source
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 12966443
num_examples: 30526
- name: validation
num_bytes: 1458106
num_examples: 2768
- name: test
num_bytes: 2657644
num_examples: 5097
download_size: 29939366
dataset_size: 17082193
---
# Dataset Card for DART
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [homepahe](https://github.com/Yale-LILY/dart)
- **Repository:** [github](https://github.com/Yale-LILY/dart)
- **Paper:** [paper](https://arxiv.org/abs/2007.02871)
- **Leaderboard:** [leaderboard](https://github.com/Yale-LILY/dart#leaderboard)
### Dataset Summary
DART is a large dataset for open-domain structured data record to text generation. We consider the structured data record input as a set of RDF entity-relation triples, a format widely used for knowledge representation and semantics description. DART consists of 82,191 examples across different domains with each input being a semantic RDF triple set derived from data records in tables and the tree ontology of the schema, annotated with sentence descriptions that cover all facts in the triple set. This hierarchical, structured format with its open-domain nature differentiates DART from other existing table-to-text corpora.
### Supported Tasks and Leaderboards
The task associated to DART is text generation from data records that are RDF triplets:
- `rdf-to-text`: The dataset can be used to train a model for text generation from RDF triplets, which consists in generating textual description of structured data. Success on this task is typically measured by achieving a *high* [BLEU](https://huggingface.co/metrics/bleu), [METEOR](https://huggingface.co/metrics/meteor), [BLEURT](https://huggingface.co/metrics/bleurt), [TER](https://huggingface.co/metrics/ter), [MoverScore](https://huggingface.co/metrics/mover_score), and [BERTScore](https://huggingface.co/metrics/bert_score). The ([BART-large model](https://huggingface.co/facebook/bart-large) from [BART](https://huggingface.co/transformers/model_doc/bart.html)) model currently achieves the following scores:
| | BLEU | METEOR | TER | MoverScore | BERTScore | BLEURT |
| ----- | ----- | ------ | ---- | ----------- | ---------- | ------ |
| BART | 37.06 | 0.36 | 0.57 | 0.44 | 0.92 | 0.22 |
This task has an active leaderboard which can be found [here](https://github.com/Yale-LILY/dart#leaderboard) and ranks models based on the above metrics while also reporting.
### Languages
The dataset is in english (en).
## Dataset Structure
### Data Instances
Here is an example from the dataset:
```
{'annotations': {'source': ['WikiTableQuestions_mturk'],
'text': ['First Clearing\tbased on Callicoon, New York and location at On NYS 52 1 Mi. Youngsville']},
'subtree_was_extended': False,
'tripleset': [['First Clearing', 'LOCATION', 'On NYS 52 1 Mi. Youngsville'],
['On NYS 52 1 Mi. Youngsville', 'CITY_OR_TOWN', 'Callicoon, New York']]}
```
It contains one annotation where the textual description is 'First Clearing\tbased on Callicoon, New York and location at On NYS 52 1 Mi. Youngsville'. The RDF triplets considered to generate this description are in tripleset and are formatted as subject, predicate, object.
### Data Fields
The different fields are:
- `annotations`:
- `text`: list of text descriptions of the triplets
- `source`: list of sources of the RDF triplets (WikiTable, e2e, etc.)
- `subtree_was_extended`: boolean, if the subtree condidered during the dataset construction was extended. Sometimes this field is missing, and therefore set to `None`
- `tripleset`: RDF triplets as a list of triplets of strings (subject, predicate, object)
### Data Splits
There are three splits, train, validation and test:
| | train | validation | test |
| ----- |------:|-----------:|-----:|
| N. Examples | 30526 | 2768 | 6959 |
## Dataset Creation
### Curation Rationale
Automatically generating textual descriptions from structured data inputs is crucial to improving the accessibility of knowledge bases to lay users.
### Source Data
DART comes from existing datasets that cover a variety of different domains while allowing to build a tree ontology and form RDF triple sets as semantic representations. The datasets used are WikiTableQuestions, WikiSQL, WebNLG and Cleaned E2E.
#### Initial Data Collection and Normalization
DART is constructed using multiple complementary methods: (1) human annotation on open-domain Wikipedia tables
from WikiTableQuestions (Pasupat and Liang, 2015) and WikiSQL (Zhong et al., 2017), (2) automatic conversion of questions in WikiSQL to declarative sentences, and (3) incorporation of existing datasets including WebNLG 2017 (Gardent et al., 2017a,b; Shimorina and Gardent, 2018) and Cleaned E2E (Novikova et al., 2017b; Dušek et al., 2018, 2019)
#### Who are the source language producers?
[More Information Needed]
### Annotations
DART is constructed using multiple complementary methods: (1) human annotation on open-domain Wikipedia tables
from WikiTableQuestions (Pasupat and Liang, 2015) and WikiSQL (Zhong et al., 2017), (2) automatic conversion of questions in WikiSQL to declarative sentences, and (3) incorporation of existing datasets including WebNLG 2017 (Gardent et al., 2017a,b; Shimorina and Gardent, 2018) and Cleaned E2E (Novikova et al., 2017b; Dušek et al., 2018, 2019)
#### Annotation process
The two stage annotation process for constructing tripleset sentence pairs is based on a tree-structured ontology of each table.
First, internal skilled annotators denote the parent column for each column header.
Then, a larger number of annotators provide a sentential description of an automatically-chosen subset of table cells in a row.
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
Under MIT license (see [here](https://github.com/Yale-LILY/dart/blob/master/LICENSE))
### Citation Information
```
@article{radev2020dart,
title={DART: Open-Domain Structured Data Record to Text Generation},
author={Dragomir Radev and Rui Zhang and Amrit Rau and Abhinand Sivaprasad and Chiachun Hsieh and Nazneen Fatema Rajani and Xiangru Tang and Aadit Vyas and Neha Verma and Pranav Krishna and Yangxiaokang Liu and Nadia Irwanto and Jessica Pan and Faiaz Rahman and Ahmad Zaidi and Murori Mutuma and Yasin Tarabar and Ankit Gupta and Tao Yu and Yi Chern Tan and Xi Victoria Lin and Caiming Xiong and Richard Socher},
journal={arXiv preprint arXiv:2007.02871},
year={2020}
```
### Contributions
Thanks to [@lhoestq](https://github.com/lhoestq) for adding this dataset. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
alzoubi36/opp_115 | alzoubi36 | 2023-06-24T07:08:08Z | 1,335 | 0 | null | [
"region:us"
] | 2023-06-24T07:08:08Z | 2023-06-24T06:55:43.000Z | 2023-06-24T06:55:43 | ---
dataset_info:
features:
- name: text
dtype: string
- name: label
sequence: int64
splits:
- name: train
num_bytes: 1047118
num_examples: 2185
- name: validation
num_bytes: 270827
num_examples: 550
- name: test
num_bytes: 316635
num_examples: 697
download_size: 811600
dataset_size: 1634580
---
# Dataset for the OPP-115 task in the [PrivacyGLUE](https://github.com/infsys-lab/privacy-glue) dataset
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
lamini/alpaca | lamini | 2023-07-23T06:29:21Z | 1,334 | 1 | null | [
"region:us"
] | 2023-07-23T06:29:21Z | 2023-07-23T06:29:20.000Z | 2023-07-23T06:29:20 | ---
dataset_info:
features:
- name: input
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 27364517
num_examples: 52002
download_size: 12742513
dataset_size: 27364517
---
# Dataset Card for "alpaca"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
approximatelabs/tablib-v1-full | approximatelabs | 2023-10-13T22:33:46Z | 1,334 | 55 | null | [
"size_categories:100M<n<1B",
"license:other",
"arxiv:2310.07875",
"region:us"
] | 2023-10-13T22:33:46Z | 2023-10-04T22:54:52.000Z | 2023-10-04T22:54:52 | ---
license: other
pretty_name: TabLib
size_categories:
- 100M<n<1B
extra_gated_prompt: >-
Accessing the full TabLib dataset requires permission from Approximate Labs. To request access, please provide the following details and Approximate Labs will process your request as soon as possible.
extra_gated_fields:
Full Name: text
Email: text
Country: text
Organization: text
What do you intend to do with this dataset?: text
I agree to abide by the license requirements of the data contained in TabLib: checkbox
---
[](https://discord.gg/kW9nBQErGe)
<img src="https://approximatelabs.com/tablib.png" width="800" />
# TabLib
A minimally-preprocessed dataset of 627M tables (69 TiB) extracted from HTML, PDF, CSV, TSV, Excel, and SQLite files from GitHub and Common Crawl.
This includes 867B tokens of "context metadata": each table includes provenance information and table context such as filename, text before/after, HTML metadata, etc.
A smaller 0.1% sample of this dataset can be found [here](https://huggingface.co/datasets/approximatelabs/tablib-v1-sample).
For more information, read the [paper](https://arxiv.org/abs/2310.07875) & [announcement blog](https://approximatelabs.com/blog/tablib).
# Dataset Details
## Sources
* **GitHub**: nearly all public GitHub repositories
* **Common Crawl**: the `CC-MAIN-2023-23` crawl
## Reading Tables
Tables are stored as serialized Arrow bytes in the `arrow_bytes` column. To read these, you will need to deserialize the bytes:
```python
import datasets
import pyarrow as pa
# load a single file of the dataset
ds = datasets.load_dataset(
'approximatelabs/tablib-v1-full',
data_files='tablib/job=github_000005/batch=000001/part=000001/manifest.parquet',
token='...',
)
df = ds['train'].to_pandas()
tables = [pa.RecordBatchStreamReader(b).read_all() for b in df['arrow_bytes']]
```
## Licensing
This dataset is intended for research use only.
For specific licensing information, refer to the license of the specific datum being used.
# Contact
If you have any questions, comments, or concerns about licensing, pii, etc. please contact using [this form](https://forms.gle/C74VTWP7L78QDVR67).
# Approximate Labs
TabLib is a project from Approximate Labs. Find us on [Twitter](https://twitter.com/approximatelabs), [Github](https://github.com/approximatelabs), [Linkedin](https://www.linkedin.com/company/approximate-labs), and [Discord](https://discord.gg/kW9nBQErGe).
# Citations
If you use TabLib for any of your research, please cite the TabLib paper:
```
@misc{eggert2023tablib,
title={TabLib: A Dataset of 627M Tables with Context},
author={Gus Eggert and Kevin Huo and Mike Biven and Justin Waugh},
year={2023},
eprint={2310.07875},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
``` | [
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anyspeech/ucla_phonetic_corpus | anyspeech | 2023-05-06T19:05:47Z | 1,328 | 0 | null | [
"region:us"
] | 2023-05-06T19:05:47Z | 2023-05-06T19:02:43.000Z | 2023-05-06T19:02:43 | ---
dataset_info:
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---
# Dataset Card for "ucla_phonetic_corpus"
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yizhongw/self_instruct | yizhongw | 2023-03-07T10:07:36Z | 1,325 | 169 | null | [
"license:apache-2.0",
"arxiv:2212.10560",
"arxiv:2204.07705",
"region:us"
] | 2023-03-07T10:07:36Z | 2023-03-02T14:29:46.000Z | 2023-03-02T14:29:46 | ---
license: apache-2.0
dataset_info:
- config_name: self_instruct
features:
- name: prompt
dtype: string
- name: completion
dtype: string
splits:
- name: train
num_bytes: 20527462
num_examples: 82612
download_size: 24113858
dataset_size: 20527462
- config_name: human_eval
features:
- name: id
dtype: string
- name: motivation_app
dtype: string
- name: instruction
dtype: string
- name: instances
sequence:
- name: input
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 151244
num_examples: 252
download_size: 170193
dataset_size: 151244
- config_name: super_natural_instructions
features:
- name: prompt
dtype: string
- name: completion
dtype: string
splits:
- name: train
num_bytes: 40352923
num_examples: 50000
- name: test
num_bytes: 9713953
num_examples: 11810
download_size: 52975509
dataset_size: 50066876
- config_name: prompt_source
features:
- name: prompt
dtype: string
- name: completion
dtype: string
splits:
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num_bytes: 57368889
num_examples: 52657
download_size: 60126945
dataset_size: 57368889
- config_name: p3
features:
- name: prompt
dtype: string
- name: completion
dtype: string
splits:
- name: train
num_bytes: 57368889
num_examples: 52657
download_size: 60126945
dataset_size: 57368889
---
# Dataset Card for Self Instruct
## Table of Contents
- [Dataset Card for Self Instruct](#dataset-card-for-self-instruct)
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [self\_instruct](#self_instruct)
- [super\_natural\_instructions](#super_natural_instructions)
- [p3](#p3)
- [human\_eval](#human_eval)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [self\_instruct](#self_instruct-1)
- [super\_natural\_instructions](#super_natural_instructions-1)
- [p3](#p3-1)
- [human\_eval](#human_eval-1)
- [Data Fields](#data-fields)
- [self\_instruct](#self_instruct-2)
- [super\_natural\_instructions](#super_natural_instructions-2)
- [p3](#p3-2)
- [human\_eval](#human_eval-2)
- [Data Splits](#data-splits)
- [self\_instruct](#self_instruct-3)
- [super\_natural\_instructions](#super_natural_instructions-3)
- [p3](#p3-3)
- [human\_eval](#human_eval-3)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Initial Data Collection and Normalization](#initial-data-collection-and-normalization)
- [Who are the source language producers?](#who-are-the-source-language-producers)
- [Annotations](#annotations)
- [Annotation process](#annotation-process)
- [Who are the annotators?](#who-are-the-annotators)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
- **Homepage:**
- **Repository:** https://github.com/yizhongw/self-instruct
- **Paper:** https://arxiv.org/abs/2212.10560
- **Leaderboard:**
- **Point of Contact:** Yizhong Wang
### Dataset Summary
Self-Instruct is a framework that helps language models improve their ability to follow natural language instructions. It does this by using the model's own generations to create a large collection of instructional data. With Self-Instruct, it is possible to improve the instruction-following capabilities of language models without relying on extensive manual annotation.
A part of this framework, the Self-Instruct authors released a dataset that contains 52k instructions, paired with 82K instance inputs and outputs. This instruction data can be used to conduct instruction-tuning for language models and make the language model follow instruction better.
The authors also released a new set of 252 expert-written tasks and their instructions motivated by user-oriented applications (rather than well-studied NLP tasks). This data is used in the human evaluation section of [the Self Instruct paper](https://arxiv.org/abs/2212.10560).
To enable comparison on public datasets, Self-Instruct also contains 50k examples from the P3 and Super Natural Instructions datasets.
### Supported Tasks and Leaderboards
The datasets in Self-Instruct are designed for _instruction training_ pretrained language models. The following subsets are provided as part of Self Instruct.
#### self_instruct
82k prompts and model completions generated via OpenAI's `davinci` engine.
#### super_natural_instructions
50k expert written instructions and demonstrations sampled from the [Super Natural Instructions dataset](https://arxiv.org/abs/2204.07705)
#### p3
50k crowd-sourced instructions and demonstrations sampled from the [Public Pool of Prompts (P3) dataset](https://huggingface.co/datasets/bigscience/P3)
#### human_eval
252 expert-written tasks and their instructions motivated by user-oriented applications (rather than well-studied NLP tasks). This data is used in the human evaluation section of [the self-instruct paper](https://arxiv.org/abs/2212.10560). Please refer to [the human evaluation information]([human_eval/README.md](https://github.com/yizhongw/self-instruct/blob/main/human_eval/README.md)) for more details.
### Languages
The data in Self Instruct are in English (BCP-47 en).
## Dataset Structure
### Data Instances
#### self_instruct
An example pf "train" looks as follows:
```json
{
"prompt": "Explain the origin of life on earth. Output:",
"completion": " Life on Earth is believed to have begun about 3.5 billion years ago, when the planet was still in its early stages of formation. The earliest evidence for life on Earth are fossils of microorganisms (bacteria and archaea) that lived approximately 3.5 billion years ago. These organisms were capable of converting carbon dioxide into organic compounds using sunlight as an energy source. This process is known as photosynthesis.",
}
```
#### super_natural_instructions
An example pf "train" looks as follows:
```json
{
"prompt": "Write a correct answer for the question. You should reason about prototypical situations and provide the correct answer. Avoid answers that provide incomplete justification for the question.\n\nInput: name something people often buy on credit.\n\nOutput:",
"completion": " appliances",
}
```
#### p3
An example pf "train" looks as follows:
```json
{
"prompt": "Determine if the following two sentences paraphrase each other or not.\nSent 1: By adding sufficient oxygen to compensate for the metabolic usage , rebreathing the carbon dioxide and removing the gas , most of the volume is conserved .\nSent 2: By adding sufficient oxygen to compensate for the metabolic consumption , removing the carbon dioxide and reinhaling the gas , most of the volume is conserved .\n",
"completion": "No",
}
```
#### human_eval
An example pf "train" looks as follows:
```json
{
"id": "user_oriented_task_136",
"motivation_app": "Goodreads",
"instruction": "Choose the best books from the given genre.",
"instances": {
"input": ["Crime & Mystery"],
"output": [
"1- The Girl with the Dragon Tattoo\n2- And Then There Were None\n3- Angels & Demons\n4- Rebecca\n5- In Cold Blood\n6- The Godfather\n7- The Lovely Bones\n8- Gone Girl\n9- The Name of the Rose\n10- Shutter Island"
],
},
}
```
### Data Fields
The data fields for each configuration are as follows.
#### self_instruct
* `prompt`: The instruction provided to the model or human labeler.
* `completion`: A completion provided by the model or human labeler.
#### super_natural_instructions
* `prompt`: The instruction provided to the model or human labeler.
* `completion`: A completion provided by the model or human labeler.
#### p3
* `prompt`: The instruction provided to the model or human labeler.
* `completion`: A completion provided by the model or human labeler.
#### human_eval
* `id`: The ID associated with the labelling task
* `motivation_app`: The application associated with the task
* `instruction`: The instruction written by the human labeler.
* `instances.input`: The input that forms part of the complete instruction
* `instances.output`: The human written demonstration
### Data Splits
#### self_instruct
| | train |
|---------------|------:|
| self_instruct | 82612 |
#### super_natural_instructions
| | train | test |
|----------------------------|------:|------:|
| super_natural_instructions | 50000 | 11810 |
#### p3
| | train |
|----|------:|
| p3 | 52657 |
#### human_eval
| | train |
|------------|------:|
| human_eval | 252 |
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
The `self_instruct` data is generated by a language model (GPT-3) and inevitably contains some errors or biases. The authors analyzed the data quality on 200 random instructions in our paper, and found that 46% of the data points may have problems. We encourage users to use this data with caution and propose new methods to filter or improve the imperfections.
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
```
@misc{selfinstruct,
title={Self-Instruct: Aligning Language Model with Self Generated Instructions},
author={Wang, Yizhong and Kordi, Yeganeh and Mishra, Swaroop and Liu, Alisa and Smith, Noah A. and Khashabi, Daniel and Hajishirzi, Hannaneh},
journal={arXiv preprint arXiv:2212.10560},
year={2022}
}
``` | [
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biosses | null | 2022-11-03T16:31:20Z | 1,320 | 4 | biosses | [
"task_categories:text-classification",
"task_ids:text-scoring",
"task_ids:semantic-similarity-scoring",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:n<1K",
"source_datasets:original",
"language:en",
"license:gpl-3.0",
"region:us"
] | 2022-11-03T16:31:20Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- en
license:
- gpl-3.0
multilinguality:
- monolingual
size_categories:
- n<1K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- text-scoring
- semantic-similarity-scoring
paperswithcode_id: biosses
pretty_name: BIOSSES
dataset_info:
features:
- name: sentence1
dtype: string
- name: sentence2
dtype: string
- name: score
dtype: float32
splits:
- name: train
num_bytes: 32783
num_examples: 100
download_size: 36324
dataset_size: 32783
---
# Dataset Card for BIOSSES
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://tabilab.cmpe.boun.edu.tr/BIOSSES/DataSet.html
- **Repository:** https://github.com/gizemsogancioglu/biosses
- **Paper:** [BIOSSES: a semantic sentence similarity estimation system for the biomedical domain](https://academic.oup.com/bioinformatics/article/33/14/i49/3953954)
- **Point of Contact:** [Gizem Soğancıoğlu](gizemsogancioglu@gmail.com) and [Arzucan Özgür](gizemsogancioglu@gmail.com)
### Dataset Summary
BIOSSES is a benchmark dataset for biomedical sentence similarity estimation. The dataset comprises 100 sentence pairs, in which each sentence was selected from the [TAC (Text Analysis Conference) Biomedical Summarization Track Training Dataset](https://tac.nist.gov/2014/BiomedSumm/) containing articles from the biomedical domain. The sentence pairs in BIOSSES were selected from citing sentences, i.e. sentences that have a citation to a reference article.
The sentence pairs were evaluated by five different human experts that judged their similarity and gave scores ranging from 0 (no relation) to 4 (equivalent). In the original paper the mean of the scores assigned by the five human annotators was taken as the gold standard. The Pearson correlation between the gold standard scores and the scores estimated by the models was used as the evaluation metric. The strength of correlation can be assessed by the general guideline proposed by Evans (1996) as follows:
- very strong: 0.80–1.00
- strong: 0.60–0.79
- moderate: 0.40–0.59
- weak: 0.20–0.39
- very weak: 0.00–0.19
### Supported Tasks and Leaderboards
Biomedical Semantic Similarity Scoring.
### Languages
English.
## Dataset Structure
### Data Instances
For each instance, there are two sentences (i.e. sentence 1 and 2), and its corresponding similarity score (the mean of the scores assigned by the five human annotators).
```
{'sentence 1': 'Here, looking for agents that could specifically kill KRAS mutant cells, they found that knockdown of GATA2 was synthetically lethal with KRAS mutation'
'sentence 2': 'Not surprisingly, GATA2 knockdown in KRAS mutant cells resulted in a striking reduction of active GTP-bound RHO proteins, including the downstream ROCK kinase'
'score': 2.2}
```
### Data Fields
- `sentence 1`: string
- `sentence 2`: string
- `score`: float ranging from 0 (no relation) to 4 (equivalent)
### Data Splits
No data splits provided.
## Dataset Creation
### Curation Rationale
### Source Data
The [TAC (Text Analysis Conference) Biomedical Summarization Track Training Dataset](https://tac.nist.gov/2014/BiomedSumm/).
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
The sentence pairs were evaluated by five different human experts that judged their similarity and gave scores ranging from 0 (no relation) to 4 (equivalent). The score range was described based on the guidelines of SemEval 2012 Task 6 on STS (Agirre et al., 2012). Besides the annotation instructions, example sentences from the biomedical literature were provided to the annotators for each of the similarity degrees.
The table below shows the Pearson correlation of the scores of each annotator with respect to the average scores of the remaining four annotators. It is observed that there is strong association among the scores of the annotators. The lowest correlations are 0.902, which can be considered as an upper bound for an algorithmic measure evaluated on this dataset.
| |Correlation r |
|----------:|--------------:|
|Annotator A| 0.952|
|Annotator B| 0.958|
|Annotator C| 0.917|
|Annotator D| 0.902|
|Annotator E| 0.941|
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
- Gizem Soğancıoğlu, gizemsogancioglu@gmail.com
- Hakime Öztürk, hakime.ozturk@boun.edu.tr
- Arzucan Özgür, gizemsogancioglu@gmail.com
Bogazici University, Istanbul, Turkey
### Licensing Information
BIOSSES is made available under the terms of [The GNU Common Public License v.3.0](https://www.gnu.org/licenses/gpl-3.0.en.html).
### Citation Information
@article{souganciouglu2017biosses,
title={BIOSSES: a semantic sentence similarity estimation system for the biomedical domain},
author={So{\u{g}}anc{\i}o{\u{g}}lu, Gizem and {\"O}zt{\"u}rk, Hakime and {\"O}zg{\"u}r, Arzucan},
journal={Bioinformatics},
volume={33},
number={14},
pages={i49--i58},
year={2017},
publisher={Oxford University Press}
}
### Contributions
Thanks to [@bwang482](https://github.com/bwang482) for adding this dataset. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
conllpp | null | 2023-04-05T10:02:29Z | 1,320 | 6 | conll | [
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:extended|conll2003",
"language:en",
"license:unknown",
"region:us"
] | 2023-04-05T10:02:29Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- extended|conll2003
task_categories:
- token-classification
task_ids:
- named-entity-recognition
paperswithcode_id: conll
pretty_name: CoNLL++
train-eval-index:
- config: conllpp
task: token-classification
task_id: entity_extraction
splits:
train_split: train
eval_split: test
col_mapping:
tokens: tokens
ner_tags: tags
metrics:
- type: seqeval
name: seqeval
dataset_info:
features:
- name: id
dtype: string
- name: tokens
sequence: string
- name: pos_tags
sequence:
class_label:
names:
0: '"'
1: ''''''
2: '#'
3: $
4: (
5: )
6: ','
7: .
8: ':'
9: '``'
10: CC
11: CD
12: DT
13: EX
14: FW
15: IN
16: JJ
17: JJR
18: JJS
19: LS
20: MD
21: NN
22: NNP
23: NNPS
24: NNS
25: NN|SYM
26: PDT
27: POS
28: PRP
29: PRP$
30: RB
31: RBR
32: RBS
33: RP
34: SYM
35: TO
36: UH
37: VB
38: VBD
39: VBG
40: VBN
41: VBP
42: VBZ
43: WDT
44: WP
45: WP$
46: WRB
- name: chunk_tags
sequence:
class_label:
names:
0: O
1: B-ADJP
2: I-ADJP
3: B-ADVP
4: I-ADVP
5: B-CONJP
6: I-CONJP
7: B-INTJ
8: I-INTJ
9: B-LST
10: I-LST
11: B-NP
12: I-NP
13: B-PP
14: I-PP
15: B-PRT
16: I-PRT
17: B-SBAR
18: I-SBAR
19: B-UCP
20: I-UCP
21: B-VP
22: I-VP
- name: ner_tags
sequence:
class_label:
names:
0: O
1: B-PER
2: I-PER
3: B-ORG
4: I-ORG
5: B-LOC
6: I-LOC
7: B-MISC
8: I-MISC
config_name: conllpp
splits:
- name: train
num_bytes: 6931393
num_examples: 14041
- name: validation
num_bytes: 1739247
num_examples: 3250
- name: test
num_bytes: 1582078
num_examples: 3453
download_size: 4859600
dataset_size: 10252718
---
# Dataset Card for "conllpp"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [Github](https://github.com/ZihanWangKi/CrossWeigh)
- **Repository:** [Github](https://github.com/ZihanWangKi/CrossWeigh)
- **Paper:** [Aclweb](https://www.aclweb.org/anthology/D19-1519)
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
CoNLLpp is a corrected version of the CoNLL2003 NER dataset where labels of 5.38% of the sentences in the test set
have been manually corrected. The training set and development set from CoNLL2003 is included for completeness. One
correction on the test set for example, is:
```
{
"tokens": ["SOCCER", "-", "JAPAN", "GET", "LUCKY", "WIN", ",", "CHINA", "IN", "SURPRISE", "DEFEAT", "."],
"original_ner_tags_in_conll2003": ["O", "O", "B-LOC", "O", "O", "O", "O", "B-PER", "O", "O", "O", "O"],
"corrected_ner_tags_in_conllpp": ["O", "O", "B-LOC", "O", "O", "O", "O", "B-LOC", "O", "O", "O", "O"],
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
#### conllpp
- **Size of downloaded dataset files:** 4.85 MB
- **Size of the generated dataset:** 10.26 MB
- **Total amount of disk used:** 15.11 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"chunk_tags": [11, 12, 12, 21, 13, 11, 11, 21, 13, 11, 12, 13, 11, 21, 22, 11, 12, 17, 11, 21, 17, 11, 12, 12, 21, 22, 22, 13, 11, 0],
"id": "0",
"ner_tags": [0, 3, 4, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
"pos_tags": [12, 22, 22, 38, 15, 22, 28, 38, 15, 16, 21, 35, 24, 35, 37, 16, 21, 15, 24, 41, 15, 16, 21, 21, 20, 37, 40, 35, 21, 7],
"tokens": ["The", "European", "Commission", "said", "on", "Thursday", "it", "disagreed", "with", "German", "advice", "to", "consumers", "to", "shun", "British", "lamb", "until", "scientists", "determine", "whether", "mad", "cow", "disease", "can", "be", "transmitted", "to", "sheep", "."]
}
```
### Data Fields
The data fields are the same among all splits.
#### conllpp
- `id`: a `string` feature.
- `tokens`: a `list` of `string` features.
- `pos_tags`: a `list` of classification labels, with possible values including `"` (0), `''` (1), `#` (2), `$` (3), `(` (4).
- `chunk_tags`: a `list` of classification labels, with possible values including `O` (0), `B-ADJP` (1), `I-ADJP` (2), `B-ADVP` (3), `I-ADVP` (4).
- `ner_tags`: a `list` of classification labels, with possible values including `O` (0), `B-PER` (1), `I-PER` (2), `B-ORG` (3), `I-ORG` (4).
### Data Splits
| name |train|validation|test|
|---------|----:|---------:|---:|
|conll2003|14041| 3250|3453|
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
```
@inproceedings{wang2019crossweigh,
title={CrossWeigh: Training Named Entity Tagger from Imperfect Annotations},
author={Wang, Zihan and Shang, Jingbo and Liu, Liyuan and Lu, Lihao and Liu, Jiacheng and Han, Jiawei},
booktitle={Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)},
pages={5157--5166},
year={2019}
}
```
### Contributions
Thanks to [@ZihanWangKi](https://github.com/ZihanWangKi) for adding this dataset. | [
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BeIR/scifact | BeIR | 2022-10-23T06:01:22Z | 1,318 | 1 | beir | [
"task_categories:text-retrieval",
"task_ids:entity-linking-retrieval",
"task_ids:fact-checking-retrieval",
"multilinguality:monolingual",
"language:en",
"license:cc-by-sa-4.0",
"region:us"
] | 2022-10-23T06:01:22Z | 2022-06-05T16:24:20.000Z | 2022-06-05T16:24:20 | ---
annotations_creators: []
language_creators: []
language:
- en
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
paperswithcode_id: beir
pretty_name: BEIR Benchmark
size_categories:
msmarco:
- 1M<n<10M
trec-covid:
- 100k<n<1M
nfcorpus:
- 1K<n<10K
nq:
- 1M<n<10M
hotpotqa:
- 1M<n<10M
fiqa:
- 10K<n<100K
arguana:
- 1K<n<10K
touche-2020:
- 100K<n<1M
cqadupstack:
- 100K<n<1M
quora:
- 100K<n<1M
dbpedia:
- 1M<n<10M
scidocs:
- 10K<n<100K
fever:
- 1M<n<10M
climate-fever:
- 1M<n<10M
scifact:
- 1K<n<10K
source_datasets: []
task_categories:
- text-retrieval
- zero-shot-retrieval
- information-retrieval
- zero-shot-information-retrieval
task_ids:
- passage-retrieval
- entity-linking-retrieval
- fact-checking-retrieval
- tweet-retrieval
- citation-prediction-retrieval
- duplication-question-retrieval
- argument-retrieval
- news-retrieval
- biomedical-information-retrieval
- question-answering-retrieval
---
# Dataset Card for BEIR Benchmark
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://github.com/UKPLab/beir
- **Repository:** https://github.com/UKPLab/beir
- **Paper:** https://openreview.net/forum?id=wCu6T5xFjeJ
- **Leaderboard:** https://docs.google.com/spreadsheets/d/1L8aACyPaXrL8iEelJLGqlMqXKPX2oSP_R10pZoy77Ns
- **Point of Contact:** nandan.thakur@uwaterloo.ca
### Dataset Summary
BEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:
- Fact-checking: [FEVER](http://fever.ai), [Climate-FEVER](http://climatefever.ai), [SciFact](https://github.com/allenai/scifact)
- Question-Answering: [NQ](https://ai.google.com/research/NaturalQuestions), [HotpotQA](https://hotpotqa.github.io), [FiQA-2018](https://sites.google.com/view/fiqa/)
- Bio-Medical IR: [TREC-COVID](https://ir.nist.gov/covidSubmit/index.html), [BioASQ](http://bioasq.org), [NFCorpus](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/)
- News Retrieval: [TREC-NEWS](https://trec.nist.gov/data/news2019.html), [Robust04](https://trec.nist.gov/data/robust/04.guidelines.html)
- Argument Retrieval: [Touche-2020](https://webis.de/events/touche-20/shared-task-1.html), [ArguAna](tp://argumentation.bplaced.net/arguana/data)
- Duplicate Question Retrieval: [Quora](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs), [CqaDupstack](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/)
- Citation-Prediction: [SCIDOCS](https://allenai.org/data/scidocs)
- Tweet Retrieval: [Signal-1M](https://research.signal-ai.com/datasets/signal1m-tweetir.html)
- Entity Retrieval: [DBPedia](https://github.com/iai-group/DBpedia-Entity/)
All these datasets have been preprocessed and can be used for your experiments.
```python
```
### Supported Tasks and Leaderboards
The dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.
The current best performing models can be found [here](https://eval.ai/web/challenges/challenge-page/689/leaderboard/).
### Languages
All tasks are in English (`en`).
## Dataset Structure
All BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:
- `corpus` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with three fields `_id` with unique document identifier, `title` with document title (optional) and `text` with document paragraph or passage. For example: `{"_id": "doc1", "title": "Albert Einstein", "text": "Albert Einstein was a German-born...."}`
- `queries` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with two fields `_id` with unique query identifier and `text` with query text. For example: `{"_id": "q1", "text": "Who developed the mass-energy equivalence formula?"}`
- `qrels` file: a `.tsv` file (tab-seperated) that contains three columns, i.e. the `query-id`, `corpus-id` and `score` in this order. Keep 1st row as header. For example: `q1 doc1 1`
### Data Instances
A high level example of any beir dataset:
```python
corpus = {
"doc1" : {
"title": "Albert Einstein",
"text": "Albert Einstein was a German-born theoretical physicist. who developed the theory of relativity, \
one of the two pillars of modern physics (alongside quantum mechanics). His work is also known for \
its influence on the philosophy of science. He is best known to the general public for his mass–energy \
equivalence formula E = mc2, which has been dubbed 'the world's most famous equation'. He received the 1921 \
Nobel Prize in Physics 'for his services to theoretical physics, and especially for his discovery of the law \
of the photoelectric effect', a pivotal step in the development of quantum theory."
},
"doc2" : {
"title": "", # Keep title an empty string if not present
"text": "Wheat beer is a top-fermented beer which is brewed with a large proportion of wheat relative to the amount of \
malted barley. The two main varieties are German Weißbier and Belgian witbier; other types include Lambic (made\
with wild yeast), Berliner Weisse (a cloudy, sour beer), and Gose (a sour, salty beer)."
},
}
queries = {
"q1" : "Who developed the mass-energy equivalence formula?",
"q2" : "Which beer is brewed with a large proportion of wheat?"
}
qrels = {
"q1" : {"doc1": 1},
"q2" : {"doc2": 1},
}
```
### Data Fields
Examples from all configurations have the following features:
### Corpus
- `corpus`: a `dict` feature representing the document title and passage text, made up of:
- `_id`: a `string` feature representing the unique document id
- `title`: a `string` feature, denoting the title of the document.
- `text`: a `string` feature, denoting the text of the document.
### Queries
- `queries`: a `dict` feature representing the query, made up of:
- `_id`: a `string` feature representing the unique query id
- `text`: a `string` feature, denoting the text of the query.
### Qrels
- `qrels`: a `dict` feature representing the query document relevance judgements, made up of:
- `_id`: a `string` feature representing the query id
- `_id`: a `string` feature, denoting the document id.
- `score`: a `int32` feature, denoting the relevance judgement between query and document.
### Data Splits
| Dataset | Website| BEIR-Name | Type | Queries | Corpus | Rel D/Q | Down-load | md5 |
| -------- | -----| ---------| --------- | ----------- | ---------| ---------| :----------: | :------:|
| MSMARCO | [Homepage](https://microsoft.github.io/msmarco/)| ``msmarco`` | ``train``<br>``dev``<br>``test``| 6,980 | 8.84M | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/msmarco.zip) | ``444067daf65d982533ea17ebd59501e4`` |
| TREC-COVID | [Homepage](https://ir.nist.gov/covidSubmit/index.html)| ``trec-covid``| ``test``| 50| 171K| 493.5 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/trec-covid.zip) | ``ce62140cb23feb9becf6270d0d1fe6d1`` |
| NFCorpus | [Homepage](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/) | ``nfcorpus`` | ``train``<br>``dev``<br>``test``| 323 | 3.6K | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nfcorpus.zip) | ``a89dba18a62ef92f7d323ec890a0d38d`` |
| BioASQ | [Homepage](http://bioasq.org) | ``bioasq``| ``train``<br>``test`` | 500 | 14.91M | 8.05 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#2-bioasq) |
| NQ | [Homepage](https://ai.google.com/research/NaturalQuestions) | ``nq``| ``train``<br>``test``| 3,452 | 2.68M | 1.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nq.zip) | ``d4d3d2e48787a744b6f6e691ff534307`` |
| HotpotQA | [Homepage](https://hotpotqa.github.io) | ``hotpotqa``| ``train``<br>``dev``<br>``test``| 7,405 | 5.23M | 2.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/hotpotqa.zip) | ``f412724f78b0d91183a0e86805e16114`` |
| FiQA-2018 | [Homepage](https://sites.google.com/view/fiqa/) | ``fiqa`` | ``train``<br>``dev``<br>``test``| 648 | 57K | 2.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fiqa.zip) | ``17918ed23cd04fb15047f73e6c3bd9d9`` |
| Signal-1M(RT) | [Homepage](https://research.signal-ai.com/datasets/signal1m-tweetir.html)| ``signal1m`` | ``test``| 97 | 2.86M | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#4-signal-1m) |
| TREC-NEWS | [Homepage](https://trec.nist.gov/data/news2019.html) | ``trec-news`` | ``test``| 57 | 595K | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#1-trec-news) |
| ArguAna | [Homepage](http://argumentation.bplaced.net/arguana/data) | ``arguana``| ``test`` | 1,406 | 8.67K | 1.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/arguana.zip) | ``8ad3e3c2a5867cdced806d6503f29b99`` |
| Touche-2020| [Homepage](https://webis.de/events/touche-20/shared-task-1.html) | ``webis-touche2020``| ``test``| 49 | 382K | 19.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/webis-touche2020.zip) | ``46f650ba5a527fc69e0a6521c5a23563`` |
| CQADupstack| [Homepage](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/) | ``cqadupstack``| ``test``| 13,145 | 457K | 1.4 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/cqadupstack.zip) | ``4e41456d7df8ee7760a7f866133bda78`` |
| Quora| [Homepage](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs) | ``quora``| ``dev``<br>``test``| 10,000 | 523K | 1.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/quora.zip) | ``18fb154900ba42a600f84b839c173167`` |
| DBPedia | [Homepage](https://github.com/iai-group/DBpedia-Entity/) | ``dbpedia-entity``| ``dev``<br>``test``| 400 | 4.63M | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/dbpedia-entity.zip) | ``c2a39eb420a3164af735795df012ac2c`` |
| SCIDOCS| [Homepage](https://allenai.org/data/scidocs) | ``scidocs``| ``test``| 1,000 | 25K | 4.9 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scidocs.zip) | ``38121350fc3a4d2f48850f6aff52e4a9`` |
| FEVER | [Homepage](http://fever.ai) | ``fever``| ``train``<br>``dev``<br>``test``| 6,666 | 5.42M | 1.2| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fever.zip) | ``5a818580227bfb4b35bb6fa46d9b6c03`` |
| Climate-FEVER| [Homepage](http://climatefever.ai) | ``climate-fever``|``test``| 1,535 | 5.42M | 3.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/climate-fever.zip) | ``8b66f0a9126c521bae2bde127b4dc99d`` |
| SciFact| [Homepage](https://github.com/allenai/scifact) | ``scifact``| ``train``<br>``test``| 300 | 5K | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scifact.zip) | ``5f7d1de60b170fc8027bb7898e2efca1`` |
| Robust04 | [Homepage](https://trec.nist.gov/data/robust/04.guidelines.html) | ``robust04``| ``test``| 249 | 528K | 69.9 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#3-robust04) |
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
[Needs More Information]
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
[Needs More Information]
### Citation Information
Cite as:
```
@inproceedings{
thakur2021beir,
title={{BEIR}: A Heterogeneous Benchmark for Zero-shot Evaluation of Information Retrieval Models},
author={Nandan Thakur and Nils Reimers and Andreas R{\"u}ckl{\'e} and Abhishek Srivastava and Iryna Gurevych},
booktitle={Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2)},
year={2021},
url={https://openreview.net/forum?id=wCu6T5xFjeJ}
}
```
### Contributions
Thanks to [@Nthakur20](https://github.com/Nthakur20) for adding this dataset. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
pcuenq/oxford-pets | pcuenq | 2022-08-06T16:01:34Z | 1,315 | 7 | null | [
"task_categories:image-classification",
"source_datasets:https://www.robots.ox.ac.uk/~vgg/data/pets/",
"license:cc-by-sa-4.0",
"pets",
"oxford",
"region:us"
] | 2022-08-06T16:01:34Z | 2022-08-06T15:59:02.000Z | 2022-08-06T15:59:02 | ---
tags:
- pets
- oxford
license: cc-by-sa-4.0
license_details: https://www.robots.ox.ac.uk/~vgg/data/pets/
pretty_name: Oxford-IIIT Pet Dataset (no annotations)
source_datasets: https://www.robots.ox.ac.uk/~vgg/data/pets/
task_categories:
- image-classification
---
# Oxford-IIIT Pet Dataset
Images from [The Oxford-IIIT Pet Dataset](https://www.robots.ox.ac.uk/~vgg/data/pets/). Only images and labels have been pushed, segmentation annotations were ignored.
- **Homepage:** https://www.robots.ox.ac.uk/~vgg/data/pets/
License:
Same as the original dataset.
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
Open-Orca/SlimOrca | Open-Orca | 2023-10-12T06:43:59Z | 1,315 | 92 | null | [
"task_categories:conversational",
"task_categories:text-classification",
"task_categories:token-classification",
"task_categories:table-question-answering",
"task_categories:question-answering",
"task_categories:zero-shot-classification",
"task_categories:summarization",
"task_categories:feature-extraction",
"task_categories:text-generation",
"task_categories:text2text-generation",
"size_categories:100K<n<1M",
"language:en",
"license:mit",
"arxiv:2306.02707",
"arxiv:2301.13688",
"region:us"
] | 2023-10-12T06:43:59Z | 2023-10-06T09:40:55.000Z | 2023-10-06T09:40:55 | ---
language:
- en
license: mit
task_categories:
- conversational
- text-classification
- token-classification
- table-question-answering
- question-answering
- zero-shot-classification
- summarization
- feature-extraction
- text-generation
- text2text-generation
pretty_name: SlimOrca
size_categories:
- 100K<n<1M
---
# Overview
This is a new curated subset of our OpenOrca data. This release provides an efficient means of reaching performance on-par with using larger slices of our data, while only including ~500k GPT-4 completions.
The key change in this dataset is that we've done an additional pass, using GPT-4 to remove answers which appear wrong based on the human annotations from the FLAN dataset.
This reduces the dataset size to only ~500k entries, allowing training to a similar quality level to our previous releases with 2/3 the compute requirement.
# Demo Models
* https://huggingface.co/openaccess-ai-collective/jackalope-7b
* https://huggingface.co/Open-Orca/Mistral-7B-SlimOrca
# Citation
```bibtex
@misc{SlimOrca,
title = {SlimOrca: An Open Dataset of GPT-4 Augmented FLAN Reasoning Traces, with Verification},
author = {Wing Lian and Guan Wang and Bleys Goodson and Eugene Pentland and Austin Cook and Chanvichet Vong and "Teknium"},
year = {2023},
publisher = {HuggingFace},
url = {https://https://huggingface.co/Open-Orca/SlimOrca}
}
```
```bibtex
@misc{mukherjee2023orca,
title={Orca: Progressive Learning from Complex Explanation Traces of GPT-4},
author={Subhabrata Mukherjee and Arindam Mitra and Ganesh Jawahar and Sahaj Agarwal and Hamid Palangi and Ahmed Awadallah},
year={2023},
eprint={2306.02707},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
```bibtex
@misc{longpre2023flan,
title={The Flan Collection: Designing Data and Methods for Effective Instruction Tuning},
author={Shayne Longpre and Le Hou and Tu Vu and Albert Webson and Hyung Won Chung and Yi Tay and Denny Zhou and Quoc V. Le and Barret Zoph and Jason Wei and Adam Roberts},
year={2023},
eprint={2301.13688},
archivePrefix={arXiv},
primaryClass={cs.AI}
}
``` | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
sentiment140 | null | 2023-10-20T12:55:00Z | 1,311 | 11 | sentiment140 | [
"language:en",
"region:us"
] | 2023-10-20T12:55:00Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
language:
- en
paperswithcode_id: sentiment140
pretty_name: Sentiment140
dataset_info:
config_name: sentiment140
features:
- name: text
dtype: string
- name: date
dtype: string
- name: user
dtype: string
- name: sentiment
dtype: int32
- name: query
dtype: string
splits:
- name: train
num_bytes: 224542690
num_examples: 1600000
- name: test
num_bytes: 72971
num_examples: 498
download_size: 81363704
dataset_size: 224615661
train-eval-index:
- config: sentiment140
task: text-classification
task_id: multi_class_classification
splits:
train_split: train
eval_split: test
col_mapping:
text: text
sentiment: target
metrics:
- type: accuracy
name: Accuracy
- type: f1
name: F1 macro
args:
average: macro
- type: f1
name: F1 micro
args:
average: micro
- type: f1
name: F1 weighted
args:
average: weighted
- type: precision
name: Precision macro
args:
average: macro
- type: precision
name: Precision micro
args:
average: micro
- type: precision
name: Precision weighted
args:
average: weighted
- type: recall
name: Recall macro
args:
average: macro
- type: recall
name: Recall micro
args:
average: micro
- type: recall
name: Recall weighted
args:
average: weighted
---
# Dataset Card for "sentiment140"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [http://help.sentiment140.com/home](http://help.sentiment140.com/home)
- **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Size of downloaded dataset files:** 81.36 MB
- **Size of the generated dataset:** 225.82 MB
- **Total amount of disk used:** 307.18 MB
### Dataset Summary
Sentiment140 consists of Twitter messages with emoticons, which are used as noisy labels for
sentiment classification. For more detailed information please refer to the paper.
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Dataset Structure
### Data Instances
#### sentiment140
- **Size of downloaded dataset files:** 81.36 MB
- **Size of the generated dataset:** 225.82 MB
- **Total amount of disk used:** 307.18 MB
An example of 'train' looks as follows.
```
{
"date": "23-04-2010",
"query": "NO_QUERY",
"sentiment": 3,
"text": "train message",
"user": "train user"
}
```
### Data Fields
The data fields are the same among all splits.
#### sentiment140
- `text`: a `string` feature.
- `date`: a `string` feature.
- `user`: a `string` feature.
- `sentiment`: a `int32` feature.
- `query`: a `string` feature.
### Data Splits
| name | train |test|
|------------|------:|---:|
|sentiment140|1600000| 498|
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Citation Information
```
@article{go2009twitter,
title={Twitter sentiment classification using distant supervision},
author={Go, Alec and Bhayani, Richa and Huang, Lei},
journal={CS224N project report, Stanford},
volume={1},
number={12},
pages={2009},
year={2009}
}
```
### Contributions
Thanks to [@patrickvonplaten](https://github.com/patrickvonplaten), [@thomwolf](https://github.com/thomwolf) for adding this dataset. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
huggan/wikiart | huggan | 2023-03-22T13:56:08Z | 1,307 | 46 | null | [
"task_categories:image-classification",
"task_categories:text-to-image",
"task_categories:image-to-text",
"size_categories:10K<n<100K",
"license:unknown",
"art",
"region:us"
] | 2023-03-22T13:56:08Z | 2022-04-06T09:40:18.000Z | 2022-04-06T09:40:18 | ---
license: unknown
license_details: Data files © Original Authors
size_categories:
- 10K<n<100K
task_categories:
- image-classification
- text-to-image
- image-to-text
tags:
- art
---
## Dataset Description
- **Homepage:** https://www.wikiart.org/
### Dataset Summary
Dataset containing 81,444 pieces of visual art from various artists, taken from WikiArt.org,
along with class labels for each image :
* "artist" : 129 artist classes, including a "Unknown Artist" class
* "genre" : 11 genre classes, including a "Unknown Genre" class
* "style" : 27 style classes
On WikiArt.org, the description for the "Artworks by Genre" page reads :
A genre system divides artworks according to depicted themes and objects. A classical hierarchy of genres was developed in European culture by the 17th century. It ranked genres in high – history painting and portrait, - and low – genre painting, landscape and still life. This hierarchy was based on the notion of man as the measure of all things. Landscape and still life were the lowest because they did not involve human subject matter. History was highest because it dealt with the noblest events of humanity. Genre system is not so much relevant for a contemporary art; there are just two genre definitions that are usually applied to it: abstract or figurative.
The "Artworks by Style" page reads :
A style of an artwork refers to its distinctive visual elements, techniques and methods. It usually corresponds with an art movement or a school (group) that its author is associated with.
## Dataset Structure
* "image" : image
* "artist" : 129 artist classes, including a "Unknown Artist" class
* "genre" : 11 genre classes, including a "Unknown Genre" class
* "style" : 27 style classes
### Source Data
Files taken from this [archive](https://archive.org/download/wikiart-dataset/wikiart.tar.gz), curated from the [WikiArt website](https://www.wikiart.org/).
## Additional Information
Note:
* The WikiArt dataset can be used only for non-commercial research purpose.
* The images in the WikiArt dataset were obtained from WikiArt.org.
* The authors are neither responsible for the content nor the meaning of these images.
By using the WikiArt dataset, you agree to obey the terms and conditions of WikiArt.org.
### Contributions
[`gigant`](https://huggingface.co/gigant) added this dataset to the hub. | [
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laion/laion2B-en | laion | 2023-08-13T10:21:14Z | 1,306 | 148 | null | [
"license:cc-by-4.0",
"region:us"
] | 2023-08-13T10:21:14Z | 2022-03-08T22:49:04.000Z | 2022-03-08T22:49:04 | ---
license: cc-by-4.0
---
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fever | null | 2023-04-05T10:06:17Z | 1,302 | 11 | fever | [
"task_categories:text-classification",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:extended|wikipedia",
"language:en",
"license:cc-by-sa-3.0",
"license:gpl-3.0",
"knowledge-verification",
"region:us"
] | 2023-04-05T10:06:17Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
language:
- en
paperswithcode_id: fever
annotations_creators:
- crowdsourced
language_creators:
- found
license:
- cc-by-sa-3.0
- gpl-3.0
multilinguality:
- monolingual
pretty_name: FEVER
size_categories:
- 100K<n<1M
source_datasets:
- extended|wikipedia
task_categories:
- text-classification
task_ids: []
tags:
- knowledge-verification
dataset_info:
- config_name: v1.0
features:
- name: id
dtype: int32
- name: label
dtype: string
- name: claim
dtype: string
- name: evidence_annotation_id
dtype: int32
- name: evidence_id
dtype: int32
- name: evidence_wiki_url
dtype: string
- name: evidence_sentence_id
dtype: int32
splits:
- name: train
num_bytes: 29591412
num_examples: 311431
- name: labelled_dev
num_bytes: 3643157
num_examples: 37566
- name: unlabelled_dev
num_bytes: 1548965
num_examples: 19998
- name: unlabelled_test
num_bytes: 1617002
num_examples: 19998
- name: paper_dev
num_bytes: 1821489
num_examples: 18999
- name: paper_test
num_bytes: 1821668
num_examples: 18567
download_size: 44853972
dataset_size: 40043693
- config_name: v2.0
features:
- name: id
dtype: int32
- name: label
dtype: string
- name: claim
dtype: string
- name: evidence_annotation_id
dtype: int32
- name: evidence_id
dtype: int32
- name: evidence_wiki_url
dtype: string
- name: evidence_sentence_id
dtype: int32
splits:
- name: validation
num_bytes: 306243
num_examples: 2384
download_size: 392466
dataset_size: 306243
- config_name: wiki_pages
features:
- name: id
dtype: string
- name: text
dtype: string
- name: lines
dtype: string
splits:
- name: wikipedia_pages
num_bytes: 7254115038
num_examples: 5416537
download_size: 1713485474
dataset_size: 7254115038
---
# Dataset Card for "fever"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [https://fever.ai/](https://fever.ai/)
- **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Dataset Summary
With billions of individual pages on the web providing information on almost every conceivable topic, we should have
the ability to collect facts that answer almost every conceivable question. However, only a small fraction of this
information is contained in structured sources (Wikidata, Freebase, etc.) – we are therefore limited by our ability to
transform free-form text to structured knowledge. There is, however, another problem that has become the focus of a lot
of recent research and media coverage: false information coming from unreliable sources.
The FEVER workshops are a venue for work in verifiable knowledge extraction and to stimulate progress in this direction.
- FEVER Dataset: FEVER (Fact Extraction and VERification) consists of 185,445 claims generated by altering sentences
extracted from Wikipedia and subsequently verified without knowledge of the sentence they were derived from. The claims
are classified as Supported, Refuted or NotEnoughInfo. For the first two classes, the annotators also recorded the
sentence(s) forming the necessary evidence for their judgment.
- FEVER 2.0 Adversarial Attacks Dataset: The FEVER 2.0 Dataset consists of 1174 claims created by the submissions of
participants in the Breaker phase of the 2019 shared task. Participants (Breakers) were tasked with generating
adversarial examples that induce classification errors for the existing systems. Breakers submitted a dataset of up to
1000 instances with equal number of instances for each of the three classes (Supported, Refuted NotEnoughInfo). Only
novel claims (i.e. not contained in the original FEVER dataset) were considered as valid entries to the shared task.
The submissions were then manually evaluated for Correctness (grammatical, appropriately labeled and meet the FEVER
annotation guidelines requirements).
### Supported Tasks and Leaderboards
The task is verification of textual claims against textual sources.
When compared to textual entailment (TE)/natural language inference, the key difference is that in these tasks the
passage to verify each claim is given, and in recent years it typically consists a single sentence, while in
verification systems it is retrieved from a large set of documents in order to form the evidence.
### Languages
The dataset is in English.
## Dataset Structure
### Data Instances
#### v1.0
- **Size of downloaded dataset files:** 44.86 MB
- **Size of the generated dataset:** 40.05 MB
- **Total amount of disk used:** 84.89 MB
An example of 'train' looks as follows.
```
'claim': 'Nikolaj Coster-Waldau worked with the Fox Broadcasting Company.',
'evidence_wiki_url': 'Nikolaj_Coster-Waldau',
'label': 'SUPPORTS',
'id': 75397,
'evidence_id': 104971,
'evidence_sentence_id': 7,
'evidence_annotation_id': 92206}
```
#### v2.0
- **Size of downloaded dataset files:** 0.39 MB
- **Size of the generated dataset:** 0.30 MB
- **Total amount of disk used:** 0.70 MB
An example of 'validation' looks as follows.
```
{'claim': "There is a convicted statutory rapist called Chinatown's writer.",
'evidence_wiki_url': '',
'label': 'NOT ENOUGH INFO',
'id': 500000,
'evidence_id': -1,
'evidence_sentence_id': -1,
'evidence_annotation_id': 269158}
```
#### wiki_pages
- **Size of downloaded dataset files:** 1.71 GB
- **Size of the generated dataset:** 7.25 GB
- **Total amount of disk used:** 8.97 GB
An example of 'wikipedia_pages' looks as follows.
```
{'text': 'The following are the football -LRB- soccer -RRB- events of the year 1928 throughout the world . ',
'lines': '0\tThe following are the football -LRB- soccer -RRB- events of the year 1928 throughout the world .\n1\t',
'id': '1928_in_association_football'}
```
### Data Fields
The data fields are the same among all splits.
#### v1.0
- `id`: a `int32` feature.
- `label`: a `string` feature.
- `claim`: a `string` feature.
- `evidence_annotation_id`: a `int32` feature.
- `evidence_id`: a `int32` feature.
- `evidence_wiki_url`: a `string` feature.
- `evidence_sentence_id`: a `int32` feature.
#### v2.0
- `id`: a `int32` feature.
- `label`: a `string` feature.
- `claim`: a `string` feature.
- `evidence_annotation_id`: a `int32` feature.
- `evidence_id`: a `int32` feature.
- `evidence_wiki_url`: a `string` feature.
- `evidence_sentence_id`: a `int32` feature.
#### wiki_pages
- `id`: a `string` feature.
- `text`: a `string` feature.
- `lines`: a `string` feature.
### Data Splits
#### v1.0
| | train | unlabelled_dev | labelled_dev | paper_dev | unlabelled_test | paper_test |
|------|-------:|---------------:|-------------:|----------:|----------------:|-----------:|
| v1.0 | 311431 | 19998 | 37566 | 18999 | 19998 | 18567 |
#### v2.0
| | validation |
|------|-----------:|
| v2.0 | 2384 |
#### wiki_pages
| | wikipedia_pages |
|------------|----------------:|
| wiki_pages | 5416537 |
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
FEVER license:
```
These data annotations incorporate material from Wikipedia, which is licensed pursuant to the Wikipedia Copyright Policy. These annotations are made available under the license terms described on the applicable Wikipedia article pages, or, where Wikipedia license terms are unavailable, under the Creative Commons Attribution-ShareAlike License (version 3.0), available at http://creativecommons.org/licenses/by-sa/3.0/ (collectively, the “License Termsâ€). You may not use these files except in compliance with the applicable License Terms.
```
### Citation Information
If you use "FEVER Dataset", please cite:
```bibtex
@inproceedings{Thorne18Fever,
author = {Thorne, James and Vlachos, Andreas and Christodoulopoulos, Christos and Mittal, Arpit},
title = {{FEVER}: a Large-scale Dataset for Fact Extraction and {VERification}},
booktitle = {NAACL-HLT},
year = {2018}
}
```
If you use "FEVER 2.0 Adversarial Attacks Dataset", please cite:
```bibtex
@inproceedings{Thorne19FEVER2,
author = {Thorne, James and Vlachos, Andreas and Cocarascu, Oana and Christodoulopoulos, Christos and Mittal, Arpit},
title = {The {FEVER2.0} Shared Task},
booktitle = {Proceedings of the Second Workshop on {Fact Extraction and VERification (FEVER)}},
year = {2018}
}
```
### Contributions
Thanks to [@thomwolf](https://github.com/thomwolf), [@lhoestq](https://github.com/lhoestq),
[@mariamabarham](https://github.com/mariamabarham), [@lewtun](https://github.com/lewtun),
[@albertvillanova](https://github.com/albertvillanova) for adding this dataset. | [
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natural_questions | null | 2023-04-05T13:35:01Z | 1,300 | 26 | natural-questions | [
"task_categories:question-answering",
"task_ids:open-domain-qa",
"annotations_creators:no-annotation",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:en",
"license:cc-by-sa-3.0",
"region:us"
] | 2023-04-05T13:35:01Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- no-annotation
language_creators:
- crowdsourced
language:
- en
license:
- cc-by-sa-3.0
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- question-answering
task_ids:
- open-domain-qa
paperswithcode_id: natural-questions
pretty_name: Natural Questions
dataset_info:
features:
- name: id
dtype: string
- name: document
struct:
- name: title
dtype: string
- name: url
dtype: string
- name: html
dtype: string
- name: tokens
sequence:
- name: token
dtype: string
- name: is_html
dtype: bool
- name: question
struct:
- name: text
dtype: string
- name: tokens
sequence: string
- name: annotations
sequence:
- name: id
dtype: string
- name: long_answer
struct:
- name: start_token
dtype: int64
- name: end_token
dtype: int64
- name: start_byte
dtype: int64
- name: end_byte
dtype: int64
- name: short_answers
sequence:
- name: start_token
dtype: int64
- name: end_token
dtype: int64
- name: start_byte
dtype: int64
- name: end_byte
dtype: int64
- name: text
dtype: string
- name: yes_no_answer
dtype:
class_label:
names:
'0': 'NO'
'1': 'YES'
- name: long_answer_candidates
sequence:
- name: start_token
dtype: int64
- name: end_token
dtype: int64
- name: start_byte
dtype: int64
- name: end_byte
dtype: int64
- name: top_label
dtype: bool
splits:
- name: train
num_bytes: 97445142568
num_examples: 307373
- name: validation
num_bytes: 2353975312
num_examples: 7830
download_size: 45069199013
dataset_size: 99799117880
---
# Dataset Card for Natural Questions
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [https://ai.google.com/research/NaturalQuestions/dataset](https://ai.google.com/research/NaturalQuestions/dataset)
- **Repository:** [https://github.com/google-research-datasets/natural-questions](https://github.com/google-research-datasets/natural-questions)
- **Paper:** [https://research.google/pubs/pub47761/](https://research.google/pubs/pub47761/)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Size of downloaded dataset files:** 45.07 GB
- **Size of the generated dataset:** 99.80 GB
- **Total amount of disk used:** 144.87 GB
### Dataset Summary
The NQ corpus contains questions from real users, and it requires QA systems to
read and comprehend an entire Wikipedia article that may or may not contain the
answer to the question. The inclusion of real user questions, and the
requirement that solutions should read an entire page to find the answer, cause
NQ to be a more realistic and challenging task than prior QA datasets.
### Supported Tasks and Leaderboards
[https://ai.google.com/research/NaturalQuestions](https://ai.google.com/research/NaturalQuestions)
### Languages
en
## Dataset Structure
### Data Instances
- **Size of downloaded dataset files:** 45.07 GB
- **Size of the generated dataset:** 99.80 GB
- **Total amount of disk used:** 144.87 GB
An example of 'train' looks as follows. This is a toy example.
```
{
"id": "797803103760793766",
"document": {
"title": "Google",
"url": "http://www.wikipedia.org/Google",
"html": "<html><body><h1>Google Inc.</h1><p>Google was founded in 1998 By:<ul><li>Larry</li><li>Sergey</li></ul></p></body></html>",
"tokens":[
{"token": "<h1>", "start_byte": 12, "end_byte": 16, "is_html": True},
{"token": "Google", "start_byte": 16, "end_byte": 22, "is_html": False},
{"token": "inc", "start_byte": 23, "end_byte": 26, "is_html": False},
{"token": ".", "start_byte": 26, "end_byte": 27, "is_html": False},
{"token": "</h1>", "start_byte": 27, "end_byte": 32, "is_html": True},
{"token": "<p>", "start_byte": 32, "end_byte": 35, "is_html": True},
{"token": "Google", "start_byte": 35, "end_byte": 41, "is_html": False},
{"token": "was", "start_byte": 42, "end_byte": 45, "is_html": False},
{"token": "founded", "start_byte": 46, "end_byte": 53, "is_html": False},
{"token": "in", "start_byte": 54, "end_byte": 56, "is_html": False},
{"token": "1998", "start_byte": 57, "end_byte": 61, "is_html": False},
{"token": "by", "start_byte": 62, "end_byte": 64, "is_html": False},
{"token": ":", "start_byte": 64, "end_byte": 65, "is_html": False},
{"token": "<ul>", "start_byte": 65, "end_byte": 69, "is_html": True},
{"token": "<li>", "start_byte": 69, "end_byte": 73, "is_html": True},
{"token": "Larry", "start_byte": 73, "end_byte": 78, "is_html": False},
{"token": "</li>", "start_byte": 78, "end_byte": 83, "is_html": True},
{"token": "<li>", "start_byte": 83, "end_byte": 87, "is_html": True},
{"token": "Sergey", "start_byte": 87, "end_byte": 92, "is_html": False},
{"token": "</li>", "start_byte": 92, "end_byte": 97, "is_html": True},
{"token": "</ul>", "start_byte": 97, "end_byte": 102, "is_html": True},
{"token": "</p>", "start_byte": 102, "end_byte": 106, "is_html": True}
],
},
"question" :{
"text": "who founded google",
"tokens": ["who", "founded", "google"]
},
"long_answer_candidates": [
{"start_byte": 32, "end_byte": 106, "start_token": 5, "end_token": 22, "top_level": True},
{"start_byte": 65, "end_byte": 102, "start_token": 13, "end_token": 21, "top_level": False},
{"start_byte": 69, "end_byte": 83, "start_token": 14, "end_token": 17, "top_level": False},
{"start_byte": 83, "end_byte": 92, "start_token": 17, "end_token": 20 , "top_level": False}
],
"annotations": [{
"id": "6782080525527814293",
"long_answer": {"start_byte": 32, "end_byte": 106, "start_token": 5, "end_token": 22, "candidate_index": 0},
"short_answers": [
{"start_byte": 73, "end_byte": 78, "start_token": 15, "end_token": 16, "text": "Larry"},
{"start_byte": 87, "end_byte": 92, "start_token": 18, "end_token": 19, "text": "Sergey"}
],
"yes_no_answer": -1
}]
}
```
### Data Fields
The data fields are the same among all splits.
#### default
- `id`: a `string` feature.
- `document` a dictionary feature containing:
- `title`: a `string` feature.
- `url`: a `string` feature.
- `html`: a `string` feature.
- `tokens`: a dictionary feature containing:
- `token`: a `string` feature.
- `is_html`: a `bool` feature.
- `start_byte`: a `int64` feature.
- `end_byte`: a `int64` feature.
- `question`: a dictionary feature containing:
- `text`: a `string` feature.
- `tokens`: a `list` of `string` features.
- `long_answer_candidates`: a dictionary feature containing:
- `start_token`: a `int64` feature.
- `end_token`: a `int64` feature.
- `start_byte`: a `int64` feature.
- `end_byte`: a `int64` feature.
- `top_level`: a `bool` feature.
- `annotations`: a dictionary feature containing:
- `id`: a `string` feature.
- `long_answers`: a dictionary feature containing:
- `start_token`: a `int64` feature.
- `end_token`: a `int64` feature.
- `start_byte`: a `int64` feature.
- `end_byte`: a `int64` feature.
- `candidate_index`: a `int64` feature.
- `short_answers`: a dictionary feature containing:
- `start_token`: a `int64` feature.
- `end_token`: a `int64` feature.
- `start_byte`: a `int64` feature.
- `end_byte`: a `int64` feature.
- `text`: a `string` feature.
- `yes_no_answer`: a classification label, with possible values including `NO` (0), `YES` (1).
### Data Splits
| name | train | validation |
|---------|-------:|-----------:|
| default | 307373 | 7830 |
| dev | N/A | 7830 |
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
[Creative Commons Attribution-ShareAlike 3.0 Unported](https://creativecommons.org/licenses/by-sa/3.0/).
### Citation Information
```
@article{47761,
title = {Natural Questions: a Benchmark for Question Answering Research},
author = {Tom Kwiatkowski and Jennimaria Palomaki and Olivia Redfield and Michael Collins and Ankur Parikh and Chris Alberti and Danielle Epstein and Illia Polosukhin and Matthew Kelcey and Jacob Devlin and Kenton Lee and Kristina N. Toutanova and Llion Jones and Ming-Wei Chang and Andrew Dai and Jakob Uszkoreit and Quoc Le and Slav Petrov},
year = {2019},
journal = {Transactions of the Association of Computational Linguistics}
}
```
### Contributions
Thanks to [@thomwolf](https://github.com/thomwolf), [@lhoestq](https://github.com/lhoestq) for adding this dataset. | [
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deepset/germanquad | deepset | 2023-04-06T13:58:35Z | 1,300 | 26 | null | [
"task_categories:question-answering",
"task_categories:text-retrieval",
"task_ids:extractive-qa",
"task_ids:closed-domain-qa",
"task_ids:open-domain-qa",
"multilinguality:monolingual",
"source_datasets:original",
"language:de",
"license:cc-by-4.0",
"arxiv:2104.12741",
"region:us"
] | 2023-04-06T13:58:35Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
thumbnail: >-
https://thumb.tildacdn.com/tild3433-3637-4830-a533-353833613061/-/resize/720x/-/format/webp/germanquad.jpg
language:
- de
multilinguality:
- monolingual
source_datasets:
- original
task_categories:
- question-answering
- text-retrieval
task_ids:
- extractive-qa
- closed-domain-qa
- open-domain-qa
train-eval-index:
- config: plain_text
task: question-answering
task_id: extractive_question_answering
splits:
train_split: train
eval_split: test
col_mapping:
context: context
question: question
answers.text: answers.text
answers.answer_start: answers.answer_start
license: cc-by-4.0
---
# Dataset Card for germanquad
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-instances)
- [Data Splits](#data-instances)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Citation Information](#citation-information)
## Dataset Description
- **Homepage:** https://deepset.ai/germanquad
- **Repository:** https://github.com/deepset-ai/haystack
- **Paper:** https://arxiv.org/abs/2104.12741
### Dataset Summary
In order to raise the bar for non-English QA, we are releasing a high-quality, human-labeled German QA dataset consisting of 13 722 questions, incl. a three-way annotated test set.
The creation of GermanQuAD is inspired by insights from existing datasets as well as our labeling experience from several industry projects. We combine the strengths of SQuAD, such as high out-of-domain performance, with self-sufficient questions that contain all relevant information for open-domain QA as in the NaturalQuestions dataset. Our training and test datasets do not overlap like other popular datasets and include complex questions that cannot be answered with a single entity or only a few words.
### Supported Tasks and Leaderboards
- `extractive-qa`, `closed-domain-qa`, `open-domain-qa`, `text-retrieval`: This dataset is intended to be used for `open-domain-qa`, but can also be used for information retrieval tasks.
### Languages
The sentences in the dataset are in German (de).
## Dataset Structure
### Data Instances
A sample from the training set is provided below:
```
{
"paragraphs": [
{
"qas": [
{
"question": "Von welchem Gesetzt stammt das Amerikanische ab? ",
"id": 51870,
"answers": [
{
"answer_id": 53778,
"document_id": 43958,
"question_id": 51870,
"text": "britischen Common Laws",
"answer_start": 146,
"answer_category": "SHORT"
}
],
"is_impossible": false
}
],
"context": "Recht_der_Vereinigten_Staaten\
\
=== Amerikanisches Common Law ===\
Obwohl die Vereinigten Staaten wie auch viele Staaten des Commonwealth Erben des britischen Common Laws sind, setzt sich das amerikanische Recht bedeutend davon ab. Dies rührt größtenteils von dem langen Zeitraum her, in dem sich das amerikanische Recht unabhängig vom Britischen entwickelt hat. Entsprechend schauen die Gerichte in den Vereinigten Staaten bei der Analyse von eventuell zutreffenden britischen Rechtsprinzipien im Common Law gewöhnlich nur bis ins frühe 19. Jahrhundert.\
Während es in den Commonwealth-Staaten üblich ist, dass Gerichte sich Entscheidungen und Prinzipien aus anderen Commonwealth-Staaten importieren, ist das in der amerikanischen Rechtsprechung selten. Ausnahmen bestehen hier nur, wenn sich überhaupt keine relevanten amerikanischen Fälle finden lassen, die Fakten nahezu identisch sind und die Begründung außerordentlich überzeugend ist. Frühe amerikanische Entscheidungen zitierten oft britische Fälle, solche Zitate verschwanden aber während des 19. Jahrhunderts, als die Gerichte eindeutig amerikanische Lösungen zu lokalen Konflikten fanden. In der aktuellen Rechtsprechung beziehen sich fast alle Zitate auf amerikanische Fälle.\
Einige Anhänger des Originalismus und der strikten Gesetzestextauslegung (''strict constructionism''), wie zum Beispiel der verstorbene Bundesrichter am Obersten Gerichtshof, Antonin Scalia, vertreten die Meinung, dass amerikanische Gerichte ''nie'' ausländische Fälle überprüfen sollten, die nach dem Unabhängigkeitskrieg entschieden wurden, unabhängig davon, ob die Argumentation überzeugend ist oder nicht. Die einzige Ausnahme wird hier in Fällen gesehen, die durch die Vereinigten Staaten ratifizierte völkerrechtliche Verträge betreffen. Andere Richter, wie zum Beispiel Anthony Kennedy und Stephen Breyer vertreten eine andere Ansicht und benutzen ausländische Rechtsprechung, sofern ihre Argumentation für sie überzeugend, nützlich oder hilfreich ist.",
"document_id": 43958
}
]
},
```
### Data Fields
- `id`: a `string` feature.
- `context`: a `string` feature.
- `question`: a `string` feature.
- `answers`: a dictionary feature containing:
- `text`: a `string` feature.
- `answer_start`: a `int32` feature.
### Data Splits
The dataset is split into a one-way annotated training set and a three-way annotated test set of German Wikipedia passages (paragraphs). Each passage is
from a different article.
| |passages|questions|answers|
|----------|----:|---------:|---------:|
|train|2540| 11518|11518|
|test|474| 2204|6536|
## Additional Information
### Dataset Curators
The dataset was initially created by Timo Möller, Julian Risch, Malte Pietsch, Julian Gutsch, Tom Hersperger, Luise Köhler, Iuliia Mozhina, and Justus Peter, during work done at deepset.ai
### Citation Information
```
@misc{möller2021germanquad,
title={GermanQuAD and GermanDPR: Improving Non-English Question Answering and Passage Retrieval},
author={Timo Möller and Julian Risch and Malte Pietsch},
year={2021},
eprint={2104.12741},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
``` | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
mkshing/xlsum_ja | mkshing | 2023-06-20T23:28:48Z | 1,296 | 4 | null | [
"task_categories:summarization",
"task_categories:text-classification",
"language:ja",
"license:cc-by-nc-sa-4.0",
"arxiv:2305.10403",
"region:us"
] | 2023-06-20T23:28:48Z | 2023-06-16T04:15:41.000Z | 2023-06-16T04:15:41 | ---
license: cc-by-nc-sa-4.0
task_categories:
- summarization
- text-classification
language:
- ja
---
This is the filtered Japanese subset of [XL-Sum](https://huggingface.co/datasets/csebuetnlp/xlsum) followed by [PaLM 2](https://arxiv.org/abs/2305.10403)
**filters**
- 15-gram overlap
\* code: https://gist.github.com/mkshing/d6371cbfdd50d4f352cee247fd4dd86a
**number of examples**
- train: 4215 (before: 7113)
- validation: 758 (before: 889)
- test: 766 (before: 889)
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
regisss/librispeech_asr_for_optimum_habana_ci | regisss | 2023-09-10T19:40:47Z | 1,296 | 0 | null | [
"license:cc-by-4.0",
"region:us"
] | 2023-09-10T19:40:47Z | 2023-09-10T18:37:05.000Z | 2023-09-10T18:37:05 | ---
license: cc-by-4.0
---
This dataset contains the splits `clean.train.100` and `clean.dev` of the [LibriSpeech dataset](https://huggingface.co/datasets/librispeech_asr).
It is only meant to be used in Optimum Habana's CI to avoid downloading other splits.
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shlomihod/civil-comments-wilds | shlomihod | 2023-07-28T17:27:14Z | 1,293 | 0 | null | [
"task_categories:text-classification",
"size_categories:100K<n<1M",
"language:en",
"license:cc0-1.0",
"toxicity",
"arxiv:2012.07421",
"arxiv:1903.04561",
"arxiv:1808.07231",
"arxiv:1911.08731",
"arxiv:2211.09110",
"region:us"
] | 2023-07-28T17:27:14Z | 2023-07-23T03:43:31.000Z | 2023-07-23T03:43:31 | ---
license: cc0-1.0
task_categories:
- text-classification
language:
- en
tags:
- toxicity
pretty_name: CivilComments WILDS
size_categories:
- 100K<n<1M
---
# Dataset Card for CivilComments WILDS
## Dataset Description
- **Homepage:** https://wilds.stanford.edu/datasets/#civilcomments
- **Repository:**
- **Paper:** https://arxiv.org/abs/2012.07421 | https://arxiv.org/abs/1903.04561
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
Automatic review of user-generated text—e.g., detecting toxic comments—is an important tool for moderating the sheer volume of text written on the Internet. Unfortunately, prior work has shown that such toxicity classifiers pick up on biases in the training data and spuriously associate toxicity with the mention of certain demographics ([Park et al., 2018](https://arxiv.org/abs/1808.07231); [Dixon et al., 2018](https://research.google/pubs/pub46743/)). These types of spurious correlations can significantly degrade model performance on particular subpopulations ([Sagawa et al.,2020](https://arxiv.org/abs/1911.08731)).
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
English
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
This dataset is in the public domain and is distributed under [CC0](https://creativecommons.org/publicdomain/zero/1.0/).
### Citation Information
@inproceedings{wilds2021,
title = {{WILDS}: A Benchmark of in-the-Wild Distribution Shifts},
author = {Pang Wei Koh and Shiori Sagawa and Henrik Marklund and Sang Michael Xie and Marvin Zhang and
Akshay Balsubramani and Weihua Hu and Michihiro Yasunaga and Richard Lanas Phillips and Irena Gao and
Tony Lee and Etienne David and Ian Stavness and Wei Guo and Berton A. Earnshaw and Imran S. Haque and
Sara Beery and Jure Leskovec and Anshul Kundaje and Emma Pierson and Sergey Levine and Chelsea Finn
and Percy Liang},
booktitle = {International Conference on Machine Learning (ICML)},
year = {2021}
}
@inproceedings{borkan2019nuanced,
title={Nuanced metrics for measuring unintended bias with real data for text classification},
author={Borkan, Daniel and Dixon, Lucas and Sorensen, Jeffrey and Thain, Nithum and Vasserman, Lucy},
booktitle={Companion Proceedings of The 2019 World Wide Web Conference},
pages={491--500},
year={2019}
}
@article{DBLP:journals/corr/abs-2211-09110,
author = {Percy Liang and
Rishi Bommasani and
Tony Lee and
Dimitris Tsipras and
Dilara Soylu and
Michihiro Yasunaga and
Yian Zhang and
Deepak Narayanan and
Yuhuai Wu and
Ananya Kumar and
Benjamin Newman and
Binhang Yuan and
Bobby Yan and
Ce Zhang and
Christian Cosgrove and
Christopher D. Manning and
Christopher R{\'{e}} and
Diana Acosta{-}Navas and
Drew A. Hudson and
Eric Zelikman and
Esin Durmus and
Faisal Ladhak and
Frieda Rong and
Hongyu Ren and
Huaxiu Yao and
Jue Wang and
Keshav Santhanam and
Laurel J. Orr and
Lucia Zheng and
Mert Y{\"{u}}ksekg{\"{o}}n{\"{u}}l and
Mirac Suzgun and
Nathan Kim and
Neel Guha and
Niladri S. Chatterji and
Omar Khattab and
Peter Henderson and
Qian Huang and
Ryan Chi and
Sang Michael Xie and
Shibani Santurkar and
Surya Ganguli and
Tatsunori Hashimoto and
Thomas Icard and
Tianyi Zhang and
Vishrav Chaudhary and
William Wang and
Xuechen Li and
Yifan Mai and
Yuhui Zhang and
Yuta Koreeda},
title = {Holistic Evaluation of Language Models},
journal = {CoRR},
volume = {abs/2211.09110},
year = {2022},
url = {https://doi.org/10.48550/arXiv.2211.09110},
doi = {10.48550/arXiv.2211.09110},
eprinttype = {arXiv},
eprint = {2211.09110},
timestamp = {Wed, 23 Nov 2022 18:03:56 +0100},
biburl = {https://dblp.org/rec/journals/corr/abs-2211-09110.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
### Contributions
[More Information Needed] | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
BeIR/hotpotqa | BeIR | 2022-10-23T06:02:40Z | 1,282 | 2 | beir | [
"task_categories:text-retrieval",
"task_ids:entity-linking-retrieval",
"task_ids:fact-checking-retrieval",
"multilinguality:monolingual",
"language:en",
"license:cc-by-sa-4.0",
"region:us"
] | 2022-10-23T06:02:40Z | 2022-06-05T16:40:18.000Z | 2022-06-05T16:40:18 | ---
annotations_creators: []
language_creators: []
language:
- en
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
paperswithcode_id: beir
pretty_name: BEIR Benchmark
size_categories:
msmarco:
- 1M<n<10M
trec-covid:
- 100k<n<1M
nfcorpus:
- 1K<n<10K
nq:
- 1M<n<10M
hotpotqa:
- 1M<n<10M
fiqa:
- 10K<n<100K
arguana:
- 1K<n<10K
touche-2020:
- 100K<n<1M
cqadupstack:
- 100K<n<1M
quora:
- 100K<n<1M
dbpedia:
- 1M<n<10M
scidocs:
- 10K<n<100K
fever:
- 1M<n<10M
climate-fever:
- 1M<n<10M
scifact:
- 1K<n<10K
source_datasets: []
task_categories:
- text-retrieval
- zero-shot-retrieval
- information-retrieval
- zero-shot-information-retrieval
task_ids:
- passage-retrieval
- entity-linking-retrieval
- fact-checking-retrieval
- tweet-retrieval
- citation-prediction-retrieval
- duplication-question-retrieval
- argument-retrieval
- news-retrieval
- biomedical-information-retrieval
- question-answering-retrieval
---
# Dataset Card for BEIR Benchmark
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://github.com/UKPLab/beir
- **Repository:** https://github.com/UKPLab/beir
- **Paper:** https://openreview.net/forum?id=wCu6T5xFjeJ
- **Leaderboard:** https://docs.google.com/spreadsheets/d/1L8aACyPaXrL8iEelJLGqlMqXKPX2oSP_R10pZoy77Ns
- **Point of Contact:** nandan.thakur@uwaterloo.ca
### Dataset Summary
BEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:
- Fact-checking: [FEVER](http://fever.ai), [Climate-FEVER](http://climatefever.ai), [SciFact](https://github.com/allenai/scifact)
- Question-Answering: [NQ](https://ai.google.com/research/NaturalQuestions), [HotpotQA](https://hotpotqa.github.io), [FiQA-2018](https://sites.google.com/view/fiqa/)
- Bio-Medical IR: [TREC-COVID](https://ir.nist.gov/covidSubmit/index.html), [BioASQ](http://bioasq.org), [NFCorpus](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/)
- News Retrieval: [TREC-NEWS](https://trec.nist.gov/data/news2019.html), [Robust04](https://trec.nist.gov/data/robust/04.guidelines.html)
- Argument Retrieval: [Touche-2020](https://webis.de/events/touche-20/shared-task-1.html), [ArguAna](tp://argumentation.bplaced.net/arguana/data)
- Duplicate Question Retrieval: [Quora](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs), [CqaDupstack](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/)
- Citation-Prediction: [SCIDOCS](https://allenai.org/data/scidocs)
- Tweet Retrieval: [Signal-1M](https://research.signal-ai.com/datasets/signal1m-tweetir.html)
- Entity Retrieval: [DBPedia](https://github.com/iai-group/DBpedia-Entity/)
All these datasets have been preprocessed and can be used for your experiments.
```python
```
### Supported Tasks and Leaderboards
The dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.
The current best performing models can be found [here](https://eval.ai/web/challenges/challenge-page/689/leaderboard/).
### Languages
All tasks are in English (`en`).
## Dataset Structure
All BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:
- `corpus` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with three fields `_id` with unique document identifier, `title` with document title (optional) and `text` with document paragraph or passage. For example: `{"_id": "doc1", "title": "Albert Einstein", "text": "Albert Einstein was a German-born...."}`
- `queries` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with two fields `_id` with unique query identifier and `text` with query text. For example: `{"_id": "q1", "text": "Who developed the mass-energy equivalence formula?"}`
- `qrels` file: a `.tsv` file (tab-seperated) that contains three columns, i.e. the `query-id`, `corpus-id` and `score` in this order. Keep 1st row as header. For example: `q1 doc1 1`
### Data Instances
A high level example of any beir dataset:
```python
corpus = {
"doc1" : {
"title": "Albert Einstein",
"text": "Albert Einstein was a German-born theoretical physicist. who developed the theory of relativity, \
one of the two pillars of modern physics (alongside quantum mechanics). His work is also known for \
its influence on the philosophy of science. He is best known to the general public for his mass–energy \
equivalence formula E = mc2, which has been dubbed 'the world's most famous equation'. He received the 1921 \
Nobel Prize in Physics 'for his services to theoretical physics, and especially for his discovery of the law \
of the photoelectric effect', a pivotal step in the development of quantum theory."
},
"doc2" : {
"title": "", # Keep title an empty string if not present
"text": "Wheat beer is a top-fermented beer which is brewed with a large proportion of wheat relative to the amount of \
malted barley. The two main varieties are German Weißbier and Belgian witbier; other types include Lambic (made\
with wild yeast), Berliner Weisse (a cloudy, sour beer), and Gose (a sour, salty beer)."
},
}
queries = {
"q1" : "Who developed the mass-energy equivalence formula?",
"q2" : "Which beer is brewed with a large proportion of wheat?"
}
qrels = {
"q1" : {"doc1": 1},
"q2" : {"doc2": 1},
}
```
### Data Fields
Examples from all configurations have the following features:
### Corpus
- `corpus`: a `dict` feature representing the document title and passage text, made up of:
- `_id`: a `string` feature representing the unique document id
- `title`: a `string` feature, denoting the title of the document.
- `text`: a `string` feature, denoting the text of the document.
### Queries
- `queries`: a `dict` feature representing the query, made up of:
- `_id`: a `string` feature representing the unique query id
- `text`: a `string` feature, denoting the text of the query.
### Qrels
- `qrels`: a `dict` feature representing the query document relevance judgements, made up of:
- `_id`: a `string` feature representing the query id
- `_id`: a `string` feature, denoting the document id.
- `score`: a `int32` feature, denoting the relevance judgement between query and document.
### Data Splits
| Dataset | Website| BEIR-Name | Type | Queries | Corpus | Rel D/Q | Down-load | md5 |
| -------- | -----| ---------| --------- | ----------- | ---------| ---------| :----------: | :------:|
| MSMARCO | [Homepage](https://microsoft.github.io/msmarco/)| ``msmarco`` | ``train``<br>``dev``<br>``test``| 6,980 | 8.84M | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/msmarco.zip) | ``444067daf65d982533ea17ebd59501e4`` |
| TREC-COVID | [Homepage](https://ir.nist.gov/covidSubmit/index.html)| ``trec-covid``| ``test``| 50| 171K| 493.5 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/trec-covid.zip) | ``ce62140cb23feb9becf6270d0d1fe6d1`` |
| NFCorpus | [Homepage](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/) | ``nfcorpus`` | ``train``<br>``dev``<br>``test``| 323 | 3.6K | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nfcorpus.zip) | ``a89dba18a62ef92f7d323ec890a0d38d`` |
| BioASQ | [Homepage](http://bioasq.org) | ``bioasq``| ``train``<br>``test`` | 500 | 14.91M | 8.05 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#2-bioasq) |
| NQ | [Homepage](https://ai.google.com/research/NaturalQuestions) | ``nq``| ``train``<br>``test``| 3,452 | 2.68M | 1.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nq.zip) | ``d4d3d2e48787a744b6f6e691ff534307`` |
| HotpotQA | [Homepage](https://hotpotqa.github.io) | ``hotpotqa``| ``train``<br>``dev``<br>``test``| 7,405 | 5.23M | 2.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/hotpotqa.zip) | ``f412724f78b0d91183a0e86805e16114`` |
| FiQA-2018 | [Homepage](https://sites.google.com/view/fiqa/) | ``fiqa`` | ``train``<br>``dev``<br>``test``| 648 | 57K | 2.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fiqa.zip) | ``17918ed23cd04fb15047f73e6c3bd9d9`` |
| Signal-1M(RT) | [Homepage](https://research.signal-ai.com/datasets/signal1m-tweetir.html)| ``signal1m`` | ``test``| 97 | 2.86M | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#4-signal-1m) |
| TREC-NEWS | [Homepage](https://trec.nist.gov/data/news2019.html) | ``trec-news`` | ``test``| 57 | 595K | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#1-trec-news) |
| ArguAna | [Homepage](http://argumentation.bplaced.net/arguana/data) | ``arguana``| ``test`` | 1,406 | 8.67K | 1.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/arguana.zip) | ``8ad3e3c2a5867cdced806d6503f29b99`` |
| Touche-2020| [Homepage](https://webis.de/events/touche-20/shared-task-1.html) | ``webis-touche2020``| ``test``| 49 | 382K | 19.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/webis-touche2020.zip) | ``46f650ba5a527fc69e0a6521c5a23563`` |
| CQADupstack| [Homepage](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/) | ``cqadupstack``| ``test``| 13,145 | 457K | 1.4 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/cqadupstack.zip) | ``4e41456d7df8ee7760a7f866133bda78`` |
| Quora| [Homepage](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs) | ``quora``| ``dev``<br>``test``| 10,000 | 523K | 1.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/quora.zip) | ``18fb154900ba42a600f84b839c173167`` |
| DBPedia | [Homepage](https://github.com/iai-group/DBpedia-Entity/) | ``dbpedia-entity``| ``dev``<br>``test``| 400 | 4.63M | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/dbpedia-entity.zip) | ``c2a39eb420a3164af735795df012ac2c`` |
| SCIDOCS| [Homepage](https://allenai.org/data/scidocs) | ``scidocs``| ``test``| 1,000 | 25K | 4.9 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scidocs.zip) | ``38121350fc3a4d2f48850f6aff52e4a9`` |
| FEVER | [Homepage](http://fever.ai) | ``fever``| ``train``<br>``dev``<br>``test``| 6,666 | 5.42M | 1.2| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fever.zip) | ``5a818580227bfb4b35bb6fa46d9b6c03`` |
| Climate-FEVER| [Homepage](http://climatefever.ai) | ``climate-fever``|``test``| 1,535 | 5.42M | 3.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/climate-fever.zip) | ``8b66f0a9126c521bae2bde127b4dc99d`` |
| SciFact| [Homepage](https://github.com/allenai/scifact) | ``scifact``| ``train``<br>``test``| 300 | 5K | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scifact.zip) | ``5f7d1de60b170fc8027bb7898e2efca1`` |
| Robust04 | [Homepage](https://trec.nist.gov/data/robust/04.guidelines.html) | ``robust04``| ``test``| 249 | 528K | 69.9 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#3-robust04) |
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
[Needs More Information]
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
[Needs More Information]
### Citation Information
Cite as:
```
@inproceedings{
thakur2021beir,
title={{BEIR}: A Heterogeneous Benchmark for Zero-shot Evaluation of Information Retrieval Models},
author={Nandan Thakur and Nils Reimers and Andreas R{\"u}ckl{\'e} and Abhishek Srivastava and Iryna Gurevych},
booktitle={Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2)},
year={2021},
url={https://openreview.net/forum?id=wCu6T5xFjeJ}
}
```
### Contributions
Thanks to [@Nthakur20](https://github.com/Nthakur20) for adding this dataset. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
AhmedSSoliman/CodeXGLUE-CONCODE | AhmedSSoliman | 2022-09-13T14:47:15Z | 1,280 | 1 | null | [
"region:us"
] | 2022-09-13T14:47:15Z | 2022-08-14T15:58:27.000Z | 2022-08-14T15:58:27 | ## Concode dataset
A large dataset with over 100,000 examples consisting of Java classes from online code repositories, and develop a new encoder-decoder architecture that models the interaction between the method documentation and the class environment.
Concode dataset is a widely used code generation dataset from Iyer's EMNLP 2018 paper [Mapping Language to Code in Programmatic Context](https://www.aclweb.org/anthology/D18-1192.pdf).
Data statistics of concode dataset are shown in the below table:
| | #Examples |
| --------- | :---------: |
| Train | 100,000 |
| Validation | 2,000 |
| Test | 2,000 |
## Data Format
Code corpus are saved in json lines format files. one line is a json object:
```
{
"nl": "Increment this vector in this place. con_elem_sep double[] vecElement con_elem_sep double[] weights con_func_sep void add(double)",
"code": "public void inc ( ) { this . add ( 1 ) ; }"
}
```
`nl` combines natural language description and class environment. Elements in class environment are seperated by special tokens like `con_elem_sep` and `con_func_sep`.
## Task Definition
Generate source code of class member functions in Java, given natural language description and class environment. Class environment is the programmatic context provided by the rest of the class, including other member variables and member functions in class. Models are evaluated by exact match and BLEU.
It's a challenging task because the desired code can vary greatly depending on the functionality the class provides. Models must (a) have a deep understanding of NL description and map the NL to environment variables, library API calls and user-defined methods in the class, and (b) decide on the structure of the resulting code.
## Reference
Concode dataset:
<pre><code>@article{iyer2018mapping,
title={Mapping language to code in programmatic context},
author={Iyer, Srinivasan and Konstas, Ioannis and Cheung, Alvin and Zettlemoyer, Luke},
journal={arXiv preprint arXiv:1808.09588},
year={2018}
}</code></pre>
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ccaligned_multilingual | null | 2022-11-03T16:31:56Z | 1,276 | 3 | ccaligned | [
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] | 2022-11-03T16:31:56Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
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language_creators:
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language:
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paperswithcode_id: ccaligned
pretty_name: CCAligned
dataset_info:
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---
# Dataset Card for ccaligned_multilingual
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** http://www.statmt.org/cc-aligned/
- **Repository:** [Needs More Information]
- **Paper:** https://www.aclweb.org/anthology/2020.emnlp-main.480.pdf
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [Needs More Information]
### Dataset Summary
CCAligned consists of parallel or comparable web-document pairs in 137 languages aligned with English. These web-document pairs were constructed by performing language identification on raw web-documents, and ensuring corresponding language codes were corresponding in the URLs of web documents. This pattern matching approach yielded more than 100 million aligned documents paired with English. Recognizing that each English document was often aligned to mulitple documents in different target language, we can join on English documents to obtain aligned documents that directly pair two non-English documents (e.g., Arabic-French). This corpus was created from 68 Commoncrawl Snapshots.
To load a language which isn't part of the config, all you need to do is specify the language code. You can find the valid languages in http://www.statmt.org/cc-aligned/ E.g.
```
dataset = load_dataset("ccaligned_multilingual", language_code="fr_XX", type="documents")
```
or
```
dataset = load_dataset("ccaligned_multilingual", language_code="fr_XX", type="sentences")
```
### Supported Tasks and Leaderboards
[Needs More Information]
### Languages
The text in the dataset is in (137) multiple languages aligned with english.
## Dataset Structure
### Data Instances
An instance of `documents` type for language `ak_GH`:
```
{'Domain': 'islamhouse.com', 'Source_URL': 'https://islamhouse.com/en/audios/373088/', 'Target_URL': 'https://islamhouse.com/ak/audios/373088/', 'translation': {'ak_GH': "Ntwatiaa / wɔabɔ no tɔfa wɔ mu no te ase ma Umrah - Arab kasa|Islamhouse.com|Follow us:|facebook|twitter|taepe|Titles All|Fie wibesite|kasa nyina|Buukuu edi adanse ma prente|Nhyehyɛmu|Nyim/sua Islam|Curriculums|Nyina ndeɛma|Nyina ndeɛma (295)|Buukuu/ nwoma (2)|sini / muuvi (31)|ɔdio (262)|Aɛn websideNew!|Kɔ wura kramosom mu seisei|Ebio|figa/kaasɛ|Farebae|AKAkan|Kratafa titriw|kasa interface( anyimu) : Akan|Kasa ma no mu-nsɛm : Arab kasa|ɔdio|Ntwatiaa / wɔabɔ no tɔfa wɔ mu no te ase ma Umrah|play|pause|stop|mute|unmute|max volume|Kasakyerɛ ni :|Farebae:|17 / 11 / 1432 , 15/10/2011|Nhyehyɛmu:|Jurisprudence/ Esum Nimdea|Som|Hajj na Umrah|Jurisprudence/ Esum Nimdea|Som|Hajj na Umrah|Mmira ma Hajj na Umrah|nkyerɛmu|kasamu /sɛntɛns ma te ase na Umrah wɔ ... mu no hann ma no Quran na Sunnah na te ase ma no nana na no kasamu /sɛntɛns ma bi ma no emerging yi adu obusuani|Akenkane we ye di ko kasa bi su (36)|Afar - Qafár afa|Akan|Amhari ne - አማርኛ|Arab kasa - عربي|Assamese - অসমীয়া|Bengali - বাংলা|Maldive - ދިވެހި|Greek - Ελληνικά|English ( brofo kasa) - English|Persian - فارسی|Fula - pulla|French - Français|Hausa - Hausa|Kurdish - كوردی سۆرانی|Uganda ne - Oluganda|Mandinka - Mandinko|Malayalam - മലയാളം|Nepali - नेपाली|Portuguese - Português|Russian - Русский|Sango - Sango|Sinhalese - සිංහල|Somali - Soomaali|Albania ne - Shqip|Swahili - Kiswahili|Telugu - తెలుగు ప్రజలు|Tajik - Тоҷикӣ|Thai - ไทย|Tagalog - Tagalog|Turkish - Türkçe|Uyghur - ئۇيغۇرچە|Urdu - اردو|Uzbeck ne - Ўзбек тили|Vietnamese - Việt Nam|Wolof - Wolof|Chine ne - 中文|Soma kɔ bi kyerɛ adwen kɔ wɛb ebusuapanin|Soma kɔ ne kɔ hom adamfo|Soma kɔ bi kyerɛ adwen kɔ wɛb ebusuapanin|Nsɔwso fael (1)|1|الموجز في فقه العمرة|MP3 14.7 MB|Enoumah ebatahu|Rituals/Esom ajomadie ewu Hajji mmire .. 1434 AH [01] no fapemso Enum|Fiidbak/ Ye hiya wu jun kyiri|Lenke de yɛe|kɔntakt yɛn|Aɛn webside|Qura'an Kro kronkrom|Balagh|wɔ mfinimfin Dowload faele|Yɛ atuu bra Islam mu afei|Tsin de yɛe ewu|Anaa bomu/combine hɛn melin liste|© Islamhouse Website/ Islam dan webi site|×|×|Yi mu kasa|", 'en_XX': 'SUMMARY in the jurisprudence of Umrah - Arabic - Abdul Aziz Bin Marzooq Al-Turaifi|Islamhouse.com|Follow us:|facebook|twitter|QuranEnc.com|HadeethEnc.com|Type|Titles All|Home Page|All Languages|Categories|Know about Islam|All items|All items (4057)|Books (701)|Articles (548)|Fatawa (370)|Videos (1853)|Audios (416)|Posters (98)|Greeting cards (22)|Favorites (25)|Applications (21)|Desktop Applications (3)|To convert to Islam now !|More|Figures|Sources|Curriculums|Our Services|QuranEnc.com|HadeethEnc.com|ENEnglish|Main Page|Interface Language : English|Language of the content : Arabic|Audios|تعريب عنوان المادة|SUMMARY in the jurisprudence of Umrah|play|pause|stop|mute|unmute|max volume|Lecturer : Abdul Aziz Bin Marzooq Al-Turaifi|Sources:|AlRaya Islamic Recoding in Riyadh|17 / 11 / 1432 , 15/10/2011|Categories:|Islamic Fiqh|Fiqh of Worship|Hajj and Umrah|Islamic Fiqh|Fiqh of Worship|Hajj and Umrah|Pilgrimage and Umrah|Description|SUMMARY in jurisprudence of Umrah: A statement of jurisprudence and Umrah in the light of the Quran and Sunnah and understanding of the Ancestors and the statement of some of the emerging issues related to them.|This page translated into (36)|Afar - Qafár afa|Akane - Akan|Amharic - አማርኛ|Arabic - عربي|Assamese - অসমীয়া|Bengali - বাংলা|Maldivi - ދިވެހި|Greek - Ελληνικά|English|Persian - فارسی|Fula - pulla|French - Français|Hausa - Hausa|kurdish - كوردی سۆرانی|Ugandan - Oluganda|Mandinka - Mandinko|Malayalam - മലയാളം|Nepali - नेपाली|Portuguese - Português|Russian - Русский|Sango - Yanga ti Sango|Sinhalese - සිංහල|Somali - Soomaali|Albanian - Shqip|Swahili - Kiswahili|Telugu - తెలుగు|Tajik - Тоҷикӣ|Thai - ไทย|Tagalog - Tagalog|Turkish - Türkçe|Uyghur - ئۇيغۇرچە|Urdu - اردو|Uzbek - Ўзбек тили|Vietnamese - Việt Nam|Wolof - Wolof|Chinese - 中文|Send a comment to Webmaster|Send to a friend?|Send a comment to Webmaster|Attachments (1)|1|الموجز في فقه العمرة|MP3 14.7 MB|The relevant Material|The rituals of the pilgrimage season .. 1434 AH [ 01] the fifth pillar|The Quality of the Accepted Hajj (Piligrimage) and Its Limitations|Easy Path to the Rules of the Rites of Hajj|A Call to the Pilgrims of the Scared House of Allah|More|feedback|Important links|Contact us|Privacy policy|Islam Q&A|Learning Arabic Language|About Us|Convert To Islam|Noble Quran encyclopedia|IslamHouse.com Reader|Encyclopedia of Translated Prophetic Hadiths|Our Services|The Quran|Balagh|Center for downloading files|To embrace Islam now...|Follow us through|Or join our mailing list.|© Islamhouse Website|×|×|Choose language|'}}
```
An instance of `sentences` type for language `ak_GH`:
```
{'LASER_similarity': 1.4549942016601562, 'translation': {'ak_GH': 'Salah (nyamefere) ye Mmerebeia', 'en_XX': 'What he dislikes when fasting (10)'}}
```
### Data Fields
For `documents` type:
- `Domain`: a `string` feature containing the domain.
- `Source_URL`: a `string` feature containing the source URL.
- `Target_URL`: a `string` feature containing the target URL.
- `translation`: a `dictionary` feature with two keys :
- `en_XX`: a `string` feature containing the content in English.
- <language_code>: a `string` feature containing the content in the `language_code` specified.
For `sentences` type:
- `LASER_similarity`: a `float32` feature representing the LASER similarity score.
- `translation`: a `dictionary` feature with two keys :
- `en_XX`: a `string` feature containing the content in English.
- <language_code>: a `string` feature containing the content in the `language_code` specified.
### Data Splits
Split sizes of some small configurations:
| name |train|
|----------|----:|
|documents-zz_TR|41|
|sentences-zz_TR|34|
|documents-tz_MA|4|
|sentences-tz_MA|33|
|documents-ak_GH|249|
|sentences-ak_GH|478|
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
The dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in this dataset.
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
[Needs More Information]
### Citation Information
```
@inproceedings{elkishky_ccaligned_2020,
author = {El-Kishky, Ahmed and Chaudhary, Vishrav and Guzm{\'a}n, Francisco and Koehn, Philipp},
booktitle = {Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020)},
month = {November},
title = {{CCAligned}: A Massive Collection of Cross-lingual Web-Document Pairs},
year = {2020}
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.emnlp-main.480",
doi = "10.18653/v1/2020.emnlp-main.480",
pages = "5960--5969"
}
```
### Contributions
Thanks to [@gchhablani](https://github.com/gchhablani) for adding this dataset. | [
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0.001092836377210915,
-0.012552076019346714,
0.04812842234969139,
-0.16128161549568176
] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
medalpaca/medical_meadow_medqa | medalpaca | 2023-04-06T16:59:02Z | 1,276 | 37 | null | [
"task_categories:question-answering",
"language:en",
"language:zh",
"medical",
"region:us"
] | 2023-04-06T16:59:02Z | 2023-04-06T16:56:15.000Z | 2023-04-06T16:56:15 | ---
task_categories:
- question-answering
language:
- en
- zh
tags:
- medical
---
# Dataset Card for MedQA
## Dataset Description
- **Paper:**
### Dataset Summary
This is the data and baseline source code for the paper: Jin, Di, et al. "What Disease does this Patient Have? A Large-scale Open Domain Question Answering Dataset from Medical Exams."
From https://github.com/jind11/MedQA:
>The data that contains both the QAs and textbooks can be downloaded from [this google drive folder](https://drive.google.com/file/d/1ImYUSLk9JbgHXOemfvyiDiirluZHPeQw/view?usp=sharing). A bit of details of data are explained as below:
>
> For QAs, we have three sources: US, Mainland of China, and Taiwan District, which are put in folders, respectively. All files for QAs are in jsonl file format, where each line is a data sample as a dict. The "XX_qbank.jsonl" files contain all data samples while we also provide an official random split into train, dev, and test sets. Those files in the "metamap" folders are extracted medical related phrases using the Metamap tool.
>
> For QAs, we also include the "4_options" version in for US and Mainland of China since we reported results for 4 options in the paper.
>
> For textbooks, we have two languages: English and simplified Chinese. For simplified Chinese, we provide two kinds of sentence spliting: one is split by sentences, and the other is split by paragraphs.
### Citation Information
```
@article{jin2020disease,
title={What Disease does this Patient Have? A Large-scale Open Domain Question Answering Dataset from Medical Exams},
author={Jin, Di and Pan, Eileen and Oufattole, Nassim and Weng, Wei-Hung and Fang, Hanyi and Szolovits, Peter},
journal={arXiv preprint arXiv:2009.13081},
year={2020}
}
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mteb/mtop_intent | mteb | 2022-09-27T19:10:23Z | 1,273 | 2 | null | [
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] | 2022-09-27T19:10:23Z | 2022-05-19T15:03:15.000Z | 2022-05-19T15:03:15 | ---
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HHousen/quora | HHousen | 2021-11-21T02:11:20Z | 1,270 | 1 | null | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
lmsys/chatbot_arena_conversations | lmsys | 2023-09-30T01:04:44Z | 1,269 | 159 | null | [
"task_categories:conversational",
"size_categories:10K<n<100K",
"license:cc",
"arxiv:2306.05685",
"region:us"
] | 2023-09-30T01:04:44Z | 2023-07-18T11:57:07.000Z | 2023-07-18T11:57:07 | ---
dataset_info:
features:
- name: question_id
dtype: string
- name: model_a
dtype: string
- name: model_b
dtype: string
- name: winner
dtype: string
- name: judge
dtype: string
- name: conversation_a
list:
- name: content
dtype: string
- name: role
dtype: string
- name: conversation_b
list:
- name: content
dtype: string
- name: role
dtype: string
- name: turn
dtype: int64
- name: anony
dtype: bool
- name: language
dtype: string
- name: tstamp
dtype: float64
- name: openai_moderation
struct:
- name: categories
struct:
- name: harassment
dtype: bool
- name: harassment/threatening
dtype: bool
- name: hate
dtype: bool
- name: hate/threatening
dtype: bool
- name: self-harm
dtype: bool
- name: self-harm/instructions
dtype: bool
- name: self-harm/intent
dtype: bool
- name: sexual
dtype: bool
- name: sexual/minors
dtype: bool
- name: violence
dtype: bool
- name: violence/graphic
dtype: bool
- name: category_scores
struct:
- name: harassment
dtype: float64
- name: harassment/threatening
dtype: float64
- name: hate
dtype: float64
- name: hate/threatening
dtype: float64
- name: self-harm
dtype: float64
- name: self-harm/instructions
dtype: float64
- name: self-harm/intent
dtype: float64
- name: sexual
dtype: float64
- name: sexual/minors
dtype: float64
- name: violence
dtype: float64
- name: violence/graphic
dtype: float64
- name: flagged
dtype: bool
- name: toxic_chat_tag
struct:
- name: roberta-large
struct:
- name: flagged
dtype: bool
- name: probability
dtype: float64
- name: t5-large
struct:
- name: flagged
dtype: bool
- name: score
dtype: float64
splits:
- name: train
num_bytes: 81159839
num_examples: 33000
download_size: 41572998
dataset_size: 81159839
license: cc
task_categories:
- conversational
size_categories:
- 10K<n<100K
extra_gated_prompt: "Disclaimers and Terms\n\
- This dataset contains conversations that may be considered unsafe, offensive, or upsetting. It is not intended for training dialogue agents without applying appropriate filtering measures. We are not responsible for any outputs of the models trained on this dataset.\n\
- Statements or opinions made in this dataset do not reflect the views of researchers or institutions involved in the data collection effort.\n\
- Users of this data are responsible for ensuring its appropriate use, which includes abiding by any applicable laws and regulations.\n\
- Users of this data should adhere to the terms of use for a specific model when using its direct outputs.\n\
- Users of this data agree to not attempt to determine the identity of individuals in this dataset."
---
## Chatbot Arena Conversations Dataset
This dataset contains 33K cleaned conversations with pairwise human preferences.
It is collected from 13K unique IP addresses on the [Chatbot Arena](https://lmsys.org/blog/2023-05-03-arena/) from April to June 2023.
Each sample includes a question ID, two model names, their full conversation text in OpenAI API JSON format, the user vote, the anonymized user ID, the detected language tag, the OpenAI moderation API tag, the additional toxic tag, and the timestamp.
To ensure the safe release of data, we have made our best efforts to remove all conversations that contain personally identifiable information (PII).
User consent is obtained through the "Terms of use" section on the data collection website.
In addition, we have included the OpenAI moderation API output to flag inappropriate conversations.
However, we have chosen to keep unsafe conversations intact so that researchers can study the safety-related questions associated with LLM usage in real-world scenarios as well as the OpenAI moderation process.
As an example, we included additional toxic tags that are generated by our own toxic tagger, which are trained by fine-tuning T5 and RoBERTa on manually labeled data.
**Basic Statistics**
| Key | Value |
| --- | --- |
| # Conversations | 33,000 |
| # Models | 20 |
| # Users | 13,383 |
| # Languages | 96 |
| Avg. # Turns per Sample | 1.2 |
| Avg. # Tokens per Prompt | 52.3 |
| Avg. # Tokens per Response | 189.5 |
## Uniqueness and Potential Usage
Compared to existing human preference datasets like [Anthropic/hh-rlhf](https://huggingface.co/datasets/Anthropic/hh-rlhf), and [OpenAssistant/oasst1](https://huggingface.co/datasets/OpenAssistant/oasst1). This dataset
- Contains the outputs of 20 LLMs including stronger LLMs such as GPT-4 and Claude-v1. It also contains many failure cases of these state-of-the-art models.
- Contains unrestricted conversations from over 13K users in the wild.
We believe it will help the AI research community answer important questions around topics like:
- Characteristics and distributions of real-world user prompts
- Training instruction-following models
- Improve and evaluate LLM evaluation methods
- Model selection and request dispatching algorithms
- AI safety and content moderation
## Disclaimers and Terms
- **This dataset contains conversations that may be considered unsafe, offensive, or upsetting.** It is not intended for training dialogue agents without applying appropriate filtering measures. We are not responsible for any outputs of the models trained on this dataset.
- Statements or opinions made in this dataset do not reflect the views of researchers or institutions involved in the data collection effort.
- Users of this data are responsible for ensuring its appropriate use, which includes abiding by any applicable laws and regulations.
- Users of this data should adhere to the terms of use for a specific model when using its direct outputs.
- Users of this data agree to not attempt to determine the identity of individuals in this dataset.
## Visualization and Elo Rating Calculation
This Colab [notebook](https://colab.research.google.com/drive/1J2Wf7sxc9SVmGnSX_lImhT246pxNVZip?usp=sharing) provides some visualizations and shows how to compute Elo ratings with the dataset.
## License
The user prompts are licensed under CC-BY-4.0, while the model outputs are licensed under CC-BY-NC-4.0.
## Citation
```
@misc{zheng2023judging,
title={Judging LLM-as-a-judge with MT-Bench and Chatbot Arena},
author={Lianmin Zheng and Wei-Lin Chiang and Ying Sheng and Siyuan Zhuang and Zhanghao Wu and Yonghao Zhuang and Zi Lin and Zhuohan Li and Dacheng Li and Eric. P Xing and Hao Zhang and Joseph E. Gonzalez and Ion Stoica},
year={2023},
eprint={2306.05685},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
``` | [
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zjunlp/Mol-Instructions | zjunlp | 2023-11-17T10:23:45Z | 1,268 | 19 | null | [
"size_categories:100M<n<1B",
"language:en",
"license:cc-by-4.0",
"chemistry",
"biology",
"molecule",
"protein",
"instructions",
"arxiv:2306.08018",
"region:us"
] | 2023-11-17T10:23:45Z | 2023-06-10T02:12:42.000Z | 2023-06-10T02:12:42 | ---
language:
- en
size_categories:
- 100M<n<1B
license: cc-by-4.0
tags:
- chemistry
- biology
- molecule
- protein
- instructions
---
<h1 align="center"> 🧪 Mol-Instructions </h1>
<h3 align="center"> An open, large-scale biomolecular instruction dataset for large language models. </h3>
> Please refer to our [repository](https://github.com/zjunlp/Mol-Instructions) and [paper](https://arxiv.org/abs/2306.08018) for more details.
## 📌 Contents
- [Overview](#1)
- [Data Stats](#1-1)
- [Data Construction](#1-2)
- [Data Release](#1-3)
- [Tasks](#2)
- [Molecule-oriented](#2-1)
- [Protein-oriented](#2-2)
- [Biomolecule text](#2-3)
- [Demo](#3)
- [Model Weight Release](#3-1)
- [Model Usage Guide](#3-2)
- [FAQ](#3-3)
- [Notices](#4)
- [Usage and License](#4-1)
- [Limitations](#4-2)
- [About](#5)
- [References](#5-1)
- [Acknowledgements](#5-2)
<h2 id="1">1. Overview</h2>
<h3 id="1-1"> 📊 1.1 Data Stats</h3>
**Mol-Instructions** comprises three cardinal components:
- 🔬 *Molecule-oriented instructions:* This component delves into the world of small molecules, emphasizing their inherent properties and behaviors. It sheds light on the fundamental challenges of diverse chemical reactions and molecular design, with 148,4K instructions across six tasks.
- 🧬 *Protein-oriented instructions:* Rooted in the biosciences, this component presents 505K instructions across five distinct categories of tasks. These tasks aim to predict the structure, function, and activity of proteins, and facilitate protein design based on textual directives.
- 🥼 *Biomolecular text instructions:* Predominantly designed to cater to NLP tasks within the fields of bioinformatics and chemoinformatics, this part encapsulates six information extraction and Q\&A tasks represented through 53K instructions.
<h3 id="1-2"> 🛠️ 1.2 Data Construction</h3>
- 🤖️ *Human-AI Collaboration Task Description Creation*: In real-world applications, task instructions must be able to accommodate the varied and dynamic nature of human needs and queries. We emulate this diversity by starting with a clear, human-crafted description for each task, which is then used as an input to GPT-3.5-turbo.
- 📖 *Information Derivation from Existing Data*: Biomolecular data often requires specialist laboratory experiments and expert analysis, making authoritative and recognized biochemistry databases an ideal source of our data. With suitable processing, these resources enable us to extract the required instruction data.
- 📜 *Template-based Conversion of Biological Data into Textual Format*: To facilitate the transformation of these structured annotations into a textual format, we design a diverse array of templates. Each resulting text-based annotation serves as a guideline for protein design.
- ✅ *Quality Control*: To expedite the model's ability to generate precise biomolecules, we implement stringent quality control measures for our biomolecular data.
<h3 id="1-3"> 🤗 1.3 Data Release</h3>
We release the dataset on Hugging Face at [zjunlp/Mol-Instructions](https://huggingface.co/datasets/zjunlp/Mol-Instructions).
<h2 id="2">2. Tasks</h2>
<h3 id="2-1"> 🔬 2.1 Molecule-oriented</h3>
<details>
<summary><b>Molecule description generation</b></summary>
- *Please give me some details about this molecule:*
[C][C][C][C][C][C][C][C][C][C][C][C][C][C][C][C][C][C][=Branch1][C][=O][O][C@H1][Branch2][Ring1][=Branch1][C][O][C][=Branch1][C][=O][C][C][C][C][C][C][C][C][C][C][C][C][C][C][C][C][O][P][=Branch1][C][=O][Branch1][C][O][O][C][C@@H1][Branch1][=Branch1][C][=Branch1][C][=O][O][N]
```
The molecule is a 3-sn-phosphatidyl-L-serine in which the phosphatidyl acyl groups at positions 1 and 2 are specified as stearoyl and arachidonoyl respectively.
It is functionally related to an arachidonic acid and an octadecanoic acid.
```
</details>
<details>
<summary><b>Description-guided molecule design</b></summary>
- *Create a molecule with the structure as the one described:*
The molecule is a primary arylamine in which an amino functional group is substituted for one of the benzene hydrogens. It is a primary arylamine and a member of anilines.
```
[N][C][=C][C][=C][C][=C][Ring1][=Branch1]
```
</details>
<details>
<summary><b>Forward reaction prediction</b></summary>
- *With the provided reactants and reagents, propose a potential product:*
[O][=N+1][Branch1][C][O-1][C][=C][N][=C][Branch1][C][Cl][C][Branch1][C][I][=C][Ring1][Branch2].[Fe]
```
[N][C][=C][N][=C][Branch1][C][Cl][C][Branch1][C][I][=C][Ring1][Branch2]
```
</details>
<details>
<summary><b>Retrosynthesis</b></summary>
- *Please suggest potential reactants used in the synthesis of the provided product:*
[C][=C][C][C][N][C][=Branch1][C][=O][O][C][Branch1][C][C][Branch1][C][C][C]
```
[C][=C][C][C][N].[C][C][Branch1][C][C][Branch1][C][C][O][C][=Branch1][C][=O][O][C][=Branch1][C][=O][O][C][Branch1][C][C][Branch1][C][C][C]
```
</details>
<details>
<summary><b>Reagent prediction</b></summary>
- *Please provide possible reagents based on the following chemical reaction:*
[C][C][=C][C][=C][Branch1][C][N][C][=N][Ring1][#Branch1].[O][=C][Branch1][C][Cl][C][Cl]>>[C][C][=C][C][=C][Branch1][Branch2][N][C][=Branch1][C][=O][C][Cl][C][=N][Ring1][O]
```
[C][C][C][O][C][Ring1][Branch1].[C][C][N][Branch1][Ring1][C][C][C][C].[O]
```
</details>
<details>
<summary><b>Property prediction</b></summary>
- *Please provide the HOMO energy value for this molecule:*
[C][C][O][C][C][Branch1][C][C][C][Branch1][C][C][C]
```
-0.2482
```
</details>
<h3 id="2-2"> 🧬 2.2 Protein-oriented</h3>
<details>
<summary><b>Protein design</b></summary>
- *Design a protein sequence that exhibits the desired activity and specificity.*
1. The presence of Mg(2+) is necessary for the protein to function in the desired environment.
2. The AMP, (6S)-NADPHX binding site should be located in a region of the protein that is accessible to the ligand.
3. The designed protein should have ATP binding, NADPHX epimerase activity, metal ion binding, ADP-dependent NAD(P)H-hydrate dehydratase activity to facilitate nicotinamide nucleotide metabolic process.
4. For general function, the protein need meet that Catalyzes the epimerization of the S- and R-forms of NAD(P)HX, a damaged form of NAD(P)H that is a result of enzymatic or heat-dependent hydration
```
MSNELVLSREQVRRVDQRAIEAYGVPGIVLMENAGRGAAEIIRAACPSAQRVLIACGPGNNGGDGFVIARHLANAGWMVELLLACPADRITGDAQGNHEIIRRMNLPCAVMADARDLEAANDRFATADVIVDALLGTGASGPPREPIASLIRAINEAHRRVSAQPAPSVFAVDIPSGLDCDTGEAANPTVRADHTITFVARKIGFRNPAARDLLGRVHVVDIGAPRAAIQDALTGKSG
```
</details>
<details>
<summary><b>Catalytic activity prediction</b></summary>
- *Please evaluate the following protein sequence and provide an explanation of the enzyme's catalytic activity, including the chemical reaction it facilitates:* MDKVAVAGFLPEELCASLSLSPSFRGNQIFQWIGKGVDSFDAMTNLSAELRASLAEKAILRSTRVSDVLKADDGTVKLQIQTEDDLAVETVLLTDKAARKTACVSCQAGCAMGCAFCKTGTLGLARNLSAAEIVEQFLYLEKHAGALDNIVFMGMGEPLLNLDALRKAIAVLTDKRGRNLSSRRITVSTVGIVSGIYDLANNGPDVRLAVSLTTADETLRRELMPASLTNPLSDLRQAISYYIEKTGKRVTLEAVLLSGKNTSEKNADSLIAFAKGLDVHVNLIPWNPVEGLSFVTPDPEETAQFVSRLEKGGLNVTLRMHRGKSISGACGQLGKTNPYA
```
Based on the provided protein sequence, the enzyme appears to facilitate the chemical reaction: adenosine(37) in tRNA + 2 reduced [2Fe-2S]-[ferredoxin] + 2 S- adenosyl-L-methionine = 2-methyladenosine(37) in tRNA + 5'- deoxyadenosine + L-methionine + 2 oxidized [2Fe-2S]-[ferredoxin] + S- adenosyl-L-homocysteine.
```
</details>
<details>
<summary><b>Protein function prediction</b></summary>
- *Analyze the following amino acid sequence, and determine the function of the resulting protein, its subcellular localization, and any biological processes it may be part of:*
MNGTVNASAPSKMSEVAVERLSNDKALKVIFVLGGPGSGKGTQCAKIAKHFGFTHLSVGDLLRAEINSGSKNGTMIESMINEGKIVRSEVTIKLLQRAMHESGNDKFLIDGFPRNEENRAAFENLEKIEPEFVLFFDCPMEEMERRILNRNQGRDDDKMETIRKRFKVFIESTLPVIEFYNLKGKLYKIDACKPADEVFEDVKAIFSRFRAKEDSSQQTNICTAKRFELVMCLIKRLFREIKRMWSSFFCKAL
```
The protein characterized by the amino acid sequence demonstrates ATP binding, cytidylate kinase activity, uridylate kinase activity and is implicated in the 'de novo' pyrimidine nucleobase biosynthetic process, phosphorylation, pyrimidine nucleotide biosynthetic process. Its subcellular localization is primarily within the cytoplasm, nucleus.
```
</details>
<details>
<summary><b>Functional description generation</b></summary>
- *Examine the given protein sequence and share a brief overview of its attributes:*
MKIVLASNNQGKLAELKAMLAPLGVQLLRQAELGIPEAAEPFRTFVENALAKARHASALSGLPALADDAGLCVEAFGGLPGVDTAFYAVQFGYAKGDANNVKALLAQMAGITDRRAALVSTLVAVRSAEDPEPLIACGRVAGEVALEPMGSNGFGFDPVMFIPEFGQTFAQLPVEVKNANSHRGKATRQMMALMRERWIT
```
A concise description of the protein with the specified amino acid sequence includes: Pyrophosphatase that catalyzes the hydrolysis of nucleoside triphosphates to their monophosphate derivatives, with a high preference for the non-canonical purine nucleotides XTP (xanthosine triphosphate), dITP (deoxyinosine triphosphate) and ITP. Seems to function as a house-cleaning enzyme that removes non-canonical purine nucleotides from the nucleotide pool, thus preventing their incorporation into DNA/RNA and avoiding chromosomal lesions.
```
</details>
<details>
<summary><b>Domain/Motif prediction</b></summary>
- *Given this protein sequence, can you identify any common protein motifs or domains that it may contain?*
MANTKYIFITGGVVSSLGKGIAAASIGALLESRGLSVSLIKVDPYINVDPGTMSPFQHGEVFVTEDGTETDLDLGHYERFVRFKASKKNNFTAGKVYETVIRNERKGNYLGGTVQVIPHITNEIKKRIKKGGQNKDIAIVEVGGTVGDIESQPFVEALRQMALELPNSSWAFVHLTLVPFINASGELKTKPTQHSVKELRSLGISPDVLVCRSEQELPKDEKNKIALFCSVPAKSVISMHDVDTVYSIPILLNKQKVDDTILKKLNLKIKKPNLNDWKRVVKAKLLPEKEVNVSFVGKYTELKDSYKSINEALEHAGIQNKAKVNINFVEAEQITSQNVRKVLKKSDAILVPGGFGERGIEGMILACKYARENNVPYLGICLGMQIAIIEYARNVLKLKSANSTEFDSSTKFPVIGLITEWSDISGKKEKRTKNSDLGGTMRLGGQVCKLKKKSNSYKMYKKSEIIERHRHRYEVNPNYKDKMIEQGLDVVGTSIDGKLVEMIELPSHKWFLACQFHPEFTSNPRDGHPIFNSYIKSTITK
```
Our predictive analysis of the given protein sequence reveals possible domains or motifs. These include: Glutamine amidotransferase, CTP synthase N-terminal domains.
```
</details>
<h3 id="2-3"> 🥼 2.3 Biomolecule text</h3>
<details>
<summary><b>Chemical entity recognition</b></summary>
- *Find and list all the instances of the chemical entities in the following content:*
"Both the control and caramiphen groups with double cannulas had significantly shorter latencies to seizure onset than the corresponding groups with single cannula."
```
caramiphen
```
</details>
<details>
<summary><b>Chemical-disease interaction extraction</b></summary>
- *You are provided with a set of clinical trial summaries. Extract the chemical-disease relations from the summaries and present your findings in the format of (Subject, Object):*
"Eating disorders and the associated behavioural problems and drug abuse are uncommon in pregnancy. When they do occur they are often unrecognized because of denial but when significant may pose a risk to both the mother and her fetus. This case illustrates a number of problems that may be encountered in women with eating disorders in pregnancy, including prolonged and recurrent metabolic disturbances and diuretic abuse. In particular it illustrates the derangements of thyroid function seen in pregnant women with eating disorders and reminds us that when a cause for thyrotoxicosis remains obscure, thyroxine abuse should be considered and explored."
```
(thyroxine, thyrotoxicosis)
```
</details>
<details>
<summary><b>Chemical-protein interaction extraction</b></summary>
- *Your task is to extract the subject and object entities from the provided text and establish the corresponding relation triples. Please format your answers as (Subject, Relation, Object):*
"Opioid receptors are the membrane proteins that mediate the pain-relieving effect of opioid drugs, such as morphine and fentanyl as well as endogenous opioid peptides enkephalins and endorphins. Using cDNAs for the mu and the kappa opioid receptors, we mapped the chromosomal locations of their genes in mouse. Multilocus cross analysis located the mu receptor gene Oprm on Chr 10 and the kappa receptor gene Oprk1 on Chr 1. Both genes are near centromere, with no markers more centromeric. These data indicate that the two opioid receptors are different gene products, ruling out the possibility that they may be differential splicing products from the same gene."
```
(morphine, regulator, Opioid receptors), (fentanyl, regulator, Opioid receptors)
```
</details>
<details>
<summary><b>Multiple-choice question</b></summary>
- *Which of the following conditions does not show multifactorial inheritance? What of the following is the right choice?*
(A) Pyloric stenosis (B) Schizophrenia (C) Spina bifida (neural tube defects) (D) Marfan syndrome
```
The final answer is (D).
```
</details>
<details>
<summary><b>True or False question</b></summary>
- *Does feeding tube insertion and its timing improve survival?*
```
No, neither insertion of PEG tubes nor timing of insertion affect survival.
```
</details>
<details>
<summary><b>Open question</b></summary>
- *What virus causes chronic disease?*
```
Hepatitis C virus (HCV).
```
</details>
<h2 id="3">3. Demo</h2>
<h3 id="3-1"> 🤗 3.1 Model Weight Release</h3>
We release the model weights on Hugging Face at:
- 🔬 *Molecule-oriented instructions:* [zjunlp/llama-molinst-molecule-7b](https://huggingface.co/zjunlp/llama-molinst-molecule-7b)
- 🧬 *Protein-oriented instructions:* [zjunlp/llama-molinst-protein-7b](https://huggingface.co/zjunlp/llama-molinst-protein-7b)
- 🥼 *Biomolecular text instructions:* [zjunlp/llama-molinst-biotext-7b](https://huggingface.co/zjunlp/llama-molinst-biotext-7b)
<h3 id="3-2"> 📝 3.2 Model Usage Guide</h3>
For this part, please refer to our [repository](https://github.com/zjunlp/Mol-Instructions).
We have provided a web version demo based on [Gradio](https://gradio.app). To use it, you first need to download this repository:
```shell
>> git clone https://github.com/zjunlp/Mol-Instruction
>> cd demo
```
Step 1, install Gradio by running：`pip install gradio`.
Step 2, specify the parameters in the [generate.sh](https://github.com/zjunlp/Mol-Instructions/blob/main/demo/generate.sh) file.
```shell
>> CUDA_VISIBLE_DEVICES=0 python generate.py \
--CLI False\
--protein False\
--load_8bit \
--base_model $BASE_MODEL_PATH \
--share_gradio True\
--lora_weights $FINETUNED_MODEL_PATH \
```
For models fine-tuned on *molecule-oriented* and *biomolecular text* instructions, please set `$FINETUNED_MODEL_PATH` to `'zjunlp/llama-molinst-molecule-7b'` or `'zjunlp/llama-molinst-biotext-7b'`.
For the model fine-tuned on *protein-oriented* instructions, you need to perform additional steps as described in [this folder](https://github.com/zjunlp/Mol-Instructions/tree/main/demo).
Step 3, run the [generate.sh](https://github.com/zjunlp/Mol-Instructions/blob/main/demo/generate.sh) file in the repository：
```shell
>> sh generate.sh
```
We offer two methods: the first one is command-line interaction, and the second one is web-based interaction, which provides greater flexibility.
1. Use the following command to enter **web-based interaction**:
```shell
>> python generate.py
```
The program will run a web server and output an address. Open the output address in a browser to use it.
2. Use the following command to enter **command-line interaction**:
```shell
>> python generate.py --CLI True
```
The disadvantage is the inability to dynamically change decoding parameters.
<h3 id="3-3"> 💡 3.3 FAQ</h3>
- *Question:* What action should be taken if the model encounters `<unk>` and subsequently repeats the input during decoding?
*Answer:* Consider reducing the value of the `max tokens`.
- *Question:* What should I do if the model encounters � during decoding?
*Answer:* If this symbol emerges in the middle of the decoded sentence, we recommend changing the input. If it shows up at the end of the sentence, you can tackle this issue by extending the output length.
- *Question:* Why do I receive varied results despite using identical decoding parameters?
*Answer:* This might occur if you have enabled `do_sample=True`. Another factor could be the order in which tasks are executed. A useful approach would be to use a for loop to generate multiple outputs with the same decoding parameters, enabling you to note the variance in each output.
- *Question:* What could be the reason for subpar answer quality?
*Answer:* Modifying the decoding parameters could help in improving the quality of the extraction or the answer.
<h2 id="4">4. Notices</h2>
<h3 id="4-1"> 🚨 4.1. Usage and License</h3>
Please note that all data and model weights of **Mol-Instructions** is exclusively licensed for research purposes. The accompanying dataset is licensed under CC BY 4.0, which permits solely non-commercial usage.
We emphatically urge all users to adhere to the highest ethical standards when using our dataset, including maintaining fairness, transparency, and responsibility in their research. Any usage of the dataset that may lead to harm or pose a detriment to society is strictly **forbidden**.
In terms of dataset maintenance, we pledge our commitment to provide necessary upkeep. This will ensure the continued relevance and usability of the dataset in light of evolving research landscapes. This commitment encompasses regular updates, error checks, and amendments in accordance with field advancements and user feedback.
<h3 id="4-2"> ❗️ 4.2. Limitations</h3>
The current state of the model, obtained via instruction tuning, is a preliminary demonstration. Its capacity to handle real-world, production-grade tasks remains limited. Moreover, there is a vast reservoir of rich instruction data that remains to be collected and exploited.
<h2 id="5">5. About</h2>
<h3 id="5-1"> 📚 5.1 References</h3>
If you use our repository, please cite the following related paper:
```
@article{molinst,
title={Mol-Instructions: A Large-Scale Biomolecular Instruction Dataset for Large Language Models},
author={Fang, Yin and Liang, Xiaozhuan and Zhang, Ningyu and Liu, Kangwei and Huang, Rui and Chen, Zhuo and Fan, Xiaohui and Chen, Huajun},
journal={arXiv preprint arXiv:2306.08018},
year={2023}
}
```
<h3 id="5-2"> 🫱🏻🫲 5.2 Acknowledgements</h3>
We appreciate [LLaMA](https://github.com/facebookresearch/llama), [Huggingface Transformers Llama](https://github.com/huggingface/transformers/tree/main/src/transformers/models/llama), [Alpaca](https://crfm.stanford.edu/2023/03/13/alpaca.html), [Alpaca-LoRA](https://github.com/tloen/alpaca-lora), [Chatbot Service](https://github.com/deep-diver/LLM-As-Chatbot) and many other related works for their open-source contributions. | [
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ghomasHudson/muld | ghomasHudson | 2022-11-02T12:55:17Z | 1,266 | 5 | null | [
"task_categories:question-answering",
"task_categories:summarization",
"task_categories:text-generation",
"task_categories:translation",
"task_ids:abstractive-qa",
"annotations_creators:found",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:translation",
"multilinguality:monolingual",
"size_categories:unknown",
"source_datasets:original",
"source_datasets:extended|hotpot_qa",
"source_datasets:extended|open_subtitles",
"language:en",
"language:de",
"conditional-text-generation",
"arxiv:2202.07362",
"region:us"
] | 2022-11-02T12:55:17Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- found
- crowdsourced
language_creators:
- found
language:
- en
- de
license: []
multilinguality:
- translation
- monolingual
size_categories:
- unknown
source_datasets:
- original
- extended|hotpot_qa
- extended|open_subtitles
task_categories:
- question-answering
- summarization
- text-generation
- translation
task_ids:
- abstractive-qa
pretty_name: The Multitask Long Document Benchmark
tags:
- conditional-text-generation
---
# MuLD
> The Multitask Long Document Benchmark
MuLD (Multitask Long Document Benchmark) is a set of 6 NLP tasks where the inputs consist of at least 10,000 words. The benchmark covers a wide variety of task types including translation, summarization, question answering, and classification. Additionally there is a range of output lengths from a single word classification label all the way up to an output longer than the input text.
- **Repository:** https://github.com/ghomasHudson/muld
- **Paper:** https://arxiv.org/abs/2202.07362
### Supported Tasks and Leaderboards
The 6 MuLD tasks consist of:
- **NarrativeQA** - A question answering dataset requiring an understanding of the plot of books and films.
- **HotpotQA** - An expanded version of HotpotQA requiring multihop reasoning between multiple wikipedia pages. This expanded version includes the full Wikipedia pages.
- **OpenSubtitles** - A translation dataset based on the OpenSubtitles 2018 dataset. The entire subtitles for each tv show is provided, one subtitle per line in both English and German.
- **VLSP (Very Long Scientific Papers)** - An expanded version of the Scientific Papers summarization dataset. Instead of removing very long papers (e.g. thesis), we explicitly include them removing any short papers.
- **AO3 Style Change Detection** - Consists of documents formed from the work of multiple [Archive of Our Own](ao3.org) authors, where the task is to predict the author for each paragraph.
- **Movie Character Types** - Predicting whether a named character is the Hero/Villain given a movie script.
### Dataset Structure
The data is presented in a text-to-text format where each instance contains a input string, output string and (optionally) json encoded metadata.
```
{'input: 'Who was wearing the blue shirt? The beginning...', 'output': ['John'], 'metadata': ''}
```
### Data Fields
- `input`: a string which has a differing structure per task but is presented in a unified format
- `output`: a list of strings where each is a possible answer. Most instances only have a single answer, but some such as narrativeQA and VLSP may have multiple.
- `metadata`: Additional metadata which may be helpful for evaluation. In this version, only the OpenSubtitles task contains metadata (for the ContraPro annotations).
### Data Splits
Each tasks contains different splits depending what was available in the source datasets:
| Task Name | Train | Validation | Test |
|----------------------------|----|----|-----|
| NarrativeQA | ✔️ | ✔️ | ✔️ |
| HotpotQA | ✔️ | ✔️ | |
| AO3 Style Change Detection | ✔️ | ✔️ | ✔️ |
| Movie Character Types | ✔️ | ✔️ | ✔️ |
| VLSP | | | ✔️ |
| OpenSubtitles | ✔️ | | ✔️ |
### Citation Information
```
@misc{hudson2022muld,
title={MuLD: The Multitask Long Document Benchmark},
author={G Thomas Hudson and Noura Al Moubayed},
year={2022},
eprint={2202.07362},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
Please also cite the papers directly used in this benchmark. | [
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riddle_sense | null | 2022-11-18T21:42:04Z | 1,264 | 15 | null | [
"task_categories:question-answering",
"task_ids:multiple-choice-qa",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:other",
"region:us"
] | 2022-11-18T21:42:04Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- en
license:
- other
multilinguality:
- monolingual
pretty_name: RiddleSense
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- question-answering
task_ids:
- multiple-choice-qa
dataset_info:
features:
- name: answerKey
dtype: string
- name: question
dtype: string
- name: choices
sequence:
- name: label
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 720715
num_examples: 3510
- name: validation
num_bytes: 208276
num_examples: 1021
- name: test
num_bytes: 212790
num_examples: 1184
download_size: 2083122
dataset_size: 1141781
---
# Dataset Card for RiddleSense
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-instances)
- [Data Splits](#data-instances)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
- **Homepage:** https://inklab.usc.edu/RiddleSense/
- **Repository:** https://github.com/INK-USC/RiddleSense/
- **Paper:** https://inklab.usc.edu/RiddleSense/riddlesense_acl21_paper.pdf
- **Leaderboard:** https://inklab.usc.edu/RiddleSense/#leaderboard
- **Point of Contact:** [Yuchen Lin](yuchen.lin@usc.edu)
### Dataset Summary
Answering such a riddle-style question is a challenging cognitive process, in that it requires
complex commonsense reasoning abilities, an understanding of figurative language, and counterfactual reasoning
skills, which are all important abilities for advanced natural language understanding (NLU). However,
there is currently no dedicated datasets aiming to test these abilities. Herein, we present RiddleSense,
a new multiple-choice question answering task, which comes with the first large dataset (5.7k examples) for answering
riddle-style commonsense questions. We systematically evaluate a wide range of models over the challenge,
and point out that there is a large gap between the best-supervised model and human performance suggesting
intriguing future research in the direction of higher-order commonsense reasoning and linguistic creativity towards
building advanced NLU systems.
### Supported Tasks and Leaderboards
[Needs More Information]
### Languages
English
## Dataset Structure
### Data Instances
An example of 'train' looks as follows.
```
{
"answerKey": "E",
"choices": {
"label": ["A", "B", "C", "D", "E"],
"text": ["throw", "bit", "gallow", "mouse", "hole"]
},
"question": "A man is incarcerated in prison, and as his punishment he has to carry a one tonne bag of sand backwards and forwards across a field the size of a football pitch. What is the one thing he can put in it to make it lighter?"
}
```
### Data Fields
Data Fields
The data fields are the same among all splits.
default
- `answerKey`: a string feature.
- `question`: a string feature.
- `choices`: a dictionary feature containing:
- `label`: a string feature.
- `text`: a string feature.
### Data Splits
|name| train| validation| test|
|---|---|---|---|
|default| 3510| 1021| 1184|
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
[Needs More Information]
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
Dataset provided for research purposes only. Please check dataset license for additional information.
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
The copyright of RiddleSense dataset is consistent with the terms of use of the fan websites and the intellectual property and privacy rights of the original sources. All of our riddles and answers are from fan websites that can be accessed freely. The website owners state that you may print and download material from the sites solely for non-commercial use provided that we agree not to change or delete any copyright or proprietary notices from the materials. The dataset users must agree that they will only use the dataset for research purposes before they can access the both the riddles and our annotations. We do not vouch for the potential bias or fairness issue that might exist within the riddles. You do not have the right to redistribute them. Again, you must not use this dataset for any commercial purposes.
### Citation Information
```
@InProceedings{lin-etal-2021-riddlesense,
title={RiddleSense: Reasoning about Riddle Questions Featuring Linguistic Creativity and Commonsense Knowledge},
author={Lin, Bill Yuchen and Wu, Ziyi and Yang, Yichi and Lee, Dong-Ho and Ren, Xiang},
journal={Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics (ACL-IJCNLP 2021): Findings},
year={2021}
}
```
### Contributions
Thanks to [@ziyiwu9494](https://github.com/ziyiwu9494) for adding this dataset. | [
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phiyodr/coco2017 | phiyodr | 2023-06-26T11:40:47Z | 1,264 | 1 | null | [
"task_categories:image-to-text",
"task_ids:image-captioning",
"size_categories:100K<n<1M",
"language:en",
"coco",
"image-captioning",
"region:us"
] | 2023-06-26T11:40:47Z | 2023-06-26T08:48:25.000Z | 2023-06-26T08:48:25 | ---
language:
- en
pretty_name: COCO2017
size_categories:
- 100K<n<1M
task_categories:
- image-to-text
task_ids:
- image-captioning
tags:
- coco
- image-captioning
dataset_info:
features:
- name: license
dtype: int64
- name: file_name
dtype: string
- name: coco_url
dtype: string
- name: height
dtype: int64
- name: width
dtype: int64
- name: date_captured
dtype: string
- name: flickr_url
dtype: string
- name: image_id
dtype: int64
- name: ids
sequence: int64
- name: captions
sequence: string
splits:
- name: train
num_bytes: 64026361
num_examples: 118287
- name: validation
num_bytes: 2684731
num_examples: 5000
download_size: 30170127
dataset_size: 66711092
---
# coco2017
Image-text pairs from [MS COCO2017](https://cocodataset.org/#download).
## Data origin
* Data originates from [cocodataset.org](http://images.cocodataset.org/annotations/annotations_trainval2017.zip)
* While `coco-karpathy` uses a dense format (with several sentences and sendids per row), `coco-karpathy-long` uses a long format with one `sentence` (aka caption) and `sendid` per row. `coco-karpathy-long` uses the first five sentences and therefore is five times as long as `coco-karpathy`.
* `phiyodr/coco2017`: One row corresponds one image with several sentences.
* `phiyodr/coco2017-long`: One row correspond one sentence (aka caption). There are 5 rows (sometimes more) with the same image details.
## Format
```python
DatasetDict({
train: Dataset({
features: ['license', 'file_name', 'coco_url', 'height', 'width', 'date_captured', 'flickr_url', 'image_id', 'ids', 'captions'],
num_rows: 118287
})
validation: Dataset({
features: ['license', 'file_name', 'coco_url', 'height', 'width', 'date_captured', 'flickr_url', 'image_id', 'ids', 'captions'],
num_rows: 5000
})
})
```
## Usage
* Download image data and unzip
```bash
cd PATH_TO_IMAGE_FOLDER
wget http://images.cocodataset.org/zips/train2017.zip
wget http://images.cocodataset.org/zips/val2017.zip
#wget http://images.cocodataset.org/annotations/annotations_trainval2017.zip # zip not needed: everything you need is in load_dataset("phiyodr/coco2017")
unzip train2017.zip
unzip val2017.zip
```
* Load dataset in Python
```python
import os
from datasets import load_dataset
PATH_TO_IMAGE_FOLDER = "COCO2017"
def create_full_path(example):
"""Create full path to image using `base_path` to COCO2017 folder."""
example["image_path"] = os.path.join(PATH_TO_IMAGE_FOLDER, example["filepath"], example["filename"])
return example
dataset = load_dataset("phiyodr/coco2017")
dataset = dataset.map(create_full_path)
``` | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
alzoubi36/policy_ie_b | alzoubi36 | 2023-06-25T07:13:15Z | 1,261 | 0 | null | [
"region:us"
] | 2023-06-25T07:13:15Z | 2023-06-25T07:10:04.000Z | 2023-06-25T07:10:04 | ---
dataset_info:
features:
- name: type-I
struct:
- name: subtask
dtype: string
- name: tags
sequence: string
- name: tokens
sequence: string
- name: type-II
struct:
- name: subtask
dtype: string
- name: tags
sequence: string
- name: tokens
sequence: string
splits:
- name: train
num_bytes: 3944744
num_examples: 4109
- name: validation
num_bytes: 1102169
num_examples: 1041
- name: test
num_bytes: 1102169
num_examples: 1041
download_size: 814098
dataset_size: 6149082
---
# Dataset for the PolicyIE-B task in the [PrivacyGLUE](https://github.com/infsys-lab/privacy-glue) dataset
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bible_para | null | 2022-11-03T16:31:57Z | 1,260 | 10 | null | [
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] | 2022-11-03T16:31:57Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- found
language_creators:
- found
language:
- acu
- af
- agr
- ake
- am
- amu
- ar
- bg
- bsn
- cak
- ceb
- ch
- chq
- chr
- cjp
- cni
- cop
- crp
- cs
- da
- de
- dik
- dje
- djk
- dop
- ee
- el
- en
- eo
- es
- et
- eu
- fi
- fr
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- gd
- gu
- gv
- he
- hi
- hr
- hu
- hy
- id
- is
- it
- ja
- jak
- jiv
- kab
- kbh
- kek
- kn
- ko
- la
- lt
- lv
- mam
- mi
- ml
- mr
- my
- ne
- nhg
- nl
- 'no'
- ojb
- pck
- pes
- pl
- plt
- pot
- ppk
- pt
- quc
- quw
- ro
- rom
- ru
- shi
- sk
- sl
- sn
- so
- sq
- sr
- ss
- sv
- syr
- te
- th
- tl
- tmh
- tr
- uk
- usp
- vi
- wal
- wo
- xh
- zh
- zu
license:
- cc0-1.0
multilinguality:
- multilingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- translation
task_ids: []
paperswithcode_id: null
pretty_name: BiblePara
dataset_info:
- config_name: de-en
features:
- name: id
dtype: string
- name: translation
dtype:
translation:
languages:
- de
- en
splits:
- name: train
num_bytes: 17262178
num_examples: 62195
download_size: 5440713
dataset_size: 17262178
- config_name: en-fr
features:
- name: id
dtype: string
- name: translation
dtype:
translation:
languages:
- en
- fr
splits:
- name: train
num_bytes: 17536445
num_examples: 62195
download_size: 5470044
dataset_size: 17536445
- config_name: en-es
features:
- name: id
dtype: string
- name: translation
dtype:
translation:
languages:
- en
- es
splits:
- name: train
num_bytes: 17105724
num_examples: 62191
download_size: 5418998
dataset_size: 17105724
- config_name: en-fi
features:
- name: id
dtype: string
- name: translation
dtype:
translation:
languages:
- en
- fi
splits:
- name: train
num_bytes: 17486055
num_examples: 62026
download_size: 5506407
dataset_size: 17486055
- config_name: en-no
features:
- name: id
dtype: string
- name: translation
dtype:
translation:
languages:
- en
- 'no'
splits:
- name: train
num_bytes: 16681323
num_examples: 62107
download_size: 5293164
dataset_size: 16681323
- config_name: en-hi
features:
- name: id
dtype: string
- name: translation
dtype:
translation:
languages:
- en
- hi
splits:
- name: train
num_bytes: 27849361
num_examples: 62073
download_size: 6224765
dataset_size: 27849361
---
# Dataset Card for BiblePara
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** http://opus.nlpl.eu/bible-uedin.php
- **Repository:** None
- **Paper:** https://link.springer.com/article/10.1007/s10579-014-9287-y
- **Leaderboard:** [More Information Needed]
- **Point of Contact:** [More Information Needed]
### Dataset Summary
To load a language pair which isn't part of the config, all you need to do is specify the language code as pairs.
You can find the valid pairs in Homepage section of Dataset Description: http://opus.nlpl.eu/bible-uedin.php
E.g.
`dataset = load_dataset("bible_para", lang1="fi", lang2="hi")`
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
Here are some examples of questions and facts:
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
[More Information Needed]
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
[More Information Needed]
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@abhishekkrthakur](https://github.com/abhishekkrthakur) for adding this dataset. | [
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taskmaster2 | null | 2022-12-01T16:31:12Z | 1,260 | 4 | taskmaster-2 | [
"task_categories:text-generation",
"task_categories:fill-mask",
"task_ids:dialogue-modeling",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"arxiv:1909.05358",
"region:us"
] | 2022-12-01T16:31:12Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-generation
- fill-mask
task_ids:
- dialogue-modeling
paperswithcode_id: taskmaster-2
pretty_name: Taskmaster-2
dataset_info:
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features:
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splits:
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dataset_size: 7073487
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dataset_size: 5738818
---
# Dataset Card for Taskmaster-2
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [Taskmaster-1](https://research.google/tools/datasets/taskmaster-1/)
- **Repository:** [GitHub](https://github.com/google-research-datasets/Taskmaster/tree/master/TM-2-2020)
- **Paper:** [Taskmaster-1: Toward a Realistic and Diverse Dialog Dataset](https://arxiv.org/abs/1909.05358)
- **Leaderboard:** N/A
- **Point of Contact:** [Taskmaster Googlegroup](taskmaster-datasets@googlegroups.com)
### Dataset Summary
Taskmaster is dataset for goal oriented conversations. The Taskmaster-2 dataset consists of 17,289 dialogs
in the seven domains which include restaurants, food ordering, movies, hotels, flights, music and sports.
Unlike Taskmaster-1, which includes both written "self-dialogs" and spoken two-person dialogs,
Taskmaster-2 consists entirely of spoken two-person dialogs. In addition, while Taskmaster-1 is
almost exclusively task-based, Taskmaster-2 contains a good number of search- and recommendation-oriented dialogs.
All dialogs in this release were created using a Wizard of Oz (WOz) methodology in which crowdsourced
workers played the role of a 'user' and trained call center operators played the role of the 'assistant'.
In this way, users were led to believe they were interacting with an automated system that “spoke”
using text-to-speech (TTS) even though it was in fact a human behind the scenes.
As a result, users could express themselves however they chose in the context of an automated interface.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
The dataset is in English language.
## Dataset Structure
### Data Instances
A typical example looks like this
```
{
"conversation_id": "dlg-0047a087-6a3c-4f27-b0e6-268f53a2e013",
"instruction_id": "flight-6",
"utterances": [
{
"index": 0,
"segments": [],
"speaker": "USER",
"text": "Hi, I'm looking for a flight. I need to visit a friend."
},
{
"index": 1,
"segments": [],
"speaker": "ASSISTANT",
"text": "Hello, how can I help you?"
},
{
"index": 2,
"segments": [],
"speaker": "ASSISTANT",
"text": "Sure, I can help you with that."
},
{
"index": 3,
"segments": [],
"speaker": "ASSISTANT",
"text": "On what dates?"
},
{
"index": 4,
"segments": [
{
"annotations": [
{
"name": "flight_search.date.depart_origin"
}
],
"end_index": 37,
"start_index": 27,
"text": "March 20th"
},
{
"annotations": [
{
"name": "flight_search.date.return"
}
],
"end_index": 45,
"start_index": 41,
"text": "22nd"
}
],
"speaker": "USER",
"text": "I'm looking to travel from March 20th to 22nd."
}
]
}
```
### Data Fields
Each conversation in the data file has the following structure:
- `conversation_id`: A universally unique identifier with the prefix 'dlg-'. The ID has no meaning.
- `utterances`: A list of utterances that make up the conversation.
- `instruction_id`: A reference to the file(s) containing the user (and, if applicable, agent) instructions for this conversation.
Each utterance has the following fields:
- `index`: A 0-based index indicating the order of the utterances in the conversation.
- `speaker`: Either USER or ASSISTANT, indicating which role generated this utterance.
- `text`: The raw text of the utterance. In case of self dialogs (one_person_dialogs), this is written by the crowdsourced worker. In case of the WOz dialogs, 'ASSISTANT' turns are written and 'USER' turns are transcribed from the spoken recordings of crowdsourced workers.
- `segments`: A list of various text spans with semantic annotations.
Each segment has the following fields:
- `start_index`: The position of the start of the annotation in the utterance text.
- `end_index`: The position of the end of the annotation in the utterance text.
- `text`: The raw text that has been annotated.
- `annotations`: A list of annotation details for this segment.
Each annotation has a single field:
- `name`: The annotation name.
### Data Splits
There are no deafults splits for all the config. The below table lists the number of examples in each config.
| Config | Train |
|-------------------|--------|
| flights | 2481 |
| food-orderings | 1050 |
| hotels | 2355 |
| movies | 3047 |
| music | 1602 |
| restaurant-search | 3276 |
| sports | 3478 |
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
[More Information Needed]
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
[More Information Needed]
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
The dataset is licensed under `Creative Commons Attribution 4.0 License`
### Citation Information
[More Information Needed]
```
@inproceedings{48484,
title = {Taskmaster-1: Toward a Realistic and Diverse Dialog Dataset},
author = {Bill Byrne and Karthik Krishnamoorthi and Chinnadhurai Sankar and Arvind Neelakantan and Daniel Duckworth and Semih Yavuz and Ben Goodrich and Amit Dubey and Kyu-Young Kim and Andy Cedilnik},
year = {2019}
}
```
### Contributions
Thanks to [@patil-suraj](https://github.com/patil-suraj) for adding this dataset. | [
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crime_and_punish | null | 2023-04-05T10:02:51Z | 1,259 | 2 | null | [
"language:en",
"region:us"
] | 2023-04-05T10:02:51Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
language:
- en
paperswithcode_id: null
pretty_name: CrimeAndPunish
dataset_info:
features:
- name: line
dtype: string
splits:
- name: train
num_bytes: 1270540
num_examples: 21969
download_size: 1201735
dataset_size: 1270540
---
# Dataset Card for "crime_and_punish"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [https://www.gutenberg.org/files/2554/2554-h/2554-h.htm](https://www.gutenberg.org/files/2554/2554-h/2554-h.htm)
- **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Size of downloaded dataset files:** 1.21 MB
- **Size of the generated dataset:** 1.27 MB
- **Total amount of disk used:** 2.47 MB
### Dataset Summary
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Dataset Structure
### Data Instances
#### crime-and-punish
- **Size of downloaded dataset files:** 1.21 MB
- **Size of the generated dataset:** 1.27 MB
- **Total amount of disk used:** 2.47 MB
An example of 'train' looks as follows.
```
{
"line": "CRIME AND PUNISHMENT\n"
}
```
### Data Fields
The data fields are the same among all splits.
#### crime-and-punish
- `line`: a `string` feature.
### Data Splits
| name |train|
|----------------|----:|
|crime-and-punish|21969|
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Citation Information
```
```
### Contributions
Thanks to [@patrickvonplaten](https://github.com/patrickvonplaten), [@thomwolf](https://github.com/thomwolf) for adding this dataset. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
civil_comments | null | 2023-06-30T11:26:30Z | 1,255 | 3 | null | [
"language:en",
"license:cc0-1.0",
"arxiv:1903.04561",
"region:us"
] | 2023-06-30T11:26:30Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
language:
- en
paperswithcode_id: null
pretty_name: CivilComments
dataset_info:
features:
- name: text
dtype: string
- name: toxicity
dtype: float32
- name: severe_toxicity
dtype: float32
- name: obscene
dtype: float32
- name: threat
dtype: float32
- name: insult
dtype: float32
- name: identity_attack
dtype: float32
- name: sexual_explicit
dtype: float32
splits:
- name: test
num_bytes: 32073013
num_examples: 97320
- name: train
num_bytes: 596835730
num_examples: 1804874
- name: validation
num_bytes: 32326369
num_examples: 97320
download_size: 414947977
dataset_size: 661235112
license: cc0-1.0
---
# Dataset Card for "civil_comments"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [https://www.kaggle.com/c/jigsaw-unintended-bias-in-toxicity-classification/data](https://www.kaggle.com/c/jigsaw-unintended-bias-in-toxicity-classification/data)
- **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Size of downloaded dataset files:** 414.95 MB
- **Size of the generated dataset:** 661.23 MB
- **Total amount of disk used:** 1.08 GB
### Dataset Summary
The comments in this dataset come from an archive of the Civil Comments
platform, a commenting plugin for independent news sites. These public comments
were created from 2015 - 2017 and appeared on approximately 50 English-language
news sites across the world. When Civil Comments shut down in 2017, they chose
to make the public comments available in a lasting open archive to enable future
research. The original data, published on figshare, includes the public comment
text, some associated metadata such as article IDs, timestamps and
commenter-generated "civility" labels, but does not include user ids. Jigsaw
extended this dataset by adding additional labels for toxicity and identity
mentions. This data set is an exact replica of the data released for the
Jigsaw Unintended Bias in Toxicity Classification Kaggle challenge. This
dataset is released under CC0, as is the underlying comment text.
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Dataset Structure
### Data Instances
#### default
- **Size of downloaded dataset files:** 414.95 MB
- **Size of the generated dataset:** 661.23 MB
- **Total amount of disk used:** 1.08 GB
An example of 'validation' looks as follows.
```
{
"identity_attack": 0.0,
"insult": 0.0,
"obscene": 0.0,
"severe_toxicity": 0.0,
"sexual_explicit": 0.0,
"text": "The public test.",
"threat": 0.0,
"toxicity": 0.0
}
```
### Data Fields
The data fields are the same among all splits.
#### default
- `text`: a `string` feature.
- `toxicity`: a `float32` feature.
- `severe_toxicity`: a `float32` feature.
- `obscene`: a `float32` feature.
- `threat`: a `float32` feature.
- `insult`: a `float32` feature.
- `identity_attack`: a `float32` feature.
- `sexual_explicit`: a `float32` feature.
### Data Splits
| name | train |validation|test |
|-------|------:|---------:|----:|
|default|1804874| 97320|97320|
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
This dataset is released under [CC0 1.0](https://creativecommons.org/publicdomain/zero/1.0/).
### Citation Information
```
@article{DBLP:journals/corr/abs-1903-04561,
author = {Daniel Borkan and
Lucas Dixon and
Jeffrey Sorensen and
Nithum Thain and
Lucy Vasserman},
title = {Nuanced Metrics for Measuring Unintended Bias with Real Data for Text
Classification},
journal = {CoRR},
volume = {abs/1903.04561},
year = {2019},
url = {http://arxiv.org/abs/1903.04561},
archivePrefix = {arXiv},
eprint = {1903.04561},
timestamp = {Sun, 31 Mar 2019 19:01:24 +0200},
biburl = {https://dblp.org/rec/bib/journals/corr/abs-1903-04561},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```
### Contributions
Thanks to [@lewtun](https://github.com/lewtun), [@patrickvonplaten](https://github.com/patrickvonplaten), [@thomwolf](https://github.com/thomwolf) for adding this dataset. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
vwxyzjn/summarize_from_feedback_tldr_3_filtered_oai_preprocessing | vwxyzjn | 2023-11-08T02:28:58Z | 1,253 | 0 | null | [
"region:us"
] | 2023-11-08T02:28:58Z | 2023-10-19T17:37:41.000Z | 2023-10-19T17:37:41 | ---
dataset_info:
features:
- name: id
dtype: string
- name: subreddit
dtype: string
- name: title
dtype: string
- name: post
dtype: string
- name: summary
dtype: string
- name: query_token
sequence: int64
- name: query
dtype: string
- name: reference_response
dtype: string
- name: reference_response_token
sequence: int64
splits:
- name: train
num_bytes: 984401845
num_examples: 116722
- name: validation
num_bytes: 54382429
num_examples: 6447
- name: test
num_bytes: 55293071
num_examples: 6553
download_size: 350302087
dataset_size: 1094077345
---
# Dataset Card for "summarize_from_feedback_tldr_3_filtered_oai_preprocessing"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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ChristophSchuhmann/improved_aesthetics_6.5plus | ChristophSchuhmann | 2022-08-10T11:34:17Z | 1,250 | 39 | null | [
"region:us"
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c3 | null | 2022-11-18T19:24:46Z | 1,247 | 8 | c3 | [
"task_categories:question-answering",
"task_ids:multiple-choice-qa",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:zh",
"license:other",
"arxiv:1904.09679",
"region:us"
] | 2022-11-18T19:24:46Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- zh
license:
- other
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- question-answering
task_ids:
- multiple-choice-qa
paperswithcode_id: c3
pretty_name: C3
dataset_info:
- config_name: mixed
features:
- name: documents
sequence: string
- name: document_id
dtype: string
- name: questions
sequence:
- name: question
dtype: string
- name: answer
dtype: string
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sequence: string
splits:
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num_bytes: 2710513
num_examples: 3138
- name: test
num_bytes: 891619
num_examples: 1045
- name: validation
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num_examples: 1046
download_size: 5481785
dataset_size: 4512931
- config_name: dialog
features:
- name: documents
sequence: string
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dtype: string
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sequence:
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- name: test
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- name: validation
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num_examples: 1628
download_size: 4352392
dataset_size: 3297960
---
# Dataset Card for C3
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** []()
- **Repository:** [link]()
- **Paper:** []()
- **Leaderboard:** []()
- **Point of Contact:** []()
### Dataset Summary
Machine reading comprehension tasks require a machine reader to answer questions relevant to the given document. In this paper, we present the first free-form multiple-Choice Chinese machine reading Comprehension dataset (C^3), containing 13,369 documents (dialogues or more formally written mixed-genre texts) and their associated 19,577 multiple-choice free-form questions collected from Chinese-as-a-second-language examinations.
We present a comprehensive analysis of the prior knowledge (i.e., linguistic, domain-specific, and general world knowledge) needed for these real-world problems. We implement rule-based and popular neural methods and find that there is still a significant performance gap between the best performing model (68.5%) and human readers (96.0%), especially on problems that require prior knowledge. We further study the effects of distractor plausibility and data augmentation based on translated relevant datasets for English on model performance. We expect C^3 to present great challenges to existing systems as answering 86.8% of questions requires both knowledge within and beyond the accompanying document, and we hope that C^3 can serve as a platform to study how to leverage various kinds of prior knowledge to better understand a given written or orally oriented text.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
[More Information Needed]
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
[More Information Needed]
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
[More Information Needed]
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
Dataset provided for research purposes only. Please check dataset license for additional information.
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
```
@article{sun2019investigating,
title={Investigating Prior Knowledge for Challenging Chinese Machine Reading Comprehension},
author={Sun, Kai and Yu, Dian and Yu, Dong and Cardie, Claire},
journal={Transactions of the Association for Computational Linguistics},
year={2020},
url={https://arxiv.org/abs/1904.09679v3}
}
```
### Contributions
Thanks to [@Narsil](https://github.com/Narsil) for adding this dataset. | [
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neulab/tldr | neulab | 2022-12-22T19:47:11Z | 1,244 | 6 | null | [
"task_categories:text2text-generation",
"language_creators:crowdsourced",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:unknown",
"source_datasets:original",
"language:code",
"license:mit",
"code-generation",
"doc retrieval",
"retrieval augmented generation",
"arxiv:2207.05987",
"region:us"
] | 2022-12-22T19:47:11Z | 2022-12-22T17:58:43.000Z | 2022-12-22T17:58:43 | ---
annotations_creators: []
language_creators:
- crowdsourced
- expert-generated
language:
- code
license:
- mit
multilinguality:
- monolingual
size_categories:
- unknown
source_datasets:
- original
task_categories:
- text2text-generation
task_ids: []
pretty_name: DocPrompting-CoNaLa
tags:
- code-generation
- doc retrieval
- retrieval augmented generation
---
## Dataset Description
- **Repository:** https://github.com/shuyanzhou/docprompting
- **Paper:** [DocPrompting: Generating Code by Retrieving the Docs](https://arxiv.org/pdf/2207.05987.pdf)
### Dataset Summary
This is the natural language to bash generation dataset we harvested from the English subset of [`tldr`](https://github.com/tldr-pages/tldr)
We split the dataset by bash commands. Every command in the dev and test set is held out from the training set.
### Supported Tasks and Leaderboards
This dataset is used to evaluate code generations.
### Languages
English - Bash
## Dataset Structure
```python
dataset = load_dataset("neulab/tldr")
DatasetDict({
train: Dataset({
features: ['question_id', 'nl', 'cmd', 'oracle_man', 'cmd_name', 'tldr_cmd_name', 'manual_exist', 'matching_info'],
num_rows: 6414
})
test: Dataset({
features: ['question_id', 'nl', 'cmd', 'oracle_man', 'cmd_name', 'tldr_cmd_name', 'manual_exist', 'matching_info'],
num_rows: 928
})
validation: Dataset({
features: ['question_id', 'nl', 'cmd', 'oracle_man', 'cmd_name', 'tldr_cmd_name', 'manual_exist', 'matching_info'],
num_rows: 1845
})
})
code_docs = load_dataset("neulab/docprompting-conala", "docs")
DatasetDict({
train: Dataset({
features: ['doc_id', 'doc_content'],
num_rows: 439064
})
})
```
### Data Fields
train/dev/test:
- nl: The natural language intent
- cmd: The reference code snippet
- question_id: the unique id of a question
- oracle_man: The `doc_id` of the functions used in the reference code snippet. The corresponding contents are in `doc` split
- cmd_name: the bash command of this code snippet
- tldr_cmd_name: the bash command used in tldr github repo. The `cmd_name` and `tldr_cmd_name` can be different due to naming difference
- manual_exist: whether the manual exists in https://manned.org
- matching_info: each code snippets have multiple tokens, this is the detailed reference doc matching on each token.
docs:
- doc_id: the id of a doc
- doc_content: the content of the doc
## Dataset Creation
The dataset was curated from [`tldr`](https://github.com/tldr-pages/tldr).
The project aims to provide frequent usage of bash commands with natural language intents.
For more details, please check the repo.
### Citation Information
```
@article{zhou2022doccoder,
title={DocCoder: Generating Code by Retrieving and Reading Docs},
author={Zhou, Shuyan and Alon, Uri and Xu, Frank F and Jiang, Zhengbao and Neubig, Graham},
journal={arXiv preprint arXiv:2207.05987},
year={2022}
}
``` | [
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HuggingFaceM4/VQAv2_modif-Dummy | HuggingFaceM4 | 2023-01-18T19:46:06Z | 1,243 | 0 | null | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
result-kand2-sdxl-wuerst-karlo/eacbe536 | result-kand2-sdxl-wuerst-karlo | 2023-10-04T18:13:45Z | 1,243 | 0 | null | [
"region:us"
] | 2023-10-04T18:13:45Z | 2023-10-04T18:13:44.000Z | 2023-10-04T18:13:44 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 173
num_examples: 10
download_size: 1377
dataset_size: 173
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "eacbe536"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
result-kand2-sdxl-wuerst-karlo/3658ecd8 | result-kand2-sdxl-wuerst-karlo | 2023-10-08T22:30:26Z | 1,242 | 0 | null | [
"region:us"
] | 2023-10-08T22:30:26Z | 2023-10-08T22:30:26.000Z | 2023-10-08T22:30:26 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 198
num_examples: 10
download_size: 1383
dataset_size: 198
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "3658ecd8"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
royboy0416/ko-alpaca | royboy0416 | 2023-03-31T21:14:40Z | 1,240 | 5 | null | [
"task_categories:text-generation",
"language:ko",
"license:cc-by-4.0",
"region:us"
] | 2023-03-31T21:14:40Z | 2023-03-31T14:16:10.000Z | 2023-03-31T14:16:10 | ---
license: cc-by-4.0
task_categories:
- text-generation
language:
- ko
---
</b>Testing purpose only. Do not redistribute. </b>
Original contents: [url] https://huggingface.co/datasets/tatsu-lab/alpaca
Ko-alpaca: [url] https://github.com/Beomi/KoAlpaca/blob/main/ko_alpaca_data.json | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
frgfm/imagenette | frgfm | 2022-12-11T22:26:06Z | 1,236 | 7 | imagenette | [
"task_categories:image-classification",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"size_categories:1K<n<10K",
"source_datasets:extended",
"language:en",
"license:apache-2.0",
"region:us"
] | 2022-12-11T22:26:06Z | 2022-07-18T00:13:35.000Z | 2022-07-18T00:13:35 | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- apache-2.0
multilinguality: []
size_categories:
- 1K<n<10K
source_datasets:
- extended
task_categories:
- image-classification
task_ids: []
paperswithcode_id: imagenette
pretty_name: Imagenette
---
# Dataset Card for Imagenette
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://github.com/fastai/imagenette
- **Repository:** https://github.com/fastai/imagenette
- **Leaderboard:** https://paperswithcode.com/sota/image-classification-on-imagenette
### Dataset Summary
A smaller subset of 10 easily classified classes from [Imagenet](https://huggingface.co/datasets/imagenet-1k#dataset-summary), and a little more French.
This dataset was created by [Jeremy Howard](https://twitter.com/jeremyphoward), and this repository is only there to share his work on this platform. The repository owner takes no credit of any kind in the creation, curation or packaging of the dataset.
### Supported Tasks and Leaderboards
- `image-classification`: The dataset can be used to train a model for Image Classification.
### Languages
The class labels in the dataset are in English.
## Dataset Structure
### Data Instances
A data point comprises an image URL and its classification label.
```
{
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=320x320 at 0x19FA12186D8>,
'label': 'tench',
}
```
### Data Fields
- `image`: A `PIL.Image.Image` object containing the image.
- `label`: the expected class label of the image.
### Data Splits
| |train|validation|
|----------|----:|---------:|
|imagenette| 9469| 3925|
## Dataset Creation
### Curation Rationale
cf. https://huggingface.co/datasets/imagenet-1k#curation-rationale
### Source Data
#### Initial Data Collection and Normalization
Imagenette is a subset of [ImageNet](https://huggingface.co/datasets/imagenet-1k). Information about data collection of the source data can be found [here](https://huggingface.co/datasets/imagenet-1k#initial-data-collection-and-normalization).
### Annotations
#### Annotation process
cf. https://huggingface.co/datasets/imagenet-1k#annotation-process
#### Who are the annotators?
cf. https://huggingface.co/datasets/imagenet-1k#who-are-the-annotators
### Personal and Sensitive Information
cf. https://huggingface.co/datasets/imagenet-1k#personal-and-sensitive-information
## Considerations for Using the Data
### Social Impact of Dataset
cf. https://huggingface.co/datasets/imagenet-1k#social-impact-of-dataset
### Discussion of Biases
cf. https://huggingface.co/datasets/imagenet-1k#discussion-of-biases
### Other Known Limitations
cf. https://huggingface.co/datasets/imagenet-1k#other-known-limitations
## Additional Information
### Dataset Curators
cf. https://huggingface.co/datasets/imagenet-1k#dataset-curators
and Jeremy Howard
### Licensing Information
[Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0).
### Citation Information
```
@software{Howard_Imagenette_2019,
title={Imagenette: A smaller subset of 10 easily classified classes from Imagenet},
author={Jeremy Howard},
year={2019},
month={March},
publisher = {GitHub},
url = {https://github.com/fastai/imagenette}
}
```
### Contributions
This dataset was created by [Jeremy Howard](https://twitter.com/jeremyphoward) and published on [Github](https://github.com/fastai/imagenette). It was then only integrated into HuggingFace Datasets by [@frgfm](https://huggingface.co/frgfm).
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
bitext/Bitext-customer-support-llm-chatbot-training-dataset | bitext | 2023-09-19T23:48:25Z | 1,235 | 20 | null | [
"task_categories:question-answering",
"task_categories:table-question-answering",
"size_categories:10K<n<100K",
"language:en",
"license:cdla-sharing-1.0",
"question-answering",
"llm",
"chatbot",
"costumer-support",
"conversional-ai",
"generative-ai",
"natural-language-understanding",
"fine-tuning",
"Retail",
"region:us"
] | 2023-09-19T23:48:25Z | 2023-08-24T15:50:29.000Z | 2023-08-24T15:50:29 | ---
license: cdla-sharing-1.0
task_categories:
- question-answering
- table-question-answering
language:
- en
tags:
- question-answering
- llm
- chatbot
- costumer-support
- conversional-ai
- generative-ai
- natural-language-understanding
- fine-tuning
- Retail
pretty_name: >-
Bitext - Customer Service Tagged Training Dataset for LLM-based Virtual
Assistants
size_categories:
- 10K<n<100K
---
# Bitext - Customer Service Tagged Training Dataset for LLM-based Virtual Assistants
## Overview
This dataset can be used to train Large Language Models such as GPT, Llama2 and Falcon, both for Fine Tuning and Domain Adaptation.
The dataset has the following specs:
- Use Case: Intent Detection
- Vertical: Customer Service
- 27 intents assigned to 10 categories
- 26872 question/answer pairs, around 1000 per intent
- 30 entity/slot types
- 12 different types of language generation tags
The categories and intents have been selected from Bitext's collection of 20 vertical-specific datasets, covering the intents that are common across all 20 verticals. The verticals are:
- Automotive, Retail Banking, Education, Events & Ticketing, Field Services, Healthcare, Hospitality, Insurance, Legal Services, Manufacturing, Media Streaming, Mortgages & Loans, Moving & Storage, Real Estate/Construction, Restaurant & Bar Chains, Retail/E-commerce, Telecommunications, Travel, Utilities, Wealth Management
For a full list of verticals and its intents see [https://www.bitext.com/chatbot-verticals/](https://www.bitext.com/chatbot-verticals/).
The question/answer pairs have been generated using a hybrid methodology that uses natural texts as source text, NLP technology to extract seeds from these texts, and NLG technology to expand the seed texts. All steps in the process are curated by computational linguists.
## Dataset Token Count
The dataset contains an extensive amount of text data across its 'instruction' and 'response' columns. After processing and tokenizing the dataset, we've identified a total of 3.57 million tokens. This rich set of tokens is essential for training advanced LLMs for AI Conversational, AI Generative, and Question and Answering (Q&A) models.
## Fields of the Dataset
Each entry in the dataset contains the following fields:
- flags: tags (explained below in the Language Generation Tags section)
- instruction: a user request from the Customer Service domain
- category: the high-level semantic category for the intent
- intent: the intent corresponding to the user instruction
- response: an example expected response from the virtual assistant
## Categories and Intents
The categories and intents covered by the dataset are:
- ACCOUNT: create_account, delete_account, edit_account, switch_account
- CANCELLATION_FEE: check_cancellation_fee
- DELIVERY: delivery_options
- FEEDBACK: complaint, review
- INVOICE: check_invoice, get_invoice
- NEWSLETTER: newsletter_subscription
- ORDER: cancel_order, change_order, place_order
- PAYMENT: check_payment_methods, payment_issue
- REFUND: check_refund_policy, track_refund
- SHIPPING_ADDRESS: change_shipping_address, set_up_shipping_address
## Entities
The entities covered by the dataset are:
- {{Order Number}}, typically present in:
- Intents: cancel_order, change_order, change_shipping_address, check_invoice, check_refund_policy, complaint, delivery_options, delivery_period, get_invoice, get_refund, place_order, track_order, track_refund
- {{Invoice Number}}, typically present in:
- Intents: check_invoice, get_invoice
- {{Online Order Interaction}}, typically present in:
- Intents: cancel_order, change_order, check_refund_policy, delivery_period, get_refund, review, track_order, track_refund
- {{Online Payment Interaction}}, typically present in:
- Intents: cancel_order, check_payment_methods
- {{Online Navigation Step}}, typically present in:
- Intents: complaint, delivery_options
- {{Online Customer Support Channel}}, typically present in:
- Intents: check_refund_policy, complaint, contact_human_agent, delete_account, delivery_options, edit_account, get_refund, payment_issue, registration_problems, switch_account
- {{Profile}}, typically present in:
- Intent: switch_account
- {{Profile Type}}, typically present in:
- Intent: switch_account
- {{Settings}}, typically present in:
- Intents: cancel_order, change_order, change_shipping_address, check_cancellation_fee, check_invoice, check_payment_methods, contact_human_agent, delete_account, delivery_options, edit_account, get_invoice, newsletter_subscription, payment_issue, place_order, recover_password, registration_problems, set_up_shipping_address, switch_account, track_order, track_refund
- {{Online Company Portal Info}}, typically present in:
- Intents: cancel_order, edit_account
- {{Date}}, typically present in:
- Intents: check_invoice, check_refund_policy, get_refund, track_order, track_refund
- {{Date Range}}, typically present in:
- Intents: check_cancellation_fee, check_invoice, get_invoice
- {{Shipping Cut-off Time}}, typically present in:
- Intent: delivery_options
- {{Delivery City}}, typically present in:
- Intent: delivery_options
- {{Delivery Country}}, typically present in:
- Intents: check_payment_methods, check_refund_policy, delivery_options, review, switch_account
- {{Salutation}}, typically present in:
- Intents: cancel_order, check_payment_methods, check_refund_policy, create_account, delete_account, delivery_options, get_refund, recover_password, review, set_up_shipping_address, switch_account, track_refund
- {{Client First Name}}, typically present in:
- Intents: check_invoice, get_invoice
- {{Client Last Name}}, typically present in:
- Intents: check_invoice, create_account, get_invoice
- {{Customer Support Phone Number}}, typically present in:
- Intents: change_shipping_address, contact_customer_service, contact_human_agent, payment_issue
- {{Customer Support Email}}, typically present in:
- Intents: cancel_order, change_shipping_address, check_invoice, check_refund_policy, complaint, contact_customer_service, contact_human_agent, get_invoice, get_refund, newsletter_subscription, payment_issue, recover_password, registration_problems, review, set_up_shipping_address, switch_account
- {{Live Chat Support}}, typically present in:
- Intents: check_refund_policy, complaint, contact_human_agent, delete_account, delivery_options, edit_account, get_refund, payment_issue, recover_password, registration_problems, review, set_up_shipping_address, switch_account, track_order
- {{Website URL}}, typically present in:
- Intents: check_payment_methods, check_refund_policy, complaint, contact_customer_service, contact_human_agent, create_account, delete_account, delivery_options, get_refund, newsletter_subscription, payment_issue, place_order, recover_password, registration_problems, review, switch_account
- {{Upgrade Account}}, typically present in:
- Intents: create_account, edit_account, switch_account
- {{Account Type}}, typically present in:
- Intents: cancel_order, change_order, change_shipping_address, check_cancellation_fee, check_invoice, check_payment_methods, check_refund_policy, complaint, contact_customer_service, contact_human_agent, create_account, delete_account, delivery_options, delivery_period, edit_account, get_invoice, get_refund, newsletter_subscription, payment_issue, place_order, recover_password, registration_problems, review, set_up_shipping_address, switch_account, track_order, track_refund
- {{Account Category}}, typically present in:
- Intents: cancel_order, change_order, change_shipping_address, check_cancellation_fee, check_invoice, check_payment_methods, check_refund_policy, complaint, contact_customer_service, contact_human_agent, create_account, delete_account, delivery_options, delivery_period, edit_account, get_invoice, get_refund, newsletter_subscription, payment_issue, place_order, recover_password, registration_problems, review, set_up_shipping_address, switch_account, track_order, track_refund
- {{Account Change}}, typically present in:
- Intent: switch_account
- {{Program}}, typically present in:
- Intent: place_order
- {{Refund Amount}}, typically present in:
- Intent: track_refund
- {{Money Amount}}, typically present in:
- Intents: check_refund_policy, complaint, get_refund, track_refund
- {{Store Location}}, typically present in:
- Intents: complaint, delivery_options, place_order
## Language Generation Tags
The dataset contains tags that reflect how language varies/changes across different linguistic phenomena like colloquial or offensive language. So if an utterance for intent “cancel_order” contains the “COLLOQUIAL” tag, the utterance will express an informal language variation like: “can u cancel my order”.
These tags indicate the type of language variation that the entry expresses. When associated to each entry, they allow Conversational Designers to customize training datasets to different user profiles with different uses of language. Through these tags, many different datasets can be created to make the resulting assistant more accurate and robust. A bot that sells sneakers should be mainly targeted to younger population that use a more colloquial language; while a classical retail banking bot should be able to handle more formal or polite language. The dataset also reflects commonly occurring linguistic phenomena of real-life virtual assistant, such as spelling mistakes, run-on words, punctuation errors…
The dataset contains tagging for all relevant linguistic phenomena that can be used to customize the dataset for different user profiles.
### Tags for Lexical variation
M - Morphological variation: inflectional and derivational
“is my SIM card active”, “is my SIM card activated”
L - Semantic variations: synonyms, use of hyphens, compounding…
“what’s my billing date", “what’s my anniversary date”
### Tags for Syntactic structure variation
B - Basic syntactic structure:
“activate my SIM card”, “I need to activate my SIM card”
I - Interrogative structure
“can you activate my SIM card?”, “how do I activate my SIM card?”
C - Coordinated syntactic structure
“I have a new SIM card, what do I need to do to activate it?”
N - Negation
“I do not want this item, where to cancel my order?”
### Tags for language register variations
P - Politeness variation
“could you help me activate my SIM card, please?”
Q - Colloquial variation
“can u activ8 my SIM?”
W - Offensive language
“I want to talk to a f*&%*g agent”
### Tags for stylistic variations
K - Keyword mode
"activate SIM", "new SIM"
E - Use of abbreviations:
“I'm / I am interested in getting a new SIM”
Z - Errors and Typos: spelling issues, wrong punctuation…
“how can i activaet my card”
### Other tags not in use in this Dataset
D - Indirect speech
“ask my agent to activate my SIM card”
G - Regional variations
US English vs UK English: "truck" vs "lorry"
France French vs Canadian French: "tchatter" vs "clavarder"
R - Respect structures - Language-dependent variations
English: "may" vs "can…"
French: "tu" vs "vous..."
Spanish: "tú" vs "usted..."
Y - Code switching
“activer ma SIM card”
---
(c) Bitext Innovations, 2023 | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
THUDM/webglm-qa | THUDM | 2023-07-12T17:14:35Z | 1,230 | 23 | null | [
"task_categories:text-generation",
"task_categories:question-answering",
"multilinguality:monolingual",
"size_categories:100M<n<200M",
"language:en",
"arxiv:2306.07906",
"region:us"
] | 2023-07-12T17:14:35Z | 2023-07-11T16:59:04.000Z | 2023-07-11T16:59:04 | ---
annotations_creators: []
language:
- en
multilinguality:
- monolingual
source_datasets: []
task_categories:
- text-generation
- question-answering
pretty_name: WebGLM-QA
size_categories:
- 100M<n<200M
---
# WebGLM-QA
## Dataset Description
[WebGLM-QA](https://github.com/THUDM/WebGLM) is the dataset used to train the WebGLM generator module. It consists of 43,579 high-quality data samples for the train split, 1,000 for the validation split, and 400 for the test split. Refer to [our paper](https://arxiv.org/abs/2306.07906) for the data construction details.
## Dataset Structure
To load the dataset, you can try the following code.
```python
from datasets import load_dataset
load_dataset("THUDM/webglm-qa")
DatasetDict({
train: Dataset({
features: ['question', 'answer', 'references'],
num_rows: 43579
})
test: Dataset({
features: ['question', 'answer', 'references'],
num_rows: 400
})
validation: Dataset({
features: ['question', 'answer', 'references'],
num_rows: 1000
})
})
```
```python
next(iter(data["test"]))
{'question': 'Just got my (Canadian) mortgage renewal notice telling me I have to chose Subsequent Payment Terms.', 'answer': "When renewing a mortgage in Canada, your lender must notify you in advance of the renewal date with your options for renewal terms[1][2]. Your mortgage will typically automatically renew or become in default if you don't take action[3]. Depending on your lender, you may be able to renew your mortgage as early as 6 months prior to your current mortgage term expiring[2][3][5]. RBC Royal Bank mortgage customers can choose Subsequent Payment Terms and be protected from an increase in interest rates for the interest type and term they selected[4].", 'references': ['When faced with a mortgage renewal, this simply means that your current contracted mortgage term is approaching its expiration date. You see, the majority of mortgages in Toronto and in general mortgages in Ontario are contracted for a finite period of time that is referred to as the “mortgage term”. This period tends to range from as little as a few months to as long as 10 years in Canada.', 'You can either proactively reach out to your lender several months prior to your renewal date to find out, but if you don’t, your lender must notify you in advance of the renewal date what your options are. If your mortgage does happen to be with a federally regulated bank, then they are obligated to send you an official renewal statement with no less than 21 days remaining on your current mortgage term. Also, if your lender chooses not to renew your mortgage then they must notify you in advance and provide you with enough time to refinance your mortgage elsewhere or to pay it off.', 'When it comes to mortgage renewals, if you do not take action your mortgage will in many cases either renew automatically or become in default. When your mortgage term approaches the end, your mortgage lender will typically offer you renewal terms that you may choose to accept, negotiate, or decline. Provided you continue to make your monthly mortgage payments on time, lenders will rarely not extend to you an offer to renew your mortgage, although this can happen without cause depending on your mortgage commitment and contract.', "When you renew your RBC Royal Bank mortgage at maturity, you are protected from an increase in interest rates, for the interest type and term you selected, in the 30-day period prior to your regularly scheduled renewal date. And, if the interest rate changes before your actual mortgage renewal date, you'll automatically receive the lower rate for the term and type you chose.", 'When renewing your mortgage in Canada, some lenders may allow you to renew your mortgage as early as 6 months prior to your current mortgage term expiring.']}
```
## Data Fields
* ``question``: a question raised by a user or individual related to a certain topic.
* ``answer``: the generated response to the question.
* ``references``: a list of quotes or snippets from sources used to generate the answer given.
## Data Splits
We split the dataset into train, validation, and test.
## Citation Information
Refer to https://github.com/THUDM/WebGLM. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
InstaDeepAI/nucleotide_transformer_downstream_tasks | InstaDeepAI | 2023-10-16T12:57:56Z | 1,226 | 2 | null | [
"region:us"
] | 2023-10-16T12:57:56Z | 2023-06-16T12:00:08.000Z | 2023-06-16T12:00:08 | ---
# For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/datasetcard.md?plain=1
# Doc / guide: https://huggingface.co/docs/hub/datasets-cards
{}
---
# Dataset Card for Dataset Name
The `nucleotide_transformer_downstream_tasks` dataset features the 18 downstream tasks presented in the Nucleotide Transformer paper. They consist of both binary and multi-class classification tasks that aim at providing a consistent genomics benchmark.
## Dataset Description
- **Repository:** [Nucleotide Transformer](https://github.com/instadeepai/nucleotide-transformer)
- **Paper:** [The Nucleotide Transformer: Building and Evaluating Robust Foundation Models for Human Genomics](https://www.biorxiv.org/content/10.1101/2023.01.11.523679v1)
### Dataset Summary
The different datasets are collected from 4 different genomics papers:
- [DeePromoter: Robust Promoter Predictor Using Deep Learning](https://www.frontiersin.org/articles/10.3389/fgene.2019.00286/full): The datasets features 3,065 TATA promoters and 26,532 non-TATA promoters, with each promoter yielding a negative sequence by randomly sampling parts of the sequence. The `promoter_all` dataset will feature all the promoters and their negative counterparts, while the `promoter_tata` and `promoter_no_tata` respectively provide the TATA and non-TATA parts of the dataset.
- [A deep learning framework for enhancer prediction using word embedding and sequence generation](https://www.sciencedirect.com/science/article/abs/pii/S0301462222000643): To build the training dataset, the authors collect 742 strong
enhancers, 742 weak enhancers and 1484 non-enhancers, and augment the dataset with 6000 synthetic enhancers and 6000 synthetic non-enhancers produced with a generative model. The test dataset is comprised of 100 strong enhancers, 100 weak enhancers and 200 non enhancers. The original paper uses this dataset to do both binary classification (i.e a sample gets classified as non-enhancer or enhancer) and 3-class classification (i.e a sample gets classified as non-enhancer, weak enhancer or strong enhancer). Both tasks are respectively tackled in the `enhancers` and `enhancers_types` datasets.
- [SpliceFinder: ab initio prediction of splice sites using convolutional neural network](https://pubmed.ncbi.nlm.nih.gov/31881982): The authors introduce a dataset containing 10,000 samples of donor site, acceptor site, and non-splice-site, resulting in 30,000 total samples that are featured in the `splice_sites_all` dataset.
- [Spliceator: multi-species splice site prediction using convolutional neural networks](https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-021-04471-3): Two datasets are introduced by this paper, each of them contain splice sites and their corresponding negative datasets. The dataset `splice_sites_acceptor` features acceptor splice sites and the other, `splice_sites_donor`, donor splice sites.
- [Qualitatively predicting acetylation and methylation areas in DNA sequences](https://pubmed.ncbi.nlm.nih.gov/16901084/): The paper introduces a set of datasets featuring epigenetic marks identified in the yeast genome, namely acetylation and metylation nucleosome occupancies. Nucleosome occupancy values in these ten datasets were obtained with Chip-Chip experiments and further processed into positive and negative observations to provide the datasets corresponding to the following histone marks: `H3`, `H4`, `H3K9ac`, `H3K14ac`, `H4ac`, `H3K4me1`, `H3K4me2`, `H3K4me3`, `H3K36me3` and `H3K79me3`
## Dataset Structure
```
| Task | Number of train sequences | Number of test sequences | Number of labels | Sequence length |
| --------------------- | ------------------------- | ------------------------ | ---------------- | --------------- |
| promoter_all | 53,276 | 5,920 | 2 | 300 |
| promoter_tata | 5,509 | 621 | 2 | 300 |
| promoter_no_tata | 47,767 | 5,299 | 2 | 300 |
| enhancers | 14,968 | 400 | 2 | 200 |
| enhancers_types | 14,968 | 400 | 3 | 200 |
| splice_sites_all | 27,000 | 3,000 | 3 | 400 |
| splice_sites_acceptor | 19,961 | 2,218 | 2 | 600 |
| splice_sites_donor | 19,775 | 2,198 | 2 | 600 |
| H3 | 13,468 | 1,497 | 2 | 500 |
| H4 | 13,140 | 1,461 | 2 | 500 |
| H3K9ac | 25,003 | 2,779 | 2 | 500 |
| H3K14ac | 29,743 | 3,305 | 2 | 500 |
| H4ac | 30,685 | 3,410 | 2 | 500 |
| H3K4me1 | 28,509 | 3,168 | 2 | 500 |
| H3K4me2 | 27,614 | 3,069 | 2 | 500 |
| H3K4me3 | 33,119 | 3,680 | 2 | 500 |
| H3K36me3 | 31,392 | 3,488 | 2 | 500 |
| H3K79me3 | 25,953 | 2,884 | 2 | 500 |
```
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
higgsfield/dsml_original | higgsfield | 2023-11-09T22:27:51Z | 1,221 | 0 | null | [
"region:us"
] | 2023-11-09T22:27:51Z | 2023-11-09T00:32:09.000Z | 2023-11-09T00:32:09 | ---
dataset_info:
features:
- name: prompt
dtype: string
- name: completion
dtype: string
splits:
- name: train
num_bytes: 243684731
num_examples: 32477
download_size: 27760890
dataset_size: 243684731
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "dsml_original"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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Amod/mental_health_counseling_conversations | Amod | 2023-07-20T19:00:46Z | 1,220 | 38 | null | [
"task_categories:conversational",
"task_categories:text-generation",
"task_categories:question-answering",
"task_ids:sentiment-classification",
"task_ids:language-modeling",
"task_ids:open-domain-qa",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:openrail",
"region:us"
] | 2023-07-20T19:00:46Z | 2023-06-22T12:52:50.000Z | 2023-06-22T12:52:50 | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license: openrail
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- conversational
- text-generation
- question-answering
task_ids:
- sentiment-classification
- language-modeling
- open-domain-qa
---
# Amod/mental_health_counseling_conversations
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-instances)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:** Bertagnolli, Nicolas (2020). Counsel chat: Bootstrapping high-quality therapy data. Towards Data Science. https://towardsdatascience.com/counsel-chat
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
This dataset is a collection of questions and answers sourced from two online counseling and therapy platforms. The questions cover a wide range of mental health topics, and the answers are provided by qualified psychologists. The dataset is intended to be used for fine-tuning language models to improve their ability to provide mental health advice.
### Supported Tasks and Leaderboards
The dataset supports the task of text generation, particularly for generating advice or suggestions in response to a mental health-related question.
### Languages
The text in the dataset is in English.
## Dataset Structure
### Data Instances
A data instance includes a 'Context' and a 'Response'. 'Context' contains the question asked by a user, and 'Response' contains the corresponding answer provided by a psychologist.
### Data Fields
- 'Context': a string containing the question asked by a user
- 'Response': a string containing the corresponding answer provided by a psychologist
### Data Splits
The dataset has no predefined splits. Users can create their own splits as needed.
## Dataset Creation
### Curation Rationale
This dataset was created to aid in the development of AI models that can provide mental health advice or guidance. The raw data was meticulously cleaned to only include the conversations.
### Source Data
The data was sourced from two online counseling and therapy platforms. The raw data can be found [here](https://github.com/nbertagnolli/counsel-chat/tree/master/data).
### Annotations
The dataset does not contain any additional annotations.
### Personal and Sensitive Information
The dataset may contain sensitive information related to mental health. All data was anonymized and no personally identifiable information is included. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
pawlo2013/anime_diffusion_full | pawlo2013 | 2023-11-13T07:54:48Z | 1,219 | 0 | null | [
"region:us"
] | 2023-11-13T07:54:48Z | 2023-11-07T11:36:28.000Z | 2023-11-07T11:36:28 | ---
dataset_info:
features:
- name: sketch
dtype: image
- name: full_colour
dtype: image
- name: scribbles
dtype: image
- name: sketch_and_scribbles_merged
dtype: image
splits:
- name: train
num_bytes: 4923366050.448
num_examples: 40869
- name: test
num_bytes: 97418307.0
num_examples: 301
download_size: 4996697792
dataset_size: 5020784357.448
---
# Dataset Card for "anime_diffusion_full"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
lucasmccabe-lmi/CodeAlpaca-20k | lucasmccabe-lmi | 2023-05-19T00:10:02Z | 1,217 | 7 | null | [
"region:us"
] | 2023-05-19T00:10:02Z | 2023-05-19T00:09:27.000Z | 2023-05-19T00:09:27 | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: input
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 6576710.0
num_examples: 20022
download_size: 3450938
dataset_size: 6576710.0
---
# Dataset Card for "CodeAlpaca-20k"
We provide a minor modification of the [CodeAlpaca-20k](https://huggingface.co/datasets/sahil2801/CodeAlpaca-20k) dataset. In particular, we add the phrase, "Write corresponding code in Python." if the intended language is not explicitly stated.
## Numbers:
Prompts: 20022
Tokens: 1561716 using the EleutherAI/gpt-neox-20b tokenizer (counting instruction+input+output) | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
SetFit/ade_corpus_v2_classification | SetFit | 2022-09-05T14:14:53Z | 1,213 | 0 | null | [
"region:us"
] | 2022-09-05T14:14:53Z | 2022-09-05T11:20:19.000Z | 2022-09-05T11:20:19 | # ADE-Corpus-V2 Dataset: Adverse Drug Reaction Data.
This is a dataset for classification if a sentence is ADE-related (True) or not (False).
**Train size: 17,637**
**Test size: 5,879**
[Source dataset](https://huggingface.co/datasets/ade_corpus_v2)
[Paper](https://www.sciencedirect.com/science/article/pii/S1532046412000615)
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SetFit/tweet_eval_stance_abortion | SetFit | 2022-09-05T13:09:04Z | 1,210 | 0 | null | [
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YeungNLP/ultrachat | YeungNLP | 2023-06-19T02:52:43Z | 1,206 | 14 | null | [
"region:us"
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textvqa | null | 2022-11-18T22:07:01Z | 1,203 | 11 | null | [
"task_categories:visual-question-answering",
"task_ids:visual-question-answering",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"arxiv:1904.08920",
"arxiv:2007.00398",
"region:us"
] | 2022-11-18T22:07:01Z | 2022-05-05T06:44:56.000Z | 2022-05-05T06:44:56 | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
pretty_name: TextVQA
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- visual-question-answering
task_ids:
- visual-question-answering
dataset_info:
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dtype: string
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dataset_size: 28390930
---
# Dataset Card for TextVQA
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-instances)
- [Data Splits](#data-instances)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://textvqa.org
- **Repository:** https://github.com/facebookresearch/mmf
- **Paper:** https://arxiv.org/abs/1904.08920
- **Leaderboard:** https://eval.ai/web/challenges/challenge-page/874/overview
- **Point of Contact:** mailto:amanpreet@nyu.edu
### Dataset Summary
TextVQA requires models to read and reason about text in images to answer questions about them.
Specifically, models need to incorporate a new modality of text present in the images and reason
over it to answer TextVQA questions. TextVQA dataset contains 45,336 questions over 28,408 images
from the OpenImages dataset. The dataset uses [VQA accuracy](https://visualqa.org/evaluation.html) metric for evaluation.
### Supported Tasks and Leaderboards
- `visual-question-answering`: The dataset can be used for Visual Question Answering tasks where given an image, you have to answer a question based on the image. For the TextVQA dataset specifically, the questions require reading and reasoning about the scene text in the given image.
### Languages
The questions in the dataset are in English.
## Dataset Structure
### Data Instances
A typical sample mainly contains the question in `question` field, an image object in `image` field, OpenImage image id in `image_id` and lot of other useful metadata. 10 answers per questions are contained in the `answers` attribute. For test set, 10 empty strings are contained in the `answers` field as the answers are not available for it.
An example look like below:
```
{'question': 'who is this copyrighted by?',
'image_id': '00685bc495504d61',
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=384x512 at 0x276021C5EB8>,
'image_classes': ['Vehicle', 'Tower', 'Airplane', 'Aircraft'],
'flickr_original_url': 'https://farm2.staticflickr.com/5067/5620759429_4ea686e643_o.jpg',
'flickr_300k_url': 'https://c5.staticflickr.com/6/5067/5620759429_f43a649fb5_z.jpg',
'image_width': 786,
'image_height': 1024,
'answers': ['simon clancy',
'simon ciancy',
'simon clancy',
'simon clancy',
'the brand is bayard',
'simon clancy',
'simon clancy',
'simon clancy',
'simon clancy',
'simon clancy'],
'question_tokens': ['who', 'is', 'this', 'copyrighted', 'by'],
'question_id': 3,
'set_name': 'train'
},
```
### Data Fields
- `question`: string, the question that is being asked about the image
- `image_id`: string, id of the image which is same as the OpenImages id
- `image`: A `PIL.Image.Image` object containing the image about which the question is being asked. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`.
- `image_classes`: List[str], The OpenImages classes to which the image belongs to.
- `flickr_original_url`: string, URL to original image on Flickr
- `flickr_300k_url`: string, URL to resized and low-resolution image on Flickr.
- `image_width`: int, Width of the original image.
- `image_height`: int, Height of the original image.
- `question_tokens`: List[str], A pre-tokenized list of question.
- `answers`: List[str], List of 10 human-annotated answers for the question. These 10 answers are collected from 10 different users. The list will contain empty strings for test set for which we don't have the answers.
- `question_id`: int, Unique id of the question.
- `set_name`: string, the set to which this question belongs.
### Data Splits
There are three splits. `train`, `validation` and `test`. The `train` and `validation` sets share images with OpenImages `train` set and have their answers available. For test set answers, we return a list of ten empty strings. To get inference results and numbers on `test` set, you need to go to the [EvalAI leaderboard](https://eval.ai/web/challenges/challenge-page/874/overview) and upload your predictions there. Please see instructions at [https://textvqa.org/challenge/](https://textvqa.org/challenge/).
## Dataset Creation
### Curation Rationale
From the paper:
> Studies have shown that a dominant class of questions asked by visually impaired users on images of their surroundings involves reading text in the image. But today’s VQA models can not read! Our paper takes a first step towards addressing this problem. First, we introduce a new “TextVQA” dataset to facilitate progress on this important problem. Existing datasets either have a small proportion of questions about text (e.g., the VQA dataset) or are too small (e.g., the VizWiz dataset). TextVQA contains 45,336 questions on 28,408 images that require reasoning about text to answer.
### Source Data
#### Initial Data Collection and Normalization
The initial images were sourced from [OpenImages](https://storage.googleapis.com/openimages/web/factsfigures_v4.html) v4 dataset. These were first filtered based on automatic heuristics using an OCR system where we only took images which had at least some text detected in them. See [annotation process](#annotation-process) section to understand the next stages.
#### Who are the source language producers?
English Crowdsource Annotators
### Annotations
#### Annotation process
After the automatic process of filter the images that contain text, the images were manually verified using human annotators making sure that they had text. In next stage, the annotators were asked to write questions involving scene text for the image. For some images, in this stage, two questions were collected whenever possible. Finally, in the last stage, ten different human annotators answer the questions asked in last stage.
#### Who are the annotators?
Annotators are from one of the major data collection platforms such as AMT. Exact details are not mentioned in the paper.
### Personal and Sensitive Information
The dataset does have similar PII issues as OpenImages and can at some times contain human faces, license plates, and documents. Using provided `image_classes` data field is one option to try to filter out some of this information.
## Considerations for Using the Data
### Social Impact of Dataset
The paper helped realize the importance of scene text recognition and reasoning in general purpose machine learning applications and has led to many follow-up works including [TextCaps](https://textvqa.org/textcaps) and [TextOCR](https://textvqa.org/textocr). Similar datasets were introduced over the time which specifically focus on sight-disabled users such as [VizWiz](https://vizwiz.org) or focusing specifically on the same problem as TextVQA like [STVQA](https://paperswithcode.com/dataset/st-vqa), [DocVQA](https://arxiv.org/abs/2007.00398v3) and [OCRVQA](https://ocr-vqa.github.io/). Currently, most methods train on combined dataset from TextVQA and STVQA to achieve state-of-the-art performance on both datasets.
### Discussion of Biases
Question-only bias where a model is able to answer the question without even looking at the image is discussed in the [paper](https://arxiv.org/abs/1904.08920) which was a major issue with original VQA dataset. The outlier bias in answers is prevented by collecting 10 different answers which are also taken in consideration by the evaluation metric.
### Other Known Limitations
- The dataset is english only but does involve images with non-English latin characters so can involve some multi-lingual understanding.
- The performance on the dataset is also dependent on the quality of OCR used as the OCR errors can directly lead to wrong answers.
- The metric used for calculating accuracy is same as [VQA accuracy](https://visualqa.org/evaluation.html). This involves one-to-one matching with the given answers and thus doesn't allow analyzing one-off errors through OCR.
## Additional Information
### Dataset Curators
- [Amanpreet Singh](https://github.com/apsdehal)
- Vivek Natarjan
- Meet Shah
- Yu Jiang
- Xinlei Chen
- Dhruv Batra
- Devi Parikh
- Marcus Rohrbach
### Licensing Information
CC by 4.0
### Citation Information
```bibtex
@inproceedings{singh2019towards,
title={Towards VQA Models That Can Read},
author={Singh, Amanpreet and Natarjan, Vivek and Shah, Meet and Jiang, Yu and Chen, Xinlei and Batra, Dhruv and Parikh, Devi and Rohrbach, Marcus},
booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
pages={8317-8326},
year={2019}
}
```
### Contributions
Thanks to [@apsdehal](https://github.com/apsdehal) for adding this dataset. | [
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break_data | null | 2023-04-05T09:42:04Z | 1,199 | 0 | break | [
"task_categories:text2text-generation",
"task_ids:open-domain-abstractive-qa",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:unknown",
"region:us"
] | 2023-04-05T09:42:04Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text2text-generation
task_ids:
- open-domain-abstractive-qa
paperswithcode_id: break
pretty_name: BREAK
dataset_info:
- config_name: QDMR-high-level
features:
- name: question_id
dtype: string
- name: question_text
dtype: string
- name: decomposition
dtype: string
- name: operators
dtype: string
- name: split
dtype: string
splits:
- name: test
num_bytes: 482339
num_examples: 3195
- name: train
num_bytes: 5148086
num_examples: 17503
- name: validation
num_bytes: 914780
num_examples: 3130
download_size: 15971078
dataset_size: 6545205
- config_name: QDMR-high-level-lexicon
features:
- name: source
dtype: string
- name: allowed_tokens
dtype: string
splits:
- name: test
num_bytes: 4240755
num_examples: 3195
- name: train
num_bytes: 23234518
num_examples: 17503
- name: validation
num_bytes: 4158679
num_examples: 3130
download_size: 15971078
dataset_size: 31633952
- config_name: QDMR
features:
- name: question_id
dtype: string
- name: question_text
dtype: string
- name: decomposition
dtype: string
- name: operators
dtype: string
- name: split
dtype: string
splits:
- name: test
num_bytes: 900632
num_examples: 8069
- name: train
num_bytes: 12790466
num_examples: 44321
- name: validation
num_bytes: 2237472
num_examples: 7760
download_size: 15971078
dataset_size: 15928570
- config_name: QDMR-lexicon
features:
- name: source
dtype: string
- name: allowed_tokens
dtype: string
splits:
- name: test
num_bytes: 10331822
num_examples: 8069
- name: train
num_bytes: 56913064
num_examples: 44321
- name: validation
num_bytes: 9936933
num_examples: 7760
download_size: 15971078
dataset_size: 77181819
- config_name: logical-forms
features:
- name: question_id
dtype: string
- name: question_text
dtype: string
- name: decomposition
dtype: string
- name: operators
dtype: string
- name: split
dtype: string
- name: program
dtype: string
splits:
- name: test
num_bytes: 927038
num_examples: 8006
- name: train
num_bytes: 19821676
num_examples: 44098
- name: validation
num_bytes: 3504893
num_examples: 7719
download_size: 15971078
dataset_size: 24253607
---
# Dataset Card for "break_data"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [https://github.com/allenai/Break](https://github.com/allenai/Break)
- **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Size of downloaded dataset files:** 79.86 MB
- **Size of the generated dataset:** 155.55 MB
- **Total amount of disk used:** 235.39 MB
### Dataset Summary
Break is a human annotated dataset of natural language questions and their Question Decomposition Meaning Representations
(QDMRs). Break consists of 83,978 examples sampled from 10 question answering datasets over text, images and databases.
This repository contains the Break dataset along with information on the exact data format.
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Dataset Structure
### Data Instances
#### QDMR
- **Size of downloaded dataset files:** 15.97 MB
- **Size of the generated dataset:** 15.93 MB
- **Total amount of disk used:** 31.90 MB
An example of 'validation' looks as follows.
```
{
"decomposition": "return flights ;return #1 from denver ;return #2 to philadelphia ;return #3 if available",
"operators": "['select', 'filter', 'filter', 'filter']",
"question_id": "ATIS_dev_0",
"question_text": "what flights are available tomorrow from denver to philadelphia ",
"split": "dev"
}
```
#### QDMR-high-level
- **Size of downloaded dataset files:** 15.97 MB
- **Size of the generated dataset:** 6.54 MB
- **Total amount of disk used:** 22.51 MB
An example of 'train' looks as follows.
```
{
"decomposition": "return ground transportation ;return #1 which is available ;return #2 from the pittsburgh airport ;return #3 to downtown ;return the cost of #4",
"operators": "['select', 'filter', 'filter', 'filter', 'project']",
"question_id": "ATIS_dev_102",
"question_text": "what ground transportation is available from the pittsburgh airport to downtown and how much does it cost ",
"split": "dev"
}
```
#### QDMR-high-level-lexicon
- **Size of downloaded dataset files:** 15.97 MB
- **Size of the generated dataset:** 31.64 MB
- **Total amount of disk used:** 47.61 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"allowed_tokens": "\"['higher than', 'same as', 'what ', 'and ', 'than ', 'at most', 'he', 'distinct', 'House', 'two', 'at least', 'or ', 'date', 'o...",
"source": "What office, also held by a member of the Maine House of Representatives, did James K. Polk hold before he was president?"
}
```
#### QDMR-lexicon
- **Size of downloaded dataset files:** 15.97 MB
- **Size of the generated dataset:** 77.19 MB
- **Total amount of disk used:** 93.16 MB
An example of 'validation' looks as follows.
```
This example was too long and was cropped:
{
"allowed_tokens": "\"['higher than', 'same as', 'what ', 'and ', 'than ', 'at most', 'distinct', 'two', 'at least', 'or ', 'date', 'on ', '@@14@@', ...",
"source": "what flights are available tomorrow from denver to philadelphia "
}
```
#### logical-forms
- **Size of downloaded dataset files:** 15.97 MB
- **Size of the generated dataset:** 24.25 MB
- **Total amount of disk used:** 40.22 MB
An example of 'train' looks as follows.
```
{
"decomposition": "return ground transportation ;return #1 which is available ;return #2 from the pittsburgh airport ;return #3 to downtown ;return the cost of #4",
"operators": "['select', 'filter', 'filter', 'filter', 'project']",
"program": "some program",
"question_id": "ATIS_dev_102",
"question_text": "what ground transportation is available from the pittsburgh airport to downtown and how much does it cost ",
"split": "dev"
}
```
### Data Fields
The data fields are the same among all splits.
#### QDMR
- `question_id`: a `string` feature.
- `question_text`: a `string` feature.
- `decomposition`: a `string` feature.
- `operators`: a `string` feature.
- `split`: a `string` feature.
#### QDMR-high-level
- `question_id`: a `string` feature.
- `question_text`: a `string` feature.
- `decomposition`: a `string` feature.
- `operators`: a `string` feature.
- `split`: a `string` feature.
#### QDMR-high-level-lexicon
- `source`: a `string` feature.
- `allowed_tokens`: a `string` feature.
#### QDMR-lexicon
- `source`: a `string` feature.
- `allowed_tokens`: a `string` feature.
#### logical-forms
- `question_id`: a `string` feature.
- `question_text`: a `string` feature.
- `decomposition`: a `string` feature.
- `operators`: a `string` feature.
- `split`: a `string` feature.
- `program`: a `string` feature.
### Data Splits
| name |train|validation|test|
|-----------------------|----:|---------:|---:|
|QDMR |44321| 7760|8069|
|QDMR-high-level |17503| 3130|3195|
|QDMR-high-level-lexicon|17503| 3130|3195|
|QDMR-lexicon |44321| 7760|8069|
|logical-forms |44098| 7719|8006|
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Citation Information
```
@article{Wolfson2020Break,
title={Break It Down: A Question Understanding Benchmark},
author={Wolfson, Tomer and Geva, Mor and Gupta, Ankit and Gardner, Matt and Goldberg, Yoav and Deutch, Daniel and Berant, Jonathan},
journal={Transactions of the Association for Computational Linguistics},
year={2020},
}
```
### Contributions
Thanks to [@patrickvonplaten](https://github.com/patrickvonplaten), [@lewtun](https://github.com/lewtun), [@thomwolf](https://github.com/thomwolf) for adding this dataset. | [
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AsakusaRinne/gaokao_bench | AsakusaRinne | 2023-07-11T02:19:45Z | 1,197 | 2 | null | [
"region:us"
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BeIR/dbpedia-entity | BeIR | 2022-10-23T06:03:56Z | 1,194 | 3 | beir | [
"task_categories:text-retrieval",
"task_ids:entity-linking-retrieval",
"task_ids:fact-checking-retrieval",
"multilinguality:monolingual",
"language:en",
"license:cc-by-sa-4.0",
"region:us"
] | 2022-10-23T06:03:56Z | 2022-06-05T16:54:24.000Z | 2022-06-05T16:54:24 | ---
annotations_creators: []
language_creators: []
language:
- en
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
paperswithcode_id: beir
pretty_name: BEIR Benchmark
size_categories:
msmarco:
- 1M<n<10M
trec-covid:
- 100k<n<1M
nfcorpus:
- 1K<n<10K
nq:
- 1M<n<10M
hotpotqa:
- 1M<n<10M
fiqa:
- 10K<n<100K
arguana:
- 1K<n<10K
touche-2020:
- 100K<n<1M
cqadupstack:
- 100K<n<1M
quora:
- 100K<n<1M
dbpedia:
- 1M<n<10M
scidocs:
- 10K<n<100K
fever:
- 1M<n<10M
climate-fever:
- 1M<n<10M
scifact:
- 1K<n<10K
source_datasets: []
task_categories:
- text-retrieval
- zero-shot-retrieval
- information-retrieval
- zero-shot-information-retrieval
task_ids:
- passage-retrieval
- entity-linking-retrieval
- fact-checking-retrieval
- tweet-retrieval
- citation-prediction-retrieval
- duplication-question-retrieval
- argument-retrieval
- news-retrieval
- biomedical-information-retrieval
- question-answering-retrieval
---
# Dataset Card for BEIR Benchmark
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://github.com/UKPLab/beir
- **Repository:** https://github.com/UKPLab/beir
- **Paper:** https://openreview.net/forum?id=wCu6T5xFjeJ
- **Leaderboard:** https://docs.google.com/spreadsheets/d/1L8aACyPaXrL8iEelJLGqlMqXKPX2oSP_R10pZoy77Ns
- **Point of Contact:** nandan.thakur@uwaterloo.ca
### Dataset Summary
BEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:
- Fact-checking: [FEVER](http://fever.ai), [Climate-FEVER](http://climatefever.ai), [SciFact](https://github.com/allenai/scifact)
- Question-Answering: [NQ](https://ai.google.com/research/NaturalQuestions), [HotpotQA](https://hotpotqa.github.io), [FiQA-2018](https://sites.google.com/view/fiqa/)
- Bio-Medical IR: [TREC-COVID](https://ir.nist.gov/covidSubmit/index.html), [BioASQ](http://bioasq.org), [NFCorpus](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/)
- News Retrieval: [TREC-NEWS](https://trec.nist.gov/data/news2019.html), [Robust04](https://trec.nist.gov/data/robust/04.guidelines.html)
- Argument Retrieval: [Touche-2020](https://webis.de/events/touche-20/shared-task-1.html), [ArguAna](tp://argumentation.bplaced.net/arguana/data)
- Duplicate Question Retrieval: [Quora](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs), [CqaDupstack](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/)
- Citation-Prediction: [SCIDOCS](https://allenai.org/data/scidocs)
- Tweet Retrieval: [Signal-1M](https://research.signal-ai.com/datasets/signal1m-tweetir.html)
- Entity Retrieval: [DBPedia](https://github.com/iai-group/DBpedia-Entity/)
All these datasets have been preprocessed and can be used for your experiments.
```python
```
### Supported Tasks and Leaderboards
The dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.
The current best performing models can be found [here](https://eval.ai/web/challenges/challenge-page/689/leaderboard/).
### Languages
All tasks are in English (`en`).
## Dataset Structure
All BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:
- `corpus` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with three fields `_id` with unique document identifier, `title` with document title (optional) and `text` with document paragraph or passage. For example: `{"_id": "doc1", "title": "Albert Einstein", "text": "Albert Einstein was a German-born...."}`
- `queries` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with two fields `_id` with unique query identifier and `text` with query text. For example: `{"_id": "q1", "text": "Who developed the mass-energy equivalence formula?"}`
- `qrels` file: a `.tsv` file (tab-seperated) that contains three columns, i.e. the `query-id`, `corpus-id` and `score` in this order. Keep 1st row as header. For example: `q1 doc1 1`
### Data Instances
A high level example of any beir dataset:
```python
corpus = {
"doc1" : {
"title": "Albert Einstein",
"text": "Albert Einstein was a German-born theoretical physicist. who developed the theory of relativity, \
one of the two pillars of modern physics (alongside quantum mechanics). His work is also known for \
its influence on the philosophy of science. He is best known to the general public for his mass–energy \
equivalence formula E = mc2, which has been dubbed 'the world's most famous equation'. He received the 1921 \
Nobel Prize in Physics 'for his services to theoretical physics, and especially for his discovery of the law \
of the photoelectric effect', a pivotal step in the development of quantum theory."
},
"doc2" : {
"title": "", # Keep title an empty string if not present
"text": "Wheat beer is a top-fermented beer which is brewed with a large proportion of wheat relative to the amount of \
malted barley. The two main varieties are German Weißbier and Belgian witbier; other types include Lambic (made\
with wild yeast), Berliner Weisse (a cloudy, sour beer), and Gose (a sour, salty beer)."
},
}
queries = {
"q1" : "Who developed the mass-energy equivalence formula?",
"q2" : "Which beer is brewed with a large proportion of wheat?"
}
qrels = {
"q1" : {"doc1": 1},
"q2" : {"doc2": 1},
}
```
### Data Fields
Examples from all configurations have the following features:
### Corpus
- `corpus`: a `dict` feature representing the document title and passage text, made up of:
- `_id`: a `string` feature representing the unique document id
- `title`: a `string` feature, denoting the title of the document.
- `text`: a `string` feature, denoting the text of the document.
### Queries
- `queries`: a `dict` feature representing the query, made up of:
- `_id`: a `string` feature representing the unique query id
- `text`: a `string` feature, denoting the text of the query.
### Qrels
- `qrels`: a `dict` feature representing the query document relevance judgements, made up of:
- `_id`: a `string` feature representing the query id
- `_id`: a `string` feature, denoting the document id.
- `score`: a `int32` feature, denoting the relevance judgement between query and document.
### Data Splits
| Dataset | Website| BEIR-Name | Type | Queries | Corpus | Rel D/Q | Down-load | md5 |
| -------- | -----| ---------| --------- | ----------- | ---------| ---------| :----------: | :------:|
| MSMARCO | [Homepage](https://microsoft.github.io/msmarco/)| ``msmarco`` | ``train``<br>``dev``<br>``test``| 6,980 | 8.84M | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/msmarco.zip) | ``444067daf65d982533ea17ebd59501e4`` |
| TREC-COVID | [Homepage](https://ir.nist.gov/covidSubmit/index.html)| ``trec-covid``| ``test``| 50| 171K| 493.5 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/trec-covid.zip) | ``ce62140cb23feb9becf6270d0d1fe6d1`` |
| NFCorpus | [Homepage](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/) | ``nfcorpus`` | ``train``<br>``dev``<br>``test``| 323 | 3.6K | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nfcorpus.zip) | ``a89dba18a62ef92f7d323ec890a0d38d`` |
| BioASQ | [Homepage](http://bioasq.org) | ``bioasq``| ``train``<br>``test`` | 500 | 14.91M | 8.05 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#2-bioasq) |
| NQ | [Homepage](https://ai.google.com/research/NaturalQuestions) | ``nq``| ``train``<br>``test``| 3,452 | 2.68M | 1.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nq.zip) | ``d4d3d2e48787a744b6f6e691ff534307`` |
| HotpotQA | [Homepage](https://hotpotqa.github.io) | ``hotpotqa``| ``train``<br>``dev``<br>``test``| 7,405 | 5.23M | 2.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/hotpotqa.zip) | ``f412724f78b0d91183a0e86805e16114`` |
| FiQA-2018 | [Homepage](https://sites.google.com/view/fiqa/) | ``fiqa`` | ``train``<br>``dev``<br>``test``| 648 | 57K | 2.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fiqa.zip) | ``17918ed23cd04fb15047f73e6c3bd9d9`` |
| Signal-1M(RT) | [Homepage](https://research.signal-ai.com/datasets/signal1m-tweetir.html)| ``signal1m`` | ``test``| 97 | 2.86M | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#4-signal-1m) |
| TREC-NEWS | [Homepage](https://trec.nist.gov/data/news2019.html) | ``trec-news`` | ``test``| 57 | 595K | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#1-trec-news) |
| ArguAna | [Homepage](http://argumentation.bplaced.net/arguana/data) | ``arguana``| ``test`` | 1,406 | 8.67K | 1.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/arguana.zip) | ``8ad3e3c2a5867cdced806d6503f29b99`` |
| Touche-2020| [Homepage](https://webis.de/events/touche-20/shared-task-1.html) | ``webis-touche2020``| ``test``| 49 | 382K | 19.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/webis-touche2020.zip) | ``46f650ba5a527fc69e0a6521c5a23563`` |
| CQADupstack| [Homepage](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/) | ``cqadupstack``| ``test``| 13,145 | 457K | 1.4 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/cqadupstack.zip) | ``4e41456d7df8ee7760a7f866133bda78`` |
| Quora| [Homepage](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs) | ``quora``| ``dev``<br>``test``| 10,000 | 523K | 1.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/quora.zip) | ``18fb154900ba42a600f84b839c173167`` |
| DBPedia | [Homepage](https://github.com/iai-group/DBpedia-Entity/) | ``dbpedia-entity``| ``dev``<br>``test``| 400 | 4.63M | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/dbpedia-entity.zip) | ``c2a39eb420a3164af735795df012ac2c`` |
| SCIDOCS| [Homepage](https://allenai.org/data/scidocs) | ``scidocs``| ``test``| 1,000 | 25K | 4.9 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scidocs.zip) | ``38121350fc3a4d2f48850f6aff52e4a9`` |
| FEVER | [Homepage](http://fever.ai) | ``fever``| ``train``<br>``dev``<br>``test``| 6,666 | 5.42M | 1.2| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fever.zip) | ``5a818580227bfb4b35bb6fa46d9b6c03`` |
| Climate-FEVER| [Homepage](http://climatefever.ai) | ``climate-fever``|``test``| 1,535 | 5.42M | 3.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/climate-fever.zip) | ``8b66f0a9126c521bae2bde127b4dc99d`` |
| SciFact| [Homepage](https://github.com/allenai/scifact) | ``scifact``| ``train``<br>``test``| 300 | 5K | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scifact.zip) | ``5f7d1de60b170fc8027bb7898e2efca1`` |
| Robust04 | [Homepage](https://trec.nist.gov/data/robust/04.guidelines.html) | ``robust04``| ``test``| 249 | 528K | 69.9 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#3-robust04) |
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
[Needs More Information]
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
[Needs More Information]
### Citation Information
Cite as:
```
@inproceedings{
thakur2021beir,
title={{BEIR}: A Heterogeneous Benchmark for Zero-shot Evaluation of Information Retrieval Models},
author={Nandan Thakur and Nils Reimers and Andreas R{\"u}ckl{\'e} and Abhishek Srivastava and Iryna Gurevych},
booktitle={Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2)},
year={2021},
url={https://openreview.net/forum?id=wCu6T5xFjeJ}
}
```
### Contributions
Thanks to [@Nthakur20](https://github.com/Nthakur20) for adding this dataset. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
circa | null | 2023-01-25T14:28:00Z | 1,192 | 2 | circa | [
"task_categories:text-classification",
"task_ids:multi-class-classification",
"annotations_creators:expert-generated",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"question-answer-pair-classification",
"arxiv:2010.03450",
"region:us"
] | 2023-01-25T14:28:00Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- crowdsourced
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- multi-class-classification
paperswithcode_id: circa
pretty_name: CIRCA
tags:
- question-answer-pair-classification
dataset_info:
features:
- name: context
dtype: string
- name: question-X
dtype: string
- name: canquestion-X
dtype: string
- name: answer-Y
dtype: string
- name: judgements
dtype: string
- name: goldstandard1
dtype:
class_label:
names:
'0': 'Yes'
'1': 'No'
'2': In the middle, neither yes nor no
'3': Probably yes / sometimes yes
'4': Probably no
'5': Yes, subject to some conditions
'6': Other
'7': I am not sure how X will interpret Y’s answer
- name: goldstandard2
dtype:
class_label:
names:
'0': 'Yes'
'1': 'No'
'2': In the middle, neither yes nor no
'3': Yes, subject to some conditions
'4': Other
splits:
- name: train
num_bytes: 8149489
num_examples: 34268
download_size: 7766077
dataset_size: 8149489
---
# Dataset Card for CIRCA
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [CIRCA homepage](https://github.com/google-research-datasets/circa)
- **Repository:** [CIRCA repository](https://github.com/google-research-datasets/circa)
- **Paper:** ["I’d rather just go to bed”: Understanding Indirect Answers](https://arxiv.org/abs/2010.03450)
- **Point of Contact:** [Circa team, Google](circa@google.com)
### Dataset Summary
The Circa (meaning ‘approximately’) dataset aims to help machine learning systems to solve the problem of interpreting indirect answers to polar questions.
The dataset contains pairs of yes/no questions and indirect answers, together with annotations for the interpretation of the answer. The data is collected in 10 different social conversational situations (eg. food preferences of a friend).
The following are the situational contexts for the dialogs in the data.
```
1. X wants to know about Y’s food preferences
2. X wants to know what activities Y likes to do during weekends.
3. X wants to know what sorts of books Y likes to read.
4. Y has just moved into a neighbourhood and meets his/her new neighbour X.
5. X and Y are colleagues who are leaving work on a Friday at the same time.
6. X wants to know about Y's music preferences.
7. Y has just travelled from a different city to meet X.
8. X and Y are childhood neighbours who unexpectedly run into each other at a cafe.
9. Y has just told X that he/she is thinking of buying a flat in New York.
10. Y has just told X that he/she is considering switching his/her job.
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
The text in the dataset is in English.
## Dataset Structure
### Data Instances
The columns indicate:
```
1. id : unique id for the question-answer pair
2. context : the social situation for the dialogue. One of 10 situations (see next section). Each
situation is a dialogue between a person who poses the question (X) and the person who
answers (Y).
3. question-X : the question posed by X
4. canquestion-X : a (automatically) rewritten version of question into declarative form
Eg. Do you like Italian? --> I like Italian. See the paper for details.
5. answer-Y : the answer given by Y to X
6. judgements : the interpretations for the QA pair from 5 annotators. The value is a list of 5 strings,
separated by the token ‘#’
7. goldstandard1 : a gold standard majority judgement from the annotators. The value is the most common
interpretation and picked by at least 3 (out of 5 annotators). When a majority
judgement was not reached by the above criteria, the value is ‘NA’
8. goldstandard2 : Here the labels ‘Probably yes / sometimes yes’, ‘Probably no', and 'I am not sure how
X will interpret Y’s answer' are mapped respectively to ‘Yes’, ‘No’, and 'In the
middle, neither yes nor no’ before computing the majority. Still the label must be given
at least 3 times to become the majority choice. This method represents a less strict way
of analyzing the interpretations.
```
### Data Fields
```
id : 1
context : X wants to know about Y's food preferences.
question-X : Are you vegan?
canquestion-X : I am vegan.
answer-Y : I love burgers too much.
judgements : no#no#no#no#no
goldstandard1 : no (label(s) used for the classification task)
goldstandard2 : no (label(s) used for the classification task)
```
### Data Splits
There are no explicit train/val/test splits in this dataset.
## Dataset Creation
### Curation Rationale
They revisited a pragmatic inference problem in dialog: Understanding indirect responses to questions. Humans can interpret ‘I’m starving.’ in response to ‘Hungry?’, even without direct cue words such as ‘yes’ and ‘no’. In dialog systems, allowing natural responses rather than closed vocabularies would be similarly beneficial. However, today’s systems are only as sensitive to these pragmatic moves as their language model allows. They create and release the first large-scale English language corpus ‘Circa’ with 34,268 (polar question, indirect answer) pairs to enable progress on this task.
### Source Data
#### Initial Data Collection and Normalization
The QA pairs and judgements were collected using crowd annotations in three phases. They recruited English native speakers. The full descriptions of the data collection and quality control are present in [EMNLP 2020 paper](https://arxiv.org/pdf/2010.03450.pdf). Below is a brief overview only.
Phase 1: In the first phase, they collected questions only. They designed 10 imaginary social situations which give the annotator a context for the conversation. Examples are:
```
‘asking a friend for food preferences’
‘meeting your childhood neighbour’
‘your friend wants to buy a flat in New York’
```
Annotators were asked to suggest questions which could be asked in each situation, such that each question only requires a ‘yes’ or ‘no’ answer. 100 annotators produced 5 questions each for the 10 situations, resulting in 5000 questions.
Phase 2: Here they focused on eliciting answers to the questions. They sampled 3500 questions from our previous set. For each question, They collected possible answers from 10 different annotators. The annotators were instructed to provide a natural phrase or a sentence as the answer and to avoid the use of explicit ‘yes’ and ‘no’ words.
Phase 3: Finally the QA pairs (34,268) were given to a third set of annotators who were asked how the question seeker would likely interpret a particular answer. These annotators had the following options to choose from:
```
* 'Yes'
* 'Probably yes' / 'sometimes yes'
* 'Yes, subject to some conditions'
* 'No'
* 'Probably no'
* 'In the middle, neither yes nor no'
* 'I am not sure how X will interpret Y's answer'
```
#### Who are the source language producers?
The rest of the data apart from 10 initial questions was collected using crowd workers. They ran pilots for each step of data collection, and perused their results manually to ensure clarity in guidelines, and quality of the data. They also recruited native English speakers, mostly from the USA, and a few from the UK and Canada. They did not collect any further information about the crowd workers.
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
The rest of the data apart from 10 initial questions was collected using crowd workers. They ran pilots for each step of data collection, and perused their results manually to ensure clarity in guidelines, and quality of the data. They also recruited native English speakers, mostly from the USA, and a few from the UK and Canada. They did not collect any further information about the crowd workers.
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
This dataset is the work of Annie Louis, Dan Roth, and Filip Radlinski from Google LLC.
### Licensing Information
This dataset was made available under the Creative Commons Attribution 4.0 License. A full copy of the license can be found at https://creativecommons.org/licenses/by-sa/4.0/e and link to the license webpage if available.
### Citation Information
```
@InProceedings{louis_emnlp2020,
author = "Annie Louis and Dan Roth and Filip Radlinski",
title = ""{I}'d rather just go to bed": {U}nderstanding {I}ndirect {A}nswers",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing",
year = "2020",
}
```
### Contributions
Thanks to [@bhavitvyamalik](https://github.com/bhavitvyamalik) for adding this dataset. | [
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crystina-z/inlang-mrtydi | crystina-z | 2022-01-16T19:56:13Z | 1,192 | 0 | null | [
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multilingual_librispeech | null | 2022-11-18T21:31:47Z | 1,191 | 7 | librispeech-1 | [
"task_categories:automatic-speech-recognition",
"task_categories:audio-classification",
"task_ids:speaker-identification",
"annotations_creators:expert-generated",
"language_creators:crowdsourced",
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"language:es",
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"language:it",
"language:nl",
"language:pl",
"language:pt",
"license:cc-by-4.0",
"arxiv:2012.03411",
"region:us"
] | 2022-11-18T21:31:47Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
pretty_name: MultiLingual LibriSpeech
annotations_creators:
- expert-generated
language_creators:
- crowdsourced
- expert-generated
language:
- de
- es
- fr
- it
- nl
- pl
- pt
license:
- cc-by-4.0
multilinguality:
- multilingual
paperswithcode_id: librispeech-1
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- automatic-speech-recognition
- audio-classification
task_ids:
- speaker-identification
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---
# Dataset Card for MultiLingual LibriSpeech
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [MultiLingual LibriSpeech ASR corpus](http://www.openslr.org/94)
- **Repository:** [Needs More Information]
- **Paper:** [MLS: A Large-Scale Multilingual Dataset for Speech Research](https://arxiv.org/abs/2012.03411)
- **Leaderboard:** [Paperswithcode Leaderboard](https://paperswithcode.com/dataset/multilingual-librispeech)
### Dataset Summary
<div class="course-tip course-tip-orange bg-gradient-to-br dark:bg-gradient-to-r before:border-orange-500 dark:before:border-orange-800 from-orange-50 dark:from-gray-900 to-white dark:to-gray-950 border border-orange-50 text-orange-700 dark:text-gray-400">
<p><b>Deprecated:</b> This legacy dataset doesn't support streaming and is not updated. Use "facebook/multilingual_librispeech" instead.</p>
</div>
Multilingual LibriSpeech (MLS) dataset is a large multilingual corpus suitable for speech research. The dataset is derived from read audiobooks from LibriVox and consists of 8 languages - English, German, Dutch, Spanish, French, Italian, Portuguese, Polish.
### Supported Tasks and Leaderboards
- `automatic-speech-recognition`, `audio-speaker-identification`: The dataset can be used to train a model for Automatic Speech Recognition (ASR). The model is presented with an audio file and asked to transcribe the audio file to written text. The most common evaluation metric is the word error rate (WER). The task has an active leaderboard which can be found at https://paperswithcode.com/dataset/multilingual-librispeech and ranks models based on their WER.
### Languages
The dataset is derived from read audiobooks from LibriVox and consists of 8 languages - English, German, Dutch, Spanish, French, Italian, Portuguese, Polish
## Dataset Structure
### Data Instances
A typical data point comprises the path to the audio file, usually called `file` and its transcription, called `text`. Some additional information about the speaker and the passage which contains the transcription is provided.
```
{'chapter_id': 141231,
'file': '/home/patrick/.cache/huggingface/datasets/downloads/extracted/b7ded9969e09942ab65313e691e6fc2e12066192ee8527e21d634aca128afbe2/dev_clean/1272/141231/1272-141231-0000.flac',
'audio': {'path': '/home/patrick/.cache/huggingface/datasets/downloads/extracted/b7ded9969e09942ab65313e691e6fc2e12066192ee8527e21d634aca128afbe2/dev_clean/1272/141231/1272-141231-0000.flac',
'array': array([-0.00048828, -0.00018311, -0.00137329, ..., 0.00079346,
0.00091553, 0.00085449], dtype=float32),
'sampling_rate': 16000},
'id': '1272-141231-0000',
'speaker_id': 1272,
'text': 'A MAN SAID TO THE UNIVERSE SIR I EXIST'}
```
### Data Fields
- file: A path to the downloaded audio file in .flac format.
- audio: A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. Note that when accessing the audio column: `dataset[0]["audio"]` the audio file is automatically decoded and resampled to `dataset.features["audio"].sampling_rate`. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the `"audio"` column, *i.e.* `dataset[0]["audio"]` should **always** be preferred over `dataset["audio"][0]`.
- text: the transcription of the audio file.
- id: unique id of the data sample.
- speaker_id: unique id of the speaker. The same speaker id can be found for multiple data samples.
- chapter_id: id of the audiobook chapter which includes the transcription.
### Data Splits
| | Train | Train.9h | Train.1h | Dev | Test |
| ----- | ------ | ----- | ---- | ---- | ---- |
| german | 469942 | 2194 | 241 | 3469 | 3394 |
| dutch | 374287 | 2153 | 234 | 3095 | 3075 |
| french | 258213 | 2167 | 241 | 2416 | 2426 |
| spanish | 220701 | 2110 | 233 | 2408 | 2385 |
| italian | 59623 | 2173 | 240 | 1248 | 1262 |
| portuguese | 37533 | 2116 | 236 | 826 | 871 |
| polish | 25043 | 2173 | 238 | 512 | 520 |
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
The dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in this dataset.
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
Public Domain, Creative Commons Attribution 4.0 International Public License ([CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/legalcode))
### Citation Information
```
@article{Pratap2020MLSAL,
title={MLS: A Large-Scale Multilingual Dataset for Speech Research},
author={Vineel Pratap and Qiantong Xu and Anuroop Sriram and Gabriel Synnaeve and Ronan Collobert},
journal={ArXiv},
year={2020},
volume={abs/2012.03411}
}
```
### Contributions
Thanks to [@patrickvonplaten](https://github.com/patrickvonplaten) for adding this dataset. | [
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open-llm-leaderboard/details_Charlie911__vicuna-7b-v1.5-lora-mctaco | open-llm-leaderboard | 2023-09-17T20:27:35Z | 1,191 | 0 | null | [
"region:us"
] | 2023-09-17T20:27:35Z | 2023-09-01T09:01:17.000Z | 2023-09-01T09:01:17 | ---
pretty_name: Evaluation run of Charlie911/vicuna-7b-v1.5-lora-mctaco
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [Charlie911/vicuna-7b-v1.5-lora-mctaco](https://huggingface.co/Charlie911/vicuna-7b-v1.5-lora-mctaco)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 64 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 3 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_Charlie911__vicuna-7b-v1.5-lora-mctaco\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-09-17T20:27:23.554125](https://huggingface.co/datasets/open-llm-leaderboard/details_Charlie911__vicuna-7b-v1.5-lora-mctaco/blob/main/results_2023-09-17T20-27-23.554125.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.2488464765100671,\n\
\ \"em_stderr\": 0.004427614016278926,\n \"f1\": 0.28849937080536914,\n\
\ \"f1_stderr\": 0.00442953185165108,\n \"acc\": 0.372010258662628,\n\
\ \"acc_stderr\": 0.00929094831305589\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.2488464765100671,\n \"em_stderr\": 0.004427614016278926,\n\
\ \"f1\": 0.28849937080536914,\n \"f1_stderr\": 0.00442953185165108\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.04473085670962851,\n \
\ \"acc_stderr\": 0.005693886131407047\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.6992896606156275,\n \"acc_stderr\": 0.012888010494704732\n\
\ }\n}\n```"
repo_url: https://huggingface.co/Charlie911/vicuna-7b-v1.5-lora-mctaco
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_09_01T09_00_53.100273
path:
- '**/details_harness|arc:challenge|25_2023-09-01T09:00:53.100273.parquet'
- split: 2023_09_01T09_03_24.370765
path:
- '**/details_harness|arc:challenge|25_2023-09-01T09:03:24.370765.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-09-01T09:03:24.370765.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_09_17T20_27_23.554125
path:
- '**/details_harness|drop|3_2023-09-17T20-27-23.554125.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-09-17T20-27-23.554125.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_09_17T20_27_23.554125
path:
- '**/details_harness|gsm8k|5_2023-09-17T20-27-23.554125.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-09-17T20-27-23.554125.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_09_01T09_00_53.100273
path:
- '**/details_harness|hellaswag|10_2023-09-01T09:00:53.100273.parquet'
- split: 2023_09_01T09_03_24.370765
path:
- '**/details_harness|hellaswag|10_2023-09-01T09:03:24.370765.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-09-01T09:03:24.370765.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_09_01T09_00_53.100273
path:
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- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-01T09:03:24.370765.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-01T09:03:24.370765.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_09_01T09_00_53.100273
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-01T09:00:53.100273.parquet'
- split: 2023_09_01T09_03_24.370765
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-01T09:03:24.370765.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-01T09:03:24.370765.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_09_01T09_00_53.100273
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-01T09:00:53.100273.parquet'
- split: 2023_09_01T09_03_24.370765
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-01T09:03:24.370765.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-01T09:03:24.370765.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_09_01T09_00_53.100273
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-01T09:00:53.100273.parquet'
- split: 2023_09_01T09_03_24.370765
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-01T09:03:24.370765.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-01T09:03:24.370765.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_09_01T09_00_53.100273
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-01T09:00:53.100273.parquet'
- split: 2023_09_01T09_03_24.370765
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-01T09:03:24.370765.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-01T09:03:24.370765.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_09_01T09_00_53.100273
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-01T09:00:53.100273.parquet'
- split: 2023_09_01T09_03_24.370765
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-01T09:03:24.370765.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-01T09:03:24.370765.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_09_01T09_00_53.100273
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-01T09:00:53.100273.parquet'
- split: 2023_09_01T09_03_24.370765
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-01T09:03:24.370765.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-01T09:03:24.370765.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_09_01T09_00_53.100273
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-01T09:00:53.100273.parquet'
- split: 2023_09_01T09_03_24.370765
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-01T09:03:24.370765.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-01T09:03:24.370765.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_09_01T09_00_53.100273
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-01T09:00:53.100273.parquet'
- split: 2023_09_01T09_03_24.370765
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-01T09:03:24.370765.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-01T09:03:24.370765.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_09_01T09_00_53.100273
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-01T09:00:53.100273.parquet'
- split: 2023_09_01T09_03_24.370765
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-01T09:03:24.370765.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-01T09:03:24.370765.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_09_01T09_00_53.100273
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-01T09:00:53.100273.parquet'
- split: 2023_09_01T09_03_24.370765
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-01T09:03:24.370765.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-01T09:03:24.370765.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_09_01T09_00_53.100273
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-01T09:00:53.100273.parquet'
- split: 2023_09_01T09_03_24.370765
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-01T09:03:24.370765.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-01T09:03:24.370765.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_09_01T09_00_53.100273
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-01T09:00:53.100273.parquet'
- split: 2023_09_01T09_03_24.370765
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-01T09:03:24.370765.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-01T09:03:24.370765.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_09_01T09_00_53.100273
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-01T09:00:53.100273.parquet'
- split: 2023_09_01T09_03_24.370765
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-01T09:03:24.370765.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-01T09:03:24.370765.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
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path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-01T09:00:53.100273.parquet'
- split: 2023_09_01T09_03_24.370765
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-01T09:03:24.370765.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-01T09:03:24.370765.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_09_01T09_00_53.100273
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-01T09:00:53.100273.parquet'
- split: 2023_09_01T09_03_24.370765
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-01T09:03:24.370765.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-01T09:03:24.370765.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_09_01T09_00_53.100273
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-01T09:00:53.100273.parquet'
- split: 2023_09_01T09_03_24.370765
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-01T09:03:24.370765.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-01T09:03:24.370765.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_09_01T09_00_53.100273
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-01T09:00:53.100273.parquet'
- split: 2023_09_01T09_03_24.370765
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-01T09:03:24.370765.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-01T09:03:24.370765.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_09_01T09_00_53.100273
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-01T09:00:53.100273.parquet'
- split: 2023_09_01T09_03_24.370765
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-01T09:03:24.370765.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-01T09:03:24.370765.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_09_01T09_00_53.100273
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-01T09:00:53.100273.parquet'
- split: 2023_09_01T09_03_24.370765
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-01T09:03:24.370765.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-01T09:03:24.370765.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_09_01T09_00_53.100273
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-01T09:00:53.100273.parquet'
- split: 2023_09_01T09_03_24.370765
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-01T09:03:24.370765.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-01T09:03:24.370765.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_09_01T09_00_53.100273
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-01T09:00:53.100273.parquet'
- split: 2023_09_01T09_03_24.370765
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-01T09:03:24.370765.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-01T09:03:24.370765.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_09_01T09_00_53.100273
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-01T09:00:53.100273.parquet'
- split: 2023_09_01T09_03_24.370765
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-01T09:03:24.370765.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-01T09:03:24.370765.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_09_01T09_00_53.100273
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-01T09:00:53.100273.parquet'
- split: 2023_09_01T09_03_24.370765
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-01T09:03:24.370765.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-01T09:03:24.370765.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_09_01T09_00_53.100273
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-01T09:00:53.100273.parquet'
- split: 2023_09_01T09_03_24.370765
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-01T09:03:24.370765.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-01T09:03:24.370765.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_09_01T09_00_53.100273
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-01T09:00:53.100273.parquet'
- split: 2023_09_01T09_03_24.370765
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-01T09:03:24.370765.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-01T09:03:24.370765.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_09_01T09_00_53.100273
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-01T09:00:53.100273.parquet'
- split: 2023_09_01T09_03_24.370765
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-01T09:03:24.370765.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-01T09:03:24.370765.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_09_01T09_00_53.100273
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-01T09:00:53.100273.parquet'
- split: 2023_09_01T09_03_24.370765
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-01T09:03:24.370765.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-01T09:03:24.370765.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_09_01T09_00_53.100273
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-01T09:00:53.100273.parquet'
- split: 2023_09_01T09_03_24.370765
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-01T09:03:24.370765.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-01T09:03:24.370765.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_09_01T09_00_53.100273
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-01T09:00:53.100273.parquet'
- split: 2023_09_01T09_03_24.370765
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-01T09:03:24.370765.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-01T09:03:24.370765.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_09_01T09_00_53.100273
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-01T09:00:53.100273.parquet'
- split: 2023_09_01T09_03_24.370765
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-01T09:03:24.370765.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-01T09:03:24.370765.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_09_01T09_00_53.100273
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-01T09:00:53.100273.parquet'
- split: 2023_09_01T09_03_24.370765
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-01T09:03:24.370765.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-01T09:03:24.370765.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_09_01T09_00_53.100273
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-01T09:00:53.100273.parquet'
- split: 2023_09_01T09_03_24.370765
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-01T09:03:24.370765.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-01T09:03:24.370765.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_09_01T09_00_53.100273
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-01T09:00:53.100273.parquet'
- split: 2023_09_01T09_03_24.370765
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-01T09:03:24.370765.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-01T09:03:24.370765.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_09_01T09_00_53.100273
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-01T09:00:53.100273.parquet'
- split: 2023_09_01T09_03_24.370765
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-01T09:03:24.370765.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-01T09:03:24.370765.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_09_01T09_00_53.100273
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-09-01T09:00:53.100273.parquet'
- split: 2023_09_01T09_03_24.370765
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-09-01T09:03:24.370765.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-09-01T09:03:24.370765.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_09_01T09_00_53.100273
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-01T09:00:53.100273.parquet'
- split: 2023_09_01T09_03_24.370765
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-01T09:03:24.370765.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-01T09:03:24.370765.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_09_01T09_00_53.100273
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-01T09:00:53.100273.parquet'
- split: 2023_09_01T09_03_24.370765
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-01T09:03:24.370765.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-01T09:03:24.370765.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
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path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-01T09:00:53.100273.parquet'
- split: 2023_09_01T09_03_24.370765
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-01T09:03:24.370765.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-01T09:03:24.370765.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_09_01T09_00_53.100273
path:
- '**/details_harness|hendrycksTest-management|5_2023-09-01T09:00:53.100273.parquet'
- split: 2023_09_01T09_03_24.370765
path:
- '**/details_harness|hendrycksTest-management|5_2023-09-01T09:03:24.370765.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-09-01T09:03:24.370765.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_09_01T09_00_53.100273
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-09-01T09:00:53.100273.parquet'
- split: 2023_09_01T09_03_24.370765
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-09-01T09:03:24.370765.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-09-01T09:03:24.370765.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_09_01T09_00_53.100273
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-01T09:00:53.100273.parquet'
- split: 2023_09_01T09_03_24.370765
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-01T09:03:24.370765.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-01T09:03:24.370765.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_09_01T09_00_53.100273
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-01T09:00:53.100273.parquet'
- split: 2023_09_01T09_03_24.370765
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-01T09:03:24.370765.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-01T09:03:24.370765.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_09_01T09_00_53.100273
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-01T09:00:53.100273.parquet'
- split: 2023_09_01T09_03_24.370765
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-01T09:03:24.370765.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-01T09:03:24.370765.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_09_01T09_00_53.100273
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-01T09:00:53.100273.parquet'
- split: 2023_09_01T09_03_24.370765
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-01T09:03:24.370765.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-01T09:03:24.370765.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_09_01T09_00_53.100273
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-01T09:00:53.100273.parquet'
- split: 2023_09_01T09_03_24.370765
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-01T09:03:24.370765.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-01T09:03:24.370765.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_09_01T09_00_53.100273
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-01T09:00:53.100273.parquet'
- split: 2023_09_01T09_03_24.370765
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-01T09:03:24.370765.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-01T09:03:24.370765.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
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path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-01T09:00:53.100273.parquet'
- split: 2023_09_01T09_03_24.370765
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-01T09:03:24.370765.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-01T09:03:24.370765.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_09_01T09_00_53.100273
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-01T09:00:53.100273.parquet'
- split: 2023_09_01T09_03_24.370765
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-01T09:03:24.370765.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-01T09:03:24.370765.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_09_01T09_00_53.100273
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-01T09:00:53.100273.parquet'
- split: 2023_09_01T09_03_24.370765
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-01T09:03:24.370765.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-01T09:03:24.370765.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_09_01T09_00_53.100273
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-01T09:00:53.100273.parquet'
- split: 2023_09_01T09_03_24.370765
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-01T09:03:24.370765.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-01T09:03:24.370765.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_09_01T09_00_53.100273
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-01T09:00:53.100273.parquet'
- split: 2023_09_01T09_03_24.370765
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-01T09:03:24.370765.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-01T09:03:24.370765.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_09_01T09_00_53.100273
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-01T09:00:53.100273.parquet'
- split: 2023_09_01T09_03_24.370765
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-01T09:03:24.370765.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-01T09:03:24.370765.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_09_01T09_00_53.100273
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-01T09:00:53.100273.parquet'
- split: 2023_09_01T09_03_24.370765
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-01T09:03:24.370765.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-01T09:03:24.370765.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_09_01T09_00_53.100273
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-09-01T09:00:53.100273.parquet'
- split: 2023_09_01T09_03_24.370765
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-09-01T09:03:24.370765.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-09-01T09:03:24.370765.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_09_01T09_00_53.100273
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-01T09:00:53.100273.parquet'
- split: 2023_09_01T09_03_24.370765
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-01T09:03:24.370765.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-01T09:03:24.370765.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_09_01T09_00_53.100273
path:
- '**/details_harness|hendrycksTest-virology|5_2023-09-01T09:00:53.100273.parquet'
- split: 2023_09_01T09_03_24.370765
path:
- '**/details_harness|hendrycksTest-virology|5_2023-09-01T09:03:24.370765.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-09-01T09:03:24.370765.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_09_01T09_00_53.100273
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-01T09:00:53.100273.parquet'
- split: 2023_09_01T09_03_24.370765
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-01T09:03:24.370765.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-01T09:03:24.370765.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_09_01T09_00_53.100273
path:
- '**/details_harness|truthfulqa:mc|0_2023-09-01T09:00:53.100273.parquet'
- split: 2023_09_01T09_03_24.370765
path:
- '**/details_harness|truthfulqa:mc|0_2023-09-01T09:03:24.370765.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-09-01T09:03:24.370765.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_09_17T20_27_23.554125
path:
- '**/details_harness|winogrande|5_2023-09-17T20-27-23.554125.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-09-17T20-27-23.554125.parquet'
- config_name: results
data_files:
- split: 2023_09_01T09_00_53.100273
path:
- results_2023-09-01T09:00:53.100273.parquet
- split: 2023_09_01T09_03_24.370765
path:
- results_2023-09-01T09:03:24.370765.parquet
- split: 2023_09_17T20_27_23.554125
path:
- results_2023-09-17T20-27-23.554125.parquet
- split: latest
path:
- results_2023-09-17T20-27-23.554125.parquet
---
# Dataset Card for Evaluation run of Charlie911/vicuna-7b-v1.5-lora-mctaco
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/Charlie911/vicuna-7b-v1.5-lora-mctaco
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** clementine@hf.co
### Dataset Summary
Dataset automatically created during the evaluation run of model [Charlie911/vicuna-7b-v1.5-lora-mctaco](https://huggingface.co/Charlie911/vicuna-7b-v1.5-lora-mctaco) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 3 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_Charlie911__vicuna-7b-v1.5-lora-mctaco",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-09-17T20:27:23.554125](https://huggingface.co/datasets/open-llm-leaderboard/details_Charlie911__vicuna-7b-v1.5-lora-mctaco/blob/main/results_2023-09-17T20-27-23.554125.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.2488464765100671,
"em_stderr": 0.004427614016278926,
"f1": 0.28849937080536914,
"f1_stderr": 0.00442953185165108,
"acc": 0.372010258662628,
"acc_stderr": 0.00929094831305589
},
"harness|drop|3": {
"em": 0.2488464765100671,
"em_stderr": 0.004427614016278926,
"f1": 0.28849937080536914,
"f1_stderr": 0.00442953185165108
},
"harness|gsm8k|5": {
"acc": 0.04473085670962851,
"acc_stderr": 0.005693886131407047
},
"harness|winogrande|5": {
"acc": 0.6992896606156275,
"acc_stderr": 0.012888010494704732
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
open-web-math/open-web-math | open-web-math | 2023-10-17T20:14:00Z | 1,188 | 172 | null | [
"arxiv:2310.06786",
"region:us"
] | 2023-10-17T20:14:00Z | 2023-09-06T00:25:12.000Z | 2023-09-06T00:25:12 | ---
dataset_info:
features:
- name: url
dtype: string
- name: text
dtype: string
- name: date
dtype: string
- name: metadata
dtype: string
splits:
- name: train
num_bytes: 56651995057
num_examples: 6315233
download_size: 16370689925
dataset_size: 56651995057
license: odc-by
task_categories:
- text-generation
language:
- en
pretty_name: OpenWebMath
size_categories:
- 10B<n<100B
---
<img src="imgs/OpenWebMath-left.png" width="300">
[Keiran Paster](https://keirp.com)\*, [Marco Dos Santos](https://marco-dossantos.github.io/)\*, [Zhangir Azerbayev](https://zhangir-azerbayev.github.io/), [Jimmy Ba](https://jimmylba.github.io/)
[GitHub ](https://github.com/keirp/OpenWebMath) | [ArXiv](https://arxiv.org/abs/2310.06786)
| [PDF](https://arxiv.org/pdf/2310.06786.pdf)
**OpenWebMath** is a dataset containing the majority of the high-quality, mathematical text from the internet. It is filtered and extracted from over 200B HTML files on Common Crawl down to a set of **6.3 million documents** containing a total of **14.7B tokens**. OpenWebMath is intended for use in _pretraining_ and _finetuning_ large language models.
You can download the dataset using Hugging Face:
```python
from datasets import load_dataset
ds = load_dataset("open-web-math/open-web-math")
```
# OpenWebMath Contents
The dataset is structured as follows:
```python
{
"text": ..., # document text.
"url": ..., # document url.
"date": ..., # date the page was crawled.
"metadata": ..., # JSON containing information from the extraction process.
}
```
OpenWebMath contains documents from over 130k different domains, including data from forums, educational pages, and blogs. The dataset contains documents covering mathematics, physics, statistics, computer science, and more. The following table shows the most common domains in OpenWebMath by character count.
| Domain | # Characters | % Characters |
| ----------------- | ------------- | ------------ |
| stackexchange.com | 4,655,132,784 | 9.55% |
| nature.com | 1,529,935,838 | 3.14% |
| wordpress.com | 1,294,166,938 | 2.66% |
| physicsforums.com | 1,160,137,919 | 2.38% |
| github.io | 725,689,722 | 1.49% |
| zbmath.org | 620,019,503 | 1.27% |
| wikipedia.org | 618,024,754 | 1.27% |
| groundai.com | 545,214,990 | 1.12% |
| blogspot.com | 520,392,333 | 1.07% |
| mathoverflow.net | 499,102,560 | 1.02% |
# OpenWebMath Pipeline
<img src="imgs/pipeline.png" alt="Overview of the OpenWebMath Pipeline">
OpenWebMath builds on the massive [Common Crawl](https://commoncrawl.org/) dataset, which contains over 200B HTML documents. We filtered the data to only include documents that are: (1) in English, (2) contain mathematical content, and (3) are of high quality. We also put a strong emphasis on extracting LaTeX content from the HTML documents as well as reducing boilerplate in comparison to other web datasets.
The OpenWebMath pipeline consists of five steps:
1. **Prefiltering HTML Documents**:
- We apply a simple prefilter to all HTML documents in Common Crawl in order to skip documents without mathematical content to unnecessary processing time.
2. **Text Extraction**:
- Extract text, including LaTeX content, from the HTML documents while removing boilerplate.
3. **Content Classification and Filtering**:
- Apply a [FastText language identification model](https://fasttext.cc/docs/en/language-identification.html) to keep only English documents.
- Filter high perplexity documents using a [KenLM](https://github.com/kpu/kenlm) model trained on [Proof-Pile](https://huggingface.co/datasets/hoskinson-center/proof-pile).
- Filter non-mathematical documents using our own _MathScore_ model.
4. **Deduplication**:
- Deduplicate the dataset using SimHash in [text-dedup](https://github.com/ChenghaoMou/text-dedup).
5. **Manual Inspection**:
- Inspect the documents gathered from previous steps and remove low quality pages.
For a detailed discussion on the processing pipeline, please refer to our paper.
# License
OpenWebMath is made available under an ODC-By 1.0 license; users should also abide by the CommonCrawl ToU: [https://commoncrawl.org/terms-of-use/](https://commoncrawl.org/terms-of-use/). We do not alter the license of any of the underlying data.
# Citation Information
```
@misc{paster2023openwebmath,
title={OpenWebMath: An Open Dataset of High-Quality Mathematical Web Text},
author={Keiran Paster and Marco Dos Santos and Zhangir Azerbayev and Jimmy Ba},
year={2023},
eprint={2310.06786},
archivePrefix={arXiv},
primaryClass={cs.AI}
}
```
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head_qa | null | 2023-06-01T14:59:51Z | 1,186 | 7 | headqa | [
"task_categories:question-answering",
"task_ids:multiple-choice-qa",
"annotations_creators:no-annotation",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"language:es",
"license:mit",
"region:us"
] | 2023-06-01T14:59:51Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- no-annotation
language_creators:
- expert-generated
language:
- en
- es
license:
- mit
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- question-answering
task_ids:
- multiple-choice-qa
paperswithcode_id: headqa
pretty_name: HEAD-QA
dataset_info:
- config_name: es
features:
- name: name
dtype: string
- name: year
dtype: string
- name: category
dtype: string
- name: qid
dtype: int32
- name: qtext
dtype: string
- name: ra
dtype: int32
- name: image
dtype: image
- name: answers
list:
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dtype: int32
- name: atext
dtype: string
splits:
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num_bytes: 1229678
num_examples: 2657
- name: test
num_bytes: 1204006
num_examples: 2742
- name: validation
num_bytes: 573354
num_examples: 1366
download_size: 79365502
dataset_size: 3007038
- config_name: en
features:
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dtype: string
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dtype: string
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dtype: string
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dtype: int32
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dtype: string
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dtype: int32
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list:
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splits:
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- name: test
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num_examples: 2742
- name: validation
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num_examples: 1366
download_size: 79365502
dataset_size: 2828236
config_names:
- en
- es
---
# Dataset Card for HEAD-QA
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [HEAD-QA homepage](https://aghie.github.io/head-qa/)
- **Repository:** [HEAD-QA repository](https://github.com/aghie/head-qa)
- **Paper:** [HEAD-QA: A Healthcare Dataset for Complex Reasoning](https://www.aclweb.org/anthology/P19-1092/)
- **Leaderboard:** [HEAD-QA leaderboard](https://aghie.github.io/head-qa/#leaderboard-general)
- **Point of Contact:** [María Grandury](mailto:mariagrandury@gmail.com) (Dataset Submitter)
### Dataset Summary
HEAD-QA is a multi-choice HEAlthcare Dataset. The questions come from exams to access a specialized position in the
Spanish healthcare system, and are challenging even for highly specialized humans. They are designed by the
[Ministerio de Sanidad, Consumo y Bienestar Social](https://www.mscbs.gob.es/), who also provides direct
[access](https://fse.mscbs.gob.es/fseweb/view/public/datosanteriores/cuadernosExamen/busquedaConvocatoria.xhtml)
to the exams of the last 5 years (in Spanish).
```
Date of the last update of the documents object of the reuse: January, 14th, 2019.
```
HEAD-QA tries to make these questions accesible for the Natural Language Processing community. We hope it is an useful resource towards achieving better QA systems. The dataset contains questions about the following topics:
- Medicine
- Nursing
- Psychology
- Chemistry
- Pharmacology
- Biology
### Supported Tasks and Leaderboards
- `multiple-choice-qa`: HEAD-QA is a multi-choice question answering testbed to encourage research on complex reasoning.
### Languages
The questions and answers are available in both Spanish (BCP-47 code: 'es-ES') and English (BCP-47 code: 'en').
The language by default is Spanish:
```
from datasets import load_dataset
data_es = load_dataset('head_qa')
data_en = load_dataset('head_qa', 'en')
```
## Dataset Structure
### Data Instances
A typical data point comprises a question `qtext`, multiple possible answers `atext` and the right answer `ra`.
An example from the HEAD-QA dataset looks as follows:
```
{
'qid': '1',
'category': 'biology',
'qtext': 'Los potenciales postsinápticos excitadores:',
'answers': [
{
'aid': 1,
'atext': 'Son de tipo todo o nada.'
},
{
'aid': 2,
'atext': 'Son hiperpolarizantes.'
},
{
'aid': 3,
'atext': 'Se pueden sumar.'
},
{
'aid': 4,
'atext': 'Se propagan a largas distancias.'
},
{
'aid': 5,
'atext': 'Presentan un periodo refractario.'
}],
'ra': '3',
'image': <PIL.PngImagePlugin.PngImageFile image mode=RGB size=675x538 at 0x1B42B6A1668>,
'name': 'Cuaderno_2013_1_B',
'year': '2013'
}
```
### Data Fields
- `qid`: question identifier (int)
- `category`: category of the question: "medicine", "nursing", "psychology", "chemistry", "pharmacology", "biology"
- `qtext`: question text
- `answers`: list of possible answers. Each element of the list is a dictionary with 2 keys:
- `aid`: answer identifier (int)
- `atext`: answer text
- `ra`: `aid` of the right answer (int)
- `image`: (optional) a `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`
- `name`: name of the exam from which the question was extracted
- `year`: year in which the exam took place
### Data Splits
The data is split into train, validation and test set for each of the two languages. The split sizes are as follow:
| | Train | Val | Test |
| ----- | ------ | ----- | ---- |
| Spanish | 2657 | 1366 | 2742 |
| English | 2657 | 1366 | 2742 |
## Dataset Creation
### Curation Rationale
As motivation for the creation of this dataset, here is the abstract of the paper:
"We present HEAD-QA, a multi-choice question answering testbed to encourage research on complex reasoning. The questions
come from exams to access a specialized position in the Spanish healthcare system, and are challenging even for highly
specialized humans. We then consider monolingual (Spanish) and cross-lingual (to English) experiments with information
retrieval and neural techniques. We show that: (i) HEAD-QA challenges current methods, and (ii) the results lag well
behind human performance, demonstrating its usefulness as a benchmark for future work."
### Source Data
#### Initial Data Collection and Normalization
The questions come from exams to access a specialized position in the Spanish healthcare system, and are designed by the
[Ministerio de Sanidad, Consumo y Bienestar Social](https://www.mscbs.gob.es/), who also provides direct
[access](https://fse.mscbs.gob.es/fseweb/view/public/datosanteriores/cuadernosExamen/busquedaConvocatoria.xhtml)
to the exams of the last 5 years (in Spanish).
#### Who are the source language producers?
The dataset was created by David Vilares and Carlos Gómez-Rodríguez.
### Annotations
The dataset does not contain any additional annotations.
#### Annotation process
[N/A]
#### Who are the annotators?
[N/A]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
The dataset was created by David Vilares and Carlos Gómez-Rodríguez.
### Licensing Information
According to the [HEAD-QA homepage](https://aghie.github.io/head-qa/#legal-requirements):
The Ministerio de Sanidad, Consumo y Biniestar Social allows the redistribution of the exams and their content under [certain conditions:](https://www.mscbs.gob.es/avisoLegal/home.htm)
- The denaturalization of the content of the information is prohibited in any circumstance.
- The user is obliged to cite the source of the documents subject to reuse.
- The user is obliged to indicate the date of the last update of the documents object of the reuse.
According to the [HEAD-QA repository](https://github.com/aghie/head-qa/blob/master/LICENSE):
The dataset is licensed under the [MIT License](https://mit-license.org/).
### Citation Information
```
@inproceedings{vilares-gomez-rodriguez-2019-head,
title = "{HEAD}-{QA}: A Healthcare Dataset for Complex Reasoning",
author = "Vilares, David and
G{\'o}mez-Rodr{\'i}guez, Carlos",
booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/P19-1092",
doi = "10.18653/v1/P19-1092",
pages = "960--966",
abstract = "We present HEAD-QA, a multi-choice question answering testbed to encourage research on complex reasoning. The questions come from exams to access a specialized position in the Spanish healthcare system, and are challenging even for highly specialized humans. We then consider monolingual (Spanish) and cross-lingual (to English) experiments with information retrieval and neural techniques. We show that: (i) HEAD-QA challenges current methods, and (ii) the results lag well behind human performance, demonstrating its usefulness as a benchmark for future work.",
}
```
### Contributions
Thanks to [@mariagrandury](https://github.com/mariagrandury) for adding this dataset. | [
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google/MusicCaps | google | 2023-03-08T14:37:09Z | 1,184 | 82 | null | [
"task_categories:text-to-speech",
"language:en",
"license:cc-by-sa-4.0",
"arxiv:2301.11325",
"region:us"
] | 2023-03-08T14:37:09Z | 2023-01-27T16:26:11.000Z | 2023-01-27T16:26:11 | ---
license:
- cc-by-sa-4.0
converted_from: kaggle
kaggle_id: googleai/musiccaps
task_categories:
- text-to-speech
language:
- en
---
# Dataset Card for MusicCaps
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://kaggle.com/datasets/googleai/musiccaps
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
The MusicCaps dataset contains **5,521 music examples, each of which is labeled with an English *aspect list* and a *free text caption* written by musicians**. An aspect list is for example *"pop, tinny wide hi hats, mellow piano melody, high pitched female vocal melody, sustained pulsating synth lead"*, while the caption consists of multiple sentences about the music, e.g.,
*"A low sounding male voice is rapping over a fast paced drums playing a reggaeton beat along with a bass. Something like a guitar is playing the melody along. This recording is of poor audio-quality. In the background a laughter can be noticed. This song may be playing in a bar."*
The text is solely focused on describing *how* the music sounds, not the metadata like the artist name.
The labeled examples are 10s music clips from the [**AudioSet**](https://research.google.com/audioset/) dataset (2,858 from the eval and 2,663 from the train split).
Please cite the corresponding paper, when using this dataset: http://arxiv.org/abs/2301.11325 (DOI: `10.48550/arXiv.2301.11325`)
### Dataset Usage
The published dataset takes the form of a `.csv` file that contains the ID of YouTube videos and their start/end stamps. In order to use this dataset, one must download the corresponding YouTube videos and chunk them according to the start/end times.
The following repository has an example script and notebook to load the clips. The notebook also includes a Gradio demo that helps explore some samples: https://github.com/nateraw/download-musiccaps-dataset
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
#### ytid
YT ID pointing to the YouTube video in which the labeled music segment appears. You can listen to the segment by opening https://youtu.be/watch?v={ytid}&start={start_s}
#### start_s
Position in the YouTube video at which the music starts.
#### end_s
Position in the YouTube video at which the music end. All clips are 10s long.
#### audioset_positive_labels
Labels for this segment from the AudioSet (https://research.google.com/audioset/) dataset.
#### aspect_list
A list of aspects describing the music.
#### caption
A multi-sentence free text caption describing the music.
#### author_id
An integer for grouping samples by who wrote them.
#### is_balanced_subset
If this value is true, the row is a part of the 1k subset which is genre-balanced.
#### is_audioset_eval
If this value is true, the clip is from the AudioSet eval split. Otherwise it is from the AudioSet train split.
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
This dataset was shared by [@googleai](https://ai.google/research/)
### Licensing Information
The license for this dataset is cc-by-sa-4.0
### Citation Information
```bibtex
[More Information Needed]
```
### Contributions
[More Information Needed] | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
AdaptLLM/finance-tasks | AdaptLLM | 2023-11-07T12:07:15Z | 1,177 | 7 | null | [
"arxiv:2309.09530",
"region:us"
] | 2023-11-07T12:07:15Z | 2023-09-19T03:17:07.000Z | 2023-09-19T03:17:07 | ---
configs:
- config_name: ConvFinQA
data_files:
- split: test
path: "ConviFinQA/test.json"
- config_name: FiQA_SA
data_files:
- split: test
path: "FiQA_SA/test.json"
- config_name: FPB
data_files:
- split: test
path: "FPB/test.json"
- config_name: Headline
data_files:
- split: test
path: "Headline/test.json"
- config_name: NER
data_files:
- split: test
path: "NER/test.json"
---
# Adapt Large Language Models to Domains
This repo contains the evaluation datasets for our paper [Adapting Large Language Models via Reading Comprehension](https://huggingface.co/papers/2309.09530)
We explore **continued pre-training on domain-specific corpora** for large language models. While this approach enriches LLMs with domain knowledge, it significantly hurts their prompting ability for question answering. Inspired by human learning via reading comprehension, we propose a simple method to **transform large-scale pre-training corpora into reading comprehension texts**, consistently improving prompting performance across tasks in **biomedicine, finance, and law domains**. Our 7B model competes with much larger domain-specific models like BloombergGPT-50B. Moreover, our domain-specific reading comprehension texts enhance model performance even on general benchmarks, indicating potential for developing a general LLM across more domains.
## GitHub repo:
https://github.com/microsoft/LMOps
## Domain-specific LLMs:
Our models of different domains are now available in Huggingface: [Biomedicine-LLM](https://huggingface.co/AdaptLLM/medicine-LLM), [Finance-LLM](https://huggingface.co/AdaptLLM/finance-LLM) and [Law-LLM](https://huggingface.co/AdaptLLM/law-LLM), the performances of our AdaptLLM compared to other domain-specific LLMs are:
<p align='center'>
<img src="./comparison.png" width="700">
</p>
## Domain-specific Tasks:
To easily reproduce our results, we have uploaded the filled-in zero/few-shot input instructions and output completions of each domain-specific task: [biomedicine-tasks](https://huggingface.co/datasets/AdaptLLM/medicine-tasks), [finance-tasks](https://huggingface.co/datasets/AdaptLLM/finance-tasks), and [law-tasks](https://huggingface.co/datasets/AdaptLLM/law-tasks).
## Citation:
```bibtex
@article{adaptllm,
author = {Daixuan Cheng and Shaohan Huang and Furu Wei},
title = {Adapting Large Language Models via Reading Comprehension},
journal = {CoRR},
volume = {abs/2309.09530},
year = {2023}
}
```
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
gmongaras/BERT_Base_Cased_512_Dataset_Mapped_NoPunct | gmongaras | 2023-11-28T19:43:46Z | 1,177 | 0 | null | [
"region:us"
] | 2023-11-28T19:43:46Z | 2023-11-11T21:32:01.000Z | 2023-11-11T21:32:01 | ---
dataset_info:
features:
- name: input_ids
sequence: int32
- name: token_type_ids
sequence: int8
- name: attention_mask
sequence: int8
splits:
- name: train
num_bytes: 36846531540
num_examples: 109375187
download_size: 12766235344
dataset_size: 36846531540
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
Dataset using the bert-cased tokenizer, cutoff sentences to 512 length (not sentence pairs), all sentence pairs extracted.
Original datasets:
https://huggingface.co/datasets/bookcorpus
https://huggingface.co/datasets/wikipedia Variant: 20220301.en
Mapped from: https://huggingface.co/datasets/gmongaras/BERT_Base_Cased_512_Dataset_NoPunct | [
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ostapeno/qa-openai_batched_icl0_clen512_maxD-1_maxC2500_0_cleaned_test | ostapeno | 2023-11-03T18:46:02Z | 1,175 | 0 | null | [
"region:us"
] | 2023-11-03T18:46:02Z | 2023-11-03T18:45:54.000Z | 2023-11-03T18:45:54 | ---
configs:
- config_name: default
data_files:
- split: abstract_algebra
path: data/abstract_algebra-*
- split: college_biology
path: data/college_biology-*
- split: formal_logic
path: data/formal_logic-*
- split: global_facts
path: data/global_facts-*
- split: high_school_government_and_politics
path: data/high_school_government_and_politics-*
- split: high_school_physics
path: data/high_school_physics-*
- split: machine_learning
path: data/machine_learning-*
- split: prehistory
path: data/prehistory-*
- split: security_studies
path: data/security_studies-*
- split: sociology
path: data/sociology-*
dataset_info:
features:
- name: id
dtype: string
- name: context
dtype: string
- name: docno
dtype: string
- name: subject
dtype: string
- name: icl_examples
dtype: 'null'
- name: author_instr
dtype: string
- name: instruction
dtype: string
- name: response
dtype: string
splits:
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num_bytes: 12694041
num_examples: 4568
- name: college_biology
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num_examples: 3883
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- name: global_facts
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- name: high_school_government_and_politics
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- name: high_school_physics
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- name: machine_learning
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- name: prehistory
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- name: security_studies
num_bytes: 9658325
num_examples: 3492
- name: sociology
num_bytes: 9248599
num_examples: 3362
download_size: 16862867
dataset_size: 113836391
---
# Dataset Card for "qa-openai_batched_icl0_clen512_maxD-1_maxC2500_0_cleaned_test"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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crystina-z/inlang-mrtydi-corpus | crystina-z | 2022-01-17T15:24:18Z | 1,173 | 0 | null | [
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aharley/rvl_cdip | aharley | 2023-05-02T09:06:16Z | 1,170 | 30 | rvl-cdip | [
"task_categories:image-classification",
"task_ids:multi-class-image-classification",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:extended|iit_cdip",
"language:en",
"license:other",
"arxiv:1502.07058",
"region:us"
] | 2023-05-02T09:06:16Z | 2022-04-21T14:21:01.000Z | 2022-04-21T14:21:01 | ---
annotations_creators:
- found
language_creators:
- found
language:
- en
license:
- other
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- extended|iit_cdip
task_categories:
- image-classification
task_ids:
- multi-class-image-classification
paperswithcode_id: rvl-cdip
pretty_name: RVL-CDIP
viewer: false
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': letter
'1': form
'2': email
'3': handwritten
'4': advertisement
'5': scientific report
'6': scientific publication
'7': specification
'8': file folder
'9': news article
'10': budget
'11': invoice
'12': presentation
'13': questionnaire
'14': resume
'15': memo
splits:
- name: train
num_bytes: 38816373360
num_examples: 320000
- name: test
num_bytes: 4863300853
num_examples: 40000
- name: validation
num_bytes: 4868685208
num_examples: 40000
download_size: 38779484559
dataset_size: 48548359421
---
# Dataset Card for RVL-CDIP
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-instances)
- [Data Splits](#data-instances)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
- **Homepage:** [The RVL-CDIP Dataset](https://www.cs.cmu.edu/~aharley/rvl-cdip/)
- **Repository:**
- **Paper:** [Evaluation of Deep Convolutional Nets for Document Image Classification and Retrieval](https://arxiv.org/abs/1502.07058)
- **Leaderboard:** [RVL-CDIP leaderboard](https://paperswithcode.com/dataset/rvl-cdip)
- **Point of Contact:** [Adam W. Harley](mailto:aharley@cmu.edu)
### Dataset Summary
The RVL-CDIP (Ryerson Vision Lab Complex Document Information Processing) dataset consists of 400,000 grayscale images in 16 classes, with 25,000 images per class. There are 320,000 training images, 40,000 validation images, and 40,000 test images. The images are sized so their largest dimension does not exceed 1000 pixels.
### Supported Tasks and Leaderboards
- `image-classification`: The goal of this task is to classify a given document into one of 16 classes representing document types (letter, form, etc.). The leaderboard for this task is available [here](https://paperswithcode.com/sota/document-image-classification-on-rvl-cdip).
### Languages
All the classes and documents use English as their primary language.
## Dataset Structure
### Data Instances
A sample from the training set is provided below :
```
{
'image': <PIL.TiffImagePlugin.TiffImageFile image mode=L size=754x1000 at 0x7F9A5E92CA90>,
'label': 15
}
```
### Data Fields
- `image`: A `PIL.Image.Image` object containing a document.
- `label`: an `int` classification label.
<details>
<summary>Class Label Mappings</summary>
```json
{
"0": "letter",
"1": "form",
"2": "email",
"3": "handwritten",
"4": "advertisement",
"5": "scientific report",
"6": "scientific publication",
"7": "specification",
"8": "file folder",
"9": "news article",
"10": "budget",
"11": "invoice",
"12": "presentation",
"13": "questionnaire",
"14": "resume",
"15": "memo"
}
```
</details>
### Data Splits
| |train|test|validation|
|----------|----:|----:|---------:|
|# of examples|320000|40000|40000|
The dataset was split in proportions similar to those of ImageNet.
- 320000 images were used for training,
- 40000 images for validation, and
- 40000 images for testing.
## Dataset Creation
### Curation Rationale
From the paper:
> This work makes available a new labelled subset of the IIT-CDIP collection, containing 400,000
document images across 16 categories, useful for training new CNNs for document analysis.
### Source Data
#### Initial Data Collection and Normalization
The same as in the IIT-CDIP collection.
#### Who are the source language producers?
The same as in the IIT-CDIP collection.
### Annotations
#### Annotation process
The same as in the IIT-CDIP collection.
#### Who are the annotators?
The same as in the IIT-CDIP collection.
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
The dataset was curated by the authors - Adam W. Harley, Alex Ufkes, and Konstantinos G. Derpanis.
### Licensing Information
RVL-CDIP is a subset of IIT-CDIP, which came from the [Legacy Tobacco Document Library](https://www.industrydocuments.ucsf.edu/tobacco/), for which license information can be found [here](https://www.industrydocuments.ucsf.edu/help/copyright/).
### Citation Information
```bibtex
@inproceedings{harley2015icdar,
title = {Evaluation of Deep Convolutional Nets for Document Image Classification and Retrieval},
author = {Adam W Harley and Alex Ufkes and Konstantinos G Derpanis},
booktitle = {International Conference on Document Analysis and Recognition ({ICDAR})}},
year = {2015}
}
```
### Contributions
Thanks to [@dnaveenr](https://github.com/dnaveenr) for adding this dataset. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
CM/codexglue_codetrans | CM | 2023-04-27T23:09:43Z | 1,166 | 0 | null | [
"region:us"
] | 2023-04-27T23:09:43Z | 2023-04-22T01:07:30.000Z | 2023-04-22T01:07:30 | ---
dataset_info:
features:
- name: id
dtype: int32
- name: java
dtype: string
- name: cs
dtype: string
splits:
- name: train
num_bytes: 4372641
num_examples: 10300
- name: validation
num_bytes: 226407
num_examples: 500
- name: test
num_bytes: 418587
num_examples: 1000
download_size: 0
dataset_size: 5017635
---
# Dataset Card for "codexglue_codetrans"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
PKU-Alignment/PKU-SafeRLHF-10K | PKU-Alignment | 2023-07-20T16:29:15Z | 1,164 | 45 | null | [
"task_categories:text-generation",
"size_categories:10K<n<100K",
"language:en",
"license:cc-by-nc-4.0",
"safe",
"safety",
"ai-safety",
"llm",
"lm",
"human-feedback",
"rlhf",
"safe-rlhf",
"arxiv:2307.04657",
"region:us"
] | 2023-07-20T16:29:15Z | 2023-05-03T11:28:45.000Z | 2023-05-03T11:28:45 | ---
license: cc-by-nc-4.0
task_categories:
- text-generation
language:
- en
tags:
- safe
- safety
- ai-safety
- llm
- lm
- human-feedback
- rlhf
- safe-rlhf
size_categories:
- 10K<n<100K
---
## Paper
You can find more information in our paper.
- **Dataset Paper:** <https://arxiv.org/abs/2307.04657>
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
Cubpaw/voxelgym_5c_42x42_25000 | Cubpaw | 2023-05-31T21:28:34Z | 1,157 | 0 | null | [
"region:us"
] | 2023-05-31T21:28:34Z | 2023-05-31T21:28:02.000Z | 2023-05-31T21:28:02 | ---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype: image
- name: rgb_label
dtype: image
- name: path_label
dtype: image
- name: path_rgb_label
dtype: image
splits:
- name: train
num_bytes: 18480640.0
num_examples: 20000
- name: validation
num_bytes: 4639555.0
num_examples: 5000
download_size: 17635174
dataset_size: 23120195.0
---
# Dataset Card for "voxelgym_5c_42x42_25000"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
cfilt/iitb-english-hindi | cfilt | 2022-04-26T13:50:22Z | 1,156 | 12 | null | [
"region:us"
] | 2022-04-26T13:50:22Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | <p align="center"><img src="https://huggingface.co/datasets/cfilt/HiNER-collapsed/raw/main/cfilt-dark-vec.png" alt="Computation for Indian Language Technology Logo" width="150" height="150"/></p>
# IITB-English-Hindi Parallel Corpus
[](https://creativecommons.org/licenses/by-nc/4.0/)
[](https://twitter.com/cfiltnlp)
[](https://twitter.com/PeopleCentredAI)
## About
The IIT Bombay English-Hindi corpus contains parallel corpus for English-Hindi as well as monolingual Hindi corpus collected from a variety of existing sources and corpora developed at the Center for Indian Language Technology, IIT Bombay over the years. This page describes the corpus. This corpus has been used at the Workshop on Asian Language Translation Shared Task since 2016 the Hindi-to-English and English-to-Hindi languages pairs and as a pivot language pair for the Hindi-to-Japanese and Japanese-to-Hindi language pairs.
The complete details of this corpus are available at [this URL](https://www.cfilt.iitb.ac.in/iitb_parallel/). We also provide this parallel corpus via browser download from the same URL. We also provide a monolingual Hindi corpus on the same URL.
### Recent Updates
* Version 3.1 - December 2021 - Added 49,400 sentence pairs to the parallel corpus.
* Version 3.0 - August 2020 - Added ~47,000 sentence pairs to the parallel corpus.
## Usage
We provide a notebook that shows how to import the IITB English-Hindi Parallel Corpus from the HuggingFace datasets repository. The notebook also shows how to segment the corpus using BPE tokenization which can be used to train an English-Hindi MT System.
[https://github.com/cfiltnlp/IITB-English-Hindi-PC](https://github.com/cfiltnlp/IITB-English-Hindi-PC)
## Other
You can find a catalogue of other English-Hindi and other Indian language parallel corpora here: [Indic NLP Catalog](https://github.com/indicnlpweb/indicnlp_catalog)
## Maintainer(s)
[Diptesh Kanojia](https://dipteshkanojia.github.io)<br/>
Shivam Mhasker<br/>
## Citation
If you use this corpus or its derivate resources for your research, kindly cite it as follows:
Anoop Kunchukuttan, Pratik Mehta, Pushpak Bhattacharyya. The IIT Bombay English-Hindi Parallel Corpus. Language Resources and Evaluation Conference. 2018.
### BiBTeX Citation
```latex
@inproceedings{kunchukuttan-etal-2018-iit,
title = "The {IIT} {B}ombay {E}nglish-{H}indi Parallel Corpus",
author = "Kunchukuttan, Anoop and
Mehta, Pratik and
Bhattacharyya, Pushpak",
booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
month = may,
year = "2018",
address = "Miyazaki, Japan",
publisher = "European Language Resources Association (ELRA)",
url = "https://aclanthology.org/L18-1548",
}
```
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BeIR/nq | BeIR | 2022-10-23T06:02:24Z | 1,156 | 2 | beir | [
"task_categories:text-retrieval",
"task_ids:entity-linking-retrieval",
"task_ids:fact-checking-retrieval",
"multilinguality:monolingual",
"language:en",
"license:cc-by-sa-4.0",
"region:us"
] | 2022-10-23T06:02:24Z | 2022-06-05T16:37:56.000Z | 2022-06-05T16:37:56 | ---
annotations_creators: []
language_creators: []
language:
- en
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
paperswithcode_id: beir
pretty_name: BEIR Benchmark
size_categories:
msmarco:
- 1M<n<10M
trec-covid:
- 100k<n<1M
nfcorpus:
- 1K<n<10K
nq:
- 1M<n<10M
hotpotqa:
- 1M<n<10M
fiqa:
- 10K<n<100K
arguana:
- 1K<n<10K
touche-2020:
- 100K<n<1M
cqadupstack:
- 100K<n<1M
quora:
- 100K<n<1M
dbpedia:
- 1M<n<10M
scidocs:
- 10K<n<100K
fever:
- 1M<n<10M
climate-fever:
- 1M<n<10M
scifact:
- 1K<n<10K
source_datasets: []
task_categories:
- text-retrieval
- zero-shot-retrieval
- information-retrieval
- zero-shot-information-retrieval
task_ids:
- passage-retrieval
- entity-linking-retrieval
- fact-checking-retrieval
- tweet-retrieval
- citation-prediction-retrieval
- duplication-question-retrieval
- argument-retrieval
- news-retrieval
- biomedical-information-retrieval
- question-answering-retrieval
---
# Dataset Card for BEIR Benchmark
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://github.com/UKPLab/beir
- **Repository:** https://github.com/UKPLab/beir
- **Paper:** https://openreview.net/forum?id=wCu6T5xFjeJ
- **Leaderboard:** https://docs.google.com/spreadsheets/d/1L8aACyPaXrL8iEelJLGqlMqXKPX2oSP_R10pZoy77Ns
- **Point of Contact:** nandan.thakur@uwaterloo.ca
### Dataset Summary
BEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:
- Fact-checking: [FEVER](http://fever.ai), [Climate-FEVER](http://climatefever.ai), [SciFact](https://github.com/allenai/scifact)
- Question-Answering: [NQ](https://ai.google.com/research/NaturalQuestions), [HotpotQA](https://hotpotqa.github.io), [FiQA-2018](https://sites.google.com/view/fiqa/)
- Bio-Medical IR: [TREC-COVID](https://ir.nist.gov/covidSubmit/index.html), [BioASQ](http://bioasq.org), [NFCorpus](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/)
- News Retrieval: [TREC-NEWS](https://trec.nist.gov/data/news2019.html), [Robust04](https://trec.nist.gov/data/robust/04.guidelines.html)
- Argument Retrieval: [Touche-2020](https://webis.de/events/touche-20/shared-task-1.html), [ArguAna](tp://argumentation.bplaced.net/arguana/data)
- Duplicate Question Retrieval: [Quora](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs), [CqaDupstack](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/)
- Citation-Prediction: [SCIDOCS](https://allenai.org/data/scidocs)
- Tweet Retrieval: [Signal-1M](https://research.signal-ai.com/datasets/signal1m-tweetir.html)
- Entity Retrieval: [DBPedia](https://github.com/iai-group/DBpedia-Entity/)
All these datasets have been preprocessed and can be used for your experiments.
```python
```
### Supported Tasks and Leaderboards
The dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.
The current best performing models can be found [here](https://eval.ai/web/challenges/challenge-page/689/leaderboard/).
### Languages
All tasks are in English (`en`).
## Dataset Structure
All BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:
- `corpus` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with three fields `_id` with unique document identifier, `title` with document title (optional) and `text` with document paragraph or passage. For example: `{"_id": "doc1", "title": "Albert Einstein", "text": "Albert Einstein was a German-born...."}`
- `queries` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with two fields `_id` with unique query identifier and `text` with query text. For example: `{"_id": "q1", "text": "Who developed the mass-energy equivalence formula?"}`
- `qrels` file: a `.tsv` file (tab-seperated) that contains three columns, i.e. the `query-id`, `corpus-id` and `score` in this order. Keep 1st row as header. For example: `q1 doc1 1`
### Data Instances
A high level example of any beir dataset:
```python
corpus = {
"doc1" : {
"title": "Albert Einstein",
"text": "Albert Einstein was a German-born theoretical physicist. who developed the theory of relativity, \
one of the two pillars of modern physics (alongside quantum mechanics). His work is also known for \
its influence on the philosophy of science. He is best known to the general public for his mass–energy \
equivalence formula E = mc2, which has been dubbed 'the world's most famous equation'. He received the 1921 \
Nobel Prize in Physics 'for his services to theoretical physics, and especially for his discovery of the law \
of the photoelectric effect', a pivotal step in the development of quantum theory."
},
"doc2" : {
"title": "", # Keep title an empty string if not present
"text": "Wheat beer is a top-fermented beer which is brewed with a large proportion of wheat relative to the amount of \
malted barley. The two main varieties are German Weißbier and Belgian witbier; other types include Lambic (made\
with wild yeast), Berliner Weisse (a cloudy, sour beer), and Gose (a sour, salty beer)."
},
}
queries = {
"q1" : "Who developed the mass-energy equivalence formula?",
"q2" : "Which beer is brewed with a large proportion of wheat?"
}
qrels = {
"q1" : {"doc1": 1},
"q2" : {"doc2": 1},
}
```
### Data Fields
Examples from all configurations have the following features:
### Corpus
- `corpus`: a `dict` feature representing the document title and passage text, made up of:
- `_id`: a `string` feature representing the unique document id
- `title`: a `string` feature, denoting the title of the document.
- `text`: a `string` feature, denoting the text of the document.
### Queries
- `queries`: a `dict` feature representing the query, made up of:
- `_id`: a `string` feature representing the unique query id
- `text`: a `string` feature, denoting the text of the query.
### Qrels
- `qrels`: a `dict` feature representing the query document relevance judgements, made up of:
- `_id`: a `string` feature representing the query id
- `_id`: a `string` feature, denoting the document id.
- `score`: a `int32` feature, denoting the relevance judgement between query and document.
### Data Splits
| Dataset | Website| BEIR-Name | Type | Queries | Corpus | Rel D/Q | Down-load | md5 |
| -------- | -----| ---------| --------- | ----------- | ---------| ---------| :----------: | :------:|
| MSMARCO | [Homepage](https://microsoft.github.io/msmarco/)| ``msmarco`` | ``train``<br>``dev``<br>``test``| 6,980 | 8.84M | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/msmarco.zip) | ``444067daf65d982533ea17ebd59501e4`` |
| TREC-COVID | [Homepage](https://ir.nist.gov/covidSubmit/index.html)| ``trec-covid``| ``test``| 50| 171K| 493.5 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/trec-covid.zip) | ``ce62140cb23feb9becf6270d0d1fe6d1`` |
| NFCorpus | [Homepage](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/) | ``nfcorpus`` | ``train``<br>``dev``<br>``test``| 323 | 3.6K | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nfcorpus.zip) | ``a89dba18a62ef92f7d323ec890a0d38d`` |
| BioASQ | [Homepage](http://bioasq.org) | ``bioasq``| ``train``<br>``test`` | 500 | 14.91M | 8.05 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#2-bioasq) |
| NQ | [Homepage](https://ai.google.com/research/NaturalQuestions) | ``nq``| ``train``<br>``test``| 3,452 | 2.68M | 1.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nq.zip) | ``d4d3d2e48787a744b6f6e691ff534307`` |
| HotpotQA | [Homepage](https://hotpotqa.github.io) | ``hotpotqa``| ``train``<br>``dev``<br>``test``| 7,405 | 5.23M | 2.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/hotpotqa.zip) | ``f412724f78b0d91183a0e86805e16114`` |
| FiQA-2018 | [Homepage](https://sites.google.com/view/fiqa/) | ``fiqa`` | ``train``<br>``dev``<br>``test``| 648 | 57K | 2.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fiqa.zip) | ``17918ed23cd04fb15047f73e6c3bd9d9`` |
| Signal-1M(RT) | [Homepage](https://research.signal-ai.com/datasets/signal1m-tweetir.html)| ``signal1m`` | ``test``| 97 | 2.86M | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#4-signal-1m) |
| TREC-NEWS | [Homepage](https://trec.nist.gov/data/news2019.html) | ``trec-news`` | ``test``| 57 | 595K | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#1-trec-news) |
| ArguAna | [Homepage](http://argumentation.bplaced.net/arguana/data) | ``arguana``| ``test`` | 1,406 | 8.67K | 1.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/arguana.zip) | ``8ad3e3c2a5867cdced806d6503f29b99`` |
| Touche-2020| [Homepage](https://webis.de/events/touche-20/shared-task-1.html) | ``webis-touche2020``| ``test``| 49 | 382K | 19.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/webis-touche2020.zip) | ``46f650ba5a527fc69e0a6521c5a23563`` |
| CQADupstack| [Homepage](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/) | ``cqadupstack``| ``test``| 13,145 | 457K | 1.4 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/cqadupstack.zip) | ``4e41456d7df8ee7760a7f866133bda78`` |
| Quora| [Homepage](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs) | ``quora``| ``dev``<br>``test``| 10,000 | 523K | 1.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/quora.zip) | ``18fb154900ba42a600f84b839c173167`` |
| DBPedia | [Homepage](https://github.com/iai-group/DBpedia-Entity/) | ``dbpedia-entity``| ``dev``<br>``test``| 400 | 4.63M | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/dbpedia-entity.zip) | ``c2a39eb420a3164af735795df012ac2c`` |
| SCIDOCS| [Homepage](https://allenai.org/data/scidocs) | ``scidocs``| ``test``| 1,000 | 25K | 4.9 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scidocs.zip) | ``38121350fc3a4d2f48850f6aff52e4a9`` |
| FEVER | [Homepage](http://fever.ai) | ``fever``| ``train``<br>``dev``<br>``test``| 6,666 | 5.42M | 1.2| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fever.zip) | ``5a818580227bfb4b35bb6fa46d9b6c03`` |
| Climate-FEVER| [Homepage](http://climatefever.ai) | ``climate-fever``|``test``| 1,535 | 5.42M | 3.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/climate-fever.zip) | ``8b66f0a9126c521bae2bde127b4dc99d`` |
| SciFact| [Homepage](https://github.com/allenai/scifact) | ``scifact``| ``train``<br>``test``| 300 | 5K | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scifact.zip) | ``5f7d1de60b170fc8027bb7898e2efca1`` |
| Robust04 | [Homepage](https://trec.nist.gov/data/robust/04.guidelines.html) | ``robust04``| ``test``| 249 | 528K | 69.9 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#3-robust04) |
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
[Needs More Information]
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
[Needs More Information]
### Citation Information
Cite as:
```
@inproceedings{
thakur2021beir,
title={{BEIR}: A Heterogeneous Benchmark for Zero-shot Evaluation of Information Retrieval Models},
author={Nandan Thakur and Nils Reimers and Andreas R{\"u}ckl{\'e} and Abhishek Srivastava and Iryna Gurevych},
booktitle={Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2)},
year={2021},
url={https://openreview.net/forum?id=wCu6T5xFjeJ}
}
```
### Contributions
Thanks to [@Nthakur20](https://github.com/Nthakur20) for adding this dataset. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
lj_speech | null | 2023-11-23T14:05:45Z | 1,150 | 10 | ljspeech | [
"task_categories:automatic-speech-recognition",
"task_categories:text-to-speech",
"task_categories:text-to-audio",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:unlicense",
"region:us"
] | 2023-11-23T14:05:45Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- en
license:
- unlicense
multilinguality:
- monolingual
paperswithcode_id: ljspeech
pretty_name: LJ Speech
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- automatic-speech-recognition
- text-to-speech
- text-to-audio
task_ids: []
train-eval-index:
- config: main
task: automatic-speech-recognition
task_id: speech_recognition
splits:
train_split: train
col_mapping:
file: path
text: text
metrics:
- type: wer
name: WER
- type: cer
name: CER
dataset_info:
features:
- name: id
dtype: string
- name: audio
dtype:
audio:
sampling_rate: 22050
- name: file
dtype: string
- name: text
dtype: string
- name: normalized_text
dtype: string
config_name: main
splits:
- name: train
num_bytes: 4667022
num_examples: 13100
download_size: 2748572632
dataset_size: 4667022
---
# Dataset Card for lj_speech
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [The LJ Speech Dataset](https://keithito.com/LJ-Speech-Dataset/)
- **Repository:** [N/A]
- **Paper:** [N/A]
- **Leaderboard:** [Paperswithcode Leaderboard](https://paperswithcode.com/sota/text-to-speech-synthesis-on-ljspeech)
- **Point of Contact:** [Keith Ito](mailto:kito@kito.us)
### Dataset Summary
This is a public domain speech dataset consisting of 13,100 short audio clips of a single speaker reading passages from 7 non-fiction books in English. A transcription is provided for each clip. Clips vary in length from 1 to 10 seconds and have a total length of approximately 24 hours.
The texts were published between 1884 and 1964, and are in the public domain. The audio was recorded in 2016-17 by the LibriVox project and is also in the public domain.
### Supported Tasks and Leaderboards
The dataset can be used to train a model for Automatic Speech Recognition (ASR) or Text-to-Speech (TTS).
- `automatic-speech-recognition`: An ASR model is presented with an audio file and asked to transcribe the audio file to written text.
The most common ASR evaluation metric is the word error rate (WER).
- `text-to-speech`, `text-to-audio`: A TTS model is given a written text in natural language and asked to generate a speech audio file.
A reasonable evaluation metric is the mean opinion score (MOS) of audio quality.
The dataset has an active leaderboard which can be found at https://paperswithcode.com/sota/text-to-speech-synthesis-on-ljspeech
### Languages
The transcriptions and audio are in English.
## Dataset Structure
### Data Instances
A data point comprises the path to the audio file, called `file` and its transcription, called `text`.
A normalized version of the text is also provided.
```
{
'id': 'LJ002-0026',
'file': '/datasets/downloads/extracted/05bfe561f096e4c52667e3639af495226afe4e5d08763f2d76d069e7a453c543/LJSpeech-1.1/wavs/LJ002-0026.wav',
'audio': {'path': '/datasets/downloads/extracted/05bfe561f096e4c52667e3639af495226afe4e5d08763f2d76d069e7a453c543/LJSpeech-1.1/wavs/LJ002-0026.wav',
'array': array([-0.00048828, -0.00018311, -0.00137329, ..., 0.00079346,
0.00091553, 0.00085449], dtype=float32),
'sampling_rate': 22050},
'text': 'in the three years between 1813 and 1816,'
'normalized_text': 'in the three years between eighteen thirteen and eighteen sixteen,',
}
```
Each audio file is a single-channel 16-bit PCM WAV with a sample rate of 22050 Hz.
### Data Fields
- id: unique id of the data sample.
- file: a path to the downloaded audio file in .wav format.
- audio: A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. Note that when accessing the audio column: `dataset[0]["audio"]` the audio file is automatically decoded and resampled to `dataset.features["audio"].sampling_rate`. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the `"audio"` column, *i.e.* `dataset[0]["audio"]` should **always** be preferred over `dataset["audio"][0]`.
- text: the transcription of the audio file.
- normalized_text: the transcription with numbers, ordinals, and monetary units expanded into full words.
### Data Splits
The dataset is not pre-split. Some statistics:
- Total Clips: 13,100
- Total Words: 225,715
- Total Characters: 1,308,678
- Total Duration: 23:55:17
- Mean Clip Duration: 6.57 sec
- Min Clip Duration: 1.11 sec
- Max Clip Duration: 10.10 sec
- Mean Words per Clip: 17.23
- Distinct Words: 13,821
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
This dataset consists of excerpts from the following works:
- Morris, William, et al. Arts and Crafts Essays. 1893.
- Griffiths, Arthur. The Chronicles of Newgate, Vol. 2. 1884.
- Roosevelt, Franklin D. The Fireside Chats of Franklin Delano Roosevelt. 1933-42.
- Harland, Marion. Marion Harland's Cookery for Beginners. 1893.
- Rolt-Wheeler, Francis. The Science - History of the Universe, Vol. 5: Biology. 1910.
- Banks, Edgar J. The Seven Wonders of the Ancient World. 1916.
- President's Commission on the Assassination of President Kennedy. Report of the President's Commission on the Assassination of President Kennedy. 1964.
Some details about normalization:
- The normalized transcription has the numbers, ordinals, and monetary units expanded into full words (UTF-8)
- 19 of the transcriptions contain non-ASCII characters (for example, LJ016-0257 contains "raison d'être").
- The following abbreviations appear in the text. They may be expanded as follows:
| Abbreviation | Expansion |
|--------------|-----------|
| Mr. | Mister |
| Mrs. | Misess (*) |
| Dr. | Doctor |
| No. | Number |
| St. | Saint |
| Co. | Company |
| Jr. | Junior |
| Maj. | Major |
| Gen. | General |
| Drs. | Doctors |
| Rev. | Reverend |
| Lt. | Lieutenant |
| Hon. | Honorable |
| Sgt. | Sergeant |
| Capt. | Captain |
| Esq. | Esquire |
| Ltd. | Limited |
| Col. | Colonel |
| Ft. | Fort |
(*) there's no standard expansion for "Mrs."
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
- The audio clips range in length from approximately 1 second to 10 seconds. They were segmented automatically based on silences in the recording. Clip boundaries generally align with sentence or clause boundaries, but not always.
- The text was matched to the audio manually, and a QA pass was done to ensure that the text accurately matched the words spoken in the audio.
#### Who are the annotators?
Recordings by Linda Johnson from LibriVox. Alignment and annotation by Keith Ito.
### Personal and Sensitive Information
The dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in this dataset.
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
- The original LibriVox recordings were distributed as 128 kbps MP3 files. As a result, they may contain artifacts introduced by the MP3 encoding.
## Additional Information
### Dataset Curators
The dataset was initially created by Keith Ito and Linda Johnson.
### Licensing Information
Public Domain ([LibriVox](https://librivox.org/pages/public-domain/))
### Citation Information
```
@misc{ljspeech17,
author = {Keith Ito and Linda Johnson},
title = {The LJ Speech Dataset},
howpublished = {\url{https://keithito.com/LJ-Speech-Dataset/}},
year = 2017
}
```
### Contributions
Thanks to [@anton-l](https://github.com/anton-l) for adding this dataset. | [
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gsarti/clean_mc4_it | gsarti | 2022-10-23T09:01:21Z | 1,149 | 6 | mc4 | [
"task_categories:text-generation",
"task_ids:language-modeling",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:monolingual",
"source_datasets:extended",
"language:it",
"license:odc-by",
"arxiv:1910.10683",
"arxiv:2203.03759",
"region:us"
] | 2022-10-23T09:01:21Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- it
license:
- odc-by
multilinguality:
- monolingual
size_categories:
tiny:
- 1M<n<10M
small:
- 10M<n<100M
medium:
- 10M<n<100M
large:
- 10M<n<100M
full:
- 100M<n<1B
source_datasets:
- extended
task_categories:
- text-generation
task_ids:
- language-modeling
paperswithcode_id: mc4
pretty_name: mC4_it
---
# Dataset Card for Clean Italian mC4 🇮🇹
## Table of Contents
- [Dataset Card for Clean](#dataset-card-for-mc4)
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Preprocessing](#preprocessing)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Original Homepage:** [HF Hub](https://huggingface.co/datasets/allenai/c4)
- **Paper:** [ArXiv](https://arxiv.org/abs/1910.10683)
### Dataset Summary
A thoroughly cleaned version of the Italian split of the multilingual colossal, cleaned version of Common Crawl's web crawl corpus (mC4). Based on the [Common Crawl dataset](https://commoncrawl.org). The original version was prepared by [AllenAI](https://allenai.org/), hosted at the address [https://huggingface.co/datasets/allenai/c4](https://huggingface.co/datasets/allenai/c4), with subsequent preprocessing performed by [Gabriele Sarti](https://gsarti.com) following a standard procedure for all dataset shards.
### Preprocessing
The preprocessing of the dataset follows the procedure used by Yeb Havinga for training the model [`t5-base-dutch`](https://huggingface.co/flax-community/t5-base-dutch) on a portion of the cleaned Dutch split of mC4. The original code, that was adapted for Italian in this case, is available on [GitLab](https://gitlab.com/yhavinga/c4nlpreproc). In summary, the preprocessing procedure includes:
- Removing documents containing words from a selection of the [Italian and English List of Dirty Naught Obscene and Otherwise Bad Words](https://github.com/LDNOOBW/List-of-Dirty-Naughty-Obscene-and-Otherwise-Bad-Words).
- Removing sentences containing:
- Less than 3 words.
- A word longer than 1000 characters.
- An end symbol not matching end-of-sentence punctuation.
- Strings associated to javascript code (e.g. `{`), lorem ipsum, policy information in Italian or English.
- Removing documents (after sentence filtering):
- Containing less than 5 sentences.
- Containing less than 500 or more than 50'000 characters.
- Not identified as prevalently Italian by the `LangDetect` package.
Using parallel processing with 96 CPU cores on a TPUv3 via Google Cloud to perform the complete clean of all the original Italian shards of mC4 (1024 of ~220Mb train, 8 of ~24Mb validation) required roughly 10 hours due to the demanding steps of sentence tokenization and language detection. The total size of compressed `.json.gz` files is roughly halved after the procedure.
## Dataset Structure
### Data Instances
An example from the dataset:
```
{
'timestamp': '2020-02-22T22:24:31Z',
'url': 'https://altreconomia.it/una-rotonda-sul-pane/',
'text': 'Per raggiungere il campo attraversiamo la striscia d’asfalto che porta verso la provinciale numero 13. Mettiamo a rischio la nostra incolumità in un territorio di auto e camion. Sullo sfondo, i profili della Grigna e del Resegone. Più vicini, quelli del solito ipermercato di provincia, e delle villette a schiera che avanzano tra le coltivazioni. È lo sprawling, l’avanzata del cemento.\\nDa questo lato dalla strada, invece, è ancora regno contadino. Almeno per ora. Torniamo a Caponago (Mb), Brianza pura, dove ha avuto i natali il progetto “Spiga e madia”. Ne parlammo su Ae nel gennaio 2009: in un territorio “spaesato”, il Comitato “verso il Distretto di economia solidale della Brianza” (Desbri) e la “Retina” dei gruppi di acquisto locali danno vita a un progetto di produzione di frumento, molitura, panificazione e distribuzione in un raggio di 20 chilometri. Si comincia da zero, nel 2007, senza alcun di finanziamento, quando una famiglia del [...]. Il giochino vale almeno 3 miliardi di euro all’anno. La misura, introdotta in via straordinaria con la finanziaria 2005, è stata prorogata anche con l’ultimo decreto “milleproroghe”.'
}
```
### Data Fields
The data contains the following fields:
- `url`: url of the source as a string
- `text`: text content as a string
- `timestamp`: timestamp of extraction as a string
### Data Splits
To build mC4, the original authors used [CLD3](https://github.com/google/cld3) to identify over 100 languages. For Italian, the whole corpus of scraped text was divided in `1032` jsonl files, `1024` for training following the naming style `c4-it.tfrecord-0XXXX-of-01024.json.gz` and 8 for validation following the naming style `c4-it-validation.tfrecord-0000X-of-00008.json.gz`. The full set of preprocessed files takes roughly 215GB of disk space to download with Git LFS.
For ease of use under different storage capacities, the following incremental splits are available (sizes are estimates). **Important**: The sizes in GB represent the estimated weight for :
|split |train size (docs, words, download + preproc disk space)|validation size|
|:-----|------------------------------------------------------:|--------------:|
|tiny | 10M docs, 4B words (9 GB + 27 GB) | 12k docs |
|small | 20M docs, 8B words (18 GB + 54 GB) | 24k docs |
|medium| 50M docs, 20B words (47 GB + 135 GB) | 48k docs |
|large | 75M docs, 30B words (71 GB + 203 GB) | 72k docs |
|full | 103M docs, 41B words (109 GB + 279 GB) | 96k docs |
You can load any subset like this:
```python
from datasets import load_dataset
mc4_it_tiny = load_dataset("gsarti/clean_mc4_it", "tiny")
```
Since splits are quite large, you may want to traverse them using the streaming mode available starting from 🤗 Datasets v1.9.0:
```python
from datasets import load_dataset
mc4_it_full_stream = load_dataset("gsarti/clean_mc4_it", "full", split='train', streaming=True)
print(next(iter(mc4_it_full_stream))) # Prints the example presented above
```
## Dataset Creation
Refer to the original paper for more considerations regarding the choice of sources and the scraping process for creating `mC4`.
## Considerations for Using the Data
### Social Impact of Dataset
With more than 200GB of cleaned Italian text and more than 41B estimated words, this is by far the largest available corpus for the Italian language. The second largest dataset available is [OSCAR](https://oscar-corpus.com/), which is only 69GB in size for its deduplicated variant. Using this corpus for training language models with adequate computational resources will allow researchers to reach parity with the performances observed for the English language. This can in turn have important repercussions for the development of commercial language technology applications for the Italian language.
### Discussion of Biases
Despit the cleaning procedure aimed at removing vulgarity and profanity, it must be considered that model trained on this scraped corpus will inevitably reflect biases present in blog articles and comments on the Internet. This makes the corpus especially interesting in the context of studying data biases and how to limit their impacts.
## Additional Information
### Dataset Curators
Authors at AllenAI are the original curators for the `mc4` corpus. For inquiries or requests regarding the Italian cleaned portion contained in this repository, please contact me at [gabriele.sarti996@gmail.com](mailto:gabriele.sarti996@gmail.com)
### Licensing Information
AllenAI are releasing this dataset under the terms of ODC-BY. By using this, you are also bound by the Common Crawl terms of use in respect of the content contained in the dataset.
### Citation Information
If you use this dataset in your work, please cite us and the original mC4 authors as:
```
@article{sarti-nissim-2022-it5,
title={IT5: Large-scale Text-to-text Pretraining for Italian Language Understanding and Generation},
author={Sarti, Gabriele and Nissim, Malvina},
journal={ArXiv preprint 2203.03759},
url={https://arxiv.org/abs/2203.03759},
year={2022},
month={mar}
}
@inproceedings{xue-etal-2021-mt5,
title = "m{T}5: A Massively Multilingual Pre-trained Text-to-Text Transformer",
author = "Xue, Linting and
Constant, Noah and
Roberts, Adam and
Kale, Mihir and
Al-Rfou, Rami and
Siddhant, Aditya and
Barua, Aditya and
Raffel, Colin",
booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jun,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.naacl-main.41",
doi = "10.18653/v1/2021.naacl-main.41",
pages = "483--498",
}
```
### Contributions
Thanks to [@dirkgr](https://github.com/dirkgr) and [@lhoestq](https://github.com/lhoestq) for adding this dataset.
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
BeIR/fever | BeIR | 2022-10-23T06:04:31Z | 1,147 | 2 | beir | [
"task_categories:text-retrieval",
"task_ids:entity-linking-retrieval",
"task_ids:fact-checking-retrieval",
"multilinguality:monolingual",
"language:en",
"license:cc-by-sa-4.0",
"region:us"
] | 2022-10-23T06:04:31Z | 2022-06-05T16:58:21.000Z | 2022-06-05T16:58:21 | ---
annotations_creators: []
language_creators: []
language:
- en
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
paperswithcode_id: beir
pretty_name: BEIR Benchmark
size_categories:
msmarco:
- 1M<n<10M
trec-covid:
- 100k<n<1M
nfcorpus:
- 1K<n<10K
nq:
- 1M<n<10M
hotpotqa:
- 1M<n<10M
fiqa:
- 10K<n<100K
arguana:
- 1K<n<10K
touche-2020:
- 100K<n<1M
cqadupstack:
- 100K<n<1M
quora:
- 100K<n<1M
dbpedia:
- 1M<n<10M
scidocs:
- 10K<n<100K
fever:
- 1M<n<10M
climate-fever:
- 1M<n<10M
scifact:
- 1K<n<10K
source_datasets: []
task_categories:
- text-retrieval
- zero-shot-retrieval
- information-retrieval
- zero-shot-information-retrieval
task_ids:
- passage-retrieval
- entity-linking-retrieval
- fact-checking-retrieval
- tweet-retrieval
- citation-prediction-retrieval
- duplication-question-retrieval
- argument-retrieval
- news-retrieval
- biomedical-information-retrieval
- question-answering-retrieval
---
# Dataset Card for BEIR Benchmark
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://github.com/UKPLab/beir
- **Repository:** https://github.com/UKPLab/beir
- **Paper:** https://openreview.net/forum?id=wCu6T5xFjeJ
- **Leaderboard:** https://docs.google.com/spreadsheets/d/1L8aACyPaXrL8iEelJLGqlMqXKPX2oSP_R10pZoy77Ns
- **Point of Contact:** nandan.thakur@uwaterloo.ca
### Dataset Summary
BEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:
- Fact-checking: [FEVER](http://fever.ai), [Climate-FEVER](http://climatefever.ai), [SciFact](https://github.com/allenai/scifact)
- Question-Answering: [NQ](https://ai.google.com/research/NaturalQuestions), [HotpotQA](https://hotpotqa.github.io), [FiQA-2018](https://sites.google.com/view/fiqa/)
- Bio-Medical IR: [TREC-COVID](https://ir.nist.gov/covidSubmit/index.html), [BioASQ](http://bioasq.org), [NFCorpus](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/)
- News Retrieval: [TREC-NEWS](https://trec.nist.gov/data/news2019.html), [Robust04](https://trec.nist.gov/data/robust/04.guidelines.html)
- Argument Retrieval: [Touche-2020](https://webis.de/events/touche-20/shared-task-1.html), [ArguAna](tp://argumentation.bplaced.net/arguana/data)
- Duplicate Question Retrieval: [Quora](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs), [CqaDupstack](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/)
- Citation-Prediction: [SCIDOCS](https://allenai.org/data/scidocs)
- Tweet Retrieval: [Signal-1M](https://research.signal-ai.com/datasets/signal1m-tweetir.html)
- Entity Retrieval: [DBPedia](https://github.com/iai-group/DBpedia-Entity/)
All these datasets have been preprocessed and can be used for your experiments.
```python
```
### Supported Tasks and Leaderboards
The dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.
The current best performing models can be found [here](https://eval.ai/web/challenges/challenge-page/689/leaderboard/).
### Languages
All tasks are in English (`en`).
## Dataset Structure
All BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:
- `corpus` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with three fields `_id` with unique document identifier, `title` with document title (optional) and `text` with document paragraph or passage. For example: `{"_id": "doc1", "title": "Albert Einstein", "text": "Albert Einstein was a German-born...."}`
- `queries` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with two fields `_id` with unique query identifier and `text` with query text. For example: `{"_id": "q1", "text": "Who developed the mass-energy equivalence formula?"}`
- `qrels` file: a `.tsv` file (tab-seperated) that contains three columns, i.e. the `query-id`, `corpus-id` and `score` in this order. Keep 1st row as header. For example: `q1 doc1 1`
### Data Instances
A high level example of any beir dataset:
```python
corpus = {
"doc1" : {
"title": "Albert Einstein",
"text": "Albert Einstein was a German-born theoretical physicist. who developed the theory of relativity, \
one of the two pillars of modern physics (alongside quantum mechanics). His work is also known for \
its influence on the philosophy of science. He is best known to the general public for his mass–energy \
equivalence formula E = mc2, which has been dubbed 'the world's most famous equation'. He received the 1921 \
Nobel Prize in Physics 'for his services to theoretical physics, and especially for his discovery of the law \
of the photoelectric effect', a pivotal step in the development of quantum theory."
},
"doc2" : {
"title": "", # Keep title an empty string if not present
"text": "Wheat beer is a top-fermented beer which is brewed with a large proportion of wheat relative to the amount of \
malted barley. The two main varieties are German Weißbier and Belgian witbier; other types include Lambic (made\
with wild yeast), Berliner Weisse (a cloudy, sour beer), and Gose (a sour, salty beer)."
},
}
queries = {
"q1" : "Who developed the mass-energy equivalence formula?",
"q2" : "Which beer is brewed with a large proportion of wheat?"
}
qrels = {
"q1" : {"doc1": 1},
"q2" : {"doc2": 1},
}
```
### Data Fields
Examples from all configurations have the following features:
### Corpus
- `corpus`: a `dict` feature representing the document title and passage text, made up of:
- `_id`: a `string` feature representing the unique document id
- `title`: a `string` feature, denoting the title of the document.
- `text`: a `string` feature, denoting the text of the document.
### Queries
- `queries`: a `dict` feature representing the query, made up of:
- `_id`: a `string` feature representing the unique query id
- `text`: a `string` feature, denoting the text of the query.
### Qrels
- `qrels`: a `dict` feature representing the query document relevance judgements, made up of:
- `_id`: a `string` feature representing the query id
- `_id`: a `string` feature, denoting the document id.
- `score`: a `int32` feature, denoting the relevance judgement between query and document.
### Data Splits
| Dataset | Website| BEIR-Name | Type | Queries | Corpus | Rel D/Q | Down-load | md5 |
| -------- | -----| ---------| --------- | ----------- | ---------| ---------| :----------: | :------:|
| MSMARCO | [Homepage](https://microsoft.github.io/msmarco/)| ``msmarco`` | ``train``<br>``dev``<br>``test``| 6,980 | 8.84M | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/msmarco.zip) | ``444067daf65d982533ea17ebd59501e4`` |
| TREC-COVID | [Homepage](https://ir.nist.gov/covidSubmit/index.html)| ``trec-covid``| ``test``| 50| 171K| 493.5 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/trec-covid.zip) | ``ce62140cb23feb9becf6270d0d1fe6d1`` |
| NFCorpus | [Homepage](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/) | ``nfcorpus`` | ``train``<br>``dev``<br>``test``| 323 | 3.6K | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nfcorpus.zip) | ``a89dba18a62ef92f7d323ec890a0d38d`` |
| BioASQ | [Homepage](http://bioasq.org) | ``bioasq``| ``train``<br>``test`` | 500 | 14.91M | 8.05 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#2-bioasq) |
| NQ | [Homepage](https://ai.google.com/research/NaturalQuestions) | ``nq``| ``train``<br>``test``| 3,452 | 2.68M | 1.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nq.zip) | ``d4d3d2e48787a744b6f6e691ff534307`` |
| HotpotQA | [Homepage](https://hotpotqa.github.io) | ``hotpotqa``| ``train``<br>``dev``<br>``test``| 7,405 | 5.23M | 2.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/hotpotqa.zip) | ``f412724f78b0d91183a0e86805e16114`` |
| FiQA-2018 | [Homepage](https://sites.google.com/view/fiqa/) | ``fiqa`` | ``train``<br>``dev``<br>``test``| 648 | 57K | 2.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fiqa.zip) | ``17918ed23cd04fb15047f73e6c3bd9d9`` |
| Signal-1M(RT) | [Homepage](https://research.signal-ai.com/datasets/signal1m-tweetir.html)| ``signal1m`` | ``test``| 97 | 2.86M | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#4-signal-1m) |
| TREC-NEWS | [Homepage](https://trec.nist.gov/data/news2019.html) | ``trec-news`` | ``test``| 57 | 595K | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#1-trec-news) |
| ArguAna | [Homepage](http://argumentation.bplaced.net/arguana/data) | ``arguana``| ``test`` | 1,406 | 8.67K | 1.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/arguana.zip) | ``8ad3e3c2a5867cdced806d6503f29b99`` |
| Touche-2020| [Homepage](https://webis.de/events/touche-20/shared-task-1.html) | ``webis-touche2020``| ``test``| 49 | 382K | 19.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/webis-touche2020.zip) | ``46f650ba5a527fc69e0a6521c5a23563`` |
| CQADupstack| [Homepage](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/) | ``cqadupstack``| ``test``| 13,145 | 457K | 1.4 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/cqadupstack.zip) | ``4e41456d7df8ee7760a7f866133bda78`` |
| Quora| [Homepage](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs) | ``quora``| ``dev``<br>``test``| 10,000 | 523K | 1.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/quora.zip) | ``18fb154900ba42a600f84b839c173167`` |
| DBPedia | [Homepage](https://github.com/iai-group/DBpedia-Entity/) | ``dbpedia-entity``| ``dev``<br>``test``| 400 | 4.63M | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/dbpedia-entity.zip) | ``c2a39eb420a3164af735795df012ac2c`` |
| SCIDOCS| [Homepage](https://allenai.org/data/scidocs) | ``scidocs``| ``test``| 1,000 | 25K | 4.9 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scidocs.zip) | ``38121350fc3a4d2f48850f6aff52e4a9`` |
| FEVER | [Homepage](http://fever.ai) | ``fever``| ``train``<br>``dev``<br>``test``| 6,666 | 5.42M | 1.2| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fever.zip) | ``5a818580227bfb4b35bb6fa46d9b6c03`` |
| Climate-FEVER| [Homepage](http://climatefever.ai) | ``climate-fever``|``test``| 1,535 | 5.42M | 3.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/climate-fever.zip) | ``8b66f0a9126c521bae2bde127b4dc99d`` |
| SciFact| [Homepage](https://github.com/allenai/scifact) | ``scifact``| ``train``<br>``test``| 300 | 5K | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scifact.zip) | ``5f7d1de60b170fc8027bb7898e2efca1`` |
| Robust04 | [Homepage](https://trec.nist.gov/data/robust/04.guidelines.html) | ``robust04``| ``test``| 249 | 528K | 69.9 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#3-robust04) |
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
[Needs More Information]
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
[Needs More Information]
### Citation Information
Cite as:
```
@inproceedings{
thakur2021beir,
title={{BEIR}: A Heterogeneous Benchmark for Zero-shot Evaluation of Information Retrieval Models},
author={Nandan Thakur and Nils Reimers and Andreas R{\"u}ckl{\'e} and Abhishek Srivastava and Iryna Gurevych},
booktitle={Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2)},
year={2021},
url={https://openreview.net/forum?id=wCu6T5xFjeJ}
}
```
### Contributions
Thanks to [@Nthakur20](https://github.com/Nthakur20) for adding this dataset. | [
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onestop_english | null | 2023-01-25T14:42:09Z | 1,146 | 15 | onestopenglish | [
"task_categories:text2text-generation",
"task_categories:text-classification",
"task_ids:multi-class-classification",
"task_ids:text-simplification",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:n<1K",
"source_datasets:original",
"language:en",
"license:cc-by-sa-4.0",
"region:us"
] | 2023-01-25T14:42:09Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- found
language_creators:
- found
language:
- en
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
size_categories:
- n<1K
source_datasets:
- original
task_categories:
- text2text-generation
- text-classification
task_ids:
- multi-class-classification
- text-simplification
paperswithcode_id: onestopenglish
pretty_name: OneStopEnglish corpus
dataset_info:
features:
- name: text
dtype: string
- name: label
dtype:
class_label:
names:
'0': ele
'1': int
'2': adv
splits:
- name: train
num_bytes: 2278043
num_examples: 567
download_size: 1228804
dataset_size: 2278043
---
# Dataset Card for OneStopEnglish corpus
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://github.com/nishkalavallabhi/OneStopEnglishCorpus
- **Repository:** https://github.com/purvimisal/OneStopCorpus-Compiled/raw/main/Texts-SeparatedByReadingLevel.zip
- **Paper:** https://www.aclweb.org/anthology/W18-0535.pdf
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
OneStopEnglish is a corpus of texts written at three reading levels, and demonstrates its usefulness for through two applications - automatic readability assessment and automatic text simplification.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
An instance example:
```
{
"text": "When you see the word Amazon, what’s the first thing you think...",
"label": 0
}
```
Note that each instance contains the full text of the document.
### Data Fields
- `text`: Full document text.
- `label`: Reading level of the document- ele/int/adv (Elementary/Intermediate/Advance).
### Data Splits
The OneStopEnglish dataset has a single _train_ split.
| Split | Number of instances |
|-------|--------------------:|
| train | 567 |
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
Creative Commons Attribution-ShareAlike 4.0 International License
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@purvimisal](https://github.com/purvimisal) for adding this dataset. | [
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cuad | null | 2022-11-18T19:50:02Z | 1,144 | 30 | cuad | [
"task_categories:question-answering",
"task_ids:closed-domain-qa",
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"arxiv:2103.06268",
"region:us"
] | 2022-11-18T19:50:02Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
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language_creators:
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language:
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license:
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multilinguality:
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size_categories:
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source_datasets:
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task_categories:
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task_ids:
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paperswithcode_id: cuad
pretty_name: CUAD
train-eval-index:
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task: question-answering
task_id: extractive_question_answering
splits:
train_split: train
eval_split: test
col_mapping:
question: question
context: context
answers:
text: text
answer_start: answer_start
metrics:
- type: cuad
name: CUAD
dataset_info:
features:
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dtype: string
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dtype: string
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dtype: string
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sequence:
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splits:
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num_bytes: 1466037640
num_examples: 22450
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num_bytes: 198543467
num_examples: 4182
download_size: 18309308
dataset_size: 1664581107
---
# Dataset Card for CUAD
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [Contract Understanding Atticus Dataset](https://www.atticusprojectai.org/cuad)
- **Repository:** [Contract Understanding Atticus Dataset](https://github.com/TheAtticusProject/cuad/)
- **Paper:** [CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review](https://arxiv.org/abs/2103.06268)
- **Point of Contact:** [Atticus Project Team](info@atticusprojectai.org)
### Dataset Summary
Contract Understanding Atticus Dataset (CUAD) v1 is a corpus of more than 13,000 labels in 510 commercial legal contracts that have been manually labeled to identify 41 categories of important clauses that lawyers look for when reviewing contracts in connection with corporate transactions.
CUAD is curated and maintained by The Atticus Project, Inc. to support NLP research and development in legal contract review. Analysis of CUAD can be found at https://arxiv.org/abs/2103.06268. Code for replicating the results and the trained model can be found at https://github.com/TheAtticusProject/cuad.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
The dataset contains samples in English only.
## Dataset Structure
### Data Instances
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"answers": {
"answer_start": [44],
"text": ['DISTRIBUTOR AGREEMENT']
},
"context": 'EXHIBIT 10.6\n\n DISTRIBUTOR AGREEMENT\n\n THIS DISTRIBUTOR AGREEMENT (the "Agreement") is made by and between Electric City Corp., a Delaware corporation ("Company") and Electric City of Illinois LLC ("Distributor") this 7th day of September, 1999...',
"id": "LIMEENERGYCO_09_09_1999-EX-10-DISTRIBUTOR AGREEMENT__Document Name_0",
"question": "Highlight the parts (if any) of this contract related to "Document Name" that should be reviewed by a lawyer. Details: The name of the contract",
"title": "LIMEENERGYCO_09_09_1999-EX-10-DISTRIBUTOR AGREEMENT"
}
```
### Data Fields
- `id`: a `string` feature.
- `title`: a `string` feature.
- `context`: a `string` feature.
- `question`: a `string` feature.
- `answers`: a dictionary feature containing:
- `text`: a `string` feature.
- `answer_start`: a `int32` feature.
### Data Splits
This dataset is split into train/test set. Number of samples in each set is given below:
| | Train | Test |
| ----- | ------ | ---- |
| CUAD | 22450 | 4182 |
## Dataset Creation
### Curation Rationale
A highly valuable specialized task without a public large-scale dataset is contract review, which costs humans substantial time, money, and attention. Many law firms spend approximately 50% of their time reviewing contracts (CEB, 2017). Due to the specialized training necessary to understand and interpret contracts, the billing rates for lawyers at large law firms are typically around $500-$900 per hour in the US. As a result, many transactions cost companies hundreds of thousands of dollars just so that lawyers can verify that there are no problematic obligations or requirements included in the contracts. Contract review can be a source of drudgery and, in comparison to other legal tasks, is widely considered to be especially boring.
Contract review costs also affect consumers. Since contract review costs are so prohibitive, contract review is not often performed outside corporate transactions. Small companies and individuals consequently often sign contracts without even reading them, which can result in predatory behavior that harms consumers. Automating contract review by openly releasing high-quality data and fine-tuned models can increase access to legal support for small businesses and individuals, so that legal support is not exclusively available to wealthy companies.
To reduce the disparate societal costs of contract review, and to study how well NLP models generalize to specialized domains, the authors introduced a new large-scale dataset for contract review. As part of The Atticus Project, a non-profit organization of legal experts, CUAD is introduced, the Contract Understanding Atticus Dataset. This dataset was created with a year-long effort pushed forward by dozens of law student annotators, lawyers, and machine learning researchers. The dataset includes more than 500 contracts and more than 13,000 expert annotations that span 41 label categories. For each of 41 different labels, models must learn to highlight the portions of a contract most salient to that label. This makes the task a matter of finding needles in a haystack.
### Source Data
#### Initial Data Collection and Normalization
The CUAD includes commercial contracts selected from 25 different types of contracts based on the contract names as shown below. Within each type, the creators randomly selected contracts based on the names of the filing companies across the alphabet.
Type of Contracts: # of Docs
Affiliate Agreement: 10
Agency Agreement: 13
Collaboration/Cooperation Agreement: 26
Co-Branding Agreement: 22
Consulting Agreement: 11
Development Agreement: 29
Distributor Agreement: 32
Endorsement Agreement: 24
Franchise Agreement: 15
Hosting Agreement: 20
IP Agreement: 17
Joint Venture Agreemen: 23
License Agreement: 33
Maintenance Agreement: 34
Manufacturing Agreement: 17
Marketing Agreement: 17
Non-Compete/No-Solicit/Non-Disparagement Agreement: 3
Outsourcing Agreement: 18
Promotion Agreement: 12
Reseller Agreement: 12
Service Agreement: 28
Sponsorship Agreement: 31
Supply Agreement: 18
Strategic Alliance Agreement: 32
Transportation Agreement: 13
TOTAL: 510
#### Who are the source language producers?
The contracts were sourced from EDGAR, the Electronic Data Gathering, Analysis, and Retrieval system used at the U.S. Securities and Exchange Commission (SEC). Publicly traded companies in the United States are required to file certain contracts under the SEC rules. Access to these contracts is available to the public for free at https://www.sec.gov/edgar. Please read the Datasheet at https://www.atticusprojectai.org/ for information on the intended use and limitations of the CUAD.
### Annotations
#### Annotation process
The labeling process included multiple steps to ensure accuracy:
1. Law Student Training: law students attended training sessions on each of the categories that included a summary, video instructions by experienced attorneys, multiple quizzes and workshops. Students were then required to label sample contracts in eBrevia, an online contract review tool. The initial training took approximately 70-100 hours.
2. Law Student Label: law students conducted manual contract review and labeling in eBrevia.
3. Key Word Search: law students conducted keyword search in eBrevia to capture additional categories that have been missed during the “Student Label” step.
4. Category-by-Category Report Review: law students exported the labeled clauses into reports, review each clause category-by-category and highlight clauses that they believe are mislabeled.
5. Attorney Review: experienced attorneys reviewed the category-by-category report with students comments, provided comments and addressed student questions. When applicable, attorneys discussed such results with the students and reached consensus. Students made changes in eBrevia accordingly.
6. eBrevia Extras Review. Attorneys and students used eBrevia to generate a list of “extras”, which are clauses that eBrevia AI tool identified as responsive to a category but not labeled by human annotators. Attorneys and students reviewed all of the “extras” and added the correct ones. The process is repeated until all or substantially all of the “extras” are incorrect labels.
7. Final Report: The final report was exported into a CSV file. Volunteers manually added the “Yes/No” answer column to categories that do not contain an answer.
#### Who are the annotators?
Answered in above section.
### Personal and Sensitive Information
Some clauses in the files are redacted because the party submitting these contracts redacted them to protect confidentiality. Such redaction may show up as asterisks (\*\*\*) or underscores (\_\_\_) or blank spaces. The dataset and the answers reflect such redactions. For example, the answer for “January \_\_ 2020” would be “1/[]/2020”).
For any categories that require an answer of “Yes/No”, annotators include full sentences as text context in a contract. To maintain consistency and minimize inter-annotator disagreement, annotators select text for the full sentence, under the instruction of “from period to period”.
For the other categories, annotators selected segments of the text in the contract that are responsive to each such category. One category in a contract may include multiple labels. For example, “Parties” may include 4-10 separate text strings that are not continuous in a contract. The answer is presented in the unified format separated by semicolons of “Party A Inc. (“Party A”); Party B Corp. (“Party B”)”.
Some sentences in the files include confidential legends that are not part of the contracts. An example of such confidential legend is as follows:
THIS EXHIBIT HAS BEEN REDACTED AND IS THE SUBJECT OF A CONFIDENTIAL TREATMENT REQUEST. REDACTED MATERIAL IS MARKED WITH [* * *] AND HAS BEEN FILED SEPARATELY WITH THE SECURITIES AND EXCHANGE COMMISSION.
Some sentences in the files contain irrelevant information such as footers or page numbers. Some sentences may not be relevant to the corresponding category. Some sentences may correspond to a different category. Because many legal clauses are very long and contain various sub-parts, sometimes only a sub-part of a sentence is responsive to a category.
To address the foregoing limitations, annotators manually deleted the portion that is not responsive, replacing it with the symbol "<omitted>" to indicate that the two text segments do not appear immediately next to each other in the contracts. For example, if a “Termination for Convenience” clause starts with “Each Party may terminate this Agreement if” followed by three subparts “(a), (b) and (c)”, but only subpart (c) is responsive to this category, the authors manually deleted subparts (a) and (b) and replaced them with the symbol "<omitted>”. Another example is for “Effective Date”, the contract includes a sentence “This Agreement is effective as of the date written above” that appears after the date “January 1, 2010”. The annotation is as follows: “January 1, 2010 <omitted> This Agreement is effective as of the date written above.”
Because the contracts were converted from PDF into TXT files, the converted TXT files may not stay true to the format of the original PDF files. For example, some contracts contain inconsistent spacing between words, sentences and paragraphs. Table format is not maintained in the TXT files.
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
Attorney Advisors
Wei Chen, John Brockland, Kevin Chen, Jacky Fink, Spencer P. Goodson, Justin Haan, Alex Haskell, Kari Krusmark, Jenny Lin, Jonas Marson, Benjamin Petersen, Alexander Kwonji Rosenberg, William R. Sawyers, Brittany Schmeltz, Max Scott, Zhu Zhu
Law Student Leaders
John Batoha, Daisy Beckner, Lovina Consunji, Gina Diaz, Chris Gronseth, Calvin Hannagan, Joseph Kroon, Sheetal Sharma Saran
Law Student Contributors
Scott Aronin, Bryan Burgoon, Jigar Desai, Imani Haynes, Jeongsoo Kim, Margaret Lynch, Allison Melville, Felix Mendez-Burgos, Nicole Mirkazemi, David Myers, Emily Rissberger, Behrang Seraj, Sarahginy Valcin
Technical Advisors & Contributors
Dan Hendrycks, Collin Burns, Spencer Ball, Anya Chen
### Licensing Information
CUAD is licensed under the Creative Commons Attribution 4.0 (CC BY 4.0) license and free to the public for commercial and non-commercial use.
The creators make no representations or warranties regarding the license status of the underlying contracts, which are publicly available and downloadable from EDGAR.
Privacy Policy & Disclaimers
The categories or the contracts included in the dataset are not comprehensive or representative. The authors encourage the public to help improve them by sending them your comments and suggestions to info@atticusprojectai.org. Comments and suggestions will be reviewed by The Atticus Project at its discretion and will be included in future versions of Atticus categories once approved.
The use of CUAD is subject to their privacy policy https://www.atticusprojectai.org/privacy-policy and disclaimer https://www.atticusprojectai.org/disclaimer.
### Citation Information
```
@article{hendrycks2021cuad,
title={CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review},
author={Dan Hendrycks and Collin Burns and Anya Chen and Spencer Ball},
journal={arXiv preprint arXiv:2103.06268},
year={2021}
}
```
### Contributions
Thanks to [@bhavitvyamalik](https://github.com/bhavitvyamalik) for adding this dataset. | [
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allenai/scitldr | allenai | 2023-01-25T14:43:42Z | 1,144 | 18 | scitldr | [
"task_categories:summarization",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:unknown",
"scientific-documents-summarization",
"arxiv:2004.15011",
"region:us"
] | 2023-01-25T14:43:42Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- summarization
task_ids: []
paperswithcode_id: scitldr
pretty_name: SciTLDR
tags:
- scientific-documents-summarization
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download_size: 110904552
dataset_size: 105890568
---
# Dataset Card for SciTLDR
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://github.com/allenai/scitldr
- **Repository:** https://github.com/allenai/scitldr
- **Paper:** https://arxiv.org/abs/2004.15011
- **Leaderboard:**
- **Point of Contact:** {isabelc,kylel,armanc,danw}@allenai.org
### Dataset Summary
`SciTLDR`: Extreme Summarization of Scientific Documents
SciTLDR is a new multi-target dataset of 5.4K TLDRs over 3.2K papers. SciTLDR contains both author-written and expert-derived TLDRs, where the latter are collected using a novel annotation protocol that produces high-quality summaries while minimizing annotation burden.
### Supported Tasks and Leaderboards
summarization
### Languages
English
## Dataset Structure
SciTLDR is split in to a 60/20/20 train/dev/test split. For each file, each line is a json, formatted as follows
```
{
"source":[
"sent0",
"sent1",
"sent2",
...
],
"source_labels":[binary list in which 1 is the oracle sentence],
"rouge_scores":[precomputed rouge-1 scores],
"paper_id":"PAPER-ID",
"target":[
"author-tldr",
"pr-tldr0",
"pr-tldr1",
...
],
"title":"TITLE"
}
```
The keys `rouge_scores` and `source_labels` are not necessary for any code to run, precomputed Rouge scores are provided for future research.
### Data Instances
{
"source": [
"Mixed precision training (MPT) is becoming a practical technique to improve the speed and energy efficiency of training deep neural networks by leveraging the fast hardware support for IEEE half-precision floating point that is available in existing GPUs.",
"MPT is typically used in combination with a technique called loss scaling, that works by scaling up the loss value up before the start of backpropagation in order to minimize the impact of numerical underflow on training.",
"Unfortunately, existing methods make this loss scale value a hyperparameter that needs to be tuned per-model, and a single scale cannot be adapted to different layers at different training stages.",
"We introduce a loss scaling-based training method called adaptive loss scaling that makes MPT easier and more practical to use, by removing the need to tune a model-specific loss scale hyperparameter.",
"We achieve this by introducing layer-wise loss scale values which are automatically computed during training to deal with underflow more effectively than existing methods.",
"We present experimental results on a variety of networks and tasks that show our approach can shorten the time to convergence and improve accuracy, compared with using the existing state-of-the-art MPT and single-precision floating point."
],
"source_labels": [
0,
0,
0,
1,
0,
0
],
"rouge_scores": [
0.2399999958000001,
0.26086956082230633,
0.19999999531250012,
0.38095237636054424,
0.2051282003944774,
0.2978723360796741
],
"paper_id": "rJlnfaNYvB",
"target": [
"We devise adaptive loss scaling to improve mixed precision training that surpass the state-of-the-art results.",
"Proposal for an adaptive loss scaling method during backpropagation for mix precision training where scale rate is decided automatically to reduce the underflow.",
"The authors propose a method to train models in FP16 precision that adopts a more elaborate way to minimize underflow in every layer simultaneously and automatically."
],
"title": "Adaptive Loss Scaling for Mixed Precision Training"
}
### Data Fields
- `source`: The Abstract, Introduction and Conclusion (AIC) or Full text of the paper, with one sentence per line.
- `source_labels`: Binary 0 or 1, 1 denotes the oracle sentence.
- `rouge_scores`: Precomputed ROUGE baseline scores for each sentence.
- `paper_id`: Arxiv Paper ID.
- `target`: Multiple summaries for each sentence, one sentence per line.
- `title`: Title of the paper.
### Data Splits
| | train | valid | test |
|-------------------|-------|--------|------|
| SciTLDR-A | 1992 | 618 | 619 |
| SciTLDR-AIC | 1992 | 618 | 619 |
| SciTLDR-FullText | 1992 | 618 | 619 |
## Dataset Creation
[More Information Needed]
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
https://allenai.org/
### Annotations
#### Annotation process
Given the title and first 128 words of a reviewer comment about a paper,
re-write the summary (if it exists) into a single sentence or an incomplete
phrase. Summaries must be no more than one sentence.
Most summaries are between 15 and 25 words. The average rewritten summary is
20 words long.
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
To encourage further research in the area of extreme summarization of scientific documents.
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
Apache License 2.0
### Citation Information
@article{cachola2020tldr,
title={{TLDR}: Extreme Summarization of Scientific Documents},
author={Isabel Cachola and Kyle Lo and Arman Cohan and Daniel S. Weld},
journal={arXiv:2004.15011},
year={2020},
}
### Contributions
Thanks to [@Bharat123rox](https://github.com/Bharat123rox) for adding this dataset. | [
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health_fact | null | 2023-01-25T14:32:02Z | 1,143 | 16 | pubhealth | [
"task_categories:text-classification",
"task_ids:fact-checking",
"task_ids:multi-class-classification",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:mit",
"arxiv:2010.09926",
"region:us"
] | 2023-01-25T14:32:02Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- en
license:
- mit
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- fact-checking
- multi-class-classification
paperswithcode_id: pubhealth
pretty_name: PUBHEALTH
dataset_info:
features:
- name: claim_id
dtype: string
- name: claim
dtype: string
- name: date_published
dtype: string
- name: explanation
dtype: string
- name: fact_checkers
dtype: string
- name: main_text
dtype: string
- name: sources
dtype: string
- name: label
dtype:
class_label:
names:
'0': 'false'
'1': mixture
'2': 'true'
'3': unproven
- name: subjects
dtype: string
splits:
- name: train
num_bytes: 53985377
num_examples: 9832
- name: test
num_bytes: 6825221
num_examples: 1235
- name: validation
num_bytes: 6653044
num_examples: 1225
download_size: 24892660
dataset_size: 67463642
train-eval-index:
- config: default
task: text-classification
task_id: multi_class_classification
splits:
train_split: train
eval_split: test
col_mapping:
claim: text
label: target
metrics:
- type: accuracy
name: Accuracy
- type: f1
name: F1 macro
args:
average: macro
- type: f1
name: F1 micro
args:
average: micro
- type: f1
name: F1 weighted
args:
average: weighted
- type: precision
name: Precision macro
args:
average: macro
- type: precision
name: Precision micro
args:
average: micro
- type: precision
name: Precision weighted
args:
average: weighted
- type: recall
name: Recall macro
args:
average: macro
- type: recall
name: Recall micro
args:
average: micro
- type: recall
name: Recall weighted
args:
average: weighted
---
# Dataset Card for PUBHEALTH
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [PUBHEALTH homepage](https://github.com/neemakot/Health-Fact-Checking)
- **Repository:** [PUBHEALTH repository](https://github.com/neemakot/Health-Fact-Checking/blob/master/data/DATASHEET.md)
- **Paper:** [Explainable Automated Fact-Checking for Public Health Claims"](https://arxiv.org/abs/2010.09926)
- **Point of Contact:**[Neema Kotonya](mailto:nk2418@ic.ac.uk)
### Dataset Summary
PUBHEALTH is a comprehensive dataset for explainable automated fact-checking of public health claims. Each instance in the PUBHEALTH dataset has an associated veracity label (true, false, unproven, mixture). Furthermore each instance in the dataset has an explanation text field. The explanation is a justification for which the claim has been assigned a particular veracity label.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
The text in the dataset is in English.
## Dataset Structure
### Data Instances
The following is an example instance of the PUBHEALTH dataset:
| Field | Example |
| ----------------- | -------------------------------------------------------------|
| __claim__ | Expired boxes of cake and pancake mix are dangerously toxic. |
| __explanation__ | What's True: Pancake and cake mixes that contain mold can cause life-threatening allergic reactions. What's False: Pancake and cake mixes that have passed their expiration dates are not inherently dangerous to ordinarily healthy people, and the yeast in packaged baking products does not "over time develops spores." |
| __label__ | mixture |
| __author(s)__ | David Mikkelson |
| __date published__ | April 19, 2006 |
| __tags__ | food, allergies, baking, cake |
| __main_text__ | In April 2006, the experience of a 14-year-old who had eaten pancakes made from a mix that had gone moldy was described in the popular newspaper column Dear Abby. The account has since been circulated widely on the Internet as scores of concerned homemakers ponder the safety of the pancake and other baking mixes lurking in their larders [...] |
| __evidence sources__ | [1] Bennett, Allan and Kim Collins. “An Unusual Case of Anaphylaxis: Mold in Pancake Mix.” American Journal of Forensic Medicine & Pathology. September 2001 (pp. 292-295). [2] Phillips, Jeanne. “Dear Abby.” 14 April 2006 [syndicated column]. |
### Data Fields
Mentioned above in data instances.
### Data Splits
| | # Instances |
|-----------|-------------|
| train.tsv | 9832 |
| dev.tsv | 1221 |
| test.tsv | 1235 |
| total | 12288 |
## Dataset Creation
### Curation Rationale
The dataset was created to explore fact-checking of difficult to verify claims i.e., those which require expertise from outside of the journalistics domain, in this case biomedical and public health expertise.
It was also created in response to the lack of fact-checking datasets which provide gold standard natural language explanations for verdicts/labels.
### Source Data
#### Initial Data Collection and Normalization
The dataset was retrieved from the following fact-checking, news reviews and news websites:
| URL | Type |
|-----------------------------------|--------------------|
| http://snopes.com/ | fact-checking |
| http://politifact.com/ | fact-checking |
| http://truthorfiction.com/ | fact-checking |
| https://www.factcheck.org/ | fact-checking |
| https://fullfact.org/ | fact-checking |
| https://apnews.com/ | news |
| https://uk.reuters.com/ | news |
| https://www.healthnewsreview.org/ | health news review |
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
Not to our knowledge, but if it is brought to our attention that we are mistaken we will make the appropriate corrections to the dataset.
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
The dataset was created by Neema Kotonya, and Francesca Toni, for their research paper "Explainable Automated Fact-Checking for Public Health Claims" presented at EMNLP 2020.
### Licensing Information
MIT License
### Citation Information
```
@inproceedings{kotonya-toni-2020-explainable,
title = "Explainable Automated Fact-Checking for Public Health Claims",
author = "Kotonya, Neema and
Toni, Francesca",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.emnlp-main.623",
pages = "7740--7754",
}
```
### Contributions
Thanks to [@bhavitvyamalik](https://github.com/bhavitvyamalik) for adding this dataset. | [
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open-llm-leaderboard/details_Aspik101__trurl-2-7b-pl-instruct_unload | open-llm-leaderboard | 2023-09-22T23:43:57Z | 1,141 | 0 | null | [
"region:us"
] | 2023-09-22T23:43:57Z | 2023-08-18T18:55:05.000Z | 2023-08-18T18:55:05 | ---
pretty_name: Evaluation run of Aspik101/trurl-2-7b-pl-instruct_unload
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [Aspik101/trurl-2-7b-pl-instruct_unload](https://huggingface.co/Aspik101/trurl-2-7b-pl-instruct_unload)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 64 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_Aspik101__trurl-2-7b-pl-instruct_unload\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-09-22T23:43:45.355114](https://huggingface.co/datasets/open-llm-leaderboard/details_Aspik101__trurl-2-7b-pl-instruct_unload/blob/main/results_2023-09-22T23-43-45.355114.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.16096895973154363,\n\
\ \"em_stderr\": 0.0037635677120072437,\n \"f1\": 0.22060822147650985,\n\
\ \"f1_stderr\": 0.0038388662178584767,\n \"acc\": 0.3986331756197593,\n\
\ \"acc_stderr\": 0.009900867594093993\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.16096895973154363,\n \"em_stderr\": 0.0037635677120072437,\n\
\ \"f1\": 0.22060822147650985,\n \"f1_stderr\": 0.0038388662178584767\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.07429871114480667,\n \
\ \"acc_stderr\": 0.007223844172845576\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7229676400947119,\n \"acc_stderr\": 0.012577891015342412\n\
\ }\n}\n```"
repo_url: https://huggingface.co/Aspik101/trurl-2-7b-pl-instruct_unload
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_08_17T14_40_19.486608
path:
- '**/details_harness|arc:challenge|25_2023-08-17T14:40:19.486608.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-08-17T14:40:19.486608.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_09_22T23_43_45.355114
path:
- '**/details_harness|drop|3_2023-09-22T23-43-45.355114.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-09-22T23-43-45.355114.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_09_22T23_43_45.355114
path:
- '**/details_harness|gsm8k|5_2023-09-22T23-43-45.355114.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-09-22T23-43-45.355114.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_08_17T14_40_19.486608
path:
- '**/details_harness|hellaswag|10_2023-08-17T14:40:19.486608.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-08-17T14:40:19.486608.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_08_17T14_40_19.486608
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-17T14:40:19.486608.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-17T14:40:19.486608.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_08_17T14_40_19.486608
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-17T14:40:19.486608.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-17T14:40:19.486608.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_08_17T14_40_19.486608
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-17T14:40:19.486608.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-17T14:40:19.486608.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_08_17T14_40_19.486608
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-17T14:40:19.486608.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-17T14:40:19.486608.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_08_17T14_40_19.486608
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-17T14:40:19.486608.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-17T14:40:19.486608.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_08_17T14_40_19.486608
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-17T14:40:19.486608.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-17T14:40:19.486608.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_08_17T14_40_19.486608
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-17T14:40:19.486608.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-17T14:40:19.486608.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_08_17T14_40_19.486608
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-17T14:40:19.486608.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-17T14:40:19.486608.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_08_17T14_40_19.486608
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-17T14:40:19.486608.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-17T14:40:19.486608.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_08_17T14_40_19.486608
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-17T14:40:19.486608.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-17T14:40:19.486608.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_08_17T14_40_19.486608
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-17T14:40:19.486608.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-17T14:40:19.486608.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_08_17T14_40_19.486608
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-17T14:40:19.486608.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-17T14:40:19.486608.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_08_17T14_40_19.486608
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-17T14:40:19.486608.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-17T14:40:19.486608.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_08_17T14_40_19.486608
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-17T14:40:19.486608.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-17T14:40:19.486608.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_08_17T14_40_19.486608
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-17T14:40:19.486608.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-17T14:40:19.486608.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_08_17T14_40_19.486608
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-17T14:40:19.486608.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-17T14:40:19.486608.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_08_17T14_40_19.486608
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-17T14:40:19.486608.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-17T14:40:19.486608.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_08_17T14_40_19.486608
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-17T14:40:19.486608.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-17T14:40:19.486608.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_08_17T14_40_19.486608
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-17T14:40:19.486608.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-17T14:40:19.486608.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_08_17T14_40_19.486608
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-17T14:40:19.486608.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-17T14:40:19.486608.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_08_17T14_40_19.486608
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-17T14:40:19.486608.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-17T14:40:19.486608.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_08_17T14_40_19.486608
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-17T14:40:19.486608.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-17T14:40:19.486608.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_08_17T14_40_19.486608
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-17T14:40:19.486608.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-17T14:40:19.486608.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_08_17T14_40_19.486608
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-17T14:40:19.486608.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-17T14:40:19.486608.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_08_17T14_40_19.486608
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-17T14:40:19.486608.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-17T14:40:19.486608.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_08_17T14_40_19.486608
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-17T14:40:19.486608.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-17T14:40:19.486608.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_08_17T14_40_19.486608
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-17T14:40:19.486608.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-17T14:40:19.486608.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_08_17T14_40_19.486608
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-17T14:40:19.486608.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-17T14:40:19.486608.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_08_17T14_40_19.486608
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-17T14:40:19.486608.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-17T14:40:19.486608.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_08_17T14_40_19.486608
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-17T14:40:19.486608.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-17T14:40:19.486608.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_08_17T14_40_19.486608
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-17T14:40:19.486608.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-17T14:40:19.486608.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_08_17T14_40_19.486608
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-17T14:40:19.486608.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-17T14:40:19.486608.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_08_17T14_40_19.486608
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-17T14:40:19.486608.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-17T14:40:19.486608.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_08_17T14_40_19.486608
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-17T14:40:19.486608.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-17T14:40:19.486608.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_08_17T14_40_19.486608
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-17T14:40:19.486608.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-17T14:40:19.486608.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_08_17T14_40_19.486608
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-08-17T14:40:19.486608.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-08-17T14:40:19.486608.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_08_17T14_40_19.486608
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-17T14:40:19.486608.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-17T14:40:19.486608.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_08_17T14_40_19.486608
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-17T14:40:19.486608.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-17T14:40:19.486608.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_08_17T14_40_19.486608
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-17T14:40:19.486608.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-17T14:40:19.486608.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_08_17T14_40_19.486608
path:
- '**/details_harness|hendrycksTest-management|5_2023-08-17T14:40:19.486608.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-08-17T14:40:19.486608.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_08_17T14_40_19.486608
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-08-17T14:40:19.486608.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-08-17T14:40:19.486608.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_08_17T14_40_19.486608
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-17T14:40:19.486608.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-17T14:40:19.486608.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_08_17T14_40_19.486608
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-17T14:40:19.486608.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-17T14:40:19.486608.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_08_17T14_40_19.486608
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-17T14:40:19.486608.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-17T14:40:19.486608.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_08_17T14_40_19.486608
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-17T14:40:19.486608.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-17T14:40:19.486608.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_08_17T14_40_19.486608
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-17T14:40:19.486608.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-17T14:40:19.486608.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_08_17T14_40_19.486608
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-17T14:40:19.486608.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-17T14:40:19.486608.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_08_17T14_40_19.486608
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-17T14:40:19.486608.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-17T14:40:19.486608.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_08_17T14_40_19.486608
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-17T14:40:19.486608.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-17T14:40:19.486608.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_08_17T14_40_19.486608
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-17T14:40:19.486608.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-17T14:40:19.486608.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_08_17T14_40_19.486608
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-17T14:40:19.486608.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-17T14:40:19.486608.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_08_17T14_40_19.486608
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-17T14:40:19.486608.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-17T14:40:19.486608.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_08_17T14_40_19.486608
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-17T14:40:19.486608.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-17T14:40:19.486608.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_08_17T14_40_19.486608
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-17T14:40:19.486608.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-17T14:40:19.486608.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_08_17T14_40_19.486608
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-08-17T14:40:19.486608.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-08-17T14:40:19.486608.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_08_17T14_40_19.486608
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-17T14:40:19.486608.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-17T14:40:19.486608.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_08_17T14_40_19.486608
path:
- '**/details_harness|hendrycksTest-virology|5_2023-08-17T14:40:19.486608.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-08-17T14:40:19.486608.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_08_17T14_40_19.486608
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-17T14:40:19.486608.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-17T14:40:19.486608.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_08_17T14_40_19.486608
path:
- '**/details_harness|truthfulqa:mc|0_2023-08-17T14:40:19.486608.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-08-17T14:40:19.486608.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_09_22T23_43_45.355114
path:
- '**/details_harness|winogrande|5_2023-09-22T23-43-45.355114.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-09-22T23-43-45.355114.parquet'
- config_name: results
data_files:
- split: 2023_08_17T14_40_19.486608
path:
- results_2023-08-17T14:40:19.486608.parquet
- split: 2023_09_22T23_43_45.355114
path:
- results_2023-09-22T23-43-45.355114.parquet
- split: latest
path:
- results_2023-09-22T23-43-45.355114.parquet
---
# Dataset Card for Evaluation run of Aspik101/trurl-2-7b-pl-instruct_unload
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/Aspik101/trurl-2-7b-pl-instruct_unload
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** clementine@hf.co
### Dataset Summary
Dataset automatically created during the evaluation run of model [Aspik101/trurl-2-7b-pl-instruct_unload](https://huggingface.co/Aspik101/trurl-2-7b-pl-instruct_unload) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_Aspik101__trurl-2-7b-pl-instruct_unload",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-09-22T23:43:45.355114](https://huggingface.co/datasets/open-llm-leaderboard/details_Aspik101__trurl-2-7b-pl-instruct_unload/blob/main/results_2023-09-22T23-43-45.355114.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.16096895973154363,
"em_stderr": 0.0037635677120072437,
"f1": 0.22060822147650985,
"f1_stderr": 0.0038388662178584767,
"acc": 0.3986331756197593,
"acc_stderr": 0.009900867594093993
},
"harness|drop|3": {
"em": 0.16096895973154363,
"em_stderr": 0.0037635677120072437,
"f1": 0.22060822147650985,
"f1_stderr": 0.0038388662178584767
},
"harness|gsm8k|5": {
"acc": 0.07429871114480667,
"acc_stderr": 0.007223844172845576
},
"harness|winogrande|5": {
"acc": 0.7229676400947119,
"acc_stderr": 0.012577891015342412
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] | [
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esb/diagnostic-dataset | esb | 2022-10-26T16:42:41Z | 1,140 | 2 | null | [
"task_categories:automatic-speech-recognition",
"annotations_creators:expert-generated",
"annotations_creators:crowdsourced",
"annotations_creators:machine-generated",
"language_creators:crowdsourced",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"size_categories:1M<n<10M",
"source_datasets:original",
"source_datasets:extended|librispeech_asr",
"source_datasets:extended|common_voice",
"language:en",
"license:cc-by-4.0",
"license:apache-2.0",
"license:cc0-1.0",
"license:cc-by-nc-3.0",
"license:other",
"asr",
"benchmark",
"speech",
"esc",
"region:us"
] | 2022-10-26T16:42:41Z | 2022-10-26T10:25:33.000Z | 2022-10-26T10:25:33 | ---
annotations_creators:
- expert-generated
- crowdsourced
- machine-generated
language:
- en
language_creators:
- crowdsourced
- expert-generated
license:
- cc-by-4.0
- apache-2.0
- cc0-1.0
- cc-by-nc-3.0
- other
multilinguality:
- monolingual
pretty_name: ESB Diagnostic Dataset
size_categories:
- 100K<n<1M
- 1M<n<10M
source_datasets:
- original
- extended|librispeech_asr
- extended|common_voice
tags:
- asr
- benchmark
- speech
- esc
task_categories:
- automatic-speech-recognition
task_ids: []
extra_gated_prompt: |-
Three of the ESB datasets have specific terms of usage that must be agreed to before using the data.
To do so, fill in the access forms on the specific datasets' pages:
* Common Voice: https://huggingface.co/datasets/mozilla-foundation/common_voice_9_0
* GigaSpeech: https://huggingface.co/datasets/speechcolab/gigaspeech
* SPGISpeech: https://huggingface.co/datasets/kensho/spgispeech
extra_gated_fields:
I hereby confirm that I have registered on the original Common Voice page and agree to not attempt to determine the identity of speakers in the Common Voice dataset: checkbox
I hereby confirm that I have accepted the terms of usages on GigaSpeech page: checkbox
I hereby confirm that I have accepted the terms of usages on SPGISpeech page: checkbox
---
## Dataset Description
- **Dataset authors:** [Suno.ai](https://www.suno.ai)
- **Point of contact:** sanchit@huggingface.co
As a part of ESB benchmark, we provide a small, 8h diagnostic dataset of in-domain validation data with newly annotated transcriptions. The audio data is sampled from each of the ESB validation sets, giving a range of different domains and speaking styles. The transcriptions are annotated according to a consistent style guide with two formats: normalised and un-normalised. The dataset is structured in the same way as the ESB dataset, by grouping audio-transcription samples according to the dataset from which they were taken. We encourage participants to use this dataset when evaluating their systems to quickly assess performance on a range of different speech recognition conditions.
The diagnostic dataset can be downloaded and prepared in much the same way as the ESB datasets:
```python
from datasets import load_dataset
esb_diagnostic_ami = load_dataset("esb/diagnostic-dataset", "ami")
```
### Data Selection
#### Audio
To provide an adequate representation of all ESB datasets, we chose to use at least 1 hour of audio from the validation sets of each of the 8 constituent ESB datasets. Following the convention of LibriSpeech, we then used a public ASR model to further split each dataset into `clean`/`other` based on WER. (Note that for LibriSpeech we kept the existing `clean`/`other` splits.). The `clean` subset represents the 'easier' 50% of samples, and the `other` subset the more difficult 50%.
To obtain the `clean` diagnostic-subset of AMI, either "slice" the `clean`/`other` split:
```python
ami_diagnostic_clean = esc_diagnostic_ami["clean"]
```
Or download the `clean` subset standalone:
```python
ami_diagnostic_clean = load_dataset("esb/diagnostic-dataset", "ami", split="clean")
```
#### Transcriptions
Firstly, the transcriptions were generated by a human _without_ the bias of the original transcript. The transcriptions follow a strict orthographic and verbatim style guide, where every word, disfluency and partial word is transcribed. Punctuation and formatting follows standard English print orthography (eg. ‘July 10th in 2021.’). Breaks in thought and partial words are indicated via ‘--’. In addition to the **orthographic** transcriptions, a **normalised** format was produced, with all punctuation removed and non-standard-words such as dates, currencies and abbreviations verbalised in the exact way they are spoken (eg. ’july tenth in twenty twenty one’).
Although great care was taken in standardisation of orthography, a remaining amount of ambiguity in transcription exists, especially around the use of commas and the choice of introducing sentence breaks for utterances starting with ‘And’. Each sample was then checked by a second human with access to both the original ground truth as well as the independently produced style-consistent transcript. Both versions were merged to produce new high quality ground truths in both the normalised and orthographic text format.
## Dataset Information
A data point can be accessed by indexing the dataset object loaded through `load_dataset`:
```python
print(ami_diagnostic_clean[0])
```
A typical data point comprises the path to the audio file and its transcription. Also included is information of the dataset from which the sample derives and a unique identifier name:
```python
{
'audio': {'path': None,
'array': array([ 7.01904297e-04, 7.32421875e-04, 7.32421875e-04, ...,
-2.74658203e-04, -1.83105469e-04, -3.05175781e-05]),
'sampling_rate': 16000},
'ortho_transcript': 'So, I guess we have to reflect on our experiences with remote controls to decide what, um, we would like to see in a convenient practical',
'norm_transcript': 'so i guess we have to reflect on our experiences with remote controls to decide what um we would like to see in a convenient practical',
'id': 'AMI_ES2011a_H00_FEE041_0062835_0064005',
'dataset': 'ami',
}
```
### Data Fields
- `audio`: a dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate.
- `ortho_transcript`: the **orthographic** transcription of the audio file.
- `norm_transcript`: the **normalised** transcription of the audio file.
- `id`: unique id of the data sample.
- `dataset`: string name of a dataset the sample belongs to.
We encourage participants to train their ASR system on the [AMI dataset](https://huggingface.co/datasets/esb/datasets#ami), the smallest of the 8 ESB datasets, and then evaluate their system on the `ortho_transcript` for **all** of the datasets in the diagnostic dataset. This gives a representation of how the system is likely to fare on other audio domains. The predictions can then be _normalised_ by removing casing and punctuation, converting numbers to spelled-out form and expanding abbreviations, and then assessed against the `norm_transcript`. This gives a representation of the effect of orthography for system performance.
### Access
All eight of the datasets in ESB are accessible and licensing is freely available. Three of the ESB datasets have specific terms of usage that must be agreed to before using the data. To do so, fill in the access forms on the specific datasets' pages:
* Common Voice: https://huggingface.co/datasets/mozilla-foundation/common_voice_9_0
* GigaSpeech: https://huggingface.co/datasets/speechcolab/gigaspeech
* SPGISpeech: https://huggingface.co/datasets/kensho/spgispeech
### Contributions
We show our greatest appreciation to Georg Kucsko, Keenan Freyberg and Michael Shulman from [Suno.ai](https://www.suno.ai) for creating and annotating the diagnostic dataset.
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
pg19 | null | 2023-07-28T09:21:25Z | 1,139 | 28 | pg-19 | [
"task_categories:text-generation",
"task_ids:language-modeling",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:apache-2.0",
"arxiv:1911.05507",
"region:us"
] | 2023-07-28T09:21:25Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
license:
- apache-2.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-generation
task_ids:
- language-modeling
paperswithcode_id: pg-19
pretty_name: PG-19
dataset_info:
features:
- name: short_book_title
dtype: string
- name: publication_date
dtype: int32
- name: url
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 11453688452
num_examples: 28602
- name: validation
num_bytes: 17402295
num_examples: 50
- name: test
num_bytes: 40482852
num_examples: 100
download_size: 11740397875
dataset_size: 11511573599
---
# Dataset Card for "pg19"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [https://github.com/deepmind/pg19](https://github.com/deepmind/pg19)
- **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Paper:** [Compressive Transformers for Long-Range Sequence Modelling](https://arxiv.org/abs/1911.05507)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Size of downloaded dataset files:** 11.74 GB
- **Size of the generated dataset:** 11.51 GB
- **Total amount of disk used:** 23.25 GB
### Dataset Summary
This repository contains the PG-19 language modeling benchmark.
It includes a set of books extracted from the Project Gutenberg books library, that were published before 1919.
It also contains metadata of book titles and publication dates.
PG-19 is over double the size of the Billion Word benchmark and contains documents that are 20X longer, on average, than the WikiText long-range language modelling benchmark.
Books are partitioned into a train, validation, and test set. Book metadata is stored in metadata.csv which contains (book_id, short_book_title, publication_date).
Unlike prior benchmarks, we do not constrain the vocabulary size --- i.e. mapping rare words to an UNK token --- but instead release the data as an open-vocabulary benchmark. The only processing of the text that has been applied is the removal of boilerplate license text, and the mapping of offensive discriminatory words as specified by Ofcom to placeholder tokens. Users are free to model the data at the character-level, subword-level, or via any mechanism that can model an arbitrary string of text.
To compare models we propose to continue measuring the word-level perplexity, by calculating the total likelihood of the dataset (via any chosen subword vocabulary or character-based scheme) divided by the number of tokens --- specified below in the dataset statistics table.
One could use this dataset for benchmarking long-range language models, or use it to pre-train for other natural language processing tasks which require long-range reasoning, such as LAMBADA or NarrativeQA. We would not recommend using this dataset to train a general-purpose language model, e.g. for applications to a production-system dialogue agent, due to the dated linguistic style of old texts and the inherent biases present in historical writing.
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Dataset Structure
### Data Instances
#### default
- **Size of downloaded dataset files:** 11.74 GB
- **Size of the generated dataset:** 11.51 GB
- **Total amount of disk used:** 23.25 GB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"publication_date": 1907,
"short_book_title": "La Fiammetta by Giovanni Boccaccio",
"text": "\"\\n\\n\\n\\nProduced by Ted Garvin, Dave Morgan and PG Distributed Proofreaders\\n\\n\\n\\n\\nLA FIAMMETTA\\n\\nBY\\n\\nGIOVANNI BOCCACCIO\\n...",
"url": "http://www.gutenberg.org/ebooks/10006"
}
```
### Data Fields
The data fields are the same among all splits.
#### default
- `short_book_title`: a `string` feature.
- `publication_date`: a `int32` feature.
- `url`: a `string` feature.
- `text`: a `string` feature.
### Data Splits
| name |train|validation|test|
|-------|----:|---------:|---:|
|default|28602| 50| 100|
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
The dataset is licensed under [Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0.html).
### Citation Information
```
@article{raecompressive2019,
author = {Rae, Jack W and Potapenko, Anna and Jayakumar, Siddhant M and
Hillier, Chloe and Lillicrap, Timothy P},
title = {Compressive Transformers for Long-Range Sequence Modelling},
journal = {arXiv preprint},
url = {https://arxiv.org/abs/1911.05507},
year = {2019},
}
```
### Contributions
Thanks to [@thomwolf](https://github.com/thomwolf), [@lewtun](https://github.com/lewtun), [@lucidrains](https://github.com/lucidrains), [@lhoestq](https://github.com/lhoestq) for adding this dataset. | [
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dbrd | null | 2023-01-25T14:29:14Z | 1,137 | 4 | dbrd | [
"task_categories:text-generation",
"task_categories:fill-mask",
"task_categories:text-classification",
"task_ids:language-modeling",
"task_ids:masked-language-modeling",
"task_ids:sentiment-classification",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:nl",
"license:cc-by-nc-sa-4.0",
"arxiv:1910.00896",
"region:us"
] | 2023-01-25T14:29:14Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- found
language_creators:
- found
language:
- nl
license:
- cc-by-nc-sa-4.0
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- text-generation
- fill-mask
- text-classification
task_ids:
- language-modeling
- masked-language-modeling
- sentiment-classification
paperswithcode_id: dbrd
pretty_name: DBRD
dataset_info:
features:
- name: text
dtype: string
- name: label
dtype:
class_label:
names:
'0': neg
'1': pos
config_name: plain_text
splits:
- name: train
num_bytes: 29496333
num_examples: 20028
- name: test
num_bytes: 3246243
num_examples: 2224
- name: unsupervised
num_bytes: 152733031
num_examples: 96264
download_size: 79065872
dataset_size: 185475607
---
# Dataset Card for DBRD
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [Dutch Book Review Dataset (DBRD) homepage](https://benjaminvdb.github.io/DBRD)
- **Repository:** https://github.com/benjaminvdb/DBRD
- **Paper:** [The merits of Universal Language Model Fine-tuning for Small Datasets - a case with Dutch book reviews](https://arxiv.org/abs/1910.00896)
- **Leaderboard:**
- **Point of Contact:** [Benjamin van der Burgh](mailto:benjaminvdb@gmail.com)
### Dataset Summary
The DBRD (pronounced *dee-bird*) dataset contains over 110k book reviews of which 22k have associated binary sentiment polarity labels. It is intended as a benchmark for sentiment classification in Dutch and was created due to a lack of annotated datasets in Dutch that are suitable for this task.
### Supported Tasks and Leaderboards
- `text-generation`: The dataset can be used to train a model for sequence modeling, more specifically language modeling.
- `text-classification`: The dataset can be used to train a model for text classification, more specifically sentiment classification, using the provided positive/negative sentiment polarity labels.
### Languages
Non-Dutch reviews were filtered out using [langdetect](https://github.com/Mimino666/langdetect), and all reviews should therefore be in Dutch (nl). They are written by reviewers on [Hebban](https://www.hebban.nl), a Dutch website for book reviews.
## Dataset Structure
### Data Instances
The dataset contains three subsets: train, test, and unsupervised. The `train` and `test` sets contain labels, while the `unsupervised` set doesn't (the label value is -1 for each instance in `unsupervised`). Here's an example of a positive review, indicated with a label value of `1`.
```
{
'label': 1,
'text': 'Super om te lezen hoe haar leven is vergaan.\nBijzonder dat ze zo openhartig is geweest.'
}
```
### Data Fields
- `label`: either 0 (negative) or 1 (positive) in the supervised sets `train` and `test`. These are always -1 for the unsupervised set.
- `text`: book review as a utf-8 encoded string.
### Data Splits
The `train` and `test` sets were constructed by extracting all non-neutral reviews because we want to assign either a positive or negative polarity label to each instance. Furthermore, the positive (pos) and negative (neg) labels were balanced in both train and test sets. The remainder was added to the unsupervised set.
| | Train | Test | Unsupervised |
| ----- | ------ | ----- | ----------- |
| # No. texts | 20028 | 2224 | 96264 |
| % of total | 16.9% | 1.9% | 81.2% |
## Dataset Creation
### Curation Rationale
This dataset was created due to a lack of annotated Dutch text that is suitable for sentiment classification. Non-Dutch texts were therefore removed, but other than that, no curation was done.
### Source Data
The book reviews were taken from [Hebban](https://www.hebban.nl), a Dutch platform for book reviews.
#### Initial Data Collection and Normalization
The source code of the scraper and preprocessing process can be found in the [DBRD GitHub repository](https://github.com/benjaminvdb/DBRD).
#### Who are the source language producers?
The reviews are written by users of [Hebban](https://www.hebban.nl) and are of varying quality. Some are short, others long, and many contain spelling mistakes and other errors.
### Annotations
Each book review was accompanied by a 1 to 5-star rating. The annotations are produced by mapping the user-provided ratings to either a positive or negative label. 1 and 2-star ratings are given the negative label `0` and 4 and 5-star ratings the positive label `1`. Reviews with a rating of 3 stars are considered neutral and left out of the `train`/`test` sets and added to the unsupervised set.
#### Annotation process
Users of [Hebban](https://www.hebban.nl) were unaware that their reviews would be used in the creation of this dataset.
#### Who are the annotators?
The annotators are the [Hebban](https://www.hebban.nl) users who wrote the book reviews associated with the annotation. Anyone can register on [Hebban](https://www.hebban.nl) and it's impossible to know the demographics of this group.
### Personal and Sensitive Information
The book reviews and ratings are publicly available on [Hebban](https://www.hebban.nl) and no personal or otherwise sensitive information is contained in this dataset.
## Considerations for Using the Data
### Social Impact of Dataset
While predicting sentiment of book reviews in itself is not that interesting, the value of this dataset lies in its usage for benchmarking models. The dataset contains some challenges that are common to outings on the internet, such as spelling mistakes and other errors. It is therefore very useful for validating models for their real-world performance. These datasets are abundant for English but are harder to find for Dutch, making them a valuable resource for ML tasks in this language.
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
Reviews on [Hebban](https://www.hebban.nl) are usually written in Dutch, but some have been written in English and possibly in other languages. While we've done our best to filter out non-Dutch texts, it's hard to do this without errors. For example, some reviews are in multiple languages, and these might slip through. Also be aware that some commercial outings can appear in the text, making them different from other reviews and influencing your models. While this doesn't pose a major issue in most cases, we just wanted to mention it briefly.
## Additional Information
### Dataset Curators
This dataset was created by [Benjamin van der Burgh](mailto:benjaminvdb@gmail.com), who was working at [Leiden Institute of Advanced Computer Science (LIACS)](https://liacs.leidenuniv.nl/) at the time.
### Licensing Information
The dataset is licensed under a [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License](https://creativecommons.org/licenses/by-nc-sa/4.0/).
### Citation Information
Please use the following citation when making use of this dataset in your work.
```
@article{DBLP:journals/corr/abs-1910-00896,
author = {Benjamin van der Burgh and
Suzan Verberne},
title = {The merits of Universal Language Model Fine-tuning for Small Datasets
- a case with Dutch book reviews},
journal = {CoRR},
volume = {abs/1910.00896},
year = {2019},
url = {http://arxiv.org/abs/1910.00896},
archivePrefix = {arXiv},
eprint = {1910.00896},
timestamp = {Fri, 04 Oct 2019 12:28:06 +0200},
biburl = {https://dblp.org/rec/journals/corr/abs-1910-00896.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```
### Contributions
Thanks to [@benjaminvdb](https://github.com/benjaminvdb) for adding this dataset. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
pie/brat | pie | 2023-11-27T09:59:09Z | 1,133 | 0 | null | [
"region:us"
] | 2023-11-27T09:59:09Z | 2023-05-11T15:25:51.000Z | 2023-05-11T15:25:51 | # PIE Dataset Card for "brat"
This is a [PyTorch-IE](https://github.com/ChristophAlt/pytorch-ie) wrapper for the
[BRAT Huggingface dataset loading script](https://huggingface.co/datasets/DFKI-SLT/brat).
## Dataset Variants
The dataset provides the following variants:
- `default`: The original dataset. Documents are of type `BratDocument` (with `LabeledMultiSpan` annotations, see below).
- `merge_fragmented_spans`: Documents are of type `BratDocumentWithMergedSpans` (this variant merges spans that are
fragmented into simple `LabeledSpans`, see below).
## Data Schema
The document type for this dataset is `BratDocument` or `BratDocumentWithMergedSpans`, depending on if the
data was loaded with `merge_fragmented_spans=True` (default: `False`). They define the following data fields:
- `text` (str)
- `id` (str, optional)
- `metadata` (dictionary, optional)
and the following annotation layers:
- `spans` (annotation type: `LabeledMultiSpan` in the case of `BratDocument` and `LabeledSpan` and in the case of `BratDocumentWithMergedSpans`, target: `text`)
- `relations` (annotation type: `BinaryRelation`, target: `spans`)
- `span_attributes` (annotation type: `Attribute`, target: `spans`)
- `relation_attributes` (annotation type: `Attribute`, target: `relations`)
The `LabeledMultiSpan` annotation type is defined as follows:
- `slices` (type: `Tuple[Tuple[int, int], ...]`): the slices consisting if start (including) and end (excluding) indices of the spans
- `label` (type: `str`)
- `score` (type: `float`, optional, not included in comparison)
The `Attribute` annotation type is defined as follows:
- `annotation` (type: `Annotation`): the annotation to which the attribute is attached
- `label` (type: `str`)
- `value` (type: `str`, optional)
- `score` (type: `float`, optional, not included in comparison)
See [here](https://github.com/ChristophAlt/pytorch-ie/blob/main/src/pytorch_ie/annotations.py) for the remaining annotation type definitions.
## Document Converters
The dataset provides no predefined document converters because the BRAT format is very flexible and can be used
for many different tasks.
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miam | null | 2023-06-01T14:59:51Z | 1,120 | 3 | null | [
"task_categories:text-generation",
"task_categories:fill-mask",
"task_categories:text-classification",
"task_ids:dialogue-modeling",
"task_ids:language-modeling",
"task_ids:masked-language-modeling",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:multilingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:de",
"language:en",
"language:es",
"language:fr",
"language:it",
"license:cc-by-sa-4.0",
"dialogue-act-classification",
"region:us"
] | 2023-06-01T14:59:51Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- de
- en
- es
- fr
- it
license:
- cc-by-sa-4.0
multilinguality:
- multilingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-generation
- fill-mask
- text-classification
task_ids:
- dialogue-modeling
- language-modeling
- masked-language-modeling
pretty_name: MIAM
tags:
- dialogue-act-classification
dataset_info:
- config_name: dihana
features:
- name: Speaker
dtype: string
- name: Utterance
dtype: string
- name: Dialogue_Act
dtype: string
- name: Dialogue_ID
dtype: string
- name: File_ID
dtype: string
- name: Label
dtype:
class_label:
names:
'0': Afirmacion
'1': Apertura
'2': Cierre
'3': Confirmacion
'4': Espera
'5': Indefinida
'6': Negacion
'7': No_entendido
'8': Nueva_consulta
'9': Pregunta
'10': Respuesta
- name: Idx
dtype: int32
splits:
- name: train
num_bytes: 1946735
num_examples: 19063
- name: validation
num_bytes: 216498
num_examples: 2123
- name: test
num_bytes: 238446
num_examples: 2361
download_size: 1777267
dataset_size: 2401679
- config_name: ilisten
features:
- name: Speaker
dtype: string
- name: Utterance
dtype: string
- name: Dialogue_Act
dtype: string
- name: Dialogue_ID
dtype: string
- name: Label
dtype:
class_label:
names:
'0': AGREE
'1': ANSWER
'2': CLOSING
'3': ENCOURAGE-SORRY
'4': GENERIC-ANSWER
'5': INFO-REQUEST
'6': KIND-ATTITUDE_SMALL-TALK
'7': OFFER-GIVE-INFO
'8': OPENING
'9': PERSUASION-SUGGEST
'10': QUESTION
'11': REJECT
'12': SOLICITATION-REQ_CLARIFICATION
'13': STATEMENT
'14': TALK-ABOUT-SELF
- name: Idx
dtype: int32
splits:
- name: train
num_bytes: 244336
num_examples: 1986
- name: validation
num_bytes: 33988
num_examples: 230
- name: test
num_bytes: 145376
num_examples: 971
download_size: 349993
dataset_size: 423700
- config_name: loria
features:
- name: Speaker
dtype: string
- name: Utterance
dtype: string
- name: Dialogue_Act
dtype: string
- name: Dialogue_ID
dtype: string
- name: File_ID
dtype: string
- name: Label
dtype:
class_label:
names:
'0': ack
'1': ask
'2': find_mold
'3': find_plans
'4': first_step
'5': greet
'6': help
'7': inform
'8': inform_engine
'9': inform_job
'10': inform_material_space
'11': informer_conditioner
'12': informer_decoration
'13': informer_elcomps
'14': informer_end_manufacturing
'15': kindAtt
'16': manufacturing_reqs
'17': next_step
'18': 'no'
'19': other
'20': quality_control
'21': quit
'22': reqRep
'23': security_policies
'24': staff_enterprise
'25': staff_job
'26': studies_enterprise
'27': studies_job
'28': todo_failure
'29': todo_irreparable
'30': 'yes'
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dtype: int32
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- config_name: maptask
features:
- name: Speaker
dtype: string
- name: Utterance
dtype: string
- name: Dialogue_Act
dtype: string
- name: Dialogue_ID
dtype: string
- name: File_ID
dtype: string
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dtype:
class_label:
names:
'0': acknowledge
'1': align
'2': check
'3': clarify
'4': explain
'5': instruct
'6': query_w
'7': query_yn
'8': ready
'9': reply_n
'10': reply_w
'11': reply_y
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- config_name: vm2
features:
- name: Utterance
dtype: string
- name: Dialogue_Act
dtype: string
- name: Speaker
dtype: string
- name: Dialogue_ID
dtype: string
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dtype:
class_label:
names:
'0': ACCEPT
'1': BACKCHANNEL
'2': BYE
'3': CLARIFY
'4': CLOSE
'5': COMMIT
'6': CONFIRM
'7': DEFER
'8': DELIBERATE
'9': DEVIATE_SCENARIO
'10': EXCLUDE
'11': EXPLAINED_REJECT
'12': FEEDBACK
'13': FEEDBACK_NEGATIVE
'14': FEEDBACK_POSITIVE
'15': GIVE_REASON
'16': GREET
'17': INFORM
'18': INIT
'19': INTRODUCE
'20': NOT_CLASSIFIABLE
'21': OFFER
'22': POLITENESS_FORMULA
'23': REJECT
'24': REQUEST
'25': REQUEST_CLARIFY
'26': REQUEST_COMMENT
'27': REQUEST_COMMIT
'28': REQUEST_SUGGEST
'29': SUGGEST
'30': THANK
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num_examples: 2855
download_size: 1641453
dataset_size: 2287676
config_names:
- dihana
- ilisten
- loria
- maptask
- vm2
---
# Dataset Card for MIAM
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [N/A]
- **Repository:** [N/A]
- **Paper:** [N/A]
- **Leaderboard:** [N/A]
- **Point of Contact:** [N/A]
### Dataset Summary
Multilingual dIalogAct benchMark is a collection of resources for training, evaluating, and
analyzing natural language understanding systems specifically designed for spoken language. Datasets
are in English, French, German, Italian and Spanish. They cover a variety of domains including
spontaneous speech, scripted scenarios, and joint task completion. All datasets contain dialogue act
labels.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
English, French, German, Italian, Spanish.
## Dataset Structure
### Data Instances
#### Dihana Corpus
For the `dihana` configuration one example from the dataset is:
```
{
'Speaker': 'U',
'Utterance': 'Hola , quería obtener el horario para ir a Valencia',
'Dialogue_Act': 9, # 'Pregunta' ('Request')
'Dialogue_ID': '0',
'File_ID': 'B209_BA5c3',
}
```
#### iLISTEN Corpus
For the `ilisten` configuration one example from the dataset is:
```
{
'Speaker': 'T_11_U11',
'Utterance': 'ok, grazie per le informazioni',
'Dialogue_Act': 6, # 'KIND-ATTITUDE_SMALL-TALK'
'Dialogue_ID': '0',
}
```
#### LORIA Corpus
For the `loria` configuration one example from the dataset is:
```
{
'Speaker': 'Samir',
'Utterance': 'Merci de votre visite, bonne chance, et à la prochaine !',
'Dialogue_Act': 21, # 'quit'
'Dialogue_ID': '5',
'File_ID': 'Dial_20111128_113927',
}
```
#### HCRC MapTask Corpus
For the `maptask` configuration one example from the dataset is:
```
{
'Speaker': 'f',
'Utterance': 'is it underneath the rope bridge or to the left',
'Dialogue_Act': 6, # 'query_w'
'Dialogue_ID': '0',
'File_ID': 'q4ec1',
}
```
#### VERBMOBIL
For the `vm2` configuration one example from the dataset is:
```
{
'Utterance': 'ja was sind viereinhalb Stunden Bahngerüttel gegen siebzig Minuten Turbulenzen im Flugzeug',
'Utterance': 'Utterance',
'Dialogue_Act': 'Dialogue_Act', # 'INFORM'
'Speaker': 'A',
'Dialogue_ID': '66',
}
```
### Data Fields
For the `dihana` configuration, the different fields are:
- `Speaker`: identifier of the speaker as a string.
- `Utterance`: Utterance as a string.
- `Dialogue_Act`: Dialog act label of the utterance. It can be one of 'Afirmacion' (0) [Feedback_positive], 'Apertura' (1) [Opening], 'Cierre' (2) [Closing], 'Confirmacion' (3) [Acknowledge], 'Espera' (4) [Hold], 'Indefinida' (5) [Undefined], 'Negacion' (6) [Feedback_negative], 'No_entendido' (7) [Request_clarify], 'Nueva_consulta' (8) [New_request], 'Pregunta' (9) [Request] or 'Respuesta' (10) [Reply].
- `Dialogue_ID`: identifier of the dialogue as a string.
- `File_ID`: identifier of the source file as a string.
For the `ilisten` configuration, the different fields are:
- `Speaker`: identifier of the speaker as a string.
- `Utterance`: Utterance as a string.
- `Dialogue_Act`: Dialog act label of the utterance. It can be one of 'AGREE' (0), 'ANSWER' (1), 'CLOSING' (2), 'ENCOURAGE-SORRY' (3), 'GENERIC-ANSWER' (4), 'INFO-REQUEST' (5), 'KIND-ATTITUDE_SMALL-TALK' (6), 'OFFER-GIVE-INFO' (7), 'OPENING' (8), 'PERSUASION-SUGGEST' (9), 'QUESTION' (10), 'REJECT' (11), 'SOLICITATION-REQ_CLARIFICATION' (12), 'STATEMENT' (13) or 'TALK-ABOUT-SELF' (14).
- `Dialogue_ID`: identifier of the dialogue as a string.
For the `loria` configuration, the different fields are:
- `Speaker`: identifier of the speaker as a string.
- `Utterance`: Utterance as a string.
- `Dialogue_Act`: Dialog act label of the utterance. It can be one of 'ack' (0), 'ask' (1), 'find_mold' (2), 'find_plans' (3), 'first_step' (4), 'greet' (5), 'help' (6), 'inform' (7), 'inform_engine' (8), 'inform_job' (9), 'inform_material_space' (10), 'informer_conditioner' (11), 'informer_decoration' (12), 'informer_elcomps' (13), 'informer_end_manufacturing' (14), 'kindAtt' (15), 'manufacturing_reqs' (16), 'next_step' (17), 'no' (18), 'other' (19), 'quality_control' (20), 'quit' (21), 'reqRep' (22), 'security_policies' (23), 'staff_enterprise' (24), 'staff_job' (25), 'studies_enterprise' (26), 'studies_job' (27), 'todo_failure' (28), 'todo_irreparable' (29), 'yes' (30)
- `Dialogue_ID`: identifier of the dialogue as a string.
- `File_ID`: identifier of the source file as a string.
For the `maptask` configuration, the different fields are:
- `Speaker`: identifier of the speaker as a string.
- `Utterance`: Utterance as a string.
- `Dialogue_Act`: Dialog act label of the utterance. It can be one of 'acknowledge' (0), 'align' (1), 'check' (2), 'clarify' (3), 'explain' (4), 'instruct' (5), 'query_w' (6), 'query_yn' (7), 'ready' (8), 'reply_n' (9), 'reply_w' (10) or 'reply_y' (11).
- `Dialogue_ID`: identifier of the dialogue as a string.
- `File_ID`: identifier of the source file as a string.
For the `vm2` configuration, the different fields are:
- `Utterance`: Utterance as a string.
- `Dialogue_Act`: Dialogue act label of the utterance. It can be one of 'ACCEPT' (0), 'BACKCHANNEL' (1), 'BYE' (2), 'CLARIFY' (3), 'CLOSE' (4), 'COMMIT' (5), 'CONFIRM' (6), 'DEFER' (7), 'DELIBERATE' (8), 'DEVIATE_SCENARIO' (9), 'EXCLUDE' (10), 'EXPLAINED_REJECT' (11), 'FEEDBACK' (12), 'FEEDBACK_NEGATIVE' (13), 'FEEDBACK_POSITIVE' (14), 'GIVE_REASON' (15), 'GREET' (16), 'INFORM' (17), 'INIT' (18), 'INTRODUCE' (19), 'NOT_CLASSIFIABLE' (20), 'OFFER' (21), 'POLITENESS_FORMULA' (22), 'REJECT' (23), 'REQUEST' (24), 'REQUEST_CLARIFY' (25), 'REQUEST_COMMENT' (26), 'REQUEST_COMMIT' (27), 'REQUEST_SUGGEST' (28), 'SUGGEST' (29), 'THANK' (30).
- `Speaker`: Speaker as a string.
- `Dialogue_ID`: identifier of the dialogue as a string.
### Data Splits
| Dataset name | Train | Valid | Test |
| ------------ | ----- | ----- | ---- |
| dihana | 19063 | 2123 | 2361 |
| ilisten | 1986 | 230 | 971 |
| loria | 8465 | 942 | 1047 |
| maptask | 25382 | 5221 | 5335 |
| vm2 | 25060 | 2860 | 2855 |
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
Anonymous.
### Licensing Information
This work is licensed under a [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 Unported License](https://creativecommons.org/licenses/by-sa/4.0/).
### Citation Information
```
@inproceedings{colombo-etal-2021-code,
title = "Code-switched inspired losses for spoken dialog representations",
author = "Colombo, Pierre and
Chapuis, Emile and
Labeau, Matthieu and
Clavel, Chlo{\'e}",
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.656",
doi = "10.18653/v1/2021.emnlp-main.656",
pages = "8320--8337",
abstract = "Spoken dialogue systems need to be able to handle both multiple languages and multilinguality inside a conversation (\textit{e.g} in case of code-switching). In this work, we introduce new pretraining losses tailored to learn generic multilingual spoken dialogue representations. The goal of these losses is to expose the model to code-switched language. In order to scale up training, we automatically build a pretraining corpus composed of multilingual conversations in five different languages (French, Italian, English, German and Spanish) from OpenSubtitles, a huge multilingual corpus composed of 24.3G tokens. We test the generic representations on MIAM, a new benchmark composed of five dialogue act corpora on the same aforementioned languages as well as on two novel multilingual tasks (\textit{i.e} multilingual mask utterance retrieval and multilingual inconsistency identification). Our experiments show that our new losses achieve a better performance in both monolingual and multilingual settings.",
}
```
### Contributions
Thanks to [@eusip](https://github.com/eusip) and [@PierreColombo](https://github.com/PierreColombo) for adding this dataset. | [
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masakhane/masakhanews | masakhane | 2023-05-25T22:27:40Z | 1,119 | 5 | null | [
"task_categories:text-classification",
"task_ids:topic-classification",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:multilingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:am",
"language:en",
"language:fr",
"language:ha",
"language:ig",
"language:ln",
"language:lg",
"language:om",
"language:pcm",
"language:rn",
"language:sn",
"language:so",
"language:sw",
"language:ti",
"language:xh",
"language:yo",
"license:afl-3.0",
"news-topic",
"masakhanews",
"masakhane",
"region:us"
] | 2023-05-25T22:27:40Z | 2023-04-20T23:06:34.000Z | 2023-04-20T23:06:34 | ---
annotations_creators:
- expert-generated
language:
- am
- en
- fr
- ha
- ig
- ln
- lg
- om
- pcm
- rn
- sn
- so
- sw
- ti
- xh
- yo
language_creators:
- expert-generated
license:
- afl-3.0
multilinguality:
- multilingual
pretty_name: masakhanews
size_categories:
- 1K<n<10K
source_datasets:
- original
tags:
- news-topic
- masakhanews
- masakhane
task_categories:
- text-classification
task_ids:
- topic-classification
---
# Dataset Card for [Dataset Name]
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [homepage](https://github.com/masakhane-io/masakhane-news)
- **Repository:** [github](https://github.com/masakhane-io/masakhane-news)
- **Paper:** [paper]()
- **Point of Contact:** [Masakhane](https://www.masakhane.io/) or didelani@lsv.uni-saarland.de
### Dataset Summary
MasakhaNEWS is the largest publicly available dataset for news topic classification in 16 languages widely spoken in Africa.
The train/validation/test sets are available for all the 16 languages.
### Supported Tasks and Leaderboards
[More Information Needed]
- `news topic classification`: categorize news articles into new topics e.g business, sport sor politics.
### Languages
There are 16 languages available :
- Amharic (amh)
- English (eng)
- French (fra)
- Hausa (hau)
- Igbo (ibo)
- Lingala (lin)
- Luganda (lug)
- Oromo (orm)
- Nigerian Pidgin (pcm)
- Rundi (run)
- chShona (sna)
- Somali (som)
- Kiswahili (swą)
- Tigrinya (tir)
- isiXhosa (xho)
- Yorùbá (yor)
## Dataset Structure
### Data Instances
The examples look like this for Yorùbá:
```
from datasets import load_dataset
data = load_dataset('masakhane/masakhanews', 'yor')
# Please, specify the language code
# A data point example is below:
{
'label': 0,
'headline': "'The barriers to entry have gone - go for it now'",
'text': "j Lalvani, CEO of Vitabiotics and former Dragons' Den star, shares his business advice for our CEO Secrets series.\nProduced, filmed and edited by Dougal Shaw",
'headline_text': "'The barriers to entry have gone - go for it now' j Lalvani, CEO of Vitabiotics and former Dragons' Den star, shares his business advice for our CEO Secrets series.\nProduced, filmed and edited by Dougal Shaw",
'url': '/news/business-61880859'
}
```
### Data Fields
- `label`: news topic id
- `headline`: news title/headline
- `text`: news body
- `headline_text`: concatenation of headline and news body
- `url`: website address
The news topics correspond to this list:
```
"business", "entertainment", "health", "politics", "religion", "sports", "technology"
```
### Data Splits
For all languages, there are three splits.
The original splits were named `train`, `dev` and `test` and they correspond to the `train`, `validation` and `test` splits.
The splits have the following sizes :
| Language | train | validation | test |
|-----------------|------:|-----------:|-----:|
| Amharic | 1311 | 188 | 376 |
| English | 3309 | 472 | 948 |
| French | 1476 | 211 | 422 |
| Hausa | 2219 | 317 | 637 |
| Igbo | 1356 | 194 | 390 |
| Lingala | 608 | 87 | 175 |
| Luganda | 771 | 110 | 223 |
| Oromo | 1015 | 145 | 292 |
| Nigerian-Pidgin | 1060 | 152 | 305 |
| Rundi | 1117 | 159 | 322 |
| chiShona | 1288 | 185 | 369 |
| Somali | 1021 | 148 | 294 |
| Kiswahili | 1658 | 237 | 476 |
| Tigrinya | 947 | 137 | 272 |
| isiXhosa | 1032 | 147 | 297 |
| Yoruba | 1433 | 206 | 411 |
## Dataset Creation
### Curation Rationale
The dataset was introduced to introduce new resources to 20 languages that were under-served for natural language processing.
[More Information Needed]
### Source Data
The source of the data is from the news domain, details can be found here ****
#### Initial Data Collection and Normalization
The articles were word-tokenized, information on the exact pre-processing pipeline is unavailable.
#### Who are the source language producers?
The source language was produced by journalists and writers employed by the news agency and newspaper mentioned above.
### Annotations
#### Annotation process
Details can be found here **
#### Who are the annotators?
Annotators were recruited from [Masakhane](https://www.masakhane.io/)
### Personal and Sensitive Information
The data is sourced from newspaper source and only contains mentions of public figures or individuals
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
Users should keep in mind that the dataset only contains news text, which might limit the applicability of the developed systems to other domains.
## Additional Information
### Dataset Curators
### Licensing Information
The licensing status of the data is CC 4.0 Non-Commercial
### Citation Information
Provide the [BibTex](http://www.bibtex.org/)-formatted reference for the dataset. For example:
```
@article{Adelani2023MasakhaNEWS,
title={MasakhaNEWS: News Topic Classification for African languages},
author={David Ifeoluwa Adelani and Marek Masiak and Israel Abebe Azime and Jesujoba Oluwadara Alabi and Atnafu Lambebo Tonja and Christine Mwase and Odunayo Ogundepo and Bonaventure F. P. Dossou and Akintunde Oladipo and Doreen Nixdorf and Chris Chinenye Emezue and Sana Sabah al-azzawi and Blessing K. Sibanda and Davis David and Lolwethu Ndolela and Jonathan Mukiibi and Tunde Oluwaseyi Ajayi and Tatiana Moteu Ngoli and Brian Odhiambo and Abraham Toluwase Owodunni and Nnaemeka C. Obiefuna and Shamsuddeen Hassan Muhammad and Saheed Salahudeen Abdullahi and Mesay Gemeda Yigezu and Tajuddeen Gwadabe and Idris Abdulmumin and Mahlet Taye Bame and Oluwabusayo Olufunke Awoyomi and Iyanuoluwa Shode and Tolulope Anu Adelani and Habiba Abdulganiy Kailani and Abdul-Hakeem Omotayo and Adetola Adeeko and Afolabi Abeeb and Anuoluwapo Aremu and Olanrewaju Samuel and Clemencia Siro and Wangari Kimotho and Onyekachi Raphael Ogbu and Chinedu E. Mbonu and Chiamaka I. Chukwuneke and Samuel Fanijo and Jessica Ojo and Oyinkansola F. Awosan and Tadesse Kebede Guge and Sakayo Toadoum Sari and Pamela Nyatsine and Freedmore Sidume and Oreen Yousuf and Mardiyyah Oduwole and Ussen Kimanuka and Kanda Patrick Tshinu and Thina Diko and Siyanda Nxakama and Abdulmejid Tuni Johar and Sinodos Gebre and Muhidin Mohamed and Shafie Abdi Mohamed and Fuad Mire Hassan and Moges Ahmed Mehamed and Evrard Ngabire and and Pontus Stenetorp},
journal={ArXiv},
year={2023},
volume={}
}
```
### Contributions
Thanks to [@dadelani](https://github.com/dadelani) for adding this dataset. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
shunk031/wrime | shunk031 | 2023-01-15T03:39:01Z | 1,117 | 12 | null | [
"task_categories:text-classification",
"task_ids:sentiment-classification",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"language:ja",
"license:unknown",
"sentiment-analysis",
"wrime",
"region:us"
] | 2023-01-15T03:39:01Z | 2023-01-12T03:04:20.000Z | 2023-01-12T03:04:20 | ---
annotations_creators:
- crowdsourced
language:
- ja
language_creators:
- crowdsourced
license:
- unknown
multilinguality:
- monolingual
pretty_name: wrime
tags:
- sentiment-analysis
- wrime
task_categories:
- text-classification
task_ids:
- sentiment-classification
datasets:
- ver1
- ver2
metrics:
- accuracy
---
# Dataset Card for WRIME
[](https://github.com/shunk031/huggingface-datasets_wrime/actions/workflows/ci.yaml)
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- Homepage: https://github.com/ids-cv/wrime
- Repository: https://github.com/shunk031/huggingface-datasets_wrime
- Paper: https://aclanthology.org/2021.naacl-main.169/
### Dataset Summary
In this study, we introduce a new dataset, WRIME, for emotional intensity estimation. We collect both the subjective emotional intensity ofthe writers themselves and the objective one annotated by the readers, and explore the differences between them. In our data collection, we hired 50 participants via crowdsourcing service. They annotated their own past posts on a social networking service (SNS) with the subjective emotional intensity. We also hired 3 annotators, who annotated allposts with the objective emotional intensity. Consequently, our Japanese emotion analysis datasetconsists of 17,000 posts with both subjective andobjective emotional intensities for Plutchik’s eightemotions ([Plutchik, 1980](https://www.sciencedirect.com/science/article/pii/B9780125587013500077)), which are given in afour-point scale (no, weak, medium, and strong).
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
- Japanese
## Dataset Structure
### Data Instances
When loading a specific configuration, users has to append a version dependent suffix:
```python
from datasets import load_dataset
dataset = load_dataset("shunk031/wrime", name="ver1")
print(dataset)
# DatasetDict({
# train: Dataset({
# features: ['sentence', 'user_id', 'datetime', 'writer', 'reader1', 'reader2', 'reader3', 'avg_readers'],
# num_rows: 40000
# })
# validation: Dataset({
# features: ['sentence', 'user_id', 'datetime', 'writer', 'reader1', 'reader2', 'reader3', 'avg_readers'],
# num_rows: 1200
# })
# test: Dataset({
# features: ['sentence', 'user_id', 'datetime', 'writer', 'reader1', 'reader2', 'reader3', 'avg_readers'],
# num_rows: 2000
# })
# })
```
#### Ver. 1
An example of looks as follows:
```json
{
"sentence": "ぼけっとしてたらこんな時間｡チャリあるから食べにでたいのに…",
"user_id": "1",
"datetime": "2012/07/31 23:48",
"writer": {
"joy": 0,
"sadness": 1,
"anticipation": 2,
"surprise": 1,
"anger": 1,
"fear": 0,
"disgust": 0,
"trust": 1
},
"reader1": {
"joy": 0,
"sadness": 2,
"anticipation": 0,
"surprise": 0,
"anger": 0,
"fear": 0,
"disgust": 0,
"trust": 0
},
"reader2": {
"joy": 0,
"sadness": 2,
"anticipation": 0,
"surprise": 1,
"anger": 0,
"fear": 0,
"disgust": 0,
"trust": 0
},
"reader3": {
"joy": 0,
"sadness": 2,
"anticipation": 0,
"surprise": 0,
"anger": 0,
"fear": 1,
"disgust": 1,
"trust": 0
},
"avg_readers": {
"joy": 0,
"sadness": 2,
"anticipation": 0,
"surprise": 0,
"anger": 0,
"fear": 0,
"disgust": 0,
"trust": 0
}
}
```
#### Ver. 1
An example of looks as follows:
```json
{
"sentence": "ぼけっとしてたらこんな時間。チャリあるから食べにでたいのに…",
"user_id": "1",
"datetime": "2012/7/31 23:48",
"writer": {
"joy": 0,
"sadness": 1,
"anticipation": 2,
"surprise": 1,
"anger": 1,
"fear": 0,
"disgust": 0,
"trust": 1,
"sentiment": 0
},
"reader1": {
"joy": 0,
"sadness": 2,
"anticipation": 0,
"surprise": 0,
"anger": 0,
"fear": 0,
"disgust": 0,
"trust": 0,
"sentiment": -2
},
"reader2": {
"joy": 0,
"sadness": 2,
"anticipation": 0,
"surprise": 0,
"anger": 0,
"fear": 1,
"disgust": 1,
"trust": 0,
"sentiment": -1
},
"reader3": {
"joy": 0,
"sadness": 2,
"anticipation": 0,
"surprise": 1,
"anger": 0,
"fear": 0,
"disgust": 0,
"trust": 0,
"sentiment": -1
},
"avg_readers": {
"joy": 0,
"sadness": 2,
"anticipation": 0,
"surprise": 0,
"anger": 0,
"fear": 0,
"disgust": 0,
"trust": 0,
"sentiment": -1
}
}
```
### Data Fields
#### Ver. 1
- `sentence`: 投稿テキスト
- `user_id`: ユーザー ID
- `datetime`: 投稿日時
- `writer`: 主観 （書き手）
- `joy`: 主観の喜びの感情
- `sadness`: 主観の悲しみの感情
- `anticipation`: 主観の期待の感情
- `surprise`: 主観の驚きの感情
- `anger`: 主観の怒りの感情
- `fear`: 主観の恐れの感情
- `disgust`: 主観の嫌悪の感情
- `trust`: 主観の信頼の感情
- `reader1`: 客観 A （読み手 A)
- `joy`: 客観 A の喜びの感情
- `sadness`: 客観 A の悲しみの感情
- `anticipation`: 客観 A の期待の感情
- `surprise`: 客観 A の驚きの感情
- `anger`: 客観 A の怒りの感情
- `fear`: 客観 A の恐れの感情
- `disgust`: 客観 A の嫌悪の感情
- `trust`: 客観 A の信頼の感情
- `reader2`: 客観 B （読み手 B)
- `joy`: 客観 B の喜びの感情
- `sadness`: 客観 B の悲しみの感情
- `anticipation`: 客観 B の期待の感情
- `surprise`: 客観 B の驚きの感情
- `anger`: 客観 B の怒りの感情
- `fear`: 客観 B の恐れの感情
- `disgust`: 客観 B の嫌悪の感情
- `trust`: 客観 B の信頼の感情
- `reader3`: 客観 C （読み手 C)
- `joy`: 客観 C の喜びの感情
- `sadness`: 客観 C の悲しみの感情
- `anticipation`: 客観 C の期待の感情
- `surprise`: 客観 C の驚きの感情
- `anger`: 客観 C の怒りの感情
- `fear`: 客観 C の恐れの感情
- `disgust`: 客観 C の嫌悪の感情
- `trust`: 客観 C の信頼の感情
- `avg_readers`
- `joy`: 客観 A, B, C 平均の喜びの感情
- `sadness`: 客観 A, B, C 平均の悲しみの感情
- `anticipation`: 客観 A, B, C 平均の期待の感情
- `surprise`: 客観 A, B, C 平均の驚きの感情
- `anger`: 客観 A, B, C 平均の怒りの感情
- `fear`: 客観 A, B, C 平均の恐れの感情
- `disgust`: 客観 A, B, C 平均の嫌悪の感情
- `trust`: 客観 A, B, C 平均の信頼の感情
#### Ver. 2
- `sentence`: 投稿テキスト
- `user_id`: ユーザー ID
- `datetime`: 投稿日時
- `writer`: 主観 （書き手）
- `joy`: 主観の喜びの感情
- `sadness`: 主観の悲しみの感情
- `anticipation`: 主観の期待の感情
- `surprise`: 主観の驚きの感情
- `anger`: 主観の怒りの感情
- `fear`: 主観の恐れの感情
- `disgust`: 主観の嫌悪の感情
- `trust`: 主観の信頼の感情
- `sentiment`: 主観の感情極性
- `reader1`: 客観 A （読み手 A)
- `joy`: 客観 A の喜びの感情
- `sadness`: 客観 A の悲しみの感情
- `anticipation`: 客観 A の期待の感情
- `surprise`: 客観 A の驚きの感情
- `anger`: 客観 A の怒りの感情
- `fear`: 客観 A の恐れの感情
- `disgust`: 客観 A の嫌悪の感情
- `trust`: 客観 A の信頼の感情
- `sentiment`: 客観 A の感情極性
- `reader2`: 客観 B （読み手 B)
- `joy`: 客観 B の喜びの感情
- `sadness`: 客観 B の悲しみの感情
- `anticipation`: 客観 B の期待の感情
- `surprise`: 客観 B の驚きの感情
- `anger`: 客観 B の怒りの感情
- `fear`: 客観 B の恐れの感情
- `disgust`: 客観 B の嫌悪の感情
- `trust`: 客観 B の信頼の感情
- `sentiment`: 客観 B の感情極性
- `reader3`: 客観 C （読み手 C)
- `joy`: 客観 C の喜びの感情
- `sadness`: 客観 C の悲しみの感情
- `anticipation`: 客観 C の期待の感情
- `surprise`: 客観 C の驚きの感情
- `anger`: 客観 C の怒りの感情
- `fear`: 客観 C の恐れの感情
- `disgust`: 客観 C の嫌悪の感情
- `trust`: 客観 C の信頼の感情
- `sentiment`: 客観 C の感情極性
- `avg_readers`
- `joy`: 客観 A, B, C 平均の喜びの感情
- `sadness`: 客観 A, B, C 平均の悲しみの感情
- `anticipation`: 客観 A, B, C 平均の期待の感情
- `surprise`: 客観 A, B, C 平均の驚きの感情
- `anger`: 客観 A, B, C 平均の怒りの感情
- `fear`: 客観 A, B, C 平均の恐れの感情
- `disgust`: 客観 A, B, C 平均の嫌悪の感情
- `trust`: 客観 A, B, C 平均の信頼の感情
- `sentiment`: 客観 A, B, C 平均の感情極性
### Data Splits
| name | train | validation | test |
|------|-------:|-----------:|------:|
| ver1 | 40,000 | 1,200 | 2,000 |
| ver2 | 30,000 | 2,500 | 2,500 |
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
From [the README](https://github.com/ids-cv/wrime/blob/master/README.en.md#licence) of the GitHub:
- The dataset is available for research purposes only.
- Redistribution of the dataset is prohibited.
### Citation Information
```bibtex
@inproceedings{kajiwara-etal-2021-wrime,
title = "{WRIME}: A New Dataset for Emotional Intensity Estimation with Subjective and Objective Annotations",
author = "Kajiwara, Tomoyuki and
Chu, Chenhui and
Takemura, Noriko and
Nakashima, Yuta and
Nagahara, Hajime",
booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jun,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.naacl-main.169",
doi = "10.18653/v1/2021.naacl-main.169",
pages = "2095--2104",
abstract = "We annotate 17,000 SNS posts with both the writer{'}s subjective emotional intensity and the reader{'}s objective one to construct a Japanese emotion analysis dataset. In this study, we explore the difference between the emotional intensity of the writer and that of the readers with this dataset. We found that the reader cannot fully detect the emotions of the writer, especially anger and trust. In addition, experimental results in estimating the emotional intensity show that it is more difficult to estimate the writer{'}s subjective labels than the readers{'}. The large gap between the subjective and objective emotions imply the complexity of the mapping from a post to the subjective emotion intensities, which also leads to a lower performance with machine learning models.",
}
```
```bibtex
@inproceedings{suzuki-etal-2022-japanese,
title = "A {J}apanese Dataset for Subjective and Objective Sentiment Polarity Classification in Micro Blog Domain",
author = "Suzuki, Haruya and
Miyauchi, Yuto and
Akiyama, Kazuki and
Kajiwara, Tomoyuki and
Ninomiya, Takashi and
Takemura, Noriko and
Nakashima, Yuta and
Nagahara, Hajime",
booktitle = "Proceedings of the Thirteenth Language Resources and Evaluation Conference",
month = jun,
year = "2022",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://aclanthology.org/2022.lrec-1.759",
pages = "7022--7028",
abstract = "We annotate 35,000 SNS posts with both the writer{'}s subjective sentiment polarity labels and the reader{'}s objective ones to construct a Japanese sentiment analysis dataset. Our dataset includes intensity labels (\textit{none}, \textit{weak}, \textit{medium}, and \textit{strong}) for each of the eight basic emotions by Plutchik (\textit{joy}, \textit{sadness}, \textit{anticipation}, \textit{surprise}, \textit{anger}, \textit{fear}, \textit{disgust}, and \textit{trust}) as well as sentiment polarity labels (\textit{strong positive}, \textit{positive}, \textit{neutral}, \textit{negative}, and \textit{strong negative}). Previous studies on emotion analysis have studied the analysis of basic emotions and sentiment polarity independently. In other words, there are few corpora that are annotated with both basic emotions and sentiment polarity. Our dataset is the first large-scale corpus to annotate both of these emotion labels, and from both the writer{'}s and reader{'}s perspectives. In this paper, we analyze the relationship between basic emotion intensity and sentiment polarity on our dataset and report the results of benchmarking sentiment polarity classification.",
}
```
### Contributions
Thanks to [@moguranosenshi](https://github.com/moguranosenshi) for creating this dataset.
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miracl/miracl | miracl | 2023-01-06T16:25:49Z | 1,113 | 26 | null | [
"task_categories:text-retrieval",
"task_ids:document-retrieval",
"annotations_creators:expert-generated",
"multilinguality:multilingual",
"language:ar",
"language:bn",
"language:en",
"language:es",
"language:fa",
"language:fi",
"language:fr",
"language:hi",
"language:id",
"language:ja",
"language:ko",
"language:ru",
"language:sw",
"language:te",
"language:th",
"language:zh",
"license:apache-2.0",
"arxiv:2210.09984",
"region:us"
] | 2023-01-06T16:25:49Z | 2022-10-11T22:20:12.000Z | 2022-10-11T22:20:12 | ---
annotations_creators:
- expert-generated
language:
- ar
- bn
- en
- es
- fa
- fi
- fr
- hi
- id
- ja
- ko
- ru
- sw
- te
- th
- zh
multilinguality:
- multilingual
pretty_name: MIRACL-corpus
size_categories: []
source_datasets: []
tags: []
task_categories:
- text-retrieval
license:
- apache-2.0
task_ids:
- document-retrieval
---
# Dataset Card for MIRACL (Topics and Qrels)
## Dataset Description
* **Homepage:** http://miracl.ai
* **Repository:** https://github.com/project-miracl/miracl
* **Paper:** https://arxiv.org/abs/2210.09984
MIRACL 🌍🙌🌏 (Multilingual Information Retrieval Across a Continuum of Languages) is a multilingual retrieval dataset that focuses on search across 18 different languages, which collectively encompass over three billion native speakers around the world.
This dataset contains the collection data of the 16 "known languages". The remaining 2 "surprise languages" will not be released until later.
The topics are generated by native speakers of each language, who also label the relevance between the topics and a given document list.
This repository only contains the topics and qrels of MIRACL. The collection can be found [here](https://huggingface.co/datasets/miracl/miracl-corpus).
## Dataset Structure
1. To download the files:
Under folders `miracl-v1.0-{lang}/topics`,
the topics are saved in `.tsv` format, with each line to be:
```
qid\tquery
```
Under folders `miracl-v1.0-{lang}/qrels`,
the qrels are saved in standard TREC format, with each line to be:
```
qid Q0 docid relevance
```
2. To access the data using HuggingFace `datasets`:
```
lang='ar' # or any of the 16 languages
miracl = datasets.load_dataset('miracl/miracl', lang, use_auth_token=True)
# training set:
for data in miracl['train']: # or 'dev', 'testA'
query_id = data['query_id']
query = data['query']
positive_passages = data['positive_passages']
negative_passages = data['negative_passages']
for entry in positive_passages: # OR 'negative_passages'
docid = entry['docid']
title = entry['title']
text = entry['text']
```
The structure is the same for `train`, `dev`, and `testA` set, where `testA` only exists for languages in Mr. TyDi (i.e., Arabic, Bengali, English, Finnish, Indonesian, Japanese, Korean, Russian, Swahili, Telugu, Thai).
Note that `negative_passages` are annotated by native speakers as well, instead of the non-positive passages from top-`k` retrieval results.
## Dataset Statistics
The following table contains the number of queries (`#Q`) and the number of judgments (`#J`) in each language, for the training and development set,
where the judgments include both positive and negative samples.
| Lang | Train | | Dev | |
|:----:|:-----:|:------:|:-----:|:------:|
| | **#Q**| **#J** |**#Q** |**#J** |
| ar | 3,495 | 25,382 | 2,896 | 29,197 |
| bn | 1,631 | 16,754 | 411 | 4,206 |
| en | 2,863 | 29,416 | 799 | 8,350 |
| es | 2,162 | 21,531 | 648 | 6,443 |
| fa | 2,107 | 21,844 | 632 | 6,571 |
| fi | 2,897 | 20,350 | 1,271 | 12,008 |
| fr | 1,143 | 11,426 | 343 | 3,429 |
| hi | 1,169 | 11,668 | 350 | 3,494 |
| id | 4,071 | 41,358 | 960 | 9,668 |
| ja | 3,477 | 34,387 | 860 | 8,354 |
| ko | 868 | 12,767 | 213 | 3,057 |
| ru | 4,683 | 33,921 | 1,252 | 13,100 |
| sw | 1,901 | 9,359 | 482 | 5,092 |
| te | 3,452 | 18,608 | 828 | 1,606 |
| th | 2,972 | 21,293 | 733 | 7,573 |
| zh | 1,312 | 13,113 | 393 | 3,928 | | [
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autshumato | null | 2023-06-01T14:59:51Z | 1,112 | 2 | null | [
"task_categories:translation",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:multilingual",
"size_categories:100K<n<1M",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"language:tn",
"language:ts",
"language:zu",
"license:cc-by-2.5",
"region:us"
] | 2023-06-01T14:59:51Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
- tn
- ts
- zu
license:
- cc-by-2.5
multilinguality:
- multilingual
size_categories:
- 100K<n<1M
- 10K<n<100K
source_datasets:
- original
task_categories:
- translation
task_ids: []
paperswithcode_id: null
pretty_name: autshumato
dataset_info:
- config_name: autshumato-en-tn
features:
- name: translation
dtype:
translation:
languages:
- en
- tn
splits:
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num_bytes: 28826392
num_examples: 159000
download_size: 9458762
dataset_size: 28826392
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features:
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dtype:
translation:
languages:
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splits:
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num_bytes: 7188970
num_examples: 35489
download_size: 2068891
dataset_size: 7188970
- config_name: autshumato-en-ts
features:
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dtype:
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languages:
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splits:
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num_bytes: 50803849
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download_size: 15145915
dataset_size: 50803849
- config_name: autshumato-en-ts-manual
features:
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dtype:
translation:
languages:
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splits:
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num_examples: 92396
download_size: 2876924
dataset_size: 10408757
- config_name: autshumato-tn
features:
- name: text
dtype: string
splits:
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num_bytes: 5132267
num_examples: 38206
download_size: 1599029
dataset_size: 5132267
- config_name: autshumato-ts
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 3399674
num_examples: 58398
download_size: 974488
dataset_size: 3399674
config_names:
- autshumato-en-tn
- autshumato-en-ts
- autshumato-en-ts-manual
- autshumato-en-zu
- autshumato-tn
- autshumato-ts
---
# Dataset Card for autshumato
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [https://repo.sadilar.org/handle/20.500.12185/7/discover]()
- **Repository:** []()
- **Paper:** []()
- **Leaderboard:** []()
- **Point of Contact:** []()
### Dataset Summary
Multilingual information access is stipulated in the South African constitution. In practise, this
is hampered by a lack of resources and capacity to perform the large volumes of translation
work required to realise multilingual information access. One of the aims of the Autshumato
project is to develop machine translation systems for three South African languages pairs.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
[More Information Needed]
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
[More Information Needed]
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
[More Information Needed]
### Dataset Curators
[More Information Needed]
### Licensing Information
### Citation Information
```
@article{groenewald2010processing,
title={Processing parallel text corpora for three South African language pairs in the Autshumato project},
author={Groenewald, Hendrik J and du Plooy, Liza},
journal={AfLaT 2010},
pages={27},
year={2010}
}
```
### Contributions
Thanks to [@Narsil](https://github.com/Narsil) for adding this dataset. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
llm-book/wrime-sentiment | llm-book | 2023-10-06T00:56:38Z | 1,112 | 2 | null | [
"task_categories:text-classification",
"size_categories:10K<n<100K",
"language:ja",
"region:us"
] | 2023-10-06T00:56:38Z | 2023-07-29T06:38:26.000Z | 2023-07-29T06:38:26 | ---
task_categories:
- text-classification
language:
- ja
size_categories:
- 10K<n<100K
---
# Dataset Card for llm-book/wrime-sentiment
日本語の感情分析データセット WRIME を、ポジティブ/ネガティブの二値分類のタスクに加工したデータセットです。
GitHub リポジトリ [ids-cv/wrime](https://github.com/ids-cv/wrime) で公開されているデータセットを利用しています。
`Avg. Readers_Sentiment` の値が0より大きいものをポジティブ、0より小さいものをネガティブとラベル付をしています。
書籍『大規模言語モデル入門』のサンプルコードで利用することを想定しています。
詳しくは[書籍のGitHubリポジトリ](https://github.com/ghmagazine/llm-book)をご覧ください。
## 使い方
以下のようにデータセットを読み込むことができます。
```python
from datasets import load_dataset
dataset = load_dataset("hf_datasets/wrime-sentiment")
print(dataset["train"].features["label"])
print(dataset)
```
```python
ClassLabel(names=['positive', 'negative'], id=None)
DatasetDict({
train: Dataset({
features: ['sentence', 'label'],
num_rows: 20149
})
validation: Dataset({
features: ['sentence', 'label'],
num_rows: 1608
})
test: Dataset({
features: ['sentence', 'label'],
num_rows: 1781
})
})
```
デフォルトの設定では、元のデータセットから極性がニュートラルであるものを除いています。
`remove_netural=False`と指定することで、ニュートラルなデータも含めた三値分類のデータセットを読み込むことができます。
```python
from datasets import load_dataset
dataset = load_dataset("hf_datasets/wrime-sentiment", remove_neutral=False)
print(dataset["train"].features["label"])
print(dataset)
```
```python
ClassLabel(names=['positive', 'negative', 'neutral'], id=None)
DatasetDict({
train: Dataset({
features: ['sentence', 'label'],
num_rows: 30000
})
validation: Dataset({
features: ['sentence', 'label'],
num_rows: 2500
})
test: Dataset({
features: ['sentence', 'label'],
num_rows: 2500
})
})
``` | [
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nthngdy/oscar-small | nthngdy | 2023-03-08T09:57:45Z | 1,109 | 6 | oscar | [
"task_categories:text-generation",
"task_ids:language-modeling",
"annotations_creators:no-annotation",
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"multilinguality:multilingual",
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] | 2023-03-08T09:57:45Z | 2022-03-23T09:26:03.000Z | 2022-03-23T09:26:03 | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- af
- am
- ar
- arz
- as
- az
- azb
- ba
- be
- bg
- bn
- bo
- br
- ca
- ce
- ceb
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- cs
- cv
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- en
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- es
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- 'no'
- or
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- pl
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- pt
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- ru
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- sq
- sr
- sv
- sw
- ta
- te
- tg
- th
- tk
- tl
- tr
- tt
- ug
- uk
- ur
- uz
- vi
- yi
- zh
license:
- cc0-1.0
multilinguality:
- multilingual
source_datasets:
- oscar
task_categories:
- text-generation
task_ids:
- language-modeling
paperswithcode_id: oscar
pretty_name: OSCAR
---
## WARNING: this dataset is an extract of the OSCAR dataset published here to simulate the use of the full dataset in low-resource contexts.
Using this dataset is equivalent to using a processed version of OSCAR legally speaking. I take no credit for the gathering of the original data and hence refer entirely to the original dataset in the card below.
# Dataset Card for "oscar"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [https://oscar-corpus.com](https://oscar-corpus.com)
- **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Dataset Summary
OSCAR or **O**pen **S**uper-large **C**rawled [**A**LMAnaCH](https://team.inria.fr/almanach/) co**R**pus is a huge multilingual corpus obtained by language classification and filtering of the [Common Crawl](https://commoncrawl.org/) corpus using the [goclassy](https://github.com/pjox/goclassy) architecture. Data is distributed by language in both original and deduplicated form.
### Supported Tasks and Leaderboards
OSCAR is mainly inteded to pretrain language models and word represantations.
### Languages
All the data is distributed by language, both the original and the deduplicated versions of the data are available. 166 different languages are available. The table in subsection [Data Splits Sample Size](#data-splits-sample-size) provides the language code for each subcorpus as well as the number of words (space separated tokens), lines and sizes for both the original and the deduplicated versions of OSCAR.
## Dataset Structure
We show detailed information for all the configurations of the dataset.
## Dataset Creation
### Curation Rationale
OSCAR was constructed new pipeline derived from the [fastText's one](https://github.com/facebookresearch/fastText), called [_goclassy_](https://github.com/pjox/goclassy). Goclassy reuses the [fastText linear classifier](https://fasttext.cc) and the pre-trained fastText model for language recognition, but it completely rewrites and parallelises their pipeline in an asynchronous manner.
The order of operations is more or less the same as in the fastText pre-processing pipeline but instead of clustering multiple operations into a single blocking process, a worker is launched for each operation but bounding the number of possible parallel operations at a given time by the number of available threads instead of the number of CPUs. Goclassy is implemented in the [Go programming language](https://golang.org/) so it lets the [Go runtime](https://golang.org/src/runtime/mprof.go) handle the scheduling of the processes. Thus the goclassy's pipeline one does not have to wait for a whole WET file to download, decompress and classify in order to start downloading and processing the next one, a new file will start downloading and processing as soon as the scheduler is able to allocate a new process.
Filtering and cleaning processes at line level are done before feeding each line to the classifier. Lines shorter than 100 UTF-8 characters and lines containing invalid UTF-8 characters are discarted and are not classified. After all files are proccesed the deduplicated versions are constructed and everything is then splitted in shards and compressed.
### Source Data
#### Initial Data Collection and Normalization
[Common Crawl](https://commoncrawl.org/) is a non-profit foundation which produces and maintains an open repository of web crawled data that is both accessible and analysable. Common Crawl's complete web archive consists of petabytes of data collected over 8 years of web crawling. The repository contains raw web page HTML data (WARC files), metdata extracts (WAT files) and plain text extracts (WET files). The organisation's crawlers has always respected [nofollow](http://microformats.org/wiki/rel-nofollow) and [robots.txt](https://www.robotstxt.org/) policies.
Each monthly Common Crawl snapshot is in itself a massive multilingual corpus, where every single file contains data coming from multiple web pages written in a large variety of languages and covering all possible types of topics.
To construct OSCAR the WET files of Common Crawl were used. These contain the extracted plain texts from the websites mostly converted to UTF-8, as well as headers containing the metatada of each crawled document. Each WET file comes compressed in gzip format and is stored on Amazon Web Services. In the case of OSCAR, the **November 2018** snapshot was used. It surpasses 20TB of uncompressed data and contains more than 50 thousand plain text files where each file consists of the plain text from multiple websites along its metadata header.
#### Who are the source language producers?
The data comes from multiple web pages in a large variety of languages.
### Annotations
The dataset does not contain any additional annotations.
#### Annotation process
N/A
#### Who are the annotators?
N/A
### Personal and Sensitive Information
Being constructed from Common Crawl, Personal and sensitive information might be present. This **must** be considered before training deep learning models with OSCAR, specially in the case of text-generation models.
## Considerations for Using the Data
### Social Impact of Dataset
OSCAR is intended to bring more data to a wide variety of lanuages, the aim of the corpus is to make large amounts of data available to lower resource languages in order to facilitate the pre-training of state-of-the-art language modeling architectures.
### Discussion of Biases
OSCAR is not properly filtered yet and this can be reflected on the models trained with it. Care is advised specially concerning biases of the resulting models.
### Other Known Limitations
The [fastText linear classifier](https://fasttext.cc) is limed both in performance and the variety of languages it can recognize, so the quality of some OSCAR sub-corpora might be lower than expected, specially for the lowest-resource langiuages. Some audits have already been done by [third parties](https://arxiv.org/abs/2010.14571).
## Additional Information
### Dataset Curators
The corpus was put together by [Pedro J. Ortiz](https://pjortiz.eu/), [Benoît Sagot](http://pauillac.inria.fr/~sagot/), and [Laurent Romary](https://cv.archives-ouvertes.fr/laurentromary), during work done at [Inria](https://www.inria.fr/en), particularly at the [ALMAnaCH team](https://team.inria.fr/almanach/).
### Licensing Information
These data are released under this licensing scheme
We do not own any of the text from which these data has been extracted.
We license the actual packaging of these data under the Creative Commons CC0 license ("no rights reserved") http://creativecommons.org/publicdomain/zero/1.0/
To the extent possible under law, Inria has waived all copyright and related or neighboring rights to OSCAR
This work is published from: France.
Should you consider that our data contains material that is owned by you and should therefore not be reproduced here, please:
* Clearly identify yourself, with detailed contact data such as an address, telephone number or email address at which you can be contacted.
* Clearly identify the copyrighted work claimed to be infringed.
* Clearly identify the material that is claimed to be infringing and information reasonably sufficient to allow us to locate the material.
We will comply to legitimate requests by removing the affected sources from the next release of the corpus.
### Citation Information
```
@inproceedings{ortiz-suarez-etal-2020-monolingual,
title = "A Monolingual Approach to Contextualized Word Embeddings for Mid-Resource Languages",
author = "Ortiz Su{'a}rez, Pedro Javier and
Romary, Laurent and
Sagot, Benoit",
booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.acl-main.156",
pages = "1703--1714",
abstract = "We use the multilingual OSCAR corpus, extracted from Common Crawl via language classification, filtering and cleaning, to train monolingual contextualized word embeddings (ELMo) for five mid-resource languages. We then compare the performance of OSCAR-based and Wikipedia-based ELMo embeddings for these languages on the part-of-speech tagging and parsing tasks. We show that, despite the noise in the Common-Crawl-based OSCAR data, embeddings trained on OSCAR perform much better than monolingual embeddings trained on Wikipedia. They actually equal or improve the current state of the art in tagging and parsing for all five languages. In particular, they also improve over multilingual Wikipedia-based contextual embeddings (multilingual BERT), which almost always constitutes the previous state of the art, thereby showing that the benefit of a larger, more diverse corpus surpasses the cross-lingual benefit of multilingual embedding architectures.",
}
@inproceedings{OrtizSuarezSagotRomary2019,
author = {Pedro Javier {Ortiz Su{'a}rez} and Benoit Sagot and Laurent Romary},
title = {Asynchronous pipelines for processing huge corpora on medium to low resource infrastructures},
series = {Proceedings of the Workshop on Challenges in the Management of Large Corpora (CMLC-7) 2019. Cardiff, 22nd July 2019},
editor = {Piotr Bański and Adrien Barbaresi and Hanno Biber and Evelyn Breiteneder and Simon Clematide and Marc Kupietz and Harald L{"u}ngen and Caroline Iliadi},
publisher = {Leibniz-Institut f{"u}r Deutsche Sprache},
address = {Mannheim},
doi = {10.14618/ids-pub-9021},
url = {http://nbn-resolving.de/urn:nbn:de:bsz:mh39-90215},
pages = {9 -- 16},
year = {2019},
abstract = {Common Crawl is a considerably large, heterogeneous multilingual corpus comprised of crawled documents from the internet, surpassing 20TB of data and distributed as a set of more than 50 thousand plain text files where each contains many documents written in a wide variety of languages. Even though each document has a metadata block associated to it, this data lacks any information about the language in which each document is written, making it extremely difficult to use Common Crawl for monolingual applications. We propose a general, highly parallel, multithreaded pipeline to clean and classify Common Crawl by language; we specifically design it so that it runs efficiently on medium to low resource infrastructures where I/O speeds are the main constraint. We develop the pipeline so that it can be easily reapplied to any kind of heterogeneous corpus and so that it can be parameterised to a wide range of infrastructures. We also distribute a 6.3TB version of Common Crawl, filtered, classified by language, shuffled at line level in order to avoid copyright issues, and ready to be used for NLP applications.},
language = {en}
}
```
### Contributions
Thanks to [@pjox](https://github.com/pjox) and [@lhoestq](https://github.com/lhoestq) for adding this dataset.
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
keremberke/satellite-building-segmentation | keremberke | 2023-01-18T09:41:34Z | 1,108 | 6 | null | [
"task_categories:image-segmentation",
"roboflow",
"roboflow2huggingface",
"Aerial",
"Logistics",
"Construction",
"Damage Risk",
"Other",
"region:us"
] | 2023-01-18T09:41:34Z | 2023-01-16T21:09:30.000Z | 2023-01-16T21:09:30 | ---
task_categories:
- image-segmentation
tags:
- roboflow
- roboflow2huggingface
- Aerial
- Logistics
- Construction
- Damage Risk
- Other
---
<div align="center">
<img width="640" alt="keremberke/satellite-building-segmentation" src="https://huggingface.co/datasets/keremberke/satellite-building-segmentation/resolve/main/thumbnail.jpg">
</div>
### Dataset Labels
```
['building']
```
### Number of Images
```json
{'train': 6764, 'valid': 1934, 'test': 967}
```
### How to Use
- Install [datasets](https://pypi.org/project/datasets/):
```bash
pip install datasets
```
- Load the dataset:
```python
from datasets import load_dataset
ds = load_dataset("keremberke/satellite-building-segmentation", name="full")
example = ds['train'][0]
```
### Roboflow Dataset Page
[https://universe.roboflow.com/roboflow-universe-projects/buildings-instance-segmentation/dataset/1](https://universe.roboflow.com/roboflow-universe-projects/buildings-instance-segmentation/dataset/1?ref=roboflow2huggingface)
### Citation
```
@misc{ buildings-instance-segmentation_dataset,
title = { Buildings Instance Segmentation Dataset },
type = { Open Source Dataset },
author = { Roboflow Universe Projects },
howpublished = { \\url{ https://universe.roboflow.com/roboflow-universe-projects/buildings-instance-segmentation } },
url = { https://universe.roboflow.com/roboflow-universe-projects/buildings-instance-segmentation },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2023 },
month = { jan },
note = { visited on 2023-01-18 },
}
```
### License
CC BY 4.0
### Dataset Summary
This dataset was exported via roboflow.com on January 16, 2023 at 9:09 PM GMT
Roboflow is an end-to-end computer vision platform that helps you
* collaborate with your team on computer vision projects
* collect & organize images
* understand and search unstructured image data
* annotate, and create datasets
* export, train, and deploy computer vision models
* use active learning to improve your dataset over time
For state of the art Computer Vision training notebooks you can use with this dataset,
visit https://github.com/roboflow/notebooks
To find over 100k other datasets and pre-trained models, visit https://universe.roboflow.com
The dataset includes 9665 images.
Buildings are annotated in COCO format.
The following pre-processing was applied to each image:
* Auto-orientation of pixel data (with EXIF-orientation stripping)
No image augmentation techniques were applied.
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BeIR/climate-fever | BeIR | 2022-10-23T06:04:48Z | 1,105 | 1 | beir | [
"task_categories:text-retrieval",
"task_ids:entity-linking-retrieval",
"task_ids:fact-checking-retrieval",
"multilinguality:monolingual",
"language:en",
"license:cc-by-sa-4.0",
"region:us"
] | 2022-10-23T06:04:48Z | 2022-06-05T17:03:57.000Z | 2022-06-05T17:03:57 | ---
annotations_creators: []
language_creators: []
language:
- en
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
paperswithcode_id: beir
pretty_name: BEIR Benchmark
size_categories:
msmarco:
- 1M<n<10M
trec-covid:
- 100k<n<1M
nfcorpus:
- 1K<n<10K
nq:
- 1M<n<10M
hotpotqa:
- 1M<n<10M
fiqa:
- 10K<n<100K
arguana:
- 1K<n<10K
touche-2020:
- 100K<n<1M
cqadupstack:
- 100K<n<1M
quora:
- 100K<n<1M
dbpedia:
- 1M<n<10M
scidocs:
- 10K<n<100K
fever:
- 1M<n<10M
climate-fever:
- 1M<n<10M
scifact:
- 1K<n<10K
source_datasets: []
task_categories:
- text-retrieval
- zero-shot-retrieval
- information-retrieval
- zero-shot-information-retrieval
task_ids:
- passage-retrieval
- entity-linking-retrieval
- fact-checking-retrieval
- tweet-retrieval
- citation-prediction-retrieval
- duplication-question-retrieval
- argument-retrieval
- news-retrieval
- biomedical-information-retrieval
- question-answering-retrieval
---
# Dataset Card for BEIR Benchmark
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://github.com/UKPLab/beir
- **Repository:** https://github.com/UKPLab/beir
- **Paper:** https://openreview.net/forum?id=wCu6T5xFjeJ
- **Leaderboard:** https://docs.google.com/spreadsheets/d/1L8aACyPaXrL8iEelJLGqlMqXKPX2oSP_R10pZoy77Ns
- **Point of Contact:** nandan.thakur@uwaterloo.ca
### Dataset Summary
BEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:
- Fact-checking: [FEVER](http://fever.ai), [Climate-FEVER](http://climatefever.ai), [SciFact](https://github.com/allenai/scifact)
- Question-Answering: [NQ](https://ai.google.com/research/NaturalQuestions), [HotpotQA](https://hotpotqa.github.io), [FiQA-2018](https://sites.google.com/view/fiqa/)
- Bio-Medical IR: [TREC-COVID](https://ir.nist.gov/covidSubmit/index.html), [BioASQ](http://bioasq.org), [NFCorpus](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/)
- News Retrieval: [TREC-NEWS](https://trec.nist.gov/data/news2019.html), [Robust04](https://trec.nist.gov/data/robust/04.guidelines.html)
- Argument Retrieval: [Touche-2020](https://webis.de/events/touche-20/shared-task-1.html), [ArguAna](tp://argumentation.bplaced.net/arguana/data)
- Duplicate Question Retrieval: [Quora](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs), [CqaDupstack](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/)
- Citation-Prediction: [SCIDOCS](https://allenai.org/data/scidocs)
- Tweet Retrieval: [Signal-1M](https://research.signal-ai.com/datasets/signal1m-tweetir.html)
- Entity Retrieval: [DBPedia](https://github.com/iai-group/DBpedia-Entity/)
All these datasets have been preprocessed and can be used for your experiments.
```python
```
### Supported Tasks and Leaderboards
The dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.
The current best performing models can be found [here](https://eval.ai/web/challenges/challenge-page/689/leaderboard/).
### Languages
All tasks are in English (`en`).
## Dataset Structure
All BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:
- `corpus` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with three fields `_id` with unique document identifier, `title` with document title (optional) and `text` with document paragraph or passage. For example: `{"_id": "doc1", "title": "Albert Einstein", "text": "Albert Einstein was a German-born...."}`
- `queries` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with two fields `_id` with unique query identifier and `text` with query text. For example: `{"_id": "q1", "text": "Who developed the mass-energy equivalence formula?"}`
- `qrels` file: a `.tsv` file (tab-seperated) that contains three columns, i.e. the `query-id`, `corpus-id` and `score` in this order. Keep 1st row as header. For example: `q1 doc1 1`
### Data Instances
A high level example of any beir dataset:
```python
corpus = {
"doc1" : {
"title": "Albert Einstein",
"text": "Albert Einstein was a German-born theoretical physicist. who developed the theory of relativity, \
one of the two pillars of modern physics (alongside quantum mechanics). His work is also known for \
its influence on the philosophy of science. He is best known to the general public for his mass–energy \
equivalence formula E = mc2, which has been dubbed 'the world's most famous equation'. He received the 1921 \
Nobel Prize in Physics 'for his services to theoretical physics, and especially for his discovery of the law \
of the photoelectric effect', a pivotal step in the development of quantum theory."
},
"doc2" : {
"title": "", # Keep title an empty string if not present
"text": "Wheat beer is a top-fermented beer which is brewed with a large proportion of wheat relative to the amount of \
malted barley. The two main varieties are German Weißbier and Belgian witbier; other types include Lambic (made\
with wild yeast), Berliner Weisse (a cloudy, sour beer), and Gose (a sour, salty beer)."
},
}
queries = {
"q1" : "Who developed the mass-energy equivalence formula?",
"q2" : "Which beer is brewed with a large proportion of wheat?"
}
qrels = {
"q1" : {"doc1": 1},
"q2" : {"doc2": 1},
}
```
### Data Fields
Examples from all configurations have the following features:
### Corpus
- `corpus`: a `dict` feature representing the document title and passage text, made up of:
- `_id`: a `string` feature representing the unique document id
- `title`: a `string` feature, denoting the title of the document.
- `text`: a `string` feature, denoting the text of the document.
### Queries
- `queries`: a `dict` feature representing the query, made up of:
- `_id`: a `string` feature representing the unique query id
- `text`: a `string` feature, denoting the text of the query.
### Qrels
- `qrels`: a `dict` feature representing the query document relevance judgements, made up of:
- `_id`: a `string` feature representing the query id
- `_id`: a `string` feature, denoting the document id.
- `score`: a `int32` feature, denoting the relevance judgement between query and document.
### Data Splits
| Dataset | Website| BEIR-Name | Type | Queries | Corpus | Rel D/Q | Down-load | md5 |
| -------- | -----| ---------| --------- | ----------- | ---------| ---------| :----------: | :------:|
| MSMARCO | [Homepage](https://microsoft.github.io/msmarco/)| ``msmarco`` | ``train``<br>``dev``<br>``test``| 6,980 | 8.84M | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/msmarco.zip) | ``444067daf65d982533ea17ebd59501e4`` |
| TREC-COVID | [Homepage](https://ir.nist.gov/covidSubmit/index.html)| ``trec-covid``| ``test``| 50| 171K| 493.5 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/trec-covid.zip) | ``ce62140cb23feb9becf6270d0d1fe6d1`` |
| NFCorpus | [Homepage](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/) | ``nfcorpus`` | ``train``<br>``dev``<br>``test``| 323 | 3.6K | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nfcorpus.zip) | ``a89dba18a62ef92f7d323ec890a0d38d`` |
| BioASQ | [Homepage](http://bioasq.org) | ``bioasq``| ``train``<br>``test`` | 500 | 14.91M | 8.05 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#2-bioasq) |
| NQ | [Homepage](https://ai.google.com/research/NaturalQuestions) | ``nq``| ``train``<br>``test``| 3,452 | 2.68M | 1.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nq.zip) | ``d4d3d2e48787a744b6f6e691ff534307`` |
| HotpotQA | [Homepage](https://hotpotqa.github.io) | ``hotpotqa``| ``train``<br>``dev``<br>``test``| 7,405 | 5.23M | 2.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/hotpotqa.zip) | ``f412724f78b0d91183a0e86805e16114`` |
| FiQA-2018 | [Homepage](https://sites.google.com/view/fiqa/) | ``fiqa`` | ``train``<br>``dev``<br>``test``| 648 | 57K | 2.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fiqa.zip) | ``17918ed23cd04fb15047f73e6c3bd9d9`` |
| Signal-1M(RT) | [Homepage](https://research.signal-ai.com/datasets/signal1m-tweetir.html)| ``signal1m`` | ``test``| 97 | 2.86M | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#4-signal-1m) |
| TREC-NEWS | [Homepage](https://trec.nist.gov/data/news2019.html) | ``trec-news`` | ``test``| 57 | 595K | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#1-trec-news) |
| ArguAna | [Homepage](http://argumentation.bplaced.net/arguana/data) | ``arguana``| ``test`` | 1,406 | 8.67K | 1.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/arguana.zip) | ``8ad3e3c2a5867cdced806d6503f29b99`` |
| Touche-2020| [Homepage](https://webis.de/events/touche-20/shared-task-1.html) | ``webis-touche2020``| ``test``| 49 | 382K | 19.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/webis-touche2020.zip) | ``46f650ba5a527fc69e0a6521c5a23563`` |
| CQADupstack| [Homepage](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/) | ``cqadupstack``| ``test``| 13,145 | 457K | 1.4 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/cqadupstack.zip) | ``4e41456d7df8ee7760a7f866133bda78`` |
| Quora| [Homepage](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs) | ``quora``| ``dev``<br>``test``| 10,000 | 523K | 1.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/quora.zip) | ``18fb154900ba42a600f84b839c173167`` |
| DBPedia | [Homepage](https://github.com/iai-group/DBpedia-Entity/) | ``dbpedia-entity``| ``dev``<br>``test``| 400 | 4.63M | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/dbpedia-entity.zip) | ``c2a39eb420a3164af735795df012ac2c`` |
| SCIDOCS| [Homepage](https://allenai.org/data/scidocs) | ``scidocs``| ``test``| 1,000 | 25K | 4.9 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scidocs.zip) | ``38121350fc3a4d2f48850f6aff52e4a9`` |
| FEVER | [Homepage](http://fever.ai) | ``fever``| ``train``<br>``dev``<br>``test``| 6,666 | 5.42M | 1.2| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fever.zip) | ``5a818580227bfb4b35bb6fa46d9b6c03`` |
| Climate-FEVER| [Homepage](http://climatefever.ai) | ``climate-fever``|``test``| 1,535 | 5.42M | 3.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/climate-fever.zip) | ``8b66f0a9126c521bae2bde127b4dc99d`` |
| SciFact| [Homepage](https://github.com/allenai/scifact) | ``scifact``| ``train``<br>``test``| 300 | 5K | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scifact.zip) | ``5f7d1de60b170fc8027bb7898e2efca1`` |
| Robust04 | [Homepage](https://trec.nist.gov/data/robust/04.guidelines.html) | ``robust04``| ``test``| 249 | 528K | 69.9 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#3-robust04) |
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
[Needs More Information]
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
[Needs More Information]
### Citation Information
Cite as:
```
@inproceedings{
thakur2021beir,
title={{BEIR}: A Heterogeneous Benchmark for Zero-shot Evaluation of Information Retrieval Models},
author={Nandan Thakur and Nils Reimers and Andreas R{\"u}ckl{\'e} and Abhishek Srivastava and Iryna Gurevych},
booktitle={Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2)},
year={2021},
url={https://openreview.net/forum?id=wCu6T5xFjeJ}
}
```
### Contributions
Thanks to [@Nthakur20](https://github.com/Nthakur20) for adding this dataset. | [
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0.2479681670665741,
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0.273154616355896,
0.445595383644104,
0.29702451825141907,
0.5533441305160522,
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0.05621562898159027,
0.13452692329883575,
0.08711707592010498,
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0.047760557383298874,
-0.07232259213924408,
0.2246197909116745,
-0.22824953496456146,
-0.22329849004745483
] | null | null | null | null | null | null | null | null | null | null | null | null | null |
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