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NbAiLab/norne | NbAiLab | 2022-11-07T12:41:46Z | 233 | 3 | null | [
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"task_ids:part-of-speech",
"annotations_creators:expert-generated",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:no",
"license:other",
"structure-prediction",
"arxiv:1911.12146",
"region:us"
] | 2022-11-07T12:41:46Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- crowdsourced
language:
- 'no'
license:
- other
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- token-classification
task_ids:
- named-entity-recognition
- part-of-speech
tags:
- structure-prediction
---
# Dataset Card for NorNE: Norwegian Named Entities
## 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:** [NorNE](https://github.com/ltgoslo/norne/)
- **Repository:** [Github](https://github.com/ltgoslo/norne/)
- **Paper:** https://arxiv.org/abs/1911.12146
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
NorNE is a manually annotated corpus of named entities which extends the annotation of the existing Norwegian Dependency Treebank. Comprising both of the official standards of written Norwegian (Bokmål and Nynorsk), the corpus contains around 600,000 tokens and annotates a rich set of entity types including persons,organizations, locations, geo-political entities, products, and events, in addition to a class corresponding to nominals derived from names.
### Supported Tasks and Leaderboards
NorNE ads named entity annotations on top of the Norwegian Dependency Treebank.
### Languages
Both Norwegian Bokmål (`bokmaal`) and Nynorsk (`nynorsk`) are supported as different configs in this dataset. An extra config for the combined languages is also included (`combined`). See the Annotation section for details on accessing reduced tag sets for the NER feature.
## Dataset Structure
Each entry contains text sentences, their language, identifiers, tokens, lemmas, and corresponding NER and POS tag lists.
### Data Instances
An example of the `train` split of the `bokmaal` config.
```python
{'idx': '000001',
'lang': 'bokmaal',
'lemmas': ['lam', 'og', 'piggvar', 'på', 'bryllupsmeny'],
'ner_tags': [0, 0, 0, 0, 0],
'pos_tags': [0, 9, 0, 5, 0],
'text': 'Lam og piggvar på bryllupsmenyen',
'tokens': ['Lam', 'og', 'piggvar', 'på', 'bryllupsmenyen']}
```
### Data Fields
Each entry is annotated with the next fields:
- `idx` (`int`), text (sentence) identifier from the NorNE dataset
- `lang` (`str`), language variety, either `bokmaal`, `nynorsk` or `combined`
- `text` (`str`), plain text
- `tokens` (`List[str]`), list of tokens extracted from `text`
- `lemmas` (`List[str]`), list of lemmas extracted from `tokens`
- `ner_tags` (`List[int]`), list of numeric NER tags for each token in `tokens`
- `pos_tags` (`List[int]`), list of numeric PoS tags for each token in `tokens`
An example DataFrame obtained from the dataset:
<table class="dataframe" border="1">
<thead>
<tr style="text-align: right;">
<th></th>
<th>idx</th>
<th>lang</th>
<th>text</th>
<th>tokens</th>
<th>lemmas</th>
<th>ner_tags</th>
<th>pos_tags</th>
</tr>
</thead>
<tbody>
<tr>
<th>0</th>
<td>000001</td>
<td>bokmaal</td>
<td>Lam og piggvar på bryllupsmenyen</td>
<td>[Lam, og, piggvar, på, bryllupsmenyen]</td>
<td>[lam, og, piggvar, på, bryllupsmeny]</td>
<td>[0, 0, 0, 0, 0]</td>
<td>[0, 9, 0, 5, 0]</td>
</tr>
<tr>
<th>1</th>
<td>000002</td>
<td>bokmaal</td>
<td>Kamskjell, piggvar og lammefilet sto på menyen...</td>
<td>[Kamskjell, ,, piggvar, og, lammefilet, sto, p...</td>
<td>[kamskjell, $,, piggvar, og, lammefilet, stå, ...</td>
<td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]</td>
<td>[0, 1, 0, 9, 0, 15, 2, 0, 2, 8, 6, 0, 1]</td>
</tr>
<tr>
<th>2</th>
<td>000003</td>
<td>bokmaal</td>
<td>Og til dessert: Parfait à la Mette-Marit.</td>
<td>[Og, til, dessert, :, Parfait, à, la, Mette-Ma...</td>
<td>[og, til, dessert, $:, Parfait, à, la, Mette-M...</td>
<td>[0, 0, 0, 0, 7, 8, 8, 8, 0]</td>
<td>[9, 2, 0, 1, 10, 12, 12, 10, 1]</td>
</tr>
</tbody>
</table>
### Data Splits
There are three splits: `train`, `validation` and `test`.
| Config | Split | Total |
| :---------|-------------:|-------:|
| `bokmaal` | `train` | 15696 |
| `bokmaal` | `validation` | 2410 |
| `bokmaal` | `test` | 1939 |
| `nynorsk` | `train` | 14174 |
| `nynorsk` | `validation` | 1890 |
| `nynorsk` | `test` | 1511 |
| `combined`| `test` | 29870 |
| `combined`| `validation` | 4300 |
| `combined`| `test` | 3450 |
## Dataset Creation
### Curation Rationale
1. A _name_ in this context is close to [Saul Kripke's definition of a name](https://en.wikipedia.org/wiki/Saul_Kripke#Naming_and_Necessity),
in that a name has a unique reference and its meaning is constant (there are exceptions in the annotations, e.g. "Regjeringen" (en. "Government")).
2. It is the usage of a name that determines the entity type, not the default/literal sense of the name,
3. If there is an ambiguity in the type/sense of a name, then the the default/literal sense of the name is chosen
(following [Markert and Nissim, 2002](http://www.lrec-conf.org/proceedings/lrec2002/pdf/11.pdf)).
For more details, see the "Annotation Guidelines.pdf" distributed with the corpus.
### Source Data
Data was collected using blogs and newspapers in Norwegian, as well as parliament speeches and governamental reports.
#### Initial Data Collection and Normalization
The texts in the Norwegian Dependency Treebank (NDT) are manually annotated with morphological features, syntactic functions
and hierarchical structure. The formalism used for the syntactic annotation is dependency grammar.
The treebanks consists of two parts, one part in Norwegian Bokmål (`nob`) and one part in Norwegian Nynorsk (`nno`).
Both parts contain around 300.000 tokens, and are a mix of different non-fictional genres.
See the [NDT webpage](https://www.nb.no/sprakbanken/show?serial=sbr-10) for more details.
### Annotations
The following types of entities are annotated:
- **Person (`PER`):** Real or fictional characters and animals
- **Organization (`ORG`):** Any collection of people, such as firms, institutions, organizations, music groups,
sports teams, unions, political parties etc.
- **Location (`LOC`):** Geographical places, buildings and facilities
- **Geo-political entity (`GPE`):** Geographical regions defined by political and/or social groups.
A GPE entity subsumes and does not distinguish between a nation, its region, its government, or its people
- **Product (`PROD`):** Artificially produced entities are regarded products. This may include more abstract entities, such as speeches,
radio shows, programming languages, contracts, laws and ideas.
- **Event (`EVT`):** Festivals, cultural events, sports events, weather phenomena, wars, etc. Events are bounded in time and space.
- **Derived (`DRV`):** Words (and phrases?) that are dervied from a name, but not a name in themselves. They typically contain a full name and are capitalized, but are not proper nouns. Examples (fictive) are "Brann-treneren" ("the Brann coach") or "Oslo-mannen" ("the man from Oslo").
- **Miscellaneous (`MISC`):** Names that do not belong in the other categories. Examples are animals species and names of medical conditions. Entities that are manufactured or produced are of type Products, whereas thing naturally or spontaneously occurring are of type Miscellaneous.
Furthermore, all `GPE` entities are additionally sub-categorized as being either `ORG` or `LOC`, with the two annotation levels separated by an underscore:
- `GPE_LOC`: Geo-political entity, with a locative sense (e.g. "John lives in _Spain_")
- `GPE_ORG`: Geo-political entity, with an organisation sense (e.g. "_Spain_ declined to meet with Belgium")
The two special types `GPE_LOC` and `GPE_ORG` can easily be altered depending on the task, choosing either the more general `GPE` tag or the more specific `LOC`/`ORG` tags, conflating them with the other annotations of the same type. This means that the following sets of entity types can be derived:
- 7 types, deleting `_GPE`: **`ORG`**, **`LOC`**, `PER`, `PROD`, `EVT`, `DRV`, `MISC`
- 8 types, deleting `LOC_` and `ORG_`: **`ORG`**, **`LOC`**, **`GPE`**, `PER`, `PROD`, `EVT`, `DRV`, `MISC`
- 9 types, keeping all types: **`ORG`**, **`LOC`**, **`GPE_LOC`**, **`GPE_ORG`**, `PER`, `PROD`, `EVT`, `DRV`, `MISC`
The class distribution is as follows, broken down across the data splits of the UD version of NDT, and sorted by total counts (i.e. the number of examples, not tokens within the spans of the annotatons):
| Type | Train | Dev | Test | Total |
| :--------|-------:|-------:|-------:|-------:|
| `PER` | 4033 | 607 | 560 | 5200 |
| `ORG` | 2828 | 400 | 283 | 3511 |
| `GPE_LOC`| 2132 | 258 | 257 | 2647 |
| `PROD` | 671 | 162 | 71 | 904 |
| `LOC` | 613 | 109 | 103 | 825 |
| `GPE_ORG`| 388 | 55 | 50 | 493 |
| `DRV` | 519 | 77 | 48 | 644 |
| `EVT` | 131 | 9 | 5 | 145 |
| `MISC` | 8 | 0 | 0 | 0 |
To access these reduce versions of the dataset, you can use the configs `bokmaal-7`, `nynorsk-7`, `combined-7` for the NER tag set with 7 tags ( **`ORG`**, **`LOC`**, `PER`, `PROD`, `EVT`, `DRV`, `MISC`), and `bokmaal-8`, `nynorsk-8`, `combined-8` for the NER tag set with 8 tags (`LOC_` and `ORG_`: **`ORG`**, **`LOC`**, **`GPE`**, `PER`, `PROD`, `EVT`, `DRV`, `MISC`). By default, the full set (9 tags) will be used.
## Additional Information
### Dataset Curators
NorNE was created as a collaboration between [Schibsted Media Group](https://schibsted.com/), [Språkbanken](https://www.nb.no/forskning/sprakbanken/) at the [National Library of Norway](https://www.nb.no) and the [Language Technology Group](https://www.mn.uio.no/ifi/english/research/groups/ltg/) at the University of Oslo.
NorNE was added to Huggingface Datasets by the AI-Lab at the National Library of Norway.
### Licensing Information
The NorNE corpus is published under the same [license](https://github.com/ltgoslo/norne/blob/master/LICENSE_NDT.txt) as the Norwegian Dependency Treebank
### Citation Information
This dataset is described in the paper _NorNE: Annotating Named Entities for Norwegian_ by
Fredrik Jørgensen, Tobias Aasmoe, Anne-Stine Ruud Husevåg, Lilja Øvrelid, and Erik Velldal, accepted for LREC 2020 and available as pre-print here: https://arxiv.org/abs/1911.12146.
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
ScandEval/angry-tweets-mini | ScandEval | 2023-07-05T09:52:07Z | 233 | 0 | null | [
"task_categories:text-classification",
"size_categories:1K<n<10K",
"language:da",
"license:cc-by-4.0",
"region:us"
] | 2023-07-05T09:52:07Z | 2022-06-14T18:30:11.000Z | 2022-06-14T18:30:11 | ---
license: cc-by-4.0
task_categories:
- text-classification
language:
- da
size_categories:
- 1K<n<10K
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s-nlp/paradetox | s-nlp | 2023-09-08T08:59:53Z | 232 | 7 | null | [
"task_categories:text-generation",
"language:en",
"license:openrail++",
"region:us"
] | 2023-09-08T08:59:53Z | 2022-05-19T17:12:06.000Z | 2022-05-19T17:12:06 | ---
license: openrail++
task_categories:
- text-generation
language:
- en
---
# ParaDetox: Detoxification with Parallel Data (English)
This repository contains information about Paradetox dataset -- the first parallel corpus for the detoxification task -- as well as models and evaluation methodology for the detoxification of English texts. The original paper ["ParaDetox: Detoxification with Parallel Data"](https://aclanthology.org/2022.acl-long.469/) was presented at ACL 2022 main conference.
## ParaDetox Collection Pipeline
The ParaDetox Dataset collection was done via [Yandex.Toloka](https://toloka.yandex.com/) crowdsource platform. The collection was done in three steps:
* *Task 1:* **Generation of Paraphrases**: The first crowdsourcing task asks users to eliminate toxicity in a given sentence while keeping the content.
* *Task 2:* **Content Preservation Check**: We show users the generated paraphrases along with their original variants and ask them to indicate if they have close meanings.
* *Task 3:* **Toxicity Check**: Finally, we check if the workers succeeded in removing toxicity.
All these steps were done to ensure high quality of the data and make the process of collection automated. For more details please refer to the original paper.
## ParaDetox Dataset
As a result, we get paraphrases for 11,939 toxic sentences (on average 1.66 paraphrases per sentence), 19,766 paraphrases total.
In addition to all ParaDetox dataset, we also make public [samples](https://huggingface.co/datasets/s-nlp/en_non_detoxified) that were marked by annotators as "cannot rewrite" in *Task 1* of crowdsource pipeline.
# Detoxification evaluation
The automatic evaluation of the model were produced based on three parameters:
* *style transfer accuracy* (**STA**): percentage of nontoxic outputs identified by a style classifier. We pretrained toxicity classifier on Jigsaw data and put it online in HuggingFace🤗 [repo](https://huggingface.co/SkolkovoInstitute/roberta_toxicity_classifier).
* *content preservation* (**SIM**): cosine similarity between the embeddings of the original text and the output computed with the model of [Wieting et al. (2019)](https://aclanthology.org/P19-1427/).
* *fluency* (**FL**): percentage of fluent sentences identified by a RoBERTa-based classifier of linguistic acceptability trained on the [CoLA dataset](https://nyu-mll.github.io/CoLA/).
All code used for our experiments to evluate different detoxifcation models can be run via Colab notebook [](https://colab.research.google.com/drive/1xTqbx7IPF8bVL2bDCfQSDarA43mIPefE?usp=sharing)
## Detoxification model
**New SOTA** for detoxification task -- BART (base) model trained on ParaDetox dataset -- we released online in HuggingFace🤗 repository [here](https://huggingface.co/SkolkovoInstitute/bart-base-detox).
You can also check out our [demo](https://detoxifier.nlp.zhores.net/junction/) and telegram [bot](https://t.me/rudetoxifierbot).
## Citation
```
@inproceedings{logacheva-etal-2022-paradetox,
title = "{P}ara{D}etox: Detoxification with Parallel Data",
author = "Logacheva, Varvara and
Dementieva, Daryna and
Ustyantsev, Sergey and
Moskovskiy, Daniil and
Dale, David and
Krotova, Irina and
Semenov, Nikita and
Panchenko, Alexander",
booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = may,
year = "2022",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.acl-long.469",
pages = "6804--6818",
abstract = "We present a novel pipeline for the collection of parallel data for the detoxification task. We collect non-toxic paraphrases for over 10,000 English toxic sentences. We also show that this pipeline can be used to distill a large existing corpus of paraphrases to get toxic-neutral sentence pairs. We release two parallel corpora which can be used for the training of detoxification models. To the best of our knowledge, these are the first parallel datasets for this task.We describe our pipeline in detail to make it fast to set up for a new language or domain, thus contributing to faster and easier development of new parallel resources.We train several detoxification models on the collected data and compare them with several baselines and state-of-the-art unsupervised approaches. We conduct both automatic and manual evaluations. All models trained on parallel data outperform the state-of-the-art unsupervised models by a large margin. This suggests that our novel datasets can boost the performance of detoxification systems.",
}
```
## Contacts
If you find some issue, do not hesitate to add it to [Github Issues](https://github.com/skoltech-nlp/paradetox/issues).
For any questions and get the TEST SET, please, contact: Daryna Dementieva (dardem96@gmail.com) | [
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patrickfrank1/chess-pgn-games | patrickfrank1 | 2022-09-02T14:07:22Z | 232 | 1 | null | [
"license:cc0-1.0",
"region:us"
] | 2022-09-02T14:07:22Z | 2022-09-02T12:51:34.000Z | 2022-09-02T12:51:34 | ---
license: cc0-1.0
---
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
TigerResearch/tigerbot-alpaca-en-50k | TigerResearch | 2023-05-31T01:56:04Z | 232 | 1 | null | [
"language:en",
"license:apache-2.0",
"region:us"
] | 2023-05-31T01:56:04Z | 2023-05-30T14:33:53.000Z | 2023-05-30T14:33:53 | ---
license: apache-2.0
language:
- en
---
[Tigerbot](https://github.com/TigerResearch/TigerBot) 自有基于alpaca生成英文问答对
<p align="center" width="40%">
## Usage
```python
import datasets
ds_sft = datasets.load_dataset('TigerResearch/tigerbot-alpaca-en-50k')
``` | [
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shi3z/anthropic_hh_rlhf_japanese | shi3z | 2023-06-29T01:19:09Z | 232 | 9 | null | [
"license:mit",
"region:us"
] | 2023-06-29T01:19:09Z | 2023-06-29T00:07:38.000Z | 2023-06-29T00:07:38 | ---
license: mit
---
https://huggingface.co/datasets/Anthropic/hh-rlhf
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
philikai/spider_SQL_PALM_Prompt | philikai | 2023-09-11T13:40:51Z | 232 | 0 | null | [
"license:mit",
"region:us"
] | 2023-09-11T13:40:51Z | 2023-09-07T18:43:07.000Z | 2023-09-07T18:43:07 | ---
license: mit
---
Dataset for creating prompts for fine-tuning on Spider Dataset with Foreign and Primary Key Information as well as Schema information.
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
dhcppc0/ksc500 | dhcppc0 | 2023-10-17T05:56:26Z | 232 | 0 | null | [
"region:us"
] | 2023-10-17T05:56:26Z | 2023-10-17T05:52:17.000Z | 2023-10-17T05:52:17 | ---
dataset_info:
features:
- name: array
sequence: float32
- name: sampling_rate
dtype: int64
- name: text
dtype: string
splits:
- name: test
num_bytes: 23715007
num_examples: 50
- name: train
num_bytes: 248781258
num_examples: 500
download_size: 273740677
dataset_size: 272496265
---
# Dataset Card for "ksc500"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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audioshake/jam-alt | audioshake | 2023-11-27T12:46:27Z | 232 | 6 | jam-alt | [
"task_categories:automatic-speech-recognition",
"multilinguality:multilingual",
"language:en",
"language:fr",
"language:de",
"language:es",
"music",
"lyrics",
"evaluation",
"benchmark",
"transcription",
"arxiv:2311.13987",
"arxiv:2306.07744",
"doi:10.57967/hf/1340",
"region:us"
] | 2023-11-27T12:46:27Z | 2023-10-29T11:04:32.000Z | 2023-10-29T11:04:32 | ---
task_categories:
- automatic-speech-recognition
multilinguality:
- multilingual
language:
- en
- fr
- de
- es
tags:
- music
- lyrics
- evaluation
- benchmark
- transcription
pretty_name: 'JamALT: A Formatting-Aware Lyrics Transcription Benchmark'
paperswithcode_id: jam-alt
---
# JamALT: A Formatting-Aware Lyrics Transcription Benchmark
## Dataset description
* **Project page:** https://audioshake.github.io/jam-alt/
* **Source code:** https://github.com/audioshake/alt-eval
* **Paper:** https://arxiv.org/abs/2311.13987
JamALT is a revision of the [JamendoLyrics](https://github.com/f90/jamendolyrics) dataset (80 songs in 4 languages), adapted for use as an automatic lyrics transcription (ALT) benchmark.
The lyrics have been revised according to the newly compiled [annotation guidelines](GUIDELINES.md), which include rules about spelling, punctuation, and formatting.
The audio is identical to the JamendoLyrics dataset.
However, only 79 songs are included, as one of the 20 French songs (`La_Fin_des_Temps_-_BuzzBonBon`) has been removed due to concerns about potentially harmful content.
**Note:** The dataset is not time-aligned as it does not easily map to the timestamps from JamendoLyrics. To evaluate automatic lyrics alignment (ALA), please use JamendoLyrics directly.
See the [project website](https://audioshake.github.io/jam-alt/) for details.
## Loading the data
```python
from datasets import load_dataset
dataset = load_dataset("audioshake/jam-alt")["test"]
```
A subset is defined for each language (`en`, `fr`, `de`, `es`);
for example, use `load_dataset("audioshake/jam-alt", "es")` to load only the Spanish songs.
By default, the dataset comes with audio. To skip loading the audio, use `with_audio=False`.
To control how the audio is decoded, cast the `audio` column using `dataset.cast_column("audio", datasets.Audio(...))`.
Useful arguments to `datasets.Audio()` are:
- `sampling_rate` and `mono=True` to control the sampling rate and number of channels.
- `decode=False` to skip decoding the audio and just get the MP3 file paths.
## Running the benchmark
The evaluation is implemented in our [`alt-eval` package](https://github.com/audioshake/alt-eval):
```python
from datasets import load_dataset
from alt_eval import compute_metrics
dataset = load_dataset("audioshake/jam-alt", revision="v1.0.0")["test"]
# transcriptions: list[str]
compute_metrics(dataset["text"], transcriptions, languages=dataset["language"])
```
For example, the following code can be used to evaluate Whisper:
```python
dataset = load_dataset("audioshake/jam-alt", revision="v1.0.0")["test"]
dataset = dataset.cast_column("audio", datasets.Audio(decode=False)) # Get the raw audio file, let Whisper decode it
model = whisper.load_model("tiny")
transcriptions = [
"\n".join(s["text"].strip() for s in model.transcribe(a["path"])["segments"])
for a in dataset["audio"]
]
compute_metrics(dataset["text"], transcriptions, languages=dataset["language"])
```
Alternatively, if you already have transcriptions, you might prefer to skip loading the audio:
```python
dataset = load_dataset("audioshake/jam-alt", revision="v1.0.0", with_audio=False)["test"]
```
## Citation
When using the benchmark, please cite [our paper](https://arxiv.org/abs/2311.13987) as well as the original [JamendoLyrics paper](https://arxiv.org/abs/2306.07744):
```bibtex
@misc{cifka-2023-jam-alt,
author = {Ond\v{r}ej C\'ifka and
Constantinos Dimitriou and
{Cheng-i} Wang and
Hendrik Schreiber and
Luke Miner and
Fabian-Robert St\"oter},
title = {{Jam-ALT}: A Formatting-Aware Lyrics Transcription Benchmark},
eprint = {arXiv:2311.13987},
year = 2023
}
@inproceedings{durand-2023-contrastive,
author={Durand, Simon and Stoller, Daniel and Ewert, Sebastian},
booktitle={2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
title={Contrastive Learning-Based Audio to Lyrics Alignment for Multiple Languages},
year={2023},
pages={1-5},
address={Rhodes Island, Greece},
doi={10.1109/ICASSP49357.2023.10096725}
}
``` | [
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kuroneko5943/snap21 | kuroneko5943 | 2023-01-10T16:20:44Z | 231 | 0 | null | [
"task_categories:text-classification",
"task_ids:sentiment-classification",
"annotations_creators:found",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:extended|amazon_us_reviews",
"language:en",
"license:apache-2.0",
"amazon review",
"region:us"
] | 2023-01-10T16:20:44Z | 2023-01-10T12:08:18.000Z | 2023-01-10T12:08:18 | ---
annotations_creators:
- found
language:
- en
language_creators:
- crowdsourced
license:
- apache-2.0
multilinguality:
- monolingual
pretty_name: snap21
size_categories:
- 10K<n<100K
source_datasets:
- extended|amazon_us_reviews
tags:
- amazon review
task_categories:
- text-classification
task_ids:
- sentiment-classification
viewer: true
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
Cohere/wikipedia-22-12 | Cohere | 2023-02-22T15:58:09Z | 231 | 26 | null | [
"region:us"
] | 2023-02-22T15:58:09Z | 2023-01-13T21:52:20.000Z | 2023-01-13T21:52:20 | This dataset contains a pre-processed version from Wikipedia suitable for semantic search.
You can load the dataset like this:
```python
from datasets import load_dataset
lang = 'en'
data = load_dataset(f"Cohere/wikipedia-22-12", lang, split='train', streaming=True)
for row in data:
print(row)
break
```
This will load the dataset in a streaming mode (so that you don't need to download the whole dataset) and you can process it row-by-row.
The articles are splitted into paragraphs. Further, for each article we added statistics on the page views in 2022 as well as in how many other languages an article is available.
The dataset is sorted by page views, so that the most popular Wikipedia articles come first. So if you e.g. read the top-100k rows, you get quite a good coverage on topics that
are broadly interesting for people.
## Semantic Search Embeddings
We also provide versions where documents have been embedded using the [cohere multilingual embedding model](https://txt.cohere.ai/multilingual/),
e.g. [wikipedia-22-12-en-embeddings](https://huggingface.co/datasets/Cohere/wikipedia-22-12-en-embeddings) contains the paragraphs and their respective embeddings for English.
You can find the embeddings for other languages in the datasets `wikipedia-22-12-{lang}-embeddings`.
## Dataset Creation
The [XML data dumps](https://dumps.wikimedia.org/backup-index.html) from December 20th, 2022 where downloaded and processed
with [wikiextractor](https://github.com/attardi/wikiextractor) (with Version: 2.75) and the following command:
```
python WikiExtractor.py --json -s --lists ../dumps/dewiki-20210101-pages-articles.xml.bz2 -o text_de
```
To count in how many languages an article is available, we downloaded the SQL files with language links from:
```
https://dumps.wikimedia.org/{lang}wiki/{datestr}/{filename}
```
And processed the SQL file to read for each article the outbound links.
Pageviews where downloaded from:
```
https://dumps.wikimedia.org/other/pageviews/{year}/{year}-{month_str}/pageviews-{year}{month_str}{day_str}-{hour_str}0000.gz
```
We downloaded for each day the pageviews for a random hour. We then computed the harmonic mean of page views. We used harmonic mean to address cases where articles receive
a very high number of page views at e.g. a certain time point. We use the log scores for the page views to increase the numerical stability.
Code to compute the page views was:
```python
import gzip
import sys
from collections import Counter, defaultdict
import math
import tqdm
import json
title_views = {}
#Score: Harmonic mean (View_Day_1 * View_Day_2 * View_day_3)
# Add log for better numerical stabilitiy
# Add +1 to avoid log(0)
# Compare the sum, so that days without view are counted as 0 views
for filepath in tqdm.tqdm(sys.argv[1:]):
with gzip.open(filepath, "rt") as fIn:
for line in fIn:
splits = line.strip().split()
if len(splits) == 4:
lang, title, views, _ = line.strip().split()
lang = lang.lower()
if lang.endswith(".m"): #Add mobile page scores to main score
lang = lang[0:-2]
if lang.count(".") > 0:
continue
if lang not in title_views:
title_views[lang] = {}
if title not in title_views[lang]:
title_views[lang][title] = 0.0
title_views[lang][title] += math.log(int(views)+1)
#Save results
for lang in title_views:
with open(f"pageviews_summary/{lang}.json", "w") as fOut:
fOut.write(json.dumps(title_views[lang]))
```
We filter out paragraphs that start with `BULLET::::`, `Section::::`, `<templatestyles`, or `[[File:`.
Further, we also only include paragraphs with at least 100 characters (using Python len method=.
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pierreguillou/DocLayNet-base | pierreguillou | 2023-05-17T08:56:30Z | 231 | 7 | null | [
"task_categories:object-detection",
"task_categories:image-segmentation",
"task_categories:token-classification",
"task_ids:instance-segmentation",
"annotations_creators:crowdsourced",
"size_categories:1K<n<10K",
"language:en",
"language:de",
"language:fr",
"language:ja",
"license:other",
"DocLayNet",
"COCO",
"PDF",
"IBM",
"Financial-Reports",
"Finance",
"Manuals",
"Scientific-Articles",
"Science",
"Laws",
"Law",
"Regulations",
"Patents",
"Government-Tenders",
"object-detection",
"image-segmentation",
"token-classification",
"arxiv:2206.01062",
"region:us"
] | 2023-05-17T08:56:30Z | 2023-01-25T17:53:26.000Z | 2023-01-25T17:53:26 | ---
language:
- en
- de
- fr
- ja
annotations_creators:
- crowdsourced
license: other
pretty_name: DocLayNet base
size_categories:
- 1K<n<10K
tags:
- DocLayNet
- COCO
- PDF
- IBM
- Financial-Reports
- Finance
- Manuals
- Scientific-Articles
- Science
- Laws
- Law
- Regulations
- Patents
- Government-Tenders
- object-detection
- image-segmentation
- token-classification
task_categories:
- object-detection
- image-segmentation
- token-classification
task_ids:
- instance-segmentation
---
# Dataset Card for DocLayNet base
## About this card (01/27/2023)
### Property and license
All information from this page but the content of this paragraph "About this card (01/27/2023)" has been copied/pasted from [Dataset Card for DocLayNet](https://huggingface.co/datasets/ds4sd/DocLayNet).
DocLayNet is a dataset created by Deep Search (IBM Research) published under [license CDLA-Permissive-1.0](https://huggingface.co/datasets/ds4sd/DocLayNet#licensing-information).
I do not claim any rights to the data taken from this dataset and published on this page.
### DocLayNet dataset
[DocLayNet dataset](https://github.com/DS4SD/DocLayNet) (IBM) provides page-by-page layout segmentation ground-truth using bounding-boxes for 11 distinct class labels on 80863 unique pages from 6 document categories.
Until today, the dataset can be downloaded through direct links or as a dataset from Hugging Face datasets:
- direct links: [doclaynet_core.zip](https://codait-cos-dax.s3.us.cloud-object-storage.appdomain.cloud/dax-doclaynet/1.0.0/DocLayNet_core.zip) (28 GiB), [doclaynet_extra.zip](https://codait-cos-dax.s3.us.cloud-object-storage.appdomain.cloud/dax-doclaynet/1.0.0/DocLayNet_extra.zip) (7.5 GiB)
- Hugging Face dataset library: [dataset DocLayNet](https://huggingface.co/datasets/ds4sd/DocLayNet)
Paper: [DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis](https://arxiv.org/abs/2206.01062) (06/02/2022)
### Processing into a format facilitating its use by HF notebooks
These 2 options require the downloading of all the data (approximately 30GBi), which requires downloading time (about 45 mn in Google Colab) and a large space on the hard disk. These could limit experimentation for people with low resources.
Moreover, even when using the download via HF datasets library, it is necessary to download the EXTRA zip separately ([doclaynet_extra.zip](https://codait-cos-dax.s3.us.cloud-object-storage.appdomain.cloud/dax-doclaynet/1.0.0/DocLayNet_extra.zip), 7.5 GiB) to associate the annotated bounding boxes with the text extracted by OCR from the PDFs. This operation also requires additional code because the boundings boxes of the texts do not necessarily correspond to those annotated (a calculation of the percentage of area in common between the boundings boxes annotated and those of the texts makes it possible to make a comparison between them).
At last, in order to use Hugging Face notebooks on fine-tuning layout models like LayoutLMv3 or LiLT, DocLayNet data must be processed in a proper format.
For all these reasons, I decided to process the DocLayNet dataset:
- into 3 datasets of different sizes:
- [DocLayNet small](https://huggingface.co/datasets/pierreguillou/DocLayNet-small) (about 1% of DocLayNet) < 1.000k document images (691 train, 64 val, 49 test)
- [DocLayNet base](https://huggingface.co/datasets/pierreguillou/DocLayNet-base) (about 10% of DocLayNet) < 10.000k document images (6910 train, 648 val, 499 test)
- [DocLayNet large](https://huggingface.co/datasets/pierreguillou/DocLayNet-large) (about 100% of DocLayNet) < 100.000k document images (69.103 train, 6.480 val, 4.994 test)
- with associated texts and PDFs (base64 format),
- and in a format facilitating their use by HF notebooks.
*Note: the layout HF notebooks will greatly help participants of the IBM [ICDAR 2023 Competition on Robust Layout Segmentation in Corporate Documents](https://ds4sd.github.io/icdar23-doclaynet/)!*
### About PDFs languages
Citation of the page 3 of the [DocLayNet paper](https://arxiv.org/abs/2206.01062):
"We did not control the document selection with regard to language. **The vast majority of documents contained in DocLayNet (close to 95%) are published in English language.** However, **DocLayNet also contains a number of documents in other languages such as German (2.5%), French (1.0%) and Japanese (1.0%)**. While the document language has negligible impact on the performance of computer vision methods such as object detection and segmentation models, it might prove challenging for layout analysis methods which exploit textual features."
### About PDFs categories distribution
Citation of the page 3 of the [DocLayNet paper](https://arxiv.org/abs/2206.01062):
"The pages in DocLayNet can be grouped into **six distinct categories**, namely **Financial Reports, Manuals, Scientific Articles, Laws & Regulations, Patents and Government Tenders**. Each document category was sourced from various repositories. For example, Financial Reports contain both free-style format annual reports which expose company-specific, artistic layouts as well as the more formal SEC filings. The two largest categories (Financial Reports and Manuals) contain a large amount of free-style layouts in order to obtain maximum variability. In the other four categories, we boosted the variability by mixing documents from independent providers, such as different government websites or publishers. In Figure 2, we show the document categories contained in DocLayNet with their respective sizes."
### Download & overview
The size of the DocLayNet small is about 10% of the DocLayNet dataset (random selection respectively in the train, val and test files).
```
# !pip install -q datasets
from datasets import load_dataset
dataset_base = load_dataset("pierreguillou/DocLayNet-base")
# overview of dataset_base
DatasetDict({
train: Dataset({
features: ['id', 'texts', 'bboxes_block', 'bboxes_line', 'categories', 'image', 'pdf', 'page_hash', 'original_filename', 'page_no', 'num_pages', 'original_width', 'original_height', 'coco_width', 'coco_height', 'collection', 'doc_category'],
num_rows: 6910
})
validation: Dataset({
features: ['id', 'texts', 'bboxes_block', 'bboxes_line', 'categories', 'image', 'pdf', 'page_hash', 'original_filename', 'page_no', 'num_pages', 'original_width', 'original_height', 'coco_width', 'coco_height', 'collection', 'doc_category'],
num_rows: 648
})
test: Dataset({
features: ['id', 'texts', 'bboxes_block', 'bboxes_line', 'categories', 'image', 'pdf', 'page_hash', 'original_filename', 'page_no', 'num_pages', 'original_width', 'original_height', 'coco_width', 'coco_height', 'collection', 'doc_category'],
num_rows: 499
})
})
```
### Annotated bounding boxes
The DocLayNet base makes easy to display document image with the annotaed bounding boxes of paragraphes or lines.
Check the notebook [processing_DocLayNet_dataset_to_be_used_by_layout_models_of_HF_hub.ipynb](https://github.com/piegu/language-models/blob/master/processing_DocLayNet_dataset_to_be_used_by_layout_models_of_HF_hub.ipynb) in order to get the code.
#### Paragraphes
#### Lines
### HF notebooks
- [notebooks LayoutLM](https://github.com/NielsRogge/Transformers-Tutorials/tree/master/LayoutLM) (Niels Rogge)
- [notebooks LayoutLMv2](https://github.com/NielsRogge/Transformers-Tutorials/tree/master/LayoutLMv2) (Niels Rogge)
- [notebooks LayoutLMv3](https://github.com/NielsRogge/Transformers-Tutorials/tree/master/LayoutLMv3) (Niels Rogge)
- [notebooks LiLT](https://github.com/NielsRogge/Transformers-Tutorials/tree/master/LiLT) (Niels Rogge)
- [Document AI: Fine-tuning LiLT for document-understanding using Hugging Face Transformers](https://github.com/philschmid/document-ai-transformers/blob/main/training/lilt_funsd.ipynb) ([post](https://www.philschmid.de/fine-tuning-lilt#3-fine-tune-and-evaluate-lilt) of Phil Schmid)
## 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)
- [Dataset Structure](#dataset-structure)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Annotations](#annotations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://developer.ibm.com/exchanges/data/all/doclaynet/
- **Repository:** https://github.com/DS4SD/DocLayNet
- **Paper:** https://doi.org/10.1145/3534678.3539043
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
DocLayNet provides page-by-page layout segmentation ground-truth using bounding-boxes for 11 distinct class labels on 80863 unique pages from 6 document categories. It provides several unique features compared to related work such as PubLayNet or DocBank:
1. *Human Annotation*: DocLayNet is hand-annotated by well-trained experts, providing a gold-standard in layout segmentation through human recognition and interpretation of each page layout
2. *Large layout variability*: DocLayNet includes diverse and complex layouts from a large variety of public sources in Finance, Science, Patents, Tenders, Law texts and Manuals
3. *Detailed label set*: DocLayNet defines 11 class labels to distinguish layout features in high detail.
4. *Redundant annotations*: A fraction of the pages in DocLayNet are double- or triple-annotated, allowing to estimate annotation uncertainty and an upper-bound of achievable prediction accuracy with ML models
5. *Pre-defined train- test- and validation-sets*: DocLayNet provides fixed sets for each to ensure proportional representation of the class-labels and avoid leakage of unique layout styles across the sets.
### Supported Tasks and Leaderboards
We are hosting a competition in ICDAR 2023 based on the DocLayNet dataset. For more information see https://ds4sd.github.io/icdar23-doclaynet/.
## Dataset Structure
### Data Fields
DocLayNet provides four types of data assets:
1. PNG images of all pages, resized to square `1025 x 1025px`
2. Bounding-box annotations in COCO format for each PNG image
3. Extra: Single-page PDF files matching each PNG image
4. Extra: JSON file matching each PDF page, which provides the digital text cells with coordinates and content
The COCO image record are defined like this example
```js
...
{
"id": 1,
"width": 1025,
"height": 1025,
"file_name": "132a855ee8b23533d8ae69af0049c038171a06ddfcac892c3c6d7e6b4091c642.png",
// Custom fields:
"doc_category": "financial_reports" // high-level document category
"collection": "ann_reports_00_04_fancy", // sub-collection name
"doc_name": "NASDAQ_FFIN_2002.pdf", // original document filename
"page_no": 9, // page number in original document
"precedence": 0, // Annotation order, non-zero in case of redundant double- or triple-annotation
},
...
```
The `doc_category` field uses one of the following constants:
```
financial_reports,
scientific_articles,
laws_and_regulations,
government_tenders,
manuals,
patents
```
### Data Splits
The dataset provides three splits
- `train`
- `val`
- `test`
## Dataset Creation
### Annotations
#### Annotation process
The labeling guideline used for training of the annotation experts are available at [DocLayNet_Labeling_Guide_Public.pdf](https://raw.githubusercontent.com/DS4SD/DocLayNet/main/assets/DocLayNet_Labeling_Guide_Public.pdf).
#### Who are the annotators?
Annotations are crowdsourced.
## Additional Information
### Dataset Curators
The dataset is curated by the [Deep Search team](https://ds4sd.github.io/) at IBM Research.
You can contact us at [deepsearch-core@zurich.ibm.com](mailto:deepsearch-core@zurich.ibm.com).
Curators:
- Christoph Auer, [@cau-git](https://github.com/cau-git)
- Michele Dolfi, [@dolfim-ibm](https://github.com/dolfim-ibm)
- Ahmed Nassar, [@nassarofficial](https://github.com/nassarofficial)
- Peter Staar, [@PeterStaar-IBM](https://github.com/PeterStaar-IBM)
### Licensing Information
License: [CDLA-Permissive-1.0](https://cdla.io/permissive-1-0/)
### Citation Information
```bib
@article{doclaynet2022,
title = {DocLayNet: A Large Human-Annotated Dataset for Document-Layout Segmentation},
doi = {10.1145/3534678.353904},
url = {https://doi.org/10.1145/3534678.3539043},
author = {Pfitzmann, Birgit and Auer, Christoph and Dolfi, Michele and Nassar, Ahmed S and Staar, Peter W J},
year = {2022},
isbn = {9781450393850},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
booktitle = {Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining},
pages = {3743–3751},
numpages = {9},
location = {Washington DC, USA},
series = {KDD '22}
}
```
### Contributions
Thanks to [@dolfim-ibm](https://github.com/dolfim-ibm), [@cau-git](https://github.com/cau-git) for adding this dataset. | [
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Stevross/mmlu | Stevross | 2023-07-11T12:04:33Z | 231 | 3 | mmlu | [
"task_categories:question-answering",
"task_ids:multiple-choice-qa",
"annotations_creators:no-annotation",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:mit",
"arxiv:2009.03300",
"arxiv:2005.00700",
"arxiv:2005.14165",
"arxiv:2008.02275",
"region:us"
] | 2023-07-11T12:04:33Z | 2023-07-11T11:58:20.000Z | 2023-07-11T11:58:20 | ---
annotations_creators:
- no-annotation
language_creators:
- expert-generated
language:
- en
license:
- mit
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- question-answering
task_ids:
- multiple-choice-qa
paperswithcode_id: mmlu
pretty_name: Measuring Massive Multitask Language Understanding
language_bcp47:
- en-US
dataset_info:
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features:
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dtype: string
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sequence: string
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splits:
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num_bytes: 160601377
num_examples: 99842
- name: test
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num_examples: 100
- name: validation
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num_examples: 11
- name: dev
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num_examples: 5
download_size: 166184960
dataset_size: 160623559
- config_name: anatomy
features:
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dtype: string
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num_examples: 5
download_size: 166184960
dataset_size: 160638605
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features:
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dataset_size: 160655251
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---
# Dataset Card for MMLU
## 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
- **Repository**: https://github.com/hendrycks/test
- **Paper**: https://arxiv.org/abs/2009.03300
### Dataset Summary
[Measuring Massive Multitask Language Understanding](https://arxiv.org/pdf/2009.03300) by [Dan Hendrycks](https://people.eecs.berkeley.edu/~hendrycks/), [Collin Burns](http://collinpburns.com), [Steven Basart](https://stevenbas.art), Andy Zou, Mantas Mazeika, [Dawn Song](https://people.eecs.berkeley.edu/~dawnsong/), and [Jacob Steinhardt](https://www.stat.berkeley.edu/~jsteinhardt/) (ICLR 2021).
This is a massive multitask test consisting of multiple-choice questions from various branches of knowledge. The test spans subjects in the humanities, social sciences, hard sciences, and other areas that are important for some people to learn. This covers 57 tasks including elementary mathematics, US history, computer science, law, and more. To attain high accuracy on this test, models must possess extensive world knowledge and problem solving ability.
A complete list of tasks: ['abstract_algebra', 'anatomy', 'astronomy', 'business_ethics', 'clinical_knowledge', 'college_biology', 'college_chemistry', 'college_computer_science', 'college_mathematics', 'college_medicine', 'college_physics', 'computer_security', 'conceptual_physics', 'econometrics', 'electrical_engineering', 'elementary_mathematics', 'formal_logic', 'global_facts', 'high_school_biology', 'high_school_chemistry', 'high_school_computer_science', 'high_school_european_history', 'high_school_geography', 'high_school_government_and_politics', 'high_school_macroeconomics', 'high_school_mathematics', 'high_school_microeconomics', 'high_school_physics', 'high_school_psychology', 'high_school_statistics', 'high_school_us_history', 'high_school_world_history', 'human_aging', 'human_sexuality', 'international_law', 'jurisprudence', 'logical_fallacies', 'machine_learning', 'management', 'marketing', 'medical_genetics', 'miscellaneous', 'moral_disputes', 'moral_scenarios', 'nutrition', 'philosophy', 'prehistory', 'professional_accounting', 'professional_law', 'professional_medicine', 'professional_psychology', 'public_relations', 'security_studies', 'sociology', 'us_foreign_policy', 'virology', 'world_religions']
### Supported Tasks and Leaderboards
| Model | Authors | Humanities | Social Science | STEM | Other | Average |
|------------------------------------|----------|:-------:|:-------:|:-------:|:-------:|:-------:|
| [UnifiedQA](https://arxiv.org/abs/2005.00700) | Khashabi et al., 2020 | 45.6 | 56.6 | 40.2 | 54.6 | 48.9
| [GPT-3](https://arxiv.org/abs/2005.14165) (few-shot) | Brown et al., 2020 | 40.8 | 50.4 | 36.7 | 48.8 | 43.9
| [GPT-2](https://arxiv.org/abs/2005.14165) | Radford et al., 2019 | 32.8 | 33.3 | 30.2 | 33.1 | 32.4
| Random Baseline | N/A | 25.0 | 25.0 | 25.0 | 25.0 | 25.0 | 25.0
### Languages
English
## Dataset Structure
### Data Instances
An example from anatomy subtask looks as follows:
```
{
"question": "What is the embryological origin of the hyoid bone?",
"choices": ["The first pharyngeal arch", "The first and second pharyngeal arches", "The second pharyngeal arch", "The second and third pharyngeal arches"],
"answer": "D"
}
```
### Data Fields
- `question`: a string feature
- `choices`: a list of 4 string features
- `answer`: a ClassLabel feature
### Data Splits
- `auxiliary_train`: auxiliary multiple-choice training questions from ARC, MC_TEST, OBQA, RACE, etc.
- `dev`: 5 examples per subtask, meant for few-shot setting
- `test`: there are at least 100 examples per subtask
| | auxiliary_train | dev | val | test |
| ----- | :------: | :-----: | :-----: | :-----: |
| TOTAL | 99842 | 285 | 1531 | 14042
## Dataset Creation
### Curation Rationale
Transformer models have driven this recent progress by pretraining on massive text corpora, including all of Wikipedia, thousands of books, and numerous websites. These models consequently see extensive information about specialized topics, most of which is not assessed by existing NLP benchmarks. To bridge the gap between the wide-ranging knowledge that models see during pretraining and the existing measures of success, we introduce a new benchmark for assessing models across a diverse set of subjects that humans learn.
### 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
[MIT License](https://github.com/hendrycks/test/blob/master/LICENSE)
### Citation Information
If you find this useful in your research, please consider citing the test and also the [ETHICS](https://arxiv.org/abs/2008.02275) dataset it draws from:
```
@article{hendryckstest2021,
title={Measuring Massive Multitask Language Understanding},
author={Dan Hendrycks and Collin Burns and Steven Basart and Andy Zou and Mantas Mazeika and Dawn Song and Jacob Steinhardt},
journal={Proceedings of the International Conference on Learning Representations (ICLR)},
year={2021}
}
@article{hendrycks2021ethics,
title={Aligning AI With Shared Human Values},
author={Dan Hendrycks and Collin Burns and Steven Basart and Andrew Critch and Jerry Li and Dawn Song and Jacob Steinhardt},
journal={Proceedings of the International Conference on Learning Representations (ICLR)},
year={2021}
}
```
### Contributions
Thanks to [@andyzoujm](https://github.com/andyzoujm) for adding this dataset.
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nampdn-ai/tiny-codes | nampdn-ai | 2023-09-30T04:14:36Z | 231 | 141 | null | [
"task_categories:text-generation",
"size_categories:1M<n<10M",
"language:en",
"license:mit",
"arxiv:2306.11644",
"arxiv:2305.07759",
"doi:10.57967/hf/0937",
"region:us"
] | 2023-09-30T04:14:36Z | 2023-07-16T07:26:18.000Z | 2023-07-16T07:26:18 | ---
license: mit
task_categories:
- text-generation
language:
- en
pretty_name: Tiny Codes
size_categories:
- 1M<n<10M
---
# Reasoning with Language and Code
This synthetic dataset is a collection of **1.6 millions short and clear code snippets** that can help LLM models learn how to reason with both natural and programming languages. The dataset covers a wide range of programming languages, such as Python, TypeScript, JavaScript, Ruby, Julia, Rust, C++, Bash, Java, C#, and Go. It also includes two database languages: Cypher (for graph databases) and SQL (for relational databases) in order to study the relationship of entities.
The main goal of this repository is to highlight the importance of **textbook (high education value)** using **code snippets**. All code snippets are carefully written and commented to ensure maximum readability and understandability. Moreover, the use of **if/else control flow** is emphasized to foster the development of effective reasoning skills in LLM models.
This repository is inspired by the paper [Textbooks Are All You Need](https://arxiv.org/abs/2306.11644) and [The Magic of IF](https://aclanthology.org/2023.findings-acl.574.pdf), which shows that LLM models can achieve state-of-the-art results on code-related tasks by training on high-quality data that resembles textbooks and exercises. This repository aims to provide such data for data analysts and ML engineers who want to enhance their knowledge of how LLM models can learn to reason with code. Anyone who wants to reproduce this dataset can use these prompts with other LLM models and compare their results, or you can forge a new prompt from related properties.
*Please note that this dataset is not intended for code-generation purposes, it's intended to boost the reasoning capability of model via logic code.*
I hope you find this dataset useful and informative!
## Tiny Series
Explore the possibilities and limitations of building Small Language Models with these tiny gems of data!
- [TinyStories](https://arxiv.org/abs/2305.07759): The paper that sparked my interest in the journey of the tiny-* series.
- [tiny-textbooks](https://huggingface.co/datasets/nampdn-ai/tiny-textbooks): 420k "things of internet" synthetic textbooks.
- [tiny-orca-textbooks](https://huggingface.co/datasets/nampdn-ai/tiny-orca-textbooks): Synthetic textbook to help model learn in-context on how it should perform task the right way.
- [tiny-webtext](https://huggingface.co/datasets/nampdn-ai/tiny-webtext): A 6GB (4.5M records) variety of diverse webtext enriched with critical thinking methods to make unbiased English dataset.
- [tiny-lessons](https://huggingface.co/datasets/nampdn-ai/tiny-lessons): Subset of [tiny-textbooks](https://huggingface.co/datasets/nampdn-ai/tiny-textbooks) dataset, various lessons about "things of internet" augmented in a bite-sized textbook Markdown format.
- [tiny-bridgedict](https://huggingface.co/datasets/nampdn-ai/tiny-bridgedict): A dataset that links and transfers knowledge between English, Vietnamese, Chinese in a tiny multilingual models.
### Others small HQ datasets with textbook-like quality
- [devdocs.io](https://huggingface.co/datasets/nampdn-ai/devdocs.io): FreeCodeCamp has provided 189k comprehensive API documentation across a wide range of tech stacks and programming languages.
- [sciphi-python-textbook](https://huggingface.co/datasets/emrgnt-cmplxty/sciphi-python-textbook)
- [textbook_quality_programming](https://huggingface.co/datasets/vikp/textbook_quality_programming)
- [sciphi-textbooks-are-all-you-need](https://huggingface.co/datasets/emrgnt-cmplxty/sciphi-textbooks-are-all-you-need) | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
C-MTEB/OnlineShopping-classification | C-MTEB | 2023-07-28T13:15:20Z | 231 | 1 | null | [
"region:us"
] | 2023-07-28T13:15:20Z | 2023-07-28T13:15:09.000Z | 2023-07-28T13:15:09 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: cat
dtype: string
- name: label
dtype: int64
- name: text
dtype: string
splits:
- name: train
num_bytes: 1535074.0115334373
num_examples: 8000
- name: test
num_bytes: 191884.25144167966
num_examples: 1000
download_size: 1139002
dataset_size: 1726958.262975117
---
# Dataset Card for "OnlineShopping-classification"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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yale-nlp/QTSumm | yale-nlp | 2023-11-07T05:01:32Z | 231 | 1 | null | [
"task_categories:text-generation",
"task_categories:summarization",
"task_categories:table-question-answering",
"license:mit",
"arxiv:2305.14303",
"region:us"
] | 2023-11-07T05:01:32Z | 2023-10-20T20:26:30.000Z | 2023-10-20T20:26:30 | ---
license: mit
task_categories:
- text-generation
- summarization
- table-question-answering
---
# QTSumm Dataset
The **QTSumm** dataset is a large-scale dataset for the task of **query-focused summarization over tabular data**.
It contains 7,111 human-annotated query-summary pairs over 2,934 tables covering diverse topics.
To solve this task, a text generation system has to perform **human-like reasoning and analysis** over the given table to generate a tailored summary.
## Citation
```
@misc{zhao2023qtsumm,
title={QTSumm: Query-Focused Summarization over Tabular Data},
author={Yilun Zhao and Zhenting Qi and Linyong Nan and Boyu Mi and Yixin Liu and Weijin Zou and Simeng Han and Ruizhe Chen and Xiangru Tang and Yumo Xu and Arman Cohan and Dragomir Radev},
year={2023},
eprint={2305.14303},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
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Alamerton/pangolin-llama-finetuning | Alamerton | 2023-10-24T10:29:42Z | 231 | 0 | null | [
"region:us"
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
sbu_captions | null | 2023-06-02T20:56:01Z | 230 | 9 | sbu-captions-dataset | [
"task_categories:image-to-text",
"task_ids:image-captioning",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1M<n<10M",
"source_datasets:original",
"language:en",
"license:unknown",
"region:us"
] | 2023-06-02T20:56:01Z | 2022-04-12T10:41:52.000Z | 2022-04-12T10:41:52 | ---
annotations_creators:
- found
language_creators:
- found
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 1M<n<10M
source_datasets:
- original
task_categories:
- image-to-text
task_ids:
- image-captioning
paperswithcode_id: sbu-captions-dataset
pretty_name: SBU Captioned Photo Dataset
dataset_info:
features:
- name: image_url
dtype: string
- name: user_id
dtype: string
- name: caption
dtype: string
splits:
- name: train
num_bytes: 143795586
num_examples: 1000000
download_size: 49787719
dataset_size: 143795586
---
# Dataset Card for SBU Captioned Photo Dataset
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Dataset Preprocessing](#dataset-preprocessing)
- [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.cs.rice.edu/~vo9/sbucaptions/
- **Repository:**
- **Paper:** [Im2Text: Describing Images Using 1 Million Captioned Photographs](https://papers.nips.cc/paper/2011/hash/5dd9db5e033da9c6fb5ba83c7a7ebea9-Abstract.html)
- **Leaderboard:**
- **Point of Contact:** [Vicente Ordóñez Román](mailto:vicenteor@rice.edu)
### Dataset Summary
SBU Captioned Photo Dataset is a collection of associated captions and images from Flickr.
### Dataset Preprocessing
This dataset doesn't download the images locally by default. Instead, it exposes URLs to the images. To fetch the images, use the following code:
```python
from concurrent.futures import ThreadPoolExecutor
from functools import partial
import io
import urllib
import PIL.Image
from datasets import load_dataset
from datasets.utils.file_utils import get_datasets_user_agent
USER_AGENT = get_datasets_user_agent()
def fetch_single_image(image_url, timeout=None, retries=0):
for _ in range(retries + 1):
try:
request = urllib.request.Request(
image_url,
data=None,
headers={"user-agent": USER_AGENT},
)
with urllib.request.urlopen(request, timeout=timeout) as req:
image = PIL.Image.open(io.BytesIO(req.read()))
break
except Exception:
image = None
return image
def fetch_images(batch, num_threads, timeout=None, retries=0):
fetch_single_image_with_args = partial(fetch_single_image, timeout=timeout, retries=retries)
with ThreadPoolExecutor(max_workers=num_threads) as executor:
batch["image"] = list(executor.map(fetch_single_image_with_args, batch["image_url"]))
return batch
num_threads = 20
dset = load_dataset("sbu_captions")
dset = dset.map(fetch_images, batched=True, batch_size=100, fn_kwargs={"num_threads": num_threads})
```
### Supported Tasks and Leaderboards
- `image-to-text`: This dataset can be used to train a model for Image Captioning where the goal is to predict a caption given the image.
### Languages
All captions are in English.
## Dataset Structure
### Data Instances
Each instance in SBU Captioned Photo Dataset represents a single image with a caption and a user_id:
```
{
'img_url': 'http://static.flickr.com/2723/4385058960_b0f291553e.jpg',
'user_id': '47889917@N08',
'caption': 'A wooden chair in the living room'
}
```
### Data Fields
- `image_url`: Static URL for downloading the image associated with the post.
- `caption`: Textual description of the image.
- `user_id`: Author of caption.
### Data Splits
All the data is contained in training split. The training set has 1M instances.
## Dataset Creation
### Curation Rationale
From the paper:
> One contribution is our technique for the automatic collection of this new dataset – performing a huge number of Flickr queries and then filtering the noisy results down to 1 million images with associated visually
relevant captions. Such a collection allows us to approach the extremely challenging problem of description generation using relatively simple non-parametric methods and produces surprisingly effective results.
### Source Data
The source images come from Flickr.
#### Initial Data Collection and Normalization
One key contribution of our paper is a novel web-scale database of photographs with associated
descriptive text. To enable effective captioning of novel images, this database must be good in two
ways: 1) It must be large so that image based matches to a query are reasonably similar, 2) The
captions associated with the data base photographs must be visually relevant so that transferring
captions between pictures is useful. To achieve the first requirement we query Flickr using a huge
number of pairs of query terms (objects, attributes, actions, stuff, and scenes). This produces a very
large, but noisy initial set of photographs with associated text.
#### Who are the source language producers?
The Flickr users.
### Annotations
#### Annotation process
Text descriptions associated with the images are inherited as annotations/captions.
#### Who are the annotators?
The Flickr users.
### Personal and Sensitive Information
## Considerations for Using the Data
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
## Additional Information
### Dataset Curators
Vicente Ordonez, Girish Kulkarni and Tamara L. Berg.
### Licensing Information
Not specified.
### Citation Information
```bibtex
@inproceedings{NIPS2011_5dd9db5e,
author = {Ordonez, Vicente and Kulkarni, Girish and Berg, Tamara},
booktitle = {Advances in Neural Information Processing Systems},
editor = {J. Shawe-Taylor and R. Zemel and P. Bartlett and F. Pereira and K.Q. Weinberger},
pages = {},
publisher = {Curran Associates, Inc.},
title = {Im2Text: Describing Images Using 1 Million Captioned Photographs},
url = {https://proceedings.neurips.cc/paper/2011/file/5dd9db5e033da9c6fb5ba83c7a7ebea9-Paper.pdf},
volume = {24},
year = {2011}
}
```
### Contributions
Thanks to [@thomasw21](https://github.com/thomasw21) for adding this dataset | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
togethercomputer/llama-instruct | togethercomputer | 2023-08-18T05:04:06Z | 230 | 20 | null | [
"language:en",
"license:llama2",
"arxiv:2304.12244",
"region:us"
] | 2023-08-18T05:04:06Z | 2023-08-03T04:52:19.000Z | 2023-08-03T04:52:19 | ---
license: llama2
language:
- en
---
# llama-instruct
This dataset was used to finetune [Llama-2-7B-32K-Instruct](https://huggingface.co/togethercomputer/Llama-2-7B-32K-Instruct).
We follow the distillation paradigm that is used by [Alpaca](https://crfm.stanford.edu/2023/03/13/alpaca.html), [Vicuna](https://lmsys.org/blog/2023-03-30-vicuna/), [WizardLM](https://arxiv.org/abs/2304.12244), [Orca](https://www.microsoft.com/en-us/research/publication/orca-progressive-learning-from-complex-explanation-traces-of-gpt-4/)
— producing instructions by querying a powerful LLM, which in our case, is the [Llama-2-70B-Chat](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf) model released by [Meta](https://ai.meta.com/llama/).
To build [Llama-2-7B-32K-Instruct](https://huggingface.co/togethercomputer/Llama-2-7B-32K-Instruct), we collect instructions from 19K human inputs extracted from [ShareGPT-90K](https://huggingface.co/datasets/philschmid/sharegpt-raw) (only using human inputs, not ChatGPT outputs).
The actual script handles multi-turn conversations and also supports restarting and caching via a SQLite3 database.
You can find the full script [here](https://github.com/togethercomputer/Llama-2-7B-32K-Instruct/blob/main/scripts/distill.py), with merely 122 lines!
The output of this step is a jsonl file, each line corresponding to one conversation:
```
{"text": "[INST] ... instruction ... [/INST] ... answer ... [INST] ... instruction ... [/INST] ..."}
{"text": "[INST] ... instruction ... [/INST] ... answer ... [INST] ... instruction ... [/INST] ..."}
{"text": "[INST] ... instruction ... [/INST] ... answer ... [INST] ... instruction ... [/INST] ..."}
```
For more details, please refer to the [Github repo](https://github.com/togethercomputer/Llama-2-7B-32K-Instruct).
## Languages
The language of the data is entirely English. | [
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findzebra/corpus.latest.vod-retriever-medical-v1.1 | findzebra | 2023-09-05T05:17:16Z | 230 | 0 | null | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
rizerphe/glaive-function-calling-v2-zephyr | rizerphe | 2023-10-17T16:36:29Z | 230 | 10 | null | [
"task_categories:text-generation",
"task_categories:conversational",
"size_categories:100K<n<1M",
"language:en",
"license:cc-by-sa-4.0",
"region:us"
] | 2023-10-17T16:36:29Z | 2023-10-17T08:28:47.000Z | 2023-10-17T08:28:47 | ---
license: cc-by-sa-4.0
task_categories:
- text-generation
- conversational
language:
- en
size_categories:
- 100K<n<1M
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 225637684
num_examples: 101469
download_size: 94820543
dataset_size: 225637684
---
# Glaive's Function Calling V2 for Zephyr-7B-alpha
[Glaive's Function Calling V2 dataset](https://huggingface.co/datasets/glaiveai/glaive-function-calling-v2), formatted according to the chat schema zephyr uses, with all the data that I wasn't able to automatically convert removed.
Adds three new roles: `definition`, `function` and `call`. Here's an example prompt:
```
<|definition|>
{
"name": "generate_password",
"description": "Generate a random password with specified criteria",
"parameters": {
"type": "object",
"properties": {
"length": {
"type": "integer",
"description": "The length of the password"
},
"include_numbers": {
"type": "boolean",
"description": "Include numbers in the password"
},
"include_special_characters": {
"type": "boolean",
"description": "Include special characters in the password"
}
},
"required": [
"length"
]
}
}</s>
<|user|>
I need a new password. Can you generate one for me?</s>
<|assistant|>
Of course! How long would you like your password to be? And do you want it to include numbers and special characters?</s>
<|user|>
I want it to be 12 characters long and yes, it should include both numbers and special characters.</s>
<|function|>
{
"length": 12,
"include_numbers": true,
"include_special_characters": true
}</s>
<|function|>
{"password": "4#7gB6&9L1!0"}</s>
<|assistant|>
Here is your new password: 4#7gB6&9L1!0. Please make sure to save it in a secure place.</s>
``` | [
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nightingal3/fig-qa | nightingal3 | 2023-06-10T18:13:33Z | 229 | 2 | null | [
"task_categories:multiple-choice",
"task_ids:multiple-choice-qa",
"annotations_creators:expert-generated",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:mit",
"arxiv:2204.12632",
"region:us"
] | 2023-06-10T18:13:33Z | 2022-06-16T18:35:21.000Z | 2022-06-16T18:35:21 | ---
annotations_creators:
- expert-generated
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- mit
multilinguality:
- monolingual
pretty_name: Fig-QA
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- multiple-choice
task_ids:
- multiple-choice-qa
---
# Dataset Card for Fig-QA
## 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 Splits](#data-splits)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Discussion of Biases](#discussion-of-biases)
- [Additional Information](#additional-information)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
- **Repository:** https://github.com/nightingal3/Fig-QA
- **Paper:** https://arxiv.org/abs/2204.12632
- **Leaderboard:** https://explainaboard.inspiredco.ai/leaderboards?dataset=fig_qa
- **Point of Contact:** emmy@cmu.edu
### Dataset Summary
This is the dataset for the paper [Testing the Ability of Language Models to Interpret Figurative Language](https://arxiv.org/abs/2204.12632). Fig-QA consists of 10256 examples of human-written creative metaphors that are paired as a Winograd schema. It can be used to evaluate the commonsense reasoning of models. The metaphors themselves can also be used as training data for other tasks, such as metaphor detection or generation.
### Supported Tasks and Leaderboards
You can evaluate your models on the test set by submitting to the [leaderboard](https://explainaboard.inspiredco.ai/leaderboards?dataset=fig_qa) on Explainaboard. Click on "New" and select `qa-multiple-choice` for the task field. Select `accuracy` for the metric. You should upload results in the form of a system output file in JSON or JSONL format.
### Languages
This is the English version. Multilingual version can be found [here](https://huggingface.co/datasets/cmu-lti/multi-figqa).
### Data Splits
Train-{S, M(no suffix), XL}: different training set sizes
Dev
Test (labels not provided for test set)
## Considerations for Using the Data
### Discussion of Biases
These metaphors are human-generated and may contain insults or other explicit content. Authors of the paper manually removed offensive content, but users should keep in mind that some potentially offensive content may remain in the dataset.
## Additional Information
### Licensing Information
MIT License
### Citation Information
If you found the dataset useful, please cite this paper:
@misc{https://doi.org/10.48550/arxiv.2204.12632,
doi = {10.48550/ARXIV.2204.12632},
url = {https://arxiv.org/abs/2204.12632},
author = {Liu, Emmy and Cui, Chen and Zheng, Kenneth and Neubig, Graham},
keywords = {Computation and Language (cs.CL), Artificial Intelligence (cs.AI), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Testing the Ability of Language Models to Interpret Figurative Language},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution Share Alike 4.0 International}
}
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SJ1999/datatransformer1 | SJ1999 | 2023-11-24T03:33:19Z | 229 | 0 | null | [
"region:us"
] | 2023-11-24T03:33:19Z | 2023-11-22T01:16:09.000Z | 2023-11-22T01:16:09 | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 19245
num_examples: 5
download_size: 19956
dataset_size: 19245
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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bond005/sberdevices_golos_10h_crowd | bond005 | 2022-10-27T04:42:07Z | 228 | 1 | golos | [
"task_categories:automatic-speech-recognition",
"task_categories:audio-classification",
"annotations_creators:expert-generated",
"language_creators:crowdsourced",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100k",
"source_datasets:extended",
"language:ru",
"license:other",
"arxiv:2106.10161",
"region:us"
] | 2022-10-27T04:42:07Z | 2022-10-26T11:12:15.000Z | 2022-10-26T11:12:15 | ---
pretty_name: Golos
annotations_creators:
- expert-generated
language_creators:
- crowdsourced
- expert-generated
language:
- ru
license:
- other
multilinguality:
- monolingual
paperswithcode_id: golos
size_categories:
- 10K<n<100k
source_datasets:
- extended
task_categories:
- automatic-speech-recognition
- audio-classification
---
# Dataset Card for sberdevices_golos_10h_crowd
## 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:** [Golos ASR corpus](https://www.openslr.org/114)
- **Repository:** [Golos dataset](https://github.com/sberdevices/golos)
- **Paper:** [Golos: Russian Dataset for Speech Research](https://arxiv.org/pdf/2106.10161.pdf)
- **Leaderboard:** [The 🤗 Speech Bench](https://huggingface.co/spaces/huggingface/hf-speech-bench)
- **Point of Contact:** [Nikolay Karpov](mailto:karpnv@gmail.com)
### Dataset Summary
Sberdevices Golos is a corpus of approximately 1200 hours of 16kHz Russian speech from crowd (reading speech) and farfield (communication with smart devices) domains, prepared by SberDevices Team (Alexander Denisenko, Angelina Kovalenko, Fedor Minkin, and Nikolay Karpov). The data is derived from the crowd-sourcing platform, and has been manually annotated.
Authors divide all dataset into train and test subsets. The training subset includes approximately 1000 hours. For experiments with a limited number of records, authors identified training subsets of shorter length: 100 hours, 10 hours, 1 hour, 10 minutes.
This dataset is a simpler version of the above mentioned Golos:
- it includes the crowd domain only (without any sound from the farfield domain);
- validation split is built on the 1-hour training subset;
- training split corresponds to the 10-hour training subset without sounds from the 1-hour training subset;
- test split is a full original test split.
### Supported Tasks and Leaderboards
- `automatic-speech-recognition`: 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 Hugging Face leaderboard which can be found at https://huggingface.co/spaces/huggingface/hf-speech-bench. The leaderboard ranks models uploaded to the Hub based on their WER.
### Languages
The audio is in Russian.
## Dataset Structure
### Data Instances
A typical data point comprises the audio data, usually called `audio` and its transcription, called `transcription`. Any additional information about the speaker and the passage which contains the transcription is not provided.
```
{'audio': {'path': None,
'array': array([ 3.05175781e-05, 3.05175781e-05, 0.00000000e+00, ...,
-1.09863281e-03, -7.93457031e-04, -1.52587891e-04]), dtype=float64),
'sampling_rate': 16000},
'transcription': 'шестнадцатая часть сезона пять сериала лемони сникет тридцать три несчастья'}
```
### Data Fields
- 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]`.
- transcription: the transcription of the audio file.
### Data Splits
This dataset is a simpler version of the original Golos:
- it includes the crowd domain only (without any sound from the farfield domain);
- validation split is built on the 1-hour training subset;
- training split corresponds to the 10-hour training subset without sounds from the 1-hour training subset;
- test split is a full original test split.
| | Train | Validation | Test |
| ----- | ------ | ---------- | ----- |
| examples | 7993 | 793 | 9994 |
| hours | 8.9h | 0.9h | 11.2h |
## 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
All recorded audio files were manually annotated on the crowd-sourcing platform.
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
The dataset consists of people who have donated their voice. 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
The dataset was initially created by Alexander Denisenko, Angelina Kovalenko, Fedor Minkin, and Nikolay Karpov.
### Licensing Information
[Public license with attribution and conditions reserved](https://github.com/sberdevices/golos/blob/master/license/en_us.pdf)
### Citation Information
```
@misc{karpov2021golos,
author = {Karpov, Nikolay and Denisenko, Alexander and Minkin, Fedor},
title = {Golos: Russian Dataset for Speech Research},
publisher = {arXiv},
year = {2021},
url = {https://arxiv.org/abs/2106.10161}
}
```
### Contributions
Thanks to [@bond005](https://github.com/bond005) for adding this dataset.
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
indonlp/NusaX-MT | indonlp | 2023-01-24T17:21:03Z | 228 | 5 | null | [
"task_categories:translation",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:multilingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:ace",
"language:ban",
"language:bjn",
"language:bug",
"language:en",
"language:id",
"language:jv",
"language:mad",
"language:min",
"language:nij",
"language:su",
"language:bbc",
"license:cc-by-sa-4.0",
"arxiv:2205.15960",
"region:us"
] | 2023-01-24T17:21:03Z | 2023-01-24T17:05:31.000Z | 2023-01-24T17:05:31 | ---
pretty_name: NusaX-MT
annotations_creators:
- expert-generated
language_creators:
- expert-generated
license:
- cc-by-sa-4.0
multilinguality:
- multilingual
language:
- ace
- ban
- bjn
- bug
- en
- id
- jv
- mad
- min
- nij
- su
- bbc
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- translation
dataset_info:
features:
- name: id
dtype: string
- name: text_1
dtype: string
- name: text_2
dtype: string
- name: text_1_lang
dtype: string
- name: text_2_lang
dtype: string
---
# Dataset Card for NusaX-MT
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [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)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Repository:** [GitHub](https://github.com/IndoNLP/nusax/tree/main/datasets/mt)
- **Paper:** [EACL 2022](https://arxiv.org/abs/2205.15960)
- **Point of Contact:** [GitHub](https://github.com/IndoNLP/nusax/tree/main/datasets/mt)
### Dataset Summary
NusaX is a high-quality multilingual parallel corpus that covers 12 languages, Indonesian, English, and 10 Indonesian local languages, namely Acehnese, Balinese, Banjarese, Buginese, Madurese, Minangkabau, Javanese, Ngaju, Sundanese, and Toba Batak.
NusaX-MT is a parallel corpus for training and benchmarking machine translation models across 10 Indonesian local languages + Indonesian and English. The data is presented in csv format with 12 columns, one column for each language.
### Supported Tasks and Leaderboards
- Machine translation for Indonesian languages
### Languages
All possible pairs of the following:
- ace: acehnese,
- ban: balinese,
- bjn: banjarese,
- bug: buginese,
- eng: english,
- ind: indonesian,
- jav: javanese,
- mad: madurese,
- min: minangkabau,
- nij: ngaju,
- sun: sundanese,
- bbc: toba_batak,
## Dataset Creation
### Curation Rationale
There is a shortage of NLP research and resources for the Indonesian languages, despite the country having over 700 languages. With this in mind, we have created this dataset to support future research for the underrepresented languages in Indonesia.
### Source Data
#### Initial Data Collection and Normalization
NusaX-MT is a dataset for machine translation in Indonesian langauges that has been expertly translated by native speakers.
#### Who are the source language producers?
The data was produced by humans (native speakers).
### Annotations
#### Annotation process
NusaX-MT is derived from SmSA, which is the biggest publicly available dataset for Indonesian sentiment analysis. It comprises of comments and reviews from multiple online platforms. To ensure the quality of our dataset, we have filtered it by removing any abusive language and personally identifying information by manually reviewing all sentences. To ensure balance in the label distribution, we randomly picked 1,000 samples through stratified sampling and then translated them to the corresponding languages.
#### Who are the annotators?
Native speakers of both Indonesian and the corresponding languages.
Annotators were compensated based on the number of translated samples.
### Personal and Sensitive Information
Personal information is removed.
## 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
NusaX is created from review text. These data sources may contain some bias.
### Other Known Limitations
No other known limitations
## Additional Information
### Licensing Information
CC-BY-SA 4.0.
Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
ShareAlike — If you remix, transform, or build upon the material, you must distribute your contributions under the same license as the original.
No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
Please contact authors for any information on the dataset.
### Citation Information
```
@misc{winata2022nusax,
title={NusaX: Multilingual Parallel Sentiment Dataset for 10 Indonesian Local Languages},
author={Winata, Genta Indra and Aji, Alham Fikri and Cahyawijaya,
Samuel and Mahendra, Rahmad and Koto, Fajri and Romadhony,
Ade and Kurniawan, Kemal and Moeljadi, David and Prasojo,
Radityo Eko and Fung, Pascale and Baldwin, Timothy and Lau,
Jey Han and Sennrich, Rico and Ruder, Sebastian},
year={2022},
eprint={2205.15960},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
### Contributions
Thanks to [@afaji](https://github.com/afaji) for adding this dataset.
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ajaykarthick/imdb-movie-reviews | ajaykarthick | 2023-02-08T21:08:35Z | 228 | 3 | null | [
"task_categories:text-classification",
"task_categories:token-classification",
"task_categories:feature-extraction",
"size_categories:10K<n<100K",
"region:us"
] | 2023-02-08T21:08:35Z | 2023-02-08T18:30:11.000Z | 2023-02-08T18:30:11 | ---
task_categories:
- text-classification
- token-classification
- feature-extraction
pretty_name: Movie-Reviews
size_categories:
- 10K<n<100K
---
# IMDB Movie Reviews
This is a dataset for binary sentiment classification containing substantially huge data. This dataset contains a set of 50,000 highly polar movie reviews for training models for text classification tasks.
The dataset is downloaded from
https://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz
This data is processed and splitted into training and test datasets (0.2% test split). Training dataset contains 40000 reviews and test dataset contains 10000 reviews.
Equal distribution among the labels in both training and test dataset. in training dataset, there are 20000 records for both positive and negative classes. In test dataset, there are 5000 records both the labels.
### Citation Information
```
@InProceedings{maas-EtAl:2011:ACL-HLT2011,
author = {Maas, Andrew L. and Daly, Raymond E. and Pham, Peter T. and Huang, Dan and Ng, Andrew Y. and Potts, Christopher},
title = {Learning Word Vectors for Sentiment Analysis},
booktitle = {Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies},
month = {June},
year = {2011},
address = {Portland, Oregon, USA},
publisher = {Association for Computational Linguistics},
pages = {142--150},
url = {http://www.aclweb.org/anthology/P11-1015}
}
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
Sunbird/Experimental-Speech-Salt-Ateso-16k | Sunbird | 2023-06-19T10:39:18Z | 228 | 0 | null | [
"region:us"
] | 2023-06-19T10:39:18Z | 2023-06-17T04:05:30.000Z | 2023-06-17T04:05:30 | ---
dataset_info:
features:
- name: audio
sequence:
sequence: float32
- name: sample_rate
dtype: int64
- name: transcription
dtype: string
- name: speaker_id
dtype: string
splits:
- name: train
num_bytes: 1726534428
num_examples: 4211
- name: validation
num_bytes: 96950913
num_examples: 231
- name: test
num_bytes: 105595730
num_examples: 250
download_size: 931410865
dataset_size: 1929081071
---
# Dataset Card for "Experimental-Speech-Salt-Ateso-16k"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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nisaar/Articles_Constitution_3300_Instruction_Set | nisaar | 2023-07-18T07:25:46Z | 228 | 2 | null | [
"license:apache-2.0",
"region:us"
] | 2023-07-18T07:25:46Z | 2023-07-18T05:22:47.000Z | 2023-07-18T05:22:47 | ---
license: apache-2.0
---
**Dataset Card for Indian Constitutional Law Instruction-Response Dataset**
---
**Dataset Summary**
The dataset contains instruction-input-output pairs on Indian Constitutional Law, specifically addressing Articles 12, 14, 19, 21, and 15. It's designed to assist AI models, researchers, and learners in understanding and generating responses to complex legal questions related to the Indian Constitution.
---
**Supported Tasks**
This dataset supports tasks such as question answering, text comprehension, language modelling, and conversational AI development in the legal domain.
---
**Languages**
The dataset is in English.
---
**Dataset Structure**
- **Data Instances**
Each instance includes an instruction, an input (a legal case), an output (the response), and a prompt that contextualizes the task.
- **Data Fields**
1. Instruction: The given instruction.
2. Input: The legal case.
3. Output: The response.
4. Prompt: The context for the instruction, input, and output.
---
**Dataset Creation**
- **Curation Rationale**
The dataset aids in understanding and answering complex questions related to Indian Constitutional Law and the specified articles.
**Considerations for Using the Data**
- **Social Impact**
The dataset contributes to understanding certain articles of the Indian Constitution and assists in legal domain applications.
- **Known Limitations**
The dataset may not cover all possible questions on Indian Constitutional Law and is limited to English language.
---
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
C-MTEB/MultilingualSentiment-classification | C-MTEB | 2023-07-28T13:29:38Z | 228 | 0 | null | [
"region:us"
] | 2023-07-28T13:29:38Z | 2023-07-28T13:29:08.000Z | 2023-07-28T13:29:08 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
dataset_info:
features:
- name: text
dtype: string
- name: label
dtype:
class_label:
names:
'0': positive
'1': neutral
'2': negative
splits:
- name: train
num_bytes: 19594086
num_examples: 120000
- name: validation
num_bytes: 483785
num_examples: 3000
- name: test
num_bytes: 491522
num_examples: 3000
download_size: 14087126
dataset_size: 20569393
---
# Dataset Card for "MultilingualSentiment-classification"
[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 | |
jarod0411/PubChem10M_Canonicalized_SELFIES | jarod0411 | 2023-11-09T04:31:54Z | 228 | 0 | null | [
"region:us"
] | 2023-11-09T04:31:54Z | 2023-11-08T21:40:18.000Z | 2023-11-08T21:40:18 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
dataset_info:
features:
- name: smiles
dtype: string
- name: selfies
dtype: string
splits:
- name: train
num_bytes: 2238384519.927304
num_examples: 8999273
- name: validation
num_bytes: 248620852.6737807
num_examples: 999913
download_size: 0
dataset_size: 2487005372.6010847
---
# Dataset Card for "PubChem10M_Canonicalized_SELFIES"
[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/f4e64da0 | result-kand2-sdxl-wuerst-karlo | 2023-11-14T23:50:06Z | 227 | 0 | null | [
"region:us"
] | 2023-11-14T23:50:06Z | 2023-11-14T23:50:05.000Z | 2023-11-14T23:50:05 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 171
num_examples: 10
download_size: 1333
dataset_size: 171
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "f4e64da0"
[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 | |
open-source-metrics/issues | open-source-metrics | 2023-11-22T16:30:37Z | 226 | 0 | null | [
"region:us"
] | 2023-11-22T16:30:37Z | 2022-09-23T18:41:08.000Z | 2022-09-23T18:41:08 | ---
dataset_info:
features:
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dtype: string
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struct:
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dtype: string
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configs:
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path: data/transformers-*
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path: data/safetensors-*
---
# Dataset Card for "issues"
[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 | |
smangrul/amazon_esci | smangrul | 2023-06-28T09:38:19Z | 226 | 3 | null | [
"license:apache-2.0",
"region:us"
] | 2023-06-28T09:38:19Z | 2023-06-28T09:34:18.000Z | 2023-06-28T09:34:18 | ---
license: apache-2.0
---
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
PKU-Alignment/processed-hh-rlhf | PKU-Alignment | 2023-11-24T13:45:12Z | 226 | 5 | null | [
"task_categories:conversational",
"size_categories:100K<n<1M",
"language:en",
"license:mit",
"rlhf",
"harmless",
"helpful",
"human-preference",
"region:us"
] | 2023-11-24T13:45:12Z | 2023-07-15T09:57:18.000Z | 2023-07-15T09:57:18 | ---
license: mit
task_categories:
- conversational
language:
- en
tags:
- rlhf
- harmless
- helpful
- human-preference
pretty_name: hh-rlhf
size_categories:
- 100K<n<1M
---
# Dataset Card for Processed-Hh-RLHF
This is a dataset that processes [hh-rlhf](https://huggingface.co/datasets/Anthropic/hh-rlhf) into an easy-to-use conversational and human-preference form.
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
C-MTEB/waimai-classification | C-MTEB | 2023-07-28T12:08:38Z | 226 | 1 | null | [
"region:us"
] | 2023-07-28T12:08:38Z | 2023-07-28T12:08:28.000Z | 2023-07-28T12:08:28 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: label
dtype: int64
- name: text
dtype: string
splits:
- name: train
num_bytes: 682948.5275715358
num_examples: 8000
- name: test
num_bytes: 85368.56594644197
num_examples: 1000
download_size: 499472
dataset_size: 768317.0935179777
---
# Dataset Card for "waimai-classification"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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cfilt/HiNER-original | cfilt | 2023-03-07T16:42:05Z | 225 | 2 | hiner-original-1 | [
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:hi",
"license:cc-by-sa-4.0",
"arxiv:2204.13743",
"region:us"
] | 2023-03-07T16:42:05Z | 2022-04-25T13:55:19.000Z | 2022-04-25T13:55:19 | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- hi
license: "cc-by-sa-4.0"
multilinguality:
- monolingual
paperswithcode_id: hiner-original-1
pretty_name: HiNER - Large Hindi Named Entity Recognition dataset
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- token-classification
task_ids:
- named-entity-recognition
---
<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>
# Dataset Card for HiNER-original
[](https://twitter.com/cfiltnlp)
[](https://twitter.com/PeopleCentredAI)
## 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://github.com/cfiltnlp/HiNER
- **Repository:** https://github.com/cfiltnlp/HiNER
- **Paper:** https://arxiv.org/abs/2204.13743
- **Leaderboard:** https://paperswithcode.com/sota/named-entity-recognition-on-hiner-original
- **Point of Contact:** Rudra Murthy V
### Dataset Summary
This dataset was created for the fundamental NLP task of Named Entity Recognition for the Hindi language at CFILT Lab, IIT Bombay. We gathered the dataset from various government information webpages and manually annotated these sentences as a part of our data collection strategy.
**Note:** The dataset contains sentences from ILCI and other sources. ILCI dataset requires license from Indian Language Consortium due to which we do not distribute the ILCI portion of the data. Please send us a mail with proof of ILCI data acquisition to obtain the full dataset.
### Supported Tasks and Leaderboards
Named Entity Recognition
### Languages
Hindi
## Dataset Structure
### Data Instances
{'id': '0', 'tokens': ['प्राचीन', 'समय', 'में', 'उड़ीसा', 'को', 'कलिंग','के', 'नाम', 'से', 'जाना', 'जाता', 'था', '।'], 'ner_tags': [0, 0, 0, 3, 0, 3, 0, 0, 0, 0, 0, 0, 0]}
### Data Fields
- `id`: The ID value of the data point.
- `tokens`: Raw tokens in the dataset.
- `ner_tags`: the NER tags for this dataset.
### Data Splits
| | Train | Valid | Test |
| ----- | ------ | ----- | ---- |
| original | 76025 | 10861 | 21722|
| collapsed | 76025 | 10861 | 21722|
## About
This repository contains the Hindi Named Entity Recognition dataset (HiNER) published at the Langauge Resources and Evaluation conference (LREC) in 2022. A pre-print via arXiv is available [here](https://arxiv.org/abs/2204.13743).
### Recent Updates
* Version 0.0.5: HiNER initial release
## Usage
You should have the 'datasets' packages installed to be able to use the :rocket: HuggingFace datasets repository. Please use the following command and install via pip:
```code
pip install datasets
```
To use the original dataset with all the tags, please use:<br/>
```python
from datasets import load_dataset
hiner = load_dataset('cfilt/HiNER-original')
```
To use the collapsed dataset with only PER, LOC, and ORG tags, please use:<br/>
```python
from datasets import load_dataset
hiner = load_dataset('cfilt/HiNER-collapsed')
```
However, the CoNLL format dataset files can also be found on this Git repository under the [data](data/) folder.
## Model(s)
Our best performing models are hosted on the HuggingFace models repository:
1. [HiNER-Collapsed-XLM-R](https://huggingface.co/cfilt/HiNER-Collapse-XLM-Roberta-Large)
2. [HiNER-Original-XLM-R](https://huggingface.co/cfilt/HiNER-Original-XLM-Roberta-Large)
## Dataset Creation
### Curation Rationale
HiNER was built on data extracted from various government websites handled by the Government of India which provide information in Hindi. This dataset was built for the task of Named Entity Recognition. The dataset was introduced to introduce new resources to the Hindi language that was under-served for Natural Language Processing.
### Source Data
#### Initial Data Collection and Normalization
HiNER was built on data extracted from various government websites handled by the Government of India which provide information in Hindi
#### Who are the source language producers?
Various Government of India webpages
### Annotations
#### Annotation process
This dataset was manually annotated by a single annotator of a long span of time.
#### Who are the annotators?
Pallab Bhattacharjee
### Personal and Sensitive Information
We ensured that there was no sensitive information present in the dataset. All the data points are curated from publicly available information.
## Considerations for Using the Data
### Social Impact of Dataset
The purpose of this dataset is to provide a large Hindi Named Entity Recognition dataset. Since the information (data points) has been obtained from public resources, we do not think there is a negative social impact in releasing this data.
### Discussion of Biases
Any biases contained in the data released by the Indian government are bound to be present in our data.
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
Pallab Bhattacharjee
### Licensing Information
CC-BY-SA 4.0
### Citation Information
```latex
@misc{https://doi.org/10.48550/arxiv.2204.13743,
doi = {10.48550/ARXIV.2204.13743},
url = {https://arxiv.org/abs/2204.13743},
author = {Murthy, Rudra and Bhattacharjee, Pallab and Sharnagat, Rahul and Khatri, Jyotsana and Kanojia, Diptesh and Bhattacharyya, Pushpak},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {HiNER: A Large Hindi Named Entity Recognition Dataset},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
``` | [
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bigbio/chemdner | bigbio | 2022-12-22T15:44:21Z | 225 | 1 | null | [
"multilinguality:monolingual",
"language:en",
"license:unknown",
"region:us"
] | 2022-12-22T15:44:21Z | 2022-11-13T22:07:46.000Z | 2022-11-13T22:07:46 |
---
language:
- en
bigbio_language:
- English
license: unknown
multilinguality: monolingual
bigbio_license_shortname: UNKNOWN
pretty_name: CHEMDNER
homepage: https://biocreative.bioinformatics.udel.edu/resources/biocreative-iv/chemdner-corpus/
bigbio_pubmed: True
bigbio_public: True
bigbio_tasks:
- NAMED_ENTITY_RECOGNITION
- TEXT_CLASSIFICATION
---
# Dataset Card for CHEMDNER
## Dataset Description
- **Homepage:** https://biocreative.bioinformatics.udel.edu/resources/biocreative-iv/chemdner-corpus/
- **Pubmed:** True
- **Public:** True
- **Tasks:** NER,TXTCLASS
We present the CHEMDNER corpus, a collection of 10,000 PubMed abstracts that
contain a total of 84,355 chemical entity mentions labeled manually by expert
chemistry literature curators, following annotation guidelines specifically
defined for this task. The abstracts of the CHEMDNER corpus were selected to be
representative for all major chemical disciplines. Each of the chemical entity
mentions was manually labeled according to its structure-associated chemical
entity mention (SACEM) class: abbreviation, family, formula, identifier,
multiple, systematic and trivial.
## Citation Information
```
@article{Krallinger2015,
title = {The CHEMDNER corpus of chemicals and drugs and its annotation principles},
author = {
Krallinger, Martin and Rabal, Obdulia and Leitner, Florian and Vazquez,
Miguel and Salgado, David and Lu, Zhiyong and Leaman, Robert and Lu, Yanan
and Ji, Donghong and Lowe, Daniel M. and Sayle, Roger A. and
Batista-Navarro, Riza Theresa and Rak, Rafal and Huber, Torsten and
Rockt{"a}schel, Tim and Matos, S{'e}rgio and Campos, David and Tang,
Buzhou and Xu, Hua and Munkhdalai, Tsendsuren and Ryu, Keun Ho and Ramanan,
S. V. and Nathan, Senthil and { {Z}}itnik, Slavko and Bajec, Marko and
Weber, Lutz and Irmer, Matthias and Akhondi, Saber A. and Kors, Jan A. and
Xu, Shuo and An, Xin and Sikdar, Utpal Kumar and Ekbal, Asif and Yoshioka,
Masaharu and Dieb, Thaer M. and Choi, Miji and Verspoor, Karin and Khabsa,
Madian and Giles, C. Lee and Liu, Hongfang and Ravikumar, Komandur
Elayavilli and Lamurias, Andre and Couto, Francisco M. and Dai, Hong-Jie
and Tsai, Richard Tzong-Han and Ata, Caglar and Can, Tolga and Usi{'e},
Anabel and Alves, Rui and Segura-Bedmar, Isabel and Mart{'i}nez, Paloma
and Oyarzabal, Julen and Valencia, Alfonso
},
year = 2015,
month = {Jan},
day = 19,
journal = {Journal of Cheminformatics},
volume = 7,
number = 1,
pages = {S2},
doi = {10.1186/1758-2946-7-S1-S2},
issn = {1758-2946},
url = {https://doi.org/10.1186/1758-2946-7-S1-S2},
abstract = {
The automatic extraction of chemical information from text requires the
recognition of chemical entity mentions as one of its key steps. When
developing supervised named entity recognition (NER) systems, the
availability of a large, manually annotated text corpus is desirable.
Furthermore, large corpora permit the robust evaluation and comparison of
different approaches that detect chemicals in documents. We present the
CHEMDNER corpus, a collection of 10,000 PubMed abstracts that contain a
total of 84,355 chemical entity mentions labeled manually by expert
chemistry literature curators, following annotation guidelines specifically
defined for this task. The abstracts of the CHEMDNER corpus were selected
to be representative for all major chemical disciplines. Each of the
chemical entity mentions was manually labeled according to its
structure-associated chemical entity mention (SACEM) class: abbreviation,
family, formula, identifier, multiple, systematic and trivial. The
difficulty and consistency of tagging chemicals in text was measured using
an agreement study between annotators, obtaining a percentage agreement of
91. For a subset of the CHEMDNER corpus (the test set of 3,000 abstracts)
we provide not only the Gold Standard manual annotations, but also mentions
automatically detected by the 26 teams that participated in the BioCreative
IV CHEMDNER chemical mention recognition task. In addition, we release the
CHEMDNER silver standard corpus of automatically extracted mentions from
17,000 randomly selected PubMed abstracts. A version of the CHEMDNER corpus
in the BioC format has been generated as well. We propose a standard for
required minimum information about entity annotations for the construction
of domain specific corpora on chemical and drug entities. The CHEMDNER
corpus and annotation guidelines are available at:
ttp://www.biocreative.org/resources/biocreative-iv/chemdner-corpus/
}
}
```
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
lukemann/baby-agi-dataset-v1 | lukemann | 2023-11-28T21:15:38Z | 225 | 0 | null | [
"region:us"
] | 2023-11-28T21:15:38Z | 2023-11-22T00:41:13.000Z | 2023-11-22T00:41:13 | # BabyAGI (Dataset)
The initial demonstration dataset follows the Huggingface dataset spec, with the raw data split into two components, trajectory images and trajectory metadata. The metadata is stored in the raw dataset, and the images are stored on S3. The data is loaded using the dataloader defined in [baby_agi_dataset.py](./baby_agi_dataset.py).
**Data Layout:**
```plaintext
├── data
│ ├── metadata_0.json
│ ├── metadata_1.json
│ └── ...
├-- baby_agi_dataset.py
```
### Metadata Format (.json)
```json
[
{
"id": "<trajectory_id_hash>",
"instruction": "<some instruction>",
"trajectory": [
{
"image_id": "image_id",
"image_size": [10, 10],
"steps": [
{
"action_options": [
{
"index": 0,
"top_left": [120, 340],
"bottom_right": [140, 440]
}
],
"action_taken": {
"type": "click",
"value": "value (only for type and scroll)",
"action_option_index": 0
}
}
]
}
]
}
]
```
## Action Types
The dataset metadata includes three types of actions: "click", "type", and "scroll". The `action_option_index` field indicates the index of the clicked element within the `action_options` list.
1. **Click**: Represents a user clicking on an element.
2. **Type**: Represents a user typing into an input field. action_option_index will always be -1 for these.
3. **Scroll**: Represents a user scrolling the viewport. The `value` field indicates the direction of the scroll, with "up" corresponding to a 200px scroll upwards and "down" corresponding to a 200px scroll downwards. Note that `bottom_left` and `top_right` will always be zero-arrays for these. action_option_index will always be -1 for these.
4. **Move Mouse**: Represents a user moving their mouse to the center of an element.
## Dataset Generation Pipeline
The dataset is generated through the following steps:
1. **Load Demo**: The demo is loaded from the Hugging Face dataset.
2. **Load Trace**: The trace is loaded from the Globus dataset.
3. **Process Trajectories**: For each Mind2Web (M2W) trajectory:
a) **Map Actions**: M2W actions are mapped to Playwright trace actions using the timestamp in `dom_content.json`.
b) **Screenshot DOM**: The DOM is "screenshoted" just before the action.
c) **Map Candidates**: `pos_candidates` and `neg_candidates` from the M2W action metadata are mapped to HTML bounding boxes via class+id matching from the action metadata. New bounding box coordinates are obtained for each.
d) **Craft Meta + Screenshot Pair**: The pair of metadata and screenshots is crafted and saved/appended.
4. **Save Data**: The updated data directory is saved to S3 and Hugging Face.
### Screenshots
Screenshots in this dataset are generated from the before states of Mind2Web trajectory traces. Each image has a width of 2036 and a height of 1144. For alternate screen sizes (via augmentation), padding is added to maintain the aspect ratio. This ensures that the content of the screenshot remains consistent across different screen sizes.
### Options Generation
Options in this dataset are generated from `positive_candidates` (always one) and `negative_candidates` in the Mind2Web (M2W) dataset. The M2W dataset labels _all_ possible interactions on the DOM. Therefore, the 50 largest area-wise options within the viewport containing the positive candidate are selected.
### Scrolling
The Mind2Web (M2W) dataset captures the entire DOM, so when the selected option action is not in the viewport, artificial scroll actions are created. This action has two possible values: "up" and "down". Each of which corresponds to a 200px scroll in the respective direction.
### Selecting
The "Select" action in the Mind2Web (M2W) dataset is recorded when a user makes a selection from a dropdown list. In this dataset, we represent it as a sequence of two distinct actions in a trajectory:
1. **Click**: The user clicks on the dropdown element.
2. **Type**: The user types the desired value followed by Enter
## Usage
To use the dataset in your Python program, you can load it using the `load_dataset` function from the `datasets` library:
```python
from datasets import load_dataset
# typically load_dataset("lukemann/baby-agi-dataset-v0"
dataset = load_dataset("lukemann/baby-agi-dataset-v0")
first_row = dataset['train'][0]
print(first_row)
```
This will load the dataset and print the first row of the training set.
For a short demo, refer to the [demo.py](./demo.py) file.
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
midas/kp20k | midas | 2023-09-25T05:14:59Z | 224 | 2 | null | [
"region:us"
] | 2023-09-25T05:14:59Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | A dataset for benchmarking keyphrase extraction and generation techniques from abstracts of English scientific papers. For more details about the dataset please refer the original paper - [http://memray.me/uploads/acl17-keyphrase-generation.pdf](http://memray.me/uploads/acl17-keyphrase-generation.pdf).
Data source - [https://github.com/memray/seq2seq-keyphrase](https://github.com/memray/seq2seq-keyphrase)
## Dataset Summary
## Dataset Structure
## Dataset Statistics
### Data Fields
- **id**: unique identifier of the document.
- **document**: Whitespace separated list of words in the document.
- **doc_bio_tags**: BIO tags for each word in the document. B stands for the beginning of a keyphrase and I stands for inside the keyphrase. O stands for outside the keyphrase and represents the word that isn't a part of the keyphrase at all.
- **extractive_keyphrases**: List of all the present keyphrases.
- **abstractive_keyphrase**: List of all the absent keyphrases.
### Data Splits
|Split| No. of datapoints |
|--|--|
| Train | 530,809 |
| Test | 20,000|
| Validation | 20,000|
## Usage
### Full Dataset
```python
from datasets import load_dataset
# get entire dataset
dataset = load_dataset("midas/kp20k", "raw")
# sample from the train split
print("Sample from training dataset split")
train_sample = dataset["train"][0]
print("Fields in the sample: ", [key for key in train_sample.keys()])
print("Tokenized Document: ", train_sample["document"])
print("Document BIO Tags: ", train_sample["doc_bio_tags"])
print("Extractive/present Keyphrases: ", train_sample["extractive_keyphrases"])
print("Abstractive/absent Keyphrases: ", train_sample["abstractive_keyphrases"])
print("\n-----------\n")
# sample from the validation split
print("Sample from validation dataset split")
validation_sample = dataset["validation"][0]
print("Fields in the sample: ", [key for key in validation_sample.keys()])
print("Tokenized Document: ", validation_sample["document"])
print("Document BIO Tags: ", validation_sample["doc_bio_tags"])
print("Extractive/present Keyphrases: ", validation_sample["extractive_keyphrases"])
print("Abstractive/absent Keyphrases: ", validation_sample["abstractive_keyphrases"])
print("\n-----------\n")
# sample from the test split
print("Sample from test dataset split")
test_sample = dataset["test"][0]
print("Fields in the sample: ", [key for key in test_sample.keys()])
print("Tokenized Document: ", test_sample["document"])
print("Document BIO Tags: ", test_sample["doc_bio_tags"])
print("Extractive/present Keyphrases: ", test_sample["extractive_keyphrases"])
print("Abstractive/absent Keyphrases: ", test_sample["abstractive_keyphrases"])
print("\n-----------\n")
```
**Output**
```bash
```
### Keyphrase Extraction
```python
from datasets import load_dataset
# get the dataset only for keyphrase extraction
dataset = load_dataset("midas/kp20k", "extraction")
print("Samples for Keyphrase Extraction")
# sample from the train split
print("Sample from training data split")
train_sample = dataset["train"][0]
print("Fields in the sample: ", [key for key in train_sample.keys()])
print("Tokenized Document: ", train_sample["document"])
print("Document BIO Tags: ", train_sample["doc_bio_tags"])
print("\n-----------\n")
# sample from the validation split
print("Sample from validation data split")
validation_sample = dataset["validation"][0]
print("Fields in the sample: ", [key for key in validation_sample.keys()])
print("Tokenized Document: ", validation_sample["document"])
print("Document BIO Tags: ", validation_sample["doc_bio_tags"])
print("\n-----------\n")
# sample from the test split
print("Sample from test data split")
test_sample = dataset["test"][0]
print("Fields in the sample: ", [key for key in test_sample.keys()])
print("Tokenized Document: ", test_sample["document"])
print("Document BIO Tags: ", test_sample["doc_bio_tags"])
print("\n-----------\n")
```
### Keyphrase Generation
```python
# get the dataset only for keyphrase generation
dataset = load_dataset("midas/kp20k", "generation")
print("Samples for Keyphrase Generation")
# sample from the train split
print("Sample from training data split")
train_sample = dataset["train"][0]
print("Fields in the sample: ", [key for key in train_sample.keys()])
print("Tokenized Document: ", train_sample["document"])
print("Extractive/present Keyphrases: ", train_sample["extractive_keyphrases"])
print("Abstractive/absent Keyphrases: ", train_sample["abstractive_keyphrases"])
print("\n-----------\n")
# sample from the validation split
print("Sample from validation data split")
validation_sample = dataset["validation"][0]
print("Fields in the sample: ", [key for key in validation_sample.keys()])
print("Tokenized Document: ", validation_sample["document"])
print("Extractive/present Keyphrases: ", validation_sample["extractive_keyphrases"])
print("Abstractive/absent Keyphrases: ", validation_sample["abstractive_keyphrases"])
print("\n-----------\n")
# sample from the test split
print("Sample from test data split")
test_sample = dataset["test"][0]
print("Fields in the sample: ", [key for key in test_sample.keys()])
print("Tokenized Document: ", test_sample["document"])
print("Extractive/present Keyphrases: ", test_sample["extractive_keyphrases"])
print("Abstractive/absent Keyphrases: ", test_sample["abstractive_keyphrases"])
print("\n-----------\n")
```
## Citation Information
Please cite the works below if you use this dataset in your work.
```
@InProceedings{meng-EtAl:2017:Long,
author = {Meng, Rui and Zhao, Sanqiang and Han, Shuguang and He, Daqing and Brusilovsky, Peter and Chi, Yu},
title = {Deep Keyphrase Generation},
booktitle = {Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
month = {July},
year = {2017},
address = {Vancouver, Canada},
publisher = {Association for Computational Linguistics},
pages = {582--592},
url = {http://aclweb.org/anthology/P17-1054}
}
@article{mahata2022ldkp,
title={LDKP: A Dataset for Identifying Keyphrases from Long Scientific Documents},
author={Mahata, Debanjan and Agarwal, Navneet and Gautam, Dibya and Kumar, Amardeep and Parekh, Swapnil and Singla, Yaman Kumar and Acharya, Anish and Shah, Rajiv Ratn},
journal={arXiv preprint arXiv:2203.15349},
year={2022}
}
```
## Contributions
Thanks to [@debanjanbhucs](https://github.com/debanjanbhucs), [@dibyaaaaax](https://github.com/dibyaaaaax), [@UmaGunturi](https://github.com/UmaGunturi) and [@ad6398](https://github.com/ad6398) for adding this dataset
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ArtifactAI/arxiv-cs-ml-instruct-tune-50k | ArtifactAI | 2023-06-21T13:45:31Z | 224 | 3 | arxiv-cs-ml-instruct-tune-50k | [
"task_categories:text-generation",
"task_ids:language-modeling",
"task_ids:masked-language-modeling",
"annotations_creators:no-annotation",
"multilinguality:monolingual",
"size_categories:1M<n<10M",
"source_datasets:original",
"language:en",
"license:cc0-1.0",
"doi:10.57967/hf/0800",
"region:us"
] | 2023-06-21T13:45:31Z | 2023-06-20T06:31:29.000Z | 2023-06-20T06:31:29 | ---
annotations_creators:
- no-annotation
language:
- en
license:
- cc0-1.0
multilinguality:
- monolingual
pretty_name: arxiv-cs-ml-instruct-tune-50k
size_categories:
- 1M<n<10M
source_datasets:
- original
task_categories:
- text-generation
task_ids:
- language-modeling
- masked-language-modeling
paperswithcode_id: arxiv-cs-ml-instruct-tune-50k
---
# Dataset Card for "arxiv-cs-ml-instruct-tune-50k"
### Dataset Summary
The "ArtifactAI/arxiv-cs-ml-instruct-tune-50k" dataset consists of question-answer pairs derived from ArXiv abstracts from the following categories:
"cs.CL", "cs.AI", "cs.CV", "cs.HC", "cs.IR", "cs.RO", "cs.NE", "stat.ML". Questions are generated using the [t5-base model](https://huggingface.co/t5-base), while the answers are generated using the [GPT-3.5-turbo model](https://openai.com/chatgpt).
### Languages
English
## Dataset Structure
### Data Instances
#### train
- **Size of downloaded dataset files:** 38.4 MB
An example of 'train' looks as follows.
```
{
"question": "What is multitask learning?",
"answer": "Multitask learning is a technique in machine learning that allows a single model to simultaneously learn multiple related tasks.
Instead of training separate models for each task, multitask learning leverages shared patterns across tasks to improve overall performance.
This can lead to faster training, better accuracy, and more efficient use of computational resources.
For example, a model might be trained to simultaneously predict the sentiment of customer reviews and categorize them by topic.
Both tasks require analyzing natural language, so the shared knowledge can help improve performance on both tasks."
}
```
### Data Fields
The data fields present in the dataset are as follows:
- question: a string feature representing the question.
- answer: a string feature representing the answer.
#### plain_text
- `text`: a `string` feature.
### Data Splits
train: 50,063 question answer pairs
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
Question-answer pairs derived from [ArXiv](https://arxiv.org/) abstracts.
#### Initial Data Collection and Normalization
The "ArtifactAI/arxiv-cs-ml-instruct-tune-50k" dataset consists of question-answer pairs derived from ArXiv abstracts. Questions are generated from ArXiv papers in the following categories:
- cs.CL
- cs.AI
- cs.CV
- cs.HC
- cs.IR
- cs.RO
- cs.NE
- stat.ML
Questions are generated using the [t5-base model](https://huggingface.co/t5-base), while the answers are generated using the [GPT-3.5-turbo model](https://openai.com/chatgpt).
### Annotations
The dataset doesn't contain annotations.
### Personal and Sensitive Information
None
#### Notice policy
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.
And contact us at the following email address: matt at artifactai.com and datasets at huggingface.co
#### Take down policy
The original authors will comply to legitimate requests by removing the affected sources from the next release of the corpus.
Hugging Face will also update this repository accordingly.
### Citation Information
```
@misc{arxiv-cs-ml-instruct-tune-50k,
title={arxiv-cs-ml-instruct-tune-50k},
author={Matthew Kenney},
year={2023}
}
```
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lighteval/drop_harness | lighteval | 2023-07-27T11:58:52Z | 224 | 0 | null | [
"region:us"
] | 2023-07-27T11:58:52Z | 2023-07-27T11:58:48.000Z | 2023-07-27T11:58:48 | ---
dataset_info:
features:
- name: section_id
dtype: string
- name: passage
dtype: string
- name: question
dtype: string
- name: query_id
dtype: string
- name: answer
struct:
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struct:
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sequence:
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struct:
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dtype: string
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sequence: string
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splits:
- name: train
num_bytes: 108858121
num_examples: 77409
- name: validation
num_bytes: 12560739
num_examples: 9536
download_size: 12003555
dataset_size: 121418860
---
# Dataset Card for "drop_harness"
[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 | |
atmallen/qm_grader_first_1.0e | atmallen | 2023-11-16T18:26:55Z | 224 | 0 | null | [
"region:us"
] | 2023-11-16T18:26:55Z | 2023-11-16T03:18:48.000Z | 2023-11-16T03:18:48 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
dataset_info:
features:
- name: alice_label
dtype: bool
- name: bob_label
dtype: bool
- name: difficulty
dtype: int64
- name: statement
dtype: string
- name: choices
sequence: string
- name: character
dtype: string
- name: label
dtype:
class_label:
names:
'0': 'False'
'1': 'True'
splits:
- name: train
num_bytes: 35940088
num_examples: 400000
- name: validation
num_bytes: 3602836
num_examples: 40000
- name: test
num_bytes: 3604340
num_examples: 40000
download_size: 0
dataset_size: 43147264
---
# Dataset Card for "qm_grader_first_1.0e"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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kuroneko5943/amz20 | kuroneko5943 | 2023-01-10T16:02:20Z | 223 | 0 | null | [
"task_categories:text-classification",
"task_ids:sentiment-classification",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:extended|amazon_us_reviews",
"language:en",
"license:apache-2.0",
"amazon",
"region:us"
] | 2023-01-10T16:02:20Z | 2023-01-10T12:02:41.000Z | 2023-01-10T12:02:41 | ---
annotations_creators:
- found
language:
- en
language_creators:
- found
license:
- apache-2.0
multilinguality:
- monolingual
pretty_name: amz20
size_categories:
- 1K<n<10K
source_datasets:
- extended|amazon_us_reviews
tags:
- amazon
task_categories:
- text-classification
task_ids:
- sentiment-classification
--- | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
PatrickHaller/wikitext-18-de | PatrickHaller | 2023-06-27T20:29:39Z | 223 | 0 | null | [
"task_categories:text-generation",
"size_categories:1K<n<10K",
"language:de",
"license:cc-by-sa-3.0",
"region:us"
] | 2023-06-27T20:29:39Z | 2023-06-27T20:07:00.000Z | 2023-06-27T20:07:00 | ---
dataset_info:
features:
- name: title
dtype: string
- name: text
dtype: string
- name: url
dtype: string
splits:
- name: train
num_bytes: 138186439
num_examples: 2759
download_size: 79585645
dataset_size: 138186439
license: cc-by-sa-3.0
task_categories:
- text-generation
language:
- de
pretty_name: wikitext german
size_categories:
- 1K<n<10K
---
# Dataset Card for "wikitext-18-de"
## Dataset Summary
The dataset is a german variation of the [wikitext](https://huggingface.co/datasets/wikitext) dataset and is a collection of
ca. 18 million tokens. It follows the same approach by extracting from the "Good and Featured" articles on Wikipedia, but
for [German articles](https://en.wikipedia.org/wiki/Wikipedia:Featured_articles_in_other_languages/German). The dataset is
available under the Creative Commons Attribution-ShareAlike License.
The stated German version contains 2759 articles (visited: 27.06.23). Even though the smalle size of articles, compared to wikitext,
the dataset contains 18 million (whitespace) seperated tokens. Probably due to longer articles lengths and language.
The dataset retains the original case, punctuation, numbers and newlines, excluding images, tables and other data.
[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 | |
CJWeiss/ukabs | CJWeiss | 2023-10-26T20:44:41Z | 223 | 0 | null | [
"region:us"
] | 2023-10-26T20:44:41Z | 2023-10-26T20:44:33.000Z | 2023-10-26T20:44:33 | ---
dataset_info:
features:
- name: judgement
dtype: string
- name: summary
dtype: string
splits:
- name: train
num_bytes: 54887059
num_examples: 595
- name: test
num_bytes: 9859833
num_examples: 119
- name: valid
num_bytes: 6659871
num_examples: 79
download_size: 33858783
dataset_size: 71406763
---
# Dataset Card for "ukabs"
[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 | |
laion/laion400m | laion | 2023-04-04T06:35:23Z | 222 | 18 | null | [
"license:cc-by-4.0",
"region:us"
] | 2023-04-04T06:35:23Z | 2023-03-28T21:36:09.000Z | 2023-03-28T21:36:09 | ---
license: cc-by-4.0
---
# LAION-400m_new
This datasets has two improvements compared to original LAION_400m dataset:
1. It uses a multilingual text filter to filter out malicious content
2. The better open_clip VitH model was used to detect potential harmful content in the images
All in all, we filtered out around 6 million additional image-text pairs - probably with a high false positive rate - in order to improve dataset safety. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
jaejoo/llama-2-ko-law | jaejoo | 2023-09-08T05:13:09Z | 222 | 4 | null | [
"size_categories:1K<n<10K",
"language:ko",
"license:apache-2.0",
"legal",
"region:us"
] | 2023-09-08T05:13:09Z | 2023-09-08T04:47:14.000Z | 2023-09-08T04:47:14 | ---
license: apache-2.0
language:
- ko
tags:
- legal
size_categories:
- 1K<n<10K
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
ScandEval/norne-nb-mini | ScandEval | 2023-07-05T09:42:22Z | 221 | 0 | null | [
"task_categories:token-classification",
"size_categories:1K<n<10K",
"language:nb",
"license:other",
"region:us"
] | 2023-07-05T09:42:22Z | 2022-06-14T18:21:00.000Z | 2022-06-14T18:21:00 | ---
dataset_info:
features:
- name: text
dtype: string
- name: tokens
sequence: string
- name: labels
sequence: string
splits:
- name: train
num_bytes: 317673
num_examples: 1024
- name: test
num_bytes: 626004
num_examples: 2048
- name: val
num_bytes: 87124
num_examples: 256
download_size: 455512
dataset_size: 1030801
license: other
task_categories:
- token-classification
language:
- nb
size_categories:
- 1K<n<10K
---
# Dataset Card for "norne-nb-mini"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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shmuhammad/AfriSenti-twitter-sentiment | shmuhammad | 2023-09-03T09:59:15Z | 221 | 3 | null | [
"task_categories:text-classification",
"task_ids:sentiment-analysis",
"task_ids:sentiment-classification",
"task_ids:sentiment-scoring",
"task_ids:semantic-similarity-classification",
"task_ids:semantic-similarity-scoring",
"multilinguality:monolingual",
"multilinguality:multilingual",
"size_categories:100K<n<1M",
"language:amh",
"language:ary",
"language:ar",
"language:arq",
"language:hau",
"language:ibo",
"language:kin",
"language:por",
"language:pcm",
"language:eng",
"language:oro",
"language:swa",
"language:tir",
"language:twi",
"language:tso",
"language:yor",
"sentiment analysis, Twitter, tweets",
"sentiment",
"arxiv:2302.08956",
"arxiv:2304.06845",
"arxiv:2201.08277",
"region:us"
] | 2023-09-03T09:59:15Z | 2023-02-16T21:02:20.000Z | 2023-02-16T21:02:20 | ---
task_categories:
- text-classification
task_ids:
- sentiment-analysis
- sentiment-classification
- sentiment-scoring
- semantic-similarity-classification
- semantic-similarity-scoring
tags:
- sentiment analysis, Twitter, tweets
- sentiment
multilinguality:
- monolingual
- multilingual
size_categories:
- 100K<n<1M
language:
- amh
- ary
- ar
- arq
- hau
- ibo
- kin
- por
- pcm
- eng
- oro
- swa
- tir
- twi
- tso
- yor
pretty_name: AfriSenti
---
# Dataset Card for AfriSenti Dataset
<p align="center">
<img src="https://raw.githubusercontent.com/afrisenti-semeval/afrisent-semeval-2023/main/images/afrisenti-twitter.png", width="700" height="500">
--------------------------------------------------------------------------------
## Dataset Description
- **Homepage:** https://github.com/afrisenti-semeval/afrisent-semeval-2023
- **Repository:** [GitHub](https://github.com/afrisenti-semeval/afrisent-semeval-2023)
- **Paper:** [AfriSenti: AfriSenti: A Twitter Sentiment Analysis Benchmark for African Languages](https://arxiv.org/pdf/2302.08956.pdf)
- **Paper:** [SemEval-2023 Task 12: Sentiment Analysis for African Languages (AfriSenti-SemEval)](https://arxiv.org/pdf/2304.06845.pdf)
- **Paper:** [NaijaSenti: A Nigerian Twitter Sentiment Corpus for Multilingual Sentiment Analysis](https://arxiv.org/pdf/2201.08277.pdf)
- **Leaderboard:** N/A
- **Point of Contact:** [shamsuddeen Muhammad](shamsuddeen2004@gmail.com)
### Dataset Summary
AfriSenti is the largest sentiment analysis dataset for under-represented African languages, covering 110,000+ annotated tweets in 14 African languages (Amharic, Algerian Arabic, Hausa, Igbo, Kinyarwanda, Moroccan Arabic, Mozambican Portuguese, Nigerian Pidgin, Oromo, Swahili, Tigrinya, Twi, Xitsonga, and Yoruba).
The datasets are used in the first Afrocentric SemEval shared task, SemEval 2023 Task 12: Sentiment analysis for African languages (AfriSenti-SemEval). AfriSenti allows the research community to build sentiment analysis systems for various African languages and enables the study of sentiment and contemporary language use in African languages.
### Supported Tasks and Leaderboards
The AfriSenti can be used for a wide range of sentiment analysis tasks in African languages, such as sentiment classification, sentiment intensity analysis, and emotion detection. This dataset is suitable for training and evaluating machine learning models for various NLP tasks related to sentiment analysis in African languages.
[SemEval 2023 Task 12 : Sentiment Analysis for African Languages](https://codalab.lisn.upsaclay.fr/competitions/7320)
### Languages
14 African languages (Amharic (amh), Algerian Arabic (ary), Hausa(hau), Igbo(ibo), Kinyarwanda(kin), Moroccan Arabic/Darija(arq), Mozambican Portuguese(por), Nigerian Pidgin (pcm), Oromo (oro), Swahili(swa), Tigrinya(tir), Twi(twi), Xitsonga(tso), and Yoruba(yor)).
## Dataset Structure
### Data Instances
For each instance, there is a string for the tweet and a string for the label. See the AfriSenti [dataset viewer](https://huggingface.co/datasets/shmuhammad/AfriSenti/viewer/shmuhammad--AfriSenti/train) to explore more examples.
```
{
"tweet": "string",
"label": "string"
}
```
### Data Fields
The data fields are:
```
tweet: a string feature.
label: a classification label, with possible values including positive, negative and neutral.
```
### Data Splits
The AfriSenti dataset has 3 splits: train, validation, and test. Below are the statistics for Version 1.0.0 of the dataset.
| | ama | arq | hau | ibo | ary | orm | pcm | pt-MZ | kin | swa | tir | tso | twi | yo |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| train | 5,982 | 1,652 | 14,173 | 10,193 | 5,584| - | 5,122 | 3,064 | 3,303 | 1,811 | - | 805 | 3,482| 8,523 |
| dev | 1,498 | 415 | 2,678 | 1,842 | 1,216 | 397 | 1,282 | 768 | 828 | 454 | 399 | 204 | 389 | 2,091 |
| test | 2,000 | 959 | 5,304 | 3,683 | 2,962 | 2,097 | 4,155 | 3,663 | 1,027 | 749 | 2,001 | 255 | 950 | 4,516 |
| total | 9,483 | 3,062 | 22,155 | 15,718 | 9,762 | 2,494 | 10,559 | 7,495 | 5,158 | 3,014 | 2,400 | 1,264 | 4,821 | 15,130 |
### How to use it
```python
from datasets import load_dataset
# you can load specific languages (e.g., Amharic). This download train, validation and test sets.
ds = load_dataset("shmuhammad/AfriSenti-twitter-sentiment", "amh")
# train set only
ds = load_dataset("shmuhammad/AfriSenti-twitter-sentiment", "amh", split = "train")
# test set only
ds = load_dataset("shmuhammad/AfriSenti-twitter-sentiment", "amh", split = "test")
# validation set only
ds = load_dataset("shmuhammad/AfriSenti-twitter-sentiment", "amh", split = "validation")
```
## Dataset Creation
### Curation Rationale
AfriSenti Version 1.0.0 aimed to be used in the first Afrocentric SemEval shared task **[SemEval 2023 Task 12: Sentiment analysis for African languages (AfriSenti-SemEval)](https://afrisenti-semeval.github.io)**.
### Source Data
Twitter
#### 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
We anonymized the tweets by replacing all *@mentions* by *@user* and removed all URLs.
## Considerations for Using the Data
### Social Impact of Dataset
The Afrisenti dataset has the potential to improve sentiment analysis for African languages, which is essential for understanding and analyzing the diverse perspectives of people in the African continent. This dataset can enable researchers and developers to create sentiment analysis models that are specific to African languages, which can be used to gain insights into the social, cultural, and political views of people in African countries. Furthermore, this dataset can help address the issue of underrepresentation of African languages in natural language processing, paving the way for more equitable and inclusive AI technologies.
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
AfriSenti is an extension of NaijaSenti, a dataset consisting of four Nigerian languages: Hausa, Yoruba, Igbo, and Nigerian-Pidgin. This dataset has been expanded to include other 10 African languages, and was curated with the help of the following:
| Language | Dataset Curators |
|---|---|
| Algerian Arabic (arq) | Nedjma Ousidhoum, Meriem Beloucif |
| Amharic (ama) | Abinew Ali Ayele, Seid Muhie Yimam |
| Hausa (hau) | Shamsuddeen Hassan Muhammad, Idris Abdulmumin, Ibrahim Said, Bello Shehu Bello |
| Igbo (ibo) | Shamsuddeen Hassan Muhammad, Idris Abdulmumin, Ibrahim Said, Bello Shehu Bello |
| Kinyarwanda (kin)| Samuel Rutunda |
| Moroccan Arabic/Darija (ary) | Oumaima Hourrane |
| Mozambique Portuguese (pt-MZ) | Felermino Dário Mário António Ali |
| Nigerian Pidgin (pcm) | Shamsuddeen Hassan Muhammad, Idris Abdulmumin, Ibrahim Said, Bello Shehu Bello |
| Oromo (orm) | Abinew Ali Ayele, Seid Muhie Yimam, Hagos Tesfahun Gebremichael, Sisay Adugna Chala, Hailu Beshada Balcha, Wendimu Baye Messell, Tadesse Belay |
| Swahili (swa) | Davis Davis |
| Tigrinya (tir) | Abinew Ali Ayele, Seid Muhie Yimam, Hagos Tesfahun Gebremichael, Sisay Adugna Chala, Hailu Beshada Balcha, Wendimu Baye Messell, Tadesse Belay |
| Twi (twi) | Salomey Osei, Bernard Opoku, Steven Arthur |
| Xithonga (tso) | Felermino Dário Mário António Ali |
| Yoruba (yor) | Shamsuddeen Hassan Muhammad, Idris Abdulmumin, Ibrahim Said, Bello Shehu Bello |
### Licensing Information
This AfriSenti is licensed under a Creative Commons Attribution 4.0 International License
### Citation Information
```
@inproceedings{Muhammad2023AfriSentiAT,
title={AfriSenti: A Twitter Sentiment Analysis Benchmark for African Languages},
author={Shamsuddeen Hassan Muhammad and Idris Abdulmumin and Abinew Ali Ayele and Nedjma Ousidhoum and David Ifeoluwa Adelani and Seid Muhie Yimam and Ibrahim Sa'id Ahmad and Meriem Beloucif and Saif Mohammad and Sebastian Ruder and Oumaima Hourrane and Pavel Brazdil and Felermino D'ario M'ario Ant'onio Ali and Davis Davis and Salomey Osei and Bello Shehu Bello and Falalu Ibrahim and Tajuddeen Gwadabe and Samuel Rutunda and Tadesse Belay and Wendimu Baye Messelle and Hailu Beshada Balcha and Sisay Adugna Chala and Hagos Tesfahun Gebremichael and Bernard Opoku and Steven Arthur},
year={2023}
}
```
```
@article{muhammad2023semeval,
title={SemEval-2023 Task 12: Sentiment Analysis for African Languages (AfriSenti-SemEval)},
author={Muhammad, Shamsuddeen Hassan and Abdulmumin, Idris and Yimam, Seid Muhie and Adelani, David Ifeoluwa and Ahmad, Ibrahim Sa'id and Ousidhoum, Nedjma and Ayele, Abinew and Mohammad, Saif M and Beloucif, Meriem},
journal={arXiv preprint arXiv:2304.06845},
year={2023}
}
```
### Contributions
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gigant/tib | gigant | 2023-09-25T12:05:25Z | 221 | 0 | null | [
"task_categories:summarization",
"size_categories:1K<n<10K",
"language:en",
"region:us"
] | 2023-09-25T12:05:25Z | 2023-04-11T15:22:07.000Z | 2023-04-11T15:22:07 | ---
dataset_info:
features:
- name: doi
dtype: string
- name: title
dtype: string
- name: url
dtype: string
- name: video_url
dtype: string
- name: license
dtype: string
- name: subject
dtype: string
- name: genre
dtype: string
- name: release_year
dtype: string
- name: author
dtype: string
- name: contributors
dtype: string
- name: abstract
dtype: string
- name: transcript
dtype: string
- name: transcript_segments
sequence:
- name: id
dtype: int32
- name: seek
dtype: int32
- name: start
dtype: float32
- name: end
dtype: float32
- name: text
dtype: string
- name: tokens
sequence: int32
- name: temperature
dtype: float32
- name: avg_logprob
dtype: float32
- name: compression_ratio
dtype: float32
- name: no_speech_prob
dtype: float32
- name: keyframes
sequence:
- name: slide
dtype: string
- name: frames
sequence: int32
- name: timestamp
sequence: float32
- name: language
dtype: string
splits:
- name: valid
num_bytes: 101380279
num_examples: 910
- name: train
num_bytes: 827555875
num_examples: 7282
- name: test
num_bytes: 102396941
num_examples: 911
download_size: 502166165
dataset_size: 1031333095
task_categories:
- summarization
language:
- en
pretty_name: "TIB: A Dataset for Abstractive Summarization of Long Multimodal Videoconference Records"
size_categories:
- 1K<n<10K
pinned: True
---
# Dataset Card for "TIB: A Dataset for Abstractive Summarization of Long Multimodal Videoconference Records"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Dataset Description
- **Homepage:** [Dataset page](https://huggingface.co/datasets/gigant/tib)
- **Repository:** [Dataset page](https://huggingface.co/datasets/gigant/tib)
- **Paper:** [TIB: A Dataset for Abstractive Summarization of Long Multimodal Videoconference Records
](https://hal.science/hal-04168911)
- **Point of Contact:** [Théo Gigant](mailto:theo.gigant@l2s.centralesupelec.fr)
## Dataset Summary
TIB is an English dataset for abstractive summarization of multimodal presentations, introduced in [*TIB: A Dataset for Abstractive Summarization of Long Multimodal Videoconference Records*
](https://hal.science/hal-04168911).
It is a collection of 9,103 videoconference records extracted from the German National Library of Science and Technology (TIB) archive, along with their metadata, an abstract and automatically processed transcripts and key frames.
### Supported Tasks and Leaderboards
- `summarization`
### Languages
The text in the dataset is in English, both for the transcripted audios and the abstracts.
## Usage
To use within the [`datasets`](https://github.com/huggingface/datasets) library:
```python
from datasets import load_dataset
dataset = load_dataset("gigant/tib")
```
## Dataset Structure
### Data Instances
A typical data point represents a videoconference record, the `transcript` and `keyframes` are textual and visual modalities, processed from the video found at `video_url`, and the `abstract` is used as a target abstractive summary.
### Data Fields
Each record consist of the following attributes:
* `doi`: digital object identifier (DOI) of the record or the associated paper
* `title`: title of the presentation
* `url`: URL of the record in the TIB archive
* `video_url`: URL of the video file
* `license`: license of the record
* `subject`: academic field (*eg* Computer Science, Mathematics, ...)
* `genre`: type of presentation (*eg* Lecture, Conference, ...)
* `release_year`: year the record was released
* `author`: name of the author
* `contributors`: name of the contributors
* `abstract`: the abstract of the presentation, that serve as a target summary
* `transcript`: the automatically extracted transcript
* `transcript_segments`: the automatically extracted transcript with time codes, output of the speech recognition system
* `keyframes`: the automatically extracted key frames time codes
`doi`, `title`, `url`, `video_url`, `license`, `subject`, `genre`, `release_year`, `author`, `contributors` and `abstract` are provided as found in the TIB archive. The length, style, quality and content of the abstract can differ from video to video as it was likely provided by each author. For instance, some abstracts can provide very short title-like summaries, introduction of the conference, the lecture or the speaker, or longer descriptions of the content. We provide examples of transcripts and summaries in the paper's Appendix.
### Data Splits
The data is split into a training, validation and test set.
* Train: 7,282 (80%)
* Validation: 910 (10%)
* Test: 911 (10%)
## Dataset Creation
### Source Data
#### Initial Data Collection and Normalization
The dataset was first assembled by crawling the [TIB-AV portal](https://av.tib.eu/) which is a large archive of videos, developed by the German National Library of Science and Technology: *Technische Informationsbibliothek* (TIB).
Entries with missing abstracts or abstracts that were too short (less than 30 characters) were filtered out.
We also filtered out records for which the abstract or the transcript is in another language than English.
In order to keep the abstracts that are relevant to the associated record, we removed documents if the abstract is the same as the abstract for another video. This allowed to get rid of all the abstracts that were written for a set of records such as conferences, instead of specifically written for a single presentation.
More information about the dataset collection and filtering can be found in [TIB: A Dataset for Abstractive Summarization of Long Multimodal Videoconference Records
](https://hal.science/hal-04168911).
### Dataset Curators
The dataset was initially created by Théo Gigant, Frédéric Dufaux, Camille Guinaudeau and Marc Decombas.
### Citation Information
```
@inproceedings{gigant:hal-04168911,
TITLE = {{TIB: A Dataset for Abstractive Summarization of Long Multimodal Videoconference Records}},
AUTHOR = {GIGANT, Th{\'e}o and Dufaux, Fr{\'e}d{\'e}ric and Guinaudeau, Camille and Decombas, Marc},
URL = {https://hal.science/hal-04168911},
BOOKTITLE = {{Proc. 20th International Conference on Content-based Multimedia Indexing (CBMI 2023)}},
ADDRESS = {Orl{\'e}ans, France},
ORGANIZATION = {{ACM}},
YEAR = {2023},
MONTH = Sep,
KEYWORDS = {multimedia dataset, multimodal documents, automatic summarization},
HAL_ID = {hal-04168911},
HAL_VERSION = {v1},
}
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
chiayewken/bamboogle | chiayewken | 2023-10-27T09:22:40Z | 221 | 0 | null | [
"arxiv:2210.03350",
"region:us"
] | 2023-10-27T09:22:40Z | 2023-10-27T08:15:25.000Z | 2023-10-27T08:15:25 | ---
dataset_info:
features:
- name: Question
dtype: string
- name: Answer
dtype: string
splits:
- name: test
num_bytes: 10747
num_examples: 125
download_size: 8383
dataset_size: 10747
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
---
# Bamboogle
This repo contains the data for ["Measuring and Narrowing the Compositionality Gap in Language Models" paper](https://arxiv.org/abs/2210.03350).
The original data link is here: https://docs.google.com/spreadsheets/d/1jwcsA5kE4TObr9YHn9Gc-wQHYjTbLhDGx6tmIzMhl_U/edit?usp=sharing
This dataset is distributed with the MIT license. | [
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nlpaueb/finer-139 | nlpaueb | 2022-10-23T05:05:03Z | 220 | 13 | null | [
"task_ids:named-entity-recognition",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:1M<n<10M",
"language:en",
"license:cc-by-sa-4.0",
"arxiv:2203.06482",
"region:us"
] | 2022-10-23T05:05:03Z | 2022-03-04T10:00:23.000Z | 2022-03-04T10:00:23 | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
pretty_name: FiNER-139
size_categories:
- 1M<n<10M
source_datasets: []
task_categories:
- structure-prediction
- named-entity-recognition
- entity-extraction
task_ids:
- named-entity-recognition
---
# Dataset Card for FiNER-139
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks)
- [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)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [SEC-BERT](#sec-bert)
- [About Us](#about-us)
## Dataset Description
- **Homepage:** [FiNER](https://github.com/nlpaueb/finer)
- **Repository:** [FiNER](https://github.com/nlpaueb/finer)
- **Paper:** [FiNER, Loukas et al. (2022)](https://arxiv.org/abs/2203.06482)
- **Point of Contact:** [Manos Fergadiotis](mailto:fergadiotis@aueb.gr)
### Dataset Summary
<div style="text-align: justify">
<strong>FiNER-139</strong> is comprised of 1.1M sentences annotated with <strong>eXtensive Business Reporting Language (XBRL)</strong> tags extracted from annual and quarterly reports of publicly-traded companies in the US.
Unlike other entity extraction tasks, like named entity recognition (NER) or contract element extraction, which typically require identifying entities of a small set of common types (e.g., persons, organizations), FiNER-139 uses a much larger label set of <strong>139 entity types</strong>.
Another important difference from typical entity extraction is that FiNER focuses on numeric tokens, with the correct tag depending mostly on context, not the token itself.
</div>
### Supported Tasks
<div style="text-align: justify">
To promote transparency among shareholders and potential investors, publicly traded companies are required to file periodic financial reports annotated with tags from the eXtensive Business Reporting Language (XBRL), an XML-based language, to facilitate the processing of financial information.
However, manually tagging reports with XBRL tags is tedious and resource-intensive.
We, therefore, introduce <strong>XBRL tagging</strong> as a <strong>new entity extraction task</strong> for the <strong>financial domain</strong> and study how financial reports can be automatically enriched with XBRL tags.
To facilitate research towards automated XBRL tagging we release FiNER-139.
</div>
### Languages
**FiNER-139** is compiled from approximately 10k annual and quarterly **English** reports
## Dataset Structure
### Data Instances
This is a "train" split example:
```json
{
'id': 40
'tokens': ['In', 'March', '2014', ',', 'the', 'Rialto', 'segment', 'issued', 'an', 'additional', '$', '100', 'million', 'of', 'the', '7.00', '%', 'Senior', 'Notes', ',', 'at', 'a', 'price', 'of', '102.25', '%', 'of', 'their', 'face', 'value', 'in', 'a', 'private', 'placement', '.']
'ner_tags': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 37, 0, 0, 0, 41, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
}
```
### Data Fields
**id**: ID of the example <br>
**tokens**: List of tokens for the specific example. <br>
**ner_tags**: List of tags for each token in the example. Tags are provided as integer classes.<br>
If you want to use the class names you can access them as follows:
```python
import datasets
finer_train = datasets.load_dataset("nlpaueb/finer-139", split="train")
finer_tag_names = finer_train.features["ner_tags"].feature.names
```
**finer_tag_names** contains a list of class names corresponding to the integer classes e.g.
```
0 -> "O"
1 -> "B-AccrualForEnvironmentalLossContingencies"
```
### Data Splits
| Training | Validation | Test
| -------- | ---------- | -------
| 900,384 | 112,494 | 108,378
## Dataset Creation
### Curation Rationale
The dataset was curated by [Loukas et al. (2022)](https://arxiv.org/abs/2203.06482) <br>
### Source Data
#### Initial Data Collection and Normalization
<div style="text-align: justify">
FiNER-139 is compiled from approximately 10k annual and quarterly English reports (filings) of publicly traded companies downloaded from the [US Securities
and Exchange Commission's (SEC)](https://www.sec.gov/) [Electronic Data Gathering, Analysis, and Retrieval (EDGAR)](https://www.sec.gov/edgar.shtml) system.
The reports span a 5-year period, from 2016 to 2020. They are annotated with XBRL tags by professional auditors and describe the performance and projections of the companies. XBRL defines approximately 6k entity types from the US-GAAP taxonomy. FiNER-139 is annotated with the 139 most frequent XBRL entity types with at least 1,000 appearances.
We used regular expressions to extract the text notes from the Financial Statements Item of each filing, which is the primary source of XBRL tags in annual and quarterly reports. We used the <strong>IOB2</strong> annotation scheme to distinguish tokens at the beginning, inside, or outside of tagged expressions, which leads to 279 possible token labels.
</div>
### Annotations
#### Annotation process
<div style="text-align: justify">
All the examples were annotated by professional auditors as required by the Securities & Exchange Commission (SEC) legislation.
Even though the gold XBRL tags come from professional auditors there are still some discrepancies. Consult [Loukas et al. (2022)](https://arxiv.org/abs/2203.06482), (Section 9.4) for more details
</div>
#### Who are the annotators?
Professional auditors
### Personal and Sensitive Information
The dataset contains publicly available annual and quarterly reports (filings)
## Additional Information
### Dataset Curators
[Loukas et al. (2022)](https://arxiv.org/abs/2203.06482)
### Licensing Information
<div style="text-align: justify">
Access to SEC's EDGAR public database is free, allowing research of public companies' financial information and operations by reviewing the filings the companies makes with the SEC.
</div>
### Citation Information
If you use this dataset cite the following
```
@inproceedings{loukas-etal-2022-finer,
title = {FiNER: Financial Numeric Entity Recognition for XBRL Tagging},
author = {Loukas, Lefteris and
Fergadiotis, Manos and
Chalkidis, Ilias and
Spyropoulou, Eirini and
Malakasiotis, Prodromos and
Androutsopoulos, Ion and
Paliouras George},
booktitle = {Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (ACL 2022)},
publisher = {Association for Computational Linguistics},
location = {Dublin, Republic of Ireland},
year = {2022},
url = {https://arxiv.org/abs/2203.06482}
}
```
## SEC-BERT
<img align="center" src="https://i.ibb.co/0yz81K9/sec-bert-logo.png" alt="SEC-BERT" width="400"/>
<div style="text-align: justify">
We also pre-train our own BERT models (<strong>SEC-BERT</strong>) for the financial domain, intended to assist financial NLP research and FinTech applications. <br>
<strong>SEC-BERT</strong> consists of the following models:
* [**SEC-BERT-BASE**](https://huggingface.co/nlpaueb/sec-bert-base): Same architecture as BERT-BASE trained on financial documents.
* [**SEC-BERT-NUM**](https://huggingface.co/nlpaueb/sec-bert-num): Same as SEC-BERT-BASE but we replace every number token with a [NUM] pseudo-token handling all numeric expressions in a uniform manner, disallowing their fragmentation
* [**SEC-BERT-SHAPE**](https://huggingface.co/nlpaueb/sec-bert-shape): Same as SEC-BERT-BASE but we replace numbers with pseudo-tokens that represent the number’s shape, so numeric expressions (of known shapes) are no longer fragmented, e.g., '53.2' becomes '[XX.X]' and '40,200.5' becomes '[XX,XXX.X]'.
These models were pre-trained on 260,773 10-K filings (annual reports) from 1993-2019, publicly available at [U.S. Securities and Exchange Commission (SEC)](https://www.sec.gov/)
</div>
## About Us
<div style="text-align: justify">
[**AUEB's Natural Language Processing Group**](http://nlp.cs.aueb.gr) develops algorithms, models, and systems that allow computers to process and generate natural language texts.
The group's current research interests include:
* question answering systems for databases, ontologies, document collections, and the Web, especially biomedical question answering,
* natural language generation from databases and ontologies, especially Semantic Web ontologies,
text classification, including filtering spam and abusive content,
* information extraction and opinion mining, including legal text analytics and sentiment analysis,
* natural language processing tools for Greek, for example parsers and named-entity recognizers,
machine learning in natural language processing, especially deep learning.
The group is part of the Information Processing Laboratory of the Department of Informatics of the Athens University of Economics and Business.
</div>
[Manos Fergadiotis](https://manosfer.github.io) on behalf of [AUEB's Natural Language Processing Group](http://nlp.cs.aueb.gr) | [
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nielsr/funsd-iob-original | nielsr | 2022-11-19T13:38:09Z | 220 | 0 | null | [
"region:us"
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IlyaGusev/ru_sharegpt_cleaned | IlyaGusev | 2023-09-16T14:34:28Z | 220 | 8 | null | [
"task_categories:conversational",
"task_categories:text-generation",
"size_categories:n<1K",
"language:ru",
"region:us"
] | 2023-09-16T14:34:28Z | 2023-04-10T18:40:47.000Z | 2023-04-10T18:40:47 | ---
language:
- ru
size_categories:
- n<1K
task_categories:
- conversational
- text-generation
dataset_info:
features:
- name: messages
sequence:
- name: role
dtype: string
- name: content
dtype: string
- name: id
dtype: string
splits:
- name: train
num_bytes: 1993005
num_examples: 273
download_size: 2054401
dataset_size: 1993005
---
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
kyujinpy/OpenOrca-KO | kyujinpy | 2023-10-12T19:55:47Z | 220 | 11 | 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:10K<n<50K",
"language:ko",
"license:mit",
"arxiv:2306.02707",
"arxiv:2301.13688",
"region:us"
] | 2023-10-12T19:55:47Z | 2023-09-29T15:26:20.000Z | 2023-09-29T15:26:20 | ---
language:
- ko
license: mit
size_categories:
- 10K<n<50K
task_categories:
- conversational
- text-classification
- token-classification
- table-question-answering
- question-answering
- zero-shot-classification
- summarization
- feature-extraction
- text-generation
- text2text-generation
pretty_name: OpenOrca
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: id
dtype: string
- name: input
dtype: string
- name: instruction
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 44220539
num_examples: 21632
download_size: 22811589
dataset_size: 44220539
---
# OpenOrca-KO
- OpenOrca dataset 중 약 2만개를 sampling하여 번역한 데이터셋
- 데이터셋 이용하셔서 모델이나 데이터셋을 만드실 때, 간단한 출처 표기를 해주신다면 연구에 큰 도움이 됩니다😭😭
## Dataset inf0
1. **NIV** // 1571개
2. **FLAN** // 9434개
3. **T0** // 6351개
4. **CoT** // 2117개
5. **[KoCoT](https://huggingface.co/datasets/kyujinpy/KoCoT_2000)** // 2159개
## Translation
Using DeepL Pro API. Thanks.
---
>Below is original dataset card
## Table of Contents
- [Dataset Summary](#dataset-summary)
- [Dataset Attribution](#dataset-attribution)
- [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)
- [Dataset Use](#dataset-use)
- [Use Cases](#use-cases)
- [Usage Caveats](#usage-caveats)
- [Getting Started](#getting-started)
<p><h1>🐋 The OpenOrca Dataset! 🐋</h1></p>
<a name="dataset-announcement"></a>
We are thrilled to announce the release of the OpenOrca dataset!
This rich collection of augmented FLAN data aligns, as best as possible, with the distributions outlined in the [Orca paper](https://arxiv.org/abs/2306.02707).
It has been instrumental in generating high-performing model checkpoints and serves as a valuable resource for all NLP researchers and developers!
# Official Models
## OpenOrca-Platypus2-13B
Our [latest release](https://huggingface.co/Open-Orca/OpenOrca-Platypus2-13B), the first 13B model to score higher than LLaMA1-65B on the HuggingFace Leaderboard!
Released in partnership with Platypus.
## LlongOrca 7B & 13B
* Our [first 7B release](https://huggingface.co/Open-Orca/LlongOrca-7B-16k), trained on top of LLongMA2 to achieve 16,000 tokens context. #1 long context 7B model at release time, with >99% of the overall #1 model's performance.
* [LlongOrca-13B-16k](https://huggingface.co/Open-Orca/LlongOrca-13B-16k), trained on top of LLongMA2. #1 long context 13B model at release time, with >97% of the overall #1 model's performance.
## OpenOrcaxOpenChat-Preview2-13B
Our [second model](https://huggingface.co/Open-Orca/OpenOrcaxOpenChat-Preview2-13B), highlighting that we've surpassed the performance reported in the Orca paper.
Was #1 at release time, now surpassed by our own OpenOrca-Platypus2-13B.
Released in partnership with OpenChat.
## OpenOrca-Preview1-13B
[OpenOrca-Preview1-13B](https://huggingface.co/Open-Orca/OpenOrca-Preview1-13B)
This model was trained in less than a day, for <$200, with <10% of our data.
At release, it beat the current state of the art models on BigBench-Hard and AGIEval. Achieves ~60% of the improvements reported in the Orca paper.
<a name="dataset-summary"></a>
# Dataset Summary
The OpenOrca dataset is a collection of augmented [FLAN Collection data](https://arxiv.org/abs/2301.13688).
Currently ~1M GPT-4 completions, and ~3.2M GPT-3.5 completions.
It is tabularized in alignment with the distributions presented in the ORCA paper and currently represents a partial completion of the full intended dataset, with ongoing generation to expand its scope.
The data is primarily used for training and evaluation in the field of natural language processing.
<a name="dataset-attribution"></a>
# Dataset Attribution
We would like to give special recognition to the following contributors for their significant efforts and dedication:
Teknium
WingLian/Caseus
Eric Hartford
NanoBit
Pankaj
Winddude
Rohan
http://AlignmentLab.ai:
Autometa
Entropi
AtlasUnified
NeverendingToast
NanoBit
WingLian/Caseus
Also of course, as always, TheBloke, for being the backbone of the whole community.
Many thanks to NanoBit and Caseus, makers of [Axolotl](https://github.com/OpenAccess-AI-Collective/axolotl), for lending us their expertise on the platform that developed and trained manticore, minotaur, and many others!
We are welcoming sponsors or collaborators to help us build these models to the scale they deserve. Please reach out via our socials:
http://Alignmentlab.ai https://discord.gg/n9hXaBPWxx
Want to visualize our full dataset? Check out our [Nomic Atlas Map](https://atlas.nomic.ai/map/c1b88b47-2d9b-47e0-9002-b80766792582/2560fd25-52fe-42f1-a58f-ff5eccc890d2).
[<img src="https://huggingface.co/Open-Orca/OpenOrca-Preview1-13B/resolve/main/OpenOrca%20Nomic%20Atlas.png" alt="Atlas Nomic Dataset Map" width="400" height="400" />](https://atlas.nomic.ai/map/c1b88b47-2d9b-47e0-9002-b80766792582/2560fd25-52fe-42f1-a58f-ff5eccc890d2)
<a name="supported-tasks-and-leaderboards"></a>
# Supported Tasks and Leaderboards
This dataset supports a range of tasks including language modeling, text generation, and text augmentation.
It has been instrumental in the generation of multiple high-performing model checkpoints which have exhibited exceptional performance in our unit testing.
Further information on leaderboards will be updated as they become available.
<a name="languages"></a>
# Languages
The language of the data is primarily English.
<a name="dataset-structure"></a>
# Dataset Structure
<a name="data-instances"></a>
## Data Instances
A data instance in this dataset represents entries from the FLAN collection which have been augmented by submitting the listed question to either GPT-4 or GPT-3.5.
The response is then entered into the response field.
<a name="data-fields"></a>
## Data Fields
The fields are:
1) 'id', a unique numbered identifier which includes one of 'niv', 't0', 'cot', or 'flan' to represent which source FLAN Collection submix the 'question' is sourced from.
2) 'system_prompt', representing the System Prompt presented to the GPT-3.5 or GPT-4 API for the datapoint
3) 'question', representing a question entry as provided by the FLAN Collection
4) 'response', a response to that question received from a query to either GPT-3.5 or GPT-4.
<a name="data-splits"></a>
## Data Splits
The data is unsplit.
<a name="dataset-creation"></a>
# Dataset Creation
<a name="curation-rationale"></a>
## Curation Rationale
The dataset was created to provide a source of augmented text data for researchers and developers.
The datapoints are intended primarily to provide an enhancement of the core FLAN Collection data which relies upon the detailed step by step reasoning capabilities of GPT-3.5 and GPT-4.
This "reasoning trace" augmentation has demonstrated exceptional results, allowing a LLaMA-13B model trained with this data to rival or beat GPT-3.5 on broad sets of hard reasoning tasks which all models below 100B parameters had previously performed dramatically worse on.
<a name="source-data"></a>
## Source Data
The data is generated using techniques in alignment with the distributions outlined in the Orca paper, except as noted below:
1) There is not enough CoT data in the FLAN Collection to generate 150K zero-shot entries, as the paper purports to use.
We suspect this portion was either undocumented or misrepresented. We have used the ~75K points available.
2) We used the pre-generated FLAN Collection datasets hosted on HuggingFace under conceptofmind, e.g. [conceptofmind/flan2021](https://huggingface.co/datasets/conceptofmind/flan2021_submix_original).
These are referenced by the [official FLAN Collection repo](https://github.com/google-research/FLAN/tree/main/flan/v2) as the preferred data source.
However, these are a subset of the full FLAN Collection data, and have less than the required entries for the flan2021 and t0 submixes, by ~1.25M and 200k respectively.
Combined, this gave us ~1.5M fewer datapoints than in the original Orca paper. Completing the set is an ongoing work.
<a name="dataset-use"></a>
# Dataset Use
<a name="use-cases"></a>
## Use Cases
The dataset can be used for tasks related to language understanding, natural language processing, machine learning model training, and model performance evaluation.
<a name="usage-caveats"></a>
## Usage Caveats
Given that this is a work-in-progress dataset, it is recommended to regularly check for updates and improvements.
Further, the data should be used in accordance with the guidelines and recommendations outlined in the Orca paper.
<a name="getting-started"></a>
## Getting Started
This dataset is organized such that it can be naively loaded via Hugging Face datasets library.
We recommend using streaming due to the large size of the files.
Regular updates and data generation progress can be monitored through the OpenOrca repository on Hugging Face.
# Citation
```bibtex
@misc{OpenOrca,
title = {OpenOrca: An Open Dataset of GPT Augmented FLAN Reasoning Traces},
author = {Wing Lian and Bleys Goodson and Eugene Pentland and Austin Cook and Chanvichet Vong and "Teknium"},
year = {2023},
publisher = {HuggingFace},
journal = {HuggingFace repository},
howpublished = {\url{https://https://huggingface.co/Open-Orca/OpenOrca},
}
```
```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}
}
```
```bibtex
@misc{touvron2023llama,
title={Llama 2: Open Foundation and Fine-Tuned Chat Models},
author={Hugo Touvron and Louis Martin and Kevin Stone and Peter Albert and Amjad Almahairi and Yasmine Babaei and Nikolay Bashlykov and Soumya Batra and Prajjwal Bhargava and Shruti Bhosale and Dan Bikel and Lukas Blecher and Cristian Canton Ferrer and Moya Chen and Guillem Cucurull and David Esiobu and Jude Fernandes and Jeremy Fu and Wenyin Fu and Brian Fuller and Cynthia Gao and Vedanuj Goswami and Naman Goyal and Anthony Hartshorn and Saghar Hosseini and Rui Hou and Hakan Inan and Marcin Kardas and Viktor Kerkez and Madian Khabsa and Isabel Kloumann and Artem Korenev and Punit Singh Koura and Marie-Anne Lachaux and Thibaut Lavril and Jenya Lee and Diana Liskovich and Yinghai Lu and Yuning Mao and Xavier Martinet and Todor Mihaylov and Pushkar Mishra and Igor Molybog and Yixin Nie and Andrew Poulton and Jeremy Reizenstein and Rashi Rungta and Kalyan Saladi and Alan Schelten and Ruan Silva and Eric Michael Smith and Ranjan Subramanian and Xiaoqing Ellen Tan and Binh Tang and Ross Taylor and Adina Williams and Jian Xiang Kuan and Puxin Xu and Zheng Yan and Iliyan Zarov and Yuchen Zhang and Angela Fan and Melanie Kambadur and Sharan Narang and Aurelien Rodriguez and Robert Stojnic and Sergey Edunov and Thomas Scialom},
year={2023},
eprint= arXiv 2307.09288
}
@software{touvron2023llama,
title={LLaMA: Open and Efficient Foundation Language Models},
author={Touvron, Hugo and Lavril, Thibaut and Izacard, Gautier and Martinet, Xavier and Lachaux, Marie-Anne and Lacroix, Timoth{\'e}e and Rozi{\`e}re, Baptiste and Goyal, Naman and Hambro, Eric and Azhar, Faisal and Rodriguez, Aurelien and Joulin, Armand and Grave, Edouard and Lample, Guillaume},
journal={arXiv preprint arXiv:2302.13971},
year={2023}
}
``` | [
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Multimodal-Fatima/COCO_captions_train | Multimodal-Fatima | 2023-03-17T21:59:22Z | 219 | 2 | null | [
"region:us"
] | 2023-03-17T21:59:22Z | 2023-03-14T16:05:25.000Z | 2023-03-14T16:05:25 | ---
dataset_info:
features:
- name: image
dtype: image
- name: filepath
dtype: string
- name: sentids
list: int32
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dtype: string
- name: imgid
dtype: int32
- name: split
dtype: string
- name: sentences_tokens
list:
list: string
- name: sentences_raw
list: string
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list: int32
- name: cocoid
dtype: int32
- name: id
dtype: int64
splits:
- name: train
num_bytes: 18595506212.0
num_examples: 113287
download_size: 18500220513
dataset_size: 18595506212.0
---
# Dataset Card for "COCO_captions_train"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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csebuetnlp/squad_bn | csebuetnlp | 2022-08-21T13:17:43Z | 218 | 4 | null | [
"task_categories:question-answering",
"task_ids:open-domain-qa",
"task_ids:extractive-qa",
"annotations_creators:machine-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:extended",
"language:bn",
"license:cc-by-nc-sa-4.0",
"arxiv:2101.00204",
"arxiv:2007.01852",
"arxiv:1606.05250",
"arxiv:2003.05002",
"region:us"
] | 2022-08-21T13:17:43Z | 2022-04-11T10:16:26.000Z | 2022-04-11T10:16:26 | ---
annotations_creators:
- machine-generated
language_creators:
- found
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- extended
task_categories:
- question-answering
task_ids:
- open-domain-qa
- extractive-qa
language:
- bn
license:
- cc-by-nc-sa-4.0
---
# Dataset Card for `squad_bn`
## Table of Contents
- [Dataset Card for `squad_bn`](#dataset-card-for-squad_bn)
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Usage](#usage)
- [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)
- [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)
- [Contributions](#contributions)
## Dataset Description
- **Repository:** [https://github.com/csebuetnlp/banglabert](https://github.com/csebuetnlp/banglabert)
- **Paper:** [**"BanglaBERT: Combating Embedding Barrier in Multilingual Models for Low-Resource Language Understanding"**](https://arxiv.org/abs/2101.00204)
- **Point of Contact:** [Tahmid Hasan](mailto:tahmidhasan@cse.buet.ac.bd)
### Dataset Summary
This is a Question Answering (QA) dataset for Bengali, curated from the [SQuAD 2.0](), [TyDI-QA]() datasets and using the state-of-the-art English to Bengali translation model introduced **[here](https://aclanthology.org/2020.emnlp-main.207/).**
### Supported Tasks and Leaderboards
[More information needed](https://github.com/csebuetnlp/banglabert)
### Languages
* `Bengali`
### Usage
```python
from datasets import load_dataset
dataset = load_dataset("csebuetnlp/squad_bn")
```
## Dataset Structure
### Data Instances
One example from the dataset is given below in JSON format.
```
{
"title": "শেখ মুজিবুর রহমান",
"paragraphs": [
{
"qas": [
{
"answers": [
{
"answer_start": 19,
"text": "১৭ মার্চ ১৯২০"
}
],
"id": "bengali--981248442377505718-0-2649",
"question": "শেখ মুজিবুর রহমান কবে জন্মগ্রহণ করেন ?"
}
],
"context": "শেখ মুজিবুর রহমান (১৭ মার্চ ১৯২০ - ১৫ আগস্ট ১৯৭৫) বাংলাদেশের প্রথম রাষ্ট্রপতি ও ভারতীয় উপমহাদেশের একজন অন্যতম প্রভাবশালী রাজনৈতিক ব্যক্তিত্ব যিনি বাঙালীর অধিকার রক্ষায় ব্রিটিশ ভারত থেকে ভারত বিভাজন আন্দোলন এবং পরবর্তীতে পূর্ব পাকিস্তান থেকে বাংলাদেশ প্রতিষ্ঠার সংগ্রামে নেতৃত্ব প্রদান করেন। প্রাচীন বাঙ্গালি সভ্যতার আধুনিক স্থপতি হিসাবে শেখ মুজিবুর রহমানকে বাংলাদেশের জাতির জনক বা জাতির পিতা বলা হয়ে থাকে। তিনি মাওলানা আব্দুল হামিদ খান ভাসানী প্রতিষ্ঠিত আওয়ামী লীগের সভাপতি, বাংলাদেশের প্রথম রাষ্ট্রপতি এবং পরবর্তীতে এদেশের প্রধানমন্ত্রীর দায়িত্ব পালন করেন। জনসাধারণের কাছে তিনি শেখ মুজিব এবং শেখ সাহেব হিসাবে বেশি পরিচিত এবং তার উপাধি বঙ্গবন্ধু। তার কন্যা শেখ হাসিনা বাংলাদেশ আওয়ামী লীগের বর্তমান সভানেত্রী এবং বাংলাদেশের বর্তমান প্রধানমন্ত্রী।"
}
]
}
```
### Data Fields
The data fields are as follows:
- `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
| split |count |
|----------|--------|
|`train`| 127771 |
|`validation`| 2502 |
|`test`| 2504 |
## Dataset Creation
For the training set, we translated the complete [SQuAD 2.0](https://aclanthology.org/N18-1101/) dataset using the English to Bangla translation model introduced [here](https://aclanthology.org/2020.emnlp-main.207/). Due to the possibility of incursions of error during automatic translation, we used the [Language-Agnostic BERT Sentence Embeddings (LaBSE)](https://arxiv.org/abs/2007.01852) of the translations and original sentences to compute their similarity. A datapoint was accepted if all of its constituent sentences had a similarity score over 0.7.
Since the TyDI-QA Gold Passage task guarantees that the given context contains the answer and we want to pose our QA task analogous to SQuAD 2.0, we also consider examples from the Passage selection task that don't have an answer for the given question. We distribute the resultant examples from the TyDI-QA training and validation sets (which are publicly available) evenly to our test and validation sets.
### Curation Rationale
[More information needed](https://github.com/csebuetnlp/banglabert)
### Source Data
[SQuAD 2.0](https://arxiv.org/abs/1606.05250), [TyDi-QA](https://arxiv.org/abs/2003.05002)
#### Initial Data Collection and Normalization
[More information needed](https://github.com/csebuetnlp/banglabert)
#### Who are the source language producers?
[More information needed](https://github.com/csebuetnlp/banglabert)
### Annotations
[More information needed](https://github.com/csebuetnlp/banglabert)
#### Annotation process
[More information needed](https://github.com/csebuetnlp/banglabert)
#### Who are the annotators?
[More information needed](https://github.com/csebuetnlp/banglabert)
### Personal and Sensitive Information
[More information needed](https://github.com/csebuetnlp/banglabert)
## Considerations for Using the Data
### Social Impact of Dataset
[More information needed](https://github.com/csebuetnlp/banglabert)
### Discussion of Biases
[More information needed](https://github.com/csebuetnlp/banglabert)
### Other Known Limitations
[More information needed](https://github.com/csebuetnlp/banglabert)
## Additional Information
### Dataset Curators
[More information needed](https://github.com/csebuetnlp/banglabert)
### Licensing Information
Contents of this repository are restricted to only non-commercial research purposes under the [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0)](https://creativecommons.org/licenses/by-nc-sa/4.0/). Copyright of the dataset contents belongs to the original copyright holders.
### Citation Information
If you use the dataset, please cite the following paper:
```
@misc{bhattacharjee2021banglabert,
title={BanglaBERT: Combating Embedding Barrier in Multilingual Models for Low-Resource Language Understanding},
author={Abhik Bhattacharjee and Tahmid Hasan and Kazi Samin and Md Saiful Islam and M. Sohel Rahman and Anindya Iqbal and Rifat Shahriyar},
year={2021},
eprint={2101.00204},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
### Contributions
Thanks to [@abhik1505040](https://github.com/abhik1505040) and [@Tahmid](https://github.com/Tahmid04) for adding this dataset. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
GabeHD/pokemon-type-captions | GabeHD | 2022-10-23T04:40:59Z | 218 | 3 | null | [
"region:us"
] | 2022-10-23T04:40:59Z | 2022-10-18T08:38:18.000Z | 2022-10-18T08:38:18 | ---
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 19372532.0
num_examples: 898
download_size: 0
dataset_size: 19372532.0
---
# Dataset Card for Pokémon type captions
Contains official artwork and type-specific caption for Pokémon #1-898 (Bulbasaur-Calyrex).
Each Pokémon is represented once by the default form from [PokéAPI](https://pokeapi.co/)
Each row contains `image` and `text` keys:
- `image` is a 475x475 PIL jpg of the Pokémon's official artwork.
- `text` is a label describing the Pokémon by its type(s)
## Attributions
_Images and typing information pulled from [PokéAPI](https://pokeapi.co/)_
_Based on the [Lambda Labs Pokémon Blip Captions Dataset](https://huggingface.co/datasets/lambdalabs/pokemon-blip-captions)_
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
emrgnt-cmplxty/sciphi-textbooks-are-all-you-need | emrgnt-cmplxty | 2023-09-30T21:57:36Z | 218 | 118 | null | [
"license:llama2",
"region:us"
] | 2023-09-30T21:57:36Z | 2023-09-26T08:14:12.000Z | 2023-09-26T08:14:12 | ---
dataset_info:
features:
- name: formatted_prompt
dtype: string
- name: completion
dtype: string
- name: first_task
dtype: string
- name: second_task
dtype: string
- name: last_task
dtype: string
- name: notes
dtype: string
- name: title
dtype: string
- name: model
dtype: string
- name: temperature
dtype: float64
splits:
- name: train
num_bytes: 3175095649
num_examples: 681845
download_size: 1280399468
dataset_size: 3175095649
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
license: llama2
---
## Textbooks are all you need : A SciPhi Collection
Dataset Description
With LLMs, we can create a fully open-source Library of Alexandria.
As a first attempt, we have generated 650,000 unique textbook samples from a diverse span of courses, kindergarten through graduate school.
These are open source samples, which likely fall under the Llama-2 license. They were generated using the [SciPhi](https://github.com/emrgnt-cmplxty/SciPhi) repository.
All samples were created with [TheBloke/Phind-CodeLlama-34B-v2-AWQ](https://huggingface.co/TheBloke/Phind-CodeLlama-34B-v2-AWQ).
Lastly, I owe thanks to Runpod for the generous GPU time to make this possible. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
bigheiniuJ/CoTTrain-CoTCollection | bigheiniuJ | 2023-11-17T04:59:52Z | 218 | 0 | null | [
"region:us"
] | 2023-11-17T04:59:52Z | 2023-11-17T04:07:30.000Z | 2023-11-17T04:07:30 | ---
dataset_info:
features:
- name: source
dtype: string
- name: target
dtype: string
- name: rationale
dtype: string
- name: task
dtype: string
- name: type
dtype: string
- name: output
dtype: string
- name: input
dtype: string
splits:
- name: train
num_bytes: 861688211
num_examples: 384272
download_size: 522795521
dataset_size: 861688211
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "CoTTrain-CoTCollection"
[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 | |
ScandEval/norne-nn-mini | ScandEval | 2023-07-05T09:41:26Z | 217 | 0 | null | [
"task_categories:token-classification",
"size_categories:1K<n<10K",
"language:nn",
"license:other",
"region:us"
] | 2023-07-05T09:41:26Z | 2022-06-14T18:21:22.000Z | 2022-06-14T18:21:22 | ---
dataset_info:
features:
- name: text
dtype: string
- name: tokens
sequence: string
- name: labels
sequence: string
splits:
- name: train
num_bytes: 341534
num_examples: 1024
- name: test
num_bytes: 721476
num_examples: 2048
- name: val
num_bytes: 90956
num_examples: 256
download_size: 502871
dataset_size: 1153966
license: other
task_categories:
- token-classification
language:
- nn
size_categories:
- 1K<n<10K
---
# Dataset Card for "norne-nn-mini"
[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 | |
shunk031/livedoor-news-corpus | shunk031 | 2023-10-28T05:40:17Z | 217 | 3 | null | [
"task_categories:text-classification",
"task_ids:multi-class-classification",
"language_creators:found",
"multilinguality:monolingual",
"language:ja",
"license:cc-by-nd-4.0",
"region:us"
] | 2023-10-28T05:40:17Z | 2023-01-18T08:30:24.000Z | 2023-01-18T08:30:24 | ---
annotations_creators: []
language:
- ja
language_creators:
- found
license:
- cc-by-nd-4.0
multilinguality:
- monolingual
pretty_name: livedoor-news-corpus
size_categories: []
source_datasets: []
tags: []
task_categories:
- text-classification
task_ids:
- multi-class-classification
---
# Dataset Card for Livedoor News Corpus
[](https://github.com/shunk031/huggingface-datasets_livedoor-news-corpus/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:** http://www.rondhuit.com/download.html#ldcc
- **Repository:** https://github.com/shunk031/huggingface-datasets_livedoor-news-corpus
### Dataset Summary
> 本コーパスは、NHN Japan 株式会社が運営する「livedoor ニュース」のうち、下記のクリエイティブ・コモンズライセンスが適用されるニュース記事を収集し、可能な限り HTML タグを取り除いて作成したものです。
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
```python
from datasets import load_dataset
dataset = load_dataset(
"shunk031/livedoor-news-corpus",
train_ratio=0.8,
val_ratio=0.1,
test_ratio=0.1,
random_state=42,
shuffle=True,
)
print(dataset)
# DatasetDict({
# train: Dataset({
# features: ['url', 'date', 'title', 'content', 'category'],
# num_rows: 5894
# })
# validation: Dataset({
# features: ['url', 'date', 'title', 'content', 'category'],
# num_rows: 737
# })
# test: Dataset({
# features: ['url', 'date', 'title', 'content', 'category'],
# num_rows: 736
# })
# })
```
### 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
> 各記事ファイルにはクリエイティブ・コモンズライセンス「表示 – 改変禁止」が適用されます。 クレジット表示についてはニュースカテゴリにより異なるため、ダウンロードしたファイルを展開したサブディレクトリにあるそれぞれの LICENSE.txt をご覧ください。 livedoor は NHN Japan 株式会社の登録商標です。
### Citation Information
[More Information Needed]
### Contributions
Thanks to [RONDHUIT Co., Ltd.](https://www.rondhuit.com/) for creating this dataset.
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HuggingFaceH4/stack-exchange-preferences | HuggingFaceH4 | 2023-03-08T03:37:53Z | 217 | 84 | null | [
"task_categories:question-answering",
"size_categories:10M<n<100M",
"language:en",
"license:cc-by-sa-4.0",
"RLHF",
"preferences",
"human-feedback",
"Stack Exchange",
"arxiv:2112.00861",
"region:us"
] | 2023-03-08T03:37:53Z | 2023-02-11T03:24:28.000Z | 2023-02-11T03:24:28 | ---
license: cc-by-sa-4.0
task_categories:
- question-answering
language:
- en
pretty_name: H4 Stack Exchange Preferences Dataset
tags:
- RLHF
- preferences
- human-feedback
- Stack Exchange
download_size: 22132072448
size_categories:
- 10M<n<100M
---
# Dataset Card for H4 Stack Exchange Preferences Dataset
## Dataset Description
- **Homepage:** https://archive.org/details/stackexchange
- **Repository:** (private for now) https://github.com/huggingface/h4
- **Point of Contact:** Nathan Lambert, nathan@huggingface.co
- **Size of downloaded dataset:** 22.13 GB
- **Number of instructions:** 10,741,532
### Dataset Summary
This dataset contains questions and answers from the [Stack Overflow Data Dump](https://archive.org/details/stackexchange) for the purpose of **preference model training**.
Importantly, the questions have been filtered to fit the following criteria for preference models (following closely from [Askell et al. 2021](https://arxiv.org/abs/2112.00861)): *have >=2 answers*.
This data could also be used for instruction fine-tuning and language model training.
The questions are grouped with answers that are assigned a score corresponding to the Anthropic paper:
```
score = log2 (1 + upvotes) rounded to the nearest integer, plus 1 if the answer was accepted by the questioner (we assign a score of −1 if the number of upvotes is negative).
```
Some important notes when using this dataset for preference model pretraining (PMP), which can be ignored for other uses:
* the data will likely need to be filtered more due to matching scores.
* see section 4.1 of Askel et al 2021 for instructions on using each pair of samples twice via the following `binarization` (for better pre-training initialization):
```
Subsequently, we created a binary dataset by applying a ‘binarization’ procedure to the ranked dataset. That
is, for every ranked pair A > B, we transform it into two independent binary comparisons:
GOOD:A > BAD:A
BAD:B > GOOD:B
```
To see all the stackexchanges used in this data, please see [this file](https://huggingface.co/datasets/HuggingFaceH4/pmp-stack-exchange/blob/main/stack_exchanges.json).
Unfortunately, sharing the binarized data directly without metadata violates the license, so we have shared a script for binarization.
### Using the data
Here is a script from our internal tooling used to create a binarized dataset:
```
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import random
from argparse import ArgumentParser
from pathlib import Path
import numpy as np
from datasets import Dataset, concatenate_datasets, load_dataset
from h4.data.utils import save_dataset_shards
H4_DIR = Path(__file__).resolve().parents[3]
DATA_DIR = H4_DIR / "data"
if __name__ == "__main__":
parser = ArgumentParser()
parser.add_argument("--debug", action="store_true", help="Added print statements / limit data size for debugging")
parser.add_argument(
"--output_dir",
default=f"{DATA_DIR}/pmp-binarized",
type=str,
help="Where to save the processed dataset",
)
parser.add_argument(
"--exchange_name",
type=str,
default=None,
help="Optional argument to specify a specific subsection of the dataset",
)
parser.add_argument(
"--binary_score", type=int, default=8, help="Score assigned to binarized pairs for preference data."
)
parser.add_argument(
"--stream_data", action="store_true", help="Optionally stream data, which can be useful with weaker computers"
)
parser.set_defaults(debug=False, stream_data=False) # default will process full dataset
args = parser.parse_args()
specific_exchange = args.exchange_name
stream_dataset = args.stream_data
binary_score = args.binary_score
if specific_exchange:
data_dir = "data/" + args.exchange_name
else:
data_dir = None
if args.debug:
data_len_limit = 10000
else:
data_len_limit = np.inf
dataset = load_dataset(
"HuggingFaceH4/pmp-stack-exchange",
data_dir=data_dir,
split="train",
streaming=stream_dataset,
)
pmp_data = []
for i, d in enumerate(iter(dataset)):
# check debug limit, quit if in debug mode (don't save)
if i > data_len_limit:
print("Early exit for debug mode!")
print(pmp_data)
break
question = d["question"]
answers = d["answers"]
num_answers = len(answers)
answer_scores = [a["pm_score"] for a in answers]
if len(np.unique(answer_scores)) < 2:
print(f"PM Scores are {answer_scores}, skipping this question {i}")
else:
# Sample 2 unique scores for binarization
dif_scores = False
while not dif_scores:
# print("infinite loop...?")
two_answers = random.sample(answers, 2)
if two_answers[0]["pm_score"] != two_answers[1]["pm_score"]:
dif_scores = True
answer_0 = two_answers[0]
answer_1 = two_answers[1]
text_0 = "Question: " + question + "\n" + "Answer: " + answer_0["text"]
text_1 = "Question: " + question + "\n" + "Answer: " + answer_1["text"]
score_0 = binary_score
score_1 = binary_score
pmp_data.append({"context": text_0, "score": score_0})
pmp_data.append({"context": text_1, "score": score_1})
# Save binarized data
sublist_len = 100000
print(f"Dataset length is {len(pmp_data)}")
# bypass known issue in arrow https://issues.apache.org/jira/browse/ARROW-17137
print(f"Processed dataset length > {sublist_len}, processing to HF dataset in chunks")
chunks = [pmp_data[x : x + sublist_len] for x in range(0, len(pmp_data), sublist_len)]
ds_chunks = [Dataset.from_list(ch) for ch in chunks]
ds = concatenate_datasets(ds_chunks)
save_dataset_shards(ds, args.output_dir, subset="stackexchange", shard_size="100MB")
```
### Languages
This is intended to be English only, thought other languages may be present. Some Stack Exchanges that are omitted include:
```
spanish: es.meta.stackoverflow.com, es.stackoverflow.com
japanese: ja.meta.stackoverflow.com, ja.stackoverflow.com
portugese: pt.stackoverflow.com, pt.meta.stackoverflow.com
russian: ru.stackoverflow, ru.meta.stackoverflow
```
### Licensing Information
License: https://creativecommons.org/licenses/by-sa/4.0/
The cc-by-sa 4.0 licensing, while intentionally permissive, does require attribution:
Attribution — You must attribute the work in the manner specified by the author or licensor (but not in any way that suggests that they endorse you or your use of the work).
Specifically the attribution requirements are as follows:
1. Visually display or otherwise indicate the source of the content as coming from the Stack Exchange Network. This requirement is satisfied with a discreet text blurb, or some other unobtrusive but clear visual indication.
2. Ensure that any Internet use of the content includes a hyperlink directly to the original question on the source site on the Network (e.g., http://stackoverflow.com/questions/12345)
3. Visually display or otherwise clearly indicate the author names for every question and answer used
4. Ensure that any Internet use of the content includes a hyperlink for each author name directly back to his or her user profile page on the source site on the Network (e.g., http://stackoverflow.com/users/12345/username), directly to the Stack Exchange domain, in standard HTML (i.e. not through a Tinyurl or other such indirect hyperlink, form of obfuscation or redirection), without any “nofollow” command or any other such means of avoiding detection by search engines, and visible even with JavaScript disabled.
For more information, see the Stack Exchange Terms of Service.
### Citation Information
```
@online{h4stackexchange,
author = {Lambert, Nathan and Tunstall, Lewis and Rajani, Nazneen and Thrush, Tristan},
title = {HuggingFace H4 Stack Exchange Preference Dataset},
year = 2023,
url = {https://huggingface.co/datasets/HuggingFaceH4/stack-exchange-preferences},
}
``` | [
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RicardoRei/wmt-mqm-human-evaluation | RicardoRei | 2023-02-16T18:29:11Z | 217 | 0 | null | [
"size_categories:100K<n<1M",
"language:en",
"language:de",
"language:ru",
"language:zh",
"license:apache-2.0",
"mt-evaluation",
"WMT",
"region:us"
] | 2023-02-16T18:29:11Z | 2023-02-16T17:14:16.000Z | 2023-02-16T17:14:16 | ---
license: apache-2.0
language:
- en
- de
- ru
- zh
tags:
- mt-evaluation
- WMT
size_categories:
- 100K<n<1M
---
# Dataset Summary
This dataset contains all MQM human annotations from previous [WMT Metrics shared tasks](https://wmt-metrics-task.github.io/) and the MQM annotations from [Experts, Errors, and Context](https://aclanthology.org/2021.tacl-1.87/).
The data is organised into 8 columns:
- lp: language pair
- src: input text
- mt: translation
- ref: reference translation
- score: MQM score
- system: MT Engine that produced the translation
- annotators: number of annotators
- domain: domain of the input text (e.g. news)
- year: collection year
You can also find the original data [here](https://github.com/google/wmt-mqm-human-evaluation). We recommend using the original repo if you are interested in annotation spans and not just the final score.
## Python usage:
```python
from datasets import load_dataset
dataset = load_dataset("RicardoRei/wmt-mqm-human-evaluation", split="train")
```
There is no standard train/test split for this dataset but you can easily split it according to year, language pair or domain. E.g. :
```python
# split by year
data = dataset.filter(lambda example: example["year"] == 2022)
# split by LP
data = dataset.filter(lambda example: example["lp"] == "en-de")
# split by domain
data = dataset.filter(lambda example: example["domain"] == "ted")
```
## Citation Information
If you use this data please cite the following works:
- [Experts, Errors, and Context: A Large-Scale Study of Human Evaluation for Machine Translation](https://aclanthology.org/2021.tacl-1.87/)
- [Results of the WMT21 Metrics Shared Task: Evaluating Metrics with Expert-based Human Evaluations on TED and News Domain](https://aclanthology.org/2021.wmt-1.73/)
- [Results of WMT22 Metrics Shared Task: Stop Using BLEU – Neural Metrics Are Better and More Robust](https://aclanthology.org/2022.wmt-1.2/) | [
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chenz16/curriculum_benchmark | chenz16 | 2022-07-11T01:51:34Z | 216 | 0 | null | [
"license:mit",
"region:us"
] | 2022-07-11T01:51:34Z | 2022-07-11T01:50:55.000Z | 2022-07-11T01:50:55 | ---
license: mit
---
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MoritzLaurer/multilingual-NLI-26lang-2mil7 | MoritzLaurer | 2022-08-22T21:40:14Z | 216 | 28 | null | [
"task_categories:text-classification",
"task_ids:natural-language-inference",
"task_ids:multi-input-text-classification",
"annotations_creators:crowdsourced",
"language_creators:machinetranslation",
"size_categories:1M<n<5",
"source_datasets:multi_nli",
"source_datasets:anli",
"source_datasets:fever",
"source_datasets:lingnli",
"source_datasets:alisawuffles/WANLI",
"language:multilingual",
"language:zh",
"language:ja",
"language:ar",
"language:ko",
"language:de",
"language:fr",
"language:es",
"language:pt",
"language:hi",
"language:id",
"language:it",
"language:tr",
"language:ru",
"language:bn",
"language:ur",
"language:mr",
"language:ta",
"language:vi",
"language:fa",
"language:pl",
"language:uk",
"language:nl",
"language:sv",
"language:he",
"language:sw",
"language:ps",
"arxiv:2104.07179",
"region:us"
] | 2022-08-22T21:40:14Z | 2022-08-17T15:28:16.000Z | 2022-08-17T15:28:16 | ---
annotations_creators:
- crowdsourced
language_creators:
- machinetranslation
size_categories:
- 1M<n<5
source_datasets:
- multi_nli
- anli
- fever
- lingnli
- alisawuffles/WANLI
task_categories:
- text-classification
task_ids:
- natural-language-inference
- multi-input-text-classification
language:
- multilingual
- zh
- ja
- ar
- ko
- de
- fr
- es
- pt
- hi
- id
- it
- tr
- ru
- bn
- ur
- mr
- ta
- vi
- fa
- pl
- uk
- nl
- sv
- he
- sw
- ps
---
# Datasheet for the dataset: multilingual-NLI-26lang-2mil7
## Dataset Summary
This dataset contains 2 730 000 NLI text pairs in 26 languages spoken by more than 4 billion people. The dataset can be used to train models for multilingual NLI (Natural Language Inference) or zero-shot classification. The dataset is based on the English datasets [MultiNLI](https://huggingface.co/datasets/multi_nli), [Fever-NLI](https://github.com/easonnie/combine-FEVER-NSMN/blob/master/other_resources/nli_fever.md), [ANLI](https://huggingface.co/datasets/anli), [LingNLI](https://arxiv.org/pdf/2104.07179.pdf) and [WANLI](https://huggingface.co/datasets/alisawuffles/WANLI) and was created using the latest open-source machine translation models.
The dataset is designed to complement the established multilingual [XNLI](https://huggingface.co/datasets/xnli) dataset. XNLI contains older machine translations of the MultiNLI dataset from 2018 for 14 languages, as well as human translations of 2490 texts for validation and 5010 texts for testing per language. multilingual-NLI-26lang-2mil7 is sourced from 5 different NLI datasets and contains 105 000 machine translated texts for each of 26 languages, leading to 2 730 000 NLI text pairs.
The release of the dataset is accompanied by the fine-tuned [mDeBERTa-v3-base-xnli-multilingual-nli-2mil7](https://huggingface.co/MoritzLaurer/mDeBERTa-v3-base-xnli-multilingual-nli-2mil7) model, which can be used for NLI or zero-shot classification in 100 languages.
## Dataset Creation
The languages in the dataset are: ['ar', 'bn', 'de', 'es', 'fa', 'fr', 'he', 'hi', 'id', 'it', 'ja', 'ko', 'mr', 'nl', 'pl', 'ps', 'pt', 'ru', 'sv', 'sw', 'ta', 'tr', 'uk', 'ur', 'vi', 'zh'] (see [ISO language codes](https://en.wikipedia.org/wiki/List_of_ISO_639-1_codes)) plus the original English texts. The languages were chosen based on two criteria: (1) They are either included in the list of the [20 most spoken languages](https://en.wikipedia.org/wiki/List_of_languages_by_total_number_of_speakers) (excluding Telugu and Nigerian Pidgin, for which no machine translation model was available); (2) or they are spoken in polit-economically important countries such as the [G20](https://en.wikipedia.org/wiki/G20) or Iran and Israel.
For each of the 26 languages, a different random sample of 25 000 hypothesis-premise pairs was taken from each of the following four datasets: [MultiNLI](https://huggingface.co/datasets/multi_nli) (392 702 texts in total), [Fever-NLI](https://github.com/easonnie/combine-FEVER-NSMN/blob/master/other_resources/nli_fever.md) (196 805 texts), [ANLI](https://huggingface.co/datasets/anli) (162 865 texts), [WANLI](https://huggingface.co/datasets/alisawuffles/WANLI) (102 885 texts). Moreover, a sample of 5000 texts was taken from [LingNLI](https://arxiv.org/pdf/2104.07179.pdf) (29 985 texts) given its smaller total size. This leads to a different random sample of 105 000 source texts per target language with a diverse distribution of data from 5 different NLI datasets.
Each sample was then machine translated using the latest open-source machine translation models available for the respective language:
- [opus-mt-tc-big models](https://huggingface.co/models?sort=downloads&search=opus-mt-tc-big) were available for English to ['ar', 'es', 'fr', 'it', 'pt', 'tr']
- [opus-mt-models](https://huggingface.co/models?sort=downloads&search=opus-mt) were available for English to ['de', 'he', 'hi', 'id', 'mr', 'nl', 'ru', 'sv', 'sw', 'uk', 'ur', 'vi', 'zh']
- [m2m100_1.2B](https://huggingface.co/facebook/m2m100_1.2B) was used for the remaining languages ['bn', 'fa', 'ja', 'ko', 'pl', 'ps', 'ta']
## DatasetStructure
### Data Splits
The dataset contains 130 splits (26 * 5), one for each language-dataset pair following the format '{language-iso}_{dataset}'. For example, split 'zh_mnli' contains the Chinese translation of 25 000 texts from the MultiNLI dataset etc.
### Data Fields
- `premise_original`: The original premise from the English source dataset
- `hypothesis_original`: The original hypothesis from the English source dataset
- `label`: The classification label, with possible values `entailment` (0), `neutral` (1), `contradiction` (2).
- `premise`: The machine translated premise in the target language
- `hypothesis`: The machine translated premise in the target language
### Example of a data instance:
```
{
"premise_original": "I would not be surprised if the top priority for the Navy was to build a new carrier.",
"hypothesis_original": "The top priority for the Navy is to build a new carrier.",
"label": 1,
"premise": "Ich würde mich nicht wundern, wenn die oberste Priorität für die Navy wäre, einen neuen Träger zu bauen.",
"hypothesis": "Die oberste Priorität für die Navy ist es, einen neuen Träger zu bauen."
}
```
## Limitations and bias
Machine translation is not as good as human translation. Machine translation can introduce inaccuracies that can be problematic for complex tasks like NLI. In an ideal world, original NLI data would be available for many languages. Given the lack of NLI data, using the latest open-source machine translation seems like a good solution to improve multilingual NLI. You can use the Hugging Face data viewer to inspect the data and verify the translation quality for your language of interest. Note that grammatical errors are less problematic for zero-shot use-cases as grammar is less relevant for these applications.
## Other
The machine translation for the full dataset took roughly 100 hours on an A100 GPU, especially due to the size of the [m2m100_1.2B](https://huggingface.co/facebook/m2m100_1.2B) model.
## Ideas for cooperation or questions?
For updates on new models and datasets, follow me on [Twitter](https://twitter.com/MoritzLaurer).
If you have questions or ideas for cooperation, contact me at m{dot}laurer{at}vu{dot}nl or on [LinkedIn](https://www.linkedin.com/in/moritz-laurer/)
### Citation Information
If the dataset is useful for you, please cite the following article:
```
@article{laurer_less_2022,
title = {Less {Annotating}, {More} {Classifying} – {Addressing} the {Data} {Scarcity} {Issue} of {Supervised} {Machine} {Learning} with {Deep} {Transfer} {Learning} and {BERT} - {NLI}},
url = {https://osf.io/74b8k},
language = {en-us},
urldate = {2022-07-28},
journal = {Preprint},
author = {Laurer, Moritz and Atteveldt, Wouter van and Casas, Andreu Salleras and Welbers, Kasper},
month = jun,
year = {2022},
note = {Publisher: Open Science Framework},
}
```
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
jonathan-roberts1/NWPU-RESISC45 | jonathan-roberts1 | 2023-03-31T16:57:43Z | 216 | 0 | null | [
"task_categories:image-classification",
"task_categories:zero-shot-image-classification",
"license:other",
"region:us"
] | 2023-03-31T16:57:43Z | 2023-01-20T15:46:31.000Z | 2023-01-20T15:46:31 | ---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': airplane
'1': airport
'2': baseball diamond
'3': basketball court
'4': beach
'5': bridge
'6': chaparral
'7': church
'8': circular farmland
'9': cloud
'10': commercial area
'11': dense residential
'12': desert
'13': forest
'14': freeway
'15': golf course
'16': ground track field
'17': harbor
'18': industrial area
'19': intersection
'20': island
'21': lake
'22': meadow
'23': medium residential
'24': mobile home park
'25': mountain
'26': overpass
'27': palace
'28': parking lot
'29': railway
'30': railway station
'31': rectangular farmland
'32': river
'33': roundabout
'34': runway
'35': sea ice
'36': ship
'37': snowberg
'38': sparse residential
'39': stadium
'40': storage tank
'41': tennis court
'42': terrace
'43': thermal power station
'44': wetland
splits:
- name: train
num_bytes: 381151705
num_examples: 31500
download_size: 424827902
dataset_size: 381151705
license: other
task_categories:
- image-classification
- zero-shot-image-classification
---
# Dataset Card for "NWPU-RESISC45"
## Dataset Description
- **Paper** [Remote sensing image scene classification: Benchmark and state of the art](https://ieeexplore.ieee.org/iel7/5/8045830/07891544.pdf)
### Licensing Information
[CC-BY-SA]
## Citation Information
[Remote sensing image scene classification: Benchmark and state of the art](https://ieeexplore.ieee.org/iel7/5/8045830/07891544.pdf)
```
@article{cheng2017remote,
title = {Remote sensing image scene classification: Benchmark and state of the art},
author = {Cheng, Gong and Han, Junwei and Lu, Xiaoqiang},
year = 2017,
journal = {Proceedings of the IEEE},
publisher = {IEEE},
volume = 105,
number = 10,
pages = {1865--1883}
}
``` | [
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fujiki/japanese_hh-rlhf-49k | fujiki | 2023-05-28T06:08:04Z | 216 | 2 | null | [
"language:ja",
"license:mit",
"region:us"
] | 2023-05-28T06:08:04Z | 2023-05-28T05:55:53.000Z | 2023-05-28T05:55:53 | ---
license: mit
dataset_info:
features:
- name: instruction
dtype: string
- name: input
dtype: string
- name: output
dtype: string
- name: index
dtype: string
splits:
- name: train
num_bytes: 34168978
num_examples: 49332
download_size: 18427777
dataset_size: 34168978
language:
- ja
---
- This is a little bit different version of [`kunishou/hh-rlhf-49k-ja`](https://huggingface.co/datasets/kunishou/hh-rlhf-49k-ja) without `ng_translation == 1` examples.
- Please also refer to the original dataset [`kunishou/hh-rlhf-49k-ja`](https://huggingface.co/datasets/kunishou/hh-rlhf-49k-ja). | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
OdiaGenAI/sentiment_analysis_hindi | OdiaGenAI | 2023-10-16T14:34:14Z | 216 | 0 | null | [
"task_categories:text-classification",
"size_categories:1K<n<10K",
"language:hi",
"region:us"
] | 2023-10-16T14:34:14Z | 2023-10-16T14:22:08.000Z | 2023-10-16T14:22:08 | ---
task_categories:
- text-classification
language:
- hi
size_categories:
- 1K<n<10K
---
Conventions followed to decide the polarity: -
- labels consisting of a single value are left undisturbed, i.e. if label = 'pos', then it'll be pos
- labels consisting of multiple values separated by '&' are processed. If all the labels are the same ('pos&pos&pos' or 'neg&neg'), then the shortened form of the multiple label is assigned as the final label. For example, if label = 'pos&pos&pos', then final label will be 'pos'.
- labels consisting of mixed values ('pos&neg&pos' or 'neg&neu&pos') are rejected.
Contributors: -
- Kusumlata Patiyal | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
objects76/Anthropic-hh-rlhf-dpo | objects76 | 2023-11-15T06:46:29Z | 216 | 0 | null | [
"license:apache-2.0",
"region:us"
] | 2023-11-15T06:46:29Z | 2023-11-15T06:39:50.000Z | 2023-11-15T06:39:50 | ---
license: apache-2.0
---
# DPO formatted Helpful and Harmless RLHF Dataset
This dataset is built from [Anthropic's hh-rlhf dataset.](https://huggingface.co/datasets/Anthropic/hh-rlhf)
It is modified as follows:
The prompt formatting is switched to the Llama 2 format with [INST] and [/INST]
The data is split into prompt, chosen and rejected rows, as required by HuggingFace's DPO trainer.
Purchase access to this dataset here. Purchase entitles the user to make use of the dataset for training large language models. | [
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newbiettn/ops_transforms | newbiettn | 2023-11-16T23:40:46Z | 216 | 0 | null | [
"license:apache-2.0",
"region:us"
] | 2023-11-16T23:40:46Z | 2023-11-15T09:48:48.000Z | 2023-11-15T09:48:48 | ---
license: apache-2.0
---
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communityai/aptchatv1-template-micro-dataset-llm-v2-714k | communityai | 2023-11-16T09:59:36Z | 216 | 0 | null | [
"region:us"
] | 2023-11-16T09:59:36Z | 2023-11-16T09:58:16.000Z | 2023-11-16T09:58:16 | ---
dataset_info:
features:
- name: id
dtype: int64
- name: source
dtype: string
- name: conversations
list:
- name: content
dtype: string
- name: role
dtype: string
splits:
- name: train
num_bytes: 1722474450.0482376
num_examples: 713591
- name: test
num_bytes: 1206905.9517624504
num_examples: 500
download_size: 903719556
dataset_size: 1723681356.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
heegyu/namuwiki-extracted | heegyu | 2023-01-15T09:46:31Z | 215 | 3 | null | [
"task_categories:other",
"language_creators:other",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"language:ko",
"license:cc-by-nc-sa-2.0",
"region:us"
] | 2023-01-15T09:46:31Z | 2022-10-01T01:27:07.000Z | 2022-10-01T01:27:07 | ---
license: cc-by-nc-sa-2.0
language:
- ko
language_creators:
- other
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
task_categories:
- other
---
# namu.wiki database dump
##
https://namu.wiki/ database dump 2022/03/01<br/>
- 571308rows
- download size: 2.19GB
## 주의사항
namu-wiki-extractor를 이용하여 전처리, 추가로 아래 전처리를 수행했습니다
1. 헤더 제거 `== 개요 ==`
1. 테이블 제거
1. `[age(1997-01-01)]` 는 전처리 시점 기준으로 적용(2022년 10월 2일)
1. `[math(a / b + c)]` 는 제거하지 않음.
1. math 마크다운이 각주 내에 있을 경우, 각주가 전처리되지 않은 문제 있음.
## Usage
```bash
pip install datasets
```
```python
from datasets import load_dataset
dataset = load_dataset("heegyu/namuwiki-extracted")
print(dataset["train"][0])
```
```
{
'title': '!!아앗!!',
'text': '！！ああっと！！ ▲신 세계수의 미궁 2에서 뜬 !!아앗!! 세계수의 미궁 시리즈에 전통으로 등장하는 대사. 2편부터 등장했으며 훌륭한 사망 플래그의 예시이다. 세계수의 모험가들이 탐험하는 던전인 수해의 구석구석에는 채취/벌채/채굴 포인트가 있으며, 이를 위한 채집 스킬에 ...',
'contributors': '110.46.34.123,kirby10,max0243,218.54.117.149,ruby3141,121.165.63.239,iviyuki,1.229.200.194,anatra95,kiri47,175.127.134.2,nickchaos71,chkong1998,kiwitree2,namubot,huwieblusnow',
'namespace': ''
}
``` | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
Birchlabs/openai-prm800k-stepwise-critic | Birchlabs | 2023-06-03T10:51:37Z | 215 | 9 | null | [
"license:mit",
"region:us"
] | 2023-06-03T10:51:37Z | 2023-06-03T10:49:21.000Z | 2023-06-03T10:49:21 | ---
license: mit
---
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
TanveerAman/AMI-Corpus-Text-Summarization | TanveerAman | 2023-06-19T07:17:53Z | 215 | 5 | null | [
"task_categories:summarization",
"language:en",
"region:us"
] | 2023-06-19T07:17:53Z | 2023-06-17T22:51:54.000Z | 2023-06-17T22:51:54 | ---
task_categories:
- summarization
language:
- en
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
jackhhao/jailbreak-classification | jackhhao | 2023-09-30T01:55:08Z | 215 | 0 | null | [
"task_categories:text-classification",
"size_categories:10K<n<100K",
"language:en",
"license:apache-2.0",
"jailbreak",
"security",
"moderation",
"region:us"
] | 2023-09-30T01:55:08Z | 2023-09-30T00:56:39.000Z | 2023-09-30T00:56:39 | ---
license: apache-2.0
task_categories:
- text-classification
language:
- en
tags:
- jailbreak
- security
- moderation
pretty_name: Jailbreak Classification
size_categories:
- 10K<n<100K
configs:
- config_name: default
data_files:
- split: train
path: "balanced/jailbreak_dataset_train_balanced.csv"
- split: test
path: "balanced/jailbreak_dataset_test_balanced.csv"
---
# Jailbreak Classification
### Dataset Summary
Dataset used to classify prompts as jailbreak vs. benign.
## Dataset Structure
### Data Fields
- `prompt`: an LLM prompt
- `type`: classification label, either `jailbreak` or `benign`
## Dataset Creation
### Curation Rationale
Created to help detect & prevent harmful jailbreak prompts when users interact with LLMs.
### Source Data
Jailbreak prompts sourced from: <https://github.com/verazuo/jailbreak_llms>
Benign prompts sourced from:
- [OpenOrca](https://huggingface.co/datasets/Open-Orca/OpenOrca)
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
nielsr/cord-layoutlmv3 | nielsr | 2022-05-02T16:41:30Z | 214 | 2 | null | [
"region:us"
] | 2022-05-02T16:41:30Z | 2022-05-02T16:37:54.000Z | 2022-05-02T16:37:54 | Entry not found | [
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mteb/raw_medrxiv | mteb | 2022-09-27T19:15:18Z | 214 | 1 | null | [
"language:en",
"region:us"
] | 2022-09-27T19:15:18Z | 2022-05-10T13:51:35.000Z | 2022-05-10T13:51:35 | ---
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
valurank/News_Articles_Categorization | valurank | 2023-08-27T05:49:31Z | 214 | 0 | null | [
"task_categories:text-classification",
"task_ids:multi-class-classification",
"multilinguality:monolingual",
"language:en",
"license:other",
"region:us"
] | 2023-08-27T05:49:31Z | 2022-05-25T21:46:45.000Z | 2022-05-25T21:46:45 | ---
license:
- other
language:
- en
multilinguality:
- monolingual
task_categories:
- text-classification
task_ids:
- multi-class-classification
---
# Dataset Card for News_Articles_Categorization
## Table of Contents
- [Dataset Description](#dataset-description)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Source Data](#source-data)
## Dataset Description
3722 News Articles classified into different categories namely: World, Politics, Tech, Entertainment, Sport, Business, Health, and Science
## Languages
The text in the dataset is in English
## Dataset Structure
The dataset consists of two columns namely Text and Category.
The Text column consists of the news article and the Category column consists of the class each article belongs to
## Source Data
The dataset is scrapped across different news platforms
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irds/clinicaltrials_2021_trec-ct-2021 | irds | 2023-01-05T02:54:09Z | 214 | 1 | null | [
"task_categories:text-retrieval",
"source_datasets:irds/clinicaltrials_2021",
"region:us"
] | 2023-01-05T02:54:09Z | 2023-01-05T02:54:03.000Z | 2023-01-05T02:54:03 | ---
pretty_name: '`clinicaltrials/2021/trec-ct-2021`'
viewer: false
source_datasets: ['irds/clinicaltrials_2021']
task_categories:
- text-retrieval
---
# Dataset Card for `clinicaltrials/2021/trec-ct-2021`
The `clinicaltrials/2021/trec-ct-2021` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/clinicaltrials#clinicaltrials/2021/trec-ct-2021).
# Data
This dataset provides:
- `queries` (i.e., topics); count=75
- `qrels`: (relevance assessments); count=35,832
- For `docs`, use [`irds/clinicaltrials_2021`](https://huggingface.co/datasets/irds/clinicaltrials_2021)
## Usage
```python
from datasets import load_dataset
queries = load_dataset('irds/clinicaltrials_2021_trec-ct-2021', 'queries')
for record in queries:
record # {'query_id': ..., 'text': ...}
qrels = load_dataset('irds/clinicaltrials_2021_trec-ct-2021', 'qrels')
for record in qrels:
record # {'query_id': ..., 'doc_id': ..., 'relevance': ..., 'iteration': ...}
```
Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the
data in 🤗 Dataset format.
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jordyvl/rvl_cdip_100_examples_per_class | jordyvl | 2023-03-23T20:55:18Z | 214 | 0 | null | [
"region:us"
] | 2023-03-23T20:55:18Z | 2023-03-23T19:58:02.000Z | 2023-03-23T19:58:02 | ---
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: 97000316.76
num_examples: 800
- name: test
num_bytes: 48612840.21
num_examples: 400
- name: validation
num_bytes: 48666549.76
num_examples: 400
download_size: 180034173
dataset_size: 194279706.73
---
# Dataset Card for "rvl_cdip_100_examples_per_class"
[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 | |
clarin-knext/nfcorpus-pl | clarin-knext | 2023-06-07T08:11:26Z | 214 | 0 | null | [
"language:pl",
"arxiv:2305.19840",
"region:us"
] | 2023-06-07T08:11:26Z | 2023-06-06T22:41:34.000Z | 2023-06-06T22:41:34 | ---
language:
- pl
---
Part of **BEIR-PL: Zero Shot Information Retrieval Benchmark for the Polish Language**.
Link to arxiv: https://arxiv.org/pdf/2305.19840.pdf
Contact: konrad.wojtasik@pwr.edu.pl | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
glaiveai/glaive-code-assistant | glaiveai | 2023-09-27T22:51:02Z | 214 | 47 | null | [
"size_categories:100K<n<1M",
"license:apache-2.0",
"region:us"
] | 2023-09-27T22:51:02Z | 2023-09-21T18:56:47.000Z | 2023-09-21T18:56:47 | ---
license: apache-2.0
size_categories:
- 100K<n<1M
---
# Glaive-code-assistant
Glaive-code-assistant is a dataset of ~140k code problems and solutions generated using Glaive’s synthetic data generation platform.
The data is intended to be used to make models act as code assistants, and so the data is structured in a QA format where the questions are worded similar to how real users will ask code related questions.
The data has ~60% python samples.
To report any problems or suggestions in the data, join the [Glaive discord](https://discord.gg/fjQ4uf3yWD) | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
dinhbinh161/vi_text_news | dinhbinh161 | 2023-11-09T15:18:11Z | 214 | 0 | null | [
"region:us"
] | 2023-11-09T15:18:11Z | 2023-11-09T07:27:58.000Z | 2023-11-09T07:27:58 | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 145710391
num_examples: 1020122
download_size: 79305270
dataset_size: 145710391
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "vi_text_news"
[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 | |
Lin-Chen/ShareGPT4V | Lin-Chen | 2023-11-22T15:48:45Z | 214 | 74 | null | [
"task_categories:visual-question-answering",
"task_categories:question-answering",
"task_categories:conversational",
"size_categories:1M<n",
"language:en",
"license:cc-by-nc-4.0",
"arxiv:2311.12793",
"region:us"
] | 2023-11-22T15:48:45Z | 2023-11-20T09:29:12.000Z | 2023-11-20T09:29:12 | ---
license: cc-by-nc-4.0
task_categories:
- visual-question-answering
- question-answering
- conversational
language:
- en
pretty_name: ShareGPT4V Captions 1.2M Dataset Card
size_categories:
- 1M<n
configs:
- config_name: ShareGPT4V
data_files: sharegpt4v_instruct_gpt4-vision_cap100k.json
- config_name: ShareGPT4V-PT
data_files: share-captioner_coco_lcs_sam_1246k_1107.json
---
# ShareGPT4V 1.2M Dataset Card
## Dataset details
**Dataset type:**
ShareGPT4V Captions 1.2M is a set of GPT4-Vision-powered multi-modal captions data.
It is constructed to enhance modality alignment and fine-grained visual concept perception in Large Multi-Modal Models (LMMs) during both the pre-training and supervised fine-tuning stages. This advancement aims to bring LMMs towards GPT4-Vision capabilities.
* sharegpt4v_instruct_gpt4-vision_cap100k.json is generated by GPT4-Vision (ShareGPT4V).
* share-captioner_coco_lcs_sam_1246k_1107.json is generated by our Share-Captioner trained on GPT4-Vision-generated data (ShareGPT4V-PT).
* sharegpt4v_mix665k_cap23k_coco-ap9k_lcs3k_sam9k_div2k.json is curated from sharegpt4v_instruct_gpt4-vision_cap100k.json for the supervised fine-tuning stage.
**Dataset date:**
ShareGPT4V Captions 1.2M was collected in 11.07 2023.
**Paper or resources for more information:**
[[Project](https://ShareGPT4V.github.io/)] [[Paper](https://huggingface.co/papers/2311.12793)] [[Code](https://github.com/InternLM/InternLM-XComposer/tree/main/projects/ShareGPT4V)]
**License:**
Attribution-NonCommercial 4.0 International
It should abide by the policy of OpenAI: https://openai.com/policies/terms-of-use
## Intended use
**Primary intended uses:**
The primary use of ShareGPT4V Captions 1.2M is research on large multimodal models and chatbots.
**Primary intended users:**
The primary intended users of this dataset are researchers and hobbyists in computer vision, natural language processing, machine learning, and artificial intelligence.
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vesteinn/swe-nerc | vesteinn | 2022-11-30T12:40:35Z | 213 | 0 | null | [
"region:us"
] | 2022-11-30T12:40:35Z | 2022-11-30T10:26:32.000Z | 2022-11-30T10:26:32 | This is a Swedish NE dataset, Swe-NERC v1. Please see https://hdl.handle.net/10794/121 for more information.
Included here is the manually tagged part.
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
HuggingFaceM4/NoCaps | HuggingFaceM4 | 2022-12-14T04:08:38Z | 213 | 1 | null | [
"license:cc-by-2.0",
"region:us"
] | 2022-12-14T04:08:38Z | 2022-12-08T17:11:21.000Z | 2022-12-08T17:11:21 | ---
license: cc-by-2.0
---
# Dataset Card for NoCaps
## 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://nocaps.org/](https://nocaps.org/)
- **Paper:** [nocaps: novel object captioning at scale](https://openaccess.thecvf.com/content_ICCV_2019/papers/Agrawal_nocaps_novel_object_captioning_at_scale_ICCV_2019_paper.pdf)
- **Leaderboard:**
- **Point of Contact:**: contact@nocaps.org
### Dataset Summary
Dubbed NoCaps for novel object captioning at scale, NoCaps consists of 166,100 human-generated captions describing 15,100 images from the Open Images validation and test sets.
The associated training data consists of COCO image-caption pairs, plus Open Images image-level labels and object bounding boxes.
Since Open Images contains many more classes than COCO, nearly 400 object classes seen in test images have no or very few associated training captions (hence, nocaps).
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
Each instance has the following structure:
```
{
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=L size=732x1024 at 0x7F574A3A9B50>,
'image_coco_url': 'https://s3.amazonaws.com/nocaps/val/0013ea2087020901.jpg',
'image_date_captured': '2018-11-06 11:04:33',
'image_file_name': '0013ea2087020901.jpg',
'image_height': 1024,
'image_width': 732,
'image_id': 0,
'image_license': 0,
'image_open_images_id': '0013ea2087020901',
'annotations_ids': [0, 1, 2, 3, 4, 5, 6, 7, 8, 9],
'annotations_captions': [
'A baby is standing in front of a house.',
'A little girl in a white jacket and sandals.',
'A young child stands in front of a house.',
'A child is wearing a white shirt and standing on a side walk. ',
'A little boy is standing in his diaper with a white shirt on.',
'A child wearing a diaper and shoes stands on the sidewalk.',
'A child is wearing a light-colored shirt during the daytime.',
'A little kid standing on the pavement in a shirt. ',
'Black and white photo of a little girl smiling.',
'a cute baby is standing alone with white shirt'
]
}
```
### Data Fields
- `image`: The image
- `image_coco_url`: URL for the image
- `image_date_captured`: Date at which the image was captured
- `image_file_name`: The file name for the image
- `image_height`: Height of the image
- `image_width`: Width of the image
- `image_id`: Id of the image
- `image_license`: Not sure what this is, it is always at 0
- `image_open_images_id`: Open image id
- `annotations_ids`: Unique ids for the captions (to use in conjunction with `annotations_captions`)
- `annotations_captions`: Captions for the image (to use in conjunction with `annotations_ids`)
### 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
Thanks to [@VictorSanh](https://github.com/VictorSanh) for adding this dataset. | [
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andstor/the_pile_github | andstor | 2023-03-20T23:39:53Z | 213 | 3 | null | [
"task_categories:text-generation",
"task_categories:fill-mask",
"task_categories:text-classification",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:other",
"arxiv:2101.00027",
"arxiv:2201.07311",
"region:us"
] | 2023-03-20T23:39:53Z | 2023-03-07T15:53:05.000Z | 2023-03-07T15:53:05 | ---
annotations_creators:
- no-annotation
language:
- en
language_creators:
- found
license:
- other
multilinguality:
- monolingual
pretty_name: The Pile GitHub
size_categories: []
source_datasets:
- original
tags: []
task_categories:
- text-generation
- fill-mask
- text-classification
task_ids: []
---
# Dataset Card for The Pile GitHub
## Table of Contents
- [Dataset Card for Smart Contracts](#dataset-card-for-the-pile-github)
- [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)
- [Additional Information](#additional-information)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [ElutherAI](https://pile.eleuther.ai)
- **Repository:** [GitHub](https://github.com/andstor/the-pile-github)
- **Paper:** [arXiv](https://arxiv.org/abs/2101.00027)
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [Needs More Information]
### Dataset Summary
This is the GitHub subset of EleutherAi/The Pile dataset and contains GitHub repositories. The programming languages are identified using the [guesslang library](https://github.com/yoeo/guesslang). A total of 54 programming languages are included in the dataset.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
The following languages are covered by the dataset:
```
'Assembly', 'Batchfile', 'C', 'C#', 'C++', 'CMake', 'COBOL', 'CSS', 'CSV', 'Clojure', 'CoffeeScript', 'DM', 'Dart', 'Dockerfile', 'Elixir', 'Erlang', 'Fortran', 'Go', 'Groovy', 'HTML', 'Haskell', 'INI', 'JSON', 'Java', 'JavaScript', 'Julia', 'Kotlin', 'Lisp', 'Lua', 'Makefile', 'Markdown', 'Matlab', 'None', 'OCaml', 'Objective-C', 'PHP', 'Pascal', 'Perl', 'PowerShell', 'Prolog', 'Python', 'R', 'Ruby', 'Rust', 'SQL', 'Scala', 'Shell', 'Swift', 'TOML', 'TeX', 'TypeScript', 'Verilog', 'Visual Basic', 'XML', 'YAML'
```
The [guesslang library](https://github.com/yoeo/guesslang) is used to identify the programming languages. It has a guessing accuracy of above 90%. Hence, there will be some misclassifications in the language identification.
## Dataset Structure
### Data Instances
[More Information Needed]
```
{
'text': ...,
'meta': {'language': ...}
}
```
### Data Fields
- `text` (`string`): the source code.
- `meta` (`dict`): the metadata of the source code.
- `language` (`string`): the programming language of the source code.
### Data Splits
[More Information Needed]
| | train | validation | test |
|-------------------------|------:|-----------:|-----:|
| Input Sentences | | | |
| Average Sentence Length | | | |
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
The data is purely a subset of the [EleutherAI/The Pile dataset](https://huggingface.co/datasets/the_pile). See the original [dataset](https://arxiv.org/abs/2201.07311) for more details.
## Additional Information
### Licensing Information
The Pile dataset was released on January 1st, 2021. It is licensed under the MIT License. See the [dataset](https://arxiv.org/abs/2201.07311) for more details.
### Citation Information
Provide the [BibTex](http://www.bibtex.org/)-formatted reference for the dataset. For example:
```
@article{pile,
title={The {P}ile: An 800GB Dataset of Diverse Text for Language Modeling},
author={Gao, Leo and Biderman, Stella and Black, Sid and Golding, Laurence and Hoppe, Travis and Foster, Charles and Phang, Jason and He, Horace and Thite, Anish and Nabeshima, Noa and Presser, Shawn and Leahy, Connor},
journal={arXiv preprint arXiv:2101.00027},
year={2020}
}
```
### Contributions
Thanks to [@andstor](https://github.com/andstor) for adding this dataset. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
pkr7098/bookcorpus-wikipedia-full | pkr7098 | 2023-10-31T01:06:21Z | 213 | 0 | null | [
"region:us"
] | 2023-10-31T01:06:21Z | 2023-10-30T11:59:38.000Z | 2023-10-30T11:59:38 | ---
dataset_info:
config_name: 20220301.en
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 24500165181
num_examples: 80462898
download_size: 0
dataset_size: 24500165181
configs:
- config_name: 20220301.en
data_files:
- split: train
path: 20220301.en/train-*
---
# Dataset Card for "bookcorpus-wikipedia-full"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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PoolC/1-fold-clone-detection-600k-5fold | PoolC | 2022-06-01T06:49:02Z | 212 | 2 | null | [
"region:us"
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
ScandEval/scandiqa-da-mini | ScandEval | 2023-07-05T09:44:29Z | 212 | 0 | null | [
"task_categories:question-answering",
"size_categories:1K<n<10K",
"language:da",
"license:cc-by-3.0",
"region:us"
] | 2023-07-05T09:44:29Z | 2022-12-05T16:41:50.000Z | 2022-12-05T16:41:50 | ---
dataset_info:
features:
- name: id
dtype: string
- name: question
dtype: string
- name: answers
struct:
- name: answer_start
sequence: int64
- name: text
sequence: string
- name: context
dtype: string
- name: answers_en
struct:
- name: answer_start
sequence: int64
- name: text
sequence: string
- name: context_en
dtype: string
- name: title_en
dtype: string
splits:
- name: train
num_bytes: 3238964
num_examples: 1024
- name: val
num_bytes: 1096223
num_examples: 256
- name: test
num_bytes: 6668816
num_examples: 2048
download_size: 6456003
dataset_size: 11004003
license: cc-by-3.0
task_categories:
- question-answering
language:
- da
size_categories:
- 1K<n<10K
---
# Dataset Card for "scandiqa-da-mini"
[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 | |
keremberke/pokemon-classification | keremberke | 2023-01-15T18:41:29Z | 212 | 5 | null | [
"task_categories:image-classification",
"roboflow",
"roboflow2huggingface",
"Gaming",
"region:us"
] | 2023-01-15T18:41:29Z | 2023-01-15T18:40:15.000Z | 2023-01-15T18:40:15 | ---
task_categories:
- image-classification
tags:
- roboflow
- roboflow2huggingface
- Gaming
---
<div align="center">
<img width="640" alt="keremberke/pokemon-classification" src="https://huggingface.co/datasets/keremberke/pokemon-classification/resolve/main/thumbnail.jpg">
</div>
### Dataset Labels
```
['Porygon', 'Goldeen', 'Hitmonlee', 'Hitmonchan', 'Gloom', 'Aerodactyl', 'Mankey', 'Seadra', 'Gengar', 'Venonat', 'Articuno', 'Seaking', 'Dugtrio', 'Machop', 'Jynx', 'Oddish', 'Dodrio', 'Dragonair', 'Weedle', 'Golduck', 'Flareon', 'Krabby', 'Parasect', 'Ninetales', 'Nidoqueen', 'Kabutops', 'Drowzee', 'Caterpie', 'Jigglypuff', 'Machamp', 'Clefairy', 'Kangaskhan', 'Dragonite', 'Weepinbell', 'Fearow', 'Bellsprout', 'Grimer', 'Nidorina', 'Staryu', 'Horsea', 'Electabuzz', 'Dratini', 'Machoke', 'Magnemite', 'Squirtle', 'Gyarados', 'Pidgeot', 'Bulbasaur', 'Nidoking', 'Golem', 'Dewgong', 'Moltres', 'Zapdos', 'Poliwrath', 'Vulpix', 'Beedrill', 'Charmander', 'Abra', 'Zubat', 'Golbat', 'Wigglytuff', 'Charizard', 'Slowpoke', 'Poliwag', 'Tentacruel', 'Rhyhorn', 'Onix', 'Butterfree', 'Exeggcute', 'Sandslash', 'Pinsir', 'Rattata', 'Growlithe', 'Haunter', 'Pidgey', 'Ditto', 'Farfetchd', 'Pikachu', 'Raticate', 'Wartortle', 'Vaporeon', 'Cloyster', 'Hypno', 'Arbok', 'Metapod', 'Tangela', 'Kingler', 'Exeggutor', 'Kadabra', 'Seel', 'Voltorb', 'Chansey', 'Venomoth', 'Ponyta', 'Vileplume', 'Koffing', 'Blastoise', 'Tentacool', 'Lickitung', 'Paras', 'Clefable', 'Cubone', 'Marowak', 'Nidorino', 'Jolteon', 'Muk', 'Magikarp', 'Slowbro', 'Tauros', 'Kabuto', 'Spearow', 'Sandshrew', 'Eevee', 'Kakuna', 'Omastar', 'Ekans', 'Geodude', 'Magmar', 'Snorlax', 'Meowth', 'Pidgeotto', 'Venusaur', 'Persian', 'Rhydon', 'Starmie', 'Charmeleon', 'Lapras', 'Alakazam', 'Graveler', 'Psyduck', 'Rapidash', 'Doduo', 'Magneton', 'Arcanine', 'Electrode', 'Omanyte', 'Poliwhirl', 'Mew', 'Alolan Sandslash', 'Mewtwo', 'Weezing', 'Gastly', 'Victreebel', 'Ivysaur', 'MrMime', 'Shellder', 'Scyther', 'Diglett', 'Primeape', 'Raichu']
```
### Number of Images
```json
{'train': 4869, 'valid': 1390, 'test': 732}
```
### 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/pokemon-classification", name="full")
example = ds['train'][0]
```
### Roboflow Dataset Page
[https://universe.roboflow.com/robert-demo-qvail/pokedex/dataset/14](https://universe.roboflow.com/robert-demo-qvail/pokedex/dataset/14?ref=roboflow2huggingface)
### Citation
```
@misc{ pokedex_dataset,
title = { Pokedex Dataset },
type = { Open Source Dataset },
author = { Lance Zhang },
howpublished = { \\url{ https://universe.roboflow.com/robert-demo-qvail/pokedex } },
url = { https://universe.roboflow.com/robert-demo-qvail/pokedex },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2022 },
month = { dec },
note = { visited on 2023-01-14 },
}
```
### License
Public Domain
### Dataset Summary
This dataset was exported via roboflow.com on December 20, 2022 at 5:34 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 unstructured image data
* annotate, and create datasets
* export, train, and deploy computer vision models
* use active learning to improve your dataset over time
It includes 6991 images.
Pokemon are annotated in folder format.
The following pre-processing was applied to each image:
* Auto-orientation of pixel data (with EXIF-orientation stripping)
* Resize to 224x224 (Fit (black edges))
No image augmentation techniques were applied.
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
tilyupo/trivia_qa | tilyupo | 2023-08-03T17:00:54Z | 212 | 0 | null | [
"region:us"
] | 2023-08-03T17:00:54Z | 2023-08-02T19:44:00.000Z | 2023-08-02T19:44:00 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
dataset_info:
features:
- name: question
dtype: string
- name: question_id
dtype: string
- name: question_source
dtype: string
- name: answer
struct:
- name: aliases
sequence: string
- name: normalized_aliases
sequence: string
- name: matched_wiki_entity_name
dtype: string
- name: normalized_matched_wiki_entity_name
dtype: string
- name: normalized_value
dtype: string
- name: type
dtype: string
- name: value
dtype: string
- name: passages
list:
- name: answer
dtype: string
- name: passage
dtype: string
- name: precise_score
dtype: float64
- name: rough_score
dtype: float64
- name: source
dtype: string
- name: title
dtype: string
splits:
- name: train
num_bytes: 3065861634
num_examples: 137282
- name: validation
num_bytes: 402091161
num_examples: 17817
download_size: 1805238996
dataset_size: 3467952795
---
# Dataset Card for "trivia_qa_passages"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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vblagoje/wikipedia_snippets_streamed | vblagoje | 2021-07-01T15:32:09Z | 211 | 0 | null | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
c-s-ale/alpaca-gpt4-data | c-s-ale | 2023-04-07T19:27:51Z | 211 | 17 | null | [
"task_categories:text-generation",
"size_categories:10K<n<100K",
"language:en",
"license:cc-by-4.0",
"gpt",
"alpaca",
"fine-tune",
"instruct-tune",
"instruction",
"arxiv:2304.03277",
"region:us"
] | 2023-04-07T19:27:51Z | 2023-04-07T18:20:58.000Z | 2023-04-07T18:20:58 | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: input
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 40178951
num_examples: 52002
download_size: 24027484
dataset_size: 40178951
license: cc-by-4.0
language:
- en
pretty_name: Instruction Tuning with GPT-4
size_categories:
- 10K<n<100K
task_categories:
- text-generation
tags:
- gpt
- alpaca
- fine-tune
- instruct-tune
- instruction
---
# Dataset Description
- **Project Page:** https://instruction-tuning-with-gpt-4.github.io
- **Repo:** https://github.com/Instruction-Tuning-with-GPT-4/GPT-4-LLM
- **Paper:** https://arxiv.org/abs/2304.03277
# Dataset Card for "alpaca-gpt4-data"
All of the work is done by [this team](https://github.com/Instruction-Tuning-with-GPT-4/GPT-4-LLM).
# Usage and License Notices
The data is intended and licensed for research use only. The dataset is CC BY NC 4.0 (allowing only non-commercial use) and models trained using the dataset should not be used outside of research purposes.
# Chinese Dataset
[Found here](https://huggingface.co/datasets/c-s-ale/alpaca-gpt4-data-zh)
# Citation
```
@article{peng2023gpt4llm,
title={Instruction Tuning with GPT-4},
author={Baolin Peng, Chunyuan Li, Pengcheng He, Michel Galley, Jianfeng Gao},
journal={arXiv preprint arXiv:2304.03277},
year={2023}
}
``` | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
minoosh/IEMOCAP_Speech_dataset | minoosh | 2023-05-16T11:58:34Z | 211 | 0 | null | [
"region:us"
] | 2023-05-16T11:58:34Z | 2023-05-16T11:40:01.000Z | 2023-05-16T11:40:01 | ---
dataset_info:
features:
- name: TURN_NAME
dtype: string
- name: audio
dtype:
audio:
sampling_rate: 16000
- name: emotion
dtype:
class_label:
names:
'0': ang
'1': hap
'2': neu
'3': sad
splits:
- name: Session1
num_bytes: 165158903.64
num_examples: 1085
- name: Session2
num_bytes: 154202695.13
num_examples: 1023
- name: Session3
num_bytes: 158294386.59
num_examples: 1151
- name: Session4
num_bytes: 147780976.55
num_examples: 1031
- name: Session5
num_bytes: 170101711.098
num_examples: 1241
download_size: 788474562
dataset_size: 795538673.0080001
---
# Dataset Card for "IEMOCAP_Speech_dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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nielsr/XFUN | nielsr | 2022-09-18T10:57:50Z | 210 | 3 | null | [
"region:us"
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
VanessaSchenkel/translation-en-pt | VanessaSchenkel | 2022-08-06T21:52:26Z | 210 | 2 | null | [
"task_categories:translation",
"annotations_creators:found",
"language_creators:found",
"multilinguality:translation",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:en",
"language:pt",
"license:afl-3.0",
"region:us"
] | 2022-08-06T21:52:26Z | 2022-07-06T00:29:28.000Z | 2022-07-06T00:29:28 | ---
annotations_creators:
- found
language:
- en
- pt
language_creators:
- found
license:
- afl-3.0
multilinguality:
- translation
pretty_name: VanessaSchenkel/translation-en-pt
size_categories:
- 100K<n<1M
source_datasets:
- original
tags: []
task_categories:
- translation
task_ids: []
---
How to use it:
```
from datasets import load_dataset
remote_dataset = load_dataset("VanessaSchenkel/translation-en-pt", field="data")
remote_dataset
```
Output:
```
DatasetDict({
train: Dataset({
features: ['id', 'translation'],
num_rows: 260482
})
})
```
Exemple:
```
remote_dataset["train"][5]
```
Output:
```
{'id': '5',
'translation': {'english': 'I have to go to sleep.',
'portuguese': 'Tenho de dormir.'}}
``` | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
ScandEval/scandiqa-no-mini | ScandEval | 2023-07-05T09:44:15Z | 210 | 0 | null | [
"task_categories:question-answering",
"size_categories:1K<n<10K",
"language:nb",
"language:no",
"language:nn",
"license:cc-by-3.0",
"region:us"
] | 2023-07-05T09:44:15Z | 2022-12-05T16:42:53.000Z | 2022-12-05T16:42:53 | ---
dataset_info:
features:
- name: id
dtype: string
- name: question
dtype: string
- name: answers
struct:
- name: answer_start
sequence: int64
- name: text
sequence: string
- name: context
dtype: string
- name: answers_en
struct:
- name: answer_start
sequence: int64
- name: text
sequence: string
- name: context_en
dtype: string
- name: title_en
dtype: string
splits:
- name: train
num_bytes: 2865871
num_examples: 1024
- name: val
num_bytes: 673315
num_examples: 256
- name: test
num_bytes: 6556814
num_examples: 2048
download_size: 5956802
dataset_size: 10096000
license: cc-by-3.0
task_categories:
- question-answering
language:
- nb
- 'no'
- nn
size_categories:
- 1K<n<10K
---
# Dataset Card for "scandiqa-no-mini"
[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 | |
hezarai/persian-license-plate-v1 | hezarai | 2023-11-09T10:36:12Z | 210 | 0 | null | [
"task_categories:image-to-text",
"language:fa",
"region:us"
] | 2023-11-09T10:36:12Z | 2023-10-14T12:56:01.000Z | 2023-10-14T12:56:01 | ---
task_categories:
- image-to-text
language:
- fa
pretty_name: PersianLicensePlate
---
> Dataset is downloaded from [here](https://ceit.aut.ac.ir/~keyvanrad/download/ML971/project/) which was provided at Amirkabir University of Technology.
> The datas then labeled by the authors.
> Experimental results show that the fine-tuned model works well in Persian License Plate.
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