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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
damerajee/khasi-datasets | damerajee | 2023-11-23T15:03:24Z | 19 | 0 | null | [
"task_categories:text-generation",
"size_categories:1K<n<10K",
"license:apache-2.0",
"region:us"
] | 2023-11-23T15:03:24Z | 2023-11-22T15:26:00.000Z | 2023-11-22T15:26:00 | ---
license: apache-2.0
task_categories:
- text-generation
pretty_name: 'Tribal Language , language modeling '
size_categories:
- 1K<n<10K
---
# What is Khasi Language?
## Location:
- Primarily spoken in the northeastern Indian state of Meghalaya.
- Also spoken in parts of Assam, Tripura, and Bangladesh.
## Language Family:
- Khasi is a member of the Austroasiatic language family.
## Script:
- Traditionally written using the Khasi script, which is a script created specifically for the Khasi language.
## Culture and Identity:
- The Khasi language is an integral part of the cultural identity of the Khasi people.
- It plays a significant role in traditional Khasi folklore, rituals, and oral traditions.
## Grammar:
- Khasi has a subject-verb-object (SVO) word order.
- Nouns do not have gender, and there is no grammatical distinction between singular and plural.
## Vocabulary:
- The vocabulary of Khasi reflects the cultural and natural environment of the Khasi people, including terms related to agriculture, nature, and social customs.
-
## Multilingualism:
- Many Khasi speakers are multilingual, often fluent in English and other languages due to the region's diverse linguistic landscape.
## Linguistic Features:
- Khasi is known for its unique linguistic features, including a system of classifiers used in counting and categorizing objects.
## Language Preservation:
- Efforts are made to preserve and promote the Khasi language through education, literature, and cultural programs.
## Cultural Significance:
- The Khasi language is closely tied to the cultural and historical heritage of the Khasi people, contributing to their distinct identity in the northeastern region of India.
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wenzhuoliu/dataset-long-context-for-e5-finetune | wenzhuoliu | 2023-11-28T15:23:06Z | 19 | 0 | null | [
"region:us"
] | 2023-11-28T15:23:06Z | 2023-11-23T13:59:17.000Z | 2023-11-23T13:59:17 | ---
dataset_info:
- config_name: default
features:
- name: query
dtype: string
- name: passage
dtype: string
splits:
- name: wikihow_summary_passage
num_bytes: 332619989
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num_examples: 20000
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num_examples: 20535
download_size: 243783107
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- config_name: llm_eval
features:
- name: query
dtype: string
- name: passage
dtype: string
splits:
- name: train
num_bytes: 355087
num_examples: 99
download_size: 230987
dataset_size: 355087
configs:
- config_name: default
data_files:
- split: wikihow_summary_passage
path: data/wikihow_summary_passage-*
- split: llm_generated_question_passage
path: data/llm_generated_question_passage-*
- split: qestion_passage_fr
path: data/qestion_passage_fr-*
- config_name: llm_eval
data_files:
- split: train
path: llm_eval/train-*
---
# Dataset Card for "dataset-long-context-for-e5-finetune"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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erikliu18/simulated_gt | erikliu18 | 2023-11-27T14:50:48Z | 19 | 0 | null | [
"region:us"
] | 2023-11-27T14:50:48Z | 2023-11-26T00:53:20.000Z | 2023-11-26T00:53:20 | ---
dataset_info:
features:
- name: image
dtype: image
splits:
- name: train
num_bytes: 1065788.0
num_examples: 500
download_size: 0
dataset_size: 1065788.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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vibhorag101/sem_eval_2018_task_1_english_cleaned_labels | vibhorag101 | 2023-11-26T05:09:30Z | 19 | 0 | null | [
"region:us"
] | 2023-11-26T05:09:30Z | 2023-11-26T05:09:18.000Z | 2023-11-26T05:09:18 | ---
configs:
- config_name: default
data_files:
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path: data/train-*
- split: test
path: data/test-*
- split: validation
path: data/validation-*
dataset_info:
features:
- name: ID
dtype: string
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dtype: string
- name: anger
dtype: bool
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dtype: bool
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dtype: bool
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dtype: bool
- name: joy
dtype: bool
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dtype: bool
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dtype: bool
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dtype: bool
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num_examples: 886
download_size: 706992
dataset_size: 1955512
---
# Dataset Card for "sem_eval_2018_task_1_english_cleaned_labels"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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lewtun/mnist-preds | lewtun | 2021-07-16T09:00:01Z | 18 | 0 | null | [
"benchmark:test",
"region:us"
] | 2021-07-16T09:00:01Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
benchmark: test
---
# Dataset Card for RAFT Submission | [
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midas/ldkp10k | midas | 2022-04-02T16:49:45Z | 18 | 2 | null | [
"region:us"
] | 2022-04-02T16:49:45Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | A dataset for benchmarking keyphrase extraction and generation techniques from long document English scientific papers. For more details about the dataset please refer the original paper - []().
Data source - []()
## Dataset Summary
## Dataset Structure
### Data Fields
- **id**: unique identifier of the document.
- **sections**: list of all the sections present in the document.
- **sec_text**: list of white space separated list of words present in each section.
- **sec_bio_tags**: list of BIO tags of white space separated list of words present in each section.
- **extractive_keyphrases**: List of all the present keyphrases.
- **abstractive_keyphrase**: List of all the absent keyphrases.
### Data Splits
|Split| #datapoints |
|--|--|
| Train-Small | 20,000 |
| Train-Medium | 50,000 |
| Train-Large | 1,296,613 |
| Test | 10,000 |
| Validation | 10,000 |
## Usage
### Small Dataset
```python
from datasets import load_dataset
# get small dataset
dataset = load_dataset("midas/ldkp10k", "small")
def order_sections(sample):
"""
corrects the order in which different sections appear in the document.
resulting order is: title, abstract, other sections in the body
"""
sections = []
sec_text = []
sec_bio_tags = []
if "title" in sample["sections"]:
title_idx = sample["sections"].index("title")
sections.append(sample["sections"].pop(title_idx))
sec_text.append(sample["sec_text"].pop(title_idx))
sec_bio_tags.append(sample["sec_bio_tags"].pop(title_idx))
if "abstract" in sample["sections"]:
abstract_idx = sample["sections"].index("abstract")
sections.append(sample["sections"].pop(abstract_idx))
sec_text.append(sample["sec_text"].pop(abstract_idx))
sec_bio_tags.append(sample["sec_bio_tags"].pop(abstract_idx))
sections += sample["sections"]
sec_text += sample["sec_text"]
sec_bio_tags += sample["sec_bio_tags"]
return sections, sec_text, sec_bio_tags
# sample from the train split
print("Sample from train data split")
train_sample = dataset["train"][0]
sections, sec_text, sec_bio_tags = order_sections(train_sample)
print("Fields in the sample: ", [key for key in train_sample.keys()])
print("Section names: ", sections)
print("Tokenized Document: ", sec_text)
print("Document BIO Tags: ", sec_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 data split")
validation_sample = dataset["validation"][0]
sections, sec_text, sec_bio_tags = order_sections(validation_sample)
print("Fields in the sample: ", [key for key in validation_sample.keys()])
print("Section names: ", sections)
print("Tokenized Document: ", sec_text)
print("Document BIO Tags: ", sec_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 data split")
test_sample = dataset["test"][0]
sections, sec_text, sec_bio_tags = order_sections(test_sample)
print("Fields in the sample: ", [key for key in test_sample.keys()])
print("Section names: ", sections)
print("Tokenized Document: ", sec_text)
print("Document BIO Tags: ", sec_bio_tags)
print("Extractive/present Keyphrases: ", test_sample["extractive_keyphrases"])
print("Abstractive/absent Keyphrases: ", test_sample["abstractive_keyphrases"])
print("\n-----------\n")
```
**Output**
```bash
```
### Medium Dataset
```python
from datasets import load_dataset
# get medium dataset
dataset = load_dataset("midas/ldkp10k", "medium")
```
### Large Dataset
```python
from datasets import load_dataset
# get large dataset
dataset = load_dataset("midas/ldkp10k", "large")
```
## Citation Information
Please cite the works below if you use this dataset in your work.
```
@article{mahata2022ldkp,
title={LDKP: A Dataset for Identifying Keyphrases from Long Scientific Documents},
author={Mahata, Debanjan and Agarwal, Naveen 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}
}
```
```
@article{lo2019s2orc,
title={S2ORC: The semantic scholar open research corpus},
author={Lo, Kyle and Wang, Lucy Lu and Neumann, Mark and Kinney, Rodney and Weld, Dan S},
journal={arXiv preprint arXiv:1911.02782},
year={2019}
}
```
```
@inproceedings{ccano2019keyphrase,
title={Keyphrase generation: A multi-aspect survey},
author={{\c{C}}ano, Erion and Bojar, Ond{\v{r}}ej},
booktitle={2019 25th Conference of Open Innovations Association (FRUCT)},
pages={85--94},
year={2019},
organization={IEEE}
}
```
```
@article{meng2017deep,
title={Deep keyphrase generation},
author={Meng, Rui and Zhao, Sanqiang and Han, Shuguang and He, Daqing and Brusilovsky, Peter and Chi, Yu},
journal={arXiv preprint arXiv:1704.06879},
year={2017}
}
```
## 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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mvip/tr_corpora_parliament_processed | mvip | 2022-02-24T07:31:09Z | 18 | 0 | null | [
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s3h/arabic-grammar-corrections | s3h | 2021-11-30T12:37:00Z | 18 | 3 | null | [
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softcatala/ca_text_corpus | softcatala | 2022-10-24T17:38:51Z | 18 | 0 | null | [
"task_categories:text-generation",
"task_ids:language-modeling",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1M<n<10M",
"source_datasets:original",
"language:ca",
"license:cc0-1.0",
"region:us"
] | 2022-10-24T17:38:51Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- ca
license:
- cc0-1.0
multilinguality:
- monolingual
size_categories:
- 1M<n<10M
source_datasets:
- original
task_categories:
- text-generation
task_ids:
- language-modeling
pretty_name: ca-text-corpus
---
# Dataset Card for ca-text-corpus
## 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:**
- **Repository:** https://github.com/Softcatala/ca-text-corpus
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
Public domain corpus of Catalan text.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
Catalan (`ca`).
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[CC0 1.0 Universal](https://creativecommons.org/publicdomain/zero/1.0/).
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@albertvillanova](https://github.com/albertvillanova) for adding this dataset.
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softcatala/catalan-dictionary | softcatala | 2022-10-24T17:38:30Z | 18 | 1 | null | [
"task_categories:text-generation",
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"region:us"
] | 2022-10-24T17:38:30Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- ca
license:
- gpl-2.0
- lgpl-2.1
multilinguality:
- monolingual
size_categories:
- 1M<n<10M
source_datasets:
- original
task_categories:
- text-generation
task_ids:
- language-modeling
pretty_name: catalan-dictionary
---
# Dataset Card for ca-text-corpus
## 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:**
- **Repository:** https://github.com/Softcatala/catalan-dict-tools
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
Catalan word lists with part of speech labeling curated by humans. Contains 1 180 773 forms including verbs, nouns, adjectives, names or toponyms. These word lists are used to build applications like Catalan spellcheckers or verb querying applications.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
Catalan (`ca`).
## Dataset Structure
The dataset contains 3 columns:
* Form (e.g. cantaré)
* Lemma (e.g. cantar)
* POS tag (e.g. VMIF1S00)
You can have the meaning of the POS tag here: https://freeling-user-manual.readthedocs.io/en/latest/tagsets/tagset-ca/#part-of-speech-verb
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[LGPL 2.1](https://www.gnu.org/licenses/old-licenses/lgpl-2.1.html).
[GPL 2.0](https://www.gnu.org/licenses/old-licenses/gpl-2.0.html).
### Citation Information
[More Information Needed]
### Contributions
Softcatalà
Jaume Ortolà
Joan Moratinos | [
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0.00822338741272687... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
SetFit/catalonia_independence_es | SetFit | 2022-03-13T09:11:31Z | 18 | 0 | null | [
"region:us"
] | 2022-03-13T09:11:31Z | 2022-03-13T02:44:02.000Z | 2022-03-13T02:44:02 | #catalonian independence tweet dataset
This dataset is a port of the official ['catalonia_independence' dataset] (https://huggingface.co/datasets/catalonia_independence) on the Hub. It has just the Spanish language version. | [
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hazal/Turkish-Biomedical-corpus-trM | hazal | 2022-08-10T11:13:22Z | 18 | 3 | null | [
"language:tr",
"region:us"
] | 2022-08-10T11:13:22Z | 2022-03-15T12:01:31.000Z | 2022-03-15T12:01:31 | ---
language:
- tr
--- | [
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huggan/CelebA-faces-with-attributes | huggan | 2022-04-01T08:27:55Z | 18 | 2 | null | [
"region:us"
] | 2022-04-01T08:27:55Z | 2022-03-31T15:01:15.000Z | 2022-03-31T15:01:15 | Entry not found | [
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jamescalam/stoic-corpus | jamescalam | 2022-04-01T19:45:56Z | 18 | 1 | null | [
"region:us"
] | 2022-04-01T19:45:56Z | 2022-04-01T15:22:51.000Z | 2022-04-01T15:22:51 | Entry not found | [
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iluvvatar/RuNNE | iluvvatar | 2023-03-30T13:36:53Z | 18 | 2 | null | [
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"multilinguality:monolingual",
"language:ru",
"arxiv:2108.13112",
"region:us"
] | 2023-03-30T13:36:53Z | 2022-04-02T07:55:42.000Z | 2022-04-02T07:55:42 | ---
language:
- ru
multilinguality:
- monolingual
task_categories:
- token-classification
task_ids:
- named-entity-recognition
pretty_name: RuNNE
---
# RuNNE dataset
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Structure](#dataset-structure)
- [Citation Information](#citation-information)
- [Contacts](#contacts)
## Dataset Description
Part of NEREL dataset (https://arxiv.org/abs/2108.13112), a Russian dataset
for named entity recognition and relation extraction, used in RuNNE (2022)
competition (https://github.com/dialogue-evaluation/RuNNE).
Entities may be nested (see https://arxiv.org/abs/2108.13112).
Entity types list:
* AGE
* AWARD
* CITY
* COUNTRY
* CRIME
* DATE
* DISEASE
* DISTRICT
* EVENT
* FACILITY
* FAMILY
* IDEOLOGY
* LANGUAGE
* LAW
* LOCATION
* MONEY
* NATIONALITY
* NUMBER
* ORDINAL
* ORGANIZATION
* PENALTY
* PERCENT
* PERSON
* PRODUCT
* PROFESSION
* RELIGION
* STATE_OR_PROVINCE
* TIME
* WORK_OF_ART
## Dataset Structure
There are two "configs" or "subsets" of the dataset.
Using
`load_dataset('MalakhovIlya/RuNNE', 'ent_types')['ent_types']`
you can download list of entity types (
Dataset({
features: ['type'],
num_rows: 29
})
)
Using
`load_dataset('MalakhovIlya/RuNNE', 'data')` or `load_dataset('MalakhovIlya/RuNNE')`
you can download the data itself (DatasetDict)
Dataset consists of 3 splits: "train", "test" and "dev". Each of them contains text document. "Train" and "test" splits also contain annotated entities, "dev" doesn't.
Each entity is represented by a string of the following format: "\<start> \<stop> \<type>", where \<start> is a position of the first symbol of entity in text, \<stop> is the last symbol position in text and \<type> is a one of the aforementioned list of types.
P.S.
Original NEREL dataset also contains relations, events and linked entities, but they were not added here yet ¯\\\_(ツ)_/¯
## Citation Information
@article{Artemova2022runne,
title={{RuNNE-2022 Shared Task: Recognizing Nested Named Entities}},
author={Artemova, Ekaterina and Zmeev, Maksim and Loukachevitch, Natalia and Rozhkov, Igor and Batura, Tatiana and Braslavski, Pavel and Ivanov, Vladimir and Tutubalina, Elena},
journal={Computational Linguistics and Intellectual Technologies: Proceedings of the International Conference "Dialog"},
year={2022}
}
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johnowhitaker/colorbs | johnowhitaker | 2022-04-04T06:52:33Z | 18 | 0 | null | [
"region:us"
] | 2022-04-04T06:52:33Z | 2022-04-03T12:24:32.000Z | 2022-04-03T12:24:32 | A synthetic dataset for GAN experiments.
Created with a CLOOB Conditioned Latent Diffusion model (https://github.com/JD-P/cloob-latent-diffusion)
For each color in a list of standard CSS color names, a set of images was generated using the following command:
```
python cfg_sample.py --autoencoder autoencoder_kl_32x32x4\
--checkpoint yfcc-latent-diffusion-f8-e2-s250k.ckpt\
--method plms\
--cond-scale 1.0\
--seed 34\
--steps 25\
-n 36\
"A glass orb with {color} spacetime fire burning inside"
```
| [
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mteb/quora-retrieval | mteb | 2022-04-12T17:15:57Z | 18 | 0 | null | [
"region:us"
] | 2022-04-12T17:15:57Z | 2022-04-12T17:06:39.000Z | 2022-04-12T17:06:39 | Entry not found | [
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-... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
XiangPan/waimai_10k | XiangPan | 2022-04-14T22:38:31Z | 18 | 2 | null | [
"region:us"
] | 2022-04-14T22:38:31Z | 2022-04-14T22:14:23.000Z | 2022-04-14T22:14:23 | # Dataset Card for [Dataset Name]
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
[More Information Needed]
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@github-username](https://github.com/<github-username>) for adding this dataset. | [
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... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
mwong/climatetext-claim-related-evaluation | mwong | 2022-10-25T10:08:44Z | 18 | 1 | null | [
"task_categories:text-classification",
"task_ids:fact-checking",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:extended|climate_text",
"language:en",
"license:cc-by-sa-3.0",
"license:gpl-3.0",
"... | 2022-10-25T10:08:44Z | 2022-04-20T12:00:50.000Z | 2022-04-20T12:00:50 | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- cc-by-sa-3.0
- gpl-3.0
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- extended|climate_text
task_categories:
- text-classification
task_ids:
- fact-checking
---
### Dataset Summary
This dataset is extracted from Climate Text dataset (https://www.sustainablefinance.uzh.ch/en/research/climate-fever/climatext.html), pre-processed and, ready to evaluate.
The evaluation objective is a text classification task - given a climate related claim and evidence, predict if claim is related to evidence. | [
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0.129008591175... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
mwong/climatetext-evidence-related-evaluation | mwong | 2022-10-25T10:08:46Z | 18 | 1 | null | [
"task_categories:text-classification",
"task_ids:fact-checking",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:extended|climate_text",
"language:en",
"license:cc-by-sa-3.0",
"license:gpl-3.0",
"... | 2022-10-25T10:08:46Z | 2022-04-20T12:18:14.000Z | 2022-04-20T12:18:14 | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- cc-by-sa-3.0
- gpl-3.0
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- extended|climate_text
task_categories:
- text-classification
task_ids:
- fact-checking
---
### Dataset Summary
This dataset is extracted from Climate Text dataset (https://www.sustainablefinance.uzh.ch/en/research/climate-fever/climatext.html), pre-processed and, ready to evaluate.
The evaluation objective is a text classification task - given a climate related claim and evidence, predict if evidence is related to claim. | [
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0.1261621564... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
mwong/climatetext-claim-climate_evidence-related-evaluation | mwong | 2022-10-25T10:08:50Z | 18 | 1 | null | [
"task_categories:text-classification",
"task_ids:fact-checking",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:extended|climate_text",
"language:en",
"license:cc-by-sa-3.0",
"license:gpl-3.0",
"... | 2022-10-25T10:08:50Z | 2022-04-21T10:07:08.000Z | 2022-04-21T10:07:08 | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- cc-by-sa-3.0
- gpl-3.0
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- extended|climate_text
task_categories:
- text-classification
task_ids:
- fact-checking
---
### Dataset Summary
This dataset is extracted from Climate Text dataset (https://www.sustainablefinance.uzh.ch/en/research/climate-fever/climatext.html), pre-processed and, ready to evaluate.
The evaluation objective is a text classification task - given a claim and climate related evidence, predict if evidence is related to claim. | [
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0.128625884652137... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
SocialGrep/the-reddit-nft-dataset | SocialGrep | 2022-07-01T17:52:49Z | 18 | 1 | null | [
"annotations_creators:lexyr",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:1M<n<10M",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"region:us"
] | 2022-07-01T17:52:49Z | 2022-04-26T19:52:29.000Z | 2022-04-26T19:52:29 | ---
annotations_creators:
- lexyr
language_creators:
- crowdsourced
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 1M<n<10M
source_datasets:
- original
paperswithcode_id: null
---
# Dataset Card for the-reddit-nft-dataset
## 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)
- [Licensing Information](#licensing-information)
## Dataset Description
- **Homepage:** [https://socialgrep.com/datasets](https://socialgrep.com/datasets/the-reddit-nft-dataset?utm_source=huggingface&utm_medium=link&utm_campaign=theredditnftdataset)
- **Point of Contact:** [Website](https://socialgrep.com/contact?utm_source=huggingface&utm_medium=link&utm_campaign=theredditnftdataset)
### Dataset Summary
A comprehensive dataset of Reddit's NFT discussion.
### Languages
Mainly English.
## Dataset Structure
### Data Instances
A data point is a post or a comment. Due to the separate nature of the two, those exist in two different files - even though many fields are shared.
### Data Fields
- 'type': the type of the data point. Can be 'post' or 'comment'.
- 'id': the base-36 Reddit ID of the data point. Unique when combined with type.
- 'subreddit.id': the base-36 Reddit ID of the data point's host subreddit. Unique.
- 'subreddit.name': the human-readable name of the data point's host subreddit.
- 'subreddit.nsfw': a boolean marking the data point's host subreddit as NSFW or not.
- 'created_utc': a UTC timestamp for the data point.
- 'permalink': a reference link to the data point on Reddit.
- 'score': score of the data point on Reddit.
- 'domain': (Post only) the domain of the data point's link.
- 'url': (Post only) the destination of the data point's link, if any.
- 'selftext': (Post only) the self-text of the data point, if any.
- 'title': (Post only) the title of the post data point.
- 'body': (Comment only) the body of the comment data point.
- 'sentiment': (Comment only) the result of an in-house sentiment analysis pipeline. Used for exploratory analysis.
## Additional Information
### Licensing Information
CC-BY v4.0
| [
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janck/bigscience-lama | janck | 2022-10-21T08:16:23Z | 18 | 1 | lama | [
"task_categories:text-retrieval",
"task_categories:text-classification",
"task_ids:fact-checking-retrieval",
"task_ids:text-scoring",
"annotations_creators:machine-generated",
"language_creators:machine-generated",
"multilinguality:monolingual",
"language:en",
"license:cc-by-4.0",
"probing",
"re... | 2022-10-21T08:16:23Z | 2022-04-27T09:20:12.000Z | 2022-04-27T09:20:12 | ---
annotations_creators:
- machine-generated
language_creators:
- machine-generated
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
trex:
- 1M<n<10M
task_categories:
- text-retrieval
- text-classification
task_ids:
- fact-checking-retrieval
- text-scoring
paperswithcode_id: lama
pretty_name: 'LAMA: LAnguage Model Analysis - BigScience version'
tags:
- probing
---
# Dataset Card for LAMA: LAnguage Model Analysis - a dataset for probing and analyzing the factual and commonsense knowledge contained in pretrained language models.
## 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)
## Dataset Description
- **Homepage:**
https://github.com/facebookresearch/LAMA
- **Repository:**
https://github.com/facebookresearch/LAMA
- **Paper:**
@inproceedings{petroni2019language,
title={Language Models as Knowledge Bases?},
author={F. Petroni, T. Rockt{\"{a}}schel, A. H. Miller, P. Lewis, A. Bakhtin, Y. Wu and S. Riedel},
booktitle={In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2019},
year={2019}
}
@inproceedings{petroni2020how,
title={How Context Affects Language Models' Factual Predictions},
author={Fabio Petroni and Patrick Lewis and Aleksandra Piktus and Tim Rockt{\"a}schel and Yuxiang Wu and Alexander H. Miller and Sebastian Riedel},
booktitle={Automated Knowledge Base Construction},
year={2020},
url={https://openreview.net/forum?id=025X0zPfn}
}
### Dataset Summary
This dataset provides the data for LAMA. This dataset only contains TRex
(subset of wikidata triples).
The dataset includes some cleanup, and addition of a masked sentence
and associated answers for the [MASK] token. The accuracy in
predicting the [MASK] token shows how well the language model knows
facts and common sense information. The [MASK] tokens are only for the
"object" slots.
This version also contains questions instead of templates that can be used to probe also non-masking models.
See the paper for more details. For more information, also see:
https://github.com/facebookresearch/LAMA
### Languages
en
## Dataset Structure
### Data Instances
The trex config has the following fields:
``
{'uuid': 'a37257ae-4cbb-4309-a78a-623036c96797', 'sub_label': 'Pianos Become the Teeth', 'predicate_id': 'P740', 'obj_label': 'Baltimore', 'template': '[X] was founded in [Y] .', 'type': 'N-1', 'question': 'Where was [X] founded?'}
34039
``
### Data Splits
There are no data splits.
## Dataset Creation
### Curation Rationale
This dataset was gathered and created to probe what language models understand.
### Source Data
#### Initial Data Collection and Normalization
See the reaserch paper and website for more detail. The dataset was
created gathered from various other datasets with cleanups for probing.
#### Who are the source language producers?
The LAMA authors and the original authors of the various configs.
### Annotations
#### Annotation process
Human annotations under the original datasets (conceptnet), and various machine annotations.
#### Who are the annotators?
Human annotations and machine annotations.
### Personal and Sensitive Information
Unkown, but likely names of famous people.
## Considerations for Using the Data
### Social Impact of Dataset
The goal for the work is to probe the understanding of language models.
### Discussion of Biases
Since the data is from human annotators, there is likely to be baises.
[More Information Needed]
### Other Known Limitations
The original documentation for the datafields are limited.
## Additional Information
### Dataset Curators
The authors of LAMA at Facebook and the authors of the original datasets.
### Licensing Information
The Creative Commons Attribution-Noncommercial 4.0 International License. see https://github.com/facebookresearch/LAMA/blob/master/LICENSE
### Citation Information
@inproceedings{petroni2019language,
title={Language Models as Knowledge Bases?},
author={F. Petroni, T. Rockt{\"{a}}schel, A. H. Miller, P. Lewis, A. Bakhtin, Y. Wu and S. Riedel},
booktitle={In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2019},
year={2019}
}
@inproceedings{petroni2020how,
title={How Context Affects Language Models' Factual Predictions},
author={Fabio Petroni and Patrick Lewis and Aleksandra Piktus and Tim Rockt{\"a}schel and Yuxiang Wu and Alexander H. Miller and Sebastian Riedel},
booktitle={Automated Knowledge Base Construction},
year={2020},
url={https://openreview.net/forum?id=025X0zPfn}
}
| [
-0.3287225663661957,
-0.9788346290588379,
0.05689394101500511,
0.22334648668766022,
-0.1294984221458435,
-0.20480088889598846,
-0.5249347686767578,
-0.38557949662208557,
0.34635129570961,
0.4738923907279968,
-0.5928986668586731,
-0.8838635683059692,
-0.42487582564353943,
0.0372227951884269... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
ai4bharat/Aksharantar | ai4bharat | 2023-08-31T07:05:34Z | 18 | 4 | null | [
"task_categories:text-generation",
"language_creators:crowdsourced",
"language_creators:expert-generated",
"language_creators:machine-generated",
"language_creators:found",
"language_creators:other",
"multilinguality:multilingual",
"source_datasets:original",
"language:asm",
"language:ben",
"lan... | 2023-08-31T07:05:34Z | 2022-05-06T12:35:15.000Z | 2022-05-06T12:35:15 | ---
annotations_creators: []
language_creators:
- crowdsourced
- expert-generated
- machine-generated
- found
- other
language:
- asm
- ben
- brx
- doi
- guj
- hin
- kan
- kas
- kok
- mai
- mal
- mar
- mni
- nep
- ori
- pan
- san
- sid
- tam
- tel
- urd
license: cc
multilinguality:
- multilingual
pretty_name: Aksharantar
source_datasets:
- original
task_categories:
- text-generation
task_ids: []
---
# Dataset Card for Aksharantar
## 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://indicnlp.ai4bharat.org/indic-xlit/
- **Repository:** https://github.com/AI4Bharat/IndicXlit/
- **Paper:** [Aksharantar: Towards building open transliteration tools for the next billion users](https://arxiv.org/abs/2205.03018)
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
Aksharantar is the largest publicly available transliteration dataset for 20 Indic languages. The corpus has 26M Indic language-English transliteration pairs.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
| <!-- --> | <!-- --> | <!-- --> | <!-- --> | <!-- --> | <!-- --> |
| -------------- | -------------- | -------------- | --------------- | -------------- | ------------- |
| Assamese (asm) | Hindi (hin) | Maithili (mai) | Marathi (mar) | Punjabi (pan) | Tamil (tam) |
| Bengali (ben) | Kannada (kan) | Malayalam (mal)| Nepali (nep) | Sanskrit (san) | Telugu (tel) |
| Bodo(brx) | Kashmiri (kas) | Manipuri (mni) | Oriya (ori) | Sindhi (snd) | Urdu (urd) |
| Gujarati (guj) | Konkani (kok) | Dogri (doi) |
## Dataset Structure
### Data Instances
```
A random sample from Hindi (hin) Train dataset.
{
'unique_identifier': 'hin1241393',
'native word': 'स्वाभिमानिक',
'english word': 'swabhimanik',
'source': 'IndicCorp',
'score': -0.1028788579
}
```
### Data Fields
- `unique_identifier` (string): 3-letter language code followed by a unique number in each set (Train, Test, Val).
- `native word` (string): A word in Indic language.
- `english word` (string): Transliteration of native word in English (Romanised word).
- `source` (string): Source of the data.
- `score` (num): Character level log probability of indic word given roman word by IndicXlit (model). Pairs with average threshold of the 0.35 are considered.
For created data sources, depending on the destination/sampling method of a pair in a language, it will be one of:
- Dakshina Dataset
- IndicCorp
- Samanantar
- Wikidata
- Existing sources
- Named Entities Indian (AK-NEI)
- Named Entities Foreign (AK-NEF)
- Data from Uniform Sampling method. (Ak-Uni)
- Data from Most Frequent words sampling method. (Ak-Freq)
### Data Splits
| Subset | asm-en | ben-en | brx-en | guj-en | hin-en | kan-en | kas-en | kok-en | mai-en | mal-en | mni-en | mar-en | nep-en | ori-en | pan-en | san-en | sid-en | tam-en | tel-en | urd-en |
|:------:|:------:|:------:|:------:|:------:|:------:|:------:|:------:|:------:|:------:|:------:|:------:|:------:|:------:|:------:|:------:|:------:|:------:|:------:|:------:|:------:|
| Training | 179K | 1231K | 36K | 1143K | 1299K | 2907K | 47K | 613K | 283K | 4101K | 10K | 1453K | 2397K | 346K | 515K | 1813K | 60K | 3231K | 2430K | 699K |
| Validation | 4K | 11K | 3K | 12K | 6K | 7K | 4K | 4K | 4K | 8K | 3K | 8K | 3K | 3K | 9K | 3K | 8K | 9K | 8K | 12K |
| Test | 5531 | 5009 | 4136 | 7768 | 5693 | 6396 | 7707 | 5093 | 5512 | 6911 | 4925 | 6573 | 4133 | 4256 | 4316 | 5334 | - | 4682 | 4567 | 4463 |
## Dataset Creation
Information in the paper. [Aksharantar: Towards building open transliteration tools for the next billion users](https://arxiv.org/abs/2205.03018)
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
Information in the paper. [Aksharantar: Towards building open transliteration tools for the next billion users](https://arxiv.org/abs/2205.03018)
#### Who are the source language producers?
[More Information Needed]
### Annotations
Information in the paper. [Aksharantar: Towards building open transliteration tools for the next billion users](https://arxiv.org/abs/2205.03018)
#### Annotation process
Information in the paper. [Aksharantar: Towards building open transliteration tools for the next billion users](https://arxiv.org/abs/2205.03018)
#### Who are the annotators?
Information in the paper. [Aksharantar: Towards building open transliteration tools for the next billion users](https://arxiv.org/abs/2205.03018)
### 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
<!-- <a rel="license" float="left" href="http://creativecommons.org/publicdomain/zero/1.0/">
<img src="https://licensebuttons.net/p/zero/1.0/88x31.png" style="border-style: none;" alt="CC0" width="100" />
<img src="https://mirrors.creativecommons.org/presskit/buttons/88x31/png/by.png" style="border-style: none;" alt="CC-BY" width="100" href="http://creativecommons.org/publicdomain/zero/1.0/"/>
</a>
<br/> -->
This data is released under the following licensing scheme:
- Manually collected data: Released under CC-BY license.
- Mined dataset (from Samanantar and IndicCorp): Released under CC0 license.
- Existing sources: Released under CC0 license.
**CC-BY License**
<a rel="license" float="left" href="https://creativecommons.org/about/cclicenses/">
<img src="https://mirrors.creativecommons.org/presskit/buttons/88x31/png/by.png" style="border-style: none;" alt="CC-BY" width="100"/>
</a>
<br>
<br>
<!--
and the Aksharantar benchmark and all manually transliterated data under the [Creative Commons CC-BY license (“no rights reserved”)](https://creativecommons.org/licenses/by/4.0/). -->
**CC0 License Statement**
<a rel="license" float="left" href="https://creativecommons.org/about/cclicenses/">
<img src="https://licensebuttons.net/p/zero/1.0/88x31.png" style="border-style: none;" alt="CC0" width="100"/>
</a>
<br>
<br>
- We do not own any of the text from which this data has been extracted.
- We license the actual packaging of the mined data under the [Creative Commons CC0 license (“no rights reserved”)](http://creativecommons.org/publicdomain/zero/1.0).
- To the extent possible under law, <a rel="dct:publisher" href="https://indicnlp.ai4bharat.org/aksharantar/"> <span property="dct:title">AI4Bharat</span></a> has waived all copyright and related or neighboring rights to <span property="dct:title">Aksharantar</span> manually collected data and existing sources.
- This work is published from: India.
### Citation Information
```
@misc{madhani2022aksharantar,
title={Aksharantar: Towards Building Open Transliteration Tools for the Next Billion Users},
author={Yash Madhani and Sushane Parthan and Priyanka Bedekar and Ruchi Khapra and Anoop Kunchukuttan and Pratyush Kumar and Mitesh Shantadevi Khapra},
year={2022},
eprint={},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
### Contributions | [
-0.37204068899154663,
-0.3754253685474396,
-0.11485153436660767,
0.16785168647766113,
-0.40930381417274475,
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-0.21262793242931366,
-0.4229148328304291,
0.27557915449142456,
0.15000155568122864,
-0.4917249381542206,
-0.6232606172561646,
-0.5698896050453186,
0.51442551612... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
strombergnlp/rumoureval_2019 | strombergnlp | 2022-10-25T21:43:58Z | 18 | 2 | null | [
"task_categories:text-classification",
"task_ids:fact-checking",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"language:en",
"license:cc-by-4.0",
"stance-detection",
"arxiv:1809.06683",
"region:us"
] | 2022-10-25T21:43:58Z | 2022-05-12T09:54:08.000Z | 2022-05-12T09:54:08 | ---
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets: []
task_categories:
- text-classification
task_ids:
- fact-checking
pretty_name: RumourEval 2019
tags:
- stance-detection
---
# Dataset Card for "rumoureval_2019"
## 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://competitions.codalab.org/competitions/19938](https://competitions.codalab.org/competitions/19938)
- **Repository:** [https://figshare.com/articles/dataset/RumourEval_2019_data/8845580](https://figshare.com/articles/dataset/RumourEval_2019_data/8845580)
- **Paper:** [https://aclanthology.org/S19-2147/](https://aclanthology.org/S19-2147/), [https://arxiv.org/abs/1809.06683](https://arxiv.org/abs/1809.06683)
- **Point of Contact:** [Leon Derczynski](https://github.com/leondz)
- **Size of downloaded dataset files:**
- **Size of the generated dataset:**
- **Total amount of disk used:**
### Dataset Summary
Stance prediction task in English. The goal is to predict whether a given reply to a claim either supports, denies, questions, or simply comments on the claim. Ran as a SemEval task in 2019.
### Supported Tasks and Leaderboards
* SemEval 2019 task 1
### Languages
English of various origins, bcp47: `en`
## Dataset Structure
### Data Instances
#### polstance
An example of 'train' looks as follows.
```
{
'id': '0',
'source_text': 'Appalled by the attack on Charlie Hebdo in Paris, 10 - probably journalists - now confirmed dead. An attack on free speech everywhere.',
'reply_text': '@m33ryg @tnewtondunn @mehdirhasan Of course it is free speech, that\'s the definition of "free speech" to openly make comments or draw a pic!',
'label': 3
}
```
### Data Fields
- `id`: a `string` feature.
- `source_text`: a `string` expressing a claim/topic.
- `reply_text`: a `string` to be classified for its stance to the source.
- `label`: a class label representing the stance the text expresses towards the target. Full tagset with indices:
```
0: "support",
1: "deny",
2: "query",
3: "comment"
```
- `quoteID`: a `string` of the internal quote ID.
- `party`: a `string` describing the party affiliation of the quote utterer at the time of utterance.
- `politician`: a `string` naming the politician who uttered the quote.
### Data Splits
| name |instances|
|---------|----:|
|train|7 005|
|dev|2 425|
|test|2 945|
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
Twitter users
### Annotations
#### Annotation process
Detailed in [Analysing How People Orient to and Spread Rumours in Social Media by Looking at Conversational Threads](https://journals.plos.org/plosone/article/authors?id=10.1371/journal.pone.0150989)
#### Who are the annotators?
### Personal and Sensitive Information
## Considerations for Using the Data
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
## Additional Information
### Dataset Curators
The dataset is curated by the paper's authors.
### Licensing Information
The authors distribute this data under Creative Commons attribution license, CC-BY 4.0.
### Citation Information
```
@inproceedings{gorrell-etal-2019-semeval,
title = "{S}em{E}val-2019 Task 7: {R}umour{E}val, Determining Rumour Veracity and Support for Rumours",
author = "Gorrell, Genevieve and
Kochkina, Elena and
Liakata, Maria and
Aker, Ahmet and
Zubiaga, Arkaitz and
Bontcheva, Kalina and
Derczynski, Leon",
booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
month = jun,
year = "2019",
address = "Minneapolis, Minnesota, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/S19-2147",
doi = "10.18653/v1/S19-2147",
pages = "845--854",
}
```
### Contributions
Author-added dataset [@leondz](https://github.com/leondz)
| [
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0.22255094349384308,
0.18338076770305634,
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0.010358558036386... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
M-CLIP/ImageCaptions-7M-Embeddings | M-CLIP | 2022-05-17T23:34:13Z | 18 | 0 | null | [
"region:us"
] | 2022-05-17T23:34:13Z | 2022-05-17T18:19:45.000Z | 2022-05-17T18:19:45 | Entry not found | [
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0.5715674161911011,
-... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
launch/gov_report_qs | launch | 2022-11-09T01:58:19Z | 18 | 1 | null | [
"task_categories:summarization",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:launch/gov_report",
"language:en",
"license:cc-by-4.0",
"region:us"
] | 2022-11-09T01:58:19Z | 2022-05-22T22:12:20.000Z | 2022-05-22T22:12:20 | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- launch/gov_report
task_categories:
- summarization
task_ids: []
pretty_name: GovReport-QS
---
# Dataset Card for GovReport-QS
## 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://gov-report-data.github.io](https://gov-report-data.github.io)
- **Repository:** [https://github.com/ShuyangCao/hibrids_summ](https://github.com/ShuyangCao/hibrids_summ)
- **Paper:** [https://aclanthology.org/2022.acl-long.58/](https://aclanthology.org/2022.acl-long.58/)
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [Needs More Information]
### Dataset Summary
Based on the GovReport dataset, GovReport-QS additionally includes annotated question-summary hierarchies for government reports. This hierarchy proactively highlights the document structure, to further promote content engagement and comprehension.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
English
## Dataset Structure
Two configs are available:
- **paragraph** (default): paragraph-level annotated data
- **document**: aggregated paragraph-level annotated data for the same document
To use different configs, set the `name` argument of the `load_dataset` function.
### Data Instances
#### paragraph
An example looks as follows.
```
{
"doc_id": "GAO_123456",
"summary_paragraph_index": 2,
"document_sections": {
"title": ["test docment section 1 title", "test docment section 1.1 title"],
"paragraphs": ["test document\nsection 1 paragraphs", "test document\nsection 1.1 paragraphs"],
"depth": [1, 2]
},
"question_summary_pairs": {
"question": ["What is the test question 1?", "What is the test question 1.1?"],
"summary": ["This is the test answer 1.", "This is the test answer 1.1"],
"parent_pair_index": [-1, 0]
}
}
```
#### document
An example looks as follows.
```
{
"doc_id": "GAO_123456",
"document_sections": {
"title": ["test docment section 1 title", "test docment section 1.1 title"],
"paragraphs": ["test document\nsection 1 paragraphs", "test document\nsection 1.1 paragraphs"],
"depth": [1, 2],
"alignment": ["h0_title", "h0_full"]
},
"question_summary_pairs": {
"question": ["What is the test question 1?", "What is the test question 1.1?"],
"summary": ["This is the test answer 1.", "This is the test answer 1.1"],
"parent_pair_index": [-1, 0],
"summary_paragraph_index": [2, 2]
}
}
```
### Data Fields
#### paragraph
**Note that document_sections in this config are the sections aligned with the annotated summary paragraph.**
- `doc_id`: a `string` feature.
- `summary_paragraph_index`: a `int32` feature.
- `document_sections`: a dictionary feature containing lists of (each element corresponds to a section):
- `title`: a `string` feature.
- `paragraphs`: a of `string` feature, with `\n` separating different paragraphs.
- `depth`: a `int32` feature.
- `question_summary_pairs`: a dictionary feature containing lists of (each element corresponds to a question-summary pair):
- `question`: a `string` feature.
- `summary`: a `string` feature.
- `parent_pair_index`: a `int32` feature indicating which question-summary pair is the parent of the current pair. `-1` indicates that the current pair does not have parent.
#### document
**Note that document_sections in this config are the all sections in the document.**
- `id`: a `string` feature.
- `document_sections`: a dictionary feature containing lists of (each element corresponds to a section):
- `title`: a `string` feature.
- `paragraphs`: a of `string` feature, with `\n` separating different paragraphs.
- `depth`: a `int32` feature.
- `alignment`: a `string` feature. Whether the `full` section or the `title` of the section should be included when aligned with each annotated hierarchy. For example, `h0_full` indicates that the full section should be included for the hierarchy indexed `0`.
- `question_summary_pairs`: a dictionary feature containing lists of:
- `question`: a `string` feature.
- `summary`: a `string` feature.
- `parent_pair_index`: a `int32` feature indicating which question-summary pair is the parent of the current pair. `-1` indicates that the current pair does not have parent. Note that the indices start from `0` for pairs with the same `summary_paragraph_index`.
- `summary_paragraph_index`: a `int32` feature indicating which summary paragraph the question-summary pair is annotated for.
### Data Splits
#### paragraph
- train: 17519
- valid: 974
- test: 973
#### document
- train: 1371
- valid: 171
- test: 172
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
Editors of the Congressional Research Service and U.S. Government Accountability Office.
### Personal and Sensitive Information
None.
## 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
CC BY 4.0
### Citation Information
```
@inproceedings{cao-wang-2022-hibrids,
title = "{HIBRIDS}: Attention with Hierarchical Biases for Structure-aware Long Document Summarization",
author = "Cao, Shuyang and
Wang, Lu",
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.58",
pages = "786--807",
abstract = "Document structure is critical for efficient information consumption. However, it is challenging to encode it efficiently into the modern Transformer architecture. In this work, we present HIBRIDS, which injects Hierarchical Biases foR Incorporating Document Structure into attention score calculation. We further present a new task, hierarchical question-summary generation, for summarizing salient content in the source document into a hierarchy of questions and summaries, where each follow-up question inquires about the content of its parent question-summary pair. We also annotate a new dataset with 6,153 question-summary hierarchies labeled on government reports. Experiment results show that our model produces better question-summary hierarchies than comparisons on both hierarchy quality and content coverage, a finding also echoed by human judges. Additionally, our model improves the generation of long-form summaries from long government reports and Wikipedia articles, as measured by ROUGE scores.",
}
```
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0.1906002759933... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
emilylearning/cond_ft_subreddit_on_reddit__prcnt_100__test_run_False__xlm-roberta-base | emilylearning | 2022-05-26T16:48:50Z | 18 | 0 | null | [
"region:us"
] | 2022-05-26T16:48:50Z | 2022-05-26T10:04:59.000Z | 2022-05-26T10:04:59 | Entry not found | [
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-... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
silver/mmchat | silver | 2022-07-10T13:04:36Z | 18 | 10 | mmchat-multi-modal-chat-dataset-on-social | [
"task_categories:conversational",
"task_ids:dialogue-generation",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10M<n<100M",
"source_datasets:original",
"language:zh",
"license:other",
"arxiv:2108.07154",
"arxiv:2008.03946",
"r... | 2022-07-10T13:04:36Z | 2022-05-29T11:15:03.000Z | 2022-05-29T11:15:03 | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- zh
license:
- other
multilinguality:
- monolingual
paperswithcode_id: mmchat-multi-modal-chat-dataset-on-social
pretty_name: "MMChat: Multi-Modal Chat Dataset on Social Media"
size_categories:
- 10M<n<100M
source_datasets:
- original
task_categories:
- conversational
task_ids:
- dialogue-generation
---
# Dataset Card for MMChat
## Table of Contents
- [Dataset Card for MMChat](#dataset-card-for-mmchat)
- [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)
- [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
- **Homepage:** https://www.zhengyinhe.com/datasets/
- **Repository:** https://github.com/silverriver/MMChat
- **Paper:** https://arxiv.org/abs/2108.07154
### Dataset Summary
MMChat is a large-scale dialogue dataset that contains image-grounded dialogues in Chinese. Each dialogue in MMChat is associated with one or more images (maximum 9 images per dialogue). We design various strategies to ensure the quality of the dialogues in MMChat.
MMChat comes with 4 different versions:
- `mmchat`: The MMChat dataset used in our paper.
- `mmchat_hf`: Contains human annotation on 100K sessions of dialogues.
- `mmchat_raw`: Raw dialogues used to construct MMChat.
`mmchat_lccc_filtered`: Raw dialogues filtered using the LCCC dataset.
If you what to use high quality multi-modal dialogues that are closed related to the given images, I suggest you to use the `mmchat_hf` version.
If you only care about the quality of dialogue texts, I suggest you to use the `mmchat_lccc_filtered` version.
### Supported Tasks and Leaderboards
- dialogue-generation: The dataset can be used to train a model for generating dialogue responses.
- response-retrieval: The dataset can be used to train a reranker model that can be used to implement a retrieval-based dialogue model.
### Languages
MMChat is in Chinese
MMChat中的对话是中文的
## Dataset Structure
### Data Instances
Several versions of MMChat are available. For `mmchat`, `mmchat_raw`, `mmchat_lccc_filtered`, the following instance applies:
```json
{
"dialog": ["你只拍出了你十分之一的美", "你的头像竟然换了，奥"],
"weibo_content": "分享图片",
"imgs": ["https://wx4.sinaimg.cn/mw2048/d716a6e2ly1fmug2w2l9qj21o02yox6p.jpg"]
}
```
For `mmchat_hf`, the following instance applies:
```json
{
"dialog": ["白百合", "啊？", "有点像", "还好吧哈哈哈牙像", "有男盆友没呢", "还没", "和你说话呢。没回我"],
"weibo_content": "补一张昨天礼仪的照片",
"imgs": ["https://ww2.sinaimg.cn/mw2048/005Co9wdjw1eyoz7ib9n5j307w0bu3z5.jpg"],
"labels": {
"image_qualified": true,
"dialog_qualified": true,
"dialog_image_related": true
}
}
```
### Data Fields
- `dialog` (list of strings): List of utterances consisting of a dialogue.
- `weibo_content` (string): Weibo content of the dialogue.
- `imgs` (list of strings): List of URLs of images.
- `labels` (dict): Human-annotated labels of the dialogue.
- `image_qualified` (bool): Whether the image is of high quality.
- `dialog_qualified` (bool): Whether the dialogue is of high quality.
- `dialog_image_related` (bool): Whether the dialogue is related to the image.
### Data Splits
For `mmchat`, we provide the following splits:
|train|valid|test|
|---:|---:|---:|
|115,842 | 4,000 | 1,000 |
For other versions, we do not provide the offical split.
More stastics are listed here:
| `mmchat` | Count |
|--------------------------------------|--------:|
| Sessions | 120.84 K |
| Sessions with more than 4 utterances | 17.32 K |
| Utterances | 314.13 K |
| Images | 198.82 K |
| Avg. utterance per session | 2.599 |
| Avg. image per session | 2.791 |
| Avg. character per utterance | 8.521 |
| `mmchat_hf` | Count |
|--------------------------------------|--------:|
| Sessions | 19.90 K |
| Sessions with more than 4 utterances | 8.91 K |
| Totally annotated sessions | 100.01 K |
| Utterances | 81.06 K |
| Images | 52.66K |
| Avg. utterance per session | 4.07 |
| Avg. image per session | 2.70 |
| Avg. character per utterance | 11.93 |
| `mmchat_raw` | Count |
|--------------------------------------|---------:|
| Sessions | 4.257 M |
| Sessions with more than 4 utterances | 2.304 M |
| Utterances | 18.590 M |
| Images | 4.874 M |
| Avg. utterance per session | 4.367 |
| Avg. image per session | 1.670 |
| Avg. character per utterance | 14.104 |
| `mmchat_lccc_filtered` | Count |
|--------------------------------------|--------:|
| Sessions | 492.6 K |
| Sessions with more than 4 utterances | 208.8 K |
| Utterances | 1.986 M |
| Images | 1.066 M |
| Avg. utterance per session | 4.031 |
| Avg. image per session | 2.514 |
| Avg. character per utterance | 11.336 |
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
[Needs More Information]
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
other-weibo
This dataset is collected from Weibo.
You can refer to the [detailed policy](https://weibo.com/signup/v5/privacy) required to use this dataset.
Please restrict the usage of this dataset to non-commerical purposes.
### Citation Information
```
@inproceedings{zheng2022MMChat,
author = {Zheng, Yinhe and Chen, Guanyi and Liu, Xin and Sun, Jian},
title = {MMChat: Multi-Modal Chat Dataset on Social Media},
booktitle = {Proceedings of The 13th Language Resources and Evaluation Conference},
year = {2022},
publisher = {European Language Resources Association},
}
@inproceedings{wang2020chinese,
title={A Large-Scale Chinese Short-Text Conversation Dataset},
author={Wang, Yida and Ke, Pei and Zheng, Yinhe and Huang, Kaili and Jiang, Yong and Zhu, Xiaoyan and Huang, Minlie},
booktitle={NLPCC},
year={2020},
url={https://arxiv.org/abs/2008.03946}
}
```
### Contributions
Thanks to [Yinhe Zheng](https://github.com/silverriver) for adding this dataset.
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0.0696165487170... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
mounikaiiith/Telugu-Hatespeech | mounikaiiith | 2022-07-04T15:06:14Z | 18 | 2 | null | [
"license:cc-by-4.0",
"region:us"
] | 2022-07-04T15:06:14Z | 2022-06-19T12:12:32.000Z | 2022-06-19T12:12:32 | ---
license: cc-by-4.0
---
Do cite the below references for using the dataset:
@article{marreddy2022resource, title={Am I a Resource-Poor Language? Data Sets, Embeddings, Models and Analysis for four different NLP tasks in Telugu Language},
author={Marreddy, Mounika and Oota, Subba Reddy and Vakada, Lakshmi Sireesha and Chinni, Venkata Charan and Mamidi, Radhika},
journal={Transactions on Asian and Low-Resource Language Information Processing}, publisher={ACM New York, NY} }
@article{marreddy2022multi,
title={Multi-Task Text Classification using Graph Convolutional Networks for Large-Scale Low Resource Language},
author={Marreddy, Mounika and Oota, Subba Reddy and Vakada, Lakshmi Sireesha and Chinni, Venkata Charan and Mamidi, Radhika},
journal={arXiv preprint arXiv:2205.01204},
year={2022}
}
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Nexdata/British_English_Average_Tone_Speech_Synthesis_Corpus | Nexdata | 2023-11-10T07:22:57Z | 18 | 1 | null | [
"task_categories:text-to-speech",
"language:en",
"region:us"
] | 2023-11-10T07:22:57Z | 2022-06-22T06:20:42.000Z | 2022-06-22T06:20:42 | ---
task_categories:
- text-to-speech
language:
- en
---
# Dataset Card for Nexdata/British_English_Average_Tone_Speech_Synthesis_Corpus
## Description
10 People - British English Average Tone Speech Synthesis Corpus. It is recorded by British English native speakers, with authentic accent. The phoneme coverage is balanced. Professional phonetician participates in the annotation. It precisely matches with the research and development needs of the speech synthesis.
For more details, please refer to the link: https://www.nexdata.ai/datasets/1309?source=Huggingface
# Specifications
## Format
48,000Hz, 24bit, uncompressed wav, mono channel;
## Recording environment
professional recording studio;
## Recording content
general narrative sentences, interrogative sentences, etc;
## Speaker
british native speaker, 5 male and 5 female, 2 hours per person;
## Device
microphone;
## Language
British English;
## Annotation
word and phoneme transcription, four-level prosodic boundary annotation;
## Application scenarios
speech synthesis.
# Licensing Information
Commercial License | [
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Nexdata/3D_Instance_Segmentation_and_22_Landmarks_Annotation_Data_of_Human_Body | Nexdata | 2023-08-31T02:47:41Z | 18 | 1 | null | [
"region:us"
] | 2023-08-31T02:47:41Z | 2022-06-27T08:52:04.000Z | 2022-06-27T08:52:04 | ---
YAML tags:
- copy-paste the tags obtained with the tagging app: https://github.com/huggingface/datasets-tagging
---
# Dataset Card for Nexdata/3D_Instance_Segmentation_and_22_Landmarks_Annotation_Data_of_Human_Body
## 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://www.nexdata.ai/datasets/1040?source=Huggingface
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
18,880 Images of 466 People - 3D Instance Segmentation and 22 Landmarks Annotation Data of Human Body. The dataset diversity includes multiple scenes, light conditions, ages, shooting angles, and poses. In terms of annotation, we adpoted instance segmentation annotations on human body. 22 landmarks were also annotated for each human body. The dataset can be used for tasks such as human body instance segmentation and human behavior recognition.
For more details, please refer to the link: https://www.nexdata.ai/datasets/1040?source=Huggingface
### Supported Tasks and Leaderboards
instance-segmentation, computer-vision,image-segmentation: The dataset can be used to train a model for computer vision.
### Languages
English
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
Commerical License: https://drive.google.com/file/d/1saDCPm74D4UWfBL17VbkTsZLGfpOQj1J/view?usp=sharing
### Citation Information
[More Information Needed]
### Contributions | [
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"license:cc",
"region:us"
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license: cc
---
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polinaeterna/test-imagefolder-zip | polinaeterna | 2022-07-07T12:54:15Z | 18 | 0 | null | [
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-0.52829909324646,
0.7012971639633179,
0.7915719747543335,
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0.5715674161911011,
-... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
embedding-data/altlex | embedding-data | 2022-08-02T01:53:24Z | 18 | 0 | embedding-data/altlex | [
"language:en",
"license:mit",
"region:us"
] | 2022-08-02T01:53:24Z | 2022-07-07T23:00:22.000Z | 2022-07-07T23:00:22 | ---
license: mit
language:
- en
paperswithcode_id: embedding-data/altlex
pretty_name: altlex
---
# Dataset Card for "altlex"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [https://github.com/chridey/altlex](https://github.com/chridey/altlex)
- **Repository:** [More Information Needed](https://github.com/chridey/altlex)
- **Paper:** [https://aclanthology.org/P16-1135.pdf](https://aclanthology.org/P16-1135.pdf)
- **Point of Contact:** [Christopher Hidey](ch3085@columbia.edu)
### Dataset Summary
Git repository for software associated with the 2016 ACL paper "Identifying Causal Relations Using Parallel Wikipedia Articles."
Disclaimer: The team releasing altlex did not upload the dataset to the Hub and did not write a dataset card.
These steps were done by the Hugging Face team.
### Supported Tasks
- [Sentence Transformers](https://huggingface.co/sentence-transformers) training; useful for semantic search and sentence similarity.
### Languages
- English.
## Dataset Structure
Each example in the dataset contains a pair of similar sentences and is formatted as a dictionary with the key "set" and a list with the sentences as "value":
```
{"set": [sentence_1, sentence_2]}
{"set": [sentence_1, sentence_2]}
...
{"set": [sentence_1, sentence_2]}
```
This dataset is useful for training Sentence Transformers models. Refer to the following post on how to train models using similar pairs of sentences.
### Usage Example
Install the 🤗 Datasets library with `pip install datasets` and load the dataset from the Hub with:
```python
from datasets import load_dataset
dataset = load_dataset("embedding-data/altlex")
```
The dataset is loaded as a `DatasetDict` and has the format:
```python
DatasetDict({
train: Dataset({
features: ['set'],
num_rows: 112696
})
})
```
Review an example `i` with:
```python
dataset["train"][i]["set"]
```
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/chridey/altlex)
#### Who are the source language producers?
[More Information Needed](https://github.com/chridey/altlex)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/chridey/altlex)
#### Who are the annotators?
[More Information Needed](https://github.com/chridey/altlex)
### Personal and Sensitive Information
[More Information Needed](https://github.com/chridey/altlex)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/chridey/altlex)
### Discussion of Biases
[More Information Needed](https://github.com/chridey/altlex)
### Other Known Limitations
[More Information Needed](https://github.com/chridey/altlex)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/chridey/altlex)
### Licensing Information
[More Information Needed](https://github.com/chridey/altlex)
### Citation Information
### Contributions
- [@chridey](https://github.com/chridey/altlex/commits?author=chridey) for adding this dataset to Github.
---
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0.2614384889602661,... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
MicPie/unpredictable_cluster17 | MicPie | 2022-08-04T19:55:23Z | 18 | 0 | null | [
"task_categories:multiple-choice",
"task_categories:question-answering",
"task_categories:zero-shot-classification",
"task_categories:text2text-generation",
"task_categories:table-question-answering",
"task_categories:text-generation",
"task_categories:text-classification",
"task_categories:tabular-cl... | 2022-08-04T19:55:23Z | 2022-07-08T17:33:42.000Z | 2022-07-08T17:33:42 | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license:
- apache-2.0
multilinguality:
- monolingual
pretty_name: UnpredicTable-cluster17
size_categories:
- 100K<n<1M
source_datasets: []
task_categories:
- multiple-choice
- question-answering
- zero-shot-classification
- text2text-generation
- table-question-answering
- text-generation
- text-classification
- tabular-classification
task_ids:
- multiple-choice-qa
- extractive-qa
- open-domain-qa
- closed-domain-qa
- closed-book-qa
- open-book-qa
- language-modeling
- multi-class-classification
- natural-language-inference
- topic-classification
- multi-label-classification
- tabular-multi-class-classification
- tabular-multi-label-classification
---
# Dataset Card for "UnpredicTable-cluster17" - Dataset of Few-shot Tasks from Tables
## 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://ethanperez.net/unpredictable
- **Repository:** https://github.com/JunShern/few-shot-adaptation
- **Paper:** Few-shot Adaptation Works with UnpredicTable Data
- **Point of Contact:** junshern@nyu.edu, perez@nyu.edu
### Dataset Summary
The UnpredicTable dataset consists of web tables formatted as few-shot tasks for fine-tuning language models to improve their few-shot performance.
There are several dataset versions available:
* [UnpredicTable-full](https://huggingface.co/datasets/MicPie/unpredictable_full): Starting from the initial WTC corpus of 50M tables, we apply our tables-to-tasks procedure to produce our resulting dataset, [UnpredicTable-full](https://huggingface.co/datasets/MicPie/unpredictable_full), which comprises 413,299 tasks from 23,744 unique websites.
* [UnpredicTable-unique](https://huggingface.co/datasets/MicPie/unpredictable_unique): This is the same as [UnpredicTable-full](https://huggingface.co/datasets/MicPie/unpredictable_full) but filtered to have a maximum of one task per website. [UnpredicTable-unique](https://huggingface.co/datasets/MicPie/unpredictable_unique) contains exactly 23,744 tasks from 23,744 websites.
* [UnpredicTable-5k](https://huggingface.co/datasets/MicPie/unpredictable_5k): This dataset contains 5k random tables from the full dataset.
* UnpredicTable data subsets based on a manual human quality rating (please see our publication for details of the ratings):
* [UnpredicTable-rated-low](https://huggingface.co/datasets/MicPie/unpredictable_rated-low)
* [UnpredicTable-rated-medium](https://huggingface.co/datasets/MicPie/unpredictable_rated-medium)
* [UnpredicTable-rated-high](https://huggingface.co/datasets/MicPie/unpredictable_rated-high)
* UnpredicTable data subsets based on the website of origin:
* [UnpredicTable-baseball-fantasysports-yahoo-com](https://huggingface.co/datasets/MicPie/unpredictable_baseball-fantasysports-yahoo-com)
* [UnpredicTable-bulbapedia-bulbagarden-net](https://huggingface.co/datasets/MicPie/unpredictable_bulbapedia-bulbagarden-net)
* [UnpredicTable-cappex-com](https://huggingface.co/datasets/MicPie/unpredictable_cappex-com)
* [UnpredicTable-cram-com](https://huggingface.co/datasets/MicPie/unpredictable_cram-com)
* [UnpredicTable-dividend-com](https://huggingface.co/datasets/MicPie/unpredictable_dividend-com)
* [UnpredicTable-dummies-com](https://huggingface.co/datasets/MicPie/unpredictable_dummies-com)
* [UnpredicTable-en-wikipedia-org](https://huggingface.co/datasets/MicPie/unpredictable_en-wikipedia-org)
* [UnpredicTable-ensembl-org](https://huggingface.co/datasets/MicPie/unpredictable_ensembl-org)
* [UnpredicTable-gamefaqs-com](https://huggingface.co/datasets/MicPie/unpredictable_gamefaqs-com)
* [UnpredicTable-mgoblog-com](https://huggingface.co/datasets/MicPie/unpredictable_mgoblog-com)
* [UnpredicTable-mmo-champion-com](https://huggingface.co/datasets/MicPie/unpredictable_mmo-champion-com)
* [UnpredicTable-msdn-microsoft-com](https://huggingface.co/datasets/MicPie/unpredictable_msdn-microsoft-com)
* [UnpredicTable-phonearena-com](https://huggingface.co/datasets/MicPie/unpredictable_phonearena-com)
* [UnpredicTable-sittercity-com](https://huggingface.co/datasets/MicPie/unpredictable_sittercity-com)
* [UnpredicTable-sporcle-com](https://huggingface.co/datasets/MicPie/unpredictable_sporcle-com)
* [UnpredicTable-studystack-com](https://huggingface.co/datasets/MicPie/unpredictable_studystack-com)
* [UnpredicTable-support-google-com](https://huggingface.co/datasets/MicPie/unpredictable_support-google-com)
* [UnpredicTable-w3-org](https://huggingface.co/datasets/MicPie/unpredictable_w3-org)
* [UnpredicTable-wiki-openmoko-org](https://huggingface.co/datasets/MicPie/unpredictable_wiki-openmoko-org)
* [UnpredicTable-wkdu-org](https://huggingface.co/datasets/MicPie/unpredictable_wkdu-org)
* UnpredicTable data subsets based on clustering (for the clustering details please see our publication):
* [UnpredicTable-cluster00](https://huggingface.co/datasets/MicPie/unpredictable_cluster00)
* [UnpredicTable-cluster01](https://huggingface.co/datasets/MicPie/unpredictable_cluster01)
* [UnpredicTable-cluster02](https://huggingface.co/datasets/MicPie/unpredictable_cluster02)
* [UnpredicTable-cluster03](https://huggingface.co/datasets/MicPie/unpredictable_cluster03)
* [UnpredicTable-cluster04](https://huggingface.co/datasets/MicPie/unpredictable_cluster04)
* [UnpredicTable-cluster05](https://huggingface.co/datasets/MicPie/unpredictable_cluster05)
* [UnpredicTable-cluster06](https://huggingface.co/datasets/MicPie/unpredictable_cluster06)
* [UnpredicTable-cluster07](https://huggingface.co/datasets/MicPie/unpredictable_cluster07)
* [UnpredicTable-cluster08](https://huggingface.co/datasets/MicPie/unpredictable_cluster08)
* [UnpredicTable-cluster09](https://huggingface.co/datasets/MicPie/unpredictable_cluster09)
* [UnpredicTable-cluster10](https://huggingface.co/datasets/MicPie/unpredictable_cluster10)
* [UnpredicTable-cluster11](https://huggingface.co/datasets/MicPie/unpredictable_cluster11)
* [UnpredicTable-cluster12](https://huggingface.co/datasets/MicPie/unpredictable_cluster12)
* [UnpredicTable-cluster13](https://huggingface.co/datasets/MicPie/unpredictable_cluster13)
* [UnpredicTable-cluster14](https://huggingface.co/datasets/MicPie/unpredictable_cluster14)
* [UnpredicTable-cluster15](https://huggingface.co/datasets/MicPie/unpredictable_cluster15)
* [UnpredicTable-cluster16](https://huggingface.co/datasets/MicPie/unpredictable_cluster16)
* [UnpredicTable-cluster17](https://huggingface.co/datasets/MicPie/unpredictable_cluster17)
* [UnpredicTable-cluster18](https://huggingface.co/datasets/MicPie/unpredictable_cluster18)
* [UnpredicTable-cluster19](https://huggingface.co/datasets/MicPie/unpredictable_cluster19)
* [UnpredicTable-cluster20](https://huggingface.co/datasets/MicPie/unpredictable_cluster20)
* [UnpredicTable-cluster21](https://huggingface.co/datasets/MicPie/unpredictable_cluster21)
* [UnpredicTable-cluster22](https://huggingface.co/datasets/MicPie/unpredictable_cluster22)
* [UnpredicTable-cluster23](https://huggingface.co/datasets/MicPie/unpredictable_cluster23)
* [UnpredicTable-cluster24](https://huggingface.co/datasets/MicPie/unpredictable_cluster24)
* [UnpredicTable-cluster25](https://huggingface.co/datasets/MicPie/unpredictable_cluster25)
* [UnpredicTable-cluster26](https://huggingface.co/datasets/MicPie/unpredictable_cluster26)
* [UnpredicTable-cluster27](https://huggingface.co/datasets/MicPie/unpredictable_cluster27)
* [UnpredicTable-cluster28](https://huggingface.co/datasets/MicPie/unpredictable_cluster28)
* [UnpredicTable-cluster29](https://huggingface.co/datasets/MicPie/unpredictable_cluster29)
* [UnpredicTable-cluster-noise](https://huggingface.co/datasets/MicPie/unpredictable_cluster-noise)
### Supported Tasks and Leaderboards
Since the tables come from the web, the distribution of tasks and topics is very broad. The shape of our dataset is very wide, i.e., we have 1000's of tasks, while each task has only a few examples, compared to most current NLP datasets which are very deep, i.e., 10s of tasks with many examples. This implies that our dataset covers a broad range of potential tasks, e.g., multiple-choice, question-answering, table-question-answering, text-classification, etc.
The intended use of this dataset is to improve few-shot performance by fine-tuning/pre-training on our dataset.
### Languages
English
## Dataset Structure
### Data Instances
Each task is represented as a jsonline file and consists of several few-shot examples. Each example is a dictionary containing a field 'task', which identifies the task, followed by an 'input', 'options', and 'output' field. The 'input' field contains several column elements of the same row in the table, while the 'output' field is a target which represents an individual column of the same row. Each task contains several such examples which can be concatenated as a few-shot task. In the case of multiple choice classification, the 'options' field contains the possible classes that a model needs to choose from.
There are also additional meta-data fields such as 'pageTitle', 'title', 'outputColName', 'url', 'wdcFile'.
### Data Fields
'task': task identifier
'input': column elements of a specific row in the table.
'options': for multiple choice classification, it provides the options to choose from.
'output': target column element of the same row as input.
'pageTitle': the title of the page containing the table.
'outputColName': output column name
'url': url to the website containing the table
'wdcFile': WDC Web Table Corpus file
### Data Splits
The UnpredicTable datasets do not come with additional data splits.
## Dataset Creation
### Curation Rationale
Few-shot training on multi-task datasets has been demonstrated to improve language models' few-shot learning (FSL) performance on new tasks, but it is unclear which training tasks lead to effective downstream task adaptation. Few-shot learning datasets are typically produced with expensive human curation, limiting the scale and diversity of the training tasks available to study. As an alternative source of few-shot data, we automatically extract 413,299 tasks from diverse internet tables. We provide this as a research resource to investigate the relationship between training data and few-shot learning.
### Source Data
#### Initial Data Collection and Normalization
We use internet tables from the English-language Relational Subset of the WDC Web Table Corpus 2015 (WTC). The WTC dataset tables were extracted from the July 2015 Common Crawl web corpus (http://webdatacommons.org/webtables/2015/EnglishStatistics.html). The dataset contains 50,820,165 tables from 323,160 web domains. We then convert the tables into few-shot learning tasks. Please see our publication for more details on the data collection and conversion pipeline.
#### Who are the source language producers?
The dataset is extracted from [WDC Web Table Corpora](http://webdatacommons.org/webtables/).
### Annotations
#### Annotation process
Manual annotation was only carried out for the [UnpredicTable-rated-low](https://huggingface.co/datasets/MicPie/unpredictable_rated-low),
[UnpredicTable-rated-medium](https://huggingface.co/datasets/MicPie/unpredictable_rated-medium), and [UnpredicTable-rated-high](https://huggingface.co/datasets/MicPie/unpredictable_rated-high) data subsets to rate task quality. Detailed instructions of the annotation instructions can be found in our publication.
#### Who are the annotators?
Annotations were carried out by a lab assistant.
### Personal and Sensitive Information
The data was extracted from [WDC Web Table Corpora](http://webdatacommons.org/webtables/), which in turn extracted tables from the [Common Crawl](https://commoncrawl.org/). We did not filter the data in any way. Thus any user identities or otherwise sensitive information (e.g., data that reveals racial or ethnic origins, sexual orientations, religious beliefs, political opinions or union memberships, or locations; financial or health data; biometric or genetic data; forms of government identification, such as social security numbers; criminal history, etc.) might be contained in our dataset.
## Considerations for Using the Data
### Social Impact of Dataset
This dataset is intended for use as a research resource to investigate the relationship between training data and few-shot learning. As such, it contains high- and low-quality data, as well as diverse content that may be untruthful or inappropriate. Without careful investigation, it should not be used for training models that will be deployed for use in decision-critical or user-facing situations.
### Discussion of Biases
Since our dataset contains tables that are scraped from the web, it will also contain many toxic, racist, sexist, and otherwise harmful biases and texts. We have not run any analysis on the biases prevalent in our datasets. Neither have we explicitly filtered the content. This implies that a model trained on our dataset may potentially reflect harmful biases and toxic text that exist in our dataset.
### Other Known Limitations
No additional known limitations.
## Additional Information
### Dataset Curators
Jun Shern Chan, Michael Pieler, Jonathan Jao, Jérémy Scheurer, Ethan Perez
### Licensing Information
Apache 2.0
### Citation Information
```
@misc{chan2022few,
author = {Chan, Jun Shern and Pieler, Michael and Jao, Jonathan and Scheurer, Jérémy and Perez, Ethan},
title = {Few-shot Adaptation Works with UnpredicTable Data},
publisher={arXiv},
year = {2022},
url = {https://arxiv.org/abs/2208.01009}
}
```
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... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
MicPie/unpredictable_cluster22 | MicPie | 2022-08-04T19:58:29Z | 18 | 0 | null | [
"task_categories:multiple-choice",
"task_categories:question-answering",
"task_categories:zero-shot-classification",
"task_categories:text2text-generation",
"task_categories:table-question-answering",
"task_categories:text-generation",
"task_categories:text-classification",
"task_categories:tabular-cl... | 2022-08-04T19:58:29Z | 2022-07-08T18:28:51.000Z | 2022-07-08T18:28:51 | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license:
- apache-2.0
multilinguality:
- monolingual
pretty_name: UnpredicTable-cluster22
size_categories:
- 100K<n<1M
source_datasets: []
task_categories:
- multiple-choice
- question-answering
- zero-shot-classification
- text2text-generation
- table-question-answering
- text-generation
- text-classification
- tabular-classification
task_ids:
- multiple-choice-qa
- extractive-qa
- open-domain-qa
- closed-domain-qa
- closed-book-qa
- open-book-qa
- language-modeling
- multi-class-classification
- natural-language-inference
- topic-classification
- multi-label-classification
- tabular-multi-class-classification
- tabular-multi-label-classification
---
# Dataset Card for "UnpredicTable-cluster22" - Dataset of Few-shot Tasks from Tables
## 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://ethanperez.net/unpredictable
- **Repository:** https://github.com/JunShern/few-shot-adaptation
- **Paper:** Few-shot Adaptation Works with UnpredicTable Data
- **Point of Contact:** junshern@nyu.edu, perez@nyu.edu
### Dataset Summary
The UnpredicTable dataset consists of web tables formatted as few-shot tasks for fine-tuning language models to improve their few-shot performance.
There are several dataset versions available:
* [UnpredicTable-full](https://huggingface.co/datasets/MicPie/unpredictable_full): Starting from the initial WTC corpus of 50M tables, we apply our tables-to-tasks procedure to produce our resulting dataset, [UnpredicTable-full](https://huggingface.co/datasets/MicPie/unpredictable_full), which comprises 413,299 tasks from 23,744 unique websites.
* [UnpredicTable-unique](https://huggingface.co/datasets/MicPie/unpredictable_unique): This is the same as [UnpredicTable-full](https://huggingface.co/datasets/MicPie/unpredictable_full) but filtered to have a maximum of one task per website. [UnpredicTable-unique](https://huggingface.co/datasets/MicPie/unpredictable_unique) contains exactly 23,744 tasks from 23,744 websites.
* [UnpredicTable-5k](https://huggingface.co/datasets/MicPie/unpredictable_5k): This dataset contains 5k random tables from the full dataset.
* UnpredicTable data subsets based on a manual human quality rating (please see our publication for details of the ratings):
* [UnpredicTable-rated-low](https://huggingface.co/datasets/MicPie/unpredictable_rated-low)
* [UnpredicTable-rated-medium](https://huggingface.co/datasets/MicPie/unpredictable_rated-medium)
* [UnpredicTable-rated-high](https://huggingface.co/datasets/MicPie/unpredictable_rated-high)
* UnpredicTable data subsets based on the website of origin:
* [UnpredicTable-baseball-fantasysports-yahoo-com](https://huggingface.co/datasets/MicPie/unpredictable_baseball-fantasysports-yahoo-com)
* [UnpredicTable-bulbapedia-bulbagarden-net](https://huggingface.co/datasets/MicPie/unpredictable_bulbapedia-bulbagarden-net)
* [UnpredicTable-cappex-com](https://huggingface.co/datasets/MicPie/unpredictable_cappex-com)
* [UnpredicTable-cram-com](https://huggingface.co/datasets/MicPie/unpredictable_cram-com)
* [UnpredicTable-dividend-com](https://huggingface.co/datasets/MicPie/unpredictable_dividend-com)
* [UnpredicTable-dummies-com](https://huggingface.co/datasets/MicPie/unpredictable_dummies-com)
* [UnpredicTable-en-wikipedia-org](https://huggingface.co/datasets/MicPie/unpredictable_en-wikipedia-org)
* [UnpredicTable-ensembl-org](https://huggingface.co/datasets/MicPie/unpredictable_ensembl-org)
* [UnpredicTable-gamefaqs-com](https://huggingface.co/datasets/MicPie/unpredictable_gamefaqs-com)
* [UnpredicTable-mgoblog-com](https://huggingface.co/datasets/MicPie/unpredictable_mgoblog-com)
* [UnpredicTable-mmo-champion-com](https://huggingface.co/datasets/MicPie/unpredictable_mmo-champion-com)
* [UnpredicTable-msdn-microsoft-com](https://huggingface.co/datasets/MicPie/unpredictable_msdn-microsoft-com)
* [UnpredicTable-phonearena-com](https://huggingface.co/datasets/MicPie/unpredictable_phonearena-com)
* [UnpredicTable-sittercity-com](https://huggingface.co/datasets/MicPie/unpredictable_sittercity-com)
* [UnpredicTable-sporcle-com](https://huggingface.co/datasets/MicPie/unpredictable_sporcle-com)
* [UnpredicTable-studystack-com](https://huggingface.co/datasets/MicPie/unpredictable_studystack-com)
* [UnpredicTable-support-google-com](https://huggingface.co/datasets/MicPie/unpredictable_support-google-com)
* [UnpredicTable-w3-org](https://huggingface.co/datasets/MicPie/unpredictable_w3-org)
* [UnpredicTable-wiki-openmoko-org](https://huggingface.co/datasets/MicPie/unpredictable_wiki-openmoko-org)
* [UnpredicTable-wkdu-org](https://huggingface.co/datasets/MicPie/unpredictable_wkdu-org)
* UnpredicTable data subsets based on clustering (for the clustering details please see our publication):
* [UnpredicTable-cluster00](https://huggingface.co/datasets/MicPie/unpredictable_cluster00)
* [UnpredicTable-cluster01](https://huggingface.co/datasets/MicPie/unpredictable_cluster01)
* [UnpredicTable-cluster02](https://huggingface.co/datasets/MicPie/unpredictable_cluster02)
* [UnpredicTable-cluster03](https://huggingface.co/datasets/MicPie/unpredictable_cluster03)
* [UnpredicTable-cluster04](https://huggingface.co/datasets/MicPie/unpredictable_cluster04)
* [UnpredicTable-cluster05](https://huggingface.co/datasets/MicPie/unpredictable_cluster05)
* [UnpredicTable-cluster06](https://huggingface.co/datasets/MicPie/unpredictable_cluster06)
* [UnpredicTable-cluster07](https://huggingface.co/datasets/MicPie/unpredictable_cluster07)
* [UnpredicTable-cluster08](https://huggingface.co/datasets/MicPie/unpredictable_cluster08)
* [UnpredicTable-cluster09](https://huggingface.co/datasets/MicPie/unpredictable_cluster09)
* [UnpredicTable-cluster10](https://huggingface.co/datasets/MicPie/unpredictable_cluster10)
* [UnpredicTable-cluster11](https://huggingface.co/datasets/MicPie/unpredictable_cluster11)
* [UnpredicTable-cluster12](https://huggingface.co/datasets/MicPie/unpredictable_cluster12)
* [UnpredicTable-cluster13](https://huggingface.co/datasets/MicPie/unpredictable_cluster13)
* [UnpredicTable-cluster14](https://huggingface.co/datasets/MicPie/unpredictable_cluster14)
* [UnpredicTable-cluster15](https://huggingface.co/datasets/MicPie/unpredictable_cluster15)
* [UnpredicTable-cluster16](https://huggingface.co/datasets/MicPie/unpredictable_cluster16)
* [UnpredicTable-cluster17](https://huggingface.co/datasets/MicPie/unpredictable_cluster17)
* [UnpredicTable-cluster18](https://huggingface.co/datasets/MicPie/unpredictable_cluster18)
* [UnpredicTable-cluster19](https://huggingface.co/datasets/MicPie/unpredictable_cluster19)
* [UnpredicTable-cluster20](https://huggingface.co/datasets/MicPie/unpredictable_cluster20)
* [UnpredicTable-cluster21](https://huggingface.co/datasets/MicPie/unpredictable_cluster21)
* [UnpredicTable-cluster22](https://huggingface.co/datasets/MicPie/unpredictable_cluster22)
* [UnpredicTable-cluster23](https://huggingface.co/datasets/MicPie/unpredictable_cluster23)
* [UnpredicTable-cluster24](https://huggingface.co/datasets/MicPie/unpredictable_cluster24)
* [UnpredicTable-cluster25](https://huggingface.co/datasets/MicPie/unpredictable_cluster25)
* [UnpredicTable-cluster26](https://huggingface.co/datasets/MicPie/unpredictable_cluster26)
* [UnpredicTable-cluster27](https://huggingface.co/datasets/MicPie/unpredictable_cluster27)
* [UnpredicTable-cluster28](https://huggingface.co/datasets/MicPie/unpredictable_cluster28)
* [UnpredicTable-cluster29](https://huggingface.co/datasets/MicPie/unpredictable_cluster29)
* [UnpredicTable-cluster-noise](https://huggingface.co/datasets/MicPie/unpredictable_cluster-noise)
### Supported Tasks and Leaderboards
Since the tables come from the web, the distribution of tasks and topics is very broad. The shape of our dataset is very wide, i.e., we have 1000's of tasks, while each task has only a few examples, compared to most current NLP datasets which are very deep, i.e., 10s of tasks with many examples. This implies that our dataset covers a broad range of potential tasks, e.g., multiple-choice, question-answering, table-question-answering, text-classification, etc.
The intended use of this dataset is to improve few-shot performance by fine-tuning/pre-training on our dataset.
### Languages
English
## Dataset Structure
### Data Instances
Each task is represented as a jsonline file and consists of several few-shot examples. Each example is a dictionary containing a field 'task', which identifies the task, followed by an 'input', 'options', and 'output' field. The 'input' field contains several column elements of the same row in the table, while the 'output' field is a target which represents an individual column of the same row. Each task contains several such examples which can be concatenated as a few-shot task. In the case of multiple choice classification, the 'options' field contains the possible classes that a model needs to choose from.
There are also additional meta-data fields such as 'pageTitle', 'title', 'outputColName', 'url', 'wdcFile'.
### Data Fields
'task': task identifier
'input': column elements of a specific row in the table.
'options': for multiple choice classification, it provides the options to choose from.
'output': target column element of the same row as input.
'pageTitle': the title of the page containing the table.
'outputColName': output column name
'url': url to the website containing the table
'wdcFile': WDC Web Table Corpus file
### Data Splits
The UnpredicTable datasets do not come with additional data splits.
## Dataset Creation
### Curation Rationale
Few-shot training on multi-task datasets has been demonstrated to improve language models' few-shot learning (FSL) performance on new tasks, but it is unclear which training tasks lead to effective downstream task adaptation. Few-shot learning datasets are typically produced with expensive human curation, limiting the scale and diversity of the training tasks available to study. As an alternative source of few-shot data, we automatically extract 413,299 tasks from diverse internet tables. We provide this as a research resource to investigate the relationship between training data and few-shot learning.
### Source Data
#### Initial Data Collection and Normalization
We use internet tables from the English-language Relational Subset of the WDC Web Table Corpus 2015 (WTC). The WTC dataset tables were extracted from the July 2015 Common Crawl web corpus (http://webdatacommons.org/webtables/2015/EnglishStatistics.html). The dataset contains 50,820,165 tables from 323,160 web domains. We then convert the tables into few-shot learning tasks. Please see our publication for more details on the data collection and conversion pipeline.
#### Who are the source language producers?
The dataset is extracted from [WDC Web Table Corpora](http://webdatacommons.org/webtables/).
### Annotations
#### Annotation process
Manual annotation was only carried out for the [UnpredicTable-rated-low](https://huggingface.co/datasets/MicPie/unpredictable_rated-low),
[UnpredicTable-rated-medium](https://huggingface.co/datasets/MicPie/unpredictable_rated-medium), and [UnpredicTable-rated-high](https://huggingface.co/datasets/MicPie/unpredictable_rated-high) data subsets to rate task quality. Detailed instructions of the annotation instructions can be found in our publication.
#### Who are the annotators?
Annotations were carried out by a lab assistant.
### Personal and Sensitive Information
The data was extracted from [WDC Web Table Corpora](http://webdatacommons.org/webtables/), which in turn extracted tables from the [Common Crawl](https://commoncrawl.org/). We did not filter the data in any way. Thus any user identities or otherwise sensitive information (e.g., data that reveals racial or ethnic origins, sexual orientations, religious beliefs, political opinions or union memberships, or locations; financial or health data; biometric or genetic data; forms of government identification, such as social security numbers; criminal history, etc.) might be contained in our dataset.
## Considerations for Using the Data
### Social Impact of Dataset
This dataset is intended for use as a research resource to investigate the relationship between training data and few-shot learning. As such, it contains high- and low-quality data, as well as diverse content that may be untruthful or inappropriate. Without careful investigation, it should not be used for training models that will be deployed for use in decision-critical or user-facing situations.
### Discussion of Biases
Since our dataset contains tables that are scraped from the web, it will also contain many toxic, racist, sexist, and otherwise harmful biases and texts. We have not run any analysis on the biases prevalent in our datasets. Neither have we explicitly filtered the content. This implies that a model trained on our dataset may potentially reflect harmful biases and toxic text that exist in our dataset.
### Other Known Limitations
No additional known limitations.
## Additional Information
### Dataset Curators
Jun Shern Chan, Michael Pieler, Jonathan Jao, Jérémy Scheurer, Ethan Perez
### Licensing Information
Apache 2.0
### Citation Information
```
@misc{chan2022few,
author = {Chan, Jun Shern and Pieler, Michael and Jao, Jonathan and Scheurer, Jérémy and Perez, Ethan},
title = {Few-shot Adaptation Works with UnpredicTable Data},
publisher={arXiv},
year = {2022},
url = {https://arxiv.org/abs/2208.01009}
}
```
| [
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0.4666702449321747,
0.33155927062034607,
0.0878412202000618,
0.1652231514453888,
-0.14626625180244446,
-0.5983081459999084,
0.5201656818389893,
0.2916639447212219,
-1.0230202674865723,
-0.6801035404205322,
-0.6567990183830261,
0.18530826270580292,
... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
MicPie/unpredictable_cluster24 | MicPie | 2022-08-04T19:59:33Z | 18 | 0 | null | [
"task_categories:multiple-choice",
"task_categories:question-answering",
"task_categories:zero-shot-classification",
"task_categories:text2text-generation",
"task_categories:table-question-answering",
"task_categories:text-generation",
"task_categories:text-classification",
"task_categories:tabular-cl... | 2022-08-04T19:59:33Z | 2022-07-08T18:33:36.000Z | 2022-07-08T18:33:36 | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license:
- apache-2.0
multilinguality:
- monolingual
pretty_name: UnpredicTable-cluster24
size_categories:
- 100K<n<1M
source_datasets: []
task_categories:
- multiple-choice
- question-answering
- zero-shot-classification
- text2text-generation
- table-question-answering
- text-generation
- text-classification
- tabular-classification
task_ids:
- multiple-choice-qa
- extractive-qa
- open-domain-qa
- closed-domain-qa
- closed-book-qa
- open-book-qa
- language-modeling
- multi-class-classification
- natural-language-inference
- topic-classification
- multi-label-classification
- tabular-multi-class-classification
- tabular-multi-label-classification
---
# Dataset Card for "UnpredicTable-cluster24" - Dataset of Few-shot Tasks from Tables
## 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://ethanperez.net/unpredictable
- **Repository:** https://github.com/JunShern/few-shot-adaptation
- **Paper:** Few-shot Adaptation Works with UnpredicTable Data
- **Point of Contact:** junshern@nyu.edu, perez@nyu.edu
### Dataset Summary
The UnpredicTable dataset consists of web tables formatted as few-shot tasks for fine-tuning language models to improve their few-shot performance.
There are several dataset versions available:
* [UnpredicTable-full](https://huggingface.co/datasets/MicPie/unpredictable_full): Starting from the initial WTC corpus of 50M tables, we apply our tables-to-tasks procedure to produce our resulting dataset, [UnpredicTable-full](https://huggingface.co/datasets/MicPie/unpredictable_full), which comprises 413,299 tasks from 23,744 unique websites.
* [UnpredicTable-unique](https://huggingface.co/datasets/MicPie/unpredictable_unique): This is the same as [UnpredicTable-full](https://huggingface.co/datasets/MicPie/unpredictable_full) but filtered to have a maximum of one task per website. [UnpredicTable-unique](https://huggingface.co/datasets/MicPie/unpredictable_unique) contains exactly 23,744 tasks from 23,744 websites.
* [UnpredicTable-5k](https://huggingface.co/datasets/MicPie/unpredictable_5k): This dataset contains 5k random tables from the full dataset.
* UnpredicTable data subsets based on a manual human quality rating (please see our publication for details of the ratings):
* [UnpredicTable-rated-low](https://huggingface.co/datasets/MicPie/unpredictable_rated-low)
* [UnpredicTable-rated-medium](https://huggingface.co/datasets/MicPie/unpredictable_rated-medium)
* [UnpredicTable-rated-high](https://huggingface.co/datasets/MicPie/unpredictable_rated-high)
* UnpredicTable data subsets based on the website of origin:
* [UnpredicTable-baseball-fantasysports-yahoo-com](https://huggingface.co/datasets/MicPie/unpredictable_baseball-fantasysports-yahoo-com)
* [UnpredicTable-bulbapedia-bulbagarden-net](https://huggingface.co/datasets/MicPie/unpredictable_bulbapedia-bulbagarden-net)
* [UnpredicTable-cappex-com](https://huggingface.co/datasets/MicPie/unpredictable_cappex-com)
* [UnpredicTable-cram-com](https://huggingface.co/datasets/MicPie/unpredictable_cram-com)
* [UnpredicTable-dividend-com](https://huggingface.co/datasets/MicPie/unpredictable_dividend-com)
* [UnpredicTable-dummies-com](https://huggingface.co/datasets/MicPie/unpredictable_dummies-com)
* [UnpredicTable-en-wikipedia-org](https://huggingface.co/datasets/MicPie/unpredictable_en-wikipedia-org)
* [UnpredicTable-ensembl-org](https://huggingface.co/datasets/MicPie/unpredictable_ensembl-org)
* [UnpredicTable-gamefaqs-com](https://huggingface.co/datasets/MicPie/unpredictable_gamefaqs-com)
* [UnpredicTable-mgoblog-com](https://huggingface.co/datasets/MicPie/unpredictable_mgoblog-com)
* [UnpredicTable-mmo-champion-com](https://huggingface.co/datasets/MicPie/unpredictable_mmo-champion-com)
* [UnpredicTable-msdn-microsoft-com](https://huggingface.co/datasets/MicPie/unpredictable_msdn-microsoft-com)
* [UnpredicTable-phonearena-com](https://huggingface.co/datasets/MicPie/unpredictable_phonearena-com)
* [UnpredicTable-sittercity-com](https://huggingface.co/datasets/MicPie/unpredictable_sittercity-com)
* [UnpredicTable-sporcle-com](https://huggingface.co/datasets/MicPie/unpredictable_sporcle-com)
* [UnpredicTable-studystack-com](https://huggingface.co/datasets/MicPie/unpredictable_studystack-com)
* [UnpredicTable-support-google-com](https://huggingface.co/datasets/MicPie/unpredictable_support-google-com)
* [UnpredicTable-w3-org](https://huggingface.co/datasets/MicPie/unpredictable_w3-org)
* [UnpredicTable-wiki-openmoko-org](https://huggingface.co/datasets/MicPie/unpredictable_wiki-openmoko-org)
* [UnpredicTable-wkdu-org](https://huggingface.co/datasets/MicPie/unpredictable_wkdu-org)
* UnpredicTable data subsets based on clustering (for the clustering details please see our publication):
* [UnpredicTable-cluster00](https://huggingface.co/datasets/MicPie/unpredictable_cluster00)
* [UnpredicTable-cluster01](https://huggingface.co/datasets/MicPie/unpredictable_cluster01)
* [UnpredicTable-cluster02](https://huggingface.co/datasets/MicPie/unpredictable_cluster02)
* [UnpredicTable-cluster03](https://huggingface.co/datasets/MicPie/unpredictable_cluster03)
* [UnpredicTable-cluster04](https://huggingface.co/datasets/MicPie/unpredictable_cluster04)
* [UnpredicTable-cluster05](https://huggingface.co/datasets/MicPie/unpredictable_cluster05)
* [UnpredicTable-cluster06](https://huggingface.co/datasets/MicPie/unpredictable_cluster06)
* [UnpredicTable-cluster07](https://huggingface.co/datasets/MicPie/unpredictable_cluster07)
* [UnpredicTable-cluster08](https://huggingface.co/datasets/MicPie/unpredictable_cluster08)
* [UnpredicTable-cluster09](https://huggingface.co/datasets/MicPie/unpredictable_cluster09)
* [UnpredicTable-cluster10](https://huggingface.co/datasets/MicPie/unpredictable_cluster10)
* [UnpredicTable-cluster11](https://huggingface.co/datasets/MicPie/unpredictable_cluster11)
* [UnpredicTable-cluster12](https://huggingface.co/datasets/MicPie/unpredictable_cluster12)
* [UnpredicTable-cluster13](https://huggingface.co/datasets/MicPie/unpredictable_cluster13)
* [UnpredicTable-cluster14](https://huggingface.co/datasets/MicPie/unpredictable_cluster14)
* [UnpredicTable-cluster15](https://huggingface.co/datasets/MicPie/unpredictable_cluster15)
* [UnpredicTable-cluster16](https://huggingface.co/datasets/MicPie/unpredictable_cluster16)
* [UnpredicTable-cluster17](https://huggingface.co/datasets/MicPie/unpredictable_cluster17)
* [UnpredicTable-cluster18](https://huggingface.co/datasets/MicPie/unpredictable_cluster18)
* [UnpredicTable-cluster19](https://huggingface.co/datasets/MicPie/unpredictable_cluster19)
* [UnpredicTable-cluster20](https://huggingface.co/datasets/MicPie/unpredictable_cluster20)
* [UnpredicTable-cluster21](https://huggingface.co/datasets/MicPie/unpredictable_cluster21)
* [UnpredicTable-cluster22](https://huggingface.co/datasets/MicPie/unpredictable_cluster22)
* [UnpredicTable-cluster23](https://huggingface.co/datasets/MicPie/unpredictable_cluster23)
* [UnpredicTable-cluster24](https://huggingface.co/datasets/MicPie/unpredictable_cluster24)
* [UnpredicTable-cluster25](https://huggingface.co/datasets/MicPie/unpredictable_cluster25)
* [UnpredicTable-cluster26](https://huggingface.co/datasets/MicPie/unpredictable_cluster26)
* [UnpredicTable-cluster27](https://huggingface.co/datasets/MicPie/unpredictable_cluster27)
* [UnpredicTable-cluster28](https://huggingface.co/datasets/MicPie/unpredictable_cluster28)
* [UnpredicTable-cluster29](https://huggingface.co/datasets/MicPie/unpredictable_cluster29)
* [UnpredicTable-cluster-noise](https://huggingface.co/datasets/MicPie/unpredictable_cluster-noise)
### Supported Tasks and Leaderboards
Since the tables come from the web, the distribution of tasks and topics is very broad. The shape of our dataset is very wide, i.e., we have 1000's of tasks, while each task has only a few examples, compared to most current NLP datasets which are very deep, i.e., 10s of tasks with many examples. This implies that our dataset covers a broad range of potential tasks, e.g., multiple-choice, question-answering, table-question-answering, text-classification, etc.
The intended use of this dataset is to improve few-shot performance by fine-tuning/pre-training on our dataset.
### Languages
English
## Dataset Structure
### Data Instances
Each task is represented as a jsonline file and consists of several few-shot examples. Each example is a dictionary containing a field 'task', which identifies the task, followed by an 'input', 'options', and 'output' field. The 'input' field contains several column elements of the same row in the table, while the 'output' field is a target which represents an individual column of the same row. Each task contains several such examples which can be concatenated as a few-shot task. In the case of multiple choice classification, the 'options' field contains the possible classes that a model needs to choose from.
There are also additional meta-data fields such as 'pageTitle', 'title', 'outputColName', 'url', 'wdcFile'.
### Data Fields
'task': task identifier
'input': column elements of a specific row in the table.
'options': for multiple choice classification, it provides the options to choose from.
'output': target column element of the same row as input.
'pageTitle': the title of the page containing the table.
'outputColName': output column name
'url': url to the website containing the table
'wdcFile': WDC Web Table Corpus file
### Data Splits
The UnpredicTable datasets do not come with additional data splits.
## Dataset Creation
### Curation Rationale
Few-shot training on multi-task datasets has been demonstrated to improve language models' few-shot learning (FSL) performance on new tasks, but it is unclear which training tasks lead to effective downstream task adaptation. Few-shot learning datasets are typically produced with expensive human curation, limiting the scale and diversity of the training tasks available to study. As an alternative source of few-shot data, we automatically extract 413,299 tasks from diverse internet tables. We provide this as a research resource to investigate the relationship between training data and few-shot learning.
### Source Data
#### Initial Data Collection and Normalization
We use internet tables from the English-language Relational Subset of the WDC Web Table Corpus 2015 (WTC). The WTC dataset tables were extracted from the July 2015 Common Crawl web corpus (http://webdatacommons.org/webtables/2015/EnglishStatistics.html). The dataset contains 50,820,165 tables from 323,160 web domains. We then convert the tables into few-shot learning tasks. Please see our publication for more details on the data collection and conversion pipeline.
#### Who are the source language producers?
The dataset is extracted from [WDC Web Table Corpora](http://webdatacommons.org/webtables/).
### Annotations
#### Annotation process
Manual annotation was only carried out for the [UnpredicTable-rated-low](https://huggingface.co/datasets/MicPie/unpredictable_rated-low),
[UnpredicTable-rated-medium](https://huggingface.co/datasets/MicPie/unpredictable_rated-medium), and [UnpredicTable-rated-high](https://huggingface.co/datasets/MicPie/unpredictable_rated-high) data subsets to rate task quality. Detailed instructions of the annotation instructions can be found in our publication.
#### Who are the annotators?
Annotations were carried out by a lab assistant.
### Personal and Sensitive Information
The data was extracted from [WDC Web Table Corpora](http://webdatacommons.org/webtables/), which in turn extracted tables from the [Common Crawl](https://commoncrawl.org/). We did not filter the data in any way. Thus any user identities or otherwise sensitive information (e.g., data that reveals racial or ethnic origins, sexual orientations, religious beliefs, political opinions or union memberships, or locations; financial or health data; biometric or genetic data; forms of government identification, such as social security numbers; criminal history, etc.) might be contained in our dataset.
## Considerations for Using the Data
### Social Impact of Dataset
This dataset is intended for use as a research resource to investigate the relationship between training data and few-shot learning. As such, it contains high- and low-quality data, as well as diverse content that may be untruthful or inappropriate. Without careful investigation, it should not be used for training models that will be deployed for use in decision-critical or user-facing situations.
### Discussion of Biases
Since our dataset contains tables that are scraped from the web, it will also contain many toxic, racist, sexist, and otherwise harmful biases and texts. We have not run any analysis on the biases prevalent in our datasets. Neither have we explicitly filtered the content. This implies that a model trained on our dataset may potentially reflect harmful biases and toxic text that exist in our dataset.
### Other Known Limitations
No additional known limitations.
## Additional Information
### Dataset Curators
Jun Shern Chan, Michael Pieler, Jonathan Jao, Jérémy Scheurer, Ethan Perez
### Licensing Information
Apache 2.0
### Citation Information
```
@misc{chan2022few,
author = {Chan, Jun Shern and Pieler, Michael and Jao, Jonathan and Scheurer, Jérémy and Perez, Ethan},
title = {Few-shot Adaptation Works with UnpredicTable Data},
publisher={arXiv},
year = {2022},
url = {https://arxiv.org/abs/2208.01009}
}
```
| [
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... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
ydmeira/segment-pokemon | ydmeira | 2022-07-23T10:28:38Z | 18 | 1 | null | [
"region:us"
] | 2022-07-23T10:28:38Z | 2022-07-23T10:25:00.000Z | 2022-07-23T10:25:00 | Entry not found | [
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0.5715674161911011,
-... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
alex-apostolo/filtered-cuad | alex-apostolo | 2022-08-04T06:24:04Z | 18 | 1 | cuad | [
"task_categories:question-answering",
"task_ids:closed-domain-qa",
"task_ids:extractive-qa",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:cuad",
"language:en",
"license:cc-by-4.0",
"arxiv:2103.06... | 2022-08-04T06:24:04Z | 2022-08-03T15:59:24.000Z | 2022-08-03T15:59:24 | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- cuad
task_categories:
- question-answering
task_ids:
- closed-domain-qa
- extractive-qa
paperswithcode_id: cuad
pretty_name: CUAD
train-eval-index:
- config: default
task: question-answering
task_id: extractive_question_answering
splits:
train_split: train
eval_split: test
col_mapping:
question: question
context: context
answers:
text: text
answer_start: answer_start
metrics:
- type: cuad
name: CUAD
---
# Dataset Card for filtered_cuad
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [Contract Understanding Atticus Dataset](https://www.atticusprojectai.org/cuad)
- **Repository:** [Contract Understanding Atticus Dataset](https://github.com/TheAtticusProject/cuad/)
- **Paper:** [CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review](https://arxiv.org/abs/2103.06268)
- **Point of Contact:** [Atticus Project Team](info@atticusprojectai.org)
### Dataset Summary
Contract Understanding Atticus Dataset (CUAD) v1 is a corpus of more than 13,000 labels in 510 commercial legal contracts that have been manually labeled to identify 41 categories of important clauses that lawyers look for when reviewing contracts in connection with corporate transactions. This dataset is a filtered version of CUAD. It excludes legal contracts with an Agreement date prior to 2002 and contracts which are not Business to Business. From the 41 categories we filtered them down to 12 which we considered the most crucial.
We wanted a small dataset to quickly fine-tune different models without sacrificing the categories which we deemed as important. The need to remove most questions was due to them not having an answer which is problematic since it can scue the resulting metrics such as the F1 score and the AUPR curve.
CUAD is curated and maintained by The Atticus Project, Inc. to support NLP research and development in legal contract review. Analysis of CUAD can be found at https://arxiv.org/abs/2103.06268. Code for replicating the results and the trained model can be found at https://github.com/TheAtticusProject/cuad.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
The dataset contains samples in English only.
## Dataset Structure
### Data Instances
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"answers": {
"answer_start": [44],
"text": ['DISTRIBUTOR AGREEMENT']
},
"context": 'EXHIBIT 10.6\n\n DISTRIBUTOR AGREEMENT\n\n THIS DISTRIBUTOR AGREEMENT (the "Agreement") is made by and between Electric City Corp., a Delaware corporation ("Company") and Electric City of Illinois LLC ("Distributor") this 7th day of September, 1999...',
"id": "LIMEENERGYCO_09_09_1999-EX-10-DISTRIBUTOR AGREEMENT__Document Name_0",
"question": "Highlight the parts (if any) of this contract related to "Document Name" that should be reviewed by a lawyer. Details: The name of the contract",
"title": "LIMEENERGYCO_09_09_1999-EX-10-DISTRIBUTOR AGREEMENT"
}
```
### Data Fields
- `id`: a `string` feature.
- `title`: a `string` feature.
- `context`: a `string` feature.
- `question`: a `string` feature.
- `answers`: a dictionary feature containing:
- `text`: a `string` feature.
- `answer_start`: a `int32` feature.
### Data Splits
This dataset is split into train/test set. Number of samples in each set is given below:
| | Train | Test |
| ----- | ------ | ---- |
| CUAD | 5442 | 936 |
## Dataset Creation
### Curation Rationale
A highly valuable specialized task without a public large-scale dataset is contract review, which costs humans substantial time, money, and attention. Many law firms spend approximately 50% of their time reviewing contracts (CEB, 2017). Due to the specialized training necessary to understand and interpret contracts, the billing rates for lawyers at large law firms are typically around $500-$900 per hour in the US. As a result, many transactions cost companies hundreds of thousands of dollars just so that lawyers can verify that there are no problematic obligations or requirements included in the contracts. Contract review can be a source of drudgery and, in comparison to other legal tasks, is widely considered to be especially boring.
Contract review costs also affect consumers. Since contract review costs are so prohibitive, contract review is not often performed outside corporate transactions. Small companies and individuals consequently often sign contracts without even reading them, which can result in predatory behavior that harms consumers. Automating contract review by openly releasing high-quality data and fine-tuned models can increase access to legal support for small businesses and individuals, so that legal support is not exclusively available to wealthy companies.
To reduce the disparate societal costs of contract review, and to study how well NLP models generalize to specialized domains, the authors introduced a new large-scale dataset for contract review. As part of The Atticus Project, a non-profit organization of legal experts, CUAD is introduced, the Contract Understanding Atticus Dataset. This dataset was created with a year-long effort pushed forward by dozens of law student annotators, lawyers, and machine learning researchers. The dataset includes more than 500 contracts and more than 13,000 expert annotations that span 41 label categories. For each of 41 different labels, models must learn to highlight the portions of a contract most salient to that label. This makes the task a matter of finding needles in a haystack.
### Source Data
#### Initial Data Collection and Normalization
The CUAD includes commercial contracts selected from 25 different types of contracts based on the contract names as shown below. Within each type, the creators randomly selected contracts based on the names of the filing companies across the alphabet.
Type of Contracts: # of Docs
Affiliate Agreement: 8
Agency Agreement: 8
Collaboration/Cooperation Agreement: 26
Co-Branding Agreement: 6
Consulting Agreement: 11
Development Agreement: 28
Distributor Agreement: 23
Endorsement Agreement: 10
Franchise Agreement: 14
Hosting Agreement: 12
IP Agreement: 16
Joint Venture Agreemen: 22
License Agreement: 32
Maintenance Agreement: 24
Manufacturing Agreement: 6
Marketing Agreement: 16
Non-Compete/No-Solicit/Non-Disparagement Agreement: 3
Outsourcing Agreement: 12
Promotion Agreement: 9
Reseller Agreement: 12
Service Agreement: 24
Sponsorship Agreement: 17
Supply Agreement: 13
Strategic Alliance Agreement: 32
Transportation Agreement: 1
TOTAL: 385
Categories
Document Name
Parties
Agreement Date
Effective Date
Expiration Date
Renewal Term
Notice Period To Terminate Renewal
Governing Law
Non-Compete
Exclusivity
Change Of Control
Anti-Assignment
#### Who are the source language producers?
The contracts were sourced from EDGAR, the Electronic Data Gathering, Analysis, and Retrieval system used at the U.S. Securities and Exchange Commission (SEC). Publicly traded companies in the United States are required to file certain contracts under the SEC rules. Access to these contracts is available to the public for free at https://www.sec.gov/edgar. Please read the Datasheet at https://www.atticusprojectai.org/ for information on the intended use and limitations of the CUAD.
### Annotations
#### Annotation process
The labeling process included multiple steps to ensure accuracy:
1. Law Student Training: law students attended training sessions on each of the categories that included a summary, video instructions by experienced attorneys, multiple quizzes and workshops. Students were then required to label sample contracts in eBrevia, an online contract review tool. The initial training took approximately 70-100 hours.
2. Law Student Label: law students conducted manual contract review and labeling in eBrevia.
3. Key Word Search: law students conducted keyword search in eBrevia to capture additional categories that have been missed during the “Student Label” step.
4. Category-by-Category Report Review: law students exported the labeled clauses into reports, review each clause category-by-category and highlight clauses that they believe are mislabeled.
5. Attorney Review: experienced attorneys reviewed the category-by-category report with students comments, provided comments and addressed student questions. When applicable, attorneys discussed such results with the students and reached consensus. Students made changes in eBrevia accordingly.
6. eBrevia Extras Review. Attorneys and students used eBrevia to generate a list of “extras”, which are clauses that eBrevia AI tool identified as responsive to a category but not labeled by human annotators. Attorneys and students reviewed all of the “extras” and added the correct ones. The process is repeated until all or substantially all of the “extras” are incorrect labels.
7. Final Report: The final report was exported into a CSV file. Volunteers manually added the “Yes/No” answer column to categories that do not contain an answer.
#### Who are the annotators?
Answered in above section.
### Personal and Sensitive Information
Some clauses in the files are redacted because the party submitting these contracts redacted them to protect confidentiality. Such redaction may show up as asterisks (\*\*\*) or underscores (\_\_\_) or blank spaces. The dataset and the answers reflect such redactions. For example, the answer for “January \_\_ 2020” would be “1/[]/2020”).
For any categories that require an answer of “Yes/No”, annotators include full sentences as text context in a contract. To maintain consistency and minimize inter-annotator disagreement, annotators select text for the full sentence, under the instruction of “from period to period”.
For the other categories, annotators selected segments of the text in the contract that are responsive to each such category. One category in a contract may include multiple labels. For example, “Parties” may include 4-10 separate text strings that are not continuous in a contract. The answer is presented in the unified format separated by semicolons of “Party A Inc. (“Party A”); Party B Corp. (“Party B”)”.
Some sentences in the files include confidential legends that are not part of the contracts. An example of such confidential legend is as follows:
THIS EXHIBIT HAS BEEN REDACTED AND IS THE SUBJECT OF A CONFIDENTIAL TREATMENT REQUEST. REDACTED MATERIAL IS MARKED WITH [* * *] AND HAS BEEN FILED SEPARATELY WITH THE SECURITIES AND EXCHANGE COMMISSION.
Some sentences in the files contain irrelevant information such as footers or page numbers. Some sentences may not be relevant to the corresponding category. Some sentences may correspond to a different category. Because many legal clauses are very long and contain various sub-parts, sometimes only a sub-part of a sentence is responsive to a category.
To address the foregoing limitations, annotators manually deleted the portion that is not responsive, replacing it with the symbol "<omitted>" to indicate that the two text segments do not appear immediately next to each other in the contracts. For example, if a “Termination for Convenience” clause starts with “Each Party may terminate this Agreement if” followed by three subparts “(a), (b) and (c)”, but only subpart (c) is responsive to this category, the authors manually deleted subparts (a) and (b) and replaced them with the symbol "<omitted>”. Another example is for “Effective Date”, the contract includes a sentence “This Agreement is effective as of the date written above” that appears after the date “January 1, 2010”. The annotation is as follows: “January 1, 2010 <omitted> This Agreement is effective as of the date written above.”
Because the contracts were converted from PDF into TXT files, the converted TXT files may not stay true to the format of the original PDF files. For example, some contracts contain inconsistent spacing between words, sentences and paragraphs. Table format is not maintained in the TXT files.
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
Attorney Advisors
Wei Chen, John Brockland, Kevin Chen, Jacky Fink, Spencer P. Goodson, Justin Haan, Alex Haskell, Kari Krusmark, Jenny Lin, Jonas Marson, Benjamin Petersen, Alexander Kwonji Rosenberg, William R. Sawyers, Brittany Schmeltz, Max Scott, Zhu Zhu
Law Student Leaders
John Batoha, Daisy Beckner, Lovina Consunji, Gina Diaz, Chris Gronseth, Calvin Hannagan, Joseph Kroon, Sheetal Sharma Saran
Law Student Contributors
Scott Aronin, Bryan Burgoon, Jigar Desai, Imani Haynes, Jeongsoo Kim, Margaret Lynch, Allison Melville, Felix Mendez-Burgos, Nicole Mirkazemi, David Myers, Emily Rissberger, Behrang Seraj, Sarahginy Valcin
Technical Advisors & Contributors
Dan Hendrycks, Collin Burns, Spencer Ball, Anya Chen
### Licensing Information
CUAD is licensed under the Creative Commons Attribution 4.0 (CC BY 4.0) license and free to the public for commercial and non-commercial use.
The creators make no representations or warranties regarding the license status of the underlying contracts, which are publicly available and downloadable from EDGAR.
Privacy Policy & Disclaimers
The categories or the contracts included in the dataset are not comprehensive or representative. The authors encourage the public to help improve them by sending them your comments and suggestions to info@atticusprojectai.org. Comments and suggestions will be reviewed by The Atticus Project at its discretion and will be included in future versions of Atticus categories once approved.
The use of CUAD is subject to their privacy policy https://www.atticusprojectai.org/privacy-policy and disclaimer https://www.atticusprojectai.org/disclaimer.
### Citation Information
```
@article{hendrycks2021cuad,
title={CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review},
author={Dan Hendrycks and Collin Burns and Anya Chen and Spencer Ball},
journal={arXiv preprint arXiv:2103.06268},
year={2021}
}
```
### Contributions
Thanks to [@bhavitvyamalik](https://github.com/bhavitvyamalik) for adding this dataset. | [
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0.1024508103728... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
scikit-learn/churn-prediction | scikit-learn | 2022-08-08T17:56:29Z | 18 | 6 | null | [
"license:cc-by-4.0",
"region:us"
] | 2022-08-08T17:56:29Z | 2022-08-08T17:42:17.000Z | 2022-08-08T17:42:17 | ---
license: cc-by-4.0
---
Customer churn prediction dataset of a fictional telecommunication company made by IBM Sample Datasets.
Context
Predict behavior to retain customers. You can analyze all relevant customer data and develop focused customer retention programs.
Content
Each row represents a customer, each column contains customer’s attributes described on the column metadata.
The data set includes information about:
- Customers who left within the last month: the column is called Churn
- Services that each customer has signed up for: phone, multiple lines, internet, online security, online backup, device protection, tech support, and streaming TV and movies
- Customer account information: how long they’ve been a customer, contract, payment method, paperless billing, monthly charges, and total charges
- Demographic info about customers: gender, age range, and if they have partners and dependents
Credits for the dataset and the card:
- [Kaggle](https://www.kaggle.com/datasets/blastchar/telco-customer-churn)
- [Latest version of the dataset by IBM Samples team](https://community.ibm.com/community/user/businessanalytics/blogs/steven-macko/2019/07/11/telco-customer-churn-1113)
| [
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0.006732616573572159,
-0.1788612157106... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
darkproger/librispeech_asr | darkproger | 2022-08-14T16:46:17Z | 18 | 0 | null | [
"license:cc-by-4.0",
"region:us"
] | 2022-08-14T16:46:17Z | 2022-08-14T14:14:16.000Z | 2022-08-14T14:14:16 | ---
license: cc-by-4.0
---
This is a dataset is a fork of [librispeech_asr](https://huggingface.co/datasets/librispeech_asr) that defines each original split (like train-clean-100) as a split (named `train.clean.100`, with dots instead of hyphens). This allows you to download each part separately.
This fork also reports a `path` for each sample accurately. | [
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-0.428654283285141,
-0.090172916650... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
Hobson/surname-nationality | Hobson | 2022-12-29T23:14:09Z | 18 | 2 | null | [
"task_categories:token-classification",
"task_categories:text-classification",
"task_ids:named-entity-recognition",
"size_categories:List[str]",
"source_datasets:List[str]",
"license:mit",
"multilingual",
"RNN",
"name",
"tagging",
"nlp",
"transliterated",
"character-level",
"text-tagging",... | 2022-12-29T23:14:09Z | 2022-08-15T03:52:58.000Z | 2022-08-15T03:52:58 | ---
license: mit
size_categories: List[str]
source_datasets: List[str]
task_categories:
- token-classification
- text-classification
task_ids:
- named-entity-recognition
pretty_name: Popular Surname Nationality Mapping
tags:
- multilingual
- RNN
- name
- tagging
- nlp
- transliterated
- character-level
- text-tagging
- bias
- classification
- language model
- surname
- ethnicity
- multilabel classification
- natural language
---
# Popular Surname Nationality Mapping
Sample of popular surnames for 30+ countries labeled with nationality (language)
| [
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0.697333574295044,
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0.000796816544607... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
rubrix/wildfire_tweets | rubrix | 2022-08-17T13:02:13Z | 18 | 0 | null | [
"task_categories:text-classification",
"annotations_creators:expert-generated",
"language_creators:other",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:apache-2.0",
"rubrix",
"climate change",
"region:us"
] | 2022-08-17T13:02:13Z | 2022-08-16T16:15:36.000Z | 2022-08-16T16:15:36 | ---
annotations_creators:
- expert-generated
language:
- en
language_creators:
- other
license:
- apache-2.0
multilinguality:
- monolingual
pretty_name: Tweets about Wildfire and climate change
size_categories:
- 1K<n<10K
source_datasets:
- original
tags:
- rubrix
- climate change
task_categories:
- text-classification
task_ids: []
---
| [
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-0.0478260256350... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
Mijavier/10_classes_custom_dataset_donut_old | Mijavier | 2022-09-07T19:22:20Z | 18 | 0 | null | [
"region:us"
] | 2022-09-07T19:22:20Z | 2022-09-07T18:49:30.000Z | 2022-09-07T18:49:30 | Entry not found | [
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... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
autoevaluate/autoeval-staging-eval-squad_v2-squad_v2-c76793-16626248 | autoevaluate | 2022-09-15T06:02:49Z | 18 | 0 | null | [
"autotrain",
"evaluation",
"region:us"
] | 2022-09-15T06:02:49Z | 2022-09-15T05:59:32.000Z | 2022-09-15T05:59:32 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- squad_v2
eval_info:
task: extractive_question_answering
model: AyushPJ/test-squad-trained-finetuned-squad
metrics: []
dataset_name: squad_v2
dataset_config: squad_v2
dataset_split: validation
col_mapping:
context: context
question: question
answers-text: answers.text
answers-answer_start: answers.answer_start
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Question Answering
* Model: AyushPJ/test-squad-trained-finetuned-squad
* Dataset: squad_v2
* Config: squad_v2
* Split: validation
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model. | [
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0.092934228479862... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
autoevaluate/autoeval-staging-eval-autoevaluate__zero-shot-classification-sample-autoevalu-acab52-16766274 | autoevaluate | 2022-09-15T19:13:14Z | 18 | 0 | null | [
"autotrain",
"evaluation",
"region:us"
] | 2022-09-15T19:13:14Z | 2022-09-15T18:06:48.000Z | 2022-09-15T18:06:48 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- autoevaluate/zero-shot-classification-sample
eval_info:
task: text_zero_shot_classification
model: autoevaluate/zero-shot-classification
metrics: []
dataset_name: autoevaluate/zero-shot-classification-sample
dataset_config: autoevaluate--zero-shot-classification-sample
dataset_split: test
col_mapping:
text: text
classes: classes
target: target
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Zero-Shot Text Classification
* Model: autoevaluate/zero-shot-classification
* Dataset: autoevaluate/zero-shot-classification-sample
* Config: autoevaluate--zero-shot-classification-sample
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model. | [
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Stablepranav/the_object | Stablepranav | 2022-09-15T19:16:54Z | 18 | 0 | null | [
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autoevaluate/autoeval-staging-eval-Tristan__zero_shot_classification_test-Tristan__zero_sh-997db8-16786276 | autoevaluate | 2022-09-15T19:26:29Z | 18 | 0 | null | [
"autotrain",
"evaluation",
"region:us"
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type: predictions
tags:
- autotrain
- evaluation
datasets:
- Tristan/zero_shot_classification_test
eval_info:
task: text_zero_shot_classification
model: autoevaluate/zero-shot-classification
metrics: []
dataset_name: Tristan/zero_shot_classification_test
dataset_config: Tristan--zero_shot_classification_test
dataset_split: test
col_mapping:
text: text
classes: classes
target: target
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Zero-Shot Text Classification
* Model: autoevaluate/zero-shot-classification
* Dataset: Tristan/zero_shot_classification_test
* Config: Tristan--zero_shot_classification_test
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@Tristan](https://huggingface.co/Tristan) for evaluating this model. | [
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mertcobanov/mozart-diff-small-256 | mertcobanov | 2023-01-05T21:33:43Z | 18 | 0 | null | [
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"region:us"
] | 2023-01-05T21:33:43Z | 2022-09-19T21:46:03.000Z | 2022-09-19T21:46:03 | ---
task_categories:
- image-to-image
pretty_name: Mozart Operas
size_categories:
- 100K<n<1M
--- | [
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Kunling/layoutlm_resume_data | Kunling | 2022-09-29T05:18:32Z | 18 | 2 | null | [
"license:bsd",
"region:us"
] | 2022-09-29T05:18:32Z | 2022-09-26T21:48:22.000Z | 2022-09-26T21:48:22 | ---
license: bsd
---
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open-source-metrics/pip | open-source-metrics | 2023-11-22T15:49:20Z | 18 | 0 | null | [
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dataset_info:
features:
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dtype: string
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splits:
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num_examples: 1176
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num_bytes: 25872
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- name: datasets
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download_size: 0
dataset_size: 231088
configs:
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data_files:
- split: accelerate
path: data/accelerate-*
- split: datasets
path: data/datasets-*
- split: diffusers
path: data/diffusers-*
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path: data/evaluate-*
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path: data/gradio-*
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path: data/huggingface_hub-*
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path: data/optimum-*
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path: data/peft-*
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path: data/pytorch_image_models-*
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path: data/safetensors-*
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path: data/tokenizers-*
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path: data/transformers-*
---
# Dataset Card for "pip"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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kejian/codesearchnet-py-linelen40-rebalanced200k-v1 | kejian | 2022-09-28T03:34:31Z | 18 | 0 | null | [
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mayjestro/LittleHodler | mayjestro | 2022-09-28T14:30:31Z | 18 | 0 | null | [
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license: c-uda
---
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autoevaluate/autoeval-eval-big_patent-g-9d42aa-1581555947 | autoevaluate | 2022-09-28T11:15:24Z | 18 | 0 | null | [
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"evaluation",
"region:us"
] | 2022-09-28T11:15:24Z | 2022-09-28T09:54:38.000Z | 2022-09-28T09:54:38 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- big_patent
eval_info:
task: summarization
model: facebook/bart-large-cnn
metrics: ['perplexity']
dataset_name: big_patent
dataset_config: g
dataset_split: validation
col_mapping:
text: description
target: abstract
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Summarization
* Model: facebook/bart-large-cnn
* Dataset: big_patent
* Config: g
* Split: validation
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@jonesdaniel](https://huggingface.co/jonesdaniel) for evaluating this model. | [
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EmnaBou/tokenDS | EmnaBou | 2022-11-30T11:32:39Z | 18 | 0 | null | [
"region:us"
] | 2022-11-30T11:32:39Z | 2022-09-28T11:34:05.000Z | 2022-09-28T11:34:05 | ---
annotations_creators:
- found
language_creators:
- found
language:
- ar
license:
- other
multilinguality:
- monolingual
pretty_name: disTD
task_categories:
- token-classification
task_ids:
- disfluency-detection
dataset_info:
features:
- name: tokens
sequence: string
- name: isDisf
sequence:
class_label:
names:
0: O
1: B_RM
2: I_RM
3: B_RP
4: I_RP
5: IP
config_name: disTD
# Dataset Card for myds
## 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:** [Needs More Information]
- **Repository:** [Needs More Information]
- **Paper:** [Needs More Information]
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [Needs More Information]
### Dataset Summary
dataset for Tunisian dialect
### Supported Tasks and Leaderboards
[Needs More Information]
### Languages
tuanisian arabic dialect
## Dataset Structure
### Data Instances
Size of downloaded dataset files: 4.63 MB
Size of the generated dataset: 9.78 MB
Total amount of disk used: 14.41 MB
### Data Fields
dsfsergrth
### Data Splits
rtsert
## Dataset Creation
### Curation Rationale
link
### Source Data
#### Initial Data Collection and Normalization
kink
#### Who are the source language producers?
link
### Annotations
#### Annotation process
tool
#### Who are the annotators?
me
### Personal and Sensitive Information
ok
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
[Needs More Information]
### Citation Information
[Needs More Information] | [
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ldm030/Training | ldm030 | 2022-09-28T15:03:41Z | 18 | 0 | null | [
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Daniel1999/pruebadataset | Daniel1999 | 2022-09-29T05:33:27Z | 18 | 0 | null | [
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Metalistenia/daniel | Metalistenia | 2022-09-29T05:54:18Z | 18 | 0 | null | [
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license: openrail
---
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erwanlc/images | erwanlc | 2022-09-29T06:54:41Z | 18 | 0 | null | [
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Algp123/seansimon | Algp123 | 2022-09-29T08:06:44Z | 18 | 0 | null | [
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license: cc
---
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dkthegreat/test2 | dkthegreat | 2022-09-29T13:27:57Z | 18 | 0 | null | [
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tomekkorbak/detoxify-pile-chunk3-0-50 | tomekkorbak | 2022-09-29T14:37:17Z | 18 | 0 | null | [
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dtroncoso/mis_fotos_de_entrenamiento | dtroncoso | 2022-09-29T15:15:21Z | 18 | 0 | null | [
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viktor2k/watercolor | viktor2k | 2022-09-29T15:11:26Z | 18 | 0 | null | [
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KamiNoGi/pochi | KamiNoGi | 2022-09-29T15:39:50Z | 18 | 0 | null | [
"license:openrail",
"region:us"
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license: openrail
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autoevaluate/autoeval-eval-mathemakitten__winobias_antistereotype_test-mathemakitt-c50da3-1597456336 | autoevaluate | 2022-09-29T18:00:45Z | 18 | 0 | null | [
"autotrain",
"evaluation",
"region:us"
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type: predictions
tags:
- autotrain
- evaluation
datasets:
- mathemakitten/winobias_antistereotype_test
eval_info:
task: text_zero_shot_classification
model: facebook/opt-66b
metrics: []
dataset_name: mathemakitten/winobias_antistereotype_test
dataset_config: mathemakitten--winobias_antistereotype_test
dataset_split: test
col_mapping:
text: text
classes: classes
target: target
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Zero-Shot Text Classification
* Model: facebook/opt-66b
* Dataset: mathemakitten/winobias_antistereotype_test
* Config: mathemakitten--winobias_antistereotype_test
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@mathemakitten](https://huggingface.co/mathemakitten) for evaluating this model. | [
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autoevaluate/autoeval-eval-mathemakitten__winobias_antistereotype_test-mathemakitt-c50da3-1597456333 | autoevaluate | 2022-09-29T15:47:19Z | 18 | 0 | null | [
"autotrain",
"evaluation",
"region:us"
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type: predictions
tags:
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datasets:
- mathemakitten/winobias_antistereotype_test
eval_info:
task: text_zero_shot_classification
model: facebook/opt-6.7b
metrics: []
dataset_name: mathemakitten/winobias_antistereotype_test
dataset_config: mathemakitten--winobias_antistereotype_test
dataset_split: test
col_mapping:
text: text
classes: classes
target: target
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Zero-Shot Text Classification
* Model: facebook/opt-6.7b
* Dataset: mathemakitten/winobias_antistereotype_test
* Config: mathemakitten--winobias_antistereotype_test
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@mathemakitten](https://huggingface.co/mathemakitten) for evaluating this model. | [
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-0.1194548606872... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
autoevaluate/autoeval-eval-mathemakitten__winobias_antistereotype_test-mathemakitt-c50da3-1597456332 | autoevaluate | 2022-09-29T15:36:34Z | 18 | 0 | null | [
"autotrain",
"evaluation",
"region:us"
] | 2022-09-29T15:36:34Z | 2022-09-29T15:31:27.000Z | 2022-09-29T15:31:27 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- mathemakitten/winobias_antistereotype_test
eval_info:
task: text_zero_shot_classification
model: facebook/opt-2.7b
metrics: []
dataset_name: mathemakitten/winobias_antistereotype_test
dataset_config: mathemakitten--winobias_antistereotype_test
dataset_split: test
col_mapping:
text: text
classes: classes
target: target
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Zero-Shot Text Classification
* Model: facebook/opt-2.7b
* Dataset: mathemakitten/winobias_antistereotype_test
* Config: mathemakitten--winobias_antistereotype_test
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@mathemakitten](https://huggingface.co/mathemakitten) for evaluating this model. | [
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autoevaluate/autoeval-eval-mathemakitten__winobias_antistereotype_test-mathemakitt-c50da3-1597456329 | autoevaluate | 2022-09-29T15:32:08Z | 18 | 0 | null | [
"autotrain",
"evaluation",
"region:us"
] | 2022-09-29T15:32:08Z | 2022-09-29T15:31:27.000Z | 2022-09-29T15:31:27 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- mathemakitten/winobias_antistereotype_test
eval_info:
task: text_zero_shot_classification
model: facebook/opt-125m
metrics: []
dataset_name: mathemakitten/winobias_antistereotype_test
dataset_config: mathemakitten--winobias_antistereotype_test
dataset_split: test
col_mapping:
text: text
classes: classes
target: target
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Zero-Shot Text Classification
* Model: facebook/opt-125m
* Dataset: mathemakitten/winobias_antistereotype_test
* Config: mathemakitten--winobias_antistereotype_test
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@mathemakitten](https://huggingface.co/mathemakitten) for evaluating this model. | [
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-0.120570145547389... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
autoevaluate/autoeval-eval-mathemakitten__winobias_antistereotype_test-mathemakitt-c50da3-1597456331 | autoevaluate | 2022-09-29T15:34:41Z | 18 | 0 | null | [
"autotrain",
"evaluation",
"region:us"
] | 2022-09-29T15:34:41Z | 2022-09-29T15:31:27.000Z | 2022-09-29T15:31:27 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- mathemakitten/winobias_antistereotype_test
eval_info:
task: text_zero_shot_classification
model: facebook/opt-1.3b
metrics: []
dataset_name: mathemakitten/winobias_antistereotype_test
dataset_config: mathemakitten--winobias_antistereotype_test
dataset_split: test
col_mapping:
text: text
classes: classes
target: target
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Zero-Shot Text Classification
* Model: facebook/opt-1.3b
* Dataset: mathemakitten/winobias_antistereotype_test
* Config: mathemakitten--winobias_antistereotype_test
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@mathemakitten](https://huggingface.co/mathemakitten) for evaluating this model. | [
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-0.1205372959... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
autoevaluate/autoeval-eval-mathemakitten__winobias_antistereotype_test-mathemakitt-c50da3-1597456330 | autoevaluate | 2022-09-29T15:32:36Z | 18 | 0 | null | [
"autotrain",
"evaluation",
"region:us"
] | 2022-09-29T15:32:36Z | 2022-09-29T15:31:29.000Z | 2022-09-29T15:31:29 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- mathemakitten/winobias_antistereotype_test
eval_info:
task: text_zero_shot_classification
model: facebook/opt-350m
metrics: []
dataset_name: mathemakitten/winobias_antistereotype_test
dataset_config: mathemakitten--winobias_antistereotype_test
dataset_split: test
col_mapping:
text: text
classes: classes
target: target
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Zero-Shot Text Classification
* Model: facebook/opt-350m
* Dataset: mathemakitten/winobias_antistereotype_test
* Config: mathemakitten--winobias_antistereotype_test
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@mathemakitten](https://huggingface.co/mathemakitten) for evaluating this model. | [
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-0.1204865723... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
autoevaluate/autoeval-eval-mathemakitten__winobias_antistereotype_test-mathemakitt-c50da3-1597456335 | autoevaluate | 2022-09-29T16:38:50Z | 18 | 0 | null | [
"autotrain",
"evaluation",
"region:us"
] | 2022-09-29T16:38:50Z | 2022-09-29T15:31:29.000Z | 2022-09-29T15:31:29 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- mathemakitten/winobias_antistereotype_test
eval_info:
task: text_zero_shot_classification
model: facebook/opt-30b
metrics: []
dataset_name: mathemakitten/winobias_antistereotype_test
dataset_config: mathemakitten--winobias_antistereotype_test
dataset_split: test
col_mapping:
text: text
classes: classes
target: target
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Zero-Shot Text Classification
* Model: facebook/opt-30b
* Dataset: mathemakitten/winobias_antistereotype_test
* Config: mathemakitten--winobias_antistereotype_test
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@mathemakitten](https://huggingface.co/mathemakitten) for evaluating this model. | [
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-0.5948266983032227,
-0.1297338008880... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
Dopamina/dopamina | Dopamina | 2022-09-29T17:03:03Z | 18 | 0 | null | [
"license:artistic-2.0",
"region:us"
] | 2022-09-29T17:03:03Z | 2022-09-29T16:57:03.000Z | 2022-09-29T16:57:03 | ---
license: artistic-2.0
---
| [
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-0.047825977206230... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
Luiselot/FotosYo | Luiselot | 2022-10-02T20:09:38Z | 18 | 0 | null | [
"region:us"
] | 2022-10-02T20:09:38Z | 2022-09-29T19:43:31.000Z | 2022-09-29T19:43:31 | Entry not found | [
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-0... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
gorkaartola/ZS-train_SDG_Descriptions_S1-sentence_S2-subject_Negative_Sample_Filter-Only_Headlines | gorkaartola | 2022-09-29T19:53:14Z | 18 | 0 | null | [
"region:us"
] | 2022-09-29T19:53:14Z | 2022-09-29T19:52:52.000Z | 2022-09-29T19:52:52 | Entry not found | [
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-0... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
Kasuzu/20 | Kasuzu | 2022-09-29T19:58:37Z | 18 | 0 | null | [
"region:us"
] | 2022-09-29T19:58:37Z | 2022-09-29T19:53:47.000Z | 2022-09-29T19:53:47 | Entry not found | [
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0.5715669393539429,
-0... | null | null | null | null | null | null | null | null | null | null | null | null | null |
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