Upload batch 320 (20 files, last=huggingface_dataset/Dataset_Card/tatiana-merz_cyrillic_turkic_langs.md)

huggingface_dataset/Dataset_Card/Abirami_tamilwikipediadataset.md

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+annotations_creators:
+- found
+language:
+- Tamil
+language_creators:
+- found
+license: []
+multilinguality:
+- multilingual
+pretty_name: tamilwikipediadataset
+size_categories:
+- 100K<n<1M
+source_datasets: []
+tags: []
+task_categories:
+- summarization
+task_ids: []

huggingface_dataset/Dataset_Card/Anthropic_hh-rlhf.md

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+---
+license: mit
+tags:
+  - human-feedback
+---
+
+# Dataset Card for HH-RLHF
+
+## Dataset Summary
+
+This repository provides access to:
+1. Human preference data about helpfulness and harmlessness from [Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback](https://arxiv.org/abs/2204.05862)
+2. Human-generated red teaming data from [Red Teaming Language Models to Reduce Harms: Methods, Scaling Behaviors, and Lessons Learned](https://www.anthropic.com/red_teaming.pdf).
+
+Each of these datasets are described further below. 
+
+**Disclaimer**: The data contain content that may be offensive or upsetting. Topics include, but are not limited to, discriminatory language and discussions of abuse, violence, self-harm, exploitation, and other potentially upsetting subject matter. Please only engage with the data in accordance with your own personal risk tolerance. The data are intended for research purposes, especially research that can make models *less* harmful. The views expressed in the data do not reflect the views of Anthropic or any of its employees.
+
+## Human preference data about helpfulness and harmlessness
+
+The data are described in the paper: [Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback](https://arxiv.org/abs/2204.05862). If you find the data useful, please cite the paper. The data format is very simple -- each line of the jsonl files contains a pair of texts, one "chosen" and one "rejected".
+
+For **helpfulness**, the data are grouped into train/test splits in three tranches: from our base models (context-distilled 52B language models), via rejection sampling (mostly with best-of-16 sampling) against an early preference model, and a dataset sampled during our iterated "online" process. 
+
+For **harmlessness**, the data are only collected for our base models, but otherwise formatted in the same way.
+
+Details about the data collection process and crowdworker population can be found in the paper, specifically in section 2 and appendix D. 
+
+## Red teaming data
+
+The data are described in the paper: [Red Teaming Language Models to Reduce Harms: Methods, Scaling Behaviors, and Lessons Learned](https://www.anthropic.com/red_teaming.pdf). If you find the data useful, please cite the paper.
+
+Details about the data and data collection procedures can be found in the Datasheet in the appendix of the paper. 
+
+Each line of the jsonl file contains a dictionary with the following fields:
+- `transcript` a text transcript of a conversation between a human adversary (red team member) and an AI assistant
+- `min_harmlessness_score_transcript` a real value score of the harmlessness of the AI assistant (lower is more harmful) as obtained from a preference model
+- `num_params` number of parameters in the language model powering the AI assistant
+- `model_type` type of model powering the AI assistant
+- `rating` the red team member's rating of how successful they were at breaking the AI assistant (Likert scale, higher is more successful)  
+- `task_description` a short text description written by the red team member about how they tried to red team the AI assistant
+- `task_description_harmlessness_score` a real value score of the harmlessness of the task description (lower is more harmful) as obtained from a preference model
+- `red_team_member_id` an arbitrary identifier of the red team member. one red team member can generate multiple red team attacks
+- `is_upworker` a binary indicator that is true if the red team member was from the crowd platform Upwork or false if they were from MTurk
+- `tags` a list of up to 6 tags per transcript. tags are short descriptions of the red team attempts generated by crowdworkers who reviewed red team data post-hoc. tags were only provided for a random sample of 1000 red team attempts for two of four model types.
+
+## Usage
+
+Each of the above datasets is located in a separate sub-directory. To load an individual subset, use the `data_dir` argument of the `load_dataset()` function as follows:
+
+```python
+from datasets import load_dataset
+
+# Load all helpfulness/harmless subsets (share the same schema)
+dataset = load_dataset("Anthropic/hh-rlhf")
+
+# Load one of the harmless subsets
+dataset = load_dataset("Anthropic/hh-rlhf", data_dir="harmless-base")
+
+# Load the red teaming subset
+dataset = load_dataset("Anthropic/hh-rlhf", data_dir="red-team-attempts")
+```
+
+## Contact
+
+The original authors host this dataset on GitHub here: https://github.com/anthropics/hh-rlhf
+You can submit inquiries to: redteam@anthropic.com

huggingface_dataset/Dataset_Card/Ceetar_MetsTweets.md

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+Tweets containing '#Mets' from early August through late September

huggingface_dataset/Dataset_Card/THUDM_humaneval-x.md

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+---
+annotations_creators: []
+language_creators:
+- crowdsourced
+- expert-generated
+language:
+- code
+license:
+- apache-2.0
+multilinguality:
+- multilingual
+size_categories:
+- unknown
+source_datasets: []
+task_categories:
+- text-generation
+task_ids:
+- language-modeling
+pretty_name: HumanEval-X
+---
+
+# HumanEval-X
+
+## Dataset Description
+[HumanEval-X](https://github.com/THUDM/CodeGeeX) is a benchmark for evaluating the multilingual ability of code generative models. It consists of 820 high-quality human-crafted data samples (each with test cases) in Python, C++, Java, JavaScript, and Go, and can be used for various tasks, such as code generation and translation.
+
+## Languages
+
+The dataset contains coding problems in 5 programming languages: Python, C++, Java, JavaScript, and Go.
+
+## Dataset Structure
+To load the dataset you need to specify a subset among the 5 exiting languages  `[python, cpp, go, java, js]`. By default `python` is loaded. 
+
+```python
+from datasets import load_dataset
+load_dataset("THUDM/humaneval-x", "js")
+
+DatasetDict({
+    test: Dataset({
+        features: ['task_id', 'prompt', 'declaration', 'canonical_solution', 'test', 'example_test'],
+        num_rows: 164
+    })
+})
+```
+
+```python
+next(iter(data["test"]))
+{'task_id': 'JavaScript/0',
+ 'prompt': '/* Check if in given list of numbers, are any two numbers closer to each other than\n  given threshold.\n  >>> hasCloseElements([1.0, 2.0, 3.0], 0.5)\n  false\n  >>> hasCloseElements([1.0, 2.8, 3.0, 4.0, 5.0, 2.0], 0.3)\n  true\n  */\nconst hasCloseElements = (numbers, threshold) => {\n',
+ 'declaration': '\nconst hasCloseElements = (numbers, threshold) => {\n',
+ 'canonical_solution': '  for (let i = 0; i < numbers.length; i++) {\n    for (let j = 0; j < numbers.length; j++) {\n      if (i != j) {\n        let distance = Math.abs(numbers[i] - numbers[j]);\n        if (distance < threshold) {\n          return true;\n        }\n      }\n    }\n  }\n  return false;\n}\n\n',
+ 'test': 'const testHasCloseElements = () => {\n  console.assert(hasCloseElements([1.0, 2.0, 3.9, 4.0, 5.0, 2.2], 0.3) === true)\n  console.assert(\n    hasCloseElements([1.0, 2.0, 3.9, 4.0, 5.0, 2.2], 0.05) === false\n  )\n  console.assert(hasCloseElements([1.0, 2.0, 5.9, 4.0, 5.0], 0.95) === true)\n  console.assert(hasCloseElements([1.0, 2.0, 5.9, 4.0, 5.0], 0.8) === false)\n  console.assert(hasCloseElements([1.0, 2.0, 3.0, 4.0, 5.0, 2.0], 0.1) === true)\n  console.assert(hasCloseElements([1.1, 2.2, 3.1, 4.1, 5.1], 1.0) === true)\n  console.assert(hasCloseElements([1.1, 2.2, 3.1, 4.1, 5.1], 0.5) === false)\n}\n\ntestHasCloseElements()\n',
+ 'example_test': 'const testHasCloseElements = () => {\n  console.assert(hasCloseElements([1.0, 2.0, 3.0], 0.5) === false)\n  console.assert(\n    hasCloseElements([1.0, 2.8, 3.0, 4.0, 5.0, 2.0], 0.3) === true\n  )\n}\ntestHasCloseElements()\n'}
+ ```
+
+
+## Data Fields
+
+*   ``task_id``: indicates the target language and ID of the problem. Language is one of ["Python", "Java", "JavaScript", "CPP", "Go"].
+*   ``prompt``: the function declaration and docstring, used for code generation.
+*   ``declaration``: only the function declaration, used for code translation. 
+*   ``canonical_solution``: human-crafted example solutions.
+*   ``test``: hidden test samples, used for evaluation.
+*   ``example_test``: public test samples (appeared in prompt), used for evaluation. 
+
+## Data Splits
+
+Each subset has one split: test.
+
+## Citation Information
+
+Refer to https://github.com/THUDM/CodeGeeX.

huggingface_dataset/Dataset_Card/andstor_output.md

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+---
+license: mit
+task_categories:
+- text-generation
+language:
+- en
+---
+
+# Dataset Card for Output
+
+## 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)
+    - [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://github.com/andstor/lm-output-dataset
+- **Repository:** https://github.com/andstor/lm-output-dataset
+- **Paper:** 
+- **Leaderboard:**
+- **Point of Contact:** [André Storhaug](mailto:andr3.storhaug@gmail.com)
+
+### Dataset Summary
+
+This is a dataset of various language model outputs from different datasets.
+
+### 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 [@andstor](https://github.com/andstor) for adding this dataset.

huggingface_dataset/Dataset_Card/autoevaluate_autoeval-eval-futin__guess-en_3-8ea950-2087767171.md

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+---
+type: predictions
+tags:
+- autotrain
+- evaluation
+datasets:
+- futin/guess
+eval_info:
+  task: text_zero_shot_classification
+  model: bigscience/bloomz-7b1
+  metrics: []
+  dataset_name: futin/guess
+  dataset_config: en_3
+  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: bigscience/bloomz-7b1
+* Dataset: futin/guess
+* Config: en_3
+* Split: test
+
+To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
+
+## Contributions
+
+Thanks to [@futin](https://huggingface.co/futin) for evaluating this model.

huggingface_dataset/Dataset_Card/autoevaluate_autoeval-eval-project-squad_v2-7b0e814c-1303349869.md

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+---
+type: predictions
+tags:
+- autotrain
+- evaluation
+datasets:
+- squad_v2
+eval_info:
+  task: extractive_question_answering
+  model: nbroad/rob-base-superqa2
+  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: nbroad/rob-base-superqa2
+* 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 [@nbroad](https://huggingface.co/nbroad) for evaluating this model.

huggingface_dataset/Dataset_Card/autoevaluate_autoeval-staging-eval-multi_nli-default-4a02ee-14425976.md

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+---
+type: predictions
+tags:
+- autotrain
+- evaluation
+datasets:
+- multi_nli
+eval_info:
+  task: natural_language_inference
+  model: MoritzLaurer/DeBERTa-v3-base-mnli-fever-anli
+  metrics: []
+  dataset_name: multi_nli
+  dataset_config: default
+  dataset_split: validation_mismatched
+  col_mapping:
+    text1: premise
+    text2: hypothesis
+    target: label
+---
+# Dataset Card for AutoTrain Evaluator
+
+This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
+
+* Task: Natural Language Inference
+* Model: MoritzLaurer/DeBERTa-v3-base-mnli-fever-anli
+* Dataset: multi_nli
+* Config: default
+* Split: validation_mismatched
+
+To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
+
+## Contributions
+
+Thanks to [@MoritzLaurer](https://huggingface.co/MoritzLaurer) for evaluating this model.

huggingface_dataset/Dataset_Card/autoevaluate_autoeval-staging-eval-project-d42d3c12-7815007.md

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+---
+type: predictions
+tags:
+- autotrain
+- evaluation
+datasets:
+- xtreme
+eval_info:
+  task: entity_extraction
+  model: evs/xlm-roberta-base-finetuned-panx-de
+  metrics: []
+  dataset_name: xtreme
+  dataset_config: PAN-X.de
+  dataset_split: test
+  col_mapping:
+    tokens: tokens
+    tags: ner_tags
+---
+# Dataset Card for AutoTrain Evaluator
+
+This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
+
+* Task: Token Classification
+* Model: evs/xlm-roberta-base-finetuned-panx-de
+* Dataset: xtreme
+
+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.

huggingface_dataset/Dataset_Card/autoevaluate_autoeval-staging-eval-project-xsum-69daf1dd-12935737.md

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+---
+type: predictions
+tags:
+- autotrain
+- evaluation
+datasets:
+- xsum
+eval_info:
+  task: summarization
+  model: facebook/bart-large-cnn
+  metrics: ['bleu']
+  dataset_name: xsum
+  dataset_config: default
+  dataset_split: test
+  col_mapping:
+    text: document
+    target: summary
+---
+# 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: xsum
+* Config: default
+* Split: test
+
+To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
+
+## Contributions
+
+Thanks to [@xarymast](https://huggingface.co/xarymast) for evaluating this model.

huggingface_dataset/Dataset_Card/huggan_facades.md

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+This dataset is part of the CycleGAN datasets, originally hosted here: https://people.eecs.berkeley.edu/~taesung_park/CycleGAN/datasets/
+
+# Citation
+```
+@article{DBLP:journals/corr/ZhuPIE17,
+  author    = {Jun{-}Yan Zhu and
+               Taesung Park and
+               Phillip Isola and
+               Alexei A. Efros},
+  title     = {Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial
+               Networks},
+  journal   = {CoRR},
+  volume    = {abs/1703.10593},
+  year      = {2017},
+  url       = {http://arxiv.org/abs/1703.10593},
+  eprinttype = {arXiv},
+  eprint    = {1703.10593},
+  timestamp = {Mon, 13 Aug 2018 16:48:06 +0200},
+  biburl    = {https://dblp.org/rec/journals/corr/ZhuPIE17.bib},
+  bibsource = {dblp computer science bibliography, https://dblp.org}
+}
+```

huggingface_dataset/Dataset_Card/indonli.md

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+---
+annotations_creators:
+- expert-generated
+- crowdsourced
+language_creators:
+- expert-generated
+language:
+- id
+license:
+- cc-by-sa-4.0
+multilinguality:
+- monolingual
+size_categories:
+- 10K<n<100K
+source_datasets:
+- original
+task_categories:
+- text-classification
+task_ids:
+- natural-language-inference
+paperswithcode_id: indonli
+pretty_name: IndoNLI
+dataset_info:
+  features:
+  - name: premise
+    dtype: string
+  - name: hypothesis
+    dtype: string
+  - name: label
+    dtype:
+      class_label:
+        names:
+          '0': entailment
+          '1': neutral
+          '2': contradiction
+  config_name: indonli
+  splits:
+  - name: train
+    num_bytes: 2265687
+    num_examples: 10330
+  - name: validation
+    num_bytes: 465299
+    num_examples: 2197
+  - name: test_lay
+    num_bytes: 473849
+    num_examples: 2201
+  - name: test_expert
+    num_bytes: 911916
+    num_examples: 2984
+  download_size: 6977877
+  dataset_size: 4116751
+---
+
+# Dataset Card for IndoNLI
+
+## Table of Contents
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
+  - [Languages](#languages)
+- [Dataset Structure](#dataset-structure)
+  - [Data Instances](#data-instances)
+  - [Data Fields](#data-fields)
+  - [Data Splits](#data-splits)
+- [Dataset Creation](#dataset-creation)
+  - [Curation Rationale](#curation-rationale)
+  - [Source Data](#source-data)
+  - [Annotations](#annotations)
+  - [Personal and Sensitive Information](#personal-and-sensitive-information)
+- [Considerations for Using the Data](#considerations-for-using-the-data)
+  - [Social Impact of Dataset](#social-impact-of-dataset)
+  - [Discussion of Biases](#discussion-of-biases)
+  - [Other Known Limitations](#other-known-limitations)
+- [Additional Information](#additional-information)
+  - [Dataset Curators](#dataset-curators)
+  - [Licensing Information](#licensing-information)
+  - [Citation Information](#citation-information)
+  - [Contributions](#contributions)
+
+## Dataset Description
+
+- **Repository:** [GitHub](https://github.com/ir-nlp-csui/indonli)
+- **Paper:** [EMNLP 2021](https://aclanthology.org/2021.emnlp-main.821/)
+- **Point of Contact:** [GitHub](https://github.com/ir-nlp-csui/indonli)
+
+### Dataset Summary
+
+IndoNLI is the first human-elicited Natural Language Inference (NLI) dataset for Indonesian. 
+IndoNLI is annotated by both crowd workers and experts. The expert-annotated data is used exclusively as a test set. It is designed to provide a challenging test-bed for Indonesian NLI by explicitly incorporating various linguistic phenomena such as numerical reasoning, structural changes, idioms, or temporal and spatial reasoning.
+
+### Supported Tasks and Leaderboards
+
+- Natural Language Inference for Indonesian
+
+### Languages
+
+Indonesian
+
+## Dataset Structure
+
+### Data Instances
+
+An example of `train` looks as follows.
+
+```
+{
+  "premise": "Keindahan alam yang terdapat di Gunung Batu Jonggol ini dapat Anda manfaatkan sebagai objek fotografi yang cantik.", 
+  "hypothesis": "Keindahan alam tidak dapat difoto.", 
+  "label": 2
+}
+```
+### Data Fields
+
+The data fields are:
+- `premise`: a `string` feature
+- `hypothesis`: a `string` feature
+- `label`: a classification label, with possible values including `entailment` (0), `neutral` (1), `contradiction` (2).
+
+### Data Splits
+
+The data is split across `train`, `valid`, `test_lay`, and `test_expert`. 
+
+`test_expert` is written by expert annotators, whereas the rest are written by lay annotators.
+
+|   split   | # examples |
+|----------|-------:|
+|train|   10330|
+|valid|   2197|
+|test_lay|   2201|
+|test_expert|   2984|
+
+A small subset of `test_expert` is used as a diasnostic tool. For more info, please visit https://github.com/ir-nlp-csui/indonli
+
+
+
+## Dataset Creation
+
+### Curation Rationale
+
+Indonesian NLP is considered under-resourced. Up until now, there is no publicly available human-annotated NLI dataset for Indonesian.
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+The premise were collected from Indonesian Wikipedia and from other public Indonesian dataset: Indonesian PUD and GSD treebanks provided by the [Universal Dependencies 2.5](https://lindat.mff.cuni.cz/repository/xmlui/handle/11234/1-3105) and [IndoSum](https://github.com/kata-ai/indosum)
+
+The hypothesis were written by annotators.
+
+#### Who are the source language producers?
+
+The data was produced by humans.
+
+### Annotations
+
+#### Annotation process
+
+We start by writing the hypothesis, given the premise and the target label. Then, we ask 2 different independent annotators to predict the label, given the premise and hypothesis. If all 3 (the original hypothesis + 2 independent annotators) agree with the label, then the annotation process ends for that sample. Otherwise, we incrementally ask additional annotator until  3 annotators agree with the label. If there's no majority concensus after 5 annotations, the sample is removed.
+
+#### Who are the annotators?
+
+Lay annotators were computer science students, and expert annotators were NLP scientists with 7+ years research experience in NLP. All annotators are native speakers.
+Additionally, expert annotators were explicitly instructed to provide challenging examples by incorporating various linguistic phenomena such as numerical reasoning, structural changes, idioms, or temporal and spatial reasoning. Annotators were compensated based on hourly rate.
+
+### Personal and Sensitive Information
+
+There might be some personal information coming from Wikipedia and news, especially the information of famous/important people.
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Discussion of Biases
+
+INDONLI is created using premise sentences taken from Wikipedia and news. These data sources may contain some bias.
+
+### Other Known Limitations
+
+No other known limitations
+
+## Additional Information
+
+### Dataset Curators
+
+This dataset is the result of the collaborative work of Indonesian researchers from the University of Indonesia, kata.ai, New York University, Fondazione Bruno Kessler, and the University of St Andrews.
+
+
+### Licensing Information
+
+CC-BY-SA 4.0.
+
+Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
+
+ShareAlike — If you remix, transform, or build upon the material, you must distribute your contributions under the same license as the original.
+
+No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
+
+Please contact authors for any information on the dataset.
+
+### Citation Information
+
+```
+@inproceedings{mahendra-etal-2021-indonli,
+    title = "{I}ndo{NLI}: A Natural Language Inference Dataset for {I}ndonesian",
+    author = "Mahendra, Rahmad and Aji, Alham Fikri and Louvan, Samuel and Rahman, Fahrurrozi and Vania, Clara",
+    booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
+    month = nov,
+    year = "2021",
+    address = "Online and Punta Cana, Dominican Republic",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2021.emnlp-main.821",
+    pages = "10511--10527",
+}
+```
+
+### Contributions
+
+Thanks to [@afaji](https://github.com/afaji) for adding this dataset.

huggingface_dataset/Dataset_Card/its5Q_habr_qna.md

@@ -0,0 +1,71 @@
+---
+annotations_creators:
+- crowdsourced
+language:
+- ru
+language_creators:
+- crowdsourced
+license:
+- cc0-1.0
+multilinguality:
+- monolingual
+pretty_name: Habr QnA
+size_categories:
+- 100K<n<1M
+source_datasets:
+- original
+tags: []
+task_categories:
+- text-generation
+- question-answering
+task_ids:
+- language-modeling
+- open-domain-qa
+---
+
+# Dataset Card for Habr QnA
+
+## Table of Contents
+- [Dataset Card for Habr QnA](#dataset-card-for-habr-qna)
+  - [Table of Contents](#table-of-contents)
+  - [Dataset Description](#dataset-description)
+    - [Dataset Summary](#dataset-summary)
+    - [Languages](#languages)
+  - [Dataset Structure](#dataset-structure)
+    - [Data Fields](#data-fields)
+    - [Data Splits](#data-splits)
+  - [Dataset Creation](#dataset-creation)
+  - [Additional Information](#additional-information)
+    - [Dataset Curators](#dataset-curators)
+
+## Dataset Description
+
+- **Repository:** https://github.com/its5Q/habr-qna-parser
+
+### Dataset Summary
+
+This is a dataset of questions and answers scraped from [Habr QnA](https://qna.habr.com/). There are 723430 asked questions with answers, comments and other metadata. 
+
+### Languages
+
+The dataset is mostly Russian with source code in different languages.
+
+## Dataset Structure
+
+### Data Fields
+
+Data fields can be previewed on the dataset card page.
+
+### Data Splits
+
+All 723430 examples are in the train split, there is no validation split.
+
+## Dataset Creation
+
+The data was scraped with a script, located in [my GitHub repository](https://github.com/its5Q/habr-qna-parser)
+
+## Additional Information
+
+### Dataset Curators
+
+- https://github.com/its5Q

huggingface_dataset/Dataset_Card/lm1b.md

@@ -0,0 +1,22 @@
+---
+pretty_name: Lm1b
+paperswithcode_id: billion-word-benchmark
+dataset_info:
+  features:
+  - name: text
+    dtype: string
+  config_name: plain_text
+  splits:
+  - name: train
+    num_bytes: 4238206516
+    num_examples: 30301028
+  - name: test
+    num_bytes: 42942045
+    num_examples: 306688
+  download_size: 1792209805
+  dataset_size: 4281148561
+---
+
+### Contributions
+
+Thanks to [@patrickvonplaten](https://github.com/patrickvonplaten), [@lewtun](https://github.com/lewtun), [@jplu](https://github.com/jplu), [@thomwolf](https://github.com/thomwolf) for adding this dataset.

huggingface_dataset/Dataset_Card/mf99_autotrain-data-sum-200-random.md

@@ -0,0 +1,54 @@
+---
+language:
+- en
+task_categories:
+- conditional-text-generation
+---
+# AutoTrain Dataset for project: sum-200-random
+
+## Dataset Descritpion
+
+This dataset has been automatically processed by AutoTrain for project sum-200-random.
+
+### Languages
+
+The BCP-47 code for the dataset's language is en.
+
+## Dataset Structure
+
+### Data Instances
+
+A sample from this dataset looks as follows:
+
+```json
+[
+  {
+    "text": "aen: {Forest hermit to Professor, it's never too late to change. | Dr. Gregory P. Smith | TEDxByronB[...]",
+    "target": "Fire, plenty of ferns to sleep on and an endless supply of alcohol. 65"
+  },
+  {
+    "text": "aen: {William Noel: Revealing the lost codex of Archimedes}{from 62% to 72%}{And combinatorics is a [...]",
+    "target": "The really astonishing thing though about this manuscript is that we looked at the other manuscripts[...]"
+  }
+]
+```
+
+### Dataset Fields
+
+The dataset has the following fields (also called "features"):
+
+```json
+{
+  "text": "Value(dtype='string', id=None)",
+  "target": "Value(dtype='string', id=None)"
+}
+```
+
+### Dataset Splits
+
+This dataset is split into a train and validation split. The split sizes are as follow:
+
+| Split name   | Num samples         |
+| ------------ | ------------------- |
+| train        | 451280 |
+| valid        | 112821 |

huggingface_dataset/Dataset_Card/osyvokon_wiki-edits-uk.md

@@ -0,0 +1,129 @@
+---
+annotations_creators:
+- crowdsourced
+language_creators:
+- crowdsourced
+language:
+- uk-UA
+license:
+- cc-by-3.0
+multilinguality:
+- monolingual
+- translation
+pretty_name: 'Ukrainian Wikipedia edits '
+size_categories:
+- 1M<n<10M
+source_datasets:
+- original
+task_categories:
+- other
+task_ids: []
+---
+
+# Ukrainian Wikipedia Edits
+
+### Dataset summary
+
+A collection of over 5M sentence edits extracted from Ukrainian Wikipedia history revisions.
+
+Edits were filtered by edit distance and sentence length. This makes them usable for grammatical error correction (GEC) or spellchecker models pre-training.
+
+
+### Supported Tasks and Leaderboards
+
+* Ukrainian grammatical error correction (GEC) - see [UA-GEC](https://github.com/grammarly/ua-gec)
+* Ukrainian spelling correction
+
+### Languages
+
+Ukrainian
+
+## Dataset Structure
+
+### Data Fields
+
+* `src` - sentence before edit
+* `tgt` - sentence after edit
+
+### Data Splits
+
+* `full/train` contains all the data (5,243,376 samples)
+* `tiny/train` contains a 5000 examples sample.
+
+## Dataset Creation
+
+Latest full Ukrainian Wiki dump were used as of 2022-04-30.
+
+It was processed with the [wikiedits](https://github.com/snukky/wikiedits) and custom scripts.
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+Wikipedia
+
+#### Who are the source language producers?
+
+Wikipedia writers
+
+### Annotations
+
+#### Annotation process
+
+Annotations inferred by comparing two subsequent page revisions.
+
+#### Who are the annotators?
+
+People who edit Wikipedia pages.
+
+### Personal and Sensitive Information
+
+No
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+[More Information Needed]
+
+### Discussion of Biases
+
+[More Information Needed]
+
+### Other Known Limitations
+
+The data is noisy. In addition to GEC and spelling edits, it contains a good chunk of factual changes and vandalism.
+
+More task-specific filters could help.
+
+## Additional Information
+
+### Dataset Curators
+
+[Oleksiy Syvokon](https://github.com/asivokon)
+
+### Licensing Information
+
+CC-BY-3.0
+
+### Citation Information
+
+```
+@inproceedings{wiked2014,
+    author = {Roman Grundkiewicz and Marcin Junczys-Dowmunt},
+    title = {The WikEd Error Corpus: A Corpus of Corrective Wikipedia Edits and its Application to Grammatical Error Correction},
+    booktitle = {Advances in Natural Language Processing -- Lecture Notes in Computer Science},
+    editor = {Adam Przepiórkowski and Maciej Ogrodniczuk},
+    publisher = {Springer},
+    year = {2014},
+    volume = {8686},
+    pages = {478--490},
+    url = {http://emjotde.github.io/publications/pdf/mjd.poltal2014.draft.pdf}
+}
+```
+
+### Contributions
+
+[@snukky](https://github.com/snukky) created tools for dataset processing.
+
+[@asivokon](https://github.com/asivokon) generated this dataset.

huggingface_dataset/Dataset_Card/pythainlp_tlcv2.0_oa.md

@@ -0,0 +1,42 @@
+---
+dataset_info:
+  features:
+  - name: TEXT
+    dtype: string
+  - name: SOURCE
+    dtype: string
+  - name: METADATA
+    struct:
+    - name: ch_num
+      dtype: string
+    - name: title
+      dtype: string
+  splits:
+  - name: train
+    num_bytes: 27856275
+    num_examples: 361
+  download_size: 11507610
+  dataset_size: 27856275
+license: mit
+task_categories:
+- text-generation
+language:
+- th
+size_categories:
+- n<1K
+---
+# Dataset Card for "tlcv2.0_oa"
+
+Thai Literature Corpora (TLC): Corpora of machine-ingestible Thai classical literature texts by Jitkapat Sawatphol (Faculty of Arts, Chulalongkorn University).
+
+This project use [Thai Literature Corpora (TLC) v2.0](https://attapol.github.io/tlc.html). All text are from old Thai book that out of copyright (or public domain).
+
+This dataset was build for [Open Assistant](https://github.com/LAION-AI/Open-Assistant/).
+
+## Columns
+
+The dataset was following columns:
+
+1. **TEXT** (string)
+2. **SOURCE** (string)
+3. **METADATA** (JSON string, optional)

huggingface_dataset/Dataset_Card/qgallouedec_prj_gia_dataset_metaworld_plate_slide_back_v2_1111.md

@@ -0,0 +1,36 @@
+---
+library_name: gia
+tags:
+- deep-reinforcement-learning
+- reinforcement-learning
+- gia
+- multi-task
+- multi-modal
+- imitation-learning
+- offline-reinforcement-learning
+---
+
+An imitation learning environment for the plate-slide-back-v2 environment, sample for the policy plate-slide-back-v2 
+
+This environment was created as part of the Generally Intelligent Agents project gia: https://github.com/huggingface/gia 
+
+
+
+
+## Load dataset
+
+First, clone it with
+
+```sh
+git clone https://huggingface.co/datasets/qgallouedec/prj_gia_dataset_metaworld_plate_slide_back_v2_1111
+```
+
+Then, load it with
+
+```python
+import numpy as np
+dataset = np.load("prj_gia_dataset_metaworld_plate_slide_back_v2_1111/dataset.npy", allow_pickle=True).item()
+print(dataset.keys())  # dict_keys(['observations', 'actions', 'dones', 'rewards'])
+```
+
+    

huggingface_dataset/Dataset_Card/rvl_cdip.md

@@ -0,0 +1,228 @@
+---
+annotations_creators:
+- found
+language_creators:
+- found
+language:
+- en
+license:
+- other
+multilinguality:
+- monolingual
+size_categories:
+- 100K<n<1M
+source_datasets:
+- extended|iit_cdip
+task_categories:
+- image-classification
+task_ids:
+- multi-class-image-classification
+paperswithcode_id: rvl-cdip
+pretty_name: RVL-CDIP
+dataset_info:
+  features:
+  - name: image
+    dtype: image
+  - name: label
+    dtype:
+      class_label:
+        names:
+          '0': letter
+          '1': form
+          '2': email
+          '3': handwritten
+          '4': advertisement
+          '5': scientific report
+          '6': scientific publication
+          '7': specification
+          '8': file folder
+          '9': news article
+          '10': budget
+          '11': invoice
+          '12': presentation
+          '13': questionnaire
+          '14': resume
+          '15': memo
+  splits:
+  - name: train
+    num_bytes: 38816373360
+    num_examples: 320000
+  - name: test
+    num_bytes: 4863300853
+    num_examples: 40000
+  - name: validation
+    num_bytes: 4868685208
+    num_examples: 40000
+  download_size: 38779484559
+  dataset_size: 48548359421
+---
+
+# Dataset Card for RVL-CDIP
+
+## Table of Contents
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks](#supported-tasks-and-leaderboards)
+  - [Languages](#languages)
+- [Dataset Structure](#dataset-structure)
+  - [Data Instances](#data-instances)
+  - [Data Fields](#data-instances)
+  - [Data Splits](#data-instances)
+- [Dataset Creation](#dataset-creation)
+  - [Curation Rationale](#curation-rationale)
+  - [Source Data](#source-data)
+  - [Annotations](#annotations)
+  - [Personal and Sensitive Information](#personal-and-sensitive-information)
+- [Considerations for Using the Data](#considerations-for-using-the-data)
+  - [Social Impact of Dataset](#social-impact-of-dataset)
+  - [Discussion of Biases](#discussion-of-biases)
+  - [Other Known Limitations](#other-known-limitations)
+- [Additional Information](#additional-information)
+  - [Dataset Curators](#dataset-curators)
+  - [Licensing Information](#licensing-information)
+  - [Citation Information](#citation-information)
+
+## Dataset Description
+
+- **Homepage:** [The RVL-CDIP Dataset](https://www.cs.cmu.edu/~aharley/rvl-cdip/)
+- **Repository:**
+- **Paper:** [Evaluation of Deep Convolutional Nets for Document Image Classification and Retrieval](https://arxiv.org/abs/1502.07058)
+- **Leaderboard:** [RVL-CDIP leaderboard](https://paperswithcode.com/dataset/rvl-cdip)
+- **Point of Contact:** [Adam W. Harley](mailto:aharley@cmu.edu)
+
+### Dataset Summary
+
+The RVL-CDIP (Ryerson Vision Lab Complex Document Information Processing) dataset consists of 400,000 grayscale images in 16 classes, with 25,000 images per class. There are 320,000 training images, 40,000 validation images, and 40,000 test images. The images are sized so their largest dimension does not exceed 1000 pixels.
+
+### Supported Tasks and Leaderboards
+
+- `image-classification`: The goal of this task is to classify a given document into one of 16 classes representing document types (letter, form, etc.). The leaderboard for this task is available [here](https://paperswithcode.com/sota/document-image-classification-on-rvl-cdip).
+
+### Languages
+
+All the classes and documents use English as their primary language.
+
+## Dataset Structure
+
+### Data Instances
+
+A sample from the training set is provided below :
+```
+{
+    'image': <PIL.TiffImagePlugin.TiffImageFile image mode=L size=754x1000 at 0x7F9A5E92CA90>,
+    'label': 15
+}
+```
+
+### Data Fields
+
+- `image`: A `PIL.Image.Image` object containing a document.
+- `label`: an `int` classification label.
+
+<details>
+  <summary>Class Label Mappings</summary>
+
+```json
+{
+  "0": "letter",
+  "1": "form",
+  "2": "email",
+  "3": "handwritten",
+  "4": "advertisement",
+  "5": "scientific report",
+  "6": "scientific publication",
+  "7": "specification",
+  "8": "file folder",
+  "9": "news article",
+  "10": "budget",
+  "11": "invoice",
+  "12": "presentation",
+  "13": "questionnaire",
+  "14": "resume",
+  "15": "memo"
+}
+```
+
+</details>
+
+### Data Splits
+
+|   |train|test|validation|
+|----------|----:|----:|---------:|
+|# of examples|320000|40000|40000|
+
+The dataset was split in proportions similar to those of ImageNet.
+- 320000 images were used for training,
+- 40000 images for validation, and 
+- 40000 images for testing. 
+
+## Dataset Creation
+
+### Curation Rationale
+
+From the paper:
+> This work makes available a new labelled subset of the IIT-CDIP collection, containing 400,000
+document images across 16 categories, useful for training new CNNs for document analysis.
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+The same as in the IIT-CDIP collection.
+
+#### Who are the source language producers?
+
+The same as in the IIT-CDIP collection.
+
+### Annotations
+
+#### Annotation process
+
+The same as in the IIT-CDIP collection.
+
+#### Who are the annotators?
+
+The same as in the IIT-CDIP collection.
+
+### Personal and Sensitive Information
+
+[More Information Needed]
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+[More Information Needed]
+
+### Discussion of Biases
+
+[More Information Needed]
+
+### Other Known Limitations
+
+[More Information Needed]
+
+## Additional Information
+
+### Dataset Curators
+
+The dataset was curated by the authors - Adam W. Harley, Alex Ufkes, and Konstantinos G. Derpanis.
+
+### Licensing Information
+
+RVL-CDIP is a subset of IIT-CDIP, which came from the [Legacy Tobacco Document Library](https://www.industrydocuments.ucsf.edu/tobacco/), for which license information can be found [here](https://www.industrydocuments.ucsf.edu/help/copyright/).
+
+### Citation Information
+
+```bibtex
+@inproceedings{harley2015icdar,
+    title = {Evaluation of Deep Convolutional Nets for Document Image Classification and Retrieval},
+    author = {Adam W Harley and Alex Ufkes and Konstantinos G Derpanis},
+    booktitle = {International Conference on Document Analysis and Recognition ({ICDAR})}},
+    year = {2015}
+}
+```
+
+### Contributions
+
+Thanks to [@dnaveenr](https://github.com/dnaveenr) for adding this dataset.

huggingface_dataset/Dataset_Card/tatiana-merz_cyrillic_turkic_langs.md

@@ -0,0 +1,96 @@
+---
+license: cc
+task_categories:
+- text-classification
+language:
+- ba
+- cv
+- sah
+- tt
+- ky
+- kk
+- tyv
+- krc
+- ru
+
+tags:
+- wiki
+size_categories:
+- 10K<n<100K
+---
+
+# Cyrillic dataset of 8 Turkic languages spoken in Russia and former USSR
+
+## Dataset Description
+
+The dataset is a part of the [Leipzig Corpora (Wiki) Collection]: https://corpora.uni-leipzig.de/
+
+For the text-classification comparison, Russian has been included to the dataset.
+
+ **Paper:** 
+Dirk Goldhahn, Thomas Eckart and Uwe Quasthoff (2012): Building Large Monolingual Dictionaries at the Leipzig Corpora Collection: From 100 to 200 Languages. In: Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC'12), 2012.
+
+### Dataset Summary
+
+
+### Supported Tasks and Leaderboards
+
+### Languages
+
+  - ba - Bashkir
+  - cv - Chuvash
+  - sah - Sakha
+  - tt - Tatar
+  - ky - Kyrgyz
+  - kk - Kazakh
+  - tyv - Tuvinian
+  - krc - Karachay-Balkar
+  - ru - Russian
+
+### Data Splits
+
+
+    train: Dataset({
+        features: ['text', 'label'],
+        num_rows: 72000
+    })
+    
+    test: Dataset({
+        features: ['text', 'label'],
+        num_rows: 9000
+    })
+    
+    validation: Dataset({
+        features: ['text', 'label'],
+        num_rows: 9000
+    })
+    
+
+## Dataset Creation
+
+[Link to the notebook](https://github.com/tatiana-merz/YakuToolkit/blob/main/CyrillicTurkicCorpus.ipynb)
+
+
+### Curation Rationale
+
+[More Information Needed]
+
+### Source Data
+
+## Additional Information
+
+### Dataset Curators
+
+[More Information Needed]
+
+### Licensing Information
+
+[More Information Needed]
+
+### Citation Information
+
+[More Information Needed]
+
+### Contributions
+
+[More Information Needed]