Upload batch 369 (20 files, last=huggingface_dataset/Dataset_Card/AdamOswald1_autotrain-data-testing.md)

huggingface_dataset/Dataset_Card/AdamOswald1_autotrain-data-testing.md

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+---
+task_categories:
+- image-classification
+
+---
+# AutoTrain Dataset for project: testing
+
+## Dataset Description
+
+This dataset has been automatically processed by AutoTrain for project testing.
+
+### Languages
+
+The BCP-47 code for the dataset's language is unk.
+
+## Dataset Structure
+
+### Data Instances
+
+A sample from this dataset looks as follows:
+
+```json
+[
+  {
+    "image": "<250x250 RGB PIL image>",
+    "target": 0
+  },
+  {
+    "image": "<1547x2048 RGB PIL image>",
+    "target": 0
+  }
+]
+```
+
+### Dataset Fields
+
+The dataset has the following fields (also called "features"):
+
+```json
+{
+  "image": "Image(decode=True, id=None)",
+  "target": "ClassLabel(num_classes=1, names=['chara'], 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        | 120 |
+| valid        | 81 |

huggingface_dataset/Dataset_Card/Heriot-WattUniversity_switchboard.md

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+# Switchboard
+
+Switchboard is a collection of telephone conversations.
+
+[[dataset link](https://catalog.ldc.upenn.edu/LDC97S62)] [[Papers with code link](https://paperswithcode.com/dataset/switchboard-1-corpus)]

huggingface_dataset/Dataset_Card/Muennighoff_flores200.md

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+---
+annotations_creators:
+- found
+language_creators:
+- expert-generated
+license:
+- cc-by-sa-4.0
+multilinguality:
+- multilingual
+- translation
+size_categories:
+- unknown
+source_datasets:
+- extended|flores
+task_categories:
+- text2text-generation
+- translation
+task_ids: []
+paperswithcode_id: flores
+pretty_name: flores200
+tags:
+- conditional-text-generation
+---
+
+# Dataset Card for Flores200
+
+## Table of Contents
+
+- [Dataset Card for Flores200](#dataset-card-for-flores200)
+  - [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)
+  - [Additional Information](#additional-information)
+    - [Dataset Curators](#dataset-curators)
+    - [Licensing Information](#licensing-information)
+    - [Citation Information](#citation-information)
+
+## Dataset Description
+
+- **Home:** [Flores](https://github.com/facebookresearch/flores)
+- **Repository:** [Github](https://github.com/facebookresearch/flores)
+
+### Dataset Summary
+
+FLORES is a benchmark dataset for machine translation between English and low-resource languages.
+
+>The creation of FLORES200 doubles the existing language coverage of FLORES-101. 
+Given the nature of the new languages, which have less standardization and require 
+more specialized professional translations, the verification process became more complex. 
+This required modifications to the translation workflow. FLORES-200 has several languages 
+which were not translated from English. Specifically, several languages were translated 
+from Spanish, French, Russian and Modern Standard Arabic. Moreover, FLORES-200 also 
+includes two script alternatives for four languages. FLORES-200 consists of translations 
+from 842 distinct web articles, totaling 3001 sentences. These sentences are divided 
+into three splits: dev, devtest, and test (hidden). On average, sentences are approximately 
+21 words long.
+
+**Disclaimer**: *The Flores200 dataset is hosted by the Facebook and licensed under the [Creative Commons Attribution-ShareAlike 4.0 International License](https://creativecommons.org/licenses/by-sa/4.0/).
+### Supported Tasks and Leaderboards
+#### Multilingual Machine Translation
+Refer to the [Dynabench leaderboard](https://dynabench.org/flores/Flores%20MT%20Evaluation%20(FULL)) for additional details on model evaluation on FLORES-101 in the context of the WMT2021 shared task on [Large-Scale Multilingual Machine Translation](http://www.statmt.org/wmt21/large-scale-multilingual-translation-task.html). Flores 200 is an extention of this.
+### Languages
+The dataset contains parallel sentences for 200 languages, as mentioned in the original [Github](https://github.com/facebookresearch/flores/blob/master/README.md) page for the project. Languages are identified with the ISO 639-3 code (e.g. `eng`, `fra`, `rus`) plus an additional code describing the script (e.g., "eng_Latn", "ukr_Cyrl"). See [the webpage for code descriptions](https://github.com/facebookresearch/flores/blob/main/flores200/README.md).
+Use the configuration `all` to access the full set of parallel sentences for all the available languages in a single command. 
+Use a hyphenated pairing to get two langauges in one datapoint (e.g., "eng_Latn-ukr_Cyrl" will provide sentences in the format below).
+## Dataset Structure
+### Data Instances
+A sample from the `dev` split for the Russian language (`ukr_Cyrl` config) is provided below. All configurations have the same structure, and all sentences are aligned across configurations and splits.
+```python
+{
+	'id': 1,
+	'sentence': 'У понеділок, науковці зі Школи медицини Стенфордського університету оголосили про винайдення нового діагностичного інструменту, що може сортувати клітини за їх видами: це малесенький друкований чіп, який можна виготовити за допомогою стандартних променевих принтерів десь по одному центу США за штуку.',
+	'URL': 'https://en.wikinews.org/wiki/Scientists_say_new_medical_diagnostic_chip_can_sort_cells_anywhere_with_an_inkjet',
+	'domain': 'wikinews',
+	'topic': 'health',
+	'has_image': 0,
+	'has_hyperlink': 0
+}
+```
+When using a hyphenated pairing or using the `all` function, data will be presented as follows:
+```python
+{
+    'id': 1, 
+    'URL': 'https://en.wikinews.org/wiki/Scientists_say_new_medical_diagnostic_chip_can_sort_cells_anywhere_with_an_inkjet', 
+    'domain': 'wikinews', 
+    'topic': 'health', 
+    'has_image': 0, 
+    'has_hyperlink': 0, 
+    'sentence_eng_Latn': 'On Monday, scientists from the Stanford University School of Medicine announced the invention of a new diagnostic tool that can sort cells by type: a tiny printable chip that can be manufactured using standard inkjet printers for possibly about one U.S. cent each.', 
+    'sentence_ukr_Cyrl': 'У понеділок, науковці зі Школи медицини Стенфордського університету оголосили про винайдення нового діагностичного інструменту, що може сортувати клітини за їх видами: це малесенький друкований чіп, який можна виготовити за допомогою стандартних променевих принтерів десь по одному центу США за штуку.'
+}
+```
+The text is provided as-in the original dataset, without further preprocessing or tokenization.
+### Data Fields
+- `id`: Row number for the data entry, starting at 1.
+- `sentence`: The full sentence in the specific language (may have _lang for pairings)
+- `URL`: The URL for the English article from which the sentence was extracted.
+- `domain`: The domain of the sentence.
+- `topic`: The topic of the sentence.
+- `has_image`: Whether the  original article contains an image.
+- `has_hyperlink`: Whether the  sentence contains a hyperlink.
+### Data Splits
+|            config| `dev`| `devtest`|
+|-----------------:|-----:|---------:|
+|all configurations|   997|     1012:|
+### Dataset Creation
+Please refer to the original article [No Language Left Behind: Scaling Human-Centered Machine Translation](https://arxiv.org/abs/2207.04672) for additional information on dataset creation.
+## Additional Information
+### Dataset Curators
+See paper for details.
+### Licensing Information
+Licensed with Creative Commons Attribution Share Alike 4.0. License available [here](https://creativecommons.org/licenses/by-sa/4.0/).
+### Citation Information
+Please cite the authors if you use these corpora in your work:
+```bibtex
+@article{nllb2022,
+  author    = {NLLB Team, Marta R. Costa-jussà, James Cross, Onur Çelebi, Maha Elbayad, Kenneth Heafield, Kevin Heffernan, Elahe Kalbassi,  Janice Lam, Daniel Licht, Jean Maillard, Anna Sun, Skyler Wang, Guillaume Wenzek, Al Youngblood, Bapi Akula, Loic Barrault, Gabriel Mejia Gonzalez, Prangthip Hansanti, John Hoffman, Semarley Jarrett, Kaushik Ram Sadagopan, Dirk Rowe, Shannon Spruit, Chau Tran, Pierre Andrews, Necip Fazil Ayan, Shruti Bhosale, Sergey Edunov, Angela Fan, Cynthia Gao, Vedanuj Goswami, Francisco Guzmán, Philipp Koehn, Alexandre Mourachko, Christophe Ropers, Safiyyah Saleem, Holger Schwenk, Jeff Wang},
+  title     = {No Language Left Behind: Scaling Human-Centered Machine Translation},
+  year      = {2022}
+}
+```

huggingface_dataset/Dataset_Card/SetFit_student-question-categories.md

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+This is the [IITJEE NEET AIIMS Students Questions Data](https://www.kaggle.com/mrutyunjaybiswal/iitjee-neet-aims-students-questions-data) dataset.
+
+It categorizes university entry questions into 4 categories: Physics, Chemistry, Biology, and Mathematics.

huggingface_dataset/Dataset_Card/Xpitfire_cmp_facade.md

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+---
+license: mit
+task_categories:
+- image-segmentation
+language:
+- en
+tags:
+- building
+- facade
+---
+
+# CMP Facade Database
+We present a dataset of facade images assembled at the Center for Machine Perception, which includes 606 rectified images of facades from various sources, which have been manually annotated. The facades are from different cities around the world and diverse architectural styles.
+Documentation
+
+Data origin, format and processing, annotation principles for 12 classes are specified in the report.
+
+- facade 
+- molding
+- cornice
+- pillar
+- window
+- door
+- sill
+- blind
+- balcony
+- shop
+- deco
+- background
+
+Link to original website:
+https://cmp.felk.cvut.cz/~tylecr1/facade/
+
+Citation
+Please use the following reference to cite the dataset:
+```latex
+@INPROCEEDINGS{Tylecek13,
+  author = {Radim Tyle{\v c}ek and Radim {\v S}{\' a}ra},
+  title = {Spatial Pattern Templates for Recognition of Objects with Regular Structure},
+  booktitle = {Proc. GCPR},
+  year = {2013},
+  address = {Saarbrucken, Germany},
+}
+```

huggingface_dataset/Dataset_Card/alisawuffles_WANLI.md

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+---
+annotations_creators:
+- crowdsourced
+language_creators:
+- other
+language:
+- en
+license:
+- cc-by-4.0
+multilinguality:
+- monolingual
+pretty_name: WANLI
+size_categories:
+- 100K<n<1M
+source_datasets:
+- original
+task_categories:
+- text-classification
+task_ids:
+- natural-language-inference
+---
+
+# Dataset Card for WANLI
+
+## 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)
+- [Additional Information](#additional-information)
+  - [Dataset Curators](#dataset-curators)
+  - [Citation Information](#citation-information)
+
+## Dataset Description
+
+- **Homepage:** [WANLI homepage](https://wanli.allenai.org/)
+- **Repository:** [Github repo](https://github.com/alisawuffles/wanli)
+- **Paper:** [arXiv](https://arxiv.org/abs/2201.05955)
+- **Point of Contact:** [Alisa Liu](mailto:alisaliu@cs.washington.edu)
+
+### Dataset Summary
+
+WANLI (**W**orker-**A**I Collaboration for **NLI**) is a collection of 108K English sentence pairs for the task of natural language inference (NLI).
+Each example is created by first identifying a "pocket" of examples in [MultiNLI (Williams et al., 2018)](https://cims.nyu.edu/~sbowman/multinli/) that share a challenging reasoning pattern, then instructing GPT-3 to write a new example with the same pattern.
+The set of generated examples are automatically filtered to contain those most likely to aid model training, and finally labeled and optionally revised by human annotators.
+
+WANLI presents unique empirical strengths compared to existing NLI datasets. Remarkably, training a model on WANLI instead of MultiNLI (which is 4 times larger) improves performance on seven out-of-domain test sets we consider, including by 11% on HANS and 9% on Adversarial NLI.
+
+### Supported Tasks and Leaderboards
+
+The dataset can be used to train a model for natural language inference, which determines whether a premise entails (i.e., implies the truth of) a hypothesis, both expressed in natural language. Success on this task is typically measured by achieving a high accuracy. A RoBERTa-large model currently achieves 75.40%.
+
+Models trained on NLI are often adapted to other downstream tasks, and NLI data can be mixed with other sources of supervision.
+
+### Languages
+
+The dataset consists of English examples generated by GPT-3 and revised by English-speaking crowdworkers located in the United States.
+
+## Dataset Structure
+
+### Data Instances
+
+Here is an example of an NLI example in `data/wanli/train.jsonl` or `data/wanli/test.jsonl`.
+```
+{
+  "id": 225295,
+  "premise": "It is a tribute to the skill of the coach that the team has been able to compete at the highest level.",
+  "hypothesis": "The coach is a good coach.",
+  "gold": "entailment",
+  "genre": "generated",
+  "pairID": "171408"
+}
+```
+
+- `id`: unique identifier for the example
+- `premise`: a piece of text 
+- `hypothesis`: a piece of text that may be true, false, or whose truth conditions may not be knowable when compared to the premise
+- `gold`: one of `entailment`, `neutral`, and `contradiction`
+- `genre`: one of `generated` and `generated_revised`, depending on whether the example was revised by annotators
+- `pairID`: id of seed MNLI example, corresponding to those in `data/mnli/train.jsonl`
+
+We also release the raw annotations for each worker, which can be found in `data/wanli/anonymized_annotations.jsonl`.
+```
+"WorkerId": "EUJ",
+"id": 271560,
+"nearest_neighbors": [
+    309783,
+    202988,
+    145310,
+    98030,
+    148759
+],
+"premise": "I don't know what I'd do without my cat. He is my only friend.",
+"hypothesis": "I would be alone.",
+"label": "neutral",
+"revised_premise": "I don't know what I'd do without my cat. He is my only friend.",
+"revised_hypothesis": "I would be alone without my cat.",
+"gold": "entailment",
+"revised": true
+```
+
+- `WorkerId`: a unique identification for each crowdworker (NOT the real worker ID from AMT)  
+- `id`: id of generated example  
+- `nearest_neighbors`: ordered ids of the group of MNLI nearest neighbors that were used as in-context examples, where the first one is seed ambiguous MNLI example. MNLI ids correspond to those in `mnli/train.jsonl`.  
+- `premise`: GPT-3 generated premise  
+- `hypothesis`: GPT-3 generated hypothesis  
+- `label`: the shared label of the in-context examples, which is the "intended" label for this generation  
+- `revised_premise`: premise after human review  
+- `revised_hypothesis`: hypothesis after human review  
+- `gold`: annotator-assigned gold label for the (potentially revised) example  
+- `revised`: whether the example was revised  
+
+### Data Splits
+
+The dataset is randomly split into a *train* and *test* set.
+
+|                         | train | test |
+|-------------------------|------:|-----:|
+| Examples                | 102885|  5000|
+
+## Dataset Creation
+
+### Curation Rationale
+
+A recurring challenge of crowdsourcing NLP datasets at scale is that human writers often rely on repetitive patterns when crafting examples, leading to a lack of linguistic diversity. On the other hand, there has been remarkable progress in open-ended text generation based on massive language models. We create WANLI to demonstrate the effectiveness an approach that leverages the best of both worlds: a language model's ability to efficiently generate diverse examples, and a human's ability to revise the examples for quality and assign a gold label.
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+Our pipeline starts with an existing dataset, MultiNLI (Williams et al., 2018). We use dataset cartography from [Swayamdipta et al. (2020)](https://aclanthology.org/2020.emnlp-main.746/) to automatically identify pockets of examples that demonstrate challenging reasoning patterns rela081 tive to a trained model. Using each group as a set of in-context examples, we leverage a pretrained language model to *generate new examples* likely to have the same pattern. We then automatically filter generations to keep those that are most likely to aid model learning. Finally, we validate the generated examples by subjecting them to human review, where crowdworkers assign a gold label and (optionally) revise for quality.
+
+#### Who are the source language producers?
+
+The GPT-3 Curie model generated examples which were then revised and labeled by crowdworkers on Amazon Mechanical Turk. 
+Workers were paid $0.12 for each example that they annotate. At the end of data collection, we aggregate the earning and time spent from each crowdworker, and find that the median hourly rate was $22.72, with 85% of workers being paid over the $15/hour target.
+
+### Annotations
+
+#### Annotation process
+
+Given an unlabeled example, annotators are asked to optionally revise it for quality (while preserving the intended meaning as much as possible through minimal revisions), and then assign a label. Alternatively, if an example would require a great deal of revision to fix *or* if it could be perceived as offensive, they were asked to discard it.
+Details about instructions, guidelines, and instructional examples can be found in Appendix D of the paper.
+
+Crowdworkers annotate a total of 118,724 examples, with two distinct workers reviewing each example.
+For examples that both annotators labeled without revision, annotators achieved a Cohen Kappa score of 0.60, indicating substantial agreement.
+
+#### Who are the annotators?
+
+Annotators were required to have a HIT approval rate of 98%, a total of 10,000 approved HITs, and be located in the United States.
+
+300 Turkers took our qualification test, of which 69 passed. Turkers who were later found to produce extremely careless annotations were removed from the qualification list (and oftentimes, their annotations were discarded, though they were still paid for their work). The number of workers who contributed to the final dataset is 62.
+
+### Personal and Sensitive Information
+
+The dataset does not contain any personal information about the authors or the crowdworkers.
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+This dataset was developed to explore the potential of worker-AI collaboration for dataset curation, train more robust NLI models, and provide more challenging evaluation of existing systems.
+
+### Discussion of Biases
+
+Text generated from large pretrained language models is susceptible to perpetuating social harms and containing toxic language.
+To partially remedy this, we ask annotators to discard any examples that may be perceived as offensive.
+Nonetheless, it is possible that harmful examples (especially if they contain subtle biases) may have been missed by annotators and included in the final dataset.
+
+## Additional Information
+
+### Dataset Curators
+
+WANLI was developed by Alisa Liu, Swabha Swayamdipta, Noah A. Smith, and Yejin Choi from the [University of Washington](https://www.cs.washington.edu/) and [AI2](https://allenai.org/).
+
+### Citation Information
+
+```
+@misc{liu-etal-2022-wanli,
+    title = "WANLI: Worker and AI Collaboration for Natural Language Inference Dataset Creation",
+    author = "Liu, Alisa  and
+      Swayamdipta, Swabha  and
+      Smith, Noah A.  and
+      Choi, Yejin",
+    month = jan,
+    year = "2022",
+    url = "https://arxiv.org/pdf/2201.05955",
+}
+```

huggingface_dataset/Dataset_Card/autoevaluate_autoeval-eval-inverse-scaling__NeQA-inverse-scaling__NeQA-1e740e-1694759588.md

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+---
+type: predictions
+tags:
+- autotrain
+- evaluation
+datasets:
+- inverse-scaling/NeQA
+eval_info:
+  task: text_zero_shot_classification
+  model: inverse-scaling/opt-30b_eval
+  metrics: []
+  dataset_name: inverse-scaling/NeQA
+  dataset_config: inverse-scaling--NeQA
+  dataset_split: train
+  col_mapping:
+    text: prompt
+    classes: classes
+    target: answer_index
+---
+# 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: inverse-scaling/opt-30b_eval
+* Dataset: inverse-scaling/NeQA
+* Config: inverse-scaling--NeQA
+* Split: train
+
+To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
+
+## Contributions
+
+Thanks to [@MicPie](https://huggingface.co/MicPie) for evaluating this model.

huggingface_dataset/Dataset_Card/autoevaluate_autoeval-eval-phpthinh__examplei-all-929d48-1748861029.md

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+---
+type: predictions
+tags:
+- autotrain
+- evaluation
+datasets:
+- phpthinh/examplei
+eval_info:
+  task: text_zero_shot_classification
+  model: bigscience/bloom-3b
+  metrics: ['f1']
+  dataset_name: phpthinh/examplei
+  dataset_config: all
+  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/bloom-3b
+* Dataset: phpthinh/examplei
+* Config: all
+* Split: test
+
+To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
+
+## Contributions
+
+Thanks to [@phpthinh](https://huggingface.co/phpthinh) for evaluating this model.

huggingface_dataset/Dataset_Card/autoevaluate_autoeval-staging-eval-launch__gov_report-plain_text-7b7f8a-16126222.md

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+---
+type: predictions
+tags:
+- autotrain
+- evaluation
+datasets:
+- launch/gov_report
+eval_info:
+  task: summarization
+  model: pszemraj/bigbird-pegasus-large-K-booksum
+  metrics: ['bertscore']
+  dataset_name: launch/gov_report
+  dataset_config: plain_text
+  dataset_split: validation
+  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: pszemraj/bigbird-pegasus-large-K-booksum
+* Dataset: launch/gov_report
+* Config: plain_text
+* Split: validation
+
+To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
+
+## Contributions
+
+Thanks to [@nonchalant-nagavalli](https://huggingface.co/nonchalant-nagavalli) for evaluating this model.

huggingface_dataset/Dataset_Card/evageon_IADD.md

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+---
+license: cc-by-4.0
+---
+# IADD
+
+IADD is an Integrated Dataset for Arabic Dialect iDentification Dataset. It contains 136,317 texts representing 5 regions (Maghrebi (MGH) , Levantine (LEV), Egypt (EGY) , Iraq (IRQ) and Gulf (GLF)) and 9 countries (Algeria, Morocco, Tunisia, Palestine, Jordan, Syria, Lebanon, Egypt and Iraq).
+
+IADD is created from the combination of subsets of five corpora: DART, SHAMI, TSAC, PADIC and AOC. The Dialectal ARabic Tweets dataset (DART) [1] has about 25,000 tweets that are annotated via crowdsourcing while the SHAMI dataset [2] consists of 117,805 sentences and covers levantine dialects spoken in Palestine, Jordan, Lebanon and Syria. TSAC [3] is a Tunisian dialect corpus of 17,000 comments collected mainly from Tunisian Facebook pages. Parallel Arabic Dialect Corpus (PADIC) [4] is made of sentences transcribed from recordings or translated from MSA. Finally, the Arabic Online Commentary (AOC) dataset [5] is based on reader commentary from the online versions of three Arabic newspapers, and it consists of 1.4M comments.
+
+IADD is stored in a JSON-like format with the following keys:
+- Sentence: contains the sentence/ text;
+- Region: stores the corresponding dialectal region (MGH, LEV, EGY, IRQ, GLF or general);
+- Country: specifies the corresponding country, if available (MAR, TUN, DZ, EGY, IRQ, SYR, JOR, PSE, LBN);
+- DataSource: indicates the source of the data (PADIC, DART, AOC, SHAMI or TSAC).
+
+
+[1] Alsarsour, I., Mohamed, E., Suwaileh, R., & Elsayed, T. (2018, May). Dart: A large dataset of dialectal arabic tweets. In Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018).
+[2] Abu Kwaik, K., Saad, M. K., Chatzikyriakidis, S., & Dobnik, S. (2018). Shami: A corpus of levantine arabic dialects. In Proceedings of the eleventh international conference on language resources and evaluation (LREC 2018).
+[3] Mdhaffar, S., Bougares, F., Esteve, Y., & Hadrich-Belguith, L. (2017, April). Sentiment analysis of tunisian dialects: Linguistic ressources and experiments. In Third Arabic Natural Language Processing Workshop (WANLP) (pp. 55-61).
+[4] Meftouh, K., Harrat, S., Jamoussi, S., Abbas, M., & Smaili, K. (2015, October). Machine translation experiments on PADIC: A parallel Arabic dialect corpus. In The 29th Pacific Asia conference on language, information and computation.
+[5] Zaidan, O., & Callison-Burch, C. (2011, June). The arabic online commentary dataset: an annotated dataset of informal arabic with high dialectal content. In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies (pp. 37-41).

huggingface_dataset/Dataset_Card/income_cqadupstack-wordpress-top-20-gen-queries.md

@@ -0,0 +1,510 @@
+---
+annotations_creators: []
+language_creators: []
+language:
+- en
+license:
+- cc-by-sa-4.0
+multilinguality:
+- monolingual
+paperswithcode_id: beir
+pretty_name: BEIR Benchmark
+size_categories:
+  msmarco:
+  - 1M<n<10M
+  trec-covid:
+  - 100k<n<1M
+  nfcorpus:
+  - 1K<n<10K
+  nq:
+  - 1M<n<10M
+  hotpotqa:
+  - 1M<n<10M
+  fiqa:
+  - 10K<n<100K
+  arguana:
+  - 1K<n<10K
+  touche-2020:
+  - 100K<n<1M
+  cqadupstack:
+  - 100K<n<1M
+  quora:
+  - 100K<n<1M
+  dbpedia:
+  - 1M<n<10M
+  scidocs:
+  - 10K<n<100K
+  fever:
+  - 1M<n<10M
+  climate-fever:
+  - 1M<n<10M
+  scifact:
+  - 1K<n<10K
+source_datasets: []
+task_categories:
+- text-retrieval
+---
+
+# NFCorpus: 20 generated queries (BEIR Benchmark)
+
+This HF dataset contains the top-20 synthetic queries generated for each passage in the above BEIR benchmark dataset.
+
+- DocT5query model used: [BeIR/query-gen-msmarco-t5-base-v1](https://huggingface.co/BeIR/query-gen-msmarco-t5-base-v1)
+- id (str): unique document id in NFCorpus in the BEIR benchmark (`corpus.jsonl`).
+- Questions generated: 20
+- Code used for generation: [evaluate_anserini_docT5query_parallel.py](https://github.com/beir-cellar/beir/blob/main/examples/retrieval/evaluation/sparse/evaluate_anserini_docT5query_parallel.py)
+
+
+Below contains the old dataset card for the BEIR benchmark.
+
+
+# Dataset Card for BEIR Benchmark
+
+## Table of Contents
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
+  - [Languages](#languages)
+- [Dataset Structure](#dataset-structure)
+  - [Data Instances](#data-instances)
+  - [Data Fields](#data-fields)
+  - [Data Splits](#data-splits)
+- [Dataset Creation](#dataset-creation)
+  - [Curation Rationale](#curation-rationale)
+  - [Source Data](#source-data)
+  - [Annotations](#annotations)
+  - [Personal and Sensitive Information](#personal-and-sensitive-information)
+- [Considerations for Using the Data](#considerations-for-using-the-data)
+  - [Social Impact of Dataset](#social-impact-of-dataset)
+  - [Discussion of Biases](#discussion-of-biases)
+  - [Other Known Limitations](#other-known-limitations)
+- [Additional Information](#additional-information)
+  - [Dataset Curators](#dataset-curators)
+  - [Licensing Information](#licensing-information)
+  - [Citation Information](#citation-information)
+  - [Contributions](#contributions)
+
+## Dataset Description
+
+- **Homepage:** https://github.com/UKPLab/beir
+- **Repository:** https://github.com/UKPLab/beir
+- **Paper:** https://openreview.net/forum?id=wCu6T5xFjeJ
+- **Leaderboard:** https://docs.google.com/spreadsheets/d/1L8aACyPaXrL8iEelJLGqlMqXKPX2oSP_R10pZoy77Ns
+- **Point of Contact:** nandan.thakur@uwaterloo.ca
+
+### Dataset Summary
+
+BEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:
+
+- Fact-checking: [FEVER](http://fever.ai), [Climate-FEVER](http://climatefever.ai), [SciFact](https://github.com/allenai/scifact)
+- Question-Answering: [NQ](https://ai.google.com/research/NaturalQuestions), [HotpotQA](https://hotpotqa.github.io), [FiQA-2018](https://sites.google.com/view/fiqa/)
+- Bio-Medical IR: [TREC-COVID](https://ir.nist.gov/covidSubmit/index.html), [BioASQ](http://bioasq.org), [NFCorpus](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/)
+- News Retrieval: [TREC-NEWS](https://trec.nist.gov/data/news2019.html), [Robust04](https://trec.nist.gov/data/robust/04.guidelines.html)
+- Argument Retrieval: [Touche-2020](https://webis.de/events/touche-20/shared-task-1.html), [ArguAna](tp://argumentation.bplaced.net/arguana/data)
+- Duplicate Question Retrieval: [Quora](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs), [CqaDupstack](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/)
+- Citation-Prediction: [SCIDOCS](https://allenai.org/data/scidocs)
+- Tweet Retrieval: [Signal-1M](https://research.signal-ai.com/datasets/signal1m-tweetir.html)
+- Entity Retrieval: [DBPedia](https://github.com/iai-group/DBpedia-Entity/)
+
+All these datasets have been preprocessed and can be used for your experiments.
+
+
+```python
+
+```
+
+### Supported Tasks and Leaderboards
+
+The dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.
+
+The current best performing models can be found [here](https://eval.ai/web/challenges/challenge-page/689/leaderboard/).
+
+### Languages
+
+All tasks are in English (`en`).
+
+## Dataset Structure
+
+All BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:
+- `corpus` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with three fields `_id` with unique document identifier, `title` with document title (optional) and `text` with document paragraph or passage. For example: `{"_id": "doc1", "title": "Albert Einstein", "text": "Albert Einstein was a German-born...."}`
+- `queries` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with two fields `_id` with unique query identifier and `text` with query text. For example: `{"_id": "q1", "text": "Who developed the mass-energy equivalence formula?"}`
+- `qrels` file: a `.tsv` file (tab-seperated) that contains three columns, i.e. the `query-id`, `corpus-id` and `score` in this order. Keep 1st row as header. For example: `q1 doc1 1`
+
+### Data Instances
+
+A high level example of any beir dataset:
+
+```python
+corpus = {
+    "doc1" : {
+        "title": "Albert Einstein", 
+        "text": "Albert Einstein was a German-born theoretical physicist. who developed the theory of relativity, \
+                 one of the two pillars of modern physics (alongside quantum mechanics). His work is also known for \
+                 its influence on the philosophy of science. He is best known to the general public for his mass–energy \
+                 equivalence formula E = mc2, which has been dubbed 'the world's most famous equation'. He received the 1921 \
+                 Nobel Prize in Physics 'for his services to theoretical physics, and especially for his discovery of the law \
+                 of the photoelectric effect', a pivotal step in the development of quantum theory."
+        },
+    "doc2" : {
+        "title": "", # Keep title an empty string if not present
+        "text": "Wheat beer is a top-fermented beer which is brewed with a large proportion of wheat relative to the amount of \
+                 malted barley. The two main varieties are German Weißbier and Belgian witbier; other types include Lambic (made\
+                 with wild yeast), Berliner Weisse (a cloudy, sour beer), and Gose (a sour, salty beer)."
+    },
+}
+
+queries = {
+    "q1" : "Who developed the mass-energy equivalence formula?",
+    "q2" : "Which beer is brewed with a large proportion of wheat?"
+}
+
+qrels = {
+    "q1" : {"doc1": 1},
+    "q2" : {"doc2": 1},
+}
+```
+
+### Data Fields
+
+Examples from all configurations have the following features:
+
+### Corpus
+- `corpus`: a `dict` feature representing the document title and passage text, made up of:
+    - `_id`: a `string` feature representing the unique document id
+        - `title`: a `string` feature, denoting the title of the document.
+        - `text`: a `string` feature, denoting the text of the document.
+
+### Queries
+- `queries`: a `dict` feature representing the query, made up of:
+    - `_id`: a `string` feature representing the unique query id
+    - `text`: a `string` feature, denoting the text of the query.
+
+### Qrels
+- `qrels`: a `dict` feature representing the query document relevance judgements, made up of:
+    - `_id`: a `string` feature representing the query id
+        - `_id`: a `string` feature, denoting the document id.
+        - `score`: a `int32` feature, denoting the relevance judgement between query and document.
+
+
+### Data Splits
+
+| Dataset   | Website| BEIR-Name | Type | Queries  | Corpus | Rel D/Q | Down-load | md5 |
+| -------- | -----| ---------| --------- | ----------- | ---------| ---------| :----------: | :------:|
+| MSMARCO    | [Homepage](https://microsoft.github.io/msmarco/)| ``msmarco`` | ``train``<br>``dev``<br>``test``|  6,980   |  8.84M     |    1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/msmarco.zip) | ``444067daf65d982533ea17ebd59501e4`` |
+| TREC-COVID |  [Homepage](https://ir.nist.gov/covidSubmit/index.html)| ``trec-covid``| ``test``| 50|  171K| 493.5 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/trec-covid.zip) | ``ce62140cb23feb9becf6270d0d1fe6d1`` |
+| NFCorpus   | [Homepage](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/) | ``nfcorpus`` | ``train``<br>``dev``<br>``test``|  323     |  3.6K     |  38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nfcorpus.zip) | ``a89dba18a62ef92f7d323ec890a0d38d`` |
+| BioASQ     | [Homepage](http://bioasq.org) | ``bioasq``|  ``train``<br>``test`` | 500    |  14.91M    |  8.05 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#2-bioasq) |
+| NQ         | [Homepage](https://ai.google.com/research/NaturalQuestions) | ``nq``| ``train``<br>``test``| 3,452   |  2.68M  |  1.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nq.zip) | ``d4d3d2e48787a744b6f6e691ff534307`` |
+| HotpotQA   | [Homepage](https://hotpotqa.github.io) | ``hotpotqa``| ``train``<br>``dev``<br>``test``|  7,405   |  5.23M  |  2.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/hotpotqa.zip)  | ``f412724f78b0d91183a0e86805e16114`` |
+| FiQA-2018  | [Homepage](https://sites.google.com/view/fiqa/) | ``fiqa`` | ``train``<br>``dev``<br>``test``|  648     |  57K    |  2.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fiqa.zip)  | ``17918ed23cd04fb15047f73e6c3bd9d9`` |
+| Signal-1M(RT) | [Homepage](https://research.signal-ai.com/datasets/signal1m-tweetir.html)| ``signal1m`` | ``test``| 97   |  2.86M  |  19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#4-signal-1m) |
+| TREC-NEWS  | [Homepage](https://trec.nist.gov/data/news2019.html) | ``trec-news``    | ``test``| 57    |  595K    |  19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#1-trec-news) |
+| ArguAna    | [Homepage](http://argumentation.bplaced.net/arguana/data) | ``arguana``| ``test`` | 1,406     |  8.67K    |  1.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/arguana.zip)  | ``8ad3e3c2a5867cdced806d6503f29b99`` |
+| Touche-2020| [Homepage](https://webis.de/events/touche-20/shared-task-1.html) | ``webis-touche2020``| ``test``| 49     |  382K    |  19.0 |  [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/webis-touche2020.zip) | ``46f650ba5a527fc69e0a6521c5a23563`` |
+| CQADupstack| [Homepage](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/) | ``cqadupstack``| ``test``| 13,145 |  457K  |  1.4 |  [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/cqadupstack.zip) | ``4e41456d7df8ee7760a7f866133bda78`` |
+| Quora| [Homepage](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs) | ``quora``| ``dev``<br>``test``| 10,000     |  523K    |  1.6 |  [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/quora.zip) | ``18fb154900ba42a600f84b839c173167`` |
+| DBPedia | [Homepage](https://github.com/iai-group/DBpedia-Entity/) | ``dbpedia-entity``| ``dev``<br>``test``| 400    |  4.63M    |  38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/dbpedia-entity.zip) | ``c2a39eb420a3164af735795df012ac2c`` |
+| SCIDOCS| [Homepage](https://allenai.org/data/scidocs) | ``scidocs``| ``test``| 1,000     |  25K    |  4.9 |  [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scidocs.zip) | ``38121350fc3a4d2f48850f6aff52e4a9`` |
+| FEVER | [Homepage](http://fever.ai) | ``fever``| ``train``<br>``dev``<br>``test``|  6,666     |  5.42M    |  1.2|  [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fever.zip)  | ``5a818580227bfb4b35bb6fa46d9b6c03`` |
+| Climate-FEVER| [Homepage](http://climatefever.ai) | ``climate-fever``|``test``|  1,535     |  5.42M |  3.0 |  [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/climate-fever.zip)  | ``8b66f0a9126c521bae2bde127b4dc99d`` |
+| SciFact| [Homepage](https://github.com/allenai/scifact) | ``scifact``| ``train``<br>``test``|  300     |  5K    |  1.1 |  [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scifact.zip)  | ``5f7d1de60b170fc8027bb7898e2efca1`` |
+| Robust04 | [Homepage](https://trec.nist.gov/data/robust/04.guidelines.html) | ``robust04``| ``test``| 249  |  528K  |  69.9 |  No  |  [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#3-robust04)  |
+
+
+## Dataset Creation
+
+### Curation Rationale
+
+[Needs More Information]
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+[Needs More Information]
+
+#### Who are the source language producers?
+
+[Needs More Information]
+
+### Annotations
+
+#### Annotation process
+
+[Needs More Information]
+
+#### Who are the annotators?
+
+[Needs More Information]
+
+### Personal and Sensitive Information
+
+[Needs More Information]
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+[Needs More Information]
+
+### Discussion of Biases
+
+[Needs More Information]
+
+### Other Known Limitations
+
+[Needs More Information]
+
+## Additional Information
+
+### Dataset Curators
+
+[Needs More Information]
+
+### Licensing Information
+
+[Needs More Information]
+
+### Citation Information
+
+Cite as:
+```
+@inproceedings{
+thakur2021beir,
+title={{BEIR}: A Heterogeneous Benchmark for Zero-shot Evaluation of Information Retrieval Models},
+author={Nandan Thakur and Nils Reimers and Andreas R{\"u}ckl{\'e} and Abhishek Srivastava and Iryna Gurevych},
+booktitle={Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2)},
+year={2021},
+url={https://openreview.net/forum?id=wCu6T5xFjeJ}
+}
+```
+
+### Contributions
+
+Thanks to [@Nthakur20](https://github.com/Nthakur20) for adding this dataset.Top-20 generated queries for every passage in NFCorpus 
+
+
+# Dataset Card for BEIR Benchmark
+
+## Table of Contents
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
+  - [Languages](#languages)
+- [Dataset Structure](#dataset-structure)
+  - [Data Instances](#data-instances)
+  - [Data Fields](#data-fields)
+  - [Data Splits](#data-splits)
+- [Dataset Creation](#dataset-creation)
+  - [Curation Rationale](#curation-rationale)
+  - [Source Data](#source-data)
+  - [Annotations](#annotations)
+  - [Personal and Sensitive Information](#personal-and-sensitive-information)
+- [Considerations for Using the Data](#considerations-for-using-the-data)
+  - [Social Impact of Dataset](#social-impact-of-dataset)
+  - [Discussion of Biases](#discussion-of-biases)
+  - [Other Known Limitations](#other-known-limitations)
+- [Additional Information](#additional-information)
+  - [Dataset Curators](#dataset-curators)
+  - [Licensing Information](#licensing-information)
+  - [Citation Information](#citation-information)
+  - [Contributions](#contributions)
+
+## Dataset Description
+
+- **Homepage:** https://github.com/UKPLab/beir
+- **Repository:** https://github.com/UKPLab/beir
+- **Paper:** https://openreview.net/forum?id=wCu6T5xFjeJ
+- **Leaderboard:** https://docs.google.com/spreadsheets/d/1L8aACyPaXrL8iEelJLGqlMqXKPX2oSP_R10pZoy77Ns
+- **Point of Contact:** nandan.thakur@uwaterloo.ca
+
+### Dataset Summary
+
+BEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:
+
+- Fact-checking: [FEVER](http://fever.ai), [Climate-FEVER](http://climatefever.ai), [SciFact](https://github.com/allenai/scifact)
+- Question-Answering: [NQ](https://ai.google.com/research/NaturalQuestions), [HotpotQA](https://hotpotqa.github.io), [FiQA-2018](https://sites.google.com/view/fiqa/)
+- Bio-Medical IR: [TREC-COVID](https://ir.nist.gov/covidSubmit/index.html), [BioASQ](http://bioasq.org), [NFCorpus](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/)
+- News Retrieval: [TREC-NEWS](https://trec.nist.gov/data/news2019.html), [Robust04](https://trec.nist.gov/data/robust/04.guidelines.html)
+- Argument Retrieval: [Touche-2020](https://webis.de/events/touche-20/shared-task-1.html), [ArguAna](tp://argumentation.bplaced.net/arguana/data)
+- Duplicate Question Retrieval: [Quora](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs), [CqaDupstack](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/)
+- Citation-Prediction: [SCIDOCS](https://allenai.org/data/scidocs)
+- Tweet Retrieval: [Signal-1M](https://research.signal-ai.com/datasets/signal1m-tweetir.html)
+- Entity Retrieval: [DBPedia](https://github.com/iai-group/DBpedia-Entity/)
+
+All these datasets have been preprocessed and can be used for your experiments.
+
+
+```python
+
+```
+
+### Supported Tasks and Leaderboards
+
+The dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.
+
+The current best performing models can be found [here](https://eval.ai/web/challenges/challenge-page/689/leaderboard/).
+
+### Languages
+
+All tasks are in English (`en`).
+
+## Dataset Structure
+
+All BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:
+- `corpus` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with three fields `_id` with unique document identifier, `title` with document title (optional) and `text` with document paragraph or passage. For example: `{"_id": "doc1", "title": "Albert Einstein", "text": "Albert Einstein was a German-born...."}`
+- `queries` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with two fields `_id` with unique query identifier and `text` with query text. For example: `{"_id": "q1", "text": "Who developed the mass-energy equivalence formula?"}`
+- `qrels` file: a `.tsv` file (tab-seperated) that contains three columns, i.e. the `query-id`, `corpus-id` and `score` in this order. Keep 1st row as header. For example: `q1 doc1 1`
+
+### Data Instances
+
+A high level example of any beir dataset:
+
+```python
+corpus = {
+    "doc1" : {
+        "title": "Albert Einstein", 
+        "text": "Albert Einstein was a German-born theoretical physicist. who developed the theory of relativity, \
+                 one of the two pillars of modern physics (alongside quantum mechanics). His work is also known for \
+                 its influence on the philosophy of science. He is best known to the general public for his mass–energy \
+                 equivalence formula E = mc2, which has been dubbed 'the world's most famous equation'. He received the 1921 \
+                 Nobel Prize in Physics 'for his services to theoretical physics, and especially for his discovery of the law \
+                 of the photoelectric effect', a pivotal step in the development of quantum theory."
+        },
+    "doc2" : {
+        "title": "", # Keep title an empty string if not present
+        "text": "Wheat beer is a top-fermented beer which is brewed with a large proportion of wheat relative to the amount of \
+                 malted barley. The two main varieties are German Weißbier and Belgian witbier; other types include Lambic (made\
+                 with wild yeast), Berliner Weisse (a cloudy, sour beer), and Gose (a sour, salty beer)."
+    },
+}
+
+queries = {
+    "q1" : "Who developed the mass-energy equivalence formula?",
+    "q2" : "Which beer is brewed with a large proportion of wheat?"
+}
+
+qrels = {
+    "q1" : {"doc1": 1},
+    "q2" : {"doc2": 1},
+}
+```
+
+### Data Fields
+
+Examples from all configurations have the following features:
+
+### Corpus
+- `corpus`: a `dict` feature representing the document title and passage text, made up of:
+    - `_id`: a `string` feature representing the unique document id
+        - `title`: a `string` feature, denoting the title of the document.
+        - `text`: a `string` feature, denoting the text of the document.
+
+### Queries
+- `queries`: a `dict` feature representing the query, made up of:
+    - `_id`: a `string` feature representing the unique query id
+    - `text`: a `string` feature, denoting the text of the query.
+
+### Qrels
+- `qrels`: a `dict` feature representing the query document relevance judgements, made up of:
+    - `_id`: a `string` feature representing the query id
+        - `_id`: a `string` feature, denoting the document id.
+        - `score`: a `int32` feature, denoting the relevance judgement between query and document.
+
+
+### Data Splits
+
+| Dataset   | Website| BEIR-Name | Type | Queries  | Corpus | Rel D/Q | Down-load | md5 |
+| -------- | -----| ---------| --------- | ----------- | ---------| ---------| :----------: | :------:|
+| MSMARCO    | [Homepage](https://microsoft.github.io/msmarco/)| ``msmarco`` | ``train``<br>``dev``<br>``test``|  6,980   |  8.84M     |    1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/msmarco.zip) | ``444067daf65d982533ea17ebd59501e4`` |
+| TREC-COVID |  [Homepage](https://ir.nist.gov/covidSubmit/index.html)| ``trec-covid``| ``test``| 50|  171K| 493.5 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/trec-covid.zip) | ``ce62140cb23feb9becf6270d0d1fe6d1`` |
+| NFCorpus   | [Homepage](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/) | ``nfcorpus`` | ``train``<br>``dev``<br>``test``|  323     |  3.6K     |  38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nfcorpus.zip) | ``a89dba18a62ef92f7d323ec890a0d38d`` |
+| BioASQ     | [Homepage](http://bioasq.org) | ``bioasq``|  ``train``<br>``test`` | 500    |  14.91M    |  8.05 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#2-bioasq) |
+| NQ         | [Homepage](https://ai.google.com/research/NaturalQuestions) | ``nq``| ``train``<br>``test``| 3,452   |  2.68M  |  1.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nq.zip) | ``d4d3d2e48787a744b6f6e691ff534307`` |
+| HotpotQA   | [Homepage](https://hotpotqa.github.io) | ``hotpotqa``| ``train``<br>``dev``<br>``test``|  7,405   |  5.23M  |  2.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/hotpotqa.zip)  | ``f412724f78b0d91183a0e86805e16114`` |
+| FiQA-2018  | [Homepage](https://sites.google.com/view/fiqa/) | ``fiqa`` | ``train``<br>``dev``<br>``test``|  648     |  57K    |  2.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fiqa.zip)  | ``17918ed23cd04fb15047f73e6c3bd9d9`` |
+| Signal-1M(RT) | [Homepage](https://research.signal-ai.com/datasets/signal1m-tweetir.html)| ``signal1m`` | ``test``| 97   |  2.86M  |  19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#4-signal-1m) |
+| TREC-NEWS  | [Homepage](https://trec.nist.gov/data/news2019.html) | ``trec-news``    | ``test``| 57    |  595K    |  19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#1-trec-news) |
+| ArguAna    | [Homepage](http://argumentation.bplaced.net/arguana/data) | ``arguana``| ``test`` | 1,406     |  8.67K    |  1.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/arguana.zip)  | ``8ad3e3c2a5867cdced806d6503f29b99`` |
+| Touche-2020| [Homepage](https://webis.de/events/touche-20/shared-task-1.html) | ``webis-touche2020``| ``test``| 49     |  382K    |  19.0 |  [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/webis-touche2020.zip) | ``46f650ba5a527fc69e0a6521c5a23563`` |
+| CQADupstack| [Homepage](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/) | ``cqadupstack``| ``test``| 13,145 |  457K  |  1.4 |  [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/cqadupstack.zip) | ``4e41456d7df8ee7760a7f866133bda78`` |
+| Quora| [Homepage](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs) | ``quora``| ``dev``<br>``test``| 10,000     |  523K    |  1.6 |  [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/quora.zip) | ``18fb154900ba42a600f84b839c173167`` |
+| DBPedia | [Homepage](https://github.com/iai-group/DBpedia-Entity/) | ``dbpedia-entity``| ``dev``<br>``test``| 400    |  4.63M    |  38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/dbpedia-entity.zip) | ``c2a39eb420a3164af735795df012ac2c`` |
+| SCIDOCS| [Homepage](https://allenai.org/data/scidocs) | ``scidocs``| ``test``| 1,000     |  25K    |  4.9 |  [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scidocs.zip) | ``38121350fc3a4d2f48850f6aff52e4a9`` |
+| FEVER | [Homepage](http://fever.ai) | ``fever``| ``train``<br>``dev``<br>``test``|  6,666     |  5.42M    |  1.2|  [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fever.zip)  | ``5a818580227bfb4b35bb6fa46d9b6c03`` |
+| Climate-FEVER| [Homepage](http://climatefever.ai) | ``climate-fever``|``test``|  1,535     |  5.42M |  3.0 |  [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/climate-fever.zip)  | ``8b66f0a9126c521bae2bde127b4dc99d`` |
+| SciFact| [Homepage](https://github.com/allenai/scifact) | ``scifact``| ``train``<br>``test``|  300     |  5K    |  1.1 |  [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scifact.zip)  | ``5f7d1de60b170fc8027bb7898e2efca1`` |
+| Robust04 | [Homepage](https://trec.nist.gov/data/robust/04.guidelines.html) | ``robust04``| ``test``| 249  |  528K  |  69.9 |  No  |  [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#3-robust04)  |
+
+
+## Dataset Creation
+
+### Curation Rationale
+
+[Needs More Information]
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+[Needs More Information]
+
+#### Who are the source language producers?
+
+[Needs More Information]
+
+### Annotations
+
+#### Annotation process
+
+[Needs More Information]
+
+#### Who are the annotators?
+
+[Needs More Information]
+
+### Personal and Sensitive Information
+
+[Needs More Information]
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+[Needs More Information]
+
+### Discussion of Biases
+
+[Needs More Information]
+
+### Other Known Limitations
+
+[Needs More Information]
+
+## Additional Information
+
+### Dataset Curators
+
+[Needs More Information]
+
+### Licensing Information
+
+[Needs More Information]
+
+### Citation Information
+
+Cite as:
+```
+@inproceedings{
+thakur2021beir,
+title={{BEIR}: A Heterogeneous Benchmark for Zero-shot Evaluation of Information Retrieval Models},
+author={Nandan Thakur and Nils Reimers and Andreas R{\"u}ckl{\'e} and Abhishek Srivastava and Iryna Gurevych},
+booktitle={Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2)},
+year={2021},
+url={https://openreview.net/forum?id=wCu6T5xFjeJ}
+}
+```
+
+### Contributions
+
+Thanks to [@Nthakur20](https://github.com/Nthakur20) for adding this dataset.

huggingface_dataset/Dataset_Card/irds_mmarco_v2_hi_dev.md

@@ -0,0 +1,49 @@
+---
+pretty_name: '`mmarco/v2/hi/dev`'
+viewer: false
+source_datasets: ['irds/mmarco_v2_hi']
+task_categories:
+- text-retrieval
+---
+
+# Dataset Card for `mmarco/v2/hi/dev`
+
+The `mmarco/v2/hi/dev` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
+For more information about the dataset, see the [documentation](https://ir-datasets.com/mmarco#mmarco/v2/hi/dev).
+
+# Data
+
+This dataset provides:
+ - `queries` (i.e., topics); count=101,093
+ - `qrels`: (relevance assessments); count=59,273
+
+ - For `docs`, use [`irds/mmarco_v2_hi`](https://huggingface.co/datasets/irds/mmarco_v2_hi)
+
+## Usage
+
+```python
+from datasets import load_dataset
+
+queries = load_dataset('irds/mmarco_v2_hi_dev', 'queries')
+for record in queries:
+    record # {'query_id': ..., 'text': ...}
+
+qrels = load_dataset('irds/mmarco_v2_hi_dev', 'qrels')
+for record in qrels:
+    record # {'query_id': ..., 'doc_id': ..., 'relevance': ..., 'iteration': ...}
+
+```
+
+Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the
+data in 🤗 Dataset format.
+
+## Citation Information
+
+```
+@article{Bonifacio2021MMarco,
+    title={{mMARCO}: A Multilingual Version of {MS MARCO} Passage Ranking Dataset},
+    author={Luiz Henrique Bonifacio and Israel Campiotti and Roberto Lotufo and Rodrigo Nogueira},
+    year={2021},
+    journal={arXiv:2108.13897}
+}
+```

huggingface_dataset/Dataset_Card/ithieund_VietNews-Abs-Sum.md

@@ -0,0 +1,39 @@
+# VietNews-Abs-Sum
+A dataset for Vietnamese Abstractive Summarization task.  
+It includes all articles from Vietnews (VNDS) dataset which was released by Van-Hau Nguyen et al.  
+The articles were collected from tuoitre.vn, vnexpress.net, and nguoiduatin.vn online newspaper by the authors.
+
+# Introduction
+This dataset was extracted from Train/Val/Test split of Vietnews dataset. All files from *test_tokenized*, *train_tokenized* and *val_tokenized* directories are fetched and preprocessed with punctuation normalization. The subsets then are stored in the *raw* director with 3 files *train.tsv*, *valid.tsv*, and *test.tsv* accordingly. These files will be considered as the original raw dataset as nothing changes except the punctuation normalization.
+
+As pointed out in *BARTpho: Pre-trained Sequence-to-Sequence Models for Vietnamese*, there are lots of duplicated samples across subsets. Therefore, we do another preprocessing process to remove all the duplicated samples. The process includes the following steps:
+- First, remove all duplicates from each subset
+- Second, merge all subsets into 1 set with the following order: test + val + train
+- Finally, remove all duplicates from that merged set and then split out into 3 new subsets
+
+The final subsets are the same to the orignal subsets but all duplicates were removed. Each subset now has total samples as follows:
+- train_no_dups.tsv: 99134 samples
+- valid_no_dups.tsv: 22184 samples
+- test_no_dups.tsv: 22498 samples
+
+Totally, we have 99134 + 22184 + 22498 = 143816 samples after filtering!  
+Note that this result is not the same as the number of samples reported in BARTpho paper, but there is no duplicate inside each subset or across subsets anymore.
+
+These filtered subsets are also exported into JSONLINE format to support future training script that requires this data format.
+
+# Directory structure
+- raw: contains 3 raw subset files fetched from Vietnews directories
+  - train.tsv
+  - val.tsv
+  - test.tsv
+- processed: contains duplicates filtered subsets
+  - test.tsv
+  - train.tsv
+  - valid.tsv
+  - test.jsonl
+  - train.jsonl
+  - valid.jsonl
+  - [and other variants]
+
+# Credits
+- Special thanks to Vietnews (VNDS) authors: https://github.com/ThanhChinhBK/vietnews

huggingface_dataset/Dataset_Card/nightingal3_fig-qa.md

@@ -0,0 +1,91 @@
+---
+annotations_creators:
+- expert-generated
+- crowdsourced
+language_creators:
+- crowdsourced
+language:
+- en
+license:
+- mit
+multilinguality:
+- monolingual
+pretty_name: Fig-QA
+size_categories:
+- 10K<n<100K
+source_datasets:
+- original
+task_categories:
+- multiple-choice
+task_ids:
+- multiple-choice-qa
+---
+
+# Dataset Card for Fig-QA
+
+## Table of Contents
+- [Table of Contents](#table-of-contents)
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
+  - [Languages](#languages)
+- [Dataset Structure](#dataset-structure)
+  - [Data Splits](#data-splits)
+- [Considerations for Using the Data](#considerations-for-using-the-data)
+  - [Discussion of Biases](#discussion-of-biases)
+- [Additional Information](#additional-information)
+  - [Licensing Information](#licensing-information)
+  - [Citation Information](#citation-information)
+
+## Dataset Description
+
+- **Repository:** https://github.com/nightingal3/Fig-QA 
+- **Paper:** https://arxiv.org/abs/2204.12632
+- **Leaderboard:** https://explainaboard.inspiredco.ai/leaderboards?dataset=fig_qa
+- **Point of Contact:** emmy@cmu.edu
+
+### Dataset Summary
+
+This is the dataset for the paper [Testing the Ability of Language Models to Interpret Figurative Language](https://arxiv.org/abs/2204.12632). Fig-QA consists of 10256 examples of human-written creative metaphors that are paired as a Winograd schema. It can be used to evaluate the commonsense reasoning of models. The metaphors themselves can also be used as training data for other tasks, such as metaphor detection or generation. 
+
+### Supported Tasks and Leaderboards
+
+You can evaluate your models on the test set by submitting to the [leaderboard](https://explainaboard.inspiredco.ai/leaderboards?dataset=fig_qa) on Explainaboard. Click on "New" and select `qa-multiple-choice` for the task field. Select `accuracy` for the metric. You should upload results in the form of a system output file in JSON or JSONL format. 
+
+### Languages
+
+English only currently
+
+### Data Splits
+
+Train-{S, M(no suffix), XL}: different training set sizes
+Dev
+Test (labels not provided for test set)
+
+## Considerations for Using the Data
+
+### Discussion of Biases
+
+These metaphors are human-generated and may contain insults or other explicit content. Authors of the paper manually removed offensive content, but users should keep in mind that some potentially offensive content may remain in the dataset.
+
+## Additional Information
+
+### Licensing Information
+
+MIT License
+
+### Citation Information
+
+If you found the dataset useful, please cite this paper:
+
+    @misc{https://doi.org/10.48550/arxiv.2204.12632,
+      doi = {10.48550/ARXIV.2204.12632},
+      url = {https://arxiv.org/abs/2204.12632},
+      author = {Liu, Emmy and Cui, Chen and Zheng, Kenneth and Neubig, Graham},
+      keywords = {Computation and Language (cs.CL), Artificial Intelligence (cs.AI), FOS: Computer and information sciences, FOS: Computer and information sciences},
+      title = {Testing the Ability of Language Models to Interpret Figurative Language},
+      publisher = {arXiv},
+      year = {2022},
+      copyright = {Creative Commons Attribution Share Alike 4.0 International}
+    }
+  

huggingface_dataset/Dataset_Card/nlphuji_flickr30k.md

@@ -0,0 +1,18 @@
+# Flickr30k
+
+Original paper: [From image descriptions to visual denotations: New similarity metrics for semantic inference over event descriptions](https://aclanthology.org/Q14-1006)
+
+Homepage: https://shannon.cs.illinois.edu/DenotationGraph/
+
+Bibtex:
+```
+@article{young2014image,
+  title={From image descriptions to visual denotations: New similarity metrics for semantic inference over event descriptions},
+  author={Young, Peter and Lai, Alice and Hodosh, Micah and Hockenmaier, Julia},
+  journal={Transactions of the Association for Computational Linguistics},
+  volume={2},
+  pages={67--78},
+  year={2014},
+  publisher={MIT Press}
+}
+```

huggingface_dataset/Dataset_Card/parsinlu_reading_comprehension.md

@@ -0,0 +1,194 @@
+---
+annotations_creators:
+- expert-generated
+language_creators:
+- expert-generated
+language:
+- fa
+license:
+- cc-by-nc-sa-4.0
+multilinguality:
+- monolingual
+size_categories:
+- 1K<n<10K
+source_datasets:
+- extended|wikipedia|google
+task_categories:
+- question-answering
+task_ids:
+- extractive-qa
+paperswithcode_id: null
+pretty_name: PersiNLU (Reading Comprehension)
+dataset_info:
+  features:
+  - name: question
+    dtype: string
+  - name: url
+    dtype: string
+  - name: context
+    dtype: string
+  - name: answers
+    sequence:
+    - name: answer_start
+      dtype: int32
+    - name: answer_text
+      dtype: string
+  config_name: parsinlu-repo
+  splits:
+  - name: train
+    num_bytes: 747679
+    num_examples: 600
+  - name: test
+    num_bytes: 681945
+    num_examples: 575
+  - name: validation
+    num_bytes: 163185
+    num_examples: 125
+  download_size: 4117863
+  dataset_size: 1592809
+---
+
+# Dataset Card for PersiNLU (Reading Comprehension)
+
+## Table of Contents
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
+  - [Languages](#languages)
+- [Dataset Structure](#dataset-structure)
+  - [Data Instances](#data-instances)
+  - [Data Fields](#data-fields)
+  - [Data Splits](#data-splits)
+- [Dataset Creation](#dataset-creation)
+  - [Curation Rationale](#curation-rationale)
+  - [Source Data](#source-data)
+  - [Annotations](#annotations)
+  - [Personal and Sensitive Information](#personal-and-sensitive-information)
+- [Considerations for Using the Data](#considerations-for-using-the-data)
+  - [Social Impact of Dataset](#social-impact-of-dataset)
+  - [Discussion of Biases](#discussion-of-biases)
+  - [Other Known Limitations](#other-known-limitations)
+- [Additional Information](#additional-information)
+  - [Dataset Curators](#dataset-curators)
+  - [Licensing Information](#licensing-information)
+  - [Citation Information](#citation-information)
+  - [Contributions](#contributions)
+
+## Dataset Description
+
+- **Homepage:** [Github](https://github.com/persiannlp/parsinlu/)
+- **Repository:** [Github](https://github.com/persiannlp/parsinlu/)
+- **Paper:** [Arxiv](https://arxiv.org/abs/2012.06154)
+- **Leaderboard:** 
+- **Point of Contact:** [email](d.khashabi@gmail.com)
+
+### Dataset Summary
+
+A Persian reading comprehenion task (generating an answer, given a question and a context paragraph). 
+The questions are mined using Google auto-complete, their answers and the corresponding evidence documents are manually annotated by native speakers. 
+
+### Supported Tasks and Leaderboards
+
+[More Information Needed]
+
+### Languages
+
+The text dataset is in Persian (`fa`).
+
+## Dataset Structure
+
+### Data Instances
+
+Here is an example from the dataset: 
+```
+{
+    'question': 'پیامبر در چه سالی به پیامبری رسید؟', 
+    'url': 'https://fa.wikipedia.org/wiki/%D9%85%D8%AD%D9%85%D8%AF', 
+    'passage': 'محمد که از روش زندگی مردم مکه ناخشنود بود، گهگاه در غار حرا در یکی از کوه\u200cهای اطراف آن دیار به تفکر و عبادت می\u200cپرداخت. به باور مسلمانان، محمد در همین مکان و در حدود ۴۰ سالگی از طرف خدا به پیامبری برگزیده، و وحی بر او فروفرستاده شد. در نظر آنان، دعوت محمد همانند دعوت دیگر پیامبرانِ کیش یکتاپرستی مبنی بر این بود که خداوند (الله) یکتاست و تسلیم شدن برابر خدا راه رسیدن به اوست.', 
+    'answers': [
+        {'answer_start': 160, 'answer_text': 'حدود ۴۰ سالگی'}
+     ]
+}
+```
+
+### Data Fields
+
+- `question`: the question, mined using Google auto-complete. 
+- `passage`: the passage that contains the answer. 
+- `url`: the url from which the passage was mined. 
+- `answers`: a list of answers, containing the string and the index of the answer with the fields `answer_start` and `answer_text`. Note that in the test set, some `answer_start` values are missing and replaced with `-1`
+
+### Data Splits
+
+The train/test split contains 600/575 samples.
+
+## Dataset Creation
+
+### Curation Rationale
+
+The question were collected via Google auto-complete.
+The answers were annotated by native speakers. 
+For more details, check [the corresponding draft](https://arxiv.org/abs/2012.06154).
+
+### 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
+
+Dataset provided for research purposes only. Please check dataset license for additional information.
+
+## Additional Information
+
+### Dataset Curators
+
+[More Information Needed]
+
+### Licensing Information
+
+CC BY-NC-SA 4.0 License 
+
+### Citation Information
+```bibtex 
+@article{huggingface:dataset,
+    title = {ParsiNLU: A Suite of Language Understanding Challenges for Persian},
+    authors = {Khashabi, Daniel and Cohan, Arman and Shakeri, Siamak and Hosseini, Pedram and Pezeshkpour, Pouya and Alikhani, Malihe and Aminnaseri, Moin and Bitaab, Marzieh and Brahman, Faeze and Ghazarian, Sarik and others},
+    year={2020}
+    journal = {arXiv e-prints},
+    eprint = {2012.06154},    
+}
+```
+
+### Contributions
+
+Thanks to [@danyaljj](https://github.com/danyaljj) for adding this dataset.

huggingface_dataset/Dataset_Card/tab_fact.md

@@ -0,0 +1,207 @@
+---
+annotations_creators:
+- crowdsourced
+language_creators:
+- crowdsourced
+language:
+- en
+license:
+- cc-by-4.0
+multilinguality:
+- monolingual
+size_categories:
+- 100K<n<1M
+source_datasets:
+- original
+task_categories:
+- text-classification
+task_ids:
+- fact-checking
+paperswithcode_id: tabfact
+pretty_name: TabFact
+dataset_info:
+- config_name: tab_fact
+  features:
+  - name: id
+    dtype: int32
+  - name: table_id
+    dtype: string
+  - name: table_text
+    dtype: string
+  - name: table_caption
+    dtype: string
+  - name: statement
+    dtype: string
+  - name: label
+    dtype:
+      class_label:
+        names:
+          '0': refuted
+          '1': entailed
+  splits:
+  - name: train
+    num_bytes: 99852664
+    num_examples: 92283
+  - name: validation
+    num_bytes: 13846872
+    num_examples: 12792
+  - name: test
+    num_bytes: 13493391
+    num_examples: 12779
+  download_size: 196508436
+  dataset_size: 127192927
+- config_name: blind_test
+  features:
+  - name: id
+    dtype: int32
+  - name: table_id
+    dtype: string
+  - name: table_text
+    dtype: string
+  - name: table_caption
+    dtype: string
+  - name: statement
+    dtype: string
+  - name: test_id
+    dtype: string
+  splits:
+  - name: test
+    num_bytes: 10954442
+    num_examples: 9750
+  download_size: 196508436
+  dataset_size: 10954442
+---
+
+# Dataset Card for TabFact
+
+## 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:** [TabFact](https://tabfact.github.io/index.html)
+- **Repository:** [GitHub](https://github.com/wenhuchen/Table-Fact-Checking)
+- **Paper:** [TabFact: A Large-scale Dataset for Table-based Fact Verification](https://arxiv.org/abs/1909.02164)
+- **Leaderboard:** [Leaderboard](https://competitions.codalab.org/competitions/21611)
+- **Point of Contact:** [Wenhu Chen](wenhuchen@cs.ucsb.edu)
+
+### Dataset Summary
+
+The problem of verifying whether a textual hypothesis holds the truth based on the given evidence, also known as fact verification, plays an important role in the study of natural language understanding and semantic representation. However, existing studies are restricted to dealing with unstructured textual evidence (e.g., sentences and passages, a pool of passages), while verification using structured forms of evidence, such as tables, graphs, and databases, remains unexplored. TABFACT is large scale dataset with 16k Wikipedia tables as evidence for 118k human annotated statements designed for fact verification with semi-structured evidence. The statements are labeled as either ENTAILED or REFUTED. TABFACT is challenging since it involves both soft linguistic reasoning and hard symbolic reasoning.
+
+### Supported Tasks and Leaderboards
+
+[More Information Needed]
+
+### Languages
+
+[More Information Needed]
+
+## Dataset Structure
+
+### Data Instances
+
+[More Information Needed]
+
+### Data Fields
+
+[More Information Needed]
+
+### Data Splits
+
+[More Information Needed]
+## Dataset Creation
+
+### Curation Rationale
+
+[More Information Needed]
+
+### Source Data
+
+[More Information Needed]
+
+#### Initial Data Collection and Normalization
+
+[More Information Needed]
+
+#### Who are the source language producers?
+
+[More Information Needed]
+
+### Annotations
+
+[More Information Needed]
+
+#### Annotation process
+
+[More Information Needed]
+
+#### Who are the annotators?
+
+[More Information Needed]
+
+### Personal and Sensitive Information
+
+[More Information Needed]
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+[More Information Needed]
+
+### Discussion of Biases
+
+[More Information Needed]
+
+### Other Known Limitations
+
+[More Information Needed]
+
+## Additional Information
+
+### Dataset Curators
+
+[More Information Needed]
+
+### Licensing Information
+
+[More Information Needed]
+
+### Citation Information
+
+```
+@inproceedings{2019TabFactA,
+  title={TabFact : A Large-scale Dataset for Table-based Fact Verification},
+  author={Wenhu Chen, Hongmin Wang, Jianshu Chen, Yunkai Zhang, Hong Wang, Shiyang Li, Xiyou Zhou and William Yang Wang},
+  booktitle = {International Conference on Learning Representations (ICLR)},
+  address = {Addis Ababa, Ethiopia},
+  month = {April},
+  year = {2020}
+}
+```
+
+### Contributions
+
+Thanks to [@patil-suraj](https://github.com/patil-suraj) for adding this dataset.

huggingface_dataset/Dataset_Card/tau_sled.md

@@ -0,0 +1,147 @@
+---
+language:
+- en
+license:
+- mit
+task_categories:
+- question-answering
+- summarization
+- text-generation
+task_ids:
+- multiple-choice-qa
+- natural-language-inference
+configs:
+- gov_report
+- summ_screen_fd
+- qmsum
+- qasper
+- narrative_qa
+- quality
+- contract_nli
+- squad
+- squad_shuffled_distractors
+- squad_ordered_distractors
+- hotpotqa
+- hotpotqa_second_only
+tags:
+- multi-hop-question-answering
+- query-based-summarization
+- long-texts
+---
+
+## Dataset Description
+- **Repository:** [SLED Github repository](https://github.com/Mivg/SLED)
+- **Paper:** [Efficient Long-Text Understanding with Short-Text Models
+](https://arxiv.org/pdf/2208.00748.pdf)
+
+# Dataset Card for SCROLLS
+
+## Overview
+This dataset is based on the [SCROLLS](https://huggingface.co/datasets/tau/scrolls) dataset ([paper](https://arxiv.org/pdf/2201.03533.pdf)), the [SQuAD 1.1](https://huggingface.co/datasets/squad) dataset and the [HotpotQA](https://huggingface.co/datasets/hotpot_qa) dataset.
+It doesn't contain any unpblished data, but includes the configuration needed for the [Efficient Long-Text Understanding with Short-Text Models
+](https://arxiv.org/pdf/2208.00748.pdf) paper.
+
+## Tasks
+The tasks included are:
+
+#### GovReport ([Huang et al., 2021](https://arxiv.org/pdf/2104.02112.pdf))
+GovReport is a summarization dataset of reports addressing various national policy issues published by the 
+Congressional Research Service and the U.S. Government Accountability Office, where each document is paired with a hand-written executive summary.
+The reports and their summaries are longer than their equivalents in other popular long-document summarization datasets; 
+for example, GovReport's documents are approximately 1.5 and 2.5 times longer than the documents in Arxiv and PubMed, respectively.
+
+#### SummScreenFD ([Chen  et al., 2021](https://arxiv.org/pdf/2104.07091.pdf))
+SummScreenFD is a summarization dataset in the domain of TV shows (e.g. Friends, Game of Thrones).
+Given a transcript of a specific episode, the goal is to produce the episode's recap.
+The original dataset is divided into two complementary subsets, based on the source of its community contributed transcripts. 
+For SCROLLS, we use the ForeverDreaming (FD) subset, as it incorporates 88 different shows, 
+making it a more diverse alternative to the TV MegaSite (TMS) subset, which has only 10 shows. 
+Community-authored recaps for the ForeverDreaming transcripts were collected from English Wikipedia and TVMaze.
+
+#### QMSum ([Zhong et al., 2021](https://arxiv.org/pdf/2104.05938.pdf))
+QMSum is a query-based summarization dataset, consisting of 232 meetings transcripts from multiple domains. 
+The corpus covers academic group meetings at the International Computer Science Institute and their summaries, industrial product meetings for designing a remote control, 
+and committee meetings of the Welsh and Canadian Parliaments, dealing with a variety of public policy issues.
+Annotators were tasked with writing queries about the broad contents of the meetings, as well as specific questions about certain topics or decisions, 
+while ensuring that the relevant text for answering each query spans at least 200 words or 10 turns.
+
+#### NarrativeQA ([Kočiský et al., 2021](https://arxiv.org/pdf/1712.07040.pdf))
+NarrativeQA (Kočiský et al., 2021) is an established question answering dataset over entire books from Project Gutenberg and movie scripts from different websites.
+Annotators were given summaries of the books and scripts obtained from Wikipedia, and asked to generate question-answer pairs, 
+resulting in about 30 questions and answers for each of the 1,567 books and scripts.
+They were encouraged to use their own words rather then copying, and avoid asking yes/no questions or ones about the cast.
+Each question was then answered by an additional annotator, providing each question with two reference answers (unless both answers are identical).
+
+#### Qasper ([Dasigi et al., 2021](https://arxiv.org/pdf/2105.03011.pdf))
+Qasper is a question answering dataset over NLP papers filtered from the Semantic Scholar Open Research Corpus (S2ORC).
+Questions were written by NLP practitioners after reading only the title and abstract of the papers, 
+while another set of NLP practitioners annotated the answers given the entire document.
+Qasper contains abstractive, extractive, and yes/no questions, as well as unanswerable ones.
+
+#### QuALITY ([Pang et al., 2021](https://arxiv.org/pdf/2112.08608.pdf))
+QuALITY is a multiple-choice question answering dataset over articles and stories sourced from Project Gutenberg, 
+the Open American National Corpus, and more.
+Experienced writers wrote questions and distractors, and were incentivized to write answerable, unambiguous questions such that in order to correctly answer them, 
+human annotators must read large portions of the given document. 
+Reference answers were then calculated using the majority vote between of the annotators and writer's answers.
+To measure the difficulty of their questions, Pang et al. conducted a speed validation process, 
+where another set of annotators were asked to answer questions given only a short period of time to skim through the document.
+As a result, 50% of the questions in QuALITY are labeled as hard, i.e. the majority of the annotators in the speed validation setting chose the wrong answer.
+
+#### ContractNLI ([Koreeda and Manning, 2021](https://arxiv.org/pdf/2110.01799.pdf))
+Contract NLI is a natural language inference dataset in the legal domain.
+Given a non-disclosure agreement (the premise), the task is to predict whether a particular legal statement (the hypothesis) is entailed, not entailed (neutral), or cannot be entailed (contradiction) from the contract.
+The NDAs were manually picked after simple filtering from the Electronic Data Gathering, Analysis, and Retrieval system (EDGAR) and Google.
+The dataset contains a total of 607 contracts and 17 unique hypotheses, which were combined to produce the dataset's 10,319 examples.
+
+#### SQuAD 1.1 ([Rajpurkar et al., 2016](https://arxiv.org/pdf/1606.05250.pdf)) 
+Stanford Question Answering Dataset (SQuAD) is a reading comprehension \
+dataset, consisting of questions posed by crowdworkers on a set of Wikipedia \
+articles, where the answer to every question is a segment of text, or span, \
+from the corresponding reading passage, or the question might be unanswerable.
+
+#### HotpotQA ([Yang et al., 2018](https://arxiv.org/pdf/1809.09600.pdf))  
+HotpotQA is a new dataset with 113k Wikipedia-based question-answer pairs with four key features:
+(1) the questions require finding and reasoning over multiple supporting documents to answer;
+(2) the questions are diverse and not constrained to any pre-existing knowledge bases or knowledge schemas;
+(3) we provide sentence-level supporting facts required for reasoning, allowingQA systems to reason with strong supervisionand explain the predictions;
+(4) we offer a new type of factoid comparison questions to testQA systems’ ability to extract relevant facts and perform necessary comparison.
+
+## Data Fields
+
+All the datasets in the benchmark are in the same input-output format
+
+- `input`: a `string` feature. The input document.
+- `input_prefix`: an optional `string` feature, for the datasets containing prefix (e.g. question)
+- `output`: a `string` feature. The target.
+- `id`: a `string` feature. Unique per input.
+- `pid`: a `string` feature. Unique per input-output pair (can differ from 'id' in NarrativeQA and Qasper, where there is more then one valid target).
+
+The dataset that contain `input_prefix` are:
+- SQuAD - the question
+- HotpotQA - the question
+- qmsum - the query
+- qasper  - the question
+- narrative_qa - the question
+- quality - the question + the four choices
+- contract_nli - the hypothesis
+
+## Controlled experiments
+To test multiple properties of SLED, we modify SQuAD 1.1 [Rajpurkar et al., 2016](https://arxiv.org/pdf/1606.05250.pdf) 
+and HotpotQA [Yang et al., 2018](https://arxiv.org/pdf/1809.09600.pdf) to create a few controlled experiments settings.
+Those are accessible via the following configurations:
+- squad - Contains the original version of SQuAD 1.1 (question + passage)
+- squad_ordered_distractors - For each example, 9 random distrctor passages are concatenated (separated by '\n')
+- squad_shuffled_distractors - For each example, 9 random distrctor passages are added (separated by '\n'), and jointly the 10 passages are randomly shuffled
+- hotpotqa - A clean version of HotpotQA, where each input contains only the two gold passages (separated by '\n')
+- hotpotqa_second_only - In each example, the input contains only the second gold passage
+
+## Citation
+If you use this dataset, **please make sure to cite all the original dataset papers as well SCROLLS.** [[bibtex](https://drive.google.com/uc?export=download&id=1IUYIzQD9DPsECw0JWkwk4Ildn8JOMtuU)]
+```
+@inproceedings{Ivgi2022EfficientLU,
+  title={Efficient Long-Text Understanding with Short-Text Models},
+  author={Maor Ivgi and Uri Shaham and Jonathan Berant},
+  year={2022}
+}
+```

huggingface_dataset/Dataset_Card/thennal_indic_tts_ml.md

@@ -0,0 +1,38 @@
+---
+dataset_info:
+  features:
+  - name: audio
+    dtype: audio
+  - name: text
+    dtype: string
+  - name: gender
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 4830182115.4
+    num_examples: 8600
+  download_size: 3966895730
+  dataset_size: 4830182115.4
+annotations_creators: []
+language:
+- ml
+language_creators: []
+license:
+- other
+multilinguality:
+- monolingual
+pretty_name: Indic TTS Malayalam Speech Corpus
+size_categories:
+- 1K<n<10K
+source_datasets: []
+tags: []
+task_categories:
+- text-to-speech
+- automatic-speech-recognition
+task_ids: []
+---
+# Indic TTS Malayalam Speech Corpus
+The Malayalam subset of [Indic TTS Corpus](https://www.iitm.ac.in/donlab/tts/index.php), taken from
+[this Kaggle database.](https://www.kaggle.com/datasets/kavyamanohar/indic-tts-malayalam-speech-corpus) The corpus contains
+one male and one female speaker, with a 2:1 ratio of samples due to missing files for the female speaker. The license is given
+in the repository.

huggingface_dataset/Dataset_Card/thennal_msc.md

@@ -0,0 +1,59 @@
+---
+annotations_creators:
+- crowdsourced
+language:
+- ml
+language_creators:
+- crowdsourced
+license:
+- cc-by-sa-4.0
+multilinguality:
+- monolingual
+pretty_name: Swathanthra Malayalam Computing Malayalam Speech Corpus
+size_categories:
+- 1K<n<10K
+source_datasets: []
+tags: []
+task_categories:
+- automatic-speech-recognition
+task_ids: []
+dataset_info:
+  features:
+  - name: speechid
+    dtype: string
+  - name: speaker_id
+    dtype: string
+  - name: review_score
+    dtype: int64
+  - name: transcript
+    dtype: string
+  - name: category
+    dtype: string
+  - name: speaker_gender
+    dtype: string
+  - name: speaker_age
+    dtype: string
+  - name: audio
+    dtype:
+      audio:
+        sampling_rate: 48000
+  splits:
+  - name: train
+    num_bytes: 581998721.306
+    num_examples: 1541
+  download_size: 422643542
+  dataset_size: 581998721.306
+---
+
+# SMC Malayalam Speech Corpus
+
+Malayalam Speech Corpus (MSC) is a repository of curated speech samples collected using MSC web application, released by Swathanthra Malayalam Computing. 
+The official blog post and source data can be found at [https://blog.smc.org.in/malayalam-speech-corpus/](https://blog.smc.org.in/malayalam-speech-corpus/).
+
+## Dataset Description
+
+- **Homepage:** [https://blog.smc.org.in/malayalam-speech-corpus/](https://blog.smc.org.in/malayalam-speech-corpus/)
+
+### Dataset Summary
+
+The first version of Malayalam Speech Corpus contains 1541 speech samples from 75 contributors amounting to 1:38:16 hours of speech. It has 482 unique sentences, 1400 unique words, 553 unique syllables and 48 unique phonemes.