Upload batch 91 (20 files, last=huggingface_dataset/Dataset_Card/p1atdev_resplash.md)

huggingface_dataset/Dataset_Card/Datatang_Multi-race_Driver_Behavior_Collection_Data.md

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+---
+YAML tags:
+- copy-paste the tags obtained with the tagging app: https://github.com/huggingface/datasets-tagging
+---
+
+# Dataset Card for Datatang/Multi-race_Driver_Behavior_Collection_Data
+
+## Table of Contents
+- [Table of Contents](#table-of-contents)
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
+  - [Languages](#languages)
+- [Dataset Structure](#dataset-structure)
+  - [Data Instances](#data-instances)
+  - [Data Fields](#data-fields)
+  - [Data Splits](#data-splits)
+- [Dataset Creation](#dataset-creation)
+  - [Curation Rationale](#curation-rationale)
+  - [Source Data](#source-data)
+  - [Annotations](#annotations)
+  - [Personal and Sensitive Information](#personal-and-sensitive-information)
+- [Considerations for Using the Data](#considerations-for-using-the-data)
+  - [Social Impact of Dataset](#social-impact-of-dataset)
+  - [Discussion of Biases](#discussion-of-biases)
+  - [Other Known Limitations](#other-known-limitations)
+- [Additional Information](#additional-information)
+  - [Dataset Curators](#dataset-curators)
+  - [Licensing Information](#licensing-information)
+  - [Citation Information](#citation-information)
+  - [Contributions](#contributions)
+
+## Dataset Description
+
+- **Homepage:** https://bit.ly/3xXaLZV
+- **Repository:**
+- **Paper:**
+- **Leaderboard:**
+- **Point of Contact:**
+
+### Dataset Summary
+
+304 People Multi-race - Driver Behavior Collection Data. The data includes multiple ages, multiple time periods and multiple races (Caucasian, Black, Indian). The driver behaviors includes dangerous behavior, fatigue behavior and visual movement behavior. In terms of device, binocular cameras of RGB and infrared channels were applied. This data can be used for tasks such as driver behavior analysis.
+                                                                                                                                  
+For more details, please refer to the link: https://bit.ly/3xXaLZV
+
+### Supported Tasks and Leaderboards
+
+face-detection, computer-vision, object-detection: The dataset can be used to train a model for face detection.
+### Languages
+English
+
+## Dataset Structure
+
+### Data Instances
+
+[More Information Needed]
+
+### Data Fields
+
+[More Information Needed]
+
+### Data Splits
+
+[More Information Needed]
+
+## Dataset Creation
+
+### Curation Rationale
+
+[More Information Needed]
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+[More Information Needed]
+
+#### Who are the source language producers?
+
+[More Information Needed]
+
+### Annotations
+
+#### Annotation process
+
+[More Information Needed]
+
+#### Who are the annotators?
+
+[More Information Needed]
+
+### Personal and Sensitive Information
+
+[More Information Needed]
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+[More Information Needed]
+
+### Discussion of Biases
+
+[More Information Needed]
+
+### Other Known Limitations
+
+[More Information Needed]
+
+## Additional Information
+
+### Dataset Curators
+
+[More Information Needed]
+
+### Licensing Information
+
+Commerical License: https://drive.google.com/file/d/1saDCPm74D4UWfBL17VbkTsZLGfpOQj1J/view?usp=sharing  
+
+### Citation Information
+
+[More Information Needed]
+
+### Contributions

huggingface_dataset/Dataset_Card/Fece228_latin-literature-dataset-170M.md

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+---
+language:
+- la
+tags:
+- text
+- linguistics
+- NLP
+- Latin
+- literature
+size_categories:
+- 100M<n<1B
+---
+This is a dataset collected from all the texts available at Corpus Corporum, which includes probably all the literary works ever written in Latin. The dataset is split in two parts: preprocessed with basic cltk tools, ready for work, and raw text data. It must be noted, however, that the latter contains text in Greek, Hebrew, and other languages, with references and contractions

huggingface_dataset/Dataset_Card/IlyaGusev_yandex_q_full.md

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+---
+dataset_info:
+  features:
+  - name: id
+    dtype: string
+  - name: id2
+    dtype: int64
+  - name: title
+    dtype: string
+  - name: text_plain
+    dtype: string
+  - name: text_html
+    dtype: string
+  - name: author
+    dtype: string
+  - name: negative_votes
+    dtype: int32
+  - name: positive_votes
+    dtype: int32
+  - name: quality
+    dtype: int8
+  - name: views
+    dtype: uint64
+  - name: votes
+    dtype: int32
+  - name: approved_answer
+    dtype: string
+  - name: timestamp
+    dtype: uint64
+  - name: tags
+    sequence: string
+  - name: answers
+    sequence:
+    - name: id
+      dtype: string
+    - name: id2
+      dtype: int64
+    - name: text_plain
+      dtype: string
+    - name: text_html
+      dtype: string
+    - name: author
+      dtype: string
+    - name: negative_votes
+      dtype: int32
+    - name: positive_votes
+      dtype: int32
+    - name: votes
+      dtype: int32
+    - name: quality
+      dtype: int8
+    - name: views
+      dtype: uint64
+    - name: reposts
+      dtype: int32
+    - name: timestamp
+      dtype: uint64
+  splits:
+  - name: train
+    num_bytes: 5468460217
+    num_examples: 1297670
+  download_size: 1130317937
+  dataset_size: 5468460217
+---
+
+Based on https://huggingface.co/datasets/its5Q/yandex-q, parsed full.jsonl.gz

huggingface_dataset/Dataset_Card/autoevaluate_autoeval-staging-eval-launch__gov_report-plain_text-7b7f8a-16126221.md

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+---
+type: predictions
+tags:
+- autotrain
+- evaluation
+datasets:
+- launch/gov_report
+eval_info:
+  task: summarization
+  model: google/bigbird-pegasus-large-pubmed
+  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: google/bigbird-pegasus-large-pubmed
+* 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/autoevaluate_autoeval-staging-eval-launch__gov_report-plain_text-cd8e90-16116210.md

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+---
+type: predictions
+tags:
+- autotrain
+- evaluation
+datasets:
+- launch/gov_report
+eval_info:
+  task: summarization
+  model: Blaise-g/longt5_tglobal_large_sumpubmed
+  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: Blaise-g/longt5_tglobal_large_sumpubmed
+* 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/autoevaluate_autoeval-staging-eval-project-5480d71b-7995089.md

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+---
+type: predictions
+tags:
+- autotrain
+- evaluation
+datasets:
+- cifar10
+eval_info:
+  task: image_multi_class_classification
+  model: tanlq/vit-base-patch16-224-in21k-finetuned-cifar10
+  metrics: []
+  dataset_name: cifar10
+  dataset_config: plain_text
+  dataset_split: test
+  col_mapping:
+    image: img
+    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: Multi-class Image Classification
+* Model: tanlq/vit-base-patch16-224-in21k-finetuned-cifar10
+* Dataset: cifar10
+
+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/clarin-pl_aspectemo.md

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+---
+annotations_creators: 
+- expert-generated 
+language_creators: 
+- other
+language: 
+- pl
+license: 
+- mit
+multilinguality: 
+- monolingual
+pretty_name: 'AspectEmo'
+size_categories: 
+- 1K
+- 1K<n<10K
+source_datasets: 
+- original
+task_categories: 
+- token-classification
+task_ids: 
+- sentiment-classification
+---
+
+# AspectEmo
+
+## Description
+
+AspectEmo Corpus is an extended version of a publicly available PolEmo 2.0 corpus of Polish customer reviews used in many projects on the use of different methods in sentiment analysis. The AspectEmo corpus consists of four subcorpora, each containing online customer reviews from the following domains: school, medicine, hotels, and products. All documents are annotated at the aspect level with six sentiment categories: strong negative (minus_m), weak negative (minus_s), neutral (zero), weak positive (plus_s), strong positive (plus_m).
+## Versions
+
+| version | config name | description                    | default | notes            |
+|---------|-------------|--------------------------------|---------|------------------|
+| 1.0     | "1.0"       | The version used in the paper. | YES     |                  |
+| 2.0     | -           | Some bugs fixed.               | NO      | work in progress |
+
+## Tasks (input, output and metrics)
+
+Aspect-based sentiment analysis (ABSA) is a text analysis method that categorizes data by  aspects and identifies the sentiment assigned to each aspect. It is the sequence tagging task. 
+
+**Input** ('*tokens'* column): sequence of tokens
+
+**Output** ('*labels'* column): sequence of predicted tokens’ classes ("O" + 6 possible classes:  strong negative (a_minus_m), weak negative (a_minus_s), neutral (a_zero), weak positive (a_plus_s), strong positive (a_plus_m),  ambiguous (a_amb) )
+
+**Domain**: school, medicine, hotels and products
+
+**Measurements**: F1-score (seqeval)
+
+**Example***:* 
+
+Input: `['Dużo', 'wymaga', ',', 'ale', 'bardzo', 'uczciwy', 'i', 'przyjazny', 'studentom', '.', 'Warto', 'chodzić', 'na', 'konsultacje', '.', 'Docenia', 'postępy', 'i', 'zaangażowanie', '.', 'Polecam', '.']`
+
+Input (translated by DeepL): `'Demands a lot , but very honest and student friendly . Worth going to consultations . Appreciates progress and commitment . I recommend .'`
+
+Output: `['O', 'a_plus_s', 'O', 'O', 'O', 'a_plus_m', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'a_zero', 'O', 'a_plus_m', 'O', 'O', 'O', 'O', 'O', 'O']`
+
+## Data splits
+
+| Subset | Cardinality (sentences) |
+|:-------|------------------------:|
+| train  |                    1173 |
+| val    |                       0 |
+| test   |                     292 |
+
+## Class distribution(without "O") 
+
+| Class     |   train |   validation |   test |
+|:----------|--------:|-------------:|-------:|
+| a_plus_m  |   0.359 |            - |  0.369 |
+| a_minus_m |   0.305 |            - |  0.377 |
+| a_zero    |   0.234 |            - |  0.182 |
+| a_minus_s |   0.037 |            - |  0.024 |
+| a_plus_s  |   0.037 |            - |  0.015 |
+| a_amb     |   0.027 |            - |  0.033 |
+
+## Citation
+
+```
+@misc{11321/849,	
+ title = {{AspectEmo} 1.0: Multi-Domain Corpus of Consumer Reviews for Aspect-Based Sentiment Analysis},	
+ author = {Koco{\'n}, Jan and Radom, Jarema and Kaczmarz-Wawryk, Ewa and Wabnic, Kamil and Zaj{\c a}czkowska, Ada and Za{\'s}ko-Zieli{\'n}ska, Monika},	
+ url = {http://hdl.handle.net/11321/849},	
+ note = {{CLARIN}-{PL} digital repository},	
+ copyright = {The {MIT} License},	
+ year = {2021}	
+}
+```
+
+## License
+
+```
+The MIT License
+```
+
+## Links
+
+[HuggingFace](https://huggingface.co/datasets/clarin-pl/aspectemo)
+
+[Source](https://clarin-pl.eu/dspace/handle/11321/849)
+
+[Paper](https://sentic.net/sentire2021kocon.pdf)
+
+## Examples
+
+### Loading
+
+```python
+from pprint import pprint
+
+from datasets import load_dataset
+
+dataset = load_dataset("clarin-pl/aspectemo")
+pprint(dataset['train'][20])
+
+# {'labels': [0, 4, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 3, 0, 5, 0, 0, 0, 0, 0, 0],
+#  'tokens': ['Dużo',
+#             'wymaga',
+#             ',',
+#             'ale',
+#             'bardzo',
+#             'uczciwy',
+#             'i',
+#             'przyjazny',
+#             'studentom',
+#             '.',
+#             'Warto',
+#             'chodzić',
+#             'na',
+#             'konsultacje',
+#             '.',
+#             'Docenia',
+#             'postępy',
+#             'i',
+#             'zaangażowanie',
+#             '.',
+#             'Polecam',
+#             '.']}
+```
+
+### Evaluation
+
+```python
+import random
+from pprint import pprint
+
+from datasets import load_dataset, load_metric
+
+dataset = load_dataset("clarin-pl/aspectemo")
+references = dataset["test"]["labels"]
+
+# generate random predictions
+predictions = [
+    [
+        random.randrange(dataset["train"].features["labels"].feature.num_classes)
+        for _ in range(len(labels))
+    ]
+    for labels in references
+]
+
+# transform to original names of labels
+references_named = [
+    [dataset["train"].features["labels"].feature.names[label] for label in labels]
+    for labels in references
+]
+predictions_named = [
+    [dataset["train"].features["labels"].feature.names[label] for label in labels]
+    for labels in predictions
+]
+
+# transform to BILOU scheme
+references_named = [
+    [f"U-{label}" if label != "O" else label for label in labels]
+    for labels in references_named
+]
+predictions_named = [
+    [f"U-{label}" if label != "O" else label for label in labels]
+    for labels in predictions_named
+]
+
+# utilise seqeval to evaluate
+seqeval = load_metric("seqeval")
+seqeval_score = seqeval.compute(
+    predictions=predictions_named,
+    references=references_named,
+    scheme="BILOU",
+    mode="strict",
+)
+
+pprint(seqeval_score)
+
+# {'a_amb': {'f1': 0.00597237775289287,
+#            'number': 91,
+#            'precision': 0.003037782418834251,
+#            'recall': 0.17582417582417584},
+#  'a_minus_m': {'f1': 0.048306148055207034,
+#                'number': 1039,
+#                'precision': 0.0288551620760727,
+#                'recall': 0.1482194417709336},
+#  'a_minus_s': {'f1': 0.004682997118155619,
+#                'number': 67,
+#                'precision': 0.0023701002734731083,
+#                'recall': 0.19402985074626866},
+#  'a_plus_m': {'f1': 0.045933014354066985,
+#               'number': 1015,
+#               'precision': 0.027402473834443386,
+#               'recall': 0.14187192118226602},
+#  'a_plus_s': {'f1': 0.0021750951604132683,
+#               'number': 41,
+#               'precision': 0.001095690284879474,
+#               'recall': 0.14634146341463414},
+#  'a_zero': {'f1': 0.025159400310184387,
+#             'number': 501,
+#             'precision': 0.013768389287061486,
+#             'recall': 0.14570858283433133},
+#  'overall_accuracy': 0.13970115681233933,
+#  'overall_f1': 0.02328248652368391,
+#  'overall_precision': 0.012639312620633834,
+#  'overall_recall': 0.14742193173565724}
+```

huggingface_dataset/Dataset_Card/codeparrot_xlcost-text-to-code.md

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+---
+annotations_creators: []
+language_creators:
+- crowdsourced
+- expert-generated
+language:
+- code
+license:
+- cc-by-sa-4.0
+multilinguality:
+- multilingual
+size_categories:
+- unknown
+source_datasets: []
+task_categories:
+- text-generation
+task_ids:
+- language-modeling
+pretty_name: xlcost-text-to-code
+---
+
+# XLCost for text-to-code synthesis
+
+## Dataset Description
+This is a subset of [XLCoST benchmark](https://github.com/reddy-lab-code-research/XLCoST), for text-to-code generation at snippet level and program level for **7** programming languages: `Python, C, C#, C++, Java, Javascript and PHP`. 
+
+## Languages
+
+The dataset contains text in English and its corresponding code translation. Each program is divided into several code snippets, so the snipppet-level subsets contain these code snippets with their corresponding comments, for program-level subsets, the comments were concatenated in one long description. Moreover, programs in all the languages are aligned at the snippet level and the comment for a particular snippet is the same across all the languages. 
+
+## Dataset Structure
+To load the dataset you need to specify a subset among the **14 exiting instances**: `LANGUAGE-snippet-level/LANGUAGE-program-level` for `LANGUAGE` in `[Python, C, Csharp, C++, Java, Javascript and PHP]`. By default `Python-snippet-level` is loaded. 
+
+```python
+from datasets import load_dataset
+load_dataset("codeparrot/xlcost-text-to-code", "Python-program-level")
+
+DatasetDict({
+    train: Dataset({
+        features: ['text', 'code'],
+        num_rows: 9263
+    })
+    test: Dataset({
+        features: ['text', 'code'],
+        num_rows: 887
+    })
+    validation: Dataset({
+        features: ['text', 'code'],
+        num_rows: 472
+    })
+})
+```
+
+```python
+next(iter(data["train"]))
+{'text': 'Maximum Prefix Sum possible by merging two given arrays | Python3 implementation of the above approach ; Stores the maximum prefix sum of the array A [ ] ; Traverse the array A [ ] ; Stores the maximum prefix sum of the array B [ ] ; Traverse the array B [ ] ; Driver code',
+ 'code': 'def maxPresum ( a , b ) : NEW_LINE INDENT X = max ( a [ 0 ] , 0 ) NEW_LINE for i in range ( 1 , len ( a ) ) : NEW_LINE INDENT a [ i ] += a [ i - 1 ] NEW_LINE X = max ( X , a [ i ] ) NEW_LINE DEDENT Y = max ( b [ 0 ] , 0 ) NEW_LINE for i in range ( 1 , len ( b ) ) : NEW_LINE INDENT b [ i ] += b [ i - 1 ] NEW_LINE Y = max ( Y , b [ i ] ) NEW_LINE DEDENT return X + Y NEW_LINE DEDENT A = [ 2 , - 1 , 4 , - 5 ] NEW_LINE B = [ 4 , - 3 , 12 , 4 , - 3 ] NEW_LINE print ( maxPresum ( A , B ) ) NEW_LINE'}
+```
+Note that the data undergo some tokenization hence the additional whitespaces and the use of NEW_LINE instead of `\n` and INDENT instead of `\t`, DEDENT to cancel indentation...
+
+## Data Fields
+
+* text: natural language description/comment
+* code: code at snippet/program level
+
+## Data Splits
+
+Each subset has three splits: train, test and validation.
+
+## Citation Information
+
+```
+@misc{zhu2022xlcost,
+     title = {XLCoST: A Benchmark Dataset for Cross-lingual Code Intelligence},
+     url = {https://arxiv.org/abs/2206.08474},
+     author = {Zhu, Ming and Jain, Aneesh and Suresh, Karthik and Ravindran, Roshan and Tipirneni, Sindhu and Reddy, Chandan K.},
+     year = {2022},
+     eprint={2206.08474},
+     archivePrefix={arXiv}
+}
+```

huggingface_dataset/Dataset_Card/ficsort_SzegedNER.md

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+---
+annotations_creators:
+- expert-generated
+language:
+- hu
+language_creators:
+- other
+license: []
+multilinguality:
+- monolingual
+paperswithcode_id: null
+pretty_name: SzegedNER
+size_categories:
+- 1K<n<10K
+source_datasets:
+- original
+tags:
+- hungarian
+- szeged
+- ner
+task_categories:
+- token-classification
+task_ids:
+- named-entity-recognition
+---
+
+# Introduction
+
+The recognition and classification of proper nouns and names in plain text is of key importance in Natural Language Processing (NLP) as it has a beneficial effect on the performance of various types of applications, including Information Extraction, Machine Translation, Syntactic Parsing/Chunking, etc.
+
+## Corpus of Business Newswire Texts (business)
+
+The Named Entity Corpus for Hungarian is a subcorpus of the Szeged Treebank, which contains full syntactic annotations done manually by linguist experts. A significant part of these texts has been annotated with Named Entity class labels in line with the annotation standards used on the CoNLL-2003 shared task.
+
+Statistical data on Named Entities occurring in the corpus:
+
+```
+       | tokens | phrases
+------ | ------ | -------
+non NE | 200067 | 	 
+PER    | 1921   | 982
+ORG    | 20433  | 10533
+LOC    | 1501   | 1294
+MISC   | 2041   | 1662
+```
+
+### Reference
+
+> György Szarvas, Richárd Farkas, László Felföldi, András Kocsor, János Csirik: Highly accurate Named Entity corpus for Hungarian. International Conference on Language Resources and Evaluation 2006, Genova (Italy)
+
+## Criminal NE corpus (criminal)
+
+The Hungarian National Corpus and its Heti Világgazdaság (HVG) subcorpus provided the basis for corpus text selection: articles related to the topic of financially liable offences were selected and annotated for the categories person, organization, location and miscellaneous.
+There are two annotated versions of the corpus. When preparing the tag-for-meaning annotation, our linguists took into consideration the context in which the Named Entity under investigation occurred, thus, it was not the primary sense of the Named Entity that determined the tag (e.g. Manchester=LOC) but its contextual reference (e.g. Manchester won the Premier League=ORG). As for tag-for-tag annotation, these cases were not differentiated: tags were always given on the basis of the primary sense.
+
+Statistical data on Named Entities occurring in the corpus:
+
+```
+       | tag-for-meaning | tag-for-tag
+------ | --------------- | -----------
+non NE | 200067          | 	 
+PER    | 8101            | 8121
+ORG    | 8782            | 9480
+LOC    | 5049            | 5391
+MISC   | 1917            | 854
+```
+
+## Metadata
+
+dataset_info:
+- config_name: business
+  features:
+  - name: id
+    dtype: string
+  - name: tokens
+    sequence: string
+  - name: ner_tags
+    sequence:
+      class_label:
+        names:
+          0: O
+          1: B-PER
+          2: I-PER
+          3: B-ORG
+          4: I-ORG
+          5: B-LOC
+          6: I-LOC
+          7: B-MISC
+          8: I-MISC
+  - name: document_id
+    dtype: string
+  - name: sentence_id
+    dtype: string
+  splits:
+  - name: original
+    num_bytes: 4452207
+    num_examples: 9573
+  - name: test
+    num_bytes: 856798
+    num_examples: 1915
+  - name: train
+    num_bytes: 3171931
+    num_examples: 6701
+  - name: validation
+    num_bytes: 423478
+    num_examples: 957
+  download_size: 0
+  dataset_size: 8904414
+- config_name: criminal
+  features:
+  - name: id
+    dtype: string
+  - name: tokens
+    sequence: string
+  - name: ner_tags
+    sequence:
+      class_label:
+        names:
+          0: O
+          1: B-PER
+          2: I-PER
+          3: B-ORG
+          4: I-ORG
+          5: B-LOC
+          6: I-LOC
+          7: B-MISC
+          8: I-MISC
+  - name: document_id
+    dtype: string
+  - name: sentence_id
+    dtype: string
+  splits:
+  - name: original
+    num_bytes: 2807970
+    num_examples: 5375
+  - name: test
+    num_bytes: 520959
+    num_examples: 1089
+  - name: train
+    num_bytes: 1989662
+    num_examples: 3760
+  - name: validation
+    num_bytes: 297349
+    num_examples: 526
+  download_size: 0
+  dataset_size: 5615940
+

huggingface_dataset/Dataset_Card/irds_mr-tydi_id.md

@@ -0,0 +1,62 @@
+---
+pretty_name: '`mr-tydi/id`'
+viewer: false
+source_datasets: []
+task_categories:
+- text-retrieval
+---
+
+# Dataset Card for `mr-tydi/id`
+
+The `mr-tydi/id` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
+For more information about the dataset, see the [documentation](https://ir-datasets.com/mr-tydi#mr-tydi/id).
+
+# Data
+
+This dataset provides:
+ - `docs` (documents, i.e., the corpus); count=1,469,399
+ - `queries` (i.e., topics); count=6,977
+ - `qrels`: (relevance assessments); count=7,087
+
+
+This dataset is used by: [`mr-tydi_id_dev`](https://huggingface.co/datasets/irds/mr-tydi_id_dev), [`mr-tydi_id_test`](https://huggingface.co/datasets/irds/mr-tydi_id_test), [`mr-tydi_id_train`](https://huggingface.co/datasets/irds/mr-tydi_id_train)
+
+
+## Usage
+
+```python
+from datasets import load_dataset
+
+docs = load_dataset('irds/mr-tydi_id', 'docs')
+for record in docs:
+    record # {'doc_id': ..., 'text': ...}
+
+queries = load_dataset('irds/mr-tydi_id', 'queries')
+for record in queries:
+    record # {'query_id': ..., 'text': ...}
+
+qrels = load_dataset('irds/mr-tydi_id', '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{Zhang2021MrTyDi,
+  title={{Mr. TyDi}: A Multi-lingual Benchmark for Dense Retrieval}, 
+  author={Xinyu Zhang and Xueguang Ma and Peng Shi and Jimmy Lin},
+  year={2021},
+  journal={arXiv:2108.08787},
+}
+@article{Clark2020TyDiQa,
+    title={{TyDi QA}: A Benchmark for Information-Seeking Question Answering in Typologically Diverse Languages},
+    author={Jonathan H. Clark and Eunsol Choi and Michael Collins and Dan Garrette and Tom Kwiatkowski and Vitaly Nikolaev and Jennimaria Palomaki},
+    year={2020},
+    journal={Transactions of the Association for Computational Linguistics}
+}
+```

huggingface_dataset/Dataset_Card/irds_mr-tydi_th.md

@@ -0,0 +1,62 @@
+---
+pretty_name: '`mr-tydi/th`'
+viewer: false
+source_datasets: []
+task_categories:
+- text-retrieval
+---
+
+# Dataset Card for `mr-tydi/th`
+
+The `mr-tydi/th` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
+For more information about the dataset, see the [documentation](https://ir-datasets.com/mr-tydi#mr-tydi/th).
+
+# Data
+
+This dataset provides:
+ - `docs` (documents, i.e., the corpus); count=568,855
+ - `queries` (i.e., topics); count=5,322
+ - `qrels`: (relevance assessments); count=5,545
+
+
+This dataset is used by: [`mr-tydi_th_dev`](https://huggingface.co/datasets/irds/mr-tydi_th_dev), [`mr-tydi_th_test`](https://huggingface.co/datasets/irds/mr-tydi_th_test), [`mr-tydi_th_train`](https://huggingface.co/datasets/irds/mr-tydi_th_train)
+
+
+## Usage
+
+```python
+from datasets import load_dataset
+
+docs = load_dataset('irds/mr-tydi_th', 'docs')
+for record in docs:
+    record # {'doc_id': ..., 'text': ...}
+
+queries = load_dataset('irds/mr-tydi_th', 'queries')
+for record in queries:
+    record # {'query_id': ..., 'text': ...}
+
+qrels = load_dataset('irds/mr-tydi_th', '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{Zhang2021MrTyDi,
+  title={{Mr. TyDi}: A Multi-lingual Benchmark for Dense Retrieval}, 
+  author={Xinyu Zhang and Xueguang Ma and Peng Shi and Jimmy Lin},
+  year={2021},
+  journal={arXiv:2108.08787},
+}
+@article{Clark2020TyDiQa,
+    title={{TyDi QA}: A Benchmark for Information-Seeking Question Answering in Typologically Diverse Languages},
+    author={Jonathan H. Clark and Eunsol Choi and Michael Collins and Dan Garrette and Tom Kwiatkowski and Vitaly Nikolaev and Jennimaria Palomaki},
+    year={2020},
+    journal={Transactions of the Association for Computational Linguistics}
+}
+```

huggingface_dataset/Dataset_Card/morteza_cogtext.md

@@ -0,0 +1,181 @@
+---
+pretty_name: CogText PubMed Abstracts
+license:
+- cc-by-4.0
+language:
+- en
+multilinguality:
+- monolingual
+task_categories:
+- text-classification
+task_ids:
+- topic-classification
+- semantic-similarity-classification
+size_categories:
+- 100K<n<1M
+paperswithcode_id: linking-theories-and-methods-in-cognitive
+inference: false
+model-index:
+- name: cogtext-pubmed
+  results: []
+source_datasets:
+- original
+language_creators:
+- found
+- expert-generated
+configs:
+- pubmed
+- pubmed20pct
+- lexicon
+- pubmed_gp3ada
+tags:
+- Cognitive Control
+- PubMed
+---
+
+# Dataset Card for CogText PubMed Abstracts
+
+## 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
+
+**CogText** dataset contains a collection of PubMed abstracts, along with their GPT-3 embeddings and topic embeddings. See [CogText on GitHub](https://github.com/morteza/cogtext) for the details and codes.
+
+- **Homepage:** https://github.com/morteza/cogtext
+- **Repository:** https://github.com/morteza/cogtext
+- **Point of Contact:** [Morteza Ansarinia](mailto:ansarinia@me.com)
+- **Paper:** https://arxiv.org/abs/2203.11016
+
+### Dataset Summary
+
+The dataset consists of 385,705 unique scientific articles that were retrieved from PubMed in December 2021. Each item includes title, abstract, some metadata,
+and embeddings generated by both GPT-3 and Top2Vec. These texts were selected based on their relevance to the cognitive control constructs or related tasks.
+
+
+### Supported Tasks and Leaderboards
+
+Topic Modeling, Text Embedding
+
+### Languages
+
+English
+
+## Dataset Structure
+
+### Data Instances
+
+522,972 scientific articles, of which 385,705 are unique.
+
+### Data Fields
+
+The CSV files contain the following fields:
+
+| Field | Description |
+| ----- | ----------- |
+| `index` | (int) Index of the article in the current dataset |
+| `pmid` | (int) PubMed ID |
+| `doi` | (str) Digital Object Identifier |
+| `year` | (int) Year of publication (yyyy format)|
+| `journal_title` | (str) Title of the journal |
+| `journal_iso_abbreviation` | (str) ISO abbreviation of the journal |
+| `title` | (str) Title of the article |
+| `abstract` | (str) Abstract of the article |
+| `category` | (enum) Category of the article, either "CognitiveTask" or "CognitiveConstruct" |
+| `label` | (enum) Label of the article, which refers to the class labels in the `ontologies/efo.owl` ontology |
+| `original_index` | (int) Index of the article in the full dataset (see `pubmed/abstracts.csv.gz`) |
+
+
+### Data Splits
+
+| Dataset | Description |
+| ------- | ----------- |
+| `pubmed/abstracts.csv.gz` | Full dataset |
+| `pubmed/abstracts20pct.csv.gz` | 20% of the dataset (stratified random sample by `label`) |
+| `gpt3/abstracts_gp3ada.nc` | GPT-3 embeddings of the entire dataset in XArray/CDF4 format, indexed by `pmid` |
+
+## Dataset Creation
+
+### Curation Rationale
+
+[Needs More Information]
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+[Needs More Information]
+
+### Annotations
+
+#### Annotation process
+
+[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]
+
+### Acknowledgments
+
+This research was supported by the Luxembourg National Research Fund (ATTRACT/2016/ID/11242114/DIGILEARN and INTER Mobility/2017-2/ID/11765868/ULALA).
+
+
+### Citation Information
+
+To cite the paper use the following entry:
+
+```
+@misc{cogtext2022,
+  author = {Morteza Ansarinia and
+            Paul Schrater and
+            Pedro Cardoso-Leite},
+  title = {Linking Theories and Methods in Cognitive Sciences via Joint Embedding of the Scientific Literature: The Example of Cognitive Control},
+  year = {2022},
+  url = {https://arxiv.org/abs/2203.11016}
+}
+```

huggingface_dataset/Dataset_Card/mrm8488_unnatural-instructions-core.md

@@ -0,0 +1,53 @@
+---
+dataset_info:
+  features:
+  - name: instruction
+    dtype: string
+  - name: instances
+    list:
+    - name: instruction_with_input
+      dtype: string
+    - name: input
+      dtype: string
+    - name: constraints
+      dtype: string
+    - name: output
+      dtype: string
+  splits:
+  - name: train
+    num_bytes: 54668900
+    num_examples: 66010
+  download_size: 28584196
+  dataset_size: 54668900
+---
+# Dataset Card for Unnatural Instructions (Core data)
+This info comes from the **Unnatural Instructions GitHub [repo](https://github.com/orhonovich/unnatural-instructions/)**.
+
+Unnatural Instructions is a dataset of instructions automatically generated by a Large Language model.
+See full details in the paper: "[Unnatural Instructions: Tuning Language Models with (Almost) No Human Labor](https://arxiv.org/abs/2212.09689)"
+
+## 🗃️ Content
+The Unnatural Instructions core dataset of 68,478 instruction-input-output triplets.
+
+## 📄 Format
+### Core data
+Each example contains:
+- `input`: An input for the task described by the `instruction`
+- `instruction_with_input`: The instruction concatenated with the `input`
+- `constraints`: The task's output space constraints
+- `output`: The output of executing `instruction` with the given `input`
+
+
+## 📘 Citation
+If you make use of Unnatural Instructions, please cite the following paper:
+```
+@misc{honovich2022unnatural,
+      title = {Unnatural Instructions: Tuning Language Models with (Almost) No Human Labor},
+      author = {Honovich, Or and Scialom, Thomas and Levy, Omer and Schick, Timo},
+      url = {https://arxiv.org/abs/2212.09689},
+      publisher = {arXiv},
+      year={2022}
+}
+```
+
+[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

huggingface_dataset/Dataset_Card/mwong_climate-claim-related.md

@@ -0,0 +1,27 @@
+---
+annotations_creators:
+- crowdsourced
+language_creators:
+- crowdsourced
+language:
+- en
+license:
+- cc-by-sa-3.0
+- gpl-3.0
+multilinguality:
+- monolingual
+paperswithcode_id: climate-fever
+pretty_name: climate-fever
+size_categories:
+- 100K<n<1M
+source_datasets:
+- extended|climate_fever
+task_categories:
+- text-classification
+task_ids:
+- fact-checking
+---
+
+### Dataset Summary
+This dataset is extracted from Climate Fever dataset (https://www.sustainablefinance.uzh.ch/en/research/climate-fever.html), pre-processed and, ready to train and evaluate.
+The training objective is a text classification task - given a claim and evidence, predict if claim is related to evidence.

huggingface_dataset/Dataset_Card/mwong_climatetext-climate_evidence-claim-related-evaluation.md

@@ -0,0 +1,25 @@
+---
+annotations_creators:
+- crowdsourced
+language_creators:
+- crowdsourced
+language:
+- en
+license:
+- cc-by-sa-3.0
+- gpl-3.0
+multilinguality:
+- monolingual
+size_categories:
+- 100K<n<1M
+source_datasets:
+- extended|climate_text
+task_categories:
+- text-classification
+task_ids:
+- fact-checking
+---
+
+### Dataset Summary
+This dataset is extracted from Climate Text dataset (https://www.sustainablefinance.uzh.ch/en/research/climate-fever/climatext.html), pre-processed and, ready to evaluate.
+The evaluation objective is a text classification task - given a claim and climate related evidence, predict if claim is related to evidence.

huggingface_dataset/Dataset_Card/mwritescode_slither-audited-smart-contracts.md

@@ -0,0 +1,138 @@
+---
+annotations_creators:
+- other
+language_creators:
+- found
+language:
+- en
+license:
+- mit
+multilinguality:
+- monolingual
+pretty_name: Slither Audited Smart Contracts
+size_categories:
+- 100K<n<1M
+source_datasets:
+- original
+task_categories:
+- text-classification
+- text-generation
+task_ids:
+- multi-label-classification
+- multi-input-text-classification
+- language-modeling
+---
+
+# Dataset Card for Slither Audited Smart Contracts
+
+## 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)
+- [Additional Information](#additional-information)
+  - [Dataset Curators](#dataset-curators)
+  - [Licensing Information](#licensing-information)
+  - [Citation Information](#citation-information)
+
+## Dataset Description
+
+- **Homepage:** https://github.com/mwritescode/slither-audited-smart-contracts
+- **Repository:** https://github.com/mwritescode/slither-audited-smart-contracts
+- **Point of Contact:** [Martina Rossini](mailto:martina.rossini704@gmail.com)
+
+### Dataset Summary
+
+This dataset contains source code and deployed bytecode for Solidity Smart Contracts that have been verified on Etherscan.io, along with a classification of their vulnerabilities according to the Slither static analysis framework.
+
+### Supported Tasks and Leaderboards
+
+- `text-classification`: The dataset can be used to train a model for both binary and multilabel text classification on smart contracts bytecode and source code. The model performance is evaluated based on the accuracy of the predicted labels as compared to the given labels in the dataset.
+- `text-generation`: The dataset can also be used to train a language model for the Solidity programming language
+- `image-classification`: By pre-processing the bytecode data to obtain RGB images, the dataset can also be used to train convolutional neural networks for code vulnerability detection and classification.
+
+### Languages
+
+The language annotations are in English, while all the source codes are in Solidity.
+
+## Dataset Structure
+
+### Data Instances
+
+Each data instance contains the following features: `address`, `source_code` and `bytecode`. The label comes in two configuration, either a plain-text cleaned up version of the output given by the Slither tool or a multi-label version, which consists in a simple list of integers, each one representing a particular vulnerability class. Label 4 indicates that the contract is safe.
+
+An example from a plain-text configuration looks as follows:
+```
+{
+'address': '0x006699d34AA3013605d468d2755A2Fe59A16B12B'
+'source_code': 'pragma solidity 0.5.4; interface IERC20 { function balanceOf(address account) external ...'
+'bytecode': '0x608060405234801561001057600080fd5b5060043610610202576000357c0100000000000000000000000000000000000000000000000000000000900...'
+'slither': '{"success": true, "error": null, "results": {"detectors": [{"check": "divide-before-multiply", "impact": "Medium", "confidence": "Medium"}]}}'
+}
+```
+
+An example from a multi-label configuration looks as follows:
+```
+{
+'address': '0x006699d34AA3013605d468d2755A2Fe59A16B12B'
+'source_code': 'pragma solidity 0.5.4; interface IERC20 { function balanceOf(address account) external ...'
+'bytecode': '0x608060405234801561001057600080fd5b5060043610610202576000357c0100000000000000000000000000000000000000000000000000000000900...'
+'slither': [ 4 ]
+}
+```
+
+### Data Fields
+
+- `address`: a string representing the address of the smart contract deployed on the Ethereum main net
+- `source_code`: a flattened version of the smart contract codebase in Solidity
+- `bytecode`: a string representing the smart contract's bytecode, obtained when calling `web3.eth.getCode()`. Note that in some cases where this was not available, the string is simply '0x'.
+- `slither`: either a cleaned up version of Slither's JSON output or a list of class labels
+
+### Data Splits
+
+The dataset comes in 6 configurations and train, test and validation splits are only provided for those configurations that do not include `all-` in their names. Test and Validation splits are both about 15% of the total.
+
+## Dataset Creation
+
+### Curation Rationale
+
+slither-audited-smart-contracts was built to provide a freely available large scale dataset for vulnerability detection and classification on verified Solidity smart contracts. Indeed, the biggest open source dataset for this task at the moment of writing is [SmartBugs Wild](https://github.com/smartbugs/smartbugs-wild), containing 47,398 smart contracts that were labeled with 9 tools withing the SmartBugs framework. 
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+The dataset was constructed started from the list of verified smart contracts provided at [Smart Contract Sanctuary](https://github.com/tintinweb/smart-contract-sanctuary-ethereum). Then, smart contract source code was either downloaded from the aforementioned repo or downloaded via [Etherscan](https://etherscan.io/apis) and flattened using the Slither contract flattener. The bytecode was downloaded using the Web3.py library, in particular the `web3.eth.getCode()` function and using [INFURA](https://infura.io/) as our endpoint.
+Finally, every smart contract was analyzed using the [Slither](https://github.com/crytic/slither) static analysis framework. The tool found 38 different vulnerability classes in the collected contracts and they were then mapped to 9 labels according to what is shown in the file `label_mappings.json`. These mappings were derived by following the guidelines at [Decentralized Application Security Project (DASP)](https://www.dasp.co/) and at [Smart Contract Weakness Classification Registry](https://swcregistry.io/). They were also inspired by the mappings used for Slither's detection by the team that labeled the SmartBugs Wild dataset, which can be found [here](https://github.com/smartbugs/smartbugs-results/blob/master/metadata/vulnerabilities_mapping.cs).
+
+## Additional Information
+
+### Dataset Curators
+
+The dataset was initially created by Martina Rossini during work done for the project of the course Blockchain and Cryptocurrencies of the University of Bologna (Italy). 
+
+### Licensing Information
+
+The license in the file LICENSE applies to all the files in this repository, except for the Solidity source code of the contracts. These are still publicly available, were obtained using the Etherscan APIs, and retain their original licenses.
+
+### Citation Information
+
+If you are using this dataset in your research and paper, here's how you can cite it:
+
+```
+@misc{rossini2022slitherauditedcontracts,
+    title = {Slither Audited Smart Contracts Dataset},
+    author={Martina Rossini},
+    year={2022}
+}
+```
+
+### Contributions
+Thanks to [@mwritescode](https://github.com/mwritescode) for adding this dataset.

huggingface_dataset/Dataset_Card/p1atdev_resplash.md

@@ -0,0 +1,69 @@
+---
+license: mit
+language:
+- en
+---
+
+# hand.json
+
+3,000 image data about "Hand" retrieved from Unsplash.
+
+# portrait.json
+
+10,000 image data about "Portrait" retrieved from Unsplash.
+
+# pose.json
+
+10,000 image data about "Pose" retrieved from Unsplash.
+
+# Tool
+
+- [unsplash-wizard](https://github.com/p1atdev/unsplash-wizard)
+
+```typescript
+deno task build
+./unsplash download ./hand.json -o ./hand --color --relatedTags --likes 50
+```
+
+# Type Definition
+
+```typescript
+interface Photo {
+  id: string
+  color: string
+  description: string | null
+  alt_description: string | null
+  tags: string[]
+  likes: number
+  urls: {
+    raw: string
+    full: string
+    regular: string
+    small: string
+    thumb: string
+    small_s3: string
+  }
+  width: number
+  height: number
+  related_tags: string[]
+  location: {
+      name: string | null
+      city: string | null
+      country: string | null
+      position: {
+          latitude: number | null
+          longitude: number | null
+      }
+  }
+  exif: {
+      make: string | null
+      model: string | null
+      exposure_time: string | null
+      aperture: string | null
+      focal_length: string | null
+      iso: number | null
+  }
+  views: number
+  downloads: number
+}
+```

huggingface_dataset/Dataset_Card/rocca_sims4-faces.md

@@ -0,0 +1,17 @@
+A collection of >200k screenshots from the Sims 4 character creator (face and upper-torso only), using the randomize button.
+
+* There are ~100k masculine faces (`masc` folder), ~100k feminine faces (`fem` folder), ~12k faces with a masculine physical frame and feminine attire/makeup (`masc2fem` folder).
+* All images are 917x917.
+* Each image is about 40kb.
+* The examples below are cropped slightly off-center, but in the actual data the characters are more centered.
+* The files are named from `1.jpg` through to `N.jpg` (no zero-padding). For `fem`, `N=101499`. For `masc`, `N=103615`. For `masc2fem`, `N=12123`.
+
+## fem examples:
+![Sims 4 feminine faces](https://i.imgur.com/O2Cu6Xg.jpg)
+
+## masc examples:
+![Sims 4 masculine faces](https://i.imgur.com/BLHlx8d.jpg)
+
+## masc2fem examples:
+![Sims 4 masc2fem faces](https://i.imgur.com/2Zuuy6g.jpg)
+

huggingface_dataset/Dataset_Card/rungalileo_20_Newsgroups_Fixed.md

@@ -0,0 +1,110 @@
+---
+annotations_creators:
+- crowdsourced
+language_creators:
+- crowdsourced
+language:
+- en
+license:
+- unknown
+multilinguality:
+- monolingual
+pretty_name: 20_Newsgroups_Fixed
+size_categories:
+- 10K<n<100K
+source_datasets:
+- original
+task_categories:
+- text-classification
+task_ids:
+- multi-class-classification
+- topic-classification
+---
+
+# Dataset Card for 20_Newsgroups_Fixed
+
+## 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
+
+- **Galileo Homepage:** [Galileo ML Data Intelligence Platform](https://www.rungalileo.io)
+- **Repository:** [Needs More Information]
+- **Dataset Blog:** [Improving Your ML Datasets With Galileo, Part 1](https://www.rungalileo.io/blog/)
+- **Leaderboard:** [Needs More Information]
+- **Point of Contact:** [Needs More Information]
+- **Sklearn Dataset:** [sklearn](https://scikit-learn.org/0.19/datasets/twenty_newsgroups.html#the-20-newsgroups-text-dataset)
+- **20 Newsgroups Homepage:** [newsgroups homepage](http://qwone.com/~jason/20Newsgroups/)
+
+### Dataset Summary
+
+This dataset is a version of the [**20 Newsgroups**](https://scikit-learn.org/0.19/datasets/twenty_newsgroups.html#the-20-newsgroups-text-dataset) dataset fixed with the help of the [**Galileo ML Data Intelligence Platform**](https://www.rungalileo.io/). In a matter of minutes, Galileo enabled us to uncover and fix a multitude of errors within the original dataset. In the end, we present this improved dataset as a new standard for natural language experimentation and benchmarking using the Newsgroups dataset. 
+
+### Curation Rationale
+
+This dataset was created to showcase the power of Galileo as a Data Intelligence Platform. Through Galileo, we identify critical error patterns within the original Newsgroups training dataset - garbage data that do not properly fit any newsgroup label category. Moreover, we observe that these errors permeate throughout the test dataset.  
+
+As a result of our analysis, we propose the addition of a new class to properly categorize and fix the labeling of garbage data samples: a "None" class. Galileo further enables us to quickly make these data sample changes within the training set (changing garbage data labels to None) and helps guide human re-annotation of the test set. 
+
+#### Total Dataset Errors Fixed: 1163 *(6.5% of the dataset)*
+|Errors / Split.      |Overall|     Train|      Test|
+|---------------------|------:|---------:|---------:|
+|Garbage samples fixed|    718|       396|       322|
+|Empty samples fixed  |    445|       254|       254|
+|Total samples fixed  |   1163|       650|       650|
+
+To learn more about the process of fixing this dataset, please refer to our [**Blog**](https://www.rungalileo.io/blog).
+
+
+## Dataset Structure
+
+### Data Instances
+
+For each data sample, there is the text of the newsgroup post, the corresponding newsgroup forum where the message was posted (label), and a data sample id. 
+
+An example from the dataset looks as follows:
+```
+{'id': 1,
+'text': 'I have win 3.0 and downloaded several icons and BMP\'s but I can\'t figure out\nhow to change the "wallpaper" or use the icons.  Any help would be appreciated.\n\n\nThanx,\n\n-Brando'
+'label': comp.os.ms-windows.misc}
+```
+
+
+### Data Fields
+
+- id: the unique numerical id associated with a data sample
+- text: a string containing the text of the newsgroups message
+- label: a string indicating the newsgroup forum where the sample was posted
+
+### Data Splits
+
+The data is split into a training and test split. To reduce bias and test generalizability across time, data samples are split between train and test depending upon whether their message was posted before or after a specific date, respectively. 
+
+### Data Classes
+
+The fixed data is organized into 20 newsgroup topics + a catch all "None" class. Some of the newsgroups are very closely related to each other (e.g. comp.sys.ibm.pc.hardware / comp.sys.mac.hardware), while others are highly unrelated (e.g misc.forsale / soc.religion.christian). Here is a list of the 21 classes, partitioned according to subject matter:
+
+| comp.graphics<br>comp.os.ms-windows.misc<br>comp.sys.ibm.pc.hardware<br>comp.sys.mac.hardware<br>comp.windows.x | rec.autos<br>rec.motorcycles<br>rec.sport.baseball<br>rec.sport.hockey | sci.crypt<br><sci.electronics<br>sci.med<br>sci.space |
+|:---|:---:|---:|
+| misc.forsale | talk.politics.misc<br>talk.politics.guns<br>talk.politics.mideast | talk.religion.misc<br>alt.atheism<br>soc.religion.christian |
+| None |

huggingface_dataset/Dataset_Card/winogrande.md

@@ -0,0 +1,364 @@
+---
+language:
+- en
+paperswithcode_id: winogrande
+pretty_name: WinoGrande
+dataset_info:
+- config_name: winogrande_xs
+  features:
+  - name: sentence
+    dtype: string
+  - name: option1
+    dtype: string
+  - name: option2
+    dtype: string
+  - name: answer
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 20704
+    num_examples: 160
+  - name: test
+    num_bytes: 227649
+    num_examples: 1767
+  - name: validation
+    num_bytes: 164199
+    num_examples: 1267
+  download_size: 3395492
+  dataset_size: 412552
+- config_name: winogrande_s
+  features:
+  - name: sentence
+    dtype: string
+  - name: option1
+    dtype: string
+  - name: option2
+    dtype: string
+  - name: answer
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 82308
+    num_examples: 640
+  - name: test
+    num_bytes: 227649
+    num_examples: 1767
+  - name: validation
+    num_bytes: 164199
+    num_examples: 1267
+  download_size: 3395492
+  dataset_size: 474156
+- config_name: winogrande_m
+  features:
+  - name: sentence
+    dtype: string
+  - name: option1
+    dtype: string
+  - name: option2
+    dtype: string
+  - name: answer
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 329001
+    num_examples: 2558
+  - name: test
+    num_bytes: 227649
+    num_examples: 1767
+  - name: validation
+    num_bytes: 164199
+    num_examples: 1267
+  download_size: 3395492
+  dataset_size: 720849
+- config_name: winogrande_l
+  features:
+  - name: sentence
+    dtype: string
+  - name: option1
+    dtype: string
+  - name: option2
+    dtype: string
+  - name: answer
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 1319576
+    num_examples: 10234
+  - name: test
+    num_bytes: 227649
+    num_examples: 1767
+  - name: validation
+    num_bytes: 164199
+    num_examples: 1267
+  download_size: 3395492
+  dataset_size: 1711424
+- config_name: winogrande_xl
+  features:
+  - name: sentence
+    dtype: string
+  - name: option1
+    dtype: string
+  - name: option2
+    dtype: string
+  - name: answer
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 5185832
+    num_examples: 40398
+  - name: test
+    num_bytes: 227649
+    num_examples: 1767
+  - name: validation
+    num_bytes: 164199
+    num_examples: 1267
+  download_size: 3395492
+  dataset_size: 5577680
+- config_name: winogrande_debiased
+  features:
+  - name: sentence
+    dtype: string
+  - name: option1
+    dtype: string
+  - name: option2
+    dtype: string
+  - name: answer
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 1203420
+    num_examples: 9248
+  - name: test
+    num_bytes: 227649
+    num_examples: 1767
+  - name: validation
+    num_bytes: 164199
+    num_examples: 1267
+  download_size: 3395492
+  dataset_size: 1595268
+---
+
+# Dataset Card for "winogrande"
+
+## 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://leaderboard.allenai.org/winogrande/submissions/get-started](https://leaderboard.allenai.org/winogrande/submissions/get-started)
+- **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+- **Size of downloaded dataset files:** 19.43 MB
+- **Size of the generated dataset:** 10.01 MB
+- **Total amount of disk used:** 29.44 MB
+
+### Dataset Summary
+
+WinoGrande is a new collection of 44k problems, inspired by Winograd Schema Challenge (Levesque, Davis, and Morgenstern
+ 2011), but adjusted to improve the scale and robustness against the dataset-specific bias. Formulated as a
+fill-in-a-blank task with binary options, the goal is to choose the right option for a given sentence which requires
+commonsense reasoning.
+
+### Supported Tasks and Leaderboards
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Languages
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+## Dataset Structure
+
+### Data Instances
+
+#### winogrande_debiased
+
+- **Size of downloaded dataset files:** 3.24 MB
+- **Size of the generated dataset:** 1.52 MB
+- **Total amount of disk used:** 4.76 MB
+
+An example of 'train' looks as follows.
+```
+
+```
+
+#### winogrande_l
+
+- **Size of downloaded dataset files:** 3.24 MB
+- **Size of the generated dataset:** 1.63 MB
+- **Total amount of disk used:** 4.87 MB
+
+An example of 'validation' looks as follows.
+```
+
+```
+
+#### winogrande_m
+
+- **Size of downloaded dataset files:** 3.24 MB
+- **Size of the generated dataset:** 0.69 MB
+- **Total amount of disk used:** 3.93 MB
+
+An example of 'validation' looks as follows.
+```
+
+```
+
+#### winogrande_s
+
+- **Size of downloaded dataset files:** 3.24 MB
+- **Size of the generated dataset:** 0.45 MB
+- **Total amount of disk used:** 3.69 MB
+
+An example of 'validation' looks as follows.
+```
+
+```
+
+#### winogrande_xl
+
+- **Size of downloaded dataset files:** 3.24 MB
+- **Size of the generated dataset:** 5.32 MB
+- **Total amount of disk used:** 8.56 MB
+
+An example of 'train' looks as follows.
+```
+
+```
+
+### Data Fields
+
+The data fields are the same among all splits.
+
+#### winogrande_debiased
+- `sentence`: a `string` feature.
+- `option1`: a `string` feature.
+- `option2`: a `string` feature.
+- `answer`: a `string` feature.
+
+#### winogrande_l
+- `sentence`: a `string` feature.
+- `option1`: a `string` feature.
+- `option2`: a `string` feature.
+- `answer`: a `string` feature.
+
+#### winogrande_m
+- `sentence`: a `string` feature.
+- `option1`: a `string` feature.
+- `option2`: a `string` feature.
+- `answer`: a `string` feature.
+
+#### winogrande_s
+- `sentence`: a `string` feature.
+- `option1`: a `string` feature.
+- `option2`: a `string` feature.
+- `answer`: a `string` feature.
+
+#### winogrande_xl
+- `sentence`: a `string` feature.
+- `option1`: a `string` feature.
+- `option2`: a `string` feature.
+- `answer`: a `string` feature.
+
+### Data Splits
+
+|       name        |train|validation|test|
+|-------------------|----:|---------:|---:|
+|winogrande_debiased| 9248|      1267|1767|
+|winogrande_l       |10234|      1267|1767|
+|winogrande_m       | 2558|      1267|1767|
+|winogrande_s       |  640|      1267|1767|
+|winogrande_xl      |40398|      1267|1767|
+
+## Dataset Creation
+
+### Curation Rationale
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+#### Who are the source language producers?
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Annotations
+
+#### Annotation process
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+#### Who are the annotators?
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Personal and Sensitive Information
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Discussion of Biases
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Other Known Limitations
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+## Additional Information
+
+### Dataset Curators
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Licensing Information
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Citation Information
+
+```
+@InProceedings{ai2:winogrande,
+title = {WinoGrande: An Adversarial Winograd Schema Challenge at Scale},
+authors={Keisuke, Sakaguchi and Ronan, Le Bras and Chandra, Bhagavatula and Yejin, Choi
+},
+year={2019}
+}
+
+```
+
+
+### Contributions
+
+Thanks to [@thomwolf](https://github.com/thomwolf), [@TevenLeScao](https://github.com/TevenLeScao), [@patrickvonplaten](https://github.com/patrickvonplaten), [@lewtun](https://github.com/lewtun) for adding this dataset.