Upload batch 361 (20 files, last=huggingface_dataset/Dataset_Card/autoevaluate_autoeval-staging-eval-Tristan__zero-shot-classification-large-test-Tristan__z-eb4ad9-22.md)

huggingface_dataset/Dataset_Card/BeIR_signal1m-generated-queries.md

@@ -0,0 +1,285 @@
+---
+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
+- zero-shot-retrieval
+- information-retrieval
+- zero-shot-information-retrieval
+task_ids:
+- passage-retrieval
+- entity-linking-retrieval
+- fact-checking-retrieval
+- tweet-retrieval
+- citation-prediction-retrieval
+- duplication-question-retrieval
+- argument-retrieval
+- news-retrieval
+- biomedical-information-retrieval
+- question-answering-retrieval
+---
+
+# 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/Jean-Baptiste_financial_news_sentiment.md

@@ -0,0 +1,42 @@
+---
+language:
+- en
+dataset_info:
+  splits:
+  - name: test
+    num_examples: 267
+  - name: train
+    num_examples: 1512
+annotations_creators:
+- expert-generated
+license:
+- mit
+multilinguality:
+- monolingual
+pretty_name: financial_news_sentiment
+size_categories:
+- 1K<n<10K
+tags: []
+task_categories:
+- text-classification
+task_ids:
+- multi-class-classification
+- sentiment-classification
+---
+# Dataset Card for "financial_news_sentiment"
+
+Manually validated sentiment for ~2000 Canadian news articles.
+
+The dataset also include a column topic which contains one of the following value:
+* acquisition
+* other
+* quaterly financial release
+* appointment to new position
+* dividend
+* corporate update
+* drillings results
+* conference
+* share repurchase program
+* grant of stocks
+
+This was generated automatically using a zero-shot classification model and **was not** reviewed manually.

huggingface_dataset/Dataset_Card/Twitter_TwitterFaveGraph.md

@@ -0,0 +1,37 @@
+---
+license: cc-by-4.0
+---
+
+# MiCRO: Multi-interest Candidate Retrieval Online
+[![PRs Welcome](https://img.shields.io/badge/PRs-welcome-green.svg?style=flat-square)](http://makeapullrequest.com)
+[![arXiv](https://img.shields.io/badge/arXiv-2201.11675-b31b1b.svg)](https://arxiv.org/abs/2210.16271)
+
+This repo contains the TwitterFaveGraph dataset from our paper [MiCRO: Multi-interest Candidate Retrieval Online](). <br />
+[[PDF]](https://arxiv.org/pdf/2210.16271.pdf)
+[[HuggingFace Datasets]](https://huggingface.co/Twitter)
+
+<a rel="license" href="http://creativecommons.org/licenses/by/4.0/"><img alt="Creative Commons License" style="border-width:0" src="https://i.creativecommons.org/l/by/4.0/88x31.png" /></a><br />This work is licensed under a <a rel="license" href="http://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International License</a>.
+
+## TwitterFaveGraph
+
+TwitterFaveGraph is a bipartite directed graph of user nodes to Tweet nodes where an edge represents a "fave" engagement. Each edge is binned into predetermined time chunks which are assigned as ordinals. These ordinals are contiguous and respect time ordering. In total TwitterFaveGraph has 6.7M user nodes, 13M Tweet nodes, and 283M edges. The maximum degree for users is 100 and the minimum degree for users is 1. The maximum
+degree for Tweets is 280k and the minimum degree for Tweets is 5.
+
+The data format is displayed below.
+
+| user_index | tweet_index | time_chunk |
+| ------------- | ------------- |  ---- |
+| 1   | 2 | 1 |
+| 2   | 1 | 1 |
+| 3   | 3 | 2 |
+
+## Citation
+If you use TwitterFaveGraph in your work, please cite the following:
+```bib
+@article{portman2022micro,
+  title={MiCRO: Multi-interest Candidate Retrieval Online},
+  author={Portman, Frank and Ragain, Stephen and El-Kishky, Ahmed},
+  journal={arXiv preprint arXiv:2210.16271},
+  year={2022}
+}
+```

huggingface_dataset/Dataset_Card/autoevaluate_autoeval-eval-WillHeld__stereoset_zero-WillHeld__stereoset_zero-7a6673-2074067135.md

@@ -0,0 +1,34 @@
+---
+type: predictions
+tags:
+- autotrain
+- evaluation
+datasets:
+- WillHeld/stereoset_zero
+eval_info:
+  task: text_zero_shot_classification
+  model: bigscience/bloom-1b1
+  metrics: []
+  dataset_name: WillHeld/stereoset_zero
+  dataset_config: WillHeld--stereoset_zero
+  dataset_split: train
+  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-1b1
+* Dataset: WillHeld/stereoset_zero
+* Config: WillHeld--stereoset_zero
+* Split: train
+
+To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
+
+## Contributions
+
+Thanks to [@WillHeld](https://huggingface.co/WillHeld) for evaluating this model.

huggingface_dataset/Dataset_Card/autoevaluate_autoeval-eval-futin__feed-top_en_-3f631c-2246071663.md

@@ -0,0 +1,34 @@
+---
+type: predictions
+tags:
+- autotrain
+- evaluation
+datasets:
+- futin/feed
+eval_info:
+  task: text_zero_shot_classification
+  model: facebook/opt-13b
+  metrics: []
+  dataset_name: futin/feed
+  dataset_config: top_en_
+  dataset_split: test
+  col_mapping:
+    text: text
+    classes: classes
+    target: target
+---
+# Dataset Card for AutoTrain Evaluator
+
+This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
+
+* Task: Zero-Shot Text Classification
+* Model: facebook/opt-13b
+* Dataset: futin/feed
+* Config: top_en_
+* Split: test
+
+To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
+
+## Contributions
+
+Thanks to [@futin](https://huggingface.co/futin) for evaluating this model.

huggingface_dataset/Dataset_Card/autoevaluate_autoeval-eval-phpthinh__exampleem-filter-918293-1728760346.md

@@ -0,0 +1,34 @@
+---
+type: predictions
+tags:
+- autotrain
+- evaluation
+datasets:
+- phpthinh/exampleem
+eval_info:
+  task: text_zero_shot_classification
+  model: bigscience/bloom-1b1
+  metrics: []
+  dataset_name: phpthinh/exampleem
+  dataset_config: filter
+  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-1b1
+* Dataset: phpthinh/exampleem
+* Config: filter
+* 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-Tristan__zero-shot-classification-large-test-Tristan__z-eb4ad9-22.md

@@ -0,0 +1,34 @@
+---
+type: predictions
+tags:
+- autotrain
+- evaluation
+datasets:
+- Tristan/zero-shot-classification-large-test
+eval_info:
+  task: text_zero_shot_classification
+  model: autoevaluate/zero-shot-classification
+  metrics: []
+  dataset_name: Tristan/zero-shot-classification-large-test
+  dataset_config: Tristan--zero-shot-classification-large-test
+  dataset_split: test
+  col_mapping:
+    text: text
+    classes: classes
+    target: target
+---
+# Dataset Card for AutoTrain Evaluator
+
+This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
+
+* Task: Zero-Shot Text Classification
+* Model: autoevaluate/zero-shot-classification
+* Dataset: Tristan/zero-shot-classification-large-test
+* Config: Tristan--zero-shot-classification-large-test
+* Split: test
+
+To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
+
+## Contributions
+
+Thanks to [@Tristan](https://huggingface.co/Tristan) for evaluating this model.

huggingface_dataset/Dataset_Card/autoevaluate_autoeval-staging-eval-project-f2158b57-4f5f-457d-9656-edbe0fb0d311-398.md

@@ -0,0 +1,35 @@
+---
+type: predictions
+tags:
+- autotrain
+- evaluation
+datasets:
+- squad_v2
+eval_info:
+  task: extractive_question_answering
+  model: autoevaluate/roberta-base-squad2
+  metrics: []
+  dataset_name: squad_v2
+  dataset_config: squad_v2
+  dataset_split: validation
+  col_mapping:
+    context: context
+    question: question
+    answers-text: answers.text
+    answers-answer_start: answers.answer_start
+---
+# Dataset Card for AutoTrain Evaluator
+
+This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
+
+* Task: Question Answering
+* Model: autoevaluate/roberta-base-squad2
+* Dataset: squad_v2
+* Config: squad_v2
+* Split: validation
+
+To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
+
+## Contributions
+
+Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model.

huggingface_dataset/Dataset_Card/bigbio_pmc_patients.md

@@ -0,0 +1,45 @@
+
+---
+language: 
+- en
+bigbio_language: 
+- English
+license: cc-by-nc-sa-4.0
+multilinguality: monolingual
+bigbio_license_shortname: CC_BY_NC_SA_4p0
+pretty_name: PMC-Patients
+homepage: https://github.com/zhao-zy15/PMC-Patients
+bigbio_pubmed: True
+bigbio_public: True
+bigbio_tasks: 
+- SEMANTIC_SIMILARITY
+---
+
+
+# Dataset Card for PMC-Patients
+
+## Dataset Description
+
+- **Homepage:** https://github.com/zhao-zy15/PMC-Patients
+- **Pubmed:** True
+- **Public:** True
+- **Tasks:** STS
+
+
+This dataset is used for calculating the similarity between two patient descriptions.
+
+
+
+## Citation Information
+
+```
+@misc{zhao2022pmcpatients,
+      title={PMC-Patients: A Large-scale Dataset of Patient Notes and Relations Extracted from Case
+          Reports in PubMed Central},
+      author={Zhengyun Zhao and Qiao Jin and Sheng Yu},
+      year={2022},
+      eprint={2202.13876},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL}
+}
+```

huggingface_dataset/Dataset_Card/conceptual_12m.md

@@ -0,0 +1,252 @@
+---
+annotations_creators:
+- found
+language_creators:
+- found
+language:
+- en
+license:
+- other
+multilinguality:
+- monolingual
+size_categories:
+- 10M<n<100M
+source_datasets:
+- original
+task_categories:
+- image-to-text
+task_ids:
+- image-captioning
+paperswithcode_id: cc12m
+pretty_name: Conceptual 12M
+dataset_info:
+  features:
+  - name: image_url
+    dtype: string
+  - name: caption
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 2794168030
+    num_examples: 12423374
+  download_size: 2707204412
+  dataset_size: 2794168030
+---
+
+# Dataset Card for Conceptual 12M
+
+## Table of Contents
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Dataset Preprocessing](#dataset-preprocessing)
+  - [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
+
+- **Repository:** [Conceptual 12M repository](https://github.com/google-research-datasets/conceptual-12m)
+- **Paper:** [Conceptual 12M: Pushing Web-Scale Image-Text Pre-Training To Recognize Long-Tail Visual Concepts](https://arxiv.org/abs/2102.08981)
+- **Point of Contact:** [Conceptual Captions e-mail](mailto:conceptual-captions@google.com)
+
+### Dataset Summary
+
+Conceptual 12M (CC12M) is a dataset with 12 million  image-text pairs specifically meant to be used for visionand-language pre-training.
+Its data collection pipeline is a relaxed version of the one used in Conceptual Captions 3M (CC3M).
+
+### Dataset Preprocessing
+
+This dataset doesn't download the images locally by default. Instead, it exposes URLs to the images. To fetch the images, use the following code:
+
+```python
+from concurrent.futures import ThreadPoolExecutor
+from functools import partial
+import io
+import urllib
+
+import PIL.Image
+
+from datasets import load_dataset
+from datasets.utils.file_utils import get_datasets_user_agent
+
+
+USER_AGENT = get_datasets_user_agent()
+
+
+def fetch_single_image(image_url, timeout=None, retries=0):
+    for _ in range(retries + 1):
+        try:
+            request = urllib.request.Request(
+                image_url,
+                data=None,
+                headers={"user-agent": USER_AGENT},
+            )
+            with urllib.request.urlopen(request, timeout=timeout) as req:
+                image = PIL.Image.open(io.BytesIO(req.read()))
+            break
+        except Exception:
+            image = None
+    return image
+
+
+def fetch_images(batch, num_threads, timeout=None, retries=0):
+    fetch_single_image_with_args = partial(fetch_single_image, timeout=timeout, retries=retries)
+    with ThreadPoolExecutor(max_workers=num_threads) as executor:
+        batch["image"] = list(executor.map(fetch_single_image_with_args, batch["image_url"]))
+    return batch
+
+
+num_threads = 20
+dset = load_dataset("conceptual_12m")
+dset = dset.map(fetch_images, batched=True, batch_size=100, fn_kwargs={"num_threads": num_threads})
+```
+
+### Supported Tasks and Leaderboards
+
+- `image-captioning`: This dataset can be used to train model for the Image Captioning task.
+
+### Languages
+
+All captions are in English.
+
+## Dataset Structure
+
+### Data Instances
+
+Each instance represents a single image with a caption:
+
+```
+{
+  'image_url': 'http://lh6.ggpht.com/-IvRtNLNcG8o/TpFyrudaT6I/AAAAAAAAM6o/_11MuAAKalQ/IMG_3422.JPG?imgmax=800',
+  'caption': 'a very typical bus station'
+}
+```
+
+### Data Fields
+
+- `image_url`: Static URL for downloading the image associated with the post.
+- `caption`: Textual description of the image.
+
+### Data Splits
+
+There is only training data, with a total of 12423374 rows
+
+## Dataset Creation
+
+### Curation Rationale
+
+Conceptual 12M shares the same pipeline with Conceptual Captions (CC3M), but relaxes some processing steps.
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+From the paper:
+> To arrive at CC12M, we keep
+the image-text filtering intact, and relax the unimodal filters only. First, for image-based filtering, we set the maximum ratio of larger to smaller dimension to 2.5 instead of 2. 
+We still keep only JPEG images with size greater than
+400 pixels, and still exclude images that trigger pornography detectors. Second, in text-based filtering, we allow text
+between 3 and 256 words in the alt-text. We still discard
+candidates with no noun or no determiner, but permit ones
+without prepositions. We discard the heuristics regarding
+high unique-word ratio covering various POS tags and word
+capitalization. We set the maximum fraction of word repetition allowed to 0.2. Given a larger pool of text due to the
+above relaxations, the threshold for counting a word type as
+rare is increased from 5 to 20
+
+> The main motivation for CC3M to
+perform text transformation is that a majority of candidate
+captions contain ultrafine-grained entities such as proper
+names (people, venues, locations, etc.), making it extremely
+difficult to learn as part of the image captioning task. In
+contrast, we are not restricted by the end task of image caption generation. Our intuition is that relatively more difficult pre-training data would lead to better transferability.
+We thus do not perform hypernimization or digit substitution. [...] The only exception to the “keep alt-texts as
+raw as possible” rule is performing person-name substitutions, which we identify as necessary to protect the privacy
+of the individuals in these images. For this step, we use the
+Google Cloud Natural Language APIs to detect all named
+entities of type Person, and substitute them by a special token <PERSON>. Around 25% of all the alt-texts in CC12M
+are transformed in this fashion.
+
+#### Who are the source language producers?
+
+Not specified.
+
+### Annotations
+
+#### Annotation process
+
+Annotations are extracted jointly with the images using the automatic pipeline.
+
+#### Who are the annotators?
+
+Not specified.
+
+### Personal and Sensitive Information
+
+From the paper:
+
+> The only exception to the “keep alt-texts as
+raw as possible” rule is performing person-name substitutions, which we identify as necessary to protect the privacy
+of the individuals in these images. For this step, we use the
+Google Cloud Natural Language APIs to detect all named
+entities of type Person, and substitute them by a special token <PERSON>. Around 25% of all the alt-texts in CC12M
+are transformed in this fashion.
+
+## 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
+
+Soravit Changpinyo, Piyush Sharma, Nan Ding and Radu Soricut.
+
+### Licensing Information
+
+The dataset may be freely used for any purpose, although acknowledgement of
+Google LLC ("Google") as the data source would be appreciated. The dataset is
+provided "AS IS" without any warranty, express or implied. Google disclaims all
+liability for any damages, direct or indirect, resulting from the use of the
+dataset.
+
+### Citation Information
+
+```bibtex
+@inproceedings{changpinyo2021cc12m,
+  title = {{Conceptual 12M}: Pushing Web-Scale Image-Text Pre-Training To Recognize Long-Tail Visual Concepts},
+  author = {Changpinyo, Soravit and Sharma, Piyush and Ding, Nan and Soricut, Radu},
+  booktitle = {CVPR},
+  year = {2021},
+}
+```
+
+### Contributions
+
+Thanks to [@thomasw21](https://github.com/thomasw21) for adding this dataset.

huggingface_dataset/Dataset_Card/mxeval_mbxp.md

@@ -0,0 +1,181 @@
+---
+license: apache-2.0
+task_categories:
+- text-generation
+language:
+- en
+tags:
+- mxeval
+- mbxp
+- mbpp
+- code-generation
+- mxeval
+pretty_name: mbxp
+size_categories:
+- 10K<n<100K
+---
+# MBXP
+
+## Table of Contents
+- [MBXP](#MBXP)
+  - [Table of Contents](#table-of-contents)
+  - [Dataset Description](#dataset-description)
+    - [Dataset Summary](#dataset-summary)
+    - [Supported Tasks and Leaderboards](#related-tasks-and-leaderboards)
+    - [Languages](#languages)
+  - [Dataset Structure](#dataset-structure)
+    - [Data Instances](#data-instances)
+    - [Data Fields](#data-fields)
+    - [Data Splits](#data-splits)
+  - [Executional Correctness](#execution)
+    - [Execution Example](#execution-example)
+    - [Considerations for Using the Data](#considerations-for-using-the-data)
+  - [Dataset Creation](#dataset-creation)
+    - [Curation Rationale](#curation-rationale)
+    - [Personal and Sensitive Information](#personal-and-sensitive-information)
+    - [Social Impact of Dataset](#social-impact-of-dataset)
+  - [Additional Information](#additional-information)
+    - [Dataset Curators](#dataset-curators)
+    - [Licensing Information](#licensing-information)
+    - [Citation Information](#citation-information)
+    - [Contributions](#contributions)
+
+# MBXP
+
+## Dataset Description
+
+- **Repository:** [GitHub Repository](https://github.com/amazon-science/mbxp-exec-eval)
+- **Paper:** [Multi-lingual Evaluation of Code Generation Models](https://openreview.net/forum?id=Bo7eeXm6An8)
+
+### Dataset Summary
+
+This repository contains data and code to perform execution-based multi-lingual evaluation of code generation capabilities and the corresponding data,
+namely, a multi-lingual benchmark MBXP, multi-lingual MathQA and multi-lingual HumanEval.
+<br>Results and findings can be found in the paper ["Multi-lingual Evaluation of Code Generation Models"](https://arxiv.org/abs/2210.14868).
+
+
+### Related Tasks and Leaderboards
+* [Multi-HumanEval](https://huggingface.co/datasets/mxeval/multi-humaneval)
+* [MBXP](https://huggingface.co/datasets/mxeval/mbxp)
+* [MathQA-X](https://huggingface.co/datasets/mxeval/mathqa-x)
+
+### Languages
+The programming problems are written in multiple programming languages and contain English natural text in comments and docstrings.
+
+
+## Dataset Structure
+To lookup currently supported datasets
+```python
+from datasets import get_dataset_config_names
+get_dataset_config_names("mxeval/mbxp")
+['python', 'csharp', 'go', 'java', 'javascript', 'kotlin', 'perl', 'php', 'ruby', 'scala', 'swift', 'typescript']
+```
+To load a specific dataset and language
+```python
+from datasets import load_dataset
+load_dataset("mxeval/mbxp", "python")
+DatasetDict({
+    test: Dataset({
+        features: ['task_id', 'language', 'prompt', 'test', 'entry_point', 'canonical_solution', 'description'],
+        num_rows: 974
+    })
+})
+```
+
+### Data Instances
+
+An example of a dataset instance:
+
+```python
+{
+  "task_id": "MBPP/1",
+  "language": "python",
+  "prompt": "\n\ndef min_cost(cost, m, n):\n\t\"\"\"\n\tWrite a function to find the minimum cost path to reach (m, n) from (0, 0) for the given cost matrix cost[][] and a position (m, n) in cost[][].\n\t>>> min_cost([[1, 2, 3], [4, 8, 2], [1, 5, 3]], 2, 2)\n\t8\n\t>>> min_cost([[2, 3, 4], [5, 9, 3], [2, 6, 4]], 2, 2)\n\t12\n\t>>> min_cost([[3, 4, 5], [6, 10, 4], [3, 7, 5]], 2, 2)\n\t16\n\t\"\"\"\n",
+  "test": "\n\nMETADATA = {}\n\n\ndef check(candidate):\n    assert candidate([[1, 2, 3], [4, 8, 2], [1, 5, 3]], 2, 2) == 8\n    assert candidate([[2, 3, 4], [5, 9, 3], [2, 6, 4]], 2, 2) == 12\n    assert candidate([[3, 4, 5], [6, 10, 4], [3, 7, 5]], 2, 2) == 16\n\n",
+  "entry_point": "min_cost",
+  "canonical_solution": "\tR = 3\n\tC = 3\n\t \n\ttc = [[0 for x in range(C)] for x in range(R)] \n\ttc[0][0] = cost[0][0] \n\tfor i in range(1, m+1): \n\t\ttc[i][0] = tc[i-1][0] + cost[i][0] \n\tfor j in range(1, n+1): \n\t\ttc[0][j] = tc[0][j-1] + cost[0][j] \n\tfor i in range(1, m+1): \n\t\tfor j in range(1, n+1): \n\t\t\ttc[i][j] = min(tc[i-1][j-1], tc[i-1][j], tc[i][j-1]) + cost[i][j] \n\treturn tc[m][n]",
+  "description": "Write a function to find the minimum cost path to reach (m, n) from (0, 0) for the given cost matrix cost[][] and a position (m, n) in cost[][]."
+}
+```
+
+### Data Fields
+
+- `task_id`: identifier for the data sample
+- `prompt`: input for the model containing function header and docstrings
+- `canonical_solution`: solution for the problem in the `prompt`
+- `description`: task description
+- `test`: contains function to test generated code for correctness
+- `entry_point`: entry point for test
+- `language`: programming lanuage identifier to call the appropriate subprocess call for program execution
+
+
+### Data Splits
+
+ - MBXP
+   - Python
+   - Java
+   - Javascript
+   - Typescript
+   - Kotlin
+   - Ruby
+   - Php
+   - Cpp
+   - Csharp
+   - Go
+   - Perl
+   - Scala
+   - Swift
+
+## Dataset Creation
+
+### Curation Rationale
+
+Since code generation models are often trained on dumps of GitHub a dataset not included in the dump was necessary to properly evaluate the model. However, since this dataset was published on GitHub it is likely to be included in future dumps.
+
+### Personal and Sensitive Information
+
+None.
+
+### Social Impact of Dataset
+With this dataset code generating models can be better evaluated which leads to fewer issues introduced when using such models.
+
+### Dataset Curators
+AWS AI Labs
+
+## Execution
+
+### Execution Example
+Install the repo [mbxp-exec-eval](https://github.com/amazon-science/mbxp-exec-eval) to execute generations or canonical solutions for the prompts from this dataset.
+
+```python
+>>> from datasets import load_dataset
+>>> from mxeval.execution import check_correctness
+>>> mbxp_python = load_dataset("mxeval/mbxp", "python", split="test")
+>>> example_problem = mbxp_python[0]
+>>> check_correctness(example_problem, example_problem["canonical_solution"], timeout=20.0)
+{'task_id': 'MBPP/1', 'passed': True, 'result': 'passed', 'completion_id': None, 'time_elapsed': 10.314226150512695}
+```
+
+### Considerations for Using the Data
+Make sure to sandbox the execution environment.
+
+### Licensing Information
+
+[LICENSE](https://huggingface.co/datasets/mxeval/mbxp/blob/main/mbxp-LICENSE) <br>
+[THIRD PARTY LICENSES](https://huggingface.co/datasets/mxeval/mbxp/blob/main/THIRD_PARTY_LICENSES)
+
+### Citation Information
+```
+@inproceedings{
+athiwaratkun2023multilingual,
+title={Multi-lingual Evaluation of Code Generation Models},
+author={Ben Athiwaratkun and Sanjay Krishna Gouda and Zijian Wang and Xiaopeng Li and Yuchen Tian and Ming Tan and Wasi Uddin Ahmad and Shiqi Wang and Qing Sun and Mingyue Shang and Sujan Kumar Gonugondla and Hantian Ding and Varun Kumar and Nathan Fulton and Arash Farahani and Siddhartha Jain and Robert Giaquinto and Haifeng Qian and Murali Krishna Ramanathan and Ramesh Nallapati and Baishakhi Ray and Parminder Bhatia and Sudipta Sengupta and Dan Roth and Bing Xiang},
+booktitle={The Eleventh International Conference on Learning Representations },
+year={2023},
+url={https://openreview.net/forum?id=Bo7eeXm6An8}
+}
+```
+
+### Contributions
+
+[skgouda@](https://github.com/sk-g) [benathi@](https://github.com/benathi)

huggingface_dataset/Dataset_Card/nateraw_pizza_not_pizza.md

@@ -0,0 +1,152 @@
+---
+license:
+- other
+kaggle_id: carlosrunner/pizza-not-pizza
+---
+
+# Dataset Card for Pizza or Not Pizza?
+
+## 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://kaggle.com/datasets/carlosrunner/pizza-not-pizza
+- **Repository:**
+- **Paper:**
+- **Leaderboard:**
+- **Point of Contact:**
+
+### Dataset Summary
+
+Who doesn't like pizza? This dataset contains about 1000 images of pizza and 1000 images of dishes other than pizza. It can be used for a simple binary image classification task.
+
+All images were rescaled to have a maximum side length of 512 pixels. 
+
+This is a subset of the Food-101 dataset. Information about the original dataset can be found in the following paper: 
+Bossard, Lukas, Matthieu Guillaumin, and Luc Van Gool. "Food-101 – Mining Discriminative Components with Random Forests." In *European conference on computer vision*, pp. 446-461. Springer, Cham, 2014.
+
+The original dataset can be found in the following locations:
+https://data.vision.ee.ethz.ch/cvl/datasets_extra/food-101/
+https://www.kaggle.com/datasets/dansbecker/food-101
+https://paperswithcode.com/dataset/food-101
+https://www.tensorflow.org/datasets/catalog/food101
+
+Number of instances in each class:
+Pizza: 983
+Not Pizza: 983
+
+##Acknowledgements
+
+The Food-101 data set consists of images from Foodspotting [1] which are not property of the Federal Institute of Technology Zurich (ETHZ). Any use beyond scientific fair use must be negociated with the respective picture owners according to the Foodspotting terms of use [2].
+
+[1] http://www.foodspotting.com/
+[2] http://www.foodspotting.com/terms/
+
+### Supported Tasks and Leaderboards
+
+[More Information Needed]
+
+### Languages
+
+[More Information Needed]
+
+## Dataset Structure
+
+### Data Instances
+
+[More Information Needed]
+
+### Data Fields
+
+[More Information Needed]
+
+### Data Splits
+
+[More Information Needed]
+
+## Dataset Creation
+
+### Curation Rationale
+
+[More Information Needed]
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+[More Information Needed]
+
+#### Who are the source language producers?
+
+[More Information Needed]
+
+### Annotations
+
+#### Annotation process
+
+[More Information Needed]
+
+#### Who are the annotators?
+
+[More Information Needed]
+
+### Personal and Sensitive Information
+
+[More Information Needed]
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+[More Information Needed]
+
+### Discussion of Biases
+
+[More Information Needed]
+
+### Other Known Limitations
+
+[More Information Needed]
+
+## Additional Information
+
+### Dataset Curators
+
+This dataset was shared by [@carlosrunner](https://kaggle.com/carlosrunner)
+
+### Licensing Information
+
+The license for this dataset is other
+
+### Citation Information
+
+```bibtex
+[More Information Needed]
+```
+
+### Contributions
+
+[More Information Needed]

huggingface_dataset/Dataset_Card/phihung_titanic.md

@@ -0,0 +1,4 @@
+---
+license: other
+---
+The legendary Titanic dataset from [this](https://www.kaggle.com/competitions/titanic/overview) Kaggle competition

huggingface_dataset/Dataset_Card/projecte-aina_casum.md

@@ -0,0 +1,166 @@
+---
+annotations_creators:
+- machine-generated
+language_creators:
+- expert-generated
+language:
+- ca
+license:
+- cc-by-nc-4.0
+multilinguality:
+- monolingual
+size_categories:
+- unknown
+source_datasets: []
+task_categories:
+- summarization
+task_ids: []
+pretty_name: casum
+---
+
+# Dataset Card for CaSum
+
+## 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
+
+- **Paper:** [Sequence to Sequence Resources for Catalan](https://arxiv.org/pdf/2202.06871.pdf)
+- **Point of Contact:** [Ona de Gibert Bonet](mailto:ona.degibert@bsc.es)
+
+
+### Dataset Summary
+
+CaSum is a summarization dataset. It is extracted from a newswire corpus crawled from the Catalan News Agency ([Agència Catalana de Notícies; ACN](https://www.acn.cat/)). The corpus consists of 217,735 instances that are composed by the headline and the body.
+ 
+### Supported Tasks and Leaderboards
+
+The dataset can be used to train a model for abstractive summarization. Success on this task is typically measured by achieving a high Rouge score. The [mbart-base-ca-casum](https://huggingface.co/projecte-aina/bart-base-ca-casum) model currently achieves a 41.39.
+
+### Languages
+
+The dataset is in Catalan (`ca-CA`).
+
+## Dataset Structure
+
+### Data Instances
+
+```
+{
+  'summary': 'Mapfre preveu ingressar 31.000 milions d’euros al tancament de 2018',
+  'text': 'L’asseguradora llançarà la seva filial Verti al mercat dels EUA a partir de 2017 ACN Madrid.-Mapfre preveu assolir uns ingressos de 31.000 milions d'euros al tancament de 2018 i destinarà a retribuir els seus accionistes com a mínim el 50% dels beneficis del grup durant el període 2016-2018, amb una rendibilitat mitjana a l’entorn del 5%, segons ha anunciat la companyia asseguradora durant la celebració aquest divendres de la seva junta general d’accionistes. La firma asseguradora també ha avançat que llançarà la seva filial d’automoció i llar al mercat dels EUA a partir de 2017. Mapfre ha recordat durant la junta que va pagar més de 540 milions d'euros en impostos el 2015, amb una taxa impositiva efectiva del 30,4 per cent. La companyia també ha posat en marxa el Pla de Sostenibilitat 2016-2018 i el Pla de Transparència Activa, “que han de contribuir a afermar la visió de Mapfre com a asseguradora global de confiança”, segons ha informat en un comunicat.'
+}
+```
+
+### Data Fields
+
+- `summary` (str): Summary of the piece of news
+- `text` (str): The text of the piece of news
+
+### Data Splits
+
+We split our dataset into train, dev and test splits
+
+- train: 197,735 examples
+- validation: 10,000 examples
+- test: 10,000 examples
+
+## Dataset Creation
+
+### Curation Rationale
+
+We created this corpus to contribute to the development of language models in Catalan, a low-resource language. There exist few resources for summarization in Catalan.
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+We obtained each headline and its corresponding body of each news piece on the Catalan News Agency ([Agència Catalana de Notícies; ACN](https://www.acn.cat/)) website and applied the following cleaning pipeline: deduplicating the documents, removing the documents with empty attributes, and deleting some boilerplate sentences. 
+
+#### Who are the source language producers?
+
+The news portal Catalan News Agency ([Agència Catalana de Notícies; ACN](https://www.acn.cat/)).
+
+### Annotations
+
+The dataset is unannotated.
+
+#### Annotation process
+
+[N/A]
+
+#### Who are the annotators?
+
+[N/A]
+
+### Personal and Sensitive Information
+
+Since all data comes from public websites, no anonymization process was performed.
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+We hope this corpus contributes to the development of summarization models in Catalan, a low-resource language.
+
+### Discussion of Biases
+
+We are aware that since the data comes from unreliable web pages, some biases may be present in the dataset. Nonetheless, we have not applied any steps to reduce their impact.
+
+### Other Known Limitations
+
+[N/A]
+
+## Additional Information
+
+### Dataset Curators
+
+Text Mining Unit (TeMU) at the Barcelona Supercomputing Center (bsc-temu@bsc.es)
+
+This work was funded by MT4All CEF project and [Departament de la Vicepresidència i de Polítiques Digitals i Territori de la Generalitat de Catalunya](https://politiquesdigitals.gencat.cat/ca/inici/index.html#googtrans(ca|en) within the framework of [Projecte AINA](https://politiquesdigitals.gencat.cat/ca/economia/catalonia-ai/aina).
+
+
+### Licensing information
+
+[Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0/).
+
+### BibTeX  citation
+
+If you use any of these resources (datasets or models) in your work, please cite our latest preprint:
+
+```bibtex
+@misc{degibert2022sequencetosequence,
+      title={Sequence-to-Sequence Resources for Catalan}, 
+      author={Ona de Gibert and Ksenia Kharitonova and Blanca Calvo Figueras and Jordi Armengol-Estapé and Maite Melero},
+      year={2022},
+      eprint={2202.06871},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL}
+}
+```
+
+### Contributions
+
+[N/A]

huggingface_dataset/Dataset_Card/projecte-aina_catalanqa.md

@@ -0,0 +1,159 @@
+---
+
+annotations_creators:
+- expert-generated
+language_creators:
+- found
+language:
+- ca
+license:
+- cc-by-sa-4.0
+multilinguality:
+- monolingual
+pretty_name: catalanqa
+size_categories:
+- 1K<n<10K
+source_datasets:
+- original
+task_categories:
+- question-answering
+task_ids:
+- extractive-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 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 Card for CatalanQA
+    
+## Dataset Description
+- **Homepage:** https://github.com/projecte-aina
+- **Point of Contact:** [Carlos Rodríguez-Penagos](mailto:carlos.rodriguez1@bsc.es) and [Carme Armentano-Oller](mailto:carme.armentano@bsc.es)
+
+### Dataset Summary
+
+This dataset can be used to build extractive-QA and Language Models. It is an aggregation and balancing of 2 previous datasets: [VilaQuAD](https://huggingface.co/datasets/projecte-aina/vilaquad) and [ViquiQuAD](https://huggingface.co/datasets/projecte-aina/viquiquad).
+
+Splits have been balanced by kind of question, and unlike other datasets like [SQuAD](http://arxiv.org/abs/1606.05250), it only contains, per record, one question and one answer for each context, although the contexts can repeat multiple times.
+
+This dataset was developed by [BSC TeMU](https://temu.bsc.es/) as part of [Projecte AINA](https://politiquesdigitals.gencat.cat/ca/economia/catalonia-ai/aina/), to enrich the [Catalan Language Understanding Benchmark (CLUB)](https://club.aina.bsc.es/).
+
+### Supported Tasks and Leaderboards
+Extractive-QA, Language Model.
+
+### Languages
+The dataset is in Catalan (`ca-CA`).
+
+## Dataset Structure
+### Data Instances
+```
+    {
+      "title": "Els 521 policies espanyols amb més mala nota a les oposicions seran enviats a Catalunya",
+      "paragraphs": [
+        {
+          "context": "El Ministeri d'Interior espanyol enviarà a Catalunya els 521 policies espanyols que han obtingut més mala nota a les oposicions. Segons que explica El País, hi havia mig miler de places vacants que s'havien de cobrir, però els agents amb més bones puntuacions han elegit destinacions diferents. En total van aprovar les oposicions 2.600 aspirants. D'aquests, en seran destinats al Principat 521 dels 560 amb més mala nota. Per l'altra banda, entre els 500 agents amb més bona nota, només 8 han triat Catalunya. Fonts de la policia espanyola que esmenta el diari ho atribueixen al procés d'independència, al Primer d'Octubre i a la 'situació social' que se'n deriva.",
+          "qas": [
+            {
+              "question": "Quants policies enviaran a Catalunya?",
+              "id": "0.5961700408283691",
+              "answers": [
+                {
+                  "text": "521",
+                  "answer_start": 57
+                }
+              ]
+            }
+          ]
+        }
+      ]
+    },
+```
+
+### Data Fields
+Follows [(Rajpurkar, Pranav et al., 2016)](http://arxiv.org/abs/1606.05250) for SQuAD v1 datasets:
+
+- `id` (str): Unique ID assigned to the question.
+- `title` (str): Title of the article.
+- `context` (str): Article text.
+- `question` (str): Question.
+- `answers` (list): Answer to the question, containing:
+  - `text` (str): Span text answering to the question.
+  - `answer_start` Starting offset of the span text answering to the question.
+  
+### Data Splits
+- train.json: 17135 question/answer pairs
+- dev.json: 2157 question/answer pairs
+- test.json: 2135 question/answer pairs
+
+## Dataset Creation
+### Curation Rationale
+
+We created this corpus to contribute to the development of language models in Catalan, a low-resource language.
+
+### Source Data
+- [VilaWeb](https://www.vilaweb.cat/) and [Catalan Wikipedia](https://ca.wikipedia.org).
+
+#### Initial Data Collection and Normalization
+This dataset is a balanced aggregation from [ViquiQuAD](https://huggingface.co/datasets/projecte-aina/viquiquad) and [VilaQuAD](https://huggingface.co/datasets/projecte-aina/vilaquad) datasets.
+
+#### Who are the source language producers?
+Volunteers from [Catalan Wikipedia](https://ca.wikipedia.org) and professional journalists from [VilaWeb](https://www.vilaweb.cat/).
+
+### Annotations
+#### Annotation process
+We did an aggregation and balancing from [ViquiQuAD](https://huggingface.co/datasets/projecte-aina/viquiquad) and [VilaQuAD](https://huggingface.co/datasets/projecte-aina/vilaquad) datasets.
+
+To annotate those datasets, we commissioned the creation of 1 to 5 questions for each context, following an adaptation of the guidelines from SQuAD 1.0 [(Rajpurkar, Pranav et al., 2016)](http://arxiv.org/abs/1606.05250).
+
+For compatibility with similar datasets in other languages, we followed as close as possible existing curation guidelines. 
+
+#### Who are the annotators?
+Annotation was commissioned by a specialized company that hired a team of native language speakers.
+
+### Personal and Sensitive Information
+No personal or sensitive information is included.
+
+## Considerations for Using the Data
+### Social Impact of Dataset
+We hope this corpus contributes to the development of language models in Catalan, a low-resource language.
+
+### Discussion of Biases
+[N/A]
+
+### Other Known Limitations
+[N/A]
+
+## Additional Information
+### Dataset Curators
+Text Mining Unit (TeMU) at the Barcelona Supercomputing Center (bsc-temu@bsc.es)
+
+This work was funded by the [Departament de la Vicepresidència i de Polítiques Digitals i Territori de la Generalitat de Catalunya](https://politiquesdigitals.gencat.cat/ca/inici/index.html#googtrans(ca|en) within the framework of [Projecte AINA](https://politiquesdigitals.gencat.cat/ca/economia/catalonia-ai/aina).
+
+### Licensing Information
+This work is licensed under a <a rel="license" href="https://creativecommons.org/licenses/by-sa/4.0/">Attribution-ShareAlike 4.0 International License</a>.
+
+### Contributions
+
+[N/A]

huggingface_dataset/Dataset_Card/research-backup_semeval2012_relational_similarity_v2.md

@@ -0,0 +1,171 @@
+---
+language:
+- en
+license:
+- other
+multilinguality:
+- monolingual
+size_categories:
+- 1K<n<10K
+pretty_name: SemEval2012 task 2 Relational Similarity
+---
+# Dataset Card for "relbert/semeval2012_relational_similarity_v2"
+## Dataset Description
+- **Repository:** [RelBERT](https://github.com/asahi417/relbert)
+- **Paper:** [https://aclanthology.org/S12-1047/](https://aclanthology.org/S12-1047/)
+- **Dataset:** SemEval2012: Relational Similarity
+
+### Dataset Summary
+
+***IMPORTANT***: This is the same dataset as [relbert/semeval2012_relational_similarity](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity),
+but with a different train/validation split.
+
+Relational similarity dataset from [SemEval2012 task 2](https://aclanthology.org/S12-1047/), compiled to fine-tune [RelBERT](https://github.com/asahi417/relbert) model.
+The dataset contains a list of positive and negative word pair from 89 pre-defined relations.
+The relation types are constructed on top of following 10 parent relation types.  
+```shell
+{
+    1: "Class Inclusion",  # Hypernym
+    2: "Part-Whole",  # Meronym, Substance Meronym
+    3: "Similar",  # Synonym, Co-hypornym
+    4: "Contrast",  # Antonym
+    5: "Attribute",  # Attribute, Event
+    6: "Non Attribute",
+    7: "Case Relation",
+    8: "Cause-Purpose",
+    9: "Space-Time",
+    10: "Representation"
+}
+```
+Each of the parent relation is further grouped into child relation types where the definition can be found [here](https://drive.google.com/file/d/0BzcZKTSeYL8VenY0QkVpZVpxYnc/view?resourcekey=0-ZP-UARfJj39PcLroibHPHw).
+
+
+## Dataset Structure
+### Data Instances
+An example of `train` looks as follows.
+```
+{
+  'relation_type': '8d',
+  'positives': [ [ "breathe", "live" ], [ "study", "learn" ], [ "speak", "communicate" ], ... ]
+  'negatives': [ [ "starving", "hungry" ], [ "clean", "bathe" ], [ "hungry", "starving" ], ... ] 
+}
+```
+
+### Data Splits
+|  name   |train|validation|
+|---------|----:|---------:|
+|semeval2012_relational_similarity_v2| 89 |      89|
+
+
+### Number of Positive/Negative Word-pairs in each Split
+
+| relation_type   |   positive (train) |   negative (train) |   positive (validation) |   negative (validation) |
+|:----------------|-------------------:|-------------------:|------------------------:|------------------------:|
+| 1               |                 40 |                592 |                      10 |                     148 |
+| 10              |                 48 |                584 |                      12 |                     146 |
+| 10a             |                  8 |                640 |                       2 |                     159 |
+| 10b             |                  8 |                638 |                       2 |                     159 |
+| 10c             |                  8 |                640 |                       2 |                     160 |
+| 10d             |                  8 |                640 |                       2 |                     159 |
+| 10e             |                  8 |                636 |                       2 |                     159 |
+| 10f             |                  8 |                640 |                       2 |                     159 |
+| 1a              |                  8 |                638 |                       2 |                     159 |
+| 1b              |                  8 |                638 |                       2 |                     159 |
+| 1c              |                  8 |                640 |                       2 |                     160 |
+| 1d              |                  8 |                638 |                       2 |                     159 |
+| 1e              |                  8 |                636 |                       2 |                     158 |
+| 2               |                 80 |                552 |                      20 |                     138 |
+| 2a              |                  8 |                640 |                       2 |                     159 |
+| 2b              |                  8 |                637 |                       2 |                     159 |
+| 2c              |                  8 |                639 |                       2 |                     159 |
+| 2d              |                  8 |                639 |                       2 |                     159 |
+| 2e              |                  8 |                640 |                       2 |                     159 |
+| 2f              |                  8 |                642 |                       2 |                     160 |
+| 2g              |                  8 |                637 |                       2 |                     159 |
+| 2h              |                  8 |                640 |                       2 |                     159 |
+| 2i              |                  8 |                640 |                       2 |                     160 |
+| 2j              |                  8 |                641 |                       2 |                     160 |
+| 3               |                 64 |                568 |                      16 |                     142 |
+| 3a              |                  8 |                640 |                       2 |                     159 |
+| 3b              |                  8 |                642 |                       2 |                     160 |
+| 3c              |                  8 |                639 |                       2 |                     159 |
+| 3d              |                  8 |                639 |                       2 |                     159 |
+| 3e              |                  8 |                642 |                       2 |                     160 |
+| 3f              |                  8 |                643 |                       2 |                     160 |
+| 3g              |                  8 |                641 |                       2 |                     160 |
+| 3h              |                  8 |                641 |                       2 |                     160 |
+| 4               |                 64 |                568 |                      16 |                     142 |
+| 4a              |                  8 |                642 |                       2 |                     160 |
+| 4b              |                  8 |                638 |                       2 |                     159 |
+| 4c              |                  8 |                640 |                       2 |                     160 |
+| 4d              |                  8 |                637 |                       2 |                     159 |
+| 4e              |                  8 |                642 |                       2 |                     160 |
+| 4f              |                  8 |                642 |                       2 |                     160 |
+| 4g              |                  8 |                639 |                       2 |                     159 |
+| 4h              |                  8 |                641 |                       2 |                     160 |
+| 5               |                 72 |                560 |                      18 |                     140 |
+| 5a              |                  8 |                639 |                       2 |                     159 |
+| 5b              |                  8 |                641 |                       2 |                     160 |
+| 5c              |                  8 |                640 |                       2 |                     159 |
+| 5d              |                  8 |                638 |                       2 |                     159 |
+| 5e              |                  8 |                641 |                       2 |                     160 |
+| 5f              |                  8 |                641 |                       2 |                     160 |
+| 5g              |                  8 |                642 |                       2 |                     160 |
+| 5h              |                  8 |                640 |                       2 |                     160 |
+| 5i              |                  8 |                640 |                       2 |                     160 |
+| 6               |                 64 |                568 |                      16 |                     142 |
+| 6a              |                  8 |                639 |                       2 |                     159 |
+| 6b              |                  8 |                641 |                       2 |                     160 |
+| 6c              |                  8 |                641 |                       2 |                     160 |
+| 6d              |                  8 |                644 |                       2 |                     160 |
+| 6e              |                  8 |                641 |                       2 |                     160 |
+| 6f              |                  8 |                640 |                       2 |                     159 |
+| 6g              |                  8 |                639 |                       2 |                     159 |
+| 6h              |                  8 |                640 |                       2 |                     159 |
+| 7               |                 64 |                568 |                      16 |                     142 |
+| 7a              |                  8 |                640 |                       2 |                     160 |
+| 7b              |                  8 |                637 |                       2 |                     159 |
+| 7c              |                  8 |                638 |                       2 |                     159 |
+| 7d              |                  8 |                640 |                       2 |                     160 |
+| 7e              |                  8 |                638 |                       2 |                     159 |
+| 7f              |                  8 |                637 |                       2 |                     159 |
+| 7g              |                  8 |                636 |                       2 |                     158 |
+| 7h              |                  8 |                636 |                       2 |                     159 |
+| 8               |                 64 |                568 |                      16 |                     142 |
+| 8a              |                  8 |                638 |                       2 |                     159 |
+| 8b              |                  8 |                641 |                       2 |                     160 |
+| 8c              |                  8 |                637 |                       2 |                     159 |
+| 8d              |                  8 |                637 |                       2 |                     159 |
+| 8e              |                  8 |                637 |                       2 |                     159 |
+| 8f              |                  8 |                638 |                       2 |                     159 |
+| 8g              |                  8 |                635 |                       2 |                     158 |
+| 8h              |                  8 |                639 |                       2 |                     159 |
+| 9               |                 72 |                560 |                      18 |                     140 |
+| 9a              |                  8 |                636 |                       2 |                     159 |
+| 9b              |                  8 |                640 |                       2 |                     159 |
+| 9c              |                  8 |                632 |                       2 |                     158 |
+| 9d              |                  8 |                643 |                       2 |                     160 |
+| 9e              |                  8 |                644 |                       2 |                     160 |
+| 9f              |                  8 |                640 |                       2 |                     159 |
+| 9g              |                  8 |                637 |                       2 |                     159 |
+| 9h              |                  8 |                640 |                       2 |                     159 |
+| 9i              |                  8 |                640 |                       2 |                     159 |
+| SUM             |               1264 |              56198 |                     316 |                   14009 |
+
+### Citation Information
+```
+@inproceedings{jurgens-etal-2012-semeval,
+    title = "{S}em{E}val-2012 Task 2: Measuring Degrees of Relational Similarity",
+    author = "Jurgens, David  and
+      Mohammad, Saif  and
+      Turney, Peter  and
+      Holyoak, Keith",
+    booktitle = "*{SEM} 2012: The First Joint Conference on Lexical and Computational Semantics {--} Volume 1: Proceedings of the main conference and the shared task, and Volume 2: Proceedings of the Sixth International Workshop on Semantic Evaluation ({S}em{E}val 2012)",
+    month = "7-8 " # jun,
+    year = "2012",
+    address = "Montr{\'e}al, Canada",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/S12-1047",
+    pages = "356--364",
+}
+```

huggingface_dataset/Dataset_Card/rpereira90_autotrain-data-guitarsproject.md

@@ -0,0 +1,53 @@
+---
+task_categories:
+- image-classification
+
+---
+# AutoTrain Dataset for project: guitarsproject
+
+## Dataset Description
+
+This dataset has been automatically processed by AutoTrain for project guitarsproject.
+
+### 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": "<1990x2520 RGB PIL image>",
+    "target": 1
+  },
+  {
+    "image": "<6000x4000 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(names=['LesPaul', 'Stratocaster'], 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        | 80 |
+| valid        | 21 |

huggingface_dataset/Dataset_Card/sedthh_gutenberg_multilang.md

@@ -0,0 +1,94 @@
+---
+dataset_info:
+  features:
+  - name: TEXT
+    dtype: string
+  - name: SOURCE
+    dtype: string
+  - name: META
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 3127637884
+    num_examples: 7907
+  download_size: 1911478917
+  dataset_size: 3127637884
+license: mit
+task_categories:
+- text-generation
+language:
+- es
+- de
+- fr
+- nl
+- it
+- pt
+- hu
+tags:
+- project gutenberg
+- e-book
+- gutenberg.org
+pretty_name: Project Gutenberg eBooks in different languages
+size_categories:
+- 1K<n<10K
+---
+# Dataset Card for Project Gutenber - Multilanguage eBooks
+
+A collection of non-english language eBooks (7907, about 75-80% of all the ES, DE, FR, NL, IT, PT, HU books available on the site) from the Project Gutenberg site with metadata removed. 
+
+Originally colected for https://github.com/LAION-AI/Open-Assistant
+
+| LANG | EBOOKS |
+|----|----|
+| ES | 717 |
+| DE | 1735 |
+| FR | 2863 |
+| NL | 904 |
+| IT | 692 |
+| PT | 501 |
+| HU | 495 |
+
+The METADATA column contains catalogue meta information on each book as a serialized JSON:
+
+| key | original column |
+|----|----|
+| language | - |
+| text_id | Text# unique book identifier on Prject Gutenberg as *int* |
+| title | Title of the book as *string* |
+| issued | Issued date as *string* |
+| authors | Authors as *string*, comma separated sometimes with dates |
+| subjects | Subjects as *string*, various formats |
+| locc | LoCC code as *string* |
+| bookshelves | Bookshelves as *string*, optional |
+
+## Source data
+
+**How was the data generated?**
+
+- A crawler (see Open-Assistant repository) downloaded the raw HTML code for 
+  each eBook based on **Text#** id in the Gutenberg catalogue (if available)
+- The metadata and the body of text are not clearly separated so an additional
+  parser attempts to split them, then remove transcriber's notes and e-book 
+  related information from the body of text (text clearly marked as copyrighted or 
+  malformed was skipped and not collected)
+- The body of cleaned TEXT as well as the catalogue METADATA is then saved as
+  a parquet file, with all columns being strings
+
+**Copyright notice:**
+
+- Some of the books are copyrighted! The crawler ignored all books
+  with an english copyright header by utilizing a regex expression, but make
+  sure to check out the metadata for each book manually to ensure they are okay
+  to use in your country! More information on copyright:
+  https://www.gutenberg.org/help/copyright.html and
+  https://www.gutenberg.org/policy/permission.html
+- Project Gutenberg has the following requests when using books without
+  metadata: _Books obtianed from the Project Gutenberg site should have the
+  following legal note next to them: "This eBook is for the use of anyone
+  anywhere in the United States and most other parts of the world at no cost and
+  with almost" no restrictions whatsoever. You may copy it, give it away or
+  re-use it under the terms of the Project Gutenberg License included with this
+  eBook or online at www.gutenberg.org. If you are not located in the United
+  States, you will have to check the laws of the country where you are located
+  before using this eBook."_
+

huggingface_dataset/Dataset_Card/wider_face.md

@@ -0,0 +1,263 @@
+---
+annotations_creators:
+- expert-generated
+language_creators:
+- found
+language:
+- en
+license:
+- cc-by-nc-nd-4.0
+multilinguality:
+- monolingual
+size_categories:
+- 10K<n<100K
+source_datasets:
+- extended|other-wider
+task_categories:
+- object-detection
+task_ids:
+- face-detection
+paperswithcode_id: wider-face-1
+pretty_name: WIDER FACE
+dataset_info:
+  features:
+  - name: image
+    dtype: image
+  - name: faces
+    sequence:
+    - name: bbox
+      sequence: float32
+      length: 4
+    - name: blur
+      dtype:
+        class_label:
+          names:
+            '0': clear
+            '1': normal
+            '2': heavy
+    - name: expression
+      dtype:
+        class_label:
+          names:
+            '0': typical
+            '1': exaggerate
+    - name: illumination
+      dtype:
+        class_label:
+          names:
+            '0': normal
+            '1': 'exaggerate '
+    - name: occlusion
+      dtype:
+        class_label:
+          names:
+            '0': 'no'
+            '1': partial
+            '2': heavy
+    - name: pose
+      dtype:
+        class_label:
+          names:
+            '0': typical
+            '1': atypical
+    - name: invalid
+      dtype: bool
+  splits:
+  - name: train
+    num_bytes: 12049881
+    num_examples: 12880
+  - name: test
+    num_bytes: 3761103
+    num_examples: 16097
+  - name: validation
+    num_bytes: 2998735
+    num_examples: 3226
+  download_size: 3676086479
+  dataset_size: 18809719
+---
+
+# Dataset Card for WIDER FACE
+
+## Table of Contents
+- [Table of Contents](#table-of-contents)
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
+  - [Languages](#languages)
+- [Dataset Structure](#dataset-structure)
+  - [Data Instances](#data-instances)
+  - [Data Fields](#data-fields)
+  - [Data Splits](#data-splits)
+- [Dataset Creation](#dataset-creation)
+  - [Curation Rationale](#curation-rationale)
+  - [Source Data](#source-data)
+  - [Annotations](#annotations)
+  - [Personal and Sensitive Information](#personal-and-sensitive-information)
+- [Considerations for Using the Data](#considerations-for-using-the-data)
+  - [Social Impact of Dataset](#social-impact-of-dataset)
+  - [Discussion of Biases](#discussion-of-biases)
+  - [Other Known Limitations](#other-known-limitations)
+- [Additional Information](#additional-information)
+  - [Dataset Curators](#dataset-curators)
+  - [Licensing Information](#licensing-information)
+  - [Citation Information](#citation-information)
+  - [Contributions](#contributions)
+
+## Dataset Description
+
+- **Homepage:** http://shuoyang1213.me/WIDERFACE/index.html
+- **Repository:**
+- **Paper:** [WIDER FACE: A Face Detection Benchmark](https://arxiv.org/abs/1511.06523)
+- **Leaderboard:** http://shuoyang1213.me/WIDERFACE/WiderFace_Results.html
+- **Point of Contact:** shuoyang.1213@gmail.com
+
+### Dataset Summary
+
+WIDER FACE dataset is a face detection benchmark dataset, of which images are
+selected from the publicly available WIDER dataset. We choose 32,203 images and
+label 393,703 faces with a high degree of variability in scale, pose and
+occlusion as depicted in the sample images. WIDER FACE dataset is organized
+based on 61 event classes. For each event class, we randomly select 40%/10%/50%
+data as training, validation and testing sets. We adopt the same evaluation
+metric employed in the PASCAL VOC dataset. Similar to MALF and Caltech datasets,
+we do not release bounding box ground truth for the test images. Users are
+required to submit final prediction files, which we shall proceed to evaluate.
+
+### Supported Tasks and Leaderboards
+
+- `face-detection`: The dataset can be used to train a model for Face Detection. More information on evaluating the model's performance can be found [here](http://shuoyang1213.me/WIDERFACE/WiderFace_Results.html).
+
+### Languages
+
+English
+
+## Dataset Structure
+
+### Data Instances
+
+A data point comprises an image and its face annotations.
+
+```
+{
+  'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=1024x755 at 0x19FA12186D8>, 'faces': {
+    'bbox': [
+      [178.0, 238.0, 55.0, 73.0],
+      [248.0, 235.0, 59.0, 73.0],
+      [363.0, 157.0, 59.0, 73.0],
+      [468.0, 153.0, 53.0, 72.0],
+      [629.0, 110.0, 56.0, 81.0],
+      [745.0, 138.0, 55.0, 77.0]
+    ], 
+    'blur': [2, 2, 2, 2, 2, 2],
+    'expression': [0, 0, 0, 0, 0, 0],
+    'illumination': [0, 0, 0, 0, 0, 0],
+    'occlusion': [1, 2, 1, 2, 1, 2],
+    'pose': [0, 0, 0, 0, 0, 0],
+    'invalid': [False, False, False, False, False, False]
+  }
+}
+```
+
+### Data Fields
+
+- `image`: A `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`
+- `faces`: a dictionary of face attributes for the faces present on the image
+  - `bbox`: the bounding box of each face (in the [coco](https://albumentations.ai/docs/getting_started/bounding_boxes_augmentation/#coco) format)
+  - `blur`: the blur level of each face, with possible values including `clear` (0), `normal` (1) and `heavy`
+  - `expression`: the facial expression of each face, with possible values including `typical` (0) and `exaggerate` (1)
+  - `illumination`: the lightning condition of each face, with possible values including `normal` (0) and `exaggerate` (1)
+  - `occlusion`: the level of occlusion of each face, with possible values including `no` (0), `partial` (1) and `heavy` (2)
+  - `pose`: the pose of each face, with possible values including `typical` (0) and `atypical` (1)
+  - `invalid`: whether the image is valid or invalid.
+
+### Data Splits
+
+The data is split into training, validation and testing set. WIDER FACE dataset is organized
+based on 61 event classes. For each event class, 40%/10%/50%
+data is randomly selected as training, validation and testing sets. The training set contains 12880 images, the validation set 3226 images and test set 16097 images.
+
+## Dataset Creation
+
+### Curation Rationale
+
+The curators state that the current face detection datasets typically contain a few thousand faces, with limited variations in pose, scale, facial expression, occlusion, and background clutters,
+making it difficult to assess for real world performance. They argue that the limitations of datasets have partially contributed to the failure of some algorithms in coping
+with heavy occlusion, small scale, and atypical pose.
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+WIDER FACE dataset is a subset of the WIDER dataset.
+The images in WIDER were collected in the following three steps: 1) Event categories
+were defined and chosen following the Large Scale Ontology for Multimedia (LSCOM) [22], which provides around 1000 concepts relevant to video event analysis. 2) Images
+are retrieved using search engines like Google and Bing. For
+each category, 1000-3000 images were collected. 3) The
+data were cleaned by manually examining all the images
+and filtering out images without human face. Then, similar
+images in each event category were removed to ensure large
+diversity in face appearance. A total of 32203 images are
+eventually included in the WIDER FACE dataset.
+
+#### Who are the source language producers?
+
+The images are selected from publicly available WIDER dataset.
+
+### Annotations
+
+#### Annotation process
+
+The curators label the bounding boxes for all
+the recognizable faces in the WIDER FACE dataset. The
+bounding box is required to tightly contain the forehead,
+chin, and cheek.. If a face is occluded, they still label it with a bounding box but with an estimation on the scale of occlusion. Similar to the PASCAL VOC dataset [6], they assign an ’Ignore’ flag to the face
+which is very difficult to be recognized due to low resolution and small scale (10 pixels or less). After annotating
+the face bounding boxes, they further annotate the following
+attributes: pose (typical, atypical) and occlusion level (partial, heavy). Each annotation is labeled by one annotator
+and cross-checked by two different people.
+
+#### Who are the annotators?
+
+Shuo Yang, Ping Luo, Chen Change Loy and Xiaoou Tang.
+
+### 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
+
+Shuo Yang, Ping Luo, Chen Change Loy and Xiaoou Tang
+
+### Licensing Information
+
+[Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)](https://creativecommons.org/licenses/by-nc-nd/4.0/).
+
+### Citation Information
+
+```
+@inproceedings{yang2016wider,
+    Author = {Yang, Shuo and Luo, Ping and Loy, Chen Change and Tang, Xiaoou},
+    Booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
+    Title = {WIDER FACE: A Face Detection Benchmark},
+    Year = {2016}}
+```
+
+### Contributions
+
+Thanks to [@mariosasko](https://github.com/mariosasko) for adding this dataset.

huggingface_dataset/Dataset_Card/zpn_clintox.md

@@ -0,0 +1,112 @@
+---
+annotations_creators:
+- machine-generated
+language_creators:
+- machine-generated
+license:
+- mit
+multilinguality:
+- monolingual
+pretty_name: clintox
+size_categories:
+- 1K<n<10K
+source_datasets: []
+tags:
+- bio
+- bio-chem
+- molnet
+- molecule-net
+- biophysics
+task_categories:
+- other
+task_ids: []
+---
+
+# Dataset Card for clintox
+
+## 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://moleculenet.org/**
+- **Repository: https://github.com/deepchem/deepchem/tree/master**
+- **Paper: https://arxiv.org/abs/1703.00564**
+
+### Dataset Summary
+
+`clintox` is a dataset included in [MoleculeNet](https://moleculenet.org/). Qualitative data of drugs approved by the FDA and those that have failed clinical trials for toxicity reasons. This uses the `CT_TOX` task.
+
+Note, there was one molecule in the training set that could not be converted to SELFIES (`*C(=O)[C@H](CCCCNC(=O)OCCOC)NC(=O)OCCOC`)
+
+## Dataset Structure
+
+### Data Fields
+
+Each split contains
+
+* `smiles`: the [SMILES](https://en.wikipedia.org/wiki/Simplified_molecular-input_line-entry_system) representation of a molecule
+* `selfies`: the [SELFIES](https://github.com/aspuru-guzik-group/selfies) representation of a molecule
+* `target`: clinical trial toxicity (or absence of toxicity) 
+
+### Data Splits
+
+The dataset is split into an 80/10/10 train/valid/test split using scaffold split. 
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+Data was originially generated by the Pande Group at Standford
+
+### Licensing Information
+
+This dataset was originally released under an MIT license
+
+### Citation Information
+
+```
+@misc{https://doi.org/10.48550/arxiv.1703.00564,
+  doi = {10.48550/ARXIV.1703.00564},
+  
+  url = {https://arxiv.org/abs/1703.00564},
+  
+  author = {Wu, Zhenqin and Ramsundar, Bharath and Feinberg, Evan N. and Gomes, Joseph and Geniesse, Caleb and Pappu, Aneesh S. and Leswing, Karl and Pande, Vijay},
+  
+  keywords = {Machine Learning (cs.LG), Chemical Physics (physics.chem-ph), Machine Learning (stat.ML), FOS: Computer and information sciences, FOS: Computer and information sciences, FOS: Physical sciences, FOS: Physical sciences},
+  
+  title = {MoleculeNet: A Benchmark for Molecular Machine Learning},
+  
+  publisher = {arXiv},
+  
+  year = {2017},
+  
+  copyright = {arXiv.org perpetual, non-exclusive license}
+}
+```
+
+### Contributions
+
+Thanks to [@zanussbaum](https://github.com/zanussbaum) for adding this dataset.