Upload batch 368 (20 files, last=huggingface_dataset/Dataset_Card/autoevaluate_autoeval-eval-Tristan__zero-shot-classification-large-test-Tristan__z-8b146c-1511954902.md)

huggingface_dataset/Dataset_Card/BeIR_quora-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/Dahoas_rm-static.md

@@ -0,0 +1,24 @@
+---
+dataset_info:
+  features:
+  - name: prompt
+    dtype: string
+  - name: response
+    dtype: string
+  - name: chosen
+    dtype: string
+  - name: rejected
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 113850006
+    num_examples: 76256
+  - name: test
+    num_bytes: 7649255
+    num_examples: 5103
+  download_size: 73006535
+  dataset_size: 121499261
+---
+# Dataset Card for "rm-static"
+
+Split of [hh-static](https://huggingface.co/datasets/Dahoas/static-hh) used for training reward models after supervised fine-tuning.

huggingface_dataset/Dataset_Card/DarwinAnim8or_grug.md

@@ -0,0 +1,32 @@
+---
+annotations_creators:
+- no-annotation
+language:
+- en
+language_creators:
+- machine-generated
+license:
+- unknown
+multilinguality:
+- monolingual
+pretty_name: 'Grug Dataset
+
+
+  This is content pulled from various archives to create a "grugbot" or sorts using
+  GPT-J. '
+size_categories: []
+source_datasets: []
+tags:
+- grug
+- internet
+- greentext
+task_categories:
+- text2text-generation
+task_ids: []
+---
+
+
+# Grug Dataset
+
+This is content pulled from various archives to create a "grugbot" or sorts using GPT-J. 
+Really, just a dumb joke I made with some friends. 

huggingface_dataset/Dataset_Card/GEM_FairytaleQA.md

@@ -0,0 +1,686 @@
+---
+annotations_creators:
+- expert-created
+language_creators:
+- unknown
+language:
+- en
+license:
+- unknown
+multilinguality:
+- unknown
+size_categories:
+- unknown
+source_datasets:
+- original
+task_categories:
+- other
+task_ids: []
+pretty_name: FairytaleQA
+tags:
+- question-generation
+---
+
+# Dataset Card for GEM/FairytaleQA
+
+## Dataset Description
+
+- **Homepage:** [Needs More Information]
+- **Repository:** https://github.com/uci-soe/FairytaleQAData
+- **Paper:** https://arxiv.org/abs/2203.13947
+- **Leaderboard:** https://paperswithcode.com/sota/question-generation-on-fairytaleqa
+- **Point of Contact:** Ying Xu, Dakuo Wang
+
+### Link to Main Data Card
+
+You can find the main data card on the [GEM Website](https://gem-benchmark.com/data_cards/FairytaleQA).
+
+### Dataset Summary 
+
+The FairytaleQA Dataset is an English-language dataset focusing on narrative comprehension of kindergarten to eighth-grade students. Generated by educational experts based on an evidence-based theoretical framework, FairytaleQA consists of 10,580 explicit and implicit questions derived from 278 children-friendly stories, covering seven types of narrative elements or relations. The Dataset was corrected to support both the tasks of Question Generation and Question Answering.
+
+You can load the dataset via:
+```
+import datasets
+data = datasets.load_dataset('GEM/FairytaleQA')
+```
+The data loader can be found [here](https://huggingface.co/datasets/GEM/FairytaleQA).
+
+#### paper
+[ArXiv](https://arxiv.org/abs/2203.13947)
+
+#### authors
+Ying Xu (University of California Irvine); Dakuo Wang (IBM Research); Mo Yu (IBM Research); Daniel Ritchie (University of California Irvine); Bingsheng Yao (Rensselaer Polytechnic Institute); Tongshuang Wu (University of Washington); Zheng Zhang (University of Notre Dame); Toby Jia-Jun Li (University of Notre Dame); Nora Bradford (University of California Irvine); Branda Sun (University of California Irvine); Tran Bao Hoang (University of California Irvine); Yisi Sang (Syracuse University); Yufang Hou (IBM Research Ireland); Xiaojuan Ma (Hong Kong Univ. of Sci and Tech); Diyi Yang (Georgia Institute of Technology); Nanyun Peng (University of California Los Angeles); Zhou Yu (Columbia University); Mark Warschauer (University of California Irvine)
+
+## Dataset Overview
+
+### Where to find the Data and its Documentation
+
+#### Download
+
+<!-- info: What is the link to where the original dataset is hosted? -->
+<!-- scope: telescope -->
+[Github](https://github.com/uci-soe/FairytaleQAData)
+
+#### Paper
+
+<!-- info: What is the link to the paper describing the dataset (open access preferred)? -->
+<!-- scope: telescope -->
+[ArXiv](https://arxiv.org/abs/2203.13947)
+
+#### BibTex
+
+<!-- info: Provide the BibTex-formatted reference for the dataset. Please use the correct published version (ACL anthology, etc.) instead of google scholar created Bibtex. -->
+<!-- scope: microscope -->
+@inproceedings{xu2022fairytaleqa,
+    author={Xu, Ying and Wang, Dakuo and Yu, Mo and Ritchie, Daniel and Yao, Bingsheng and Wu, Tongshuang and Zhang, Zheng and Li, Toby Jia-Jun and Bradford, Nora and Sun, Branda and Hoang, Tran Bao and Sang, Yisi and Hou, Yufang and Ma, Xiaojuan and Yang, Diyi and Peng, Nanyun and Yu, Zhou and Warschauer, Mark},
+    title = {Fantastic Questions and Where to Find Them: Fairytale{QA} -- An Authentic Dataset for Narrative Comprehension},
+    publisher = {Association for Computational Linguistics},
+    year = {2022}
+}
+
+#### Contact Name
+
+<!-- quick -->
+<!-- info: If known, provide the name of at least one person the reader can contact for questions about the dataset. -->
+<!-- scope: periscope -->
+Ying Xu, Dakuo Wang
+
+#### Contact Email
+
+<!-- info: If known, provide the email of at least one person the reader can contact for questions about the dataset. -->
+<!-- scope: periscope -->
+ying.xu@uci.edu, dakuo.wang@ibm.com
+
+#### Has a Leaderboard?
+
+<!-- info: Does the dataset have an active leaderboard? -->
+<!-- scope: telescope -->
+yes
+
+#### Leaderboard Link
+
+<!-- info: Provide a link to the leaderboard. -->
+<!-- scope: periscope -->
+[PapersWithCode](https://paperswithcode.com/sota/question-generation-on-fairytaleqa)
+
+#### Leaderboard Details
+
+<!-- info: Briefly describe how the leaderboard evaluates models. -->
+<!-- scope: microscope -->
+The task was to generate questions corresponding to the given answers and the story context. Success on the Question Generation task is typically measured by achieving a high ROUGE-L score to the reference ground-truth question.
+
+
+### Languages and Intended Use
+
+#### Multilingual?
+
+<!-- quick -->
+<!-- info: Is the dataset multilingual? -->
+<!-- scope: telescope -->
+no
+
+#### Covered Dialects
+
+<!-- info: What dialects are covered? Are there multiple dialects per language? -->
+<!-- scope: periscope -->
+[N/A]
+
+#### Covered Languages
+
+<!-- quick -->
+<!-- info: What languages/dialects are covered in the dataset? -->
+<!-- scope: telescope -->
+`English`
+
+#### Whose Language?
+
+<!-- info: Whose language is in the dataset? -->
+<!-- scope: periscope -->
+[N/A]
+
+#### License
+
+<!-- quick -->
+<!-- info: What is the license of the dataset? -->
+<!-- scope: telescope -->
+unknown: License information unavailable
+
+#### Intended Use
+
+<!-- info: What is the intended use of the dataset? -->
+<!-- scope: microscope -->
+The purpose of this dataset is to help develop systems to facilitate assessment and training of narrative comprehension skills for children in education domain. The dataset distinguishes fine-grained reading skills, such as the understanding of varying narrative elements, and contains high-quality QA-pairs generated by education experts with sufficient training and education domain knowledge to create valid QA-pairs in a consistent way. 
+
+This dataset is suitable for developing models to automatically generate questions and QA-Pairs that satisfy the need for a continuous supply of new questions, which can potentially enable large-scale development of AI-supported interactive platforms for the learning and assessment of reading comprehension skills.
+
+#### Primary Task
+
+<!-- info: What primary task does the dataset support? -->
+<!-- scope: telescope -->
+Question Generation
+
+#### Communicative Goal
+
+<!-- quick -->
+<!-- info: Provide a short description of the communicative goal of a model trained for this task on this dataset. -->
+<!-- scope: periscope -->
+The task was to generate questions corresponding to the given answers and the story context. Models trained for this task can potentially enable large-scale development of AI-supported interactive platforms for the learning and assessment of reading comprehension skills.
+
+
+### Credit
+
+#### Curation Organization Type(s)
+
+<!-- info: In what kind of organization did the dataset curation happen? -->
+<!-- scope: telescope -->
+`academic`
+
+#### Curation Organization(s)
+
+<!-- info: Name the organization(s). -->
+<!-- scope: periscope -->
+University of California Irvine
+
+#### Dataset Creators
+
+<!-- info: Who created the original dataset? List the people involved in collecting the dataset and their affiliation(s). -->
+<!-- scope: microscope -->
+Ying Xu (University of California Irvine); Dakuo Wang (IBM Research); Mo Yu (IBM Research); Daniel Ritchie (University of California Irvine); Bingsheng Yao (Rensselaer Polytechnic Institute); Tongshuang Wu (University of Washington); Zheng Zhang (University of Notre Dame); Toby Jia-Jun Li (University of Notre Dame); Nora Bradford (University of California Irvine); Branda Sun (University of California Irvine); Tran Bao Hoang (University of California Irvine); Yisi Sang (Syracuse University); Yufang Hou (IBM Research Ireland); Xiaojuan Ma (Hong Kong Univ. of Sci and Tech); Diyi Yang (Georgia Institute of Technology); Nanyun Peng (University of California Los Angeles); Zhou Yu (Columbia University); Mark Warschauer (University of California Irvine)
+
+#### Funding
+
+<!-- info: Who funded the data creation? -->
+<!-- scope: microscope -->
+Schmidt Futures
+
+#### Who added the Dataset to GEM?
+
+<!-- info: Who contributed to the data card and adding the dataset to GEM? List the people+affiliations involved in creating this data card and who helped integrate this dataset into GEM. -->
+<!-- scope: microscope -->
+Dakuo Wang (IBM Research); Bingsheng Yao (Rensselaer Polytechnic Institute); Ying Xu (University of California Irvine)
+
+
+### Dataset Structure
+
+#### Data Fields
+
+<!-- info: List and describe the fields present in the dataset. -->
+<!-- scope: telescope -->
+- `story_name`: a string of the story name to which the story section content belongs. Full story data can be found [here](https://github.com/uci-soe/FairytaleQAData).
+
+- `content`: a string of the story section(s) content related to the experts' labeled QA-pair. Used as the input for both Question Generation and Question Answering tasks.  
+
+- `question`: a string of the question content. Used as the input for Question Answering task and as the output for Question Generation task.  
+
+- `answer`: a string of the answer content for all splits. Used as the input for Question Generation task and as the output for Question Answering task.
+
+- `gem_id`: a string of id follows GEM naming convention ```GEM-${DATASET_NAME}-${SPLIT-NAME}-${id}``` where id is an incrementing number starting at 1
+
+- `target`: a string of the question content being used for training
+
+- `references`: a list of string containing the question content being used for automatic eval
+
+- `local_or_sum`: a string of either local or summary, indicating whether the QA is related to one story section or multiple sections  
+
+- `attribute`: a string of one of character, causal relationship, action, setting, feeling, prediction, or outcome resolution. Classification of the QA by education experts annotators via 7 narrative elements on an established framework
+ 
+- `ex_or_im`: a string of either explicit or implicit, indicating whether the answers can be directly found in the story content or cannot be directly from the story content.
+
+
+#### Reason for Structure
+
+<!-- info: How was the dataset structure determined? -->
+<!-- scope: microscope -->
+[N/A]
+
+#### How were labels chosen?
+
+<!-- info: How were the labels chosen? -->
+<!-- scope: microscope -->
+A typical data point comprises a question, the corresponding story content, and one answer. Education expert annotators labeled whether the answer is locally relevant to one story section or requires summarization capabilities from multiple story sections, and whether the answers are explicit (can be directly found in the stories) or implicit (cannot be directly found in the story text). Additionally, education expert annotators categorize the QA-pairs via 7 narrative elements from an establish framework.
+
+#### Example Instance
+
+<!-- info: Provide a JSON formatted example of a typical instance in the dataset. -->
+<!-- scope: periscope -->
+{'story_name': 'self-did-it', 
+'content': '" what is your name ? " asked the girl from underground . " self is my name , " said the woman . that seemed a curious name to the girl , and she once more began to pull the fire apart . then the woman grew angry and began to scold , and built it all up again . thus they went on for a good while ; but at last , while they were in the midst of their pulling apart and building up of the fire , the woman upset the tar - barrel on the girl from underground . then the latter screamed and ran away , crying : " father , father ! self burned me ! " " nonsense , if self did it , then self must suffer for it ! " came the answer from below the hill .', 
+'answer': 'the woman told the girl her name was self .', 
+'question': "why did the girl's father think the girl burned herself ?", 
+'gem_id': 'GEM-FairytaleQA-test-1006', 
+'target': "why did the girl's father think the girl burned herself ?", 
+'references': ["why did the girl's father think the girl burned herself ?"], 
+'local_or_sum': 'local', 
+'attribute': 'causal relationship', 
+'ex_or_im': 'implicit'}
+
+#### Data Splits
+
+<!-- info: Describe and name the splits in the dataset if there are more than one. -->
+<!-- scope: periscope -->
+The data is split into a train, validation, and test split randomly. The final split sizes are as follows:
+
+|            | Train | Validation | Test  |
+| -----      | ----- | -----      | ----- |
+| # Books    | 232   | 23         | 23    |
+| # QA-Pairs | 8548  | 1025       |1007   |
+
+#### Splitting Criteria
+
+<!-- info: Describe any criteria for splitting the data, if used. If there are differences between the splits (e.g., if the training annotations are machine-generated and the dev and test ones are created by humans, or if different numbers of annotators contributed to each example), describe them here. -->
+<!-- scope: microscope -->
+The books are randomly split into train/validation/test splits. We control the ratio of QA-pair numbers in train:validation:test splits close to 8:1:1
+
+#### 
+
+<!-- info: What does an outlier of the dataset in terms of length/perplexity/embedding look like? -->
+<!-- scope: microscope -->
+[N/A]
+
+
+
+## Dataset in GEM
+
+### Rationale for Inclusion in GEM
+
+#### Why is the Dataset in GEM?
+
+<!-- info: What does this dataset contribute toward better generation evaluation and why is it part of GEM? -->
+<!-- scope: microscope -->
+The dataset distinguishes fine-grained reading skills, such as the understanding of varying narrative elements, and contains high-quality QA-pairs generated by education experts with sufficient training and education domain knowledge to create valid QA-pairs in a consistent way.
+
+
+
+#### Similar Datasets
+
+<!-- info: Do other datasets for the high level task exist? -->
+<!-- scope: telescope -->
+no
+
+#### Ability that the Dataset measures
+
+<!-- info: What aspect of model ability can be measured with this dataset? -->
+<!-- scope: periscope -->
+This dataset is suitable for developing models to automatically generate questions or QA-pairs that satisfy the need for a continuous supply of new questions, which can potentially enable large-scale development of AI-supported interactive platforms for the learning and assessment of reading comprehension skills.
+
+
+### GEM-Specific Curation
+
+#### Modificatied for GEM?
+
+<!-- info: Has the GEM version of the dataset been modified in any way (data, processing, splits) from the original curated data? -->
+<!-- scope: telescope -->
+yes
+
+#### GEM Modifications
+
+<!-- info: What changes have been made to he original dataset? -->
+<!-- scope: periscope -->
+`data points removed`
+
+#### Modification Details
+
+<!-- info: For each of these changes, described them in more details and provided the intended purpose of the modification -->
+<!-- scope: microscope -->
+The original data contains two answers by different annotators in validation/test splits, we removed the 2nd answer for GEM version because it is not being used for the Question Generation task.  
+
+#### Additional Splits?
+
+<!-- info: Does GEM provide additional splits to the dataset? -->
+<!-- scope: telescope -->
+no
+
+
+### Getting Started with the Task
+
+#### Pointers to Resources
+
+<!-- info: Getting started with in-depth research on the task. Add relevant pointers to resources that researchers can consult when they want to get started digging deeper into the task. -->
+<!-- scope: microscope -->
+[N/A]
+
+
+
+## Previous Results
+
+### Previous Results
+
+#### Measured Model Abilities
+
+<!-- info: What aspect of model ability can be measured with this dataset? -->
+<!-- scope: telescope -->
+We are able to measure model's capabilities of generating various types of questions that corresponds to different narrative elements with the FairytaleQA dataset on the Question Generation Task
+
+#### Metrics
+
+<!-- info: What metrics are typically used for this task? -->
+<!-- scope: periscope -->
+`ROUGE`
+
+#### Proposed Evaluation
+
+<!-- info: List and describe the purpose of the metrics and evaluation methodology (including human evaluation) that the dataset creators used when introducing this task. -->
+<!-- scope: microscope -->
+The task was to generate questions corresponding to the given answers and the story context. Success on this task is typically measured by achieving a high [ROUGE](https://huggingface.co/metrics/rouge) score to the reference ground-truth questions. 
+
+#### Previous results available?
+
+<!-- info: Are previous results available? -->
+<!-- scope: telescope -->
+yes
+
+#### Relevant Previous Results
+
+<!-- info: What are the most relevant previous results for this task/dataset? -->
+<!-- scope: microscope -->
+A [BART-based model](https://huggingface.co/facebook/bart-large) currently achieves a [ROUGE-L of 0.527/0.527](https://github.com/uci-soe/FairytaleQAData) on valid/test splits, which is reported as the baseline experiment for the dataset [paper](https://arxiv.org/pdf/2203.13947.pdf).
+
+
+
+## Dataset Curation
+
+### Original Curation
+
+#### Original Curation Rationale
+
+<!-- info: Original curation rationale -->
+<!-- scope: telescope -->
+FairytaleQA was built to focus on comprehension of narratives in the education domain, targeting students from kindergarten to eighth grade. We focus on narrative comprehension for 1. it is a high-level comprehension skill strongly predictive of reading achievement and plays a central role in daily life as people frequently encounter narratives in different forms, 2. narrative stories have a clear structure of specific elements and relations among these elements, and there are existing validated narrative comprehension frameworks around this structure, which provides a basis for developing the annotation schema for our dataset.
+
+#### Communicative Goal
+
+<!-- info: What was the communicative goal? -->
+<!-- scope: periscope -->
+The purpose of this dataset is to help develop systems to facilitate assessment and training of narrative comprehension skills for children in education domain.
+
+#### Sourced from Different Sources
+
+<!-- info: Is the dataset aggregated from different data sources? -->
+<!-- scope: telescope -->
+no
+
+
+### Language Data
+
+#### How was Language Data Obtained?
+
+<!-- info: How was the language data obtained? -->
+<!-- scope: telescope -->
+`Found`
+
+#### Where was it found?
+
+<!-- info: If found, where from? -->
+<!-- scope: telescope -->
+`Single website`
+
+#### Language Producers
+
+<!-- info: What further information do we have on the language producers? -->
+<!-- scope: microscope -->
+The fairytale story texts are from the [Project Gutenberg](https://www.gutenberg.org/) website
+
+#### Topics Covered
+
+<!-- info: Does the language in the dataset focus on specific topics? How would you describe them? -->
+<!-- scope: periscope -->
+We gathered the text from the Project Gutenberg website, using “fairytale” as the search term. 
+
+#### Data Validation
+
+<!-- info: Was the text validated by a different worker or a data curator? -->
+<!-- scope: telescope -->
+validated by data curator
+
+#### Data Preprocessing
+
+<!-- info: How was the text data pre-processed? (Enter N/A if the text was not pre-processed) -->
+<!-- scope: microscope -->
+Due to a large number of fairytales found, we used the most popular stories based on the number of downloads since these stories are presumably of higher quality. To ensure the readability of the text, we made a small number of minor revisions to some obviously outdated vocabulary (e.g., changing “ere” to “before”) and the unconventional use of punctuation (e.g., changing consecutive semi-colons to periods). 
+
+These texts were broken down into small sections based on their semantic content by our annotators. The annotators were instructed to split the story into sections of 100-300 words that also contain meaningful content and are separated at natural story breaks. An initial annotator would split the story, and this would be reviewed by a cross-checking annotator. Most of the resulting sections were one natural paragraph of the original text.
+
+#### Was Data Filtered?
+
+<!-- info: Were text instances selected or filtered? -->
+<!-- scope: telescope -->
+manually
+
+#### Filter Criteria
+
+<!-- info: What were the selection criteria? -->
+<!-- scope: microscope -->
+For each story, we evaluated the reading difficulty level using the [textstat](https://pypi.org/project/textstat/) Python package, primarily based on sentence length, word length, and commonness of words. We excluded stories that are at 10th grade level or above.
+
+
+### Structured Annotations
+
+#### Additional Annotations?
+
+<!-- quick -->
+<!-- info: Does the dataset have additional annotations for each instance? -->
+<!-- scope: telescope -->
+expert created
+
+#### Number of Raters
+
+<!-- info: What is the number of raters -->
+<!-- scope: telescope -->
+2<n<10
+
+#### Rater Qualifications
+
+<!-- info: Describe the qualifications required of an annotator. -->
+<!-- scope: periscope -->
+All of these annotators have a B.A. degree in education, psychology, or cognitive science and have substantial experience in teaching and reading assessment. These annotators were supervised by three experts in literacy education.
+
+#### Raters per Training Example
+
+<!-- info: How many annotators saw each training example? -->
+<!-- scope: periscope -->
+2
+
+#### Raters per Test Example
+
+<!-- info: How many annotators saw each test example? -->
+<!-- scope: periscope -->
+3
+
+#### Annotation Service?
+
+<!-- info: Was an annotation service used? -->
+<!-- scope: telescope -->
+no
+
+#### Annotation Values
+
+<!-- info: Purpose and values for each annotation -->
+<!-- scope: microscope -->
+The dataset annotation distinguishes fine-grained reading skills, such as the understanding of varying narrative elements, and contains high-quality QA-pairs generated by education experts with sufficient training and education domain knowledge to create valid QA-pairs in a consistent way.
+
+#### Any Quality Control?
+
+<!-- info: Quality control measures? -->
+<!-- scope: telescope -->
+validated by data curators
+
+#### Quality Control Details
+
+<!-- info: Describe the quality control measures that were taken. -->
+<!-- scope: microscope -->
+The annotators were instructed to imagine that they were creating questions to test elementary or middle school students in the process of reading a complete story. We required the annotators to generate only natural, open-ended questions, avoiding “yes-” or “no-” questions. We also instructed them to provide a diverse set of questions about 7 different narrative elements, and with both implicit and explicit questions. 
+
+We asked the annotators to also generate answers for each of their questions. We asked them to provide the shortest possible answers but did not restrict them to complete sentences or short phrases. We also asked the annotators to label which section(s) the question and answer was from.
+
+All annotators received a two-week training in which each of them was familiarized with the coding template and conducted practice coding on the same five stories. The practice QA pairs were then reviewed by the other annotators and the three experts, and discrepancies among annotators were discussed. During the annotation process, the team met once every week to review and discuss each member’s work. All QA pairs were cross-checked by two annotators, and 10% of the QA pairs were additionally checked by the expert supervisor.
+
+For the 46 stories used as the evaluation set, we annotate a second reference answer by asking an annotator to independently read the story and answer the questions generated by others.
+
+
+### Consent
+
+#### Any Consent Policy?
+
+<!-- info: Was there a consent policy involved when gathering the data? -->
+<!-- scope: telescope -->
+yes
+
+#### Consent Policy Details
+
+<!-- info: What was the consent policy? -->
+<!-- scope: microscope -->
+During the annotation process, the team met once every week to review and discuss each member’s work. All QA pairs were cross-checked by two annotators, and 10% of the QA pairs were additionally checked by the expert supervisor.
+
+#### Other Consented Downstream Use
+
+<!-- info: What other downstream uses of the data did the original data creators and the data curators consent to? -->
+<!-- scope: microscope -->
+Aside from Question Generation task, the data creators and curators used this data for Question Answering, and QA-Pair Generation tasks, and to identify social stereotypes represented in story narratives.
+
+
+### Private Identifying Information (PII)
+
+#### Contains PII?
+
+<!-- quick -->
+<!-- info: Does the source language data likely contain Personal Identifying Information about the data creators or subjects? -->
+<!-- scope: telescope -->
+no PII
+
+#### Justification for no PII
+
+<!-- info: Provide a justification for selecting `no PII` above. -->
+<!-- scope: periscope -->
+The story content is from publically available knowledge website and the annotated QA-pairs are about general knowledge to the story content without references to the author or to any persons
+
+
+### Maintenance
+
+#### Any Maintenance Plan?
+
+<!-- info: Does the original dataset have a maintenance plan? -->
+<!-- scope: telescope -->
+yes
+
+#### Maintenance Plan Details
+
+<!-- info: Describe the original dataset's maintenance plan. -->
+<!-- scope: microscope -->
+We plan to host various splits for the FairytaleQA dataset to better serve various types of research interests. We have the original data for 2 different split approaches including train/validation/test splits and split by fairytale origins. We are also plan to host the dataset on multiple platforms for various tasks. 
+
+#### Maintainer Contact Information
+
+<!-- info: Provide contact information of a person responsible for the dataset maintenance -->
+<!-- scope: periscope -->
+Daniel Ritchie
+
+#### Any Contestation Mechanism?
+
+<!-- info: Does the maintenance plan include a contestation mechanism allowing individuals to request removal fo content? -->
+<!-- scope: periscope -->
+no mechanism
+
+
+
+## Broader Social Context
+
+### Previous Work on the Social Impact of the Dataset
+
+#### Usage of Models based on the Data
+
+<!-- info: Are you aware of cases where models trained on the task featured in this dataset ore related tasks have been used in automated systems? -->
+<!-- scope: telescope -->
+yes - models trained on this dataset
+
+#### Social Impact Observations
+
+<!-- info: Did any of these previous uses result in observations about the social impact of the systems? In particular, has there been work outlining the risks and limitations of the system? Provide links and descriptions here. -->
+<!-- scope: microscope -->
+[N/A]
+
+#### Changes as Consequence of Social Impact
+
+<!-- info: Have any changes been made to the dataset as a result of these observations? -->
+<!-- scope: periscope -->
+[N/A]
+
+
+### Impact on Under-Served Communities
+
+#### Addresses needs of underserved Communities?
+
+<!-- info: Does this dataset address the needs of communities that are traditionally underserved in language technology, and particularly language generation technology? Communities may be underserved for exemple because their language, language variety, or social or geographical context is underepresented in NLP and NLG resources (datasets and models). -->
+<!-- scope: telescope -->
+yes
+
+#### Details on how Dataset Addresses the Needs
+
+<!-- info: Describe how this dataset addresses the needs of underserved communities. -->
+<!-- scope: microscope -->
+From the educational perspective, given that reading comprehension is a multicomponent skill, it is ideal for comprehension questions to be able to identify students’ performance in specific sub-skills, thus allowing teachers to provide tailored guidance. 
+
+
+### Discussion of Biases
+
+#### Any Documented Social Biases?
+
+<!-- info: Are there documented social biases in the dataset? Biases in this context are variations in the ways members of different social categories are represented that can have harmful downstream consequences for members of the more disadvantaged group. -->
+<!-- scope: telescope -->
+unsure
+
+#### Are the Language Producers Representative of the Language?
+
+<!-- info: Does the distribution of language producers in the dataset accurately represent the full distribution of speakers of the language world-wide? If not, how does it differ? -->
+<!-- scope: periscope -->
+[N/A]
+
+
+
+## Considerations for Using the Data
+
+### PII Risks and Liability
+
+#### Potential PII Risk
+
+<!-- info: Considering your answers to the PII part of the Data Curation Section, describe any potential privacy to the data subjects and creators risks when using the dataset. -->
+<!-- scope: microscope -->
+[N/A]
+
+
+### Licenses
+
+#### Copyright Restrictions on the Dataset
+
+<!-- info: Based on your answers in the Intended Use part of the Data Overview Section, which of the following best describe the copyright and licensing status of the dataset? -->
+<!-- scope: periscope -->
+`research use only`
+
+#### Copyright Restrictions on the Language Data
+
+<!-- info: Based on your answers in the Language part of the Data Curation Section, which of the following best describe the copyright and licensing status of the underlying language data? -->
+<!-- scope: periscope -->
+`public domain`
+
+
+### Known Technical Limitations
+
+#### Technical Limitations
+
+<!-- info: Describe any known technical limitations, such as spurrious correlations, train/test overlap, annotation biases, or mis-annotations, and cite the works that first identified these limitations when possible. -->
+<!-- scope: microscope -->
+We noticed that human results are obtained via cross-estimation between the two annotated answers, thus are underestimated. One possibility for future work is to conduct a large-scale human annotation to collect more answers per question and then leverage the massively annotated answers to better establish a human performance evaluation.
+
+#### Unsuited Applications
+
+<!-- info: When using a model trained on this dataset in a setting where users or the public may interact with its predictions, what are some pitfalls to look out for? In particular, describe some applications of the general task featured in this dataset that its curation or properties make it less suitable for. -->
+<!-- scope: microscope -->
+The QA-pairs annotated by education experts are targeting the audience of children from kindergarten to eighth grade, so the difficulty of QA-pairs are not suitable to compare with other existing dataset that are sourced from knowledge graphs or knowledge bases like Wikipedia.
+
+#### Discouraged Use Cases
+
+<!-- info: What are some discouraged use cases of a model trained to maximize the proposed metrics on this dataset? In particular, think about settings where decisions made by a model that performs reasonably well on the metric my still have strong negative consequences for user or members of the public. -->
+<!-- scope: microscope -->
+[N/A]
+
+

huggingface_dataset/Dataset_Card/HighCWu_mpii_100_openpose.md

@@ -0,0 +1,123 @@
+---
+license: bsd
+dataset_info:
+  features:
+  - name: image
+    dtype: image
+  - name: guide
+    dtype: image
+  - name: text
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 51273540
+    num_examples: 100
+  download_size: 49905504
+  dataset_size: 51273540
+task_categories:
+- text-to-image
+language:
+- en
+size_categories:
+- n<1K
+---
+
+# Dataset Card for Dataset Name
+
+## Dataset Description
+
+- **Homepage:** 
+- **Repository:** 
+- **Paper:** 
+- **Leaderboard:** 
+- **Point of Contact:** 
+
+### Dataset Summary
+
+This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1).
+
+### 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
+
+[mpii](http://human-pose.mpi-inf.mpg.de/)
+
+#### Initial Data Collection and Normalization
+
+[More Information Needed]
+
+#### Who are the source language producers?
+
+[More Information Needed]
+
+### Annotations
+
+#### Annotation process
+
+[More Information Needed]
+
+#### Who are the annotators?
+
+[More Information Needed]
+
+### Personal and Sensitive Information
+
+[More Information Needed]
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+[More Information Needed]
+
+### Discussion of Biases
+
+[More Information Needed]
+
+### Other Known Limitations
+
+[More Information Needed]
+
+## Additional Information
+
+### Dataset Curators
+
+[More Information Needed]
+
+### Licensing Information
+
+[More Information Needed]
+
+### Citation Information
+
+[More Information Needed]
+
+### Contributions
+
+[More Information Needed]

huggingface_dataset/Dataset_Card/Mediocreatmybest_Example.md

@@ -0,0 +1,11 @@
+---
+license: cc0-1.0
+---
+
+Inital example files to test an easy way to store and manage data text and images. 
+Created from python scripts available at https://github.com/mediocreatmybest/gaslightingeveryone/tree/main/tools
+
+Creation script: https://github.com/mediocreatmybest/gaslightingeveryone/blob/main/tools/images2parq.py
+Extraction script: https://github.com/mediocreatmybest/gaslightingeveryone/blob/main/tools/parq2folder.py
+
+

huggingface_dataset/Dataset_Card/PlanTL-GOB-ES_UD_Spanish-AnCora.md

@@ -0,0 +1,180 @@
+---
+YAML tags:
+
+annotations_creators:
+- expert-generated
+language:
+- es
+language_creators:
+- found
+license:
+- cc-by-4.0
+multilinguality:
+- monolingual
+pretty_name: UD_Spanish-AnCora
+size_categories: []
+source_datasets: []
+tags: []
+task_categories:
+- token-classification
+task_ids:
+- part-of-speech
+
+---
+
+
+# UD_Spanish-AnCora
+
+## 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
+- **Website:** https://github.com/UniversalDependencies/UD_Spanish-AnCora
+- **Point of Contact:** [Daniel Zeman](zeman@ufal.mff.cuni.cz) 
+
+
+### Dataset Summary
+
+This dataset is composed of the annotations from the [AnCora corpus](http://clic.ub.edu/corpus/), projected on the [Universal Dependencies treebank](https://universaldependencies.org/). We use the POS annotations of this corpus as part of the EvalEs Spanish language benchmark. 
+
+### Supported Tasks and Leaderboards
+
+POS tagging
+
+### Languages
+
+The dataset is in Spanish (`es-ES`)
+
+## Dataset Structure
+
+### Data Instances
+
+Three conllu files.
+
+Annotations are encoded in plain text files (UTF-8, normalized to NFC, using only the LF character as line break, including an LF character at the end of file) with three types of lines:
+
+1) Word lines containing the annotation of a word/token in 10 fields separated by single tab characters (see below).
+2) Blank lines marking sentence boundaries.
+3) Comment lines starting with hash (#).
+
+### Data Fields
+Word lines contain the following fields:
+
+1) ID: Word index, integer starting at 1 for each new sentence; may be a range for multiword tokens; may be a decimal number for empty nodes (decimal numbers can be lower than 1 but must be greater than 0).
+2) FORM: Word form or punctuation symbol.
+3) LEMMA: Lemma or stem of word form.
+4) UPOS: Universal part-of-speech tag.
+5) XPOS: Language-specific part-of-speech tag; underscore if not available.
+6) FEATS: List of morphological features from the universal feature inventory or from a defined language-specific extension; underscore if not available.
+7) HEAD: Head of the current word, which is either a value of ID or zero (0).
+8) DEPREL: Universal dependency relation to the HEAD (root iff HEAD = 0) or a defined language-specific subtype of one.
+9) DEPS: Enhanced dependency graph in the form of a list of head-deprel pairs.
+10) MISC: Any other annotation.
+  
+From: [https://universaldependencies.org](https://universaldependencies.org/guidelines.html)
+
+### Data Splits
+
+- es_ancora-ud-train.conllu
+- es_ancora-ud-dev.conllu
+- es_ancora-ud-test.conllu
+
+## Dataset Creation
+
+### Curation Rationale
+[N/A] 
+
+### Source Data
+
+[UD_Spanish-AnCora](https://github.com/UniversalDependencies/UD_Spanish-AnCora)
+
+#### Initial Data Collection and Normalization
+
+The original annotation was done in a constituency framework as a part of the [AnCora project](http://clic.ub.edu/corpus/) at the University of Barcelona. It was converted to dependencies by the [Universal Dependencies team](https://universaldependencies.org/) and used in the CoNLL 2009 shared task. The CoNLL 2009 version was later converted to HamleDT and to Universal Dependencies.
+
+For more information on the AnCora project, visit the [AnCora site](http://clic.ub.edu/corpus/).
+
+To learn about the Universal Dependences, visit the webpage [https://universaldependencies.org](https://universaldependencies.org)
+
+#### Who are the source language producers?
+
+For more information on the AnCora corpus and its sources, visit the [AnCora site](http://clic.ub.edu/corpus/).
+
+### Annotations
+
+#### Annotation process
+
+For more information on the first AnCora annotation, visit the [AnCora site](http://clic.ub.edu/corpus/).
+
+#### Who are the annotators?
+
+For more information on the AnCora annotation team, visit the [AnCora site](http://clic.ub.edu/corpus/).
+
+### Personal and Sensitive Information
+
+No personal or sensitive information included.
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+This dataset contributes to the development of language models in Spanish.
+
+### Discussion of Biases
+
+[N/A]
+
+### Other Known Limitations
+
+[N/A]
+
+## Additional Information
+
+### Dataset Curators
+
+[N/A]
+
+
+### Licensing Information
+
+This work is licensed under a <a rel="license" href="https://creativecommons.org/licenses/by/4.0/">CC Attribution 4.0 International License</a>.
+
+### Citation Information
+
+The following paper must be cited when using this corpus:
+
+Taulé, M., M.A. Martí, M. Recasens (2008) 'Ancora: Multilevel Annotated Corpora for Catalan and Spanish', Proceedings of 6th International Conference on Language Resources and Evaluation. Marrakesh (Morocco).
+
+To cite the Universal Dependencies project:
+
+Rueter, J. (Creator), Erina, O. (Contributor), Klementeva, J. (Contributor), Ryabov, I. (Contributor), Tyers, F. M. (Contributor), Zeman, D. (Contributor), Nivre, J. (Creator) (15 Nov 2020). Universal Dependencies version 2.7 Erzya JR. Universal Dependencies Consortium. 
+
+
+### Contributions
+
+[N/A]
+
+

huggingface_dataset/Dataset_Card/TheGreatRambler_mm2_user.md

@@ -0,0 +1,472 @@
+---
+language:
+- multilingual
+license:
+- cc-by-nc-sa-4.0
+multilinguality:
+- multilingual
+size_categories:
+- 1M<n<10M
+source_datasets:
+- original
+task_categories:
+- other
+- object-detection
+- text-retrieval
+- token-classification
+- text-generation
+task_ids: []
+pretty_name: Mario Maker 2 users
+tags:
+- text-mining
+---
+
+# Mario Maker 2 users
+Part of the [Mario Maker 2 Dataset Collection](https://tgrcode.com/posts/mario_maker_2_datasets)
+
+## Dataset Description
+The Mario Maker 2 users dataset consists of 6 million users from Nintendo's online service totaling around 1.2GB of data. The dataset was created using the self-hosted [Mario Maker 2 api](https://tgrcode.com/posts/mario_maker_2_api) over the course of 1 month in February 2022.
+
+### How to use it
+The Mario Maker 2 users dataset is a very large dataset so for most use cases it is recommended to make use of the streaming API of `datasets`. You can load and iterate through the dataset with the following code:
+
+```python
+from datasets import load_dataset
+
+ds = load_dataset("TheGreatRambler/mm2_user", streaming=True, split="train")
+print(next(iter(ds)))
+
+#OUTPUT:
+{
+ 'pid': '14608829447232141607',
+ 'data_id': 1,
+ 'region': 0,
+ 'name': 'げんまい',
+ 'country': 'JP',
+ 'last_active': 1578384457,
+ 'mii_data': [some binary data],
+ 'mii_image': '000f165d6574777a7881949e9da1acc1cac7cacad3dad9e0eff2f9faf900430a151c25384258637084878e8b96a0b0',
+ 'pose': 0,
+ 'hat': 0,
+ 'shirt': 0,
+ 'pants': 0,
+ 'wearing_outfit': 0,
+ 'courses_played': 12,
+ 'courses_cleared': 10,
+ 'courses_attempted': 23,
+ 'courses_deaths': 13,
+ 'likes': 0,
+ 'maker_points': 0,
+ 'easy_highscore': 0,
+ 'normal_highscore': 0,
+ 'expert_highscore': 0,
+ 'super_expert_highscore': 0,
+ 'versus_rating': 0,
+ 'versus_rank': 1,
+ 'versus_won': 0,
+ 'versus_lost': 1,
+ 'versus_win_streak': 0,
+ 'versus_lose_streak': 1,
+ 'versus_plays': 1,
+ 'versus_disconnected': 0,
+ 'coop_clears': 1,
+ 'coop_plays': 1,
+ 'recent_performance': 1383,
+ 'versus_kills': 0,
+ 'versus_killed_by_others': 0,
+ 'multiplayer_unk13': 286,
+ 'multiplayer_unk14': 5999927,
+ 'first_clears': 0,
+ 'world_records': 0,
+ 'unique_super_world_clears': 0,
+ 'uploaded_levels': 0,
+ 'maximum_uploaded_levels': 100,
+ 'weekly_maker_points': 0,
+ 'last_uploaded_level': 1561555201,
+ 'is_nintendo_employee': 0,
+ 'comments_enabled': 1,
+ 'tags_enabled': 0,
+ 'super_world_id': '',
+ 'unk3': 0,
+ 'unk12': 0,
+ 'unk16': 0
+}
+```
+Each row is a unique play in the level denoted by the `data_id` done by the player denoted by the `pid`, `pid` is a 64 bit integer stored within a string from database limitations. `cleared` and `liked` denote if the player successfully cleared the level during their play and/or liked the level during their play. Every level has only one unique play per player.
+
+Each row is a unique user associated denoted by the `pid`. `data_id` is not used by Nintendo but, like levels, it counts up sequentially and can be used to determine account age. `mii_data` is a `charinfo` type Switch Mii. `mii_image` can be used with Nintendo's online studio API to generate images:
+
+```python
+from datasets import load_dataset
+
+ds = load_dataset("TheGreatRambler/mm2_user", streaming=True, split="train")
+
+mii_image = next(iter(ds))["mii_image"]
+print("Face: https://studio.mii.nintendo.com/miis/image.png?data=%s&type=face&width=512&instanceCount=1" % mii_image)
+print("Body: https://studio.mii.nintendo.com/miis/image.png?data=%s&type=all_body&width=512&instanceCount=1" % mii_image)
+print("Face (x16): https://studio.mii.nintendo.com/miis/image.png?data=%s&type=face&width=512&instanceCount=16" % mii_image)
+print("Body (x16): https://studio.mii.nintendo.com/miis/image.png?data=%s&type=all_body&width=512&instanceCount=16" % mii_image)
+```
+
+`pose`, `hat`, `shirt` and `pants` has associated enums described below. `last_active` and `last_uploaded_level` are UTC timestamps. `super_world_id`, if not empty, provides the ID of a super world in `TheGreatRambler/mm2_world`.
+
+You can also download the full dataset. Note that this will download ~1.2GB:
+```python
+ds = load_dataset("TheGreatRambler/mm2_user", split="train")
+```
+
+## Data Structure
+
+### Data Instances
+
+```python
+{
+ 'pid': '14608829447232141607',
+ 'data_id': 1,
+ 'region': 0,
+ 'name': 'げんまい',
+ 'country': 'JP',
+ 'last_active': 1578384457,
+ 'mii_data': [some binary data],
+ 'mii_image': '000f165d6574777a7881949e9da1acc1cac7cacad3dad9e0eff2f9faf900430a151c25384258637084878e8b96a0b0',
+ 'pose': 0,
+ 'hat': 0,
+ 'shirt': 0,
+ 'pants': 0,
+ 'wearing_outfit': 0,
+ 'courses_played': 12,
+ 'courses_cleared': 10,
+ 'courses_attempted': 23,
+ 'courses_deaths': 13,
+ 'likes': 0,
+ 'maker_points': 0,
+ 'easy_highscore': 0,
+ 'normal_highscore': 0,
+ 'expert_highscore': 0,
+ 'super_expert_highscore': 0,
+ 'versus_rating': 0,
+ 'versus_rank': 1,
+ 'versus_won': 0,
+ 'versus_lost': 1,
+ 'versus_win_streak': 0,
+ 'versus_lose_streak': 1,
+ 'versus_plays': 1,
+ 'versus_disconnected': 0,
+ 'coop_clears': 1,
+ 'coop_plays': 1,
+ 'recent_performance': 1383,
+ 'versus_kills': 0,
+ 'versus_killed_by_others': 0,
+ 'multiplayer_unk13': 286,
+ 'multiplayer_unk14': 5999927,
+ 'first_clears': 0,
+ 'world_records': 0,
+ 'unique_super_world_clears': 0,
+ 'uploaded_levels': 0,
+ 'maximum_uploaded_levels': 100,
+ 'weekly_maker_points': 0,
+ 'last_uploaded_level': 1561555201,
+ 'is_nintendo_employee': 0,
+ 'comments_enabled': 1,
+ 'tags_enabled': 0,
+ 'super_world_id': '',
+ 'unk3': 0,
+ 'unk12': 0,
+ 'unk16': 0
+}
+```
+
+### Data Fields
+
+|Field|Type|Description|
+|---|---|---|
+|pid|string|The player ID of this user, an unsigned 64 bit integer as a string|
+|data_id|int|The data ID of this user, while not used internally user codes are generated using this|
+|region|int|User region, enum below|
+|name|string|User name|
+|country|string|User country as a 2 letter ALPHA-2 code|
+|last_active|int|UTC timestamp of when this user was last active, not known what constitutes active|
+|mii_data|bytes|The CHARINFO blob of this user's Mii|
+|mii_image|string|A string that can be fed into Nintendo's studio API to generate an image|
+|pose|int|Pose, enum below|
+|hat|int|Hat, enum below|
+|shirt|int|Shirt, enum below|
+|pants|int|Pants, enum below|
+|wearing_outfit|bool|Whether this user is wearing pants|
+|courses_played|int|How many courses this user has played|
+|courses_cleared|int|How many courses this user has cleared|
+|courses_attempted|int|How many courses this user has attempted|
+|courses_deaths|int|How many times this user has died|
+|likes|int|How many likes this user has recieved|
+|maker_points|int|Maker points|
+|easy_highscore|int|Easy highscore|
+|normal_highscore|int|Normal highscore|
+|expert_highscore|int|Expert highscore|
+|super_expert_highscore|int|Super expert high score|
+|versus_rating|int|Versus rating|
+|versus_rank|int|Versus rank, enum below|
+|versus_won|int|How many courses this user has won in versus|
+|versus_lost|int|How many courses this user has lost in versus|
+|versus_win_streak|int|Versus win streak|
+|versus_lose_streak|int|Versus lose streak|
+|versus_plays|int|Versus plays|
+|versus_disconnected|int|Times user has disconnected in versus|
+|coop_clears|int|Coop clears|
+|coop_plays|int|Coop plays|
+|recent_performance|int|Unknown variable relating to versus performance|
+|versus_kills|int|Kills in versus, unknown what activities constitute a kill|
+|versus_killed_by_others|int|Deaths in versus from other users, little is known about what activities constitute a death|
+|multiplayer_unk13|int|Unknown, relating to multiplayer|
+|multiplayer_unk14|int|Unknown, relating to multiplayer|
+|first_clears|int|First clears|
+|world_records|int|World records|
+|unique_super_world_clears|int|Super world clears|
+|uploaded_levels|int|Number of uploaded levels|
+|maximum_uploaded_levels|int|Maximum number of levels this user may upload|
+|weekly_maker_points|int|Weekly maker points|
+|last_uploaded_level|int|UTC timestamp of when this user last uploaded a level|
+|is_nintendo_employee|bool|Whether this user is an official Nintendo account|
+|comments_enabled|bool|Whether this user has comments enabled on their levels|
+|tags_enabled|bool|Whether this user has tags enabled on their levels|
+|super_world_id|string|The ID of this user's super world, blank if they do not have one|
+|unk3|int|Unknown|
+|unk12|int|Unknown|
+|unk16|int|Unknown|
+
+### Data Splits
+
+The dataset only contains a train split.
+
+## Enums
+
+The dataset contains some enum integer fields. This can be used to convert back to their string equivalents:
+
+```python
+Regions = {
+	0: "Asia",
+	1: "Americas",
+	2: "Europe",
+	3: "Other"
+}
+
+MultiplayerVersusRanks = {
+	1: "D",
+	2: "C",
+	3: "B",
+	4: "A",
+	5: "S",
+	6: "S+"
+}
+
+UserPose = {
+	0: "Normal",
+	15: "Fidgety",
+	17: "Annoyed",
+	18: "Buoyant",
+	19: "Thrilled",
+	20: "Let's go!",
+	21: "Hello!",
+	29: "Show-Off",
+	31: "Cutesy",
+	39: "Hyped!"
+}
+
+UserHat = {
+	0: "None",
+	1: "Mario Cap",
+	2: "Luigi Cap",
+	4: "Mushroom Hairclip",
+	5: "Bowser Headpiece",
+	8: "Princess Peach Wig",
+	11: "Builder Hard Hat",
+	12: "Bowser Jr. Headpiece",
+	13: "Pipe Hat",
+	15: "Cat Mario Headgear",
+	16: "Propeller Mario Helmet",
+	17: "Cheep Cheep Hat",
+	18: "Yoshi Hat",
+	21: "Faceplant",
+	22: "Toad Cap",
+	23: "Shy Cap",
+	24: "Magikoopa Hat",
+	25: "Fancy Top Hat",
+	26: "Doctor Headgear",
+	27: "Rocky Wrench Manhold Lid",
+	28: "Super Star Barrette",
+	29: "Rosalina Wig",
+	30: "Fried-Chicken Headgear",
+	31: "Royal Crown",
+	32: "Edamame Barrette",
+	33: "Superball Mario Hat",
+	34: "Robot Cap",
+	35: "Frog Cap",
+	36: "Cheetah Headgear",
+	37: "Ninji Cap",
+	38: "Super Acorn Hat",
+	39: "Pokey Hat",
+	40: "Snow Pokey Hat"
+}
+
+UserShirt = {
+	0: "Nintendo Shirt",
+	1: "Mario Outfit",
+	2: "Luigi Outfit",
+	3: "Super Mushroom Shirt",
+	5: "Blockstripe Shirt",
+	8: "Bowser Suit",
+	12: "Builder Mario Outfit",
+	13: "Princess Peach Dress",
+	16: "Nintendo Uniform",
+	17: "Fireworks Shirt",
+	19: "Refreshing Shirt",
+	21: "Reset Dress",
+	22: "Thwomp Suit",
+	23: "Slobbery Shirt",
+	26: "Cat Suit",
+	27: "Propeller Mario Clothes",
+	28: "Banzai Bill Shirt",
+	29: "Staredown Shirt",
+	31: "Yoshi Suit",
+	33: "Midnight Dress",
+	34: "Magikoopa Robes",
+	35: "Doctor Coat",
+	37: "Chomp-Dog Shirt",
+	38: "Fish Bone Shirt",
+	40: "Toad Outfit",
+	41: "Googoo Onesie",
+	42: "Matrimony Dress",
+	43: "Fancy Tuxedo",
+	44: "Koopa Troopa Suit",
+	45: "Laughing Shirt",
+	46: "Running Shirt",
+	47: "Rosalina Dress",
+	49: "Angry Sun Shirt",
+	50: "Fried-Chicken Hoodie",
+	51: "? Block Hoodie",
+	52: "Edamame Camisole",
+	53: "I-Like-You Camisole",
+	54: "White Tanktop",
+	55: "Hot Hot Shirt",
+	56: "Royal Attire",
+	57: "Superball Mario Suit",
+	59: "Partrick Shirt",
+	60: "Robot Suit",
+	61: "Superb Suit",
+	62: "Yamamura Shirt",
+	63: "Princess Peach Tennis Outfit",
+	64: "1-Up Hoodie",
+	65: "Cheetah Tanktop",
+	66: "Cheetah Suit",
+	67: "Ninji Shirt",
+	68: "Ninji Garb",
+	69: "Dash Block Hoodie",
+	70: "Fire Mario Shirt",
+	71: "Raccoon Mario Shirt",
+	72: "Cape Mario Shirt",
+	73: "Flying Squirrel Mario Shirt",
+	74: "Cat Mario Shirt",
+	75: "World Wear",
+	76: "Koopaling Hawaiian Shirt",
+	77: "Frog Mario Raincoat",
+	78: "Phanto Hoodie"
+}
+
+UserPants = {
+	0: "Black Short-Shorts",
+	1: "Denim Jeans",
+	5: "Denim Skirt",
+	8: "Pipe Skirt",
+	9: "Skull Skirt",
+	10: "Burner Skirt",
+	11: "Cloudwalker",
+	12: "Platform Skirt",
+	13: "Parent-and-Child Skirt",
+	17: "Mario Swim Trunks",
+	22: "Wind-Up Shoe",
+	23: "Hoverclown",
+	24: "Big-Spender Shorts",
+	25: "Shorts of Doom!",
+	26: "Doorduroys",
+	27: "Antsy Corduroys",
+	28: "Bouncy Skirt",
+	29: "Stingby Skirt",
+	31: "Super Star Flares",
+	32: "Cheetah Runners",
+	33: "Ninji Slacks"
+}
+
+# Checked against user's shirt
+UserIsOutfit = {
+	0: False,
+	1: True,
+	2: True,
+	3: False,
+	5: False,
+	8: True,
+	12: True,
+	13: True,
+	16: False,
+	17: False,
+	19: False,
+	21: True,
+	22: True,
+	23: False,
+	26: True,
+	27: True,
+	28: False,
+	29: False,
+	31: True,
+	33: True,
+	34: True,
+	35: True,
+	37: False,
+	38: False,
+	40: True,
+	41: True,
+	42: True,
+	43: True,
+	44: True,
+	45: False,
+	46: False,
+	47: True,
+	49: False,
+	50: False,
+	51: False,
+	52: False,
+	53: False,
+	54: False,
+	55: False,
+	56: True,
+	57: True,
+	59: False,
+	60: True,
+	61: True,
+	62: False,
+	63: True,
+	64: False,
+	65: False,
+	66: True,
+	67: False,
+	68: True,
+	69: False,
+	70: False,
+	71: False,
+	72: False,
+	73: False,
+	74: False,
+	75: True,
+	76: False,
+	77: True,
+	78: False
+}
+```
+
+<!-- TODO create detailed statistics -->
+
+## Dataset Creation
+
+The dataset was created over a little more than a month in Febuary 2022 using the self hosted [Mario Maker 2 api](https://tgrcode.com/posts/mario_maker_2_api). As requests made to Nintendo's servers require authentication the process had to be done with upmost care and limiting download speed as to not overload the API and risk a ban. There are no intentions to create an updated release of this dataset.
+
+## Considerations for Using the Data
+
+The dataset consists of many different Mario Maker 2 players globally and as such their names could contain harmful language. Harmful depictions could also be present in their Miis, should you choose to render it.

huggingface_dataset/Dataset_Card/VLyb_FB15k.md

@@ -0,0 +1,16 @@
+---
+license: unlicense
+language:
+- en
+tags:
+- link-prediction
+pretty_name: FB15k
+size_categories:
+- 10K<n<100K
+---
+
+# FB15k Dataset
+
+The details of it can be got by this paper titled:
+
++ [Translating Embeddings for Modeling Multi-relational Data](http://dl.acm.org/doi/10.5555/2999792.2999923)

huggingface_dataset/Dataset_Card/argilla_twitter-coronavirus.md

@@ -0,0 +1,103 @@
+---
+language:
+- en
+license:
+- unknown
+size_categories:
+- 10K<n<100K
+source_datasets:
+- original
+task_categories:
+- text-classification
+task_ids:
+- sentiment-classification
+- sentiment-analysis
+dataset_info:
+  features:
+  - name: text
+    dtype: string
+  - name: inputs
+    struct:
+    - name: text
+      dtype: string
+  - name: prediction
+    list:
+    - name: label
+      dtype: string
+    - name: score
+      dtype: float64
+  - name: prediction_agent
+    dtype: string
+  - name: annotation
+    dtype: 'null'
+  - name: annotation_agent
+    dtype: 'null'
+  - name: multi_label
+    dtype: bool
+  - name: explanation
+    dtype: 'null'
+  - name: id
+    dtype: string
+  - name: metadata
+    struct:
+    - name: location
+      dtype: string
+    - name: screen_name
+      dtype: int64
+    - name: split
+      dtype: string
+    - name: user_name
+      dtype: int64
+  - name: status
+    dtype: string
+  - name: event_timestamp
+    dtype: timestamp[us]
+  - name: metrics
+    struct:
+    - name: text_length
+      dtype: int64
+  splits:
+  - name: train
+    num_bytes: 25394534
+    num_examples: 44955
+  download_size: 15712627
+  dataset_size: 25394534
+---
+# Dataset Card for "twitter-coronavirus"
+
+## Dataset Description
+
+- **Homepage:** Kaggle Challenge
+- **Repository:** https://www.kaggle.com/datasets/datatattle/covid-19-nlp-text-classification
+- **Paper:** N.A.
+- **Leaderboard:** N.A.
+- **Point of Contact:** N.A.
+
+### Dataset Summary
+
+Perform Text Classification on the data. The tweets have been pulled from Twitter and manual tagging has been done then.
+The names and usernames have been given codes to avoid any privacy concerns.
+
+Columns:
+1) Location
+2) Tweet At
+3) Original Tweet
+4) Label
+  - Extremely Negative
+  - Negative
+  - Neutral
+  - Positive
+  - Extremely Positive
+
+### Languages
+
+english 
+
+### Citation Information
+
+https://www.kaggle.com/datasets/datatattle/covid-19-nlp-text-classification
+
+
+### Contributions
+
+Thanks to [@davidberenstein1957](https://github.com/davidberenstein1957) for adding this dataset.

huggingface_dataset/Dataset_Card/autoevaluate_autoeval-eval-Tristan__zero-shot-classification-large-test-Tristan__z-8b146c-1511954902.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: Tristan/opt-30b-copy
+  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: Tristan/opt-30b-copy
+* 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-eval-phpthinh__exampletx-constructive-7f6ba0-1708559812.md

@@ -0,0 +1,34 @@
+---
+type: predictions
+tags:
+- autotrain
+- evaluation
+datasets:
+- phpthinh/exampletx
+eval_info:
+  task: text_zero_shot_classification
+  model: bigscience/bloom-560m
+  metrics: []
+  dataset_name: phpthinh/exampletx
+  dataset_config: constructive
+  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-560m
+* Dataset: phpthinh/exampletx
+* Config: constructive
+* 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-project-636a44ed-fa98-4717-b181-b742a86b03be-4846.md

@@ -0,0 +1,33 @@
+---
+type: predictions
+tags:
+- autotrain
+- evaluation
+datasets:
+- emotion
+eval_info:
+  task: multi_class_classification
+  model: autoevaluate/multi-class-classification
+  metrics: ['matthews_correlation']
+  dataset_name: emotion
+  dataset_config: default
+  dataset_split: test
+  col_mapping:
+    text: text
+    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 Text Classification
+* Model: autoevaluate/multi-class-classification
+* Dataset: emotion
+* Config: default
+* Split: test
+
+To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
+
+## Contributions
+
+Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model.

huggingface_dataset/Dataset_Card/autoevaluate_autoeval-staging-eval-project-f87a1758-7384798.md

@@ -0,0 +1,30 @@
+---
+type: predictions
+tags:
+- autotrain
+- evaluation
+datasets:
+- banking77
+eval_info:
+  task: multi_class_classification
+  model: philschmid/RoBERTa-Banking77
+  dataset_name: banking77
+  dataset_config: default
+  dataset_split: test
+  col_mapping:
+    text: text
+    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 Text Classification
+* Model: philschmid/RoBERTa-Banking77
+* Dataset: banking77
+
+To run new evaluation jobs, visit Hugging Face's [automatic evaluation service](https://huggingface.co/spaces/autoevaluate/model-evaluator).
+
+## Contributions
+
+Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model.

huggingface_dataset/Dataset_Card/autoevaluate_autoeval-staging-eval-samsum-samsum-fbc19a-15816179.md

@@ -0,0 +1,33 @@
+---
+type: predictions
+tags:
+- autotrain
+- evaluation
+datasets:
+- samsum
+eval_info:
+  task: summarization
+  model: google/pegasus-xsum
+  metrics: []
+  dataset_name: samsum
+  dataset_config: samsum
+  dataset_split: validation
+  col_mapping:
+    text: dialogue
+    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/pegasus-xsum
+* Dataset: samsum
+* Config: samsum
+* Split: validation
+
+To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
+
+## Contributions
+
+Thanks to [@samuelallen123](https://huggingface.co/samuelallen123) for evaluating this model.

huggingface_dataset/Dataset_Card/cannlytics_aggregated-cannabis-test-results.md

@@ -0,0 +1,40 @@
+---
+license: cc-by-4.0
+---
+
+# Aggregated Cannabis Laboratory Test Results
+
+Lab results are arguably among the most valuable data that you can come by in the cannabis industry. Due to the complexity of the data and, until now, a lack of standardization, the lab results that are publicly available have been just out of reach. Cannlytics puts these lab results in your hands, fresh, clean, and standardized, ripe for you to calculate juicy cannabis statistics.
+
+## Algorithms
+
+| Algorithm | URL |
+|-----------|-----|
+| MCR Labs Data Collection Routine | <https://github.com/cannlytics/cannlytics/tree/main/ai/curation/get_mcr_labs_data> |
+| PSI Labs Data Collection Routine | <https://github.com/cannlytics/cannlytics/tree/main/ai/curation/get_psi_labs_data> |
+| SC Labs Data Collection Routine | <https://github.com/cannlytics/cannlytics/tree/main/ai/curation/get_sc_labs_data> |
+
+## Data Sources
+
+| Data Source | URL |
+|-------------|-----|
+| MCR Labs Test Results | <https://reports.mcrlabs.com> |
+| PSI Labs Test Results | <https://results.psilabs.org/test-results/> |
+| SC Labs Test Results | <https://client.sclabs.com/>
+
+## License
+
+```
+Copyright (c) 2022 Cannlytics and the Cannabis Data Science Team
+
+The files associated with this dataset are licensed under a 
+Creative Commons Attribution 4.0 International license.
+
+You can share, copy and modify this dataset so long as you give
+appropriate credit, provide a link to the CC BY license, and
+indicate if changes were made, but you may not do so in a way
+that suggests the rights holder has endorsed you or your use of
+the dataset. Note that further permission may be required for
+any content within the dataset that is identified as belonging
+to a third party.
+```

huggingface_dataset/Dataset_Card/chitra_contradictionNLI.md

@@ -0,0 +1,2 @@
+This data can help in solving contradiction detection problem. this data is picked from kaggle. 
+reference - Contradictory, My DWatson

huggingface_dataset/Dataset_Card/lmqg_qa_squad.md

@@ -0,0 +1,65 @@
+---
+license: cc-by-4.0
+pretty_name: SQuAD with QG split.
+language: en
+multilinguality: monolingual
+size_categories: 1M<
+source_datasets:
+- extended|wikipedia
+task_categories:
+- question-answering
+task_ids:
+- extractive-qa
+---
+
+# Dataset Card for "lmqg/qa_squad"
+
+## Dataset Description
+- **Repository:** [https://github.com/asahi417/lm-question-generation](https://github.com/asahi417/lm-question-generation)
+- **Paper:** [https://rajpurkar.github.io/SQuAD-explorer/](https://rajpurkar.github.io/SQuAD-explorer/)
+- **Point of Contact:** [Asahi Ushio](http://asahiushio.com/)
+
+### Dataset Summary
+This is the SQuAD v1 dataset with the train/validatio/test split used in [qg_squad](https://huggingface.co/datasets/lmqg/qg_squad).
+
+
+### Supported Tasks and Leaderboards
+* `question-answering`
+
+### Languages
+English (en)
+
+## Dataset Structure
+
+### Data Fields
+The data fields are the same among all splits.
+
+#### plain_text
+
+- `id`: a `string` feature of id
+- `title`: a `string` feature of title of the paragraph 
+- `context`: a `string` feature of paragraph 
+- `question`: a `string` feature of question
+- `answers`: a `json` feature of answers
+
+### Data Splits
+
+|train    |validation|test    |
+|--------:|---------:|-------:|
+|   75,722|    10,570|  11,877|
+
+## Citation Information
+
+```
+@article{2016arXiv160605250R,
+       author = {{Rajpurkar}, Pranav and {Zhang}, Jian and {Lopyrev},
+                 Konstantin and {Liang}, Percy},
+        title = "{SQuAD: 100,000+ Questions for Machine Comprehension of Text}",
+      journal = {arXiv e-prints},
+         year = 2016,
+          eid = {arXiv:1606.05250},
+        pages = {arXiv:1606.05250},
+archivePrefix = {arXiv},
+       eprint = {1606.05250},
+}
+```

huggingface_dataset/Dataset_Card/mwong_climatetext-evidence-claim-pair-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 climate related evidence and claim, predict if pair is related.

huggingface_dataset/Dataset_Card/tner_mit_movie_trivia.md

@@ -0,0 +1,79 @@
+---
+language:
+- en
+license:
+- other
+multilinguality:
+- monolingual
+size_categories:
+- 1K<n<10K
+task_categories:
+- token-classification
+task_ids:
+- named-entity-recognition
+pretty_name: MIT Movie
+---
+
+# Dataset Card for "tner/mit_movie_trivia"
+
+## Dataset Description
+
+- **Repository:** [T-NER](https://github.com/asahi417/tner)
+- **Dataset:** MIT Movie
+- **Domain:** Movie
+- **Number of Entity:** 12
+
+### Dataset Summary
+MIT Movie NER dataset formatted in a part of [TNER](https://github.com/asahi417/tner) project.
+
+- Entity Types: `Actor`, `Plot`, `Opinion`, `Award`, `Year`, `Genre`, `Origin`, `Director`, `Soundtrack`, `Relationship`, `Character_Name`, `Quote`
+
+## Dataset Structure
+
+### Data Instances
+An example of `train` looks as follows.
+
+```
+{
+    'tags': [0, 13, 14, 0, 0, 0, 3, 4, 4, 4, 4, 4, 4, 4, 4],
+    'tokens': ['a', 'steven', 'spielberg', 'film', 'featuring', 'a', 'bluff', 'called', 'devil', 's', 'tower', 'and', 'a', 'spectacular', 'mothership']
+}
+```
+
+### Label ID
+The label2id dictionary can be found at [here](https://huggingface.co/datasets/tner/mit_movie_trivia/raw/main/dataset/label.json).
+```python
+{
+    "O": 0,
+    "B-Actor": 1,
+    "I-Actor": 2,
+    "B-Plot": 3,
+    "I-Plot": 4,
+    "B-Opinion": 5,
+    "I-Opinion": 6,
+    "B-Award": 7,
+    "I-Award": 8,
+    "B-Year": 9,
+    "B-Genre": 10,
+    "B-Origin": 11,
+    "I-Origin": 12,
+    "B-Director": 13,
+    "I-Director": 14,
+    "I-Genre": 15,
+    "I-Year": 16,
+    "B-Soundtrack": 17,
+    "I-Soundtrack": 18,
+    "B-Relationship": 19,
+    "I-Relationship": 20,
+    "B-Character_Name": 21,
+    "I-Character_Name": 22,
+    "B-Quote": 23,
+    "I-Quote": 24
+}
+```
+
+### Data Splits
+
+|  name   |train|validation|test|
+|---------|----:|---------:|---:|
+|mit_movie_trivia      |6816  |       1000| 1953|