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huggingface_dataset/Dataset_Card/Datatang_Mandarin_Speech_Data_by_Mobile_Phone.md

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+---
+YAML tags:
+- copy-paste the tags obtained with the tagging app: https://github.com/huggingface/datasets-tagging
+---
+
+# Dataset Card for Datatang/Mandarin_Speech_Data_by_Mobile_Phone
+
+## Table of Contents
+- [Table of Contents](#table-of-contents)
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
+  - [Languages](#languages)
+- [Dataset Structure](#dataset-structure)
+  - [Data Instances](#data-instances)
+  - [Data Fields](#data-fields)
+  - [Data Splits](#data-splits)
+- [Dataset Creation](#dataset-creation)
+  - [Curation Rationale](#curation-rationale)
+  - [Source Data](#source-data)
+  - [Annotations](#annotations)
+  - [Personal and Sensitive Information](#personal-and-sensitive-information)
+- [Considerations for Using the Data](#considerations-for-using-the-data)
+  - [Social Impact of Dataset](#social-impact-of-dataset)
+  - [Discussion of Biases](#discussion-of-biases)
+  - [Other Known Limitations](#other-known-limitations)
+- [Additional Information](#additional-information)
+  - [Dataset Curators](#dataset-curators)
+  - [Licensing Information](#licensing-information)
+  - [Citation Information](#citation-information)
+  - [Contributions](#contributions)
+
+## Dataset Description
+
+- **Homepage:** https://bit.ly/3bj7xZh
+- **Repository:**
+- **Paper:**
+- **Leaderboard:**
+- **Point of Contact:**
+
+### Dataset Summary
+
+4,787 Chinese native speakers participated in the recording with equal gender. Speakers are from various provinces of China. The recording content is rich, covering mobile phone voice assistant interaction, smart home command and control, In-car command and control, numbers, and other fields, which is accurately matching the smart home, intelligent car, and other practical application scenarios.
+                           
+For more details, please refer to the link: https://bit.ly/3bj7xZh
+
+### Supported Tasks and Leaderboards
+
+automatic-speech-recognition, audio-speaker-identification: The dataset can be used to train a model for Automatic Speech Recognition (ASR).
+
+### Languages
+
+Chinese Mandarin
+## Dataset Structure
+
+### Data Instances
+
+[More Information Needed]
+
+### Data Fields
+
+[More Information Needed]
+
+### Data Splits
+
+[More Information Needed]
+
+## Dataset Creation
+
+### Curation Rationale
+
+[More Information Needed]
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+[More Information Needed]
+
+#### Who are the source language producers?
+
+[More Information Needed]
+
+### Annotations
+
+#### Annotation process
+
+[More Information Needed]
+
+#### Who are the annotators?
+
+[More Information Needed]
+
+### Personal and Sensitive Information
+
+[More Information Needed]
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+[More Information Needed]
+
+### Discussion of Biases
+
+[More Information Needed]
+
+### Other Known Limitations
+
+[More Information Needed]
+
+## Additional Information
+
+### Dataset Curators
+
+[More Information Needed]
+
+### Licensing Information
+
+Commerical License: https://drive.google.com/file/d/1saDCPm74D4UWfBL17VbkTsZLGfpOQj1J/view?usp=sharing  
+
+### Citation Information
+
+[More Information Needed]
+
+### Contributions

huggingface_dataset/Dataset_Card/allenai_scicite.md

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+---
+annotations_creators:
+- crowdsourced
+- expert-generated
+language_creators:
+- found
+language:
+- en
+license:
+- unknown
+multilinguality:
+- monolingual
+size_categories:
+- 10K<n<100K
+source_datasets:
+- original
+task_categories:
+- text-classification
+task_ids:
+- intent-classification
+- multi-class-classification
+paperswithcode_id: scicite
+pretty_name: SciCite
+dataset_info:
+  features:
+  - name: string
+    dtype: string
+  - name: sectionName
+    dtype: string
+  - name: label
+    dtype:
+      class_label:
+        names:
+          '0': method
+          '1': background
+          '2': result
+  - name: citingPaperId
+    dtype: string
+  - name: citedPaperId
+    dtype: string
+  - name: excerpt_index
+    dtype: int32
+  - name: isKeyCitation
+    dtype: bool
+  - name: label2
+    dtype:
+      class_label:
+        names:
+          '0': supportive
+          '1': not_supportive
+          '2': cant_determine
+          '3': none
+  - name: citeEnd
+    dtype: int64
+  - name: citeStart
+    dtype: int64
+  - name: source
+    dtype:
+      class_label:
+        names:
+          '0': properNoun
+          '1': andPhrase
+          '2': acronym
+          '3': etAlPhrase
+          '4': explicit
+          '5': acronymParen
+          '6': nan
+  - name: label_confidence
+    dtype: float32
+  - name: label2_confidence
+    dtype: float32
+  - name: id
+    dtype: string
+  splits:
+  - name: test
+    num_bytes: 870809
+    num_examples: 1859
+  - name: train
+    num_bytes: 3843904
+    num_examples: 8194
+  - name: validation
+    num_bytes: 430296
+    num_examples: 916
+  download_size: 23189911
+  dataset_size: 5145009
+---
+
+# Dataset Card for "scicite"
+
+## 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:**
+- **Repository:** https://github.com/allenai/scicite
+- **Paper:** [Structural Scaffolds for Citation Intent Classification in Scientific Publications](https://arxiv.org/abs/1904.01608)
+- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+- **Size of downloaded dataset files:** 22.12 MB
+- **Size of the generated dataset:** 4.91 MB
+- **Total amount of disk used:** 27.02 MB
+
+### Dataset Summary
+
+This is a dataset for classifying citation intents in academic papers.
+The main citation intent label for each Json object is specified with the label
+key while the citation context is specified in with a context key. Example:
+{
+ 'string': 'In chacma baboons, male-infant relationships can be linked to both
+    formation of friendships and paternity success [30,31].'
+ 'sectionName': 'Introduction',
+ 'label': 'background',
+ 'citingPaperId': '7a6b2d4b405439',
+ 'citedPaperId': '9d1abadc55b5e0',
+ ...
+ }
+You may obtain the full information about the paper using the provided paper ids
+with the Semantic Scholar API (https://api.semanticscholar.org/).
+The labels are:
+Method, Background, Result
+
+### Supported Tasks and Leaderboards
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Languages
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+## Dataset Structure
+
+### Data Instances
+
+#### default
+
+- **Size of downloaded dataset files:** 22.12 MB
+- **Size of the generated dataset:** 4.91 MB
+- **Total amount of disk used:** 27.02 MB
+
+An example of 'validation' looks as follows.
+```
+{
+    "citeEnd": 68,
+    "citeStart": 64,
+    "citedPaperId": "5e413c7872f5df231bf4a4f694504384560e98ca",
+    "citingPaperId": "8f1fbe460a901d994e9b81d69f77bfbe32719f4c",
+    "excerpt_index": 0,
+    "id": "8f1fbe460a901d994e9b81d69f77bfbe32719f4c>5e413c7872f5df231bf4a4f694504384560e98ca",
+    "isKeyCitation": false,
+    "label": 2,
+    "label2": 0,
+    "label2_confidence": 0.0,
+    "label_confidence": 0.0,
+    "sectionName": "Discussion",
+    "source": 4,
+    "string": "These results are in contrast with the findings of Santos et al.(16), who reported a significant association between low sedentary time and healthy CVF among Portuguese"
+}
+```
+
+### Data Fields
+
+The data fields are the same among all splits.
+
+#### default
+- `string`: a `string` feature.
+- `sectionName`: a `string` feature.
+- `label`: a classification label, with possible values including `method` (0), `background` (1), `result` (2).
+- `citingPaperId`: a `string` feature.
+- `citedPaperId`: a `string` feature.
+- `excerpt_index`: a `int32` feature.
+- `isKeyCitation`: a `bool` feature.
+- `label2`: a classification label, with possible values including `supportive` (0), `not_supportive` (1), `cant_determine` (2), `none` (3).
+- `citeEnd`: a `int64` feature.
+- `citeStart`: a `int64` feature.
+- `source`: a classification label, with possible values including `properNoun` (0), `andPhrase` (1), `acronym` (2), `etAlPhrase` (3), `explicit` (4).
+- `label_confidence`: a `float32` feature.
+- `label2_confidence`: a `float32` feature.
+- `id`: a `string` feature.
+
+### Data Splits
+
+| name  |train|validation|test|
+|-------|----:|---------:|---:|
+|default| 8194|       916|1859|
+
+## Dataset Creation
+
+### Curation Rationale
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+#### Who are the source language producers?
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Annotations
+
+#### Annotation process
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+#### Who are the annotators?
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Personal and Sensitive Information
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Discussion of Biases
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Other Known Limitations
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+## Additional Information
+
+### Dataset Curators
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Licensing Information
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Citation Information
+
+```
+@inproceedings{cohan-etal-2019-structural,
+    title = "Structural Scaffolds for Citation Intent Classification in Scientific Publications",
+    author = "Cohan, Arman  and
+      Ammar, Waleed  and
+      van Zuylen, Madeleine  and
+      Cady, Field",
+    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
+    month = jun,
+    year = "2019",
+    address = "Minneapolis, Minnesota",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/N19-1361",
+    doi = "10.18653/v1/N19-1361",
+    pages = "3586--3596",
+}
+```
+
+### Contributions
+
+Thanks to [@lewtun](https://github.com/lewtun), [@patrickvonplaten](https://github.com/patrickvonplaten), [@mariamabarham](https://github.com/mariamabarham), [@thomwolf](https://github.com/thomwolf) for adding this dataset.

huggingface_dataset/Dataset_Card/autoevaluate_autoeval-eval-phpthinh__examplei-mismatch-1389aa-1748961033.md

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+---
+type: predictions
+tags:
+- autotrain
+- evaluation
+datasets:
+- phpthinh/examplei
+eval_info:
+  task: text_zero_shot_classification
+  model: bigscience/bloom-560m
+  metrics: ['f1']
+  dataset_name: phpthinh/examplei
+  dataset_config: mismatch
+  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/examplei
+* Config: mismatch
+* 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-0839fa4f-7534859.md

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+---
+type: predictions
+tags:
+- autotrain
+- evaluation
+datasets:
+- ag_news
+eval_info:
+  task: multi_class_classification
+  model: nateraw/bert-base-uncased-ag-news
+  metrics: []
+  dataset_name: ag_news
+  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: nateraw/bert-base-uncased-ag-news
+* Dataset: ag_news
+
+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-cnn_dailymail-c1b20bff-12875715.md

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+---
+type: predictions
+tags:
+- autotrain
+- evaluation
+datasets:
+- cnn_dailymail
+eval_info:
+  task: summarization
+  model: facebook/bart-large-cnn
+  metrics: []
+  dataset_name: cnn_dailymail
+  dataset_config: 3.0.0
+  dataset_split: test
+  col_mapping:
+    text: article
+    target: highlights
+---
+# Dataset Card for AutoTrain Evaluator
+
+This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
+
+* Task: Summarization
+* Model: facebook/bart-large-cnn
+* Dataset: cnn_dailymail
+* Config: 3.0.0
+* Split: test
+
+To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
+
+## Contributions
+
+Thanks to [@grapplerulrich](https://huggingface.co/grapplerulrich) for evaluating this model.

huggingface_dataset/Dataset_Card/bigbio_genia_ptm_event_corpus.md

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+
+---
+language: 
+- en
+bigbio_language: 
+- English
+license: other
+multilinguality: monolingual
+bigbio_license_shortname: GENIA_PROJECT_LICENSE
+pretty_name: PTM Events
+homepage: http://www.geniaproject.org/other-corpora/ptm-event-corpus
+bigbio_pubmed: True
+bigbio_public: True
+bigbio_tasks: 
+- NAMED_ENTITY_RECOGNITION
+- COREFERENCE_RESOLUTION
+- EVENT_EXTRACTION
+---
+
+
+# Dataset Card for PTM Events
+
+## Dataset Description
+
+- **Homepage:** http://www.geniaproject.org/other-corpora/ptm-event-corpus
+- **Pubmed:** True
+- **Public:** True
+- **Tasks:** NER,COREF,EE
+
+
+Post-translational-modifications (PTM), amino acid modifications of proteins after translation, are one of the posterior processes of protein biosynthesis for many proteins, and they are critical for determining protein function such as its activity state, localization, turnover and interactions with other biomolecules. While there have been many studies of information extraction targeting individual PTM types, there was until recently little effort to address extraction of multiple PTM types at once in a unified framework.
+
+
+
+## Citation Information
+
+```
+@inproceedings{ohta-etal-2010-event,
+    title = "Event Extraction for Post-Translational Modifications",
+    author = "Ohta, Tomoko  and
+      Pyysalo, Sampo  and
+      Miwa, Makoto  and
+      Kim, Jin-Dong  and
+      Tsujii, Jun{'}ichi",
+    booktitle = "Proceedings of the 2010 Workshop on Biomedical Natural Language Processing",
+    month = jul,
+    year = "2010",
+    address = "Uppsala, Sweden",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/W10-1903",
+    pages = "19--27",
+}
+
+```

huggingface_dataset/Dataset_Card/ciempiess_ciempiess_test.md

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+---
+annotations_creators:
+- expert-generated
+language:
+- es
+language_creators:
+- other
+license:
+- cc-by-sa-4.0
+multilinguality:
+- monolingual
+pretty_name: 'CIEMPIESS TEST CORPUS: Audio and Transcripts of Mexican Spanish Broadcast Conversations.'
+size_categories:
+- 1K<n<10K
+source_datasets:
+- original
+tags:
+- ciempiess
+- spanish
+- mexican spanish
+- test set
+- ciempiess project
+- ciempiess-unam project
+- ciempiess test
+task_categories:
+- automatic-speech-recognition
+task_ids: []
+---
+
+
+# Dataset Card for ciempiess_test
+## Table of Contents
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks](#supported-tasks-and-leaderboards)
+  - [Languages](#languages)
+- [Dataset Structure](#dataset-structure)
+  - [Data Instances](#data-instances)
+  - [Data Fields](#data-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:** [CIEMPIESS-UNAM Project](http://www.ciempiess.org/)
+- **Repository:** [CIEMPIESS-TEST is part of LDC2019S07](https://catalog.ldc.upenn.edu/LDC2019S07)
+- **Paper:** [Creating Mexican Spanish Language Resources through the Social Service Program](https://aclanthology.org/2022.nidcp-1.4.pdf)
+- **Point of Contact:** [Carlos Mena](mailto:carlos.mena@ciempiess.org)
+
+### Dataset Summary
+
+When developing automatic speech recognition engines and any other machine learning system is a good practice to separate the test from the training data and never combined them. So, the CIEMPIESS TEST Corpus was created by this necessity of having an standard test set destined to measure the advances of the community of users of the CIEMPIESS datasets and we strongly recommend not to use the CIEMPIESS TEST for any other purpose.
+
+The CIEMPIESS TEST Corpus is a gender balanced corpus designed to test acoustic models for the speech recognition task. It was created by recordings and human transcripts of 10 male and 10 female speakers.
+
+The CIEMPIESS TEST Corpus is considered a CIEMPIESS dataset because it only contains audio from the same source of the first [CIEMPIESS Corpus](https://catalog.ldc.upenn.edu/LDC2015S07) and it has the word "TEST" in its name, obviously because it is recommended for test purposes only.
+
+### Example Usage
+The CIEMPIESS TEST contains only the test split:
+```python
+from datasets import load_dataset
+ciempiess_test = load_dataset("ciempiess/ciempiess_test")
+```
+It is also valid to do:
+```python
+from datasets import load_dataset
+ciempiess_test = load_dataset("ciempiess/ciempiess_test",split="test")
+```
+
+### Supported Tasks
+automatic-speech-recognition: The dataset can be used to test a model for Automatic Speech Recognition (ASR). The model is presented with an audio file and asked to transcribe the audio file to written text. The most common evaluation metric is the word error rate (WER).
+
+### Languages
+The language of the corpus is Spanish with the accent of Central Mexico except for the speaker M_09 that comes from El Salvador.
+
+## Dataset Structure
+
+### Data Instances
+```python
+{
+  'audio_id': 'CMPT_M_07_0074', 
+  'audio': {
+    'path': '/home/carlos/.cache/HuggingFace/datasets/downloads/extracted/86a30fdc762ba3fad1e38fbe6900ea4940d6f0070af8d56aa483701faa050d51/test/male/M_07/CMPT_M_07_0074.flac',  
+    'array': array([-0.00192261, -0.00234985, -0.00158691, ..., -0.00839233,
+       -0.00900269, -0.00698853], dtype=float32), 
+    'sampling_rate': 16000
+  }, 
+  'speaker_id': 'M_07', 
+  'gender': 'male', 
+  'duration': 7.510000228881836, 
+  'normalized_text': 'pues está la libertá de las posiciones de a ver quién es pasivo quién es activo blablablá muchas cosas no pero'
+}
+```
+
+### Data Fields
+* `audio_id` (string) - id of audio segment
+* `audio` (datasets.Audio) - a dictionary containing the path to the audio, the decoded audio array, and the sampling rate. In non-streaming mode (default), the path points to the locally extracted audio. In streaming mode, the path is the relative path of an audio inside its archive (as files are not downloaded and extracted locally).
+* `speaker_id` (string) - id of speaker
+* `gender` (string) - gender of speaker (male or female)
+* `duration` (float32) - duration of the audio file in seconds.
+* `normalized_text` (string) - normalized audio segment transcription
+
+### Data Splits
+
+The corpus counts just with the test split which has a total of 3558 speech files from 10 male speakers and 10 female speakers with a total duration of 8 hours and 8 minutes.
+
+## Dataset Creation
+
+### Curation Rationale
+
+The CIEMPIESS TEST (CT) Corpus has the following characteristics:
+
+* The CT has a total of 3558 audio files of 10 male speakers and 10 female speakers. It has a total duration of 8 hours and 8 minutes.
+
+* The total number of audio files that come from male speakers is 1694 with a total duration of 4 hours and 3 minutes. The total number of audio files that come from female speakers is 1864 with a total duration of 4 hours and 4 minutes. So CT is perfectly balanced in gender.
+
+* All of the speakers in the CT come from Mexico, except for the speaker M_09 that comes from El Salvador.
+
+* Every audio file in the CT has a duration between 5 and 10 seconds approximately.
+
+* Data in CT is classified by gender and also by speaker, so one can easily select audios from a particular set of speakers to do experiments.
+
+* Audio files in the CT and the first [CIEMPIESS](https://catalog.ldc.upenn.edu/LDC2015S07) are all of the same type. In both, speakers talk about legal and lawyer issues. They also talk about things related to the [UNAM University](https://www.unam.mx/) and the ["Facultad de Derecho de la UNAM"](https://www.derecho.unam.mx/).
+
+* As in the first CIEMPIESS Corpus, transcriptions in the CT were made by humans.
+
+* Speakers in the CT are not present in any other CIEMPIESS dataset.
+
+* Audio files in the CT are distributed in a 16khz@16bit mono format.
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+The CIEMPIESS TEST is a Radio Corpus designed to test acoustic models of automatic speech recognition and it is made out of recordings of spontaneous conversations in Spanish between a radio moderator and his guests. Most of the speech in these conversations has the accent of Central Mexico.
+
+All the recordings that constitute the CIEMPIESS TEST come from ["RADIO-IUS"](http://www.derecho.unam.mx/cultura-juridica/radio.php), a radio station belonging to UNAM. Recordings were donated by Lic. Cesar Gabriel Alanis Merchand and Mtro. Ricardo Rojas Arevalo from the "Facultad de Derecho de la UNAM" with the condition that they have to be used for academic and research purposes only.
+
+### Annotations
+#### Annotation process
+
+The annotation process is at follows:
+
+* 1. A whole podcast is manually segmented keeping just the portions containing good quality speech.
+* 2. A second pass os segmentation is performed; this time to separate speakers and put them in different folders.
+* 3. The resulting speech files between 5 and 10 seconds are transcribed by students from different departments (computing, engineering, linguistics). Most of them are native speakers but not with a particular training as transcribers.
+
+#### Who are the annotators?
+
+The CIEMPIESS TEST Corpus was created by the social service program ["Desarrollo de Tecnologías del Habla"](http://profesores.fi-b.unam.mx/carlos_mena/servicio.html) of the ["Facultad de Ingeniería"](https://www.ingenieria.unam.mx/) (FI) in the ["Universidad Nacional Autónoma de México"](https://www.unam.mx/) (UNAM) between 2016 and 2018 by Carlos Daniel Hernández Mena, head of the program.
+
+### Personal and Sensitive Information
+
+The dataset could contain names revealing the identity of some speakers; on the other side, the recordings come from publicly available podcasts, so, there is not a real intent of the participants to be anonymized. Anyway, you agree to not attempt to determine the identity of speakers in this dataset.
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+This dataset is challenging because it contains spontaneous speech; so, it will be helpful for the ASR community to evaluate their acoustic models in Spanish with it.
+
+### Discussion of Biases
+
+The dataset intents to be gender balanced. It is comprised of 10 male speakers and 10 female speakers. On the other hand the vocabulary is limited to legal issues.
+
+### Other Known Limitations
+
+The transcriptions in this dataset were revised by Mónica Alejandra Ruiz López during 2022 and they are slightly different from the transcriptions found at [LDC](https://catalog.ldc.upenn.edu/LDC2019S07) or at the [CIEMPIESS-UNAM Project](http://www.ciempiess.org/) official website. We strongly recommend to use these updated transcriptions; we will soon update the transcriptions in the rest of the repositories.
+
+### Dataset Curators
+
+The dataset was collected by students belonging to the social service program ["Desarrollo de Tecnologías del Habla"](http://profesores.fi-b.unam.mx/carlos_mena/servicio.html), it was curated by Carlos Daniel Hernández Mena and its transcriptions were manually verified by Mónica Alejandra Ruiz López during 2022.
+
+### Licensing Information
+[CC-BY-SA-4.0](https://creativecommons.org/licenses/by-sa/4.0/)
+
+### Citation Information
+```
+@misc{carlosmenaciempiesstest2019,
+      title={CIEMPIESS TEST CORPUS: Audio and Transcripts of Mexican Spanish Broadcast Conversations.}, 
+      ldc_catalog_no={LDC2019S07},
+      DOI={https://doi.org/10.35111/xdx5-n815},
+      author={Hernandez Mena, Carlos Daniel},
+      journal={Linguistic Data Consortium, Philadelphia},
+      year={2019},
+      url={https://catalog.ldc.upenn.edu/LDC2019S07},
+}
+```
+### Contributions
+
+The authors want to thank to Alejandro V. Mena, Elena Vera and Angélica Gutiérrez for their support to the social service program: "Desarrollo de Tecnologías del Habla." We also thank to the social service students for all the hard work.
+
+We also thank to Lic. Cesar Gabriel Alanis Merchand and Mtro. Ricardo Rojas Arevalo from the "Facultad de Derecho de la UNAM" for donating all the recordings that constitute the CIEMPIESS TEST Corpus.
+
+Special thanks to Mónica Alejandra Ruiz López who performed a meticulous verification of the transcriptions of this dataset during 2022.

huggingface_dataset/Dataset_Card/codkiller0911_kotlin_code.md

@@ -0,0 +1,107 @@
+---
+language:
+- en
+tags:
+- kotlin
+- android
+size_categories:
+- 1K<n<10K
+---
+
+# Dataset Card for Dataset kotlin_code
+
+## Dataset Description
+
+- **Homepage:** 
+- **Repository:** 
+- **Paper:** 
+- **Leaderboard:** 
+- **Point of Contact:** 
+
+### Dataset Summary
+
+This Dataset contains Kotlin functions with there documentation. This dataset can be useful in fine-tuning or creating new models for developing models which can generate the code documentaiton 
+
+### Supported Tasks and Leaderboards
+
+[More Information Needed]
+
+### Languages
+
+[More Information Needed]
+
+## Dataset Structure
+
+### Data Instances
+
+[More Information Needed]
+
+### Data Fields
+
+[More Information Needed]
+
+### Data Splits
+
+[More Information Needed]
+
+## Dataset Creation
+
+### Curation Rationale
+
+[More Information Needed]
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+[More Information Needed]
+
+#### Who are the source language producers?
+
+[More Information Needed]
+
+### Annotations
+
+#### Annotation process
+
+[More Information Needed]
+
+#### Who are the annotators?
+
+[More Information Needed]
+
+### Personal and Sensitive Information
+
+[More Information Needed]
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+[More Information Needed]
+
+### Discussion of Biases
+
+[More Information Needed]
+
+### Other Known Limitations
+
+[More Information Needed]
+
+## Additional Information
+
+### Dataset Curators
+
+[More Information Needed]
+
+### Licensing Information
+
+[More Information Needed]
+
+### Citation Information
+
+[More Information Needed]
+
+### Contributions
+
+[More Information Needed]

huggingface_dataset/Dataset_Card/cstrathe435_Task2Dial.md

@@ -0,0 +1,178 @@
+# Dataset Card for Task2Dial
+
+## Table of Contents
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks](#supported-tasks-and-leaderboards)
+  - [Languages](#languages)
+- [Dataset Structure](#dataset-structure)
+  - [Data Instances](#data-instances)
+  - [Data Fields](#data-instances)
+- [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)
+  - [Acknowledgements] (#funding-information)
+
+## Dataset Description
+
+- **Homepage:** [Needs More Information]
+- **Repository:** [Needs More Information]
+- **Paper:** https://aclanthology.org/2021.icnlsp-1.28/
+- **Leaderboard:** [Needs More Information]
+- **Point of Contact:** [Needs More Information]
+
+### Dataset Summary
+
+The Task2Dial dataset includes (1) a set of recipe documents with 353 individual dialogues; and (2) conversations between an IG and an IF, which are grounded in the associated recipe documents. Presents sample utterances from a dialogue along with the associated recipe. It demonstrates some important features of the dataset, such as mentioning entities not present in the recipe document; re-composition of the original text to focus on the important steps and the breakdown of the recipe into manageable and appropriate steps. Following recent efforts in the field to standardise NLG research, we have made the dataset freely available.
+
+### Supported Tasks and Leaderboards
+
+We demonstrate the task of implementing the Task2Dial in a conversational agent called chefbot in the following git repo: https://github.com/carlstrath/ChefBot
+
+### Languages
+
+English
+
+### Data Fields
+
+Dataset.1: Task2Dial main, 353 cooking recipes modelled on real conversations between an IF and IG.
+
+Dataset. 2: A list of alternative ingredients for every swappable ingredient in the Task2Dial dataset.
+
+Dataset. 3. A list of objects and utensils with explanations, comparisons, handling and common storage location information.
+
+## Dataset Creation
+
+The proposed task considers the recipe-following scenario with an information giver
+(IG) and an information follower (IF), where the IG has access to the recipe and gives
+instructions to the IF. The IG might choose to omit irrelevant information, simplify
+the content of a recipe or provide it as is. The IF will either follow the task or ask
+for further information. The IG might have to rely on information outside the given
+document (i.e. commonsense) to enhance understanding and success of the task. In
+addition, the IG decides on how to present the recipe steps, i.e. split them into sub-
+steps or merge them together, often diverging from the original number of recipe steps.
+The task is regarded as successful when the IG has successfully followed/understood
+the recipe. Hence, other dialogue-focused metrics, such as the number of turns, are
+not appropriate here. Formally, Task2Dial can be defined as follows: Given a recipe
+𝑅𝑖 from 𝑅 =𝑅1, 𝑅2, 𝑅3,..., 𝑅𝑛, an ontology or ontologies 𝑂𝑖 =𝑂11,𝑂2,...,𝑂𝑛 of
+cooking-related concepts, a history of the conversation ℎ, predict the response 𝑟 of
+the IG.
+
+### Curation Rationale
+
+Text selection was dependent on the quality of the information
+provided in the existing recipes. Too little information and the transcription and
+interpretation of the text became diffused with missing or incorrect knowledge.
+Conversely, providing too much information in the text resulted in a lack of creativity
+and commonsense reasoning by the data curators. Thus, the goal of the curation was
+to identify text that contained all the relevant information to complete the cooking
+task (tools, ingredients, weights, timings, servings) but not in such detail that it
+subtracted from the creativity, commonsense and imagination of the annotators.
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+Three open-source and creative commons licensed
+cookery websites6 were identified for data extraction, which permits any use or non-
+commercial use of data for research purposes. As content submission to the
+cooking websites was unrestricted, data appropriateness was ratified by the ratings
+and reviews given to each recipe by the public, highly rated recipes with a positive
+feedback were given preference over recipes with low scores and poor reviews [38].
+From this, a list of 353 recipes was compiled and divided amongst the annotators
+for the data collection. As mentioned earlier, annotators were asked to take on the
+roles of both IF and IG, rather than a multi-turn WoZ approach, to allow flexibility
+in the utterances. This approach allowed the annotators additional time to formulate
+detailed and concise responses.
+
+#### Who are the source language producers?
+
+Undergraduate RAs were recruited through email.
+The participants were paid an hourly rate based on a university pay scale which is
+above the living wage and corresponds to the real living wage, following ethical
+guidelines for responsible innovation. The annotation team was composed of
+two males and one female data curators, under the age of 25 of mixed ethnicity’s with
+experience in AI and computing. This minimised the gender bias that is frequently
+observed in crowdsourcing platforms.
+
+#### Annotation process
+
+Each annotator was provided with a detailed list of instructions, an example dialogue and an IF/IG template (see Appendix A). The annotators were asked to read both the example dialogue and the original recipe to understand the text, context, composition, translation and annotation. The instructions included information handling and storage of data, text formatting, metadata and examples of high-quality and poor dialogues. An administrator was on hand throughout the data collection to support and guide the annotators. This approach reduced the number of low-quality dialogues associated with large crowdsourcing platforms that are often discarded post evaluation, as demonstrated in the data collection of the Doc2Dial dataset.
+
+#### Who are the annotators?
+
+Research assistants (RAs) from the School of Computing were employed on temporary contracts to construct and format the dataset. After an initial meeting to discuss the job role and determine suitability, the RAs were asked to complete a paid trial, this was evaluated and further advice was given on how to write dialogues and format the data to ensure high quality. After the successful completion of the trial, the RAs were permitted to continue with the remainder of the data collection. To ensure the high quality of the dataset, samples of the dialogues were often reviewed and further feedback was provided. 
+
+### Personal and Sensitive Information
+
+An ethics request was submitted for review by the board of ethics at our university. No personal or other data that may be used to identify an individual was collected in this study. 
+
+## Considerations for Using the Data
+
+The Task2Dial dataset is currently only for the cooking domain, but using the methodologies provided other tasks can be modelled for example, furniture assembly and maintenance tasks.
+
+### Social Impact of Dataset
+
+Our proposed task aims to motivate research for modern dialogue systems that
+address the following challenges. Firstly, modern dialogue systems should be flexible
+and allow for "off-script" scenarios in order to emulate real-world phenomena, such
+as the ones present in human-human communication. This will require new ways
+of encoding user intents and new approaches to dialogue management in general.
+Secondly, as dialogue systems find different domain applications, the complexity
+of the dialogues might increase as well as the reliance on domain knowledge that
+can be encoded in structured or unstructured ways, such as documents, databases
+etc. Many applications, might require access to different domain knowledge sources
+in a course of a dialogue, and in such context, selection might prove beneficial in
+choosing "what to say".
+
+### Discussion of Biases
+
+Prior to data collection, we performed three pilot studies.
+In the first, two participants assumed the roles of IG and IF respectively, where the
+IG had access to a recipe and provided recipe instructions to the IF (who did not have
+access to the recipe) over the phone, recording the session and then transcribing it.
+Next, we repeated the process with text-based dialogue through an online platform
+following a similar setup, however, the interaction was solely chat-based. The final
+study used self-dialogue, with one member of the team writing entire dialogues
+assuming both the IF and IG roles. We found that self-dialogue results were proximal
+to the results of two-person studies. However, time and cost were higher for producing
+two-person dialogues, with the additional time needed for transcribing and correction,
+thus, we opted to use self-dialogue.
+
+## Additional Information
+
+Video: https://www.youtube.com/watch?v=zISkwn95RXs&ab_channel=ICNLSPConference
+
+### Dataset Curators
+
+The recipes are composed by people of a different races
+/ ethnicity, nationalities, socioeconomic status, abilities, age, gender and language
+with significant variation in pronunciations, structure, language and grammar. This
+provided the annotators with unique linguistic content for each recipe to interpret
+the data and configure the text into an IF/IG format. To help preserve sociolinguistic
+patterns in speech, the data curators retained the underlying language when para-
+phrasing, to intercede social and regional dialects with their own interpretation of
+the data to enhance the lexical richness.
+
+### Licensing Information
+
+CC
+
+### Citation Information
+
+https://aclanthology.org/2021.icnlsp-1.28/
+
+### Acknowledgements
+
+The research is supported under the EPSRC projects CiViL (EP/T014598/1) and
+NLG for low-resource domains (EP/T024917/1).

huggingface_dataset/Dataset_Card/huggingartists_agata-christie.md

@@ -0,0 +1,204 @@
+---
+language:
+- en
+tags:
+- huggingartists
+- lyrics
+---
+
+# Dataset Card for "huggingartists/agata-christie"
+
+## Table of Contents
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
+  - [Languages](#languages)
+- [How to use](#how-to-use)
+- [Dataset Structure](#dataset-structure)
+  - [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)
+- [About](#about)
+
+## Dataset Description
+
+- **Homepage:** [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists)
+- **Repository:** [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists)
+- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+- **Size of the generated dataset:** 0.143508 MB
+
+
+ <div class="inline-flex flex-col" style="line-height: 1.5;">
+    <div class="flex">
+        <div style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;https://images.genius.com/61b6b0a0b7f6587d1b33542d5c18ad3c.489x489x1.jpg&#39;)">
+        </div>
+    </div>
+    <a href="https://huggingface.co/huggingartists/agata-christie">
+      <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 HuggingArtists Model 🤖</div>
+    </a>
+    <div style="text-align: center; font-size: 16px; font-weight: 800">Агата Кристи (Agata Christie)</div>
+    <a href="https://genius.com/artists/agata-christie">
+    	<div style="text-align: center; font-size: 14px;">@agata-christie</div>
+    </a>
+</div>
+
+### Dataset Summary
+
+ The Lyrics dataset parsed from Genius. This dataset is designed to generate lyrics with HuggingArtists.
+ Model is available [here](https://huggingface.co/huggingartists/agata-christie).
+
+### Supported Tasks and Leaderboards
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Languages
+
+en
+
+## How to use
+
+How to load this dataset directly with the datasets library:
+
+```python
+from datasets import load_dataset
+
+dataset = load_dataset("huggingartists/agata-christie")
+```
+
+## Dataset Structure
+
+An example of 'train' looks as follows.
+```
+This example was too long and was cropped:
+
+{
+    "text": "Look, I was gonna go easy on you\nNot to hurt your feelings\nBut I'm only going to get this one chance\nSomething's wrong, I can feel it..."
+}
+```
+
+### Data Fields
+
+The data fields are the same among all splits.
+
+- `text`: a `string` feature.
+
+
+### Data Splits
+
+| train |validation|test|
+|------:|---------:|---:|
+|78|         -|   -|
+
+'Train' can be easily divided into 'train' & 'validation' & 'test' with few lines of code:
+
+```python
+from datasets import load_dataset, Dataset, DatasetDict
+import numpy as np
+
+datasets = load_dataset("huggingartists/agata-christie")
+
+train_percentage = 0.9
+validation_percentage = 0.07
+test_percentage = 0.03
+
+train, validation, test = np.split(datasets['train']['text'], [int(len(datasets['train']['text'])*train_percentage), int(len(datasets['train']['text'])*(train_percentage + validation_percentage))])
+
+datasets = DatasetDict(
+    {
+        'train': Dataset.from_dict({'text': list(train)}),
+        'validation': Dataset.from_dict({'text': list(validation)}),
+        'test': Dataset.from_dict({'text': list(test)})
+    }
+)
+```
+
+## Dataset Creation
+
+### Curation Rationale
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+#### Who are the source language producers?
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Annotations
+
+#### Annotation process
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+#### Who are the annotators?
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Personal and Sensitive Information
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Discussion of Biases
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Other Known Limitations
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+## Additional Information
+
+### Dataset Curators
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Licensing Information
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Citation Information
+
+```
+@InProceedings{huggingartists,
+    author={Aleksey Korshuk}
+    year=2021
+}
+```
+
+
+## About
+
+*Built by Aleksey Korshuk*
+
+[![Follow](https://img.shields.io/github/followers/AlekseyKorshuk?style=social)](https://github.com/AlekseyKorshuk)
+
+[![Follow](https://img.shields.io/twitter/follow/alekseykorshuk?style=social)](https://twitter.com/intent/follow?screen_name=alekseykorshuk)
+
+[![Follow](https://img.shields.io/badge/dynamic/json?color=blue&label=Telegram%20Channel&query=%24.result&url=https%3A%2F%2Fapi.telegram.org%2Fbot1929545866%3AAAFGhV-KKnegEcLiyYJxsc4zV6C-bdPEBtQ%2FgetChatMemberCount%3Fchat_id%3D-1001253621662&style=social&logo=telegram)](https://t.me/joinchat/_CQ04KjcJ-4yZTky)
+
+For more details, visit the project repository.
+
+[![GitHub stars](https://img.shields.io/github/stars/AlekseyKorshuk/huggingartists?style=social)](https://github.com/AlekseyKorshuk/huggingartists)

huggingface_dataset/Dataset_Card/irds_neumarco_ru_train.md

@@ -0,0 +1,46 @@
+---
+pretty_name: '`neumarco/ru/train`'
+viewer: false
+source_datasets: ['irds/neumarco_ru']
+task_categories:
+- text-retrieval
+---
+
+# Dataset Card for `neumarco/ru/train`
+
+The `neumarco/ru/train` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
+For more information about the dataset, see the [documentation](https://ir-datasets.com/neumarco#neumarco/ru/train).
+
+# Data
+
+This dataset provides:
+ - `queries` (i.e., topics); count=808,731
+ - `qrels`: (relevance assessments); count=532,761
+ - `docpairs`; count=269,919,004
+
+ - For `docs`, use [`irds/neumarco_ru`](https://huggingface.co/datasets/irds/neumarco_ru)
+
+This dataset is used by: [`neumarco_ru_train_judged`](https://huggingface.co/datasets/irds/neumarco_ru_train_judged)
+
+
+## Usage
+
+```python
+from datasets import load_dataset
+
+queries = load_dataset('irds/neumarco_ru_train', 'queries')
+for record in queries:
+    record # {'query_id': ..., 'text': ...}
+
+qrels = load_dataset('irds/neumarco_ru_train', 'qrels')
+for record in qrels:
+    record # {'query_id': ..., 'doc_id': ..., 'relevance': ..., 'iteration': ...}
+
+docpairs = load_dataset('irds/neumarco_ru_train', 'docpairs')
+for record in docpairs:
+    record # {'query_id': ..., 'doc_id_a': ..., 'doc_id_b': ...}
+
+```
+
+Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the
+data in 🤗 Dataset format.

huggingface_dataset/Dataset_Card/irds_wikiclir_de.md

@@ -0,0 +1,63 @@
+---
+pretty_name: '`wikiclir/de`'
+viewer: false
+source_datasets: []
+task_categories:
+- text-retrieval
+---
+
+# Dataset Card for `wikiclir/de`
+
+The `wikiclir/de` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
+For more information about the dataset, see the [documentation](https://ir-datasets.com/wikiclir#wikiclir/de).
+
+# Data
+
+This dataset provides:
+ - `docs` (documents, i.e., the corpus); count=2,091,278
+ - `queries` (i.e., topics); count=938,217
+ - `qrels`: (relevance assessments); count=5,550,454
+
+
+## Usage
+
+```python
+from datasets import load_dataset
+
+docs = load_dataset('irds/wikiclir_de', 'docs')
+for record in docs:
+    record # {'doc_id': ..., 'title': ..., 'text': ...}
+
+queries = load_dataset('irds/wikiclir_de', 'queries')
+for record in queries:
+    record # {'query_id': ..., 'text': ...}
+
+qrels = load_dataset('irds/wikiclir_de', 'qrels')
+for record in qrels:
+    record # {'query_id': ..., 'doc_id': ..., 'relevance': ..., 'iteration': ...}
+
+```
+
+Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the
+data in 🤗 Dataset format.
+
+## Citation Information
+
+```
+@inproceedings{sasaki-etal-2018-cross,
+    title = "Cross-Lingual Learning-to-Rank with Shared Representations",
+    author = "Sasaki, Shota  and
+      Sun, Shuo  and
+      Schamoni, Shigehiko  and
+      Duh, Kevin  and
+      Inui, Kentaro",
+    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)",
+    month = jun,
+    year = "2018",
+    address = "New Orleans, Louisiana",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/N18-2073",
+    doi = "10.18653/v1/N18-2073",
+    pages = "458--463"
+}
+```

huggingface_dataset/Dataset_Card/miracl_miracl-corpus.md

@@ -0,0 +1,98 @@
+---
+annotations_creators:
+- expert-generated
+
+language:
+- ar
+- bn
+- en
+- es
+- fa
+- fi
+- fr
+- hi
+- id
+- ja
+- ko
+- ru
+- sw
+- te
+- th
+- zh
+
+
+multilinguality:
+- multilingual
+
+pretty_name: MIRACL-corpus
+size_categories: []
+source_datasets: []
+tags: []
+
+task_categories:
+- text-retrieval
+
+license:
+- apache-2.0
+
+task_ids:
+- document-retrieval
+---
+
+# Dataset Card for MIRACL Corpus
+
+
+## Dataset Description
+* **Homepage:** http://miracl.ai
+* **Repository:** https://github.com/project-miracl/miracl
+* **Paper:** https://arxiv.org/abs/2210.09984
+
+MIRACL 🌍🙌🌏 (Multilingual Information Retrieval Across a Continuum of Languages) is a multilingual retrieval dataset that focuses on search across 18 different languages, which collectively encompass over three billion native speakers around the world.
+
+This dataset contains the collection data of the 16 "known languages". The remaining 2 "surprise languages" will not be released until later.
+
+The corpus for each language is prepared from a Wikipedia dump, where we keep only the plain text and discard images, tables, etc. Each article is segmented into multiple passages using WikiExtractor based on natural discourse units (e.g., `\n\n` in the wiki markup). Each of these passages comprises a "document" or unit of retrieval. We preserve the Wikipedia article title of each passage.
+
+## Dataset Structure
+Each retrieval unit contains three fields: `docid`, `title`, and `text`. Consider an example from the English corpus:
+
+```
+{
+    "docid": "39#0",
+    "title": "Albedo", 
+    "text": "Albedo (meaning 'whiteness') is the measure of the diffuse reflection of solar radiation out of the total solar radiation received by an astronomical body (e.g. a planet like Earth). It is dimensionless and measured on a scale from 0 (corresponding to a black body that absorbs all incident radiation) to 1 (corresponding to a body that reflects all incident radiation)."
+}
+```
+The `docid` has the schema `X#Y`, where all passages with the same `X` come from the same Wikipedia article, whereas `Y` denotes the passage within that article, numbered sequentially. The text field contains the text of the passage. The title field contains the name of the article the passage comes from.
+
+
+The collection can be loaded using:
+```
+lang='ar'  # or any of the 16 languages
+miracl_corpus = datasets.load_dataset('miracl/miracl-corpus', lang)['train']
+for doc in miracl_corpus:
+   docid = doc['docid']
+   title = doc['title']
+   text = doc['text']
+```
+
+## Dataset Statistics and Links
+The following table contains the number of passage and Wikipedia articles in the collection of each language, along with the links to the datasets and raw Wikipedia dumps.
+| Language        | # of Passages | # of Articles | Links | Raw Wiki Dump |
+|:----------------|--------------:|--------------:|:------|:------|
+| Arabic (ar)     |     2,061,414 |       656,982 | [🤗](https://huggingface.co/datasets/miracl/miracl-corpus/tree/main/miracl-corpus-v1.0-ar) | [🌏](https://archive.org/download/arwiki-20190201/arwiki-20190201-pages-articles-multistream.xml.bz2)
+| Bengali (bn)    |       297,265 |        63,762 | [🤗](https://huggingface.co/datasets/miracl/miracl-corpus/tree/main/miracl-corpus-v1.0-bn) | [🌏](https://archive.org/download/bnwiki-20190201/bnwiki-20190201-pages-articles-multistream.xml.bz2)
+| English (en)    |    32,893,221 |     5,758,285 | [🤗](https://huggingface.co/datasets/miracl/miracl-corpus/tree/main/miracl-corpus-v1.0-en) | [🌏](https://archive.org/download/enwiki-20190201/enwiki-20190201-pages-articles-multistream.xml.bz2)
+| Spanish (es)    |    10,373,953 |     1,669,181 | [🤗](https://huggingface.co/datasets/miracl/miracl-corpus/tree/main/miracl-corpus-v1.0-es) | [🌏](https://archive.org/download/eswiki-20220301/eswiki-20220301-pages-articles-multistream.xml.bz2)
+| Persian (fa)    |     2,207,172 |       857,827 | [🤗](https://huggingface.co/datasets/miracl/miracl-corpus/tree/main/miracl-corpus-v1.0-fa) | [🌏](https://archive.org/download/fawiki-20220301/fawiki-20220301-pages-articles-multistream.xml.bz2)
+| Finnish (fi)    |     1,883,509 |       447,815 | [🤗](https://huggingface.co/datasets/miracl/miracl-corpus/tree/main/miracl-corpus-v1.0-fi) | [🌏](https://archive.org/download/fiwiki-20190201/fiwiki-20190201-pages-articles-multistream.xml.bz2)
+| French (fr)     |    14,636,953 |     2,325,608 | [🤗](https://huggingface.co/datasets/miracl/miracl-corpus/tree/main/miracl-corpus-v1.0-fr) | [🌏](https://archive.org/download/frwiki-20220301/frwiki-20220301-pages-articles-multistream.xml.bz2)
+| Hindi (hi)      |       506,264 |       148,107 | [🤗](https://huggingface.co/datasets/miracl/miracl-corpus/tree/main/miracl-corpus-v1.0-hi) | [🌏](https://archive.org/download/hiwiki-20220301/hiwiki-20220301-pages-articles-multistream.xml.bz2)
+| Indonesian (id) |     1,446,315 |       446,330 | [🤗](https://huggingface.co/datasets/miracl/miracl-corpus/tree/main/miracl-corpus-v1.0-id) | [🌏](https://archive.org/download/idwiki-20190201/idwiki-20190201-pages-articles-multistream.xml.bz2)
+| Japanese (ja)   |     6,953,614 |     1,133,444 | [🤗](https://huggingface.co/datasets/miracl/miracl-corpus/tree/main/miracl-corpus-v1.0-ja) | [🌏](https://archive.org/download/jawiki-20190201/jawiki-20190201-pages-articles-multistream.xml.bz2)
+| Korean (ko)     |     1,486,752 |       437,373 | [🤗](https://huggingface.co/datasets/miracl/miracl-corpus/tree/main/miracl-corpus-v1.0-ko) | [🌏](https://archive.org/download/kowiki-20190201/kowiki-20190201-pages-articles-multistream.xml.bz2)
+| Russian (ru)    |     9,543,918 |     1,476,045 | [🤗](https://huggingface.co/datasets/miracl/miracl-corpus/tree/main/miracl-corpus-v1.0-ru) | [🌏](https://archive.org/download/ruwiki-20190201/ruwiki-20190201-pages-articles-multistream.xml.bz2)
+| Swahili (sw)    |       131,924 |        47,793 | [🤗](https://huggingface.co/datasets/miracl/miracl-corpus/tree/main/miracl-corpus-v1.0-sw) | [🌏](https://archive.org/download/swwiki-20190201/swwiki-20190201-pages-articles-multistream.xml.bz2)
+| Telugu (te)     |       518,079 |        66,353 | [🤗](https://huggingface.co/datasets/miracl/miracl-corpus/tree/main/miracl-corpus-v1.0-te) | [🌏](https://archive.org/download/tewiki-20190201/tewiki-20190201-pages-articles-multistream.xml.bz2)
+| Thai (th)       |       542,166 |       128,179 | [🤗](https://huggingface.co/datasets/miracl/miracl-corpus/tree/main/miracl-corpus-v1.0-th) | [🌏](https://archive.org/download/thwiki-20190101/thwiki-20190101-pages-articles-multistream.xml.bz2)
+| Chinese (zh)    |     4,934,368 |     1,246,389 | [🤗](https://huggingface.co/datasets/miracl/miracl-corpus/tree/main/miracl-corpus-v1.0-zh) | [🌏](https://archive.org/download/zhwiki-20220301/zhwiki-20220301-pages-articles-multistream.xml.bz2)

huggingface_dataset/Dataset_Card/monash_tsf.md

@@ -0,0 +1,1035 @@
+---
+annotations_creators:
+- no-annotation
+language_creators:
+- found
+license:
+- cc-by-4.0
+multilinguality:
+- monolingual
+pretty_name: Monash Time Series Forecasting Repository
+size_categories:
+- 1K<n<10K
+source_datasets:
+- original
+task_categories:
+- time-series-forecasting
+task_ids:
+- univariate-time-series-forecasting
+- multivariate-time-series-forecasting
+dataset_info:
+- config_name: weather
+  features:
+  - name: start
+    dtype: timestamp[s]
+  - name: target
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+    num_examples: 3010
+  download_size: 38820451
+  dataset_size: 531798677
+- config_name: tourism_yearly
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+- config_name: us_births
+  features:
+  - name: start
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+- config_name: solar_4_seconds
+  features:
+  - name: start
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+- config_name: wind_4_seconds
+  features:
+  - name: start
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+    sequence: uint64
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+  dataset_size: 91539064
+- config_name: rideshare
+  features:
+  - name: start
+    dtype: timestamp[s]
+  - name: target
+    sequence:
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+  - name: feat_static_cat
+    sequence: uint64
+  - name: feat_dynamic_real
+    sequence:
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+  - name: item_id
+    dtype: string
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+  - name: validation
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+    num_examples: 156
+  download_size: 1031826
+  dataset_size: 14115729
+- config_name: oikolab_weather
+  features:
+  - name: start
+    dtype: timestamp[s]
+  - name: target
+    sequence: float32
+  - name: feat_static_cat
+    sequence: uint64
+  - name: feat_dynamic_real
+    sequence:
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+  - name: item_id
+    dtype: string
+  splits:
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+    num_examples: 8
+  - name: validation
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+    num_examples: 8
+  download_size: 1326101
+  dataset_size: 9902178
+- config_name: temperature_rain
+  features:
+  - name: start
+    dtype: timestamp[s]
+  - name: target
+    sequence:
+      sequence: float32
+  - name: feat_static_cat
+    sequence: uint64
+  - name: feat_dynamic_real
+    sequence:
+      sequence: float32
+  - name: item_id
+    dtype: string
+  splits:
+  - name: train
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+    num_examples: 422
+  - name: test
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+    num_examples: 422
+  - name: validation
+    num_bytes: 92090376
+    num_examples: 422
+  download_size: 25747139
+  dataset_size: 276271128
+---
+
+# Dataset Card for Monash Time Series Forecasting Repository
+
+## 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:** [Monash Time Series Forecasting Repository](https://forecastingdata.org/)
+- **Repository:** [Monash Time Series Forecasting Repository code repository](https://github.com/rakshitha123/TSForecasting)
+- **Paper:** [Monash Time Series Forecasting Archive](https://openreview.net/pdf?id=wEc1mgAjU-)
+- **Leaderboard:** [Baseline Results](https://forecastingdata.org/#results)
+- **Point of Contact:** [Rakshitha Godahewa](mailto:rakshitha.godahewa@monash.edu)
+
+### Dataset Summary
+
+The first comprehensive time series forecasting repository containing datasets of related time series to facilitate the evaluation of global forecasting models. All datasets are intended to use only for research purpose. Our repository contains 30 datasets including both publicly available time series datasets (in different formats) and datasets curated by us. Many datasets have different versions based on the frequency and the inclusion of missing values, making the total number of dataset variations to 58. Furthermore, it includes both real-world and competition time series datasets covering varied domains.
+
+The following table shows a list of datasets available:
+
+| Name                          | Domain    | No. of series | Freq.  | Pred. Len. | Source                                                                                                                              |
+|-------------------------------|-----------|---------------|--------|------------|-------------------------------------------------------------------------------------------------------------------------------------|
+| weather                       | Nature    | 3010          | 1D     | 30         | [Sparks et al., 2020](https://cran.r-project.org/web/packages/bomrang)                                                              |
+| tourism_yearly                | Tourism   | 1311          | 1Y     | 4          | [Athanasopoulos et al., 2011](https://doi.org/10.1016/j.ijforecast.2010.04.009)                                                     |
+| tourism_quarterly             | Tourism   | 1311          | 1Q-JAN | 8          | [Athanasopoulos et al., 2011](https://doi.org/10.1016/j.ijforecast.2010.04.009)                                                     |
+| tourism_monthly               | Tourism   | 1311          | 1M     | 24         | [Athanasopoulos et al., 2011](https://doi.org/10.1016/j.ijforecast.2010.04.009)                                                     |
+| cif_2016                      | Banking   | 72            | 1M     | 12         | [Stepnicka and Burda, 2017](https://doi.org/10.1109/FUZZ-IEEE.2017.8015455)                                                         |
+| london_smart_meters           | Energy    | 5560          | 30T    | 60         | [Jean-Michel, 2019](https://www.kaggle.com/jeanmidev/smart-meters-in-london)                                                        |
+| australian_electricity_demand | Energy    | 5             | 30T    | 60         | [Godahewa et al. 2021](https://openreview.net/pdf?id=wEc1mgAjU-)                                                                    |
+| wind_farms_minutely           | Energy    | 339           | 1T     | 60         | [Godahewa et al. 2021](https://openreview.net/pdf?id=wEc1mgAjU- )                                                                   |
+| bitcoin                       | Economic  | 18            | 1D     | 30         | [Godahewa et al. 2021](https://openreview.net/pdf?id=wEc1mgAjU- )                                                                   |
+| pedestrian_counts             | Transport | 66            | 1H     | 48         | [City of Melbourne, 2020](https://data.melbourne.vic.gov.au/Transport/Pedestrian-Counting-System-Monthly-counts-per-hour/b2ak-trbp) |
+| vehicle_trips                 | Transport | 329           | 1D     | 30         | [fivethirtyeight, 2015](https://github.com/fivethirtyeight/uber-tlc-foil-response)                                                  |
+| kdd_cup_2018                  | Nature    | 270           | 1H     | 48         | [KDD Cup, 2018](https://www.kdd.org/kdd2018/kdd-cup)                                                                                |
+| nn5_daily                     | Banking   | 111           | 1D     | 56         | [Ben Taieb et al., 2012](https://doi.org/10.1016/j.eswa.2012.01.039)                                                                |
+| nn5_weekly                    | Banking   | 111           | 1W-MON | 8          | [Ben Taieb et al., 2012](https://doi.org/10.1016/j.eswa.2012.01.039)                                                                |
+| kaggle_web_traffic            | Web       | 145063        | 1D     | 59         | [Google, 2017](https://www.kaggle.com/c/web-traffic-time-series-forecasting)                                                        |
+| kaggle_web_traffic_weekly     | Web       | 145063        | 1W-WED | 8          | [Google, 2017](https://www.kaggle.com/c/web-traffic-time-series-forecasting)                                                        |
+| solar_10_minutes              | Energy    | 137           | 10T    | 60         | [Solar, 2020](https://www.nrel.gov/grid/solar-power-data.html)                                                                      |
+| solar_weekly                  | Energy    | 137           | 1W-SUN | 5          | [Solar, 2020](https://www.nrel.gov/grid/solar-power-data.html)                                                                      |
+| car_parts                     | Sales     | 2674          | 1M     | 12         | [Hyndman, 2015](https://cran.r-project.org/web/packages/expsmooth/)                                                                 |
+| fred_md                       | Economic  | 107           | 1M     | 12         | [McCracken and Ng, 2016](https://doi.org/10.1080/07350015.2015.1086655)                                                             |
+| traffic_hourly                | Transport | 862           | 1H     | 48         | [Caltrans, 2020](http://pems.dot.ca.gov/)                                                                                           |
+| traffic_weekly                | Transport | 862           | 1W-WED | 8          | [Caltrans, 2020](http://pems.dot.ca.gov/)                                                                                           |
+| hospital                      | Health    | 767           | 1M     | 12         | [Hyndman, 2015](https://cran.r-project.org/web/packages/expsmooth/)                                                                 |
+| covid_deaths                  | Health    | 266           | 1D     | 30         | [Johns Hopkins University, 2020](https://github.com/CSSEGISandData/COVID-19)                                                        |
+| sunspot                       | Nature    | 1             | 1D     | 30         | [Sunspot, 2015](http://www.sidc.be/silso/newdataset)                                                                                |
+| saugeenday                    | Nature    | 1             | 1D     | 30         | [McLeod and Gweon, 2013](http://www.jenvstat.org/v04/i11)                                                                           |
+| us_births                     | Health    | 1             | 1D     | 30         | [Pruim et al., 2020](https://cran.r-project.org/web/packages/mosaicData)                                                            |
+| solar_4_seconds               | Energy    | 1             | 4S     | 60         | [Godahewa et al. 2021](https://openreview.net/pdf?id=wEc1mgAjU- )                                                                   |
+| wind_4_seconds                | Energy    | 1             | 4S     | 60         | [Godahewa et al. 2021](https://openreview.net/pdf?id=wEc1mgAjU- )                                                                   |
+| rideshare                     | Transport | 2304          | 1H     | 48         | [Godahewa et al. 2021](https://openreview.net/pdf?id=wEc1mgAjU- )                                                                   |
+| oikolab_weather               | Nature    | 8             | 1H     | 48         | [Oikolab](https://oikolab.com/)                                                                                                     |
+| temperature_rain              | Nature    | 32072         | 1D     | 30         | [Godahewa et al. 2021](https://openreview.net/pdf?id=wEc1mgAjU- )      
+
+
+### Dataset Usage
+
+To load a particular dataset just specify its name from the table above e.g.:
+
+```python
+load_dataset("monash_tsf", "nn5_daily")
+```
+> Notes:
+> - Data might contain missing values as in the original datasets.
+> - The prediction length is either specified in the dataset or a default value depending on the frequency is used as in the original repository benchmark.
+
+
+### Supported Tasks and Leaderboards
+
+#### `time-series-forecasting`
+
+##### `univariate-time-series-forecasting`
+
+The univariate time series forecasting tasks involves learning the future one dimensional `target` values of a time series in a dataset for some `prediction_length` time steps. The performance of the forecast models can then be validated via the ground truth in the `validation` split and tested via the `test` split.
+
+##### `multivariate-time-series-forecasting`
+
+The multivariate time series forecasting task involves learning the future vector of `target` values of a time series in a dataset for some `prediction_length` time steps. Similar to the univariate setting the performance of a multivariate model can be validated via the ground truth in the `validation` split and tested via the `test` split.
+
+### Languages
+
+## Dataset Structure
+
+### Data Instances
+
+A sample from the training set is provided below:
+
+```python
+{
+  'start': datetime.datetime(2012, 1, 1, 0, 0),
+  'target': [14.0, 18.0, 21.0, 20.0, 22.0, 20.0, ...],
+  'feat_static_cat': [0], 
+  'feat_dynamic_real': [[0.3, 0.4], [0.1, 0.6], ...],
+  'item_id': '0'
+}
+```
+
+### Data Fields
+
+For the univariate regular time series each series has the following keys:
+
+* `start`: a datetime of the first entry of each time series in the dataset
+* `target`: an array[float32] of the actual target values
+* `feat_static_cat`: an array[uint64] which contains a categorical identifier of each time series in the dataset
+* `feat_dynamic_real`: optional array of covariate features
+* `item_id`: a string identifier of each time series in a dataset for reference
+
+For the multivariate time series the `target` is a vector of the multivariate dimension for each time point.
+
+### Data Splits
+
+The datasets are split in time depending on the prediction length specified in the datasets. In particular for each time series in a dataset there is a prediction length window of the future in the validation split and another prediction length more in the test split.
+
+
+## Dataset Creation
+
+### Curation Rationale
+
+To facilitate the evaluation of global forecasting models. All datasets in our repository are intended for research purposes and to evaluate the performance of new forecasting algorithms.
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+Out of the 30 datasets, 23 were already publicly available in different platforms with different data formats. The original sources of all datasets are mentioned in the datasets table above.
+
+After extracting and curating these datasets, we analysed them individually to identify the datasets containing series with different frequencies and missing observations. Nine datasets contain time series belonging to different frequencies and the archive contains a separate dataset per each frequency.
+
+#### Who are the source language producers?
+
+The data comes from the datasets listed in the table above.
+
+### Annotations
+
+#### Annotation process
+
+The annotations come from the datasets listed in the table above.
+
+#### 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
+
+* [Rakshitha Godahewa](mailto:rakshitha.godahewa@monash.edu)
+* [Christoph Bergmeir](mailto:christoph.bergmeir@monash.edu)
+* [Geoff Webb](mailto:geoff.webb@monash.edu)
+* [Rob Hyndman](mailto:rob.hyndman@monash.edu)
+* [Pablo Montero-Manso](mailto:pablo.monteromanso@sydney.edu.au)
+
+### Licensing Information
+
+[Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0/legalcode)
+
+### Citation Information
+
+```tex
+@InProceedings{godahewa2021monash,
+    author = "Godahewa, Rakshitha and Bergmeir, Christoph and Webb, Geoffrey I. and Hyndman, Rob J. and Montero-Manso, Pablo",
+    title = "Monash Time Series Forecasting Archive",
+    booktitle = "Neural Information Processing Systems Track on Datasets and Benchmarks",
+    year = "2021",
+    note = "forthcoming"
+}
+```
+
+### Contributions
+
+Thanks to [@kashif](https://github.com/kashif) for adding this dataset.

huggingface_dataset/Dataset_Card/optimum_documentation-images.md

@@ -0,0 +1 @@
+This dataset contains images used in the documentation of HuggingFace's Optimum library.

huggingface_dataset/Dataset_Card/parivartanayurveda_Malesexproblemsayurvedictreatment.md

@@ -0,0 +1 @@
+Best ayurvedic medicine for erectile dysfunction. More Info :-  https://www.parivartanayurveda.com/male-sexual-problems.php

huggingface_dataset/Dataset_Card/pszemraj_SQuALITY-v1.3.md

@@ -0,0 +1,58 @@
+---
+license: apache-2.0
+language:
+- en
+task_categories:
+- summarization
+- text2text-generation
+tags:
+- summarization
+- long-document
+pretty_name: SQuALITY v1.3
+size_categories:
+- n<1K
+---
+
+
+# SQuALITY - v1.3
+
+> Original paper [here](https://arxiv.org/abs/2205.11465)
+
+This is v1.3, the 'text' edition `.jsonl` files. See description from the [original repo](https://github.com/nyu-mll/SQuALITY):
+
+> v1.3 fixes some bugs in v1.2. In v1.2, 10 out of 127 articles (each ~5k-word-long) are missing a few hundreds words each, so summaries may not be fully contained in the article. To fix this issue, we have updated the 10 articles.
+
+## contents 
+
+> again, this is taken from the repo
+
+Each data file ({train/dev/test}.jsonl) is formatted as a JSON lines file. Each row in the data file is a JSON dictionary with the following fields:
+
+- metadata: the Gutenberg story ID, an internal UID, and the Project Gutenberg license
+- document: the Gutenberg story
+questions: a list of questions and accompanying responses
+  - question text
+  - question number: the order in which that question was answered by the writers
+  - responses: list of worker's response, where each response is a dictionary containing the (anonymized) worker ID, an internal UID, and their response to the question
+ 
+
+### dataset contents
+
+
+```python
+DatasetDict({
+    train: Dataset({
+        features: ['metadata', 'document', 'questions'],
+        num_rows: 50
+    })
+    test: Dataset({
+        features: ['metadata', 'document', 'questions'],
+        num_rows: 52
+    })
+    validation: Dataset({
+        features: ['metadata', 'document', 'questions'],
+        num_rows: 25
+    })
+})
+```
+

huggingface_dataset/Dataset_Card/qgallouedec_prj_gia_dataset_metaworld_hammer_v2_1111.md

@@ -0,0 +1,36 @@
+---
+library_name: gia
+tags:
+- deep-reinforcement-learning
+- reinforcement-learning
+- gia
+- multi-task
+- multi-modal
+- imitation-learning
+- offline-reinforcement-learning
+---
+
+An imitation learning environment for the hammer-v2 environment, sample for the policy hammer-v2 
+
+This environment was created as part of the Generally Intelligent Agents project gia: https://github.com/huggingface/gia 
+
+
+
+
+## Load dataset
+
+First, clone it with
+
+```sh
+git clone https://huggingface.co/datasets/qgallouedec/prj_gia_dataset_metaworld_hammer_v2_1111
+```
+
+Then, load it with
+
+```python
+import numpy as np
+dataset = np.load("prj_gia_dataset_metaworld_hammer_v2_1111/dataset.npy", allow_pickle=True).item()
+print(dataset.keys())  # dict_keys(['observations', 'actions', 'dones', 'rewards'])
+```
+
+    

huggingface_dataset/Dataset_Card/swedish_reviews.md

@@ -0,0 +1,181 @@
+---
+annotations_creators:
+- found
+language_creators:
+- found
+language:
+- sv
+license:
+- unknown
+multilinguality:
+- monolingual
+size_categories:
+- 100K<n<1M
+source_datasets:
+- original
+task_categories:
+- text-classification
+task_ids:
+- sentiment-classification
+pretty_name: Swedish Reviews
+dataset_info:
+  features:
+  - name: text
+    dtype: string
+  - name: label
+    dtype:
+      class_label:
+        names:
+          '0': negative
+          '1': positive
+  config_name: plain_text
+  splits:
+  - name: test
+    num_bytes: 6296541
+    num_examples: 20697
+  - name: validation
+    num_bytes: 6359227
+    num_examples: 20696
+  - name: train
+    num_bytes: 18842891
+    num_examples: 62089
+  download_size: 11841056
+  dataset_size: 31498659
+---
+
+# Dataset Card for Swedish Reviews
+
+## 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:** [swedish_reviews homepage](https://github.com/timpal0l/swedish-sentiment)
+- **Repository:** [swedish_reviews repository](https://github.com/timpal0l/swedish-sentiment)
+- **Point of Contact:** [Tim Isbister](mailto:timisbisters@gmail.com)
+
+### Dataset Summary
+
+The dataset is scraped from various Swedish websites where reviews are present. The dataset consists of 103 482 samples split between `train`, `valid` and `test`. It is a sample of the full dataset, where this sample is balanced to the minority class (negative). The original data dump was heavly skewved to positive samples with a 95/5 ratio.  
+
+### Supported Tasks and Leaderboards
+
+This dataset can be used to evaluate sentiment classification on Swedish.
+
+### Languages
+
+The text in the dataset is in Swedish.
+
+## Dataset Structure
+
+### Data Instances
+
+What a sample looks like:
+```
+{
+ 'text': 'Jag tycker huggingface är ett grymt project!',
+ 'label': 1,
+}
+```
+
+### Data Fields
+
+- `text`:  A text where the sentiment expression is present.
+- `label`: a int representing the label `0`for negative and `1`for positive.
+
+### Data Splits
+
+The data is split into a training, validation and test set. The final split sizes are as follow:
+
+| Train   | Valid  | Test  |
+| ------  | -----  | ----  |
+| 62089   |  20696 | 20697 |
+
+## Dataset Creation
+
+### Curation Rationale
+
+[More Information Needed]
+
+### Source Data
+
+Various Swedish websites with product reviews.
+
+#### Initial Data Collection and Normalization
+
+#### Who are the source language producers?
+
+Swedish
+
+### Annotations
+
+[More Information Needed]
+
+#### Annotation process
+
+Automatically annotated based on user reviews on a scale 1-5, where 1-2 is considered `negative` and 4-5 is `positive`, 3 is skipped as it tends to be more neutral. 
+
+#### Who are the annotators?
+
+The users who have been using the products.
+
+### Personal and Sensitive Information
+
+[More Information Needed]
+
+## Considerations for Using the Data
+
+[More Information Needed]
+
+### Social Impact of Dataset
+
+[More Information Needed]
+
+### Discussion of Biases
+
+[More Information Needed]
+
+### Other Known Limitations
+
+[More Information Needed]
+
+## Additional Information
+
+[More Information Needed]
+
+### Dataset Curators
+
+The corpus was scraped by @timpal0l
+
+### Licensing Information
+
+Research only.
+
+### Citation Information
+
+No paper exists currently.
+
+### Contributions
+
+Thanks to [@timpal0l](https://github.com/timpal0l) for adding this dataset.

huggingface_dataset/Dataset_Card/wiki_atomic_edits.md

@@ -0,0 +1,439 @@
+---
+annotations_creators:
+- found
+language_creators:
+- found
+language:
+- de
+- en
+- es
+- fr
+- it
+- ja
+- ru
+- zh
+license:
+- cc-by-sa-4.0
+multilinguality:
+- multilingual
+size_categories:
+- 100K<n<1M
+- 10M<n<100M
+- 1M<n<10M
+source_datasets:
+- original
+task_categories:
+- summarization
+task_ids: []
+paperswithcode_id: wikiatomicedits
+pretty_name: WikiAtomicEdits
+configs:
+- chinese_deletions
+- chinese_insertions
+- english_deletions
+- english_insertions
+- french_deletions
+- french_insertions
+- german_deletions
+- german_insertions
+- italian_deletions
+- italian_insertions
+- japanese_deletions
+- japanese_insertions
+- russian_deletions
+- russian_insertions
+- spanish_deletions
+- spanish_insertions
+dataset_info:
+- config_name: german_insertions
+  features:
+  - name: id
+    dtype: int32
+  - name: base_sentence
+    dtype: string
+  - name: phrase
+    dtype: string
+  - name: edited_sentence
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 1072443082
+    num_examples: 3343403
+  download_size: 274280387
+  dataset_size: 1072443082
+- config_name: german_deletions
+  features:
+  - name: id
+    dtype: int32
+  - name: base_sentence
+    dtype: string
+  - name: phrase
+    dtype: string
+  - name: edited_sentence
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 624070402
+    num_examples: 1994329
+  download_size: 160133549
+  dataset_size: 624070402
+- config_name: english_insertions
+  features:
+  - name: id
+    dtype: int32
+  - name: base_sentence
+    dtype: string
+  - name: phrase
+    dtype: string
+  - name: edited_sentence
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 4258411914
+    num_examples: 13737796
+  download_size: 1090652177
+  dataset_size: 4258411914
+- config_name: english_deletions
+  features:
+  - name: id
+    dtype: int32
+  - name: base_sentence
+    dtype: string
+  - name: phrase
+    dtype: string
+  - name: edited_sentence
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 2865754626
+    num_examples: 9352389
+  download_size: 736560902
+  dataset_size: 2865754626
+- config_name: spanish_insertions
+  features:
+  - name: id
+    dtype: int32
+  - name: base_sentence
+    dtype: string
+  - name: phrase
+    dtype: string
+  - name: edited_sentence
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 481145004
+    num_examples: 1380934
+  download_size: 118837934
+  dataset_size: 481145004
+- config_name: spanish_deletions
+  features:
+  - name: id
+    dtype: int32
+  - name: base_sentence
+    dtype: string
+  - name: phrase
+    dtype: string
+  - name: edited_sentence
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 317253196
+    num_examples: 908276
+  download_size: 78485695
+  dataset_size: 317253196
+- config_name: french_insertions
+  features:
+  - name: id
+    dtype: int32
+  - name: base_sentence
+    dtype: string
+  - name: phrase
+    dtype: string
+  - name: edited_sentence
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 651525210
+    num_examples: 2038305
+  download_size: 160442894
+  dataset_size: 651525210
+- config_name: french_deletions
+  features:
+  - name: id
+    dtype: int32
+  - name: base_sentence
+    dtype: string
+  - name: phrase
+    dtype: string
+  - name: edited_sentence
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 626323354
+    num_examples: 2060242
+  download_size: 155263358
+  dataset_size: 626323354
+- config_name: italian_insertions
+  features:
+  - name: id
+    dtype: int32
+  - name: base_sentence
+    dtype: string
+  - name: phrase
+    dtype: string
+  - name: edited_sentence
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 372950256
+    num_examples: 1078814
+  download_size: 92302006
+  dataset_size: 372950256
+- config_name: italian_deletions
+  features:
+  - name: id
+    dtype: int32
+  - name: base_sentence
+    dtype: string
+  - name: phrase
+    dtype: string
+  - name: edited_sentence
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 198598618
+    num_examples: 583316
+  download_size: 49048596
+  dataset_size: 198598618
+- config_name: japanese_insertions
+  features:
+  - name: id
+    dtype: int32
+  - name: base_sentence
+    dtype: string
+  - name: phrase
+    dtype: string
+  - name: edited_sentence
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 765754162
+    num_examples: 2249527
+  download_size: 185766012
+  dataset_size: 765754162
+- config_name: japanese_deletions
+  features:
+  - name: id
+    dtype: int32
+  - name: base_sentence
+    dtype: string
+  - name: phrase
+    dtype: string
+  - name: edited_sentence
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 459683880
+    num_examples: 1352162
+  download_size: 110513593
+  dataset_size: 459683880
+- config_name: russian_insertions
+  features:
+  - name: id
+    dtype: int32
+  - name: base_sentence
+    dtype: string
+  - name: phrase
+    dtype: string
+  - name: edited_sentence
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 790822192
+    num_examples: 1471638
+  download_size: 152985812
+  dataset_size: 790822192
+- config_name: russian_deletions
+  features:
+  - name: id
+    dtype: int32
+  - name: base_sentence
+    dtype: string
+  - name: phrase
+    dtype: string
+  - name: edited_sentence
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 514750186
+    num_examples: 960976
+  download_size: 100033230
+  dataset_size: 514750186
+- config_name: chinese_insertions
+  features:
+  - name: id
+    dtype: int32
+  - name: base_sentence
+    dtype: string
+  - name: phrase
+    dtype: string
+  - name: edited_sentence
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 233367646
+    num_examples: 746509
+  download_size: 66124094
+  dataset_size: 233367646
+- config_name: chinese_deletions
+  features:
+  - name: id
+    dtype: int32
+  - name: base_sentence
+    dtype: string
+  - name: phrase
+    dtype: string
+  - name: edited_sentence
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 144269112
+    num_examples: 467271
+  download_size: 40898651
+  dataset_size: 144269112
+---
+
+# Dataset Card for WikiAtomicEdits
+
+## 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:** None
+- **Repository:** https://github.com/google-research-datasets/wiki-atomic-edits
+- **Paper:** https://www.aclweb.org/anthology/D18-1028/
+- **Leaderboard:** [More Information Needed]
+- **Point of Contact:** [More Information Needed]
+
+### Dataset Summary
+
+[More Information Needed]
+
+### Supported Tasks and Leaderboards
+
+[More Information Needed]
+
+### Languages
+
+The languages in the dataset are:
+- de
+- en
+- es
+- fr
+- it
+- jp: Japanese (`ja`)
+- ru
+- zh
+
+## Dataset Structure
+
+### Data Instances
+
+Here are some examples of questions and facts:
+
+
+### Data Fields
+
+[More Information Needed]
+
+### Data Splits
+
+[More Information Needed]
+
+## Dataset Creation
+
+### Curation Rationale
+
+[More Information Needed]
+
+### Source Data
+
+[More Information Needed]
+
+#### Initial Data Collection and Normalization
+
+[More Information Needed]
+
+#### Who are the source language producers?
+
+[More Information Needed]
+
+### Annotations
+
+[More Information Needed]
+
+#### Annotation process
+
+[More Information Needed]
+
+#### Who are the annotators?
+
+[More Information Needed]
+
+### Personal and Sensitive Information
+
+[More Information Needed]
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+[More Information Needed]
+
+### Discussion of Biases
+
+[More Information Needed]
+
+### Other Known Limitations
+
+[More Information Needed]
+
+## Additional Information
+
+### Dataset Curators
+
+[More Information Needed]
+
+### Licensing Information
+
+[More Information Needed]
+
+### Citation Information
+
+[More Information Needed]
+
+### Contributions
+
+Thanks to [@abhishekkrthakur](https://github.com/abhishekkrthakur) for adding this dataset.