Upload batch 190 (20 files, last=huggingface_dataset/Dataset_Card/autoevaluate_autoeval-eval-project-quoref-bbfe943f-1305449898.md)

huggingface_dataset/Dataset_Card/AIARTCHAN_lora-bmpmi.md

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+---
+license: creativeml-openrail-m
+tags:
+- lora
+- aiartchan
+- stable-diffusion
+---
+
+# Lora - 븝미
+
+## Dataset Description
+
+- **원본** [19) 븜미 로라](https://arca.live/b/aiart/71610355)
+
+강도 0.6 ~ 0.7 사용
+
+프롬프트 : bmpmi, red hair, red eyes, long hair (필요시 Twintails 추가)
+
+[다운로드](https://huggingface.co/datasets/AIARTCHAN/lora-bmpmi/resolve/main/Bmpmi.safetensors)

huggingface_dataset/Dataset_Card/AdamOswald1_autotrain-data-failure.md

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

huggingface_dataset/Dataset_Card/GEM-submissions_lewtun__this-is-a-test__1647247409.md

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+---
+benchmark: gem
+type: prediction
+submission_name: This is a test
+tags:
+- evaluation
+- benchmark
+---
+# GEM Submission
+
+Submission name: This is a test
+

huggingface_dataset/Dataset_Card/Seledorn_SwissProt-GO.md

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+---
+language: 
+  - protein sequences
+datasets:
+- Swissprot
+tags: 
+- Protein
+- Gene Ontology
+- GO
+---
+
+Swissprot is a high quality manually annotated protein database. The dataset contains annotations with the functional properties of the proteins. Here we extract proteins with Gene Ontology labels.
+
+The dataset is ported from Protinfer: https://github.com/google-research/proteinfer.
+
+The GO-labels are extracted and indexed, the mapping is provided in `idx_mapping.json`. Proteins without GO tags are removed.

huggingface_dataset/Dataset_Card/allegro_klej-dyk.md

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+---
+annotations_creators:
+- expert-generated
+language_creators:
+- other
+language:
+- pl
+license:
+- cc-by-sa-3.0
+multilinguality:
+- monolingual
+size_categories:
+- 1K<n<10K
+source_datasets:
+- original
+task_categories:
+- question-answering
+task_ids:
+- open-domain-qa
+pretty_name: Did you know?
+---
+
+# klej-dyk
+
+## Description
+
+The Czy wiesz? (eng. Did you know?) the dataset consists of almost 5k question-answer pairs obtained from Czy wiesz... section of Polish Wikipedia. Each question is written by a Wikipedia collaborator and is answered with a link to a relevant Wikipedia article. In huggingface version of this dataset, they chose the negatives which have the largest token overlap with a question.
+
+## Tasks (input, output, and metrics)
+
+The task is to predict if the answer to the given question is correct or not. 
+
+**Input** ('question sentence', 'answer' columns): question and answer sentences
+
+**Output** ('target' column): 1 if the answer is correct, 0 otherwise. 
+
+**Domain**: Wikipedia
+
+**Measurements**: F1-Score
+
+**Example**:
+
+Input: `Czym zajmowali się świątnicy?` ; `Świątnik – osoba, która dawniej zajmowała się
+obsługą kościoła (świątyni).`  
+
+Input (translated by DeepL): `What did the sacristans do?` ; `A sacristan - a person who used to be in charge of the handling the church (temple).`
+
+Output: `1` (the answer is correct)
+
+## Data splits
+
+| Subset      | Cardinality |
+| ----------- | ----------: |
+| train       | 4154        |
+| val         | 0           |
+| test        | 1029        |
+
+## Class distribution
+
+|     Class |   train |   validation |   test |
+|:----------|--------:|-------------:|-------:|
+| incorrect |   0.831 |            - |  0.831 |
+|   correct |   0.169 |            - |  0.169 |
+
+## Citation
+
+```
+@misc{11321/39,	
+ title = {Pytania i odpowiedzi z serwisu wikipedyjnego "Czy wiesz", wersja 1.1},	
+ author = {Marci{\'n}czuk, Micha{\l} and Piasecki, Dominik and Piasecki, Maciej and Radziszewski, Adam},	
+ url = {http://hdl.handle.net/11321/39},	
+ note = {{CLARIN}-{PL} digital repository},	
+ year = {2013}	
+}
+```
+
+## License
+
+```
+Creative Commons Attribution ShareAlike 3.0 licence (CC-BY-SA 3.0)
+```
+
+## Links
+
+[HuggingFace](https://huggingface.co/datasets/dyk)
+
+[Source](http://nlp.pwr.wroc.pl/en/tools-and-resources/resources/czy-wiesz-question-answering-dataset)
+[Source #2](https://clarin-pl.eu/dspace/handle/11321/39) 
+
+[Paper](https://www.researchgate.net/publication/272685895_Open_dataset_for_development_of_Polish_Question_Answering_systems)
+
+## Examples
+
+### Loading
+
+```python
+from pprint import pprint
+
+from datasets import load_dataset
+
+dataset = load_dataset("allegro/klej-dyk")
+pprint(dataset['train'][100])
+
+#{'answer': '"W wyborach prezydenckich w 2004 roku, Moroz przekazał swoje '
+#           'poparcie Wiktorowi Juszczence. Po wyborach w 2006 socjaliści '
+#           'początkowo tworzyli ""pomarańczową koalicję"" z Naszą Ukrainą i '
+#           'Blokiem Julii Tymoszenko."',
+# 'q_id': 'czywiesz4362',
+# 'question': 'ile partii tworzy powołaną przez Wiktora Juszczenkę koalicję '
+#             'Blok Nasza Ukraina?',
+# 'target': 0}
+```
+
+### Evaluation
+
+```python
+import random
+from pprint import pprint
+
+from datasets import load_dataset, load_metric
+
+dataset = load_dataset("allegro/klej-dyk")
+dataset = dataset.class_encode_column("target")
+references = dataset["test"]["target"]
+
+# generate random predictions
+predictions = [random.randrange(max(references) + 1) for _ in range(len(references))]
+
+acc = load_metric("accuracy")
+f1 = load_metric("f1")
+
+acc_score = acc.compute(predictions=predictions, references=references)
+f1_score = f1.compute(predictions=predictions, references=references, average="macro")
+
+pprint(acc_score)
+pprint(f1_score)
+
+# {'accuracy': 0.5286686103012633}
+# {'f1': 0.46700507614213194}
+
+```

huggingface_dataset/Dataset_Card/arjunpatel_best-selling-video-games.md

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+# Dataset Card for [best-selling-video-games]
+
+## 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:**
+- **Repository:**
+- **Paper:**
+- **Leaderboard:**
+- **Point of Contact:**
+
+### Dataset Summary
+
+[More Information Needed]
+
+### 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
+
+Thanks to [@arjunpatel7](https://github.com/<github-username>) for adding this dataset.

huggingface_dataset/Dataset_Card/autoevaluate_autoeval-eval-Tristan__zero-shot-classification-large-test-Tristan__z-7873ce-1486054319.md

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

huggingface_dataset/Dataset_Card/autoevaluate_autoeval-eval-inverse-scaling__hindsight-neglect-10shot-inverse-scali-383fe9-1695459612.md

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+---
+type: predictions
+tags:
+- autotrain
+- evaluation
+datasets:
+- inverse-scaling/hindsight-neglect-10shot
+eval_info:
+  task: text_zero_shot_classification
+  model: inverse-scaling/opt-30b_eval
+  metrics: []
+  dataset_name: inverse-scaling/hindsight-neglect-10shot
+  dataset_config: inverse-scaling--hindsight-neglect-10shot
+  dataset_split: train
+  col_mapping:
+    text: prompt
+    classes: classes
+    target: answer_index
+---
+# Dataset Card for AutoTrain Evaluator
+
+This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
+
+* Task: Zero-Shot Text Classification
+* Model: inverse-scaling/opt-30b_eval
+* Dataset: inverse-scaling/hindsight-neglect-10shot
+* Config: inverse-scaling--hindsight-neglect-10shot
+* Split: train
+
+To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
+
+## Contributions
+
+Thanks to [@MicPie](https://huggingface.co/MicPie) for evaluating this model.

huggingface_dataset/Dataset_Card/autoevaluate_autoeval-eval-project-quoref-bbfe943f-1305449898.md

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+---
+type: predictions
+tags:
+- autotrain
+- evaluation
+datasets:
+- quoref
+eval_info:
+  task: extractive_question_answering
+  model: nbroad/deb-base-gc2
+  metrics: []
+  dataset_name: quoref
+  dataset_config: default
+  dataset_split: validation
+  col_mapping:
+    context: context
+    question: question
+    answers-text: answers.text
+    answers-answer_start: answers.answer_start
+---
+# Dataset Card for AutoTrain Evaluator
+
+This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
+
+* Task: Question Answering
+* Model: nbroad/deb-base-gc2
+* Dataset: quoref
+* Config: default
+* Split: validation
+
+To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
+
+## Contributions
+
+Thanks to [@nbroad](https://huggingface.co/nbroad) for evaluating this model.

huggingface_dataset/Dataset_Card/autoevaluate_autoeval-eval-squad_v2-squad_v2-ea058a-1765461442.md

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+---
+type: predictions
+tags:
+- autotrain
+- evaluation
+datasets:
+- squad_v2
+eval_info:
+  task: extractive_question_answering
+  model: husnu/bert-base-turkish-128k-cased-finetuned_lr-2e-05_epochs-3TQUAD2-finetuned_lr-2e-05_epochs-1
+  metrics: []
+  dataset_name: squad_v2
+  dataset_config: squad_v2
+  dataset_split: validation
+  col_mapping:
+    context: context
+    question: question
+    answers-text: answers.text
+    answers-answer_start: answers.answer_start
+---
+# Dataset Card for AutoTrain Evaluator
+
+This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
+
+* Task: Question Answering
+* Model: husnu/bert-base-turkish-128k-cased-finetuned_lr-2e-05_epochs-3TQUAD2-finetuned_lr-2e-05_epochs-1
+* Dataset: squad_v2
+* Config: squad_v2
+* Split: validation
+
+To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
+
+## Contributions
+
+Thanks to [@Jets](https://huggingface.co/Jets) for evaluating this model.

huggingface_dataset/Dataset_Card/autoevaluate_autoeval-staging-eval-emotion-default-73111d-17386348.md

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+---
+type: predictions
+tags:
+- autotrain
+- evaluation
+datasets:
+- emotion
+eval_info:
+  task: multi_class_classification
+  model: lewtun/sagemaker-distilbert-emotion
+  metrics: []
+  dataset_name: emotion
+  dataset_config: default
+  dataset_split: test
+  col_mapping:
+    text: text
+    target: label
+---
+# Dataset Card for AutoTrain Evaluator
+
+This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
+
+* Task: Multi-class Text Classification
+* Model: lewtun/sagemaker-distilbert-emotion
+* Dataset: emotion
+* Config: default
+* Split: test
+
+To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
+
+## Contributions
+
+Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model.

huggingface_dataset/Dataset_Card/bond005_sberdevices_golos_10h_crowd.md

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+---
+pretty_name: Golos
+annotations_creators:
+- expert-generated
+language_creators:
+- crowdsourced
+- expert-generated
+language:
+- ru
+license:
+- other
+multilinguality:
+- monolingual
+paperswithcode_id: golos
+size_categories:
+- 10K<n<100k
+source_datasets:
+- extended
+task_categories:
+- automatic-speech-recognition
+- audio-classification
+---
+
+
+# Dataset Card for sberdevices_golos_10h_crowd
+## 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:** [Golos ASR corpus](https://www.openslr.org/114)
+- **Repository:** [Golos dataset](https://github.com/sberdevices/golos)
+- **Paper:** [Golos: Russian Dataset for Speech Research](https://arxiv.org/pdf/2106.10161.pdf)
+- **Leaderboard:** [The 🤗 Speech Bench](https://huggingface.co/spaces/huggingface/hf-speech-bench)
+- **Point of Contact:** [Nikolay Karpov](mailto:karpnv@gmail.com)
+### Dataset Summary
+Sberdevices Golos is a corpus of approximately 1200 hours of 16kHz Russian speech from crowd (reading speech) and farfield (communication with smart devices) domains, prepared by SberDevices Team (Alexander Denisenko, Angelina Kovalenko, Fedor Minkin, and Nikolay Karpov). The data is derived from the crowd-sourcing platform, and has been manually annotated.
+
+Authors divide all dataset into train and test subsets. The training subset includes approximately 1000 hours. For experiments with a limited number of records, authors identified training subsets of shorter length: 100 hours, 10 hours, 1 hour, 10 minutes.
+
+This dataset is a simpler version of the above mentioned Golos:
+
+- it includes the crowd domain only (without any sound from the farfield domain);
+- validation split is built on the 1-hour training subset;
+- training split corresponds to the 10-hour training subset without sounds from the 1-hour training subset;
+- test split is a full original test split.
+
+### Supported Tasks and Leaderboards
+- `automatic-speech-recognition`: The dataset can be used to train 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). The task has an active Hugging Face leaderboard which can be found at https://huggingface.co/spaces/huggingface/hf-speech-bench. The leaderboard ranks models uploaded to the Hub based on their WER.
+### Languages
+The audio is in Russian.
+## Dataset Structure
+### Data Instances
+A typical data point comprises the audio data, usually called `audio` and its transcription, called `transcription`. Any additional information about the speaker and the passage which contains the transcription is not provided.
+```
+{'audio': {'path': None,
+  'array': array([ 3.05175781e-05,  3.05175781e-05,  0.00000000e+00, ...,
+                  -1.09863281e-03, -7.93457031e-04, -1.52587891e-04]), dtype=float64),
+  'sampling_rate': 16000},
+ 'transcription': 'шестнадцатая часть сезона пять сериала лемони сникет тридцать три несчастья'}
+```
+### Data Fields
+- audio: A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. Note that when accessing the audio column: `dataset[0]["audio"]` the audio file is automatically decoded and resampled to `dataset.features["audio"].sampling_rate`. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the `"audio"` column, *i.e.* `dataset[0]["audio"]` should **always** be preferred over `dataset["audio"][0]`.
+- transcription: the transcription of the audio file.
+### Data Splits
+This dataset is a simpler version of the original Golos:
+
+- it includes the crowd domain only (without any sound from the farfield domain);
+- validation split is built on the 1-hour training subset;
+- training split corresponds to the 10-hour training subset without sounds from the 1-hour training subset;
+- test split is a full original test split.
+
+|                             | Train  | Validation |  Test |
+| -----                       | ------ | ---------- | ----- |
+| examples                    |  7993  |        793 |  9994 |
+| hours                       |  8.9h  |       0.9h | 11.2h |
+## Dataset Creation
+### Curation Rationale
+[Needs More Information]
+### Source Data
+#### Initial Data Collection and Normalization
+[Needs More Information]
+#### Who are the source language producers?
+[Needs More Information]
+### Annotations
+#### Annotation process
+All recorded audio files were manually annotated on the crowd-sourcing platform.
+#### Who are the annotators?
+[Needs More Information]
+### Personal and Sensitive Information
+The dataset consists of people who have donated their voice. You agree to not attempt to determine the identity of speakers in this dataset.
+## Considerations for Using the Data
+### Social Impact of Dataset
+[More Information Needed]
+### Discussion of Biases
+[More Information Needed]
+### Other Known Limitations
+[Needs More Information]
+## Additional Information
+### Dataset Curators
+The dataset was initially created by Alexander Denisenko, Angelina Kovalenko, Fedor Minkin, and Nikolay Karpov.
+### Licensing Information
+[Public license with attribution and conditions reserved](https://github.com/sberdevices/golos/blob/master/license/en_us.pdf)
+### Citation Information
+```
+@misc{karpov2021golos,
+  author = {Karpov, Nikolay and Denisenko, Alexander and Minkin, Fedor},
+  title = {Golos: Russian Dataset for Speech Research},
+  publisher = {arXiv},
+  year = {2021},
+  url = {https://arxiv.org/abs/2106.10161}
+}
+
+```
+### Contributions
+Thanks to [@bond005](https://github.com/bond005) for adding this dataset.
+

huggingface_dataset/Dataset_Card/hrwac.md

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+---
+annotations_creators:
+- no-annotation
+language_creators:
+- found
+language:
+- hr
+license:
+- cc-by-sa-3.0
+multilinguality:
+- monolingual
+size_categories:
+- 1B<n<10B
+source_datasets:
+- original
+task_categories:
+- text-generation
+- fill-mask
+task_ids:
+- language-modeling
+- masked-language-modeling
+paperswithcode_id: null
+pretty_name: HrWac
+dataset_info:
+  features:
+  - name: sentence
+    dtype: string
+  config_name: hrwac
+  splits:
+  - name: train
+    num_bytes: 43994569015
+    num_examples: 1736944727
+  download_size: 9217221471
+  dataset_size: 43994569015
+---
+
+# Dataset Card for HrWac
+
+## Table of Contents
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
+  - [Languages](#languages)
+- [Dataset Structure](#dataset-structure)
+  - [Data Instances](#data-instances)
+  - [Data Fields](#data-fields)
+  - [Data Splits](#data-splits)
+- [Dataset Creation](#dataset-creation)
+  - [Curation Rationale](#curation-rationale)
+  - [Source Data](#source-data)
+  - [Annotations](#annotations)
+  - [Personal and Sensitive Information](#personal-and-sensitive-information)
+- [Considerations for Using the Data](#considerations-for-using-the-data)
+  - [Social Impact of Dataset](#social-impact-of-dataset)
+  - [Discussion of Biases](#discussion-of-biases)
+  - [Other Known Limitations](#other-known-limitations)
+- [Additional Information](#additional-information)
+  - [Dataset Curators](#dataset-curators)
+  - [Licensing Information](#licensing-information)
+  - [Citation Information](#citation-information)
+  - [Contributions](#contributions)
+
+## Dataset Description
+
+- **Homepage:** http://nlp.ffzg.hr/resources/corpora/hrwac/
+- **Repository:** https://www.clarin.si/repository/xmlui/handle/11356/1064
+- **Paper:** http://nlp.ffzg.hr/data/publications/nljubesi/ljubesic11-hrwac.pdf
+- **Leaderboard:**
+- **Point of Contact:** [Nikola Ljubešič](mailto:nikola.ljubesic@ffzg.hr)
+
+### Dataset Summary
+
+The Croatian web corpus hrWaC was built by crawling the .hr top-level domain in 2011 and again in 2014. The corpus was near-deduplicated on paragraph level, normalised via diacritic restoration, morphosyntactically annotated and lemmatised. The corpus is shuffled by paragraphs. Each paragraph contains metadata on the URL, domain and language identification (Croatian vs. Serbian).
+
+### Supported Tasks and Leaderboards
+
+[More Information Needed]
+
+### Languages
+
+Dataset is monolingual in Croatian language.
+
+## Dataset Structure
+
+### Data Instances
+
+[More Information Needed]
+
+### Data Fields
+
+- sentence: sentences as strings
+
+### 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
+
+Dataset is under the [CC-BY-SA 3.0](http://creativecommons.org/licenses/by-sa/3.0/) license.
+
+### Citation Information
+
+```
+ @misc{11356/1064,
+ title = {Croatian web corpus {hrWaC} 2.1},
+ author = {Ljube{\v s}i{\'c}, Nikola and Klubi{\v c}ka, Filip},
+ url = {http://hdl.handle.net/11356/1064},
+ note = {Slovenian language resource repository {CLARIN}.{SI}},
+ copyright = {Creative Commons - Attribution-{ShareAlike} 4.0 International ({CC} {BY}-{SA} 4.0)},
+ year = {2016} }
+```
+
+### Contributions
+
+Thanks to [@IvanZidov](https://github.com/IvanZidov) for adding this dataset.

huggingface_dataset/Dataset_Card/huggingartists_fear-factory.md

@@ -0,0 +1,204 @@
+---
+language:
+- en
+tags:
+- huggingartists
+- lyrics
+---
+
+# Dataset Card for "huggingartists/fear-factory"
+
+## 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:** 0.178617 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/5c2952ca198d8eda91b478829b867fd6.1000x1000x1.jpg&#39;)">
+        </div>
+    </div>
+    <a href="https://huggingface.co/huggingartists/fear-factory">
+      <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">Fear Factory</div>
+    <a href="https://genius.com/artists/fear-factory">
+    	<div style="text-align: center; font-size: 14px;">@fear-factory</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/fear-factory).
+
+### 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/fear-factory")
+```
+
+## 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|
+|------:|---------:|---:|
+|197|         -|   -|
+
+'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/fear-factory")
+
+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/kmewhort_quickdraw-bins-50M.md

@@ -0,0 +1,418 @@
+---
+dataset_info:
+  features:
+  - name: label
+    dtype:
+      class_label:
+        names:
+          '0': The Eiffel Tower
+          '1': The Great Wall of China
+          '2': The Mona Lisa
+          '3': aircraft carrier
+          '4': airplane
+          '5': alarm clock
+          '6': ambulance
+          '7': angel
+          '8': animal migration
+          '9': ant
+          '10': anvil
+          '11': apple
+          '12': arm
+          '13': asparagus
+          '14': axe
+          '15': backpack
+          '16': banana
+          '17': bandage
+          '18': barn
+          '19': baseball
+          '20': baseball bat
+          '21': basket
+          '22': basketball
+          '23': bat
+          '24': bathtub
+          '25': beach
+          '26': bear
+          '27': beard
+          '28': bed
+          '29': bee
+          '30': belt
+          '31': bench
+          '32': bicycle
+          '33': binoculars
+          '34': bird
+          '35': birthday cake
+          '36': blackberry
+          '37': blueberry
+          '38': book
+          '39': boomerang
+          '40': bottlecap
+          '41': bowtie
+          '42': bracelet
+          '43': brain
+          '44': bread
+          '45': bridge
+          '46': broccoli
+          '47': broom
+          '48': bucket
+          '49': bulldozer
+          '50': bus
+          '51': bush
+          '52': butterfly
+          '53': cactus
+          '54': cake
+          '55': calculator
+          '56': calendar
+          '57': camel
+          '58': camera
+          '59': camouflage
+          '60': campfire
+          '61': candle
+          '62': cannon
+          '63': canoe
+          '64': car
+          '65': carrot
+          '66': castle
+          '67': cat
+          '68': ceiling fan
+          '69': cell phone
+          '70': cello
+          '71': chair
+          '72': chandelier
+          '73': church
+          '74': circle
+          '75': clarinet
+          '76': clock
+          '77': cloud
+          '78': coffee cup
+          '79': compass
+          '80': computer
+          '81': cookie
+          '82': cooler
+          '83': couch
+          '84': cow
+          '85': crab
+          '86': crayon
+          '87': crocodile
+          '88': crown
+          '89': cruise ship
+          '90': cup
+          '91': diamond
+          '92': dishwasher
+          '93': diving board
+          '94': dog
+          '95': dolphin
+          '96': donut
+          '97': door
+          '98': dragon
+          '99': dresser
+          '100': drill
+          '101': drums
+          '102': duck
+          '103': dumbbell
+          '104': ear
+          '105': elbow
+          '106': elephant
+          '107': envelope
+          '108': eraser
+          '109': eye
+          '110': eyeglasses
+          '111': face
+          '112': fan
+          '113': feather
+          '114': fence
+          '115': finger
+          '116': fire hydrant
+          '117': fireplace
+          '118': firetruck
+          '119': fish
+          '120': flamingo
+          '121': flashlight
+          '122': flip flops
+          '123': floor lamp
+          '124': flower
+          '125': flying saucer
+          '126': foot
+          '127': fork
+          '128': frog
+          '129': frying pan
+          '130': garden
+          '131': garden hose
+          '132': giraffe
+          '133': goatee
+          '134': golf club
+          '135': grapes
+          '136': grass
+          '137': guitar
+          '138': hamburger
+          '139': hammer
+          '140': hand
+          '141': harp
+          '142': hat
+          '143': headphones
+          '144': hedgehog
+          '145': helicopter
+          '146': helmet
+          '147': hexagon
+          '148': hockey puck
+          '149': hockey stick
+          '150': horse
+          '151': hospital
+          '152': hot air balloon
+          '153': hot dog
+          '154': hot tub
+          '155': hourglass
+          '156': house
+          '157': house plant
+          '158': hurricane
+          '159': ice cream
+          '160': jacket
+          '161': jail
+          '162': kangaroo
+          '163': key
+          '164': keyboard
+          '165': knee
+          '166': knife
+          '167': ladder
+          '168': lantern
+          '169': laptop
+          '170': leaf
+          '171': leg
+          '172': light bulb
+          '173': lighter
+          '174': lighthouse
+          '175': lightning
+          '176': line
+          '177': lion
+          '178': lipstick
+          '179': lobster
+          '180': lollipop
+          '181': mailbox
+          '182': map
+          '183': marker
+          '184': matches
+          '185': megaphone
+          '186': mermaid
+          '187': microphone
+          '188': microwave
+          '189': monkey
+          '190': moon
+          '191': mosquito
+          '192': motorbike
+          '193': mountain
+          '194': mouse
+          '195': moustache
+          '196': mouth
+          '197': mug
+          '198': mushroom
+          '199': nail
+          '200': necklace
+          '201': nose
+          '202': ocean
+          '203': octagon
+          '204': octopus
+          '205': onion
+          '206': oven
+          '207': owl
+          '208': paint can
+          '209': paintbrush
+          '210': palm tree
+          '211': panda
+          '212': pants
+          '213': paper clip
+          '214': parachute
+          '215': parrot
+          '216': passport
+          '217': peanut
+          '218': pear
+          '219': peas
+          '220': pencil
+          '221': penguin
+          '222': piano
+          '223': pickup truck
+          '224': picture frame
+          '225': pig
+          '226': pillow
+          '227': pineapple
+          '228': pizza
+          '229': pliers
+          '230': police car
+          '231': pond
+          '232': pool
+          '233': popsicle
+          '234': postcard
+          '235': potato
+          '236': power outlet
+          '237': purse
+          '238': rabbit
+          '239': raccoon
+          '240': radio
+          '241': rain
+          '242': rainbow
+          '243': rake
+          '244': remote control
+          '245': rhinoceros
+          '246': rifle
+          '247': river
+          '248': roller coaster
+          '249': rollerskates
+          '250': sailboat
+          '251': sandwich
+          '252': saw
+          '253': saxophone
+          '254': school bus
+          '255': scissors
+          '256': scorpion
+          '257': screwdriver
+          '258': sea turtle
+          '259': see saw
+          '260': shark
+          '261': sheep
+          '262': shoe
+          '263': shorts
+          '264': shovel
+          '265': sink
+          '266': skateboard
+          '267': skull
+          '268': skyscraper
+          '269': sleeping bag
+          '270': smiley face
+          '271': snail
+          '272': snake
+          '273': snorkel
+          '274': snowflake
+          '275': snowman
+          '276': soccer ball
+          '277': sock
+          '278': speedboat
+          '279': spider
+          '280': spoon
+          '281': spreadsheet
+          '282': square
+          '283': squiggle
+          '284': squirrel
+          '285': stairs
+          '286': star
+          '287': steak
+          '288': stereo
+          '289': stethoscope
+          '290': stitches
+          '291': stop sign
+          '292': stove
+          '293': strawberry
+          '294': streetlight
+          '295': string bean
+          '296': submarine
+          '297': suitcase
+          '298': sun
+          '299': swan
+          '300': sweater
+          '301': swing set
+          '302': sword
+          '303': syringe
+          '304': t-shirt
+          '305': table
+          '306': teapot
+          '307': teddy-bear
+          '308': telephone
+          '309': television
+          '310': tennis racquet
+          '311': tent
+          '312': tiger
+          '313': toaster
+          '314': toe
+          '315': toilet
+          '316': tooth
+          '317': toothbrush
+          '318': toothpaste
+          '319': tornado
+          '320': tractor
+          '321': traffic light
+          '322': train
+          '323': tree
+          '324': triangle
+          '325': trombone
+          '326': truck
+          '327': trumpet
+          '328': umbrella
+          '329': underwear
+          '330': van
+          '331': vase
+          '332': violin
+          '333': washing machine
+          '334': watermelon
+          '335': waterslide
+          '336': whale
+          '337': wheel
+          '338': windmill
+          '339': wine bottle
+          '340': wine glass
+          '341': wristwatch
+          '342': yoga
+          '343': zebra
+          '344': zigzag
+  - name: packed_drawing
+    dtype: binary
+  splits:
+  - name: train
+    num_bytes: 5196066788.157136
+    num_examples: 40341012
+  - name: test
+    num_bytes: 1299016825.8428645
+    num_examples: 10085254
+  download_size: 6290637578
+  dataset_size: 6495083614.0
+---
+# Quick!Draw! Dataset (per-row bin format)
+
+This is the full 50M-row dataset from [QuickDraw! dataset](https://github.com/googlecreativelab/quickdraw-dataset).  The row for each drawing contains a byte-encoded packed representation of the drawing and data, which you can unpack using the following snippet:
+
+```
+def unpack_drawing(file_handle):
+    key_id, = unpack('Q', file_handle.read(8))
+    country_code, = unpack('2s', file_handle.read(2))
+    recognized, = unpack('b', file_handle.read(1))
+    timestamp, = unpack('I', file_handle.read(4))
+    n_strokes, = unpack('H', file_handle.read(2))
+    image = []
+    n_bytes = 17
+    for i in range(n_strokes):
+        n_points, = unpack('H', file_handle.read(2))
+        fmt = str(n_points) + 'B'
+        x = unpack(fmt, file_handle.read(n_points))
+        y = unpack(fmt, file_handle.read(n_points))
+        image.append((x, y))
+        n_bytes += 2 + 2*n_points
+    result = {
+        'key_id': key_id,
+        'country_code': country_code,
+        'recognized': recognized,
+        'timestamp': timestamp,
+        'image': image,
+    }
+    return result
+```
+
+The `image` in the above is still in line vector format.  To convert render this to a raster image (I recommend you do this on-the-fly in a pre-processor):
+
+```
+# packed bin -> RGB PIL
+def binToPIL(packed_drawing):
+    padding = 8
+    radius = 7
+    scale = (224.0-(2*padding)) / 256
+    
+    unpacked = unpack_drawing(io.BytesIO(packed_drawing))
+    unpacked_image = unpacked['image']
+    image = np.full((224,224), 255, np.uint8)
+    for stroke in unpacked['image']:
+        prevX = round(stroke[0][0]*scale)
+        prevY = round(stroke[1][0]*scale)
+        for i in range(1, len(stroke[0])):
+            x = round(stroke[0][i]*scale)
+            y = round(stroke[1][i]*scale)
+            cv2.line(image, (padding+prevX, padding+prevY), (padding+x, padding+y), 0, radius, -1)
+            prevX = x
+            prevY = y
+    pilImage = Image.fromarray(image).convert("RGB")     
+    return pilImage
+```

huggingface_dataset/Dataset_Card/lm4pt_bpsad.md

@@ -0,0 +1,218 @@
+---
+annotations_creators: []
+language:
+- pt
+language_creators:
+- other
+license:
+- unknown
+multilinguality:
+- monolingual
+pretty_name: bpsad
+size_categories:
+- 1M<n<10M
+source_datasets: []
+tags: []
+task_categories:
+- text-classification
+task_ids:
+- multi-class-classification
+- sentiment-classification
+- sentiment-scoring
+- sentiment-analysis
+---
+
+# Dataset Card for Brazilian Portuguese Sentiment Analysis Dataset
+
+## 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:** [Kaggle Dataset](https://www.kaggle.com/datasets/fredericods/ptbr-sentiment-analysis-datasets)
+- **Paper:** [Sentiment Analysis on Brazilian Portuguese User Reviews](https://ieeexplore.ieee.org/abstract/document/9769838)
+- **Point of Contact:** [Frederico Dias Souza](fredericods@poli.ufrj.br)
+
+### Dataset Summary
+
+**Disclaimer:** *The team releasing the dataset did not write a dataset card
+for this dataset so this dataset card has been written by the contributors.*
+
+The Brazilian Portuguese Sentiment Analysis Dataset (BPSAD) is composed
+by the concatenation of 5 differents sources (Olist, B2W Digital, Buscapé,
+UTLC-Apps and UTLC-Movies), each one is composed by evaluation sentences
+classified according to the polarity (0: negative; 1: positive) and ratings
+(1, 2, 3, 4 and 5 stars).
+
+This dataset requires manual download:
+1. Download the `concatenated` file from dataset homepage.
+2. Extract the file inside `<path/to/manual/data>`.
+3. Load the dataset using the command:
+```python
+datasets.load_dataset(
+    path="lm4pt/bpsad",
+    name='<polarity|rating>',
+    data_dir='<path/to/manual/data>')
+```
+
+A detailed description about the dataset and the processing steps can be
+found at the [dataset homepage](https://www.kaggle.com/datasets/fredericods/ptbr-sentiment-analysis-datasets).
+
+
+### Supported Tasks and Leaderboards
+
+The dataset contains two configurations that represents the possible tasks
+related to sentiment analysis. The polarity classification is a binary
+classification problem where the sentences must be classified as positive (1)
+or negative (0) reviews. The rating prediction is a multiclass problem
+with values ranging from 1 to 5 stars.
+
+
+### Languages
+
+The texts are in Brazilian Portuguese, as spoken by users of different e-commerces and Filmow social network.
+
+## Dataset Structure
+
+### Data Instances
+
+#### polarity
+
+```
+{
+    "review_text": "Bem macio e felpudo...recomendo.  Preço imbatível e entrega rápida. Compraria outro quando precisar",
+    "polarity": 1
+}
+```
+
+#### rating
+
+```
+{
+    "review_text": "Bem macio e felpudo...recomendo.  Preço imbatível e entrega rápida. Compraria outro quando precisar",
+    "rating": 4
+}
+```
+
+### Data Fields
+
+#### polarity
+- `review_text`: a `string` feature with product or movie review.
+- `polarity`: an `integer` value that represents positive (1) or negative (0) reviews.
+
+#### rating
+
+- `review_text`: a `string` feature with product or movie review.
+- `rating`: an `integer` value that represents the number of stars given by the reviewer. Possible values are 1, 2, 3, 4 and 5.
+
+### Data Splits
+
+Data splits are created based on the original `kfold` column of each configuration, following the original authors recomendations:
+- train: folds 1 to 8
+- validation: fold 9
+- test: fold 10
+
+|          |  train  | validation |  test  |
+|----------|--------:|-----------:|-------:|
+| polarity | 1908937 |     238614 | 238613 |
+|   rating | 2228877 |     278608 | 278607 |
+
+More information about sentence size and label distribution can be found in the [dataset homepage](https://www.kaggle.com/datasets/fredericods/ptbr-sentiment-analysis-datasets).
+
+## 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
+
+```
+@inproceedings{souza2021sentiment,
+    author={
+        Souza, Frederico Dias and
+        Baptista de Oliveira e Souza Filho, João},
+    booktitle={
+        2021 IEEE Latin American Conference on
+        Computational Intelligence (LA-CCI)}, 
+    title={
+        Sentiment Analysis on Brazilian Portuguese User Reviews}, 
+    year={2021},
+    pages={1-6},
+    doi={10.1109/LA-CCI48322.2021.9769838}
+}
+```
+
+### Contributions
+
+Thanks to [@guilhermelmello](https://huggingface.co/guilhermelmello) and [@DominguesPH](https://huggingface.co/DominguesPH) for adding this dataset.

huggingface_dataset/Dataset_Card/parambharat_mile_dataset.md

@@ -0,0 +1,161 @@
+---
+annotations_creators:
+- expert-generated
+language:
+- ta
+language_creators:
+- expert-generated
+license:
+- cc-by-2.0
+multilinguality:
+- monolingual
+pretty_name: IISc-MILE Tamil ASR Corpus
+size_categories:
+- 10K<n<100K
+source_datasets:
+- original
+tags:
+- Tamil ASR
+- Speech Recognition
+task_categories:
+- automatic-speech-recognition
+task_ids: []
+---
+
+# Dataset Card for [Dataset Name]
+
+## 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://www.openslr.org/127/
+- **Repository:** https://github.com/MILE-IISc
+- **Paper:** https://arxiv.org/abs/2207.13331
+- **Leaderboard:**
+- **Point of Contact:**
+
+### Dataset Summary
+
+Tamil transcribed speech corpus for ASR 
+
+### Supported Tasks and Leaderboards
+
+[More Information Needed]
+
+### Languages
+
+- Tamil
+
+## 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
+
+Attribution 2.0 Generic (CC BY 2.0) 
+
+### Citation Information
+@misc{mile_1,
+  doi = {10.48550/ARXIV.2207.13331},
+  url = {https://arxiv.org/abs/2207.13331},
+  author = {A, Madhavaraj and Pilar, Bharathi and G, Ramakrishnan A},
+  title = {Subword Dictionary Learning and Segmentation Techniques for Automatic Speech Recognition in Tamil and Kannada},
+  publisher = {arXiv},
+  year = {2022},
+}
+
+@misc{mile_2,
+  doi = {10.48550/ARXIV.2207.13333},
+  url = {https://arxiv.org/abs/2207.13333},
+  author = {A, Madhavaraj and Pilar, Bharathi and G, Ramakrishnan A},
+  title = {Knowledge-driven Subword Grammar Modeling for Automatic Speech Recognition in Tamil and Kannada},
+  publisher = {arXiv},
+  year = {2022},
+}
+
+### Contributions
+
+Thanks to [@parambharat](https://github.com/parambharat) for adding this dataset.

huggingface_dataset/Dataset_Card/relbert_semeval2012_relational_similarity.md

@@ -0,0 +1,73 @@
+---
+language:
+- en
+license:
+- other
+multilinguality:
+- monolingual
+size_categories:
+- n<1K
+pretty_name: SemEval2012 relational similarity dataset
+---
+# Dataset Card for "relbert/semeval2012_relational_similarity"
+## Dataset Description
+- **Repository:** [RelBERT](https://github.com/asahi417/relbert)
+- **Paper:** [https://aclanthology.org/S12-1047/](https://aclanthology.org/S12-1047/)
+- **Dataset:** SemEval2012 relational similarity dataset 
+
+### Dataset Summary
+Relational similarity dataset from [SemEval2012 task 2](https://aclanthology.org/S12-1047/), compiled to fine-tune [RelBERT](https://github.com/asahi417/relbert) model.
+The dataset contains a list of positive and negative word pair from 89 pre-defined relations.
+The relation types are constructed on top of following 10 parent relation types.  
+```shell
+{
+    1: "Class Inclusion",  # Hypernym
+    2: "Part-Whole",  # Meronym, Substance Meronym
+    3: "Similar",  # Synonym, Co-hypornym
+    4: "Contrast",  # Antonym
+    5: "Attribute",  # Attribute, Event
+    6: "Non Attribute",
+    7: "Case Relation",
+    8: "Cause-Purpose",
+    9: "Space-Time",
+    10: "Representation"
+}
+```
+Each of the parent relation is further grouped into child relation types where the definition can be found [here](https://drive.google.com/file/d/0BzcZKTSeYL8VenY0QkVpZVpxYnc/view?resourcekey=0-ZP-UARfJj39PcLroibHPHw).
+
+
+## Dataset Structure
+### Data Instances
+An example of `train` looks as follows.
+```shell
+{
+  'relation_type': '8d',
+  'positives': [ [ "breathe", "live" ], [ "study", "learn" ], [ "speak", "communicate" ], ... ]
+  'negatives': [ [ "starving", "hungry" ], [ "clean", "bathe" ], [ "hungry", "starving" ], ... ] 
+}
+```
+
+### Data Splits
+
+|train|validation|
+|----:|---------:|
+|  79 |       79 |
+
+
+## Citation Information
+```
+@inproceedings{jurgens-etal-2012-semeval,
+    title = "{S}em{E}val-2012 Task 2: Measuring Degrees of Relational Similarity",
+    author = "Jurgens, David  and
+      Mohammad, Saif  and
+      Turney, Peter  and
+      Holyoak, Keith",
+    booktitle = "*{SEM} 2012: The First Joint Conference on Lexical and Computational Semantics {--} Volume 1: Proceedings of the main conference and the shared task, and Volume 2: Proceedings of the Sixth International Workshop on Semantic Evaluation ({S}em{E}val 2012)",
+    month = "7-8 " # jun,
+    year = "2012",
+    address = "Montr{\'e}al, Canada",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/S12-1047",
+    pages = "356--364",
+}
+```

huggingface_dataset/Dataset_Card/sayakpaul_diode-subset-train.md

@@ -0,0 +1,9 @@
+---
+license: mit
+tags:
+- depth-estimation
+---
+
+DIODE dataset: https://diode-dataset.org/
+
+Code to prepare the archive: TBA

huggingface_dataset/Dataset_Card/wmt20_mlqe_task3.md

@@ -0,0 +1,419 @@
+---
+annotations_creators:
+- expert-generated
+- machine-generated
+language_creators:
+- found
+language:
+- en
+- fr
+license:
+- unknown
+multilinguality:
+- translation
+size_categories:
+- 1K<n<10K
+source_datasets:
+- extended|amazon_us_reviews
+task_categories:
+- translation
+task_ids: []
+pretty_name: WMT20 - MultiLingual Quality Estimation (MLQE) Task3
+dataset_info:
+  features:
+  - name: document_id
+    dtype: string
+  - name: source_segments
+    sequence: string
+  - name: source_tokenized
+    sequence: string
+  - name: mt_segments
+    sequence: string
+  - name: mt_tokenized
+    sequence: string
+  - name: annotations
+    sequence:
+    - name: segment_id
+      sequence: int32
+    - name: annotation_start
+      sequence: int32
+    - name: annotation_length
+      sequence: int32
+    - name: severity
+      dtype:
+        class_label:
+          names:
+            '0': minor
+            '1': major
+            '2': critical
+    - name: severity_weight
+      dtype: float32
+    - name: category
+      dtype:
+        class_label:
+          names:
+            '0': Addition
+            '1': Agreement
+            '2': Ambiguous Translation
+            '3': Capitalization
+            '4': Character Encoding
+            '5': Company Terminology
+            '6': Date/Time
+            '7': Diacritics
+            '8': Duplication
+            '9': False Friend
+            '10': Grammatical Register
+            '11': Hyphenation
+            '12': Inconsistency
+            '13': Lexical Register
+            '14': Lexical Selection
+            '15': Named Entity
+            '16': Number
+            '17': Omitted Auxiliary Verb
+            '18': Omitted Conjunction
+            '19': Omitted Determiner
+            '20': Omitted Preposition
+            '21': Omitted Pronoun
+            '22': Orthography
+            '23': Other POS Omitted
+            '24': Over-translation
+            '25': Overly Literal
+            '26': POS
+            '27': Punctuation
+            '28': Shouldn't Have Been Translated
+            '29': Shouldn't have been translated
+            '30': Spelling
+            '31': Tense/Mood/Aspect
+            '32': Under-translation
+            '33': Unidiomatic
+            '34': Unintelligible
+            '35': Unit Conversion
+            '36': Untranslated
+            '37': Whitespace
+            '38': Word Order
+            '39': Wrong Auxiliary Verb
+            '40': Wrong Conjunction
+            '41': Wrong Determiner
+            '42': Wrong Language Variety
+            '43': Wrong Preposition
+            '44': Wrong Pronoun
+  - name: token_annotations
+    sequence:
+    - name: segment_id
+      sequence: int32
+    - name: first_token
+      sequence: int32
+    - name: last_token
+      sequence: int32
+    - name: token_after_gap
+      sequence: int32
+    - name: severity
+      dtype:
+        class_label:
+          names:
+            '0': minor
+            '1': major
+            '2': critical
+    - name: category
+      dtype:
+        class_label:
+          names:
+            '0': Addition
+            '1': Agreement
+            '2': Ambiguous Translation
+            '3': Capitalization
+            '4': Character Encoding
+            '5': Company Terminology
+            '6': Date/Time
+            '7': Diacritics
+            '8': Duplication
+            '9': False Friend
+            '10': Grammatical Register
+            '11': Hyphenation
+            '12': Inconsistency
+            '13': Lexical Register
+            '14': Lexical Selection
+            '15': Named Entity
+            '16': Number
+            '17': Omitted Auxiliary Verb
+            '18': Omitted Conjunction
+            '19': Omitted Determiner
+            '20': Omitted Preposition
+            '21': Omitted Pronoun
+            '22': Orthography
+            '23': Other POS Omitted
+            '24': Over-translation
+            '25': Overly Literal
+            '26': POS
+            '27': Punctuation
+            '28': Shouldn't Have Been Translated
+            '29': Shouldn't have been translated
+            '30': Spelling
+            '31': Tense/Mood/Aspect
+            '32': Under-translation
+            '33': Unidiomatic
+            '34': Unintelligible
+            '35': Unit Conversion
+            '36': Untranslated
+            '37': Whitespace
+            '38': Word Order
+            '39': Wrong Auxiliary Verb
+            '40': Wrong Conjunction
+            '41': Wrong Determiner
+            '42': Wrong Language Variety
+            '43': Wrong Preposition
+            '44': Wrong Pronoun
+  - name: token_index
+    sequence:
+      sequence:
+        sequence: int32
+  - name: total_words
+    dtype: int32
+  config_name: plain_text
+  splits:
+  - name: train
+    num_bytes: 10762355
+    num_examples: 1448
+  - name: test
+    num_bytes: 745260
+    num_examples: 180
+  - name: validation
+    num_bytes: 1646596
+    num_examples: 200
+  download_size: 3534634
+  dataset_size: 13154211
+---
+
+# Dataset Card for WMT20 - MultiLingual Quality Estimation (MLQE) Task3
+
+## 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:** [WMT20 Quality Estimation Shared Task](http://www.statmt.org/wmt20/quality-estimation-task.html)
+- **Repository**: [Github repository](https://github.com/deep-spin/deep-spin.github.io/tree/master/docs/data/wmt2020_qe)
+- **Paper:** *Not available*
+
+### Dataset Summary
+
+From the homepage:
+
+*This shared task (part of WMT20) will build on its previous editions to further examine automatic methods for estimating the quality of neural machine translation output at run-time, without relying on reference translations. As in previous years, we cover estimation at various levels. Important elements introduced this year include: a new task where sentences are annotated with Direct Assessment (DA) scores instead of labels based on post-editing; a new multilingual sentence-level dataset mainly from Wikipedia articles, where the source articles can be retrieved for document-wide context; the availability of NMT models to explore system-internal information for the task.*
+
+*The goal of this task 3 is to predict document-level quality scores as well as fine-grained annotations.*
+
+*Each document has a product title and its description, and is annotated for translation errors according to the MQM framework. Each error annotation has:*
+- ***Word span(s).*** *Errors may consist of one or more words, not necessarily contiguous.*
+- ***Severity.*** *An error can be minor (if it doesn't lead to a loss of meaning and it doesn't confuse or mislead the user), major (if it changes the meaning) or critical (if it changes the meaning and carry any type of implication, or could be seen as offensive).*
+- ***Type.*** *A label specifying the error type, such as wrong word order, missing words, agreement, etc. They may provide additional information, but systems don't need to predict them.*
+
+### Supported Tasks and Leaderboards
+
+From the homepage:
+
+*Submissions will be evaluated as in Task 1, in terms of Pearson's correlation between the true and predicted MQM document-level scores. Additionally, the predicted annotations will be evaluated in terms of their F1 scores with respect to the gold annotations. The [official evaluation scripts](https://github.com/sheffieldnlp/qe-eval-scripts) are available.*
+
+### Languages
+
+There is a single language pair in the dataset: English (`en`) - French (`fr`).
+
+## Dataset Structure
+
+### Data Instances
+
+An example looks like this:
+```
+{
+  'document_id': 'B0000568SY',
+  'source_segments': ['Razor Scooter Replacement Wheels Set with Bearings', 'Scooter Wheels w/Bearings-Blue'],
+  'source_tokenized': ['Razor Scooter Replacement Wheels Set with Bearings', 'Scooter Wheels w / Bearings-Blue'],
+  'mt_segments': ['Roues de rechange Razor Scooter sertie de roulements', 'Roues de scooter w/roulements-bleu'],
+  'mt_tokenized': ['Roues de rechange Razor Scooter sertie de roulements', 'Roues de scooter w / roulements-bleu'],
+  'annotations': {
+    'segment_id': [[0], [1], [1], [0, 0], [0], [1], [1]],
+    'annotation_start': [[42], [19], [9], [0, 32], [9], [17], [30]],
+    'annotation_length': [[10], [10], [7], [5, 6], [8], [1], [4]],
+    'severity': [0, 0, 0, 0, 0, 1, 0],
+    'severity_weight': [1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 1.0]
+    'category': [3, 3, 3, 1, 3, 36, 3],
+  },
+  'token_annotations': {
+    'category': [3, 3, 3, 1, 3, 36, 3],
+    'first_token': [[7], [5], [2], [0, 5], [2], [3], [5]],
+    'last_token': [[7], [5], [2], [0, 5], [2], [3], [5]],
+    'segment_id': [[0], [1], [1], [0, 0], [0], [1], [1]],
+    'severity': [0, 0, 0, 0, 0, 1, 0],
+    'token_after_gap': [[-1], [-1], [-1], [-1, -1], [-1], [-1], [-1]]
+  },
+  'token_index': [[[0, 5], [6, 2], [9, 8], [18, 5], [24, 7], [32, 6], [39, 2], [42, 10]], [[0, 5], [6, 2], [9, 7], [17, 1], [18, 1], [19, 15]]],
+  'total_words': 16
+}
+```
+
+### Data Fields
+
+- `document_id`: the document id (name of the folder).
+- `source_segments`: the original source text, one sentence per line (i.e. per element of the list).
+- `source_tokenized`: a tokenized version of `source_segments`.
+- `mt_segments`: the original machine-translated text, one sentence per line (i.e. per element of the list).
+- `mt_tokenized`: a tokenized version of `mt_segments`. Default value is `[]` when this information is not available (it happens 3 times in the train set: `B0001BW0PQ`, `B0001GS19U` and `B000A6SMJ0`).
+- `annotations`: error annotations for the document. Each item of the list corresponds to an error annotation, which in turn may contain one or more error spans. Error fields are encoded in a dictionary. In the case of a multi-span error, multiple starting positions and lengths are encoded in the list. Note that these positions points to `mt.segments`, not `mt_tokenized`.
+  - `segment_id`: List of list of integers. Id of each error.
+  - `annotation_start`: List of list of integers. Start of each error.
+  - `annotation_length`: List of list of intergers. Length of each error.
+  - `severity`: List of one hot. Severity category of each error.
+  - `severity_weight`: List of floats. Severity weight of each error.
+  - `category`: List of one hot. Category of each error. See the 45 categories in `_ANNOTATION_CATEGORIES_MAPPING`.
+- `token_annotations`: tokenized version of `annotations`. Each error span that contains one or more tokens has a "first token" and "last token". Again, multi-span errors have their first and last tokens encoded in a list. When a span is over a gap between two tokens, the "first" and "last" positions are `-1` (encoded as `-` in the original data), and instead the `token_after_gap` column points to the token immediately after the gap. In case of a gap occurring at the end of the sentence, this value will be equal to the number of tokens.
+  - `segment_id`: List of list of integers. Id of each error.
+  - `first_token`: List of list of integers. Start of each error.
+  - `last_token`: List of list of intergers. End of each error.
+  - `token_after_gap`: List of list of integers. Token after gap of each error.
+  - `severity`: List of one hot. Severity category of each error.
+  - `category`: List of one hot. Category of each error. See the 45 categories in `_ANNOTATION_CATEGORIES_MAPPING`.
+- `token_index`: a mapping of tokens to their start and ending positions in `mt_segments`. For each token, a start and end value are encoded in a list of length 2, and all tokens represent one item in the list.
+- `total_words`: total number of words in the document
+
+```
+_ANNOTATION_CATEGORIES_MAPPING = {
+  0: 'Addition',
+  1: 'Agreement',
+  2: 'Ambiguous Translation',
+  3: 'Capitalization',
+  4: 'Character Encoding',
+  5: 'Company Terminology',
+  6: 'Date/Time',
+  7: 'Diacritics',
+  8: 'Duplication',
+  9: 'False Friend',
+  10: 'Grammatical Register',
+  11: 'Hyphenation',
+  12: 'Inconsistency',
+  13: 'Lexical Register',
+  14: 'Lexical Selection',
+  15: 'Named Entity',
+  16: 'Number',
+  17: 'Omitted Auxiliary Verb',
+  18: 'Omitted Conjunction',
+  19: 'Omitted Determiner',
+  20: 'Omitted Preposition',
+  21: 'Omitted Pronoun',
+  22: 'Orthography',
+  23: 'Other POS Omitted',
+  24: 'Over-translation',
+  25: 'Overly Literal',
+  26: 'POS',
+  27: 'Punctuation',
+  28: "Shouldn't Have Been Translated",
+  29: "Shouldn't have been translated",
+  30: 'Spelling',
+  31: 'Tense/Mood/Aspect',
+  32: 'Under-translation',
+  33: 'Unidiomatic',
+  34: 'Unintelligible',
+  35: 'Unit Conversion',
+  36: 'Untranslated',
+  37: 'Whitespace',
+  38: 'Word Order',
+  39: 'Wrong Auxiliary Verb',
+  40: 'Wrong Conjunction',
+  41: 'Wrong Determiner',
+  42: 'Wrong Language Variety',
+  43: 'Wrong Preposition',
+  44: 'Wrong Pronoun'
+}
+```
+
+### Data Splits
+
+The dataset contains 1,448 documents for training, 200 documents for validation and 180 for (blind) test (all English-French).
+
+## Dataset Creation
+
+### Curation Rationale
+
+The data is dervied from the [Amazon Product Reviews dataset](http://jmcauley.ucsd.edu/data/amazon/).
+
+### 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
+
+Unknown
+
+### Citation Information
+
+```
+Not available.
+```
+
+### Contributions
+
+Thanks to [@VictorSanh](https://github.com/VictorSanh) for adding this dataset.