Upload batch 83 (20 files, last=huggingface_dataset/Dataset_Card/autoevaluate_autoeval-eval-futin__feed-top_en-c0540d-2175569974.md)

huggingface_dataset/Dataset_Card/EdBianchi_SmokeFire.md

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+---
+dataset_info:
+  features:
+  - name: image
+    dtype: image
+  - name: label
+    dtype:
+      class_label:
+        names:
+          '0': Fire
+          '1': Normal
+          '2': Smoke
+  splits:
+  - name: train
+    num_bytes: 166216842.46
+    num_examples: 6060
+  - name: test
+    num_bytes: 89193578.0
+    num_examples: 759
+  - name: validation
+    num_bytes: 75838884.0
+    num_examples: 756
+  download_size: 890673915
+  dataset_size: 331249304.46000004
+---
+# Dataset Card for "SmokeFire"
+Wildfires or forest fires are unpredictable catastrophic and destructive events that affect rural areas. The impact of these events affects both vegetation and wildlife.
+This dataset can be used to train networks able to detect smoke and/or fire in forest environments.
+
+## Data Sources & Description
+- **This dataset consist of sample from two datasets hosted on Kaggle:**
+  - [Forest Fire](https://www.kaggle.com/datasets/kutaykutlu/forest-fire?select=train_fire)
+  - [Forest Fire Images](https://www.kaggle.com/datasets/mohnishsaiprasad/forest-fire-images)
+- **The datasets consist of:**
+  - 2525 **Fire** samples
+  - 2525 **Smoke** samples
+  - 2525 **Normal** samples
+- **The dataset is splitted into:**
+  - Train Set -> 6060 samples
+  - Validation Set -> 756 samples
+  - Test Set -> 759 samples

huggingface_dataset/Dataset_Card/Heriot-WattUniversity_CANDOR-corpus.md

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+# CANDOR Corpus
+
+### CANDOR = Conversation: A Naturalistic Dataset of Online Recordings
+
+The CANDOR corpus is a large, novel, multimodal corpus of 1,656 recorded conversations in spoken English. This 7+ million word, 850 hour corpus totals over 1TB of audio, video, and transcripts, with moment-to-moment measures of vocal, facial, and semantic expression, along with an extensive survey of speaker post conversation reflections.
+
+This corpus was first introduced by Reece et al. in [Advancing an Interdisciplinary Science of Conversation: Insights from a Large Multimodal Corpus of Human Speech](https://paperswithcode.com/paper/advancing-an-interdisciplinary-science-of)

huggingface_dataset/Dataset_Card/alighasemi_fa-paraphrase.md

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+---
+Tasks:
+- Text2Text Generation
+Fine-Grained Tasks:
+- paraphrase
+- query-paraphrasing
+Languages:
+- Persian
+Multilinguality:
+- monolingual
+- fa
+- fa-IR
+Sizes:
+- n>1M
+dataset_info:
+  features:
+  - name: sentence1
+    dtype: string
+  - name: sentence2
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 139373682.4
+    num_examples: 881408
+  - name: test
+    num_bytes: 17421710.3
+    num_examples: 110176
+  - name: validation
+    num_bytes: 17421710.3
+    num_examples: 110176
+  download_size: 98032993
+  dataset_size: 174217103.00000003
+---
+# Dataset Card for "fa-paraphrase"
+
+This dataset contains over 1.1 million rows. Each row contains a pair of Farsi sentences which are a paraphrase of each other. The datasets used to create this dataset can be found here:
+
+* [tapaco](https://huggingface.co/datasets/tapaco)
+* [kaggle](https://www.kaggle.com/datasets/armannikkhah/persian-paraphrase-dataset)
+
+[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

huggingface_dataset/Dataset_Card/autoevaluate_autoeval-eval-adversarial_qa-adversarialQA-cadd10-1947965536.md

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+---
+type: predictions
+tags:
+- autotrain
+- evaluation
+datasets:
+- adversarial_qa
+eval_info:
+  task: extractive_question_answering
+  model: 123tarunanand/roberta-base-finetuned
+  metrics: []
+  dataset_name: adversarial_qa
+  dataset_config: adversarialQA
+  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: 123tarunanand/roberta-base-finetuned
+* Dataset: adversarial_qa
+* Config: adversarialQA
+* Split: validation
+
+To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
+
+## Contributions
+
+Thanks to [@MHassanSaleem](https://huggingface.co/MHassanSaleem) for evaluating this model.

huggingface_dataset/Dataset_Card/autoevaluate_autoeval-eval-futin__feed-top_en-c0540d-2175569974.md

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+---
+type: predictions
+tags:
+- autotrain
+- evaluation
+datasets:
+- futin/feed
+eval_info:
+  task: text_zero_shot_classification
+  model: facebook/opt-2.7b
+  metrics: []
+  dataset_name: futin/feed
+  dataset_config: top_en
+  dataset_split: test
+  col_mapping:
+    text: text
+    classes: classes
+    target: target
+---
+# Dataset Card for AutoTrain Evaluator
+
+This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
+
+* Task: Zero-Shot Text Classification
+* Model: facebook/opt-2.7b
+* Dataset: futin/feed
+* Config: top_en
+* Split: test
+
+To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
+
+## Contributions
+
+Thanks to [@futin](https://huggingface.co/futin) for evaluating this model.

huggingface_dataset/Dataset_Card/autoevaluate_autoeval-eval-futin__feed-top_vi-b5257d-2174969942.md

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+---
+type: predictions
+tags:
+- autotrain
+- evaluation
+datasets:
+- futin/feed
+eval_info:
+  task: text_zero_shot_classification
+  model: bigscience/bloom-7b1
+  metrics: []
+  dataset_name: futin/feed
+  dataset_config: top_vi
+  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-7b1
+* Dataset: futin/feed
+* Config: top_vi
+* Split: test
+
+To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
+
+## Contributions
+
+Thanks to [@futin](https://huggingface.co/futin) for evaluating this model.

huggingface_dataset/Dataset_Card/autoevaluate_autoeval-eval-futin__guess-vi_3-74fd83-2087367157.md

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+---
+type: predictions
+tags:
+- autotrain
+- evaluation
+datasets:
+- futin/guess
+eval_info:
+  task: text_zero_shot_classification
+  model: bigscience/bloom-1b7
+  metrics: []
+  dataset_name: futin/guess
+  dataset_config: vi_3
+  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-1b7
+* Dataset: futin/guess
+* Config: vi_3
+* Split: test
+
+To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
+
+## Contributions
+
+Thanks to [@futin](https://huggingface.co/futin) for evaluating this model.

huggingface_dataset/Dataset_Card/autoevaluate_autoeval-eval-multi_news-default-e22c67-2252871793.md

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+---
+type: predictions
+tags:
+- autotrain
+- evaluation
+datasets:
+- multi_news
+eval_info:
+  task: summarization
+  model: pszemraj/led-base-book-summary
+  metrics: []
+  dataset_name: multi_news
+  dataset_config: default
+  dataset_split: test
+  col_mapping:
+    text: document
+    target: summary
+---
+# Dataset Card for AutoTrain Evaluator
+
+This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
+
+* Task: Summarization
+* Model: pszemraj/led-base-book-summary
+* Dataset: multi_news
+* 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 [@pszemraj](https://huggingface.co/pszemraj) for evaluating this model.

huggingface_dataset/Dataset_Card/autoevaluate_autoeval-staging-eval-squad_v2-squad_v2-07bda3-16636249.md

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+---
+type: predictions
+tags:
+- autotrain
+- evaluation
+datasets:
+- squad_v2
+eval_info:
+  task: extractive_question_answering
+  model: haritzpuerto/MiniLM-L12-H384-uncased-squad
+  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: haritzpuerto/MiniLM-L12-H384-uncased-squad
+* 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 [@timbmg](https://huggingface.co/timbmg) for evaluating this model.

huggingface_dataset/Dataset_Card/cardiffnlp_tweet_topic_single.md

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+---
+language:
+- en
+license:
+- other
+multilinguality:
+- monolingual
+size_categories:
+- 1k<10K
+task_categories:
+- text-classification
+task_ids:
+- sentiment-classification
+pretty_name: TweetTopicSingle
+---
+
+# Dataset Card for "cardiffnlp/tweet_topic_single"
+
+## Dataset Description
+
+- **Paper:** [https://arxiv.org/abs/2209.09824](https://arxiv.org/abs/2209.09824)
+- **Dataset:** Tweet Topic Dataset
+- **Domain:** Twitter
+- **Number of Class:** 6
+
+
+### Dataset Summary
+This is the official repository of TweetTopic (["Twitter Topic Classification
+, COLING main conference 2022"](https://arxiv.org/abs/2209.09824)), a topic classification dataset on Twitter with 6 labels.
+Each instance of TweetTopic comes with a timestamp which distributes from September 2019 to August 2021.
+See [cardiffnlp/tweet_topic_multi](https://huggingface.co/datasets/cardiffnlp/tweet_topic_multi) for multi label version of TweetTopic.
+The tweet collection used in TweetTopic is same as what used in [TweetNER7](https://huggingface.co/datasets/tner/tweetner7).
+The dataset is integrated in [TweetNLP](https://tweetnlp.org/) too.
+
+### Preprocessing
+We pre-process tweets before the annotation to normalize some artifacts, converting URLs into a special token `{{URL}}` and non-verified usernames into `{{USERNAME}}`.
+For verified usernames, we replace its display name (or account name) with symbols `{@}`.
+For example, a tweet
+```
+Get the all-analog Classic Vinyl Edition
+of "Takin' Off" Album from @herbiehancock
+via @bluenoterecords link below: 
+http://bluenote.lnk.to/AlbumOfTheWeek
+```
+is transformed into the following text.
+```
+Get the all-analog Classic Vinyl Edition
+of "Takin' Off" Album from {@herbiehancock@}
+via {@bluenoterecords@} link below: {{URL}}
+```
+A simple function to format tweet follows below.
+```python
+import re
+from urlextract import URLExtract
+extractor = URLExtract()
+def format_tweet(tweet):
+    # mask web urls
+    urls = extractor.find_urls(tweet)
+    for url in urls:
+        tweet = tweet.replace(url, "{{URL}}")
+    # format twitter account
+    tweet = re.sub(r"\b(\s*)(@[\S]+)\b", r'\1{\2@}', tweet)
+    return tweet
+target = """Get the all-analog Classic Vinyl Edition of "Takin' Off" Album from @herbiehancock via @bluenoterecords link below: http://bluenote.lnk.to/AlbumOfTheWeek"""
+target_format = format_tweet(target)
+print(target_format)
+'Get the all-analog Classic Vinyl Edition of "Takin\' Off" Album from {@herbiehancock@} via {@bluenoterecords@} link below: {{URL}}'
+```
+
+
+### Data Splits
+
+| split                   | number of texts | description |
+|:------------------------|-----:|------:|
+| test_2020               |  376 | test dataset from September 2019 to August 2020 |
+| test_2021               | 1693 | test dataset from September 2020 to August 2021 |
+| train_2020              | 2858 | training dataset from September 2019 to August 2020 |
+| train_2021              | 1516 | training dataset from September 2020 to August 2021 |
+| train_all               | 4374 | combined training dataset of `train_2020` and `train_2021` |
+| validation_2020         |  352 | validation dataset from September 2019 to August 2020 |
+| validation_2021         |  189 | validation dataset from September 2020 to August 2021 | 
+| train_random            | 2830 | randomly sampled training dataset with the same size as `train_2020` from `train_all` |
+| validation_random       |  354 | randomly sampled training dataset with the same size as `validation_2020` from `validation_all` |
+| test_coling2022_random  | 3399 | random split used in the COLING 2022 paper |
+| train_coling2022_random | 3598 | random split used in the COLING 2022 paper |
+| test_coling2022         | 3399 | temporal split used in the COLING 2022 paper |
+| train_coling2022        | 3598 | temporal split used in the COLING 2022 paper |
+
+For the temporal-shift setting, model should be trained on `train_2020` with `validation_2020` and evaluate on `test_2021`.
+In general, model would be trained on `train_all`, the most representative training set with `validation_2021` and evaluate on `test_2021`.
+
+**IMPORTANT NOTE:** To get a result that is comparable with the results of the COLING 2022 Tweet Topic paper, please use `train_coling2022` and `test_coling2022` for temporal-shift, and `train_coling2022_random` and `test_coling2022_random` fir random split (the coling2022 split does not have validation set).
+
+### Models
+
+| model                                                                                                                                                       | training data     |       F1 |   F1 (macro) |   Accuracy |
+|:------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------|---------:|-------------:|-----------:|
+| [cardiffnlp/roberta-large-tweet-topic-single-all](https://huggingface.co/cardiffnlp/roberta-large-tweet-topic-single-all)                                   | all (2020 + 2021) | 0.896043 |     0.800061 |   0.896043 |
+| [cardiffnlp/roberta-base-tweet-topic-single-all](https://huggingface.co/cardiffnlp/roberta-base-tweet-topic-single-all)                                     | all (2020 + 2021) | 0.887773 |     0.79793  |   0.887773 |
+| [cardiffnlp/twitter-roberta-base-2019-90m-tweet-topic-single-all](https://huggingface.co/cardiffnlp/twitter-roberta-base-2019-90m-tweet-topic-single-all)   | all (2020 + 2021) | 0.892499 |     0.774494 |   0.892499 |
+| [cardiffnlp/twitter-roberta-base-dec2020-tweet-topic-single-all](https://huggingface.co/cardiffnlp/twitter-roberta-base-dec2020-tweet-topic-single-all)     | all (2020 + 2021) | 0.890136 |     0.776025 |   0.890136 |
+| [cardiffnlp/twitter-roberta-base-dec2021-tweet-topic-single-all](https://huggingface.co/cardiffnlp/twitter-roberta-base-dec2021-tweet-topic-single-all)     | all (2020 + 2021) | 0.894861 |     0.800952 |   0.894861 |
+| [cardiffnlp/roberta-large-tweet-topic-single-2020](https://huggingface.co/cardiffnlp/roberta-large-tweet-topic-single-2020)                                 | 2020 only         | 0.878913 |     0.70565  |   0.878913 |
+| [cardiffnlp/roberta-base-tweet-topic-single-2020](https://huggingface.co/cardiffnlp/roberta-base-tweet-topic-single-2020)                                   | 2020 only         | 0.868281 |     0.729667 |   0.868281 |
+| [cardiffnlp/twitter-roberta-base-2019-90m-tweet-topic-single-2020](https://huggingface.co/cardiffnlp/twitter-roberta-base-2019-90m-tweet-topic-single-2020) | 2020 only         | 0.882457 |     0.740187 |   0.882457 |
+| [cardiffnlp/twitter-roberta-base-dec2020-tweet-topic-single-2020](https://huggingface.co/cardiffnlp/twitter-roberta-base-dec2020-tweet-topic-single-2020)   | 2020 only         | 0.87596  |     0.746275 |   0.87596  |
+| [cardiffnlp/twitter-roberta-base-dec2021-tweet-topic-single-2020](https://huggingface.co/cardiffnlp/twitter-roberta-base-dec2021-tweet-topic-single-2020)   | 2020 only         | 0.877732 |     0.746119 |   0.877732 |
+
+Model fine-tuning script can be found [here](https://huggingface.co/datasets/cardiffnlp/tweet_topic_single/blob/main/lm_finetuning.py).
+
+## Dataset Structure
+
+### Data Instances
+An example of `train` looks as follows.
+
+```python
+{
+    "text": "Game day for {{USERNAME}} U18\u2019s against {{USERNAME}} U18\u2019s. Even though it\u2019s a \u2018home\u2019 game for the people that have settled in Mid Wales it\u2019s still a 4 hour round trip for us up to Colwyn Bay. Still enjoy it though!",
+    "date": "2019-09-08",
+    "label": 4,
+    "id": "1170606779568463874",
+    "label_name": "sports_&_gaming"
+}
+```
+
+### Label ID
+The label2id dictionary can be found at [here](https://huggingface.co/datasets/tner/tweet_topic_single/raw/main/dataset/label.single.json).
+```python
+{
+    "arts_&_culture": 0,
+    "business_&_entrepreneurs": 1,
+    "pop_culture": 2,
+    "daily_life": 3,
+    "sports_&_gaming": 4,
+    "science_&_technology": 5
+}
+```
+
+### Citation Information
+
+```
+@inproceedings{dimosthenis-etal-2022-twitter,
+    title = "{T}witter {T}opic {C}lassification",
+    author = "Antypas, Dimosthenis  and
+    Ushio, Asahi  and
+    Camacho-Collados, Jose  and
+    Neves, Leonardo  and
+    Silva, Vitor  and
+    Barbieri, Francesco",
+    booktitle = "Proceedings of the 29th International Conference on Computational Linguistics",
+    month = oct,
+    year = "2022",
+    address = "Gyeongju, Republic of Korea",
+    publisher = "International Committee on Computational Linguistics"
+}
+```

huggingface_dataset/Dataset_Card/dane.md

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+---
+annotations_creators:
+- expert-generated
+language_creators:
+- found
+language:
+- da
+license:
+- cc-by-sa-4.0
+multilinguality:
+- monolingual
+size_categories:
+- 1K<n<10K
+source_datasets:
+- extended|other-Danish-Universal-Dependencies-treebank
+task_categories:
+- token-classification
+task_ids:
+- named-entity-recognition
+- part-of-speech
+paperswithcode_id: dane
+pretty_name: DaNE
+dataset_info:
+  features:
+  - name: sent_id
+    dtype: string
+  - name: text
+    dtype: string
+  - name: tok_ids
+    sequence: int64
+  - name: tokens
+    sequence: string
+  - name: lemmas
+    sequence: string
+  - name: pos_tags
+    sequence:
+      class_label:
+        names:
+          '0': NUM
+          '1': CCONJ
+          '2': PRON
+          '3': VERB
+          '4': INTJ
+          '5': AUX
+          '6': ADJ
+          '7': PROPN
+          '8': PART
+          '9': ADV
+          '10': PUNCT
+          '11': ADP
+          '12': NOUN
+          '13': X
+          '14': DET
+          '15': SYM
+          '16': SCONJ
+  - name: morph_tags
+    sequence: string
+  - name: dep_ids
+    sequence: int64
+  - name: dep_labels
+    sequence:
+      class_label:
+        names:
+          '0': parataxis
+          '1': mark
+          '2': nummod
+          '3': discourse
+          '4': compound:prt
+          '5': reparandum
+          '6': vocative
+          '7': list
+          '8': obj
+          '9': dep
+          '10': det
+          '11': obl:loc
+          '12': flat
+          '13': iobj
+          '14': cop
+          '15': expl
+          '16': obl
+          '17': conj
+          '18': nmod
+          '19': root
+          '20': acl:relcl
+          '21': goeswith
+          '22': appos
+          '23': fixed
+          '24': obl:tmod
+          '25': xcomp
+          '26': advmod
+          '27': nmod:poss
+          '28': aux
+          '29': ccomp
+          '30': amod
+          '31': cc
+          '32': advcl
+          '33': nsubj
+          '34': punct
+          '35': case
+  - name: ner_tags
+    sequence:
+      class_label:
+        names:
+          '0': O
+          '1': B-PER
+          '2': I-PER
+          '3': B-ORG
+          '4': I-ORG
+          '5': B-LOC
+          '6': I-LOC
+          '7': B-MISC
+          '8': I-MISC
+  splits:
+  - name: train
+    num_bytes: 7311212
+    num_examples: 4383
+  - name: test
+    num_bytes: 909699
+    num_examples: 565
+  - name: validation
+    num_bytes: 940413
+    num_examples: 564
+  download_size: 1209710
+  dataset_size: 9161324
+---
+
+# Dataset Card for DaNE
+
+## 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:** [DaNE homepage](https://danlp-alexandra.readthedocs.io/en/latest/docs/datasets.html#dane)
+- **Repository:** [Github](https://github.com/alexandrainst/danlp)
+- **Paper:** [Aclweb](https://www.aclweb.org/anthology/2020.lrec-1.565)
+- **Leaderboard:**
+- **Point of Contact:**
+
+### Dataset Summary
+
+The Danish Dependency Treebank (DaNE) is a named entity annotation for the Danish Universal Dependencies treebank using the CoNLL-2003 annotation scheme.
+
+The Danish UD treebank (Johannsen et al., 2015, UD-DDT) is a conversion of the Danish Dependency Treebank (Buch-Kromann et al. 2003) based on texts from Parole (Britt, 1998). UD-DDT has annotations for dependency parsing and part-of-speech (POS) tagging. The dataset was annotated with Named Entities for PER, ORG, and LOC by the Alexandra Institute in the DaNE dataset (Hvingelby et al. 2020). 
+
+### Supported Tasks and Leaderboards
+
+Parts-of-speech tagging, dependency parsing and named entitity recognition.
+
+### Languages
+
+Danish
+
+## Dataset Structure
+
+### Data Instances
+
+This is an example in the "train" split:
+```python
+{
+  'sent_id': 'train-v2-0\n', 
+  'lemmas': ['på', 'fredag', 'have', 'SiD', 'invitere', 'til', 'reception', 'i', 'SID-hus', 'i', 'anledning', 'af', 'at', 'formand', 'Kjeld', 'Christensen', 'gå', 'ind', 'i', 'den', 'glad', 'tresser', '.'],
+  'dep_labels': [35, 16, 28, 33, 19, 35, 16, 35, 18, 35, 18, 1, 1, 33, 22, 12, 32, 11, 35, 10, 30, 16, 34],
+  'ner_tags': [0, 0, 0, 3, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0],
+  'morph_tags': ['AdpType=Prep', 'Definite=Ind|Gender=Com|Number=Sing', 'Mood=Ind|Tense=Pres|VerbForm=Fin|Voice=Act', '_', 'Definite=Ind|Number=Sing|Tense=Past|VerbForm=Part', 'AdpType=Prep', 'Definite=Ind|Gender=Com|Number=Sing', 'AdpType=Prep', 'Definite=Def|Gender=Neut|Number=Sing', 'AdpType=Prep', 'Definite=Ind|Gender=Com|Number=Sing', 'AdpType=Prep', '_', 'Definite=Def|Gender=Com|Number=Sing', '_', '_', 'Mood=Ind|Tense=Pres|VerbForm=Fin|Voice=Act', '_', 'AdpType=Prep', 'Number=Plur|PronType=Dem', 'Degree=Pos|Number=Plur', 'Definite=Ind|Gender=Com|Number=Plur', '_'], 
+  'dep_ids': [2, 5, 5, 5, 0, 7, 5, 9, 7, 11, 7, 17, 17, 17, 14, 15, 11, 17, 22, 22, 22, 18, 5], 
+  'pos_tags': [11, 12, 5, 7, 3, 11, 12, 11, 12, 11, 12, 11, 16, 12, 7, 7, 3, 9, 11, 14, 6, 12, 10], 
+  'text': 'På fredag har SID inviteret til reception i SID-huset i anledning af at formanden Kjeld Christensen går ind i de glade tressere.\n', 
+  'tokens': ['På', 'fredag', 'har', 'SID', 'inviteret', 'til', 'reception', 'i', 'SID-huset', 'i', 'anledning', 'af', 'at', 'formanden', 'Kjeld', 'Christensen', 'går', 'ind', 'i', 'de', 'glade', 'tressere', '.'], 
+  'tok_ids': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23]
+}
+```
+
+### Data Fields
+
+Data Fields:
+  - q_id: a string question identifier for each example, corresponding to its ID in the Pushshift.io Reddit submission dumps.
+  - subreddit: One of explainlikeimfive, askscience, or AskHistorians, indicating which subreddit the question came from
+  - title: title of the question, with URLs extracted and replaced by URL_n tokens
+  - title_urls: list of the extracted URLs, the nth element of the list was replaced by URL_n
+  - sent_id: a string identifier for each example
+  - text: a string, the original sentence (not tokenized)
+  - tok_ids: a list of ids (int), one for each token
+  - tokens: a list of strings, the tokens
+  - lemmas: a list of strings, the lemmas of the tokens
+  - pos_tags: a list of strings, the part-of-speech tags of the tokens
+  - morph_tags: a list of strings, the morphological tags of the tokens
+  - dep_ids: a list of ids (int), the id of the head of the incoming dependency for each token
+  - dep_labels: a list of strings, the dependency labels
+  - ner_tags: a list of strings, the named entity tags (BIO format)
+
+### Data Splits
+
+|             |  train | validation |   test |
+|-------------|-------:|-----------:|-------:|
+| # sentences |   4383 |        564 |    565 |
+| # tokens    | 80 378 |     10 322 | 10 023 |
+
+## 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]
+
+### Citation Information
+
+```
+@inproceedings{hvingelby-etal-2020-dane,
+    title = "{D}a{NE}: A Named Entity Resource for {D}anish",
+    author = "Hvingelby, Rasmus  and
+      Pauli, Amalie Brogaard  and
+      Barrett, Maria  and
+      Rosted, Christina  and
+      Lidegaard, Lasse Malm  and
+      S{\o}gaard, Anders",
+    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
+    month = may,
+    year = "2020",
+    address = "Marseille, France",
+    publisher = "European Language Resources Association",
+    url = "https://aclanthology.org/2020.lrec-1.565",
+    pages = "4597--4604",
+    abstract = "We present a named entity annotation for the Danish Universal Dependencies treebank using the CoNLL-2003 annotation scheme: DaNE. It is the largest publicly available, Danish named entity gold annotation. We evaluate the quality of our annotations intrinsically by double annotating the entire treebank and extrinsically by comparing our annotations to a recently released named entity annotation of the validation and test sections of the Danish Universal Dependencies treebank. We benchmark the new resource by training and evaluating competitive architectures for supervised named entity recognition (NER), including FLAIR, monolingual (Danish) BERT and multilingual BERT. We explore cross-lingual transfer in multilingual BERT from five related languages in zero-shot and direct transfer setups, and we show that even with our modestly-sized training set, we improve Danish NER over a recent cross-lingual approach, as well as over zero-shot transfer from five related languages. Using multilingual BERT, we achieve higher performance by fine-tuning on both DaNE and a larger Bokm{\aa}l (Norwegian) training set compared to only using DaNE. However, the highest performance isachieved by using a Danish BERT fine-tuned on DaNE. Our dataset enables improvements and applicability for Danish NER beyond cross-lingual methods. We employ a thorough error analysis of the predictions of the best models for seen and unseen entities, as well as their robustness on un-capitalized text. The annotated dataset and all the trained models are made publicly available.",
+    language = "English",
+    ISBN = "979-10-95546-34-4",
+}
+```
+
+### Contributions
+
+Thanks to [@ophelielacroix](https://github.com/ophelielacroix), [@lhoestq](https://github.com/lhoestq) for adding this dataset.

huggingface_dataset/Dataset_Card/deepklarity_top-npm-packages.md

@@ -0,0 +1,17 @@
+---
+license: cc
+---
+
+**Top NPM Packages Dataset**
+
+This dataset contains a snapshot of Top 3000 popular node packages hosted on [Node Package Manager](https://www.npmjs.com/)
+
+The dataset was scraped in `July-2022`. This includes a combination of data gathered by [Libraries.io](https://libraries.io/) and [npm](https://www.npmjs.com/)
+
+We aim to use this dataset to perform analysis and identify trends and get a bird's eye view of nodejs ecosystem.
+
+#### Mantainers:
+- [Keshaw Soni](https://twitter.com/SoniKeshaw)
+- [Somya Gautam](http://linkedin.in/in/somya-gautam)
+- [Kondrolla Dinesh Reddy](https://twitter.com/KondrollaR)
+

huggingface_dataset/Dataset_Card/id_puisi.md

@@ -0,0 +1,213 @@
+---
+annotations_creators:
+- no-annotation
+language_creators:
+- found
+language:
+- id
+license:
+- mit
+multilinguality:
+- monolingual
+size_categories:
+- 1K<n<10K
+source_datasets:
+- original
+task_categories:
+- text2text-generation
+- text-generation
+- fill-mask
+task_ids: []
+paperswithcode_id: null
+pretty_name: Indonesian Puisi
+tags:
+- poem-generation
+dataset_info:
+  features:
+  - name: title
+    dtype: string
+  - name: author
+    dtype: string
+  - name: puisi
+    dtype: string
+  - name: puisi_with_header
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 10613475
+    num_examples: 7223
+  download_size: 10558108
+  dataset_size: 10613475
+---
+
+# Dataset Card for id_puisi
+
+## 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:** [puisi-pantun-generator](https://github.com/ilhamfp/puisi-pantun-generator)
+- **Repository:** [puisi-pantun-generator](https://github.com/ilhamfp/puisi-pantun-generator)
+- **Paper:** [N/A]
+- **Leaderboard:** [N/A]
+- **Point of Contact:** [Ilham Firdausi Putra](ilhamfputra31@gmail.com)
+
+### Dataset Summary
+
+Puisi (poem) is an Indonesian poetic form. The dataset contains 7223 Indonesian puisi with its title and author.
+
+### Supported Tasks and Leaderboards
+
+[More Information Needed]
+
+### Languages
+
+Indonesian
+
+## Dataset Structure
+
+### Data Instances
+```
+{
+  'puisi_with_header': 'TEPERANGKAP 
+ Oleh Mangku Langit Jingga 
+  
+ Mungkin kau membiarkan aku 
+ Membiarkan perasaan ini larut 
+ Memberi ruang jiwaku hampa 
+ Agar tetap terbiasa nikmati 
+  
+ Perangkap yang kau buat 
+ Perisai yang kau banggakan 
+ Takkan jadi tameng bagimu 
+ Aku mengerti betapa hebatnya 
+  
+ Perangkap mu hei sang dewi 
+ Ku akan terus merasa terbiasa 
+ Dengan pesona indahmu 
+ Ku masih akan nikmati hadirmu 
+  
+ Berjalanlah pada hati yang sama 
+ Satu hati denganku 
+ Walau ku terperangkap 
+ Namunku nikmati dan jalani',
+
+  'title': 'TEPERANGKAP',
+
+  'author': 'Oleh Mangku Langit Jingga',
+
+  'puisi': 'Mungkin kau membiarkan aku 
+ Membiarkan perasaan ini larut 
+ Memberi ruang jiwaku hampa 
+ Agar tetap terbiasa nikmati 
+  
+ Perangkap yang kau buat 
+ Perisai yang kau banggakan 
+ Takkan jadi tameng bagimu 
+ Aku mengerti betapa hebatnya 
+  
+ Perangkap mu hei sang dewi 
+ Ku akan terus merasa terbiasa 
+ Dengan pesona indahmu 
+ Ku masih akan nikmati hadirmu 
+  
+ Berjalanlah pada hati yang sama 
+ Satu hati denganku 
+ Walau ku terperangkap 
+ Namunku nikmati dan jalani',
+}
+```
+### Data Fields
+
+- `puisi_with_header`: the raw text from scraping
+- `title`: the title extracted from the raw text using regex
+- `author`: the author extracted from the raw text using regex
+- `puisi`: the poem with title and author extracted out using regex
+
+### Data Splits
+
+The dataset contains only a train set.
+
+## Dataset Creation
+
+### Curation Rationale
+
+The dataset was initially collected as an experiment to generate an Indonesian poem using GPT-2.
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+The dataset was scraped using BeautifulSoup from lokerpuisi.web.id (the data no longer exist on the original blog). The title and author column was produced using regex match from puisi_with_header column.
+
+#### Who are the source language producers?
+
+The poems were generated by humans. The users of the original blog voluntarily submit their original poems to get published on the blog.
+
+### Annotations
+
+#### Annotation process
+
+[N/A]
+
+#### Who are the annotators?
+
+[N/A]
+
+### 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
+
+The regex match used to extract the title & author from the raw text is not perfect. Some title & text is still failed to get extracted.
+
+## Additional Information
+
+### Dataset Curators
+
+Ilham Firdausi Putra
+
+### Licensing Information
+
+MIT License
+
+### Citation Information
+
+[N/A]
+
+### Contributions
+
+Thanks to [@ilhamfp](https://github.com/ilhamfp) for adding this dataset.

huggingface_dataset/Dataset_Card/irds_beir_webis-touche2020.md

@@ -0,0 +1,61 @@
+---
+pretty_name: '`beir/webis-touche2020`'
+viewer: false
+source_datasets: []
+task_categories:
+- text-retrieval
+---
+
+# Dataset Card for `beir/webis-touche2020`
+
+The `beir/webis-touche2020` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
+For more information about the dataset, see the [documentation](https://ir-datasets.com/beir#beir/webis-touche2020).
+
+# Data
+
+This dataset provides:
+ - `docs` (documents, i.e., the corpus); count=382,545
+ - `queries` (i.e., topics); count=49
+ - `qrels`: (relevance assessments); count=2,962
+
+
+## Usage
+
+```python
+from datasets import load_dataset
+
+docs = load_dataset('irds/beir_webis-touche2020', 'docs')
+for record in docs:
+    record # {'doc_id': ..., 'text': ..., 'title': ..., 'stance': ..., 'url': ...}
+
+queries = load_dataset('irds/beir_webis-touche2020', 'queries')
+for record in queries:
+    record # {'query_id': ..., 'text': ..., 'description': ..., 'narrative': ...}
+
+qrels = load_dataset('irds/beir_webis-touche2020', '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{Bondarenko2020Tuche,
+  title={Overview of Touch{\'e} 2020: Argument Retrieval},
+  author={Alexander Bondarenko and Maik Fr{\"o}be and Meriem Beloucif and Lukas Gienapp and Yamen Ajjour and Alexander Panchenko and Christian Biemann and Benno Stein and Henning Wachsmuth and Martin Potthast and Matthias Hagen},
+  booktitle={CLEF},
+  year={2020}
+}
+@article{Thakur2021Beir,
+  title = "BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval Models",
+  author = "Thakur, Nandan and Reimers, Nils and Rücklé, Andreas and Srivastava, Abhishek and Gurevych, Iryna", 
+  journal= "arXiv preprint arXiv:2104.08663",
+  month = "4",
+  year = "2021",
+  url = "https://arxiv.org/abs/2104.08663",
+}
+```

huggingface_dataset/Dataset_Card/irds_wikir_fr14k.md

@@ -0,0 +1,49 @@
+---
+pretty_name: '`wikir/fr14k`'
+viewer: false
+source_datasets: []
+task_categories:
+- text-retrieval
+---
+
+# Dataset Card for `wikir/fr14k`
+
+The `wikir/fr14k` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
+For more information about the dataset, see the [documentation](https://ir-datasets.com/wikir#wikir/fr14k).
+
+# Data
+
+This dataset provides:
+ - `docs` (documents, i.e., the corpus); count=736,616
+
+
+## Usage
+
+```python
+from datasets import load_dataset
+
+docs = load_dataset('irds/wikir_fr14k', 'docs')
+for record in docs:
+    record # {'doc_id': ..., 'text': ...}
+
+```
+
+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{Frej2020Wikir,
+  title={WIKIR: A Python toolkit for building a large-scale Wikipedia-based English Information Retrieval Dataset},
+  author={Jibril Frej and Didier Schwab and Jean-Pierre Chevallet},
+  booktitle={LREC},
+  year={2020}
+}
+@inproceedings{Frej2020MlWikir,
+  title={MLWIKIR: A Python Toolkit for Building Large-scale Wikipedia-based Information Retrieval Datasets in Chinese, English, French, Italian, Japanese, Spanish and More},
+  author={Jibril Frej and Didier Schwab and Jean-Pierre Chevallet},
+  booktitle={CIRCLE},
+  year={2020}
+}
+```

huggingface_dataset/Dataset_Card/linhd-postdata_pulpo.md

@@ -0,0 +1,43 @@
+## PULPO
+
+PULPO, the Prodigious Unannotated Literary Poetry Corpus, is a set of multilingual corpora of verses and stanzas with over 95M words.
+
+The following corpora has been downloaded using the [Averell](https://github.com/linhd-postdata/averell/) tool, developed by the [POSTDATA](https://postdata.linhd.uned.es/) team:
+
+### Spanish
+- [Disco v3](https://github.com/pruizf/disco)
+- [Corpus of Spanish Golden-Age Sonnets](https://github.com/bncolorado/CorpusSonetosSigloDeOro)
+- [Corpus general de poesía lírica castellana del Siglo de Oro](https://github.com/bncolorado/CorpusGeneralPoesiaLiricaCastellanaDelSigloDeOro)
+- [Gongocorpus](https://github.com/linhd-postdata/gongocorpus) - [source](http://obvil.sorbonne-universite.site/corpus/gongora/gongora_obra-poetica)
+### English
+- [Eighteenth-Century Poetry Archive (ECPA)](https://github.com/alhuber1502/ECPA)
+- [For better for verse](https://github.com/waynegraham/for_better_for_verse)
+### French
+- [Métrique en Ligne](https://crisco2.unicaen.fr/verlaine/index.php?navigation=accueil) - [source](https://github.com/linhd-postdata/metrique-en-ligne)
+### Italian
+- [Biblioteca italiana](https://github.com/linhd-postdata/biblioteca_italiana) - [source](http://www.bibliotecaitaliana.it/)
+### Czech
+- [Corpus of Czech Verse](https://github.com/versotym/corpusCzechVerse)
+### Portuguese
+- [Stichotheque](https://gitlab.com/stichotheque/stichotheque-pt)
+
+Also, we obtained the following corpora from these sources:
+### Spanish 
+- [Poesi.as](https://github.com/linhd-postdata/poesi.as) - [source](http://www.poesi.as/)
+### English
+- [A Gutenberg Poetry Corpus](https://github.com/aparrish/gutenberg-poetry-corpus)
+### Arabic
+- [Arabic Poetry dataset](https://www.kaggle.com/ahmedabelal/arabic-poetry)
+### Chinese
+- [THU Chinese Classical Poetry Corpus](https://github.com/THUNLP-AIPoet/Datasets/tree/master/CCPC)
+### Finnish
+- [SKVR](https://github.com/sks190/SKVR)
+### German
+- [TextGrid Poetry Corpus](https://github.com/linhd-postdata/textgrid-poetry) - [source](https://textgrid.de/en/digitale-bibliothek)
+- [German Rhyme Corpus](https://github.com/tnhaider/german-rhyme-corpus)
+### Hungarian
+- [verskorpusz](https://github.com/ELTE-DH/verskorpusz)
+### Portuguese
+- [Poems in Portuguese](https://www.kaggle.com/oliveirasp6/poems-in-portuguese)
+### Russian
+- [19 000 Russian poems](https://www.kaggle.com/grafstor/19-000-russian-poems)

huggingface_dataset/Dataset_Card/mbazaNLP_Kinyarwanda_English_parallel_dataset.md

@@ -0,0 +1,10 @@
+---
+license: cc-by-4.0
+---
+## Kinyarwanda-English parallel text 
+
+This dataset contains 55,000 Kinyarwanda-English sentence pairs, obtained by scraping web data from religious sources such as:
+[Bible](https://servervideos.hopto.org/XMLBible/EnglishKJBible.xml) 
+[Quran](https://quranenc.com/en/home/download/csv/kinyarwanda_assoc)
+
+This dataset has not been curated only cleaned.

huggingface_dataset/Dataset_Card/severo_danish-wit.md

@@ -0,0 +1,158 @@
+---
+pretty_name: Danish WIT
+language:
+- da
+license:
+- cc-by-sa-4.0
+size_categories:
+- 100K<n<1M
+source_datasets:
+- wikimedia/wit_base
+task_categories:
+- image-to-text
+- zero-shot-image-classification
+- feature-extraction
+task_ids:
+- image-captioning
+---
+
+# Dataset Card for Danish WIT
+
+## Dataset Description
+
+- **Repository:** <https://gist.github.com/saattrupdan/bb6c9c52d9f4b35258db2b2456d31224>
+- **Point of Contact:** [Dan Saattrup Nielsen](mailto:dan.nielsen@alexandra.dk)
+- **Size of downloaded dataset files:** 7.5 GB
+- **Size of the generated dataset:** 7.8 GB
+- **Total amount of disk used:** 15.3 GB
+
+### Dataset Summary
+
+Google presented the Wikipedia Image Text (WIT) dataset in [July
+2021](https://dl.acm.org/doi/abs/10.1145/3404835.3463257), a dataset which contains
+scraped images from Wikipedia along with their descriptions. WikiMedia released
+WIT-Base in [September
+2021](https://techblog.wikimedia.org/2021/09/09/the-wikipedia-image-caption-matching-challenge-and-a-huge-release-of-image-data-for-research/),
+being a modified version of WIT where they have removed the images with empty
+"reference descriptions", as well as removing images where a person's face covers more
+than 10% of the image surface, along with inappropriate images that are candidate for
+deletion. This dataset is the Danish portion of the WIT-Base dataset, consisting of
+roughly 160,000 images with associated Danish descriptions. We release the dataset
+under the [CC BY-SA 4.0 license](https://creativecommons.org/licenses/by-sa/4.0/), in
+accordance with WIT-Base's [identical
+license](https://huggingface.co/datasets/wikimedia/wit_base#licensing-information).
+
+
+### Supported Tasks and Leaderboards
+
+Training machine learning models for caption generation, zero-shot image classification
+and text-image search are the intended tasks for this dataset. No leaderboard is active
+at this point.
+
+
+### Languages
+
+The dataset is available in Danish (`da`).
+
+
+## Dataset Structure
+
+### Data Instances
+
+- **Size of downloaded dataset files:** 7.5 GB
+- **Size of the generated dataset:** 7.8 GB
+- **Total amount of disk used:** 15.3 GB
+
+An example from the `train` split looks as follows.
+```
+{
+    "image": {
+        "bytes": b"\xff\xd8\xff\xe0\x00\x10JFIF...",
+        "path": None
+    },
+    "image_url": "https://upload.wikimedia.org/wikipedia/commons/4/45/Bispen_-_inside.jpg",
+    "embedding": [2.8568285, 2.9562542, 0.33794892, 8.753725, ...],
+    "metadata_url": "http://commons.wikimedia.org/wiki/File:Bispen_-_inside.jpg",
+    "original_height": 3161,
+    "original_width": 2316,
+    "mime_type": "image/jpeg",
+    "caption_attribution_description": "Kulturhuset Bispen set indefra. Biblioteket er til venstre",
+    "page_url": "https://da.wikipedia.org/wiki/Bispen",
+    "attribution_passes_lang_id": True,
+    "caption_alt_text_description": None,
+    "caption_reference_description": "Bispen set indefra fra 1. sal, hvor ....",
+    "caption_title_and_reference_description": "Bispen [SEP] Bispen set indefra ...",
+    "context_page_description": "Bispen er navnet på det offentlige kulturhus i ...",
+    "context_section_description": "Bispen er navnet på det offentlige kulturhus i ...",
+    "hierarchical_section_title": "Bispen",
+    "is_main_image": True,
+    "page_changed_recently": True,
+    "page_title": "Bispen",
+    "section_title": None
+}
+```
+
+### Data Fields
+
+The data fields are the same among all splits.
+
+- `image`: a `dict` feature.
+- `image_url`: a `str` feature.
+- `embedding`: a `list` feature.
+- `metadata_url`: a `str` feature.
+- `original_height`: an `int` or `NaN` feature.
+- `original_width`: an `int` or `NaN` feature.
+- `mime_type`: a `str` or `None` feature.
+- `caption_attribution_description`: a `str` or `None` feature.
+- `page_url`: a `str` feature.
+- `attribution_passes_lang_id`: a `bool` or `None` feature.
+- `caption_alt_text_description`: a `str` or `None` feature.
+- `caption_reference_description`: a `str` or `None` feature.
+- `caption_title_and_reference_description`: a `str` or `None` feature.
+- `context_page_description`: a `str` or `None` feature.
+- `context_section_description`: a `str` or `None` feature.
+- `hierarchical_section_title`: a `str` feature.
+- `is_main_image`: a `bool` or `None` feature.
+- `page_changed_recently`: a `bool` or `None` feature.
+- `page_title`: a `str` feature.
+- `section_title`: a `str` or `None` feature.
+
+### Data Splits
+
+Roughly 2.60% of the WIT-Base dataset comes from the Danish Wikipedia. We have split
+the resulting 168,740 samples into a training set, validation set and testing set of
+the following sizes:
+
+|   split | samples |
+|---------|--------:|
+| train   | 167,460 |
+| val     | 256     |
+| test    | 1,024   |
+
+
+## Dataset Creation
+
+### Curation Rationale
+
+It is quite cumbersome to extract the Danish portion of the WIT-Base dataset,
+especially as the dataset takes up 333 GB of disk space, so the curation of Danish-WIT
+is purely to make it easier to work with the Danish portion of it.
+
+### Source Data
+
+The original data was collected from WikiMedia's
+[WIT-Base](https://huggingface.co/datasets/wikimedia/wit_base) dataset, which in turn
+comes from Google's [WIT](https://huggingface.co/datasets/google/wit) dataset.
+
+
+## Additional Information
+
+### Dataset Curators
+
+[Dan Saattrup Nielsen](https://saattrupdan.github.io/) from the [The Alexandra
+Institute](https://alexandra.dk/) curated this dataset.
+
+### Licensing Information
+
+The dataset is licensed under the [CC BY-SA 4.0
+license](https://creativecommons.org/licenses/by-sa/4.0/).

huggingface_dataset/Dataset_Card/sheikh_FCD_lmv2.md

@@ -0,0 +1,133 @@
+annotations_creators:
+- other
+language:
+- en
+language_creators:
+- machine-generated
+license:
+- unknown
+multilinguality:
+- monolingual
+pretty_name: FCD
+size_categories: []
+source_datasets: []
+task_categories:
+- feature-extraction
+task_ids: []
+
+# Dataset Card for FCD
+
+## Table of Contents
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks](#supported-tasks-and-leaderboards)
+  - [Languages](#languages)
+- [Dataset Structure](#dataset-structure)
+  - [Data Instances](#data-instances)
+  - [Data Fields](#data-instances)
+  - [Data Splits](#data-instances)
+- [Dataset Creation](#dataset-creation)
+  - [Curation Rationale](#curation-rationale)
+  - [Source Data](#source-data)
+  - [Annotations](#annotations)
+  - [Personal and Sensitive Information](#personal-and-sensitive-information)
+- [Considerations for Using the Data](#considerations-for-using-the-data)
+  - [Social Impact of Dataset](#social-impact-of-dataset)
+  - [Discussion of Biases](#discussion-of-biases)
+  - [Other Known Limitations](#other-known-limitations)
+- [Additional Information](#additional-information)
+  - [Dataset Curators](#dataset-curators)
+  - [Licensing Information](#licensing-information)
+  - [Citation Information](#citation-information)
+
+## Dataset Description
+
+- **Homepage:** [Needs More Information]
+- **Repository:** [Needs More Information]
+- **Paper:** [Needs More Information]
+- **Leaderboard:** [Needs More Information]
+- **Point of Contact:** [Needs More Information]
+
+### Dataset Summary
+
+FCD dataset
+
+### Supported Tasks and Leaderboards
+
+NLP
+
+### Languages
+
+en
+
+## Dataset Structure
+
+### Data Instances
+
+[Needs More Information]
+
+### Data Fields
+
+[Needs More Information]
+
+### Data Splits
+
+[Needs More Information]
+
+## Dataset Creation
+
+### Curation Rationale
+
+[Needs More Information]
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+[Needs More Information]
+
+#### Who are the source language producers?
+
+[Needs More Information]
+
+### Annotations
+
+#### Annotation process
+
+[Needs More Information]
+
+#### Who are the annotators?
+
+[Needs More Information]
+
+### Personal and Sensitive Information
+
+[Needs More Information]
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+[Needs More Information]
+
+### Discussion of Biases
+
+[Needs More Information]
+
+### Other Known Limitations
+
+[Needs More Information]
+
+## Additional Information
+
+### Dataset Curators
+
+[Needs More Information]
+
+### Licensing Information
+
+[Needs More Information]
+
+### Citation Information
+
+[Needs More Information]

huggingface_dataset/Dataset_Card/stas_openwebtext-10k.md

@@ -0,0 +1,31 @@
+10K slice of OpenWebText - An open-source replication of the WebText dataset from OpenAI.
+
+This is a small subset representing the first 10K records from the original dataset - created for testing.
+
+The full 8M-record dataset is [here](https://huggingface.co/datasets/openwebtext).
+
+```
+$ python -c "from datasets import load_dataset; ds=load_dataset('stas/openwebtext-10k'); print(ds)"
+DatasetDict({
+    train: Dataset({
+        features: ['text'],
+        num_rows: 10000
+    })
+})
+```
+
+* Records: 10,000
+* compressed size: ~15MB
+* uncompressed size: 50MB
+
+To convert to jsonlines:
+
+```
+from datasets import load_dataset
+dataset_name = "stas/openwebtext-10k"
+name = dataset_name.split('/')[-1]
+ds = load_dataset(dataset_name, split='train')
+ds.to_json(f"{name}.jsonl", orient="records", lines=True)
+```
+
+To see how this subset was created, here is the [instructions file](https://huggingface.co/datasets/stas/openwebtext-10k/blob/main/process.txt).