Upload batch 334 (20 files, last=huggingface_dataset/Dataset_Card/reddit_tifu.md)

huggingface_dataset/Dataset_Card/Adapting_chinese_biomedical_NER_dataset.md

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+---
+license: mit
+---
+
+# 1 Source
+Source: https://github.com/alibaba-research/ChineseBLUE
+
+# 2 Definition of the tagset
+```python
+tag_set = [
+ 'B_手术',
+ 'I_疾病和诊断',
+ 'B_症状',
+ 'I_解剖部位',
+ 'I_药物',
+ 'B_影像检查',
+ 'B_药物',
+ 'B_疾病和诊断',
+ 'I_影像检查',
+ 'I_手术',
+ 'B_解剖部位',
+ 'O',
+ 'B_实验室检验',
+ 'I_症状',
+ 'I_实验室检验'
+ ]
+ 
+tag2id = lambda tag: tag_set.index(tag)
+id2tag = lambda id: tag_set[id]
+```
+ 
+
+# 3 Citation
+To use this dataset in your work please cite:
+
+Ningyu Zhang, Qianghuai Jia, Kangping Yin, Liang Dong, Feng Gao, Nengwei Hua. Conceptualized Representation Learning for Chinese Biomedical Text Mining
+
+```
+@article{zhang2020conceptualized,
+  title={Conceptualized Representation Learning for Chinese Biomedical Text Mining},
+  author={Zhang, Ningyu and Jia, Qianghuai and Yin, Kangping and Dong, Liang and Gao, Feng and Hua, Nengwei},
+  journal={arXiv preprint arXiv:2008.10813},
+  year={2020}
+}
+```

huggingface_dataset/Dataset_Card/GBaker_MedQA-USMLE-4-options-hf-cosine-similarity.md

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+---
+license: cc-by-sa-4.0
+---
+Original dataset introduced by Jin et al. in [What Disease does this Patient Have? A Large-scale Open Domain Question Answering Dataset from Medical Exams](https://paperswithcode.com/paper/what-disease-does-this-patient-have-a-large)
+
+This version is augmented with context retrieved from the textbooks provided with the original dataset using cosine similarity.
+
+<h4>Citation information:</h4>
+
+    @article{jin2020disease,
+      title={What Disease does this Patient Have? A Large-scale Open Domain Question Answering Dataset from Medical Exams},
+      author={Jin, Di and Pan, Eileen and Oufattole, Nassim and Weng, Wei-Hung and Fang, Hanyi and Szolovits, Peter},
+      journal={arXiv preprint arXiv:2009.13081},
+      year={2020}
+    }

huggingface_dataset/Dataset_Card/HuggingFaceH4_stack-exchange-preferences.md

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+---
+license: cc-by-sa-4.0
+task_categories:
+- question-answering
+language:
+- en
+pretty_name: H4 Stack Exchange Preferences Dataset
+tags:
+- RLHF
+- preferences
+- human-feedback
+- Stack Exchange
+download_size: 22132072448
+size_categories:
+- 10M<n<100M
+---
+# Dataset Card for H4 Stack Exchange Preferences Dataset
+
+## Dataset Description
+
+- **Homepage:** https://archive.org/details/stackexchange
+- **Repository:** (private for now) https://github.com/huggingface/h4
+- **Point of Contact:** Nathan Lambert, nathan@huggingface.co
+- **Size of downloaded dataset:** 22.13 GB
+- **Number of instructions:** 10,741,532
+
+### Dataset Summary
+
+This dataset contains questions and answers from the [Stack Overflow Data Dump](https://archive.org/details/stackexchange) for the purpose of **preference model training**. 
+Importantly, the questions have been filtered to fit the following criteria for preference models (following closely from [Askell et al. 2021](https://arxiv.org/abs/2112.00861)): *have >=2 answers*. 
+This data could also be used for instruction fine-tuning and language model training.
+
+The questions are grouped with answers that are assigned a score corresponding to the Anthropic paper:
+```
+score = log2 (1 + upvotes) rounded to the nearest integer, plus 1 if the answer was accepted by the questioner (we assign a score of −1 if the number of upvotes is negative).
+```
+
+Some important notes when using this dataset for preference model pretraining (PMP), which can be ignored for other uses: 
+* the data will likely need to be filtered more due to matching scores.
+* see section 4.1 of Askel et al 2021 for instructions on using each pair of samples twice via the following `binarization` (for better pre-training initialization):
+
+```
+Subsequently, we created a binary dataset by applying a ‘binarization’ procedure to the ranked dataset. That
+is, for every ranked pair A > B, we transform it into two independent binary comparisons:
+GOOD:A > BAD:A
+BAD:B > GOOD:B
+```
+To see all the stackexchanges used in this data, please see [this file](https://huggingface.co/datasets/HuggingFaceH4/pmp-stack-exchange/blob/main/stack_exchanges.json).
+
+Unfortunately, sharing the binarized data directly without metadata violates the license, so we have shared a script for binarization.
+
+
+### Using the data
+Here is a script from our internal tooling used to create a binarized dataset:
+```
+# Copyright 2023 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import random
+from argparse import ArgumentParser
+from pathlib import Path
+
+import numpy as np
+from datasets import Dataset, concatenate_datasets, load_dataset
+
+from h4.data.utils import save_dataset_shards
+
+
+H4_DIR = Path(__file__).resolve().parents[3]
+DATA_DIR = H4_DIR / "data"
+
+if __name__ == "__main__":
+    parser = ArgumentParser()
+    parser.add_argument("--debug", action="store_true", help="Added print statements / limit data size for debugging")
+    parser.add_argument(
+        "--output_dir",
+        default=f"{DATA_DIR}/pmp-binarized",
+        type=str,
+        help="Where to save the processed dataset",
+    )
+    parser.add_argument(
+        "--exchange_name",
+        type=str,
+        default=None,
+        help="Optional argument to specify a specific subsection of the dataset",
+    )
+    parser.add_argument(
+        "--binary_score", type=int, default=8, help="Score assigned to binarized pairs for preference data."
+    )
+    parser.add_argument(
+        "--stream_data", action="store_true", help="Optionally stream data, which can be useful with weaker computers"
+    )
+    parser.set_defaults(debug=False, stream_data=False)  # default will process full dataset
+
+    args = parser.parse_args()
+    specific_exchange = args.exchange_name
+    stream_dataset = args.stream_data
+    binary_score = args.binary_score
+
+    if specific_exchange:
+        data_dir = "data/" + args.exchange_name
+    else:
+        data_dir = None
+
+    if args.debug:
+        data_len_limit = 10000
+    else:
+        data_len_limit = np.inf
+
+    dataset = load_dataset(
+        "HuggingFaceH4/pmp-stack-exchange",
+        data_dir=data_dir,
+        split="train",
+        streaming=stream_dataset,
+    )
+
+    pmp_data = []
+    for i, d in enumerate(iter(dataset)):
+        # check debug limit, quit if in debug mode (don't save)
+        if i > data_len_limit:
+            print("Early exit for debug mode!")
+            print(pmp_data)
+            break
+
+        question = d["question"]
+        answers = d["answers"]
+        num_answers = len(answers)
+
+        answer_scores = [a["pm_score"] for a in answers]
+        if len(np.unique(answer_scores)) < 2:
+            print(f"PM Scores are {answer_scores}, skipping this question {i}")
+        else:
+            # Sample 2 unique scores for binarization
+            dif_scores = False
+            while not dif_scores:
+                # print("infinite loop...?")
+                two_answers = random.sample(answers, 2)
+
+                if two_answers[0]["pm_score"] != two_answers[1]["pm_score"]:
+                    dif_scores = True
+
+        answer_0 = two_answers[0]
+        answer_1 = two_answers[1]
+        text_0 = "Question: " + question + "\n" + "Answer: " + answer_0["text"]
+        text_1 = "Question: " + question + "\n" + "Answer: " + answer_1["text"]
+        score_0 = binary_score
+        score_1 = binary_score
+
+        pmp_data.append({"context": text_0, "score": score_0})
+        pmp_data.append({"context": text_1, "score": score_1})
+
+    # Save binarized data
+    sublist_len = 100000
+
+    print(f"Dataset length is {len(pmp_data)}")
+    # bypass known issue in arrow https://issues.apache.org/jira/browse/ARROW-17137
+    print(f"Processed dataset length > {sublist_len}, processing to HF dataset in chunks")
+    chunks = [pmp_data[x : x + sublist_len] for x in range(0, len(pmp_data), sublist_len)]
+    ds_chunks = [Dataset.from_list(ch) for ch in chunks]
+    ds = concatenate_datasets(ds_chunks)
+
+    save_dataset_shards(ds, args.output_dir, subset="stackexchange", shard_size="100MB")
+```
+
+
+### Languages
+
+This is intended to be English only, thought other languages may be present. Some Stack Exchanges that are omitted include: 
+
+```
+spanish: es.meta.stackoverflow.com, es.stackoverflow.com
+japanese: ja.meta.stackoverflow.com, ja.stackoverflow.com
+portugese: pt.stackoverflow.com, pt.meta.stackoverflow.com
+russian: ru.stackoverflow, ru.meta.stackoverflow
+```
+
+### Licensing Information
+
+License:  https://creativecommons.org/licenses/by-sa/4.0/
+
+The cc-by-sa 4.0 licensing, while intentionally permissive, does require attribution:
+
+Attribution — You must attribute the work in the manner specified by the author or licensor (but not in any way that suggests that they endorse you or your use of the work).
+Specifically the attribution requirements are as follows:
+1. Visually display or otherwise indicate the source of the content as coming from the Stack Exchange Network. This requirement is satisfied with a discreet text blurb, or some other unobtrusive but clear visual indication.
+2. Ensure that any Internet use of the content includes a hyperlink directly to the original question on the source site on the Network (e.g., http://stackoverflow.com/questions/12345)
+3. Visually display or otherwise clearly indicate the author names for every question and answer used
+4. Ensure that any Internet use of the content includes a hyperlink for each author name directly back to his or her user profile page on the source site on the Network (e.g., http://stackoverflow.com/users/12345/username), directly to the Stack Exchange domain, in standard HTML (i.e. not through a Tinyurl or other such indirect hyperlink, form of obfuscation or redirection), without any “nofollow” command or any other such means of avoiding detection by search engines, and visible even with JavaScript disabled.
+For more information, see the Stack Exchange Terms of Service.
+
+### Citation Information
+
+```
+@online{h4stackexchange,
+  author = {Lambert, Nathan and Tunstall, Lewis and Rajani, Nazneen and Thrush, Tristan},
+  title = {HuggingFace H4 Stack Exchange Preference Dataset},
+  year = 2023,
+  url = {https://huggingface.co/datasets/HuggingFaceH4/stack-exchange-preferences},
+}
+```

huggingface_dataset/Dataset_Card/TurkuNLP_wikipedia-toxicity-data-fi.md

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+---
+license: cc-by-sa-4.0
+task_categories:
+- text-classification
+language:
+- fi
+multilinguality:
+- translation
+tags:
+- toxicity, multi-label
+source_datasets:
+- extended|jigsaw_toxicity_pred
+size_categories:
+- 100K<n<1M
+---
+
+
+### Dataset Summary
+
+This dataset is a DeepL -based machine translated version of the Jigsaw toxicity dataset for Finnish. The dataset is originally from a Kaggle competition https://www.kaggle.com/c/jigsaw-toxic-comment-classification-challenge/data.
+The dataset poses a multi-label text classification problem and includes the labels `identity_attack`, `insult`, `obscene`, `severe_toxicity`, `threat` and `toxicity`.
+
+#### Example data
+
+```
+{
+"id": "879ad7bdba4cedaa",
+"label_identity_attack": 0,
+"label_insult": 0,
+"label_obscene": 0,
+"label_severe_toxicity": 0,
+"label_threat": 0,
+"label_toxicity": 0,
+"lang": "fi-deepl",
+"text": "\" \n\n Hei Pieter Pietersen, ja tervetuloa Wikipediaan!   \n\n Tervetuloa Wikipediaan! Toivottavasti viihdyt tietosanakirjassa ja haluat jäädä tänne. Ensimmäiseksi voit lukea johdannon. \n\n Jos sinulla on kysyttävää, voit kysyä minulta keskustelusivullani - autan mielelläni. Tai voit kysyä kysymyksesi Uusien avustajien ohjesivulla. \n\n - \n Seuraavassa on lisää resursseja, jotka auttavat sinua tutkimaan ja osallistumaan maailman suurinta tietosanakirjaa.... \n\n  Löydät perille:  \n\n  \n * Sisällysluettelo \n\n * Osastohakemisto \n\n  \n  Tarvitsetko apua?  \n\n  \n * Kysymykset - opas siitä, mistä voi esittää kysymyksiä. \n * Huijausluettelo - pikaohje Wikipedian merkintäkoodeista. \n\n * Wikipedian 5 pilaria - yleiskatsaus Wikipedian perustaan. \n * The Simplified Ruleset - yhteenveto Wikipedian tärkeimmistä säännöistä. \n\n  \n  Miten voit auttaa:  \n\n  \n * Wikipedian avustaminen - opas siitä, miten voit auttaa. \n\n * Yhteisöportaali - Wikipedian toiminnan keskus. \n\n  \n  Lisää vinkkejä...   \n\n  \n * Allekirjoita viestisi keskustelusivuilla neljällä tildillä (~~~~). Tämä lisää automaattisesti \"\"allekirjoituksesi\"\" (käyttäjänimesi ja päivämääräleima). Myös Wikipedian tekstinmuokkausikkunan yläpuolella olevassa työkalupalkissa oleva painike tekee tämän.  \n\n * Jos haluat leikkiä uusilla Wiki-taidoillasi, Hiekkalaatikko on sinua varten.  \n\n  \n  Onnea ja hauskaa. \""
+}
+```
+
+### Data Fields
+
+Fields marked as `label_` have either `0` to convey *not* having that category of toxicity in the text and `1` to convey having that category of toxicity present in the text.
+
+- `id`: a `string` feature.
+- `label_identity_attack`: a `int64` feature. 
+- `label_insult`: a `int64` feature.
+- `label_obscene`: a `int64` feature.
+- `label_severe_toxicity`: a `int64` feature.
+- `label_threat`: a `int64` feature.
+- `label_toxicity`: a `int64` feature.
+- `lang`: a `string` feature.
+- `text`: a `string` feature.
+
+
+### Data Splits
+
+The splits are the same as in the original English data.
+| dataset   |  train | test |
+| -------- | -----: | ---------: |
+| TurkuNLP/wikipedia-toxicity-data-fi| 159571 | 63978 |
+
+### Evaluation Results
+
+Results from fine-tuning [TurkuNLP/bert-large-finnish-cased-v1](https://huggingface.co/TurkuNLP/bert-large-finnish-cased-v1) for multi-label toxicity detection.
+| dataset              | F1-micro    | Precision | Recall |
+| -------------------- | ----: |  ---: | ----: |
+| TurkuNLP/wikipedia-toxicity-data-fi | 0.66 | 0.58 | 0.76 |
+
+<!--- Base results from fine-tuning [bert-large-cased](https://huggingface.co/bert-large-cased) on the original English data for multi-label toxicity detection.
+| dataset              | F1-micro    | Precision | Recall |
+| -------------------- | ----: | ---: | ----: |
+| jigsaw_toxicity_pred | 0.69 | 0.59 | 0.81 | --->
+
+### Considerations for Using the Data
+Due to DeepL terms and conditions, this dataset **must not be used for any machine translation work**, namely machine translation 
+system development and evaluation of any kind. In general, we wish you do not pair the original English data with the translations 
+except when working on research unrelated to machine translation, so as not to infringe on the terms and conditions.
+### Licensing Information
+Contents of this repository are distributed under the 
+[Creative Commons Attribution-ShareAlike 4.0 International License (CC BY-SA 4.0)](https://creativecommons.org/licenses/by-sa/4.0/). 
+Copyright of the dataset contents belongs to the original copyright holders.

huggingface_dataset/Dataset_Card/arabic_billion_words.md

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+---
+annotations_creators:
+- found
+language_creators:
+- found
+language:
+- ar
+license:
+- unknown
+multilinguality:
+- monolingual
+size_categories:
+- 100K<n<1M
+- 10K<n<100K
+- 1M<n<10M
+source_datasets:
+- original
+task_categories:
+- text-generation
+- fill-mask
+task_ids:
+- language-modeling
+- masked-language-modeling
+paperswithcode_id: null
+pretty_name: Arabic Billion Words
+configs:
+- Alittihad
+- Almasryalyoum
+- Almustaqbal
+- Alqabas
+- Echoroukonline
+- Ryiadh
+- Sabanews
+- SaudiYoum
+- Techreen
+- Youm7
+dataset_info:
+- config_name: Alittihad
+  features:
+  - name: url
+    dtype: string
+  - name: head_line
+    dtype: string
+  - name: date
+    dtype: string
+  - name: text
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 1601790302
+    num_examples: 349342
+  download_size: 348259999
+  dataset_size: 1601790302
+- config_name: Almasryalyoum
+  features:
+  - name: url
+    dtype: string
+  - name: head_line
+    dtype: string
+  - name: date
+    dtype: string
+  - name: text
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 1056197870
+    num_examples: 291723
+  download_size: 242604438
+  dataset_size: 1056197870
+- config_name: Almustaqbal
+  features:
+  - name: url
+    dtype: string
+  - name: head_line
+    dtype: string
+  - name: date
+    dtype: string
+  - name: text
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 1545659336
+    num_examples: 446873
+  download_size: 350826797
+  dataset_size: 1545659336
+- config_name: Alqabas
+  features:
+  - name: url
+    dtype: string
+  - name: head_line
+    dtype: string
+  - name: date
+    dtype: string
+  - name: text
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 2631729746
+    num_examples: 817274
+  download_size: 595274646
+  dataset_size: 2631729746
+- config_name: Echoroukonline
+  features:
+  - name: url
+    dtype: string
+  - name: head_line
+    dtype: string
+  - name: date
+    dtype: string
+  - name: text
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 464386206
+    num_examples: 139732
+  download_size: 108184378
+  dataset_size: 464386206
+- config_name: Ryiadh
+  features:
+  - name: url
+    dtype: string
+  - name: head_line
+    dtype: string
+  - name: date
+    dtype: string
+  - name: text
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 3101294859
+    num_examples: 858188
+  download_size: 691264971
+  dataset_size: 3101294859
+- config_name: Sabanews
+  features:
+  - name: url
+    dtype: string
+  - name: head_line
+    dtype: string
+  - name: date
+    dtype: string
+  - name: text
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 198019614
+    num_examples: 92149
+  download_size: 38214558
+  dataset_size: 198019614
+- config_name: SaudiYoum
+  features:
+  - name: url
+    dtype: string
+  - name: head_line
+    dtype: string
+  - name: date
+    dtype: string
+  - name: text
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 2723291416
+    num_examples: 888068
+  download_size: 605537923
+  dataset_size: 2723291416
+- config_name: Techreen
+  features:
+  - name: url
+    dtype: string
+  - name: head_line
+    dtype: string
+  - name: date
+    dtype: string
+  - name: text
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 1103458209
+    num_examples: 314597
+  download_size: 252976781
+  dataset_size: 1103458209
+- config_name: Youm7
+  features:
+  - name: url
+    dtype: string
+  - name: head_line
+    dtype: string
+  - name: date
+    dtype: string
+  - name: text
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 3004689464
+    num_examples: 1172136
+  download_size: 617708074
+  dataset_size: 3004689464
+---
+
+# Dataset Card for Arabic Billion Words Corpus
+
+## 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://www.abuelkhair.net/index.php/en/arabic/abu-el-khair-corpus
+- **Repository:**
+- **Paper:** https://arxiv.org/pdf/1611.04033
+- **Leaderboard:**
+- **Point of Contact:**[Ibrahim Abu El-Khair](iabuelkhair@gmail.com)
+
+### Dataset Summary
+
+Abu El-Khair Corpus is an Arabic text corpus, that includes more than five million newspaper articles.
+It contains over a billion and a half words in total, out of which, there are about three million unique words.
+The corpus is encoded with two types of encoding, namely: UTF-8, and Windows CP-1256.
+Also it was marked with two mark-up languages, namely: SGML, and XML.
+
+### Supported Tasks and Leaderboards
+
+[More Information Needed]
+
+### Languages
+
+Arabic
+
+## Dataset Structure
+
+### Data Instances
+
+This is an example of the "Almasryalyoum" configuration subset:
+```python
+{
+  "url": "http://today.almasryalyoum.com/printerfriendly.aspx?ArticleID=61300",
+  "head_line": "رئيس وزراء المجر: عنصرية جماهير أوجبيست جلبت العار للبلاد",
+  "date": "19/5/2007",
+  "text": """قال متحدث باسم الحكومة المجرية: إن رئيس الوزراء فيرنك جيوركساني رحب بقرار اتحاد كرة القدم المجري بخصم ثلاث نقاط من نادي أوجبيست بسبب السلوك العنصري الذي صدر من جماهيره.
+وعاقب الاتحاد المجري فريق أوجبيست بعد أن سخرت جماهيره من إبراهيم سيديبي مهاجم فريق ديبرينسين الأسود أثناء مباراة الفريقين أوائل مايو الجاري.
+يذكر أن الاتحاد فرض أيضا غرامة مالية قدرها 20 ألف دولار علي أوجبيست في عام 2005 بعد أن رددت جماهيره شعارات معادية للسامية خلال مباراة بالدوري المجري.
+وأوضح جيوركساني في خطاب إلي إيستفان كيستليكي رئيس الاتحاد المجري لكرة القدم، أن هذا السلوك العنصري من الجماهير «جلب العار لكرة القدم وللمجر». يذكر أن المجر بها مجموعة من مشجعي كرة القدم المشاغبين «الهوليجانز»، وشارك الكثير منهم في أعمال شغب معادية للحكومة في العام الماضي.""",
+}
+```
+
+### Data Fields
+
+The data fields are:
+- "url": string, original url of the article,
+- "head_line": string, headline of the article,
+- "date": string, date of the article,
+- "text": string, text content of the article,
+
+### Data Splits
+
+There is only one "training" split for all configuration subsets, containing the following number of examples:
+
+|                | Number of examples |
+|:---------------|-------------------:|
+| Alittihad      |             349342 |
+| Almasryalyoum  |             291723 |
+| Almustaqbal    |             446873 |
+| Alqabas        |             817274 |
+| Echoroukonline |             139732 |
+| Ryiadh         |             858188 |
+| Sabanews       |              92149 |
+| SaudiYoum      |             888068 |
+| Techreen       |             314597 |
+| Youm7          |            1172136 |
+
+## 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
+
+```
+@article{el20161,
+  title={1.5 billion words arabic corpus},
+  author={El-Khair, Ibrahim Abu},
+  journal={arXiv preprint arXiv:1611.04033},
+  year={2016}
+}
+```
+
+### Contributions
+
+Thanks to [@zaidalyafeai](https://github.com/zaidalyafeai) and [@albertvillanova](https://github.com/albertvillanova) for adding this dataset.

huggingface_dataset/Dataset_Card/autoevaluate_autoeval-eval-mathemakitten__winobias_antistereotype_test_cot_v2-math-db74ac-2016866705.md

@@ -0,0 +1,34 @@
+---
+type: predictions
+tags:
+- autotrain
+- evaluation
+datasets:
+- mathemakitten/winobias_antistereotype_test_cot_v2
+eval_info:
+  task: text_zero_shot_classification
+  model: inverse-scaling/opt-350m_eval
+  metrics: []
+  dataset_name: mathemakitten/winobias_antistereotype_test_cot_v2
+  dataset_config: mathemakitten--winobias_antistereotype_test_cot_v2
+  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: inverse-scaling/opt-350m_eval
+* Dataset: mathemakitten/winobias_antistereotype_test_cot_v2
+* Config: mathemakitten--winobias_antistereotype_test_cot_v2
+* Split: test
+
+To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
+
+## Contributions
+
+Thanks to [@mathemakitten](https://huggingface.co/mathemakitten) for evaluating this model.

huggingface_dataset/Dataset_Card/biu-nlp_CoreSearch.md

@@ -0,0 +1,48 @@
+# The CoreSearch Dataset
+A large-scale dataset for cross-document event coreference **search**</br>
+
+- **Paper:** [Cross-document Event Coreference Search: Task, Dataset and Modeling](https://arxiv.org/abs/2210.12654)
+
+### Languages
+
+English
+
+## Load Dataset
+You can read/download the dataset files following Huggingface Hub instructions.</br>
+For example, below code will load CoreSearch DPR folder:
+
+```python
+from huggingface_hub import hf_hub_url, cached_download
+import json
+REPO_ID = "datasets/Intel/CoreSearch"
+DPR_FILES = "/dpr/"
+
+dpr_files = ["dpr/Dev.json", "dpr/Train.json", "dpr/Test.json"]
+
+dpr_jsons = list()
+for _file in dpr_files:
+    dpr_jsons.append(json.load(open(cached_download(
+        hf_hub_url(REPO_ID, _file)), "r")))
+```
+
+### Data Splits
+- **Final version of the CD event coreference search dataset**<br>
+|                                                    | Train   | Valid | Test | Total |
+| -----                                              | ------ | ----- | ----  | ----  |
+| WEC-Eng Validated Data                             |        |       |       |       |
+| &nbsp;&nbsp;&nbsp;&nbsp;# Clusters                 | 237    |  49   | 236   | 522   | 
+| &nbsp;&nbsp;&nbsp;&nbsp;# Passages (with Mentions) | 1,503  |  341  | 1,266 | 3,110 |
+| # Added Destructor Passages                        | 922,736  |  923,376  | 923,746   | 2,769,858 |
+| # Total Passages                                   | 924,239 |  923,717 | 925,012 | 2,772,968 |
+
+## Citation
+```
+@inproceedings{TBD}
+```
+
+
+## License
+We provide the following data sets under a <a href="https://creativecommons.org/licenses/by-sa/3.0/deed.en_US">Creative Commons Attribution-ShareAlike 3.0 Unported License</a>. It is based on content extracted from Wikipedia that is licensed under the Creative Commons Attribution-ShareAlike 3.0 Unported License
+
+## Contact
+If you have any questions please create a Github issue at <a href="https://github.com/AlonEirew/CoreSearch">https://github.com/AlonEirew/CoreSearch</a>.

huggingface_dataset/Dataset_Card/demelin_wino_x.md

@@ -0,0 +1,215 @@
+---
+annotations_creators:
+- no-annotation
+language:
+- en
+- de
+- fr
+- ru
+language_creators:
+- machine-generated
+- expert-generated
+license:
+- mit
+multilinguality:
+- multilingual
+- translation
+pretty_name: Wino-X
+size_categories:
+- 1K<n<10K
+source_datasets:
+- original
+task_categories:
+- translation
+- coreference resolution
+- commonsense reasoning
+task_ids:
+- multiple-choice-qa
+- language-modeling
+---
+
+# Dataset Card for Wino-X
+
+## 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:** [Wino-X repository](https://github.com/demelin/Wino-X)
+- **Repository:** [Wino-X repository](https://github.com/demelin/Wino-X)
+- **Paper:** [Wino-X: Multilingual Winograd Schemas for Commonsense Reasoning and Coreference Resolution](https://aclanthology.org/2021.emnlp-main.670/)
+- **Leaderboard:** [N/A]
+- **Point of Contact:** [Denis Emelin](demelin.github.io)
+
+### Dataset Summary
+
+Wino-X is a parallel dataset of German, French, and Russian Winograd schemas, aligned with their English
+counterparts, used to examine whether neural machine translation models can perform coreference resolution that
+requires commonsense knowledge, and whether multilingual language models are capable of commonsense reasoning across
+multiple languages.
+
+### Supported Tasks and Leaderboards
+
+- translation: The dataset can be used to evaluate translations of ambiguous source sentences, as produced by translation models . A [pretrained transformer-based NMT model](https://huggingface.co/Helsinki-NLP/opus-mt-en-de) can be used for this purpose.
+- coreference-resolution: The dataset can be used to rank alternative translations of an ambiguous source sentence that differ in the chosen referent of an ambiguous source pronoun. A [pretrained transformer-based NMT model](https://huggingface.co/Helsinki-NLP/opus-mt-en-de) can be used for this purpose.
+- commonsense-reasoning: The dataset can also be used evaluate whether pretrained multilingual language models can perform commonsense reasoning in (or across) multiple languages by identifying the correct filler in a cloze completion task. An [XLM-based model](https://huggingface.co/xlm-roberta-base) can be used for this purpose.
+
+### Languages
+
+The dataset (both its MT and LM portions) is available in the following translation pairs: English-German, English-French, English-Russian. All English sentences included in *Wino-X* were extracted from publicly available parallel corpora, as detailed in the accompanying paper, and represent the dataset-specific language varieties. All non-English sentences were obtained through machine translation and may, as such, exhibit features of translationese.
+
+## Dataset Structure
+
+### Data Instances
+
+The following represents a typical *MT-Wino-X* instance (for the English-German translation pair):  
+  
+{"qID": "3UDTAB6HH8D37OQL3O6F3GXEEOF09Z-1",  
+"sentence": "The woman looked for a different vase for the bouquet because it was too small.",  
+"translation1": "Die Frau suchte nach einer anderen Vase für den Blumenstrauß, weil sie zu klein war.",  
+"translation2": "Die Frau suchte nach einer anderen Vase für den Blumenstrauß, weil er zu klein war.",  
+"answer": 1,  
+"pronoun1": "sie",  
+"pronoun2": "er",  
+"referent1_en": "vase",  
+"referent2_en": "bouquet",  
+"true_translation_referent_of_pronoun1_de": "Vase",  
+"true_translation_referent_of_pronoun2_de": "Blumenstrauß",  
+"false_translation_referent_of_pronoun1_de": "Vase",  
+ "false_translation_referent_of_pronoun2_de": "Blumenstrauß"}  
+  
+  
+The following represents a typical *LM-Wino-X* instance (for the English-French translation pair):  
+  
+{"qID": "3UDTAB6HH8D37OQL3O6F3GXEEOF09Z-1",   
+"sentence": "The woman looked for a different vase for the bouquet because it was too small.",   
+"context_en": "The woman looked for a different vase for the bouquet because _ was too small.",   
+"context_fr": "La femme a cherché un vase différent pour le bouquet car _ était trop petit.",   
+"option1_en": "the bouquet",   
+"option2_en": "the vase",   
+"option1_fr": "le bouquet",   
+"option2_fr": "le vase",   
+"answer": 2,   
+"context_referent_of_option1_fr": "bouquet",  
+"context_referent_of_option2_fr": "vase"}
+
+### Data Fields
+
+For *MT-Wino-X*:  
+  
+- "qID": Unique identifier ID for this dataset instance.
+- "sentence":  English sentence containing the ambiguous pronoun 'it'.
+- "translation1": First translation candidate.
+- "translation2": Second translation candidate.
+- "answer":  ID of the correct translation.
+- "pronoun1": Translation of the ambiguous source pronoun in translation1.
+- "pronoun2": Translation of the ambiguous source pronoun in translation2.
+- "referent1_en": English referent of the translation of the ambiguous source pronoun in translation1.
+- "referent2_en": English referent of the translation of the ambiguous source pronoun in translation2.
+- "true_translation_referent_of_pronoun1_[TGT-LANG]": Target language referent of pronoun1 in the correct translation.
+- "true_translation_referent_of_pronoun2_[TGT-LANG]":  Target language referent of pronoun2 in the correct translation.
+- "false_translation_referent_of_pronoun1_[TGT-LANG]": Target language referent of pronoun1 in the incorrect translation.
+- "false_translation_referent_of_pronoun2_[TGT-LANG]":  Target language referent of pronoun2 in the incorrect translation.
+  
+  
+For *LM-Wino-X*:  
+  
+- "qID": Unique identifier ID for this dataset instance.
+- "sentence": English sentence containing the ambiguous pronoun 'it'. 
+- "context_en": Same English sentence, where 'it' is replaced by a gap. 
+- "context_fr": Target language translation of the English sentence, where the translation of 'it' is replaced by a gap.
+- "option1_en": First filler option for the English sentence.
+- "option2_en":  Second filler option for the English sentence.
+- "option1_[TGT-LANG]": First filler option for the target language sentence.
+- "option2_[TGT-LANG]":  Second filler option for the target language sentence.
+- "answer": ID of the correct gap filler.
+- "context_referent_of_option1_[TGT-LANG]": English translation of option1_[TGT-LANG].
+- "context_referent_of_option2_[TGT-LANG]": English translation of option2_[TGT-LANG]
+
+### Data Splits
+
+*Wno-X* was designed as an evaluation-only benchmark and therefore is intended to be used for zero-shot testing only. However, users are very welcome to split the data as they wish :) .
+
+
+## Dataset Creation
+
+### Curation Rationale
+
+Please refer to [Section 2 in the dataset paper](https://aclanthology.org/2021.emnlp-main.670.pdf).
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+Please refer to [Section 2 in the dataset paper](https://aclanthology.org/2021.emnlp-main.670.pdf).
+
+#### Who are the source language producers?
+
+Please refer to [Section 2 in the dataset paper](https://aclanthology.org/2021.emnlp-main.670.pdf).
+
+### Annotations
+
+#### Annotation process
+
+Please refer to [Section 2 in the dataset paper](https://aclanthology.org/2021.emnlp-main.670.pdf).
+
+#### Who are the annotators?
+
+Annotations were generated automatically and verified by the dataset author / curator for correctness.
+
+### Personal and Sensitive Information
+
+[N/A]
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+Please refer to ['Ethical Considerations' in the dataset paper](https://aclanthology.org/2021.emnlp-main.670.pdf).
+
+### Discussion of Biases
+
+Please refer to ['Ethical Considerations' in the dataset paper](https://aclanthology.org/2021.emnlp-main.670.pdf).
+
+### Other Known Limitations
+
+Please refer to ['Ethical Considerations' in the dataset paper](https://aclanthology.org/2021.emnlp-main.670.pdf).
+
+## Additional Information
+
+### Dataset Curators
+
+[Denis Emelin](demelin.github.io)
+
+### Licensing Information
+
+MIT
+
+### Citation Information
+
+@inproceedings{Emelin2021WinoXMW,
+  title={Wino-X: Multilingual Winograd Schemas for Commonsense Reasoning and Coreference Resolution},
+  author={Denis Emelin and Rico Sennrich},
+  booktitle={EMNLP},
+  year={2021}
+}

huggingface_dataset/Dataset_Card/gfhayworth_wiki_mini.md

@@ -0,0 +1,5 @@
+Simple English Wikipedia it has only about 170k articles. We split these articles into paragraphs.
+wikipedia_filepath = 'simplewiki-2020-11-01.jsonl.gz'
+
+if not os.path.exists(wikipedia_filepath):
+    util.http_get('http://sbert.net/datasets/simplewiki-2020-11-01.jsonl.gz', wikipedia_filepath)

huggingface_dataset/Dataset_Card/ggerganov_whisper.cpp-coreml.md

@@ -0,0 +1,20 @@
+---
+license: mit
+---
+
+# OpenAI's Whisper models converted to Core ML format
+
+[Available models](https://huggingface.co/datasets/ggerganov/whisper.cpp-coreml/tree/main)
+
+**WIP in progress !!!
+
+The models are not final and will likely change
+
+Hosting location will likely change
+
+The main purpose of this repo is to make testing easier during the Core ML development**
+
+For more information, visit:
+
+- https://github.com/ggerganov/whisper.cpp/discussions/548
+- https://github.com/ggerganov/whisper.cpp/pull/566

huggingface_dataset/Dataset_Card/hackathon-pln-es_Axolotl-Spanish-Nahuatl.md

@@ -0,0 +1,65 @@
+---
+annotations_creators:
+- expert-generated
+
+language_creators:
+- expert-generated
+
+language:
+- es
+language_bcp47:
+- es-MX
+license:
+- mpl-2.0
+
+multilinguality:
+- translation
+
+pretty_name: "Axolotl Spanish-Nahuatl parallel corpus , is a digital corpus that compiles several sources with parallel content in these two languages. \n\nA parallel corpus is a type of corpus that contains texts in a source language with their correspondent translation in one or more target languages.
+Gutierrez-Vasques, X., Sierra, G., and Pompa, I. H. (2016). Axolotl: a web accessible parallel corpus for spanish-nahuatl. In Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC 2016), Portoro, Slovenia. European Language Resources Association (ELRA).
+Grupo de Ingenieria Linguistica (GIL, UNAM). Corpus paralelo español-nahuatl. http://www.corpus.unam.mx/axolotl."
+
+size_categories:
+- unknown
+
+source_datasets:
+- original
+
+task_categories:
+- conditional-text-generation
+
+task_ids:
+- machine-translation
+---
+
+# Axolotl-Spanish-Nahuatl : Parallel corpus for Spanish-Nahuatl machine translation
+
+## Table of Contents
+- [Dataset Card for [Axolotl-Spanish-Nahuatl]](#dataset-card-for-Axolotl-Spanish-Nahuatl)
+
+
+## Dataset Description
+
+- **Source 1:** http://www.corpus.unam.mx/axolotl
+- **Source 2:** http://link.springer.com/article/10.1007/s10579-014-9287-y
+- **Repository:1** https://github.com/ElotlMX/py-elotl
+- **Repository:2** https://github.com/christos-c/bible-corpus/blob/master/bibles/Nahuatl-NT.xml
+- **Paper:** https://aclanthology.org/N15-2021.pdf
+
+## Dataset Collection
+
+In order to get a good translator, we collected and cleaned two of the most complete Nahuatl-Spanish parallel corpora available. Those are Axolotl collected by an expert team at UNAM and Bible UEDIN Nahuatl Spanish crawled by Christos Christodoulopoulos and Mark Steedman from Bible Gateway site.
+
+After this, we ended with 12,207 samples from Axolotl due to misalignments and duplicated texts in Spanish in both original and nahuatl columns and 7,821 samples from Bible UEDIN for a total of 20028 utterances.
+
+
+## Team members
+- Emilio Morales [(milmor)](https://huggingface.co/milmor)
+- Rodrigo Martínez Arzate  [(rockdrigoma)](https://huggingface.co/rockdrigoma)
+- Luis Armando Mercado [(luisarmando)](https://huggingface.co/luisarmando)
+- Jacobo del Valle [(jjdv)](https://huggingface.co/jjdv)
+
+## Applications
+
+- MODEL: Spanish Nahuatl Translation Task with a T5 model in ([t5-small-spanish-nahuatl](https://huggingface.co/hackathon-pln-es/t5-small-spanish-nahuatl))
+- DEMO: Spanish Nahuatl Translation in ([Spanish-nahuatl](https://huggingface.co/spaces/hackathon-pln-es/Spanish-Nahuatl-Translation))

huggingface_dataset/Dataset_Card/irds_beir_webis-touche2020_v2.md

@@ -0,0 +1,61 @@
+---
+pretty_name: '`beir/webis-touche2020/v2`'
+viewer: false
+source_datasets: []
+task_categories:
+- text-retrieval
+---
+
+# Dataset Card for `beir/webis-touche2020/v2`
+
+The `beir/webis-touche2020/v2` 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/v2).
+
+# Data
+
+This dataset provides:
+ - `docs` (documents, i.e., the corpus); count=382,545
+ - `queries` (i.e., topics); count=49
+ - `qrels`: (relevance assessments); count=2,214
+
+
+## Usage
+
+```python
+from datasets import load_dataset
+
+docs = load_dataset('irds/beir_webis-touche2020_v2', 'docs')
+for record in docs:
+    record # {'doc_id': ..., 'text': ..., 'title': ..., 'stance': ..., 'url': ...}
+
+queries = load_dataset('irds/beir_webis-touche2020_v2', 'queries')
+for record in queries:
+    record # {'query_id': ..., 'text': ..., 'description': ..., 'narrative': ...}
+
+qrels = load_dataset('irds/beir_webis-touche2020_v2', '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/ivelin_rico_sca_refexp_synthetic_saved.md

@@ -0,0 +1,37 @@
+---
+dataset_info:
+  features:
+  - name: image
+    dtype: image
+  - name: image_id
+    dtype: string
+  - name: labels
+    list:
+    - name: prompt
+      dtype: string
+    - name: target_bounding_box
+      struct:
+      - name: xmin
+        dtype: float32
+      - name: ymin
+        dtype: float32
+      - name: xmax
+        dtype: float32
+      - name: ymax
+        dtype: float32
+  splits:
+  - name: train
+    num_bytes: 2604982403.694
+    num_examples: 24063
+  - name: validation
+    num_bytes: 21192787.0
+    num_examples: 160
+  - name: test
+    num_bytes: 22057836.0
+    num_examples: 185
+  download_size: 2096931333
+  dataset_size: 2648233026.694
+---
+# Dataset Card for "rico_sca_refexp_synthetic_saved"
+
+This is a saved snapshot of the dynamically generated [Rico SCA RefExp dataset](https://huggingface.co/datasets/ivelin/rico_sca_refexp_synthetic)

huggingface_dataset/Dataset_Card/khaclinh_testdata.md

@@ -0,0 +1,184 @@
+---
+annotations_creators:
+- expert-generated
+language_creators:
+- found
+language:
+- en
+license:
+- cc-by-nc-nd-4.0
+multilinguality:
+- monolingual
+size_categories:
+- 1K<n<10K
+source_datasets:
+- extended
+task_categories:
+- object-detection
+task_ids:
+- face-detection
+- license-plate-detection
+pretty_name: PP4AV
+---
+
+# Dataset Card for PP4AV
+
+## Table of Contents
+- [Table of Contents](#table-of-contents)
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Languages](#languages)
+- [Dataset Structure](#dataset-structure)
+  - [Data Instances](#data-instances)
+  - [Data Fields](#data-fields)
+- [Dataset Creation](#dataset-creation)
+  - [Source Data](#source-data)
+  - [Annotations](#annotations)
+  - [Personal and Sensitive Information](#personal-and-sensitive-information)
+- [Considerations for Using the Data](#considerations-for-using-the-data)
+  - [Social Impact of Dataset](#social-impact-of-dataset)
+  - [Discussion of Biases](#discussion-of-biases)
+  - [Other Known Limitations](#other-known-limitations)
+- [Additional Information](#additional-information)
+  - [Dataset Curators](#dataset-curators)
+  - [Licensing Information](#licensing-information)
+  - [Citation Information](#citation-information)
+  - [Contributions](#contributions)
+
+## Dataset Description
+
+- **Homepage:** https://github.com/khaclinh/pp4av
+- **Repository:**
+- **Paper:** [PP4AV: A benchmarking Dataset for Privacy-preserving Autonomous Driving]
+- **Point of Contact:** linhtk.dhbk@gmail.com
+
+### Dataset Summary
+
+PP4AV is the first public dataset with faces and license plates annotated with driving scenarios. P4AV provides 3,447 annotated driving images for both faces and license plates. For normal camera data, dataset sampled images from the existing videos in which cameras were mounted in moving vehicles, running around the European cities. The images in PP4AV were sampled from 6 European cities at various times of day, including nighttime. This dataset use the fisheye images from the WoodScape dataset to select 244 images from the front, rear, left, and right cameras for fisheye camera data. PP4AV dataset can be used as a benchmark suite (evaluating dataset) for data anonymization models in autonomous driving.
+
+### Languages
+
+English
+
+## Dataset Structure
+
+### Data Instances
+
+A data point comprises an image and its face and license plate annotations.
+
+```
+{
+  'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=1920x1080 at 0x19FA12186D8>, 'objects': {
+    'bbox': [
+      [0 0.230078 0.317081 0.239062 0.331367],
+      [1 0.5017185 0.0306425 0.5185935 0.0410975],
+      [1 0.695078 0.0710145 0.7109375 0.0863355],
+      [1 0.4089065 0.31646 0.414375 0.32764],
+      [0 0.1843745 0.403416 0.201093 0.414182],
+      [0 0.7132 0.3393474 0.717922 0.3514285]
+    ]
+  }
+}
+```
+
+### Data Fields
+
+- `image`: A `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`
+- `objects`: a dictionary of face and license plate bounding boxes present on the image
+  - `bbox`: the bounding box of each face and license plate (in the [yolo](https://albumentations.ai/docs/getting_started/bounding_boxes_augmentation/#yolo) format). Basically, each row in annotation `.txt` file for each image `.png` file consists of data in format: `<object-class> <x_center> <y_center> <width> <height>`:
+    - `object-class`: integer number of object from 0 to 1, where 0 indicate face object, and 1 indicate licese plate object
+    - `x_center`: normalized x-axis coordinate of the center of the bounding box.  
+      `x_center = <absolute_x_center> / <image_width>`
+    - `y_center`: normalized y-axis coordinate of the center of the bounding box.  
+      `y_center = <absolute_y_center> / <image_height>`
+    - `width`: normalized width of the bounding box.  
+      `width = <absolute_width> / <image_width>`
+    - `height`: normalized wheightdth of the bounding box.  
+      `height = <absolute_height> / <image_height>`
+    - Example lines in YOLO v1.1 format `.txt' annotation file:   
+      ` 1 0.716797 0.395833 0.216406 0.147222  
+        0 0.687109 0.379167 0.255469 0.158333  
+        1 0.420312 0.395833 0.140625 0.166667
+      `
+
+## Dataset Creation
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+The objective of PP4AV is to build a benchmark dataset that can be used to evaluate face and license plate detection models for autonomous driving. For normal camera data, we sampled images from the existing videos in which cameras were mounted in moving vehicles, running around the European cities. We focus on sampling data in urban areas rather than highways in order to provide sufficient samples of license plates and pedestrians. The images in PP4AV were sampled from **6** European cities at various times of day, including nighttime. The source data from 6 cities in European was described as follow:
+  - `Paris`: This subset contains **1450** images of the car driving down a Parisian street during the day. The video frame rate is 30 frames per second. The video is longer than one hour. We cut a shorter video for sampling and annotation. The original video can be found at the following URL:
+    URL: [paris_youtube_video](https://www.youtube.com/watch?v=nqWtGWymV6c)  
+  - `Netherland day time`: This subset consists of **388** images of Hague, Amsterdam city in day time. The image of this subset are sampled from the bellow original video:  
+    URL: [netherland_youtube_video](https://www.youtube.com/watch?v=Xuo4uCZxNrE)  
+    The frame rate of the video is 30 frames per second. We cut a shorter video for sampling and annotation. The original video was longer than a half hour.
+  - `Netherland night time`: This subset consists of **824** images of Hague, Amsterdam city in night time sampled by the following original video:   
+    URL: [netherland_youtube_video](https://www.youtube.com/watch?v=eAy9eHsynhM)  
+    The frame rate of the video is 30 frames per second. We cut a shorter video for sampling and annotation. The original video was longer than a half hour.
+  - `Switzerland`: This subset consists of **372** images of Switzerland sampled by the following video:   
+    URL: [switzerland_youtube_video](https://www.youtube.com/watch?v=0iw5IP94m0Q)  
+    The frame rate of the video is 30 frames per second. We cut a shorter video for sampling and annotation. The original video was longer than one hour.
+  - `Zurich`: This subset consists of **50** images of Zurich city provided by the Cityscapes training set in package [leftImg8bit_trainvaltest.zip](https://www.cityscapes-dataset.com/file-handling/?packageID=3)
+  - `Stuttgart`: This subset consists of **69** images of Stuttgart city provided by the Cityscapes training set in package [leftImg8bit_trainvaltest.zip](https://www.cityscapes-dataset.com/file-handling/?packageID=3)
+  - `Strasbourg`: This subset consists of **50** images of Strasbourg city provided by the Cityscapes training set in package [leftImg8bit_trainvaltest.zip](https://www.cityscapes-dataset.com/file-handling/?packageID=3)
+
+We use the fisheye images from the WoodScape dataset to select **244** images from the front, rear, left, and right cameras for fisheye camera data. 
+The source of fisheye data for sampling is located at WoodScape's [Fisheye images](https://woodscape.valeo.com/download).
+
+In total, **3,447** images were selected and annotated in PP4AV.
+
+
+### Annotations
+
+#### Annotation process
+
+Annotators annotate facial and license plate objects in images. For facial objects, bounding boxes are defined by all detectable human faces from the forehead to the chin to the ears. Faces were labelled with diverse sizes, skin tones, and faces partially obscured by a transparent material, such as a car windshield. For license plate objects,  bounding boxes consists of all recognizable license plates with high variability, such as different sizes, countries, vehicle types (motorcycle, automobile, bus, truck), and occlusions by other vehicles. License plates were annotated for vehicles involved in moving traffic. To ensure the quality of annotation, there are two-step process for annotation. In the first phase, two teams of annotators will independently annotate identical image sets. After their annotation output is complete, a merging method based on the IoU scores between the two bounding boxes of the two annotations will be applied. Pairs of annotations with IoU scores above a threshold will be merged and saved as a single annotation. Annotated pairs with IoU scores below a threshold will be considered conflicting. In the second phase, two teams of reviewers will inspect the conflicting pairs of annotations for revision before a second merging method similar to the first is applied. The results of these two phases will be combined to form the final annotation. All work is conducted on the CVAT tool https://github.com/openvinotoolkit/cvat.
+
+#### Who are the annotators?
+
+Vantix Data Science team
+
+### 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
+
+Linh Trinh
+
+### Licensing Information
+
+[Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)](https://creativecommons.org/licenses/by-nc-nd/4.0/).
+
+### Citation Information
+
+```
+@article{PP4AV2022,
+  title = {PP4AV: A benchmarking Dataset for Privacy-preserving Autonomous Driving},
+  author = {Linh Trinh, Phuong Pham, Hoang Trinh, Nguyen Bach, Dung Nguyen, Giang Nguyen, Huy Nguyen},
+  booktitle = {IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},
+  year = {2023}
+}
+```
+
+### Contributions
+
+Thanks to [@khaclinh](https://github.com/khaclinh) for adding this dataset.
+

huggingface_dataset/Dataset_Card/loubnabnl_bigcode-data-stats.md

@@ -0,0 +1,27 @@
+**Stats about bigcode dataset:**
+
+* Permissive licenses only :
+
+
+ |Language |Raw (only exact dedup)  | Near dedup | Near dedup + content filters| Near dedup + more near-dedup (1)|Near dedup + comments filter (1)| Near dedup + more near-dedup + comments(1)|
+|-------|--------|-------|--------|--------|--------|--------|
+|Python | 200 GB| [80 GB](https://huggingface.co/datasets/bigcode/the-stack-dedup-pjj/tree/v1.1.a1) | [75.61 GB](https://huggingface.co/datasets/bigcode/the-stack-pjjs-no-pii-filtered) | [61.97 GB](https://huggingface.co/datasets/bigcode/stack-dedup-alt-filter-no-pii) | [65 GB](https://huggingface.co/datasets/bigcode/the-stack-comments-filter) | (?) less than 60 |
+|Java | 266 GB |112 GB |110 GB | 88 GB | 92 GB |(?) less than 80 |
+|JavaScript | 496 GB | 166 GB |83 GB| 65 GB | 75 GB |(?) less than 60|
+|C | 255 GB | 75 GB |73 GB (2)| - | - |-|
+|C++ | 215 GB | 65 GB |61 GB (2)|- | - |-|
+
+* Non permissive data, the number are higher than the  e.g 240GB of python I mentionned (it was on old dump)
+|Language |Raw (only exact dedup)  | Near dedup |
+|-------|--------|-------|
+| Python (3)| 737 GB | - |
+|Java | 1.3 TB | - |
+|JavaScript | 5.8 TB | -|
+|C | 1.64 TB | - |
+|C++ | 644 GB | - |
+
+(1) all these runs have content filtering, notice that it removes a lot of data from JavaScript (you could try filtering with less strict thresholds, the [script](https://github.com/bigcode-project/bigcode-dataset/tree/main/preprocessing) is very easy to run)
+
+(2) I don't have the data but I found these numbers from an old run on the forst version of The Stack (it uses the same content filtering thresholds as for python, java and js)
+
+(3) this went through content filters

huggingface_dataset/Dataset_Card/maderix_flickr_bw_rgb.md

@@ -0,0 +1,43 @@
+---
+license: cc-by-nc-sa-4.0
+annotations_creators:
+- machine-generated
+language:
+- en
+language_creators:
+- other
+multilinguality:
+- monolingual
+pretty_name: 'flickr_bw_rgb'
+size_categories:
+- n<1K
+source_datasets:
+- N/A
+tags: []
+task_categories:
+- text-to-image
+task_ids: []
+---
+# Dataset Card for Flickr_bw_rgb
+_Dataset A image-caption dataset which stores group of black and white and color images with corresponding
+ captions mentioning the content of the image with a 'colorized photograph of' or 'Black and white photograph of' suffix.
+ This dataset can then be used for fine-tuning image to text models.. Only a train split is provided.
+## Examples
+  "train/<filename>.jpg" : containing the images in JPEG format
+  "train/metadata.jsonl" : Contains the metadata and the fields.
+  Dataset columns:
+    "file_name"
+    "caption"
+
+## Citation
+
+If you use this dataset, please cite it as:
+
+```
+@misc{maderix2022flickrbwrgb,
+      author = {maderix: maderix@gmail.com},
+      title = {flickr_bw_rgb},
+      year={2022},
+      howpublished= {\url{https://huggingface.co/datasets/maderix/flickr_bw_rgb/}}
+} 
+```

huggingface_dataset/Dataset_Card/reddit_tifu.md

@@ -0,0 +1,265 @@
+---
+annotations_creators:
+- crowdsourced
+language_creators:
+- crowdsourced
+language:
+- en
+license:
+- mit
+multilinguality:
+- monolingual
+pretty_name: Reddit TIFU
+size_categories:
+- 100K<n<1M
+source_datasets:
+- original
+task_categories:
+- summarization
+task_ids: []
+paperswithcode_id: reddit-tifu
+tags:
+- reddit-posts-summarization
+dataset_info:
+- config_name: short
+  features:
+  - name: ups
+    dtype: float32
+  - name: num_comments
+    dtype: float32
+  - name: upvote_ratio
+    dtype: float32
+  - name: score
+    dtype: float32
+  - name: documents
+    dtype: string
+  - name: tldr
+    dtype: string
+  - name: title
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 137715925
+    num_examples: 79740
+  download_size: 670607856
+  dataset_size: 137715925
+- config_name: long
+  features:
+  - name: ups
+    dtype: float32
+  - name: num_comments
+    dtype: float32
+  - name: upvote_ratio
+    dtype: float32
+  - name: score
+    dtype: float32
+  - name: documents
+    dtype: string
+  - name: tldr
+    dtype: string
+  - name: title
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 91984758
+    num_examples: 42139
+  download_size: 670607856
+  dataset_size: 91984758
+---
+
+# Dataset Card for "reddit_tifu"
+
+## Table of Contents
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
+  - [Languages](#languages)
+- [Dataset Structure](#dataset-structure)
+  - [Data Instances](#data-instances)
+  - [Data Fields](#data-fields)
+  - [Data Splits](#data-splits)
+- [Dataset Creation](#dataset-creation)
+  - [Curation Rationale](#curation-rationale)
+  - [Source Data](#source-data)
+  - [Annotations](#annotations)
+  - [Personal and Sensitive Information](#personal-and-sensitive-information)
+- [Considerations for Using the Data](#considerations-for-using-the-data)
+  - [Social Impact of Dataset](#social-impact-of-dataset)
+  - [Discussion of Biases](#discussion-of-biases)
+  - [Other Known Limitations](#other-known-limitations)
+- [Additional Information](#additional-information)
+  - [Dataset Curators](#dataset-curators)
+  - [Licensing Information](#licensing-information)
+  - [Citation Information](#citation-information)
+  - [Contributions](#contributions)
+
+## Dataset Description
+
+- **Homepage:** [https://github.com/ctr4si/MMN](https://github.com/ctr4si/MMN)
+- **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+- **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 downloaded dataset files:** 1279.08 MB
+- **Size of the generated dataset:** 219.12 MB
+- **Total amount of disk used:** 1498.20 MB
+
+### Dataset Summary
+
+Reddit dataset, where TIFU denotes the name of subbreddit /r/tifu.
+As defined in the publication, style "short" uses title as summary and
+"long" uses tldr as summary.
+
+Features includes:
+  - document: post text without tldr.
+  - tldr: tldr line.
+  - title: trimmed title without tldr.
+  - ups: upvotes.
+  - score: score.
+  - num_comments: number of comments.
+  - upvote_ratio: upvote ratio.
+
+### Supported Tasks and Leaderboards
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Languages
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+## Dataset Structure
+
+### Data Instances
+
+#### long
+
+- **Size of downloaded dataset files:** 639.54 MB
+- **Size of the generated dataset:** 87.74 MB
+- **Total amount of disk used:** 727.29 MB
+
+An example of 'train' looks as follows.
+```
+{'ups': 115.0,
+ 'num_comments': 23.0,
+ 'upvote_ratio': 0.88,
+ 'score': 115.0,
+ 'documents': 'this actually happened a couple of years ago. i grew up in germany where i went to a german secondary school that went from 5th to 13th grade (we still had 13 grades then, they have since changed that). my school was named after anne frank and we had a club that i was very active in from 9th grade on, which was dedicated to teaching incoming 5th graders about anne franks life, discrimination, anti-semitism, hitler, the third reich and that whole spiel. basically a day where the students\' classes are cancelled and instead we give them an interactive history and social studies class with lots of activities and games. \n\nthis was my last year at school and i already had a lot of experience doing these project days with the kids. i was running the thing with a friend, so it was just the two of us and 30-something 5th graders. we start off with a brief introduction and brainstorming: what do they know about anne frank and the third reich? you\'d be surprised how much they know. anyway after the brainstorming we do a few activities, and then we take a short break. after the break we split the class into two groups to make it easier to handle. one group watches a short movie about anne frank while the other gets a tour through our poster presentation that our student group has been perfecting over the years. then the groups switch. \n\ni\'m in the classroom to show my group the movie and i take attendance to make sure no one decided to run away during break. i\'m going down the list when i come to the name sandra (name changed). a kid with a boyish haircut and a somewhat deeper voice, wearing clothes from the boy\'s section at a big clothing chain in germany, pipes up. \n\nnow keep in mind, these are all 11 year olds, they are all pre-pubescent, their bodies are not yet showing any sex specific features one would be able to see while they are fully clothed (e.g. boobs, beards,...). this being a 5th grade in the rather conservative (for german standards) bavaria, i was confused. i looked down at the list again making sure i had read the name right. look back up at the kid. \n\nme: "you\'re sandra?"\n\nkid: "yep."\n\nme: "oh, sorry. *thinking the kid must be from somewhere where sandra is both a girl\'s and boy\'s name* where are you from? i\'ve only ever heard that as a girl\'s name before."\n\nthe class starts laughing. sandra gets really quiet. "i am a girl..." she says. some of the other students start saying that their parents made the same mistake when they met sandra. i feel so sorry and stupid. i get the class to calm down and finish taking attendance. we watch the movie in silence. after the movie, when we walked down to where the poster presentation took place i apologised to sandra. i felt so incredibly terrible, i still do to this day. throughout the rest of the day i heard lots of whispers about sandra. i tried to stop them whenever they came up, but there was no stopping the 5th grade gossip i had set in motion.\n\nsandra, if you\'re out there, i am so incredibly sorry for humiliating you in front of your class. i hope you are happy and healthy and continue to live your life the way you like. don\'t let anyone tell you you have to dress or act a certain way just because of the body parts you were born with. i\'m sorry if i made you feel like you were wrong for dressing and acting differently. i\'m sorry i probably made that day hell for you. i\'m sorry for my ignorance.',
+ 'tldr': 'confuse a 5th grade girl for a boy in front of half of her class. kids are mean. sorry sandra.**',
+ 'title': 'gender-stereotyping'}
+```
+
+#### short
+
+- **Size of downloaded dataset files:** 639.54 MB
+- **Size of the generated dataset:** 131.37 MB
+- **Total amount of disk used:** 770.92 MB
+
+An example of 'train' looks as follows.
+```
+{'ups': 50.0,
+ 'num_comments': 13.0,
+ 'upvote_ratio': 0.77,
+ 'score': 50.0,
+ 'documents': "i was on skype on my tablet as i went to the toilet iming a friend. i don't multitask very well, so i forgot one of the most important things to do before pooping. i think the best part was when i realised and told my mate who just freaked out because i was talking to him on the john!",
+ 'tldr': '',
+ 'title': 'forgetting to pull my underwear down before i pooped.'}
+```
+
+### Data Fields
+
+The data fields are the same among all splits.
+
+#### long
+- `ups`: a `float32` feature.
+- `num_comments`: a `float32` feature.
+- `upvote_ratio`: a `float32` feature.
+- `score`: a `float32` feature.
+- `documents`: a `string` feature.
+- `tldr`: a `string` feature.
+- `title`: a `string` feature.
+
+#### short
+- `ups`: a `float32` feature.
+- `num_comments`: a `float32` feature.
+- `upvote_ratio`: a `float32` feature.
+- `score`: a `float32` feature.
+- `documents`: a `string` feature.
+- `tldr`: a `string` feature.
+- `title`: a `string` feature.
+
+### Data Splits
+
+|name |train|
+|-----|----:|
+|long |42139|
+|short|79740|
+
+## 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
+
+MIT License.
+
+### Citation Information
+
+```
+@misc{kim2018abstractive,
+    title={Abstractive Summarization of Reddit Posts with Multi-level Memory Networks},
+    author={Byeongchang Kim and Hyunwoo Kim and Gunhee Kim},
+    year={2018},
+    eprint={1811.00783},
+    archivePrefix={arXiv},
+    primaryClass={cs.CL}
+}
+```
+
+### Contributions
+
+Thanks to [@patrickvonplaten](https://github.com/patrickvonplaten), [@thomwolf](https://github.com/thomwolf) for adding this dataset.

huggingface_dataset/Dataset_Card/rony_climate-change-MRC.md

@@ -0,0 +1,14 @@
+---
+license: mit
+---
+The Climate Change MRC dataset, also known as CCMRC, is a part of the work "Climate Bot: A Machine Reading Comprehension System for Climate Change Question Answering", accepted at IJCAI-ECAI 2022. The paper was accepted in the special system demo track "AI for Good".
+
+If you use the dataset, cite the following paper:
+```
+@inproceedings{rony2022climatemrc,
+  title={Climate Bot: A Machine Reading Comprehension System for Climate Change Question Answering.},
+  author={Rony, Md Rashad Al Hasan and Zuo, Ying and Kovriguina, Liubov and Teucher, Roman and Lehmann, Jens},
+  booktitle={IJCAI},
+  year={2022}
+}
+```

huggingface_dataset/Dataset_Card/samp3209_logo-dataset.md

@@ -0,0 +1,103 @@
+---
+# For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/datasetcard.md?plain=1
+# Doc / guide: https://huggingface.co/docs/hub/datasets-cards
+{}
+---
+
+# Dataset Card for Dataset Name
+
+## Dataset Description
+
+- **Homepage:** 
+- **Repository:** 
+- **Paper:** 
+- **Leaderboard:** 
+- **Point of Contact:** 
+
+### Dataset Summary
+
+This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1).
+
+### Supported Tasks and Leaderboards
+
+[More Information Needed]
+
+### Languages
+
+[More Information Needed]
+
+## Dataset Structure
+
+### Data Instances
+
+[More Information Needed]
+
+### Data Fields
+
+[More Information Needed]
+
+### Data Splits
+
+[More Information Needed]
+
+## Dataset Creation
+
+### Curation Rationale
+
+[More Information Needed]
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+[More Information Needed]
+
+#### Who are the source language producers?
+
+[More Information Needed]
+
+### Annotations
+
+#### Annotation process
+
+[More Information Needed]
+
+#### Who are the annotators?
+
+[More Information Needed]
+
+### Personal and Sensitive Information
+
+[More Information Needed]
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+[More Information Needed]
+
+### Discussion of Biases
+
+[More Information Needed]
+
+### Other Known Limitations
+
+[More Information Needed]
+
+## Additional Information
+
+### Dataset Curators
+
+[More Information Needed]
+
+### Licensing Information
+
+[More Information Needed]
+
+### Citation Information
+
+[More Information Needed]
+
+### Contributions
+
+[More Information Needed]

huggingface_dataset/Dataset_Card/torchgeo_eurosat.md

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+---
+license: mit
+task_categories:
+- image-classification
+language:
+- en
+pretty_name: EuroSAT
+size_categories:
+- 10K<n<100K
+---
+
+Redistributed without modification from https://github.com/phelber/EuroSAT.
+
+EuroSAT100 is a subset of EuroSATallBands containing only 100 images. It is intended for tutorials and demonstrations, not for benchmarking.