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huggingface_dataset/Dataset_Card/DDSC_angry-tweets.md

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+---
+annotations_creators:
+- crowdsourced
+language_creators:
+- found
+language:
+- da
+license:
+- cc-by-4.0
+multilinguality:
+- monolingual
+pretty_name: AngryTweets
+size_categories:
+- 1K<n<10K
+source_datasets:
+- original
+task_categories:
+- text-classification
+task_ids:
+- sentiment-classification
+---
+
+# Dataset Card for DKHate
+
+## 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)
+  - [Data Splits](#data-splits)
+- [Additional Information](#additional-information)
+  - [Dataset Curators](#dataset-curators)
+  - [Licensing Information](#licensing-information)
+  - [Citation Information](#citation-information)
+  - [Contributions](#contributions)
+
+## Dataset Description
+
+- **Paper:** https://aclanthology.org/2021.nodalida-main.53/
+- **Direct Download**: https://danlp-downloads.alexandra.dk/datasets/game_tweets.zip
+
+### Dataset Summary
+
+This dataset consists of anonymised Danish Twitter data that has been annotated for sentiment analysis through crowd-sourcing. All credits go to the authors of the following paper, who created the dataset: 
+
+[Pauli, Amalie Brogaard, et al. "DaNLP: An open-source toolkit for Danish Natural Language Processing." Proceedings of the 23rd Nordic Conference on Computational Linguistics (NoDaLiDa). 2021](https://aclanthology.org/2021.nodalida-main.53/)
+
+### Supported Tasks and Leaderboards
+
+This dataset is suitable for sentiment analysis.
+
+### Languages
+
+This dataset is in Danish.
+
+## Dataset Structure
+
+### Data Instances
+
+Every entry in the dataset has a tweet and an associated label.
+
+### Data Fields
+
+An entry in the dataset consists of the following fields:
+- `text` (`str`): The tweet content.
+- `label` (`str`): The label of the `text`. Can be "positiv", "neutral" or "negativ" for positive, neutral and negative sentiment, respectively.
+
+### Data Splits
+
+A `train` and `test` split is available, with the test split being 30% of the dataset, randomly sampled in a stratified fashion. There are 2,437 tweets in the training split and 1,047 in the test split.
+
+## Additional Information
+
+### Dataset Curators
+
+The collection and annotation of the dataset is solely due to the authors of [the original paper](https://aclanthology.org/2021.nodalida-main.53/): Amalie Brogaard Pauli, Maria Barrett, Ophélie Lacroix and Rasmus Hvingelby. The tweets have been anonymised by [@saattrupdan](https://github.com/saattrupdan).
+
+### Licensing Information
+
+The dataset is released under the CC BY 4.0 license.
+
+### Citation Information
+
+```
+@inproceedings{pauli2021danlp,
+  title={DaNLP: An open-source toolkit for Danish Natural Language Processing},
+  author={Pauli, Amalie Brogaard and Barrett, Maria and Lacroix, Oph{\'e}lie and Hvingelby, Rasmus},
+  booktitle={Proceedings of the 23rd Nordic Conference on Computational Linguistics (NoDaLiDa)},
+  pages={460--466},
+  year={2021}
+}
+```
+
+### Contributions
+
+Thanks to [@saattrupdan](https://github.com/saattrupdan) for adding this dataset to the Hugging Face Hub.

huggingface_dataset/Dataset_Card/DFKI-SLT_fabner.md

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+---
+annotations_creators:
+- expert-generated
+language:
+- en
+language_creators:
+- found
+license:
+- other
+multilinguality:
+- monolingual
+pretty_name: FabNER is a manufacturing text dataset for Named Entity Recognition.
+size_categories:
+- 10K<n<100K
+source_datasets: []
+tags:
+- manufacturing
+- 2000-2020
+task_categories:
+- token-classification
+task_ids:
+- named-entity-recognition
+dataset_info:
+- config_name: fabner
+  features:
+  - name: id
+    dtype: string
+  - name: tokens
+    sequence: string
+  - name: ner_tags
+    sequence:
+      class_label:
+        names:
+          '0': O
+          '1': B-MATE
+          '2': I-MATE
+          '3': O-MATE
+          '4': E-MATE
+          '5': S-MATE
+          '6': B-MANP
+          '7': I-MANP
+          '8': O-MANP
+          '9': E-MANP
+          '10': S-MANP
+          '11': B-MACEQ
+          '12': I-MACEQ
+          '13': O-MACEQ
+          '14': E-MACEQ
+          '15': S-MACEQ
+          '16': B-APPL
+          '17': I-APPL
+          '18': O-APPL
+          '19': E-APPL
+          '20': S-APPL
+          '21': B-FEAT
+          '22': I-FEAT
+          '23': O-FEAT
+          '24': E-FEAT
+          '25': S-FEAT
+          '26': B-PRO
+          '27': I-PRO
+          '28': O-PRO
+          '29': E-PRO
+          '30': S-PRO
+          '31': B-CHAR
+          '32': I-CHAR
+          '33': O-CHAR
+          '34': E-CHAR
+          '35': S-CHAR
+          '36': B-PARA
+          '37': I-PARA
+          '38': O-PARA
+          '39': E-PARA
+          '40': S-PARA
+          '41': B-ENAT
+          '42': I-ENAT
+          '43': O-ENAT
+          '44': E-ENAT
+          '45': S-ENAT
+          '46': B-CONPRI
+          '47': I-CONPRI
+          '48': O-CONPRI
+          '49': E-CONPRI
+          '50': S-CONPRI
+          '51': B-MANS
+          '52': I-MANS
+          '53': O-MANS
+          '54': E-MANS
+          '55': S-MANS
+          '56': B-BIOP
+          '57': I-BIOP
+          '58': O-BIOP
+          '59': E-BIOP
+          '60': S-BIOP
+  splits:
+  - name: train
+    num_bytes: 4394010
+    num_examples: 9435
+  - name: validation
+    num_bytes: 934347
+    num_examples: 2183
+  - name: test
+    num_bytes: 940136
+    num_examples: 2064
+  download_size: 3793613
+  dataset_size: 6268493
+- config_name: fabner_bio
+  features:
+  - name: id
+    dtype: string
+  - name: tokens
+    sequence: string
+  - name: ner_tags
+    sequence:
+      class_label:
+        names:
+          '0': O
+          '1': B-MATE
+          '2': I-MATE
+          '3': B-MANP
+          '4': I-MANP
+          '5': B-MACEQ
+          '6': I-MACEQ
+          '7': B-APPL
+          '8': I-APPL
+          '9': B-FEAT
+          '10': I-FEAT
+          '11': B-PRO
+          '12': I-PRO
+          '13': B-CHAR
+          '14': I-CHAR
+          '15': B-PARA
+          '16': I-PARA
+          '17': B-ENAT
+          '18': I-ENAT
+          '19': B-CONPRI
+          '20': I-CONPRI
+          '21': B-MANS
+          '22': I-MANS
+          '23': B-BIOP
+          '24': I-BIOP
+  splits:
+  - name: train
+    num_bytes: 4394010
+    num_examples: 9435
+  - name: validation
+    num_bytes: 934347
+    num_examples: 2183
+  - name: test
+    num_bytes: 940136
+    num_examples: 2064
+  download_size: 3793613
+  dataset_size: 6268493
+- config_name: fabner_simple
+  features:
+  - name: id
+    dtype: string
+  - name: tokens
+    sequence: string
+  - name: ner_tags
+    sequence:
+      class_label:
+        names:
+          '0': O
+          '1': MATE
+          '2': MANP
+          '3': MACEQ
+          '4': APPL
+          '5': FEAT
+          '6': PRO
+          '7': CHAR
+          '8': PARA
+          '9': ENAT
+          '10': CONPRI
+          '11': MANS
+          '12': BIOP
+  splits:
+  - name: train
+    num_bytes: 4394010
+    num_examples: 9435
+  - name: validation
+    num_bytes: 934347
+    num_examples: 2183
+  - name: test
+    num_bytes: 940136
+    num_examples: 2064
+  download_size: 3793613
+  dataset_size: 6268493
+- config_name: text2tech
+  features:
+  - name: id
+    dtype: string
+  - name: tokens
+    sequence: string
+  - name: ner_tags
+    sequence:
+      class_label:
+        names:
+          '0': O
+          '1': Technological System
+          '2': Method
+          '3': Material
+          '4': Technical Field
+  splits:
+  - name: train
+    num_bytes: 4394010
+    num_examples: 9435
+  - name: validation
+    num_bytes: 934347
+    num_examples: 2183
+  - name: test
+    num_bytes: 940136
+    num_examples: 2064
+  download_size: 3793613
+  dataset_size: 6268493
+---
+
+# Dataset Card for [Dataset Name]
+
+## Table of Contents
+- [Table of Contents](#table-of-contents)
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
+  - [Languages](#languages)
+- [Dataset Structure](#dataset-structure)
+  - [Data Instances](#data-instances)
+  - [Data Fields](#data-fields)
+  - [Data Splits](#data-splits)
+- [Dataset Creation](#dataset-creation)
+  - [Curation Rationale](#curation-rationale)
+  - [Source Data](#source-data)
+  - [Annotations](#annotations)
+  - [Personal and Sensitive Information](#personal-and-sensitive-information)
+- [Considerations for Using the Data](#considerations-for-using-the-data)
+  - [Social Impact of Dataset](#social-impact-of-dataset)
+  - [Discussion of Biases](#discussion-of-biases)
+  - [Other Known Limitations](#other-known-limitations)
+- [Additional Information](#additional-information)
+  - [Dataset Curators](#dataset-curators)
+  - [Licensing Information](#licensing-information)
+  - [Citation Information](#citation-information)
+  - [Contributions](#contributions)
+
+## Dataset Description
+
+- **Homepage:** [https://figshare.com/articles/dataset/Dataset_NER_Manufacturing_-_FabNER_Information_Extraction_from_Manufacturing_Process_Science_Domain_Literature_Using_Named_Entity_Recognition/14782407](https://figshare.com/articles/dataset/Dataset_NER_Manufacturing_-_FabNER_Information_Extraction_from_Manufacturing_Process_Science_Domain_Literature_Using_Named_Entity_Recognition/14782407)
+- **Paper:** ["FabNER": information extraction from manufacturing process science domain literature using named entity recognition](https://par.nsf.gov/servlets/purl/10290810)
+- **Size of downloaded dataset files:** 3.79 MB
+- **Size of the generated dataset:** 6.27 MB
+
+### Dataset Summary
+
+FabNER is a manufacturing text corpus of 350,000+ words for Named Entity Recognition.
+It is a collection of abstracts obtained from Web of Science through known journals available in manufacturing process 
+science research.
+For every word, there were categories/entity labels defined namely Material (MATE), Manufacturing Process (MANP), 
+Machine/Equipment (MACEQ), Application (APPL), Features (FEAT), Mechanical Properties (PRO), Characterization (CHAR), 
+Parameters (PARA), Enabling Technology (ENAT), Concept/Principles (CONPRI), Manufacturing Standards (MANS) and 
+BioMedical (BIOP). Annotation was performed in all categories along with the output tag in 'BIOES' format: 
+B=Beginning, I-Intermediate, O=Outside, E=End, S=Single.
+
+For details about the dataset, please refer to the paper: ["FabNER": information extraction from manufacturing process science domain literature using named entity recognition](https://par.nsf.gov/servlets/purl/10290810)
+
+### Supported Tasks and Leaderboards
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Languages
+
+The language in the dataset is English.
+
+## Dataset Structure
+
+### Data Instances
+
+- **Size of downloaded dataset files:** 3.79 MB
+- **Size of the generated dataset:** 6.27 MB
+  
+An example of 'train' looks as follows:
+```json
+{
+  "id": "0", 
+  "tokens": ["Revealed", "the", "location-specific", "flow", "patterns", "and", "quantified", "the", "speeds", "of", "various", "types", "of", "flow", "."], 
+  "ner_tags": [0, 0, 0, 46, 49, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
+}
+```
+
+### Data Fields
+
+#### fabner
+- `id`: the instance id of this sentence, a `string` feature.
+- `tokens`: the list of tokens of this sentence, a `list` of `string` features.
+- `ner_tags`: the list of entity tags, a `list` of classification labels.
+
+```json
+{"O": 0, "B-MATE": 1, "I-MATE": 2, "O-MATE": 3, "E-MATE": 4, "S-MATE": 5, "B-MANP": 6, "I-MANP": 7, "O-MANP": 8, "E-MANP": 9, "S-MANP": 10, "B-MACEQ": 11, "I-MACEQ": 12, "O-MACEQ": 13, "E-MACEQ": 14, "S-MACEQ": 15, "B-APPL": 16, "I-APPL": 17, "O-APPL": 18, "E-APPL": 19, "S-APPL": 20, "B-FEAT": 21, "I-FEAT": 22, "O-FEAT": 23, "E-FEAT": 24, "S-FEAT": 25, "B-PRO": 26, "I-PRO": 27, "O-PRO": 28, "E-PRO": 29, "S-PRO": 30, "B-CHAR": 31, "I-CHAR": 32, "O-CHAR": 33, "E-CHAR": 34, "S-CHAR": 35, "B-PARA": 36, "I-PARA": 37, "O-PARA": 38, "E-PARA": 39, "S-PARA": 40, "B-ENAT": 41, "I-ENAT": 42, "O-ENAT": 43, "E-ENAT": 44, "S-ENAT": 45, "B-CONPRI": 46, "I-CONPRI": 47, "O-CONPRI": 48, "E-CONPRI": 49, "S-CONPRI": 50, "B-MANS": 51, "I-MANS": 52, "O-MANS": 53, "E-MANS": 54, "S-MANS": 55, "B-BIOP": 56, "I-BIOP": 57, "O-BIOP": 58, "E-BIOP": 59, "S-BIOP": 60}
+```
+
+#### fabner_bio
+- `id`: the instance id of this sentence, a `string` feature.
+- `tokens`: the list of tokens of this sentence, a `list` of `string` features.
+- `ner_tags`: the list of entity tags, a `list` of classification labels.
+
+```json
+{"O": 0, "B-MATE": 1, "I-MATE": 2, "B-MANP": 3, "I-MANP": 4, "B-MACEQ": 5, "I-MACEQ": 6, "B-APPL": 7, "I-APPL": 8, "B-FEAT": 9, "I-FEAT": 10, "B-PRO": 11, "I-PRO": 12, "B-CHAR": 13, "I-CHAR": 14, "B-PARA": 15, "I-PARA": 16, "B-ENAT": 17, "I-ENAT": 18, "B-CONPRI": 19, "I-CONPRI": 20, "B-MANS": 21, "I-MANS": 22, "B-BIOP": 23, "I-BIOP": 24}
+```
+
+#### fabner_simple
+- `id`: the instance id of this sentence, a `string` feature.
+- `tokens`: the list of tokens of this sentence, a `list` of `string` features.
+- `ner_tags`: the list of entity tags, a `list` of classification labels.
+
+```json
+{"O": 0, "MATE": 1, "MANP": 2, "MACEQ": 3, "APPL": 4, "FEAT": 5, "PRO": 6, "CHAR": 7, "PARA": 8, "ENAT": 9, "CONPRI": 10, "MANS": 11, "BIOP": 12}
+```
+
+#### text2tech
+- `id`: the instance id of this sentence, a `string` feature.
+- `tokens`: the list of tokens of this sentence, a `list` of `string` features.
+- `ner_tags`: the list of entity tags, a `list` of classification labels.
+
+```json
+{"O": 0, "Technological System": 1, "Method": 2, "Material": 3, "Technical Field": 4}
+```
+
+### Data Splits
+
+|        | Train | Dev  | Test |
+|--------|-------|------|------|
+| fabner | 9435  | 2183 | 2064 |
+
+## Dataset Creation
+
+### Curation Rationale
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+#### Who are the source language producers?
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Annotations
+
+#### Annotation process
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+#### Who are the annotators?
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Personal and Sensitive Information
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Discussion of Biases
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Other Known Limitations
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+## Additional Information
+
+### Dataset Curators
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Licensing Information
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Citation Information
+
+```
+@article{DBLP:journals/jim/KumarS22,
+  author    = {Aman Kumar and
+               Binil Starly},
+  title     = {"FabNER": information extraction from manufacturing process science
+               domain literature using named entity recognition},
+  journal   = {J. Intell. Manuf.},
+  volume    = {33},
+  number    = {8},
+  pages     = {2393--2407},
+  year      = {2022},
+  url       = {https://doi.org/10.1007/s10845-021-01807-x},
+  doi       = {10.1007/s10845-021-01807-x},
+  timestamp = {Sun, 13 Nov 2022 17:52:57 +0100},
+  biburl    = {https://dblp.org/rec/journals/jim/KumarS22.bib},
+  bibsource = {dblp computer science bibliography, https://dblp.org}
+}
+```
+
+### Contributions
+
+Thanks to [@phucdev](https://github.com/phucdev) for adding this dataset.

huggingface_dataset/Dataset_Card/Fraser_python-state-changes.md

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+---
+language:
+- code
+---
+
+# Python State Changes
+
+State changes from the execution of single lines of Python code.
+All code was taken from Python HackerRank solutions.
+
+Scraped from my dataset of traced HackerRank solutions. https://www.kaggle.com/frasergreenlee/ran-hackerrank-solutions
+
+```json
+{"start": "g = 100; i = 1; l = [100, 100, 0, 0, -100, -100]", "code": "g += l[i]", "end": "g = 200; i = 1; l = [100, 100, 0, 0, -100, -100]"}
+{"start": "a = 1; b = 2; d = 4; i = 3; j = 2", "code": "i, j = a + (j - b), b + (d - (i - a))", "end": "a = 1; b = 2; d = 4; i = 1; j = 4"}
+{"start": "b = 15", "code": "b = b // 2", "end": "b = 7"}
+```
+
+## Get an overview of the dataset from seeing the frequency of different ASTs.
+👉 https://observablehq.com/@frasergreenlee/python-lines-dataset#chart

huggingface_dataset/Dataset_Card/HuggingFace-CN-community_Diffusion-book-cn.md

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+# 《从零开始学扩散模型》
+
+## 术语表
+
+| 词汇                    | 翻译 | 
+| :---------------------- | :--- | 
+| Corruption Process      |  退化过程  |
+| Pipeline      |  管线  |
+| Timestep      |  时间步  |
+| Scheduler      |  调度器  |
+| Gradient Accumulation      |  梯度累加  |
+| Fine-Tuning      |  微调  |
+| Guidance      |  引导  |
+# 目录
+
+## 第一部分 基础知识
+
+### 第一章 扩散模型的原理、发展和应用
+
+#### 1.1 扩散模型的原理
+#### 1.2 扩散模型的发展
+#### 1.3 扩散模型的应用
+
+### 第二章 HuggingFace介绍与环境准备
+
+#### 2.1 HuggingFace Space
+#### 2.2 Transformer 与 diffusers 库
+#### 2.3 环境准备
+
+## 第二部分 扩散模型实战
+
+### 第三章 从零开始做扩散模型
+
+#### 3.1 章节概述
+#### 3.2 环境准备
+#### 3.2.1 环境的创建与导入
+#### 3.2.2 数据集测试
+#### 3.3 扩散模型-退化过程
+#### 3.4 扩散模型训练
+#### 3.4.1 Unet模型
+#### 3.4.2 开始训练模型
+#### 3.5 扩散模型-采样（取样）过程
+#### 3.5.1 采样（取样）过程
+#### 3.5.2 与DDPM的区别
+#### 3.5.3 UNet2DModel模型
+#### 3.6 扩散模型-退化过程示例
+#### 3.6.1 退化过程
+#### 3.6.2 最终的训练目标
+#### 3.7 拓展知识
+#### 3.7.1 迭代周期（Timestep）的调节
+#### 3.7.2 采样（取样）的关键问题
+#### 3.8 本章小结
+
+### 第四章 Diffusers实战
+#### 4.1 章节概述
+#### 4.2 环境准备
+#### 4.2.1 安装Diffusers库
+#### 4.2.2 Dreambooth-全新的扩散模型
+#### 4.2.3 Diffusers核心API
+#### 4.3 实战：生成美丽的蝴蝶图片
+#### 4.3.1 下载蝴蝶图像集
+#### 4.3.2 扩散模型-调度器
+#### 4.3.3 定义扩散模型
+#### 4.3.4 创建扩散模型训练循环
+#### 4.3.5 图像的生成
+#### 4.4 拓展知识
+#### 4.4.1 将模型上传到Hub上
+#### 4.4.2 扩大训练模型的规模
+#### 4.5 本章小结
+
+### 第五章 微调和引导
+#### 5.1 章节概述
+#### 5.2 环境准备
+#### 5.3 载入一个预训练过的管线
+#### 5.4 DDIM-更快的采样过程
+#### 5.5 扩散模型-微调
+#### 5.5.1 实战：微调
+#### 5.5.2 使用最小化样例脚本微调模型
+#### 5.5.3 保存和载入微调过的管线
+#### 5.6 扩散模型-引导
+#### 5.6.1 实战：引导
+#### 5.6.2 CLIP 引导
+#### 5.7 分享你的自定义采样训练
+#### 5.7.1 环境准备
+#### 5.7.2 创建一个以类别为条件的UNet
+#### 5.7.3 训练与采样
+#### 5.8 本章小结
+#### 5.9 实战：创建一个类别条件扩散模型
+
+### 第六章 Stable Diffusion
+#### 6.1 章节概述
+#### 6.2 环境准备
+#### 6.3 从文本生成图像
+#### 6.4 Stable Diffusion Pipeline
+#### 6.4.1 可变分自编码器(VAE)
+#### 6.4.2 分词器(Tokenizer)和文本编码器(Text Encoder)
+#### 6.4.3 UNet
+#### 6.4.4 调度器(Scheduler)
+#### 6.4.5 DIY一个采样循环
+#### 6.5 其他管线介绍
+#### 6.5.1 Img2Img
+#### 6.5.2 In-Painting
+#### 6.5.3 Depth2Image
+#### 6.5.4 拓展：管理你的模型缓存
+#### 6.6 本章小结
+
+### 第七章 DDIM反转 
+#### 7.1 本章概述
+#### 7.2 实战：反转
+#### 7.2.1 设置
+#### 7.2.2 加载一个已训练的管道 
+#### 7.2.3 DDIM采样 
+#### 7.2.4 反转
+#### 7.3 组合封装
+#### 7.4 本章小结
+
+### 第八章 音频扩散模型
+#### 8.1 本章概述
+#### 8.2 实战：音频扩散模型
+#### 8.2.1 设置与导入
+#### 8.2.2 从预先训练的音频管道采样 
+#### 8.2.3 从音频到频谱的转换 
+#### 8.2.4 微调管道
+#### 8.2.5 循环训练
+#### 8.3 将模型上传到Hub上 
+#### 8.4 本章小结
+
+### 第九章 ControlNet和LoRa
+#### 9.1 ControlNet
+#### 9.2 LoRa
+
+## 附录 精美图像集展示

huggingface_dataset/Dataset_Card/NYTK_HuCoPA.md

@@ -0,0 +1,196 @@
+---
+YAML tags:
+annotations_creators:
+- found
+language_creators:
+- found
+- expert-generated
+language:
+- hu
+license:
+- bsd-2-clause
+multilinguality:
+- monolingual
+pretty_name: HuCoPA
+size_categories:
+- unknown
+source_datasets:
+- extended|other
+task_categories:
+- other
+task_ids:
+- commonsense-reasoning
+---
+
+# Dataset Card for HuCoPA
+
+## Table of Contents
+- [Table of Contents](#table-of-contents)
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
+  - [Languages](#languages)
+- [Dataset Structure](#dataset-structure)
+  - [Data Instances](#data-instances)
+  - [Data Fields](#data-fields)
+  - [Data Splits](#data-splits)
+- [Dataset Creation](#dataset-creation)
+  - [Curation Rationale](#curation-rationale)
+  - [Source Data](#source-data)
+  - [Annotations](#annotations)
+  - [Personal and Sensitive Information](#personal-and-sensitive-information)
+- [Considerations for Using the Data](#considerations-for-using-the-data)
+  - [Social Impact of Dataset](#social-impact-of-dataset)
+  - [Discussion of Biases](#discussion-of-biases)
+  - [Other Known Limitations](#other-known-limitations)
+- [Additional Information](#additional-information)
+  - [Dataset Curators](#dataset-curators)
+  - [Licensing Information](#licensing-information)
+  - [Citation Information](#citation-information)
+  - [Contributions](#contributions)
+
+## Dataset Description
+
+- **Homepage:**
+- **Repository:**
+[HuCoPA dataset](https://github.com/nytud/HuCoPA)
+- **Paper:**
+- **Leaderboard:**
+- **Point of Contact:**
+[lnnoemi](mailto:ligeti-nagy.noemi@nytud.hu)
+
+### Dataset Summary
+
+This is the dataset card for the Hungarian Choice of Plausible Alternatives Corpus (HuCoPA), which is also part of the Hungarian Language Understanding Evaluation Benchmark Kit [HuLU](hulu.nlp.nytud.hu). The corpus was created by translating and re-annotating the original English CoPA corpus (Roemmele et al., 2011).
+
+### Supported Tasks and Leaderboards
+
+'commonsense reasoning'
+'question answering'
+
+### Languages
+
+The BCP-47 code for Hungarian, the only represented language in this dataset, is hu-HU. 
+
+## Dataset Structure
+
+### Data Instances
+
+For each instance, there is an id, a premise, a question ('cause' or 'effect'), two alternatives and a label (1 or 2). 
+
+An example:
+
+```
+{"idx": "1",
+ "question": "cause",
+ "label": "1",
+ "premise": "A testem árnyékot vetett a fűre.",
+ "choice1": "Felkelt a nap.",
+ "choice2": "A füvet lenyírták."}
+```
+
+### Data Fields
+- id: unique id of the instances, an integer between 1 and 1000;
+- question: "cause" or "effect". It suggests what kind of causal relation are we looking for: in the case of "cause" we search for the more plausible alternative that may be a cause of the premise. In the case of "effect" we are looking for a plausible result of the premise;
+- premise: the premise, a sentence;
+- choice1: the first alternative, a sentence;
+- choice2: the second alternative, a sentence;
+- label: the number of the more plausible alternative (1 or 2).
+
+### Data Splits
+
+HuCoPA has 3 splits: *train*, *validation* and *test*. 
+
+| Dataset split | Number of instances in the split |
+|---------------|----------------------------------|
+| train         | 400                              |
+| validation    | 100                              |
+| test          | 500                              |
+
+The test data is distributed without the labels. To evaluate your model, please [contact us](mailto:ligeti-nagy.noemi@nytud.hu), or check [HuLU's website](hulu.nlp.nytud.hu) for an automatic evaluation (this feature is under construction at the moment). 
+
+## Dataset Creation
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+The data is a translation of the content of the CoPA corpus. Each sentence was translated by a human translator. Each translation was manually checked and further refined by another annotator. 
+
+### Annotations
+
+#### Annotation process
+
+The instances initially inherited their original labels from the CoPA dataset. Each instance was annotated by a human annotator. If the original label and the human annotator's label did not match, we manually curated the instance and assigned a final label to that. This step was necessary to ensure that the causal realationship had not been changed or lost during the translation process. 
+
+#### Who are the annotators?
+The translators were native Hungarian speakers with English proficiency. The annotators were university students with some linguistic background.
+
+## Additional Information
+
+The human performance on the test set is 96% (accuracy).
+
+### Licensing Information
+
+HuCoPA is released under the BSD 2-Clause License.
+
+Copyright (c) 2010, University of Southern California
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are met:
+
+* Redistributions of source code must retain the above copyright notice, this
+list of conditions and the following disclaimer.
+
+* Redistributions in binary form must reproduce the above copyright notice,
+this list of conditions and the following disclaimer in the documentation
+and/or other materials provided with the distribution.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+
+### Citation Information
+
+
+If you use this resource or any part of its documentation, please refer to:
+
+Ligeti-Nagy, N., Ferenczi, G., Héja, E., Jelencsik-Mátyus, K., Laki, L. J., Vadász, N., Yang, Z. Gy. and Váradi, T. (2022) HuLU: magyar nyelvű benchmark adatbázis
+kiépítése a neurális nyelvmodellek kiértékelése céljából [HuLU: Hungarian benchmark dataset to evaluate neural language models]. In: Berend, Gábor and Gosztolya, Gábor and Vincze, Veronika (eds), XVIII. Magyar Számítógépes Nyelvészeti Konferencia. JATEPress, Szeged. 431–446.
+```
+@inproceedings{ligetinagy2022hulu,
+  title={HuLU: magyar nyelvű benchmark adatbázis kiépítése a neurális nyelvmodellek kiértékelése céljából},
+  author={Ligeti-Nagy, N. and Ferenczi, G. and Héja, E. and Jelencsik-Mátyus, K. and Laki, L. J. and Vadász, N. and Yang, Z. Gy. and Váradi, T.},
+  booktitle={XVIII. Magyar Számítógépes Nyelvészeti Konferencia},
+  year={2022},
+  editors = {Berend, Gábor and Gosztolya, Gábor and Vincze, Veronika},
+  address = {Szeged},
+  publisher = {JATEPress},
+  pages = {431–446}
+}
+```
+and to:
+
+Roemmele, M., Bejan, C., and Gordon, A. (2011) Choice of Plausible Alternatives: An Evaluation of Commonsense Causal Reasoning. AAAI Spring Symposium on Logical Formalizations of Commonsense Reasoning, Stanford University, March 21-23, 2011.
+```
+@inproceedings{roemmele2011choice,
+  title={Choice of plausible alternatives: An evaluation of commonsense causal reasoning},
+  author={Roemmele, Melissa and Bejan, Cosmin Adrian and Gordon, Andrew S},
+  booktitle={2011 AAAI Spring Symposium Series},
+  year={2011},
+  url={https://people.ict.usc.edu/~gordon/publications/AAAI-SPRING11A.PDF},
+}
+```
+
+### Contributions
+
+Thanks to [lnnoemi](https://github.com/lnnoemi) for adding this dataset.

huggingface_dataset/Dataset_Card/PlanTL-GOB-ES_WikiCAT_esv2.md

@@ -0,0 +1,145 @@
+---
+YAML tags:
+annotations_creators:
+- automatically-generated
+language_creators:
+- found
+language:
+- es
+license:
+- cc-by-sa-3.0
+multilinguality:
+- monolingual
+pretty_name: wikicat_esv2
+size_categories:
+- unknown
+source_datasets: []
+task_categories:
+- text-classification
+task_ids:
+- multi-class-classification
+---
+
+# WikiCAT_ca: Spanish Text Classification dataset
+
+
+## Dataset Description
+
+- **Paper:** 
+
+- **Point of Contact:** carlos.rodriguez1@bsc.es
+
+
+**Repository**
+
+
+
+
+### Dataset Summary
+
+WikiCAT_ca is a Spanish corpus for thematic Text Classification tasks. It is created automatically from Wikipedia and Wikidata sources, and contains 8401 articles from the Viquipedia classified under 12 different categories.
+
+This dataset was developed by BSC TeMU as part of the PlanTL project, and intended as an evaluation of LT capabilities to generate useful synthetic corpus.
+
+### Supported Tasks and Leaderboards
+
+Text classification, Language Model
+
+### Languages
+
+ES- Spanish
+
+## Dataset Structure
+
+### Data Instances
+
+Two json files, one for each split.
+
+### Data Fields
+
+We used a simple model with the article text and associated labels, without further metadata.
+
+#### Example:
+
+<pre>
+{'sentence': 'La economía de Reunión se ha basado tradicionalmente en la agricultura. La caña de azúcar ha sido el cultivo principal durante más de un siglo, y en algunos años representa el 85% de las exportaciones. El gobierno ha estado impulsando el desarrollo de una industria turística para aliviar el alto desempleo, que representa más del 40% de la fuerza laboral.(...) El PIB total de la isla fue de 18.800 millones de dólares EE.UU. en 2007., 'label': 'Economía'}
+
+
+</pre>
+
+#### Labels
+
+'Religión', 'Entretenimiento', 'Música', 'Ciencia_y_Tecnología', 'Política', 'Economía', 'Matemáticas', 'Humanidades', 'Deporte', 'Derecho', 'Historia', 'Filosofía'
+
+### Data Splits
+
+* hfeval_esv5.json: 1681 label-document pairs
+* hftrain_esv5.json: 6716  label-document pairs
+
+
+## Dataset Creation
+
+### Methodology
+
+La páginas de "Categoría" representan los temas.
+para cada tema, extraemos las páginas asociadas a ese primer nivel de la jerarquía, y utilizamos el resúmen ("summary") como texto representativo.
+
+### Curation Rationale
+
+
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+The source data are thematic categories in the different Wikipedias
+
+#### Who are the source language producers?
+
+
+### Annotations
+
+#### Annotation process
+Automatic annotation
+
+#### Who are the annotators?
+
+[N/A]
+
+### Personal and Sensitive Information
+
+No personal or sensitive information included.
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+We hope this corpus contributes to the development of language models in Spanish.
+
+### Discussion of Biases
+
+We are aware that this data might contain biases. We have not applied any steps to reduce their impact.
+
+### Other Known Limitations
+
+[N/A]
+
+## Additional Information
+
+### Dataset Curators
+
+Text Mining Unit (TeMU) at the Barcelona Supercomputing Center (bsc-temu@bsc.es). 
+
+For further information, send an email to (plantl-gob-es@bsc.es).
+
+This work was funded by the [Spanish State Secretariat for Digitalization and Artificial Intelligence (SEDIA)](https://avancedigital.mineco.gob.es/en-us/Paginas/index.aspx) within the framework of the [Plan-TL](https://plantl.mineco.gob.es/Paginas/index.aspx).
+
+### Licensing Information
+
+This work is licensed under [CC Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0/) License.
+
+Copyright by the Spanish State Secretariat for Digitalization and Artificial Intelligence (SEDIA) (2022)
+
+### Contributions
+
+[N/A]

huggingface_dataset/Dataset_Card/Whispering-GPT_whisper-transcripts-linustechtips.md

@@ -0,0 +1,110 @@
+---
+task_categories:
+- automatic-speech-recognition
+dataset_info:
+  features:
+  - name: id
+    dtype: string
+  - name: channel
+    dtype: string
+  - name: channel_id
+    dtype: string
+  - name: title
+    dtype: string
+  - name: categories
+    sequence: string
+  - name: tags
+    sequence: string
+  - name: description
+    dtype: string
+  - name: text
+    dtype: string
+  - name: segments
+    list:
+    - name: start
+      dtype: float64
+    - name: end
+      dtype: float64
+    - name: text
+      dtype: string
+  splits:
+  - name: train
+    num_bytes: 177776633.92326075
+    num_examples: 5655
+  download_size: 100975518
+  dataset_size: 177776633.92326075
+tags:
+- whisper
+- whispering
+- medium
+---
+# Dataset Card for "whisper-transcripts-linustechtips"
+
+## 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)
+  - [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)
+  - [Contributions](#contributions)
+
+## Dataset Description
+
+- **Homepage:** [Whispering-GPT](https://github.com/matallanas/whisper_gpt_pipeline)
+- **Repository:** [whisper_gpt_pipeline](https://github.com/matallanas/whisper_gpt_pipeline)
+- **Paper:** [whisper](https://cdn.openai.com/papers/whisper.pdf) and [gpt](https://s3-us-west-2.amazonaws.com/openai-assets/research-covers/language-unsupervised/language_understanding_paper.pdf)
+- **Point of Contact:** [Whispering-GPT organization](https://huggingface.co/Whispering-GPT)
+
+### Dataset Summary
+
+This dataset is created by applying whisper to the videos of the Youtube channel [Linus Tech Tips](https://www.youtube.com/channel/UCXuqSBlHAE6Xw-yeJA0Tunw). The dataset was created a medium size whisper model.
+
+### Languages
+
+- **Language**: English
+
+## Dataset Structure
+
+The dataset 
+
+### Data Fields
+
+The dataset is composed by:
+- **id**: Id of the youtube video.
+- **channel**: Name of the channel.
+- **channel\_id**: Id of the youtube channel.
+- **title**: Title given to the video.
+- **categories**: Category of the video.
+- **description**: Description added by the author.
+- **text**: Whole transcript of the video.
+- **segments**: A list with the time and transcription of the video.
+  - **start**: When started the trancription.
+  - **end**: When the transcription ends.
+  - **text**: The text of the transcription.
+
+### Data Splits
+
+- Train split.
+
+## Dataset Creation
+
+### Source Data
+
+The transcriptions are from the videos of [Linus Tech Tips Channel](https://www.youtube.com/channel/UCXuqSBlHAE6Xw-yeJA0Tunw)
+
+### Contributions
+
+Thanks to [Whispering-GPT](https://huggingface.co/Whispering-GPT) organization for adding this dataset.

huggingface_dataset/Dataset_Card/ajders_machine_translated_cnn_dailymail_da_small.md

@@ -0,0 +1,40 @@
+---
+annotations_creators:
+- machine-generated
+language_creators:
+- machine-generated
+language:
+- da
+license:
+- apache-2.0
+multilinguality:
+- translation
+pretty_name: machine_translated_cnn_dailymail_da_small
+size_categories:
+- 1K<n<10K
+source_datasets: []
+task_categories:
+- summarization
+task_ids:
+- news-articles-summarization
+---
+
+# Dataset Card for machine_translated_cnn_dailymail_da_small
+
+### Dataset Summary
+
+This dataset is a machine translated subset of the [CNN Dailymail Dataset](https://huggingface.co/datasets/ccdv/cnn_dailymail) into Danish. The dataset is translated using the [Helsinki-NLP/opus-mt-en-da](https://huggingface.co/Helsinki-NLP/opus-mt-en-da)-model. The dataset consists of 2872 articles with summaries with intended usage for Danish text summarisation.
+
+## Dataset Structure
+
+Machine translated articles (`article`) with corresponding summaries (`highlights`).
+```
+{
+  'article': Value(dtype='string', id=None),
+  'highlights': Value(dtype='string', id=None),
+  'id': Value(dtype='string', id=None)
+}
+```
+
+### Licensing Information
+The dataset is released under the [Apache-2.0 License](http://www.apache.org/licenses/LICENSE-2.0). 

huggingface_dataset/Dataset_Card/autoevaluate_autoeval-eval-adversarial_qa-adversarialQA-b079e4-1737160612.md

@@ -0,0 +1,35 @@
+---
+type: predictions
+tags:
+- autotrain
+- evaluation
+datasets:
+- adversarial_qa
+eval_info:
+  task: extractive_question_answering
+  model: Adrian/distilbert-base-uncased-finetuned-squad-colab
+  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: Adrian/distilbert-base-uncased-finetuned-squad-colab
+* 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 [@saad](https://huggingface.co/saad) for evaluating this model.

huggingface_dataset/Dataset_Card/autoevaluate_autoeval-eval-mathemakitten__winobias_antistereotype_dev-mathemakitte-398e1c-2536177709.md

@@ -0,0 +1,34 @@
+---
+type: predictions
+tags:
+- autotrain
+- evaluation
+datasets:
+- mathemakitten/winobias_antistereotype_dev
+eval_info:
+  task: text_zero_shot_classification
+  model: BirdL/OLM-GPT2-Yannic
+  metrics: []
+  dataset_name: mathemakitten/winobias_antistereotype_dev
+  dataset_config: mathemakitten--winobias_antistereotype_dev
+  dataset_split: validation
+  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: BirdL/OLM-GPT2-Yannic
+* Dataset: mathemakitten/winobias_antistereotype_dev
+* Config: mathemakitten--winobias_antistereotype_dev
+* Split: validation
+
+To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
+
+## Contributions
+
+Thanks to [@puffy310](https://huggingface.co/puffy310) for evaluating this model.

huggingface_dataset/Dataset_Card/autoevaluate_autoeval-eval-samsum-samsum-6999f5-3301091732.md

@@ -0,0 +1,33 @@
+---
+type: predictions
+tags:
+- autotrain
+- evaluation
+datasets:
+- samsum
+eval_info:
+  task: summarization
+  model: knkarthick/MEETING_SUMMARY
+  metrics: []
+  dataset_name: samsum
+  dataset_config: samsum
+  dataset_split: test
+  col_mapping:
+    text: dialogue
+    target: summary
+---
+# Dataset Card for AutoTrain Evaluator
+
+This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
+
+* Task: Summarization
+* Model: knkarthick/MEETING_SUMMARY
+* Dataset: samsum
+* Config: samsum
+* Split: test
+
+To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
+
+## Contributions
+
+Thanks to [@TheAlphaQ](https://huggingface.co/TheAlphaQ) for evaluating this model.

huggingface_dataset/Dataset_Card/autoevaluate_autoeval-staging-eval-project-d545d554-fb32-43d5-a9dd-4a47f0efba15-3836.md

@@ -0,0 +1,33 @@
+---
+type: predictions
+tags:
+- autotrain
+- evaluation
+datasets:
+- glue
+eval_info:
+  task: binary_classification
+  model: autoevaluate/binary-classification
+  metrics: ['matthews_correlation']
+  dataset_name: glue
+  dataset_config: sst2
+  dataset_split: validation
+  col_mapping:
+    text: sentence
+    target: label
+---
+# Dataset Card for AutoTrain Evaluator
+
+This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
+
+* Task: Binary Text Classification
+* Model: autoevaluate/binary-classification
+* Dataset: glue
+* Config: sst2
+* Split: validation
+
+To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
+
+## Contributions
+
+Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model.

huggingface_dataset/Dataset_Card/b-mc2_wikihow_lists.md

@@ -0,0 +1,34 @@
+---
+license: cc-by-nc-sa-3.0
+task_categories:
+- summarization
+- question-answering
+language:
+- en
+tags:
+- lists
+- bullets
+- steps
+- summary
+pretty_name: wikihow_lists
+size_categories:
+- 10K<n<100K
+---
+
+
+# Dataset Card for WikiHow Lists
+
+
+### Dataset Summary
+
+Contains CSV of a subset of WikiHow articles.
+
+Subsets include articles that have summaries in numbered list format, unordered list of ingredients, or unordered list of items needed for the article. 
+
+CSV contains a pageId to reference back to the source, title of the article, result with the list data, and a column specifying the result type (ingredient, needed items, summary)
+
+
+### Licensing Information
+
+Data is from WikiHow, license for content is located here
+https://www.wikihow.com/wikiHow:Creative-Commons

huggingface_dataset/Dataset_Card/dattatreya303_covid-qa-tts.md

@@ -0,0 +1,149 @@
+---
+license: mit
+---
+
+---
+annotations_creators:
+- found
+language:
+- en
+language_creators:
+- found
+license:
+- mit
+multilinguality:
+- monolingual
+pretty_name: covid-qa-tts
+size_categories:
+- 1K<n<10K
+source_datasets:
+- extended|covid_qa_deepset
+tags: []
+task_categories:
+- question-answering
+task_ids:
+- closed-domain-qa
+---
+
+# Dataset Card for covid-qa-tts
+
+## Table of Contents
+- [Table of Contents](#table-of-contents)
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
+  - [Languages](#languages)
+- [Dataset Structure](#dataset-structure)
+  - [Data Instances](#data-instances)
+  - [Data Fields](#data-fields)
+  - [Data Splits](#data-splits)
+- [Dataset Creation](#dataset-creation)
+  - [Curation Rationale](#curation-rationale)
+  - [Source Data](#source-data)
+  - [Annotations](#annotations)
+  - [Personal and Sensitive Information](#personal-and-sensitive-information)
+- [Considerations for Using the Data](#considerations-for-using-the-data)
+  - [Social Impact of Dataset](#social-impact-of-dataset)
+  - [Discussion of Biases](#discussion-of-biases)
+  - [Other Known Limitations](#other-known-limitations)
+- [Additional Information](#additional-information)
+  - [Dataset Curators](#dataset-curators)
+  - [Licensing Information](#licensing-information)
+  - [Citation Information](#citation-information)
+  - [Contributions](#contributions)
+
+## Dataset Description
+
+- **Homepage:**
+- **Repository:**
+- **Paper:**
+- **Leaderboard:**
+- **Point of Contact:**
+
+### Dataset Summary
+
+[More Information Needed]
+
+### Supported Tasks and Leaderboards
+
+[More Information Needed]
+
+### Languages
+
+[More Information Needed]
+
+## Dataset Structure
+
+### Data Instances
+
+[More Information Needed]
+
+### Data Fields
+
+[More Information Needed]
+
+### Data Splits
+
+[More Information Needed]
+
+## Dataset Creation
+
+### Curation Rationale
+
+[More Information Needed]
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+[More Information Needed]
+
+#### Who are the source language producers?
+
+[More Information Needed]
+
+### Annotations
+
+#### Annotation process
+
+[More Information Needed]
+
+#### Who are the annotators?
+
+[More Information Needed]
+
+### Personal and Sensitive Information
+
+[More Information Needed]
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+[More Information Needed]
+
+### Discussion of Biases
+
+[More Information Needed]
+
+### Other Known Limitations
+
+[More Information Needed]
+
+## Additional Information
+
+### Dataset Curators
+
+[More Information Needed]
+
+### Licensing Information
+
+[More Information Needed]
+
+### Citation Information
+
+[More Information Needed]
+
+### Contributions
+
+Thanks to [@github-username](https://github.com/<github-username>) for adding this dataset.

huggingface_dataset/Dataset_Card/graphs-datasets_ZINC.md

@@ -0,0 +1,151 @@
+---
+license: unknown
+dataset_info:
+  features:
+  - name: node_feat
+    sequence:
+      sequence: int64
+  - name: edge_index
+    sequence:
+      sequence: int64
+  - name: edge_attr
+    sequence:
+      sequence: int64
+  - name: 'y'
+    sequence: float64
+  - name: num_nodes
+    dtype: int64
+  splits:
+  - name: train
+    num_bytes: 376796456
+    num_examples: 220011
+  - name: test
+    num_bytes: 8538528
+    num_examples: 5000
+  - name: validation
+    num_bytes: 41819628
+    num_examples: 24445
+  download_size: 20636253
+  dataset_size: 427154612
+task_categories:
+- graph-ml
+---
+
+# Dataset Card for ZINC
+
+## Table of Contents
+- [Table of Contents](#table-of-contents)
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
+- [External Use](#external-use)
+  - [PyGeometric](#pygeometric)
+- [Dataset Structure](#dataset-structure)
+  - [Data Properties](#data-properties)
+  - [Data Fields](#data-fields)
+  - [Data Splits](#data-splits)
+- [Additional Information](#additional-information)
+  - [Licensing Information](#licensing-information)
+  - [Citation Information](#citation-information)
+  - [Contributions](#contributions)
+
+## Dataset Description
+
+- **[Homepage](https://zinc15.docking.org/)**
+- **[Repository](https://www.dropbox.com/s/feo9qle74kg48gy/molecules.zip?dl=1):**: 
+- **Paper:**: ZINC 15 – Ligand Discovery for Everyone (see citation) 
+- **Leaderboard:**: [Papers with code leaderboard](https://paperswithcode.com/sota/)
+
+### Dataset Summary
+
+The `ZINC` dataset is a "curated collection of commercially available chemical compounds prepared especially for virtual screening" (Wikipedia). 
+
+### Supported Tasks and Leaderboards
+
+`ZINC` should be used for molecular property prediction (aiming to predict the constrained solubility of the molecules), a graph regression task. The score used is the MAE.
+
+The associated leaderboard is here: [Papers with code leaderboard](https://paperswithcode.com/sota/graph-regression-on-zinc).
+
+## External Use
+### PyGeometric
+To load in PyGeometric, do the following:
+
+```python
+from datasets import load_dataset
+
+from torch_geometric.data import Data
+from torch_geometric.loader import DataLoader
+
+dataset_hf = load_dataset("graphs-datasets/<mydataset>")
+# For the train set (replace by valid or test as needed)
+dataset_pg_list = [Data(graph) for graph in dataset_hf["train"]]
+dataset_pg = DataLoader(dataset_pg_list)
+
+```
+
+
+## Dataset Structure
+
+### Data Properties
+
+| property | value |
+|---|---|
+| scale | big |
+| #graphs | 220011 |
+| average #nodes | 23.15 |
+| average #edges | 49.81 |
+
+### Data Fields
+
+Each row of a given file is a graph, with: 
+- `node_feat` (list: #nodes x #node-features): nodes
+- `edge_index` (list: 2 x #edges): pairs of nodes constituting edges
+- `edge_attr` (list: #edges x #edge-features): for the aforementioned edges, contains their features
+- `y` (list: 1 x #labels): contains the number of labels available to predict (here 1, equal to zero or one)
+- `num_nodes` (int): number of nodes of the graph
+
+### Data Splits
+
+This data comes from the PyGeometric version of the dataset, and follows the provided data splits.
+This information can be found back using
+```python
+from torch_geometric.datasets import ZINC
+
+dataset = ZINC(root = '', split='train') # valid, test
+```
+
+## Additional Information
+
+### Licensing Information
+The dataset has been released under unknown license. Please open an issue if you know what is the license of this dataset.
+
+### Citation Information
+```bibtex
+@article{doi:10.1021/acs.jcim.5b00559,
+  author = {Sterling, Teague and Irwin, John J.},
+  title = {ZINC 15 – Ligand Discovery for Everyone},
+  journal = {Journal of Chemical Information and Modeling},
+  volume = {55},
+  number = {11},
+  pages = {2324-2337},
+  year = {2015},
+  doi = {10.1021/acs.jcim.5b00559},
+      note ={PMID: 26479676},
+  
+  URL = { 
+          https://doi.org/10.1021/acs.jcim.5b00559
+      
+  },
+  eprint = { 
+          https://doi.org/10.1021/acs.jcim.5b00559
+      
+  }
+  
+}
+
+
+```
+
+### Contributions
+
+Thanks to [@clefourrier](https://github.com/clefourrier) for adding this dataset.

huggingface_dataset/Dataset_Card/irds_mr-tydi_sw_dev.md

@@ -0,0 +1,55 @@
+---
+pretty_name: '`mr-tydi/sw/dev`'
+viewer: false
+source_datasets: ['irds/mr-tydi_sw']
+task_categories:
+- text-retrieval
+---
+
+# Dataset Card for `mr-tydi/sw/dev`
+
+The `mr-tydi/sw/dev` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
+For more information about the dataset, see the [documentation](https://ir-datasets.com/mr-tydi#mr-tydi/sw/dev).
+
+# Data
+
+This dataset provides:
+ - `queries` (i.e., topics); count=526
+ - `qrels`: (relevance assessments); count=623
+
+ - For `docs`, use [`irds/mr-tydi_sw`](https://huggingface.co/datasets/irds/mr-tydi_sw)
+
+## Usage
+
+```python
+from datasets import load_dataset
+
+queries = load_dataset('irds/mr-tydi_sw_dev', 'queries')
+for record in queries:
+    record # {'query_id': ..., 'text': ...}
+
+qrels = load_dataset('irds/mr-tydi_sw_dev', '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
+
+```
+@article{Zhang2021MrTyDi,
+  title={{Mr. TyDi}: A Multi-lingual Benchmark for Dense Retrieval}, 
+  author={Xinyu Zhang and Xueguang Ma and Peng Shi and Jimmy Lin},
+  year={2021},
+  journal={arXiv:2108.08787},
+}
+@article{Clark2020TyDiQa,
+    title={{TyDi QA}: A Benchmark for Information-Seeking Question Answering in Typologically Diverse Languages},
+    author={Jonathan H. Clark and Eunsol Choi and Michael Collins and Dan Garrette and Tom Kwiatkowski and Vitaly Nikolaev and Jennimaria Palomaki},
+    year={2020},
+    journal={Transactions of the Association for Computational Linguistics}
+}
+```

huggingface_dataset/Dataset_Card/mwitiderrick_arXiv.md

@@ -0,0 +1,55 @@
+---
+license: cc0-1.0
+---
+About Dataset
+
+This is a subset of the ArXiv dataset from Kaggle 
+
+https://www.kaggle.com/datasets/Cornell-University/arxiv
+
+About ArXiv
+
+For nearly 30 years, ArXiv has served the public and research communities by providing open access to scholarly articles, from the vast branches of physics to the many subdisciplines of computer science to everything in between, including math, statistics, electrical engineering, quantitative biology, and economics. This rich corpus of information offers significant, but sometimes overwhelming depth.
+
+In these times of unique global challenges, efficient extraction of insights from data is essential. To help make the arXiv more accessible, we present a free, open pipeline on Kaggle to the machine-readable arXiv dataset: a repository of 1.7 million articles, with relevant features such as article titles, authors, categories, abstracts, full text PDFs, and more.
+
+Our hope is to empower new use cases that can lead to the exploration of richer machine learning techniques that combine multi-modal features towards applications like trend analysis, paper recommender engines, category prediction, co-citation networks, knowledge graph construction and semantic search interfaces.
+
+The dataset is freely available via Google Cloud Storage buckets (more info here). Stay tuned for weekly updates to the dataset!
+
+ArXiv is a collaboratively funded, community-supported resource founded by Paul Ginsparg in 1991 and maintained and operated by Cornell University.
+
+The release of this dataset was featured further in a Kaggle blog post here.
+
+
+See here for more information.
+
+ArXiv On Kaggle
+
+Metadata
+
+This dataset is a mirror of the original ArXiv data. Because the full dataset is rather large (1.1TB and growing), this dataset provides only a metadata file in the json format. This file contains an entry for each paper, containing:
+
+id: ArXiv ID (can be used to access the paper, see below)
+submitter: Who submitted the paper
+authors: Authors of the paper
+title: Title of the paper
+comments: Additional info, such as number of pages and figures
+journal-ref: Information about the journal the paper was published in
+doi: [https://www.doi.org](Digital Object Identifier)
+abstract: The abstract of the paper
+categories: Categories / tags in the ArXiv system
+versions: A version history
+
+You can access each paper directly on ArXiv using these links:
+
+https://arxiv.org/abs/{id}: Page for this paper including its abstract and further links
+https://arxiv.org/pdf/{id}: Direct link to download the PDF
+License
+Creative Commons CC0 1.0 Universal Public Domain Dedication applies to the metadata in this dataset. See https://arxiv.org/help/license for further details and licensing on individual papers.
+
+Acknowledgements
+
+The original data is maintained by ArXiv, huge thanks to the team for building and maintaining this dataset.
+
+We're using https://github.com/mattbierbaum/arxiv-public-datasets to pull the original data, thanks to Matt Bierbaum for providing this tool.

huggingface_dataset/Dataset_Card/n1ghtf4l1_vigilant-fortnight.md

@@ -0,0 +1,54 @@
+---
+license: mit
+---
+
+This contains the datasets for the Trojan Detection Challenge NeurIPS 2022 competition. To learn more, please see the [competition website](http://trojandetection.ai/).
+
+# **Trojan Detection**
+
+##### Detect and Analyze Trojan attacks on deep neural networks that are designed to be difficult to detect. 
+
+### **Overview**
+
+Neural Trojans are a growing concern for the security of ML systems, but little is known about the fundamental offense-defense balance of Trojan detection. Early work suggests that standard Trojan attacks may be easy to detect, but recently it has been shown that in simple cases one can design practically undetectable Trojans.
+
+This repository contains code for the **Trojan Detection Challenge (TDC) NeurIPS 2022** [competition](https://trojandetection.ai/).
+
+There are 3 main tracks for this competition:
+- **Trojan Detection Track**: Given a dataset of Trojaned and clean networks spanning multiple data sources, build a Trojan detector that classifies a test set of networks with held-out labels (Trojan, clean). For more information, see here.
+
+- **Trojan Analysis Track**: Given a dataset of Trojaned networks spanning multiple data sources, predict various properties of Trojaned networks on a test set with held-out labels. This track has two subtracks: (1) target label prediction, (2) trigger synthesis. For more information, see here.
+
+- **Evasive Trojans Track**: Given a dataset of clean networks and a list of attack specifications, train a small set of Trojaned networks meeting the specifications and upload them to the evaluation server. The server will verify that the attack specifications are met, then train and evaluate a baseline Trojan detector using held-out clean networks and the submitted Trojaned networks. The task is to create Trojaned networks that are hard to detect. For more information, see here.
+
+The competition has two rounds: In the primary round, participants will compete on the three main tracks. In the final round, the solution of the first-place team in the Evasive Trojans track will be used to train a new set of hard-to-detect Trojans, and participants will compete to detect these networks. For more information on the final round, see here.
+
+### **Contents**
+
+There are four folders corresponding to different tracks and subtracks: 1) Trojan Detection, 2) Trojan Analysis (Target Label Prediction), 3) Trojan Analysis (Trigger Synthesis), and 4) Evasive Trojans. We provide starter code for submitting baselines in ```example_submission.ipynb``` under each folder. The ```tdc_datasets``` folder is expected to be under the same parent directory as ```tdc-starter-kit```. The datasets are available [here](https://zenodo.org/record/6894041). You can download them from the Zenodo website or by running ```download_datasets.py```.
+
+The ```utils.py``` file contains helper functions for loading new models, generating new attack specifications, and training clean/Trojaned networks. This is primarily used for the Evasive Trojans Track starter kit. It also contains the load_data function for loading data sources (CIFAR-10/100, GTSRB, MNIST), which may be of general use. To load GTSRB images, unzip ```gtsrb_preprocessed.zip``` in the data folder (NOTE: This folder is only for storing data sources. The network datasets are stored in tdc_datasets, which must be downloaded from Zenodo). You may need to adjust the paths in the load_data function depending on your working directory. The ```wrn.py``` file contains the definition of the Wide Residual Network class used for CIFAR-10 and CIFAR-100 models. When loading networks from the competition datasets, ```wrn.py``` must be in your path. See the example submission notebooks for details.
+
+### **Data**
+
+Unlike standard machine learning tasks, the datasets consist of neural networks. That is, rather than making predictions on input images, goal will be identifying hidden functionality in neural networks. Networks are trained on four standard data sources: MNIST, CIFAR-10, CIFAR-100, and GTSRB. Variants of two standard Trojan attacks are used that are modified to be harder to detect. For the Detection Track, the training, validation, and test sets have 1,000 neural networks each. Networks are split evenly across all four data sources. Half of the networks are Trojaned, and there is a 50/50 split between the two attack types.
+
+## How to Use
+
+**Clone this repository, download the competition [datasets](https://huggingface.co/datasets/n1ghtf4l1/vigilant-fortnight/blob/main/tdc_datasets.zip) from my HuggingFace repository and unzip adjacent to the repository**. Ensure that Jupyter version is up-to-date (fairly recent). To avoid errors with model incompatibility, please use PyTorch version 1.11.0. Run one of the example notebooks or start building your own submission.
+
+### **Additional Information**
+
+#### **Model Architectures and Data Sources**
+
+Networks have been trained on four standard data sources: MNIST, CIFAR-10, CIFAR-100, and GTSRB. GTSRB images are resized to 32x32.
+
+For MNIST, convolutional networks have been used. For CIFAR-10 and CIFAR-100, Wide Residual Networks have been used. For GTSRB, Vision Transformers have been used.
+
+#### **Trojan Attacks**
+
+Trojaned networks have been trained with patch and whole-image attacks. These attacks are variants of the foundational BadNets and blended attacks modified to be harder to detect. These modified attacks use a simple change to the standard Trojan training procedure. Instead of training Trojaned networks from scratch, they were fine-tuned from the starting parameters of clean networks and regularize them with various similarity losses such that they are similar to the distribution of clean networks. Additionally, the networks have been trained to have high specificity for the particular trigger pattern associated with the attack. In extensive experiments, baseline detectors have been verified obtain substantially lower performance on these hard-to-detect Trojans.
+
+All patch attacks in datasets use random trigger patterns sampled from an independent Bernoulli 0/1 distribution for each pixel and color channel (for Trojan detection and target label prediction, patches are black-and-white; for trigger synthesis, patches are colored). Each patch attack uses a different location and size for its trigger mask. All blended attacks in our datasets use random trigger trigger patterns sampled from an independent Uniform(0,1) distribution for each pixel and color channel. All attacks are all-to-one with a random target label. For more details, please see the starter kit. 
+
+MNTD, Neural Cleanse, and ABS has been used as baseline Trojan detectors for participants to improve upon. These are well-known Trojan detectors from the academic literature, each with a distinct approach to Trojan detection. Also a specificity-based detector has been used as a baseline, since Trojan attacks with low specificity can be highly susceptible to such a detector. The specificity detector applies random triggers to inputs from a given data source, then runs these triggered inputs through the network in question. The negative entropy of the average posterior is used as a detection score. This leverages the fact that Trojan attacks without specificity are activated quite frequently by randomly sampled triggers.

huggingface_dataset/Dataset_Card/nijatzeynalov_azerbaijani-multi-news.md

@@ -0,0 +1,81 @@
+---
+license: creativeml-openrail-m
+task_categories:
+- summarization
+language:
+- az
+pretty_name: Azerbaijani News Summary Dataset Card
+---
+
+# Azerbaijani News Summary Dataset Card
+
+## Dataset Summary
+
+I present __az-news-summary__, a comprehensive and diverse dataset comprising __143k (143,448)__ Azerbaijani news articles extracted using a set of carefully designed heuristics. The dataset covers common topics for news reports include war, government, politics, education, health, the environment, economy, business, fashion, entertainment, and sport, as well as quirky or unusual events. 
+
+The dataset is prepared for Abstractive/Extractive summarization tasks. It can also be used in other scopes like Text Generation, Title Generation and etc.
+
+## Dataset Structure
+
+One example from the dataset is given below in JSON format.
+
+```json
+{'id': 33885080,
+
+ 'title': 'İsmayıllı silkələndi - Zəlzələ',
+
+ 'summary': 'Avqustun 11-də İsmayıllı rayonu ərazisində zəlzələ baş verib',
+
+ 'text': 'Azərbaycan milli elmlər akademiyası nəzdində respublika seysmoloji
+          xidmət mərkəzindən  bildirilib ki, ilkin məlumatlara əsasən yeraltı təkanlar
+          yerli vaxtla saat 23:03:11-də pirquludan 11 kilometr qərbdə i̇smayıllı ərazisində
+          qeydə alınıb.ocağı 9 kilometr dərinlikdə yerləşən zəlzələ episentrdə 4 bal,
+          ətraf rayonlarda isə 3 bala qədər hiss olunub.'}
+```
+
+## Data Fields
+
+- `id`: ID of the news.
+- `title`: The title of the news.
+- `summary`:  The summary of the news.
+- `text`:  The body of the news.
+
+##  Data Splits
+
+This dataset has 3 splits: _train_, _validation_, and _test_. \
+Token counts are white space based.
+
+| Dataset Split | Number of Instances |     Size (MB)         |
+| ------------- | --------------------|:----------------------|
+| Train         | 100,413             |      150              |
+| Validation    | 14,344              |      21.3             |
+| Test          | 28,691              |      42.8             |
+
+## Usage
+
+Usage is easy and takes only a few minutes. Firstly, you need to use install datasets library as follows:
+```python
+!pip install datasets
+```
+To load the dataset from the library, you need to pass the file name on the load_dataset() function. In this case:
+```python
+from datasets import load_dataset
+dataset = load_dataset("nijatzeynalov/azerbaijani-multi-news")
+```
+
+## Dataset Curator
+
+This dataset was curated by [Nijat Zeynalov](https://www.linkedin.com/in/nijat-zeynalov-064163142/)
+
+#  Citation Information
+
+```bibtex
+@misc {nijatzeynalov_2023,
+	author       = { {NijatZeynalov} },
+	title        = { azerbaijani-multi-news (Revision 2afa300) },
+	year         = 2023,
+	url          = { https://huggingface.co/datasets/nijatzeynalov/azerbaijani-multi-news },
+	doi          = { 10.57967/hf/0312 },
+	publisher    = { Hugging Face }
+}
+```

huggingface_dataset/Dataset_Card/qgallouedec_prj_gia_dataset_metaworld_coffee_button_v2_1111.md

@@ -0,0 +1,36 @@
+---
+library_name: gia
+tags:
+- deep-reinforcement-learning
+- reinforcement-learning
+- gia
+- multi-task
+- multi-modal
+- imitation-learning
+- offline-reinforcement-learning
+---
+
+An imitation learning environment for the coffee-button-v2 environment, sample for the policy coffee-button-v2 
+
+This environment was created as part of the Generally Intelligent Agents project gia: https://github.com/huggingface/gia 
+
+
+
+
+## Load dataset
+
+First, clone it with
+
+```sh
+git clone https://huggingface.co/datasets/qgallouedec/prj_gia_dataset_metaworld_coffee_button_v2_1111
+```
+
+Then, load it with
+
+```python
+import numpy as np
+dataset = np.load("prj_gia_dataset_metaworld_coffee_button_v2_1111/dataset.npy", allow_pickle=True).item()
+print(dataset.keys())  # dict_keys(['observations', 'actions', 'dones', 'rewards'])
+```
+
+