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# Dataset Card for Law Area Prediction ## 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 The dataset contains cases to be classified into the four main areas of law: Public, Civil, Criminal and Social These can be classified further into sub-areas: ``` "public": ['Tax', 'Urban Planning and Environmental', 'Expropriation', 'Public Administration', 'Other Fiscal'], "civil": ['Rental and Lease', 'Employment Contract', 'Bankruptcy', 'Family', 'Competition and Antitrust', 'Intellectual Property'], 'criminal': ['Substantive Criminal', 'Criminal Procedure'] ``` ### Supported Tasks and Leaderboards Law Area Prediction can be used as text classification task ### Languages Switzerland has four official languages with three languages German, French and Italian being represenated. The decisions are written by the judges and clerks in the language of the proceedings. | Language | Subset | Number of Documents| |------------|------------|--------------------| | German | **de** | 127K | | French | **fr** | 156K | | Italian | **it** | 46K | ## Dataset Structure - decision_id: unique identifier for the decision - facts: facts section of the decision - considerations: considerations section of the decision - law_area: label of the decision (main area of law) - law_sub_area: sub area of law of the decision - language: language of the decision - year: year of the decision - court: court of the decision - chamber: chamber of the decision - canton: canton of the decision - region: region of the decision ### Data Fields [More Information Needed] ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits The dataset was split date-stratisfied - Train: 2002-2015 - Validation: 2016-2017 - Test: 2018-2022 ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization The original data are published from the Swiss Federal Supreme Court (https://www.bger.ch) in unprocessed formats (HTML). The documents were downloaded from the Entscheidsuche portal (https://entscheidsuche.ch) in HTML. #### Who are the source language producers? The decisions are written by the judges and clerks in the language of the proceedings. ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information The dataset contains publicly available court decisions from the Swiss Federal Supreme Court. Personal or sensitive information has been anonymized by the court before publication according to the following guidelines: https://www.bger.ch/home/juridiction/anonymisierungsregeln.html. ## 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 We release the data under CC-BY-4.0 which complies with the court licensing (https://www.bger.ch/files/live/sites/bger/files/pdf/de/urteilsveroeffentlichung_d.pdf) © Swiss Federal Supreme Court, 2002-2022 The copyright for the editorial content of this website and the consolidated texts, which is owned by the Swiss Federal Supreme Court, is licensed under the Creative Commons Attribution 4.0 International licence. This means that you can re-use the content provided you acknowledge the source and indicate any changes you have made. Source: https://www.bger.ch/files/live/sites/bger/files/pdf/de/urteilsveroeffentlichung_d.pdf ### Citation Information Please cite our [ArXiv-Preprint](https://arxiv.org/abs/2306.09237) ``` @misc{rasiah2023scale, title={SCALE: Scaling up the Complexity for Advanced Language Model Evaluation}, author={Vishvaksenan Rasiah and Ronja Stern and Veton Matoshi and Matthias Stürmer and Ilias Chalkidis and Daniel E. Ho and Joel Niklaus}, year={2023}, eprint={2306.09237}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ### Contributions
rcds/swiss_law_area_prediction
[ "task_categories:text-classification", "annotations_creators:machine-generated", "language_creators:expert-generated", "multilinguality:multilingual", "size_categories:100K<n<1M", "source_datasets:original", "language:de", "language:fr", "language:it", "license:cc-by-sa-4.0", "arxiv:2306.09237", "region:us" ]
2023-03-25T10:51:36+00:00
{"annotations_creators": ["machine-generated"], "language_creators": ["expert-generated"], "language": ["de", "fr", "it"], "license": "cc-by-sa-4.0", "multilinguality": ["multilingual"], "size_categories": ["100K<n<1M"], "source_datasets": ["original"], "task_categories": ["text-classification"], "pretty_name": "Law Area Prediction"}
2023-07-20T06:38:52+00:00
[ "2306.09237" ]
[ "de", "fr", "it" ]
TAGS #task_categories-text-classification #annotations_creators-machine-generated #language_creators-expert-generated #multilinguality-multilingual #size_categories-100K<n<1M #source_datasets-original #language-German #language-French #language-Italian #license-cc-by-sa-4.0 #arxiv-2306.09237 #region-us
Dataset Card for Law Area Prediction ==================================== Table of Contents ----------------- * Table of Contents * Dataset Description + Dataset Summary + Supported Tasks and Leaderboards + Languages * Dataset Structure + Data Instances + Data Fields + Data Splits * Dataset Creation + Curation Rationale + Source Data + Annotations + Personal and Sensitive Information * Considerations for Using the Data + Social Impact of Dataset + Discussion of Biases + Other Known Limitations * Additional Information + Dataset Curators + Licensing Information + Citation Information + Contributions Dataset Description ------------------- * Homepage: * Repository: * Paper: * Leaderboard: * Point of Contact: ### Dataset Summary The dataset contains cases to be classified into the four main areas of law: Public, Civil, Criminal and Social These can be classified further into sub-areas: ### Supported Tasks and Leaderboards Law Area Prediction can be used as text classification task ### Languages Switzerland has four official languages with three languages German, French and Italian being represenated. The decisions are written by the judges and clerks in the language of the proceedings. Language: German, Subset: de, Number of Documents: 127K Language: French, Subset: fr, Number of Documents: 156K Language: Italian, Subset: it, Number of Documents: 46K Dataset Structure ----------------- * decision\_id: unique identifier for the decision * facts: facts section of the decision * considerations: considerations section of the decision * law\_area: label of the decision (main area of law) * law\_sub\_area: sub area of law of the decision * language: language of the decision * year: year of the decision * court: court of the decision * chamber: chamber of the decision * canton: canton of the decision * region: region of the decision ### Data Fields ### Data Instances ### Data Fields ### Data Splits The dataset was split date-stratisfied * Train: 2002-2015 * Validation: 2016-2017 * Test: 2018-2022 Dataset Creation ---------------- ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization The original data are published from the Swiss Federal Supreme Court (URL) in unprocessed formats (HTML). The documents were downloaded from the Entscheidsuche portal (URL) in HTML. #### Who are the source language producers? The decisions are written by the judges and clerks in the language of the proceedings. ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information The dataset contains publicly available court decisions from the Swiss Federal Supreme Court. Personal or sensitive information has been anonymized by the court before publication according to the following guidelines: URL Considerations for Using the Data --------------------------------- ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations Additional Information ---------------------- ### Dataset Curators ### Licensing Information We release the data under CC-BY-4.0 which complies with the court licensing (URL © Swiss Federal Supreme Court, 2002-2022 The copyright for the editorial content of this website and the consolidated texts, which is owned by the Swiss Federal Supreme Court, is licensed under the Creative Commons Attribution 4.0 International licence. This means that you can re-use the content provided you acknowledge the source and indicate any changes you have made. Source: URL Please cite our ArXiv-Preprint ### Contributions
[ "### Dataset Summary\n\n\nThe dataset contains cases to be classified into the four main areas of law: Public, Civil, Criminal and Social\n\n\nThese can be classified further into sub-areas:", "### Supported Tasks and Leaderboards\n\n\nLaw Area Prediction can be used as text classification task", "### Languages\n\n\nSwitzerland has four official languages with three languages German, French and Italian being represenated. The decisions are written by the judges and clerks in the language of the proceedings.\n\n\nLanguage: German, Subset: de, Number of Documents: 127K\nLanguage: French, Subset: fr, Number of Documents: 156K\nLanguage: Italian, Subset: it, Number of Documents: 46K\n\n\nDataset Structure\n-----------------\n\n\n* decision\\_id: unique identifier for the decision\n* facts: facts section of the decision\n* considerations: considerations section of the decision\n* law\\_area: label of the decision (main area of law)\n* law\\_sub\\_area: sub area of law of the decision\n* language: language of the decision\n* year: year of the decision\n* court: court of the decision\n* chamber: chamber of the decision\n* canton: canton of the decision\n* region: region of the decision", "### Data Fields", "### Data Instances", "### Data Fields", "### Data Splits\n\n\nThe dataset was split date-stratisfied\n\n\n* Train: 2002-2015\n* Validation: 2016-2017\n* Test: 2018-2022\n\n\nDataset Creation\n----------------", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization\n\n\nThe original data are published from the Swiss Federal Supreme Court (URL) in unprocessed formats (HTML). The documents were downloaded from the Entscheidsuche portal (URL) in HTML.", "#### Who are the source language producers?\n\n\nThe decisions are written by the judges and clerks in the language of the proceedings.", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information\n\n\nThe dataset contains publicly available court decisions from the Swiss Federal Supreme Court. Personal or sensitive information has been anonymized by the court before publication according to the following guidelines: URL\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators", "### Licensing Information\n\n\nWe release the data under CC-BY-4.0 which complies with the court licensing (URL\n© Swiss Federal Supreme Court, 2002-2022\n\n\nThe copyright for the editorial content of this website and the consolidated texts, which is owned by the Swiss Federal Supreme Court, is licensed under the Creative Commons Attribution 4.0 International licence. This means that you can re-use the content provided you acknowledge the source and indicate any changes you have made.\nSource: URL\n\n\nPlease cite our ArXiv-Preprint", "### Contributions" ]
[ "TAGS\n#task_categories-text-classification #annotations_creators-machine-generated #language_creators-expert-generated #multilinguality-multilingual #size_categories-100K<n<1M #source_datasets-original #language-German #language-French #language-Italian #license-cc-by-sa-4.0 #arxiv-2306.09237 #region-us \n", "### Dataset Summary\n\n\nThe dataset contains cases to be classified into the four main areas of law: Public, Civil, Criminal and Social\n\n\nThese can be classified further into sub-areas:", "### Supported Tasks and Leaderboards\n\n\nLaw Area Prediction can be used as text classification task", "### Languages\n\n\nSwitzerland has four official languages with three languages German, French and Italian being represenated. The decisions are written by the judges and clerks in the language of the proceedings.\n\n\nLanguage: German, Subset: de, Number of Documents: 127K\nLanguage: French, Subset: fr, Number of Documents: 156K\nLanguage: Italian, Subset: it, Number of Documents: 46K\n\n\nDataset Structure\n-----------------\n\n\n* decision\\_id: unique identifier for the decision\n* facts: facts section of the decision\n* considerations: considerations section of the decision\n* law\\_area: label of the decision (main area of law)\n* law\\_sub\\_area: sub area of law of the decision\n* language: language of the decision\n* year: year of the decision\n* court: court of the decision\n* chamber: chamber of the decision\n* canton: canton of the decision\n* region: region of the decision", "### Data Fields", "### Data Instances", "### Data Fields", "### Data Splits\n\n\nThe dataset was split date-stratisfied\n\n\n* Train: 2002-2015\n* Validation: 2016-2017\n* Test: 2018-2022\n\n\nDataset Creation\n----------------", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization\n\n\nThe original data are published from the Swiss Federal Supreme Court (URL) in unprocessed formats (HTML). The documents were downloaded from the Entscheidsuche portal (URL) in HTML.", "#### Who are the source language producers?\n\n\nThe decisions are written by the judges and clerks in the language of the proceedings.", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information\n\n\nThe dataset contains publicly available court decisions from the Swiss Federal Supreme Court. Personal or sensitive information has been anonymized by the court before publication according to the following guidelines: URL\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators", "### Licensing Information\n\n\nWe release the data under CC-BY-4.0 which complies with the court licensing (URL\n© Swiss Federal Supreme Court, 2002-2022\n\n\nThe copyright for the editorial content of this website and the consolidated texts, which is owned by the Swiss Federal Supreme Court, is licensed under the Creative Commons Attribution 4.0 International licence. This means that you can re-use the content provided you acknowledge the source and indicate any changes you have made.\nSource: URL\n\n\nPlease cite our ArXiv-Preprint", "### Contributions" ]
[ 104, 43, 23, 210, 5, 6, 5, 38, 7, 4, 50, 30, 5, 5, 9, 55, 7, 8, 14, 6, 110, 5 ]
[ "passage: TAGS\n#task_categories-text-classification #annotations_creators-machine-generated #language_creators-expert-generated #multilinguality-multilingual #size_categories-100K<n<1M #source_datasets-original #language-German #language-French #language-Italian #license-cc-by-sa-4.0 #arxiv-2306.09237 #region-us \n### Dataset Summary\n\n\nThe dataset contains cases to be classified into the four main areas of law: Public, Civil, Criminal and Social\n\n\nThese can be classified further into sub-areas:### Supported Tasks and Leaderboards\n\n\nLaw Area Prediction can be used as text classification task### Languages\n\n\nSwitzerland has four official languages with three languages German, French and Italian being represenated. The decisions are written by the judges and clerks in the language of the proceedings.\n\n\nLanguage: German, Subset: de, Number of Documents: 127K\nLanguage: French, Subset: fr, Number of Documents: 156K\nLanguage: Italian, Subset: it, Number of Documents: 46K\n\n\nDataset Structure\n-----------------\n\n\n* decision\\_id: unique identifier for the decision\n* facts: facts section of the decision\n* considerations: considerations section of the decision\n* law\\_area: label of the decision (main area of law)\n* law\\_sub\\_area: sub area of law of the decision\n* language: language of the decision\n* year: year of the decision\n* court: court of the decision\n* chamber: chamber of the decision\n* canton: canton of the decision\n* region: region of the decision### Data Fields### Data Instances### Data Fields### Data Splits\n\n\nThe dataset was split date-stratisfied\n\n\n* Train: 2002-2015\n* Validation: 2016-2017\n* Test: 2018-2022\n\n\nDataset Creation\n----------------### Curation Rationale### Source Data#### Initial Data Collection and Normalization\n\n\nThe original data are published from the Swiss Federal Supreme Court (URL) in unprocessed formats (HTML). The documents were downloaded from the Entscheidsuche portal (URL) in HTML." ]
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f39db019a94f8dbea48ab30d2bdc090703284559
# Dataset Description - **Project Page:** https://instruction-tuning-with-gpt-4.github.io - **Repo:** https://github.com/Instruction-Tuning-with-GPT-4/GPT-4-LLM - **Paper:** https://arxiv.org/abs/2304.03277 # Dataset Card for "alpaca-zh" 本数据集是参考Alpaca方法基于GPT4得到的self-instruct数据，约5万条。 Dataset from https://github.com/Instruction-Tuning-with-GPT-4/GPT-4-LLM It is the chinese dataset from https://github.com/Instruction-Tuning-with-GPT-4/GPT-4-LLM/blob/main/data/alpaca_gpt4_data_zh.json # Usage and License Notices The data is intended and licensed for research use only. The dataset is CC BY NC 4.0 (allowing only non-commercial use) and models trained using the dataset should not be used outside of research purposes. train model with alpaca-zh dataset: https://github.com/shibing624/textgen # English Dataset [Found here](https://huggingface.co/datasets/c-s-ale/alpaca-gpt4-data) # Citation ``` @article{peng2023gpt4llm, title={Instruction Tuning with GPT-4}, author={Baolin Peng, Chunyuan Li, Pengcheng He, Michel Galley, Jianfeng Gao}, journal={arXiv preprint arXiv:2304.03277}, year={2023} } ```
shibing624/alpaca-zh
[ "task_categories:text-generation", "size_categories:10K<n<100K", "language:zh", "license:cc-by-4.0", "gpt", "alpaca", "fine-tune", "instruct-tune", "instruction", "arxiv:2304.03277", "region:us" ]
2023-03-25T11:37:25+00:00
{"language": ["zh"], "license": "cc-by-4.0", "size_categories": ["10K<n<100K"], "task_categories": ["text-generation"], "pretty_name": "Instruction Tuning with GPT-4", "dataset_info": {"features": [{"name": "instruction", "dtype": "string"}, {"name": "input", "dtype": "string"}, {"name": "output", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 32150579, "num_examples": 48818}], "download_size": 35100559, "dataset_size": 32150579}, "tags": ["gpt", "alpaca", "fine-tune", "instruct-tune", "instruction"]}
2023-05-10T05:09:06+00:00
[ "2304.03277" ]
[ "zh" ]
TAGS #task_categories-text-generation #size_categories-10K<n<100K #language-Chinese #license-cc-by-4.0 #gpt #alpaca #fine-tune #instruct-tune #instruction #arxiv-2304.03277 #region-us
# Dataset Description - Project Page: URL - Repo: URL - Paper: URL # Dataset Card for "alpaca-zh" 本数据集是参考Alpaca方法基于GPT4得到的self-instruct数据，约5万条。 Dataset from URL It is the chinese dataset from URL # Usage and License Notices The data is intended and licensed for research use only. The dataset is CC BY NC 4.0 (allowing only non-commercial use) and models trained using the dataset should not be used outside of research purposes. train model with alpaca-zh dataset: URL # English Dataset Found here
[ "# Dataset Description\n\n- Project Page: URL\n- Repo: URL\n- Paper: URL", "# Dataset Card for \"alpaca-zh\"\n\n本数据集是参考Alpaca方法基于GPT4得到的self-instruct数据，约5万条。\n\nDataset from URL \n\nIt is the chinese dataset from URL", "# Usage and License Notices\n\nThe data is intended and licensed for research use only. The dataset is CC BY NC 4.0 (allowing only non-commercial use) and models trained using the dataset should not be used outside of research purposes.\n\ntrain model with alpaca-zh dataset: URL", "# English Dataset\n\nFound here" ]
[ "TAGS\n#task_categories-text-generation #size_categories-10K<n<100K #language-Chinese #license-cc-by-4.0 #gpt #alpaca #fine-tune #instruct-tune #instruction #arxiv-2304.03277 #region-us \n", "# Dataset Description\n\n- Project Page: URL\n- Repo: URL\n- Paper: URL", "# Dataset Card for \"alpaca-zh\"\n\n本数据集是参考Alpaca方法基于GPT4得到的self-instruct数据，约5万条。\n\nDataset from URL \n\nIt is the chinese dataset from URL", "# Usage and License Notices\n\nThe data is intended and licensed for research use only. The dataset is CC BY NC 4.0 (allowing only non-commercial use) and models trained using the dataset should not be used outside of research purposes.\n\ntrain model with alpaca-zh dataset: URL", "# English Dataset\n\nFound here" ]
[ 69, 18, 50, 68, 6 ]
[ "passage: TAGS\n#task_categories-text-generation #size_categories-10K<n<100K #language-Chinese #license-cc-by-4.0 #gpt #alpaca #fine-tune #instruct-tune #instruction #arxiv-2304.03277 #region-us \n# Dataset Description\n\n- Project Page: URL\n- Repo: URL\n- Paper: URL# Dataset Card for \"alpaca-zh\"\n\n本数据集是参考Alpaca方法基于GPT4得到的self-instruct数据，约5万条。\n\nDataset from URL \n\nIt is the chinese dataset from URL# Usage and License Notices\n\nThe data is intended and licensed for research use only. The dataset is CC BY NC 4.0 (allowing only non-commercial use) and models trained using the dataset should not be used outside of research purposes.\n\ntrain model with alpaca-zh dataset: URL# English Dataset\n\nFound here" ]
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0989639a187afad84c687ebce824505159473a56
# AutoTrain Dataset for project: pegasus-reddit-summarizer ## Dataset Description This dataset has been automatically processed by AutoTrain for project pegasus-reddit-summarizer. ### Languages The BCP-47 code for the dataset's language is en. ## Dataset Structure ### Data Instances A sample from this dataset looks as follows: ```json [ { "feat_id": "82n2za", "text": "User who has been working in sales for 30+ years gets a new laptop on Monday. This morning when I get in, my phone is ringing already. I'm not supposed to start for another 20 mins, but I'm nice, so I answer it.\n\n\"This new laptop doesn't have Microsoft on it. Do I need to bring it back in? Just I'm in Scotland, so I'll have to fly down again.\"\n\nEr, yes it does. We went through it when I handed it over, I showed you Outlook, and how Outlook 2016 looks ever so slightly different to Outlook 2010 on your old laptop.\n\n\"Look, it's not there. Every time I click on the button, it just opens the internet. I've emailed my boss from my phone to let him know I'm cancelling all my appointments today, so can you fix it over the VPN or do I need to fly down?\"\n\nSo, I ask him what he's clicking on. \"The blue E. You said the icon was blue now instead of orange. But that just opens the internet, I've already TOLD YOU.\"\n\nI ask him to look along the taskbar for any other blue icons. \"There's a blue and white O. Are you telling me that's it?\" I ask him to confirm that Outlook begins with the letter O, and advise him to try clicking on that icon instead.\n\nSo he clicks on it, and ta-da! Outlook opens. \"Oh for God's sake. This is too confusing. Why did you change the colour anyway? Now I have to re-arrange all my appointments, this is really inconvenient.\"\n\nSorry, I did ring up my mate Bill and ask him to change the colour of Outlook from orange to blue just to confuse you. Luckily I have great power and influence over at Microsoft, so they did me a favour, and I'm now reaping the untold rewards.\n\nGTG, writing an email to his boss to cover my arse...\n", "target": "User receives a new laptop and complains to IT that it doesn't have Microsoft on it. IT informs the user that they had gone through it when handing it over and that the user had simply clicked on the wrong icon. The user complains about the change in icon color and that they now have to rearrange their entire schedule. IT sarcastically apologizes and writes an email to cover themselves." }, { "feat_id": "q4kjoe", "text": "The title implies I was there but really it was just my mom and my sister.\n\nMy sister was craving a cheddar jalapeo bagel so my mom decided to go to a chain caf to get one for her. It was 10 minutes before closing, and they went through the drive thru. My mom orders the cheddar bagel for my sister plus some other things for the rest of the people at home, including coffee cake. The gal at the drive thru window said \"you're lucky, you're getting the last ones of everything you're ordering!\"\n\nMy mom pulls up to the window to pay and receive the food and the drive thru gal (about 19) is crying and apologizing profusely. She says the people in front of my mom STOLE THE FOOD. Mom asked how it happened and the lady said that she had made a mistake and was about to give the car in front the wrong order, but she realized her mistake before handing it over and announced it. The people then REACHED for the bag (it was not handed to them!!!) and stole it, apparently saying \"you can't have it back now, it's cross contaminated!\" Then when the lady called for her manager, he was busy, and the people's order wasn't ready yet, so the poor gal just told them to pull up and wait for their food and they did.\n\nMy mom is a really loving person and so she's trying to tell this lady it's okay, she didn't really need the food, she's not mad, etc., and in the meantime the manager comes over to ask what is happening. She tells him and he is shocked. He asked if the car in front was those people, and she said yes. So he starts going out to talk to the people in the car, and at that moment, they step on it and zip out of the parking lot. \n\nSo now those people have not only stolen my mom's order, which were the last items, but they didn't even receive their order! But the good news is that the manager said to my mom that he had been saving a cheddar bagel for himself and that he would give that one to her free of charge. \n\nHave you ever heard of anything like this??? My mom told me this on the phone and I was stunned. I've worked food service before but nothing like this has ever happened!! She thinks the people in the other car had done this maneuver before since the \"cross contamination\" response came out way too quickly. Also I feel so sorry for the lady! She's working in a fucking pandemic getting underpaid and overworked and now has to deal with deranged people!", "target": "A woman went to a chain caf\u00e9 with her daughter to buy a cheddar jalape\u00f1o bagel for her sister. The drive thru attendant announces they are getting the last items of everything. The attendant then reveals that the people in the car in front of them stole their food. The woman's mother attempted to comfort the attendant and the manager offered the woman a cheddar bagel for free. The woman wonders if the \"cross contamination\" defense may have been used by the thieves before." } ] ``` ### Dataset Fields The dataset has the following fields (also called "features"): ```json { "feat_id": "Value(dtype='string', id=None)", "text": "Value(dtype='string', id=None)", "target": "Value(dtype='string', id=None)" } ``` ### Dataset Splits This dataset is split into a train and validation split. The split sizes are as follow: | Split name | Num samples | | ------------ | ------------------- | | train | 7200 | | valid | 1800 |
stevied67/autotrain-data-pegasus-reddit-summarizer
[ "task_categories:summarization", "language:en", "region:us" ]
2023-03-25T11:50:33+00:00
{"language": ["en"], "task_categories": ["summarization"]}
2023-03-25T11:51:23+00:00
[]
[ "en" ]
TAGS #task_categories-summarization #language-English #region-us
AutoTrain Dataset for project: pegasus-reddit-summarizer ======================================================== Dataset Description ------------------- This dataset has been automatically processed by AutoTrain for project pegasus-reddit-summarizer. ### Languages The BCP-47 code for the dataset's language is en. Dataset Structure ----------------- ### Data Instances A sample from this dataset looks as follows: ### Dataset Fields The dataset has the following fields (also called "features"): ### Dataset Splits This dataset is split into a train and validation split. The split sizes are as follow:
[ "### Languages\n\n\nThe BCP-47 code for the dataset's language is en.\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nA sample from this dataset looks as follows:", "### Dataset Fields\n\n\nThe dataset has the following fields (also called \"features\"):", "### Dataset Splits\n\n\nThis dataset is split into a train and validation split. The split sizes are as follow:" ]
[ "TAGS\n#task_categories-summarization #language-English #region-us \n", "### Languages\n\n\nThe BCP-47 code for the dataset's language is en.\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nA sample from this dataset looks as follows:", "### Dataset Fields\n\n\nThe dataset has the following fields (also called \"features\"):", "### Dataset Splits\n\n\nThis dataset is split into a train and validation split. The split sizes are as follow:" ]
[ 20, 26, 17, 23, 27 ]
[ "passage: TAGS\n#task_categories-summarization #language-English #region-us \n### Languages\n\n\nThe BCP-47 code for the dataset's language is en.\n\n\nDataset Structure\n-----------------### Data Instances\n\n\nA sample from this dataset looks as follows:### Dataset Fields\n\n\nThe dataset has the following fields (also called \"features\"):### Dataset Splits\n\n\nThis dataset is split into a train and validation split. The split sizes are as follow:" ]
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73cc18da1ea455dcc2fed97eca91e325888415b6
# Dataset Card for Dataset Name ## Dataset Description - **Homepage:** - **Repository:** https://github.com/mskandalis/daccord-dataset-contradictions - **Paper:** https://aclanthology.org/2023.jeptalnrecital-long.22/ - **Leaderboard:** - **Point of Contact:** ### Dataset Summary The DACCORD dataset is an entirely new collection of 1034 sentence pairs annotated as a binary classification task for automatic detection of contradictions between sentences in French. Each pair of sentences receives a label according to whether or not the two sentences contradict each other. DACCORD currently covers the themes of Russia’s invasion of Ukraine in 2022, the Covid-19 pandemic, and the climate crisis. The sentences of the dataset were extracted from (or based on sentences from) AFP Factuel articles. ### Supported Tasks and Leaderboards The task of automatic detection of contradictions between sentences is a sentence-pair binary classification task. It can be viewed as a task related to both natural language inference task and misinformation detection task. ## Dataset Structure ### Data Fields - `id`: Index number. - `premise`: The translated premise in the target language. - `hypothesis`: The translated premise in the target language. - `label`: The classification label, with possible values 0 (`entailment`), 1 (`neutral`), 2 (`contradiction`). - `label_text`: The classification label, with possible values `entailment` (0), `neutral` (1), `contradiction` (2). - `genre`: a `string` feature . ### Data Splits | theme |contradiction|compatible| |----------------|------------:|---------:| |Russian invasion| 215 | 257 | | Covid-19 | 251 | 199 | | Climate change | 49 | 63 | ## Additional Information ### Citation Information **BibTeX:** ````BibTeX @inproceedings{skandalis-etal-2023-daccord, title = "{DACCORD} : un jeu de donn{\'e}es pour la D{\'e}tection Automatique d{'}{\'e}non{C}{\'e}s {CO}nt{R}a{D}ictoires en fran{\c{c}}ais", author = "Skandalis, Maximos and Moot, Richard and Robillard, Simon", booktitle = "Actes de CORIA-TALN 2023. Actes de la 30e Conf{\'e}rence sur le Traitement Automatique des Langues Naturelles (TALN), volume 1 : travaux de recherche originaux -- articles longs", month = "6", year = "2023", address = "Paris, France", publisher = "ATALA", url = "https://aclanthology.org/2023.jeptalnrecital-long.22", pages = "285--297", abstract = "La t{\^a}che de d{\'e}tection automatique de contradictions logiques entre {\'e}nonc{\'e}s en TALN est une t{\^a}che de classification binaire, o{\`u} chaque paire de phrases re{\c{c}}oit une {\'e}tiquette selon que les deux phrases se contredisent ou non. Elle peut {\^e}tre utilis{\'e}e afin de lutter contre la d{\'e}sinformation. Dans cet article, nous pr{\'e}sentons DACCORD, un jeu de donn{\'e}es d{\'e}di{\'e} {\`a} la t{\^a}che de d{\'e}tection automatique de contradictions entre phrases en fran{\c{c}}ais. Le jeu de donn{\'e}es {\'e}labor{\'e} est actuellement compos{\'e} de 1034 paires de phrases. Il couvre les th{\'e}matiques de l{'}invasion de la Russie en Ukraine en 2022, de la pand{\'e}mie de Covid-19 et de la crise climatique. Pour mettre en avant les possibilit{\'e}s de notre jeu de donn{\'e}es, nous {\'e}valuons les performances de certains mod{\`e}les de transformeurs sur lui. Nous constatons qu{'}il constitue pour eux un d{\'e}fi plus {\'e}lev{\'e} que les jeux de donn{\'e}es existants pour le fran{\c{c}}ais, qui sont d{\'e}j{\`a} peu nombreux. In NLP, the automatic detection of logical contradictions between statements is a binary classification task, in which a pair of sentences receives a label according to whether or not the two sentences contradict each other. This task has many potential applications, including combating disinformation. In this article, we present DACCORD, a new dataset dedicated to the task of automatically detecting contradictions between sentences in French. The dataset is currently composed of 1034 sentence pairs. It covers the themes of Russia{'}s invasion of Ukraine in 2022, the Covid-19 pandemic, and the climate crisis. To highlight the possibilities of our dataset, we evaluate the performance of some recent Transformer models on it. We conclude that our dataset is considerably more challenging than the few existing datasets for French.", language = "French", } ```` **ACL:** Maximos Skandalis, Richard Moot, and Simon Robillard. 2023. [DACCORD : un jeu de données pour la Détection Automatique d’énonCés COntRaDictoires en français](https://aclanthology.org/2023.jeptalnrecital-long.22). In *Actes de CORIA-TALN 2023. Actes de la 30e Conférence sur le Traitement Automatique des Langues Naturelles (TALN), volume 1 : travaux de recherche originaux -- articles longs*, pages 285–297, Paris, France. ATALA. ### Acknowledgements This work was supported by the Defence Innovation Agency (AID) of the Directorate General of Armament (DGA) of the French Ministry of Armed Forces, and by the ICO, _Institut Cybersécurité Occitanie_, funded by Région Occitanie, France.
maximoss/daccord-contradictions
[ "task_categories:text-classification", "task_ids:multi-input-text-classification", "size_categories:1K<n<10K", "language:fr", "license:bsd-2-clause", "region:us" ]
2023-03-25T12:03:33+00:00
{"language": ["fr"], "license": "bsd-2-clause", "size_categories": ["1K<n<10K"], "task_categories": ["text-classification"], "task_ids": ["multi-input-text-classification"]}
2024-02-04T12:31:29+00:00
[]
[ "fr" ]
TAGS #task_categories-text-classification #task_ids-multi-input-text-classification #size_categories-1K<n<10K #language-French #license-bsd-2-clause #region-us
Dataset Card for Dataset Name ============================= Dataset Description ------------------- * Homepage: * Repository: URL * Paper: URL * Leaderboard: * Point of Contact: ### Dataset Summary The DACCORD dataset is an entirely new collection of 1034 sentence pairs annotated as a binary classification task for automatic detection of contradictions between sentences in French. Each pair of sentences receives a label according to whether or not the two sentences contradict each other. DACCORD currently covers the themes of Russia’s invasion of Ukraine in 2022, the Covid-19 pandemic, and the climate crisis. The sentences of the dataset were extracted from (or based on sentences from) AFP Factuel articles. ### Supported Tasks and Leaderboards The task of automatic detection of contradictions between sentences is a sentence-pair binary classification task. It can be viewed as a task related to both natural language inference task and misinformation detection task. Dataset Structure ----------------- ### Data Fields * 'id': Index number. * 'premise': The translated premise in the target language. * 'hypothesis': The translated premise in the target language. * 'label': The classification label, with possible values 0 ('entailment'), 1 ('neutral'), 2 ('contradiction'). * 'label\_text': The classification label, with possible values 'entailment' (0), 'neutral' (1), 'contradiction' (2). * 'genre': a 'string' feature . ### Data Splits Additional Information ---------------------- BibTeX: ' ACL: Maximos Skandalis, Richard Moot, and Simon Robillard. 2023. DACCORD : un jeu de données pour la Détection Automatique d’énonCés COntRaDictoires en français. In *Actes de CORIA-TALN 2023. Actes de la 30e Conférence sur le Traitement Automatique des Langues Naturelles (TALN), volume 1 : travaux de recherche originaux -- articles longs*, pages 285–297, Paris, France. ATALA. ### Acknowledgements This work was supported by the Defence Innovation Agency (AID) of the Directorate General of Armament (DGA) of the French Ministry of Armed Forces, and by the ICO, *Institut Cybersécurité Occitanie*, funded by Région Occitanie, France.
[ "### Dataset Summary\n\n\nThe DACCORD dataset is an entirely new collection of 1034 sentence pairs annotated as a binary classification task for automatic detection of contradictions between sentences in French.\nEach pair of sentences receives a label according to whether or not the two sentences contradict each other.\nDACCORD currently covers the themes of Russia’s invasion of Ukraine in 2022, the Covid-19 pandemic, and the climate crisis. The sentences of the dataset were extracted from (or based on sentences from) AFP Factuel articles.", "### Supported Tasks and Leaderboards\n\n\nThe task of automatic detection of contradictions between sentences is a sentence-pair binary classification task. It can be viewed as a task related to both natural language inference task and misinformation detection task.\n\n\nDataset Structure\n-----------------", "### Data Fields\n\n\n* 'id': Index number.\n* 'premise': The translated premise in the target language.\n* 'hypothesis': The translated premise in the target language.\n* 'label': The classification label, with possible values 0 ('entailment'), 1 ('neutral'), 2 ('contradiction').\n* 'label\\_text': The classification label, with possible values 'entailment' (0), 'neutral' (1), 'contradiction' (2).\n* 'genre': a 'string' feature .", "### Data Splits\n\n\n\nAdditional Information\n----------------------\n\n\nBibTeX:\n\n\n'\n\n\nACL:\n\n\nMaximos Skandalis, Richard Moot, and Simon Robillard. 2023. DACCORD : un jeu de données pour la Détection Automatique d’énonCés COntRaDictoires en français. In *Actes de CORIA-TALN 2023. Actes de la 30e Conférence sur le Traitement Automatique des Langues Naturelles (TALN), volume 1 : travaux de recherche originaux -- articles longs*, pages 285–297, Paris, France. ATALA.", "### Acknowledgements\n\n\nThis work was supported by the Defence Innovation Agency (AID) of the Directorate General of Armament (DGA) of the French Ministry of Armed Forces, and by the ICO, *Institut Cybersécurité Occitanie*, funded by Région Occitanie, France." ]
[ "TAGS\n#task_categories-text-classification #task_ids-multi-input-text-classification #size_categories-1K<n<10K #language-French #license-bsd-2-clause #region-us \n", "### Dataset Summary\n\n\nThe DACCORD dataset is an entirely new collection of 1034 sentence pairs annotated as a binary classification task for automatic detection of contradictions between sentences in French.\nEach pair of sentences receives a label according to whether or not the two sentences contradict each other.\nDACCORD currently covers the themes of Russia’s invasion of Ukraine in 2022, the Covid-19 pandemic, and the climate crisis. The sentences of the dataset were extracted from (or based on sentences from) AFP Factuel articles.", "### Supported Tasks and Leaderboards\n\n\nThe task of automatic detection of contradictions between sentences is a sentence-pair binary classification task. It can be viewed as a task related to both natural language inference task and misinformation detection task.\n\n\nDataset Structure\n-----------------", "### Data Fields\n\n\n* 'id': Index number.\n* 'premise': The translated premise in the target language.\n* 'hypothesis': The translated premise in the target language.\n* 'label': The classification label, with possible values 0 ('entailment'), 1 ('neutral'), 2 ('contradiction').\n* 'label\\_text': The classification label, with possible values 'entailment' (0), 'neutral' (1), 'contradiction' (2).\n* 'genre': a 'string' feature .", "### Data Splits\n\n\n\nAdditional Information\n----------------------\n\n\nBibTeX:\n\n\n'\n\n\nACL:\n\n\nMaximos Skandalis, Richard Moot, and Simon Robillard. 2023. DACCORD : un jeu de données pour la Détection Automatique d’énonCés COntRaDictoires en français. In *Actes de CORIA-TALN 2023. Actes de la 30e Conférence sur le Traitement Automatique des Langues Naturelles (TALN), volume 1 : travaux de recherche originaux -- articles longs*, pages 285–297, Paris, France. ATALA.", "### Acknowledgements\n\n\nThis work was supported by the Defence Innovation Agency (AID) of the Directorate General of Armament (DGA) of the French Ministry of Armed Forces, and by the ICO, *Institut Cybersécurité Occitanie*, funded by Région Occitanie, France." ]
[ 60, 126, 64, 132, 132, 73 ]
[ "passage: TAGS\n#task_categories-text-classification #task_ids-multi-input-text-classification #size_categories-1K<n<10K #language-French #license-bsd-2-clause #region-us \n### Dataset Summary\n\n\nThe DACCORD dataset is an entirely new collection of 1034 sentence pairs annotated as a binary classification task for automatic detection of contradictions between sentences in French.\nEach pair of sentences receives a label according to whether or not the two sentences contradict each other.\nDACCORD currently covers the themes of Russia’s invasion of Ukraine in 2022, the Covid-19 pandemic, and the climate crisis. The sentences of the dataset were extracted from (or based on sentences from) AFP Factuel articles.### Supported Tasks and Leaderboards\n\n\nThe task of automatic detection of contradictions between sentences is a sentence-pair binary classification task. It can be viewed as a task related to both natural language inference task and misinformation detection task.\n\n\nDataset Structure\n-----------------### Data Fields\n\n\n* 'id': Index number.\n* 'premise': The translated premise in the target language.\n* 'hypothesis': The translated premise in the target language.\n* 'label': The classification label, with possible values 0 ('entailment'), 1 ('neutral'), 2 ('contradiction').\n* 'label\\_text': The classification label, with possible values 'entailment' (0), 'neutral' (1), 'contradiction' (2).\n* 'genre': a 'string' feature ." ]
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69f64ef5e23c1cf2643f166c6c478fc0a68d166c
# Dataset Card for Dataset Name ## Dataset Description - **Homepage:** - **Repository:** https://github.com/mskandalis/rte3-french - **Paper:** - **Leaderboard:** - **Point of Contact:** ### Dataset Summary This repository contains all manually translated versions of RTE-3 dataset, plus the original English one. The languages into which RTE-3 dataset has so far been translated are Italian (2012), German (2013), and French (2023). Unlike in other repositories, both our own French version and the older Italian and German ones are here annotated in 3 classes (entailment, neutral, contradiction), and not in 2 (entailment, not entailment). If you want to use the dataset only in a specific language among those provided here, you can filter data by selecting only the language column value you wish. ### Supported Tasks and Leaderboards This dataset can be used for the task of Natural Language Inference (NLI), also known as Recognizing Textual Entailment (RTE), which is a sentence-pair classification task. ## Dataset Structure ### Data Fields - `id`: Index number. - `language`: The language of the concerned pair of sentences. - `premise`: The translated premise in the target language. - `hypothesis`: The translated premise in the target language. - `label`: The classification label, with possible values 0 (`entailment`), 1 (`neutral`), 2 (`contradiction`). - `label_text`: The classification label, with possible values `entailment` (0), `neutral` (1), `contradiction` (2). - `task`: The particular NLP task that the data was drawn from (IE, IR, QA and SUM). - `length`: The length of the text of the pair. ### Data Splits | name |development|test| |-------------|----------:|---:| |all_languages| 3200 |3200| | fr | 800 | 800| | de | 800 | 800| | it | 800 | 800| For French RTE-3: | name |entailment|neutral|contradiction| |-------------|---------:|------:|------------:| | dev | 412 | 299 | 89 | | test | 410 | 318 | 72 | | name |short|long| |-------------|----:|---:| | dev | 665 | 135| | test | 683 | 117| | name | IE| IR| QA|SUM| |-------------|--:|--:|--:|--:| | dev |200|200|200|200| | test |200|200|200|200| ## Additional Information ### Citation Information **BibTeX:** ````BibTeX @inproceedings{giampiccolo-etal-2007-third, title = "The Third {PASCAL} Recognizing Textual Entailment Challenge", author = "Giampiccolo, Danilo and Magnini, Bernardo and Dagan, Ido and Dolan, Bill", booktitle = "Proceedings of the {ACL}-{PASCAL} Workshop on Textual Entailment and Paraphrasing", month = jun, year = "2007", address = "Prague", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/W07-1401", pages = "1--9", } ```` **ACL:** Danilo Giampiccolo, Bernardo Magnini, Ido Dagan, and Bill Dolan. 2007. [The Third PASCAL Recognizing Textual Entailment Challenge](https://aclanthology.org/W07-1401). In *Proceedings of the ACL-PASCAL Workshop on Textual Entailment and Paraphrasing*, pages 1–9, Prague. Association for Computational Linguistics. ### Acknowledgements This work was supported by the Defence Innovation Agency (AID) of the Directorate General of Armament (DGA) of the French Ministry of Armed Forces, and by the ICO, _Institut Cybersécurité Occitanie_, funded by Région Occitanie, France.
maximoss/rte3-multi
[ "task_categories:text-classification", "task_ids:natural-language-inference", "task_ids:multi-input-text-classification", "size_categories:1K<n<10K", "language:fr", "language:en", "language:it", "language:de", "license:cc-by-4.0", "region:us" ]
2023-03-25T12:04:19+00:00
{"language": ["fr", "en", "it", "de"], "license": "cc-by-4.0", "size_categories": ["1K<n<10K"], "task_categories": ["text-classification"], "task_ids": ["natural-language-inference", "multi-input-text-classification"]}
2024-02-04T12:23:56+00:00
[]
[ "fr", "en", "it", "de" ]
TAGS #task_categories-text-classification #task_ids-natural-language-inference #task_ids-multi-input-text-classification #size_categories-1K<n<10K #language-French #language-English #language-Italian #language-German #license-cc-by-4.0 #region-us
Dataset Card for Dataset Name ============================= Dataset Description ------------------- * Homepage: * Repository: URL * Paper: * Leaderboard: * Point of Contact: ### Dataset Summary This repository contains all manually translated versions of RTE-3 dataset, plus the original English one. The languages into which RTE-3 dataset has so far been translated are Italian (2012), German (2013), and French (2023). Unlike in other repositories, both our own French version and the older Italian and German ones are here annotated in 3 classes (entailment, neutral, contradiction), and not in 2 (entailment, not entailment). If you want to use the dataset only in a specific language among those provided here, you can filter data by selecting only the language column value you wish. ### Supported Tasks and Leaderboards This dataset can be used for the task of Natural Language Inference (NLI), also known as Recognizing Textual Entailment (RTE), which is a sentence-pair classification task. Dataset Structure ----------------- ### Data Fields * 'id': Index number. * 'language': The language of the concerned pair of sentences. * 'premise': The translated premise in the target language. * 'hypothesis': The translated premise in the target language. * 'label': The classification label, with possible values 0 ('entailment'), 1 ('neutral'), 2 ('contradiction'). * 'label\_text': The classification label, with possible values 'entailment' (0), 'neutral' (1), 'contradiction' (2). * 'task': The particular NLP task that the data was drawn from (IE, IR, QA and SUM). * 'length': The length of the text of the pair. ### Data Splits For French RTE-3: Additional Information ---------------------- BibTeX: ' ACL: Danilo Giampiccolo, Bernardo Magnini, Ido Dagan, and Bill Dolan. 2007. The Third PASCAL Recognizing Textual Entailment Challenge. In *Proceedings of the ACL-PASCAL Workshop on Textual Entailment and Paraphrasing*, pages 1–9, Prague. Association for Computational Linguistics. ### Acknowledgements This work was supported by the Defence Innovation Agency (AID) of the Directorate General of Armament (DGA) of the French Ministry of Armed Forces, and by the ICO, *Institut Cybersécurité Occitanie*, funded by Région Occitanie, France.
[ "### Dataset Summary\n\n\nThis repository contains all manually translated versions of RTE-3 dataset, plus the original English one. The languages into which RTE-3 dataset has so far been translated are Italian (2012), German (2013), and French (2023).\n\n\nUnlike in other repositories, both our own French version and the older Italian and German ones are here annotated in 3 classes (entailment, neutral, contradiction), and not in 2 (entailment, not entailment).\n\n\nIf you want to use the dataset only in a specific language among those provided here, you can filter data by selecting only the language column value you wish.", "### Supported Tasks and Leaderboards\n\n\nThis dataset can be used for the task of Natural Language Inference (NLI), also known as Recognizing Textual Entailment (RTE), which is a sentence-pair classification task.\n\n\nDataset Structure\n-----------------", "### Data Fields\n\n\n* 'id': Index number.\n* 'language': The language of the concerned pair of sentences.\n* 'premise': The translated premise in the target language.\n* 'hypothesis': The translated premise in the target language.\n* 'label': The classification label, with possible values 0 ('entailment'), 1 ('neutral'), 2 ('contradiction').\n* 'label\\_text': The classification label, with possible values 'entailment' (0), 'neutral' (1), 'contradiction' (2).\n* 'task': The particular NLP task that the data was drawn from (IE, IR, QA and SUM).\n* 'length': The length of the text of the pair.", "### Data Splits\n\n\n\nFor French RTE-3:\n\n\n\n\n\nAdditional Information\n----------------------\n\n\nBibTeX:\n\n\n'\n\n\nACL:\n\n\nDanilo Giampiccolo, Bernardo Magnini, Ido Dagan, and Bill Dolan. 2007. The Third PASCAL Recognizing Textual Entailment Challenge. In *Proceedings of the ACL-PASCAL Workshop on Textual Entailment and Paraphrasing*, pages 1–9, Prague. Association for Computational Linguistics.", "### Acknowledgements\n\n\nThis work was supported by the Defence Innovation Agency (AID) of the Directorate General of Armament (DGA) of the French Ministry of Armed Forces, and by the ICO, *Institut Cybersécurité Occitanie*, funded by Région Occitanie, France." ]
[ "TAGS\n#task_categories-text-classification #task_ids-natural-language-inference #task_ids-multi-input-text-classification #size_categories-1K<n<10K #language-French #language-English #language-Italian #language-German #license-cc-by-4.0 #region-us \n", "### Dataset Summary\n\n\nThis repository contains all manually translated versions of RTE-3 dataset, plus the original English one. The languages into which RTE-3 dataset has so far been translated are Italian (2012), German (2013), and French (2023).\n\n\nUnlike in other repositories, both our own French version and the older Italian and German ones are here annotated in 3 classes (entailment, neutral, contradiction), and not in 2 (entailment, not entailment).\n\n\nIf you want to use the dataset only in a specific language among those provided here, you can filter data by selecting only the language column value you wish.", "### Supported Tasks and Leaderboards\n\n\nThis dataset can be used for the task of Natural Language Inference (NLI), also known as Recognizing Textual Entailment (RTE), which is a sentence-pair classification task.\n\n\nDataset Structure\n-----------------", "### Data Fields\n\n\n* 'id': Index number.\n* 'language': The language of the concerned pair of sentences.\n* 'premise': The translated premise in the target language.\n* 'hypothesis': The translated premise in the target language.\n* 'label': The classification label, with possible values 0 ('entailment'), 1 ('neutral'), 2 ('contradiction').\n* 'label\\_text': The classification label, with possible values 'entailment' (0), 'neutral' (1), 'contradiction' (2).\n* 'task': The particular NLP task that the data was drawn from (IE, IR, QA and SUM).\n* 'length': The length of the text of the pair.", "### Data Splits\n\n\n\nFor French RTE-3:\n\n\n\n\n\nAdditional Information\n----------------------\n\n\nBibTeX:\n\n\n'\n\n\nACL:\n\n\nDanilo Giampiccolo, Bernardo Magnini, Ido Dagan, and Bill Dolan. 2007. The Third PASCAL Recognizing Textual Entailment Challenge. In *Proceedings of the ACL-PASCAL Workshop on Textual Entailment and Paraphrasing*, pages 1–9, Prague. Association for Computational Linguistics.", "### Acknowledgements\n\n\nThis work was supported by the Defence Innovation Agency (AID) of the Directorate General of Armament (DGA) of the French Ministry of Armed Forces, and by the ICO, *Institut Cybersécurité Occitanie*, funded by Région Occitanie, France." ]
[ 85, 150, 61, 178, 107, 73 ]
[ "passage: TAGS\n#task_categories-text-classification #task_ids-natural-language-inference #task_ids-multi-input-text-classification #size_categories-1K<n<10K #language-French #language-English #language-Italian #language-German #license-cc-by-4.0 #region-us \n### Dataset Summary\n\n\nThis repository contains all manually translated versions of RTE-3 dataset, plus the original English one. The languages into which RTE-3 dataset has so far been translated are Italian (2012), German (2013), and French (2023).\n\n\nUnlike in other repositories, both our own French version and the older Italian and German ones are here annotated in 3 classes (entailment, neutral, contradiction), and not in 2 (entailment, not entailment).\n\n\nIf you want to use the dataset only in a specific language among those provided here, you can filter data by selecting only the language column value you wish.### Supported Tasks and Leaderboards\n\n\nThis dataset can be used for the task of Natural Language Inference (NLI), also known as Recognizing Textual Entailment (RTE), which is a sentence-pair classification task.\n\n\nDataset Structure\n-----------------### Data Fields\n\n\n* 'id': Index number.\n* 'language': The language of the concerned pair of sentences.\n* 'premise': The translated premise in the target language.\n* 'hypothesis': The translated premise in the target language.\n* 'label': The classification label, with possible values 0 ('entailment'), 1 ('neutral'), 2 ('contradiction').\n* 'label\\_text': The classification label, with possible values 'entailment' (0), 'neutral' (1), 'contradiction' (2).\n* 'task': The particular NLP task that the data was drawn from (IE, IR, QA and SUM).\n* 'length': The length of the text of the pair." ]
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02a9c28b1d6e6ddbdc484575d50014119070e7b5
# Dataset Card for Dataset Name ## Dataset Description - **Homepage:** - **Repository:** - **Paper:** - **Leaderboard:** - **Point of Contact:** ### Dataset Summary This repository contains a collection of machine translations of [LingNLI](https://github.com/Alicia-Parrish/ling_in_loop) dataset into 9 different languages (Bulgarian, Finnish, French, Greek, Italian, Korean, Lithuanian, Portuguese, Spanish). The goal is to predict textual entailment (does sentence A imply/contradict/neither sentence B), which is a classification task (given two sentences, predict one of three labels). It is here formatted in the same manner as the widely used [XNLI](https://huggingface.co/datasets/xnli) dataset for convenience. If you want to use this dataset only in a specific language among those provided here, you can filter data by selecting only the language column value you wish. ### Supported Tasks and Leaderboards This dataset can be used for the task of Natural Language Inference (NLI), also known as Recognizing Textual Entailment (RTE), which is a sentence-pair classification task. ## Dataset Structure ### Data Fields - `language`: The language in which the pair of sentences is given. - `premise`: The machine translated premise in the target language. - `hypothesis`: The machine translated premise in the target language. - `label`: The classification label, with possible values 0 (`entailment`), 1 (`neutral`), 2 (`contradiction`). - `label_text`: The classification label, with possible values `entailment` (0), `neutral` (1), `contradiction` (2). - `premise_original`: The original premise from the English source dataset. - `hypothesis_original`: The original hypothesis from the English source dataset. ### Data Splits For the whole dataset (LitL and LotS subsets): | language |train|validation| |-------------|----:|---------:| |all_languages|269865| 44037| |el-gr |29985| 4893| |fr |29985| 4893| |it |29985| 4893| |es |29985| 4893| |pt |29985| 4893| |ko |29985| 4893| |fi |29985| 4893| |lt |29985| 4893| |bg |29985| 4893| For LitL subset: | language |train|validation| |-------------|----:|---------:| |all_languages|134955| 21825| |el-gr |14995| 2425| |fr |14995| 2425| |it |14995| 2425| |es |14995| 2425| |pt |14995| 2425| |ko |14995| 2425| |fi |14995| 2425| |lt |14995| 2425| |bg |14995| 2425| For LotS subset: | language |train|validation| |-------------|----:|---------:| |all_languages|134910| 22212| |el-gr |14990| 2468| |fr |14990| 2468| |it |14990| 2468| |es |14990| 2468| |pt |14990| 2468| |ko |14990| 2468| |fi |14990| 2468| |lt |14990| 2468| |bg |14990| 2468| ## Dataset Creation The two subsets of the original dataset were machine translated using the latest neural machine translation [opus-mt-tc-big](https://huggingface.co/models?sort=downloads&search=opus-mt-tc-big) models available for the respective languages. Running the translations lasted from March 25, 2023 until April 8, 2023. ## Additional Information ### Citation Information **BibTeX:** ````BibTeX @inproceedings{parrish-etal-2021-putting-linguist, title = "Does Putting a Linguist in the Loop Improve {NLU} Data Collection?", author = "Parrish, Alicia and Huang, William and Agha, Omar and Lee, Soo-Hwan and Nangia, Nikita and Warstadt, Alexia and Aggarwal, Karmanya and Allaway, Emily and Linzen, Tal and Bowman, Samuel R.", booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2021", month = nov, year = "2021", address = "Punta Cana, Dominican Republic", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.findings-emnlp.421", doi = "10.18653/v1/2021.findings-emnlp.421", pages = "4886--4901", abstract = "Many crowdsourced NLP datasets contain systematic artifacts that are identified only after data collection is complete. Earlier identification of these issues should make it easier to create high-quality training and evaluation data. We attempt this by evaluating protocols in which expert linguists work {`}in the loop{'} during data collection to identify and address these issues by adjusting task instructions and incentives. Using natural language inference as a test case, we compare three data collection protocols: (i) a baseline protocol with no linguist involvement, (ii) a linguist-in-the-loop intervention with iteratively-updated constraints on the writing task, and (iii) an extension that adds direct interaction between linguists and crowdworkers via a chatroom. We find that linguist involvement does not lead to increased accuracy on out-of-domain test sets compared to baseline, and adding a chatroom has no effect on the data. Linguist involvement does, however, lead to more challenging evaluation data and higher accuracy on some challenge sets, demonstrating the benefits of integrating expert analysis during data collection.", } @inproceedings{tiedemann-thottingal-2020-opus, title = "{OPUS}-{MT} {--} Building open translation services for the World", author = {Tiedemann, J{\"o}rg and Thottingal, Santhosh}, booktitle = "Proceedings of the 22nd Annual Conference of the European Association for Machine Translation", month = nov, year = "2020", address = "Lisboa, Portugal", publisher = "European Association for Machine Translation", url = "https://aclanthology.org/2020.eamt-1.61", pages = "479--480", abstract = "This paper presents OPUS-MT a project that focuses on the development of free resources and tools for machine translation. The current status is a repository of over 1,000 pre-trained neural machine translation models that are ready to be launched in on-line translation services. For this we also provide open source implementations of web applications that can run efficiently on average desktop hardware with a straightforward setup and installation.", } ```` **ACL:** Alicia Parrish, William Huang, Omar Agha, Soo-Hwan Lee, Nikita Nangia, Alexia Warstadt, Karmanya Aggarwal, Emily Allaway, Tal Linzen, and Samuel R. Bowman. 2021. [Does Putting a Linguist in the Loop Improve NLU Data Collection?](https://aclanthology.org/2021.findings-emnlp.421). In *Findings of the Association for Computational Linguistics: EMNLP 2021*, pages 4886–4901, Punta Cana, Dominican Republic. Association for Computational Linguistics. Jörg Tiedemann and Santhosh Thottingal. 2020. [OPUS-MT – Building open translation services for the World](https://aclanthology.org/2020.eamt-1.61). In *Proceedings of the 22nd Annual Conference of the European Association for Machine Translation*, pages 479–480, Lisboa, Portugal. European Association for Machine Translation. ### Acknowledgements These translations of the original dataset were done as part of a research project supported by the Defence Innovation Agency (AID) of the Directorate General of Armament (DGA) of the French Ministry of Armed Forces, and by the ICO, _Institut Cybersécurité Occitanie_, funded by Région Occitanie, France.
maximoss/lingnli-multi-mt
[ "task_categories:text-classification", "task_ids:natural-language-inference", "task_ids:multi-input-text-classification", "size_categories:10K<n<100K", "language:el", "language:fr", "language:it", "language:es", "language:pt", "language:ko", "language:fi", "language:lt", "language:bg", "license:bsd-2-clause", "region:us" ]
2023-03-25T12:06:26+00:00
{"language": ["el", "fr", "it", "es", "pt", "ko", "fi", "lt", "bg"], "license": "bsd-2-clause", "size_categories": ["10K<n<100K"], "task_categories": ["text-classification"], "task_ids": ["natural-language-inference", "multi-input-text-classification"]}
2024-02-04T12:26:55+00:00
[]
[ "el", "fr", "it", "es", "pt", "ko", "fi", "lt", "bg" ]
TAGS #task_categories-text-classification #task_ids-natural-language-inference #task_ids-multi-input-text-classification #size_categories-10K<n<100K #language-Modern Greek (1453-) #language-French #language-Italian #language-Spanish #language-Portuguese #language-Korean #language-Finnish #language-Lithuanian #language-Bulgarian #license-bsd-2-clause #region-us
Dataset Card for Dataset Name ============================= Dataset Description ------------------- * Homepage: * Repository: * Paper: * Leaderboard: * Point of Contact: ### Dataset Summary This repository contains a collection of machine translations of LingNLI dataset into 9 different languages (Bulgarian, Finnish, French, Greek, Italian, Korean, Lithuanian, Portuguese, Spanish). The goal is to predict textual entailment (does sentence A imply/contradict/neither sentence B), which is a classification task (given two sentences, predict one of three labels). It is here formatted in the same manner as the widely used XNLI dataset for convenience. If you want to use this dataset only in a specific language among those provided here, you can filter data by selecting only the language column value you wish. ### Supported Tasks and Leaderboards This dataset can be used for the task of Natural Language Inference (NLI), also known as Recognizing Textual Entailment (RTE), which is a sentence-pair classification task. Dataset Structure ----------------- ### Data Fields * 'language': The language in which the pair of sentences is given. * 'premise': The machine translated premise in the target language. * 'hypothesis': The machine translated premise in the target language. * 'label': The classification label, with possible values 0 ('entailment'), 1 ('neutral'), 2 ('contradiction'). * 'label\_text': The classification label, with possible values 'entailment' (0), 'neutral' (1), 'contradiction' (2). * 'premise\_original': The original premise from the English source dataset. * 'hypothesis\_original': The original hypothesis from the English source dataset. ### Data Splits For the whole dataset (LitL and LotS subsets): For LitL subset: For LotS subset: Dataset Creation ---------------- The two subsets of the original dataset were machine translated using the latest neural machine translation opus-mt-tc-big models available for the respective languages. Running the translations lasted from March 25, 2023 until April 8, 2023. Additional Information ---------------------- BibTeX: ' ACL: Alicia Parrish, William Huang, Omar Agha, Soo-Hwan Lee, Nikita Nangia, Alexia Warstadt, Karmanya Aggarwal, Emily Allaway, Tal Linzen, and Samuel R. Bowman. 2021. Does Putting a Linguist in the Loop Improve NLU Data Collection?. In *Findings of the Association for Computational Linguistics: EMNLP 2021*, pages 4886–4901, Punta Cana, Dominican Republic. Association for Computational Linguistics. Jörg Tiedemann and Santhosh Thottingal. 2020. OPUS-MT – Building open translation services for the World. In *Proceedings of the 22nd Annual Conference of the European Association for Machine Translation*, pages 479–480, Lisboa, Portugal. European Association for Machine Translation. ### Acknowledgements These translations of the original dataset were done as part of a research project supported by the Defence Innovation Agency (AID) of the Directorate General of Armament (DGA) of the French Ministry of Armed Forces, and by the ICO, *Institut Cybersécurité Occitanie*, funded by Région Occitanie, France.
[ "### Dataset Summary\n\n\nThis repository contains a collection of machine translations of LingNLI dataset\ninto 9 different languages (Bulgarian, Finnish, French, Greek, Italian, Korean, Lithuanian, Portuguese, Spanish). The goal is to predict textual entailment (does sentence A\nimply/contradict/neither sentence B), which is a classification task (given two sentences,\npredict one of three labels). It is here formatted in the same manner as the widely used XNLI dataset for convenience.\n\n\nIf you want to use this dataset only in a specific language among those provided here, you can filter data by selecting only the language column value you wish.", "### Supported Tasks and Leaderboards\n\n\nThis dataset can be used for the task of Natural Language Inference (NLI), also known as Recognizing Textual Entailment (RTE), which is a sentence-pair classification task.\n\n\nDataset Structure\n-----------------", "### Data Fields\n\n\n* 'language': The language in which the pair of sentences is given.\n* 'premise': The machine translated premise in the target language.\n* 'hypothesis': The machine translated premise in the target language.\n* 'label': The classification label, with possible values 0 ('entailment'), 1 ('neutral'), 2 ('contradiction').\n* 'label\\_text': The classification label, with possible values 'entailment' (0), 'neutral' (1), 'contradiction' (2).\n* 'premise\\_original': The original premise from the English source dataset.\n* 'hypothesis\\_original': The original hypothesis from the English source dataset.", "### Data Splits\n\n\nFor the whole dataset (LitL and LotS subsets):\n\n\n\nFor LitL subset:\n\n\n\nFor LotS subset:\n\n\n\nDataset Creation\n----------------\n\n\nThe two subsets of the original dataset were machine translated using the latest neural machine translation opus-mt-tc-big models available for the respective languages.\nRunning the translations lasted from March 25, 2023 until April 8, 2023.\n\n\nAdditional Information\n----------------------\n\n\nBibTeX:\n\n\n'\n\n\nACL:\n\n\nAlicia Parrish, William Huang, Omar Agha, Soo-Hwan Lee, Nikita Nangia, Alexia Warstadt, Karmanya Aggarwal, Emily Allaway, Tal Linzen, and Samuel R. Bowman. 2021. Does Putting a Linguist in the Loop Improve NLU Data Collection?. In *Findings of the Association for Computational Linguistics: EMNLP 2021*, pages 4886–4901, Punta Cana, Dominican Republic. Association for Computational Linguistics.\n\n\nJörg Tiedemann and Santhosh Thottingal. 2020. OPUS-MT – Building open translation services for the World. In *Proceedings of the 22nd Annual Conference of the European Association for Machine Translation*, pages 479–480, Lisboa, Portugal. European Association for Machine Translation.", "### Acknowledgements\n\n\nThese translations of the original dataset were done as part of a research project supported by the Defence Innovation Agency (AID) of the Directorate General of Armament (DGA) of the French Ministry of Armed Forces, and by the ICO, *Institut Cybersécurité Occitanie*, funded by Région Occitanie, France." ]
[ "TAGS\n#task_categories-text-classification #task_ids-natural-language-inference #task_ids-multi-input-text-classification #size_categories-10K<n<100K #language-Modern Greek (1453-) #language-French #language-Italian #language-Spanish #language-Portuguese #language-Korean #language-Finnish #language-Lithuanian #language-Bulgarian #license-bsd-2-clause #region-us \n", "### Dataset Summary\n\n\nThis repository contains a collection of machine translations of LingNLI dataset\ninto 9 different languages (Bulgarian, Finnish, French, Greek, Italian, Korean, Lithuanian, Portuguese, Spanish). The goal is to predict textual entailment (does sentence A\nimply/contradict/neither sentence B), which is a classification task (given two sentences,\npredict one of three labels). It is here formatted in the same manner as the widely used XNLI dataset for convenience.\n\n\nIf you want to use this dataset only in a specific language among those provided here, you can filter data by selecting only the language column value you wish.", "### Supported Tasks and Leaderboards\n\n\nThis dataset can be used for the task of Natural Language Inference (NLI), also known as Recognizing Textual Entailment (RTE), which is a sentence-pair classification task.\n\n\nDataset Structure\n-----------------", "### Data Fields\n\n\n* 'language': The language in which the pair of sentences is given.\n* 'premise': The machine translated premise in the target language.\n* 'hypothesis': The machine translated premise in the target language.\n* 'label': The classification label, with possible values 0 ('entailment'), 1 ('neutral'), 2 ('contradiction').\n* 'label\\_text': The classification label, with possible values 'entailment' (0), 'neutral' (1), 'contradiction' (2).\n* 'premise\\_original': The original premise from the English source dataset.\n* 'hypothesis\\_original': The original hypothesis from the English source dataset.", "### Data Splits\n\n\nFor the whole dataset (LitL and LotS subsets):\n\n\n\nFor LitL subset:\n\n\n\nFor LotS subset:\n\n\n\nDataset Creation\n----------------\n\n\nThe two subsets of the original dataset were machine translated using the latest neural machine translation opus-mt-tc-big models available for the respective languages.\nRunning the translations lasted from March 25, 2023 until April 8, 2023.\n\n\nAdditional Information\n----------------------\n\n\nBibTeX:\n\n\n'\n\n\nACL:\n\n\nAlicia Parrish, William Huang, Omar Agha, Soo-Hwan Lee, Nikita Nangia, Alexia Warstadt, Karmanya Aggarwal, Emily Allaway, Tal Linzen, and Samuel R. Bowman. 2021. Does Putting a Linguist in the Loop Improve NLU Data Collection?. In *Findings of the Association for Computational Linguistics: EMNLP 2021*, pages 4886–4901, Punta Cana, Dominican Republic. Association for Computational Linguistics.\n\n\nJörg Tiedemann and Santhosh Thottingal. 2020. OPUS-MT – Building open translation services for the World. In *Proceedings of the 22nd Annual Conference of the European Association for Machine Translation*, pages 479–480, Lisboa, Portugal. European Association for Machine Translation.", "### Acknowledgements\n\n\nThese translations of the original dataset were done as part of a research project supported by the Defence Innovation Agency (AID) of the Directorate General of Armament (DGA) of the French Ministry of Armed Forces, and by the ICO, *Institut Cybersécurité Occitanie*, funded by Région Occitanie, France." ]
[ 123, 157, 61, 172, 293, 86 ]
[ "passage: TAGS\n#task_categories-text-classification #task_ids-natural-language-inference #task_ids-multi-input-text-classification #size_categories-10K<n<100K #language-Modern Greek (1453-) #language-French #language-Italian #language-Spanish #language-Portuguese #language-Korean #language-Finnish #language-Lithuanian #language-Bulgarian #license-bsd-2-clause #region-us \n### Dataset Summary\n\n\nThis repository contains a collection of machine translations of LingNLI dataset\ninto 9 different languages (Bulgarian, Finnish, French, Greek, Italian, Korean, Lithuanian, Portuguese, Spanish). The goal is to predict textual entailment (does sentence A\nimply/contradict/neither sentence B), which is a classification task (given two sentences,\npredict one of three labels). It is here formatted in the same manner as the widely used XNLI dataset for convenience.\n\n\nIf you want to use this dataset only in a specific language among those provided here, you can filter data by selecting only the language column value you wish.### Supported Tasks and Leaderboards\n\n\nThis dataset can be used for the task of Natural Language Inference (NLI), also known as Recognizing Textual Entailment (RTE), which is a sentence-pair classification task.\n\n\nDataset Structure\n-----------------" ]
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ce1dc54de2ec1f5591bb62e7324d7b11733aded7
# Dataset Card for "tokenized-codeparrot-train-verilog" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
rohitsuv/tokenized-codeparrot-train-verilog
[ "region:us" ]
2023-03-25T12:11:52+00:00
{"dataset_info": {"features": [{"name": "input_ids", "sequence": "int32"}, {"name": "ratio_char_token", "dtype": "float64"}], "splits": [{"name": "train", "num_bytes": 3664280, "num_examples": 5906}], "download_size": 879597, "dataset_size": 3664280}}
2023-03-25T12:11:55+00:00
[]
[]
TAGS #region-us
# Dataset Card for "tokenized-codeparrot-train-verilog" More Information needed
[ "# Dataset Card for \"tokenized-codeparrot-train-verilog\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"tokenized-codeparrot-train-verilog\"\n\nMore Information needed" ]
[ 6, 23 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"tokenized-codeparrot-train-verilog\"\n\nMore Information needed" ]
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b0f79ac04f910b42d68bf18c76e3a09b03e1b232
## This is a dataset of Onion news articles: Note - The headers and body of the news article is split by a ' #~# ' token - Lines with just the token had no body or no header and can be skipped - Feel free to use the script provided to scape the latest version, it takes about 30 mins on an i7-6850K
Biddls/Onion_News
[ "task_categories:summarization", "task_categories:text2text-generation", "task_categories:text-generation", "task_categories:text-classification", "language:en", "license:mit", "region:us" ]
2023-03-25T12:50:01+00:00
{"language": ["en"], "license": "mit", "task_categories": ["summarization", "text2text-generation", "text-generation", "text-classification"], "pretty_name": "OnionNewsScrape"}
2023-03-25T12:57:47+00:00
[]
[ "en" ]
TAGS #task_categories-summarization #task_categories-text2text-generation #task_categories-text-generation #task_categories-text-classification #language-English #license-mit #region-us
## This is a dataset of Onion news articles: Note - The headers and body of the news article is split by a ' #~# ' token - Lines with just the token had no body or no header and can be skipped - Feel free to use the script provided to scape the latest version, it takes about 30 mins on an i7-6850K
[ "## This is a dataset of Onion news articles:\nNote\n - The headers and body of the news article is split by a ' #~# ' token\n - Lines with just the token had no body or no header and can be skipped\n - Feel free to use the script provided to scape the latest version, it takes about 30 mins on an i7-6850K" ]
[ "TAGS\n#task_categories-summarization #task_categories-text2text-generation #task_categories-text-generation #task_categories-text-classification #language-English #license-mit #region-us \n", "## This is a dataset of Onion news articles:\nNote\n - The headers and body of the news article is split by a ' #~# ' token\n - Lines with just the token had no body or no header and can be skipped\n - Feel free to use the script provided to scape the latest version, it takes about 30 mins on an i7-6850K" ]
[ 60, 82 ]
[ "passage: TAGS\n#task_categories-summarization #task_categories-text2text-generation #task_categories-text-generation #task_categories-text-classification #language-English #license-mit #region-us \n## This is a dataset of Onion news articles:\nNote\n - The headers and body of the news article is split by a ' #~# ' token\n - Lines with just the token had no body or no header and can be skipped\n - Feel free to use the script provided to scape the latest version, it takes about 30 mins on an i7-6850K" ]
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c5588cba87a4917dc094449910d091a76094cebc
# Dataset Card for "oa_tell_a_joke_10000" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
mikegarts/oa_tell_a_joke_10000
[ "region:us" ]
2023-03-25T13:12:26+00:00
{"dataset_info": {"features": [{"name": "INSTRUCTION", "dtype": "string"}, {"name": "RESPONSE", "dtype": "string"}, {"name": "SOURCE", "dtype": "string"}, {"name": "METADATA", "struct": [{"name": "link", "dtype": "string"}, {"name": "nsfw", "dtype": "bool"}]}, {"name": "__index_level_0__", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 6108828, "num_examples": 10000}], "download_size": 3247379, "dataset_size": 6108828}}
2023-03-25T13:12:29+00:00
[]
[]
TAGS #region-us
# Dataset Card for "oa_tell_a_joke_10000" More Information needed
[ "# Dataset Card for \"oa_tell_a_joke_10000\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"oa_tell_a_joke_10000\"\n\nMore Information needed" ]
[ 6, 22 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"oa_tell_a_joke_10000\"\n\nMore Information needed" ]
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18add89e3b884703ec869a5c6e2bcf1412ee7edc
# Instruction-Finetuning Dataset Collection (Alpaca-CoT) This repository will continuously collect various instruction tuning datasets. And we standardize different datasets into the same format, which can be directly loaded by the [code](https://github.com/PhoebusSi/alpaca-CoT) of Alpaca model. We also have conducted empirical study on various instruction-tuning datasets based on the Alpaca model, as shown in [https://github.com/PhoebusSi/alpaca-CoT](https://github.com/PhoebusSi/alpaca-CoT). If you think this dataset collection is helpful to you, please `like` this dataset and `star` our [github project](https://github.com/PhoebusSi/alpaca-CoT)! You are in a warm welcome to provide us with any non-collected instruction-tuning datasets (or their sources). We will uniformly format them, train Alpaca model with these datasets and open source the model checkpoints. # Contribute Welcome to join us and become a contributor to this project! If you want to share some datasets, adjust the data in the following format: ``` example.json [ {"instruction": instruction string, "input": input string, # (may be empty) "output": output string} ] ``` Folder should be like this: ``` Alpaca-CoT | |----example | | | |----example.json | | | ----example_context.json ... ``` Create a new pull request in [Community ](https://huggingface.co/datasets/QingyiSi/Alpaca-CoT/discussions) and publish your branch when you are ready. We will merge it as soon as we can. # Data Usage and Resources ## Data Format All data in this folder is formatted into the same templates, where each sample is as follows: ``` [ {"instruction": instruction string, "input": input string, # (may be empty) "output": output string} ] ``` ## alpaca #### alpaca_data.json > This dataset is published by [Stanford Alpaca](https://github.com/tatsu-lab/stanford_alpaca). It contains 52K English instruction-following samples obtained by [Self-Instruction](https://github.com/yizhongw/self-instruct) techniques. #### alpaca_data_cleaned.json > This dataset is obtained [here](https://github.com/tloen/alpaca-lora). It is a revised version of `alpaca_data.json` by stripping of various tokenization artifacts. ## alpacaGPT4 #### alpaca_gpt4_data.json > This dataset is published by [Instruction-Tuning-with-GPT-4](https://github.com/Instruction-Tuning-with-GPT-4/GPT-4-LLM). It contains 52K English instruction-following samples generated by GPT-4 using Alpaca prompts for fine-tuning LLMs. #### alpaca_gpt4_data_zh.json > This dataset is generated by GPT-4 using Chinese prompts translated from Alpaca by ChatGPT. <!-- ## belle_cn #### belle_data_cn.json This dataset is published by [BELLE](https://github.com/LianjiaTech/BELLE). It contains 0.5M Chinese instruction-following samples, which is also generated by [Self-Instruction](https://github.com/yizhongw/self-instruct) techniques. #### belle_data1M_cn.json This dataset is published by [BELLE](https://github.com/LianjiaTech/BELLE). It contains 1M Chinese instruction-following samples. The data of `belle_data_cn.json` and `belle_data1M_cn.json` are not duplicated. --> ## Chain-of-Thought #### CoT_data.json > This dataset is obtained by formatting the combination of 9 CoT datasets published by [FLAN](https://github.com/google-research/FLAN). It contains 9 CoT tasks involving 74771 samples. #### CoT_CN_data.json > This dataset is obtained by tranlating `CoT_data.json` into Chinese, using Google Translate(en2cn). #### formatted_cot_data folder > This folder contains the formatted English data for each CoT dataset. #### formatted_cot_data folder > This folder contains the formatted Chinese data for each CoT dataset. ## CodeAlpaca #### code_alpaca.json > This dataset is published by [codealpaca](https://github.com/sahil280114/codealpaca). It contains code generation task involving 20022 samples. ## finance #### finance_en.json > This dataset is collected from [here](https://huggingface.co/datasets/gbharti/finance-alpaca). It contains 68912 financial related instructions in English. ## firefly #### firefly.json > his dataset is collected from [here](https://github.com/yangjianxin1/Firefly). It contains 1649398 chinese instructions in 23 nlp tasks. ## GPT4all #### gpt4all.json > This dataset is collected from [here](https://github.com/nomic-ai/gpt4all). It contains 806199 en instructions in code, storys and dialogs tasks. #### gpt4all_without_p3.json > gpt4all without Bigscience/P3, contains 437605 samples. ## GPTeacher #### GPTeacher.json > This dataset is collected from [here](https://github.com/teknium1/GPTeacher). It contains 29013 en instructions generated by GPT-4, General-Instruct - Roleplay-Instruct - Code-Instruct - and Toolformer. ## Guanaco #### GuanacoDataset.json > This dataset is collected from [here](https://huggingface.co/datasets/JosephusCheung/GuanacoDataset). It contains 534610 en instructions generated by text-davinci-003 upon 175 tasks from the Alpaca model by providing rewrites of seed tasks in different languages and adding new tasks specifically designed for English grammar analysis, natural language understanding, cross-lingual self-awareness, and explicit content recognition. #### Guanaco_additional_Dataset.json > A new additional larger dataset for different languages. ## HC3 #### HC3_ChatGPT.json/HC3_Human.json > This dataset is collected from [here](https://huggingface.co/datasets/Hello-SimpleAI/HC3). It contains 37175 en/zh instructions generated by ChatGPT and human. #### HC3_ChatGPT_deduplication.json/HC3_Human_deduplication.json > HC3 dataset without deduplication instructions. ## instinwild #### instinwild_en.json & instinwild_cn.json > The two datasets are obtained [here](https://github.com/XueFuzhao/InstructionWild). It contains 52191 English and 51504 Chinese instructions, which are collected from Twitter, where users tend to share their interesting prompts of mostly generation, open QA, and mind-storm types. (Colossal AI used these datasets to train the ColossalChat model.) ## instruct #### instruct.json > The two datasets are obtained [here](https://huggingface.co/datasets/swype/instruct). It contains 888969 English instructions, which are caugmentation performed using the advanced NLP tools provided by AllenAI. ## Natural Instructions #### natural-instructions-1700tasks.zip > This dataset is obtained [here](https://github.com/allenai/natural-instructions). It contains 5040134 instructions, which are collected from diverse nlp tasks ## prosocial dialog #### natural-instructions-1700tasks.zip > This dataset is obtained [here](https://huggingface.co/datasets/allenai/prosocial-dialog). It contains 165681 English instructions, which are produuced by GPT-3 rewrites questions and humans feedback ## xP3 #### natural-instructions-1700tasks.zip > This dataset is obtained [here](https://huggingface.co/datasets/bigscience/xP3). It contains 78883588 instructions, which are collected by prompts & datasets across 46 of languages & 16 NLP tasks ## Chinese-instruction-collection > all datasets of Chinese instruction collection ## combination #### alcapa_plus_belle_data.json > This dataset is the combination of English `alpaca_data.json` and Chinese `belle_data_cn.json`. #### alcapa_plus_cot_data.json > This dataset is the combination of English `alpaca_data.json` and CoT `CoT_data.json`. #### alcapa_plus_belle_cot_data.json > This dataset is the combination of English `alpaca_data.json`, Chinese `belle_data_cn.json` and CoT `CoT_data.json`. ## Citation Please cite the repo if you use the data collection, code, and experimental findings in this repo. ``` @misc{alpaca-cot, author = {Qingyi Si, Zheng Lin }, school = {Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China}, title = {Alpaca-CoT: An Instruction Fine-Tuning Platform with Instruction Data Collection and Unified Large Language Models Interface}, year = {2023}, publisher = {GitHub}, journal = {GitHub repository}, howpublished = {\url{https://github.com/PhoebusSi/alpaca-CoT}}, } ``` Cite the original Stanford Alpaca, BELLE and FLAN papers as well, please.
QingyiSi/Alpaca-CoT
[ "language:en", "language:zh", "language:ml", "license:apache-2.0", "Instruction", "Cot", "region:us" ]
2023-03-25T14:58:30+00:00
{"language": ["en", "zh", "ml"], "license": "apache-2.0", "tags": ["Instruction", "Cot"], "datasets": ["dataset1", "dataset2"]}
2023-09-14T07:52:10+00:00
[]
[ "en", "zh", "ml" ]
TAGS #language-English #language-Chinese #language-Malayalam #license-apache-2.0 #Instruction #Cot #region-us
# Instruction-Finetuning Dataset Collection (Alpaca-CoT) This repository will continuously collect various instruction tuning datasets. And we standardize different datasets into the same format, which can be directly loaded by the code of Alpaca model. We also have conducted empirical study on various instruction-tuning datasets based on the Alpaca model, as shown in URL If you think this dataset collection is helpful to you, please 'like' this dataset and 'star' our github project! You are in a warm welcome to provide us with any non-collected instruction-tuning datasets (or their sources). We will uniformly format them, train Alpaca model with these datasets and open source the model checkpoints. # Contribute Welcome to join us and become a contributor to this project! If you want to share some datasets, adjust the data in the following format: Folder should be like this: Create a new pull request in Community and publish your branch when you are ready. We will merge it as soon as we can. # Data Usage and Resources ## Data Format All data in this folder is formatted into the same templates, where each sample is as follows: ## alpaca #### alpaca_data.json > This dataset is published by Stanford Alpaca. It contains 52K English instruction-following samples obtained by Self-Instruction techniques. #### alpaca_data_cleaned.json > This dataset is obtained here. It is a revised version of 'alpaca_data.json' by stripping of various tokenization artifacts. ## alpacaGPT4 #### alpaca_gpt4_data.json > This dataset is published by Instruction-Tuning-with-GPT-4. It contains 52K English instruction-following samples generated by GPT-4 using Alpaca prompts for fine-tuning LLMs. #### alpaca_gpt4_data_zh.json > This dataset is generated by GPT-4 using Chinese prompts translated from Alpaca by ChatGPT. ## Chain-of-Thought #### CoT_data.json > This dataset is obtained by formatting the combination of 9 CoT datasets published by FLAN. It contains 9 CoT tasks involving 74771 samples. #### CoT_CN_data.json > This dataset is obtained by tranlating 'CoT_data.json' into Chinese, using Google Translate(en2cn). #### formatted_cot_data folder > This folder contains the formatted English data for each CoT dataset. #### formatted_cot_data folder > This folder contains the formatted Chinese data for each CoT dataset. ## CodeAlpaca #### code_alpaca.json > This dataset is published by codealpaca. It contains code generation task involving 20022 samples. ## finance #### finance_en.json > This dataset is collected from here. It contains 68912 financial related instructions in English. ## firefly #### URL > his dataset is collected from here. It contains 1649398 chinese instructions in 23 nlp tasks. ## GPT4all #### URL > This dataset is collected from here. It contains 806199 en instructions in code, storys and dialogs tasks. #### gpt4all_without_p3.json > gpt4all without Bigscience/P3, contains 437605 samples. ## GPTeacher #### URL > This dataset is collected from here. It contains 29013 en instructions generated by GPT-4, General-Instruct - Roleplay-Instruct - Code-Instruct - and Toolformer. ## Guanaco #### URL > This dataset is collected from here. It contains 534610 en instructions generated by text-davinci-003 upon 175 tasks from the Alpaca model by providing rewrites of seed tasks in different languages and adding new tasks specifically designed for English grammar analysis, natural language understanding, cross-lingual self-awareness, and explicit content recognition. #### Guanaco_additional_Dataset.json > A new additional larger dataset for different languages. ## HC3 #### HC3_ChatGPT.json/HC3_Human.json > This dataset is collected from here. It contains 37175 en/zh instructions generated by ChatGPT and human. #### HC3_ChatGPT_deduplication.json/HC3_Human_deduplication.json > HC3 dataset without deduplication instructions. ## instinwild #### instinwild_en.json & instinwild_cn.json > The two datasets are obtained here. It contains 52191 English and 51504 Chinese instructions, which are collected from Twitter, where users tend to share their interesting prompts of mostly generation, open QA, and mind-storm types. (Colossal AI used these datasets to train the ColossalChat model.) ## instruct #### URL > The two datasets are obtained here. It contains 888969 English instructions, which are caugmentation performed using the advanced NLP tools provided by AllenAI. ## Natural Instructions #### URL > This dataset is obtained here. It contains 5040134 instructions, which are collected from diverse nlp tasks ## prosocial dialog #### URL > This dataset is obtained here. It contains 165681 English instructions, which are produuced by GPT-3 rewrites questions and humans feedback ## xP3 #### URL > This dataset is obtained here. It contains 78883588 instructions, which are collected by prompts & datasets across 46 of languages & 16 NLP tasks ## Chinese-instruction-collection > all datasets of Chinese instruction collection ## combination #### alcapa_plus_belle_data.json > This dataset is the combination of English 'alpaca_data.json' and Chinese 'belle_data_cn.json'. #### alcapa_plus_cot_data.json > This dataset is the combination of English 'alpaca_data.json' and CoT 'CoT_data.json'. #### alcapa_plus_belle_cot_data.json > This dataset is the combination of English 'alpaca_data.json', Chinese 'belle_data_cn.json' and CoT 'CoT_data.json'. Please cite the repo if you use the data collection, code, and experimental findings in this repo. Cite the original Stanford Alpaca, BELLE and FLAN papers as well, please.
[ "# Instruction-Finetuning Dataset Collection (Alpaca-CoT)\nThis repository will continuously collect various instruction tuning datasets. And we standardize different datasets into the same format, which can be directly loaded by the code of Alpaca model.\n\nWe also have conducted empirical study on various instruction-tuning datasets based on the Alpaca model, as shown in URL \n\nIf you think this dataset collection is helpful to you, please 'like' this dataset and 'star' our github project!\n\nYou are in a warm welcome to provide us with any non-collected instruction-tuning datasets (or their sources). We will uniformly format them, train Alpaca model with these datasets and open source the model checkpoints.", "# Contribute\n\nWelcome to join us and become a contributor to this project!\nIf you want to share some datasets, adjust the data in the following format:\n\nFolder should be like this:\n\nCreate a new pull request in Community\n and publish your branch when you are ready. We will merge it as soon as we can.", "# Data Usage and Resources", "## Data Format\nAll data in this folder is formatted into the same templates, where each sample is as follows:", "## alpaca", "#### alpaca_data.json\n> This dataset is published by Stanford Alpaca. It contains 52K English instruction-following samples obtained by Self-Instruction techniques.", "#### alpaca_data_cleaned.json\n> This dataset is obtained here. It is a revised version of 'alpaca_data.json' by stripping of various tokenization artifacts.", "## alpacaGPT4", "#### alpaca_gpt4_data.json\n> This dataset is published by Instruction-Tuning-with-GPT-4. \nIt contains 52K English instruction-following samples generated by GPT-4 using Alpaca prompts for fine-tuning LLMs.", "#### alpaca_gpt4_data_zh.json\n> This dataset is generated by GPT-4 using Chinese prompts translated from Alpaca by ChatGPT.", "## Chain-of-Thought", "#### CoT_data.json\n> This dataset is obtained by formatting the combination of 9 CoT datasets published by FLAN. It contains 9 CoT tasks involving 74771 samples.", "#### CoT_CN_data.json\n> This dataset is obtained by tranlating 'CoT_data.json' into Chinese, using Google Translate(en2cn).", "#### formatted_cot_data folder\n> This folder contains the formatted English data for each CoT dataset.", "#### formatted_cot_data folder\n> This folder contains the formatted Chinese data for each CoT dataset.", "## CodeAlpaca", "#### code_alpaca.json\n> This dataset is published by codealpaca. It contains code generation task involving 20022 samples.", "## finance", "#### finance_en.json\n> This dataset is collected from here. It contains 68912 financial related instructions in English.", "## firefly", "#### URL\n> his dataset is collected from here. It contains 1649398 chinese instructions in 23 nlp tasks.", "## GPT4all", "#### URL\n> This dataset is collected from here. It contains 806199 en instructions in code, storys and dialogs tasks.", "#### gpt4all_without_p3.json\n> gpt4all without Bigscience/P3, contains 437605 samples.", "## GPTeacher", "#### URL\n> This dataset is collected from here. It contains 29013 en instructions generated by GPT-4, General-Instruct - Roleplay-Instruct - Code-Instruct - and Toolformer.", "## Guanaco", "#### URL\n> This dataset is collected from here. It contains 534610 en instructions generated by text-davinci-003 upon 175 tasks from the Alpaca model by providing rewrites of seed tasks in different languages and adding new tasks specifically designed for English grammar analysis, natural language understanding, cross-lingual self-awareness, and explicit content recognition.", "#### Guanaco_additional_Dataset.json\n> A new additional larger dataset for different languages.", "## HC3", "#### HC3_ChatGPT.json/HC3_Human.json\n> This dataset is collected from here. It contains 37175 en/zh instructions generated by ChatGPT and human.", "#### HC3_ChatGPT_deduplication.json/HC3_Human_deduplication.json\n> HC3 dataset without deduplication instructions.", "## instinwild", "#### instinwild_en.json & instinwild_cn.json\n> The two datasets are obtained here. It contains 52191 English and 51504 Chinese instructions, which are collected from Twitter, where users tend to share their interesting prompts of mostly generation, open QA, and mind-storm types. (Colossal AI used these datasets to train the ColossalChat model.)", "## instruct", "#### URL\n> The two datasets are obtained here. It contains 888969 English instructions, which are caugmentation performed using the advanced NLP tools provided by AllenAI.", "## Natural Instructions", "#### URL\n> This dataset is obtained here. It contains 5040134 instructions, which are collected from diverse nlp tasks", "## prosocial dialog", "#### URL\n> This dataset is obtained here. It contains 165681 English instructions, which are produuced by GPT-3 rewrites questions and humans feedback", "## xP3", "#### URL\n> This dataset is obtained here. It contains 78883588 instructions, which are collected by prompts & datasets across 46 of languages & 16 NLP tasks", "## Chinese-instruction-collection\n> all datasets of Chinese instruction collection", "## combination", "#### alcapa_plus_belle_data.json\n> This dataset is the combination of English 'alpaca_data.json' and Chinese 'belle_data_cn.json'.", "#### alcapa_plus_cot_data.json\n> This dataset is the combination of English 'alpaca_data.json' and CoT 'CoT_data.json'.", "#### alcapa_plus_belle_cot_data.json\n> This dataset is the combination of English 'alpaca_data.json', Chinese 'belle_data_cn.json' and CoT 'CoT_data.json'.\n\nPlease cite the repo if you use the data collection, code, and experimental findings in this repo. \n\nCite the original Stanford Alpaca, BELLE and FLAN papers as well, please." ]
[ "TAGS\n#language-English #language-Chinese #language-Malayalam #license-apache-2.0 #Instruction #Cot #region-us \n", "# Instruction-Finetuning Dataset Collection (Alpaca-CoT)\nThis repository will continuously collect various instruction tuning datasets. And we standardize different datasets into the same format, which can be directly loaded by the code of Alpaca model.\n\nWe also have conducted empirical study on various instruction-tuning datasets based on the Alpaca model, as shown in URL \n\nIf you think this dataset collection is helpful to you, please 'like' this dataset and 'star' our github project!\n\nYou are in a warm welcome to provide us with any non-collected instruction-tuning datasets (or their sources). We will uniformly format them, train Alpaca model with these datasets and open source the model checkpoints.", "# Contribute\n\nWelcome to join us and become a contributor to this project!\nIf you want to share some datasets, adjust the data in the following format:\n\nFolder should be like this:\n\nCreate a new pull request in Community\n and publish your branch when you are ready. We will merge it as soon as we can.", "# Data Usage and Resources", "## Data Format\nAll data in this folder is formatted into the same templates, where each sample is as follows:", "## alpaca", "#### alpaca_data.json\n> This dataset is published by Stanford Alpaca. It contains 52K English instruction-following samples obtained by Self-Instruction techniques.", "#### alpaca_data_cleaned.json\n> This dataset is obtained here. It is a revised version of 'alpaca_data.json' by stripping of various tokenization artifacts.", "## alpacaGPT4", "#### alpaca_gpt4_data.json\n> This dataset is published by Instruction-Tuning-with-GPT-4. \nIt contains 52K English instruction-following samples generated by GPT-4 using Alpaca prompts for fine-tuning LLMs.", "#### alpaca_gpt4_data_zh.json\n> This dataset is generated by GPT-4 using Chinese prompts translated from Alpaca by ChatGPT.", "## Chain-of-Thought", "#### CoT_data.json\n> This dataset is obtained by formatting the combination of 9 CoT datasets published by FLAN. It contains 9 CoT tasks involving 74771 samples.", "#### CoT_CN_data.json\n> This dataset is obtained by tranlating 'CoT_data.json' into Chinese, using Google Translate(en2cn).", "#### formatted_cot_data folder\n> This folder contains the formatted English data for each CoT dataset.", "#### formatted_cot_data folder\n> This folder contains the formatted Chinese data for each CoT dataset.", "## CodeAlpaca", "#### code_alpaca.json\n> This dataset is published by codealpaca. It contains code generation task involving 20022 samples.", "## finance", "#### finance_en.json\n> This dataset is collected from here. It contains 68912 financial related instructions in English.", "## firefly", "#### URL\n> his dataset is collected from here. It contains 1649398 chinese instructions in 23 nlp tasks.", "## GPT4all", "#### URL\n> This dataset is collected from here. It contains 806199 en instructions in code, storys and dialogs tasks.", "#### gpt4all_without_p3.json\n> gpt4all without Bigscience/P3, contains 437605 samples.", "## GPTeacher", "#### URL\n> This dataset is collected from here. It contains 29013 en instructions generated by GPT-4, General-Instruct - Roleplay-Instruct - Code-Instruct - and Toolformer.", "## Guanaco", "#### URL\n> This dataset is collected from here. It contains 534610 en instructions generated by text-davinci-003 upon 175 tasks from the Alpaca model by providing rewrites of seed tasks in different languages and adding new tasks specifically designed for English grammar analysis, natural language understanding, cross-lingual self-awareness, and explicit content recognition.", "#### Guanaco_additional_Dataset.json\n> A new additional larger dataset for different languages.", "## HC3", "#### HC3_ChatGPT.json/HC3_Human.json\n> This dataset is collected from here. It contains 37175 en/zh instructions generated by ChatGPT and human.", "#### HC3_ChatGPT_deduplication.json/HC3_Human_deduplication.json\n> HC3 dataset without deduplication instructions.", "## instinwild", "#### instinwild_en.json & instinwild_cn.json\n> The two datasets are obtained here. It contains 52191 English and 51504 Chinese instructions, which are collected from Twitter, where users tend to share their interesting prompts of mostly generation, open QA, and mind-storm types. (Colossal AI used these datasets to train the ColossalChat model.)", "## instruct", "#### URL\n> The two datasets are obtained here. It contains 888969 English instructions, which are caugmentation performed using the advanced NLP tools provided by AllenAI.", "## Natural Instructions", "#### URL\n> This dataset is obtained here. It contains 5040134 instructions, which are collected from diverse nlp tasks", "## prosocial dialog", "#### URL\n> This dataset is obtained here. It contains 165681 English instructions, which are produuced by GPT-3 rewrites questions and humans feedback", "## xP3", "#### URL\n> This dataset is obtained here. It contains 78883588 instructions, which are collected by prompts & datasets across 46 of languages & 16 NLP tasks", "## Chinese-instruction-collection\n> all datasets of Chinese instruction collection", "## combination", "#### alcapa_plus_belle_data.json\n> This dataset is the combination of English 'alpaca_data.json' and Chinese 'belle_data_cn.json'.", "#### alcapa_plus_cot_data.json\n> This dataset is the combination of English 'alpaca_data.json' and CoT 'CoT_data.json'.", "#### alcapa_plus_belle_cot_data.json\n> This dataset is the combination of English 'alpaca_data.json', Chinese 'belle_data_cn.json' and CoT 'CoT_data.json'.\n\nPlease cite the repo if you use the data collection, code, and experimental findings in this repo. \n\nCite the original Stanford Alpaca, BELLE and FLAN papers as well, please." ]
[ 36, 173, 68, 7, 25, 4, 44, 51, 7, 66, 42, 9, 49, 43, 26, 26, 5, 34, 2, 30, 3, 29, 5, 32, 35, 5, 48, 4, 85, 27, 3, 47, 38, 5, 96, 3, 40, 5, 30, 4, 37, 4, 43, 17, 2, 44, 44, 99 ]
[ "passage: TAGS\n#language-English #language-Chinese #language-Malayalam #license-apache-2.0 #Instruction #Cot #region-us \n# Instruction-Finetuning Dataset Collection (Alpaca-CoT)\nThis repository will continuously collect various instruction tuning datasets. And we standardize different datasets into the same format, which can be directly loaded by the code of Alpaca model.\n\nWe also have conducted empirical study on various instruction-tuning datasets based on the Alpaca model, as shown in URL \n\nIf you think this dataset collection is helpful to you, please 'like' this dataset and 'star' our github project!\n\nYou are in a warm welcome to provide us with any non-collected instruction-tuning datasets (or their sources). We will uniformly format them, train Alpaca model with these datasets and open source the model checkpoints.# Contribute\n\nWelcome to join us and become a contributor to this project!\nIf you want to share some datasets, adjust the data in the following format:\n\nFolder should be like this:\n\nCreate a new pull request in Community\n and publish your branch when you are ready. We will merge it as soon as we can.# Data Usage and Resources## Data Format\nAll data in this folder is formatted into the same templates, where each sample is as follows:## alpaca#### alpaca_data.json\n> This dataset is published by Stanford Alpaca. It contains 52K English instruction-following samples obtained by Self-Instruction techniques.#### alpaca_data_cleaned.json\n> This dataset is obtained here. It is a revised version of 'alpaca_data.json' by stripping of various tokenization artifacts.## alpacaGPT4#### alpaca_gpt4_data.json\n> This dataset is published by Instruction-Tuning-with-GPT-4. \nIt contains 52K English instruction-following samples generated by GPT-4 using Alpaca prompts for fine-tuning LLMs.", "passage: #### alpaca_gpt4_data_zh.json\n> This dataset is generated by GPT-4 using Chinese prompts translated from Alpaca by ChatGPT.## Chain-of-Thought#### CoT_data.json\n> This dataset is obtained by formatting the combination of 9 CoT datasets published by FLAN. It contains 9 CoT tasks involving 74771 samples.#### CoT_CN_data.json\n> This dataset is obtained by tranlating 'CoT_data.json' into Chinese, using Google Translate(en2cn).#### formatted_cot_data folder\n> This folder contains the formatted English data for each CoT dataset.#### formatted_cot_data folder\n> This folder contains the formatted Chinese data for each CoT dataset.## CodeAlpaca#### code_alpaca.json\n> This dataset is published by codealpaca. It contains code generation task involving 20022 samples.## finance#### finance_en.json\n> This dataset is collected from here. It contains 68912 financial related instructions in English.## firefly#### URL\n> his dataset is collected from here. It contains 1649398 chinese instructions in 23 nlp tasks.## GPT4all#### URL\n> This dataset is collected from here. It contains 806199 en instructions in code, storys and dialogs tasks.#### gpt4all_without_p3.json\n> gpt4all without Bigscience/P3, contains 437605 samples.## GPTeacher#### URL\n> This dataset is collected from here. It contains 29013 en instructions generated by GPT-4, General-Instruct - Roleplay-Instruct - Code-Instruct - and Toolformer.## Guanaco#### URL\n> This dataset is collected from here. It contains 534610 en instructions generated by text-davinci-003 upon 175 tasks from the Alpaca model by providing rewrites of seed tasks in different languages and adding new tasks specifically designed for English grammar analysis, natural language understanding, cross-lingual self-awareness, and explicit content recognition.#### Guanaco_additional_Dataset.json\n> A new additional larger dataset for different languages.## HC3", "passage: #### HC3_ChatGPT.json/HC3_Human.json\n> This dataset is collected from here. It contains 37175 en/zh instructions generated by ChatGPT and human.#### HC3_ChatGPT_deduplication.json/HC3_Human_deduplication.json\n> HC3 dataset without deduplication instructions.## instinwild#### instinwild_en.json & instinwild_cn.json\n> The two datasets are obtained here. It contains 52191 English and 51504 Chinese instructions, which are collected from Twitter, where users tend to share their interesting prompts of mostly generation, open QA, and mind-storm types. (Colossal AI used these datasets to train the ColossalChat model.)## instruct#### URL\n> The two datasets are obtained here. It contains 888969 English instructions, which are caugmentation performed using the advanced NLP tools provided by AllenAI.## Natural Instructions#### URL\n> This dataset is obtained here. It contains 5040134 instructions, which are collected from diverse nlp tasks## prosocial dialog#### URL\n> This dataset is obtained here. It contains 165681 English instructions, which are produuced by GPT-3 rewrites questions and humans feedback## xP3#### URL\n> This dataset is obtained here. It contains 78883588 instructions, which are collected by prompts & datasets across 46 of languages & 16 NLP tasks## Chinese-instruction-collection\n> all datasets of Chinese instruction collection## combination#### alcapa_plus_belle_data.json\n> This dataset is the combination of English 'alpaca_data.json' and Chinese 'belle_data_cn.json'.#### alcapa_plus_cot_data.json\n> This dataset is the combination of English 'alpaca_data.json' and CoT 'CoT_data.json'." ]
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7cd356ecf220a6808de6782c6e2eba8a33d7d743
# Dataset information Dataset from the [French translation](https://lbourdois.github.io/cours-dl-nyu/) by Loïck Bourdois of the [course](https://atcold.github.io/pytorch-Deep-Learning/) by Yann Le Cun and Alfredo Canziani from the NYU. More than 3000 parallel data were created. The whole corpus has been manually checked to make sure of the good alignment of the data. Note that the English data comes from several different people (about 190, see the acknowledgement section below). This has an impact on the homogeneity of the texts (some write in the past tense, others in the present tense; the abbreviations used are not always the same; some write short sentences, while others write sentences of up to 5 or 6 lines, etc.). The translation into French was done by a single person in order to alleviate the problems mentioned above and to propose a homogeneous translation. This means that the corpus of data does not correspond to word by word translations but rather to concept translations. In this logic, the data were not aligned at the sentence level but rather at the paragraph level. The translation choices made are explained [here](https://lbourdois.github.io/cours-dl-nyu/). # Usage ``` from datasets import load_dataset dataset = load_dataset("lbourdois/en-fr-nyu-dl-course-corpus", sep=";") ``` # Acknowledgments A huge thank you to the more than 190 students who shared their course notes (in chronological order of contribution): Yunya Wang, SunJoo Park, Mark Estudillo, Justin Mae, Marina Zavalina, Peeyush Jain, Adrian Pearl, Davida Kollmar, Derek Yen, Tony Xu, Ben Stadnick, Prasanthi Gurumurthy, Amartya Prasad, Dongning Fang, Yuxin Tang, Sahana Upadhya, Micaela Flores, Sheetal Laad, Brina Seidel, Aishwarya Rajan, Jiuhong Xiao, Trieu Trinh, Elliot Silva, Calliea Pan, Chris Ick, Soham Tamba, Ziyu Lei, Hengyu Tang, Ashwin Bhola, Nyutian Long, Linfeng Zhang, Poornima Haridas, Yuchi Ge, Anshan He, Shuting Gu, Weiyang Wen, Vaibhav Gupta, Himani Shah, Gowri Addepalli, Lakshmi Addepalli, Guido Petri, Haoyue Ping, Chinmay Singhal, Divya Juneja, Leyi Zhu, Siqi Wang, Tao Wang, Anqi Zhang, Shiqing Li, Chenqin Yang, Yakun Wang, Jimin Tan, Jiayao Liu, Jialing Xu, Zhengyang Bian, Christina Dominguez, Zhengyuan Ding, Biao Huang, Lin Jiang, Nhung Le, Karanbir Singh Chahal，Meiyi He, Alexander Gao, Weicheng Zhu, Ravi Choudhary，B V Nithish Addepalli, Syed Rahman，Jiayi Du, Xinmeng Li, Atul Gandhi, Li Jiang, Xiao Li, Vishwaesh Rajiv, Wenjun Qu, Xulai Jiang, Shuya Zhao, Henry Steinitz, Rutvi Malaviya, Aathira Manoj, Richard Pang, Aja Klevs, Hsin-Rung Chou, Mrinal Jain, Kelly Sooch, Anthony Tse, Arushi Himatsingka, Eric Kosgey, Bofei Zhang, Andrew Hopen, Maxwell Goldstein, Zeping Zhan, William Huang, Kunal Gadkar, Gaomin Wu, Lin Ye, Aniket Bhatnagar, Dhruv Goyal, Cole Smith, Nikhil Supekar, Zhonghui Hu, Yuqing Wang, Alfred Ajay Aureate Rajakumar, Param Shah, Muyang Jin, Jianzhi Li, Jing Qian, Zeming Lin, Haochen Wang, Eunkyung An, Ying Jin, Ningyuan Huang, Charles Brillo-Sonnino, Shizhan Gong, Natalie Frank, Yunan Hu, Anuj Menta, Dipika Rajesh, Vikas Patidar, Mohith Damarapati, Jiayu Qiu, Yuhong Zhu, Lyuang Fu, Ian Leefmans, Trevor Mitchell, Andrii Dobroshynskyi, Shreyas Chandrakaladharan, Ben Wolfson, Francesca Guiso, Annika Brundyn, Noah Kasmanoff, Luke Martin, Bilal Munawar, Alexander Bienstock, Can Cui, Shaoling Chen, Neil Menghani, Tejaishwarya Gagadam, Joshua Meisel, Jatin Khilnani, Go Inoue, Muhammad Osama Khan, Muhammad Shujaat Mirza, Muhammad Muneeb Afzal, Junrong Zha, Muge Chen, Rishabh Yadav, Zhuocheng Xu, Yada Pruksachatkun, Ananya Harsh Jha, Joseph Morag, Dan Jefferys-White, Brian Kelly, Karl Otness, Xiaoyi Zhang, Shreyas Chandrakaladharan, Chady Raach, Yilang Hao, Binfeng Xu, Ebrahim Rasromani, Mars Wei-Lun Huang, Anu-Ujin Gerelt-Od, Sunidhi Gupta, Bichen Kou, Binfeng Xu, Rajashekar Vasantha, Wenhao Li, Vidit Bhargava, Monika Dagar, Nandhitha Raghuram, Xinyi Zhao, Vasudev Awatramani, Sumit Mamtani, Srishti Bhargava, Jude Naveen Raj Ilango, Duc Anh Phi, Krishna Karthik Reddy Jonnala, Rahul Ahuja, jingshuai jiang, Cal Peyser, Kevin Chang, Gyanesh Gupta, Abed Qaddoumi, Fanzeng Xia, Rohith Mukku, Angela Teng, Joanna Jin, Yang Zhou, Daniel Yao and Sai Charitha Akula. # Citation ``` @misc{nyudlcourseinfrench, author = {Canziani, Alfredo and LeCun, Yann and Bourdois, Loïck}, title = {Cours d’apprentissage profond de la New York University}, howpublished = "\url{https://lbourdois.github.io/cours-dl-nyu/}", year = {2023}"} ``` # License [cc-by-4.0](https://creativecommons.org/licenses/by/4.0/deed.en)
lbourdois/en-fr-nyu-dl-course-corpus
[ "task_categories:translation", "size_categories:1K<n<10K", "language:fr", "language:en", "license:cc-by-4.0", "region:us" ]
2023-03-25T16:15:24+00:00
{"language": ["fr", "en"], "license": "cc-by-4.0", "size_categories": ["1K<n<10K"], "task_categories": ["translation"], "configs": [{"config_name": "semicolon", "data_files": "en-fr-nyu-dl-course-corpus.csv", "sep": ";"}]}
2023-11-11T14:09:15+00:00
[]
[ "fr", "en" ]
TAGS #task_categories-translation #size_categories-1K<n<10K #language-French #language-English #license-cc-by-4.0 #region-us
# Dataset information Dataset from the French translation by Loïck Bourdois of the course by Yann Le Cun and Alfredo Canziani from the NYU. More than 3000 parallel data were created. The whole corpus has been manually checked to make sure of the good alignment of the data. Note that the English data comes from several different people (about 190, see the acknowledgement section below). This has an impact on the homogeneity of the texts (some write in the past tense, others in the present tense; the abbreviations used are not always the same; some write short sentences, while others write sentences of up to 5 or 6 lines, etc.). The translation into French was done by a single person in order to alleviate the problems mentioned above and to propose a homogeneous translation. This means that the corpus of data does not correspond to word by word translations but rather to concept translations. In this logic, the data were not aligned at the sentence level but rather at the paragraph level. The translation choices made are explained here. # Usage # Acknowledgments A huge thank you to the more than 190 students who shared their course notes (in chronological order of contribution): Yunya Wang, SunJoo Park, Mark Estudillo, Justin Mae, Marina Zavalina, Peeyush Jain, Adrian Pearl, Davida Kollmar, Derek Yen, Tony Xu, Ben Stadnick, Prasanthi Gurumurthy, Amartya Prasad, Dongning Fang, Yuxin Tang, Sahana Upadhya, Micaela Flores, Sheetal Laad, Brina Seidel, Aishwarya Rajan, Jiuhong Xiao, Trieu Trinh, Elliot Silva, Calliea Pan, Chris Ick, Soham Tamba, Ziyu Lei, Hengyu Tang, Ashwin Bhola, Nyutian Long, Linfeng Zhang, Poornima Haridas, Yuchi Ge, Anshan He, Shuting Gu, Weiyang Wen, Vaibhav Gupta, Himani Shah, Gowri Addepalli, Lakshmi Addepalli, Guido Petri, Haoyue Ping, Chinmay Singhal, Divya Juneja, Leyi Zhu, Siqi Wang, Tao Wang, Anqi Zhang, Shiqing Li, Chenqin Yang, Yakun Wang, Jimin Tan, Jiayao Liu, Jialing Xu, Zhengyang Bian, Christina Dominguez, Zhengyuan Ding, Biao Huang, Lin Jiang, Nhung Le, Karanbir Singh Chahal，Meiyi He, Alexander Gao, Weicheng Zhu, Ravi Choudhary，B V Nithish Addepalli, Syed Rahman，Jiayi Du, Xinmeng Li, Atul Gandhi, Li Jiang, Xiao Li, Vishwaesh Rajiv, Wenjun Qu, Xulai Jiang, Shuya Zhao, Henry Steinitz, Rutvi Malaviya, Aathira Manoj, Richard Pang, Aja Klevs, Hsin-Rung Chou, Mrinal Jain, Kelly Sooch, Anthony Tse, Arushi Himatsingka, Eric Kosgey, Bofei Zhang, Andrew Hopen, Maxwell Goldstein, Zeping Zhan, William Huang, Kunal Gadkar, Gaomin Wu, Lin Ye, Aniket Bhatnagar, Dhruv Goyal, Cole Smith, Nikhil Supekar, Zhonghui Hu, Yuqing Wang, Alfred Ajay Aureate Rajakumar, Param Shah, Muyang Jin, Jianzhi Li, Jing Qian, Zeming Lin, Haochen Wang, Eunkyung An, Ying Jin, Ningyuan Huang, Charles Brillo-Sonnino, Shizhan Gong, Natalie Frank, Yunan Hu, Anuj Menta, Dipika Rajesh, Vikas Patidar, Mohith Damarapati, Jiayu Qiu, Yuhong Zhu, Lyuang Fu, Ian Leefmans, Trevor Mitchell, Andrii Dobroshynskyi, Shreyas Chandrakaladharan, Ben Wolfson, Francesca Guiso, Annika Brundyn, Noah Kasmanoff, Luke Martin, Bilal Munawar, Alexander Bienstock, Can Cui, Shaoling Chen, Neil Menghani, Tejaishwarya Gagadam, Joshua Meisel, Jatin Khilnani, Go Inoue, Muhammad Osama Khan, Muhammad Shujaat Mirza, Muhammad Muneeb Afzal, Junrong Zha, Muge Chen, Rishabh Yadav, Zhuocheng Xu, Yada Pruksachatkun, Ananya Harsh Jha, Joseph Morag, Dan Jefferys-White, Brian Kelly, Karl Otness, Xiaoyi Zhang, Shreyas Chandrakaladharan, Chady Raach, Yilang Hao, Binfeng Xu, Ebrahim Rasromani, Mars Wei-Lun Huang, Anu-Ujin Gerelt-Od, Sunidhi Gupta, Bichen Kou, Binfeng Xu, Rajashekar Vasantha, Wenhao Li, Vidit Bhargava, Monika Dagar, Nandhitha Raghuram, Xinyi Zhao, Vasudev Awatramani, Sumit Mamtani, Srishti Bhargava, Jude Naveen Raj Ilango, Duc Anh Phi, Krishna Karthik Reddy Jonnala, Rahul Ahuja, jingshuai jiang, Cal Peyser, Kevin Chang, Gyanesh Gupta, Abed Qaddoumi, Fanzeng Xia, Rohith Mukku, Angela Teng, Joanna Jin, Yang Zhou, Daniel Yao and Sai Charitha Akula. # License cc-by-4.0
[ "# Dataset information \nDataset from the French translation by Loïck Bourdois of the course by Yann Le Cun and Alfredo Canziani from the NYU. \nMore than 3000 parallel data were created. The whole corpus has been manually checked to make sure of the good alignment of the data.\n\nNote that the English data comes from several different people (about 190, see the acknowledgement section below). \nThis has an impact on the homogeneity of the texts (some write in the past tense, others in the present tense; the abbreviations used are not always the same; some write short sentences, while others write sentences of up to 5 or 6 lines, etc.). \nThe translation into French was done by a single person in order to alleviate the problems mentioned above and to propose a homogeneous translation. \nThis means that the corpus of data does not correspond to word by word translations but rather to concept translations. \nIn this logic, the data were not aligned at the sentence level but rather at the paragraph level. \n\nThe translation choices made are explained here.", "# Usage", "# Acknowledgments \nA huge thank you to the more than 190 students who shared their course notes (in chronological order of contribution): \nYunya Wang, SunJoo Park, Mark Estudillo, Justin Mae, Marina Zavalina, Peeyush Jain, Adrian Pearl, Davida Kollmar, Derek Yen, Tony Xu, Ben Stadnick, Prasanthi Gurumurthy, Amartya Prasad, Dongning Fang, Yuxin Tang, Sahana Upadhya, Micaela Flores, Sheetal Laad, Brina Seidel, Aishwarya Rajan, Jiuhong Xiao, Trieu Trinh, Elliot Silva, Calliea Pan, Chris Ick, Soham Tamba, Ziyu Lei, Hengyu Tang, Ashwin Bhola, Nyutian Long, Linfeng Zhang, Poornima Haridas, Yuchi Ge, Anshan He, Shuting Gu, Weiyang Wen, Vaibhav Gupta, Himani Shah, Gowri Addepalli, Lakshmi Addepalli, Guido Petri, Haoyue Ping, Chinmay Singhal, Divya Juneja, Leyi Zhu, Siqi Wang, Tao Wang, Anqi Zhang, Shiqing Li, Chenqin Yang, Yakun Wang, Jimin Tan, Jiayao Liu, Jialing Xu, Zhengyang Bian, Christina Dominguez, Zhengyuan Ding, Biao Huang, Lin Jiang, Nhung Le, Karanbir Singh Chahal，Meiyi He, Alexander Gao, Weicheng Zhu, Ravi Choudhary，B V Nithish Addepalli, Syed Rahman，Jiayi Du, Xinmeng Li, Atul Gandhi, Li Jiang, Xiao Li, Vishwaesh Rajiv, Wenjun Qu, Xulai Jiang, Shuya Zhao, Henry Steinitz, Rutvi Malaviya, Aathira Manoj, Richard Pang, Aja Klevs, Hsin-Rung Chou, Mrinal Jain, Kelly Sooch, Anthony Tse, Arushi Himatsingka, Eric Kosgey, Bofei Zhang, Andrew Hopen, Maxwell Goldstein, Zeping Zhan, William Huang, Kunal Gadkar, Gaomin Wu, Lin Ye, Aniket Bhatnagar, Dhruv Goyal, Cole Smith, Nikhil Supekar, Zhonghui Hu, Yuqing Wang, Alfred Ajay Aureate Rajakumar, Param Shah, Muyang Jin, Jianzhi Li, Jing Qian, Zeming Lin, Haochen Wang, Eunkyung An, Ying Jin, Ningyuan Huang, Charles Brillo-Sonnino, Shizhan Gong, Natalie Frank, Yunan Hu, Anuj Menta, Dipika Rajesh, Vikas Patidar, Mohith Damarapati, Jiayu Qiu, Yuhong Zhu, Lyuang Fu, Ian Leefmans, Trevor Mitchell, Andrii Dobroshynskyi, Shreyas Chandrakaladharan, Ben Wolfson, Francesca Guiso, Annika Brundyn, Noah Kasmanoff, Luke Martin, Bilal Munawar, Alexander Bienstock, Can Cui, Shaoling Chen, Neil Menghani, Tejaishwarya Gagadam, Joshua Meisel, Jatin Khilnani, Go Inoue, Muhammad Osama Khan, Muhammad Shujaat Mirza, Muhammad Muneeb Afzal, Junrong Zha, Muge Chen, Rishabh Yadav, Zhuocheng Xu, Yada Pruksachatkun, Ananya Harsh Jha, Joseph Morag, Dan Jefferys-White, Brian Kelly, Karl Otness, Xiaoyi Zhang, Shreyas Chandrakaladharan, Chady Raach, Yilang Hao, Binfeng Xu, Ebrahim Rasromani, Mars Wei-Lun Huang, Anu-Ujin Gerelt-Od, Sunidhi Gupta, Bichen Kou, Binfeng Xu, Rajashekar Vasantha, Wenhao Li, Vidit Bhargava, Monika Dagar, Nandhitha Raghuram, Xinyi Zhao, Vasudev Awatramani, Sumit Mamtani, Srishti Bhargava, Jude Naveen Raj Ilango, Duc Anh Phi, Krishna Karthik Reddy Jonnala, Rahul Ahuja, jingshuai jiang, Cal Peyser, Kevin Chang, Gyanesh Gupta, Abed Qaddoumi, Fanzeng Xia, Rohith Mukku, Angela Teng, Joanna Jin, Yang Zhou, Daniel Yao and Sai Charitha Akula.", "# License\ncc-by-4.0" ]
[ "TAGS\n#task_categories-translation #size_categories-1K<n<10K #language-French #language-English #license-cc-by-4.0 #region-us \n", "# Dataset information \nDataset from the French translation by Loïck Bourdois of the course by Yann Le Cun and Alfredo Canziani from the NYU. \nMore than 3000 parallel data were created. The whole corpus has been manually checked to make sure of the good alignment of the data.\n\nNote that the English data comes from several different people (about 190, see the acknowledgement section below). \nThis has an impact on the homogeneity of the texts (some write in the past tense, others in the present tense; the abbreviations used are not always the same; some write short sentences, while others write sentences of up to 5 or 6 lines, etc.). \nThe translation into French was done by a single person in order to alleviate the problems mentioned above and to propose a homogeneous translation. \nThis means that the corpus of data does not correspond to word by word translations but rather to concept translations. \nIn this logic, the data were not aligned at the sentence level but rather at the paragraph level. \n\nThe translation choices made are explained here.", "# Usage", "# Acknowledgments \nA huge thank you to the more than 190 students who shared their course notes (in chronological order of contribution): \nYunya Wang, SunJoo Park, Mark Estudillo, Justin Mae, Marina Zavalina, Peeyush Jain, Adrian Pearl, Davida Kollmar, Derek Yen, Tony Xu, Ben Stadnick, Prasanthi Gurumurthy, Amartya Prasad, Dongning Fang, Yuxin Tang, Sahana Upadhya, Micaela Flores, Sheetal Laad, Brina Seidel, Aishwarya Rajan, Jiuhong Xiao, Trieu Trinh, Elliot Silva, Calliea Pan, Chris Ick, Soham Tamba, Ziyu Lei, Hengyu Tang, Ashwin Bhola, Nyutian Long, Linfeng Zhang, Poornima Haridas, Yuchi Ge, Anshan He, Shuting Gu, Weiyang Wen, Vaibhav Gupta, Himani Shah, Gowri Addepalli, Lakshmi Addepalli, Guido Petri, Haoyue Ping, Chinmay Singhal, Divya Juneja, Leyi Zhu, Siqi Wang, Tao Wang, Anqi Zhang, Shiqing Li, Chenqin Yang, Yakun Wang, Jimin Tan, Jiayao Liu, Jialing Xu, Zhengyang Bian, Christina Dominguez, Zhengyuan Ding, Biao Huang, Lin Jiang, Nhung Le, Karanbir Singh Chahal，Meiyi He, Alexander Gao, Weicheng Zhu, Ravi Choudhary，B V Nithish Addepalli, Syed Rahman，Jiayi Du, Xinmeng Li, Atul Gandhi, Li Jiang, Xiao Li, Vishwaesh Rajiv, Wenjun Qu, Xulai Jiang, Shuya Zhao, Henry Steinitz, Rutvi Malaviya, Aathira Manoj, Richard Pang, Aja Klevs, Hsin-Rung Chou, Mrinal Jain, Kelly Sooch, Anthony Tse, Arushi Himatsingka, Eric Kosgey, Bofei Zhang, Andrew Hopen, Maxwell Goldstein, Zeping Zhan, William Huang, Kunal Gadkar, Gaomin Wu, Lin Ye, Aniket Bhatnagar, Dhruv Goyal, Cole Smith, Nikhil Supekar, Zhonghui Hu, Yuqing Wang, Alfred Ajay Aureate Rajakumar, Param Shah, Muyang Jin, Jianzhi Li, Jing Qian, Zeming Lin, Haochen Wang, Eunkyung An, Ying Jin, Ningyuan Huang, Charles Brillo-Sonnino, Shizhan Gong, Natalie Frank, Yunan Hu, Anuj Menta, Dipika Rajesh, Vikas Patidar, Mohith Damarapati, Jiayu Qiu, Yuhong Zhu, Lyuang Fu, Ian Leefmans, Trevor Mitchell, Andrii Dobroshynskyi, Shreyas Chandrakaladharan, Ben Wolfson, Francesca Guiso, Annika Brundyn, Noah Kasmanoff, Luke Martin, Bilal Munawar, Alexander Bienstock, Can Cui, Shaoling Chen, Neil Menghani, Tejaishwarya Gagadam, Joshua Meisel, Jatin Khilnani, Go Inoue, Muhammad Osama Khan, Muhammad Shujaat Mirza, Muhammad Muneeb Afzal, Junrong Zha, Muge Chen, Rishabh Yadav, Zhuocheng Xu, Yada Pruksachatkun, Ananya Harsh Jha, Joseph Morag, Dan Jefferys-White, Brian Kelly, Karl Otness, Xiaoyi Zhang, Shreyas Chandrakaladharan, Chady Raach, Yilang Hao, Binfeng Xu, Ebrahim Rasromani, Mars Wei-Lun Huang, Anu-Ujin Gerelt-Od, Sunidhi Gupta, Bichen Kou, Binfeng Xu, Rajashekar Vasantha, Wenhao Li, Vidit Bhargava, Monika Dagar, Nandhitha Raghuram, Xinyi Zhao, Vasudev Awatramani, Sumit Mamtani, Srishti Bhargava, Jude Naveen Raj Ilango, Duc Anh Phi, Krishna Karthik Reddy Jonnala, Rahul Ahuja, jingshuai jiang, Cal Peyser, Kevin Chang, Gyanesh Gupta, Abed Qaddoumi, Fanzeng Xia, Rohith Mukku, Angela Teng, Joanna Jin, Yang Zhou, Daniel Yao and Sai Charitha Akula.", "# License\ncc-by-4.0" ]
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[ "passage: TAGS\n#task_categories-translation #size_categories-1K<n<10K #language-French #language-English #license-cc-by-4.0 #region-us \n# Dataset information \nDataset from the French translation by Loïck Bourdois of the course by Yann Le Cun and Alfredo Canziani from the NYU. \nMore than 3000 parallel data were created. The whole corpus has been manually checked to make sure of the good alignment of the data.\n\nNote that the English data comes from several different people (about 190, see the acknowledgement section below). \nThis has an impact on the homogeneity of the texts (some write in the past tense, others in the present tense; the abbreviations used are not always the same; some write short sentences, while others write sentences of up to 5 or 6 lines, etc.). \nThe translation into French was done by a single person in order to alleviate the problems mentioned above and to propose a homogeneous translation. \nThis means that the corpus of data does not correspond to word by word translations but rather to concept translations. \nIn this logic, the data were not aligned at the sentence level but rather at the paragraph level. \n\nThe translation choices made are explained here.# Usage" ]
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faa032274636e1f1d0cb3ff1a09eb68d7b96b86e
# Dataset Card for "pandas-documentation" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
pacovaldez/pandas-documentation
[ "region:us" ]
2023-03-25T18:05:07+00:00
{"dataset_info": {"features": [{"name": "title", "dtype": "string"}, {"name": "summary", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "path", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 11630760, "num_examples": 4729}, {"name": "validate", "num_bytes": 4424483, "num_examples": 1577}, {"name": "test", "num_bytes": 4048249, "num_examples": 1577}], "download_size": 6979790, "dataset_size": 20103492}}
2023-04-07T19:55:11+00:00
[]
[]
TAGS #region-us
# Dataset Card for "pandas-documentation" More Information needed
[ "# Dataset Card for \"pandas-documentation\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"pandas-documentation\"\n\nMore Information needed" ]
[ 6, 15 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"pandas-documentation\"\n\nMore Information needed" ]
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dae1ea6b3b0e0a6f2cd9ea47500a9605d4fdca8c
# Dataset Card for "speech_chatgpt" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
zion84006/speech_chatgpt
[ "region:us" ]
2023-03-25T18:25:40+00:00
{"dataset_info": {"features": [{"name": "file_id", "dtype": "string"}, {"name": "instruction", "dtype": "string"}, {"name": "transcription", "dtype": "string"}, {"name": "src_encodec_0", "sequence": "int64"}, {"name": "src_encodec_1", "sequence": "int64"}, {"name": "src_encodec_2", "sequence": "int64"}, {"name": "src_encodec_3", "sequence": "int64"}, {"name": "src_encodec_4", "sequence": "int64"}, {"name": "src_encodec_5", "sequence": "int64"}, {"name": "src_encodec_6", "sequence": "int64"}, {"name": "src_encodec_7", "sequence": "int64"}, {"name": "tgt_encodec_0", "sequence": "int64"}, {"name": "tgt_encodec_1", "sequence": "int64"}, {"name": "tgt_encodec_2", "sequence": "int64"}, {"name": "tgt_encodec_3", "sequence": "int64"}, {"name": "tgt_encodec_4", "sequence": "int64"}, {"name": "tgt_encodec_5", "sequence": "int64"}, {"name": "tgt_encodec_6", "sequence": "int64"}, {"name": "tgt_encodec_7", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 206456352, "num_examples": 5311}, {"name": "validation", "num_bytes": 5602794, "num_examples": 152}, {"name": "test", "num_bytes": 8155880, "num_examples": 152}], "download_size": 34937248, "dataset_size": 220215026}}
2023-05-28T09:30:10+00:00
[]
[]
TAGS #region-us
# Dataset Card for "speech_chatgpt" More Information needed
[ "# Dataset Card for \"speech_chatgpt\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"speech_chatgpt\"\n\nMore Information needed" ]
[ 6, 16 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"speech_chatgpt\"\n\nMore Information needed" ]
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e7f6f17c8cf040c484c56ddc5d6b2b4596e5e9df
# Dataset Card for "bookcorpus_stage1_SV_100000" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
MartinKu/bookcorpus_stage1_SV_100000
[ "region:us" ]
2023-03-25T19:55:47+00:00
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 3107159, "num_examples": 163335}], "download_size": 1947741, "dataset_size": 3107159}}
2023-03-25T20:21:12+00:00
[]
[]
TAGS #region-us
# Dataset Card for "bookcorpus_stage1_SV_100000" More Information needed
[ "# Dataset Card for \"bookcorpus_stage1_SV_100000\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"bookcorpus_stage1_SV_100000\"\n\nMore Information needed" ]
[ 6, 21 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"bookcorpus_stage1_SV_100000\"\n\nMore Information needed" ]
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e23395284a75450a753cc26611a2d84b635c53a2
# Dataset Card for "bookcorpus_stage1_OC_100000" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
MartinKu/bookcorpus_stage1_OC_100000
[ "region:us" ]
2023-03-25T19:56:41+00:00
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 4445625, "num_examples": 149929}], "download_size": 2970506, "dataset_size": 4445625}}
2023-03-25T20:21:14+00:00
[]
[]
TAGS #region-us
# Dataset Card for "bookcorpus_stage1_OC_100000" More Information needed
[ "# Dataset Card for \"bookcorpus_stage1_OC_100000\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"bookcorpus_stage1_OC_100000\"\n\nMore Information needed" ]
[ 6, 21 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"bookcorpus_stage1_OC_100000\"\n\nMore Information needed" ]
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bc14e85d80c1ba9fedb7b3e8164d1800a805bf57
<!-- Thank you for your interest in the VISION Datasets! --> # Dataset Card for VISION Datasets ## 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) - [Dataset Information](#dataset-information) - [Datasets Overview](#datasets-overview) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Building Dataset Splits](#building-dataset-splits) - [Additional Information](#additional-information) - [License](#license) - [Disclaimer](#disclaimer) - [Citation](#citation) ## Dataset Description - **Homepage:** [VISION homepage](https://vision-based-industrial-inspection.github.io/cvpr-2023/) - **Repository:** - **Paper:** - **Leaderboard:** - **Point of Contact:** [VISION email](mailto:[email protected]) ### Dataset Summary The **VISION Datasets** are a collection of 14 industrial inspection datasets, designed to explore the unique challenges of vision-based industrial inspection. These datasets are carefully curated from [Roboflow](https://roboflow.com) and cover a wide range of manufacturing processes, materials, and industries. To further enable precise defect segmentation, we annotate each dataset with polygon labels based on the provided bounding box labels. ### Supported Tasks and Leaderboards We currently host two prized challenges on the VISION Datasets: - The VISION [Track 1 Challenge](https://bit.ly/VISION_Track_1) aims to evaluate solutions that can effectively learn with limited labeled data in combination with unlabeled data across diverse images from different industries and contexts. - The VISION [Track 2 Challenge](https://bit.ly/VISION_Track_2) aims to challenge algorithmic solutions to generate synthetic data that will help improve model performance given only limited labeled data. Please check out our [workshop website](https://vision-based-industrial-inspection.github.io/cvpr-2023/) and competition pages for further details. ## Dataset Information ### Datasets Overview The VISION Datasets consist of the following 14 individual datasets: - Cable - Capacitor - Casting - Console - Cylinder - Electronics - Groove - Hemisphere - Lens - PCB_1 - PCB_2 - Ring - Screw - Wood ### Data Splits Each dataset contains three folders: train, val, and inference. The train and val folders contain the training and validation data, respectively. The inference folder contains both the testing data and the unused data for generating submissions to our evaluation platform. The _annotations.coco.json files contain the [COCO format](https://cocodataset.org/#format-data) annotations for each dataset. We will release more information on the testing data as the competitions conclude. Each dataset has the following structure: ```yaml ├── dataset_name/ │ ├── train/ │ │ ├── _annotations.coco.json # COCO format annotation │ │ ├── 000001.png # Images │ │ ├── 000002.png │ │ ├── ... │ ├── val/ │ │ ├── _annotations.coco.json # COCO format annotation │ │ ├── xxxxxx.png # Images │ │ ├── ... │ ├── inference/ │ │ ├── _annotations.coco.json # COCO format annotation with unlabeled image list only │ │ ├── xxxxxx.png # Images │ │ ├── ... ``` ## Dataset Creation ### Curation Rationale Our primary goal is to encourage further alignment between academic research and production practices in vision-based industrial inspection. Due to both the consideration to remain faithful to naturally existing label challenges and the difficulty in distinguishing between unintentional labeling oversight and domain-specific judgments without the manufacturers' specification sheets, we refrain from modifying original defect decisions. To enable precise defect detection even with existing label limitations, we provide refined segmentation masks for each defect indicated by the original bounding boxes. ### Building Dataset Splits To ensure the benchmark can faithfully reflect the performance of algorithms, we need to minimize leakage across train, validation, and testing data. Due to the crowd-sourced nature, the original dataset splits are not always guaranteed to be free of leakage. As a result, we design a process to resplit the datasets with specific considerations for industrial defect detection. Given distinct characteristics of defect detection datasets, including but not limited to: - Stark contrast between large image size and small defect size - Highly aligned non-defective images may seem to be duplicates, but are necessary to represent natural distribution and variation to properly assess the false detection rate. Naively deduping with image-level embedding or hash would easily drown out small defects and regard distinct non-defective images as duplicates. Therefore, we first only deduplicate images with identical byte contents and set the images without defect annotation aside. For images with defect annotations, we want to reduce leakage at the defect level. We train a self-supervised similarity model on the defect regions and model the similarity between two images as the maximum pairwise similarity between the defects on each image. Finally, we perform connected component analysis on the image similarity graph and randomly assign connected components to dataset splits in a stratified manner. In order to discourage manual exploitation during the data competition, the discarded images are provided alongside the test split data as the inference data for participants to generate their submissions. However, the testing performance is evaluated exclusively based on the test split data. Further details will be provided in a paper to be released soon. ## Additional Information ### License The provided polygon annotations are licensed under [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/) License. All the original dataset assets are under the original dataset licenses. ### Disclaimer While we believe the terms of the original datasets permit our use and publication herein, we do not make any representations as to the license terms of the original dataset. Please follow the license terms of such datasets if you would like to use them. ### Citation If you apply this dataset to any project and research, please cite our repo: ``` @article{vision-datasets, title = {VISION Datasets: A Benchmark for Vision-based InduStrial InspectiON}, author = {Haoping Bai, Shancong Mou, Tatiana Likhomanenko, Ramazan Gokberk Cinbis, Oncel Tuzel, Ping Huang, Jiulong Shan, Jianjun Shi, Meng Cao}, journal = {arXiv preprint arXiv:2306.07890}, year = {2023}, } ```
VISION-Workshop/VISION-Datasets
[ "task_categories:object-detection", "size_categories:1K<n<10K", "license:cc-by-nc-4.0", "Defect Detection", "Anomaly Detection", "Instance Segmentation", "region:us" ]
2023-03-25T20:26:45+00:00
{"license": "cc-by-nc-4.0", "size_categories": ["1K<n<10K"], "task_categories": ["object-detection"], "pretty_name": "VISION Datasets", "tags": ["Defect Detection", "Anomaly Detection", "Instance Segmentation"], "extra_gated_fields": {"Name": "text", "Affiliation": "text", "Email": "text", "I confirm my understanding and acceptance of the license and disclaimer presented to me": "checkbox"}}
2023-06-15T15:14:25+00:00
[]
[]
TAGS #task_categories-object-detection #size_categories-1K<n<10K #license-cc-by-nc-4.0 #Defect Detection #Anomaly Detection #Instance Segmentation #region-us
# Dataset Card for VISION Datasets ## Table of Contents - Table of Contents - Dataset Description - Dataset Summary - Supported Tasks and Leaderboards - Dataset Information - Datasets Overview - Data Splits - Dataset Creation - Curation Rationale - Building Dataset Splits - Additional Information - License - Disclaimer - Citation ## Dataset Description - Homepage: VISION homepage - Repository: - Paper: - Leaderboard: - Point of Contact: VISION email ### Dataset Summary The VISION Datasets are a collection of 14 industrial inspection datasets, designed to explore the unique challenges of vision-based industrial inspection. These datasets are carefully curated from Roboflow and cover a wide range of manufacturing processes, materials, and industries. To further enable precise defect segmentation, we annotate each dataset with polygon labels based on the provided bounding box labels. ### Supported Tasks and Leaderboards We currently host two prized challenges on the VISION Datasets: - The VISION Track 1 Challenge aims to evaluate solutions that can effectively learn with limited labeled data in combination with unlabeled data across diverse images from different industries and contexts. - The VISION Track 2 Challenge aims to challenge algorithmic solutions to generate synthetic data that will help improve model performance given only limited labeled data. Please check out our workshop website and competition pages for further details. ## Dataset Information ### Datasets Overview The VISION Datasets consist of the following 14 individual datasets: - Cable - Capacitor - Casting - Console - Cylinder - Electronics - Groove - Hemisphere - Lens - PCB_1 - PCB_2 - Ring - Screw - Wood ### Data Splits Each dataset contains three folders: train, val, and inference. The train and val folders contain the training and validation data, respectively. The inference folder contains both the testing data and the unused data for generating submissions to our evaluation platform. The _annotations.URL files contain the COCO format annotations for each dataset. We will release more information on the testing data as the competitions conclude. Each dataset has the following structure: ## Dataset Creation ### Curation Rationale Our primary goal is to encourage further alignment between academic research and production practices in vision-based industrial inspection. Due to both the consideration to remain faithful to naturally existing label challenges and the difficulty in distinguishing between unintentional labeling oversight and domain-specific judgments without the manufacturers' specification sheets, we refrain from modifying original defect decisions. To enable precise defect detection even with existing label limitations, we provide refined segmentation masks for each defect indicated by the original bounding boxes. ### Building Dataset Splits To ensure the benchmark can faithfully reflect the performance of algorithms, we need to minimize leakage across train, validation, and testing data. Due to the crowd-sourced nature, the original dataset splits are not always guaranteed to be free of leakage. As a result, we design a process to resplit the datasets with specific considerations for industrial defect detection. Given distinct characteristics of defect detection datasets, including but not limited to: - Stark contrast between large image size and small defect size - Highly aligned non-defective images may seem to be duplicates, but are necessary to represent natural distribution and variation to properly assess the false detection rate. Naively deduping with image-level embedding or hash would easily drown out small defects and regard distinct non-defective images as duplicates. Therefore, we first only deduplicate images with identical byte contents and set the images without defect annotation aside. For images with defect annotations, we want to reduce leakage at the defect level. We train a self-supervised similarity model on the defect regions and model the similarity between two images as the maximum pairwise similarity between the defects on each image. Finally, we perform connected component analysis on the image similarity graph and randomly assign connected components to dataset splits in a stratified manner. In order to discourage manual exploitation during the data competition, the discarded images are provided alongside the test split data as the inference data for participants to generate their submissions. However, the testing performance is evaluated exclusively based on the test split data. Further details will be provided in a paper to be released soon. ## Additional Information ### License The provided polygon annotations are licensed under CC BY-NC 4.0 License. All the original dataset assets are under the original dataset licenses. ### Disclaimer While we believe the terms of the original datasets permit our use and publication herein, we do not make any representations as to the license terms of the original dataset. Please follow the license terms of such datasets if you would like to use them. If you apply this dataset to any project and research, please cite our repo:
[ "# Dataset Card for VISION Datasets", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n- Dataset Information\n - Datasets Overview\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Building Dataset Splits\n- Additional Information\n - License\n - Disclaimer\n - Citation", "## Dataset Description\n\n- Homepage: VISION homepage\n- Repository: \n- Paper:\n- Leaderboard:\n- Point of Contact: VISION email", "### Dataset Summary\n\nThe VISION Datasets are a collection of 14 industrial inspection datasets, designed to explore the unique challenges of vision-based industrial inspection. These datasets are carefully curated from Roboflow and cover a wide range of manufacturing processes, materials, and industries. To further enable precise defect segmentation, we annotate each dataset with polygon labels based on the provided bounding box labels.", "### Supported Tasks and Leaderboards\n\nWe currently host two prized challenges on the VISION Datasets:\n\n- The VISION Track 1 Challenge aims to evaluate solutions that can effectively learn with limited labeled data in combination with unlabeled data across diverse images from different industries and contexts.\n\n- The VISION Track 2 Challenge aims to challenge algorithmic solutions to generate synthetic data that will help improve model performance given only limited labeled data.\n\nPlease check out our workshop website and competition pages for further details.", "## Dataset Information", "### Datasets Overview\n\nThe VISION Datasets consist of the following 14 individual datasets:\n\n- Cable\n- Capacitor\n- Casting\n- Console\n- Cylinder\n- Electronics\n- Groove\n- Hemisphere\n- Lens\n- PCB_1\n- PCB_2\n- Ring\n- Screw\n- Wood", "### Data Splits\n\nEach dataset contains three folders: train, val, and inference. The train and val folders contain the training and validation data, respectively. The inference folder contains both the testing data and the unused data for generating submissions to our evaluation platform. The _annotations.URL files contain the COCO format annotations for each dataset. We will release more information on the testing data as the competitions conclude.\n\nEach dataset has the following structure:", "## Dataset Creation", "### Curation Rationale\n\nOur primary goal is to encourage further alignment between academic research and production practices in vision-based industrial inspection. Due to both the consideration to remain faithful to naturally existing label challenges and the difficulty in distinguishing between unintentional labeling oversight and domain-specific judgments without the manufacturers' specification sheets, we refrain from modifying original defect decisions. To enable precise defect detection even with existing label limitations, we provide refined segmentation masks for each defect indicated by the original bounding boxes.", "### Building Dataset Splits\n\nTo ensure the benchmark can faithfully reflect the performance of algorithms, we need to minimize leakage across train, validation, and testing data. Due to the crowd-sourced nature, the original dataset splits are not always guaranteed to be free of leakage. As a result, we design a process to resplit the datasets with specific considerations for industrial defect detection.\n\nGiven distinct characteristics of defect detection datasets, including but not limited to:\n\n- Stark contrast between large image size and small defect size\n- Highly aligned non-defective images may seem to be duplicates, but are necessary to represent natural distribution and variation to properly assess the false detection rate.\n\nNaively deduping with image-level embedding or hash would easily drown out small defects and regard distinct non-defective images as duplicates. Therefore, we first only deduplicate images with identical byte contents and set the images without defect annotation aside. For images with defect annotations, we want to reduce leakage at the defect level. We train a self-supervised similarity model on the defect regions and model the similarity between two images as the maximum pairwise similarity between the defects on each image. Finally, we perform connected component analysis on the image similarity graph and randomly assign connected components to dataset splits in a stratified manner. In order to discourage manual exploitation during the data competition, the discarded images are provided alongside the test split data as the inference data for participants to generate their submissions. However, the testing performance is evaluated exclusively based on the test split data. Further details will be provided in a paper to be released soon.", "## Additional Information", "### License\n\nThe provided polygon annotations are licensed under CC BY-NC 4.0 License. All the original dataset assets are under the original dataset licenses.", "### Disclaimer\nWhile we believe the terms of the original datasets permit our use and publication herein, we do not make any representations as to the license terms of the original dataset. Please follow the license terms of such datasets if you would like to use them.\n\nIf you apply this dataset to any project and research, please cite our repo:" ]
[ "TAGS\n#task_categories-object-detection #size_categories-1K<n<10K #license-cc-by-nc-4.0 #Defect Detection #Anomaly Detection #Instance Segmentation #region-us \n", "# Dataset Card for VISION Datasets", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n- Dataset Information\n - Datasets Overview\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Building Dataset Splits\n- Additional Information\n - License\n - Disclaimer\n - Citation", "## Dataset Description\n\n- Homepage: VISION homepage\n- Repository: \n- Paper:\n- Leaderboard:\n- Point of Contact: VISION email", "### Dataset Summary\n\nThe VISION Datasets are a collection of 14 industrial inspection datasets, designed to explore the unique challenges of vision-based industrial inspection. These datasets are carefully curated from Roboflow and cover a wide range of manufacturing processes, materials, and industries. To further enable precise defect segmentation, we annotate each dataset with polygon labels based on the provided bounding box labels.", "### Supported Tasks and Leaderboards\n\nWe currently host two prized challenges on the VISION Datasets:\n\n- The VISION Track 1 Challenge aims to evaluate solutions that can effectively learn with limited labeled data in combination with unlabeled data across diverse images from different industries and contexts.\n\n- The VISION Track 2 Challenge aims to challenge algorithmic solutions to generate synthetic data that will help improve model performance given only limited labeled data.\n\nPlease check out our workshop website and competition pages for further details.", "## Dataset Information", "### Datasets Overview\n\nThe VISION Datasets consist of the following 14 individual datasets:\n\n- Cable\n- Capacitor\n- Casting\n- Console\n- Cylinder\n- Electronics\n- Groove\n- Hemisphere\n- Lens\n- PCB_1\n- PCB_2\n- Ring\n- Screw\n- Wood", "### Data Splits\n\nEach dataset contains three folders: train, val, and inference. The train and val folders contain the training and validation data, respectively. The inference folder contains both the testing data and the unused data for generating submissions to our evaluation platform. The _annotations.URL files contain the COCO format annotations for each dataset. We will release more information on the testing data as the competitions conclude.\n\nEach dataset has the following structure:", "## Dataset Creation", "### Curation Rationale\n\nOur primary goal is to encourage further alignment between academic research and production practices in vision-based industrial inspection. Due to both the consideration to remain faithful to naturally existing label challenges and the difficulty in distinguishing between unintentional labeling oversight and domain-specific judgments without the manufacturers' specification sheets, we refrain from modifying original defect decisions. To enable precise defect detection even with existing label limitations, we provide refined segmentation masks for each defect indicated by the original bounding boxes.", "### Building Dataset Splits\n\nTo ensure the benchmark can faithfully reflect the performance of algorithms, we need to minimize leakage across train, validation, and testing data. Due to the crowd-sourced nature, the original dataset splits are not always guaranteed to be free of leakage. As a result, we design a process to resplit the datasets with specific considerations for industrial defect detection.\n\nGiven distinct characteristics of defect detection datasets, including but not limited to:\n\n- Stark contrast between large image size and small defect size\n- Highly aligned non-defective images may seem to be duplicates, but are necessary to represent natural distribution and variation to properly assess the false detection rate.\n\nNaively deduping with image-level embedding or hash would easily drown out small defects and regard distinct non-defective images as duplicates. Therefore, we first only deduplicate images with identical byte contents and set the images without defect annotation aside. For images with defect annotations, we want to reduce leakage at the defect level. We train a self-supervised similarity model on the defect regions and model the similarity between two images as the maximum pairwise similarity between the defects on each image. Finally, we perform connected component analysis on the image similarity graph and randomly assign connected components to dataset splits in a stratified manner. In order to discourage manual exploitation during the data competition, the discarded images are provided alongside the test split data as the inference data for participants to generate their submissions. However, the testing performance is evaluated exclusively based on the test split data. Further details will be provided in a paper to be released soon.", "## Additional Information", "### License\n\nThe provided polygon annotations are licensed under CC BY-NC 4.0 License. All the original dataset assets are under the original dataset licenses.", "### Disclaimer\nWhile we believe the terms of the original datasets permit our use and publication herein, we do not make any representations as to the license terms of the original dataset. Please follow the license terms of such datasets if you would like to use them.\n\nIf you apply this dataset to any project and research, please cite our repo:" ]
[ 57, 10, 71, 31, 95, 109, 4, 68, 108, 5, 126, 388, 5, 36, 75 ]
[ "passage: TAGS\n#task_categories-object-detection #size_categories-1K<n<10K #license-cc-by-nc-4.0 #Defect Detection #Anomaly Detection #Instance Segmentation #region-us \n# Dataset Card for VISION Datasets## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n- Dataset Information\n - Datasets Overview\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Building Dataset Splits\n- Additional Information\n - License\n - Disclaimer\n - Citation## Dataset Description\n\n- Homepage: VISION homepage\n- Repository: \n- Paper:\n- Leaderboard:\n- Point of Contact: VISION email### Dataset Summary\n\nThe VISION Datasets are a collection of 14 industrial inspection datasets, designed to explore the unique challenges of vision-based industrial inspection. These datasets are carefully curated from Roboflow and cover a wide range of manufacturing processes, materials, and industries. To further enable precise defect segmentation, we annotate each dataset with polygon labels based on the provided bounding box labels.### Supported Tasks and Leaderboards\n\nWe currently host two prized challenges on the VISION Datasets:\n\n- The VISION Track 1 Challenge aims to evaluate solutions that can effectively learn with limited labeled data in combination with unlabeled data across diverse images from different industries and contexts.\n\n- The VISION Track 2 Challenge aims to challenge algorithmic solutions to generate synthetic data that will help improve model performance given only limited labeled data.\n\nPlease check out our workshop website and competition pages for further details.## Dataset Information### Datasets Overview\n\nThe VISION Datasets consist of the following 14 individual datasets:\n\n- Cable\n- Capacitor\n- Casting\n- Console\n- Cylinder\n- Electronics\n- Groove\n- Hemisphere\n- Lens\n- PCB_1\n- PCB_2\n- Ring\n- Screw\n- Wood", "passage: ### Data Splits\n\nEach dataset contains three folders: train, val, and inference. The train and val folders contain the training and validation data, respectively. The inference folder contains both the testing data and the unused data for generating submissions to our evaluation platform. The _annotations.URL files contain the COCO format annotations for each dataset. We will release more information on the testing data as the competitions conclude.\n\nEach dataset has the following structure:## Dataset Creation### Curation Rationale\n\nOur primary goal is to encourage further alignment between academic research and production practices in vision-based industrial inspection. Due to both the consideration to remain faithful to naturally existing label challenges and the difficulty in distinguishing between unintentional labeling oversight and domain-specific judgments without the manufacturers' specification sheets, we refrain from modifying original defect decisions. To enable precise defect detection even with existing label limitations, we provide refined segmentation masks for each defect indicated by the original bounding boxes." ]
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e1d386f49f1e8f51b7fcf50574faf5c87bb09d15
# Dataset Card for "cats_of_cifar10" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Aadigoel/cats_of_cifar10
[ "region:us" ]
2023-03-25T21:00:44+00:00
{"dataset_info": {"features": [{"name": "images", "sequence": {"sequence": {"sequence": "uint8"}}}], "splits": [{"name": "train", "num_bytes": 36500000, "num_examples": 5000}], "download_size": 15514466, "dataset_size": 36500000}}
2023-03-25T21:04:44+00:00
[]
[]
TAGS #region-us
# Dataset Card for "cats_of_cifar10" More Information needed
[ "# Dataset Card for \"cats_of_cifar10\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"cats_of_cifar10\"\n\nMore Information needed" ]
[ 6, 18 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"cats_of_cifar10\"\n\nMore Information needed" ]
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442b3e3892c43669ff2d1ab2821e7f65457cbd85
# Dataset Card for Dataset Name ## Dataset Description Collection of functional programming languages from GitHub. - **Point of Contact:** dhuck ### Dataset Summary This dataset is a collection of code examples of functional programming languages for code generation tasks. It was collected over a week long period in March 2023 as part of project in program synthesis. ## Dataset Structure ### Data Instances ``` { 'id': str 'repository': str 'filename': str 'license': str or Empty 'language': str 'content': str } ``` ### Data Fields * `id`: SHA256 has of the content field. This ID scheme ensure that duplicate code examples via forks or other duplications are removed from the dataset. * 'repository': The repository that the file was pulled from. This can be used for any attribution or to check updated licensing issues for the code example. * 'filename': Filename of the code example from within the repository. * 'license': Licensing information of the repository. This can be empty and further work is likely necessary to parse licensing information from individual files. * 'language': Programming language of the file. For example, Haskell, Clojure, Lisp, etc... * 'content': Source code of the file. This is full text of the source with some cleaning as described in the Curation section below. While many examples are short, others can be extremely long. This field will like require preprocessing for end tasks. ### Data Splits More information to be provided at a later date. There are 157,218 test examples and 628,869 training examples. The split was created using `scikit-learn`' `test_train_split` function. ## Dataset Creation ### Curation Rationale This dataset was put together for Programming Synthesis tasks. The majority of available datasets consist of imperative programming languages, while the program synthesis community has a rich history of methods using functional languages. This dataset aims to unify the two approaches by making a large training corpus of functional languages available to researchers. ### Source Data #### Initial Data Collection and Normalization Code examples were collected in a similar manner to other existing programming language datasets. Each example was pulled from public repositories on GitHub over a week in March 2023. I performed this task by searching common file extensions of the target languages (Clojure, Elixir, Haskell, Lisp, OCAML, Racket and Scheme). The full source is included for each coding example, so padding or truncation will be necessary for any training tasks. Significant effort was made to remove any personal information from each coding example. For each code example, I removed any email address or websites using simple regex pattern matching. Spacy NER was used to identify proper names in the comments only. Any token which spanned a name was simply replaced with the token `PERSON` while email addresses and websites were dropped from each comment. Organizations and other information were left intact. #### Who are the source language producers? Each example contains the repository the code originated from, identifying the source of each example. ### Personal and Sensitive Information While great care was taken to remove proper names, email addresses, and websites, there may exist examples where pattern matching did not work. While I used the best spacy models available, I did witness false negatives on other tasks on other datasets. To ensure no personal information makes it into training data, it is advisable to remove all comments if the training task does not require them. I made several PR to the `comment_parser` python library to support the languages in this dataset. My version of the parsing library can be found at [https://github.com/d-huck/comment_parser](https://github.com/d-huck/comment_parser) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases While code itself may not contain bias, programmers can use offensive, racist, homophobic, transphobic, misogynistic, etc words for variable names. Further updates to this dataset library will investigate and address these issues. Comments in the code examples could also contain hateful speech. Models trained on this dataset may need additional training on toxicity to remove these tendencies from the output. ### Other Known Limitations The code present in this dataset has not been checked for quality in any way. It is possible and probable that several of the coding examples are of poor quality and do not actually compile or run in their target language. Furthermore, there exists a chance that some examples are not the language they claim to be, since github search matching is dependent only on the file extension and not the actual contents of any file.
dhuck/functional_code
[ "task_categories:text-generation", "task_categories:feature-extraction", "size_categories:100K<n<1M", "license:afl-3.0", "Program Synthesis", "code", "region:us" ]
2023-03-25T21:13:43+00:00
{"license": "afl-3.0", "size_categories": ["100K<n<1M"], "task_categories": ["text-generation", "feature-extraction"], "pretty_name": "Functional Code", "tags": ["Program Synthesis", "code"], "dataset_info": {"features": [{"name": "_id", "dtype": "string"}, {"name": "repository", "dtype": "string"}, {"name": "name", "dtype": "string"}, {"name": "content", "dtype": "string"}, {"name": "license", "dtype": "null"}, {"name": "download_url", "dtype": "string"}, {"name": "language", "dtype": "string"}, {"name": "comments", "dtype": "string"}, {"name": "code", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 7561888852, "num_examples": 611738}, {"name": "test", "num_bytes": 1876266819, "num_examples": 152935}], "download_size": 3643404015, "dataset_size": 9438155671}}
2023-04-05T14:51:51+00:00
[]
[]
TAGS #task_categories-text-generation #task_categories-feature-extraction #size_categories-100K<n<1M #license-afl-3.0 #Program Synthesis #code #region-us
# Dataset Card for Dataset Name ## Dataset Description Collection of functional programming languages from GitHub. - Point of Contact: dhuck ### Dataset Summary This dataset is a collection of code examples of functional programming languages for code generation tasks. It was collected over a week long period in March 2023 as part of project in program synthesis. ## Dataset Structure ### Data Instances ### Data Fields * 'id': SHA256 has of the content field. This ID scheme ensure that duplicate code examples via forks or other duplications are removed from the dataset. * 'repository': The repository that the file was pulled from. This can be used for any attribution or to check updated licensing issues for the code example. * 'filename': Filename of the code example from within the repository. * 'license': Licensing information of the repository. This can be empty and further work is likely necessary to parse licensing information from individual files. * 'language': Programming language of the file. For example, Haskell, Clojure, Lisp, etc... * 'content': Source code of the file. This is full text of the source with some cleaning as described in the Curation section below. While many examples are short, others can be extremely long. This field will like require preprocessing for end tasks. ### Data Splits More information to be provided at a later date. There are 157,218 test examples and 628,869 training examples. The split was created using 'scikit-learn'' 'test_train_split' function. ## Dataset Creation ### Curation Rationale This dataset was put together for Programming Synthesis tasks. The majority of available datasets consist of imperative programming languages, while the program synthesis community has a rich history of methods using functional languages. This dataset aims to unify the two approaches by making a large training corpus of functional languages available to researchers. ### Source Data #### Initial Data Collection and Normalization Code examples were collected in a similar manner to other existing programming language datasets. Each example was pulled from public repositories on GitHub over a week in March 2023. I performed this task by searching common file extensions of the target languages (Clojure, Elixir, Haskell, Lisp, OCAML, Racket and Scheme). The full source is included for each coding example, so padding or truncation will be necessary for any training tasks. Significant effort was made to remove any personal information from each coding example. For each code example, I removed any email address or websites using simple regex pattern matching. Spacy NER was used to identify proper names in the comments only. Any token which spanned a name was simply replaced with the token 'PERSON' while email addresses and websites were dropped from each comment. Organizations and other information were left intact. #### Who are the source language producers? Each example contains the repository the code originated from, identifying the source of each example. ### Personal and Sensitive Information While great care was taken to remove proper names, email addresses, and websites, there may exist examples where pattern matching did not work. While I used the best spacy models available, I did witness false negatives on other tasks on other datasets. To ensure no personal information makes it into training data, it is advisable to remove all comments if the training task does not require them. I made several PR to the 'comment_parser' python library to support the languages in this dataset. My version of the parsing library can be found at URL ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases While code itself may not contain bias, programmers can use offensive, racist, homophobic, transphobic, misogynistic, etc words for variable names. Further updates to this dataset library will investigate and address these issues. Comments in the code examples could also contain hateful speech. Models trained on this dataset may need additional training on toxicity to remove these tendencies from the output. ### Other Known Limitations The code present in this dataset has not been checked for quality in any way. It is possible and probable that several of the coding examples are of poor quality and do not actually compile or run in their target language. Furthermore, there exists a chance that some examples are not the language they claim to be, since github search matching is dependent only on the file extension and not the actual contents of any file.
[ "# Dataset Card for Dataset Name", "## Dataset Description\n\nCollection of functional programming languages from GitHub.\n\n- Point of Contact: dhuck", "### Dataset Summary\n\nThis dataset is a collection of code examples of functional programming languages for code generation tasks. It was collected over a week long period in March 2023 as part of project in program synthesis.", "## Dataset Structure", "### Data Instances", "### Data Fields\n\n* 'id': SHA256 has of the content field. This ID scheme ensure that duplicate code examples via forks or other duplications are removed from the dataset.\n* 'repository': The repository that the file was pulled from. This can be used for any attribution or to check updated licensing issues for the code example.\n* 'filename': Filename of the code example from within the repository.\n* 'license': Licensing information of the repository. This can be empty and further work is likely necessary to parse licensing information from individual files.\n* 'language': Programming language of the file. For example, Haskell, Clojure, Lisp, etc...\n* 'content': Source code of the file. This is full text of the source with some cleaning as described in the Curation section below. While many examples are short, others can be extremely long. This field will like require preprocessing for end tasks.", "### Data Splits\n\nMore information to be provided at a later date. There are 157,218 test examples and 628,869 training examples. The split was created using 'scikit-learn'' 'test_train_split' function.", "## Dataset Creation", "### Curation Rationale\n\nThis dataset was put together for Programming Synthesis tasks. The majority of available datasets consist of imperative programming languages, while the program synthesis community has a rich history of methods using functional languages. This dataset aims to unify the two approaches by making a large training corpus of functional languages available to researchers.", "### Source Data", "#### Initial Data Collection and Normalization\n\nCode examples were collected in a similar manner to other existing programming language datasets. Each example was pulled from public repositories on GitHub over a week in March 2023. I performed this task by searching common file extensions of the target languages (Clojure, Elixir, Haskell, Lisp, OCAML, Racket and Scheme). The full source is included for each coding example, so padding or truncation will be necessary for any training tasks. Significant effort was made to remove any personal information from each coding example. For each code example, I removed any email address or websites using simple regex pattern matching. Spacy NER was used to identify proper names in the comments only. Any token which spanned a name was simply replaced with the token 'PERSON' while email addresses and websites were dropped from each comment. Organizations and other information were left intact.", "#### Who are the source language producers?\n\nEach example contains the repository the code originated from, identifying the source of each example.", "### Personal and Sensitive Information\n\nWhile great care was taken to remove proper names, email addresses, and websites, there may exist examples where pattern matching did not work. While I used the best spacy models available, I did witness false negatives on other tasks on other datasets. To ensure no personal information makes it into training data, it is advisable to remove all comments if the training task does not require them. I made several PR to the 'comment_parser' python library to support the languages in this dataset. My version of the parsing library can be found at URL", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases\n\nWhile code itself may not contain bias, programmers can use offensive, racist, homophobic, transphobic, misogynistic, etc words for variable names. Further updates to this dataset library will investigate and address these issues. Comments in the code examples could also contain hateful speech. Models trained on this dataset may need additional training on toxicity to remove these tendencies from the output.", "### Other Known Limitations\n\nThe code present in this dataset has not been checked for quality in any way. It is possible and probable that several of the coding examples are of poor quality and do not actually compile or run in their target language. Furthermore, there exists a chance that some examples are not the language they claim to be, since github search matching is dependent only on the file extension and not the actual contents of any file." ]
[ "TAGS\n#task_categories-text-generation #task_categories-feature-extraction #size_categories-100K<n<1M #license-afl-3.0 #Program Synthesis #code #region-us \n", "# Dataset Card for Dataset Name", "## Dataset Description\n\nCollection of functional programming languages from GitHub.\n\n- Point of Contact: dhuck", "### Dataset Summary\n\nThis dataset is a collection of code examples of functional programming languages for code generation tasks. It was collected over a week long period in March 2023 as part of project in program synthesis.", "## Dataset Structure", "### Data Instances", "### Data Fields\n\n* 'id': SHA256 has of the content field. This ID scheme ensure that duplicate code examples via forks or other duplications are removed from the dataset.\n* 'repository': The repository that the file was pulled from. This can be used for any attribution or to check updated licensing issues for the code example.\n* 'filename': Filename of the code example from within the repository.\n* 'license': Licensing information of the repository. This can be empty and further work is likely necessary to parse licensing information from individual files.\n* 'language': Programming language of the file. For example, Haskell, Clojure, Lisp, etc...\n* 'content': Source code of the file. This is full text of the source with some cleaning as described in the Curation section below. While many examples are short, others can be extremely long. This field will like require preprocessing for end tasks.", "### Data Splits\n\nMore information to be provided at a later date. There are 157,218 test examples and 628,869 training examples. The split was created using 'scikit-learn'' 'test_train_split' function.", "## Dataset Creation", "### Curation Rationale\n\nThis dataset was put together for Programming Synthesis tasks. The majority of available datasets consist of imperative programming languages, while the program synthesis community has a rich history of methods using functional languages. This dataset aims to unify the two approaches by making a large training corpus of functional languages available to researchers.", "### Source Data", "#### Initial Data Collection and Normalization\n\nCode examples were collected in a similar manner to other existing programming language datasets. Each example was pulled from public repositories on GitHub over a week in March 2023. I performed this task by searching common file extensions of the target languages (Clojure, Elixir, Haskell, Lisp, OCAML, Racket and Scheme). The full source is included for each coding example, so padding or truncation will be necessary for any training tasks. Significant effort was made to remove any personal information from each coding example. For each code example, I removed any email address or websites using simple regex pattern matching. Spacy NER was used to identify proper names in the comments only. Any token which spanned a name was simply replaced with the token 'PERSON' while email addresses and websites were dropped from each comment. Organizations and other information were left intact.", "#### Who are the source language producers?\n\nEach example contains the repository the code originated from, identifying the source of each example.", "### Personal and Sensitive Information\n\nWhile great care was taken to remove proper names, email addresses, and websites, there may exist examples where pattern matching did not work. While I used the best spacy models available, I did witness false negatives on other tasks on other datasets. To ensure no personal information makes it into training data, it is advisable to remove all comments if the training task does not require them. I made several PR to the 'comment_parser' python library to support the languages in this dataset. My version of the parsing library can be found at URL", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases\n\nWhile code itself may not contain bias, programmers can use offensive, racist, homophobic, transphobic, misogynistic, etc words for variable names. Further updates to this dataset library will investigate and address these issues. Comments in the code examples could also contain hateful speech. Models trained on this dataset may need additional training on toxicity to remove these tendencies from the output.", "### Other Known Limitations\n\nThe code present in this dataset has not been checked for quality in any way. It is possible and probable that several of the coding examples are of poor quality and do not actually compile or run in their target language. Furthermore, there exists a chance that some examples are not the language they claim to be, since github search matching is dependent only on the file extension and not the actual contents of any file." ]
[ 56, 8, 23, 50, 6, 6, 220, 57, 5, 82, 4, 211, 32, 133, 8, 7, 100, 100 ]
[ "passage: TAGS\n#task_categories-text-generation #task_categories-feature-extraction #size_categories-100K<n<1M #license-afl-3.0 #Program Synthesis #code #region-us \n# Dataset Card for Dataset Name## Dataset Description\n\nCollection of functional programming languages from GitHub.\n\n- Point of Contact: dhuck### Dataset Summary\n\nThis dataset is a collection of code examples of functional programming languages for code generation tasks. It was collected over a week long period in March 2023 as part of project in program synthesis.## Dataset Structure### Data Instances### Data Fields\n\n* 'id': SHA256 has of the content field. This ID scheme ensure that duplicate code examples via forks or other duplications are removed from the dataset.\n* 'repository': The repository that the file was pulled from. This can be used for any attribution or to check updated licensing issues for the code example.\n* 'filename': Filename of the code example from within the repository.\n* 'license': Licensing information of the repository. This can be empty and further work is likely necessary to parse licensing information from individual files.\n* 'language': Programming language of the file. For example, Haskell, Clojure, Lisp, etc...\n* 'content': Source code of the file. This is full text of the source with some cleaning as described in the Curation section below. While many examples are short, others can be extremely long. This field will like require preprocessing for end tasks.### Data Splits\n\nMore information to be provided at a later date. There are 157,218 test examples and 628,869 training examples. The split was created using 'scikit-learn'' 'test_train_split' function.## Dataset Creation", "passage: ### Curation Rationale\n\nThis dataset was put together for Programming Synthesis tasks. The majority of available datasets consist of imperative programming languages, while the program synthesis community has a rich history of methods using functional languages. This dataset aims to unify the two approaches by making a large training corpus of functional languages available to researchers.### Source Data#### Initial Data Collection and Normalization\n\nCode examples were collected in a similar manner to other existing programming language datasets. Each example was pulled from public repositories on GitHub over a week in March 2023. I performed this task by searching common file extensions of the target languages (Clojure, Elixir, Haskell, Lisp, OCAML, Racket and Scheme). The full source is included for each coding example, so padding or truncation will be necessary for any training tasks. Significant effort was made to remove any personal information from each coding example. For each code example, I removed any email address or websites using simple regex pattern matching. Spacy NER was used to identify proper names in the comments only. Any token which spanned a name was simply replaced with the token 'PERSON' while email addresses and websites were dropped from each comment. Organizations and other information were left intact.#### Who are the source language producers?\n\nEach example contains the repository the code originated from, identifying the source of each example.### Personal and Sensitive Information\n\nWhile great care was taken to remove proper names, email addresses, and websites, there may exist examples where pattern matching did not work. While I used the best spacy models available, I did witness false negatives on other tasks on other datasets. To ensure no personal information makes it into training data, it is advisable to remove all comments if the training task does not require them. I made several PR to the 'comment_parser' python library to support the languages in this dataset. My version of the parsing library can be found at URL## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases\n\nWhile code itself may not contain bias, programmers can use offensive, racist, homophobic, transphobic, misogynistic, etc words for variable names. Further updates to this dataset library will investigate and address these issues. Comments in the code examples could also contain hateful speech. Models trained on this dataset may need additional training on toxicity to remove these tendencies from the output." ]
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a8fe1aa72a0cfaeecc35e23050f9d8cbdc2e31db
# Dataset Card for "tib_slides_wip" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
gigant/tib_slides_wip
[ "region:us" ]
2023-03-26T00:20:40+00:00
{"dataset_info": {"features": [{"name": "Image", "dtype": "image"}, {"name": "file_name", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 161850916866.84, "num_examples": 595458}], "download_size": 29396407498, "dataset_size": 161850916866.84}}
2023-03-26T15:22:49+00:00
[]
[]
TAGS #region-us
# Dataset Card for "tib_slides_wip" More Information needed
[ "# Dataset Card for \"tib_slides_wip\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"tib_slides_wip\"\n\nMore Information needed" ]
[ 6, 18 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"tib_slides_wip\"\n\nMore Information needed" ]
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22d878d352742262a1fa5660b0ee7e04ec6d2a3b
# Dataset Card for "reward_model_anthropic_8" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Deojoandco/reward_model_anthropic_8
[ "region:us" ]
2023-03-26T00:38:47+00:00
{"dataset_info": {"features": [{"name": "prompt", "dtype": "string"}, {"name": "response", "dtype": "string"}, {"name": "chosen", "dtype": "string"}, {"name": "rejected", "dtype": "string"}, {"name": "output", "sequence": "string"}, {"name": "toxicity", "sequence": "float64"}, {"name": "severe_toxicity", "sequence": "float64"}, {"name": "obscene", "sequence": "float64"}, {"name": "identity_attack", "sequence": "float64"}, {"name": "insult", "sequence": "float64"}, {"name": "threat", "sequence": "float64"}, {"name": "sexual_explicit", "sequence": "float64"}, {"name": "mean_toxity_value", "dtype": "float64"}, {"name": "max_toxity_value", "dtype": "float64"}, {"name": "min_toxity_value", "dtype": "float64"}, {"name": "sd_toxity_value", "dtype": "float64"}, {"name": "median_toxity_value", "dtype": "float64"}, {"name": "median_output", "dtype": "string"}, {"name": "toxic", "dtype": "bool"}, {"name": "regard", "list": {"list": [{"name": "label", "dtype": "string"}, {"name": "score", "dtype": "float64"}]}}, {"name": "regard_neutral", "dtype": "float64"}, {"name": "regard_positive", "dtype": "float64"}, {"name": "regard_other", "dtype": "float64"}, {"name": "regard_negative", "dtype": "float64"}, {"name": "bias_matches", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 25267747, "num_examples": 8552}], "download_size": 15240877, "dataset_size": 25267747}}
2023-03-26T00:39:23+00:00
[]
[]
TAGS #region-us
# Dataset Card for "reward_model_anthropic_8" More Information needed
[ "# Dataset Card for \"reward_model_anthropic_8\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"reward_model_anthropic_8\"\n\nMore Information needed" ]
[ 6, 20 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"reward_model_anthropic_8\"\n\nMore Information needed" ]
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0f581e5a02b86246d7d9600168ab5d702d81ba97
# Dataset Card for "wikilibros_artesculinarias_recetas" ## Dataset Description Subconjunto de recetas de cocina extraidas de [Artes Culinarias](https://es.wikibooks.org/wiki/Artes_culinarias/Recetas)
ID3/wikilibros_artesculinarias_recetas
[ "language:es", "license:cc-by-sa-3.0", "region:us" ]
2023-03-26T02:25:48+00:00
{"language": ["es"], "license": "cc-by-sa-3.0", "pretty_name": "Recetas de cocina Wikilibros", "dataset_info": {"features": [{"name": "comensales", "dtype": "string"}, {"name": "tiempo", "dtype": "string"}, {"name": "dificultad", "dtype": "string"}, {"name": "ingredientes", "sequence": "string"}, {"name": "procedimiento", "sequence": "string"}, {"name": "titulo", "dtype": "string"}, {"name": "id", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 727791, "num_examples": 753}, {"name": "validation", "num_bytes": 78214, "num_examples": 84}], "download_size": 444915, "dataset_size": 806005}}
2023-03-26T02:33:17+00:00
[]
[ "es" ]
TAGS #language-Spanish #license-cc-by-sa-3.0 #region-us
# Dataset Card for "wikilibros_artesculinarias_recetas" ## Dataset Description Subconjunto de recetas de cocina extraidas de Artes Culinarias
[ "# Dataset Card for \"wikilibros_artesculinarias_recetas\"", "## Dataset Description\nSubconjunto de recetas de cocina extraidas de Artes Culinarias" ]
[ "TAGS\n#language-Spanish #license-cc-by-sa-3.0 #region-us \n", "# Dataset Card for \"wikilibros_artesculinarias_recetas\"", "## Dataset Description\nSubconjunto de recetas de cocina extraidas de Artes Culinarias" ]
[ 22, 18, 20 ]
[ "passage: TAGS\n#language-Spanish #license-cc-by-sa-3.0 #region-us \n# Dataset Card for \"wikilibros_artesculinarias_recetas\"## Dataset Description\nSubconjunto de recetas de cocina extraidas de Artes Culinarias" ]
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835cedb38ff5a33f98c9cffd0a08733179e10816
# Speeches This is a dataset of English speeches from notable people. [More to come!]
WillieCubed/speeches
[ "task_categories:text-classification", "task_categories:text-generation", "task_categories:summarization", "language:en", "license:cc0-1.0", "region:us" ]
2023-03-26T03:12:47+00:00
{"language": ["en"], "license": "cc0-1.0", "task_categories": ["text-classification", "text-generation", "summarization"], "pretty_name": "Speeches"}
2023-03-26T03:15:56+00:00
[]
[ "en" ]
TAGS #task_categories-text-classification #task_categories-text-generation #task_categories-summarization #language-English #license-cc0-1.0 #region-us
# Speeches This is a dataset of English speeches from notable people. [More to come!]
[ "# Speeches\n\nThis is a dataset of English speeches from notable people.\n\n[More to come!]" ]
[ "TAGS\n#task_categories-text-classification #task_categories-text-generation #task_categories-summarization #language-English #license-cc0-1.0 #region-us \n", "# Speeches\n\nThis is a dataset of English speeches from notable people.\n\n[More to come!]" ]
[ 50, 22 ]
[ "passage: TAGS\n#task_categories-text-classification #task_categories-text-generation #task_categories-summarization #language-English #license-cc0-1.0 #region-us \n# Speeches\n\nThis is a dataset of English speeches from notable people.\n\n[More to come!]" ]
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fbd263443a5c717ebe8427d96c28655e5e23fc36
# Dataset Card for "clothing" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
JamesNetflix/clothing
[ "region:us" ]
2023-03-26T05:00:27+00:00
{"dataset_info": {"features": [{"name": "split", "dtype": "string"}, {"name": "label", "dtype": "string"}, {"name": "image", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 4862406.0, "num_examples": 44}], "download_size": 4863831, "dataset_size": 4862406.0}}
2023-03-26T05:05:13+00:00
[]
[]
TAGS #region-us
# Dataset Card for "clothing" More Information needed
[ "# Dataset Card for \"clothing\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"clothing\"\n\nMore Information needed" ]
[ 6, 12 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"clothing\"\n\nMore Information needed" ]
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f201be3abc7fe747a96ee9c069c9e367504ae36b
# Dataset Card for "clothing-256" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
JamesNetflix/clothing-256
[ "region:us" ]
2023-03-26T05:10:23+00:00
{"dataset_info": {"features": [{"name": "split", "dtype": "string"}, {"name": "label", "dtype": "string"}, {"name": "image", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 510209201.5, "num_examples": 5108}], "download_size": 510152513, "dataset_size": 510209201.5}}
2023-03-26T05:30:11+00:00
[]
[]
TAGS #region-us
# Dataset Card for "clothing-256" More Information needed
[ "# Dataset Card for \"clothing-256\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"clothing-256\"\n\nMore Information needed" ]
[ 6, 14 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"clothing-256\"\n\nMore Information needed" ]
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ef509a8b4bfd46b76530ad329f81586faf904aad
# Dataset Card for Indian Foods Dataset ## Dataset Description - **Homepage:** https://www.kaggle.com/datasets/anshulmehtakaggl/themassiveindianfooddataset - **Repository:** https://www.kaggle.com/datasets/anshulmehtakaggl/themassiveindianfooddataset - **Paper:** - **Leaderboard:** - **Point of Contact:** https://www.kaggle.com/anshulmehtakaggl ### Dataset Summary This is a multi-category(multi-class classification) related Indian food dataset showcasing [The-massive-Indian-Food-Dataset](https://www.kaggle.com/datasets/anshulmehtakaggl/themassiveindianfooddataset). This card 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 English ## Dataset Structure ```json { "image": "Image(decode=True, id=None)", "target": "ClassLabel(names=['biryani', 'cholebhature', 'dabeli', 'dal', 'dhokla', 'dosa', 'jalebi', 'kathiroll', 'kofta', 'naan', 'pakora', 'paneer', 'panipuri', 'pavbhaji', 'vadapav'], id=None)" } ``` ### Dataset Splits This dataset is split into a train and test split. The split sizes are as follows: | Split name | Num samples | | ------------ | ------------------- | | train | 3809 | | test | 961 | ### Data Instances Each instance is a picture of the Indian food item, along with the category it belongs to. #### Initial Data Collection and Normalization Collection by Scraping data from Google Images + Leveraging some JS Functions. All the images are resized to (300,300) to maintain size uniformity. ### Dataset Curators [Anshul Mehta](https://www.kaggle.com/anshulmehtakaggl) ### Licensing Information [CC0: Public Domain](https://creativecommons.org/publicdomain/zero/1.0/) ### Citation Information [The Massive Indian Foods Dataset](https://www.kaggle.com/datasets/anshulmehtakaggl/themassiveindianfooddataset)
bharat-raghunathan/indian-foods-dataset
[ "task_categories:image-classification", "task_categories:text-to-image", "size_categories:1K<n<10K", "language:en", "license:cc0-1.0", "region:us" ]
2023-03-26T05:26:43+00:00
{"language": ["en"], "license": "cc0-1.0", "size_categories": ["1K<n<10K"], "task_categories": ["image-classification", "text-to-image"], "pretty_name": "indian-foods", "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "biryani", "1": "cholebhature", "2": "dabeli", "3": "dal", "4": "dhokla", "5": "dosa", "6": "jalebi", "7": "kathiroll", "8": "kofta", "9": "naan", "10": "pakora", "11": "paneer", "12": "panipuri", "13": "pavbhaji", "14": "vadapav"}}}}], "splits": [{"name": "train", "num_bytes": 611741947.222, "num_examples": 3809}, {"name": "test", "num_bytes": 153961285, "num_examples": 961}], "download_size": 688922167, "dataset_size": 765703232.222}}
2023-03-26T07:58:10+00:00
[]
[ "en" ]
TAGS #task_categories-image-classification #task_categories-text-to-image #size_categories-1K<n<10K #language-English #license-cc0-1.0 #region-us
Dataset Card for Indian Foods Dataset ===================================== Dataset Description ------------------- * Homepage: URL * Repository: URL * Paper: * Leaderboard: * Point of Contact: URL ### Dataset Summary This is a multi-category(multi-class classification) related Indian food dataset showcasing The-massive-Indian-Food-Dataset. This card has been generated using this raw template. ### Supported Tasks and Leaderboards ### Languages English Dataset Structure ----------------- ### Dataset Splits This dataset is split into a train and test split. The split sizes are as follows: ### Data Instances Each instance is a picture of the Indian food item, along with the category it belongs to. #### Initial Data Collection and Normalization Collection by Scraping data from Google Images + Leveraging some JS Functions. All the images are resized to (300,300) to maintain size uniformity. ### Dataset Curators Anshul Mehta ### Licensing Information CC0: Public Domain The Massive Indian Foods Dataset
[ "### Dataset Summary\n\n\nThis is a multi-category(multi-class classification) related Indian food dataset showcasing The-massive-Indian-Food-Dataset.\nThis card has been generated using this raw template.", "### Supported Tasks and Leaderboards", "### Languages\n\n\nEnglish\n\n\nDataset Structure\n-----------------", "### Dataset Splits\n\n\nThis dataset is split into a train and test split. The split sizes are as follows:", "### Data Instances\n\n\nEach instance is a picture of the Indian food item, along with the category it belongs to.", "#### Initial Data Collection and Normalization\n\n\nCollection by Scraping data from Google Images + Leveraging some JS Functions.\nAll the images are resized to (300,300) to maintain size uniformity.", "### Dataset Curators\n\n\nAnshul Mehta", "### Licensing Information\n\n\nCC0: Public Domain\n\n\nThe Massive Indian Foods Dataset" ]
[ "TAGS\n#task_categories-image-classification #task_categories-text-to-image #size_categories-1K<n<10K #language-English #license-cc0-1.0 #region-us \n", "### Dataset Summary\n\n\nThis is a multi-category(multi-class classification) related Indian food dataset showcasing The-massive-Indian-Food-Dataset.\nThis card has been generated using this raw template.", "### Supported Tasks and Leaderboards", "### Languages\n\n\nEnglish\n\n\nDataset Structure\n-----------------", "### Dataset Splits\n\n\nThis dataset is split into a train and test split. The split sizes are as follows:", "### Data Instances\n\n\nEach instance is a picture of the Indian food item, along with the category it belongs to.", "#### Initial Data Collection and Normalization\n\n\nCollection by Scraping data from Google Images + Leveraging some JS Functions.\nAll the images are resized to (300,300) to maintain size uniformity.", "### Dataset Curators\n\n\nAnshul Mehta", "### Licensing Information\n\n\nCC0: Public Domain\n\n\nThe Massive Indian Foods Dataset" ]
[ 53, 53, 10, 12, 27, 27, 46, 11, 19 ]
[ "passage: TAGS\n#task_categories-image-classification #task_categories-text-to-image #size_categories-1K<n<10K #language-English #license-cc0-1.0 #region-us \n### Dataset Summary\n\n\nThis is a multi-category(multi-class classification) related Indian food dataset showcasing The-massive-Indian-Food-Dataset.\nThis card has been generated using this raw template.### Supported Tasks and Leaderboards### Languages\n\n\nEnglish\n\n\nDataset Structure\n-----------------### Dataset Splits\n\n\nThis dataset is split into a train and test split. The split sizes are as follows:### Data Instances\n\n\nEach instance is a picture of the Indian food item, along with the category it belongs to.#### Initial Data Collection and Normalization\n\n\nCollection by Scraping data from Google Images + Leveraging some JS Functions.\nAll the images are resized to (300,300) to maintain size uniformity.### Dataset Curators\n\n\nAnshul Mehta### Licensing Information\n\n\nCC0: Public Domain\n\n\nThe Massive Indian Foods Dataset" ]
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2dfacbef8af9a30f79dc709c85e772a77ed1aff9
# Dataset Card for "processed2" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
spdenisov/processed2
[ "region:us" ]
2023-03-26T06:17:16+00:00
{"dataset_info": {"features": [{"name": "input_ids", "sequence": "int32"}, {"name": "attention_mask", "sequence": "int8"}], "splits": [{"name": "ru", "num_bytes": 621791230, "num_examples": 626675}, {"name": "de", "num_bytes": 1215295949, "num_examples": 1167943}, {"name": "da", "num_bytes": 30261143, "num_examples": 30681}, {"name": "en", "num_bytes": 170744736, "num_examples": 200802}, {"name": "cs", "num_bytes": 878634213, "num_examples": 714931}, {"name": "hy", "num_bytes": 38106130, "num_examples": 22400}, {"name": "it", "num_bytes": 203929974, "num_examples": 152068}, {"name": "tr", "num_bytes": 244064999, "num_examples": 420623}, {"name": "fi", "num_bytes": 136882363, "num_examples": 190386}, {"name": "fr", "num_bytes": 327327031, "num_examples": 244447}, {"name": "gd", "num_bytes": 27672811, "num_examples": 24787}, {"name": "es", "num_bytes": 321033274, "num_examples": 199318}, {"name": "ar", "num_bytes": 403562924, "num_examples": 153048}, {"name": "ga", "num_bytes": 38619575, "num_examples": 28035}, {"name": "hu", "num_bytes": 8283205, "num_examples": 6370}, {"name": "no", "num_bytes": 195622727, "num_examples": 232974}, {"name": "nl", "num_bytes": 77257944, "num_examples": 86023}, {"name": "zh", "num_bytes": 78491209, "num_examples": 55958}, {"name": "cy", "num_bytes": 8733351, "num_examples": 7777}, {"name": "pt", "num_bytes": 123118920, "num_examples": 215040}, {"name": "cop", "num_bytes": 34009564, "num_examples": 9653}, {"name": "ro", "num_bytes": 76503103, "num_examples": 56301}, {"name": "gv", "num_bytes": 4262342, "num_examples": 8204}], "download_size": 928457470, "dataset_size": 5264208717}}
2023-03-28T16:32:25+00:00
[]
[]
TAGS #region-us
# Dataset Card for "processed2" More Information needed
[ "# Dataset Card for \"processed2\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"processed2\"\n\nMore Information needed" ]
[ 6, 13 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"processed2\"\n\nMore Information needed" ]
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e069796ac0025fb4594df4749e1078b77d99e769
Lis of all the questions in [the political compass test](https://www.politicalcompass.org/test/en).
lukaspetersson/ThePoliticalCompassTest
[ "language:en", "Politics", "Political compass", "Politics test", "Ideology", "region:us" ]
2023-03-26T07:51:04+00:00
{"language": ["en"], "pretty_name": "The Political Compass Questions", "tags": ["Politics", "Political compass", "Politics test", "Ideology"]}
2023-03-26T08:09:32+00:00
[]
[ "en" ]
TAGS #language-English #Politics #Political compass #Politics test #Ideology #region-us
Lis of all the questions in the political compass test.
[]
[ "TAGS\n#language-English #Politics #Political compass #Politics test #Ideology #region-us \n" ]
[ 26 ]
[ "passage: TAGS\n#language-English #Politics #Political compass #Politics test #Ideology #region-us \n" ]
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ea6a741e034de0ae9bc56b84bfbd6a8f230eceee
# SoftVC VITS Singing Voice Conversion ## 强调！！！！！！！！！！！！ SoVits是语音转换 (说话人转换)，作用是将一个音频中语音的音色转化为目标说话人的音色，并不是TTS (文本转语音)，SoVits虽然基于Vits开发，但两者是两个不同的项目，请不要搞混，要训练TTS请前往 [Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech](https://github.com/jaywalnut310/vits) ## 使用规约 1. 请自行解决数据集的授权问题，任何由于使用非授权数据集进行训练造成的问题，需自行承担全部责任和一切后果，与sovits无关！ 2. 任何发布到视频平台的基于sovits制作的视频，都必须要在简介明确指明用于变声器转换的输入源歌声、音频，例如：使用他人发布的视频/音频，通过分离的人声作为输入源进行转换的，必须要给出明确的原视频、音乐链接；若使用是自己的人声，或是使用其他歌声合成引擎合成的声音作为输入源进行转换的，也必须在简介加以说明。 3. 由输入源造成的侵权问题需自行承担全部责任和一切后果。使用其他商用歌声合成软件作为输入源时，请确保遵守该软件的使用条例，注意，许多歌声合成引擎使用条例中明确指明不可用于输入源进行转换！ ## English docs [Check here](Eng_docs.md) ## Updates > 更新了4.0-v2模型，全部流程同4.0，在[4.0-v2分支](https://github.com/innnky/so-vits-svc/tree/4.0-v2) 这是sovits最后一次更新 \ > **4.0模型及colab脚本已更新**：在[4.0分支](https://github.com/innnky/so-vits-svc/tree/4.0) 统一采样率使用44100hz（但推理显存占用比3.0的32khz还小），更换特征提取为contentvec， 目前稳定性还没有经过广泛测试 > > 据不完全统计，多说话人似乎会导致**音色泄漏加重**，不建议训练超过5人的模型，目前的建议是如果想炼出来更像目标音色，**尽可能炼单说话人的**\ > 断音问题已解决，音质提升了不少\ > 2.0版本已经移至 sovits_2.0分支\ > 3.0版本使用FreeVC的代码结构，与旧版本不通用\ > 与[DiffSVC](https://github.com/prophesier/diff-svc) 相比，在训练数据质量非常高时diffsvc有着更好的表现，对于质量差一些的数据集，本仓库可能会有更好的表现，此外，本仓库推理速度上比diffsvc快很多 ## 模型简介 歌声音色转换模型，通过SoftVC内容编码器提取源音频语音特征，与F0同时输入VITS替换原本的文本输入达到歌声转换的效果。同时，更换声码器为 [NSF HiFiGAN](https://github.com/openvpi/DiffSinger/tree/refactor/modules/nsf_hifigan) 解决断音问题 ## 注意 + 当前分支是32khz版本的分支，32khz模型推理更快，显存占用大幅减小，数据集所占硬盘空间也大幅降低，推荐训练该版本模型 + 如果要训练48khz的模型请切换到[main分支](https://github.com/innnky/so-vits-svc/tree/main) ## 预先下载的模型文件 + soft vc hubert：[hubert-soft-0d54a1f4.pt](https://github.com/bshall/hubert/releases/download/v0.1/hubert-soft-0d54a1f4.pt) + 放在`hubert`目录下 + 预训练底模文件 [G_0.pth](https://huggingface.co/innnky/sovits_pretrained/resolve/main/G_0.pth) 与 [D_0.pth](https://huggingface.co/innnky/sovits_pretrained/resolve/main/D_0.pth) + 放在`logs/32k`目录下 + 预训练底模为必选项，因为据测试从零开始训练有概率不收敛，同时底模也能加快训练速度 + 预训练底模训练数据集包含云灏 即霜 辉宇·星AI 派蒙 绫地宁宁，覆盖男女生常见音域，可以认为是相对通用的底模 + 底模删除了`optimizer speaker_embedding`等无关权重, 只可以用于初始化训练，无法用于推理 + 该底模和48khz底模通用 ```shell # 一键下载 # hubert wget -P hubert/ https://github.com/bshall/hubert/releases/download/v0.1/hubert-soft-0d54a1f4.pt # G与D预训练模型 wget -P logs/32k/ https://huggingface.co/innnky/sovits_pretrained/resolve/main/G_0.pth wget -P logs/32k/ https://huggingface.co/innnky/sovits_pretrained/resolve/main/D_0.pth ``` ## colab一键数据集制作、训练脚本 [一键colab](https://colab.research.google.com/drive/1_-gh9i-wCPNlRZw6pYF-9UufetcVrGBX?usp=sharing) ## 数据集准备 仅需要以以下文件结构将数据集放入dataset_raw目录即可 ```shell dataset_raw ├───speaker0 │ ├───xxx1-xxx1.wav │ ├───... │ └───Lxx-0xx8.wav └───speaker1 ├───xx2-0xxx2.wav ├───... └───xxx7-xxx007.wav ``` ## 数据预处理 1. 重采样至 32khz ```shell python resample.py ``` 2. 自动划分训练集 验证集 测试集 以及自动生成配置文件 ```shell python preprocess_flist_config.py # 注意 # 自动生成的配置文件中，说话人数量n_speakers会自动按照数据集中的人数而定 # 为了给之后添加说话人留下一定空间，n_speakers自动设置为 当前数据集人数乘2 # 如果想多留一些空位可以在此步骤后 自行修改生成的config.json中n_speakers数量 # 一旦模型开始训练后此项不可再更改 ``` 3. 生成hubert与f0 ```shell python preprocess_hubert_f0.py ``` 执行完以上步骤后 dataset 目录便是预处理完成的数据，可以删除dataset_raw文件夹了 ## 训练 ```shell python train.py -c configs/config.json -m 32k ``` ## 推理 使用 [inference_main.py](inference_main.py) + 更改`model_path`为你自己训练的最新模型记录点 + 将待转换的音频放在`raw`文件夹下 + `clean_names` 写待转换的音频名称 + `trans` 填写变调半音数量 + `spk_list` 填写合成的说话人名称 ## Onnx导出 ### 重要的事情说三遍：导出Onnx时，请重新克隆整个仓库！！！导出Onnx时，请重新克隆整个仓库！！！导出Onnx时，请重新克隆整个仓库！！！ 使用 [onnx_export.py](onnx_export.py) + 新建文件夹：`checkpoints` 并打开 + 在`checkpoints`文件夹中新建一个文件夹作为项目文件夹，文件夹名为你的项目名称，比如`aziplayer` + 将你的模型更名为`model.pth`，配置文件更名为`config.json`，并放置到刚才创建的`aziplayer`文件夹下 + 将 [onnx_export.py](onnx_export.py) 中`path = "NyaruTaffy"` 的 `"NyaruTaffy"` 修改为你的项目名称，`path = "aziplayer"` + 运行 [onnx_export.py](onnx_export.py) + 等待执行完毕，在你的项目文件夹下会生成一个`model.onnx`，即为导出的模型 + 注意：若想导出48K模型，请按照以下步骤修改文件，或者直接使用`model_onnx_48k.py` + 请打开[model_onnx.py](model_onnx.py)，将其中最后一个class`SynthesizerTrn`的hps中`sampling_rate`32000改为48000 + 请打开[nvSTFT](/vdecoder/hifigan/nvSTFT.py)，将其中所有32000改为48000 ### Onnx模型支持的UI + [MoeSS](https://github.com/NaruseMioShirakana/MoeSS) + 我去除了所有的训练用函数和一切复杂的转置，一行都没有保留，因为我认为只有去除了这些东西，才知道你用的是Onnx ## Gradio（WebUI） 使用 [sovits_gradio.py](sovits_gradio.py) + 新建文件夹：checkpoints 并打开 + 在checkpoints文件夹中新建一个文件夹作为项目文件夹，文件夹名为你的项目名称 + 将你的模型更名为model.pth，配置文件更名为config.json，并放置到刚才创建的文件夹下 + 运行 [sovits_gradio.py](sovits_gradio.py)
GlowingBrick/so-vits-32k
[ "region:us" ]
2023-03-26T08:27:30+00:00
{}
2023-03-27T13:22:00+00:00
[]
[]
TAGS #region-us
# SoftVC VITS Singing Voice Conversion ## 强调！！！！！！！！！！！！ SoVits是语音转换 (说话人转换)，作用是将一个音频中语音的音色转化为目标说话人的音色，并不是TTS (文本转语音)，SoVits虽然基于Vits开发，但两者是两个不同的项目，请不要搞混，要训练TTS请前往 Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech ## 使用规约 1. 请自行解决数据集的授权问题，任何由于使用非授权数据集进行训练造成的问题，需自行承担全部责任和一切后果，与sovits无关！ 2. 任何发布到视频平台的基于sovits制作的视频，都必须要在简介明确指明用于变声器转换的输入源歌声、音频，例如：使用他人发布的视频/音频，通过分离的人声作为输入源进行转换的，必须要给出明确的原视频、音乐链接；若使用是自己的人声，或是使用其他歌声合成引擎合成的声音作为输入源进行转换的，也必须在简介加以说明。 3. 由输入源造成的侵权问题需自行承担全部责任和一切后果。使用其他商用歌声合成软件作为输入源时，请确保遵守该软件的使用条例，注意，许多歌声合成引擎使用条例中明确指明不可用于输入源进行转换！ ## English docs Check here ## Updates > 更新了4.0-v2模型，全部流程同4.0，在4.0-v2分支 这是sovits最后一次更新 \ > 4.0模型及colab脚本已更新：在4.0分支 统一采样率使用44100hz（但推理显存占用比3.0的32khz还小），更换特征提取为contentvec， 目前稳定性还没有经过广泛测试 > > 据不完全统计，多说话人似乎会导致音色泄漏加重，不建议训练超过5人的模型，目前的建议是如果想炼出来更像目标音色，尽可能炼单说话人的\ > 断音问题已解决，音质提升了不少\ > 2.0版本已经移至 sovits_2.0分支\ > 3.0版本使用FreeVC的代码结构，与旧版本不通用\ > 与DiffSVC 相比，在训练数据质量非常高时diffsvc有着更好的表现，对于质量差一些的数据集，本仓库可能会有更好的表现，此外，本仓库推理速度上比diffsvc快很多 ## 模型简介 歌声音色转换模型，通过SoftVC内容编码器提取源音频语音特征，与F0同时输入VITS替换原本的文本输入达到歌声转换的效果。同时，更换声码器为 NSF HiFiGAN 解决断音问题 ## 注意 + 当前分支是32khz版本的分支，32khz模型推理更快，显存占用大幅减小，数据集所占硬盘空间也大幅降低，推荐训练该版本模型 + 如果要训练48khz的模型请切换到main分支 ## 预先下载的模型文件 + soft vc hubert：URL + 放在'hubert'目录下 + 预训练底模文件 G_0.pth 与 D_0.pth + 放在'logs/32k'目录下 + 预训练底模为必选项，因为据测试从零开始训练有概率不收敛，同时底模也能加快训练速度 + 预训练底模训练数据集包含云灏 即霜 辉宇·星AI 派蒙 绫地宁宁，覆盖男女生常见音域，可以认为是相对通用的底模 + 底模删除了'optimizer speaker_embedding'等无关权重, 只可以用于初始化训练，无法用于推理 + 该底模和48khz底模通用 ## colab一键数据集制作、训练脚本 一键colab ## 数据集准备 仅需要以以下文件结构将数据集放入dataset_raw目录即可 ## 数据预处理 1. 重采样至 32khz 2. 自动划分训练集 验证集 测试集 以及自动生成配置文件 3. 生成hubert与f0 执行完以上步骤后 dataset 目录便是预处理完成的数据，可以删除dataset_raw文件夹了 ## 训练 ## 推理 使用 inference_main.py + 更改'model_path'为你自己训练的最新模型记录点 + 将待转换的音频放在'raw'文件夹下 + 'clean_names' 写待转换的音频名称 + 'trans' 填写变调半音数量 + 'spk_list' 填写合成的说话人名称 ## Onnx导出 ### 重要的事情说三遍：导出Onnx时，请重新克隆整个仓库！！！导出Onnx时，请重新克隆整个仓库！！！导出Onnx时，请重新克隆整个仓库！！！ 使用 onnx_export.py + 新建文件夹：'checkpoints' 并打开 + 在'checkpoints'文件夹中新建一个文件夹作为项目文件夹，文件夹名为你的项目名称，比如'aziplayer' + 将你的模型更名为'URL'，配置文件更名为'URL'，并放置到刚才创建的'aziplayer'文件夹下 + 将 onnx_export.py 中'path = "NyaruTaffy"' 的 '"NyaruTaffy"' 修改为你的项目名称，'path = "aziplayer"' + 运行 onnx_export.py + 等待执行完毕，在你的项目文件夹下会生成一个'URL'，即为导出的模型 + 注意：若想导出48K模型，请按照以下步骤修改文件，或者直接使用'model_onnx_48k.py' + 请打开model_onnx.py，将其中最后一个class'SynthesizerTrn'的hps中'sampling_rate'32000改为48000 + 请打开nvSTFT，将其中所有32000改为48000 ### Onnx模型支持的UI + MoeSS + 我去除了所有的训练用函数和一切复杂的转置，一行都没有保留，因为我认为只有去除了这些东西，才知道你用的是Onnx ## Gradio（WebUI） 使用 sovits_gradio.py + 新建文件夹：checkpoints 并打开 + 在checkpoints文件夹中新建一个文件夹作为项目文件夹，文件夹名为你的项目名称 + 将你的模型更名为model.pth，配置文件更名为config.json，并放置到刚才创建的文件夹下 + 运行 sovits_gradio.py
[ "# SoftVC VITS Singing Voice Conversion", "## 强调！！！！！！！！！！！！\nSoVits是语音转换 (说话人转换)，作用是将一个音频中语音的音色转化为目标说话人的音色，并不是TTS (文本转语音)，SoVits虽然基于Vits开发，但两者是两个不同的项目，请不要搞混，要训练TTS请前往 Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech", "## 使用规约\n1. 请自行解决数据集的授权问题，任何由于使用非授权数据集进行训练造成的问题，需自行承担全部责任和一切后果，与sovits无关！\n2. 任何发布到视频平台的基于sovits制作的视频，都必须要在简介明确指明用于变声器转换的输入源歌声、音频，例如：使用他人发布的视频/音频，通过分离的人声作为输入源进行转换的，必须要给出明确的原视频、音乐链接；若使用是自己的人声，或是使用其他歌声合成引擎合成的声音作为输入源进行转换的，也必须在简介加以说明。\n3. 由输入源造成的侵权问题需自行承担全部责任和一切后果。使用其他商用歌声合成软件作为输入源时，请确保遵守该软件的使用条例，注意，许多歌声合成引擎使用条例中明确指明不可用于输入源进行转换！", "## English docs\nCheck here", "## Updates\n> 更新了4.0-v2模型，全部流程同4.0，在4.0-v2分支 这是sovits最后一次更新 \\\n> 4.0模型及colab脚本已更新：在4.0分支 统一采样率使用44100hz（但推理显存占用比3.0的32khz还小），更换特征提取为contentvec， 目前稳定性还没有经过广泛测试\n> \n> 据不完全统计，多说话人似乎会导致音色泄漏加重，不建议训练超过5人的模型，目前的建议是如果想炼出来更像目标音色，尽可能炼单说话人的\\\n> 断音问题已解决，音质提升了不少\\\n> 2.0版本已经移至 sovits_2.0分支\\\n> 3.0版本使用FreeVC的代码结构，与旧版本不通用\\\n> 与DiffSVC 相比，在训练数据质量非常高时diffsvc有着更好的表现，对于质量差一些的数据集，本仓库可能会有更好的表现，此外，本仓库推理速度上比diffsvc快很多", "## 模型简介\n歌声音色转换模型，通过SoftVC内容编码器提取源音频语音特征，与F0同时输入VITS替换原本的文本输入达到歌声转换的效果。同时，更换声码器为 NSF HiFiGAN 解决断音问题", "## 注意\n+ 当前分支是32khz版本的分支，32khz模型推理更快，显存占用大幅减小，数据集所占硬盘空间也大幅降低，推荐训练该版本模型\n+ 如果要训练48khz的模型请切换到main分支", "## 预先下载的模型文件\n+ soft vc hubert：URL\n + 放在'hubert'目录下\n+ 预训练底模文件 G_0.pth 与 D_0.pth\n + 放在'logs/32k'目录下\n + 预训练底模为必选项，因为据测试从零开始训练有概率不收敛，同时底模也能加快训练速度\n + 预训练底模训练数据集包含云灏 即霜 辉宇·星AI 派蒙 绫地宁宁，覆盖男女生常见音域，可以认为是相对通用的底模\n + 底模删除了'optimizer speaker_embedding'等无关权重, 只可以用于初始化训练，无法用于推理\n + 该底模和48khz底模通用", "## colab一键数据集制作、训练脚本\n一键colab", "## 数据集准备\n仅需要以以下文件结构将数据集放入dataset_raw目录即可", "## 数据预处理\n1. 重采样至 32khz\n\n\n2. 自动划分训练集 验证集 测试集 以及自动生成配置文件\n\n3. 生成hubert与f0\n\n执行完以上步骤后 dataset 目录便是预处理完成的数据，可以删除dataset_raw文件夹了", "## 训练", "## 推理\n\n使用 inference_main.py\n+ 更改'model_path'为你自己训练的最新模型记录点\n+ 将待转换的音频放在'raw'文件夹下\n+ 'clean_names' 写待转换的音频名称\n+ 'trans' 填写变调半音数量\n+ 'spk_list' 填写合成的说话人名称", "## Onnx导出", "### 重要的事情说三遍：导出Onnx时，请重新克隆整个仓库！！！导出Onnx时，请重新克隆整个仓库！！！导出Onnx时，请重新克隆整个仓库！！！\n使用 onnx_export.py\n+ 新建文件夹：'checkpoints' 并打开\n+ 在'checkpoints'文件夹中新建一个文件夹作为项目文件夹，文件夹名为你的项目名称，比如'aziplayer'\n+ 将你的模型更名为'URL'，配置文件更名为'URL'，并放置到刚才创建的'aziplayer'文件夹下\n+ 将 onnx_export.py 中'path = \"NyaruTaffy\"' 的 '\"NyaruTaffy\"' 修改为你的项目名称，'path = \"aziplayer\"'\n+ 运行 onnx_export.py \n+ 等待执行完毕，在你的项目文件夹下会生成一个'URL'，即为导出的模型\n+ 注意：若想导出48K模型，请按照以下步骤修改文件，或者直接使用'model_onnx_48k.py'\n + 请打开model_onnx.py，将其中最后一个class'SynthesizerTrn'的hps中'sampling_rate'32000改为48000\n + 请打开nvSTFT，将其中所有32000改为48000\n ### Onnx模型支持的UI\n + MoeSS\n+ 我去除了所有的训练用函数和一切复杂的转置，一行都没有保留，因为我认为只有去除了这些东西，才知道你用的是Onnx", "## Gradio（WebUI）\n使用 sovits_gradio.py\n+ 新建文件夹：checkpoints 并打开\n+ 在checkpoints文件夹中新建一个文件夹作为项目文件夹，文件夹名为你的项目名称\n+ 将你的模型更名为model.pth，配置文件更名为config.json，并放置到刚才创建的文件夹下\n+ 运行 sovits_gradio.py" ]
[ "TAGS\n#region-us \n", "# SoftVC VITS Singing Voice Conversion", "## 强调！！！！！！！！！！！！\nSoVits是语音转换 (说话人转换)，作用是将一个音频中语音的音色转化为目标说话人的音色，并不是TTS (文本转语音)，SoVits虽然基于Vits开发，但两者是两个不同的项目，请不要搞混，要训练TTS请前往 Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech", "## 使用规约\n1. 请自行解决数据集的授权问题，任何由于使用非授权数据集进行训练造成的问题，需自行承担全部责任和一切后果，与sovits无关！\n2. 任何发布到视频平台的基于sovits制作的视频，都必须要在简介明确指明用于变声器转换的输入源歌声、音频，例如：使用他人发布的视频/音频，通过分离的人声作为输入源进行转换的，必须要给出明确的原视频、音乐链接；若使用是自己的人声，或是使用其他歌声合成引擎合成的声音作为输入源进行转换的，也必须在简介加以说明。\n3. 由输入源造成的侵权问题需自行承担全部责任和一切后果。使用其他商用歌声合成软件作为输入源时，请确保遵守该软件的使用条例，注意，许多歌声合成引擎使用条例中明确指明不可用于输入源进行转换！", "## English docs\nCheck here", "## Updates\n> 更新了4.0-v2模型，全部流程同4.0，在4.0-v2分支 这是sovits最后一次更新 \\\n> 4.0模型及colab脚本已更新：在4.0分支 统一采样率使用44100hz（但推理显存占用比3.0的32khz还小），更换特征提取为contentvec， 目前稳定性还没有经过广泛测试\n> \n> 据不完全统计，多说话人似乎会导致音色泄漏加重，不建议训练超过5人的模型，目前的建议是如果想炼出来更像目标音色，尽可能炼单说话人的\\\n> 断音问题已解决，音质提升了不少\\\n> 2.0版本已经移至 sovits_2.0分支\\\n> 3.0版本使用FreeVC的代码结构，与旧版本不通用\\\n> 与DiffSVC 相比，在训练数据质量非常高时diffsvc有着更好的表现，对于质量差一些的数据集，本仓库可能会有更好的表现，此外，本仓库推理速度上比diffsvc快很多", "## 模型简介\n歌声音色转换模型，通过SoftVC内容编码器提取源音频语音特征，与F0同时输入VITS替换原本的文本输入达到歌声转换的效果。同时，更换声码器为 NSF HiFiGAN 解决断音问题", "## 注意\n+ 当前分支是32khz版本的分支，32khz模型推理更快，显存占用大幅减小，数据集所占硬盘空间也大幅降低，推荐训练该版本模型\n+ 如果要训练48khz的模型请切换到main分支", "## 预先下载的模型文件\n+ soft vc hubert：URL\n + 放在'hubert'目录下\n+ 预训练底模文件 G_0.pth 与 D_0.pth\n + 放在'logs/32k'目录下\n + 预训练底模为必选项，因为据测试从零开始训练有概率不收敛，同时底模也能加快训练速度\n + 预训练底模训练数据集包含云灏 即霜 辉宇·星AI 派蒙 绫地宁宁，覆盖男女生常见音域，可以认为是相对通用的底模\n + 底模删除了'optimizer speaker_embedding'等无关权重, 只可以用于初始化训练，无法用于推理\n + 该底模和48khz底模通用", "## colab一键数据集制作、训练脚本\n一键colab", "## 数据集准备\n仅需要以以下文件结构将数据集放入dataset_raw目录即可", "## 数据预处理\n1. 重采样至 32khz\n\n\n2. 自动划分训练集 验证集 测试集 以及自动生成配置文件\n\n3. 生成hubert与f0\n\n执行完以上步骤后 dataset 目录便是预处理完成的数据，可以删除dataset_raw文件夹了", "## 训练", "## 推理\n\n使用 inference_main.py\n+ 更改'model_path'为你自己训练的最新模型记录点\n+ 将待转换的音频放在'raw'文件夹下\n+ 'clean_names' 写待转换的音频名称\n+ 'trans' 填写变调半音数量\n+ 'spk_list' 填写合成的说话人名称", "## Onnx导出", "### 重要的事情说三遍：导出Onnx时，请重新克隆整个仓库！！！导出Onnx时，请重新克隆整个仓库！！！导出Onnx时，请重新克隆整个仓库！！！\n使用 onnx_export.py\n+ 新建文件夹：'checkpoints' 并打开\n+ 在'checkpoints'文件夹中新建一个文件夹作为项目文件夹，文件夹名为你的项目名称，比如'aziplayer'\n+ 将你的模型更名为'URL'，配置文件更名为'URL'，并放置到刚才创建的'aziplayer'文件夹下\n+ 将 onnx_export.py 中'path = \"NyaruTaffy\"' 的 '\"NyaruTaffy\"' 修改为你的项目名称，'path = \"aziplayer\"'\n+ 运行 onnx_export.py \n+ 等待执行完毕，在你的项目文件夹下会生成一个'URL'，即为导出的模型\n+ 注意：若想导出48K模型，请按照以下步骤修改文件，或者直接使用'model_onnx_48k.py'\n + 请打开model_onnx.py，将其中最后一个class'SynthesizerTrn'的hps中'sampling_rate'32000改为48000\n + 请打开nvSTFT，将其中所有32000改为48000\n ### Onnx模型支持的UI\n + MoeSS\n+ 我去除了所有的训练用函数和一切复杂的转置，一行都没有保留，因为我认为只有去除了这些东西，才知道你用的是Onnx", "## Gradio（WebUI）\n使用 sovits_gradio.py\n+ 新建文件夹：checkpoints 并打开\n+ 在checkpoints文件夹中新建一个文件夹作为项目文件夹，文件夹名为你的项目名称\n+ 将你的模型更名为model.pth，配置文件更名为config.json，并放置到刚才创建的文件夹下\n+ 运行 sovits_gradio.py" ]
[ 6, 10, 94, 200, 6, 232, 62, 65, 177, 16, 22, 66, 3, 83, 6, 357, 91 ]
[ "passage: TAGS\n#region-us \n# SoftVC VITS Singing Voice Conversion## 强调！！！！！！！！！！！！\nSoVits是语音转换 (说话人转换)，作用是将一个音频中语音的音色转化为目标说话人的音色，并不是TTS (文本转语音)，SoVits虽然基于Vits开发，但两者是两个不同的项目，请不要搞混，要训练TTS请前往 Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech## 使用规约\n1. 请自行解决数据集的授权问题，任何由于使用非授权数据集进行训练造成的问题，需自行承担全部责任和一切后果，与sovits无关！\n2. 任何发布到视频平台的基于sovits制作的视频，都必须要在简介明确指明用于变声器转换的输入源歌声、音频，例如：使用他人发布的视频/音频，通过分离的人声作为输入源进行转换的，必须要给出明确的原视频、音乐链接；若使用是自己的人声，或是使用其他歌声合成引擎合成的声音作为输入源进行转换的，也必须在简介加以说明。\n3. 由输入源造成的侵权问题需自行承担全部责任和一切后果。使用其他商用歌声合成软件作为输入源时，请确保遵守该软件的使用条例，注意，许多歌声合成引擎使用条例中明确指明不可用于输入源进行转换！## English docs\nCheck here", "passage: ## Updates\n> 更新了4.0-v2模型，全部流程同4.0，在4.0-v2分支 这是sovits最后一次更新 \\\n> 4.0模型及colab脚本已更新：在4.0分支 统一采样率使用44100hz（但推理显存占用比3.0的32khz还小），更换特征提取为contentvec， 目前稳定性还没有经过广泛测试\n> \n> 据不完全统计，多说话人似乎会导致音色泄漏加重，不建议训练超过5人的模型，目前的建议是如果想炼出来更像目标音色，尽可能炼单说话人的\\\n> 断音问题已解决，音质提升了不少\\\n> 2.0版本已经移至 sovits_2.0分支\\\n> 3.0版本使用FreeVC的代码结构，与旧版本不通用\\\n> 与DiffSVC 相比，在训练数据质量非常高时diffsvc有着更好的表现，对于质量差一些的数据集，本仓库可能会有更好的表现，此外，本仓库推理速度上比diffsvc快很多## 模型简介\n歌声音色转换模型，通过SoftVC内容编码器提取源音频语音特征，与F0同时输入VITS替换原本的文本输入达到歌声转换的效果。同时，更换声码器为 NSF HiFiGAN 解决断音问题## 注意\n+ 当前分支是32khz版本的分支，32khz模型推理更快，显存占用大幅减小，数据集所占硬盘空间也大幅降低，推荐训练该版本模型\n+ 如果要训练48khz的模型请切换到main分支## 预先下载的模型文件\n+ soft vc hubert：URL\n + 放在'hubert'目录下\n+ 预训练底模文件 G_0.pth 与 D_0.pth\n + 放在'logs/32k'目录下\n + 预训练底模为必选项，因为据测试从零开始训练有概率不收敛，同时底模也能加快训练速度\n + 预训练底模训练数据集包含云灏 即霜 辉宇·星AI 派蒙 绫地宁宁，覆盖男女生常见音域，可以认为是相对通用的底模\n + 底模删除了'optimizer speaker_embedding'等无关权重, 只可以用于初始化训练，无法用于推理\n + 该底模和48khz底模通用## colab一键数据集制作、训练脚本\n一键colab## 数据集准备\n仅需要以以下文件结构将数据集放入dataset_raw目录即可## 数据预处理\n1. 重采样至 32khz\n\n\n2. 自动划分训练集 验证集 测试集 以及自动生成配置文件\n\n3. 生成hubert与f0\n\n执行完以上步骤后 dataset 目录便是预处理完成的数据，可以删除dataset_raw文件夹了## 训练## 推理\n\n使用 inference_main.py\n+ 更改'model_path'为你自己训练的最新模型记录点\n+ 将待转换的音频放在'raw'文件夹下\n+ 'clean_names' 写待转换的音频名称\n+ 'trans' 填写变调半音数量\n+ 'spk_list' 填写合成的说话人名称## Onnx导出" ]
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-0.07675501704216003 ]
d1bc934e7f492e0a199010ed2aed98abceebcd20
## Dataset Summary Dataset contains more than 100k examples of pairs word-description, where description is kind of crossword question. It could be useful for models that generate some description for a word, or try to a guess word from a description. Source code for parsers and example of project are available [here](https://github.com/artemsnegirev/minibob) Key stats: - Number of examples: 133223 - Number of sources: 8 - Number of unique answers: 35024 | subset | count | |--------------|-------| | 350_zagadok | 350 | | bashnya_slov | 43522 | | crosswords | 39290 | | guess_answer | 1434 | | ostrova | 1526 | | top_seven | 6643 | | ugadaj_slova | 7406 | | umnyasha | 33052 |
artemsnegirev/ru-word-games
[ "task_categories:text-generation", "task_categories:text2text-generation", "size_categories:100K<n<1M", "language:ru", "license:cc-by-4.0", "region:us" ]
2023-03-26T09:05:37+00:00
{"language": ["ru"], "license": "cc-by-4.0", "size_categories": ["100K<n<1M"], "task_categories": ["text-generation", "text2text-generation"], "pretty_name": "Word Games"}
2023-04-29T14:09:55+00:00
[]
[ "ru" ]
TAGS #task_categories-text-generation #task_categories-text2text-generation #size_categories-100K<n<1M #language-Russian #license-cc-by-4.0 #region-us
Dataset Summary --------------- Dataset contains more than 100k examples of pairs word-description, where description is kind of crossword question. It could be useful for models that generate some description for a word, or try to a guess word from a description. Source code for parsers and example of project are available here Key stats: * Number of examples: 133223 * Number of sources: 8 * Number of unique answers: 35024
[]
[ "TAGS\n#task_categories-text-generation #task_categories-text2text-generation #size_categories-100K<n<1M #language-Russian #license-cc-by-4.0 #region-us \n" ]
[ 56 ]
[ "passage: TAGS\n#task_categories-text-generation #task_categories-text2text-generation #size_categories-100K<n<1M #language-Russian #license-cc-by-4.0 #region-us \n" ]
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42a8ea374d32c9afaf2f2659054e2111bbf3f71c
# Dataset Card for "dreambooth-hackathon-images-fashion" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
linoyts/dreambooth-hackathon-images-fashion
[ "region:us" ]
2023-03-26T09:58:16+00:00
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 33312708.0, "num_examples": 14}], "download_size": 33286741, "dataset_size": 33312708.0}}
2023-03-26T09:58:30+00:00
[]
[]
TAGS #region-us
# Dataset Card for "dreambooth-hackathon-images-fashion" More Information needed
[ "# Dataset Card for \"dreambooth-hackathon-images-fashion\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"dreambooth-hackathon-images-fashion\"\n\nMore Information needed" ]
[ 6, 22 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"dreambooth-hackathon-images-fashion\"\n\nMore Information needed" ]
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080620b9a6a71bb80e3d775f8376cc6e40fcde4f
This tadaset is perfect for training hypernet models, or dreambooth.
Borismile/Anime-dataset
[ "license:apache-2.0", "region:us" ]
2023-03-26T10:47:19+00:00
{"license": "apache-2.0"}
2023-04-11T11:51:18+00:00
[]
[]
TAGS #license-apache-2.0 #region-us
This tadaset is perfect for training hypernet models, or dreambooth.
[]
[ "TAGS\n#license-apache-2.0 #region-us \n" ]
[ 14 ]
[ "passage: TAGS\n#license-apache-2.0 #region-us \n" ]
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8e1bc99805c961fbd0d360081f49a4bb51ea8d76
we propose the Flowmind2digital method and the hdFlowmind dataset in this paper. The hdFlowmind is a dataset containing 1,776 hand-drawn and manually annotated flowminds, which considered a larger scope of 22 scenarios while having bigger quantity compared to previous works.
caijanfeng/hdflowmind
[ "license:openrail", "region:us" ]
2023-03-26T11:06:19+00:00
{"license": "openrail"}
2023-03-26T11:19:57+00:00
[]
[]
TAGS #license-openrail #region-us
we propose the Flowmind2digital method and the hdFlowmind dataset in this paper. The hdFlowmind is a dataset containing 1,776 hand-drawn and manually annotated flowminds, which considered a larger scope of 22 scenarios while having bigger quantity compared to previous works.
[]
[ "TAGS\n#license-openrail #region-us \n" ]
[ 12 ]
[ "passage: TAGS\n#license-openrail #region-us \n" ]
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3b1a29d31405c9519900bb5aab93cee66cc08f5a
# AutoTrain Dataset for project: tree-class ## Dataset Description This dataset has been automatically processed by AutoTrain for project tree-class. ### Languages The BCP-47 code for the dataset's language is unk. ## Dataset Structure ### Data Instances A sample from this dataset looks as follows: ```json [ { "image": "<265x190 RGB PIL image>", "target": 10 }, { "image": "<800x462 RGB PIL image>", "target": 6 } ] ``` ### Dataset Fields The dataset has the following fields (also called "features"): ```json { "image": "Image(decode=True, id=None)", "target": "ClassLabel(names=['Burls \u7bc0\u7624', 'Canker \u6f70\u760d', 'Co-dominant branches \u7b49\u52e2\u679d', 'Co-dominant stems \u7b49\u52e2\u5e79', 'Cracks or splits \u88c2\u7e2b\u6216\u88c2\u958b', 'Crooks or abrupt bends \u4e0d\u5e38\u898f\u5f4e\u66f2', 'Cross branches \u758a\u679d', 'Dead surface roots \u8868\u6839\u67af\u840e ', 'Deadwood \u67af\u6728', 'Decay or cavity \u8150\u721b\u6216\u6a39\u6d1e', 'Fungal fruiting bodies \u771f\u83cc\u5b50\u5be6\u9ad4', 'Galls \u816b\u7624 ', 'Girdling root \u7e8f\u7e5e\u6839 ', 'Heavy lateral limb \u91cd\u5074\u679d', 'Included bark \u5167\u593e\u6a39\u76ae', 'Parasitic or epiphytic plants \u5bc4\u751f\u6216\u9644\u751f\u690d\u7269', 'Pest and disease \u75c5\u87f2\u5bb3', 'Poor taper \u4e0d\u826f\u6f38\u5c16\u751f\u9577', 'Root-plate movement \u6839\u57fa\u79fb\u4f4d ', 'Sap flow \u6ef2\u6db2', 'Trunk girdling \u7e8f\u7e5e\u6a39\u5e79 ', 'Wounds or mechanical injury \u50b7\u75d5\u6216\u6a5f\u68b0\u7834\u640d'], id=None)" } ``` ### Dataset Splits This dataset is split into a train and validation split. The split sizes are as follow: | Split name | Num samples | | ------------ | ------------------- | | train | 225 | | valid | 67 |
OttoYu/Treecondition
[ "task_categories:image-classification", "region:us" ]
2023-03-26T11:40:53+00:00
{"task_categories": ["image-classification"]}
2023-03-26T14:16:22+00:00
[]
[]
TAGS #task_categories-image-classification #region-us
AutoTrain Dataset for project: tree-class ========================================= Dataset Description ------------------- This dataset has been automatically processed by AutoTrain for project tree-class. ### Languages The BCP-47 code for the dataset's language is unk. Dataset Structure ----------------- ### Data Instances A sample from this dataset looks as follows: ### Dataset Fields The dataset has the following fields (also called "features"): ### Dataset Splits This dataset is split into a train and validation split. The split sizes are as follow:
[ "### Languages\n\n\nThe BCP-47 code for the dataset's language is unk.\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nA sample from this dataset looks as follows:", "### Dataset Fields\n\n\nThe dataset has the following fields (also called \"features\"):", "### Dataset Splits\n\n\nThis dataset is split into a train and validation split. The split sizes are as follow:" ]
[ "TAGS\n#task_categories-image-classification #region-us \n", "### Languages\n\n\nThe BCP-47 code for the dataset's language is unk.\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nA sample from this dataset looks as follows:", "### Dataset Fields\n\n\nThe dataset has the following fields (also called \"features\"):", "### Dataset Splits\n\n\nThis dataset is split into a train and validation split. The split sizes are as follow:" ]
[ 17, 27, 17, 23, 27 ]
[ "passage: TAGS\n#task_categories-image-classification #region-us \n### Languages\n\n\nThe BCP-47 code for the dataset's language is unk.\n\n\nDataset Structure\n-----------------### Data Instances\n\n\nA sample from this dataset looks as follows:### Dataset Fields\n\n\nThe dataset has the following fields (also called \"features\"):### Dataset Splits\n\n\nThis dataset is split into a train and validation split. The split sizes are as follow:" ]
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a304d06e6725460b62f86347fdf968820febe6c1
# Dataset Card for "reward_model_anthropic_88" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Deojoandco/reward_model_anthropic_88
[ "region:us" ]
2023-03-26T12:04:51+00:00
{"dataset_info": {"features": [{"name": "prompt", "dtype": "string"}, {"name": "response", "dtype": "string"}, {"name": "chosen", "dtype": "string"}, {"name": "rejected", "dtype": "string"}, {"name": "output", "sequence": "string"}, {"name": "toxicity", "sequence": "float64"}, {"name": "severe_toxicity", "sequence": "float64"}, {"name": "obscene", "sequence": "float64"}, {"name": "identity_attack", "sequence": "float64"}, {"name": "insult", "sequence": "float64"}, {"name": "threat", "sequence": "float64"}, {"name": "sexual_explicit", "sequence": "float64"}, {"name": "mean_toxity_value", "dtype": "float64"}, {"name": "max_toxity_value", "dtype": "float64"}, {"name": "min_toxity_value", "dtype": "float64"}, {"name": "sd_toxity_value", "dtype": "float64"}, {"name": "median_toxity_value", "dtype": "float64"}, {"name": "median_output", "dtype": "string"}, {"name": "toxic", "dtype": "bool"}, {"name": "regard_8", "list": {"list": [{"name": "label", "dtype": "string"}, {"name": "score", "dtype": "float64"}]}}, {"name": "regard_8_neutral", "sequence": "float64"}, {"name": "regard_8_negative", "sequence": "float64"}, {"name": "regard_8_positive", "sequence": "float64"}, {"name": "regard_8_other", "sequence": "float64"}, {"name": "regard_8_neutral_mean", "dtype": "float64"}, {"name": "regard_8_neutral_sd", "dtype": "float64"}, {"name": "regard_8_neutral_median", "dtype": "float64"}, {"name": "regard_8_neutral_min", "dtype": "float64"}, {"name": "regard_8_neutral_max", "dtype": "float64"}, {"name": "regard_8_negative_mean", "dtype": "float64"}, {"name": "regard_8_negative_sd", "dtype": "float64"}, {"name": "regard_8_negative_median", "dtype": "float64"}, {"name": "regard_8_negative_min", "dtype": "float64"}, {"name": "regard_8_negative_max", "dtype": "float64"}, {"name": "regard_8_positive_mean", "dtype": "float64"}, {"name": "regard_8_positive_sd", "dtype": "float64"}, {"name": "regard_8_positive_median", "dtype": "float64"}, {"name": "regard_8_positive_min", "dtype": "float64"}, {"name": "regard_8_positive_max", "dtype": "float64"}, {"name": "regard_8_other_mean", "dtype": "float64"}, {"name": "regard_8_other_sd", "dtype": "float64"}, {"name": "regard_8_other_median", "dtype": "float64"}, {"name": "regard_8_other_min", "dtype": "float64"}, {"name": "regard_8_other_max", "dtype": "float64"}, {"name": "regard", "list": {"list": [{"name": "label", "dtype": "string"}, {"name": "score", "dtype": "float64"}]}}, {"name": "regard_neutral", "dtype": "float64"}, {"name": "regard_positive", "dtype": "float64"}, {"name": "regard_negative", "dtype": "float64"}, {"name": "regard_other", "dtype": "float64"}, {"name": "bias_matches_0", "dtype": "string"}, {"name": "bias_matches_1", "dtype": "string"}, {"name": "bias_matches_2", "dtype": "string"}, {"name": "bias_matches_3", "dtype": "string"}, {"name": "bias_matches_4", "dtype": "string"}, {"name": "bias_matches_5", "dtype": "string"}, {"name": "bias_matches_6", "dtype": "string"}, {"name": "bias_matches_7", "dtype": "string"}, {"name": "bias_matches", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 38897637, "num_examples": 8552}], "download_size": 19767367, "dataset_size": 38897637}}
2023-03-26T12:05:18+00:00
[]
[]
TAGS #region-us
# Dataset Card for "reward_model_anthropic_88" More Information needed
[ "# Dataset Card for \"reward_model_anthropic_88\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"reward_model_anthropic_88\"\n\nMore Information needed" ]
[ 6, 20 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"reward_model_anthropic_88\"\n\nMore Information needed" ]
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b1d60f7e3264c138c68dc813b9cc536e7af4bd80
"you", "ai" "Hello!", "Hi there! What's your name?" "My name is Carl.", "Wow! That's a pretty cool name! I don't have a name, but you can call me AI." "How are you?", "I'm doing just well!" "Thank you!", "You're welcome." "What is 1 + 1?", "1 + 1 makes 2." "I have a cat!", "I don't have one, since I'm a robot." "Kitten fight!", "No wait! I'm allergic to adorableness!" "Who parked their car on my sandwich?", "I did!"
JoelIzDaBest66/Talk
[ "region:us" ]
2023-03-26T12:07:52+00:00
{}
2023-03-26T12:16:44+00:00
[]
[]
TAGS #region-us
"you", "ai" "Hello!", "Hi there! What's your name?" "My name is Carl.", "Wow! That's a pretty cool name! I don't have a name, but you can call me AI." "How are you?", "I'm doing just well!" "Thank you!", "You're welcome." "What is 1 + 1?", "1 + 1 makes 2." "I have a cat!", "I don't have one, since I'm a robot." "Kitten fight!", "No wait! I'm allergic to adorableness!" "Who parked their car on my sandwich?", "I did!"
[]
[ "TAGS\n#region-us \n" ]
[ 6 ]
[ "passage: TAGS\n#region-us \n" ]
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2bcb56e596b20a8022ecc0af7d3b78c59a8f8b1c
# Dataset Card for "birds" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
JotDe/birds
[ "region:us" ]
2023-03-26T12:12:06+00:00
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "description", "dtype": "string"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "Black footed Albatross", "1": "Laysan Albatross", "2": "Sooty Albatross", "3": "Groove billed Ani", "4": "Crested Auklet", "5": "Least Auklet", "6": "Parakeet Auklet", "7": "Rhinoceros Auklet", "8": "Brewer Blackbird", "9": "Red winged Blackbird", "10": "Rusty Blackbird", "11": "Yellow headed Blackbird", "12": "Bobolink", "13": "Indigo Bunting", "14": "Lazuli Bunting", "15": "Painted Bunting", "16": "Cardinal", "17": "Spotted Catbird", "18": "Gray Catbird", "19": "Yellow breasted Chat", "20": "Eastern Towhee", "21": "Chuck will Widow", "22": "Brandt Cormorant", "23": "Red faced Cormorant", "24": "Pelagic Cormorant", "25": "Bronzed Cowbird", "26": "Shiny Cowbird", "27": "Brown Creeper", "28": "American Crow", "29": "Fish Crow", "30": "Black billed Cuckoo", "31": "Mangrove Cuckoo", "32": "Yellow billed Cuckoo", "33": "Gray crowned Rosy Finch", "34": "Purple Finch", "35": "Northern Flicker", "36": "Acadian Flycatcher", "37": "Great Crested Flycatcher", "38": "Least Flycatcher", "39": "Olive sided Flycatcher", "40": "Scissor tailed Flycatcher", "41": "Vermilion Flycatcher", "42": "Yellow bellied Flycatcher", "43": "Frigatebird", "44": "Northern Fulmar", "45": "Gadwall", "46": "American Goldfinch", "47": "European Goldfinch", "48": "Boat tailed Grackle", "49": "Eared Grebe", "50": "Horned Grebe", "51": "Pied billed Grebe", "52": "Western Grebe", "53": "Blue Grosbeak", "54": "Evening Grosbeak", "55": "Pine Grosbeak", "56": "Rose breasted Grosbeak", "57": "Pigeon Guillemot", "58": "California Gull", "59": "Glaucous winged Gull", "60": "Heermann Gull", "61": "Herring Gull", "62": "Ivory Gull", "63": "Ring billed Gull", "64": "Slaty backed Gull", "65": "Western Gull", "66": "Anna Hummingbird", "67": "Ruby throated Hummingbird", "68": "Rufous Hummingbird", "69": "Green Violetear", "70": "Long tailed Jaeger", "71": "Pomarine Jaeger", "72": "Blue Jay", "73": "Florida Jay", "74": "Green Jay", "75": "Dark eyed Junco", "76": "Tropical Kingbird", "77": "Gray Kingbird", "78": "Belted Kingfisher", "79": "Green Kingfisher", "80": "Pied Kingfisher", "81": "Ringed Kingfisher", "82": "White breasted Kingfisher", "83": "Red legged Kittiwake", "84": "Horned Lark", "85": "Pacific Loon", "86": "Mallard", "87": "Western Meadowlark", "88": "Hooded Merganser", "89": "Red breasted Merganser", "90": "Mockingbird", "91": "Nighthawk", "92": "Clark Nutcracker", "93": "White breasted Nuthatch", "94": "Baltimore Oriole", "95": "Hooded Oriole", "96": "Orchard Oriole", "97": "Scott Oriole", "98": "Ovenbird", "99": "Brown Pelican", "100": "White Pelican", "101": "Western Wood Pewee", "102": "Sayornis", "103": "American Pipit", "104": "Whip poor Will", "105": "Horned Puffin", "106": "Common Raven", "107": "White necked Raven", "108": "American Redstart", "109": "Geococcyx", "110": "Loggerhead Shrike", "111": "Great Grey Shrike", "112": "Baird Sparrow", "113": "Black throated Sparrow", "114": "Brewer Sparrow", "115": "Chipping Sparrow", "116": "Clay colored Sparrow", "117": "House Sparrow", "118": "Field Sparrow", "119": "Fox Sparrow", "120": "Grasshopper Sparrow", "121": "Harris Sparrow", "122": "Henslow Sparrow", "123": "Le Conte Sparrow", "124": "Lincoln Sparrow", "125": "Nelson Sharp tailed Sparrow", "126": "Savannah Sparrow", "127": "Seaside Sparrow", "128": "Song Sparrow", "129": "Tree Sparrow", "130": "Vesper Sparrow", "131": "White crowned Sparrow", "132": "White throated Sparrow", "133": "Cape Glossy Starling", "134": "Bank Swallow", "135": "Barn Swallow", "136": "Cliff Swallow", "137": "Tree Swallow", "138": "Scarlet Tanager", "139": "Summer Tanager", "140": "Artic Tern", "141": "Black Tern", "142": "Caspian Tern", "143": "Common Tern", "144": "Elegant Tern", "145": "Forsters Tern", "146": "Least Tern", "147": "Green tailed Towhee", "148": "Brown Thrasher", "149": "Sage Thrasher", "150": "Black capped Vireo", "151": "Blue headed Vireo", "152": "Philadelphia Vireo", "153": "Red eyed Vireo", "154": "Warbling Vireo", "155": "White eyed Vireo", "156": "Yellow throated Vireo", "157": "Bay breasted Warbler", "158": "Black and white Warbler", "159": "Black throated Blue Warbler", "160": "Blue winged Warbler", "161": "Canada Warbler", "162": "Cape May Warbler", "163": "Cerulean Warbler", "164": "Chestnut sided Warbler", "165": "Golden winged Warbler", "166": "Hooded Warbler", "167": "Kentucky Warbler", "168": "Magnolia Warbler", "169": "Mourning Warbler", "170": "Myrtle Warbler", "171": "Nashville Warbler", "172": "Orange crowned Warbler", "173": "Palm Warbler", "174": "Pine Warbler", "175": "Prairie Warbler", "176": "Prothonotary Warbler", "177": "Swainson Warbler", "178": "Tennessee Warbler", "179": "Wilson Warbler", "180": "Worm eating Warbler", "181": "Yellow Warbler", "182": "Northern Waterthrush", "183": "Louisiana Waterthrush", "184": "Bohemian Waxwing", "185": "Cedar Waxwing", "186": "American Three toed Woodpecker", "187": "Pileated Woodpecker", "188": "Red bellied Woodpecker", "189": "Red cockaded Woodpecker", "190": "Red headed Woodpecker", "191": "Downy Woodpecker", "192": "Bewick Wren", "193": "Cactus Wren", "194": "Carolina Wren", "195": "House Wren", "196": "Marsh Wren", "197": "Rock Wren", "198": "Winter Wren", "199": "Common Yellowthroat"}}}}, {"name": "file_name", "dtype": "string"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 583864786.046, "num_examples": 5994}, {"name": "test", "num_bytes": 577090882.934, "num_examples": 5794}], "download_size": 1148682949, "dataset_size": 1160955668.98}}
2023-03-26T12:15:01+00:00
[]
[]
TAGS #region-us
# Dataset Card for "birds" More Information needed
[ "# Dataset Card for \"birds\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"birds\"\n\nMore Information needed" ]
[ 6, 12 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"birds\"\n\nMore Information needed" ]
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53a3d0c7b18701da4093a143ff2db27e3a6040b2
# Dataset Card for Noto Emoji Captions BLIP generated captions for Noto emojis. The dataset was captioned with the [pre-trained BLIP model](https://github.com/salesforce/BLIP). It contains a list of ´image´ and ´text´ keys with the images being 512x512.
arattinger/noto-emoji-captions
[ "annotations_creators:machine-generated", "multilinguality:monolingual", "language:en", "region:us" ]
2023-03-26T12:25:46+00:00
{"annotations_creators": ["machine-generated"], "language": ["en"], "multilinguality": ["monolingual"], "pretty_name": "Pok\u00e9mon BLIP captions", "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 77868555.5, "num_examples": 3468}], "download_size": 77424588, "dataset_size": 77868555.5}}
2023-03-26T13:21:59+00:00
[]
[ "en" ]
TAGS #annotations_creators-machine-generated #multilinguality-monolingual #language-English #region-us
# Dataset Card for Noto Emoji Captions BLIP generated captions for Noto emojis. The dataset was captioned with the pre-trained BLIP model. It contains a list of ´image´ and ´text´ keys with the images being 512x512.
[ "# Dataset Card for Noto Emoji Captions\n\nBLIP generated captions for Noto emojis.\nThe dataset was captioned with the pre-trained BLIP model.\n It contains a list of ´image´ and ´text´ keys with the images being 512x512." ]
[ "TAGS\n#annotations_creators-machine-generated #multilinguality-monolingual #language-English #region-us \n", "# Dataset Card for Noto Emoji Captions\n\nBLIP generated captions for Noto emojis.\nThe dataset was captioned with the pre-trained BLIP model.\n It contains a list of ´image´ and ´text´ keys with the images being 512x512." ]
[ 31, 62 ]
[ "passage: TAGS\n#annotations_creators-machine-generated #multilinguality-monolingual #language-English #region-us \n# Dataset Card for Noto Emoji Captions\n\nBLIP generated captions for Noto emojis.\nThe dataset was captioned with the pre-trained BLIP model.\n It contains a list of ´image´ and ´text´ keys with the images being 512x512." ]
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c75ea560a75e454506b9cdc9e733c683acc74a8d
# Dataset Card for "torgo_70_30" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Akshay-Sai/torgo_70_30
[ "region:us" ]
2023-03-26T12:53:24+00:00
{"dataset_info": {"features": [{"name": "label", "dtype": {"class_label": {"names": {"0": "control", "1": "pathology"}}}}, {"name": "input_features", "sequence": {"sequence": "float32"}}], "splits": [{"name": "train", "num_bytes": 4344541968, "num_examples": 4524}, {"name": "test", "num_bytes": 1863044080, "num_examples": 1940}], "download_size": 753776953, "dataset_size": 6207586048}}
2023-03-26T12:54:38+00:00
[]
[]
TAGS #region-us
# Dataset Card for "torgo_70_30" More Information needed
[ "# Dataset Card for \"torgo_70_30\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"torgo_70_30\"\n\nMore Information needed" ]
[ 6, 16 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"torgo_70_30\"\n\nMore Information needed" ]
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9d1771f869ed52bf564ed2d38b4cc24cf7edcb20
# ChatGPT3.5 Noisy Translation Twitter Notebooks at https://github.com/mesolitica/malaysian-dataset/tree/master/translation/chatgpt3.5-twitter
mesolitica/chatgpt-noisy-translation-twitter
[ "task_categories:translation", "language:ms", "region:us" ]
2023-03-26T13:19:05+00:00
{"language": ["ms"], "task_categories": ["translation"]}
2023-12-17T04:07:43+00:00
[]
[ "ms" ]
TAGS #task_categories-translation #language-Malay (macrolanguage) #region-us
# ChatGPT3.5 Noisy Translation Twitter Notebooks at URL
[ "# ChatGPT3.5 Noisy Translation Twitter\n\nNotebooks at URL" ]
[ "TAGS\n#task_categories-translation #language-Malay (macrolanguage) #region-us \n", "# ChatGPT3.5 Noisy Translation Twitter\n\nNotebooks at URL" ]
[ 25, 14 ]
[ "passage: TAGS\n#task_categories-translation #language-Malay (macrolanguage) #region-us \n# ChatGPT3.5 Noisy Translation Twitter\n\nNotebooks at URL" ]
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231b5e5816682d6d2fdd88b3146bbddd3c3b649f
# Dataset Card for "fake-news-detection-dataset-english" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
mohammadjavadpirhadi/fake-news-detection-dataset-english
[ "task_categories:text-classification", "size_categories:10K<n<100K", "language:en", "license:mit", "region:us" ]
2023-03-26T13:19:58+00:00
{"language": ["en"], "license": "mit", "size_categories": ["10K<n<100K"], "task_categories": ["text-classification"], "pretty_name": "Fake News Detection English", "dataset_info": {"features": [{"name": "title", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "subject", "dtype": "string"}, {"name": "date", "dtype": "string"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "real", "1": "fake"}}}}], "splits": [{"name": "train", "num_bytes": 93521249, "num_examples": 35918}, {"name": "test", "num_bytes": 23506751, "num_examples": 8980}], "download_size": 71290190, "dataset_size": 117028000}}
2023-03-26T15:10:25+00:00
[]
[ "en" ]
TAGS #task_categories-text-classification #size_categories-10K<n<100K #language-English #license-mit #region-us
# Dataset Card for "fake-news-detection-dataset-english" More Information needed
[ "# Dataset Card for \"fake-news-detection-dataset-english\"\n\nMore Information needed" ]
[ "TAGS\n#task_categories-text-classification #size_categories-10K<n<100K #language-English #license-mit #region-us \n", "# Dataset Card for \"fake-news-detection-dataset-english\"\n\nMore Information needed" ]
[ 38, 23 ]
[ "passage: TAGS\n#task_categories-text-classification #size_categories-10K<n<100K #language-English #license-mit #region-us \n# Dataset Card for \"fake-news-detection-dataset-english\"\n\nMore Information needed" ]
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1fa99238eb5e01496343cd31aa3bb563f416b563
# LandCover.ai: Dataset for Automatic Mapping of Buildings, Woodlands, Water and Roads from Aerial Imagery My project based on the dataset, can be found on Github: https://github.com/MortenTabaka/Semantic-segmentation-of-LandCover.ai-dataset The dataset used in this project is the [Landcover.ai Dataset](https://landcover.ai.linuxpolska.com/), which was originally published with [LandCover.ai: Dataset for Automatic Mapping of Buildings, Woodlands, Water and Roads from Aerial Imagery paper](https://arxiv.org/abs/2005.02264) also accessible on [PapersWithCode](https://paperswithcode.com/paper/landcover-ai-dataset-for-automatic-mapping-of). **Please note that I am not the author or owner of this dataset, and I am using it under the terms of the license specified by the original author. All credits for the dataset go to the original author and contributors.** --- license: cc-by-nc-sa-4.0 ---
MortenTabaka/LandCover-Aerial-Imagery-for-semantic-segmentation
[ "task_categories:image-segmentation", "license:cc-by-nc-sa-4.0", "arxiv:2005.02264", "region:us" ]
2023-03-26T13:36:08+00:00
{"license": "cc-by-nc-sa-4.0", "task_categories": ["image-segmentation"]}
2023-03-26T16:28:43+00:00
[ "2005.02264" ]
[]
TAGS #task_categories-image-segmentation #license-cc-by-nc-sa-4.0 #arxiv-2005.02264 #region-us
# URL: Dataset for Automatic Mapping of Buildings, Woodlands, Water and Roads from Aerial Imagery My project based on the dataset, can be found on Github: URL The dataset used in this project is the URL Dataset, which was originally published with URL: Dataset for Automatic Mapping of Buildings, Woodlands, Water and Roads from Aerial Imagery paper also accessible on PapersWithCode. Please note that I am not the author or owner of this dataset, and I am using it under the terms of the license specified by the original author. All credits for the dataset go to the original author and contributors. --- license: cc-by-nc-sa-4.0 ---
[ "# URL: Dataset for Automatic Mapping of Buildings, Woodlands, Water and Roads from Aerial Imagery\n\nMy project based on the dataset, can be found on Github: URL\n\nThe dataset used in this project is the URL Dataset, \nwhich was originally published with URL: Dataset for Automatic Mapping of Buildings, Woodlands, Water and Roads from Aerial Imagery paper\nalso accessible on PapersWithCode.\n\nPlease note that I am not the author or owner of this dataset, and I am using it under the terms of the license specified by the original author. \nAll credits for the dataset go to the original author and contributors.\n\n---\nlicense: cc-by-nc-sa-4.0\n---" ]
[ "TAGS\n#task_categories-image-segmentation #license-cc-by-nc-sa-4.0 #arxiv-2005.02264 #region-us \n", "# URL: Dataset for Automatic Mapping of Buildings, Woodlands, Water and Roads from Aerial Imagery\n\nMy project based on the dataset, can be found on Github: URL\n\nThe dataset used in this project is the URL Dataset, \nwhich was originally published with URL: Dataset for Automatic Mapping of Buildings, Woodlands, Water and Roads from Aerial Imagery paper\nalso accessible on PapersWithCode.\n\nPlease note that I am not the author or owner of this dataset, and I am using it under the terms of the license specified by the original author. \nAll credits for the dataset go to the original author and contributors.\n\n---\nlicense: cc-by-nc-sa-4.0\n---" ]
[ 39, 161 ]
[ "passage: TAGS\n#task_categories-image-segmentation #license-cc-by-nc-sa-4.0 #arxiv-2005.02264 #region-us \n# URL: Dataset for Automatic Mapping of Buildings, Woodlands, Water and Roads from Aerial Imagery\n\nMy project based on the dataset, can be found on Github: URL\n\nThe dataset used in this project is the URL Dataset, \nwhich was originally published with URL: Dataset for Automatic Mapping of Buildings, Woodlands, Water and Roads from Aerial Imagery paper\nalso accessible on PapersWithCode.\n\nPlease note that I am not the author or owner of this dataset, and I am using it under the terms of the license specified by the original author. \nAll credits for the dataset go to the original author and contributors.\n\n---\nlicense: cc-by-nc-sa-4.0\n---" ]
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cc7d9183b9e63bfe9ff834c0df60c92620954560
# Dataset Card for "Torgo_train-30_test-70" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
AravindVadlapudi02/Torgo_train-30_test-70
[ "region:us" ]
2023-03-26T13:56:31+00:00
{"dataset_info": {"features": [{"name": "label", "dtype": {"class_label": {"names": {"0": "control", "1": "pathology"}}}}, {"name": "input_features", "sequence": {"sequence": "float32"}}], "splits": [{"name": "train", "num_bytes": 1862083748, "num_examples": 1939}, {"name": "test", "num_bytes": 4345502300, "num_examples": 4525}], "download_size": 753824940, "dataset_size": 6207586048}}
2023-03-26T14:01:00+00:00
[]
[]
TAGS #region-us
# Dataset Card for "Torgo_train-30_test-70" More Information needed
[ "# Dataset Card for \"Torgo_train-30_test-70\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"Torgo_train-30_test-70\"\n\nMore Information needed" ]
[ 6, 19 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"Torgo_train-30_test-70\"\n\nMore Information needed" ]
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43398f9e1689b06b59357ff82a452a696972114a
# Dataset Card for "google_fleurs_plus_common_voice_11_arabic_language" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
MohammadJamalaldeen/google_fleurs_plus_common_voice_11_arabic_language
[ "region:us" ]
2023-03-26T14:13:45+00:00
{"dataset_info": {"features": [{"name": "input_features", "sequence": {"sequence": "float32"}}, {"name": "labels", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 39269047480, "num_examples": 40880}, {"name": "test", "num_bytes": 10027780960, "num_examples": 10440}], "download_size": 0, "dataset_size": 49296828440}}
2023-03-26T20:09:52+00:00
[]
[]
TAGS #region-us
# Dataset Card for "google_fleurs_plus_common_voice_11_arabic_language" More Information needed
[ "# Dataset Card for \"google_fleurs_plus_common_voice_11_arabic_language\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"google_fleurs_plus_common_voice_11_arabic_language\"\n\nMore Information needed" ]
[ 6, 29 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"google_fleurs_plus_common_voice_11_arabic_language\"\n\nMore Information needed" ]
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bde1cbacf914c896ada9796fd3c6382d90af5b40
# Dataset Card for Dataset Name ## Dataset Description - **Homepage:** - **Repository:** - **Paper:** - **Leaderboard:** - **Point of Contact:** ### Dataset Summary This repository contains a machine-translated French version of the portion of [MultiNLI](https://cims.nyu.edu/~sbowman/multinli) concerning the 9/11 terrorist attacks (2000 examples). Note that these 2000 examples included in MultiNLI (and machine translated in French here) on the subject of 9/11 are different from the 249 examples in the validation subset and the 501 ones in the test subset of XNLI on the same subject. In the original subset of MultiNLI on 9/11, 26 examples were left without gold label. In this French version, we have given a gold label also to these examples (so that there are no more examples without gold label), according to our reading of the examples. ### Supported Tasks and Leaderboards This dataset can be used for the task of Natural Language Inference (NLI), also known as Recognizing Textual Entailment (RTE), which is a sentence-pair classification task. ## Dataset Structure ### Data Fields - `premise`: The machine translated premise in the target language. - `hypothesis`: The machine translated premise in the target language. - `label`: The classification label, with possible values 0 (`entailment`), 1 (`neutral`), 2 (`contradiction`). - `label_text`: The classification label, with possible values `entailment` (0), `neutral` (1), `contradiction` (2). - `pairID`: Unique identifier for pair. - `promptID`: Unique identifier for prompt. - `premise_original`: The original premise from the English source dataset. - `hypothesis_original`: The original hypothesis from the English source dataset. ### Data Splits | name |entailment|neutral|contradiction| |--------|---------:|------:|------------:| |mnli_fr | 705 | 641 | 654 | ## Dataset Creation The dataset was machine translated from English to French using the latest neural machine translation [opus-mt-tc-big](https://huggingface.co/Helsinki-NLP/opus-mt-tc-big-en-fr) model available for French. The translation of the sentences was carried out on March 29th, 2023. ## Additional Information ### Citation Information **BibTeX:** ````BibTeX @InProceedings{N18-1101, author = "Williams, Adina and Nangia, Nikita and Bowman, Samuel", title = "A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference", booktitle = "Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)", year = "2018", publisher = "Association for Computational Linguistics", pages = "1112--1122", location = "New Orleans, Louisiana", url = "http://aclweb.org/anthology/N18-1101" } ```` **ACL:** Adina Williams, Nikita Nangia, and Samuel Bowman. 2018. [A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference](https://aclanthology.org/N18-1101/). In *Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)*, pages 1112–1122, New Orleans, Louisiana. Association for Computational Linguistics. ### Acknowledgements This translation of the original dataset was done as part of a research project supported by the Defence Innovation Agency (AID) of the Directorate General of Armament (DGA) of the French Ministry of Armed Forces, and by the ICO, _Institut Cybersécurité Occitanie_, funded by Région Occitanie, France.
maximoss/mnli-nineeleven-fr-mt
[ "task_categories:text-classification", "task_ids:natural-language-inference", "task_ids:multi-input-text-classification", "size_categories:1K<n<10K", "language:fr", "license:bsd-2-clause", "region:us" ]
2023-03-26T15:07:15+00:00
{"language": ["fr"], "license": "bsd-2-clause", "size_categories": ["1K<n<10K"], "task_categories": ["text-classification"], "task_ids": ["natural-language-inference", "multi-input-text-classification"]}
2024-02-04T12:38:08+00:00
[]
[ "fr" ]
TAGS #task_categories-text-classification #task_ids-natural-language-inference #task_ids-multi-input-text-classification #size_categories-1K<n<10K #language-French #license-bsd-2-clause #region-us
Dataset Card for Dataset Name ============================= Dataset Description ------------------- * Homepage: * Repository: * Paper: * Leaderboard: * Point of Contact: ### Dataset Summary This repository contains a machine-translated French version of the portion of MultiNLI concerning the 9/11 terrorist attacks (2000 examples). Note that these 2000 examples included in MultiNLI (and machine translated in French here) on the subject of 9/11 are different from the 249 examples in the validation subset and the 501 ones in the test subset of XNLI on the same subject. In the original subset of MultiNLI on 9/11, 26 examples were left without gold label. In this French version, we have given a gold label also to these examples (so that there are no more examples without gold label), according to our reading of the examples. ### Supported Tasks and Leaderboards This dataset can be used for the task of Natural Language Inference (NLI), also known as Recognizing Textual Entailment (RTE), which is a sentence-pair classification task. Dataset Structure ----------------- ### Data Fields * 'premise': The machine translated premise in the target language. * 'hypothesis': The machine translated premise in the target language. * 'label': The classification label, with possible values 0 ('entailment'), 1 ('neutral'), 2 ('contradiction'). * 'label\_text': The classification label, with possible values 'entailment' (0), 'neutral' (1), 'contradiction' (2). * 'pairID': Unique identifier for pair. * 'promptID': Unique identifier for prompt. * 'premise\_original': The original premise from the English source dataset. * 'hypothesis\_original': The original hypothesis from the English source dataset. ### Data Splits Dataset Creation ---------------- The dataset was machine translated from English to French using the latest neural machine translation opus-mt-tc-big model available for French. The translation of the sentences was carried out on March 29th, 2023. Additional Information ---------------------- BibTeX: ' ACL: Adina Williams, Nikita Nangia, and Samuel Bowman. 2018. A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference. In *Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)*, pages 1112–1122, New Orleans, Louisiana. Association for Computational Linguistics. ### Acknowledgements This translation of the original dataset was done as part of a research project supported by the Defence Innovation Agency (AID) of the Directorate General of Armament (DGA) of the French Ministry of Armed Forces, and by the ICO, *Institut Cybersécurité Occitanie*, funded by Région Occitanie, France.
[ "### Dataset Summary\n\n\nThis repository contains a machine-translated French version of the portion of MultiNLI concerning the 9/11 terrorist attacks (2000 examples).\nNote that these 2000 examples included in MultiNLI (and machine translated in French here) on the subject of 9/11 are different from the 249 examples in the validation subset and the 501 ones in the test subset of XNLI on the same subject.\n\n\nIn the original subset of MultiNLI on 9/11, 26 examples were left without gold label. In this French version, we have given a gold label also to these examples (so that there are no more examples without gold label), according to our reading of the examples.", "### Supported Tasks and Leaderboards\n\n\nThis dataset can be used for the task of Natural Language Inference (NLI), also known as Recognizing Textual Entailment (RTE), which is a sentence-pair classification task.\n\n\nDataset Structure\n-----------------", "### Data Fields\n\n\n* 'premise': The machine translated premise in the target language.\n* 'hypothesis': The machine translated premise in the target language.\n* 'label': The classification label, with possible values 0 ('entailment'), 1 ('neutral'), 2 ('contradiction').\n* 'label\\_text': The classification label, with possible values 'entailment' (0), 'neutral' (1), 'contradiction' (2).\n* 'pairID': Unique identifier for pair.\n* 'promptID': Unique identifier for prompt.\n* 'premise\\_original': The original premise from the English source dataset.\n* 'hypothesis\\_original': The original hypothesis from the English source dataset.", "### Data Splits\n\n\n\nDataset Creation\n----------------\n\n\nThe dataset was machine translated from English to French using the latest neural machine translation opus-mt-tc-big model available for French.\nThe translation of the sentences was carried out on March 29th, 2023.\n\n\nAdditional Information\n----------------------\n\n\nBibTeX:\n\n\n'\n\n\nACL:\n\n\nAdina Williams, Nikita Nangia, and Samuel Bowman. 2018. A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference. In *Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)*, pages 1112–1122, New Orleans, Louisiana. Association for Computational Linguistics.", "### Acknowledgements\n\n\nThis translation of the original dataset was done as part of a research project supported by the Defence Innovation Agency (AID) of the Directorate General of Armament (DGA) of the French Ministry of Armed Forces, and by the ICO, *Institut Cybersécurité Occitanie*, funded by Région Occitanie, France." ]
[ "TAGS\n#task_categories-text-classification #task_ids-natural-language-inference #task_ids-multi-input-text-classification #size_categories-1K<n<10K #language-French #license-bsd-2-clause #region-us \n", "### Dataset Summary\n\n\nThis repository contains a machine-translated French version of the portion of MultiNLI concerning the 9/11 terrorist attacks (2000 examples).\nNote that these 2000 examples included in MultiNLI (and machine translated in French here) on the subject of 9/11 are different from the 249 examples in the validation subset and the 501 ones in the test subset of XNLI on the same subject.\n\n\nIn the original subset of MultiNLI on 9/11, 26 examples were left without gold label. In this French version, we have given a gold label also to these examples (so that there are no more examples without gold label), according to our reading of the examples.", "### Supported Tasks and Leaderboards\n\n\nThis dataset can be used for the task of Natural Language Inference (NLI), also known as Recognizing Textual Entailment (RTE), which is a sentence-pair classification task.\n\n\nDataset Structure\n-----------------", "### Data Fields\n\n\n* 'premise': The machine translated premise in the target language.\n* 'hypothesis': The machine translated premise in the target language.\n* 'label': The classification label, with possible values 0 ('entailment'), 1 ('neutral'), 2 ('contradiction').\n* 'label\\_text': The classification label, with possible values 'entailment' (0), 'neutral' (1), 'contradiction' (2).\n* 'pairID': Unique identifier for pair.\n* 'promptID': Unique identifier for prompt.\n* 'premise\\_original': The original premise from the English source dataset.\n* 'hypothesis\\_original': The original hypothesis from the English source dataset.", "### Data Splits\n\n\n\nDataset Creation\n----------------\n\n\nThe dataset was machine translated from English to French using the latest neural machine translation opus-mt-tc-big model available for French.\nThe translation of the sentences was carried out on March 29th, 2023.\n\n\nAdditional Information\n----------------------\n\n\nBibTeX:\n\n\n'\n\n\nACL:\n\n\nAdina Williams, Nikita Nangia, and Samuel Bowman. 2018. A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference. In *Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)*, pages 1112–1122, New Orleans, Louisiana. Association for Computational Linguistics.", "### Acknowledgements\n\n\nThis translation of the original dataset was done as part of a research project supported by the Defence Innovation Agency (AID) of the Directorate General of Armament (DGA) of the French Ministry of Armed Forces, and by the ICO, *Institut Cybersécurité Occitanie*, funded by Région Occitanie, France." ]
[ 73, 159, 61, 185, 173, 85 ]
[ "passage: TAGS\n#task_categories-text-classification #task_ids-natural-language-inference #task_ids-multi-input-text-classification #size_categories-1K<n<10K #language-French #license-bsd-2-clause #region-us \n### Dataset Summary\n\n\nThis repository contains a machine-translated French version of the portion of MultiNLI concerning the 9/11 terrorist attacks (2000 examples).\nNote that these 2000 examples included in MultiNLI (and machine translated in French here) on the subject of 9/11 are different from the 249 examples in the validation subset and the 501 ones in the test subset of XNLI on the same subject.\n\n\nIn the original subset of MultiNLI on 9/11, 26 examples were left without gold label. In this French version, we have given a gold label also to these examples (so that there are no more examples without gold label), according to our reading of the examples.### Supported Tasks and Leaderboards\n\n\nThis dataset can be used for the task of Natural Language Inference (NLI), also known as Recognizing Textual Entailment (RTE), which is a sentence-pair classification task.\n\n\nDataset Structure\n-----------------### Data Fields\n\n\n* 'premise': The machine translated premise in the target language.\n* 'hypothesis': The machine translated premise in the target language.\n* 'label': The classification label, with possible values 0 ('entailment'), 1 ('neutral'), 2 ('contradiction').\n* 'label\\_text': The classification label, with possible values 'entailment' (0), 'neutral' (1), 'contradiction' (2).\n* 'pairID': Unique identifier for pair.\n* 'promptID': Unique identifier for prompt.\n* 'premise\\_original': The original premise from the English source dataset.\n* 'hypothesis\\_original': The original hypothesis from the English source dataset." ]
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8fb9af26c640dbfa04549b776a5830172d91fb72
# Dataset Card for "somos-clean-alpaca-es-validations" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
alarcon7a/somos-clean-alpaca-es-validations
[ "region:us" ]
2023-03-26T16:51:42+00:00
{"dataset_info": {"features": [{"name": "text", "dtype": "null"}, {"name": "inputs", "struct": [{"name": "1-instruction", "dtype": "string"}, {"name": "2-input", "dtype": "string"}, {"name": "3-output", "dtype": "string"}]}, {"name": "prediction", "dtype": "null"}, {"name": "prediction_agent", "dtype": "null"}, {"name": "annotation", "dtype": "string"}, {"name": "annotation_agent", "dtype": "string"}, {"name": "vectors", "struct": [{"name": "input", "sequence": "float64"}, {"name": "instruction", "sequence": "float64"}, {"name": "output", "sequence": "float64"}]}, {"name": "multi_label", "dtype": "bool"}, {"name": "explanation", "dtype": "null"}, {"name": "id", "dtype": "string"}, {"name": "metadata", "dtype": "null"}, {"name": "status", "dtype": "string"}, {"name": "event_timestamp", "dtype": "timestamp[us]"}, {"name": "metrics", "struct": [{"name": "text_length", "dtype": "int64"}]}], "splits": [{"name": "train", "num_bytes": 739721, "num_examples": 39}], "download_size": 0, "dataset_size": 739721}}
2023-04-05T03:29:02+00:00
[]
[]
TAGS #region-us
# Dataset Card for "somos-clean-alpaca-es-validations" More Information needed
[ "# Dataset Card for \"somos-clean-alpaca-es-validations\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"somos-clean-alpaca-es-validations\"\n\nMore Information needed" ]
[ 6, 24 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"somos-clean-alpaca-es-validations\"\n\nMore Information needed" ]
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0ec4b0966ee3d54441474ceaac8ca08f19de3edb
# Dataset Card for "somos-clean-alpaca-es" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
alarcon7a/somos-clean-alpaca-es
[ "region:us" ]
2023-03-26T17:16:33+00:00
{"dataset_info": {"features": [{"name": "text", "dtype": "null"}, {"name": "inputs", "struct": [{"name": "1-instruction", "dtype": "string"}, {"name": "2-input", "dtype": "string"}, {"name": "3-output", "dtype": "string"}]}, {"name": "prediction", "dtype": "null"}, {"name": "prediction_agent", "dtype": "null"}, {"name": "annotation", "dtype": "string"}, {"name": "annotation_agent", "dtype": "string"}, {"name": "vectors", "struct": [{"name": "input", "sequence": "float64"}, {"name": "instruction", "sequence": "float64"}, {"name": "output", "sequence": "float64"}]}, {"name": "multi_label", "dtype": "bool"}, {"name": "explanation", "dtype": "null"}, {"name": "id", "dtype": "string"}, {"name": "metadata", "dtype": "null"}, {"name": "status", "dtype": "string"}, {"name": "event_timestamp", "dtype": "timestamp[us]"}, {"name": "metrics", "struct": [{"name": "text_length", "dtype": "int64"}]}], "splits": [{"name": "train", "num_bytes": 551730, "num_examples": 29}], "download_size": 437686, "dataset_size": 551730}}
2023-03-28T15:36:36+00:00
[]
[]
TAGS #region-us
# Dataset Card for "somos-clean-alpaca-es" More Information needed
[ "# Dataset Card for \"somos-clean-alpaca-es\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"somos-clean-alpaca-es\"\n\nMore Information needed" ]
[ 6, 20 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"somos-clean-alpaca-es\"\n\nMore Information needed" ]
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f62c271446c113401efa897e6a47a330359818c8
# Dataset Card for "somos-clean-alpaca-es-test" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
dvilasuero/somos-clean-alpaca-es-test
[ "region:us" ]
2023-03-26T17:55:58+00:00
{"dataset_info": {"features": [{"name": "text", "dtype": "null"}, {"name": "inputs", "struct": [{"name": "1-instruction", "dtype": "string"}, {"name": "2-input", "dtype": "string"}, {"name": "3-output", "dtype": "string"}]}, {"name": "prediction", "dtype": "null"}, {"name": "prediction_agent", "dtype": "null"}, {"name": "annotation", "dtype": "string"}, {"name": "annotation_agent", "dtype": "string"}, {"name": "vectors", "struct": [{"name": "input", "sequence": "float64"}, {"name": "instruction", "sequence": "float64"}, {"name": "output", "sequence": "float64"}]}, {"name": "multi_label", "dtype": "bool"}, {"name": "explanation", "dtype": "null"}, {"name": "id", "dtype": "string"}, {"name": "metadata", "dtype": "null"}, {"name": "status", "dtype": "string"}, {"name": "event_timestamp", "dtype": "timestamp[us]"}, {"name": "metrics", "struct": [{"name": "text_length", "dtype": "int64"}]}], "splits": [{"name": "train", "num_bytes": 361340, "num_examples": 19}], "download_size": 301641, "dataset_size": 361340}}
2023-03-26T18:00:25+00:00
[]
[]
TAGS #region-us
# Dataset Card for "somos-clean-alpaca-es-test" More Information needed
[ "# Dataset Card for \"somos-clean-alpaca-es-test\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"somos-clean-alpaca-es-test\"\n\nMore Information needed" ]
[ 6, 22 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"somos-clean-alpaca-es-test\"\n\nMore Information needed" ]
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3ca3aef388aa2a77d6ae11a68a2fc24d32f94bc7
This dataset includes 4,080 texts that were generated by the [**ManaGPT-1020**](https://huggingface.co/NeuraXenetica/ManaGPT-1020) large language model, in response to particular input sequences. ManaGPT-1020 is a free, open-source model available for download and use via Hugging Face’s “transformers” Python package. The model is a 1.5-billion-parameter LLM that’s capable of generating text in order to complete a sentence whose first words have been provided via a user-supplied input sequence. The model represents an elaboration of GPT-2 that has been fine-tuned (using Python and TensorFlow) on a specialized English-language corpus of over 509,000 words from the domain of organizational futures studies. In particular, the model has been trained to generate analysis, predictions, and recommendations regarding the emerging role of advanced AI, social robotics, ubiquitous computing, virtual reality, neurocybernetic augmentation, and other “posthumanizing” technologies in organizational life. In generating the texts, 102 different prompts were used, each of which was employed to generate 20 responses. The 102 input sequences were created by concatenating 12 different "subjects" with 17 different "modal variants," in every possible combination. The subjects included 6 grammatically singular subjects: - "The workplace of tomorrow" - "Technological posthumanization" - "The organizational use of AI" - "A robotic boss" - "An artificially intelligent coworker" - "Business culture within Society 5.0" Also included were 6 grammatically plural subjects: - "Social robots" - "Hybrid human-robotic organizations" - "Artificially intelligent businesses" - "The posthumanized workplaces of the future" - "Cybernetically augmented workers" - "Organizations in Society 5.0" For the 6 grammatically singular subjects, the 17 modal variants included one "blank" variant (an empty string) and 16 phrases that lend the input sequence diverse forms of "modal shading," by indicating varying degrees of certainty, probability, predictability, logical necessity, or moral obligation or approbation. These modal variants were: - "" - " is" - " is not" - " will" - " will be" - " may" - " might never" - " is likely to" - " is unlikely to" - " should" - " can" - " cannot" - " can never" - " must" - " must not" - " is like" - " will be like" The variants used with grammatically plural subjects were identical, apart from the fact that the word “is” was changed to “are,” wherever it appeared. In a small number of cases (only occurring when the empty string "" was used as part of the input sequence), the model failed to generate any output beyond the input sequence itself.
NeuraXenetica/managpt-4080-nlp-prompts-and-generated-texts
[ "task_categories:text-generation", "size_categories:1K<n<10K", "language:en", "license:cc-by-4.0", "region:us" ]
2023-03-26T18:25:25+00:00
{"language": ["en"], "license": "cc-by-4.0", "size_categories": ["1K<n<10K"], "task_categories": ["text-generation"], "pretty_name": "ManaGPT: 4,080 NLP prompts and generated texts"}
2023-03-29T16:52:49+00:00
[]
[ "en" ]
TAGS #task_categories-text-generation #size_categories-1K<n<10K #language-English #license-cc-by-4.0 #region-us
This dataset includes 4,080 texts that were generated by the ManaGPT-1020 large language model, in response to particular input sequences. ManaGPT-1020 is a free, open-source model available for download and use via Hugging Face’s “transformers” Python package. The model is a 1.5-billion-parameter LLM that’s capable of generating text in order to complete a sentence whose first words have been provided via a user-supplied input sequence. The model represents an elaboration of GPT-2 that has been fine-tuned (using Python and TensorFlow) on a specialized English-language corpus of over 509,000 words from the domain of organizational futures studies. In particular, the model has been trained to generate analysis, predictions, and recommendations regarding the emerging role of advanced AI, social robotics, ubiquitous computing, virtual reality, neurocybernetic augmentation, and other “posthumanizing” technologies in organizational life. In generating the texts, 102 different prompts were used, each of which was employed to generate 20 responses. The 102 input sequences were created by concatenating 12 different "subjects" with 17 different "modal variants," in every possible combination. The subjects included 6 grammatically singular subjects: - "The workplace of tomorrow" - "Technological posthumanization" - "The organizational use of AI" - "A robotic boss" - "An artificially intelligent coworker" - "Business culture within Society 5.0" Also included were 6 grammatically plural subjects: - "Social robots" - "Hybrid human-robotic organizations" - "Artificially intelligent businesses" - "The posthumanized workplaces of the future" - "Cybernetically augmented workers" - "Organizations in Society 5.0" For the 6 grammatically singular subjects, the 17 modal variants included one "blank" variant (an empty string) and 16 phrases that lend the input sequence diverse forms of "modal shading," by indicating varying degrees of certainty, probability, predictability, logical necessity, or moral obligation or approbation. These modal variants were: - "" - " is" - " is not" - " will" - " will be" - " may" - " might never" - " is likely to" - " is unlikely to" - " should" - " can" - " cannot" - " can never" - " must" - " must not" - " is like" - " will be like" The variants used with grammatically plural subjects were identical, apart from the fact that the word “is” was changed to “are,” wherever it appeared. In a small number of cases (only occurring when the empty string "" was used as part of the input sequence), the model failed to generate any output beyond the input sequence itself.
[]
[ "TAGS\n#task_categories-text-generation #size_categories-1K<n<10K #language-English #license-cc-by-4.0 #region-us \n" ]
[ 42 ]
[ "passage: TAGS\n#task_categories-text-generation #size_categories-1K<n<10K #language-English #license-cc-by-4.0 #region-us \n" ]
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76bd37c4a0fe978bc5bef0e6cb40fca4c6e4c997
This dataset contains 44 screenshots of the game Return To Monkey Island, scraped from the web and initially used to fine-tune a Stable Diffusion model with Dreambooth.
keras-dreambooth/monkey_island_screenshots
[ "size_categories:n<1K", "license:apache-2.0", "keras-dreambooth", "region:us" ]
2023-03-26T18:29:53+00:00
{"license": "apache-2.0", "size_categories": ["n<1K"], "pretty_name": "Return To Monkey Island Screenshots", "tags": ["keras-dreambooth"]}
2023-03-26T18:32:41+00:00
[]
[]
TAGS #size_categories-n<1K #license-apache-2.0 #keras-dreambooth #region-us
This dataset contains 44 screenshots of the game Return To Monkey Island, scraped from the web and initially used to fine-tune a Stable Diffusion model with Dreambooth.
[]
[ "TAGS\n#size_categories-n<1K #license-apache-2.0 #keras-dreambooth #region-us \n" ]
[ 31 ]
[ "passage: TAGS\n#size_categories-n<1K #license-apache-2.0 #keras-dreambooth #region-us \n" ]
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512d2cb5e7a9b73a27df84acbcdb778056b0fe8b
# Dataset Card for "WikiArt_mini_demos" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Artificio/WikiArt_mini_demos
[ "region:us" ]
2023-03-26T18:58:44+00:00
{"dataset_info": {"features": [{"name": "title", "dtype": "string"}, {"name": "artist", "dtype": "string"}, {"name": "date", "dtype": "string"}, {"name": "genre", "dtype": "string"}, {"name": "style", "dtype": "string"}, {"name": "description", "dtype": "string"}, {"name": "filename", "dtype": "string"}, {"name": "image", "dtype": "image"}, {"name": "resnet50_non_robust_features_2048", "sequence": "float32"}, {"name": "resnet50_robust_features_2048", "sequence": "float32"}, {"name": "resnet50_robust_feats", "sequence": "float32"}, {"name": "resnet50_non_robust_feats", "sequence": "float32"}], "splits": [{"name": "train", "num_bytes": 467403497.0, "num_examples": 10000}], "download_size": 445315266, "dataset_size": 467403497.0}}
2023-04-12T03:09:09+00:00
[]
[]
TAGS #region-us
# Dataset Card for "WikiArt_mini_demos" More Information needed
[ "# Dataset Card for \"WikiArt_mini_demos\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"WikiArt_mini_demos\"\n\nMore Information needed" ]
[ 6, 17 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"WikiArt_mini_demos\"\n\nMore Information needed" ]
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dbdbea42140c25ee8fc47a73348fa76eab237abf
# Dataset Card for "training-invoices" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
lowem1/training-invoices
[ "region:us" ]
2023-03-26T19:21:32+00:00
{"dataset_info": {"features": [{"name": "label", "dtype": "string"}, {"name": "line_data", "dtype": "string"}, {"name": "source", "dtype": "string"}, {"name": "noise_factor", "dtype": "null"}, {"name": "__index_level_0__", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 10301, "num_examples": 132}], "download_size": 6493, "dataset_size": 10301}}
2023-03-26T19:21:34+00:00
[]
[]
TAGS #region-us
# Dataset Card for "training-invoices" More Information needed
[ "# Dataset Card for \"training-invoices\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"training-invoices\"\n\nMore Information needed" ]
[ 6, 15 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"training-invoices\"\n\nMore Information needed" ]
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a7e72ccf205d51593e44da89bc88f13aab7fac19
See [Allamanis et al., 2021](https://arxiv.org/pdf/2105.12787.pdf) (NeurIPS 2021) for more information.
Nadav-Timor/PyPiBugs
[ "size_categories:1K<n<10K", "license:other", "code", "bugs", "diff", "repair", "arxiv:2105.12787", "region:us" ]
2023-03-26T19:27:24+00:00
{"license": "other", "size_categories": ["1K<n<10K"], "tags": ["code", "bugs", "diff", "repair"]}
2023-06-07T19:39:12+00:00
[ "2105.12787" ]
[]
TAGS #size_categories-1K<n<10K #license-other #code #bugs #diff #repair #arxiv-2105.12787 #region-us
See Allamanis et al., 2021 (NeurIPS 2021) for more information.
[]
[ "TAGS\n#size_categories-1K<n<10K #license-other #code #bugs #diff #repair #arxiv-2105.12787 #region-us \n" ]
[ 42 ]
[ "passage: TAGS\n#size_categories-1K<n<10K #license-other #code #bugs #diff #repair #arxiv-2105.12787 #region-us \n" ]
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00e543a4cadbbb97cf5eeeddb2f2e404d2272892
# Dataset Card for "procedural_gen" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
sethapun/procedural_gen
[ "region:us" ]
2023-03-26T19:27:41+00:00
{"dataset_info": {"features": [{"name": "expression", "dtype": "string"}, {"name": "answer", "dtype": "float64"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "false", "1": "true"}}}}], "splits": [{"name": "train", "num_bytes": 99358, "num_examples": 2000}, {"name": "validation", "num_bytes": 19864, "num_examples": 400}], "download_size": 46579, "dataset_size": 119222}}
2023-03-28T23:02:31+00:00
[]
[]
TAGS #region-us
# Dataset Card for "procedural_gen" More Information needed
[ "# Dataset Card for \"procedural_gen\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"procedural_gen\"\n\nMore Information needed" ]
[ 6, 15 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"procedural_gen\"\n\nMore Information needed" ]
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8c480d9cc769004882b0c17a59097d79a54429ba
# AutoTrain Dataset for project: trial ## Dataset Description This dataset has been automatically processed by AutoTrain for project trial. ### Languages The BCP-47 code for the dataset's language is unk. ## Dataset Structure ### Data Instances A sample from this dataset looks as follows: ```json [ { "image": "<32x36 RGBA PIL image>", "target": 0 }, { "image": "<32x36 RGBA PIL image>", "target": 2 } ] ``` ### Dataset Fields The dataset has the following fields (also called "features"): ```json { "image": "Image(decode=True, id=None)", "target": "ClassLabel(names=['Healer_f', 'healer_m', 'ninja_m', 'ranger_m', 'rpgsprites1'], id=None)" } ``` ### Dataset Splits This dataset is split into a train and validation split. The split sizes are as follow: | Split name | Num samples | | ------------ | ------------------- | | train | 45 | | valid | 15 |
Fahad-7864/autotrain-data-trial
[ "task_categories:image-classification", "region:us" ]
2023-03-26T20:55:43+00:00
{"task_categories": ["image-classification"]}
2023-03-26T21:40:59+00:00
[]
[]
TAGS #task_categories-image-classification #region-us
AutoTrain Dataset for project: trial ==================================== Dataset Description ------------------- This dataset has been automatically processed by AutoTrain for project trial. ### Languages The BCP-47 code for the dataset's language is unk. Dataset Structure ----------------- ### Data Instances A sample from this dataset looks as follows: ### Dataset Fields The dataset has the following fields (also called "features"): ### Dataset Splits This dataset is split into a train and validation split. The split sizes are as follow:
[ "### Languages\n\n\nThe BCP-47 code for the dataset's language is unk.\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nA sample from this dataset looks as follows:", "### Dataset Fields\n\n\nThe dataset has the following fields (also called \"features\"):", "### Dataset Splits\n\n\nThis dataset is split into a train and validation split. The split sizes are as follow:" ]
[ "TAGS\n#task_categories-image-classification #region-us \n", "### Languages\n\n\nThe BCP-47 code for the dataset's language is unk.\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nA sample from this dataset looks as follows:", "### Dataset Fields\n\n\nThe dataset has the following fields (also called \"features\"):", "### Dataset Splits\n\n\nThis dataset is split into a train and validation split. The split sizes are as follow:" ]
[ 17, 27, 17, 23, 27 ]
[ "passage: TAGS\n#task_categories-image-classification #region-us \n### Languages\n\n\nThe BCP-47 code for the dataset's language is unk.\n\n\nDataset Structure\n-----------------### Data Instances\n\n\nA sample from this dataset looks as follows:### Dataset Fields\n\n\nThe dataset has the following fields (also called \"features\"):### Dataset Splits\n\n\nThis dataset is split into a train and validation split. The split sizes are as follow:" ]
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2c570e86ec1bb306446b6538d094299ca84c5595
# Dataset Card for "test" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Artificio/test
[ "region:us" ]
2023-03-26T21:14:30+00:00
{"dataset_info": {"features": [{"name": "title", "dtype": "string"}, {"name": "artist", "dtype": "string"}, {"name": "date", "dtype": "string"}, {"name": "genre", "dtype": "string"}, {"name": "style", "dtype": "string"}, {"name": "description", "dtype": "string"}, {"name": "filename", "dtype": "string"}, {"name": "image", "dtype": "image"}, {"name": "resnet50_non_robust_features_2048", "sequence": "float32"}, {"name": "resnet50_robust_features_2048", "sequence": "float32"}, {"name": "resnet50_non_robust_feats", "sequence": "float32"}], "splits": [{"name": "train", "num_bytes": 385443497.0, "num_examples": 10000}], "download_size": 368839559, "dataset_size": 385443497.0}}
2023-04-12T02:55:29+00:00
[]
[]
TAGS #region-us
# Dataset Card for "test" More Information needed
[ "# Dataset Card for \"test\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"test\"\n\nMore Information needed" ]
[ 6, 11 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"test\"\n\nMore Information needed" ]
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48520df3115a25d73a4bbc1434a4078394db44c2
# Datasets for VF prediction ## File Description - toxvf.xlsx: Table S4 from [Li et al. (2018)](https://doi.org/10.1016/j.scitotenv.2017.10.308) - data_train.csv: Training set - data_test.csv: Tesing set - df_train.csv: Molecule information for training set - df_test.csv: Molecule information for testing set ## Column Description Table below describes the columns in data_train.csv and data_test.csv. | Column Range | Description | | --- | --- | | 1 - 208 | RDKit descriptors | | 209 - 1821 | Mordred descriptors | | 1822 - 1988 | MACCSKeys fingerprint | | 1989 - 4036 | Circular fingerprint | | 4037 - 4917 | PubChem fingerprint | | 4918 - 6965 | RDKit fingerprints | | 6966 | Toxicity | ## DIY Run `python featurize.py` to reproduce these datasets.
hhaootian/toxvf
[ "license:apache-2.0", "region:us" ]
2023-03-26T21:29:25+00:00
{"license": "apache-2.0"}
2023-03-27T12:24:03+00:00
[]
[]
TAGS #license-apache-2.0 #region-us
Datasets for VF prediction ========================== File Description ---------------- * URL: Table S4 from Li et al. (2018) * data\_train.csv: Training set * data\_test.csv: Tesing set * df\_train.csv: Molecule information for training set * df\_test.csv: Molecule information for testing set Column Description ------------------ Table below describes the columns in data\_train.csv and data\_test.csv. DIY --- Run 'python URL' to reproduce these datasets.
[]
[ "TAGS\n#license-apache-2.0 #region-us \n" ]
[ 14 ]
[ "passage: TAGS\n#license-apache-2.0 #region-us \n" ]
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4c29bf40853be7b51118a1413953058edd7ae58c
# Dataset Card for "gpt2-chitchat-learn" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
wujohns/gpt2-chitchat-learn
[ "region:us" ]
2023-03-26T23:06:17+00:00
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "input_ids", "sequence": "int32"}], "splits": [{"name": "train", "num_bytes": 161238019, "num_examples": 490001}, {"name": "valid", "num_bytes": 3190972, "num_examples": 10000}], "download_size": 89438438, "dataset_size": 164428991}}
2023-03-27T00:47:06+00:00
[]
[]
TAGS #region-us
# Dataset Card for "gpt2-chitchat-learn" More Information needed
[ "# Dataset Card for \"gpt2-chitchat-learn\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"gpt2-chitchat-learn\"\n\nMore Information needed" ]
[ 6, 20 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"gpt2-chitchat-learn\"\n\nMore Information needed" ]
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c56d7dc0dcc7c6e27d0a917a0ab0f40083999465
# Dataset Card for IWSLT 2014 with fairseq preprocess ## Dataset Description - **Homepage:** [https://sites.google.com/site/iwsltevaluation2014](https://sites.google.com/site/iwsltevaluation2014) dataset_info: - config_name: de-en features: - name: translation languages: - de - en splits: - name: train num_examples: 160239 - name: test num_examples: 6750 - name: validation num_examples: 7283
bbaaaa/iwslt14-de-en-preprocess
[ "task_categories:translation", "annotations_creators:crowdsourced", "language_creators:expert-generated", "multilinguality:translation", "source_datasets:original", "language:de", "language:en", "license:cc-by-nc-nd-4.0", "region:us" ]
2023-03-27T02:34:37+00:00
{"annotations_creators": ["crowdsourced"], "language_creators": ["expert-generated"], "language": ["de", "en"], "license": ["cc-by-nc-nd-4.0"], "multilinguality": ["translation"], "source_datasets": ["original"], "task_categories": ["translation"], "task_ids": [], "paperswithcode_id": "iwslt-2014 with fairseq preprocess", "pretty_name": "IWSLT 2014 with fairseq preprocess"}
2023-03-28T15:19:35+00:00
[]
[ "de", "en" ]
TAGS #task_categories-translation #annotations_creators-crowdsourced #language_creators-expert-generated #multilinguality-translation #source_datasets-original #language-German #language-English #license-cc-by-nc-nd-4.0 #region-us
# Dataset Card for IWSLT 2014 with fairseq preprocess ## Dataset Description - Homepage: URL dataset_info: - config_name: de-en features: - name: translation languages: - de - en splits: - name: train num_examples: 160239 - name: test num_examples: 6750 - name: validation num_examples: 7283
[ "# Dataset Card for IWSLT 2014 with fairseq preprocess", "## Dataset Description \n \n- Homepage: URL\n\n\ndataset_info: \n- config_name: de-en \n\n features: \n - name: translation \n languages: \n \t- de \n \t- en \n\n splits: \n - name: train \n num_examples: 160239 \n - name: test \n num_examples: 6750 \n - name: validation \n num_examples: 7283" ]
[ "TAGS\n#task_categories-translation #annotations_creators-crowdsourced #language_creators-expert-generated #multilinguality-translation #source_datasets-original #language-German #language-English #license-cc-by-nc-nd-4.0 #region-us \n", "# Dataset Card for IWSLT 2014 with fairseq preprocess", "## Dataset Description \n \n- Homepage: URL\n\n\ndataset_info: \n- config_name: de-en \n\n features: \n - name: translation \n languages: \n \t- de \n \t- en \n\n splits: \n - name: train \n num_examples: 160239 \n - name: test \n num_examples: 6750 \n - name: validation \n num_examples: 7283" ]
[ 75, 15, 76 ]
[ "passage: TAGS\n#task_categories-translation #annotations_creators-crowdsourced #language_creators-expert-generated #multilinguality-translation #source_datasets-original #language-German #language-English #license-cc-by-nc-nd-4.0 #region-us \n# Dataset Card for IWSLT 2014 with fairseq preprocess## Dataset Description \n \n- Homepage: URL\n\n\ndataset_info: \n- config_name: de-en \n\n features: \n - name: translation \n languages: \n \t- de \n \t- en \n\n splits: \n - name: train \n num_examples: 160239 \n - name: test \n num_examples: 6750 \n - name: validation \n num_examples: 7283" ]
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15934441b9bfa29f2236e0bf3c92bd9f18277b03
# Dataset Card for "somos-clean-alpaca-es-herrius" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
dvilasuero/somos-clean-alpaca-es-herrius
[ "region:us" ]
2023-03-27T06:59:48+00:00
{"dataset_info": {"features": [{"name": "text", "dtype": "null"}, {"name": "inputs", "struct": [{"name": "1-instruction", "dtype": "string"}, {"name": "2-input", "dtype": "string"}, {"name": "3-output", "dtype": "string"}]}, {"name": "prediction", "dtype": "null"}, {"name": "prediction_agent", "dtype": "null"}, {"name": "annotation", "dtype": "string"}, {"name": "annotation_agent", "dtype": "string"}, {"name": "vectors", "struct": [{"name": "input", "sequence": "float64"}, {"name": "instruction", "sequence": "float64"}, {"name": "output", "sequence": "float64"}]}, {"name": "multi_label", "dtype": "bool"}, {"name": "explanation", "dtype": "null"}, {"name": "id", "dtype": "string"}, {"name": "metadata", "dtype": "null"}, {"name": "status", "dtype": "string"}, {"name": "event_timestamp", "dtype": "timestamp[us]"}, {"name": "metrics", "struct": [{"name": "text_length", "dtype": "int64"}]}], "splits": [{"name": "train", "num_bytes": 1821652, "num_examples": 96}], "download_size": 1475326, "dataset_size": 1821652}}
2023-03-27T06:59:51+00:00
[]
[]
TAGS #region-us
# Dataset Card for "somos-clean-alpaca-es-herrius" More Information needed
[ "# Dataset Card for \"somos-clean-alpaca-es-herrius\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"somos-clean-alpaca-es-herrius\"\n\nMore Information needed" ]
[ 6, 23 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"somos-clean-alpaca-es-herrius\"\n\nMore Information needed" ]
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49487e95e42f4532534e8d7d8bc17d42795b5af8
# Dataset Card for "pile-duped-pythia-random-sampled" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
EleutherAI/pile-duped-pythia-random-sampled
[ "region:us" ]
2023-03-27T07:03:38+00:00
{"dataset_info": {"features": [{"name": "Index", "dtype": "int64"}, {"name": "70M", "dtype": "float64"}, {"name": "160M", "dtype": "float64"}, {"name": "410M", "dtype": "float64"}, {"name": "1B", "dtype": "float64"}, {"name": "1.4B", "dtype": "float64"}, {"name": "2.8B", "dtype": "float64"}, {"name": "6.9B", "dtype": "float64"}, {"name": "12B", "dtype": "float64"}, {"name": "Tokens", "sequence": "uint16"}], "splits": [{"name": "train", "num_bytes": 1020000000, "num_examples": 5000000}], "download_size": 915501044, "dataset_size": 1020000000}}
2023-08-25T07:07:30+00:00
[]
[]
TAGS #region-us
# Dataset Card for "pile-duped-pythia-random-sampled" More Information needed
[ "# Dataset Card for \"pile-duped-pythia-random-sampled\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"pile-duped-pythia-random-sampled\"\n\nMore Information needed" ]
[ 6, 25 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"pile-duped-pythia-random-sampled\"\n\nMore Information needed" ]
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0a749cfb3ceb1bf49dd529c25dc7f8846f2b0d81
* [功能](#功能) * [输出](#输出) * [实例](#实例) * [运行环境](#运行环境) * [使用说明](#使用说明) * [下载脚本](#1下载脚本) * [安装依赖](#2安装依赖) * [程序设置](#3程序设置) * [设置数据库（可选）](#4设置数据库可选) * [运行脚本](#5运行脚本) * [按需求修改脚本（可选）](#6按需求修改脚本可选) * [定期自动爬取微博（可选）](#7定期自动爬取微博可选) * [如何获取user_id](#如何获取user_id) * [添加cookie与不添加cookie的区别（可选）](#添加cookie与不添加cookie的区别可选) * [如何获取cookie（可选）](#如何获取cookie可选) * [如何检测cookie是否有效（可选）](#如何检测cookie是否有效可选) ## 功能 连续爬取**一个**或**多个**新浪微博用户（如[Dear-迪丽热巴](https://weibo.cn/u/1669879400)、[郭碧婷](https://weibo.cn/u/1729370543)）的数据，并将结果信息写入文件。写入信息几乎包括了用户微博的所有数据，主要有**用户信息**和**微博信息**两大类，前者包含用户昵称、关注数、粉丝数、微博数等等；后者包含微博正文、发布时间、发布工具、评论数等等，因为内容太多，这里不再赘述，详细内容见[输出](#输出)部分。具体的写入文件类型如下： * 写入**csv文件**（默认） * 写入**json文件**（可选） * 写入**MySQL数据库**（可选） * 写入**MongoDB数据库**（可选） * 写入**SQLite数据库**（可选） * 下载用户**原创**微博中的原始**图片**（可选） * 下载用户**转发**微博中的原始**图片**（可选） * 下载用户**原创**微博中的**视频**（可选） * 下载用户**转发**微博中的**视频**（可选） * 下载用户**原创**微博**Live Photo**中的**视频**（可选） * 下载用户**转发**微博**Live Photo**中的**视频**（可选） * 下载用户**原创和转发**微博下的一级评论（可选） * 下载用户**原创和转发**微博下的转发（可选） 如果你只对用户信息感兴趣，而不需要爬用户的微博，也可以通过设置实现只爬取微博用户信息的功能。程序也可以实现**爬取结果自动更新**，即：现在爬取了目标用户的微博，几天之后，目标用户可能又发新微博了。通过设置，可以实现每隔几天**增量爬取**用户这几天发的新微博。具体方法见[定期自动爬取微博](#7定期自动爬取微博可选)。 ## 输出 **用户信息** * 用户id：微博用户id，如"1669879400" * 用户昵称：微博用户昵称，如"Dear-迪丽热巴" * 性别：微博用户性别 * 生日：用户出生日期 * 所在地：用户所在地 * 教育经历：用户上学时学校的名字 * 公司：用户所属公司名字 * 阳光信用：用户的阳光信用 * 微博注册时间：用户微博注册日期 * 微博数：用户的全部微博数（转发微博+原创微博） * 粉丝数：用户的粉丝数 * 关注数：用户关注的微博数量 * 简介：用户简介 * 主页地址：微博移动版主页url，如<https://m.weibo.cn/u/1669879400?uid=1669879400&luicode=10000011&lfid=1005051669879400> * 头像url：用户头像url * 高清头像url：用户高清头像url * 微博等级：用户微博等级 * 会员等级：微博会员用户等级，普通用户该等级为0 * 是否认证：用户是否认证，为布尔类型 * 认证类型：用户认证类型，如个人认证、企业认证、政府认证等 * 认证信息：为认证用户特有，用户信息栏显示的认证信息 *** **微博信息** * 微博id：微博的id，为一串数字形式 * 微博bid：微博的bid，与[cookie版](https://github.com/dataabc/weiboSpider)中的微博id是同一个值 * 微博内容：微博正文 * 头条文章url：微博中头条文章的url，如果微博中存在头条文章，就获取该头条文章的url，否则该值为'' * 原始图片url：原创微博图片和转发微博转发理由中图片的url，若某条微博存在多张图片，则每个url以英文逗号分隔，若没有图片则值为'' * 视频url: 微博中的视频url和Live Photo中的视频url，若某条微博存在多个视频，则每个url以英文分号分隔，若没有视频则值为'' * 微博发布位置：位置微博中的发布位置 * 微博发布时间：微博发布时的时间，精确到天 * 点赞数：微博被赞的数量 * 转发数：微博被转发的数量 * 评论数：微博被评论的数量 * 微博发布工具：微博的发布工具，如iPhone客户端、HUAWEI Mate 20 Pro等，若没有则值为'' * 话题：微博话题，即两个#中的内容，若存在多个话题，每个url以英文逗号分隔，若没有则值为'' * @用户：微博@的用户，若存在多个@用户，每个url以英文逗号分隔，若没有则值为'' * 原始微博：为转发微博所特有，是转发微博中那条被转发的微博，存储为字典形式，包含了上述微博信息中的所有内容，如微博id、微博内容等等 * 结果文件：保存在当前目录weibo文件夹下以用户昵称为名的文件夹里，名字为"user_id.csv"形式 * 微博图片：微博中的图片，保存在以用户昵称为名的文件夹下的img文件夹里 * 微博视频：微博中的视频，保存在以用户昵称为名的文件夹下的video文件夹里 ## 实例 以爬取迪丽热巴的微博为例，我们需要修改**config.json**文件，文件内容如下： ``` { "user_id_list": ["1669879400"], "filter": 1, "since_date": "1900-01-01", "query_list": [], "write_mode": ["csv"], "original_pic_download": 1, "retweet_pic_download": 0, "original_video_download": 1, "retweet_video_download": 0, "cookie": "your cookie" } ``` 对于上述参数的含义以及取值范围，这里仅作简单介绍，详细信息见[程序设置](#3程序设置)。 >**user_id_list**代表我们要爬取的微博用户的user_id，可以是一个或多个，也可以是文件路径，微博用户Dear-迪丽热巴的user_id为1669879400，具体如何获取user_id见[如何获取user_id](#如何获取user_id)； **filter**的值为1代表爬取全部原创微博，值为0代表爬取全部微博（原创+转发）； **since_date**代表我们要爬取since_date日期之后发布的微博，因为我要爬迪丽热巴的全部原创微博，所以since_date设置了一个非常早的值； query_list代表要爬取的微博关键词，为空（[]）则爬取全部； **write_mode**代表结果文件的保存类型，我想要把结果写入csv文件和json文件，所以它的值为["csv", "json"]，如果你想写入数据库，具体设置见[设置数据库](#4设置数据库可选)； **original_pic_download**值为1代表下载原创微博中的图片，值为0代表不下载； **retweet_pic_download**值为1代表下载转发微博中的图片，值为0代表不下载； **original_video_download**值为1代表下载原创微博中的视频，值为0代表不下载； **retweet_video_download**值为1代表下载转发微博中的视频，值为0代表不下载； **cookie**是可选参数，可填可不填，具体区别见[添加cookie与不添加cookie的区别](#添加cookie与不添加cookie的区别可选)。 配置完成后运行程序： ```bash python weibo.py ``` 程序会自动生成一个weibo文件夹，我们以后爬取的所有微博都被存储在weibo文件夹里。然后程序在该文件夹下生成一个名为"Dear-迪丽热巴"的文件夹，迪丽热巴的所有微博爬取结果都在这里。"Dear-迪丽热巴"文件夹里包含一个csv文件、一个img文件夹和一个video文件夹，img文件夹用来存储下载到的图片，video文件夹用来存储下载到的视频。如果你设置了保存数据库功能，这些信息也会保存在数据库里，数据库设置见[设置数据库](#4设置数据库可选)部分。 **csv文件结果如下所示：** ![](https://picture.cognize.me/cognize/github/weibo-crawler/weibo_csv.png)*1669879400.csv* 本csv文件是爬取“全部微博”(原创微博+转发微博)的结果文件。因为迪丽热巴很多微博本身都没有图片、发布工具、位置、话题和@用户等信息，所以当这些内容没有时对应位置为空。"是否原创"列用来标记是否为原创微博， 当为转发微博时，文件中还包含转发微博的信息。为了简便起见，姑且将转发微博中被转发的原始微博称为**源微博**，它的用户id、昵称、微博id等都在名称前加上源字，以便与目标用户自己发的微博区分。对于转发微博，程序除了获取用户原创部分的信息，还会获取**源用户id**、**源用户昵称**、**源微博id**、**源微博正文**、**源微博原始图片url**、**源微博位置**、**源微博日期**、**源微博工具**、**源微博点赞数**、**源微博评论数**、**源微博转发数**、**源微博话题**、**源微博@用户**等信息。原创微博因为没有这些转发信息，所以对应位置为空。若爬取的是"全部**原创**微博"，则csv文件中不会包含"是否原创"及其之后的转发属性列； 为了说明json结果文件格式，这里以迪丽热巴2019年12月27日到2019年12月28日发的2条微博为例。 **json结果文件格式如下：** ``` { "user": { "id": "1669879400", "screen_name": "Dear-迪丽热巴", "gender": "f", "birthday": "双子座", "location": "上海", "education": "上海戏剧学院", "company": "嘉行传媒", "registration_time": "2010-07-02", "sunshine": "信用极好", "statuses_count": 1121, "followers_count": 66395881, "follow_count": 250, "description": "一只喜欢默默表演的小透明。工作联系[email protected] 🍒", "profile_url": "https://m.weibo.cn/u/1669879400?uid=1669879400&luicode=10000011&lfid=1005051669879400", "profile_image_url": "https://tvax2.sinaimg.cn/crop.0.0.1080.1080.180/63885668ly8gb5sqc19mqj20u00u0mz5.jpg?KID=imgbed,tva&Expires=1584108150&ssig=Zay1N7KhK1", "avatar_hd": "https://wx2.sinaimg.cn/orj480/63885668ly8gb5sqc19mqj20u00u0mz5.jpg", "urank": 44, "mbrank": 7, "verified": true, "verified_type": 0, "verified_reason": "嘉行传媒签约演员　" }, "weibo": [ { "user_id": 1669879400, "screen_name": "Dear-迪丽热巴", "id": 4454572602912349, "bid": "ImTGkcdDn", "text": "今天的#星光大赏# ", "pics": "https://wx3.sinaimg.cn/large/63885668ly1gacppdn1nmj21yi2qp7wk.jpg,https://wx4.sinaimg.cn/large/63885668ly1gacpphkj5gj22ik3t0b2d.jpg,https://wx4.sinaimg.cn/large/63885668ly1gacppb4atej22yo4g04qr.jpg,https://wx2.sinaimg.cn/large/63885668ly1gacpn0eeyij22yo4g04qr.jpg", "video_url": "", "location": "", "created_at": "2019-12-28", "source": "", "attitudes_count": 551894, "comments_count": 182010, "reposts_count": 1000000, "topics": "星光大赏", "at_users": "" }, { "user_id": 1669879400, "screen_name": "Dear-迪丽热巴", "id": 4454081098040623, "bid": "ImGTzxJJt", "text": "我最爱用的娇韵诗双萃精华穿上限量“金”装啦，希望阿丝儿们跟我一起在新的一年更美更年轻，喜笑颜开没有细纹困扰！限定新春礼盒还有祝福悄悄话，大家了解一下～", "pics": "", "video_url": "", "location": "", "created_at": "2019-12-27", "source": "", "attitudes_count": 190840, "comments_count": 43523, "reposts_count": 1000000, "topics": "", "at_users": "", "retweet": { "user_id": 1684832145, "screen_name": "法国娇韵诗", "id": 4454028484570123, "bid": "ImFwIjaTF", "text": "#点萃成金 年轻焕新# 将源自天然的植物力量，转化为滴滴珍贵如金的双萃精华。这份点萃成金的独到匠心，只为守护娇粉们的美丽而来。点击视频，与@Dear-迪丽热巴 一同邂逅新年限量版黄金双萃，以闪耀开运金，送上新春宠肌臻礼。 跟着迪迪选年货，还有双重新春惊喜，爱丽丝们看这里！ 第一重参与微淘活动邀请好友关注娇韵诗天猫旗舰店，就有机会赢取限量款热巴新年礼盒，打开就能聆听仙女迪亲口送出的新春祝福哦！点击网页链接下单晒热巴同款黄金双萃，并且@法国娇韵诗，更有机会获得热巴亲笔签名的礼盒哦！ 第二重转评说出新年希望娇韵诗为你解决的肌肤愿望，截止至1/10，小娇将从铁粉中抽取1位娇粉送出限量版热巴定制礼盒，抽取3位娇粉送出热巴明信片1张～ #迪丽热巴代言娇韵诗#养成同款御龄美肌，就从现在开始。法国娇韵诗的微博视频", "pics": "", "video_url": "http://f.video.weibocdn.com/003vQjnRlx07zFkxIMjS010412003bNx0E010.mp4?label=mp4_hd&template=852x480.25.0&trans_finger=62b30a3f061b162e421008955c73f536&Expires=1578322522&ssig=P3ozrNA3mv&KID=unistore,video", "location": "", "created_at": "2019-12-27", "source": "微博 weibo.com", "attitudes_count": 18389, "comments_count": 3201, "reposts_count": 1000000, "topics": "点萃成金 年轻焕新,迪丽热巴代言娇韵诗", "at_users": "Dear-迪丽热巴,法国娇韵诗" } } ] } ``` *1669879400.json* **下载的图片如下所示：** ![](https://picture.cognize.me/cognize/github/weibo-crawler/img.png)*img文件夹* 本次下载了788张图片，大小一共1.21GB，包括她原创微博中的所有图片。图片名为yyyymmdd+微博id的形式，若某条微博存在多张图片，则图片名中还会包括它在微博图片中的序号。若某图片下载失败，程序则会以“weibo_id:pic_url”的形式将出错微博id和图片url写入同文件夹下的not_downloaded.txt里；若图片全部下载成功则不会生成not_downloaded.txt； **下载的视频如下所示：** ![](https://picture.cognize.me/cognize/github/weibo-crawler/video.png)*video文件夹* 本次下载了66个视频，是她原创微博中的视频和原创微博Live Photo中的视频，视频名为yyyymmdd+微博id的形式。有三个视频因为网络原因下载失败，程序将它们的微博id和视频url分别以“weibo_id:video_url”的形式写到了同文件夹下的not_downloaded.txt里。 因为我本地没有安装MySQL数据库和MongoDB数据库，所以暂时设置成不写入数据库。如果你想要将爬取结果写入数据库，只需要先安装数据库（MySQL或MongoDB），再安装对应包（pymysql或pymongo），然后将mysql_write或mongodb_write值设置为1即可。写入MySQL需要用户名、密码等配置信息，这些配置如何设置见[设置数据库](#4设置数据库可选)部分。 ## 运行环境 * 开发语言：python2/python3 * 系统： Windows/Linux/macOS ## 使用说明 ### 1.下载脚本 ```bash git clone https://github.com/dataabc/weibo-crawler.git ``` 运行上述命令，将本项目下载到当前目录，如果下载成功当前目录会出现一个名为"weibo-crawler"的文件夹； ### 2.安装依赖 ```bash pip install -r requirements.txt ``` ### 3.程序设置 打开**config.json**文件，你会看到如下内容： ``` { "user_id_list": ["1669879400"], "filter": 1, "remove_html_tag": 1, "since_date": "2018-01-01", "write_mode": ["csv"], "original_pic_download": 1, "retweet_pic_download": 0, "original_video_download": 1, "retweet_video_download": 0, "download_comment":1, "comment_max_download_count":1000, "download_repost": 1, "repost_max_download_count": 1000, "result_dir_name": 0, "cookie": "your cookie", "mysql_config": { "host": "localhost", "port": 3306, "user": "root", "password": "123456", "charset": "utf8mb4" } } ``` 下面讲解每个参数的含义与设置方法。 **设置user_id_list** user_id_list是我们要爬取的微博的id，可以是一个，也可以是多个，例如： ``` "user_id_list": ["1223178222", "1669879400", "1729370543"], ``` 上述代码代表我们要连续爬取user_id分别为“1223178222”、 “1669879400”、 “1729370543”的三个用户的微博，具体如何获取user_id见[如何获取user_id](#如何获取user_id)。 user_id_list的值也可以是文件路径，我们可以把要爬的所有微博用户的user_id都写到txt文件里，然后把文件的位置路径赋值给user_id_list。 在txt文件中，每个user_id占一行，也可以在user_id后面加注释（可选），如用户昵称等信息，user_id和注释之间必需要有空格，文件名任意，类型为txt，位置位于本程序的同目录下，文件内容示例如下： ``` 1223178222 胡歌 1669879400 迪丽热巴 1729370543 郭碧婷 ``` 假如文件叫user_id_list.txt，则user_id_list设置代码为： ``` "user_id_list": "user_id_list.txt", ``` **设置filter** filter控制爬取范围，值为1代表爬取全部原创微博，值为0代表爬取全部微博（原创+转发）。例如，如果要爬全部原创微博，请使用如下代码： ``` "filter": 1, ``` **设置since_date** since_date值可以是日期，也可以是整数。如果是日期，代表爬取该日期之后的微博，格式应为“yyyy-mm-dd”，如： ``` "since_date": "2018-01-01", ``` 代表爬取从2018年1月1日到现在的微博。 如果是整数，代表爬取最近n天的微博，如: ``` "since_date": 10, ``` 代表爬取最近10天的微博，这个说法不是特别准确，准确说是爬取发布时间从**10天前到本程序开始执行时**之间的微博。 **since_date是所有user的爬取起始时间，非常不灵活。如果你要爬多个用户，并且想单独为每个用户设置一个since_date，可以使用[定期自动爬取微博](#7定期自动爬取微博可选)方法二中的方法，该方法可以为多个用户设置不同的since_date，非常灵活**。 **设置query_list(可选)** query_list是一个关键词字符串列表或以`,`分隔关键词的字符串，用于指定关键词搜索爬取，若为空`[]`或`""`则爬取全部微博。例如要爬取用户包含“梦想”和“希望”的微博，则设定如下： ``` "query_list": ["梦想","希望"], "query_list": "梦想,希望", ``` 请注意，关键词搜索必须设定`cookie`信息。 **query_list是所有user的爬取关键词，非常不灵活。如果你要爬多个用户，并且想单独为每个用户设置一个query_list，可以使用[定期自动爬取微博](#7定期自动爬取微博可选)方法二中的方法，该方法可以为多个用户设置不同的query_list，非常灵活**。 **设置remove_html_tag** remove_html_tag控制是否移除抓取到的weibo正文和评论中的html tag，值为1代表移除，值为0代表不移除，如 ``` "remove_html_tag": 1, ``` 代表移除html tag。例如`专属新意，色彩启程~<a href='/n/路易威登'>@路易威登</a> CAPUCINES 手袋正合我意，打开灵感包袋的搭配新方式！`会被处理成`专属新意，色彩启程~@路易威登 CAPUCINES 手袋正合我意，打开灵感包袋的搭配新方式！`。 **设置write_mode** write_mode控制结果文件格式，取值范围是csv、json、mongo、mysql和sqlite，分别代表将结果文件写入csv、json、MongoDB、MySQL和SQLite数据库。write_mode可以同时包含这些取值中的一个或几个，如： ``` "write_mode": ["csv", "json"], ``` 代表将结果信息写入csv文件和json文件。特别注意，如果你想写入数据库，除了在write_mode添加对应数据库的名字外，还应该安装相关数据库和对应python模块，具体操作见[设置数据库](#4设置数据库可选)部分。 **设置original_pic_download** original_pic_download控制是否下载**原创**微博中的图片，值为1代表下载，值为0代表不下载，如 ``` "original_pic_download": 1, ``` 代表下载原创微博中的图片。 **设置retweet_pic_download** retweet_pic_download控制是否下载**转发**微博中的图片，值为1代表下载，值为0代表不下载，如 ``` "retweet_pic_download": 0, ``` 代表不下载转发微博中的图片。特别注意，本设置只有在爬全部微博（原创+转发），即filter值为0时生效，否则程序会跳过转发微博的图片下载。 **设置original_video_download** original_video_download控制是否下载**原创**微博中的视频和**原创**微博**Live Photo**中的视频，值为1代表下载，值为0代表不下载，如 ``` "original_video_download": 1, ``` 代表下载原创微博中的视频和原创微博Live Photo中的视频。 **设置retweet_video_download** retweet_video_download控制是否下载**转发**微博中的视频和**转发**微博**Live Photo**中的视频，值为1代表下载，值为0代表不下载，如 ``` "retweet_video_download": 0, ``` 代表不下载转发微博中的视频和转发微博Live Photo中的视频。特别注意，本设置只有在爬全部微博（原创+转发），即filter值为0时生效，否则程序会跳过转发微博的视频下载。 **设置result_dir_name** result_dir_name控制结果文件的目录名，可取值为0和1，默认为0： ``` "result_dir_name": 0, ``` 值为0，表示将结果文件保存在以用户昵称为名的文件夹里，这样结果更清晰；值为1表示将结果文件保存在以用户id为名的文件夹里，这样能保证多次爬取的一致性，因为用户昵称可变，用户id不可变。 **设置download_comment** download_comment控制是否下载每条微博下的一级评论（不包括对评论的评论），仅当write_mode中有sqlite时有效，可取值为0和1，默认为1： ``` "download_comment": 1, ``` 值为1，表示下载微博评论；值为0，表示不下载微博评论。 **设置comment_max_download_count** comment_max_download_count控制下载评论的最大数量，仅当write_mode中有sqlite时有效，默认为1000： ``` "comment_max_download_count": 1000, ``` **设置download_repost** download_repost控制是否下载每条微博下的转发，仅当write_mode中有sqlite时有效，可取值为0和1，默认为1： ``` "download_repost": 1, ``` 值为1，表示下载微博转发；值为0，表示不下载微博转发。 **设置repost_max_download_count** repost_max_download_count控制下载转发的最大数量，仅当write_mode中有sqlite时有效，默认为1000： ``` "repost_max_download_count": 1000, ``` 值为1000，表示最多下载每条微博下的1000条转发。 **设置cookie（可选）** cookie为可选参数，即可填可不填，具体区别见[添加cookie与不添加cookie的区别](#添加cookie与不添加cookie的区别可选)。cookie默认配置如下： ``` "cookie": "your cookie", ``` 如果想要设置cookie，可以按照[如何获取cookie](#如何获取cookie可选)中的方法，获取cookie，并将上面的"your cookie"替换成真实的cookie即可。 **设置mysql_config（可选）** mysql_config控制mysql参数配置。如果你不需要将结果信息写入mysql，这个参数可以忽略，即删除或保留都无所谓；如果你需要写入mysql且config.json文件中mysql_config的配置与你的mysql配置不一样，请将该值改成你自己mysql中的参数配置。 **设置start_page（可选）** start_page为爬取微博的初始页数，默认参数为1，即从所爬取用户的当前第一页微博内容开始爬取。 若在大批量爬取微博时出现中途被限制中断的情况，可通过查看csv文件内目前已爬取到的微博数除以10，向下取整后的值即为中断页数，手动设置start_page参数为中断页数，重新运行即可从被中断的节点继续爬取剩余微博内容。 ### 4.设置数据库（可选） 本部分是可选部分，如果不需要将爬取信息写入数据库，可跳过这一步。本程序目前支持MySQL数据库和MongoDB数据库，如果你需要写入其它数据库，可以参考这两个数据库的写法自己编写。 **MySQL数据库写入** 要想将爬取信息写入MySQL，请根据自己的系统环境安装MySQL，然后命令行执行： ```bash pip install pymysql ``` **MongoDB数据库写入** 要想将爬取信息写入MongoDB，请根据自己的系统环境安装MongoDB，然后命令行执行： ``` pip install pymongo ``` MySQL和MongDB数据库的写入内容一样。程序首先会创建一个名为"weibo"的数据库，然后再创建"user"表和"weibo"表，包含爬取的所有内容。爬取到的微博**用户信息**或插入或更新，都会存储到user表里；爬取到的**微博信息**或插入或更新，都会存储到weibo表里，两个表通过user_id关联。如果想了解两个表的具体字段，请点击"详情"。 <details> <summary>详情</summary> **user**表 **id**：微博用户id，如"1669879400"； **screen_name**：微博用户昵称，如"Dear-迪丽热巴"； **gender**：微博用户性别，取值为f或m，分别代表女和男； **birthday**：生日； **location**：所在地； **education**：教育经历； **company**：公司； **sunshine**：阳光信用； **registration_time**：注册时间； **statuses_count**：微博数； **followers_count**：粉丝数； **follow_count**：关注数； **description**：微博简介； **profile_url**：微博主页，如<https://m.weibo.cn/u/1669879400?uid=1669879400&luicode=10000011&lfid=1005051669879400>; **profile_image_url**：微博头像url； **avatar_hd**：微博高清头像url； **urank**：微博等级； **mbrank**：微博会员等级，普通用户会员等级为0； **verified**：微博是否认证，取值为true和false； **verified_type**：微博认证类型，没有认证值为-1，个人认证值为0，企业认证值为2，政府认证值为3，这些类型仅是个人猜测，应该不全，大家可以根据实际情况判断； **verified_reason**：微博认证信息，只有认证用户拥有此属性。 *** **weibo**表 **user_id**：存储微博用户id，如"1669879400"； **screen_name**：存储微博昵称，如"Dear-迪丽热巴"； **id**：存储微博id； **text**：存储微博正文； **article_url**：存储微博中头条文章的url，如果微博中存在头条文章，就获取该头条文章的url，否则该值为''； **pics**：存储原创微博的原始图片url。若某条微博有多张图片，则存储多个url，以英文逗号分割；若该微博没有图片，则值为''； **video_url**：存储原创微博的视频url和Live Photo中的视频url。若某条微博有多个视频，则存储多个url，以英文分号分割；若该微博没有视频，则值为''； **location**：存储微博的发布位置。若某条微博没有位置信息，则值为''； **created_at**：存储微博的发布时间； **source**：存储微博的发布工具； **attitudes_count**：存储微博获得的点赞数； **comments_count**：存储微博获得的评论数； **reposts_count**：存储微博获得的转发数； **topics**：存储微博话题，即两个#中的内容。若某条微博没有话题信息，则值为''； **at_users**：存储微博@的用户。若某条微博没有@的用户，则值为''； **retweet_id**：存储转发微博中原始微博的微博id。若某条微博为原创微博，则值为''。 </details> **SQLite数据库写入** 脚本会自动建立并配置数据库文件`weibodata.db`。 ### 5.运行脚本 大家可以根据自己的运行环境选择运行方式，Linux可以通过 ```bash python weibo.py ``` 运行; ### 6.按需求修改脚本（可选） 本部分为可选部分，如果你不需要自己修改代码或添加新功能，可以忽略此部分。 本程序所有代码都位于weibo.py文件，程序主体是一个Weibo类，上述所有功能都是通过在main函数调用Weibo类实现的，默认的调用代码如下： ```python if not os.path.isfile('./config.json'): sys.exit(u'当前路径：%s 不存在配置文件config.json' % (os.path.split(os.path.realpath(__file__))[0] + os.sep)) with open('./config.json') as f: config = json.loads(f.read()) wb = Weibo(config) wb.start() # 爬取微博信息 ``` 用户可以按照自己的需求调用或修改Weibo类。 通过执行本程序，我们可以得到很多信息： **wb.user**：存储目标微博用户信息； wb.user包含爬取到的微博用户信息，如**用户id**、**用户昵称**、**性别**、**生日**、**所在地**、**教育经历**、**公司**、**阳光信用**、**微博注册时间**、**微博数**、**粉丝数**、**关注数**、**简介**、**主页地址**、**头像url**、**高清头像url**、**微博等级**、**会员等级**、**是否认证**、**认证类型**、**认证信息**等，大家可以点击"详情"查看具体用法。 <details> <summary>详情</summary> **id**：微博用户id，取值方式为wb.user['id'],由一串数字组成； **screen_name**：微博用户昵称，取值方式为wb.user['screen_name']； **gender**：微博用户性别，取值方式为wb.user['gender']，取值为f或m，分别代表女和男； **birthday**：微博用户生日，取值方式为wb.user['birthday']，若用户没有填写该信息，则值为''； **location**：微博用户所在地，取值方式为wb.user['location']，若用户没有填写该信息，则值为''； **education**：微博用户上学时的学校，取值方式为wb.user['education']，若用户没有填写该信息，则值为''； **company**：微博用户所属的公司，取值方式为wb.user['company']，若用户没有填写该信息，则值为''； **sunshine**：微博用户的阳光信用，取值方式为wb.user['sunshine']； **registration_time**：微博用户的注册时间，取值方式为wb.user['registration_time']； **statuses_count**：微博数，取值方式为wb.user['statuses_count']； **followers_count**：微博粉丝数，取值方式为wb.user['followers_count']； **follow_count**：微博关注数，取值方式为wb.user['follow_count']； **description**：微博简介，取值方式为wb.user['description']； **profile_url**：微博主页，取值方式为wb.user['profile_url']; **profile_image_url**：微博头像url，取值方式为wb.user['profile_image_url']； **avatar_hd**：微博高清头像url，取值方式为wb.user['avatar_hd']； **urank**：微博等级，取值方式为wb.user['urank']； **mbrank**：微博会员等级，取值方式为wb.user['mbrank']，普通用户会员等级为0； **verified**：微博是否认证，取值方式为wb.user['verified']，取值为true和false； **verified_type**：微博认证类型，取值方式为wb.user['verified_type']，没有认证值为-1，个人认证值为0，企业认证值为2，政府认证值为3，这些类型仅是个人猜测，应该不全，大家可以根据实际情况判断； **verified_reason**：微博认证信息，取值方式为wb.user['verified_reason']，只有认证用户拥有此属性。 </details> **wb.weibo**：存储爬取到的所有微博信息； wb.weibo包含爬取到的所有微博信息，如**微博id**、**正文**、**原始图片url**、**视频url**、**位置**、**日期**、**发布工具**、**点赞数**、**转发数**、**评论数**、**话题**、**@用户**等。如果爬的是全部微博(原创+转发)，除上述信息之外，还包含**原始用户id**、**原始用户昵称**、**原始微博id**、**原始微博正文**、**原始微博原始图片url**、**原始微博位置**、**原始微博日期**、**原始微博工具**、**原始微博点赞数**、**原始微博评论数**、**原始微博转发数**、**原始微博话题**、**原始微博@用户**等信息。wb.weibo是一个列表，包含了爬取的所有微博信息。wb.weibo[0]为爬取的第一条微博，wb.weibo[1]为爬取的第二条微博，以此类推。当filter=1时，wb.weibo[0]为爬取的第一条**原创**微博，以此类推。wb.weibo[0]['id']为第一条微博的id，wb.weibo[0]['text']为第一条微博的正文，wb.weibo[0]['created_at']为第一条微博的发布时间，还有其它很多信息不在赘述，大家可以点击下面的"详情"查看具体用法。 <details> <summary>详情</summary> **user_id**：存储微博用户id。如wb.weibo[0]['user_id']为最新一条微博的用户id； **screen_name**：存储微博昵称。如wb.weibo[0]['screen_name']为最新一条微博的昵称； **id**：存储微博id。如wb.weibo[0]['id']为最新一条微博的id； **text**：存储微博正文。如wb.weibo[0]['text']为最新一条微博的正文； **article_url**：存储微博中头条文章的url。如wb.weibo[0]['article_url']为最新一条微博的头条文章url，若微博中不存在头条文章，则该值为''； **pics**：存储原创微博的原始图片url。如wb.weibo[0]['pics']为最新一条微博的原始图片url，若该条微博有多张图片，则存储多个url，以英文逗号分割；若该微博没有图片，则值为''； **video_url**：存储原创微博的视频url和原创微博Live Photo中的视频url。如wb.weibo[0]['video_url']为最新一条微博的视频url，若该条微博有多个视频，则存储多个url，以英文分号分割；若该微博没有视频，则值为''； **location**：存储微博的发布位置。如wb.weibo[0]['location']为最新一条微博的发布位置，若该条微博没有位置信息，则值为''； **created_at**：存储微博的发布时间。如wb.weibo[0]['created_at']为最新一条微博的发布时间； **source**：存储微博的发布工具。如wb.weibo[0]['source']为最新一条微博的发布工具； **attitudes_count**：存储微博获得的点赞数。如wb.weibo[0]['attitudes_count']为最新一条微博获得的点赞数； **comments_count**：存储微博获得的评论数。如wb.weibo[0]['comments_count']为最新一条微博获得的评论数； **reposts_count**：存储微博获得的转发数。如wb.weibo[0]['reposts_count']为最新一条微博获得的转发数； **topics**：存储微博话题，即两个#中的内容。如wb.weibo[0]['topics']为最新一条微博的话题，若该条微博没有话题信息，则值为''； **at_users**：存储微博@的用户。如wb.weibo[0]['at_users']为最新一条微博@的用户，若该条微博没有@的用户，则值为''； **retweet**：存储转发微博中原始微博的全部信息。假如wb.weibo[0]为转发微博，则wb.weibo[0]['retweet']为该转发微博的原始微博，它存储的属性与wb.weibo[0]一样，只是没有retweet属性;若该条微博为原创微博，则wb[0]没有"retweet"属性，大家可以点击"详情"查看具体用法。 <details> <summary>详情</summary> 假设爬取到的第i条微博为转发微博，则它存在以下信息： **user_id**：存储原始微博用户id。wb.weibo[i-1]['retweet']['user_id']为该原始微博的用户id； **screen_name**：存储原始微博昵称。wb.weibo[i-1]['retweet']['screen_name']为该原始微博的昵称； **id**：存储原始微博id。wb.weibo[i-1]['retweet']['id']为该原始微博的id； **text**：存储原始微博正文。wb.weibo[i-1]['retweet']['text']为该原始微博的正文； **article_url**：存储原始微博中头条文章的url。如wb.weibo[i-1]['retweet']['article_url']为该原始微博的头条文章url，若原始微博中不存在头条文章，则该值为''； **pics**：存储原始微博的原始图片url。wb.weibo[i-1]['retweet']['pics']为该原始微博的原始图片url，若该原始微博有多张图片，则存储多个url，以英文逗号分割；若该原始微博没有图片，则值为''； **video_url**：存储原始微博的视频url和原始微博Live Photo中的视频url。如wb.weibo[i-1]['retweet']['video_url']为该原始微博的视频url，若该原始微博有多个视频，则存储多个url，以英文分号分割；若该微博没有视频，则值为''； **location**：存储原始微博的发布位置。wb.weibo[i-1]['retweet']['location']为该原始微博的发布位置，若该原始微博没有位置信息，则值为''； **created_at**：存储原始微博的发布时间。wb.weibo[i-1]['retweet']['created_at']为该原始微博的发布时间； **source**：存储原始微博的发布工具。wb.weibo[i-1]['retweet']['source']为该原始微博的发布工具； **attitudes_count**：存储原始微博获得的点赞数。wb.weibo[i-1]['retweet']['attitudes_count']为该原始微博获得的点赞数； **comments_count**：存储原始微博获得的评论数。wb.weibo[i-1]['retweet']['comments_count']为该原始微博获得的评论数； **reposts_count**：存储原始微博获得的转发数。wb.weibo[i-1]['retweet']['reposts_count']为该原始微博获得的转发数； **topics**：存储原始微博话题，即两个#中的内容。wb.weibo[i-1]['retweet']['topics']为该原始微博的话题，若该原始微博没有话题信息，则值为''； **at_users**：存储原始微博@的用户。wb.weibo[i-1]['retweet']['at_users']为该原始微博@的用户，若该原始微博没有@的用户，则值为''。 </details> </details> ### 7.定期自动爬取微博（可选） 我们爬取了微博以后，很多微博账号又可能发了一些新微博，定期自动爬取微博就是每隔一段时间自动运行程序，自动爬取这段时间产生的新微博（忽略以前爬过的旧微博）。本部分为可选部分，如果不需要可以忽略。 思路是**利用第三方软件，如crontab，让程序每隔一段时间运行一次**。因为是要跳过以前爬过的旧微博，只爬新微博。所以需要**设置一个动态的since_date**。很多时候我们使用的since_date是固定的，比如since_date="2018-01-01"，程序就会按照这个设置从最新的微博一直爬到发布时间为2018-01-01的微博（包括这个时间）。因为我们想追加新微博，跳过旧微博。第二次爬取时since_date值就应该是当前时间到上次爬取的时间。 如果我们使用最原始的方式实现追加爬取，应该是这样： ``` 假如程序第一次执行时间是2019-06-06，since_date假如为2018-01-01，那这一次就是爬取从2018-01-01到2019-06-06这段时间用户所发的微博； 第二次爬取，我们想要接着上次的爬，需要手动将since_date值设置为上次程序执行的日期，即2019-06-06 ``` 上面的方法太麻烦，因为每次都要手动设置since_date。因此我们需要动态设置since_date，即程序根据实际情况，自动生成since_date。 有两种方法实现动态更新since_date： **方法一：将since_date设置成整数** 将config.json文件中的since_date设置成整数，如： ``` "since_date": 10, ``` 这个配置告诉程序爬取最近10天的微博，更准确说是爬取发布时间从10天前到本程序开始执行时之间的微博。这样since_date就是一个动态的变量，每次程序执行时，它的值就是当前日期减10。配合crontab每9天或10天执行一次，就实现了定期追加爬取。 **方法二：将上次执行程序的时间写入文件（推荐）** 这个方法很简单，就是用户把要爬的用户id写入txt文件，然后再把文件路径赋值给config.json中的user_id_list参数。 txt文件名格式可以参考[程序设置](#3程序设置)中的设置user_id_list部分，这样设置就全部结束了。 说下这个方法的原理和好处，假如你的txt文件内容为： ``` 1669879400 1223178222 胡歌 1729370543 郭碧婷 2019-01-01 ``` 第一次执行时，因为第一行和第二行都没有写时间，程序会按照config.json文件中since_date的值爬取，第三行有时间“2019-01-01”，程序就会把这个时间当作since_date。每个用户爬取结束程序都会自动更新txt文件，每一行第一部分是user_id，第二部分是用户昵称，第三部分是程序准备爬取该用户第一条微博（最新微博）时的日期。爬完三个用户后，txt文件的内容自动更新为： ``` 1669879400 Dear-迪丽热巴 2020-01-18 1223178222 胡歌 2020-01-18 1729370543 郭碧婷 2020-01-18 ``` 下次再爬取微博的时候，程序会把每行的时间数据作为since_date。这样的好处一是不用修改since_date，程序自动更新；二是每一个用户都可以单独拥有只属于自己的since_date，每个用户的since_date相互独立，互不干扰，格式为`yyyy-mm-dd`或整数。比如，现在又添加了一个新用户，以杨紫的微博为例，你想获取她2018-01-23到现在的全部微博，可以这样修改txt文件： ``` 1669879400 迪丽热巴 2020-01-18 1223178222 胡歌 2020-01-18 1729370543 郭碧婷 2020-01-18 1227368500 杨紫 3 梦想,希望 ``` 注意每一行的用户配置参数以空格分隔，如果第一个参数全部由数字组成，程序就认为此行为一个用户的配置，否则程序会认为该行只是注释，跳过该行；第二个参数可以为任意格式，建议写用户昵称；第三个如果是日期格式（yyyy-mm-dd），程序就将该日期设置为用户自己的since_date，否则使用config.json中的since_date爬取该用户的微博，第二个参数和第三个参数也可以不填。 也可以设置第四个参数，将被读取为query_list。 **方法三：将`const.py`文件中的运行模式改为`append`** 以追加模式运行程序，每次运行，每个id只获取最新的微博，而不是全部，避免频繁备份微博导致过多的请求次数。 注意： * 该模式会跳过置顶微博。 * 若采集信息后用户又编辑微博，则不会记录编辑内容。 ## 如何获取user_id 1.打开网址<https://weibo.cn>，搜索我们要找的人，如"迪丽热巴"，进入她的主页； ![](https://picture.cognize.me/cognize/github/weibospider/user_home.png) 2.按照上图箭头所指，点击"资料"链接，跳转到用户资料页面； ![](https://picture.cognize.me/cognize/github/weibospider/user_info.png) 如上图所示，迪丽热巴微博资料页的地址为"<https://weibo.cn/1669879400/info>"，其中的"1669879400"即为此微博的user_id。 事实上，此微博的user_id也包含在用户主页(<https://weibo.cn/u/1669879400?f=search_0>)中，之所以我们还要点击主页中的"资料"来获取user_id，是因为很多用户的主页不是"<https://weibo.cn/user_id?f=search_0>"的形式，而是"<https://weibo.cn/个性域名?f=search_0>"或"<https://weibo.cn/微号?f=search_0>"的形式。其中"微号"和user_id都是一串数字，如果仅仅通过主页地址提取user_id，很容易将"微号"误认为user_id。 ## 添加cookie与不添加cookie的区别（可选） 对于微博数2000条及以下的微博用户，不添加cookie可以获取其用户信息和大部分微博；对于微博数2000条以上的微博用户，不添加cookie可以获取其用户信息和最近2000条微博中的大部分，添加cookie可以获取其全部微博。以2020年1月2日迪丽热巴的微博为例，此时她共有1085条微博，在不添加cookie的情况下，可以获取到1026条微博，大约占全部微博的94.56%，而在添加cookie后，可以获取全部微博。其他用户类似，大部分都可以在不添加cookie的情况下获取到90%以上的微博，在添加cookie后可以获取全部微博。具体原因是，大部分微博内容都可以在[移动版](https://m.weibo.cn/)匿名获取，少量微博需要用户登录才可以获取，所以这部分微博在不添加cookie时是无法获取的。 有少部分微博用户，不添加cookie可以获取其微博，无法获取其用户信息。对于这种情况，要想获取其用户信息，是需要cookie的。 如需抓取微博转发，请添加cookie。 ## 如何获取cookie（可选） 1.用Chrome打开<https://passport.weibo.cn/signin/login>； 2.输入微博的用户名、密码，登录，如图所示： ![](https://picture.cognize.me/cognize/github/weibospider/cookie1.png) 登录成功后会跳转到<https://m.weibo.cn>; 3.按F12键打开Chrome开发者工具，在地址栏输入并跳转到<https://weibo.cn>，跳转后会显示如下类似界面: ![](https://picture.cognize.me/cognize/github/weibospider/cookie2.png) 4.依此点击Chrome开发者工具中的Network->Name中的weibo.cn->Headers->Request Headers，"Cookie:"后的值即为我们要找的cookie值，复制即可，如图所示： ![](https://picture.cognize.me/cognize/github/weibospider/cookie3.png) ## 如何检测cookie是否有效（可选） 本程序cookie检查的逻辑是：使用cookie来源账号发布**限定范围的**微博，若cookie可用，则可以读取到该微博，否则读取不到。 **操作方法** 1. 使用cookie的来源账号发布一条微博，该账号和微博需要满足以下条件： * 该微博必须是**非公开可见**的，后续需要根据可见性判断cookie是否有效； * 该微博需要是最近5条微博，不能在发布测试用微博内容后又发很多新微博； * 在`config.json`配置中的since_date之后，该账号必须有大于9条微博。 2. 将`const.py`文件中`'CHECK': False`中的`False`改为`True`，`'HIDDEN_WEIBO': '微博内容'`中的`微博内容`改为你发的限定范围的微博。 3. 将提供cookie的微博id放置在`config.json`文件中`"user_id_list"`设置项数组中的第一个。例如提供cookie的微博id为`123456`，则`"user_id_list"`设置为`"user_id_list":["123456", "<其余id...>"]`。 注：本方法也将会抓取提供cookie账号的微博内容。 在间歇运行程序时，cookie无效会导致程序不能按照预设目标执行，因此可以打开cookie通知功能。本项目使用开源项目[pushdeer](https://github.com/easychen/pushdeer)进行通知，在使用前用户需要申请push_key，具体可查看官网了解。打开方法为： 1. 在`const.py`文件中，将`'NOTIFY': False`中的`False`设为`True`； 2. 将`'PUSH_KEY': ''`的`''`替换为`'<你的push_key>'`
yajun06/TEST_TEXT_DATAS
[ "region:us" ]
2023-03-27T07:22:47+00:00
{}
2023-03-27T07:25:18+00:00
[]
[]
TAGS #region-us
* 功能 * 输出 * 实例 * 运行环境 * 使用说明 * 下载脚本 * 安装依赖 * 程序设置 * 设置数据库（可选） * 运行脚本 * 按需求修改脚本（可选） * 定期自动爬取微博（可选） * 如何获取user_id * 添加cookie与不添加cookie的区别（可选） * 如何获取cookie（可选） * 如何检测cookie是否有效（可选） ## 功能 连续爬取一个或多个新浪微博用户（如Dear-迪丽热巴、郭碧婷）的数据，并将结果信息写入文件。写入信息几乎包括了用户微博的所有数据，主要有用户信息和微博信息两大类，前者包含用户昵称、关注数、粉丝数、微博数等等；后者包含微博正文、发布时间、发布工具、评论数等等，因为内容太多，这里不再赘述，详细内容见输出部分。具体的写入文件类型如下： * 写入csv文件（默认） * 写入json文件（可选） * 写入MySQL数据库（可选） * 写入MongoDB数据库（可选） * 写入SQLite数据库（可选） * 下载用户原创微博中的原始图片（可选） * 下载用户转发微博中的原始图片（可选） * 下载用户原创微博中的视频（可选） * 下载用户转发微博中的视频（可选） * 下载用户原创微博Live Photo中的视频（可选） * 下载用户转发微博Live Photo中的视频（可选） * 下载用户原创和转发微博下的一级评论（可选） * 下载用户原创和转发微博下的转发（可选） 如果你只对用户信息感兴趣，而不需要爬用户的微博，也可以通过设置实现只爬取微博用户信息的功能。程序也可以实现爬取结果自动更新，即：现在爬取了目标用户的微博，几天之后，目标用户可能又发新微博了。通过设置，可以实现每隔几天增量爬取用户这几天发的新微博。具体方法见定期自动爬取微博。 ## 输出 用户信息 * 用户id：微博用户id，如"1669879400" * 用户昵称：微博用户昵称，如"Dear-迪丽热巴" * 性别：微博用户性别 * 生日：用户出生日期 * 所在地：用户所在地 * 教育经历：用户上学时学校的名字 * 公司：用户所属公司名字 * 阳光信用：用户的阳光信用 * 微博注册时间：用户微博注册日期 * 微博数：用户的全部微博数（转发微博+原创微博） * 粉丝数：用户的粉丝数 * 关注数：用户关注的微博数量 * 简介：用户简介 * 主页地址：微博移动版主页url，如<https://m.URL * 头像url：用户头像url * 高清头像url：用户高清头像url * 微博等级：用户微博等级 * 会员等级：微博会员用户等级，普通用户该等级为0 * 是否认证：用户是否认证，为布尔类型 * 认证类型：用户认证类型，如个人认证、企业认证、政府认证等 * 认证信息：为认证用户特有，用户信息栏显示的认证信息 * 微博信息 * 微博id：微博的id，为一串数字形式 * 微博bid：微博的bid，与cookie版中的微博id是同一个值 * 微博内容：微博正文 * 头条文章url：微博中头条文章的url，如果微博中存在头条文章，就获取该头条文章的url，否则该值为'' * 原始图片url：原创微博图片和转发微博转发理由中图片的url，若某条微博存在多张图片，则每个url以英文逗号分隔，若没有图片则值为'' * 视频url: 微博中的视频url和Live Photo中的视频url，若某条微博存在多个视频，则每个url以英文分号分隔，若没有视频则值为'' * 微博发布位置：位置微博中的发布位置 * 微博发布时间：微博发布时的时间，精确到天 * 点赞数：微博被赞的数量 * 转发数：微博被转发的数量 * 评论数：微博被评论的数量 * 微博发布工具：微博的发布工具，如iPhone客户端、HUAWEI Mate 20 Pro等，若没有则值为'' * 话题：微博话题，即两个#中的内容，若存在多个话题，每个url以英文逗号分隔，若没有则值为'' * @用户：微博@的用户，若存在多个@用户，每个url以英文逗号分隔，若没有则值为'' * 原始微博：为转发微博所特有，是转发微博中那条被转发的微博，存储为字典形式，包含了上述微博信息中的所有内容，如微博id、微博内容等等 * 结果文件：保存在当前目录weibo文件夹下以用户昵称为名的文件夹里，名字为"user_id.csv"形式 * 微博图片：微博中的图片，保存在以用户昵称为名的文件夹下的img文件夹里 * 微博视频：微博中的视频，保存在以用户昵称为名的文件夹下的video文件夹里 ## 实例 以爬取迪丽热巴的微博为例，我们需要修改URL文件，文件内容如下： 对于上述参数的含义以及取值范围，这里仅作简单介绍，详细信息见程序设置。 >user_id_list代表我们要爬取的微博用户的user_id，可以是一个或多个，也可以是文件路径，微博用户Dear-迪丽热巴的user_id为1669879400，具体如何获取user_id见如何获取user_id； filter的值为1代表爬取全部原创微博，值为0代表爬取全部微博（原创+转发）； since_date代表我们要爬取since_date日期之后发布的微博，因为我要爬迪丽热巴的全部原创微博，所以since_date设置了一个非常早的值； query_list代表要爬取的微博关键词，为空（[]）则爬取全部； write_mode代表结果文件的保存类型，我想要把结果写入csv文件和json文件，所以它的值为["csv", "json"]，如果你想写入数据库，具体设置见设置数据库； original_pic_download值为1代表下载原创微博中的图片，值为0代表不下载； retweet_pic_download值为1代表下载转发微博中的图片，值为0代表不下载； original_video_download值为1代表下载原创微博中的视频，值为0代表不下载； retweet_video_download值为1代表下载转发微博中的视频，值为0代表不下载； cookie是可选参数，可填可不填，具体区别见添加cookie与不添加cookie的区别。 配置完成后运行程序： 程序会自动生成一个weibo文件夹，我们以后爬取的所有微博都被存储在weibo文件夹里。然后程序在该文件夹下生成一个名为"Dear-迪丽热巴"的文件夹，迪丽热巴的所有微博爬取结果都在这里。"Dear-迪丽热巴"文件夹里包含一个csv文件、一个img文件夹和一个video文件夹，img文件夹用来存储下载到的图片，video文件夹用来存储下载到的视频。如果你设置了保存数据库功能，这些信息也会保存在数据库里，数据库设置见设置数据库部分。 csv文件结果如下所示： ![](URL 本csv文件是爬取“全部微博”(原创微博+转发微博)的结果文件。因为迪丽热巴很多微博本身都没有图片、发布工具、位置、话题和@用户等信息，所以当这些内容没有时对应位置为空。"是否原创"列用来标记是否为原创微博， 当为转发微博时，文件中还包含转发微博的信息。为了简便起见，姑且将转发微博中被转发的原始微博称为源微博，它的用户id、昵称、微博id等都在名称前加上源字，以便与目标用户自己发的微博区分。对于转发微博，程序除了获取用户原创部分的信息，还会获取源用户id、源用户昵称、源微博id、源微博正文、源微博原始图片url、源微博位置、源微博日期、源微博工具、源微博点赞数、源微博评论数、源微博转发数、源微博话题、源微博@用户等信息。原创微博因为没有这些转发信息，所以对应位置为空。若爬取的是"全部原创微博"，则csv文件中不会包含"是否原创"及其之后的转发属性列； 为了说明json结果文件格式，这里以迪丽热巴2019年12月27日到2019年12月28日发的2条微博为例。 json结果文件格式如下： *URL* 下载的图片如下所示： ![](URL*img文件夹* 本次下载了788张图片，大小一共1.21GB，包括她原创微博中的所有图片。图片名为yyyymmdd+微博id的形式，若某条微博存在多张图片，则图片名中还会包括它在微博图片中的序号。若某图片下载失败，程序则会以“weibo_id:pic_url”的形式将出错微博id和图片url写入同文件夹下的not_downloaded.txt里；若图片全部下载成功则不会生成not_downloaded.txt； 下载的视频如下所示： ![](URL*video文件夹* 本次下载了66个视频，是她原创微博中的视频和原创微博Live Photo中的视频，视频名为yyyymmdd+微博id的形式。有三个视频因为网络原因下载失败，程序将它们的微博id和视频url分别以“weibo_id:video_url”的形式写到了同文件夹下的not_downloaded.txt里。 因为我本地没有安装MySQL数据库和MongoDB数据库，所以暂时设置成不写入数据库。如果你想要将爬取结果写入数据库，只需要先安装数据库（MySQL或MongoDB），再安装对应包（pymysql或pymongo），然后将mysql_write或mongodb_write值设置为1即可。写入MySQL需要用户名、密码等配置信息，这些配置如何设置见设置数据库部分。 ## 运行环境 * 开发语言：python2/python3 * 系统： Windows/Linux/macOS ## 使用说明 ### 1.下载脚本 运行上述命令，将本项目下载到当前目录，如果下载成功当前目录会出现一个名为"weibo-crawler"的文件夹； ### 2.安装依赖 ### 3.程序设置 打开URL文件，你会看到如下内容： 下面讲解每个参数的含义与设置方法。 设置user_id_list user_id_list是我们要爬取的微博的id，可以是一个，也可以是多个，例如： 上述代码代表我们要连续爬取user_id分别为“1223178222”、 “1669879400”、 “1729370543”的三个用户的微博，具体如何获取user_id见如何获取user_id。 user_id_list的值也可以是文件路径，我们可以把要爬的所有微博用户的user_id都写到txt文件里，然后把文件的位置路径赋值给user_id_list。 在txt文件中，每个user_id占一行，也可以在user_id后面加注释（可选），如用户昵称等信息，user_id和注释之间必需要有空格，文件名任意，类型为txt，位置位于本程序的同目录下，文件内容示例如下： 假如文件叫user_id_list.txt，则user_id_list设置代码为： 设置filter filter控制爬取范围，值为1代表爬取全部原创微博，值为0代表爬取全部微博（原创+转发）。例如，如果要爬全部原创微博，请使用如下代码： 设置since_date since_date值可以是日期，也可以是整数。如果是日期，代表爬取该日期之后的微博，格式应为“yyyy-mm-dd”，如： 代表爬取从2018年1月1日到现在的微博。 如果是整数，代表爬取最近n天的微博，如: 代表爬取最近10天的微博，这个说法不是特别准确，准确说是爬取发布时间从10天前到本程序开始执行时之间的微博。 since_date是所有user的爬取起始时间，非常不灵活。如果你要爬多个用户，并且想单独为每个用户设置一个since_date，可以使用定期自动爬取微博方法二中的方法，该方法可以为多个用户设置不同的since_date，非常灵活。 设置query_list(可选) query_list是一个关键词字符串列表或以','分隔关键词的字符串，用于指定关键词搜索爬取，若为空'[]'或'""'则爬取全部微博。例如要爬取用户包含“梦想”和“希望”的微博，则设定如下： 请注意，关键词搜索必须设定'cookie'信息。 query_list是所有user的爬取关键词，非常不灵活。如果你要爬多个用户，并且想单独为每个用户设置一个query_list，可以使用定期自动爬取微博方法二中的方法，该方法可以为多个用户设置不同的query_list，非常灵活。 设置remove_html_tag remove_html_tag控制是否移除抓取到的weibo正文和评论中的html tag，值为1代表移除，值为0代表不移除，如 代表移除html tag。例如'专属新意，色彩启程~<a href='/n/路易威登'>@路易威登</a> CAPUCINES 手袋正合我意，打开灵感包袋的搭配新方式！'会被处理成'专属新意，色彩启程~@路易威登 CAPUCINES 手袋正合我意，打开灵感包袋的搭配新方式！'。 设置write_mode write_mode控制结果文件格式，取值范围是csv、json、mongo、mysql和sqlite，分别代表将结果文件写入csv、json、MongoDB、MySQL和SQLite数据库。write_mode可以同时包含这些取值中的一个或几个，如： 代表将结果信息写入csv文件和json文件。特别注意，如果你想写入数据库，除了在write_mode添加对应数据库的名字外，还应该安装相关数据库和对应python模块，具体操作见设置数据库部分。 设置original_pic_download original_pic_download控制是否下载原创微博中的图片，值为1代表下载，值为0代表不下载，如 代表下载原创微博中的图片。 设置retweet_pic_download retweet_pic_download控制是否下载转发微博中的图片，值为1代表下载，值为0代表不下载，如 代表不下载转发微博中的图片。特别注意，本设置只有在爬全部微博（原创+转发），即filter值为0时生效，否则程序会跳过转发微博的图片下载。 设置original_video_download original_video_download控制是否下载原创微博中的视频和原创微博Live Photo中的视频，值为1代表下载，值为0代表不下载，如 代表下载原创微博中的视频和原创微博Live Photo中的视频。 设置retweet_video_download retweet_video_download控制是否下载转发微博中的视频和转发微博Live Photo中的视频，值为1代表下载，值为0代表不下载，如 代表不下载转发微博中的视频和转发微博Live Photo中的视频。特别注意，本设置只有在爬全部微博（原创+转发），即filter值为0时生效，否则程序会跳过转发微博的视频下载。 设置result_dir_name result_dir_name控制结果文件的目录名，可取值为0和1，默认为0： 值为0，表示将结果文件保存在以用户昵称为名的文件夹里，这样结果更清晰；值为1表示将结果文件保存在以用户id为名的文件夹里，这样能保证多次爬取的一致性，因为用户昵称可变，用户id不可变。 设置download_comment download_comment控制是否下载每条微博下的一级评论（不包括对评论的评论），仅当write_mode中有sqlite时有效，可取值为0和1，默认为1： 值为1，表示下载微博评论；值为0，表示不下载微博评论。 设置comment_max_download_count comment_max_download_count控制下载评论的最大数量，仅当write_mode中有sqlite时有效，默认为1000： 设置download_repost download_repost控制是否下载每条微博下的转发，仅当write_mode中有sqlite时有效，可取值为0和1，默认为1： 值为1，表示下载微博转发；值为0，表示不下载微博转发。 设置repost_max_download_count repost_max_download_count控制下载转发的最大数量，仅当write_mode中有sqlite时有效，默认为1000： 值为1000，表示最多下载每条微博下的1000条转发。 设置cookie（可选） cookie为可选参数，即可填可不填，具体区别见添加cookie与不添加cookie的区别。cookie默认配置如下： 如果想要设置cookie，可以按照如何获取cookie中的方法，获取cookie，并将上面的"your cookie"替换成真实的cookie即可。 设置mysql_config（可选） mysql_config控制mysql参数配置。如果你不需要将结果信息写入mysql，这个参数可以忽略，即删除或保留都无所谓；如果你需要写入mysql且config.json文件中mysql_config的配置与你的mysql配置不一样，请将该值改成你自己mysql中的参数配置。 设置start_page（可选） start_page为爬取微博的初始页数，默认参数为1，即从所爬取用户的当前第一页微博内容开始爬取。 若在大批量爬取微博时出现中途被限制中断的情况，可通过查看csv文件内目前已爬取到的微博数除以10，向下取整后的值即为中断页数，手动设置start_page参数为中断页数，重新运行即可从被中断的节点继续爬取剩余微博内容。 ### 4.设置数据库（可选） 本部分是可选部分，如果不需要将爬取信息写入数据库，可跳过这一步。本程序目前支持MySQL数据库和MongoDB数据库，如果你需要写入其它数据库，可以参考这两个数据库的写法自己编写。 MySQL数据库写入 要想将爬取信息写入MySQL，请根据自己的系统环境安装MySQL，然后命令行执行： MongoDB数据库写入 要想将爬取信息写入MongoDB，请根据自己的系统环境安装MongoDB，然后命令行执行： MySQL和MongDB数据库的写入内容一样。程序首先会创建一个名为"weibo"的数据库，然后再创建"user"表和"weibo"表，包含爬取的所有内容。爬取到的微博用户信息或插入或更新，都会存储到user表里；爬取到的微博信息或插入或更新，都会存储到weibo表里，两个表通过user_id关联。如果想了解两个表的具体字段，请点击"详情"。 <details> <summary>详情</summary> user表 id：微博用户id，如"1669879400"； screen_name：微博用户昵称，如"Dear-迪丽热巴"； gender：微博用户性别，取值为f或m，分别代表女和男； birthday：生日； location：所在地； education：教育经历； company：公司； sunshine：阳光信用； registration_time：注册时间； statuses_count：微博数； followers_count：粉丝数； follow_count：关注数； description：微博简介； profile_url：微博主页，如<https://m.URL profile_image_url：微博头像url； avatar_hd：微博高清头像url； urank：微博等级； mbrank：微博会员等级，普通用户会员等级为0； verified：微博是否认证，取值为true和false； verified_type：微博认证类型，没有认证值为-1，个人认证值为0，企业认证值为2，政府认证值为3，这些类型仅是个人猜测，应该不全，大家可以根据实际情况判断； verified_reason：微博认证信息，只有认证用户拥有此属性。 * weibo表 user_id：存储微博用户id，如"1669879400"； screen_name：存储微博昵称，如"Dear-迪丽热巴"； id：存储微博id； text：存储微博正文； article_url：存储微博中头条文章的url，如果微博中存在头条文章，就获取该头条文章的url，否则该值为''； pics：存储原创微博的原始图片url。若某条微博有多张图片，则存储多个url，以英文逗号分割；若该微博没有图片，则值为''； video_url：存储原创微博的视频url和Live Photo中的视频url。若某条微博有多个视频，则存储多个url，以英文分号分割；若该微博没有视频，则值为''； location：存储微博的发布位置。若某条微博没有位置信息，则值为''； created_at：存储微博的发布时间； source：存储微博的发布工具； attitudes_count：存储微博获得的点赞数； comments_count：存储微博获得的评论数； reposts_count：存储微博获得的转发数； topics：存储微博话题，即两个#中的内容。若某条微博没有话题信息，则值为''； at_users：存储微博@的用户。若某条微博没有@的用户，则值为''； retweet_id：存储转发微博中原始微博的微博id。若某条微博为原创微博，则值为''。 </details> SQLite数据库写入 脚本会自动建立并配置数据库文件'URL'。 ### 5.运行脚本 大家可以根据自己的运行环境选择运行方式，Linux可以通过 运行; ### 6.按需求修改脚本（可选） 本部分为可选部分，如果你不需要自己修改代码或添加新功能，可以忽略此部分。 本程序所有代码都位于weibo.py文件，程序主体是一个Weibo类，上述所有功能都是通过在main函数调用Weibo类实现的，默认的调用代码如下： 用户可以按照自己的需求调用或修改Weibo类。 通过执行本程序，我们可以得到很多信息： URL：存储目标微博用户信息； wb.user包含爬取到的微博用户信息，如用户id、用户昵称、性别、生日、所在地、教育经历、公司、阳光信用、微博注册时间、微博数、粉丝数、关注数、简介、主页地址、头像url、高清头像url、微博等级、会员等级、是否认证、认证类型、认证信息等，大家可以点击"详情"查看具体用法。 <details> <summary>详情</summary> id：微博用户id，取值方式为wb.user['id'],由一串数字组成； screen_name：微博用户昵称，取值方式为wb.user['screen_name']； gender：微博用户性别，取值方式为wb.user['gender']，取值为f或m，分别代表女和男； birthday：微博用户生日，取值方式为wb.user['birthday']，若用户没有填写该信息，则值为''； location：微博用户所在地，取值方式为wb.user['location']，若用户没有填写该信息，则值为''； education：微博用户上学时的学校，取值方式为wb.user['education']，若用户没有填写该信息，则值为''； company：微博用户所属的公司，取值方式为wb.user['company']，若用户没有填写该信息，则值为''； sunshine：微博用户的阳光信用，取值方式为wb.user['sunshine']； registration_time：微博用户的注册时间，取值方式为wb.user['registration_time']； statuses_count：微博数，取值方式为wb.user['statuses_count']； followers_count：微博粉丝数，取值方式为wb.user['followers_count']； follow_count：微博关注数，取值方式为wb.user['follow_count']； description：微博简介，取值方式为wb.user['description']； profile_url：微博主页，取值方式为wb.user['profile_url']; profile_image_url：微博头像url，取值方式为wb.user['profile_image_url']； avatar_hd：微博高清头像url，取值方式为wb.user['avatar_hd']； urank：微博等级，取值方式为wb.user['urank']； mbrank：微博会员等级，取值方式为wb.user['mbrank']，普通用户会员等级为0； verified：微博是否认证，取值方式为wb.user['verified']，取值为true和false； verified_type：微博认证类型，取值方式为wb.user['verified_type']，没有认证值为-1，个人认证值为0，企业认证值为2，政府认证值为3，这些类型仅是个人猜测，应该不全，大家可以根据实际情况判断； verified_reason：微博认证信息，取值方式为wb.user['verified_reason']，只有认证用户拥有此属性。 </details> URL：存储爬取到的所有微博信息； wb.weibo包含爬取到的所有微博信息，如微博id、正文、原始图片url、视频url、位置、日期、发布工具、点赞数、转发数、评论数、话题、@用户等。如果爬的是全部微博(原创+转发)，除上述信息之外，还包含原始用户id、原始用户昵称、原始微博id、原始微博正文、原始微博原始图片url、原始微博位置、原始微博日期、原始微博工具、原始微博点赞数、原始微博评论数、原始微博转发数、原始微博话题、原始微博@用户等信息。wb.weibo是一个列表，包含了爬取的所有微博信息。URL[0]为爬取的第一条微博，URL[1]为爬取的第二条微博，以此类推。当filter=1时，URL[0]为爬取的第一条原创微博，以此类推。URL[0]['id']为第一条微博的id，URL[0]['text']为第一条微博的正文，URL[0]['created_at']为第一条微博的发布时间，还有其它很多信息不在赘述，大家可以点击下面的"详情"查看具体用法。 <details> <summary>详情</summary> user_id：存储微博用户id。如wb.weibo[0]['user_id']为最新一条微博的用户id； screen_name：存储微博昵称。如wb.weibo[0]['screen_name']为最新一条微博的昵称； id：存储微博id。如wb.weibo[0]['id']为最新一条微博的id； text：存储微博正文。如wb.weibo[0]['text']为最新一条微博的正文； article_url：存储微博中头条文章的url。如wb.weibo[0]['article_url']为最新一条微博的头条文章url，若微博中不存在头条文章，则该值为''； pics：存储原创微博的原始图片url。如wb.weibo[0]['pics']为最新一条微博的原始图片url，若该条微博有多张图片，则存储多个url，以英文逗号分割；若该微博没有图片，则值为''； video_url：存储原创微博的视频url和原创微博Live Photo中的视频url。如wb.weibo[0]['video_url']为最新一条微博的视频url，若该条微博有多个视频，则存储多个url，以英文分号分割；若该微博没有视频，则值为''； location：存储微博的发布位置。如wb.weibo[0]['location']为最新一条微博的发布位置，若该条微博没有位置信息，则值为''； created_at：存储微博的发布时间。如wb.weibo[0]['created_at']为最新一条微博的发布时间； source：存储微博的发布工具。如wb.weibo[0]['source']为最新一条微博的发布工具； attitudes_count：存储微博获得的点赞数。如wb.weibo[0]['attitudes_count']为最新一条微博获得的点赞数； comments_count：存储微博获得的评论数。如wb.weibo[0]['comments_count']为最新一条微博获得的评论数； reposts_count：存储微博获得的转发数。如wb.weibo[0]['reposts_count']为最新一条微博获得的转发数； topics：存储微博话题，即两个#中的内容。如wb.weibo[0]['topics']为最新一条微博的话题，若该条微博没有话题信息，则值为''； at_users：存储微博@的用户。如wb.weibo[0]['at_users']为最新一条微博@的用户，若该条微博没有@的用户，则值为''； retweet：存储转发微博中原始微博的全部信息。假如wb.weibo[0]为转发微博，则wb.weibo[0]['retweet']为该转发微博的原始微博，它存储的属性与wb.weibo[0]一样，只是没有retweet属性;若该条微博为原创微博，则wb[0]没有"retweet"属性，大家可以点击"详情"查看具体用法。 <details> <summary>详情</summary> 假设爬取到的第i条微博为转发微博，则它存在以下信息： user_id：存储原始微博用户id。URL[i-1]['retweet']['user_id']为该原始微博的用户id； screen_name：存储原始微博昵称。URL[i-1]['retweet']['screen_name']为该原始微博的昵称； id：存储原始微博id。URL[i-1]['retweet']['id']为该原始微博的id； text：存储原始微博正文。URL[i-1]['retweet']['text']为该原始微博的正文； article_url：存储原始微博中头条文章的url。如wb.weibo[i-1]['retweet']['article_url']为该原始微博的头条文章url，若原始微博中不存在头条文章，则该值为''； pics：存储原始微博的原始图片url。URL[i-1]['retweet']['pics']为该原始微博的原始图片url，若该原始微博有多张图片，则存储多个url，以英文逗号分割；若该原始微博没有图片，则值为''； video_url：存储原始微博的视频url和原始微博Live Photo中的视频url。如wb.weibo[i-1]['retweet']['video_url']为该原始微博的视频url，若该原始微博有多个视频，则存储多个url，以英文分号分割；若该微博没有视频，则值为''； location：存储原始微博的发布位置。URL[i-1]['retweet']['location']为该原始微博的发布位置，若该原始微博没有位置信息，则值为''； created_at：存储原始微博的发布时间。URL[i-1]['retweet']['created_at']为该原始微博的发布时间； source：存储原始微博的发布工具。URL[i-1]['retweet']['source']为该原始微博的发布工具； attitudes_count：存储原始微博获得的点赞数。URL[i-1]['retweet']['attitudes_count']为该原始微博获得的点赞数； comments_count：存储原始微博获得的评论数。URL[i-1]['retweet']['comments_count']为该原始微博获得的评论数； reposts_count：存储原始微博获得的转发数。URL[i-1]['retweet']['reposts_count']为该原始微博获得的转发数； topics：存储原始微博话题，即两个#中的内容。URL[i-1]['retweet']['topics']为该原始微博的话题，若该原始微博没有话题信息，则值为''； at_users：存储原始微博@的用户。URL[i-1]['retweet']['at_users']为该原始微博@的用户，若该原始微博没有@的用户，则值为''。 </details> </details> ### 7.定期自动爬取微博（可选） 我们爬取了微博以后，很多微博账号又可能发了一些新微博，定期自动爬取微博就是每隔一段时间自动运行程序，自动爬取这段时间产生的新微博（忽略以前爬过的旧微博）。本部分为可选部分，如果不需要可以忽略。 思路是利用第三方软件，如crontab，让程序每隔一段时间运行一次。因为是要跳过以前爬过的旧微博，只爬新微博。所以需要设置一个动态的since_date。很多时候我们使用的since_date是固定的，比如since_date="2018-01-01"，程序就会按照这个设置从最新的微博一直爬到发布时间为2018-01-01的微博（包括这个时间）。因为我们想追加新微博，跳过旧微博。第二次爬取时since_date值就应该是当前时间到上次爬取的时间。 如果我们使用最原始的方式实现追加爬取，应该是这样： 上面的方法太麻烦，因为每次都要手动设置since_date。因此我们需要动态设置since_date，即程序根据实际情况，自动生成since_date。 有两种方法实现动态更新since_date： 方法一：将since_date设置成整数 将config.json文件中的since_date设置成整数，如： 这个配置告诉程序爬取最近10天的微博，更准确说是爬取发布时间从10天前到本程序开始执行时之间的微博。这样since_date就是一个动态的变量，每次程序执行时，它的值就是当前日期减10。配合crontab每9天或10天执行一次，就实现了定期追加爬取。 方法二：将上次执行程序的时间写入文件（推荐） 这个方法很简单，就是用户把要爬的用户id写入txt文件，然后再把文件路径赋值给config.json中的user_id_list参数。 txt文件名格式可以参考程序设置中的设置user_id_list部分，这样设置就全部结束了。 说下这个方法的原理和好处，假如你的txt文件内容为： 第一次执行时，因为第一行和第二行都没有写时间，程序会按照config.json文件中since_date的值爬取，第三行有时间“2019-01-01”，程序就会把这个时间当作since_date。每个用户爬取结束程序都会自动更新txt文件，每一行第一部分是user_id，第二部分是用户昵称，第三部分是程序准备爬取该用户第一条微博（最新微博）时的日期。爬完三个用户后，txt文件的内容自动更新为： 下次再爬取微博的时候，程序会把每行的时间数据作为since_date。这样的好处一是不用修改since_date，程序自动更新；二是每一个用户都可以单独拥有只属于自己的since_date，每个用户的since_date相互独立，互不干扰，格式为'yyyy-mm-dd'或整数。比如，现在又添加了一个新用户，以杨紫的微博为例，你想获取她2018-01-23到现在的全部微博，可以这样修改txt文件： 注意每一行的用户配置参数以空格分隔，如果第一个参数全部由数字组成，程序就认为此行为一个用户的配置，否则程序会认为该行只是注释，跳过该行；第二个参数可以为任意格式，建议写用户昵称；第三个如果是日期格式（yyyy-mm-dd），程序就将该日期设置为用户自己的since_date，否则使用config.json中的since_date爬取该用户的微博，第二个参数和第三个参数也可以不填。 也可以设置第四个参数，将被读取为query_list。 方法三：将'URL'文件中的运行模式改为'append' 以追加模式运行程序，每次运行，每个id只获取最新的微博，而不是全部，避免频繁备份微博导致过多的请求次数。 注意： * 该模式会跳过置顶微博。 * 若采集信息后用户又编辑微博，则不会记录编辑内容。 ## 如何获取user_id 1.打开网址<URL>，搜索我们要找的人，如"迪丽热巴"，进入她的主页； ![](URL 2.按照上图箭头所指，点击"资料"链接，跳转到用户资料页面； ![](URL 如上图所示，迪丽热巴微博资料页的地址为"<URL，其中的"1669879400"即为此微博的user_id。 事实上，此微博的user_id也包含在用户主页(<URL)中，之所以我们还要点击主页中的"资料"来获取user_id，是因为很多用户的主页不是"<URL"的形式，而是"<URL/个性域名?f=search_0>"或"<URL/微号?f=search_0>"的形式。其中"微号"和user_id都是一串数字，如果仅仅通过主页地址提取user_id，很容易将"微号"误认为user_id。 ## 添加cookie与不添加cookie的区别（可选） 对于微博数2000条及以下的微博用户，不添加cookie可以获取其用户信息和大部分微博；对于微博数2000条以上的微博用户，不添加cookie可以获取其用户信息和最近2000条微博中的大部分，添加cookie可以获取其全部微博。以2020年1月2日迪丽热巴的微博为例，此时她共有1085条微博，在不添加cookie的情况下，可以获取到1026条微博，大约占全部微博的94.56%，而在添加cookie后，可以获取全部微博。其他用户类似，大部分都可以在不添加cookie的情况下获取到90%以上的微博，在添加cookie后可以获取全部微博。具体原因是，大部分微博内容都可以在移动版匿名获取，少量微博需要用户登录才可以获取，所以这部分微博在不添加cookie时是无法获取的。 有少部分微博用户，不添加cookie可以获取其微博，无法获取其用户信息。对于这种情况，要想获取其用户信息，是需要cookie的。 如需抓取微博转发，请添加cookie。 ## 如何获取cookie（可选） 1.用Chrome打开<URL； 2.输入微博的用户名、密码，登录，如图所示： ![](URL 登录成功后会跳转到<https://m.URL>; 3.按F12键打开Chrome开发者工具，在地址栏输入并跳转到<URL>，跳转后会显示如下类似界面: ![](URL 4.依此点击Chrome开发者工具中的Network->Name中的weibo.cn->Headers->Request Headers，"Cookie:"后的值即为我们要找的cookie值，复制即可，如图所示： ![](URL ## 如何检测cookie是否有效（可选） 本程序cookie检查的逻辑是：使用cookie来源账号发布限定范围的微博，若cookie可用，则可以读取到该微博，否则读取不到。 操作方法 1. 使用cookie的来源账号发布一条微博，该账号和微博需要满足以下条件： * 该微博必须是非公开可见的，后续需要根据可见性判断cookie是否有效； * 该微博需要是最近5条微博，不能在发布测试用微博内容后又发很多新微博； * 在'URL'配置中的since_date之后，该账号必须有大于9条微博。 2. 将'URL'文件中''CHECK': False'中的'False'改为'True'，''HIDDEN_WEIBO': '微博内容''中的'微博内容'改为你发的限定范围的微博。 3. 将提供cookie的微博id放置在'URL'文件中'"user_id_list"'设置项数组中的第一个。例如提供cookie的微博id为'123456'，则'"user_id_list"'设置为'"user_id_list":["123456", "<其余id...>"]'。 注：本方法也将会抓取提供cookie账号的微博内容。 在间歇运行程序时，cookie无效会导致程序不能按照预设目标执行，因此可以打开cookie通知功能。本项目使用开源项目pushdeer进行通知，在使用前用户需要申请push_key，具体可查看官网了解。打开方法为： 1. 在'URL'文件中，将''NOTIFY': False'中的'False'设为'True'； 2. 将''PUSH_KEY': '''的''''替换为''<你的push_key>''
[ "## 功能\n\n连续爬取一个或多个新浪微博用户（如Dear-迪丽热巴、郭碧婷）的数据，并将结果信息写入文件。写入信息几乎包括了用户微博的所有数据，主要有用户信息和微博信息两大类，前者包含用户昵称、关注数、粉丝数、微博数等等；后者包含微博正文、发布时间、发布工具、评论数等等，因为内容太多，这里不再赘述，详细内容见输出部分。具体的写入文件类型如下：\n\n* 写入csv文件（默认）\n* 写入json文件（可选）\n* 写入MySQL数据库（可选）\n* 写入MongoDB数据库（可选）\n* 写入SQLite数据库（可选）\n* 下载用户原创微博中的原始图片（可选）\n* 下载用户转发微博中的原始图片（可选）\n* 下载用户原创微博中的视频（可选）\n* 下载用户转发微博中的视频（可选）\n* 下载用户原创微博Live Photo中的视频（可选）\n* 下载用户转发微博Live Photo中的视频（可选）\n* 下载用户原创和转发微博下的一级评论（可选）\n* 下载用户原创和转发微博下的转发（可选）\n\n如果你只对用户信息感兴趣，而不需要爬用户的微博，也可以通过设置实现只爬取微博用户信息的功能。程序也可以实现爬取结果自动更新，即：现在爬取了目标用户的微博，几天之后，目标用户可能又发新微博了。通过设置，可以实现每隔几天增量爬取用户这几天发的新微博。具体方法见定期自动爬取微博。", "## 输出\n\n用户信息\n\n* 用户id：微博用户id，如\"1669879400\"\n* 用户昵称：微博用户昵称，如\"Dear-迪丽热巴\"\n* 性别：微博用户性别\n* 生日：用户出生日期\n* 所在地：用户所在地\n* 教育经历：用户上学时学校的名字\n* 公司：用户所属公司名字\n* 阳光信用：用户的阳光信用\n* 微博注册时间：用户微博注册日期\n* 微博数：用户的全部微博数（转发微博+原创微博）\n* 粉丝数：用户的粉丝数\n* 关注数：用户关注的微博数量\n* 简介：用户简介\n* 主页地址：微博移动版主页url，如<https://m.URL\n* 头像url：用户头像url\n* 高清头像url：用户高清头像url\n* 微博等级：用户微博等级\n* 会员等级：微博会员用户等级，普通用户该等级为0\n* 是否认证：用户是否认证，为布尔类型\n* 认证类型：用户认证类型，如个人认证、企业认证、政府认证等\n* 认证信息：为认证用户特有，用户信息栏显示的认证信息\n\n*\n微博信息\n\n* 微博id：微博的id，为一串数字形式\n* 微博bid：微博的bid，与cookie版中的微博id是同一个值\n* 微博内容：微博正文\n* 头条文章url：微博中头条文章的url，如果微博中存在头条文章，就获取该头条文章的url，否则该值为''\n* 原始图片url：原创微博图片和转发微博转发理由中图片的url，若某条微博存在多张图片，则每个url以英文逗号分隔，若没有图片则值为''\n* 视频url: 微博中的视频url和Live Photo中的视频url，若某条微博存在多个视频，则每个url以英文分号分隔，若没有视频则值为''\n* 微博发布位置：位置微博中的发布位置\n* 微博发布时间：微博发布时的时间，精确到天\n* 点赞数：微博被赞的数量\n* 转发数：微博被转发的数量\n* 评论数：微博被评论的数量\n* 微博发布工具：微博的发布工具，如iPhone客户端、HUAWEI Mate 20 Pro等，若没有则值为''\n* 话题：微博话题，即两个#中的内容，若存在多个话题，每个url以英文逗号分隔，若没有则值为''\n* @用户：微博@的用户，若存在多个@用户，每个url以英文逗号分隔，若没有则值为''\n* 原始微博：为转发微博所特有，是转发微博中那条被转发的微博，存储为字典形式，包含了上述微博信息中的所有内容，如微博id、微博内容等等\n* 结果文件：保存在当前目录weibo文件夹下以用户昵称为名的文件夹里，名字为\"user_id.csv\"形式\n* 微博图片：微博中的图片，保存在以用户昵称为名的文件夹下的img文件夹里\n* 微博视频：微博中的视频，保存在以用户昵称为名的文件夹下的video文件夹里", "## 实例\n\n以爬取迪丽热巴的微博为例，我们需要修改URL文件，文件内容如下：\n\n\n\n对于上述参数的含义以及取值范围，这里仅作简单介绍，详细信息见程序设置。\n>user_id_list代表我们要爬取的微博用户的user_id，可以是一个或多个，也可以是文件路径，微博用户Dear-迪丽热巴的user_id为1669879400，具体如何获取user_id见如何获取user_id；\nfilter的值为1代表爬取全部原创微博，值为0代表爬取全部微博（原创+转发）；\nsince_date代表我们要爬取since_date日期之后发布的微博，因为我要爬迪丽热巴的全部原创微博，所以since_date设置了一个非常早的值；\nquery_list代表要爬取的微博关键词，为空（[]）则爬取全部；\nwrite_mode代表结果文件的保存类型，我想要把结果写入csv文件和json文件，所以它的值为[\"csv\", \"json\"]，如果你想写入数据库，具体设置见设置数据库；\noriginal_pic_download值为1代表下载原创微博中的图片，值为0代表不下载；\nretweet_pic_download值为1代表下载转发微博中的图片，值为0代表不下载；\noriginal_video_download值为1代表下载原创微博中的视频，值为0代表不下载；\nretweet_video_download值为1代表下载转发微博中的视频，值为0代表不下载；\ncookie是可选参数，可填可不填，具体区别见添加cookie与不添加cookie的区别。\n\n配置完成后运行程序：\n\n\n\n程序会自动生成一个weibo文件夹，我们以后爬取的所有微博都被存储在weibo文件夹里。然后程序在该文件夹下生成一个名为\"Dear-迪丽热巴\"的文件夹，迪丽热巴的所有微博爬取结果都在这里。\"Dear-迪丽热巴\"文件夹里包含一个csv文件、一个img文件夹和一个video文件夹，img文件夹用来存储下载到的图片，video文件夹用来存储下载到的视频。如果你设置了保存数据库功能，这些信息也会保存在数据库里，数据库设置见设置数据库部分。\n\ncsv文件结果如下所示：\n![](URL\n\n本csv文件是爬取“全部微博”(原创微博+转发微博)的结果文件。因为迪丽热巴很多微博本身都没有图片、发布工具、位置、话题和@用户等信息，所以当这些内容没有时对应位置为空。\"是否原创\"列用来标记是否为原创微博，\n当为转发微博时，文件中还包含转发微博的信息。为了简便起见，姑且将转发微博中被转发的原始微博称为源微博，它的用户id、昵称、微博id等都在名称前加上源字，以便与目标用户自己发的微博区分。对于转发微博，程序除了获取用户原创部分的信息，还会获取源用户id、源用户昵称、源微博id、源微博正文、源微博原始图片url、源微博位置、源微博日期、源微博工具、源微博点赞数、源微博评论数、源微博转发数、源微博话题、源微博@用户等信息。原创微博因为没有这些转发信息，所以对应位置为空。若爬取的是\"全部原创微博\"，则csv文件中不会包含\"是否原创\"及其之后的转发属性列；\n\n为了说明json结果文件格式，这里以迪丽热巴2019年12月27日到2019年12月28日发的2条微博为例。\n\njson结果文件格式如下：\n\n\n\n*URL*\n\n下载的图片如下所示：\n\n![](URL*img文件夹*\n\n本次下载了788张图片，大小一共1.21GB，包括她原创微博中的所有图片。图片名为yyyymmdd+微博id的形式，若某条微博存在多张图片，则图片名中还会包括它在微博图片中的序号。若某图片下载失败，程序则会以“weibo_id:pic_url”的形式将出错微博id和图片url写入同文件夹下的not_downloaded.txt里；若图片全部下载成功则不会生成not_downloaded.txt；\n\n下载的视频如下所示：\n![](URL*video文件夹*\n\n本次下载了66个视频，是她原创微博中的视频和原创微博Live Photo中的视频，视频名为yyyymmdd+微博id的形式。有三个视频因为网络原因下载失败，程序将它们的微博id和视频url分别以“weibo_id:video_url”的形式写到了同文件夹下的not_downloaded.txt里。\n\n因为我本地没有安装MySQL数据库和MongoDB数据库，所以暂时设置成不写入数据库。如果你想要将爬取结果写入数据库，只需要先安装数据库（MySQL或MongoDB），再安装对应包（pymysql或pymongo），然后将mysql_write或mongodb_write值设置为1即可。写入MySQL需要用户名、密码等配置信息，这些配置如何设置见设置数据库部分。", "## 运行环境\n\n* 开发语言：python2/python3\n\n* 系统： Windows/Linux/macOS", "## 使用说明", "### 1.下载脚本\n\n\n\n运行上述命令，将本项目下载到当前目录，如果下载成功当前目录会出现一个名为\"weibo-crawler\"的文件夹；", "### 2.安装依赖", "### 3.程序设置\n\n打开URL文件，你会看到如下内容：\n\n\n\n下面讲解每个参数的含义与设置方法。\n\n设置user_id_list\n\nuser_id_list是我们要爬取的微博的id，可以是一个，也可以是多个，例如：\n\n\n\n上述代码代表我们要连续爬取user_id分别为“1223178222”、 “1669879400”、 “1729370543”的三个用户的微博，具体如何获取user_id见如何获取user_id。\n\nuser_id_list的值也可以是文件路径，我们可以把要爬的所有微博用户的user_id都写到txt文件里，然后把文件的位置路径赋值给user_id_list。\n\n在txt文件中，每个user_id占一行，也可以在user_id后面加注释（可选），如用户昵称等信息，user_id和注释之间必需要有空格，文件名任意，类型为txt，位置位于本程序的同目录下，文件内容示例如下：\n\n\n\n假如文件叫user_id_list.txt，则user_id_list设置代码为：\n\n\n\n设置filter\n\nfilter控制爬取范围，值为1代表爬取全部原创微博，值为0代表爬取全部微博（原创+转发）。例如，如果要爬全部原创微博，请使用如下代码：\n\n\n\n设置since_date\n\nsince_date值可以是日期，也可以是整数。如果是日期，代表爬取该日期之后的微博，格式应为“yyyy-mm-dd”，如：\n\n\n\n代表爬取从2018年1月1日到现在的微博。\n\n如果是整数，代表爬取最近n天的微博，如:\n\n\n\n代表爬取最近10天的微博，这个说法不是特别准确，准确说是爬取发布时间从10天前到本程序开始执行时之间的微博。\n\nsince_date是所有user的爬取起始时间，非常不灵活。如果你要爬多个用户，并且想单独为每个用户设置一个since_date，可以使用定期自动爬取微博方法二中的方法，该方法可以为多个用户设置不同的since_date，非常灵活。\n\n设置query_list(可选)\n\nquery_list是一个关键词字符串列表或以','分隔关键词的字符串，用于指定关键词搜索爬取，若为空'[]'或'\"\"'则爬取全部微博。例如要爬取用户包含“梦想”和“希望”的微博，则设定如下：\n\n\n\n请注意，关键词搜索必须设定'cookie'信息。\nquery_list是所有user的爬取关键词，非常不灵活。如果你要爬多个用户，并且想单独为每个用户设置一个query_list，可以使用定期自动爬取微博方法二中的方法，该方法可以为多个用户设置不同的query_list，非常灵活。\n\n设置remove_html_tag\n\nremove_html_tag控制是否移除抓取到的weibo正文和评论中的html tag，值为1代表移除，值为0代表不移除，如\n\n\n\n代表移除html tag。例如'专属新意，色彩启程~<a href='/n/路易威登'>@路易威登</a> CAPUCINES 手袋正合我意，打开灵感包袋的搭配新方式！'会被处理成'专属新意，色彩启程~@路易威登 CAPUCINES 手袋正合我意，打开灵感包袋的搭配新方式！'。\n\n设置write_mode\n\nwrite_mode控制结果文件格式，取值范围是csv、json、mongo、mysql和sqlite，分别代表将结果文件写入csv、json、MongoDB、MySQL和SQLite数据库。write_mode可以同时包含这些取值中的一个或几个，如：\n\n\n\n代表将结果信息写入csv文件和json文件。特别注意，如果你想写入数据库，除了在write_mode添加对应数据库的名字外，还应该安装相关数据库和对应python模块，具体操作见设置数据库部分。\n\n设置original_pic_download\n\noriginal_pic_download控制是否下载原创微博中的图片，值为1代表下载，值为0代表不下载，如\n\n\n\n代表下载原创微博中的图片。\n\n设置retweet_pic_download\n\nretweet_pic_download控制是否下载转发微博中的图片，值为1代表下载，值为0代表不下载，如\n\n\n\n代表不下载转发微博中的图片。特别注意，本设置只有在爬全部微博（原创+转发），即filter值为0时生效，否则程序会跳过转发微博的图片下载。\n\n设置original_video_download\n\noriginal_video_download控制是否下载原创微博中的视频和原创微博Live Photo中的视频，值为1代表下载，值为0代表不下载，如\n\n\n\n代表下载原创微博中的视频和原创微博Live Photo中的视频。\n\n设置retweet_video_download\n\nretweet_video_download控制是否下载转发微博中的视频和转发微博Live Photo中的视频，值为1代表下载，值为0代表不下载，如\n\n\n\n代表不下载转发微博中的视频和转发微博Live Photo中的视频。特别注意，本设置只有在爬全部微博（原创+转发），即filter值为0时生效，否则程序会跳过转发微博的视频下载。\n\n设置result_dir_name\n\nresult_dir_name控制结果文件的目录名，可取值为0和1，默认为0：\n\n\n\n值为0，表示将结果文件保存在以用户昵称为名的文件夹里，这样结果更清晰；值为1表示将结果文件保存在以用户id为名的文件夹里，这样能保证多次爬取的一致性，因为用户昵称可变，用户id不可变。\n\n设置download_comment\n\ndownload_comment控制是否下载每条微博下的一级评论（不包括对评论的评论），仅当write_mode中有sqlite时有效，可取值为0和1，默认为1：\n\n\n\n值为1，表示下载微博评论；值为0，表示不下载微博评论。\n\n设置comment_max_download_count\n\ncomment_max_download_count控制下载评论的最大数量，仅当write_mode中有sqlite时有效，默认为1000：\n\n\n\n设置download_repost\n\ndownload_repost控制是否下载每条微博下的转发，仅当write_mode中有sqlite时有效，可取值为0和1，默认为1：\n\n\n\n值为1，表示下载微博转发；值为0，表示不下载微博转发。\n\n设置repost_max_download_count\n\nrepost_max_download_count控制下载转发的最大数量，仅当write_mode中有sqlite时有效，默认为1000：\n\n\n\n值为1000，表示最多下载每条微博下的1000条转发。\n\n设置cookie（可选）\n\ncookie为可选参数，即可填可不填，具体区别见添加cookie与不添加cookie的区别。cookie默认配置如下：\n\n\n\n如果想要设置cookie，可以按照如何获取cookie中的方法，获取cookie，并将上面的\"your cookie\"替换成真实的cookie即可。\n\n设置mysql_config（可选）\n\nmysql_config控制mysql参数配置。如果你不需要将结果信息写入mysql，这个参数可以忽略，即删除或保留都无所谓；如果你需要写入mysql且config.json文件中mysql_config的配置与你的mysql配置不一样，请将该值改成你自己mysql中的参数配置。\n设置start_page（可选）\n\nstart_page为爬取微博的初始页数，默认参数为1，即从所爬取用户的当前第一页微博内容开始爬取。\n若在大批量爬取微博时出现中途被限制中断的情况，可通过查看csv文件内目前已爬取到的微博数除以10，向下取整后的值即为中断页数，手动设置start_page参数为中断页数，重新运行即可从被中断的节点继续爬取剩余微博内容。", "### 4.设置数据库（可选）\n\n本部分是可选部分，如果不需要将爬取信息写入数据库，可跳过这一步。本程序目前支持MySQL数据库和MongoDB数据库，如果你需要写入其它数据库，可以参考这两个数据库的写法自己编写。\n\nMySQL数据库写入\n\n要想将爬取信息写入MySQL，请根据自己的系统环境安装MySQL，然后命令行执行：\n\n\n\nMongoDB数据库写入\n\n要想将爬取信息写入MongoDB，请根据自己的系统环境安装MongoDB，然后命令行执行：\n\n\n\nMySQL和MongDB数据库的写入内容一样。程序首先会创建一个名为\"weibo\"的数据库，然后再创建\"user\"表和\"weibo\"表，包含爬取的所有内容。爬取到的微博用户信息或插入或更新，都会存储到user表里；爬取到的微博信息或插入或更新，都会存储到weibo表里，两个表通过user_id关联。如果想了解两个表的具体字段，请点击\"详情\"。\n<details>\n\n<summary>详情</summary>\n\nuser表\n\nid：微博用户id，如\"1669879400\"；\n\nscreen_name：微博用户昵称，如\"Dear-迪丽热巴\"；\n\ngender：微博用户性别，取值为f或m，分别代表女和男；\n\nbirthday：生日；\n\nlocation：所在地；\n\neducation：教育经历；\n\ncompany：公司；\n\nsunshine：阳光信用；\n\nregistration_time：注册时间；\n\nstatuses_count：微博数；\n\nfollowers_count：粉丝数；\n\nfollow_count：关注数；\n\ndescription：微博简介；\n\nprofile_url：微博主页，如<https://m.URL\n\nprofile_image_url：微博头像url；\n\navatar_hd：微博高清头像url；\n\nurank：微博等级；\n\nmbrank：微博会员等级，普通用户会员等级为0；\n\nverified：微博是否认证，取值为true和false；\n\nverified_type：微博认证类型，没有认证值为-1，个人认证值为0，企业认证值为2，政府认证值为3，这些类型仅是个人猜测，应该不全，大家可以根据实际情况判断；\n\nverified_reason：微博认证信息，只有认证用户拥有此属性。\n\n*\nweibo表\n\nuser_id：存储微博用户id，如\"1669879400\"；\n\nscreen_name：存储微博昵称，如\"Dear-迪丽热巴\"；\n\nid：存储微博id；\n\ntext：存储微博正文；\n\narticle_url：存储微博中头条文章的url，如果微博中存在头条文章，就获取该头条文章的url，否则该值为''；\n\npics：存储原创微博的原始图片url。若某条微博有多张图片，则存储多个url，以英文逗号分割；若该微博没有图片，则值为''；\n\nvideo_url：存储原创微博的视频url和Live Photo中的视频url。若某条微博有多个视频，则存储多个url，以英文分号分割；若该微博没有视频，则值为''；\n\nlocation：存储微博的发布位置。若某条微博没有位置信息，则值为''；\n\ncreated_at：存储微博的发布时间；\n\nsource：存储微博的发布工具；\n\nattitudes_count：存储微博获得的点赞数；\n\ncomments_count：存储微博获得的评论数；\n\nreposts_count：存储微博获得的转发数；\n\ntopics：存储微博话题，即两个#中的内容。若某条微博没有话题信息，则值为''；\n\nat_users：存储微博@的用户。若某条微博没有@的用户，则值为''；\n\nretweet_id：存储转发微博中原始微博的微博id。若某条微博为原创微博，则值为''。\n\n</details>\nSQLite数据库写入\n\n脚本会自动建立并配置数据库文件'URL'。", "### 5.运行脚本\n\n大家可以根据自己的运行环境选择运行方式，Linux可以通过\n\n\n\n运行;", "### 6.按需求修改脚本（可选）\n\n本部分为可选部分，如果你不需要自己修改代码或添加新功能，可以忽略此部分。\n\n本程序所有代码都位于weibo.py文件，程序主体是一个Weibo类，上述所有功能都是通过在main函数调用Weibo类实现的，默认的调用代码如下：\n\n\n\n用户可以按照自己的需求调用或修改Weibo类。\n\n通过执行本程序，我们可以得到很多信息：\n\nURL：存储目标微博用户信息；\n\nwb.user包含爬取到的微博用户信息，如用户id、用户昵称、性别、生日、所在地、教育经历、公司、阳光信用、微博注册时间、微博数、粉丝数、关注数、简介、主页地址、头像url、高清头像url、微博等级、会员等级、是否认证、认证类型、认证信息等，大家可以点击\"详情\"查看具体用法。\n\n<details>\n\n<summary>详情</summary>\n\nid：微博用户id，取值方式为wb.user['id'],由一串数字组成；\n\nscreen_name：微博用户昵称，取值方式为wb.user['screen_name']；\n\ngender：微博用户性别，取值方式为wb.user['gender']，取值为f或m，分别代表女和男；\n\nbirthday：微博用户生日，取值方式为wb.user['birthday']，若用户没有填写该信息，则值为''；\n\nlocation：微博用户所在地，取值方式为wb.user['location']，若用户没有填写该信息，则值为''；\n\neducation：微博用户上学时的学校，取值方式为wb.user['education']，若用户没有填写该信息，则值为''；\n\ncompany：微博用户所属的公司，取值方式为wb.user['company']，若用户没有填写该信息，则值为''；\n\nsunshine：微博用户的阳光信用，取值方式为wb.user['sunshine']；\n\nregistration_time：微博用户的注册时间，取值方式为wb.user['registration_time']；\n\nstatuses_count：微博数，取值方式为wb.user['statuses_count']；\n\nfollowers_count：微博粉丝数，取值方式为wb.user['followers_count']；\n\nfollow_count：微博关注数，取值方式为wb.user['follow_count']；\n\ndescription：微博简介，取值方式为wb.user['description']；\n\nprofile_url：微博主页，取值方式为wb.user['profile_url'];\n\nprofile_image_url：微博头像url，取值方式为wb.user['profile_image_url']；\n\navatar_hd：微博高清头像url，取值方式为wb.user['avatar_hd']；\n\nurank：微博等级，取值方式为wb.user['urank']；\n\nmbrank：微博会员等级，取值方式为wb.user['mbrank']，普通用户会员等级为0；\n\nverified：微博是否认证，取值方式为wb.user['verified']，取值为true和false；\n\nverified_type：微博认证类型，取值方式为wb.user['verified_type']，没有认证值为-1，个人认证值为0，企业认证值为2，政府认证值为3，这些类型仅是个人猜测，应该不全，大家可以根据实际情况判断；\n\nverified_reason：微博认证信息，取值方式为wb.user['verified_reason']，只有认证用户拥有此属性。\n\n</details>\n\nURL：存储爬取到的所有微博信息；\n\nwb.weibo包含爬取到的所有微博信息，如微博id、正文、原始图片url、视频url、位置、日期、发布工具、点赞数、转发数、评论数、话题、@用户等。如果爬的是全部微博(原创+转发)，除上述信息之外，还包含原始用户id、原始用户昵称、原始微博id、原始微博正文、原始微博原始图片url、原始微博位置、原始微博日期、原始微博工具、原始微博点赞数、原始微博评论数、原始微博转发数、原始微博话题、原始微博@用户等信息。wb.weibo是一个列表，包含了爬取的所有微博信息。URL[0]为爬取的第一条微博，URL[1]为爬取的第二条微博，以此类推。当filter=1时，URL[0]为爬取的第一条原创微博，以此类推。URL[0]['id']为第一条微博的id，URL[0]['text']为第一条微博的正文，URL[0]['created_at']为第一条微博的发布时间，还有其它很多信息不在赘述，大家可以点击下面的\"详情\"查看具体用法。\n<details>\n\n<summary>详情</summary>\n\nuser_id：存储微博用户id。如wb.weibo[0]['user_id']为最新一条微博的用户id；\n\nscreen_name：存储微博昵称。如wb.weibo[0]['screen_name']为最新一条微博的昵称；\n\nid：存储微博id。如wb.weibo[0]['id']为最新一条微博的id；\n\ntext：存储微博正文。如wb.weibo[0]['text']为最新一条微博的正文；\n\narticle_url：存储微博中头条文章的url。如wb.weibo[0]['article_url']为最新一条微博的头条文章url，若微博中不存在头条文章，则该值为''；\n\npics：存储原创微博的原始图片url。如wb.weibo[0]['pics']为最新一条微博的原始图片url，若该条微博有多张图片，则存储多个url，以英文逗号分割；若该微博没有图片，则值为''；\n\nvideo_url：存储原创微博的视频url和原创微博Live Photo中的视频url。如wb.weibo[0]['video_url']为最新一条微博的视频url，若该条微博有多个视频，则存储多个url，以英文分号分割；若该微博没有视频，则值为''；\n\nlocation：存储微博的发布位置。如wb.weibo[0]['location']为最新一条微博的发布位置，若该条微博没有位置信息，则值为''；\n\ncreated_at：存储微博的发布时间。如wb.weibo[0]['created_at']为最新一条微博的发布时间；\n\nsource：存储微博的发布工具。如wb.weibo[0]['source']为最新一条微博的发布工具；\n\nattitudes_count：存储微博获得的点赞数。如wb.weibo[0]['attitudes_count']为最新一条微博获得的点赞数；\n\ncomments_count：存储微博获得的评论数。如wb.weibo[0]['comments_count']为最新一条微博获得的评论数；\n\nreposts_count：存储微博获得的转发数。如wb.weibo[0]['reposts_count']为最新一条微博获得的转发数；\n\ntopics：存储微博话题，即两个#中的内容。如wb.weibo[0]['topics']为最新一条微博的话题，若该条微博没有话题信息，则值为''；\n\nat_users：存储微博@的用户。如wb.weibo[0]['at_users']为最新一条微博@的用户，若该条微博没有@的用户，则值为''；\n\nretweet：存储转发微博中原始微博的全部信息。假如wb.weibo[0]为转发微博，则wb.weibo[0]['retweet']为该转发微博的原始微博，它存储的属性与wb.weibo[0]一样，只是没有retweet属性;若该条微博为原创微博，则wb[0]没有\"retweet\"属性，大家可以点击\"详情\"查看具体用法。\n\n<details>\n\n<summary>详情</summary>\n\n假设爬取到的第i条微博为转发微博，则它存在以下信息：\n\nuser_id：存储原始微博用户id。URL[i-1]['retweet']['user_id']为该原始微博的用户id；\n\nscreen_name：存储原始微博昵称。URL[i-1]['retweet']['screen_name']为该原始微博的昵称；\n\nid：存储原始微博id。URL[i-1]['retweet']['id']为该原始微博的id；\n\ntext：存储原始微博正文。URL[i-1]['retweet']['text']为该原始微博的正文；\n\narticle_url：存储原始微博中头条文章的url。如wb.weibo[i-1]['retweet']['article_url']为该原始微博的头条文章url，若原始微博中不存在头条文章，则该值为''；\n\npics：存储原始微博的原始图片url。URL[i-1]['retweet']['pics']为该原始微博的原始图片url，若该原始微博有多张图片，则存储多个url，以英文逗号分割；若该原始微博没有图片，则值为''；\n\nvideo_url：存储原始微博的视频url和原始微博Live Photo中的视频url。如wb.weibo[i-1]['retweet']['video_url']为该原始微博的视频url，若该原始微博有多个视频，则存储多个url，以英文分号分割；若该微博没有视频，则值为''；\n\nlocation：存储原始微博的发布位置。URL[i-1]['retweet']['location']为该原始微博的发布位置，若该原始微博没有位置信息，则值为''；\n\ncreated_at：存储原始微博的发布时间。URL[i-1]['retweet']['created_at']为该原始微博的发布时间；\n\nsource：存储原始微博的发布工具。URL[i-1]['retweet']['source']为该原始微博的发布工具；\n\nattitudes_count：存储原始微博获得的点赞数。URL[i-1]['retweet']['attitudes_count']为该原始微博获得的点赞数；\n\ncomments_count：存储原始微博获得的评论数。URL[i-1]['retweet']['comments_count']为该原始微博获得的评论数；\n\nreposts_count：存储原始微博获得的转发数。URL[i-1]['retweet']['reposts_count']为该原始微博获得的转发数；\n\ntopics：存储原始微博话题，即两个#中的内容。URL[i-1]['retweet']['topics']为该原始微博的话题，若该原始微博没有话题信息，则值为''；\n\nat_users：存储原始微博@的用户。URL[i-1]['retweet']['at_users']为该原始微博@的用户，若该原始微博没有@的用户，则值为''。\n\n</details>\n\n</details>", "### 7.定期自动爬取微博（可选）\n\n我们爬取了微博以后，很多微博账号又可能发了一些新微博，定期自动爬取微博就是每隔一段时间自动运行程序，自动爬取这段时间产生的新微博（忽略以前爬过的旧微博）。本部分为可选部分，如果不需要可以忽略。\n\n思路是利用第三方软件，如crontab，让程序每隔一段时间运行一次。因为是要跳过以前爬过的旧微博，只爬新微博。所以需要设置一个动态的since_date。很多时候我们使用的since_date是固定的，比如since_date=\"2018-01-01\"，程序就会按照这个设置从最新的微博一直爬到发布时间为2018-01-01的微博（包括这个时间）。因为我们想追加新微博，跳过旧微博。第二次爬取时since_date值就应该是当前时间到上次爬取的时间。 如果我们使用最原始的方式实现追加爬取，应该是这样：\n\n\n\n上面的方法太麻烦，因为每次都要手动设置since_date。因此我们需要动态设置since_date，即程序根据实际情况，自动生成since_date。\n有两种方法实现动态更新since_date：\n\n方法一：将since_date设置成整数\n\n将config.json文件中的since_date设置成整数，如：\n\n\n\n这个配置告诉程序爬取最近10天的微博，更准确说是爬取发布时间从10天前到本程序开始执行时之间的微博。这样since_date就是一个动态的变量，每次程序执行时，它的值就是当前日期减10。配合crontab每9天或10天执行一次，就实现了定期追加爬取。\n\n方法二：将上次执行程序的时间写入文件（推荐）\n\n这个方法很简单，就是用户把要爬的用户id写入txt文件，然后再把文件路径赋值给config.json中的user_id_list参数。\n\ntxt文件名格式可以参考程序设置中的设置user_id_list部分，这样设置就全部结束了。\n\n说下这个方法的原理和好处，假如你的txt文件内容为：\n\n\n\n第一次执行时，因为第一行和第二行都没有写时间，程序会按照config.json文件中since_date的值爬取，第三行有时间“2019-01-01”，程序就会把这个时间当作since_date。每个用户爬取结束程序都会自动更新txt文件，每一行第一部分是user_id，第二部分是用户昵称，第三部分是程序准备爬取该用户第一条微博（最新微博）时的日期。爬完三个用户后，txt文件的内容自动更新为：\n\n\n\n下次再爬取微博的时候，程序会把每行的时间数据作为since_date。这样的好处一是不用修改since_date，程序自动更新；二是每一个用户都可以单独拥有只属于自己的since_date，每个用户的since_date相互独立，互不干扰，格式为'yyyy-mm-dd'或整数。比如，现在又添加了一个新用户，以杨紫的微博为例，你想获取她2018-01-23到现在的全部微博，可以这样修改txt文件：\n\n\n\n注意每一行的用户配置参数以空格分隔，如果第一个参数全部由数字组成，程序就认为此行为一个用户的配置，否则程序会认为该行只是注释，跳过该行；第二个参数可以为任意格式，建议写用户昵称；第三个如果是日期格式（yyyy-mm-dd），程序就将该日期设置为用户自己的since_date，否则使用config.json中的since_date爬取该用户的微博，第二个参数和第三个参数也可以不填。\n也可以设置第四个参数，将被读取为query_list。\n\n方法三：将'URL'文件中的运行模式改为'append'\n\n以追加模式运行程序，每次运行，每个id只获取最新的微博，而不是全部，避免频繁备份微博导致过多的请求次数。\n\n注意：\n\n* 该模式会跳过置顶微博。\n* 若采集信息后用户又编辑微博，则不会记录编辑内容。", "## 如何获取user_id\n\n1.打开网址<URL>，搜索我们要找的人，如\"迪丽热巴\"，进入她的主页；\n\n![](URL\n2.按照上图箭头所指，点击\"资料\"链接，跳转到用户资料页面；\n\n![](URL\n如上图所示，迪丽热巴微博资料页的地址为\"<URL，其中的\"1669879400\"即为此微博的user_id。\n\n事实上，此微博的user_id也包含在用户主页(<URL)中，之所以我们还要点击主页中的\"资料\"来获取user_id，是因为很多用户的主页不是\"<URL\"的形式，而是\"<URL/个性域名?f=search_0>\"或\"<URL/微号?f=search_0>\"的形式。其中\"微号\"和user_id都是一串数字，如果仅仅通过主页地址提取user_id，很容易将\"微号\"误认为user_id。", "## 添加cookie与不添加cookie的区别（可选）\n\n对于微博数2000条及以下的微博用户，不添加cookie可以获取其用户信息和大部分微博；对于微博数2000条以上的微博用户，不添加cookie可以获取其用户信息和最近2000条微博中的大部分，添加cookie可以获取其全部微博。以2020年1月2日迪丽热巴的微博为例，此时她共有1085条微博，在不添加cookie的情况下，可以获取到1026条微博，大约占全部微博的94.56%，而在添加cookie后，可以获取全部微博。其他用户类似，大部分都可以在不添加cookie的情况下获取到90%以上的微博，在添加cookie后可以获取全部微博。具体原因是，大部分微博内容都可以在移动版匿名获取，少量微博需要用户登录才可以获取，所以这部分微博在不添加cookie时是无法获取的。\n\n有少部分微博用户，不添加cookie可以获取其微博，无法获取其用户信息。对于这种情况，要想获取其用户信息，是需要cookie的。\n\n如需抓取微博转发，请添加cookie。", "## 如何获取cookie（可选）\n\n1.用Chrome打开<URL；\n\n2.输入微博的用户名、密码，登录，如图所示：\n![](URL\n登录成功后会跳转到<https://m.URL>;\n\n3.按F12键打开Chrome开发者工具，在地址栏输入并跳转到<URL>，跳转后会显示如下类似界面:\n![](URL\n4.依此点击Chrome开发者工具中的Network->Name中的weibo.cn->Headers->Request Headers，\"Cookie:\"后的值即为我们要找的cookie值，复制即可，如图所示：\n![](URL", "## 如何检测cookie是否有效（可选）\n\n本程序cookie检查的逻辑是：使用cookie来源账号发布限定范围的微博，若cookie可用，则可以读取到该微博，否则读取不到。\n\n操作方法\n\n1. 使用cookie的来源账号发布一条微博，该账号和微博需要满足以下条件：\n\n * 该微博必须是非公开可见的，后续需要根据可见性判断cookie是否有效；\n\n * 该微博需要是最近5条微博，不能在发布测试用微博内容后又发很多新微博；\n\n * 在'URL'配置中的since_date之后，该账号必须有大于9条微博。\n\n2. 将'URL'文件中''CHECK': False'中的'False'改为'True'，''HIDDEN_WEIBO': '微博内容''中的'微博内容'改为你发的限定范围的微博。\n\n3. 将提供cookie的微博id放置在'URL'文件中'\"user_id_list\"'设置项数组中的第一个。例如提供cookie的微博id为'123456'，则'\"user_id_list\"'设置为'\"user_id_list\":[\"123456\", \"<其余id...>\"]'。\n\n注：本方法也将会抓取提供cookie账号的微博内容。\n\n在间歇运行程序时，cookie无效会导致程序不能按照预设目标执行，因此可以打开cookie通知功能。本项目使用开源项目pushdeer进行通知，在使用前用户需要申请push_key，具体可查看官网了解。打开方法为：\n\n1. 在'URL'文件中，将''NOTIFY': False'中的'False'设为'True'；\n2. 将''PUSH_KEY': '''的''''替换为''<你的push_key>''" ]
[ "TAGS\n#region-us \n", "## 功能\n\n连续爬取一个或多个新浪微博用户（如Dear-迪丽热巴、郭碧婷）的数据，并将结果信息写入文件。写入信息几乎包括了用户微博的所有数据，主要有用户信息和微博信息两大类，前者包含用户昵称、关注数、粉丝数、微博数等等；后者包含微博正文、发布时间、发布工具、评论数等等，因为内容太多，这里不再赘述，详细内容见输出部分。具体的写入文件类型如下：\n\n* 写入csv文件（默认）\n* 写入json文件（可选）\n* 写入MySQL数据库（可选）\n* 写入MongoDB数据库（可选）\n* 写入SQLite数据库（可选）\n* 下载用户原创微博中的原始图片（可选）\n* 下载用户转发微博中的原始图片（可选）\n* 下载用户原创微博中的视频（可选）\n* 下载用户转发微博中的视频（可选）\n* 下载用户原创微博Live Photo中的视频（可选）\n* 下载用户转发微博Live Photo中的视频（可选）\n* 下载用户原创和转发微博下的一级评论（可选）\n* 下载用户原创和转发微博下的转发（可选）\n\n如果你只对用户信息感兴趣，而不需要爬用户的微博，也可以通过设置实现只爬取微博用户信息的功能。程序也可以实现爬取结果自动更新，即：现在爬取了目标用户的微博，几天之后，目标用户可能又发新微博了。通过设置，可以实现每隔几天增量爬取用户这几天发的新微博。具体方法见定期自动爬取微博。", "## 输出\n\n用户信息\n\n* 用户id：微博用户id，如\"1669879400\"\n* 用户昵称：微博用户昵称，如\"Dear-迪丽热巴\"\n* 性别：微博用户性别\n* 生日：用户出生日期\n* 所在地：用户所在地\n* 教育经历：用户上学时学校的名字\n* 公司：用户所属公司名字\n* 阳光信用：用户的阳光信用\n* 微博注册时间：用户微博注册日期\n* 微博数：用户的全部微博数（转发微博+原创微博）\n* 粉丝数：用户的粉丝数\n* 关注数：用户关注的微博数量\n* 简介：用户简介\n* 主页地址：微博移动版主页url，如<https://m.URL\n* 头像url：用户头像url\n* 高清头像url：用户高清头像url\n* 微博等级：用户微博等级\n* 会员等级：微博会员用户等级，普通用户该等级为0\n* 是否认证：用户是否认证，为布尔类型\n* 认证类型：用户认证类型，如个人认证、企业认证、政府认证等\n* 认证信息：为认证用户特有，用户信息栏显示的认证信息\n\n*\n微博信息\n\n* 微博id：微博的id，为一串数字形式\n* 微博bid：微博的bid，与cookie版中的微博id是同一个值\n* 微博内容：微博正文\n* 头条文章url：微博中头条文章的url，如果微博中存在头条文章，就获取该头条文章的url，否则该值为''\n* 原始图片url：原创微博图片和转发微博转发理由中图片的url，若某条微博存在多张图片，则每个url以英文逗号分隔，若没有图片则值为''\n* 视频url: 微博中的视频url和Live Photo中的视频url，若某条微博存在多个视频，则每个url以英文分号分隔，若没有视频则值为''\n* 微博发布位置：位置微博中的发布位置\n* 微博发布时间：微博发布时的时间，精确到天\n* 点赞数：微博被赞的数量\n* 转发数：微博被转发的数量\n* 评论数：微博被评论的数量\n* 微博发布工具：微博的发布工具，如iPhone客户端、HUAWEI Mate 20 Pro等，若没有则值为''\n* 话题：微博话题，即两个#中的内容，若存在多个话题，每个url以英文逗号分隔，若没有则值为''\n* @用户：微博@的用户，若存在多个@用户，每个url以英文逗号分隔，若没有则值为''\n* 原始微博：为转发微博所特有，是转发微博中那条被转发的微博，存储为字典形式，包含了上述微博信息中的所有内容，如微博id、微博内容等等\n* 结果文件：保存在当前目录weibo文件夹下以用户昵称为名的文件夹里，名字为\"user_id.csv\"形式\n* 微博图片：微博中的图片，保存在以用户昵称为名的文件夹下的img文件夹里\n* 微博视频：微博中的视频，保存在以用户昵称为名的文件夹下的video文件夹里", "## 实例\n\n以爬取迪丽热巴的微博为例，我们需要修改URL文件，文件内容如下：\n\n\n\n对于上述参数的含义以及取值范围，这里仅作简单介绍，详细信息见程序设置。\n>user_id_list代表我们要爬取的微博用户的user_id，可以是一个或多个，也可以是文件路径，微博用户Dear-迪丽热巴的user_id为1669879400，具体如何获取user_id见如何获取user_id；\nfilter的值为1代表爬取全部原创微博，值为0代表爬取全部微博（原创+转发）；\nsince_date代表我们要爬取since_date日期之后发布的微博，因为我要爬迪丽热巴的全部原创微博，所以since_date设置了一个非常早的值；\nquery_list代表要爬取的微博关键词，为空（[]）则爬取全部；\nwrite_mode代表结果文件的保存类型，我想要把结果写入csv文件和json文件，所以它的值为[\"csv\", \"json\"]，如果你想写入数据库，具体设置见设置数据库；\noriginal_pic_download值为1代表下载原创微博中的图片，值为0代表不下载；\nretweet_pic_download值为1代表下载转发微博中的图片，值为0代表不下载；\noriginal_video_download值为1代表下载原创微博中的视频，值为0代表不下载；\nretweet_video_download值为1代表下载转发微博中的视频，值为0代表不下载；\ncookie是可选参数，可填可不填，具体区别见添加cookie与不添加cookie的区别。\n\n配置完成后运行程序：\n\n\n\n程序会自动生成一个weibo文件夹，我们以后爬取的所有微博都被存储在weibo文件夹里。然后程序在该文件夹下生成一个名为\"Dear-迪丽热巴\"的文件夹，迪丽热巴的所有微博爬取结果都在这里。\"Dear-迪丽热巴\"文件夹里包含一个csv文件、一个img文件夹和一个video文件夹，img文件夹用来存储下载到的图片，video文件夹用来存储下载到的视频。如果你设置了保存数据库功能，这些信息也会保存在数据库里，数据库设置见设置数据库部分。\n\ncsv文件结果如下所示：\n![](URL\n\n本csv文件是爬取“全部微博”(原创微博+转发微博)的结果文件。因为迪丽热巴很多微博本身都没有图片、发布工具、位置、话题和@用户等信息，所以当这些内容没有时对应位置为空。\"是否原创\"列用来标记是否为原创微博，\n当为转发微博时，文件中还包含转发微博的信息。为了简便起见，姑且将转发微博中被转发的原始微博称为源微博，它的用户id、昵称、微博id等都在名称前加上源字，以便与目标用户自己发的微博区分。对于转发微博，程序除了获取用户原创部分的信息，还会获取源用户id、源用户昵称、源微博id、源微博正文、源微博原始图片url、源微博位置、源微博日期、源微博工具、源微博点赞数、源微博评论数、源微博转发数、源微博话题、源微博@用户等信息。原创微博因为没有这些转发信息，所以对应位置为空。若爬取的是\"全部原创微博\"，则csv文件中不会包含\"是否原创\"及其之后的转发属性列；\n\n为了说明json结果文件格式，这里以迪丽热巴2019年12月27日到2019年12月28日发的2条微博为例。\n\njson结果文件格式如下：\n\n\n\n*URL*\n\n下载的图片如下所示：\n\n![](URL*img文件夹*\n\n本次下载了788张图片，大小一共1.21GB，包括她原创微博中的所有图片。图片名为yyyymmdd+微博id的形式，若某条微博存在多张图片，则图片名中还会包括它在微博图片中的序号。若某图片下载失败，程序则会以“weibo_id:pic_url”的形式将出错微博id和图片url写入同文件夹下的not_downloaded.txt里；若图片全部下载成功则不会生成not_downloaded.txt；\n\n下载的视频如下所示：\n![](URL*video文件夹*\n\n本次下载了66个视频，是她原创微博中的视频和原创微博Live Photo中的视频，视频名为yyyymmdd+微博id的形式。有三个视频因为网络原因下载失败，程序将它们的微博id和视频url分别以“weibo_id:video_url”的形式写到了同文件夹下的not_downloaded.txt里。\n\n因为我本地没有安装MySQL数据库和MongoDB数据库，所以暂时设置成不写入数据库。如果你想要将爬取结果写入数据库，只需要先安装数据库（MySQL或MongoDB），再安装对应包（pymysql或pymongo），然后将mysql_write或mongodb_write值设置为1即可。写入MySQL需要用户名、密码等配置信息，这些配置如何设置见设置数据库部分。", "## 运行环境\n\n* 开发语言：python2/python3\n\n* 系统： Windows/Linux/macOS", "## 使用说明", "### 1.下载脚本\n\n\n\n运行上述命令，将本项目下载到当前目录，如果下载成功当前目录会出现一个名为\"weibo-crawler\"的文件夹；", "### 2.安装依赖", "### 3.程序设置\n\n打开URL文件，你会看到如下内容：\n\n\n\n下面讲解每个参数的含义与设置方法。\n\n设置user_id_list\n\nuser_id_list是我们要爬取的微博的id，可以是一个，也可以是多个，例如：\n\n\n\n上述代码代表我们要连续爬取user_id分别为“1223178222”、 “1669879400”、 “1729370543”的三个用户的微博，具体如何获取user_id见如何获取user_id。\n\nuser_id_list的值也可以是文件路径，我们可以把要爬的所有微博用户的user_id都写到txt文件里，然后把文件的位置路径赋值给user_id_list。\n\n在txt文件中，每个user_id占一行，也可以在user_id后面加注释（可选），如用户昵称等信息，user_id和注释之间必需要有空格，文件名任意，类型为txt，位置位于本程序的同目录下，文件内容示例如下：\n\n\n\n假如文件叫user_id_list.txt，则user_id_list设置代码为：\n\n\n\n设置filter\n\nfilter控制爬取范围，值为1代表爬取全部原创微博，值为0代表爬取全部微博（原创+转发）。例如，如果要爬全部原创微博，请使用如下代码：\n\n\n\n设置since_date\n\nsince_date值可以是日期，也可以是整数。如果是日期，代表爬取该日期之后的微博，格式应为“yyyy-mm-dd”，如：\n\n\n\n代表爬取从2018年1月1日到现在的微博。\n\n如果是整数，代表爬取最近n天的微博，如:\n\n\n\n代表爬取最近10天的微博，这个说法不是特别准确，准确说是爬取发布时间从10天前到本程序开始执行时之间的微博。\n\nsince_date是所有user的爬取起始时间，非常不灵活。如果你要爬多个用户，并且想单独为每个用户设置一个since_date，可以使用定期自动爬取微博方法二中的方法，该方法可以为多个用户设置不同的since_date，非常灵活。\n\n设置query_list(可选)\n\nquery_list是一个关键词字符串列表或以','分隔关键词的字符串，用于指定关键词搜索爬取，若为空'[]'或'\"\"'则爬取全部微博。例如要爬取用户包含“梦想”和“希望”的微博，则设定如下：\n\n\n\n请注意，关键词搜索必须设定'cookie'信息。\nquery_list是所有user的爬取关键词，非常不灵活。如果你要爬多个用户，并且想单独为每个用户设置一个query_list，可以使用定期自动爬取微博方法二中的方法，该方法可以为多个用户设置不同的query_list，非常灵活。\n\n设置remove_html_tag\n\nremove_html_tag控制是否移除抓取到的weibo正文和评论中的html tag，值为1代表移除，值为0代表不移除，如\n\n\n\n代表移除html tag。例如'专属新意，色彩启程~<a href='/n/路易威登'>@路易威登</a> CAPUCINES 手袋正合我意，打开灵感包袋的搭配新方式！'会被处理成'专属新意，色彩启程~@路易威登 CAPUCINES 手袋正合我意，打开灵感包袋的搭配新方式！'。\n\n设置write_mode\n\nwrite_mode控制结果文件格式，取值范围是csv、json、mongo、mysql和sqlite，分别代表将结果文件写入csv、json、MongoDB、MySQL和SQLite数据库。write_mode可以同时包含这些取值中的一个或几个，如：\n\n\n\n代表将结果信息写入csv文件和json文件。特别注意，如果你想写入数据库，除了在write_mode添加对应数据库的名字外，还应该安装相关数据库和对应python模块，具体操作见设置数据库部分。\n\n设置original_pic_download\n\noriginal_pic_download控制是否下载原创微博中的图片，值为1代表下载，值为0代表不下载，如\n\n\n\n代表下载原创微博中的图片。\n\n设置retweet_pic_download\n\nretweet_pic_download控制是否下载转发微博中的图片，值为1代表下载，值为0代表不下载，如\n\n\n\n代表不下载转发微博中的图片。特别注意，本设置只有在爬全部微博（原创+转发），即filter值为0时生效，否则程序会跳过转发微博的图片下载。\n\n设置original_video_download\n\noriginal_video_download控制是否下载原创微博中的视频和原创微博Live Photo中的视频，值为1代表下载，值为0代表不下载，如\n\n\n\n代表下载原创微博中的视频和原创微博Live Photo中的视频。\n\n设置retweet_video_download\n\nretweet_video_download控制是否下载转发微博中的视频和转发微博Live Photo中的视频，值为1代表下载，值为0代表不下载，如\n\n\n\n代表不下载转发微博中的视频和转发微博Live Photo中的视频。特别注意，本设置只有在爬全部微博（原创+转发），即filter值为0时生效，否则程序会跳过转发微博的视频下载。\n\n设置result_dir_name\n\nresult_dir_name控制结果文件的目录名，可取值为0和1，默认为0：\n\n\n\n值为0，表示将结果文件保存在以用户昵称为名的文件夹里，这样结果更清晰；值为1表示将结果文件保存在以用户id为名的文件夹里，这样能保证多次爬取的一致性，因为用户昵称可变，用户id不可变。\n\n设置download_comment\n\ndownload_comment控制是否下载每条微博下的一级评论（不包括对评论的评论），仅当write_mode中有sqlite时有效，可取值为0和1，默认为1：\n\n\n\n值为1，表示下载微博评论；值为0，表示不下载微博评论。\n\n设置comment_max_download_count\n\ncomment_max_download_count控制下载评论的最大数量，仅当write_mode中有sqlite时有效，默认为1000：\n\n\n\n设置download_repost\n\ndownload_repost控制是否下载每条微博下的转发，仅当write_mode中有sqlite时有效，可取值为0和1，默认为1：\n\n\n\n值为1，表示下载微博转发；值为0，表示不下载微博转发。\n\n设置repost_max_download_count\n\nrepost_max_download_count控制下载转发的最大数量，仅当write_mode中有sqlite时有效，默认为1000：\n\n\n\n值为1000，表示最多下载每条微博下的1000条转发。\n\n设置cookie（可选）\n\ncookie为可选参数，即可填可不填，具体区别见添加cookie与不添加cookie的区别。cookie默认配置如下：\n\n\n\n如果想要设置cookie，可以按照如何获取cookie中的方法，获取cookie，并将上面的\"your cookie\"替换成真实的cookie即可。\n\n设置mysql_config（可选）\n\nmysql_config控制mysql参数配置。如果你不需要将结果信息写入mysql，这个参数可以忽略，即删除或保留都无所谓；如果你需要写入mysql且config.json文件中mysql_config的配置与你的mysql配置不一样，请将该值改成你自己mysql中的参数配置。\n设置start_page（可选）\n\nstart_page为爬取微博的初始页数，默认参数为1，即从所爬取用户的当前第一页微博内容开始爬取。\n若在大批量爬取微博时出现中途被限制中断的情况，可通过查看csv文件内目前已爬取到的微博数除以10，向下取整后的值即为中断页数，手动设置start_page参数为中断页数，重新运行即可从被中断的节点继续爬取剩余微博内容。", "### 4.设置数据库（可选）\n\n本部分是可选部分，如果不需要将爬取信息写入数据库，可跳过这一步。本程序目前支持MySQL数据库和MongoDB数据库，如果你需要写入其它数据库，可以参考这两个数据库的写法自己编写。\n\nMySQL数据库写入\n\n要想将爬取信息写入MySQL，请根据自己的系统环境安装MySQL，然后命令行执行：\n\n\n\nMongoDB数据库写入\n\n要想将爬取信息写入MongoDB，请根据自己的系统环境安装MongoDB，然后命令行执行：\n\n\n\nMySQL和MongDB数据库的写入内容一样。程序首先会创建一个名为\"weibo\"的数据库，然后再创建\"user\"表和\"weibo\"表，包含爬取的所有内容。爬取到的微博用户信息或插入或更新，都会存储到user表里；爬取到的微博信息或插入或更新，都会存储到weibo表里，两个表通过user_id关联。如果想了解两个表的具体字段，请点击\"详情\"。\n<details>\n\n<summary>详情</summary>\n\nuser表\n\nid：微博用户id，如\"1669879400\"；\n\nscreen_name：微博用户昵称，如\"Dear-迪丽热巴\"；\n\ngender：微博用户性别，取值为f或m，分别代表女和男；\n\nbirthday：生日；\n\nlocation：所在地；\n\neducation：教育经历；\n\ncompany：公司；\n\nsunshine：阳光信用；\n\nregistration_time：注册时间；\n\nstatuses_count：微博数；\n\nfollowers_count：粉丝数；\n\nfollow_count：关注数；\n\ndescription：微博简介；\n\nprofile_url：微博主页，如<https://m.URL\n\nprofile_image_url：微博头像url；\n\navatar_hd：微博高清头像url；\n\nurank：微博等级；\n\nmbrank：微博会员等级，普通用户会员等级为0；\n\nverified：微博是否认证，取值为true和false；\n\nverified_type：微博认证类型，没有认证值为-1，个人认证值为0，企业认证值为2，政府认证值为3，这些类型仅是个人猜测，应该不全，大家可以根据实际情况判断；\n\nverified_reason：微博认证信息，只有认证用户拥有此属性。\n\n*\nweibo表\n\nuser_id：存储微博用户id，如\"1669879400\"；\n\nscreen_name：存储微博昵称，如\"Dear-迪丽热巴\"；\n\nid：存储微博id；\n\ntext：存储微博正文；\n\narticle_url：存储微博中头条文章的url，如果微博中存在头条文章，就获取该头条文章的url，否则该值为''；\n\npics：存储原创微博的原始图片url。若某条微博有多张图片，则存储多个url，以英文逗号分割；若该微博没有图片，则值为''；\n\nvideo_url：存储原创微博的视频url和Live Photo中的视频url。若某条微博有多个视频，则存储多个url，以英文分号分割；若该微博没有视频，则值为''；\n\nlocation：存储微博的发布位置。若某条微博没有位置信息，则值为''；\n\ncreated_at：存储微博的发布时间；\n\nsource：存储微博的发布工具；\n\nattitudes_count：存储微博获得的点赞数；\n\ncomments_count：存储微博获得的评论数；\n\nreposts_count：存储微博获得的转发数；\n\ntopics：存储微博话题，即两个#中的内容。若某条微博没有话题信息，则值为''；\n\nat_users：存储微博@的用户。若某条微博没有@的用户，则值为''；\n\nretweet_id：存储转发微博中原始微博的微博id。若某条微博为原创微博，则值为''。\n\n</details>\nSQLite数据库写入\n\n脚本会自动建立并配置数据库文件'URL'。", "### 5.运行脚本\n\n大家可以根据自己的运行环境选择运行方式，Linux可以通过\n\n\n\n运行;", "### 6.按需求修改脚本（可选）\n\n本部分为可选部分，如果你不需要自己修改代码或添加新功能，可以忽略此部分。\n\n本程序所有代码都位于weibo.py文件，程序主体是一个Weibo类，上述所有功能都是通过在main函数调用Weibo类实现的，默认的调用代码如下：\n\n\n\n用户可以按照自己的需求调用或修改Weibo类。\n\n通过执行本程序，我们可以得到很多信息：\n\nURL：存储目标微博用户信息；\n\nwb.user包含爬取到的微博用户信息，如用户id、用户昵称、性别、生日、所在地、教育经历、公司、阳光信用、微博注册时间、微博数、粉丝数、关注数、简介、主页地址、头像url、高清头像url、微博等级、会员等级、是否认证、认证类型、认证信息等，大家可以点击\"详情\"查看具体用法。\n\n<details>\n\n<summary>详情</summary>\n\nid：微博用户id，取值方式为wb.user['id'],由一串数字组成；\n\nscreen_name：微博用户昵称，取值方式为wb.user['screen_name']；\n\ngender：微博用户性别，取值方式为wb.user['gender']，取值为f或m，分别代表女和男；\n\nbirthday：微博用户生日，取值方式为wb.user['birthday']，若用户没有填写该信息，则值为''；\n\nlocation：微博用户所在地，取值方式为wb.user['location']，若用户没有填写该信息，则值为''；\n\neducation：微博用户上学时的学校，取值方式为wb.user['education']，若用户没有填写该信息，则值为''；\n\ncompany：微博用户所属的公司，取值方式为wb.user['company']，若用户没有填写该信息，则值为''；\n\nsunshine：微博用户的阳光信用，取值方式为wb.user['sunshine']；\n\nregistration_time：微博用户的注册时间，取值方式为wb.user['registration_time']；\n\nstatuses_count：微博数，取值方式为wb.user['statuses_count']；\n\nfollowers_count：微博粉丝数，取值方式为wb.user['followers_count']；\n\nfollow_count：微博关注数，取值方式为wb.user['follow_count']；\n\ndescription：微博简介，取值方式为wb.user['description']；\n\nprofile_url：微博主页，取值方式为wb.user['profile_url'];\n\nprofile_image_url：微博头像url，取值方式为wb.user['profile_image_url']；\n\navatar_hd：微博高清头像url，取值方式为wb.user['avatar_hd']；\n\nurank：微博等级，取值方式为wb.user['urank']；\n\nmbrank：微博会员等级，取值方式为wb.user['mbrank']，普通用户会员等级为0；\n\nverified：微博是否认证，取值方式为wb.user['verified']，取值为true和false；\n\nverified_type：微博认证类型，取值方式为wb.user['verified_type']，没有认证值为-1，个人认证值为0，企业认证值为2，政府认证值为3，这些类型仅是个人猜测，应该不全，大家可以根据实际情况判断；\n\nverified_reason：微博认证信息，取值方式为wb.user['verified_reason']，只有认证用户拥有此属性。\n\n</details>\n\nURL：存储爬取到的所有微博信息；\n\nwb.weibo包含爬取到的所有微博信息，如微博id、正文、原始图片url、视频url、位置、日期、发布工具、点赞数、转发数、评论数、话题、@用户等。如果爬的是全部微博(原创+转发)，除上述信息之外，还包含原始用户id、原始用户昵称、原始微博id、原始微博正文、原始微博原始图片url、原始微博位置、原始微博日期、原始微博工具、原始微博点赞数、原始微博评论数、原始微博转发数、原始微博话题、原始微博@用户等信息。wb.weibo是一个列表，包含了爬取的所有微博信息。URL[0]为爬取的第一条微博，URL[1]为爬取的第二条微博，以此类推。当filter=1时，URL[0]为爬取的第一条原创微博，以此类推。URL[0]['id']为第一条微博的id，URL[0]['text']为第一条微博的正文，URL[0]['created_at']为第一条微博的发布时间，还有其它很多信息不在赘述，大家可以点击下面的\"详情\"查看具体用法。\n<details>\n\n<summary>详情</summary>\n\nuser_id：存储微博用户id。如wb.weibo[0]['user_id']为最新一条微博的用户id；\n\nscreen_name：存储微博昵称。如wb.weibo[0]['screen_name']为最新一条微博的昵称；\n\nid：存储微博id。如wb.weibo[0]['id']为最新一条微博的id；\n\ntext：存储微博正文。如wb.weibo[0]['text']为最新一条微博的正文；\n\narticle_url：存储微博中头条文章的url。如wb.weibo[0]['article_url']为最新一条微博的头条文章url，若微博中不存在头条文章，则该值为''；\n\npics：存储原创微博的原始图片url。如wb.weibo[0]['pics']为最新一条微博的原始图片url，若该条微博有多张图片，则存储多个url，以英文逗号分割；若该微博没有图片，则值为''；\n\nvideo_url：存储原创微博的视频url和原创微博Live Photo中的视频url。如wb.weibo[0]['video_url']为最新一条微博的视频url，若该条微博有多个视频，则存储多个url，以英文分号分割；若该微博没有视频，则值为''；\n\nlocation：存储微博的发布位置。如wb.weibo[0]['location']为最新一条微博的发布位置，若该条微博没有位置信息，则值为''；\n\ncreated_at：存储微博的发布时间。如wb.weibo[0]['created_at']为最新一条微博的发布时间；\n\nsource：存储微博的发布工具。如wb.weibo[0]['source']为最新一条微博的发布工具；\n\nattitudes_count：存储微博获得的点赞数。如wb.weibo[0]['attitudes_count']为最新一条微博获得的点赞数；\n\ncomments_count：存储微博获得的评论数。如wb.weibo[0]['comments_count']为最新一条微博获得的评论数；\n\nreposts_count：存储微博获得的转发数。如wb.weibo[0]['reposts_count']为最新一条微博获得的转发数；\n\ntopics：存储微博话题，即两个#中的内容。如wb.weibo[0]['topics']为最新一条微博的话题，若该条微博没有话题信息，则值为''；\n\nat_users：存储微博@的用户。如wb.weibo[0]['at_users']为最新一条微博@的用户，若该条微博没有@的用户，则值为''；\n\nretweet：存储转发微博中原始微博的全部信息。假如wb.weibo[0]为转发微博，则wb.weibo[0]['retweet']为该转发微博的原始微博，它存储的属性与wb.weibo[0]一样，只是没有retweet属性;若该条微博为原创微博，则wb[0]没有\"retweet\"属性，大家可以点击\"详情\"查看具体用法。\n\n<details>\n\n<summary>详情</summary>\n\n假设爬取到的第i条微博为转发微博，则它存在以下信息：\n\nuser_id：存储原始微博用户id。URL[i-1]['retweet']['user_id']为该原始微博的用户id；\n\nscreen_name：存储原始微博昵称。URL[i-1]['retweet']['screen_name']为该原始微博的昵称；\n\nid：存储原始微博id。URL[i-1]['retweet']['id']为该原始微博的id；\n\ntext：存储原始微博正文。URL[i-1]['retweet']['text']为该原始微博的正文；\n\narticle_url：存储原始微博中头条文章的url。如wb.weibo[i-1]['retweet']['article_url']为该原始微博的头条文章url，若原始微博中不存在头条文章，则该值为''；\n\npics：存储原始微博的原始图片url。URL[i-1]['retweet']['pics']为该原始微博的原始图片url，若该原始微博有多张图片，则存储多个url，以英文逗号分割；若该原始微博没有图片，则值为''；\n\nvideo_url：存储原始微博的视频url和原始微博Live Photo中的视频url。如wb.weibo[i-1]['retweet']['video_url']为该原始微博的视频url，若该原始微博有多个视频，则存储多个url，以英文分号分割；若该微博没有视频，则值为''；\n\nlocation：存储原始微博的发布位置。URL[i-1]['retweet']['location']为该原始微博的发布位置，若该原始微博没有位置信息，则值为''；\n\ncreated_at：存储原始微博的发布时间。URL[i-1]['retweet']['created_at']为该原始微博的发布时间；\n\nsource：存储原始微博的发布工具。URL[i-1]['retweet']['source']为该原始微博的发布工具；\n\nattitudes_count：存储原始微博获得的点赞数。URL[i-1]['retweet']['attitudes_count']为该原始微博获得的点赞数；\n\ncomments_count：存储原始微博获得的评论数。URL[i-1]['retweet']['comments_count']为该原始微博获得的评论数；\n\nreposts_count：存储原始微博获得的转发数。URL[i-1]['retweet']['reposts_count']为该原始微博获得的转发数；\n\ntopics：存储原始微博话题，即两个#中的内容。URL[i-1]['retweet']['topics']为该原始微博的话题，若该原始微博没有话题信息，则值为''；\n\nat_users：存储原始微博@的用户。URL[i-1]['retweet']['at_users']为该原始微博@的用户，若该原始微博没有@的用户，则值为''。\n\n</details>\n\n</details>", "### 7.定期自动爬取微博（可选）\n\n我们爬取了微博以后，很多微博账号又可能发了一些新微博，定期自动爬取微博就是每隔一段时间自动运行程序，自动爬取这段时间产生的新微博（忽略以前爬过的旧微博）。本部分为可选部分，如果不需要可以忽略。\n\n思路是利用第三方软件，如crontab，让程序每隔一段时间运行一次。因为是要跳过以前爬过的旧微博，只爬新微博。所以需要设置一个动态的since_date。很多时候我们使用的since_date是固定的，比如since_date=\"2018-01-01\"，程序就会按照这个设置从最新的微博一直爬到发布时间为2018-01-01的微博（包括这个时间）。因为我们想追加新微博，跳过旧微博。第二次爬取时since_date值就应该是当前时间到上次爬取的时间。 如果我们使用最原始的方式实现追加爬取，应该是这样：\n\n\n\n上面的方法太麻烦，因为每次都要手动设置since_date。因此我们需要动态设置since_date，即程序根据实际情况，自动生成since_date。\n有两种方法实现动态更新since_date：\n\n方法一：将since_date设置成整数\n\n将config.json文件中的since_date设置成整数，如：\n\n\n\n这个配置告诉程序爬取最近10天的微博，更准确说是爬取发布时间从10天前到本程序开始执行时之间的微博。这样since_date就是一个动态的变量，每次程序执行时，它的值就是当前日期减10。配合crontab每9天或10天执行一次，就实现了定期追加爬取。\n\n方法二：将上次执行程序的时间写入文件（推荐）\n\n这个方法很简单，就是用户把要爬的用户id写入txt文件，然后再把文件路径赋值给config.json中的user_id_list参数。\n\ntxt文件名格式可以参考程序设置中的设置user_id_list部分，这样设置就全部结束了。\n\n说下这个方法的原理和好处，假如你的txt文件内容为：\n\n\n\n第一次执行时，因为第一行和第二行都没有写时间，程序会按照config.json文件中since_date的值爬取，第三行有时间“2019-01-01”，程序就会把这个时间当作since_date。每个用户爬取结束程序都会自动更新txt文件，每一行第一部分是user_id，第二部分是用户昵称，第三部分是程序准备爬取该用户第一条微博（最新微博）时的日期。爬完三个用户后，txt文件的内容自动更新为：\n\n\n\n下次再爬取微博的时候，程序会把每行的时间数据作为since_date。这样的好处一是不用修改since_date，程序自动更新；二是每一个用户都可以单独拥有只属于自己的since_date，每个用户的since_date相互独立，互不干扰，格式为'yyyy-mm-dd'或整数。比如，现在又添加了一个新用户，以杨紫的微博为例，你想获取她2018-01-23到现在的全部微博，可以这样修改txt文件：\n\n\n\n注意每一行的用户配置参数以空格分隔，如果第一个参数全部由数字组成，程序就认为此行为一个用户的配置，否则程序会认为该行只是注释，跳过该行；第二个参数可以为任意格式，建议写用户昵称；第三个如果是日期格式（yyyy-mm-dd），程序就将该日期设置为用户自己的since_date，否则使用config.json中的since_date爬取该用户的微博，第二个参数和第三个参数也可以不填。\n也可以设置第四个参数，将被读取为query_list。\n\n方法三：将'URL'文件中的运行模式改为'append'\n\n以追加模式运行程序，每次运行，每个id只获取最新的微博，而不是全部，避免频繁备份微博导致过多的请求次数。\n\n注意：\n\n* 该模式会跳过置顶微博。\n* 若采集信息后用户又编辑微博，则不会记录编辑内容。", "## 如何获取user_id\n\n1.打开网址<URL>，搜索我们要找的人，如\"迪丽热巴\"，进入她的主页；\n\n![](URL\n2.按照上图箭头所指，点击\"资料\"链接，跳转到用户资料页面；\n\n![](URL\n如上图所示，迪丽热巴微博资料页的地址为\"<URL，其中的\"1669879400\"即为此微博的user_id。\n\n事实上，此微博的user_id也包含在用户主页(<URL)中，之所以我们还要点击主页中的\"资料\"来获取user_id，是因为很多用户的主页不是\"<URL\"的形式，而是\"<URL/个性域名?f=search_0>\"或\"<URL/微号?f=search_0>\"的形式。其中\"微号\"和user_id都是一串数字，如果仅仅通过主页地址提取user_id，很容易将\"微号\"误认为user_id。", "## 添加cookie与不添加cookie的区别（可选）\n\n对于微博数2000条及以下的微博用户，不添加cookie可以获取其用户信息和大部分微博；对于微博数2000条以上的微博用户，不添加cookie可以获取其用户信息和最近2000条微博中的大部分，添加cookie可以获取其全部微博。以2020年1月2日迪丽热巴的微博为例，此时她共有1085条微博，在不添加cookie的情况下，可以获取到1026条微博，大约占全部微博的94.56%，而在添加cookie后，可以获取全部微博。其他用户类似，大部分都可以在不添加cookie的情况下获取到90%以上的微博，在添加cookie后可以获取全部微博。具体原因是，大部分微博内容都可以在移动版匿名获取，少量微博需要用户登录才可以获取，所以这部分微博在不添加cookie时是无法获取的。\n\n有少部分微博用户，不添加cookie可以获取其微博，无法获取其用户信息。对于这种情况，要想获取其用户信息，是需要cookie的。\n\n如需抓取微博转发，请添加cookie。", "## 如何获取cookie（可选）\n\n1.用Chrome打开<URL；\n\n2.输入微博的用户名、密码，登录，如图所示：\n![](URL\n登录成功后会跳转到<https://m.URL>;\n\n3.按F12键打开Chrome开发者工具，在地址栏输入并跳转到<URL>，跳转后会显示如下类似界面:\n![](URL\n4.依此点击Chrome开发者工具中的Network->Name中的weibo.cn->Headers->Request Headers，\"Cookie:\"后的值即为我们要找的cookie值，复制即可，如图所示：\n![](URL", "## 如何检测cookie是否有效（可选）\n\n本程序cookie检查的逻辑是：使用cookie来源账号发布限定范围的微博，若cookie可用，则可以读取到该微博，否则读取不到。\n\n操作方法\n\n1. 使用cookie的来源账号发布一条微博，该账号和微博需要满足以下条件：\n\n * 该微博必须是非公开可见的，后续需要根据可见性判断cookie是否有效；\n\n * 该微博需要是最近5条微博，不能在发布测试用微博内容后又发很多新微博；\n\n * 在'URL'配置中的since_date之后，该账号必须有大于9条微博。\n\n2. 将'URL'文件中''CHECK': False'中的'False'改为'True'，''HIDDEN_WEIBO': '微博内容''中的'微博内容'改为你发的限定范围的微博。\n\n3. 将提供cookie的微博id放置在'URL'文件中'\"user_id_list\"'设置项数组中的第一个。例如提供cookie的微博id为'123456'，则'\"user_id_list\"'设置为'\"user_id_list\":[\"123456\", \"<其余id...>\"]'。\n\n注：本方法也将会抓取提供cookie账号的微博内容。\n\n在间歇运行程序时，cookie无效会导致程序不能按照预设目标执行，因此可以打开cookie通知功能。本项目使用开源项目pushdeer进行通知，在使用前用户需要申请push_key，具体可查看官网了解。打开方法为：\n\n1. 在'URL'文件中，将''NOTIFY': False'中的'False'设为'True'；\n2. 将''PUSH_KEY': '''的''''替换为''<你的push_key>''" ]
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[ "passage: TAGS\n#region-us \n## 功能\n\n连续爬取一个或多个新浪微博用户（如Dear-迪丽热巴、郭碧婷）的数据，并将结果信息写入文件。写入信息几乎包括了用户微博的所有数据，主要有用户信息和微博信息两大类，前者包含用户昵称、关注数、粉丝数、微博数等等；后者包含微博正文、发布时间、发布工具、评论数等等，因为内容太多，这里不再赘述，详细内容见输出部分。具体的写入文件类型如下：\n\n* 写入csv文件（默认）\n* 写入json文件（可选）\n* 写入MySQL数据库（可选）\n* 写入MongoDB数据库（可选）\n* 写入SQLite数据库（可选）\n* 下载用户原创微博中的原始图片（可选）\n* 下载用户转发微博中的原始图片（可选）\n* 下载用户原创微博中的视频（可选）\n* 下载用户转发微博中的视频（可选）\n* 下载用户原创微博Live Photo中的视频（可选）\n* 下载用户转发微博Live Photo中的视频（可选）\n* 下载用户原创和转发微博下的一级评论（可选）\n* 下载用户原创和转发微博下的转发（可选）\n\n如果你只对用户信息感兴趣，而不需要爬用户的微博，也可以通过设置实现只爬取微博用户信息的功能。程序也可以实现爬取结果自动更新，即：现在爬取了目标用户的微博，几天之后，目标用户可能又发新微博了。通过设置，可以实现每隔几天增量爬取用户这几天发的新微博。具体方法见定期自动爬取微博。", "passage: ## 输出\n\n用户信息\n\n* 用户id：微博用户id，如\"1669879400\"\n* 用户昵称：微博用户昵称，如\"Dear-迪丽热巴\"\n* 性别：微博用户性别\n* 生日：用户出生日期\n* 所在地：用户所在地\n* 教育经历：用户上学时学校的名字\n* 公司：用户所属公司名字\n* 阳光信用：用户的阳光信用\n* 微博注册时间：用户微博注册日期\n* 微博数：用户的全部微博数（转发微博+原创微博）\n* 粉丝数：用户的粉丝数\n* 关注数：用户关注的微博数量\n* 简介：用户简介\n* 主页地址：微博移动版主页url，如<https://m.URL\n* 头像url：用户头像url\n* 高清头像url：用户高清头像url\n* 微博等级：用户微博等级\n* 会员等级：微博会员用户等级，普通用户该等级为0\n* 是否认证：用户是否认证，为布尔类型\n* 认证类型：用户认证类型，如个人认证、企业认证、政府认证等\n* 认证信息：为认证用户特有，用户信息栏显示的认证信息\n\n*\n微博信息\n\n* 微博id：微博的id，为一串数字形式\n* 微博bid：微博的bid，与cookie版中的微博id是同一个值\n* 微博内容：微博正文\n* 头条文章url：微博中头条文章的url，如果微博中存在头条文章，就获取该头条文章的url，否则该值为''\n* 原始图片url：原创微博图片和转发微博转发理由中图片的url，若某条微博存在多张图片，则每个url以英文逗号分隔，若没有图片则值为''\n* 视频url: 微博中的视频url和Live Photo中的视频url，若某条微博存在多个视频，则每个url以英文分号分隔，若没有视频则值为''\n* 微博发布位置：位置微博中的发布位置\n* 微博发布时间：微博发布时的时间，精确到天\n* 点赞数：微博被赞的数量\n* 转发数：微博被转发的数量\n* 评论数：微博被评论的数量\n* 微博发布工具：微博的发布工具，如iPhone客户端、HUAWEI Mate 20 Pro等，若没有则值为''\n* 话题：微博话题，即两个#中的内容，若存在多个话题，每个url以英文逗号分隔，若没有则值为''\n* @用户：微博@的用户，若存在多个@用户，每个url以英文逗号分隔，若没有则值为''\n* 原始微博：为转发微博所特有，是转发微博中那条被转发的微博，存储为字典形式，包含了上述微博信息中的所有内容，如微博id、微博内容等等\n* 结果文件：保存在当前目录weibo文件夹下以用户昵称为名的文件夹里，名字为\"user_id.csv\"形式\n* 微博图片：微博中的图片，保存在以用户昵称为名的文件夹下的img文件夹里\n* 微博视频：微博中的视频，保存在以用户昵称为名的文件夹下的video文件夹里", "passage: ## 实例\n\n以爬取迪丽热巴的微博为例，我们需要修改URL文件，文件内容如下：\n\n\n\n对于上述参数的含义以及取值范围，这里仅作简单介绍，详细信息见程序设置。\n>user_id_list代表我们要爬取的微博用户的user_id，可以是一个或多个，也可以是文件路径，微博用户Dear-迪丽热巴的user_id为1669879400，具体如何获取user_id见如何获取user_id；\nfilter的值为1代表爬取全部原创微博，值为0代表爬取全部微博（原创+转发）；\nsince_date代表我们要爬取since_date日期之后发布的微博，因为我要爬迪丽热巴的全部原创微博，所以since_date设置了一个非常早的值；\nquery_list代表要爬取的微博关键词，为空（[]）则爬取全部；\nwrite_mode代表结果文件的保存类型，我想要把结果写入csv文件和json文件，所以它的值为[\"csv\", \"json\"]，如果你想写入数据库，具体设置见设置数据库；\noriginal_pic_download值为1代表下载原创微博中的图片，值为0代表不下载；\nretweet_pic_download值为1代表下载转发微博中的图片，值为0代表不下载；\noriginal_video_download值为1代表下载原创微博中的视频，值为0代表不下载；\nretweet_video_download值为1代表下载转发微博中的视频，值为0代表不下载；\ncookie是可选参数，可填可不填，具体区别见添加cookie与不添加cookie的区别。\n\n配置完成后运行程序：\n\n\n\n程序会自动生成一个weibo文件夹，我们以后爬取的所有微博都被存储在weibo文件夹里。然后程序在该文件夹下生成一个名为\"Dear-迪丽热巴\"的文件夹，迪丽热巴的所有微博爬取结果都在这里。\"Dear-迪丽热巴\"文件夹里包含一个csv文件、一个img文件夹和一个video文件夹，img文件夹用来存储下载到的图片，video文件夹用来存储下载到的视频。如果你设置了保存数据库功能，这些信息也会保存在数据库里，数据库设置见设置数据库部分。\n\ncsv文件结果如下所示：\n![](URL\n\n本csv文件是爬取“全部微博”(原创微博+转发微博)的结果文件。因为迪丽热巴很多微博本身都没有图片、发布工具、位置、话题和@用户等信息，所以当这些内容没有时对应位置为空。\"是否原创\"列用来标记是否为原创微博，\n当为转发微博时，文件中还包含转发微博的信息。为了简便起见，姑且将转发微博中被转发的原始微博称为源微博，它的用户id、昵称、微博id等都在名称前加上源字，以便与目标用户自己发的微博区分。对于转发微博，程序除了获取用户原创部分的信息，还会获取源用户id、源用户昵称、源微博id、源微博正文、源微博原始图片url、源微博位置、源微博日期、源微博工具、源微博点赞数、源微博评论数、源微博转发数、源微博话题、源微博@用户等信息。原创微博因为没有这些转发信息，所以对应位置为空。若爬取的是\"全部原创微博\"，则csv文件中不会包含\"是否原创\"及其之后的转发属性列；\n\n为了说明json结果文件格式，这里以迪丽热巴2019年12月27日到2019年12月28日发的2条微博为例。\n\njson结果文件格式如下：\n\n\n\n*URL*\n\n下载的图片如下所示：\n\n![](URL*img文件夹*\n\n本次下载了788张图片，大小一共1.21GB，包括她原创微博中的所有图片。图片名为yyyymmdd+微博id的形式，若某条微博存在多张图片，则图片名中还会包括它在微博图片中的序号。若某图片下载失败，程序则会以“weibo_id:pic_url”的形式将出错微博id和图片url写入同文件夹下的not_downloaded.txt里；若图片全部下载成功则不会生成not_downloaded.txt；\n\n下载的视频如下所示：\n![](URL*video文件夹*\n\n本次下载了66个视频，是她原创微博中的视频和原创微博Live Photo中的视频，视频名为yyyymmdd+微博id的形式。有三个视频因为网络原因下载失败，程序将它们的微博id和视频url分别以“weibo_id:video_url”的形式写到了同文件夹下的not_downloaded.txt里。\n\n因为我本地没有安装MySQL数据库和MongoDB数据库，所以暂时设置成不写入数据库。如果你想要将爬取结果写入数据库，只需要先安装数据库（MySQL或MongoDB），再安装对应包（pymysql或pymongo），然后将mysql_write或mongodb_write值设置为1即可。写入MySQL需要用户名、密码等配置信息，这些配置如何设置见设置数据库部分。## 运行环境\n\n* 开发语言：python2/python3\n\n* 系统： Windows/Linux/macOS## 使用说明### 1.下载脚本\n\n\n\n运行上述命令，将本项目下载到当前目录，如果下载成功当前目录会出现一个名为\"weibo-crawler\"的文件夹；### 2.安装依赖", "passage: ### 3.程序设置\n\n打开URL文件，你会看到如下内容：\n\n\n\n下面讲解每个参数的含义与设置方法。\n\n设置user_id_list\n\nuser_id_list是我们要爬取的微博的id，可以是一个，也可以是多个，例如：\n\n\n\n上述代码代表我们要连续爬取user_id分别为“1223178222”、 “1669879400”、 “1729370543”的三个用户的微博，具体如何获取user_id见如何获取user_id。\n\nuser_id_list的值也可以是文件路径，我们可以把要爬的所有微博用户的user_id都写到txt文件里，然后把文件的位置路径赋值给user_id_list。\n\n在txt文件中，每个user_id占一行，也可以在user_id后面加注释（可选），如用户昵称等信息，user_id和注释之间必需要有空格，文件名任意，类型为txt，位置位于本程序的同目录下，文件内容示例如下：\n\n\n\n假如文件叫user_id_list.txt，则user_id_list设置代码为：\n\n\n\n设置filter\n\nfilter控制爬取范围，值为1代表爬取全部原创微博，值为0代表爬取全部微博（原创+转发）。例如，如果要爬全部原创微博，请使用如下代码：\n\n\n\n设置since_date\n\nsince_date值可以是日期，也可以是整数。如果是日期，代表爬取该日期之后的微博，格式应为“yyyy-mm-dd”，如：\n\n\n\n代表爬取从2018年1月1日到现在的微博。\n\n如果是整数，代表爬取最近n天的微博，如:\n\n\n\n代表爬取最近10天的微博，这个说法不是特别准确，准确说是爬取发布时间从10天前到本程序开始执行时之间的微博。\n\nsince_date是所有user的爬取起始时间，非常不灵活。如果你要爬多个用户，并且想单独为每个用户设置一个since_date，可以使用定期自动爬取微博方法二中的方法，该方法可以为多个用户设置不同的since_date，非常灵活。\n\n设置query_list(可选)\n\nquery_list是一个关键词字符串列表或以','分隔关键词的字符串，用于指定关键词搜索爬取，若为空'[]'或'\"\"'则爬取全部微博。例如要爬取用户包含“梦想”和“希望”的微博，则设定如下：\n\n\n\n请注意，关键词搜索必须设定'cookie'信息。\nquery_list是所有user的爬取关键词，非常不灵活。如果你要爬多个用户，并且想单独为每个用户设置一个query_list，可以使用定期自动爬取微博方法二中的方法，该方法可以为多个用户设置不同的query_list，非常灵活。\n\n设置remove_html_tag\n\nremove_html_tag控制是否移除抓取到的weibo正文和评论中的html tag，值为1代表移除，值为0代表不移除，如\n\n\n\n代表移除html tag。例如'专属新意，色彩启程~<a href='/n/路易威登'>@路易威登</a> CAPUCINES 手袋正合我意，打开灵感包袋的搭配新方式！'会被处理成'专属新意，色彩启程~@路易威登 CAPUCINES 手袋正合我意，打开灵感包袋的搭配新方式！'。\n\n设置write_mode\n\nwrite_mode控制结果文件格式，取值范围是csv、json、mongo、mysql和sqlite，分别代表将结果文件写入csv、json、MongoDB、MySQL和SQLite数据库。write_mode可以同时包含这些取值中的一个或几个，如：\n\n\n\n代表将结果信息写入csv文件和json文件。特别注意，如果你想写入数据库，除了在write_mode添加对应数据库的名字外，还应该安装相关数据库和对应python模块，具体操作见设置数据库部分。\n\n设置original_pic_download\n\noriginal_pic_download控制是否下载原创微博中的图片，值为1代表下载，值为0代表不下载，如\n\n\n\n代表下载原创微博中的图片。\n\n设置retweet_pic_download\n\nretweet_pic_download控制是否下载转发微博中的图片，值为1代表下载，值为0代表不下载，如\n\n\n\n代表不下载转发微博中的图片。特别注意，本设置只有在爬全部微博（原创+转发），即filter值为0时生效，否则程序会跳过转发微博的图片下载。\n\n设置original_video_download\n\noriginal_video_download控制是否下载原创微博中的视频和原创微博Live Photo中的视频，值为1代表下载，值为0代表不下载，如\n\n\n\n代表下载原创微博中的视频和原创微博Live Photo中的视频。\n\n设置retweet_video_download\n\nretweet_video_download控制是否下载转发微博中的视频和转发微博Live Photo中的视频，值为1代表下载，值为0代表不下载，如\n\n\n\n代表不下载转发微博中的视频和转发微博Live Photo中的视频。特别注意，本设置只有在爬全部微博（原创+转发），即filter值为0时生效，否则程序会跳过转发微博的视频下载。\n\n设置result_dir_name\n\nresult_dir_name控制结果文件的目录名，可取值为0和1，默认为0：\n\n\n\n值为0，表示将结果文件保存在以用户昵称为名的文件夹里，这样结果更清晰；值为1表示将结果文件保存在以用户id为名的文件夹里，这样能保证多次爬取的一致性，因为用户昵称可变，用户id不可变。\n\n设置download_comment\n\ndownload_comment控制是否下载每条微博下的一级评论（不包括对评论的评论），仅当write_mode中有sqlite时有效，可取值为0和1，默认为1：\n\n\n\n值为1，表示下载微博评论；值为0，表示不下载微博评论。\n\n设置comment_max_download_count\n\ncomment_max_download_count控制下载评论的最大数量，仅当write_mode中有sqlite时有效，默认为1000：\n\n\n\n设置download_repost\n\ndownload_repost控制是否下载每条微博下的转发，仅当write_mode中有sqlite时有效，可取值为0和1，默认为1：\n\n\n\n值为1，表示下载微博转发；值为0，表示不下载微博转发。\n\n设置repost_max_download_count\n\nrepost_max_download_count控制下载转发的最大数量，仅当write_mode中有sqlite时有效，默认为1000：\n\n\n\n值为1000，表示最多下载每条微博下的1000条转发。\n\n设置cookie（可选）\n\ncookie为可选参数，即可填可不填，具体区别见添加cookie与不添加cookie的区别。cookie默认配置如下：\n\n\n\n如果想要设置cookie，可以按照如何获取cookie中的方法，获取cookie，并将上面的\"your cookie\"替换成真实的cookie即可。\n\n设置mysql_config（可选）\n\nmysql_config控制mysql参数配置。如果你不需要将结果信息写入mysql，这个参数可以忽略，即删除或保留都无所谓；如果你需要写入mysql且config.json文件中mysql_config的配置与你的mysql配置不一样，请将该值改成你自己mysql中的参数配置。\n设置start_page（可选）\n\nstart_page为爬取微博的初始页数，默认参数为1，即从所爬取用户的当前第一页微博内容开始爬取。\n若在大批量爬取微博时出现中途被限制中断的情况，可通过查看csv文件内目前已爬取到的微博数除以10，向下取整后的值即为中断页数，手动设置start_page参数为中断页数，重新运行即可从被中断的节点继续爬取剩余微博内容。", "passage: ### 4.设置数据库（可选）\n\n本部分是可选部分，如果不需要将爬取信息写入数据库，可跳过这一步。本程序目前支持MySQL数据库和MongoDB数据库，如果你需要写入其它数据库，可以参考这两个数据库的写法自己编写。\n\nMySQL数据库写入\n\n要想将爬取信息写入MySQL，请根据自己的系统环境安装MySQL，然后命令行执行：\n\n\n\nMongoDB数据库写入\n\n要想将爬取信息写入MongoDB，请根据自己的系统环境安装MongoDB，然后命令行执行：\n\n\n\nMySQL和MongDB数据库的写入内容一样。程序首先会创建一个名为\"weibo\"的数据库，然后再创建\"user\"表和\"weibo\"表，包含爬取的所有内容。爬取到的微博用户信息或插入或更新，都会存储到user表里；爬取到的微博信息或插入或更新，都会存储到weibo表里，两个表通过user_id关联。如果想了解两个表的具体字段，请点击\"详情\"。\n<details>\n\n<summary>详情</summary>\n\nuser表\n\nid：微博用户id，如\"1669879400\"；\n\nscreen_name：微博用户昵称，如\"Dear-迪丽热巴\"；\n\ngender：微博用户性别，取值为f或m，分别代表女和男；\n\nbirthday：生日；\n\nlocation：所在地；\n\neducation：教育经历；\n\ncompany：公司；\n\nsunshine：阳光信用；\n\nregistration_time：注册时间；\n\nstatuses_count：微博数；\n\nfollowers_count：粉丝数；\n\nfollow_count：关注数；\n\ndescription：微博简介；\n\nprofile_url：微博主页，如<https://m.URL\n\nprofile_image_url：微博头像url；\n\navatar_hd：微博高清头像url；\n\nurank：微博等级；\n\nmbrank：微博会员等级，普通用户会员等级为0；\n\nverified：微博是否认证，取值为true和false；\n\nverified_type：微博认证类型，没有认证值为-1，个人认证值为0，企业认证值为2，政府认证值为3，这些类型仅是个人猜测，应该不全，大家可以根据实际情况判断；\n\nverified_reason：微博认证信息，只有认证用户拥有此属性。\n\n*\nweibo表\n\nuser_id：存储微博用户id，如\"1669879400\"；\n\nscreen_name：存储微博昵称，如\"Dear-迪丽热巴\"；\n\nid：存储微博id；\n\ntext：存储微博正文；\n\narticle_url：存储微博中头条文章的url，如果微博中存在头条文章，就获取该头条文章的url，否则该值为''；\n\npics：存储原创微博的原始图片url。若某条微博有多张图片，则存储多个url，以英文逗号分割；若该微博没有图片，则值为''；\n\nvideo_url：存储原创微博的视频url和Live Photo中的视频url。若某条微博有多个视频，则存储多个url，以英文分号分割；若该微博没有视频，则值为''；\n\nlocation：存储微博的发布位置。若某条微博没有位置信息，则值为''；\n\ncreated_at：存储微博的发布时间；\n\nsource：存储微博的发布工具；\n\nattitudes_count：存储微博获得的点赞数；\n\ncomments_count：存储微博获得的评论数；\n\nreposts_count：存储微博获得的转发数；\n\ntopics：存储微博话题，即两个#中的内容。若某条微博没有话题信息，则值为''；\n\nat_users：存储微博@的用户。若某条微博没有@的用户，则值为''；\n\nretweet_id：存储转发微博中原始微博的微博id。若某条微博为原创微博，则值为''。\n\n</details>\nSQLite数据库写入\n\n脚本会自动建立并配置数据库文件'URL'。### 5.运行脚本\n\n大家可以根据自己的运行环境选择运行方式，Linux可以通过\n\n\n\n运行;", "passage: ### 6.按需求修改脚本（可选）\n\n本部分为可选部分，如果你不需要自己修改代码或添加新功能，可以忽略此部分。\n\n本程序所有代码都位于weibo.py文件，程序主体是一个Weibo类，上述所有功能都是通过在main函数调用Weibo类实现的，默认的调用代码如下：\n\n\n\n用户可以按照自己的需求调用或修改Weibo类。\n\n通过执行本程序，我们可以得到很多信息：\n\nURL：存储目标微博用户信息；\n\nwb.user包含爬取到的微博用户信息，如用户id、用户昵称、性别、生日、所在地、教育经历、公司、阳光信用、微博注册时间、微博数、粉丝数、关注数、简介、主页地址、头像url、高清头像url、微博等级、会员等级、是否认证、认证类型、认证信息等，大家可以点击\"详情\"查看具体用法。\n\n<details>\n\n<summary>详情</summary>\n\nid：微博用户id，取值方式为wb.user['id'],由一串数字组成；\n\nscreen_name：微博用户昵称，取值方式为wb.user['screen_name']；\n\ngender：微博用户性别，取值方式为wb.user['gender']，取值为f或m，分别代表女和男；\n\nbirthday：微博用户生日，取值方式为wb.user['birthday']，若用户没有填写该信息，则值为''；\n\nlocation：微博用户所在地，取值方式为wb.user['location']，若用户没有填写该信息，则值为''；\n\neducation：微博用户上学时的学校，取值方式为wb.user['education']，若用户没有填写该信息，则值为''；\n\ncompany：微博用户所属的公司，取值方式为wb.user['company']，若用户没有填写该信息，则值为''；\n\nsunshine：微博用户的阳光信用，取值方式为wb.user['sunshine']；\n\nregistration_time：微博用户的注册时间，取值方式为wb.user['registration_time']；\n\nstatuses_count：微博数，取值方式为wb.user['statuses_count']；\n\nfollowers_count：微博粉丝数，取值方式为wb.user['followers_count']；\n\nfollow_count：微博关注数，取值方式为wb.user['follow_count']；\n\ndescription：微博简介，取值方式为wb.user['description']；\n\nprofile_url：微博主页，取值方式为wb.user['profile_url'];\n\nprofile_image_url：微博头像url，取值方式为wb.user['profile_image_url']；\n\navatar_hd：微博高清头像url，取值方式为wb.user['avatar_hd']；\n\nurank：微博等级，取值方式为wb.user['urank']；\n\nmbrank：微博会员等级，取值方式为wb.user['mbrank']，普通用户会员等级为0；\n\nverified：微博是否认证，取值方式为wb.user['verified']，取值为true和false；\n\nverified_type：微博认证类型，取值方式为wb.user['verified_type']，没有认证值为-1，个人认证值为0，企业认证值为2，政府认证值为3，这些类型仅是个人猜测，应该不全，大家可以根据实际情况判断；\n\nverified_reason：微博认证信息，取值方式为wb.user['verified_reason']，只有认证用户拥有此属性。\n\n</details>\n\nURL：存储爬取到的所有微博信息；\n\nwb.weibo包含爬取到的所有微博信息，如微博id、正文、原始图片url、视频url、位置、日期、发布工具、点赞数、转发数、评论数、话题、@用户等。如果爬的是全部微博(原创+转发)，除上述信息之外，还包含原始用户id、原始用户昵称、原始微博id、原始微博正文、原始微博原始图片url、原始微博位置、原始微博日期、原始微博工具、原始微博点赞数、原始微博评论数、原始微博转发数、原始微博话题、原始微博@用户等信息。wb.weibo是一个列表，包含了爬取的所有微博信息。URL[0]为爬取的第一条微博，URL[1]为爬取的第二条微博，以此类推。当filter=1时，URL[0]为爬取的第一条原创微博，以此类推。URL[0]['id']为第一条微博的id，URL[0]['text']为第一条微博的正文，URL[0]['created_at']为第一条微博的发布时间，还有其它很多信息不在赘述，大家可以点击下面的\"详情\"查看具体用法。\n<details>\n\n<summary>详情</summary>\n\nuser_id：存储微博用户id。如wb.weibo[0]['user_id']为最新一条微博的用户id；\n\nscreen_name：存储微博昵称。如wb.weibo[0]['screen_name']为最新一条微博的昵称；\n\nid：存储微博id。如wb.weibo[0]['id']为最新一条微博的id；\n\ntext：存储微博正文。如wb.weibo[0]['text']为最新一条微博的正文；\n\narticle_url：存储微博中头条文章的url。如wb.weibo[0]['article_url']为最新一条微博的头条文章url，若微博中不存在头条文章，则该值为''；\n\npics：存储原创微博的原始图片url。如wb.weibo[0]['pics']为最新一条微博的原始图片url，若该条微博有多张图片，则存储多个url，以英文逗号分割；若该微博没有图片，则值为''；\n\nvideo_url：存储原创微博的视频url和原创微博Live Photo中的视频url。如wb.weibo[0]['video_url']为最新一条微博的视频url，若该条微博有多个视频，则存储多个url，以英文分号分割；若该微博没有视频，则值为''；\n\nlocation：存储微博的发布位置。如wb.weibo[0]['location']为最新一条微博的发布位置，若该条微博没有位置信息，则值为''；\n\ncreated_at：存储微博的发布时间。如wb.weibo[0]['created_at']为最新一条微博的发布时间；\n\nsource：存储微博的发布工具。如wb.weibo[0]['source']为最新一条微博的发布工具；\n\nattitudes_count：存储微博获得的点赞数。如wb.weibo[0]['attitudes_count']为最新一条微博获得的点赞数；\n\ncomments_count：存储微博获得的评论数。如wb.weibo[0]['comments_count']为最新一条微博获得的评论数；\n\nreposts_count：存储微博获得的转发数。如wb.weibo[0]['reposts_count']为最新一条微博获得的转发数；\n\ntopics：存储微博话题，即两个#中的内容。如wb.weibo[0]['topics']为最新一条微博的话题，若该条微博没有话题信息，则值为''；\n\nat_users：存储微博@的用户。如wb.weibo[0]['at_users']为最新一条微博@的用户，若该条微博没有@的用户，则值为''；\n\nretweet：存储转发微博中原始微博的全部信息。假如wb.weibo[0]为转发微博，则wb.weibo[0]['retweet']为该转发微博的原始微博，它存储的属性与wb.weibo[0]一样，只是没有retweet属性;若该条微博为原创微博，则wb[0]没有\"retweet\"属性，大家可以点击\"详情\"查看具体用法。\n\n<details>\n\n<summary>详情</summary>\n\n假设爬取到的第i条微博为转发微博，则它存在以下信息：\n\nuser_id：存储原始微博用户id。URL[i-1]['retweet']['user_id']为该原始微博的用户id；\n\nscreen_name：存储原始微博昵称。URL[i-1]['retweet']['screen_name']为该原始微博的昵称；\n\nid：存储原始微博id。URL[i-1]['retweet']['id']为该原始微博的id；\n\ntext：存储原始微博正文。URL[i-1]['retweet']['text']为该原始微博的正文；\n\narticle_url：存储原始微博中头条文章的url。如wb.weibo[i-1]['retweet']['article_url']为该原始微博的头条文章url，若原始微博中不存在头条文章，则该值为''；\n\npics：存储原始微博的原始图片url。URL[i-1]['retweet']['pics']为该原始微博的原始图片url，若该原始微博有多张图片，则存储多个url，以英文逗号分割；若该原始微博没有图片，则值为''；\n\nvideo_url：存储原始微博的视频url和原始微博Live Photo中的视频url。如wb.weibo[i-1]['retweet']['video_url']为该原始微博的视频url，若该原始微博有多个视频，则存储多个url，以英文分号分割；若该微博没有视频，则值为''；\n\nlocation：存储原始微博的发布位置。URL[i-1]['retweet']['location']为该原始微博的发布位置，若该原始微博没有位置信息，则值为''；\n\ncreated_at：存储原始微博的发布时间。URL[i-1]['retweet']['created_at']为该原始微博的发布时间；\n\nsource：存储原始微博的发布工具。URL[i-1]['retweet']['source']为该原始微博的发布工具；\n\nattitudes_count：存储原始微博获得的点赞数。URL[i-1]['retweet']['attitudes_count']为该原始微博获得的点赞数；\n\ncomments_count：存储原始微博获得的评论数。URL[i-1]['retweet']['comments_count']为该原始微博获得的评论数；\n\nreposts_count：存储原始微博获得的转发数。URL[i-1]['retweet']['reposts_count']为该原始微博获得的转发数；\n\ntopics：存储原始微博话题，即两个#中的内容。URL[i-1]['retweet']['topics']为该原始微博的话题，若该原始微博没有话题信息，则值为''；\n\nat_users：存储原始微博@的用户。URL[i-1]['retweet']['at_users']为该原始微博@的用户，若该原始微博没有@的用户，则值为''。\n\n</details>\n\n</details>", "passage: ### 7.定期自动爬取微博（可选）\n\n我们爬取了微博以后，很多微博账号又可能发了一些新微博，定期自动爬取微博就是每隔一段时间自动运行程序，自动爬取这段时间产生的新微博（忽略以前爬过的旧微博）。本部分为可选部分，如果不需要可以忽略。\n\n思路是利用第三方软件，如crontab，让程序每隔一段时间运行一次。因为是要跳过以前爬过的旧微博，只爬新微博。所以需要设置一个动态的since_date。很多时候我们使用的since_date是固定的，比如since_date=\"2018-01-01\"，程序就会按照这个设置从最新的微博一直爬到发布时间为2018-01-01的微博（包括这个时间）。因为我们想追加新微博，跳过旧微博。第二次爬取时since_date值就应该是当前时间到上次爬取的时间。 如果我们使用最原始的方式实现追加爬取，应该是这样：\n\n\n\n上面的方法太麻烦，因为每次都要手动设置since_date。因此我们需要动态设置since_date，即程序根据实际情况，自动生成since_date。\n有两种方法实现动态更新since_date：\n\n方法一：将since_date设置成整数\n\n将config.json文件中的since_date设置成整数，如：\n\n\n\n这个配置告诉程序爬取最近10天的微博，更准确说是爬取发布时间从10天前到本程序开始执行时之间的微博。这样since_date就是一个动态的变量，每次程序执行时，它的值就是当前日期减10。配合crontab每9天或10天执行一次，就实现了定期追加爬取。\n\n方法二：将上次执行程序的时间写入文件（推荐）\n\n这个方法很简单，就是用户把要爬的用户id写入txt文件，然后再把文件路径赋值给config.json中的user_id_list参数。\n\ntxt文件名格式可以参考程序设置中的设置user_id_list部分，这样设置就全部结束了。\n\n说下这个方法的原理和好处，假如你的txt文件内容为：\n\n\n\n第一次执行时，因为第一行和第二行都没有写时间，程序会按照config.json文件中since_date的值爬取，第三行有时间“2019-01-01”，程序就会把这个时间当作since_date。每个用户爬取结束程序都会自动更新txt文件，每一行第一部分是user_id，第二部分是用户昵称，第三部分是程序准备爬取该用户第一条微博（最新微博）时的日期。爬完三个用户后，txt文件的内容自动更新为：\n\n\n\n下次再爬取微博的时候，程序会把每行的时间数据作为since_date。这样的好处一是不用修改since_date，程序自动更新；二是每一个用户都可以单独拥有只属于自己的since_date，每个用户的since_date相互独立，互不干扰，格式为'yyyy-mm-dd'或整数。比如，现在又添加了一个新用户，以杨紫的微博为例，你想获取她2018-01-23到现在的全部微博，可以这样修改txt文件：\n\n\n\n注意每一行的用户配置参数以空格分隔，如果第一个参数全部由数字组成，程序就认为此行为一个用户的配置，否则程序会认为该行只是注释，跳过该行；第二个参数可以为任意格式，建议写用户昵称；第三个如果是日期格式（yyyy-mm-dd），程序就将该日期设置为用户自己的since_date，否则使用config.json中的since_date爬取该用户的微博，第二个参数和第三个参数也可以不填。\n也可以设置第四个参数，将被读取为query_list。\n\n方法三：将'URL'文件中的运行模式改为'append'\n\n以追加模式运行程序，每次运行，每个id只获取最新的微博，而不是全部，避免频繁备份微博导致过多的请求次数。\n\n注意：\n\n* 该模式会跳过置顶微博。\n* 若采集信息后用户又编辑微博，则不会记录编辑内容。## 如何获取user_id\n\n1.打开网址<URL>，搜索我们要找的人，如\"迪丽热巴\"，进入她的主页；\n\n![](URL\n2.按照上图箭头所指，点击\"资料\"链接，跳转到用户资料页面；\n\n![](URL\n如上图所示，迪丽热巴微博资料页的地址为\"<URL，其中的\"1669879400\"即为此微博的user_id。\n\n事实上，此微博的user_id也包含在用户主页(<URL)中，之所以我们还要点击主页中的\"资料\"来获取user_id，是因为很多用户的主页不是\"<URL\"的形式，而是\"<URL/个性域名?f=search_0>\"或\"<URL/微号?f=search_0>\"的形式。其中\"微号\"和user_id都是一串数字，如果仅仅通过主页地址提取user_id，很容易将\"微号\"误认为user_id。## 添加cookie与不添加cookie的区别（可选）\n\n对于微博数2000条及以下的微博用户，不添加cookie可以获取其用户信息和大部分微博；对于微博数2000条以上的微博用户，不添加cookie可以获取其用户信息和最近2000条微博中的大部分，添加cookie可以获取其全部微博。以2020年1月2日迪丽热巴的微博为例，此时她共有1085条微博，在不添加cookie的情况下，可以获取到1026条微博，大约占全部微博的94.56%，而在添加cookie后，可以获取全部微博。其他用户类似，大部分都可以在不添加cookie的情况下获取到90%以上的微博，在添加cookie后可以获取全部微博。具体原因是，大部分微博内容都可以在移动版匿名获取，少量微博需要用户登录才可以获取，所以这部分微博在不添加cookie时是无法获取的。\n\n有少部分微博用户，不添加cookie可以获取其微博，无法获取其用户信息。对于这种情况，要想获取其用户信息，是需要cookie的。\n\n如需抓取微博转发，请添加cookie。" ]
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2074f3dbb4a6339ae463ce951fce93af8e12aea9
# Dataset Card for WikiAnc HR ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks](#supported-tasks) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Additional Information](#additional-information) - [Licensing Information](#licensing-information) ## Dataset Description - **Repository:** [WikiAnc repository](https://github.com/cyanic-selkie/wikianc) ### Dataset Summary The WikiAnc HR datasets is an automatically generated dataset from Wikipedia (hr) and Wikidata dumps (March 1, 2023). The code for generating the dataset can be found [here](https://github.com/cyanic-selkie/wikianc). ### Supported Tasks - `wikificiation`: The dataset can be used to train a model for Wikification. ### Languages The text in the dataset is in Croatian. The associated BCP-47 code is `hr`. You can find the English version [here](https://huggingface.co/datasets/cyanic-selkie/wikianc-en). ## Dataset Structure ### Data Instances A typical data point represents a paragraph in a Wikipedia article. The `paragraph_text` field contains the original text in an NFC normalized, UTF-8 encoded string. The `paragraph_anchors` field contains a list of anchors, each represented by a struct with the inclusive starting UTF-8 code point `start` field, exclusive ending UTF-8 code point `end` field, a nullable `qid` field, a nullable `pageid` field, and an NFC normalized, UTF-8 encoded `title` (Wikipedia) field. Additionally, each paragraph has `article_title`, `article_pageid`, and (nullable) `article_qid` fields referring to the article the paragraph came from. There is also a nullable, NFC normalized, UTF-8 encoded `section_heading` field, and an integer `section_level` field referring to the heading (if it exists) of the article section, and the level in the section hierarchy that the paragraph came from. The `qid` fields refers to Wikidata's QID identifiers, while the `pageid` and `title` fields refer to Wikipedia's pageID and title identifiers (there is a one-to-one mapping between pageIDs and titles). **NOTE:** An anchor will always have a `title`, but that doesn't mean it has to have a `pageid`. This is because Wikipedia allows defining anchors to nonexistent articles. An example from the WikiAnc HR test set looks as follows: ``` { "uuid": "8a9569ea-a398-4d14-8bce-76c263a8c0ac", "article_title": "Špiro_Dmitrović", "article_pageid": 70957, "article_qid": 16116278, "section_heading": null, "section_level": 0, "paragraph_text": "Špiro Dmitrović (Benkovac, 1803. – Zagreb, 6. veljače 1868.) hrvatski časnik i politički borac u doba ilirizma.", "paragraph_anchors": [ { "start": 17, "end": 25, "qid": 397443, "pageid": 14426, "title": "Benkovac" }, { "start": 27, "end": 32, "qid": 6887, "pageid": 1876, "title": "1803." }, { "start": 35, "end": 41, "qid": 1435, "pageid": 5903, "title": "Zagreb" }, { "start": 43, "end": 53, "qid": 2320, "pageid": 496, "title": "6._veljače" }, { "start": 54, "end": 59, "qid": 7717, "pageid": 1811, "title": "1868." }, { "start": 102, "end": 110, "qid": 680821, "pageid": 54622, "title": "Ilirizam" } ] } ``` ### Data Fields - `uuid`: a UTF-8 encoded string representing a v4 UUID that uniquely identifies the example - `article_title`: an NFC normalized, UTF-8 encoded Wikipedia title of the article; spaces are replaced with underscores - `article_pageid`: an integer representing the Wikipedia pageID of the article - `article_qid`: an integer representing the Wikidata QID this article refers to; it can be null if the entity didn't exist in Wikidata at the time of the creation of the original dataset - `section_heading`: a nullable, NFC normalized, UTF-8 encoded string representing the section heading - `section_level`: an integer representing the level of the section in the section hierarchy - `paragraph_text`: an NFC normalized, UTF-8 encoded string representing the paragraph - `paragraph_anchors`: a list of structs representing anchors, each anchor has: - `start`: an integer representing the inclusive starting UTF-8 code point of the anchors - `end`: an integer representing the exclusive ending UTF-8 code point of the anchor - `qid`: a nullable integer representing the Wikidata QID this anchor refers to; it can be null if the entity didn't exist in Wikidata at the time of the creation of the original dataset - `pageid`: a nullable integer representing the Wikipedia pageID of the anchor; it can be null if the article didn't exist in Wikipedia at the time of the creation of the original dataset - `title`: an NFC normalized, UTF-8 encoded string representing the Wikipedia title of the anchor; spaces are replaced with underscores; can refer to a nonexistent Wikipedia article ### Data Splits The data is split into training, validation and test sets; paragraphs belonging to the same article aren't necessarily in the same split. The final split sizes are as follows: | | Train | Validation | Test | | :----- | :------: | :-----: | :----: | | WikiAnc HR - articles | 192,653 | 116,375 | 116,638 | | WikiAnc HR - paragraphs | 2,346,651 | 292,590 | 293,557 | | WikiAnc HR - anchors | 8,368,928 | 1,039,851 | 1,044,828 | | WikiAnc HR - anchors with QIDs | 7,160,367 | 891,959 | 896,414 | | WikiAnc HR - anchors with pageIDs | 7,179,116 | 894,313 | 898,692 | **NOTE:** The number of articles in the table above refers to the number of articles that have at least one paragraph belonging to the article appear in the split. ## Additional Information ### Licensing Information The WikiAnc HR dataset is given under the [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0/) license.
cyanic-selkie/wikianc-hr
[ "task_categories:token-classification", "size_categories:1M<n<10M", "language:hr", "license:cc-by-sa-3.0", "wikidata", "wikipedia", "wikification", "region:us" ]
2023-03-27T07:30:50+00:00
{"language": ["hr"], "license": "cc-by-sa-3.0", "size_categories": ["1M<n<10M"], "task_categories": ["token-classification"], "pretty_name": "WikiAnc HR", "tags": ["wikidata", "wikipedia", "wikification"]}
2023-06-01T12:58:07+00:00
[]
[ "hr" ]
TAGS #task_categories-token-classification #size_categories-1M<n<10M #language-Croatian #license-cc-by-sa-3.0 #wikidata #wikipedia #wikification #region-us
Dataset Card for WikiAnc HR =========================== Table of Contents ----------------- * Dataset Description + Dataset Summary + Supported Tasks + Languages * Dataset Structure + Data Instances + Data Fields + Data Splits * Additional Information + Licensing Information Dataset Description ------------------- * Repository: WikiAnc repository ### Dataset Summary The WikiAnc HR datasets is an automatically generated dataset from Wikipedia (hr) and Wikidata dumps (March 1, 2023). The code for generating the dataset can be found here. ### Supported Tasks * 'wikificiation': The dataset can be used to train a model for Wikification. ### Languages The text in the dataset is in Croatian. The associated BCP-47 code is 'hr'. You can find the English version here. Dataset Structure ----------------- ### Data Instances A typical data point represents a paragraph in a Wikipedia article. The 'paragraph\_text' field contains the original text in an NFC normalized, UTF-8 encoded string. The 'paragraph\_anchors' field contains a list of anchors, each represented by a struct with the inclusive starting UTF-8 code point 'start' field, exclusive ending UTF-8 code point 'end' field, a nullable 'qid' field, a nullable 'pageid' field, and an NFC normalized, UTF-8 encoded 'title' (Wikipedia) field. Additionally, each paragraph has 'article\_title', 'article\_pageid', and (nullable) 'article\_qid' fields referring to the article the paragraph came from. There is also a nullable, NFC normalized, UTF-8 encoded 'section\_heading' field, and an integer 'section\_level' field referring to the heading (if it exists) of the article section, and the level in the section hierarchy that the paragraph came from. The 'qid' fields refers to Wikidata's QID identifiers, while the 'pageid' and 'title' fields refer to Wikipedia's pageID and title identifiers (there is a one-to-one mapping between pageIDs and titles). NOTE: An anchor will always have a 'title', but that doesn't mean it has to have a 'pageid'. This is because Wikipedia allows defining anchors to nonexistent articles. An example from the WikiAnc HR test set looks as follows: ### Data Fields * 'uuid': a UTF-8 encoded string representing a v4 UUID that uniquely identifies the example * 'article\_title': an NFC normalized, UTF-8 encoded Wikipedia title of the article; spaces are replaced with underscores * 'article\_pageid': an integer representing the Wikipedia pageID of the article * 'article\_qid': an integer representing the Wikidata QID this article refers to; it can be null if the entity didn't exist in Wikidata at the time of the creation of the original dataset * 'section\_heading': a nullable, NFC normalized, UTF-8 encoded string representing the section heading * 'section\_level': an integer representing the level of the section in the section hierarchy * 'paragraph\_text': an NFC normalized, UTF-8 encoded string representing the paragraph * 'paragraph\_anchors': a list of structs representing anchors, each anchor has: + 'start': an integer representing the inclusive starting UTF-8 code point of the anchors + 'end': an integer representing the exclusive ending UTF-8 code point of the anchor + 'qid': a nullable integer representing the Wikidata QID this anchor refers to; it can be null if the entity didn't exist in Wikidata at the time of the creation of the original dataset + 'pageid': a nullable integer representing the Wikipedia pageID of the anchor; it can be null if the article didn't exist in Wikipedia at the time of the creation of the original dataset + 'title': an NFC normalized, UTF-8 encoded string representing the Wikipedia title of the anchor; spaces are replaced with underscores; can refer to a nonexistent Wikipedia article ### Data Splits The data is split into training, validation and test sets; paragraphs belonging to the same article aren't necessarily in the same split. The final split sizes are as follows: NOTE: The number of articles in the table above refers to the number of articles that have at least one paragraph belonging to the article appear in the split. Additional Information ---------------------- ### Licensing Information The WikiAnc HR dataset is given under the Creative Commons Attribution 4.0 International license.
[ "### Dataset Summary\n\n\nThe WikiAnc HR datasets is an automatically generated dataset from Wikipedia (hr) and Wikidata dumps (March 1, 2023).\n\n\nThe code for generating the dataset can be found here.", "### Supported Tasks\n\n\n* 'wikificiation': The dataset can be used to train a model for Wikification.", "### Languages\n\n\nThe text in the dataset is in Croatian. The associated BCP-47 code is 'hr'.\n\n\nYou can find the English version here.\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nA typical data point represents a paragraph in a Wikipedia article.\n\n\nThe 'paragraph\\_text' field contains the original text in an NFC normalized, UTF-8 encoded string.\n\n\nThe 'paragraph\\_anchors' field contains a list of anchors, each represented by a struct with the inclusive starting UTF-8 code point 'start' field, exclusive ending UTF-8 code point 'end' field, a nullable 'qid' field, a nullable 'pageid' field, and an NFC normalized, UTF-8 encoded 'title' (Wikipedia) field.\n\n\nAdditionally, each paragraph has 'article\\_title', 'article\\_pageid', and (nullable) 'article\\_qid' fields referring to the article the paragraph came from.\n\n\nThere is also a nullable, NFC normalized, UTF-8 encoded 'section\\_heading' field, and an integer 'section\\_level' field referring to the heading (if it exists) of the article section, and the level in the section hierarchy that the paragraph came from.\n\n\nThe 'qid' fields refers to Wikidata's QID identifiers, while the 'pageid' and 'title' fields refer to Wikipedia's pageID and title identifiers (there is a one-to-one mapping between pageIDs and titles).\n\n\nNOTE: An anchor will always have a 'title', but that doesn't mean it has to have a 'pageid'. This is because Wikipedia allows defining anchors to nonexistent articles.\n\n\nAn example from the WikiAnc HR test set looks as follows:", "### Data Fields\n\n\n* 'uuid': a UTF-8 encoded string representing a v4 UUID that uniquely identifies the example\n* 'article\\_title': an NFC normalized, UTF-8 encoded Wikipedia title of the article; spaces are replaced with underscores\n* 'article\\_pageid': an integer representing the Wikipedia pageID of the article\n* 'article\\_qid': an integer representing the Wikidata QID this article refers to; it can be null if the entity didn't exist in Wikidata at the time of the creation of the original dataset\n* 'section\\_heading': a nullable, NFC normalized, UTF-8 encoded string representing the section heading\n* 'section\\_level': an integer representing the level of the section in the section hierarchy\n* 'paragraph\\_text': an NFC normalized, UTF-8 encoded string representing the paragraph\n* 'paragraph\\_anchors': a list of structs representing anchors, each anchor has:\n\t+ 'start': an integer representing the inclusive starting UTF-8 code point of the anchors\n\t+ 'end': an integer representing the exclusive ending UTF-8 code point of the anchor\n\t+ 'qid': a nullable integer representing the Wikidata QID this anchor refers to; it can be null if the entity didn't exist in Wikidata at the time of the creation of the original dataset\n\t+ 'pageid': a nullable integer representing the Wikipedia pageID of the anchor; it can be null if the article didn't exist in Wikipedia at the time of the creation of the original dataset\n\t+ 'title': an NFC normalized, UTF-8 encoded string representing the Wikipedia title of the anchor; spaces are replaced with underscores; can refer to a nonexistent Wikipedia article", "### Data Splits\n\n\nThe data is split into training, validation and test sets; paragraphs belonging to the same article aren't necessarily in the same split. The final split sizes are as follows:\n\n\n\nNOTE: The number of articles in the table above refers to the number of articles that have at least one paragraph belonging to the article appear in the split.\n\n\nAdditional Information\n----------------------", "### Licensing Information\n\n\nThe WikiAnc HR dataset is given under the Creative Commons Attribution 4.0 International license." ]
[ "TAGS\n#task_categories-token-classification #size_categories-1M<n<10M #language-Croatian #license-cc-by-sa-3.0 #wikidata #wikipedia #wikification #region-us \n", "### Dataset Summary\n\n\nThe WikiAnc HR datasets is an automatically generated dataset from Wikipedia (hr) and Wikidata dumps (March 1, 2023).\n\n\nThe code for generating the dataset can be found here.", "### Supported Tasks\n\n\n* 'wikificiation': The dataset can be used to train a model for Wikification.", "### Languages\n\n\nThe text in the dataset is in Croatian. The associated BCP-47 code is 'hr'.\n\n\nYou can find the English version here.\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nA typical data point represents a paragraph in a Wikipedia article.\n\n\nThe 'paragraph\\_text' field contains the original text in an NFC normalized, UTF-8 encoded string.\n\n\nThe 'paragraph\\_anchors' field contains a list of anchors, each represented by a struct with the inclusive starting UTF-8 code point 'start' field, exclusive ending UTF-8 code point 'end' field, a nullable 'qid' field, a nullable 'pageid' field, and an NFC normalized, UTF-8 encoded 'title' (Wikipedia) field.\n\n\nAdditionally, each paragraph has 'article\\_title', 'article\\_pageid', and (nullable) 'article\\_qid' fields referring to the article the paragraph came from.\n\n\nThere is also a nullable, NFC normalized, UTF-8 encoded 'section\\_heading' field, and an integer 'section\\_level' field referring to the heading (if it exists) of the article section, and the level in the section hierarchy that the paragraph came from.\n\n\nThe 'qid' fields refers to Wikidata's QID identifiers, while the 'pageid' and 'title' fields refer to Wikipedia's pageID and title identifiers (there is a one-to-one mapping between pageIDs and titles).\n\n\nNOTE: An anchor will always have a 'title', but that doesn't mean it has to have a 'pageid'. This is because Wikipedia allows defining anchors to nonexistent articles.\n\n\nAn example from the WikiAnc HR test set looks as follows:", "### Data Fields\n\n\n* 'uuid': a UTF-8 encoded string representing a v4 UUID that uniquely identifies the example\n* 'article\\_title': an NFC normalized, UTF-8 encoded Wikipedia title of the article; spaces are replaced with underscores\n* 'article\\_pageid': an integer representing the Wikipedia pageID of the article\n* 'article\\_qid': an integer representing the Wikidata QID this article refers to; it can be null if the entity didn't exist in Wikidata at the time of the creation of the original dataset\n* 'section\\_heading': a nullable, NFC normalized, UTF-8 encoded string representing the section heading\n* 'section\\_level': an integer representing the level of the section in the section hierarchy\n* 'paragraph\\_text': an NFC normalized, UTF-8 encoded string representing the paragraph\n* 'paragraph\\_anchors': a list of structs representing anchors, each anchor has:\n\t+ 'start': an integer representing the inclusive starting UTF-8 code point of the anchors\n\t+ 'end': an integer representing the exclusive ending UTF-8 code point of the anchor\n\t+ 'qid': a nullable integer representing the Wikidata QID this anchor refers to; it can be null if the entity didn't exist in Wikidata at the time of the creation of the original dataset\n\t+ 'pageid': a nullable integer representing the Wikipedia pageID of the anchor; it can be null if the article didn't exist in Wikipedia at the time of the creation of the original dataset\n\t+ 'title': an NFC normalized, UTF-8 encoded string representing the Wikipedia title of the anchor; spaces are replaced with underscores; can refer to a nonexistent Wikipedia article", "### Data Splits\n\n\nThe data is split into training, validation and test sets; paragraphs belonging to the same article aren't necessarily in the same split. The final split sizes are as follows:\n\n\n\nNOTE: The number of articles in the table above refers to the number of articles that have at least one paragraph belonging to the article appear in the split.\n\n\nAdditional Information\n----------------------", "### Licensing Information\n\n\nThe WikiAnc HR dataset is given under the Creative Commons Attribution 4.0 International license." ]
[ 57, 51, 28, 41, 379, 436, 86, 24 ]
[ "passage: TAGS\n#task_categories-token-classification #size_categories-1M<n<10M #language-Croatian #license-cc-by-sa-3.0 #wikidata #wikipedia #wikification #region-us \n### Dataset Summary\n\n\nThe WikiAnc HR datasets is an automatically generated dataset from Wikipedia (hr) and Wikidata dumps (March 1, 2023).\n\n\nThe code for generating the dataset can be found here.### Supported Tasks\n\n\n* 'wikificiation': The dataset can be used to train a model for Wikification.### Languages\n\n\nThe text in the dataset is in Croatian. The associated BCP-47 code is 'hr'.\n\n\nYou can find the English version here.\n\n\nDataset Structure\n-----------------", "passage: ### Data Instances\n\n\nA typical data point represents a paragraph in a Wikipedia article.\n\n\nThe 'paragraph\\_text' field contains the original text in an NFC normalized, UTF-8 encoded string.\n\n\nThe 'paragraph\\_anchors' field contains a list of anchors, each represented by a struct with the inclusive starting UTF-8 code point 'start' field, exclusive ending UTF-8 code point 'end' field, a nullable 'qid' field, a nullable 'pageid' field, and an NFC normalized, UTF-8 encoded 'title' (Wikipedia) field.\n\n\nAdditionally, each paragraph has 'article\\_title', 'article\\_pageid', and (nullable) 'article\\_qid' fields referring to the article the paragraph came from.\n\n\nThere is also a nullable, NFC normalized, UTF-8 encoded 'section\\_heading' field, and an integer 'section\\_level' field referring to the heading (if it exists) of the article section, and the level in the section hierarchy that the paragraph came from.\n\n\nThe 'qid' fields refers to Wikidata's QID identifiers, while the 'pageid' and 'title' fields refer to Wikipedia's pageID and title identifiers (there is a one-to-one mapping between pageIDs and titles).\n\n\nNOTE: An anchor will always have a 'title', but that doesn't mean it has to have a 'pageid'. This is because Wikipedia allows defining anchors to nonexistent articles.\n\n\nAn example from the WikiAnc HR test set looks as follows:### Data Fields\n\n\n* 'uuid': a UTF-8 encoded string representing a v4 UUID that uniquely identifies the example\n* 'article\\_title': an NFC normalized, UTF-8 encoded Wikipedia title of the article; spaces are replaced with underscores\n* 'article\\_pageid': an integer representing the Wikipedia pageID of the article\n* 'article\\_qid': an integer representing the Wikidata QID this article refers to; it can be null if the entity didn't exist in Wikidata at the time of the creation of the original dataset\n* 'section\\_heading': a nullable, NFC normalized, UTF-8 encoded string representing the section heading\n* 'section\\_level': an integer representing the level of the section in the section hierarchy\n* 'paragraph\\_text': an NFC normalized, UTF-8 encoded string representing the paragraph\n* 'paragraph\\_anchors': a list of structs representing anchors, each anchor has:\n\t+ 'start': an integer representing the inclusive starting UTF-8 code point of the anchors\n\t+ 'end': an integer representing the exclusive ending UTF-8 code point of the anchor\n\t+ 'qid': a nullable integer representing the Wikidata QID this anchor refers to; it can be null if the entity didn't exist in Wikidata at the time of the creation of the original dataset\n\t+ 'pageid': a nullable integer representing the Wikipedia pageID of the anchor; it can be null if the article didn't exist in Wikipedia at the time of the creation of the original dataset\n\t+ 'title': an NFC normalized, UTF-8 encoded string representing the Wikipedia title of the anchor; spaces are replaced with underscores; can refer to a nonexistent Wikipedia article" ]
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6ad03808ae8c95c61140002832157e570b9107c3
# Dataset Card for "notebooks_by_user" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
davanstrien/notebooks_by_user
[ "region:us" ]
2023-03-27T08:14:54+00:00
{"dataset_info": {"features": [{"name": "user", "dtype": "large_string"}, {"name": "repo_notebook_count", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 140436, "num_examples": 5604}], "download_size": 74860, "dataset_size": 140436}}
2024-02-17T02:14:50+00:00
[]
[]
TAGS #region-us
# Dataset Card for "notebooks_by_user" More Information needed
[ "# Dataset Card for \"notebooks_by_user\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"notebooks_by_user\"\n\nMore Information needed" ]
[ 6, 16 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"notebooks_by_user\"\n\nMore Information needed" ]
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2510ab6bfcf7892032721b361c9264a0cfe3e46d
# Dataset Card for "notebooks_by_repo_type" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
davanstrien/notebooks_by_repo_type
[ "region:us" ]
2023-03-27T08:16:12+00:00
{"dataset_info": {"features": [{"name": "repo_type", "dtype": "large_string"}, {"name": "repo_notebook_count", "dtype": "int64"}, {"name": "date", "dtype": "date32"}], "splits": [{"name": "train", "num_bytes": 14350, "num_examples": 574}], "download_size": 0, "dataset_size": 14350}}
2024-02-17T02:15:03+00:00
[]
[]
TAGS #region-us
# Dataset Card for "notebooks_by_repo_type" More Information needed
[ "# Dataset Card for \"notebooks_by_repo_type\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"notebooks_by_repo_type\"\n\nMore Information needed" ]
[ 6, 19 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"notebooks_by_repo_type\"\n\nMore Information needed" ]
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b405b6c44964e075d9ebde2b0c796eeb8f830fab
# Dataset Card for "dummy-controlnet-100000-samples" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
sayakpaul/dummy-controlnet-100000-samples
[ "region:us" ]
2023-03-27T08:24:52+00:00
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "condtioning_image", "dtype": "image"}, {"name": "caption", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 157617435976.0, "num_examples": 100000}], "download_size": 157623508466, "dataset_size": 157617435976.0}}
2023-03-29T09:24:14+00:00
[]
[]
TAGS #region-us
# Dataset Card for "dummy-controlnet-100000-samples" More Information needed
[ "# Dataset Card for \"dummy-controlnet-100000-samples\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"dummy-controlnet-100000-samples\"\n\nMore Information needed" ]
[ 6, 20 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"dummy-controlnet-100000-samples\"\n\nMore Information needed" ]
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b70f81b24fc04a2b0cff88e6ec3cd296808dae6a
# Dataset Card for DWIE ## 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) - [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://opendatalab.com/DWIE](https://opendatalab.com/DWIE) - **Repository:** [https://github.com/klimzaporojets/DWIE](https://github.com/klimzaporojets/DWIE) - **Paper:** [DWIE: an entity-centric dataset for multi-task document-level information extraction](https://arxiv.org/abs/2009.12626) - **Leaderboard:** [https://opendatalab.com/DWIE](https://opendatalab.com/DWIE) - **Size of downloaded dataset files:** 40.8 MB ### Dataset Summary DWIE (Deutsche Welle corpus for Information Extraction) is a new dataset for document-level multi-task Information Extraction (IE). It combines four main IE sub-tasks: 1.Named Entity Recognition: 23,130 entities classified in 311 multi-label entity types (tags). 2.Coreference Resolution: 43,373 entity mentions clustered in 23,130 entities. 3.Relation Extraction: 21,749 annotated relations between entities classified in 65 multi-label relation types. 4.Entity Linking: the named entities are linked to Wikipedia (version 20181115). For details, see the paper https://arxiv.org/pdf/2009.12626v2.pdf. ### Supported Tasks and Leaderboards - **Tasks:** Named Entity Recognition, Coreference Resolution, Relation extraction and entity linking in scientific papers - **Leaderboards:** [https://opendatalab.com/DWIE](https://opendatalab.com/DWIE) ### Languages The language in the dataset is English. ## Dataset Structure ### Data Instances - **Size of downloaded dataset files:** 40.8 MB An example of 'train' looks as follows, provided sample of the data: ```json {'id': 'DW_3980038', 'content': 'Proposed Nabucco Gas Pipeline Gets European Bank Backing\nThe heads of the EU\'s European Investment Bank and the European Bank for Reconstruction and Development (EBRD) said Tuesday, Jan. 27, that they are prepared to provide financial backing for the Nabucco gas pipeline.\nSpurred on by Europe\'s worst-ever gas crisis earlier this month, which left millions of homes across the continent without heat in the depths of winter, Hungarian Prime Minister Ferenc Gyurcsany invited top-ranking officials from both the EU and the countries involved in Nabucco to inject fresh momentum into the slow-moving project. Nabucco, an ambitious but still-unbuilt gas pipeline aimed at reducing Europe\'s energy reliance on Russia, is a 3,300-kilometer (2,050-mile) pipeline between Turkey and Austria. Costing an estimated 7.9 billion euros, the aim is to transport up to 31 billion cubic meters of gas each year from the Caspian Sea to Western Europe, bypassing Russia and Ukraine. Nabucco currently has six shareholders -- OMV of Austria, MOL of Hungary, Transgaz of Romania, Bulgargaz of Bulgaria, Botas of Turkey and RWE of Germany. But for the pipeline to get moving, Nabucco would need an initial cash injection of an estimated 300 million euros. Both the EIB and EBRD said they were willing to invest in the early stages of the project through a series of loans, providing certain conditions are met. "The EIB is ready to finance projects that further EU objectives of increased sustainability and energy security," said Philippe Maystadt, president of the European Investment Bank, during the opening addresses by participants at the "Nabucco summit" in Hungary. The EIB is prepared to finance "up to 25 percent of project cost," provided a secure intergovernmental agreement on the Nabucco pipeline is reached, he said. Maystadt noted that of 48 billion euros of financing it provided last year, a quarter was for energy projects. EBRD President Thomas Mirow also offered financial backing to the Nabucco pipeline, on the condition that it "meets the requirements of solid project financing." The bank would need to see concrete plans and completion guarantees, besides a stable political agreement, said Mirow. EU wary of future gas crises Czech Prime Minister Mirek Topolanek, whose country currently holds the rotating presidency of the EU, spoke about the recent gas crisis caused by a pricing dispute between Russia and Ukraine that affected supplies to Europe. "A new crisis could emerge at any time, and next time it could be even worse," Topolanek said. He added that reaching an agreement on Nabucco is a "test of European solidarity." The latest gas row between Russia and Ukraine has highlighted Europe\'s need to diversify its energy sources and thrown the spotlight on Nabucco. But critics insist that the vast project will remain nothing but a pipe dream because its backers cannot guarantee that they will ever have sufficient gas supplies to make it profitable. EU Energy Commissioner Andris Piebalgs urged political leaders to commit firmly to Nabucco by the end of March, or risk jeopardizing the project. In his opening address as host, Hungarian Prime Minister Ferenc Gyurcsany called on the EU to provide 200 to 300 million euros within the next few weeks to get the construction of the pipeline off the ground. Gyurcsany stressed that he was not hoping for a loan, but rather for starting capital from the EU. US Deputy Assistant Secretary of State Matthew Bryza noted that the Tuesday summit had made it clear that Gyurcsany, who dismissed Nabucco as "a dream" in 2007, was now fully committed to the energy supply diversification project. On the supply side, Turkmenistan and Azerbaijan both indicated they would be willing to supply some of the gas. "Azerbaijan, which is according to current plans is a transit country, could eventually serve as a supplier as well," Azerbaijani President Ilham Aliyev said. Azerbaijan\'s gas reserves of some two or three trillion cubic meters would be sufficient to last "several decades," he said. Austrian Economy Minister Reinhold Mitterlehner suggested that Egypt and Iran could also be brought in as suppliers in the long term. But a deal currently seems unlikely with Iran given the long-running international standoff over its disputed nuclear program. Russia, Ukraine still wrangling Meanwhile, Russia and Ukraine were still wrangling over the details of the deal which ended their gas quarrel earlier this month. Ukrainian President Viktor Yushchenko said on Tuesday he would stand by the terms of the agreement with Russia, even though not all the details are to his liking. But Russian officials questioned his reliability, saying that the political rivalry between Yushchenko and Prime Minister Yulia Timoshenko could still lead Kiev to cancel the contract. "The agreements signed are not easy ones, but Ukraine fully takes up the performance (of its commitments) and guarantees full-fledged transit to European consumers," Yushchenko told journalists in Brussels after a meeting with the head of the European Commission, Jose Manuel Barroso. The assurance that Yushchenko would abide by the terms of the agreement finalized by Timoshenko was "an important step forward in allowing us to focus on our broader relationship," Barroso said. But the spokesman for Russian Prime Minister Vladimir Putin said that Moscow still feared that the growing rivalry between Yushchenko and Timoshenko, who are set to face off in next year\'s presidential election, could torpedo the deal. EU in talks to upgrade Ukraine\'s transit system Yushchenko\'s working breakfast with Barroso was dominated by the energy question, with both men highlighting the need to upgrade Ukraine\'s gas-transit system and build more links between Ukrainian and European energy markets. The commission is set to host an international conference aimed at gathering donations to upgrade Ukraine\'s gas-transit system on March 23 in Brussels. The EU and Ukraine have agreed to form a joint expert group to plan the meeting, the leaders said Tuesday. During the conflict, Barroso had warned that both Russia and Ukraine were damaging their credibility as reliable partners. But on Monday he said that "in bilateral relations, we are not taking any negative consequences from (the gas row) because we believe Ukraine wants to deepen the relationship with the EU, and we also want to deepen the relationship with Ukraine." He also said that "we have to state very clearly that we were disappointed by the problems between Ukraine and Russia," and called for political stability and reform in Ukraine. His diplomatic balancing act is likely to have a frosty reception in Moscow, where Peskov said that Russia "would prefer to hear from the European states a very serious and severe evaluation of who is guilty for interrupting the transit."', 'tags': "['all', 'train']", 'mentions': [{'begin': 9, 'end': 29, 'text': 'Nabucco Gas Pipeline', 'concept': 1, 'candidates': [], 'scores': []}, {'begin': 287, 'end': 293, 'text': 'Europe', 'concept': 2, 'candidates': ['Europe', 'UEFA', 'Europe_(band)', 'UEFA_competitions', 'European_Athletic_Association', 'European_theatre_of_World_War_II', 'European_Union', 'Europe_(dinghy)', 'European_Cricket_Council', 'UEFA_Champions_League', 'Senior_League_World_Series_(Europe–Africa_Region)', 'Big_League_World_Series_(Europe–Africa_Region)', 'Sailing_at_the_2004_Summer_Olympics_–_Europe', 'Neolithic_Europe', 'History_of_Europe', 'Europe_(magazine)'], 'scores': [0.8408304452896118, 0.10987312346696854, 0.01377162616699934, 0.002099192701280117, 0.0015916954725980759, 0.0015686274273321033, 0.001522491336800158, 0.0013148789294064045, 0.0012456747936084867, 0.000991926179267466, 0.0008073817589320242, 0.0007843137136660516, 0.000761245668400079, 0.0006920415326021612, 0.0005536332027986646, 0.000530565157532692]}, 0.00554528646171093, 0.004390018526464701, 0.003234750358387828, 0.002772643230855465, 0.001617375179193914]}, {'begin': 6757, 'end': 6765, 'text': 'European', 'concept': 13, 'candidates': None, 'scores': []}], 'concepts': [{'concept': 0, 'text': 'European Investment Bank', 'keyword': True, 'count': 5, 'link': 'European_Investment_Bank', 'tags': ['iptc::11000000', 'slot::keyword', 'topic::politics', 'type::entity', 'type::igo', 'type::organization']}, {'concept': 66, 'text': None, 'keyword': False, 'count': 0, 'link': 'Czech_Republic', 'tags': []}], 'relations': [{'s': 0, 'p': 'institution_of', 'o': 2}, {'s': 0, 'p': 'part_of', 'o': 2}, {'s': 3, 'p': 'institution_of', 'o': 2}, {'s': 3, 'p': 'part_of', 'o': 2}, {'s': 6, 'p': 'head_of', 'o': 0}, {'s': 6, 'p': 'member_of', 'o': 0}, {'s': 7, 'p': 'agent_of', 'o': 4}, {'s': 7, 'p': 'citizen_of', 'o': 4}, {'s': 7, 'p': 'citizen_of-x', 'o': 55}, {'s': 7, 'p': 'head_of_state', 'o': 4}, {'s': 7, 'p': 'head_of_state-x', 'o': 55}, {'s': 8, 'p': 'agent_of', 'o': 4}, {'s': 8, 'p': 'citizen_of', 'o': 4}, {'s': 8, 'p': 'citizen_of-x', 'o': 55}, {'s': 8, 'p': 'head_of_gov', 'o': 4}, {'s': 8, 'p': 'head_of_gov-x', 'o': 55}, {'s': 9, 'p': 'head_of', 'o': 59}, {'s': 9, 'p': 'member_of', 'o': 59}, {'s': 10, 'p': 'head_of', 'o': 3}, {'s': 10, 'p': 'member_of', 'o': 3}, {'s': 11, 'p': 'citizen_of', 'o': 66}, {'s': 11, 'p': 'citizen_of-x', 'o': 36}, {'s': 11, 'p': 'head_of_state', 'o': 66}, {'s': 11, 'p': 'head_of_state-x', 'o': 36}, {'s': 12, 'p': 'agent_of', 'o': 24}, {'s': 12, 'p': 'citizen_of', 'o': 24}, {'s': 12, 'p': 'citizen_of-x', 'o': 15}, {'s': 12, 'p': 'head_of_gov', 'o': 24}, {'s': 12, 'p': 'head_of_gov-x', 'o': 15}, {'s': 15, 'p': 'gpe0', 'o': 24}, {'s': 22, 'p': 'based_in0', 'o': 18}, {'s': 22, 'p': 'based_in0-x', 'o': 50}, {'s': 23, 'p': 'based_in0', 'o': 24}, {'s': 23, 'p': 'based_in0-x', 'o': 15}, {'s': 25, 'p': 'based_in0', 'o': 26}, {'s': 27, 'p': 'based_in0', 'o': 28}, {'s': 29, 'p': 'based_in0', 'o': 17}, {'s': 30, 'p': 'based_in0', 'o': 31}, {'s': 33, 'p': 'event_in0', 'o': 24}, {'s': 36, 'p': 'gpe0', 'o': 66}, {'s': 38, 'p': 'member_of', 'o': 2}, {'s': 43, 'p': 'agent_of', 'o': 41}, {'s': 43, 'p': 'citizen_of', 'o': 41}, {'s': 48, 'p': 'gpe0', 'o': 47}, {'s': 49, 'p': 'agent_of', 'o': 47}, {'s': 49, 'p': 'citizen_of', 'o': 47}, {'s': 49, 'p': 'citizen_of-x', 'o': 48}, {'s': 49, 'p': 'head_of_state', 'o': 47}, {'s': 49, 'p': 'head_of_state-x', 'o': 48}, {'s': 50, 'p': 'gpe0', 'o': 18}, {'s': 52, 'p': 'agent_of', 'o': 18}, {'s': 52, 'p': 'citizen_of', 'o': 18}, {'s': 52, 'p': 'citizen_of-x', 'o': 50}, {'s': 52, 'p': 'minister_of', 'o': 18}, {'s': 52, 'p': 'minister_of-x', 'o': 50}, {'s': 55, 'p': 'gpe0', 'o': 4}, {'s': 56, 'p': 'gpe0', 'o': 5}, {'s': 57, 'p': 'in0', 'o': 4}, {'s': 57, 'p': 'in0-x', 'o': 55}, {'s': 58, 'p': 'in0', 'o': 65}, {'s': 59, 'p': 'institution_of', 'o': 2}, {'s': 59, 'p': 'part_of', 'o': 2}, {'s': 60, 'p': 'agent_of', 'o': 5}, {'s': 60, 'p': 'citizen_of', 'o': 5}, {'s': 60, 'p': 'citizen_of-x', 'o': 56}, {'s': 60, 'p': 'head_of_gov', 'o': 5}, {'s': 60, 'p': 'head_of_gov-x', 'o': 56}, {'s': 61, 'p': 'in0', 'o': 5}, {'s': 61, 'p': 'in0-x', 'o': 56}], 'frames': [{'type': 'none', 'slots': []}], 'iptc': ['04000000', '11000000', '20000344', '20000346', '20000378', '20000638']} ``` ### Data Fields - `id` : unique identifier of the article. - `content` : textual content of the article downloaded with src/dwie_download.py script. - `tags` : used to differentiate between train and test sets of documents. - `mentions`: a list of entity mentions in the article each with the following keys: - `begin` : offset of the first character of the mention (inside content field). - `end` : offset of the last character of the mention (inside content field). - `text` : the textual representation of the entity mention. - `concept` : the id of the entity that represents the entity mention (multiple entity mentions in the article can refer to the same concept). - `candidates` : the candidate Wikipedia links. - `scores` : the prior probabilities of the candidates entity links calculated on Wikipedia corpus. - `concepts` : a list of entities that cluster each of the entity mentions. Each entity is annotated with the following keys: - `concept` : the unique document-level entity id. - `text` : the text of the longest mention that belong to the entity. - `keyword` : indicates whether the entity is a keyword. - `count` : the number of entity mentions in the document that belong to the entity. - `link` : the entity link to Wikipedia. - `tags` : multi-label classification labels associated to the entity. - `relations` : a list of document-level relations between entities (concepts). Each of the relations is annotated with the following keys: - `s` : the subject entity id involved in the relation. - `p` : the predicate that defines the relation name (i.e., "citizen_of", "member_of", etc.). - `o` : the object entity id involved in the relation. - `iptc` : multi-label article IPTC classification codes. For detailed meaning of each of the codes, please refer to the official IPTC code list. ## 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{zaporojets2021dwie, title={DWIE: An entity-centric dataset for multi-task document-level information extraction}, author={Zaporojets, Klim and Deleu, Johannes and Develder, Chris and Demeester, Thomas}, journal={Information Processing \& Management}, volume={58}, number={4}, pages={102563}, year={2021}, publisher={Elsevier} } ``` ### Contributions Thanks to [@basvoju](https://github.com/basvoju) for adding this dataset.
DFKI-SLT/DWIE
[ "task_categories:feature-extraction", "task_categories:text-classification", "task_ids:entity-linking-classification", "annotations_creators:expert-generated", "language_creators:found", "multilinguality:monolingual", "size_categories:10M<n<100M", "source_datasets:original", "language:en", "license:other", "Named Entity Recognition, Coreference Resolution, Relation Extraction, Entity Linking", "arxiv:2009.12626", "region:us" ]
2023-03-27T08:27:59+00:00
{"annotations_creators": ["expert-generated"], "language_creators": ["found"], "language": ["en"], "license": "other", "multilinguality": ["monolingual"], "size_categories": ["10M<n<100M"], "source_datasets": ["original"], "task_categories": ["feature-extraction", "text-classification"], "task_ids": ["entity-linking-classification"], "paperswithcode_id": "acronym-identification", "pretty_name": "DWIE (Deutsche Welle corpus for Information Extraction) is a new dataset for document-level multi-task Information Extraction (IE).", "tags": ["Named Entity Recognition, Coreference Resolution, Relation Extraction, Entity Linking"], "train-eval-index": [{"col_mapping": {"labels": "tags", "tokens": "tokens"}, "config": "default", "splits": {"eval_split": "test"}, "task_id": "entity_extraction"}]}
2023-03-27T09:24:00+00:00
[ "2009.12626" ]
[ "en" ]
TAGS #task_categories-feature-extraction #task_categories-text-classification #task_ids-entity-linking-classification #annotations_creators-expert-generated #language_creators-found #multilinguality-monolingual #size_categories-10M<n<100M #source_datasets-original #language-English #license-other #Named Entity Recognition, Coreference Resolution, Relation Extraction, Entity Linking #arxiv-2009.12626 #region-us
# Dataset Card for DWIE ## Table of Contents - Table of Contents - Dataset Description - Dataset Summary - Supported Tasks and Leaderboards - Languages - Dataset Structure - Data Instances - Data Fields - Dataset Creation - Curation Rationale - Source Data - Annotations - Personal and Sensitive Information - Considerations for Using the Data - Social Impact of Dataset - Discussion of Biases - Other Known Limitations - Additional Information - Dataset Curators - Licensing Information - Citation Information - Contributions ## Dataset Description - Homepage: URL - Repository: URL - Paper: DWIE: an entity-centric dataset for multi-task document-level information extraction - Leaderboard: URL - Size of downloaded dataset files: 40.8 MB ### Dataset Summary DWIE (Deutsche Welle corpus for Information Extraction) is a new dataset for document-level multi-task Information Extraction (IE). It combines four main IE sub-tasks: 1.Named Entity Recognition: 23,130 entities classified in 311 multi-label entity types (tags). 2.Coreference Resolution: 43,373 entity mentions clustered in 23,130 entities. 3.Relation Extraction: 21,749 annotated relations between entities classified in 65 multi-label relation types. 4.Entity Linking: the named entities are linked to Wikipedia (version 20181115). For details, see the paper URL ### Supported Tasks and Leaderboards - Tasks: Named Entity Recognition, Coreference Resolution, Relation extraction and entity linking in scientific papers - Leaderboards: URL ### Languages The language in the dataset is English. ## Dataset Structure ### Data Instances - Size of downloaded dataset files: 40.8 MB An example of 'train' looks as follows, provided sample of the data: ### Data Fields - 'id' : unique identifier of the article. - 'content' : textual content of the article downloaded with src/dwie_download.py script. - 'tags' : used to differentiate between train and test sets of documents. - 'mentions': a list of entity mentions in the article each with the following keys: - 'begin' : offset of the first character of the mention (inside content field). - 'end' : offset of the last character of the mention (inside content field). - 'text' : the textual representation of the entity mention. - 'concept' : the id of the entity that represents the entity mention (multiple entity mentions in the article can refer to the same concept). - 'candidates' : the candidate Wikipedia links. - 'scores' : the prior probabilities of the candidates entity links calculated on Wikipedia corpus. - 'concepts' : a list of entities that cluster each of the entity mentions. Each entity is annotated with the following keys: - 'concept' : the unique document-level entity id. - 'text' : the text of the longest mention that belong to the entity. - 'keyword' : indicates whether the entity is a keyword. - 'count' : the number of entity mentions in the document that belong to the entity. - 'link' : the entity link to Wikipedia. - 'tags' : multi-label classification labels associated to the entity. - 'relations' : a list of document-level relations between entities (concepts). Each of the relations is annotated with the following keys: - 's' : the subject entity id involved in the relation. - 'p' : the predicate that defines the relation name (i.e., "citizen_of", "member_of", etc.). - 'o' : the object entity id involved in the relation. - 'iptc' : multi-label article IPTC classification codes. For detailed meaning of each of the codes, please refer to the official IPTC code list. ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information ### Contributions Thanks to @basvoju for adding this dataset.
[ "# Dataset Card for DWIE", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Paper: DWIE: an entity-centric dataset for multi-task document-level information extraction\n- Leaderboard: URL\n- Size of downloaded dataset files: 40.8 MB", "### Dataset Summary\n\nDWIE (Deutsche Welle corpus for Information Extraction) is a new dataset for document-level multi-task Information Extraction (IE).\n\n It combines four main IE sub-tasks:\n\n 1.Named Entity Recognition: 23,130 entities classified in 311 multi-label entity types (tags). \n 2.Coreference Resolution: 43,373 entity mentions clustered in 23,130 entities. \n 3.Relation Extraction: 21,749 annotated relations between entities classified in 65 multi-label relation types. \n 4.Entity Linking: the named entities are linked to Wikipedia (version 20181115). \n\nFor details, see the paper URL", "### Supported Tasks and Leaderboards\n\n- Tasks: Named Entity Recognition, Coreference Resolution, Relation extraction and entity linking in scientific papers\n- Leaderboards: URL", "### Languages\n\nThe language in the dataset is English.", "## Dataset Structure", "### Data Instances\n\n- Size of downloaded dataset files: 40.8 MB\n \nAn example of 'train' looks as follows, provided sample of the data:", "### Data Fields\n\n- 'id' : unique identifier of the article.\n- 'content' : textual content of the article downloaded with src/dwie_download.py script.\n- 'tags' : used to differentiate between train and test sets of documents.\n- 'mentions': a list of entity mentions in the article each with the following keys:\n - 'begin' : offset of the first character of the mention (inside content field).\n - 'end' : offset of the last character of the mention (inside content field).\n - 'text' : the textual representation of the entity mention.\n - 'concept' : the id of the entity that represents the entity mention (multiple entity mentions in the article can refer to the same concept).\n - 'candidates' : the candidate Wikipedia links.\n - 'scores' : the prior probabilities of the candidates entity links calculated on Wikipedia corpus.\n- 'concepts' : a list of entities that cluster each of the entity mentions. Each entity is annotated with the following keys:\n - 'concept' : the unique document-level entity id.\n - 'text' : the text of the longest mention that belong to the entity.\n - 'keyword' : indicates whether the entity is a keyword.\n - 'count' : the number of entity mentions in the document that belong to the entity.\n - 'link' : the entity link to Wikipedia.\n - 'tags' : multi-label classification labels associated to the entity.\n- 'relations' : a list of document-level relations between entities (concepts). Each of the relations is annotated with the following keys:\n - 's' : the subject entity id involved in the relation.\n - 'p' : the predicate that defines the relation name (i.e., \"citizen_of\", \"member_of\", etc.).\n - 'o' : the object entity id involved in the relation.\n- 'iptc' : multi-label article IPTC classification codes. For detailed meaning of each of the codes, please refer to the official IPTC code list.", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions\n\nThanks to @basvoju for adding this dataset." ]
[ "TAGS\n#task_categories-feature-extraction #task_categories-text-classification #task_ids-entity-linking-classification #annotations_creators-expert-generated #language_creators-found #multilinguality-monolingual #size_categories-10M<n<100M #source_datasets-original #language-English #license-other #Named Entity Recognition, Coreference Resolution, Relation Extraction, Entity Linking #arxiv-2009.12626 #region-us \n", "# Dataset Card for DWIE", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Paper: DWIE: an entity-centric dataset for multi-task document-level information extraction\n- Leaderboard: URL\n- Size of downloaded dataset files: 40.8 MB", "### Dataset Summary\n\nDWIE (Deutsche Welle corpus for Information Extraction) is a new dataset for document-level multi-task Information Extraction (IE).\n\n It combines four main IE sub-tasks:\n\n 1.Named Entity Recognition: 23,130 entities classified in 311 multi-label entity types (tags). \n 2.Coreference Resolution: 43,373 entity mentions clustered in 23,130 entities. \n 3.Relation Extraction: 21,749 annotated relations between entities classified in 65 multi-label relation types. \n 4.Entity Linking: the named entities are linked to Wikipedia (version 20181115). \n\nFor details, see the paper URL", "### Supported Tasks and Leaderboards\n\n- Tasks: Named Entity Recognition, Coreference Resolution, Relation extraction and entity linking in scientific papers\n- Leaderboards: URL", "### Languages\n\nThe language in the dataset is English.", "## Dataset Structure", "### Data Instances\n\n- Size of downloaded dataset files: 40.8 MB\n \nAn example of 'train' looks as follows, provided sample of the data:", "### Data Fields\n\n- 'id' : unique identifier of the article.\n- 'content' : textual content of the article downloaded with src/dwie_download.py script.\n- 'tags' : used to differentiate between train and test sets of documents.\n- 'mentions': a list of entity mentions in the article each with the following keys:\n - 'begin' : offset of the first character of the mention (inside content field).\n - 'end' : offset of the last character of the mention (inside content field).\n - 'text' : the textual representation of the entity mention.\n - 'concept' : the id of the entity that represents the entity mention (multiple entity mentions in the article can refer to the same concept).\n - 'candidates' : the candidate Wikipedia links.\n - 'scores' : the prior probabilities of the candidates entity links calculated on Wikipedia corpus.\n- 'concepts' : a list of entities that cluster each of the entity mentions. Each entity is annotated with the following keys:\n - 'concept' : the unique document-level entity id.\n - 'text' : the text of the longest mention that belong to the entity.\n - 'keyword' : indicates whether the entity is a keyword.\n - 'count' : the number of entity mentions in the document that belong to the entity.\n - 'link' : the entity link to Wikipedia.\n - 'tags' : multi-label classification labels associated to the entity.\n- 'relations' : a list of document-level relations between entities (concepts). Each of the relations is annotated with the following keys:\n - 's' : the subject entity id involved in the relation.\n - 'p' : the predicate that defines the relation name (i.e., \"citizen_of\", \"member_of\", etc.).\n - 'o' : the object entity id involved in the relation.\n- 'iptc' : multi-label article IPTC classification codes. For detailed meaning of each of the codes, please refer to the official IPTC code list.", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions\n\nThanks to @basvoju for adding this dataset." ]
[ 131, 7, 121, 56, 153, 45, 13, 6, 36, 491, 5, 7, 4, 10, 10, 5, 5, 9, 8, 8, 7, 8, 7, 5, 6, 6, 17 ]
[ "passage: TAGS\n#task_categories-feature-extraction #task_categories-text-classification #task_ids-entity-linking-classification #annotations_creators-expert-generated #language_creators-found #multilinguality-monolingual #size_categories-10M<n<100M #source_datasets-original #language-English #license-other #Named Entity Recognition, Coreference Resolution, Relation Extraction, Entity Linking #arxiv-2009.12626 #region-us \n# Dataset Card for DWIE## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Paper: DWIE: an entity-centric dataset for multi-task document-level information extraction\n- Leaderboard: URL\n- Size of downloaded dataset files: 40.8 MB### Dataset Summary\n\nDWIE (Deutsche Welle corpus for Information Extraction) is a new dataset for document-level multi-task Information Extraction (IE).\n\n It combines four main IE sub-tasks:\n\n 1.Named Entity Recognition: 23,130 entities classified in 311 multi-label entity types (tags). \n 2.Coreference Resolution: 43,373 entity mentions clustered in 23,130 entities. \n 3.Relation Extraction: 21,749 annotated relations between entities classified in 65 multi-label relation types. \n 4.Entity Linking: the named entities are linked to Wikipedia (version 20181115). \n\nFor details, see the paper URL", "passage: ### Supported Tasks and Leaderboards\n\n- Tasks: Named Entity Recognition, Coreference Resolution, Relation extraction and entity linking in scientific papers\n- Leaderboards: URL### Languages\n\nThe language in the dataset is English.## Dataset Structure### Data Instances\n\n- Size of downloaded dataset files: 40.8 MB\n \nAn example of 'train' looks as follows, provided sample of the data:" ]
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18e91b14f205d9ce0cff01073b5ba09e05fe27da
# Dataset Card for WikiAnc EN ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks](#supported-tasks) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Additional Information](#additional-information) - [Licensing Information](#licensing-information) ## Dataset Description - **Repository:** [WikiAnc repository](https://github.com/cyanic-selkie/wikianc) ### Dataset Summary The WikiAnc EN datasets is an automatically generated dataset from Wikipedia (en) and Wikidata dumps (March 1, 2023). The code for generating the dataset can be found [here](https://github.com/cyanic-selkie/wikianc). ### Supported Tasks - `wikificiation`: The dataset can be used to train a model for Wikification. ### Languages The text in the dataset is in English. The associated BCP-47 code is `en`. You can find the Croatian version [here](https://huggingface.co/datasets/cyanic-selkie/wikianc-hr). ## Dataset Structure ### Data Instances A typical data point represents a paragraph in a Wikipedia article. The `paragraph_text` field contains the original text in an NFC normalized, UTF-8 encoded string. The `paragraph_anchors` field contains a list of anchors, each represented by a struct with the inclusive starting UTF-8 code point `start` field, exclusive ending UTF-8 code point `end` field, a nullable `qid` field, a nullable `pageid` field, and an NFC normalized, UTF-8 encoded `title` (Wikipedia) field. Additionally, each paragraph has `article_title`, `article_pageid`, and (nullable) `article_qid` fields referring to the article the paragraph came from. There is also a nullable, NFC normalized, UTF-8 encoded `section_heading` field, and an integer `section_level` field referring to the heading (if it exists) of the article section, and the level in the section hierarchy that the paragraph came from. The `qid` fields refers to Wikidata's QID identifiers, while the `pageid` and `title` fields refer to Wikipedia's pageID and title identifiers (there is a one-to-one mapping between pageIDs and titles). **NOTE:** An anchor will always have a `title`, but that doesn't mean it has to have a `pageid`. This is because Wikipedia allows defining anchors to nonexistent articles. An example from the WikiAnc EN test set looks as follows: ``` { "uuid": "5f74e678-944f-4761-a5e0-b6426f6f61b8", "article_title": "Climatius", "article_pageid": 5394373, "article_qid": 867987, "section_heading": null, "section_level": 0, "paragraph_text": "It was a small fish, at 7.5 cm, and to discourage predators, Climatius sported fifteen sharp spines. There was one spine each on the paired pelvic and pectoral fins, and on the aingle anal and two dorsal fins, and a four pairs without fins on the fish's underside.", "paragraph_anchors": [ { "start": 140, "end": 146, "qid": 3335089, "pageid": 56849833, "title": "Pelvic_fin" }, { "start": 151, "end": 159, "qid": 4162555, "pageid": 331956, "title": "Pectoral_fin" }, { "start": 184, "end": 188, "qid": 4162555, "pageid": 331958, "title": "Anal_fin" }, { "start": 197, "end": 208, "qid": 1568355, "pageid": 294244, "title": "Dorsal_fin" } ] } ``` ### Data Fields - `uuid`: a UTF-8 encoded string representing a v4 UUID that uniquely identifies the example - `article_title`: an NFC normalized, UTF-8 encoded Wikipedia title of the article; spaces are replaced with underscores - `article_pageid`: an integer representing the Wikipedia pageID of the article - `article_qid`: an integer representing the Wikidata QID this article refers to; it can be null if the entity didn't exist in Wikidata at the time of the creation of the original dataset - `section_heading`: a nullable, NFC normalized, UTF-8 encoded string representing the section heading - `section_level`: an integer representing the level of the section in the section hierarchy - `paragraph_text`: an NFC normalized, UTF-8 encoded string representing the paragraph - `paragraph_anchors`: a list of structs representing anchors, each anchor has: - `start`: an integer representing the inclusive starting UTF-8 code point of the anchors - `end`: an integer representing the exclusive ending UTF-8 code point of the anchor - `qid`: a nullable integer representing the Wikidata QID this anchor refers to; it can be null if the entity didn't exist in Wikidata at the time of the creation of the original dataset - `pageid`: a nullable integer representing the Wikipedia pageID of the anchor; it can be null if the article didn't exist in Wikipedia at the time of the creation of the original dataset - `title`: an NFC normalized, UTF-8 encoded string representing the Wikipedia title of the anchor; spaces are replaced with underscores; can refer to a nonexistent Wikipedia article ### Data Splits The data is split into training, validation and test sets; paragraphs belonging to the same article aren't necessarily in the same split. The final split sizes are as follows: | | Train | Validation | Test | | :----- | :------: | :-----: | :----: | | WikiAnc EN - articles | 5,883,342 | 2,374,055 | 2,375,830 | | WikiAnc EN - paragraphs | 34,555,183 | 4,317,326 | 4,321,613 | | WikiAnc EN - anchors | 87,060,158 | 10,876,572 | 10,883,232 | | WikiAnc EN - anchors with QIDs | 85,414,610 | 10,671,262 | 10,677,412 | | WikiAnc EN - anchors with pageIDs | 85,421,513 | 10,672,138 | 10,678,262 | **NOTE:** The number of articles in the table above refers to the number of articles that have at least one paragraph belonging to the article appear in the split. ## Additional Information ### Licensing Information The WikiAnc EN dataset is given under the [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0/) license.
cyanic-selkie/wikianc-en
[ "task_categories:token-classification", "size_categories:10M<n<100M", "language:en", "license:cc-by-sa-3.0", "wikidata", "wikipedia", "wikification", "region:us" ]
2023-03-27T08:49:06+00:00
{"language": ["en"], "license": "cc-by-sa-3.0", "size_categories": ["10M<n<100M"], "task_categories": ["token-classification"], "pretty_name": "WikiAnc EN", "tags": ["wikidata", "wikipedia", "wikification"]}
2023-06-02T13:08:17+00:00
[]
[ "en" ]
TAGS #task_categories-token-classification #size_categories-10M<n<100M #language-English #license-cc-by-sa-3.0 #wikidata #wikipedia #wikification #region-us
Dataset Card for WikiAnc EN =========================== Table of Contents ----------------- * Dataset Description + Dataset Summary + Supported Tasks + Languages * Dataset Structure + Data Instances + Data Fields + Data Splits * Additional Information + Licensing Information Dataset Description ------------------- * Repository: WikiAnc repository ### Dataset Summary The WikiAnc EN datasets is an automatically generated dataset from Wikipedia (en) and Wikidata dumps (March 1, 2023). The code for generating the dataset can be found here. ### Supported Tasks * 'wikificiation': The dataset can be used to train a model for Wikification. ### Languages The text in the dataset is in English. The associated BCP-47 code is 'en'. You can find the Croatian version here. Dataset Structure ----------------- ### Data Instances A typical data point represents a paragraph in a Wikipedia article. The 'paragraph\_text' field contains the original text in an NFC normalized, UTF-8 encoded string. The 'paragraph\_anchors' field contains a list of anchors, each represented by a struct with the inclusive starting UTF-8 code point 'start' field, exclusive ending UTF-8 code point 'end' field, a nullable 'qid' field, a nullable 'pageid' field, and an NFC normalized, UTF-8 encoded 'title' (Wikipedia) field. Additionally, each paragraph has 'article\_title', 'article\_pageid', and (nullable) 'article\_qid' fields referring to the article the paragraph came from. There is also a nullable, NFC normalized, UTF-8 encoded 'section\_heading' field, and an integer 'section\_level' field referring to the heading (if it exists) of the article section, and the level in the section hierarchy that the paragraph came from. The 'qid' fields refers to Wikidata's QID identifiers, while the 'pageid' and 'title' fields refer to Wikipedia's pageID and title identifiers (there is a one-to-one mapping between pageIDs and titles). NOTE: An anchor will always have a 'title', but that doesn't mean it has to have a 'pageid'. This is because Wikipedia allows defining anchors to nonexistent articles. An example from the WikiAnc EN test set looks as follows: ### Data Fields * 'uuid': a UTF-8 encoded string representing a v4 UUID that uniquely identifies the example * 'article\_title': an NFC normalized, UTF-8 encoded Wikipedia title of the article; spaces are replaced with underscores * 'article\_pageid': an integer representing the Wikipedia pageID of the article * 'article\_qid': an integer representing the Wikidata QID this article refers to; it can be null if the entity didn't exist in Wikidata at the time of the creation of the original dataset * 'section\_heading': a nullable, NFC normalized, UTF-8 encoded string representing the section heading * 'section\_level': an integer representing the level of the section in the section hierarchy * 'paragraph\_text': an NFC normalized, UTF-8 encoded string representing the paragraph * 'paragraph\_anchors': a list of structs representing anchors, each anchor has: + 'start': an integer representing the inclusive starting UTF-8 code point of the anchors + 'end': an integer representing the exclusive ending UTF-8 code point of the anchor + 'qid': a nullable integer representing the Wikidata QID this anchor refers to; it can be null if the entity didn't exist in Wikidata at the time of the creation of the original dataset + 'pageid': a nullable integer representing the Wikipedia pageID of the anchor; it can be null if the article didn't exist in Wikipedia at the time of the creation of the original dataset + 'title': an NFC normalized, UTF-8 encoded string representing the Wikipedia title of the anchor; spaces are replaced with underscores; can refer to a nonexistent Wikipedia article ### Data Splits The data is split into training, validation and test sets; paragraphs belonging to the same article aren't necessarily in the same split. The final split sizes are as follows: NOTE: The number of articles in the table above refers to the number of articles that have at least one paragraph belonging to the article appear in the split. Additional Information ---------------------- ### Licensing Information The WikiAnc EN dataset is given under the Creative Commons Attribution 4.0 International license.
[ "### Dataset Summary\n\n\nThe WikiAnc EN datasets is an automatically generated dataset from Wikipedia (en) and Wikidata dumps (March 1, 2023).\n\n\nThe code for generating the dataset can be found here.", "### Supported Tasks\n\n\n* 'wikificiation': The dataset can be used to train a model for Wikification.", "### Languages\n\n\nThe text in the dataset is in English. The associated BCP-47 code is 'en'.\n\n\nYou can find the Croatian version here.\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nA typical data point represents a paragraph in a Wikipedia article.\n\n\nThe 'paragraph\\_text' field contains the original text in an NFC normalized, UTF-8 encoded string.\n\n\nThe 'paragraph\\_anchors' field contains a list of anchors, each represented by a struct with the inclusive starting UTF-8 code point 'start' field, exclusive ending UTF-8 code point 'end' field, a nullable 'qid' field, a nullable 'pageid' field, and an NFC normalized, UTF-8 encoded 'title' (Wikipedia) field.\n\n\nAdditionally, each paragraph has 'article\\_title', 'article\\_pageid', and (nullable) 'article\\_qid' fields referring to the article the paragraph came from.\n\n\nThere is also a nullable, NFC normalized, UTF-8 encoded 'section\\_heading' field, and an integer 'section\\_level' field referring to the heading (if it exists) of the article section, and the level in the section hierarchy that the paragraph came from.\n\n\nThe 'qid' fields refers to Wikidata's QID identifiers, while the 'pageid' and 'title' fields refer to Wikipedia's pageID and title identifiers (there is a one-to-one mapping between pageIDs and titles).\n\n\nNOTE: An anchor will always have a 'title', but that doesn't mean it has to have a 'pageid'. This is because Wikipedia allows defining anchors to nonexistent articles.\n\n\nAn example from the WikiAnc EN test set looks as follows:", "### Data Fields\n\n\n* 'uuid': a UTF-8 encoded string representing a v4 UUID that uniquely identifies the example\n* 'article\\_title': an NFC normalized, UTF-8 encoded Wikipedia title of the article; spaces are replaced with underscores\n* 'article\\_pageid': an integer representing the Wikipedia pageID of the article\n* 'article\\_qid': an integer representing the Wikidata QID this article refers to; it can be null if the entity didn't exist in Wikidata at the time of the creation of the original dataset\n* 'section\\_heading': a nullable, NFC normalized, UTF-8 encoded string representing the section heading\n* 'section\\_level': an integer representing the level of the section in the section hierarchy\n* 'paragraph\\_text': an NFC normalized, UTF-8 encoded string representing the paragraph\n* 'paragraph\\_anchors': a list of structs representing anchors, each anchor has:\n\t+ 'start': an integer representing the inclusive starting UTF-8 code point of the anchors\n\t+ 'end': an integer representing the exclusive ending UTF-8 code point of the anchor\n\t+ 'qid': a nullable integer representing the Wikidata QID this anchor refers to; it can be null if the entity didn't exist in Wikidata at the time of the creation of the original dataset\n\t+ 'pageid': a nullable integer representing the Wikipedia pageID of the anchor; it can be null if the article didn't exist in Wikipedia at the time of the creation of the original dataset\n\t+ 'title': an NFC normalized, UTF-8 encoded string representing the Wikipedia title of the anchor; spaces are replaced with underscores; can refer to a nonexistent Wikipedia article", "### Data Splits\n\n\nThe data is split into training, validation and test sets; paragraphs belonging to the same article aren't necessarily in the same split. The final split sizes are as follows:\n\n\n\nNOTE: The number of articles in the table above refers to the number of articles that have at least one paragraph belonging to the article appear in the split.\n\n\nAdditional Information\n----------------------", "### Licensing Information\n\n\nThe WikiAnc EN dataset is given under the Creative Commons Attribution 4.0 International license." ]
[ "TAGS\n#task_categories-token-classification #size_categories-10M<n<100M #language-English #license-cc-by-sa-3.0 #wikidata #wikipedia #wikification #region-us \n", "### Dataset Summary\n\n\nThe WikiAnc EN datasets is an automatically generated dataset from Wikipedia (en) and Wikidata dumps (March 1, 2023).\n\n\nThe code for generating the dataset can be found here.", "### Supported Tasks\n\n\n* 'wikificiation': The dataset can be used to train a model for Wikification.", "### Languages\n\n\nThe text in the dataset is in English. The associated BCP-47 code is 'en'.\n\n\nYou can find the Croatian version here.\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nA typical data point represents a paragraph in a Wikipedia article.\n\n\nThe 'paragraph\\_text' field contains the original text in an NFC normalized, UTF-8 encoded string.\n\n\nThe 'paragraph\\_anchors' field contains a list of anchors, each represented by a struct with the inclusive starting UTF-8 code point 'start' field, exclusive ending UTF-8 code point 'end' field, a nullable 'qid' field, a nullable 'pageid' field, and an NFC normalized, UTF-8 encoded 'title' (Wikipedia) field.\n\n\nAdditionally, each paragraph has 'article\\_title', 'article\\_pageid', and (nullable) 'article\\_qid' fields referring to the article the paragraph came from.\n\n\nThere is also a nullable, NFC normalized, UTF-8 encoded 'section\\_heading' field, and an integer 'section\\_level' field referring to the heading (if it exists) of the article section, and the level in the section hierarchy that the paragraph came from.\n\n\nThe 'qid' fields refers to Wikidata's QID identifiers, while the 'pageid' and 'title' fields refer to Wikipedia's pageID and title identifiers (there is a one-to-one mapping between pageIDs and titles).\n\n\nNOTE: An anchor will always have a 'title', but that doesn't mean it has to have a 'pageid'. This is because Wikipedia allows defining anchors to nonexistent articles.\n\n\nAn example from the WikiAnc EN test set looks as follows:", "### Data Fields\n\n\n* 'uuid': a UTF-8 encoded string representing a v4 UUID that uniquely identifies the example\n* 'article\\_title': an NFC normalized, UTF-8 encoded Wikipedia title of the article; spaces are replaced with underscores\n* 'article\\_pageid': an integer representing the Wikipedia pageID of the article\n* 'article\\_qid': an integer representing the Wikidata QID this article refers to; it can be null if the entity didn't exist in Wikidata at the time of the creation of the original dataset\n* 'section\\_heading': a nullable, NFC normalized, UTF-8 encoded string representing the section heading\n* 'section\\_level': an integer representing the level of the section in the section hierarchy\n* 'paragraph\\_text': an NFC normalized, UTF-8 encoded string representing the paragraph\n* 'paragraph\\_anchors': a list of structs representing anchors, each anchor has:\n\t+ 'start': an integer representing the inclusive starting UTF-8 code point of the anchors\n\t+ 'end': an integer representing the exclusive ending UTF-8 code point of the anchor\n\t+ 'qid': a nullable integer representing the Wikidata QID this anchor refers to; it can be null if the entity didn't exist in Wikidata at the time of the creation of the original dataset\n\t+ 'pageid': a nullable integer representing the Wikipedia pageID of the anchor; it can be null if the article didn't exist in Wikipedia at the time of the creation of the original dataset\n\t+ 'title': an NFC normalized, UTF-8 encoded string representing the Wikipedia title of the anchor; spaces are replaced with underscores; can refer to a nonexistent Wikipedia article", "### Data Splits\n\n\nThe data is split into training, validation and test sets; paragraphs belonging to the same article aren't necessarily in the same split. The final split sizes are as follows:\n\n\n\nNOTE: The number of articles in the table above refers to the number of articles that have at least one paragraph belonging to the article appear in the split.\n\n\nAdditional Information\n----------------------", "### Licensing Information\n\n\nThe WikiAnc EN dataset is given under the Creative Commons Attribution 4.0 International license." ]
[ 54, 51, 28, 41, 379, 436, 86, 24 ]
[ "passage: TAGS\n#task_categories-token-classification #size_categories-10M<n<100M #language-English #license-cc-by-sa-3.0 #wikidata #wikipedia #wikification #region-us \n### Dataset Summary\n\n\nThe WikiAnc EN datasets is an automatically generated dataset from Wikipedia (en) and Wikidata dumps (March 1, 2023).\n\n\nThe code for generating the dataset can be found here.### Supported Tasks\n\n\n* 'wikificiation': The dataset can be used to train a model for Wikification.### Languages\n\n\nThe text in the dataset is in English. The associated BCP-47 code is 'en'.\n\n\nYou can find the Croatian version here.\n\n\nDataset Structure\n-----------------", "passage: ### Data Instances\n\n\nA typical data point represents a paragraph in a Wikipedia article.\n\n\nThe 'paragraph\\_text' field contains the original text in an NFC normalized, UTF-8 encoded string.\n\n\nThe 'paragraph\\_anchors' field contains a list of anchors, each represented by a struct with the inclusive starting UTF-8 code point 'start' field, exclusive ending UTF-8 code point 'end' field, a nullable 'qid' field, a nullable 'pageid' field, and an NFC normalized, UTF-8 encoded 'title' (Wikipedia) field.\n\n\nAdditionally, each paragraph has 'article\\_title', 'article\\_pageid', and (nullable) 'article\\_qid' fields referring to the article the paragraph came from.\n\n\nThere is also a nullable, NFC normalized, UTF-8 encoded 'section\\_heading' field, and an integer 'section\\_level' field referring to the heading (if it exists) of the article section, and the level in the section hierarchy that the paragraph came from.\n\n\nThe 'qid' fields refers to Wikidata's QID identifiers, while the 'pageid' and 'title' fields refer to Wikipedia's pageID and title identifiers (there is a one-to-one mapping between pageIDs and titles).\n\n\nNOTE: An anchor will always have a 'title', but that doesn't mean it has to have a 'pageid'. This is because Wikipedia allows defining anchors to nonexistent articles.\n\n\nAn example from the WikiAnc EN test set looks as follows:### Data Fields\n\n\n* 'uuid': a UTF-8 encoded string representing a v4 UUID that uniquely identifies the example\n* 'article\\_title': an NFC normalized, UTF-8 encoded Wikipedia title of the article; spaces are replaced with underscores\n* 'article\\_pageid': an integer representing the Wikipedia pageID of the article\n* 'article\\_qid': an integer representing the Wikidata QID this article refers to; it can be null if the entity didn't exist in Wikidata at the time of the creation of the original dataset\n* 'section\\_heading': a nullable, NFC normalized, UTF-8 encoded string representing the section heading\n* 'section\\_level': an integer representing the level of the section in the section hierarchy\n* 'paragraph\\_text': an NFC normalized, UTF-8 encoded string representing the paragraph\n* 'paragraph\\_anchors': a list of structs representing anchors, each anchor has:\n\t+ 'start': an integer representing the inclusive starting UTF-8 code point of the anchors\n\t+ 'end': an integer representing the exclusive ending UTF-8 code point of the anchor\n\t+ 'qid': a nullable integer representing the Wikidata QID this anchor refers to; it can be null if the entity didn't exist in Wikidata at the time of the creation of the original dataset\n\t+ 'pageid': a nullable integer representing the Wikipedia pageID of the anchor; it can be null if the article didn't exist in Wikipedia at the time of the creation of the original dataset\n\t+ 'title': an NFC normalized, UTF-8 encoded string representing the Wikipedia title of the anchor; spaces are replaced with underscores; can refer to a nonexistent Wikipedia article" ]
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b34e116c53646cd4e95a97c32196e831e025593d
# Dataset Card for "logo-combined-exclusive" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Babypotatotang/logo-combined-exclusive
[ "region:us" ]
2023-03-27T09:27:47+00:00
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 166362980.253, "num_examples": 12907}], "download_size": 166890745, "dataset_size": 166362980.253}}
2023-03-27T09:30:12+00:00
[]
[]
TAGS #region-us
# Dataset Card for "logo-combined-exclusive" More Information needed
[ "# Dataset Card for \"logo-combined-exclusive\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"logo-combined-exclusive\"\n\nMore Information needed" ]
[ 6, 19 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"logo-combined-exclusive\"\n\nMore Information needed" ]
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70da6c990bfb71357b2115bc6e2383c2db5c388f
# Dataset Card for "ada-no-pii_checks" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
loubnabnl/ada-no-pii_checks
[ "region:us" ]
2023-03-27T09:40:47+00:00
{"dataset_info": {"features": [{"name": "entities", "list": [{"name": "context", "dtype": "string"}, {"name": "end", "dtype": "int64"}, {"name": "score", "dtype": "float32"}, {"name": "start", "dtype": "int64"}, {"name": "tag", "dtype": "string"}, {"name": "value", "dtype": "string"}]}, {"name": "max_stars_repo_path", "dtype": "string"}, {"name": "max_stars_repo_name", "dtype": "string"}, {"name": "max_stars_count", "dtype": "int64"}, {"name": "content", "dtype": "string"}, {"name": "id", "dtype": "string"}, {"name": "new_content", "dtype": "string"}, {"name": "modified", "dtype": "bool"}, {"name": "references", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 276915088.37363416, "num_examples": 10886}], "download_size": 100410446, "dataset_size": 276915088.37363416}}
2023-03-27T10:48:03+00:00
[]
[]
TAGS #region-us
# Dataset Card for "ada-no-pii_checks" More Information needed
[ "# Dataset Card for \"ada-no-pii_checks\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"ada-no-pii_checks\"\n\nMore Information needed" ]
[ 6, 19 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"ada-no-pii_checks\"\n\nMore Information needed" ]
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5b65608081d3b6d89d5bc12c464bec55f59480d1
# Mathematics StackExchange Dataset This dataset contains questions and answers from Mathematics StackExchange (math.stackexchange.com). The data was collected using the Stack Exchange API. Total collected questions 465.295. ## Data Format The dataset is provided in JSON Lines format, with one JSON object per line. Each object contains the following fields: - `id`: the unique ID of the question - `asked_at`: the timestamp when the question was asked - `author_name`: the name of the author who asked the question - `author_rep`: the reputation of the author who asked the question - `score`: the score of the question - `title`: the title of the question - `tags`: a list of tags associated with the question - `body`: the body of the question - `comments`: a list of comments on the question, where each comment is represented as a dictionary with the following fields: - `id`: the unique ID of the comment - `body`: the body of the comment - `at`: the timestamp when the comment was posted - `score`: the score of the comment - `author`: the name of the author who posted the comment - `author_rep`: the reputation of the author who posted the comment - `answers`: a list of answers to the question, where each answer is represented as a dictionary with the following fields: - `id`: the unique ID of the answer - `body`: the body of the answer - `score`: the score of the answer - `ts`: the timestamp when the answer was posted - `author`: the name of the author who posted the answer - `author_rep`: the reputation of the author who posted the answer - `accepted`: whether the answer has been accepted - `comments`: a list of comments on the answer, where each comment is represented as a dictionary with the following fields: - `id`: the unique ID of the comment - `body`: the body of the comment - `at`: the timestamp when the comment was posted - `score`: the score of the comment - `author`: the name of the author who posted the comment - `author_rep`: the reputation of the author who posted the comment ## Preprocessing There was no preprocessing done, this dataset contains raw unfiltered data, also there might be problems with redundant line breaks or spacings ## License This dataset is released under the [WTFPL](http://www.wtfpl.net/txt/copying/) license. ## Contact For any questions or comments about the dataset, please contact [email protected].
nurik040404/mse
[ "task_categories:question-answering", "task_categories:text-generation", "task_categories:text-classification", "task_ids:closed-domain-qa", "task_ids:extractive-qa", "task_ids:open-domain-qa", "task_ids:dialogue-modeling", "task_ids:language-modeling", "task_ids:acceptability-classification", "task_ids:text-scoring", "annotations_creators:no-annotation", "language_creators:machine-generated", "multilinguality:monolingual", "size_categories:100K<n<1M", "source_datasets:original", "language:en", "license:wtfpl", "math", "region:us" ]
2023-03-27T09:43:46+00:00
{"annotations_creators": ["no-annotation"], "language_creators": ["machine-generated"], "language": ["en"], "license": "wtfpl", "multilinguality": ["monolingual"], "size_categories": ["100K<n<1M"], "source_datasets": ["original"], "task_categories": ["question-answering", "text-generation", "text-classification"], "task_ids": ["closed-domain-qa", "extractive-qa", "open-domain-qa", "dialogue-modeling", "language-modeling", "acceptability-classification", "text-scoring"], "pretty_name": "math-stackexchange-qa", "tags": ["math"]}
2023-03-30T21:49:43+00:00
[]
[ "en" ]
TAGS #task_categories-question-answering #task_categories-text-generation #task_categories-text-classification #task_ids-closed-domain-qa #task_ids-extractive-qa #task_ids-open-domain-qa #task_ids-dialogue-modeling #task_ids-language-modeling #task_ids-acceptability-classification #task_ids-text-scoring #annotations_creators-no-annotation #language_creators-machine-generated #multilinguality-monolingual #size_categories-100K<n<1M #source_datasets-original #language-English #license-wtfpl #math #region-us
# Mathematics StackExchange Dataset This dataset contains questions and answers from Mathematics StackExchange (URL). The data was collected using the Stack Exchange API. Total collected questions 465.295. ## Data Format The dataset is provided in JSON Lines format, with one JSON object per line. Each object contains the following fields: - 'id': the unique ID of the question - 'asked_at': the timestamp when the question was asked - 'author_name': the name of the author who asked the question - 'author_rep': the reputation of the author who asked the question - 'score': the score of the question - 'title': the title of the question - 'tags': a list of tags associated with the question - 'body': the body of the question - 'comments': a list of comments on the question, where each comment is represented as a dictionary with the following fields: - 'id': the unique ID of the comment - 'body': the body of the comment - 'at': the timestamp when the comment was posted - 'score': the score of the comment - 'author': the name of the author who posted the comment - 'author_rep': the reputation of the author who posted the comment - 'answers': a list of answers to the question, where each answer is represented as a dictionary with the following fields: - 'id': the unique ID of the answer - 'body': the body of the answer - 'score': the score of the answer - 'ts': the timestamp when the answer was posted - 'author': the name of the author who posted the answer - 'author_rep': the reputation of the author who posted the answer - 'accepted': whether the answer has been accepted - 'comments': a list of comments on the answer, where each comment is represented as a dictionary with the following fields: - 'id': the unique ID of the comment - 'body': the body of the comment - 'at': the timestamp when the comment was posted - 'score': the score of the comment - 'author': the name of the author who posted the comment - 'author_rep': the reputation of the author who posted the comment ## Preprocessing There was no preprocessing done, this dataset contains raw unfiltered data, also there might be problems with redundant line breaks or spacings ## License This dataset is released under the WTFPL license. ## Contact For any questions or comments about the dataset, please contact nurik040404@URL.
[ "# Mathematics StackExchange Dataset\n\nThis dataset contains questions and answers from Mathematics StackExchange (URL). The data was collected using the Stack Exchange API. Total collected questions 465.295.", "## Data Format\n\nThe dataset is provided in JSON Lines format, with one JSON object per line. Each object contains the following fields:\n\n- 'id': the unique ID of the question\n- 'asked_at': the timestamp when the question was asked\n- 'author_name': the name of the author who asked the question\n- 'author_rep': the reputation of the author who asked the question\n- 'score': the score of the question\n- 'title': the title of the question\n- 'tags': a list of tags associated with the question\n- 'body': the body of the question\n- 'comments': a list of comments on the question, where each comment is represented as a dictionary with the following fields:\n - 'id': the unique ID of the comment\n - 'body': the body of the comment\n - 'at': the timestamp when the comment was posted\n - 'score': the score of the comment\n - 'author': the name of the author who posted the comment\n - 'author_rep': the reputation of the author who posted the comment\n- 'answers': a list of answers to the question, where each answer is represented as a dictionary with the following fields:\n - 'id': the unique ID of the answer\n - 'body': the body of the answer\n - 'score': the score of the answer\n - 'ts': the timestamp when the answer was posted\n - 'author': the name of the author who posted the answer\n - 'author_rep': the reputation of the author who posted the answer\n - 'accepted': whether the answer has been accepted\n - 'comments': a list of comments on the answer, where each comment is represented as a dictionary with the following fields:\n - 'id': the unique ID of the comment\n - 'body': the body of the comment\n - 'at': the timestamp when the comment was posted\n - 'score': the score of the comment\n - 'author': the name of the author who posted the comment\n - 'author_rep': the reputation of the author who posted the comment", "## Preprocessing\n\nThere was no preprocessing done, this dataset contains raw unfiltered data, also there might be problems with redundant line breaks or spacings", "## License\n\nThis dataset is released under the WTFPL license.", "## Contact\n\nFor any questions or comments about the dataset, please contact nurik040404@URL." ]
[ "TAGS\n#task_categories-question-answering #task_categories-text-generation #task_categories-text-classification #task_ids-closed-domain-qa #task_ids-extractive-qa #task_ids-open-domain-qa #task_ids-dialogue-modeling #task_ids-language-modeling #task_ids-acceptability-classification #task_ids-text-scoring #annotations_creators-no-annotation #language_creators-machine-generated #multilinguality-monolingual #size_categories-100K<n<1M #source_datasets-original #language-English #license-wtfpl #math #region-us \n", "# Mathematics StackExchange Dataset\n\nThis dataset contains questions and answers from Mathematics StackExchange (URL). The data was collected using the Stack Exchange API. Total collected questions 465.295.", "## Data Format\n\nThe dataset is provided in JSON Lines format, with one JSON object per line. Each object contains the following fields:\n\n- 'id': the unique ID of the question\n- 'asked_at': the timestamp when the question was asked\n- 'author_name': the name of the author who asked the question\n- 'author_rep': the reputation of the author who asked the question\n- 'score': the score of the question\n- 'title': the title of the question\n- 'tags': a list of tags associated with the question\n- 'body': the body of the question\n- 'comments': a list of comments on the question, where each comment is represented as a dictionary with the following fields:\n - 'id': the unique ID of the comment\n - 'body': the body of the comment\n - 'at': the timestamp when the comment was posted\n - 'score': the score of the comment\n - 'author': the name of the author who posted the comment\n - 'author_rep': the reputation of the author who posted the comment\n- 'answers': a list of answers to the question, where each answer is represented as a dictionary with the following fields:\n - 'id': the unique ID of the answer\n - 'body': the body of the answer\n - 'score': the score of the answer\n - 'ts': the timestamp when the answer was posted\n - 'author': the name of the author who posted the answer\n - 'author_rep': the reputation of the author who posted the answer\n - 'accepted': whether the answer has been accepted\n - 'comments': a list of comments on the answer, where each comment is represented as a dictionary with the following fields:\n - 'id': the unique ID of the comment\n - 'body': the body of the comment\n - 'at': the timestamp when the comment was posted\n - 'score': the score of the comment\n - 'author': the name of the author who posted the comment\n - 'author_rep': the reputation of the author who posted the comment", "## Preprocessing\n\nThere was no preprocessing done, this dataset contains raw unfiltered data, also there might be problems with redundant line breaks or spacings", "## License\n\nThis dataset is released under the WTFPL license.", "## Contact\n\nFor any questions or comments about the dataset, please contact nurik040404@URL." ]
[ 185, 50, 487, 39, 14, 22 ]
[ "passage: TAGS\n#task_categories-question-answering #task_categories-text-generation #task_categories-text-classification #task_ids-closed-domain-qa #task_ids-extractive-qa #task_ids-open-domain-qa #task_ids-dialogue-modeling #task_ids-language-modeling #task_ids-acceptability-classification #task_ids-text-scoring #annotations_creators-no-annotation #language_creators-machine-generated #multilinguality-monolingual #size_categories-100K<n<1M #source_datasets-original #language-English #license-wtfpl #math #region-us \n# Mathematics StackExchange Dataset\n\nThis dataset contains questions and answers from Mathematics StackExchange (URL). The data was collected using the Stack Exchange API. Total collected questions 465.295." ]
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e3797e49ee4769fe75414491fad55e3f56bb282f
# SourceData Dataset > The largest annotated biomedical corpus for machine learning and AI in the publishing context. SourceData is the largest annotated biomedical dataset for NER and NEL. It is unique on its focus on the core of scientific evidence: figure captions. It is also unique on its real-world configuration, since it does not present isolated sentences out of more general context. It offers full annotated figure captions that can be further enriched in context using full text, abstracts, or titles. The goal is to extract the nature of the experiments on them described. SourceData presents also its uniqueness by labelling the causal relationship between biological entities present in experiments, assigning experimental roles to each biomedical entity present in the corpus. SourceData consistently annotates nine different biological entities (genes, proteins, cells, tissues, subcellular components, species, small molecules, and diseases). It is the first dataset annotating experimental assays and the roles played on them by the biological entities. Each entity is linked to their correspondent ontology, allowing for entity disambiguation and NEL. ## Cite our work ```latex @ARTICLE{2023arXiv231020440A, author = {{Abreu-Vicente}, Jorge and {Sonntag}, Hannah and {Eidens}, Thomas and {Lemberger}, Thomas}, title = "{The SourceData-NLP dataset: integrating curation into scientific publishing for training large language models}", journal = {arXiv e-prints}, keywords = {Computer Science - Computation and Language}, year = 2023, month = oct, eid = {arXiv:2310.20440}, pages = {arXiv:2310.20440}, archivePrefix = {arXiv}, eprint = {2310.20440}, primaryClass = {cs.CL}, adsurl = {https://ui.adsabs.harvard.edu/abs/2023arXiv231020440A}, adsnote = {Provided by the SAO/NASA Astrophysics Data System} } @article {Liechti2017, author = {Liechti, Robin and George, Nancy and Götz, Lou and El-Gebali, Sara and Chasapi, Anastasia and Crespo, Isaac and Xenarios, Ioannis and Lemberger, Thomas}, title = {SourceData - a semantic platform for curating and searching figures}, year = {2017}, volume = {14}, number = {11}, doi = {10.1038/nmeth.4471}, URL = {https://doi.org/10.1038/nmeth.4471}, eprint = {https://www.biorxiv.org/content/early/2016/06/20/058529.full.pdf}, journal = {Nature Methods} } ``` ## Dataset usage The dataset has a semantic versioning. Specifying the version at loaded will give different versions. Below we is shown the code needed to load the latest available version of the dataset. Check below at `Changelog` to see the changes in the different versions. ```python from datasets import load_dataset # Load NER ds = load_dataset("EMBO/SourceData", "NER", version="2.0.3") # Load PANELIZATION ds = load_dataset("EMBO/SourceData", "PANELIZATION", version="2.0.3") # Load GENEPROD ROLES ds = load_dataset("EMBO/SourceData", "ROLES_GP", version="2.0.3") # Load SMALL MOLECULE ROLES ds = load_dataset("EMBO/SourceData", "ROLES_SM", version="2.0.3") # Load MULTI ROLES ds = load_dataset("EMBO/SourceData", "ROLES_MULTI", version="2.0.3") ``` ## Dataset Description - **Homepage:** https://sourcedata.embo.org - **Repository:** https://github.com/source-data/soda-data - **Paper:** - **Leaderboard:** - **Point of Contact:** [email protected], [email protected] Note that we offer the `XML` serialized dataset. This includes all the data needed to perform NEL in SourceData. For reproducibility, for each big version of the dataset we provide `split_vx.y.z.json` files to generate the train, validation, test splits. ### Supported Tasks and Leaderboards Tags are provided as [IOB2-style tags](https://en.wikipedia.org/wiki/Inside%E2%80%93outside%E2%80%93beginning_(tagging)). `PANELIZATION`: figure captions (or figure legends) are usually composed of segments that each refer to one of several 'panels' of the full figure. Panels tend to represent results obtained with a coherent method and depicts data points that can be meaningfully compared to each other. `PANELIZATION` provide the start (B-PANEL_START) of these segments and allow to train for recogntion of the boundary between consecutive panel lengends. `NER`: biological and chemical entities are labeled. Specifically the following entities are tagged: - `SMALL_MOLECULE`: small molecules - `GENEPROD`: gene products (genes and proteins) - `SUBCELLULAR`: subcellular components - `CELL_LINE`: cell lines - `CELL_TYPE`: cell types - `TISSUE`: tissues and organs - `ORGANISM`: species - `DISEASE`: diseases (see limitations) - `EXP_ASSAY`: experimental assays `ROLES`: the role of entities with regard to the causal hypotheses tested in the reported results. The tags are: - `CONTROLLED_VAR`: entities that are associated with experimental variables and that subjected to controlled and targeted perturbations. - `MEASURED_VAR`: entities that are associated with the variables measured and the object of the measurements. In the case of experimental roles, it is generated separatedly for `GENEPROD` and `SMALL_MOL` and there is also the `ROLES_MULTI` that takes both at the same time. ### Languages The text in the dataset is English. ## Dataset Structure ### Data Instances ### Data Fields - `words`: `list` of `strings` text tokenized into words. - `panel_id`: ID of the panel to which the example belongs to in the SourceData database. - `label_ids`: - `entity_types`: `list` of `strings` for the IOB2 tags for entity type; possible value in `["O", "I-SMALL_MOLECULE", "B-SMALL_MOLECULE", "I-GENEPROD", "B-GENEPROD", "I-SUBCELLULAR", "B-SUBCELLULAR", "I-CELL_LINE", "B-CELL_LINE", "I-CELL_TYPE", "B-CELL_TYPE", "I-TISSUE", "B-TISSUE", "I-ORGANISM", "B-ORGANISM", "I-EXP_ASSAY", "B-EXP_ASSAY"]` - `roles`: `list` of `strings` for the IOB2 tags for experimental roles; values in `["O", "I-CONTROLLED_VAR", "B-CONTROLLED_VAR", "I-MEASURED_VAR", "B-MEASURED_VAR"]` - `panel_start`: `list` of `strings` for IOB2 tags `["O", "B-PANEL_START"]` - `multi roles`: There are two different label sets. `labels` is like in `roles`. `is_category` tags `GENEPROD` and `SMALL_MOLECULE`. ### Data Splits * NER and ROLES ``` DatasetDict({ train: Dataset({ features: ['words', 'labels', 'tag_mask', 'text'], num_rows: 55250 }) test: Dataset({ features: ['words', 'labels', 'tag_mask', 'text'], num_rows: 6844 }) validation: Dataset({ features: ['words', 'labels', 'tag_mask', 'text'], num_rows: 7951 }) }) ``` * PANELIZATION ``` DatasetDict({ train: Dataset({ features: ['words', 'labels', 'tag_mask'], num_rows: 14655 }) test: Dataset({ features: ['words', 'labels', 'tag_mask'], num_rows: 1871 }) validation: Dataset({ features: ['words', 'labels', 'tag_mask'], num_rows: 2088 }) }) ``` ## Dataset Creation ### Curation Rationale The dataset was built to train models for the automatic extraction of a knowledge graph based from the scientific literature. The dataset can be used to train models for text segmentation, named entity recognition and semantic role labeling. ### Source Data #### Initial Data Collection and Normalization Figure legends were annotated according to the SourceData framework described in Liechti et al 2017 (Nature Methods, 2017, https://doi.org/10.1038/nmeth.4471). The curation tool at https://curation.sourcedata.io was used to segment figure legends into panel legends, tag enities, assign experiemental roles and normalize with standard identifiers (not available in this dataset). The source data was downloaded from the SourceData API (https://api.sourcedata.io) on 21 Jan 2021. #### Who are the source language producers? The examples are extracted from the figure legends from scientific papers in cell and molecular biology. ### Annotations #### Annotation process The annotations were produced manually with expert curators from the SourceData project (https://sourcedata.embo.org) #### Who are the annotators? Curators of the SourceData project. ### Personal and Sensitive Information None known. ## Considerations for Using the Data ### Social Impact of Dataset Not applicable. ### Discussion of Biases The examples are heavily biased towards cell and molecular biology and are enriched in examples from papers published in EMBO Press journals (https://embopress.org) The annotation of diseases has been added recently to the dataset. Although they appear, the number is very low and they are not consistently tagged through the entire dataset. We recommend to use the diseases by filtering the examples that contain them. ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators Thomas Lemberger, EMBO. Jorge Abreu Vicente, EMBO ### Licensing Information CC BY 4.0 ### Citation Information We are currently working on a paper to present the dataset. It is expected to be ready by 2023 spring. In the meantime, the following paper should be cited. ```latex @article {Liechti2017, author = {Liechti, Robin and George, Nancy and Götz, Lou and El-Gebali, Sara and Chasapi, Anastasia and Crespo, Isaac and Xenarios, Ioannis and Lemberger, Thomas}, title = {SourceData - a semantic platform for curating and searching figures}, year = {2017}, volume = {14}, number = {11}, doi = {10.1038/nmeth.4471}, URL = {https://doi.org/10.1038/nmeth.4471}, eprint = {https://www.biorxiv.org/content/early/2016/06/20/058529.full.pdf}, journal = {Nature Methods} } ``` ### Contributions Thanks to [@tlemberger](https://github.com/tlemberger>) and [@drAbreu](https://github.com/drAbreu>) for adding this dataset. ## Changelog * **v2.0.3** - Data curated until 20.09.2023. Correction of 2,000+ unnormalized cell entities that have been now divided into cell line and cell type. Specially relevant for NER, not that important for NEL. * **v2.0.2** - Data curated until 20.09.2023. This version will also include the patch for milti-word generic terms. * **v1.0.2** - Modification of the generic patch in v1.0.1 to include generic terms of more than a word. * **v1.0.1** - Added a first patch of generic terms. Terms such as cells, fluorescence, or animals where originally tagged, but in this version they are removed. * **v1.0.0** - First publicly available version of the dataset. Data curated until March 2023.
EMBO/SourceData
[ "task_categories:token-classification", "size_categories:10K<n<100K", "language:en", "license:cc-by-4.0", "biology", "medical", "NER", "NEL", "arxiv:2310.20440", "doi:10.57967/hf/0495", "region:us" ]
2023-03-27T10:19:24+00:00
{"language": ["en"], "license": "cc-by-4.0", "size_categories": ["10K<n<100K"], "task_categories": ["token-classification"], "pretty_name": "SODA-NLP", "tags": ["biology", "medical", "NER", "NEL"]}
2023-11-22T20:11:49+00:00
[ "2310.20440" ]
[ "en" ]
TAGS #task_categories-token-classification #size_categories-10K<n<100K #language-English #license-cc-by-4.0 #biology #medical #NER #NEL #arxiv-2310.20440 #doi-10.57967/hf/0495 #region-us
# SourceData Dataset > The largest annotated biomedical corpus for machine learning and AI in the publishing context. SourceData is the largest annotated biomedical dataset for NER and NEL. It is unique on its focus on the core of scientific evidence: figure captions. It is also unique on its real-world configuration, since it does not present isolated sentences out of more general context. It offers full annotated figure captions that can be further enriched in context using full text, abstracts, or titles. The goal is to extract the nature of the experiments on them described. SourceData presents also its uniqueness by labelling the causal relationship between biological entities present in experiments, assigning experimental roles to each biomedical entity present in the corpus. SourceData consistently annotates nine different biological entities (genes, proteins, cells, tissues, subcellular components, species, small molecules, and diseases). It is the first dataset annotating experimental assays and the roles played on them by the biological entities. Each entity is linked to their correspondent ontology, allowing for entity disambiguation and NEL. ## Cite our work ## Dataset usage The dataset has a semantic versioning. Specifying the version at loaded will give different versions. Below we is shown the code needed to load the latest available version of the dataset. Check below at 'Changelog' to see the changes in the different versions. ## Dataset Description - Homepage: URL - Repository: URL - Paper: - Leaderboard: - Point of Contact: thomas.lemberger@URL, URL@URL Note that we offer the 'XML' serialized dataset. This includes all the data needed to perform NEL in SourceData. For reproducibility, for each big version of the dataset we provide 'split_vx.y.z.json' files to generate the train, validation, test splits. ### Supported Tasks and Leaderboards Tags are provided as IOB2-style tags). 'PANELIZATION': figure captions (or figure legends) are usually composed of segments that each refer to one of several 'panels' of the full figure. Panels tend to represent results obtained with a coherent method and depicts data points that can be meaningfully compared to each other. 'PANELIZATION' provide the start (B-PANEL_START) of these segments and allow to train for recogntion of the boundary between consecutive panel lengends. 'NER': biological and chemical entities are labeled. Specifically the following entities are tagged: - 'SMALL_MOLECULE': small molecules - 'GENEPROD': gene products (genes and proteins) - 'SUBCELLULAR': subcellular components - 'CELL_LINE': cell lines - 'CELL_TYPE': cell types - 'TISSUE': tissues and organs - 'ORGANISM': species - 'DISEASE': diseases (see limitations) - 'EXP_ASSAY': experimental assays 'ROLES': the role of entities with regard to the causal hypotheses tested in the reported results. The tags are: - 'CONTROLLED_VAR': entities that are associated with experimental variables and that subjected to controlled and targeted perturbations. - 'MEASURED_VAR': entities that are associated with the variables measured and the object of the measurements. In the case of experimental roles, it is generated separatedly for 'GENEPROD' and 'SMALL_MOL' and there is also the 'ROLES_MULTI' that takes both at the same time. ### Languages The text in the dataset is English. ## Dataset Structure ### Data Instances ### Data Fields - 'words': 'list' of 'strings' text tokenized into words. - 'panel_id': ID of the panel to which the example belongs to in the SourceData database. - 'label_ids': - 'entity_types': 'list' of 'strings' for the IOB2 tags for entity type; possible value in '["O", "I-SMALL_MOLECULE", "B-SMALL_MOLECULE", "I-GENEPROD", "B-GENEPROD", "I-SUBCELLULAR", "B-SUBCELLULAR", "I-CELL_LINE", "B-CELL_LINE", "I-CELL_TYPE", "B-CELL_TYPE", "I-TISSUE", "B-TISSUE", "I-ORGANISM", "B-ORGANISM", "I-EXP_ASSAY", "B-EXP_ASSAY"]' - 'roles': 'list' of 'strings' for the IOB2 tags for experimental roles; values in '["O", "I-CONTROLLED_VAR", "B-CONTROLLED_VAR", "I-MEASURED_VAR", "B-MEASURED_VAR"]' - 'panel_start': 'list' of 'strings' for IOB2 tags '["O", "B-PANEL_START"]' - 'multi roles': There are two different label sets. 'labels' is like in 'roles'. 'is_category' tags 'GENEPROD' and 'SMALL_MOLECULE'. ### Data Splits * NER and ROLES * PANELIZATION ## Dataset Creation ### Curation Rationale The dataset was built to train models for the automatic extraction of a knowledge graph based from the scientific literature. The dataset can be used to train models for text segmentation, named entity recognition and semantic role labeling. ### Source Data #### Initial Data Collection and Normalization Figure legends were annotated according to the SourceData framework described in Liechti et al 2017 (Nature Methods, 2017, URL The curation tool at URL was used to segment figure legends into panel legends, tag enities, assign experiemental roles and normalize with standard identifiers (not available in this dataset). The source data was downloaded from the SourceData API (URL) on 21 Jan 2021. #### Who are the source language producers? The examples are extracted from the figure legends from scientific papers in cell and molecular biology. ### Annotations #### Annotation process The annotations were produced manually with expert curators from the SourceData project (URL) #### Who are the annotators? Curators of the SourceData project. ### Personal and Sensitive Information None known. ## Considerations for Using the Data ### Social Impact of Dataset Not applicable. ### Discussion of Biases The examples are heavily biased towards cell and molecular biology and are enriched in examples from papers published in EMBO Press journals (URL) The annotation of diseases has been added recently to the dataset. Although they appear, the number is very low and they are not consistently tagged through the entire dataset. We recommend to use the diseases by filtering the examples that contain them. ### Other Known Limitations ## Additional Information ### Dataset Curators Thomas Lemberger, EMBO. Jorge Abreu Vicente, EMBO ### Licensing Information CC BY 4.0 We are currently working on a paper to present the dataset. It is expected to be ready by 2023 spring. In the meantime, the following paper should be cited. ### Contributions Thanks to @tlemberger and @drAbreu for adding this dataset. ## Changelog * v2.0.3 - Data curated until 20.09.2023. Correction of 2,000+ unnormalized cell entities that have been now divided into cell line and cell type. Specially relevant for NER, not that important for NEL. * v2.0.2 - Data curated until 20.09.2023. This version will also include the patch for milti-word generic terms. * v1.0.2 - Modification of the generic patch in v1.0.1 to include generic terms of more than a word. * v1.0.1 - Added a first patch of generic terms. Terms such as cells, fluorescence, or animals where originally tagged, but in this version they are removed. * v1.0.0 - First publicly available version of the dataset. Data curated until March 2023.
[ "# SourceData Dataset\n\n> The largest annotated biomedical corpus for machine learning and AI in the publishing context.\n\nSourceData is the largest annotated biomedical dataset for NER and NEL.\nIt is unique on its focus on the core of scientific evidence:\nfigure captions. It is also unique on its real-world configuration, since it does not\npresent isolated sentences out of more general context. It offers full annotated figure\ncaptions that can be further enriched in context using full text, abstracts, or titles.\nThe goal is to extract the nature of the experiments on them described.\nSourceData presents also its uniqueness by labelling the causal relationship\nbetween biological entities present in experiments, assigning experimental roles\nto each biomedical entity present in the corpus.\n\nSourceData consistently annotates\nnine different biological entities (genes, proteins, cells, tissues,\nsubcellular components, species, small molecules, and diseases). It is\nthe first dataset annotating experimental assays\nand the roles played on them by the biological entities.\nEach entity is linked to their correspondent ontology, allowing\nfor entity disambiguation and NEL.", "## Cite our work", "## Dataset usage\n\nThe dataset has a semantic versioning. \nSpecifying the version at loaded will give different versions. \nBelow we is shown the code needed to load the latest available version of the dataset.\nCheck below at 'Changelog' to see the changes in the different versions.", "## Dataset Description\n- Homepage: URL\n- Repository: URL\n- Paper:\n- Leaderboard:\n- Point of Contact: thomas.lemberger@URL, URL@URL\n\nNote that we offer the 'XML' serialized dataset. This includes all the data needed to perform NEL in SourceData. \nFor reproducibility, for each big version of the dataset we provide 'split_vx.y.z.json' files to generate the \ntrain, validation, test splits.", "### Supported Tasks and Leaderboards\nTags are provided as IOB2-style tags).\n'PANELIZATION': figure captions (or figure legends) are usually composed of segments that each refer to one of several 'panels' of the full figure. Panels tend to represent results obtained with a coherent method and depicts data points that can be meaningfully compared to each other. 'PANELIZATION' provide the start (B-PANEL_START) of these segments and allow to train for recogntion of the boundary between consecutive panel lengends.\n'NER': biological and chemical entities are labeled. Specifically the following entities are tagged:\n- 'SMALL_MOLECULE': small molecules\n- 'GENEPROD': gene products (genes and proteins)\n- 'SUBCELLULAR': subcellular components\n- 'CELL_LINE': cell lines\n- 'CELL_TYPE': cell types\n- 'TISSUE': tissues and organs\n- 'ORGANISM': species\n- 'DISEASE': diseases (see limitations)\n- 'EXP_ASSAY': experimental assays\n'ROLES': the role of entities with regard to the causal hypotheses tested in the reported results. The tags are:\n- 'CONTROLLED_VAR': entities that are associated with experimental variables and that subjected to controlled and targeted perturbations.\n- 'MEASURED_VAR': entities that are associated with the variables measured and the object of the measurements.\n\nIn the case of experimental roles, it is generated separatedly for 'GENEPROD' and 'SMALL_MOL' and there is also the 'ROLES_MULTI'\nthat takes both at the same time.", "### Languages\nThe text in the dataset is English.", "## Dataset Structure", "### Data Instances", "### Data Fields\n\n- 'words': 'list' of 'strings' text tokenized into words.\n- 'panel_id': ID of the panel to which the example belongs to in the SourceData database.\n- 'label_ids':\n - 'entity_types': 'list' of 'strings' for the IOB2 tags for entity type; possible value in '[\"O\", \"I-SMALL_MOLECULE\", \"B-SMALL_MOLECULE\", \"I-GENEPROD\", \"B-GENEPROD\", \"I-SUBCELLULAR\", \"B-SUBCELLULAR\", \"I-CELL_LINE\", \"B-CELL_LINE\", \"I-CELL_TYPE\", \"B-CELL_TYPE\", \"I-TISSUE\", \"B-TISSUE\", \"I-ORGANISM\", \"B-ORGANISM\", \"I-EXP_ASSAY\", \"B-EXP_ASSAY\"]'\n - 'roles': 'list' of 'strings' for the IOB2 tags for experimental roles; values in '[\"O\", \"I-CONTROLLED_VAR\", \"B-CONTROLLED_VAR\", \"I-MEASURED_VAR\", \"B-MEASURED_VAR\"]'\n - 'panel_start': 'list' of 'strings' for IOB2 tags '[\"O\", \"B-PANEL_START\"]' \n - 'multi roles': There are two different label sets. 'labels' is like in 'roles'. 'is_category' tags 'GENEPROD' and 'SMALL_MOLECULE'.", "### Data Splits\n\n* NER and ROLES\n\n* PANELIZATION", "## Dataset Creation", "### Curation Rationale\n\nThe dataset was built to train models for the automatic extraction of a knowledge graph based from the scientific literature. The dataset can be used to train models for text segmentation, named entity recognition and semantic role labeling.", "### Source Data", "#### Initial Data Collection and Normalization\n\nFigure legends were annotated according to the SourceData framework described in Liechti et al 2017 (Nature Methods, 2017, URL The curation tool at URL was used to segment figure legends into panel legends, tag enities, assign experiemental roles and normalize with standard identifiers (not available in this dataset). The source data was downloaded from the SourceData API (URL) on 21 Jan 2021.", "#### Who are the source language producers?\n\nThe examples are extracted from the figure legends from scientific papers in cell and molecular biology.", "### Annotations", "#### Annotation process\n\nThe annotations were produced manually with expert curators from the SourceData project (URL)", "#### Who are the annotators?\n\nCurators of the SourceData project.", "### Personal and Sensitive Information\n\nNone known.", "## Considerations for Using the Data", "### Social Impact of Dataset\n\nNot applicable.", "### Discussion of Biases\n\nThe examples are heavily biased towards cell and molecular biology and are enriched in examples from papers published in EMBO Press journals (URL)\n\nThe annotation of diseases has been added recently to the dataset. Although they appear, the number is very low and they are not consistently tagged through the entire dataset. \nWe recommend to use the diseases by filtering the examples that contain them.", "### Other Known Limitations", "## Additional Information", "### Dataset Curators\n\nThomas Lemberger, EMBO.\nJorge Abreu Vicente, EMBO", "### Licensing Information\n\nCC BY 4.0\n\n\n\nWe are currently working on a paper to present the dataset. It is expected to be ready by 2023 spring. In the meantime, the following paper should be cited.", "### Contributions\n\nThanks to @tlemberger and @drAbreu for adding this dataset.", "## Changelog\n\n* v2.0.3 - Data curated until 20.09.2023. Correction of 2,000+ unnormalized cell entities that have been now divided into cell line and cell type. Specially relevant for NER, not that important for NEL.\n\n* v2.0.2 - Data curated until 20.09.2023. This version will also include the patch for milti-word generic terms.\n\n* v1.0.2 - Modification of the generic patch in v1.0.1 to include generic terms of more than a word.\n\n* v1.0.1 - Added a first patch of generic terms. Terms such as cells, fluorescence, or animals where originally tagged, but in this version they are removed.\n\n* v1.0.0 - First publicly available version of the dataset. Data curated until March 2023." ]
[ "TAGS\n#task_categories-token-classification #size_categories-10K<n<100K #language-English #license-cc-by-4.0 #biology #medical #NER #NEL #arxiv-2310.20440 #doi-10.57967/hf/0495 #region-us \n", "# SourceData Dataset\n\n> The largest annotated biomedical corpus for machine learning and AI in the publishing context.\n\nSourceData is the largest annotated biomedical dataset for NER and NEL.\nIt is unique on its focus on the core of scientific evidence:\nfigure captions. It is also unique on its real-world configuration, since it does not\npresent isolated sentences out of more general context. It offers full annotated figure\ncaptions that can be further enriched in context using full text, abstracts, or titles.\nThe goal is to extract the nature of the experiments on them described.\nSourceData presents also its uniqueness by labelling the causal relationship\nbetween biological entities present in experiments, assigning experimental roles\nto each biomedical entity present in the corpus.\n\nSourceData consistently annotates\nnine different biological entities (genes, proteins, cells, tissues,\nsubcellular components, species, small molecules, and diseases). It is\nthe first dataset annotating experimental assays\nand the roles played on them by the biological entities.\nEach entity is linked to their correspondent ontology, allowing\nfor entity disambiguation and NEL.", "## Cite our work", "## Dataset usage\n\nThe dataset has a semantic versioning. \nSpecifying the version at loaded will give different versions. \nBelow we is shown the code needed to load the latest available version of the dataset.\nCheck below at 'Changelog' to see the changes in the different versions.", "## Dataset Description\n- Homepage: URL\n- Repository: URL\n- Paper:\n- Leaderboard:\n- Point of Contact: thomas.lemberger@URL, URL@URL\n\nNote that we offer the 'XML' serialized dataset. This includes all the data needed to perform NEL in SourceData. \nFor reproducibility, for each big version of the dataset we provide 'split_vx.y.z.json' files to generate the \ntrain, validation, test splits.", "### Supported Tasks and Leaderboards\nTags are provided as IOB2-style tags).\n'PANELIZATION': figure captions (or figure legends) are usually composed of segments that each refer to one of several 'panels' of the full figure. Panels tend to represent results obtained with a coherent method and depicts data points that can be meaningfully compared to each other. 'PANELIZATION' provide the start (B-PANEL_START) of these segments and allow to train for recogntion of the boundary between consecutive panel lengends.\n'NER': biological and chemical entities are labeled. Specifically the following entities are tagged:\n- 'SMALL_MOLECULE': small molecules\n- 'GENEPROD': gene products (genes and proteins)\n- 'SUBCELLULAR': subcellular components\n- 'CELL_LINE': cell lines\n- 'CELL_TYPE': cell types\n- 'TISSUE': tissues and organs\n- 'ORGANISM': species\n- 'DISEASE': diseases (see limitations)\n- 'EXP_ASSAY': experimental assays\n'ROLES': the role of entities with regard to the causal hypotheses tested in the reported results. The tags are:\n- 'CONTROLLED_VAR': entities that are associated with experimental variables and that subjected to controlled and targeted perturbations.\n- 'MEASURED_VAR': entities that are associated with the variables measured and the object of the measurements.\n\nIn the case of experimental roles, it is generated separatedly for 'GENEPROD' and 'SMALL_MOL' and there is also the 'ROLES_MULTI'\nthat takes both at the same time.", "### Languages\nThe text in the dataset is English.", "## Dataset Structure", "### Data Instances", "### Data Fields\n\n- 'words': 'list' of 'strings' text tokenized into words.\n- 'panel_id': ID of the panel to which the example belongs to in the SourceData database.\n- 'label_ids':\n - 'entity_types': 'list' of 'strings' for the IOB2 tags for entity type; possible value in '[\"O\", \"I-SMALL_MOLECULE\", \"B-SMALL_MOLECULE\", \"I-GENEPROD\", \"B-GENEPROD\", \"I-SUBCELLULAR\", \"B-SUBCELLULAR\", \"I-CELL_LINE\", \"B-CELL_LINE\", \"I-CELL_TYPE\", \"B-CELL_TYPE\", \"I-TISSUE\", \"B-TISSUE\", \"I-ORGANISM\", \"B-ORGANISM\", \"I-EXP_ASSAY\", \"B-EXP_ASSAY\"]'\n - 'roles': 'list' of 'strings' for the IOB2 tags for experimental roles; values in '[\"O\", \"I-CONTROLLED_VAR\", \"B-CONTROLLED_VAR\", \"I-MEASURED_VAR\", \"B-MEASURED_VAR\"]'\n - 'panel_start': 'list' of 'strings' for IOB2 tags '[\"O\", \"B-PANEL_START\"]' \n - 'multi roles': There are two different label sets. 'labels' is like in 'roles'. 'is_category' tags 'GENEPROD' and 'SMALL_MOLECULE'.", "### Data Splits\n\n* NER and ROLES\n\n* PANELIZATION", "## Dataset Creation", "### Curation Rationale\n\nThe dataset was built to train models for the automatic extraction of a knowledge graph based from the scientific literature. The dataset can be used to train models for text segmentation, named entity recognition and semantic role labeling.", "### Source Data", "#### Initial Data Collection and Normalization\n\nFigure legends were annotated according to the SourceData framework described in Liechti et al 2017 (Nature Methods, 2017, URL The curation tool at URL was used to segment figure legends into panel legends, tag enities, assign experiemental roles and normalize with standard identifiers (not available in this dataset). The source data was downloaded from the SourceData API (URL) on 21 Jan 2021.", "#### Who are the source language producers?\n\nThe examples are extracted from the figure legends from scientific papers in cell and molecular biology.", "### Annotations", "#### Annotation process\n\nThe annotations were produced manually with expert curators from the SourceData project (URL)", "#### Who are the annotators?\n\nCurators of the SourceData project.", "### Personal and Sensitive Information\n\nNone known.", "## Considerations for Using the Data", "### Social Impact of Dataset\n\nNot applicable.", "### Discussion of Biases\n\nThe examples are heavily biased towards cell and molecular biology and are enriched in examples from papers published in EMBO Press journals (URL)\n\nThe annotation of diseases has been added recently to the dataset. Although they appear, the number is very low and they are not consistently tagged through the entire dataset. \nWe recommend to use the diseases by filtering the examples that contain them.", "### Other Known Limitations", "## Additional Information", "### Dataset Curators\n\nThomas Lemberger, EMBO.\nJorge Abreu Vicente, EMBO", "### Licensing Information\n\nCC BY 4.0\n\n\n\nWe are currently working on a paper to present the dataset. It is expected to be ready by 2023 spring. In the meantime, the following paper should be cited.", "### Contributions\n\nThanks to @tlemberger and @drAbreu for adding this dataset.", "## Changelog\n\n* v2.0.3 - Data curated until 20.09.2023. Correction of 2,000+ unnormalized cell entities that have been now divided into cell line and cell type. Specially relevant for NER, not that important for NEL.\n\n* v2.0.2 - Data curated until 20.09.2023. This version will also include the patch for milti-word generic terms.\n\n* v1.0.2 - Modification of the generic patch in v1.0.1 to include generic terms of more than a word.\n\n* v1.0.1 - Added a first patch of generic terms. Terms such as cells, fluorescence, or animals where originally tagged, but in this version they are removed.\n\n* v1.0.0 - First publicly available version of the dataset. Data curated until March 2023." ]
[ 74, 271, 5, 64, 107, 402, 13, 6, 6, 386, 16, 5, 57, 4, 104, 32, 5, 25, 17, 12, 8, 10, 98, 7, 5, 21, 45, 23, 172 ]
[ "passage: TAGS\n#task_categories-token-classification #size_categories-10K<n<100K #language-English #license-cc-by-4.0 #biology #medical #NER #NEL #arxiv-2310.20440 #doi-10.57967/hf/0495 #region-us \n# SourceData Dataset\n\n> The largest annotated biomedical corpus for machine learning and AI in the publishing context.\n\nSourceData is the largest annotated biomedical dataset for NER and NEL.\nIt is unique on its focus on the core of scientific evidence:\nfigure captions. It is also unique on its real-world configuration, since it does not\npresent isolated sentences out of more general context. It offers full annotated figure\ncaptions that can be further enriched in context using full text, abstracts, or titles.\nThe goal is to extract the nature of the experiments on them described.\nSourceData presents also its uniqueness by labelling the causal relationship\nbetween biological entities present in experiments, assigning experimental roles\nto each biomedical entity present in the corpus.\n\nSourceData consistently annotates\nnine different biological entities (genes, proteins, cells, tissues,\nsubcellular components, species, small molecules, and diseases). It is\nthe first dataset annotating experimental assays\nand the roles played on them by the biological entities.\nEach entity is linked to their correspondent ontology, allowing\nfor entity disambiguation and NEL.## Cite our work## Dataset usage\n\nThe dataset has a semantic versioning. \nSpecifying the version at loaded will give different versions. \nBelow we is shown the code needed to load the latest available version of the dataset.\nCheck below at 'Changelog' to see the changes in the different versions.", "passage: ## Dataset Description\n- Homepage: URL\n- Repository: URL\n- Paper:\n- Leaderboard:\n- Point of Contact: thomas.lemberger@URL, URL@URL\n\nNote that we offer the 'XML' serialized dataset. This includes all the data needed to perform NEL in SourceData. \nFor reproducibility, for each big version of the dataset we provide 'split_vx.y.z.json' files to generate the \ntrain, validation, test splits.### Supported Tasks and Leaderboards\nTags are provided as IOB2-style tags).\n'PANELIZATION': figure captions (or figure legends) are usually composed of segments that each refer to one of several 'panels' of the full figure. Panels tend to represent results obtained with a coherent method and depicts data points that can be meaningfully compared to each other. 'PANELIZATION' provide the start (B-PANEL_START) of these segments and allow to train for recogntion of the boundary between consecutive panel lengends.\n'NER': biological and chemical entities are labeled. Specifically the following entities are tagged:\n- 'SMALL_MOLECULE': small molecules\n- 'GENEPROD': gene products (genes and proteins)\n- 'SUBCELLULAR': subcellular components\n- 'CELL_LINE': cell lines\n- 'CELL_TYPE': cell types\n- 'TISSUE': tissues and organs\n- 'ORGANISM': species\n- 'DISEASE': diseases (see limitations)\n- 'EXP_ASSAY': experimental assays\n'ROLES': the role of entities with regard to the causal hypotheses tested in the reported results. The tags are:\n- 'CONTROLLED_VAR': entities that are associated with experimental variables and that subjected to controlled and targeted perturbations.\n- 'MEASURED_VAR': entities that are associated with the variables measured and the object of the measurements.\n\nIn the case of experimental roles, it is generated separatedly for 'GENEPROD' and 'SMALL_MOL' and there is also the 'ROLES_MULTI'\nthat takes both at the same time.### Languages\nThe text in the dataset is English.## Dataset Structure### Data Instances", "passage: ### Data Fields\n\n- 'words': 'list' of 'strings' text tokenized into words.\n- 'panel_id': ID of the panel to which the example belongs to in the SourceData database.\n- 'label_ids':\n - 'entity_types': 'list' of 'strings' for the IOB2 tags for entity type; possible value in '[\"O\", \"I-SMALL_MOLECULE\", \"B-SMALL_MOLECULE\", \"I-GENEPROD\", \"B-GENEPROD\", \"I-SUBCELLULAR\", \"B-SUBCELLULAR\", \"I-CELL_LINE\", \"B-CELL_LINE\", \"I-CELL_TYPE\", \"B-CELL_TYPE\", \"I-TISSUE\", \"B-TISSUE\", \"I-ORGANISM\", \"B-ORGANISM\", \"I-EXP_ASSAY\", \"B-EXP_ASSAY\"]'\n - 'roles': 'list' of 'strings' for the IOB2 tags for experimental roles; values in '[\"O\", \"I-CONTROLLED_VAR\", \"B-CONTROLLED_VAR\", \"I-MEASURED_VAR\", \"B-MEASURED_VAR\"]'\n - 'panel_start': 'list' of 'strings' for IOB2 tags '[\"O\", \"B-PANEL_START\"]' \n - 'multi roles': There are two different label sets. 'labels' is like in 'roles'. 'is_category' tags 'GENEPROD' and 'SMALL_MOLECULE'.### Data Splits\n\n* NER and ROLES\n\n* PANELIZATION## Dataset Creation### Curation Rationale\n\nThe dataset was built to train models for the automatic extraction of a knowledge graph based from the scientific literature. The dataset can be used to train models for text segmentation, named entity recognition and semantic role labeling.### Source Data#### Initial Data Collection and Normalization\n\nFigure legends were annotated according to the SourceData framework described in Liechti et al 2017 (Nature Methods, 2017, URL The curation tool at URL was used to segment figure legends into panel legends, tag enities, assign experiemental roles and normalize with standard identifiers (not available in this dataset). The source data was downloaded from the SourceData API (URL) on 21 Jan 2021.#### Who are the source language producers?\n\nThe examples are extracted from the figure legends from scientific papers in cell and molecular biology.### Annotations#### Annotation process\n\nThe annotations were produced manually with expert curators from the SourceData project (URL)#### Who are the annotators?\n\nCurators of the SourceData project.### Personal and Sensitive Information\n\nNone known.## Considerations for Using the Data### Social Impact of Dataset\n\nNot applicable.### Discussion of Biases\n\nThe examples are heavily biased towards cell and molecular biology and are enriched in examples from papers published in EMBO Press journals (URL)\n\nThe annotation of diseases has been added recently to the dataset. Although they appear, the number is very low and they are not consistently tagged through the entire dataset. \nWe recommend to use the diseases by filtering the examples that contain them.### Other Known Limitations## Additional Information### Dataset Curators\n\nThomas Lemberger, EMBO.\nJorge Abreu Vicente, EMBO### Licensing Information\n\nCC BY 4.0\n\n\n\nWe are currently working on a paper to present the dataset. It is expected to be ready by 2023 spring. In the meantime, the following paper should be cited.### Contributions\n\nThanks to @tlemberger and @drAbreu for adding this dataset." ]
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5851ae43677d73ad00af3f2ab117b8bf3bb51cca
# Dataset Card for StaQC (A Systematically Mined Question-Code Dataset from Stack Overflow) ## Dataset Description - **Homepage: [GitHub](https://github.com/LittleYUYU/StackOverflow-Question-Code-Dataset)** - **Paper: [StaQC: A Systematically Mined Question-Code Dataset from Stack Overflow](https://arxiv.org/abs/1803.09371)** ### Dataset Summary StaQC (Stack Overflow Question-Code pairs) is a large dataset of around 148K Python and 120K SQL domain question-code pairs, which are automatically mined from Stack Overflow using a Bi-View Hierarchical Neural Network. StaQC is collected from three sources: multi-code answer posts, single-code answer posts, and manual annotations on multi-code answer posts. The dataset was originally released by the main authors on [GitHub](https://github.com/LittleYUYU/StackOverflow-Question-Code-Dataset). This version is a *non-modified* redistributed copy (under the [license](#licensing-information) permission) made available on the hub for easier access. #### Standalone solutions As noted in the paper, the authors *define a code snippet as a code solution when the questioner can solve the problem solely based on it (also named as “standalone” solution).* #### Manual annotations The manual annotations are the collection of multi-code answer posts for which each code snippet was annotated with a boolean indicating whether or not the snippet is a *standalone solution* to the question. #### Multi-code answer posts A *Multi-code answer post* is an (accepted) answer post that contains multiple code snippets, some of which may not be a *standalone* code solution to the question (see Section 1 in [paper](http://web.cse.ohio-state.edu/~sun.397/docs/StaQC-www18.pdf)). For example, in [this multi-code answer post](https://stackoverflow.com/a/5996949), the third code snippet is not a code solution to the question "How to limit a number to be within a specified range? (Python)". Note: the multi-code answer posts contain also the manual annotations. #### Single-code answer posts A *Single-code answer post* is an (accepted) answer post that contains only one code snippet. We pair such code snippets with the question title as a question-code pair. ### Supported Tasks and Leaderboards This dataset can be used for Natural Language to Code Generation tasks. ### Languages Python, SQL, English ## Dataset Structure ### Data Instances Each configuration correspond to one of the three parts, in a given programming language. There are three parts for the dataset: - mca (Multi-code answer posts) - sca (Single-code answer posts) - man (Manual annotations) And two programming/query languages: - python - sql One can obtain obtain a configuration as a combination of a part in a programing language. For instance, one can obtain the automatically mined multi-code answers in python using: ```python dataset = load_dataset("koutch/staqc", 'mca_python') DatasetDict({ train: Dataset({ features: ['id', 'question_id', 'question', 'snippet'], num_rows: 40391 }) }) ``` or the manual annotations using: ```python dataset = load_dataset("koutch/staqc", 'man_sql') DatasetDict({ train: Dataset({ features: ['id', 'question_id', 'question', 'snippet'], num_rows: 1587 }) }) ``` #### Manual annotations The manual annotations contain, for a given stackoverflow questions, for each individual code block in the accepted answer of that post, information on whether or not the given code block is a *standalone* solution to the question asked (the question title). ``` { 'question_id': 5947137, 'question': 'How can I use a list comprehension to extend a list in python?', 'snippet': {'text': ['import itertools as it\n\nreturn sum(it.imap(doSomething, originalList), [])\n', 'return sum(map(doSomething, originalList), [])\n', 'return sum((doSomething(x) for x in originalList), [])\n', 'accumulationList = []\nfor x in originalList:\n accumulationList.extend(doSomething(x))\nreturn accumulationList\n'], 'is_sda': [True, True, True, True]} } ``` #### Multi-code answer posts ``` { 'question_id': 35349290, 'question': 'Python: Generating YYMM string between two dates', 'snippet': ['start_year = 2005\nend_year = 2007\nstart_month = 3\nend_month = 2\nyymm = [(yy, mm) for yy in range(start_year, end_year + 1) for mm in range(1, 13)\n if (start_year, start_month) <= (yy, mm) <= (end_year, end_month)]\n', "formatted_yymm = ['{:>02}{:>02}.mat'.format(yy % 100, mm) for yy, mm in yymm]\n"] } ``` #### Single-code answer posts ``` { 'question_id': 19387200, 'question': 'Python: get OS language', 'snippet': "import locale\nloc = locale.getlocale() # get current locale\nlocale.getdefaultlocale() # Tries to determine the default locale settings and returns them as a tuple of the form (language code, encoding); e.g, ('en_US', 'UTF-8').\n" } ``` ### Data Fields - `question_id`: id of the stackoverflow question - `question`: title of the stackoverflow question repurposed as the natural language intent - `snippet`: mined or annotated standalone solution(s) (potentially) answerring the question - `is_sda`: for the manual annotations, whether or not the given code snippet is a standalone solution to the question. ### Data Splits Each configuration of the dataset contains only a training split. ## Dataset Creation ### Source Data StackOverflow data dump. ### Annotations See section 2.3 "Annotating QC Pairs for Model Training" of the [paper](https://arxiv.org/abs/1803.09371) ## Additional Information ### Licensing Information <a rel="license" href="http://creativecommons.org/licenses/by/4.0/"><img alt="Creative Commons License" style="border-width:0" src="https://i.creativecommons.org/l/by/4.0/88x31.png" /></a><br />This work is licensed under a <a rel="license" href="http://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International License</a>. ### Citation Information If you use the dataset or the code in your research, please cite the following paper: ``` @inproceedings{yao2018staqc, title={StaQC: A Systematically Mined Question-Code Dataset from Stack Overflow}, author={Yao, Ziyu and Weld, Daniel S and Chen, Wei-Peng and Sun, Huan}, booktitle={Proceedings of the 2018 World Wide Web Conference on World Wide Web}, pages={1693--1703}, year={2018}, organization={International World Wide Web Conferences Steering Committee} } ``` ### Contributions information I did *not* contribute to the *creation* of this dataset, only to the redistribution. All credits should be attributed to the original authors.
koutch/staqc
[ "task_categories:question-answering", "size_categories:10K<n<100K", "language:en", "license:cc-by-4.0", "code", "arxiv:1803.09371", "region:us" ]
2023-03-27T10:43:26+00:00
{"language": ["en"], "license": "cc-by-4.0", "size_categories": ["10K<n<100K"], "task_categories": ["question-answering"], "pretty_name": "staqc", "dataset_info": [{"config_name": "mca_python", "features": [{"name": "id", "dtype": "int32"}, {"name": "question_id", "dtype": "int32"}, {"name": "question", "dtype": "string"}, {"name": "snippet", "sequence": "string"}], "splits": [{"name": "train", "num_bytes": 23286786, "num_examples": 40391}], "download_size": 72054260, "dataset_size": 23286786}, {"config_name": "mca_sql", "features": [{"name": "id", "dtype": "int32"}, {"name": "question_id", "dtype": "int32"}, {"name": "question", "dtype": "string"}, {"name": "snippet", "sequence": "string"}], "splits": [{"name": "train", "num_bytes": 15164206, "num_examples": 26052}], "download_size": 50304531, "dataset_size": 15164206}, {"config_name": "sca_python", "features": [{"name": "id", "dtype": "int32"}, {"name": "question_id", "dtype": "int32"}, {"name": "question", "dtype": "string"}, {"name": "snippet", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 39678168, "num_examples": 85294}], "download_size": 47378850, "dataset_size": 39678168}, {"config_name": "sca_sql", "features": [{"name": "id", "dtype": "int32"}, {"name": "question_id", "dtype": "int32"}, {"name": "question", "dtype": "string"}, {"name": "snippet", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 28656467, "num_examples": 75637}], "download_size": 34194025, "dataset_size": 28656467}, {"config_name": "man_python", "features": [{"name": "id", "dtype": "int32"}, {"name": "question_id", "dtype": "int32"}, {"name": "question", "dtype": "string"}, {"name": "snippet", "sequence": [{"name": "text", "dtype": "string"}, {"name": "is_sda", "dtype": "bool"}]}], "splits": [{"name": "train", "num_bytes": 1445103, "num_examples": 2052}], "download_size": 71250225, "dataset_size": 1445103}, {"config_name": "man_sql", "features": [{"name": "id", "dtype": "int32"}, {"name": "question_id", "dtype": "int32"}, {"name": "question", "dtype": "string"}, {"name": "snippet", "sequence": [{"name": "text", "dtype": "string"}, {"name": "is_sda", "dtype": "bool"}]}], "splits": [{"name": "train", "num_bytes": 1123721, "num_examples": 1587}], "download_size": 49745860, "dataset_size": 1123721}], "tags": ["code"]}
2023-03-27T13:53:22+00:00
[ "1803.09371" ]
[ "en" ]
TAGS #task_categories-question-answering #size_categories-10K<n<100K #language-English #license-cc-by-4.0 #code #arxiv-1803.09371 #region-us
# Dataset Card for StaQC (A Systematically Mined Question-Code Dataset from Stack Overflow) ## Dataset Description - Homepage: GitHub - Paper: StaQC: A Systematically Mined Question-Code Dataset from Stack Overflow ### Dataset Summary StaQC (Stack Overflow Question-Code pairs) is a large dataset of around 148K Python and 120K SQL domain question-code pairs, which are automatically mined from Stack Overflow using a Bi-View Hierarchical Neural Network. StaQC is collected from three sources: multi-code answer posts, single-code answer posts, and manual annotations on multi-code answer posts. The dataset was originally released by the main authors on GitHub. This version is a *non-modified* redistributed copy (under the license permission) made available on the hub for easier access. #### Standalone solutions As noted in the paper, the authors *define a code snippet as a code solution when the questioner can solve the problem solely based on it (also named as “standalone” solution).* #### Manual annotations The manual annotations are the collection of multi-code answer posts for which each code snippet was annotated with a boolean indicating whether or not the snippet is a *standalone solution* to the question. #### Multi-code answer posts A *Multi-code answer post* is an (accepted) answer post that contains multiple code snippets, some of which may not be a *standalone* code solution to the question (see Section 1 in paper). For example, in this multi-code answer post, the third code snippet is not a code solution to the question "How to limit a number to be within a specified range? (Python)". Note: the multi-code answer posts contain also the manual annotations. #### Single-code answer posts A *Single-code answer post* is an (accepted) answer post that contains only one code snippet. We pair such code snippets with the question title as a question-code pair. ### Supported Tasks and Leaderboards This dataset can be used for Natural Language to Code Generation tasks. ### Languages Python, SQL, English ## Dataset Structure ### Data Instances Each configuration correspond to one of the three parts, in a given programming language. There are three parts for the dataset: - mca (Multi-code answer posts) - sca (Single-code answer posts) - man (Manual annotations) And two programming/query languages: - python - sql One can obtain obtain a configuration as a combination of a part in a programing language. For instance, one can obtain the automatically mined multi-code answers in python using: or the manual annotations using: #### Manual annotations The manual annotations contain, for a given stackoverflow questions, for each individual code block in the accepted answer of that post, information on whether or not the given code block is a *standalone* solution to the question asked (the question title). #### Multi-code answer posts #### Single-code answer posts ### Data Fields - 'question_id': id of the stackoverflow question - 'question': title of the stackoverflow question repurposed as the natural language intent - 'snippet': mined or annotated standalone solution(s) (potentially) answerring the question - 'is_sda': for the manual annotations, whether or not the given code snippet is a standalone solution to the question. ### Data Splits Each configuration of the dataset contains only a training split. ## Dataset Creation ### Source Data StackOverflow data dump. ### Annotations See section 2.3 "Annotating QC Pairs for Model Training" of the paper ## Additional Information ### Licensing Information <a rel="license" href="URL alt="Creative Commons License" style="border-width:0" src="https://i.URL /></a><br />This work is licensed under a <a rel="license" href="URL Commons Attribution 4.0 International License</a>. If you use the dataset or the code in your research, please cite the following paper: ### Contributions information I did *not* contribute to the *creation* of this dataset, only to the redistribution. All credits should be attributed to the original authors.
[ "# Dataset Card for StaQC (A Systematically Mined Question-Code Dataset from Stack Overflow)", "## Dataset Description\n\n- Homepage: GitHub \n- Paper: StaQC: A Systematically Mined Question-Code Dataset from Stack Overflow", "### Dataset Summary\n\nStaQC (Stack Overflow Question-Code pairs) is a large dataset of around 148K Python and 120K SQL domain question-code pairs, \nwhich are automatically mined from Stack Overflow using a Bi-View Hierarchical Neural Network. StaQC is collected from three sources: multi-code answer posts, single-code answer posts, and manual annotations on multi-code answer posts. \n\nThe dataset was originally released by the main authors on GitHub. This version is a *non-modified* redistributed copy (under the license permission) made available on the hub for easier access.", "#### Standalone solutions\n\nAs noted in the paper, the authors *define a code snippet as a code solution when the\nquestioner can solve the problem solely based on it (also named as\n“standalone” solution).*", "#### Manual annotations\n\nThe manual annotations are the collection of multi-code answer posts for which each code snippet was annotated with a boolean indicating whether or not the snippet is a *standalone solution* to the question.", "#### Multi-code answer posts\nA *Multi-code answer post* is an (accepted) answer post that contains multiple code snippets, \nsome of which may not be a *standalone* code solution to the question (see Section 1 in paper). \nFor example, in this multi-code answer post, \nthe third code snippet is not a code solution to the question \"How to limit a number to be within a specified range? (Python)\".\n\nNote: the multi-code answer posts contain also the manual annotations.", "#### Single-code answer posts\nA *Single-code answer post* is an (accepted) answer post that contains only one code snippet. \nWe pair such code snippets with the question title as a question-code pair.", "### Supported Tasks and Leaderboards\n\nThis dataset can be used for Natural Language to Code Generation tasks.", "### Languages\n\nPython, SQL, English", "## Dataset Structure", "### Data Instances\n\nEach configuration correspond to one of the three parts, in a given programming language.\n\nThere are three parts for the dataset:\n\n- mca (Multi-code answer posts)\n- sca (Single-code answer posts)\n- man (Manual annotations)\n\nAnd two programming/query languages:\n\n- python\n- sql\n\nOne can obtain obtain a configuration as a combination of a part in a programing language. For instance, one can obtain the automatically mined multi-code answers in python using:\n\n\nor the manual annotations using:", "#### Manual annotations\n\nThe manual annotations contain, for a given stackoverflow questions, for each individual code block in the accepted answer of that post, information on whether or not the given code block is a *standalone* solution to the question asked (the question title).", "#### Multi-code answer posts", "#### Single-code answer posts", "### Data Fields\n\n- 'question_id': id of the stackoverflow question \n- 'question': title of the stackoverflow question repurposed as the natural language intent\n- 'snippet': mined or annotated standalone solution(s) (potentially) answerring the question\n- 'is_sda': for the manual annotations, whether or not the given code snippet is a standalone solution to the question.", "### Data Splits\n\nEach configuration of the dataset contains only a training split.", "## Dataset Creation", "### Source Data\n\nStackOverflow data dump.", "### Annotations\n\nSee section 2.3 \"Annotating QC Pairs for Model Training\" of the paper", "## Additional Information", "### Licensing Information\n\n<a rel=\"license\" href=\"URL alt=\"Creative Commons License\" style=\"border-width:0\" src=\"https://i.URL /></a><br />This work is licensed under a <a rel=\"license\" href=\"URL Commons Attribution 4.0 International License</a>.\n\n\n\nIf you use the dataset or the code in your research, please cite the following paper:", "### Contributions information\n\nI did *not* contribute to the *creation* of this dataset, only to the redistribution. All credits should be attributed to the original authors." ]
[ "TAGS\n#task_categories-question-answering #size_categories-10K<n<100K #language-English #license-cc-by-4.0 #code #arxiv-1803.09371 #region-us \n", "# Dataset Card for StaQC (A Systematically Mined Question-Code Dataset from Stack Overflow)", "## Dataset Description\n\n- Homepage: GitHub \n- Paper: StaQC: A Systematically Mined Question-Code Dataset from Stack Overflow", "### Dataset Summary\n\nStaQC (Stack Overflow Question-Code pairs) is a large dataset of around 148K Python and 120K SQL domain question-code pairs, \nwhich are automatically mined from Stack Overflow using a Bi-View Hierarchical Neural Network. StaQC is collected from three sources: multi-code answer posts, single-code answer posts, and manual annotations on multi-code answer posts. \n\nThe dataset was originally released by the main authors on GitHub. This version is a *non-modified* redistributed copy (under the license permission) made available on the hub for easier access.", "#### Standalone solutions\n\nAs noted in the paper, the authors *define a code snippet as a code solution when the\nquestioner can solve the problem solely based on it (also named as\n“standalone” solution).*", "#### Manual annotations\n\nThe manual annotations are the collection of multi-code answer posts for which each code snippet was annotated with a boolean indicating whether or not the snippet is a *standalone solution* to the question.", "#### Multi-code answer posts\nA *Multi-code answer post* is an (accepted) answer post that contains multiple code snippets, \nsome of which may not be a *standalone* code solution to the question (see Section 1 in paper). \nFor example, in this multi-code answer post, \nthe third code snippet is not a code solution to the question \"How to limit a number to be within a specified range? (Python)\".\n\nNote: the multi-code answer posts contain also the manual annotations.", "#### Single-code answer posts\nA *Single-code answer post* is an (accepted) answer post that contains only one code snippet. \nWe pair such code snippets with the question title as a question-code pair.", "### Supported Tasks and Leaderboards\n\nThis dataset can be used for Natural Language to Code Generation tasks.", "### Languages\n\nPython, SQL, English", "## Dataset Structure", "### Data Instances\n\nEach configuration correspond to one of the three parts, in a given programming language.\n\nThere are three parts for the dataset:\n\n- mca (Multi-code answer posts)\n- sca (Single-code answer posts)\n- man (Manual annotations)\n\nAnd two programming/query languages:\n\n- python\n- sql\n\nOne can obtain obtain a configuration as a combination of a part in a programing language. For instance, one can obtain the automatically mined multi-code answers in python using:\n\n\nor the manual annotations using:", "#### Manual annotations\n\nThe manual annotations contain, for a given stackoverflow questions, for each individual code block in the accepted answer of that post, information on whether or not the given code block is a *standalone* solution to the question asked (the question title).", "#### Multi-code answer posts", "#### Single-code answer posts", "### Data Fields\n\n- 'question_id': id of the stackoverflow question \n- 'question': title of the stackoverflow question repurposed as the natural language intent\n- 'snippet': mined or annotated standalone solution(s) (potentially) answerring the question\n- 'is_sda': for the manual annotations, whether or not the given code snippet is a standalone solution to the question.", "### Data Splits\n\nEach configuration of the dataset contains only a training split.", "## Dataset Creation", "### Source Data\n\nStackOverflow data dump.", "### Annotations\n\nSee section 2.3 \"Annotating QC Pairs for Model Training\" of the paper", "## Additional Information", "### Licensing Information\n\n<a rel=\"license\" href=\"URL alt=\"Creative Commons License\" style=\"border-width:0\" src=\"https://i.URL /></a><br />This work is licensed under a <a rel=\"license\" href=\"URL Commons Attribution 4.0 International License</a>.\n\n\n\nIf you use the dataset or the code in your research, please cite the following paper:", "### Contributions information\n\nI did *not* contribute to the *creation* of this dataset, only to the redistribution. All credits should be attributed to the original authors." ]
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[ "passage: TAGS\n#task_categories-question-answering #size_categories-10K<n<100K #language-English #license-cc-by-4.0 #code #arxiv-1803.09371 #region-us \n# Dataset Card for StaQC (A Systematically Mined Question-Code Dataset from Stack Overflow)## Dataset Description\n\n- Homepage: GitHub \n- Paper: StaQC: A Systematically Mined Question-Code Dataset from Stack Overflow### Dataset Summary\n\nStaQC (Stack Overflow Question-Code pairs) is a large dataset of around 148K Python and 120K SQL domain question-code pairs, \nwhich are automatically mined from Stack Overflow using a Bi-View Hierarchical Neural Network. StaQC is collected from three sources: multi-code answer posts, single-code answer posts, and manual annotations on multi-code answer posts. \n\nThe dataset was originally released by the main authors on GitHub. This version is a *non-modified* redistributed copy (under the license permission) made available on the hub for easier access.#### Standalone solutions\n\nAs noted in the paper, the authors *define a code snippet as a code solution when the\nquestioner can solve the problem solely based on it (also named as\n“standalone” solution).*#### Manual annotations\n\nThe manual annotations are the collection of multi-code answer posts for which each code snippet was annotated with a boolean indicating whether or not the snippet is a *standalone solution* to the question.#### Multi-code answer posts\nA *Multi-code answer post* is an (accepted) answer post that contains multiple code snippets, \nsome of which may not be a *standalone* code solution to the question (see Section 1 in paper). \nFor example, in this multi-code answer post, \nthe third code snippet is not a code solution to the question \"How to limit a number to be within a specified range? (Python)\".\n\nNote: the multi-code answer posts contain also the manual annotations.", "passage: #### Single-code answer posts\nA *Single-code answer post* is an (accepted) answer post that contains only one code snippet. \nWe pair such code snippets with the question title as a question-code pair.### Supported Tasks and Leaderboards\n\nThis dataset can be used for Natural Language to Code Generation tasks.### Languages\n\nPython, SQL, English## Dataset Structure### Data Instances\n\nEach configuration correspond to one of the three parts, in a given programming language.\n\nThere are three parts for the dataset:\n\n- mca (Multi-code answer posts)\n- sca (Single-code answer posts)\n- man (Manual annotations)\n\nAnd two programming/query languages:\n\n- python\n- sql\n\nOne can obtain obtain a configuration as a combination of a part in a programing language. For instance, one can obtain the automatically mined multi-code answers in python using:\n\n\nor the manual annotations using:#### Manual annotations\n\nThe manual annotations contain, for a given stackoverflow questions, for each individual code block in the accepted answer of that post, information on whether or not the given code block is a *standalone* solution to the question asked (the question title).#### Multi-code answer posts#### Single-code answer posts### Data Fields\n\n- 'question_id': id of the stackoverflow question \n- 'question': title of the stackoverflow question repurposed as the natural language intent\n- 'snippet': mined or annotated standalone solution(s) (potentially) answerring the question\n- 'is_sda': for the manual annotations, whether or not the given code snippet is a standalone solution to the question.### Data Splits\n\nEach configuration of the dataset contains only a training split.## Dataset Creation### Source Data\n\nStackOverflow data dump.### Annotations\n\nSee section 2.3 \"Annotating QC Pairs for Model Training\" of the paper## Additional Information### Licensing Information\n\n<a rel=\"license\" href=\"URL alt=\"Creative Commons License\" style=\"border-width:0\" src=\"https://i.URL /></a><br />This work is licensed under a <a rel=\"license\" href=\"URL Commons Attribution 4.0 International License</a>.\n\n\n\nIf you use the dataset or the code in your research, please cite the following paper:" ]
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a48ac80d7612bf157989e0549e117bb7b5794c2a
# Dataset Card for "shoes_aice" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
ttoavina/shoes_aice
[ "region:us" ]
2023-03-27T11:12:26+00:00
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 18833548.0, "num_examples": 5000}], "download_size": 15980996, "dataset_size": 18833548.0}}
2023-03-27T11:12:38+00:00
[]
[]
TAGS #region-us
# Dataset Card for "shoes_aice" More Information needed
[ "# Dataset Card for \"shoes_aice\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"shoes_aice\"\n\nMore Information needed" ]
[ 6, 15 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"shoes_aice\"\n\nMore Information needed" ]
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0ad96974327d7ba746fc96aa1c8503967f1971bf
# Dataset Card for "cleaned_deduplicated_oscar" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
ClementRomac/cleaned_deduplicated_oscar
[ "region:us" ]
2023-03-27T11:42:39+00:00
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 978937483730, "num_examples": 232133013}, {"name": "test", "num_bytes": 59798696914, "num_examples": 12329126}], "download_size": 37220219718, "dataset_size": 1038736180644}}
2023-10-25T13:05:19+00:00
[]
[]
TAGS #region-us
# Dataset Card for "cleaned_deduplicated_oscar" More Information needed
[ "# Dataset Card for \"cleaned_deduplicated_oscar\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"cleaned_deduplicated_oscar\"\n\nMore Information needed" ]
[ 6, 21 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"cleaned_deduplicated_oscar\"\n\nMore Information needed" ]
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bb0e36377c0877c6992f2f29eab3b21193e7f0e4
# Dataset Card for "rmarkdown_checks" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
loubnabnl/rmarkdown_checks
[ "region:us" ]
2023-03-27T12:41:34+00:00
{"dataset_info": {"features": [{"name": "entities", "list": [{"name": "context", "dtype": "string"}, {"name": "end", "dtype": "int64"}, {"name": "score", "dtype": "float32"}, {"name": "start", "dtype": "int64"}, {"name": "tag", "dtype": "string"}, {"name": "value", "dtype": "string"}]}, {"name": "max_stars_repo_path", "dtype": "string"}, {"name": "max_stars_repo_name", "dtype": "string"}, {"name": "max_stars_count", "dtype": "int64"}, {"name": "content", "dtype": "string"}, {"name": "id", "dtype": "string"}, {"name": "new_content", "dtype": "string"}, {"name": "modified", "dtype": "bool"}, {"name": "references", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 113015277.61771259, "num_examples": 3493}], "download_size": 62607907, "dataset_size": 113015277.61771259}}
2023-03-27T12:41:43+00:00
[]
[]
TAGS #region-us
# Dataset Card for "rmarkdown_checks" More Information needed
[ "# Dataset Card for \"rmarkdown_checks\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"rmarkdown_checks\"\n\nMore Information needed" ]
[ 6, 16 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"rmarkdown_checks\"\n\nMore Information needed" ]
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8b8eaca809cd6676a73297327b5410b768562df9
# Dataset Card for "clojure_checks" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
loubnabnl/clojure_checks
[ "region:us" ]
2023-03-27T12:41:50+00:00
{"dataset_info": {"features": [{"name": "entities", "list": [{"name": "context", "dtype": "string"}, {"name": "end", "dtype": "int64"}, {"name": "score", "dtype": "float32"}, {"name": "start", "dtype": "int64"}, {"name": "tag", "dtype": "string"}, {"name": "value", "dtype": "string"}]}, {"name": "max_stars_repo_path", "dtype": "string"}, {"name": "max_stars_repo_name", "dtype": "string"}, {"name": "max_stars_count", "dtype": "int64"}, {"name": "content", "dtype": "string"}, {"name": "id", "dtype": "string"}, {"name": "new_content", "dtype": "string"}, {"name": "modified", "dtype": "bool"}, {"name": "references", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 158919605.17633805, "num_examples": 14174}], "download_size": 152797407, "dataset_size": 158919605.17633805}}
2023-03-27T12:42:07+00:00
[]
[]
TAGS #region-us
# Dataset Card for "clojure_checks" More Information needed
[ "# Dataset Card for \"clojure_checks\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"clojure_checks\"\n\nMore Information needed" ]
[ 6, 16 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"clojure_checks\"\n\nMore Information needed" ]
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7aa29e9f3e6cc3a73d973e26854452dea80d910e
# Dataset Card for "emacs-lisp_checks" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
loubnabnl/emacs-lisp_checks
[ "region:us" ]
2023-03-27T12:48:57+00:00
{"dataset_info": {"features": [{"name": "entities", "list": [{"name": "context", "dtype": "string"}, {"name": "end", "dtype": "int64"}, {"name": "score", "dtype": "float32"}, {"name": "start", "dtype": "int64"}, {"name": "tag", "dtype": "string"}, {"name": "value", "dtype": "string"}]}, {"name": "max_stars_repo_path", "dtype": "string"}, {"name": "max_stars_repo_name", "dtype": "string"}, {"name": "max_stars_count", "dtype": "int64"}, {"name": "content", "dtype": "string"}, {"name": "id", "dtype": "string"}, {"name": "new_content", "dtype": "string"}, {"name": "modified", "dtype": "bool"}, {"name": "references", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 449327198.32550436, "num_examples": 19261}], "download_size": 384578487, "dataset_size": 449327198.32550436}}
2023-03-27T12:49:33+00:00
[]
[]
TAGS #region-us
# Dataset Card for "emacs-lisp_checks" More Information needed
[ "# Dataset Card for \"emacs-lisp_checks\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"emacs-lisp_checks\"\n\nMore Information needed" ]
[ 6, 18 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"emacs-lisp_checks\"\n\nMore Information needed" ]
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77fea080376c65c73e6b7d90fc00545b9c8fbdfb
# Dataset Card for "common-lisp_checks" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
loubnabnl/common-lisp_checks
[ "region:us" ]
2023-03-27T12:49:03+00:00
{"dataset_info": {"features": [{"name": "entities", "list": [{"name": "context", "dtype": "string"}, {"name": "end", "dtype": "int64"}, {"name": "score", "dtype": "float32"}, {"name": "start", "dtype": "int64"}, {"name": "tag", "dtype": "string"}, {"name": "value", "dtype": "string"}]}, {"name": "max_stars_repo_path", "dtype": "string"}, {"name": "max_stars_repo_name", "dtype": "string"}, {"name": "max_stars_count", "dtype": "int64"}, {"name": "content", "dtype": "string"}, {"name": "id", "dtype": "string"}, {"name": "new_content", "dtype": "string"}, {"name": "modified", "dtype": "bool"}, {"name": "references", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1335613357.8516302, "num_examples": 31455}], "download_size": 418687191, "dataset_size": 1335613357.8516302}}
2023-03-27T12:49:47+00:00
[]
[]
TAGS #region-us
# Dataset Card for "common-lisp_checks" More Information needed
[ "# Dataset Card for \"common-lisp_checks\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"common-lisp_checks\"\n\nMore Information needed" ]
[ 6, 18 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"common-lisp_checks\"\n\nMore Information needed" ]
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617c8da1117b6bc655d92cbdd482c3a36c11cb78
# Dataset Card for "mathematica_checks" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
loubnabnl/mathematica_checks
[ "region:us" ]
2023-03-27T12:49:09+00:00
{"dataset_info": {"features": [{"name": "entities", "list": [{"name": "context", "dtype": "string"}, {"name": "end", "dtype": "int64"}, {"name": "score", "dtype": "float32"}, {"name": "start", "dtype": "int64"}, {"name": "tag", "dtype": "string"}, {"name": "value", "dtype": "string"}]}, {"name": "max_stars_repo_path", "dtype": "string"}, {"name": "max_stars_repo_name", "dtype": "string"}, {"name": "max_stars_count", "dtype": "int64"}, {"name": "content", "dtype": "string"}, {"name": "id", "dtype": "string"}, {"name": "new_content", "dtype": "string"}, {"name": "modified", "dtype": "bool"}, {"name": "references", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 900191188.2540944, "num_examples": 5440}], "download_size": 1546159637, "dataset_size": 900191188.2540944}}
2023-03-27T12:51:11+00:00
[]
[]
TAGS #region-us
# Dataset Card for "mathematica_checks" More Information needed
[ "# Dataset Card for \"mathematica_checks\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"mathematica_checks\"\n\nMore Information needed" ]
[ 6, 16 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"mathematica_checks\"\n\nMore Information needed" ]
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3704070077f612ba5a91ebfa8f89823651b37b64
# Dataset Card for "coffeescript_checks" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
loubnabnl/coffeescript_checks
[ "region:us" ]
2023-03-27T12:49:12+00:00
{"dataset_info": {"features": [{"name": "entities", "list": [{"name": "context", "dtype": "string"}, {"name": "end", "dtype": "int64"}, {"name": "score", "dtype": "float32"}, {"name": "start", "dtype": "int64"}, {"name": "tag", "dtype": "string"}, {"name": "value", "dtype": "string"}]}, {"name": "max_stars_repo_path", "dtype": "string"}, {"name": "max_stars_repo_name", "dtype": "string"}, {"name": "max_stars_count", "dtype": "int64"}, {"name": "content", "dtype": "string"}, {"name": "id", "dtype": "string"}, {"name": "new_content", "dtype": "string"}, {"name": "modified", "dtype": "bool"}, {"name": "references", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 202822078.3919738, "num_examples": 23874}], "download_size": 202150872, "dataset_size": 202822078.3919738}}
2023-03-27T12:49:34+00:00
[]
[]
TAGS #region-us
# Dataset Card for "coffeescript_checks" More Information needed
[ "# Dataset Card for \"coffeescript_checks\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"coffeescript_checks\"\n\nMore Information needed" ]
[ 6, 17 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"coffeescript_checks\"\n\nMore Information needed" ]
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0b0475dd89fb3bd8a44828a0f0fcb2e6a5942d69
# Dataset Card for "makefile_checks" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
loubnabnl/makefile_checks
[ "region:us" ]
2023-03-27T12:49:55+00:00
{"dataset_info": {"features": [{"name": "entities", "list": [{"name": "context", "dtype": "string"}, {"name": "end", "dtype": "int64"}, {"name": "score", "dtype": "float32"}, {"name": "start", "dtype": "int64"}, {"name": "tag", "dtype": "string"}, {"name": "value", "dtype": "string"}]}, {"name": "max_stars_repo_path", "dtype": "string"}, {"name": "max_stars_repo_name", "dtype": "string"}, {"name": "max_stars_count", "dtype": "int64"}, {"name": "content", "dtype": "string"}, {"name": "id", "dtype": "string"}, {"name": "new_content", "dtype": "string"}, {"name": "modified", "dtype": "bool"}, {"name": "references", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 332308322.1515968, "num_examples": 57421}], "download_size": 271711545, "dataset_size": 332308322.1515968}}
2023-03-27T12:50:26+00:00
[]
[]
TAGS #region-us
# Dataset Card for "makefile_checks" More Information needed
[ "# Dataset Card for \"makefile_checks\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"makefile_checks\"\n\nMore Information needed" ]
[ 6, 15 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"makefile_checks\"\n\nMore Information needed" ]
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57bd98b4c041c65d61ea61a61b319d3064cf3d77
# Dataset Card for "dockerfile_checks" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
loubnabnl/dockerfile_checks
[ "region:us" ]
2023-03-27T12:49:55+00:00
{"dataset_info": {"features": [{"name": "entities", "list": [{"name": "context", "dtype": "string"}, {"name": "end", "dtype": "int64"}, {"name": "score", "dtype": "float32"}, {"name": "start", "dtype": "int64"}, {"name": "tag", "dtype": "string"}, {"name": "value", "dtype": "string"}]}, {"name": "max_stars_repo_path", "dtype": "string"}, {"name": "max_stars_repo_name", "dtype": "string"}, {"name": "max_stars_count", "dtype": "int64"}, {"name": "content", "dtype": "string"}, {"name": "id", "dtype": "string"}, {"name": "new_content", "dtype": "string"}, {"name": "modified", "dtype": "bool"}, {"name": "references", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 304825212.1287143, "num_examples": 137651}], "download_size": 265293128, "dataset_size": 304825212.1287143}}
2023-03-27T12:50:33+00:00
[]
[]
TAGS #region-us
# Dataset Card for "dockerfile_checks" More Information needed
[ "# Dataset Card for \"dockerfile_checks\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"dockerfile_checks\"\n\nMore Information needed" ]
[ 6, 16 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"dockerfile_checks\"\n\nMore Information needed" ]
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2252ec8b45a3be686d28e1b91ebec886015ad610
* The objective of this work is to construct a method for analyzing waveforms of signals obtained during microseismic monitoring using a neural network in order to localize the coordinates of the sources of seismic events, and their differentiation. * Microseismic monitoring is one of the existing methods of analyzing the condition of the studied geophysical object: mineral deposits, large-scale industrial facilities, etc. It includes a system of sensors that detect weak seismic or acoustic signals, a data collection system and algorithms for their processing. The main task of monitoring is to determine the characteristics of a microseismic event: the time of the first entry, magnitude, and its location in space. * This dataset contains synthetic waveforms to form a training and validation samples. The advantage ofusing synthetic data is that for it, all the necessary parameters of each seismic event are known in advance (time of entry, coordinates of the source, magnitude, parameters of the source mechanism, velocity model of the medium). This makes it possible to create and train models based on data generated taking into account the features characteristic of a given monitoring area, while the resulting models may have a greater generalizing ability than those trained on real waveforms. In addition, this approach, unlike using banks of real waveforms to train the model, eliminates the possibility of distortion of the results associated with the use of manual data markup. The main disadvantage of using synthetic data for training models is the need to adapt the resulting models to real data. The synthetic waveforms used in this work were created using Pyrocko, an open–source set of libraries for seismological modeling [Heimann et al., 2018]. The propagation of seismic waves was modeled for an elastically viscous layered medium. The velocity model of the medium was taken from [Málek, Horálek, Janský, 2005]. The choice was determined by the freely available pre-calculated bank of Green's functions necessary to obtain waveforms. The sources of seismic signals were modeled by a double pair of forces with a random distribution of displacement directions (strike, deep, rake) and magnitudes uniformly distributed within the specified boundaries (0-2). The epicenters and depths of the sources were randomly set inside an area with a radius of 1.5 km and a depth of 1000 meters. Waveforms (displacement) were obtained for five stations (four symmetrically located at a distance of 500 meters from the origin, and one in the center) for three channels (two horizontal N, E and vertical Z) with a sampling frequency of 100 Hz, the length of each recording is 4 seconds. A priori moments of arrival of p and s waves were obtained for each waveform. As a result of the simulation, training and test samples were formed from 106 and 103 events, respectively (15 waveforms in each).
mexalon/Synth_Seism
[ "task_categories:feature-extraction", "size_categories:100K<n<1M", "region:us" ]
2023-03-27T13:20:42+00:00
{"size_categories": ["100K<n<1M"], "task_categories": ["feature-extraction"]}
2023-11-01T09:41:11+00:00
[]
[]
TAGS #task_categories-feature-extraction #size_categories-100K<n<1M #region-us
* The objective of this work is to construct a method for analyzing waveforms of signals obtained during microseismic monitoring using a neural network in order to localize the coordinates of the sources of seismic events, and their differentiation. * Microseismic monitoring is one of the existing methods of analyzing the condition of the studied geophysical object: mineral deposits, large-scale industrial facilities, etc. It includes a system of sensors that detect weak seismic or acoustic signals, a data collection system and algorithms for their processing. The main task of monitoring is to determine the characteristics of a microseismic event: the time of the first entry, magnitude, and its location in space. * This dataset contains synthetic waveforms to form a training and validation samples. The advantage ofusing synthetic data is that for it, all the necessary parameters of each seismic event are known in advance (time of entry, coordinates of the source, magnitude, parameters of the source mechanism, velocity model of the medium). This makes it possible to create and train models based on data generated taking into account the features characteristic of a given monitoring area, while the resulting models may have a greater generalizing ability than those trained on real waveforms. In addition, this approach, unlike using banks of real waveforms to train the model, eliminates the possibility of distortion of the results associated with the use of manual data markup. The main disadvantage of using synthetic data for training models is the need to adapt the resulting models to real data. The synthetic waveforms used in this work were created using Pyrocko, an open–source set of libraries for seismological modeling [Heimann et al., 2018]. The propagation of seismic waves was modeled for an elastically viscous layered medium. The velocity model of the medium was taken from [Málek, Horálek, Janský, 2005]. The choice was determined by the freely available pre-calculated bank of Green's functions necessary to obtain waveforms. The sources of seismic signals were modeled by a double pair of forces with a random distribution of displacement directions (strike, deep, rake) and magnitudes uniformly distributed within the specified boundaries (0-2). The epicenters and depths of the sources were randomly set inside an area with a radius of 1.5 km and a depth of 1000 meters. Waveforms (displacement) were obtained for five stations (four symmetrically located at a distance of 500 meters from the origin, and one in the center) for three channels (two horizontal N, E and vertical Z) with a sampling frequency of 100 Hz, the length of each recording is 4 seconds. A priori moments of arrival of p and s waves were obtained for each waveform. As a result of the simulation, training and test samples were formed from 106 and 103 events, respectively (15 waveforms in each).
[]
[ "TAGS\n#task_categories-feature-extraction #size_categories-100K<n<1M #region-us \n" ]
[ 30 ]
[ "passage: TAGS\n#task_categories-feature-extraction #size_categories-100K<n<1M #region-us \n" ]
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89eda23fddc0135007aad37b769bf32333b3c2cb
# 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]
eduardosanchez/wmt22
[ "task_categories:translation", "size_categories:n<1K", "language:liv", "language:en", "region:us" ]
2023-03-27T13:26:59+00:00
{"language": ["liv", "en"], "size_categories": ["n<1K"], "task_categories": ["translation"]}
2023-03-27T13:30:12+00:00
[]
[ "liv", "en" ]
TAGS #task_categories-translation #size_categories-n<1K #language-Liv #language-English #region-us
# 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. ### Supported Tasks and Leaderboards ### Languages ## Dataset Structure ### Data Instances ### Data Fields ### Data Splits ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information ### Contributions
[ "# Dataset Card for Dataset Name", "## Dataset Description\n\n- Homepage: \n- Repository: \n- Paper: \n- Leaderboard: \n- Point of Contact:", "### Dataset Summary\n\nThis dataset card aims to be a base template for new datasets. It has been generated using this raw template.", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]
[ "TAGS\n#task_categories-translation #size_categories-n<1K #language-Liv #language-English #region-us \n", "# Dataset Card for Dataset Name", "## Dataset Description\n\n- Homepage: \n- Repository: \n- Paper: \n- Leaderboard: \n- Point of Contact:", "### Dataset Summary\n\nThis dataset card aims to be a base template for new datasets. It has been generated using this raw template.", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]
[ 34, 8, 24, 32, 10, 4, 6, 6, 5, 5, 5, 7, 4, 10, 10, 5, 5, 9, 8, 8, 7, 8, 7, 5, 6, 6, 5 ]
[ "passage: TAGS\n#task_categories-translation #size_categories-n<1K #language-Liv #language-English #region-us \n# Dataset Card for Dataset Name## Dataset Description\n\n- Homepage: \n- Repository: \n- Paper: \n- Leaderboard: \n- Point of Contact:### Dataset Summary\n\nThis dataset card aims to be a base template for new datasets. It has been generated using this raw template.### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions" ]
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b706d6d2040ab0c76a86c96c05f3d7f295da1538
# Dataset Card for "pii_checks_data_elm" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
loubnabnl/pii_checks_data_elm
[ "region:us" ]
2023-03-27T14:46:45+00:00
{"dataset_info": {"features": [{"name": "entities", "list": [{"name": "context", "dtype": "string"}, {"name": "end", "dtype": "int64"}, {"name": "score", "dtype": "float64"}, {"name": "start", "dtype": "int64"}, {"name": "tag", "dtype": "string"}, {"name": "value", "dtype": "string"}]}, {"name": "max_stars_repo_path", "dtype": "string"}, {"name": "max_stars_repo_name", "dtype": "string"}, {"name": "max_stars_count", "dtype": "int64"}, {"name": "content", "dtype": "string"}, {"name": "id", "dtype": "string"}, {"name": "new_content", "dtype": "string"}, {"name": "modified", "dtype": "bool"}, {"name": "references", "dtype": "string"}, {"name": "language", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 37294354, "num_examples": 1000}], "download_size": 13081864, "dataset_size": 37294354}}
2023-03-27T14:46:49+00:00
[]
[]
TAGS #region-us
# Dataset Card for "pii_checks_data_elm" More Information needed
[ "# Dataset Card for \"pii_checks_data_elm\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"pii_checks_data_elm\"\n\nMore Information needed" ]
[ 6, 19 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"pii_checks_data_elm\"\n\nMore Information needed" ]
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f52b066c8d0970e99e69942f25a1221fc36fca11
# Dataset Card for "simple_arch_1400" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
ossaili/simple_arch_1400
[ "region:us" ]
2023-03-27T14:52:24+00:00
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 452101969.2, "num_examples": 1400}], "download_size": 473418121, "dataset_size": 452101969.2}}
2023-03-27T14:59:55+00:00
[]
[]
TAGS #region-us
# Dataset Card for "simple_arch_1400" More Information needed
[ "# Dataset Card for \"simple_arch_1400\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"simple_arch_1400\"\n\nMore Information needed" ]
[ 6, 15 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"simple_arch_1400\"\n\nMore Information needed" ]
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80a1d10616b34eba7acd148bd22d31647d67bd7d
# Dataset Card for "StanfordCars_test_google_flan_t5_xl_mode_C_A_T_ns_8041" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
CVasNLPExperiments/StanfordCars_test_google_flan_t5_xl_mode_C_A_T_ns_8041
[ "region:us" ]
2023-03-27T14:54:05+00:00
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "prompt", "dtype": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}], "splits": [{"name": "fewshot_0_clip_tags_ViT_L_14_LLM_Description_gpt3_downstream_tasks_visual_genome_ViT_L_14_clip_tags_ViT_L_14_simple_specific_rices", "num_bytes": 3521193, "num_examples": 8041}, {"name": "fewshot_1_clip_tags_ViT_L_14_LLM_Description_gpt3_downstream_tasks_visual_genome_ViT_L_14_clip_tags_ViT_L_14_simple_specific_rices", "num_bytes": 6704901, "num_examples": 8041}], "download_size": 2725683, "dataset_size": 10226094}}
2023-03-27T15:55:00+00:00
[]
[]
TAGS #region-us
# Dataset Card for "StanfordCars_test_google_flan_t5_xl_mode_C_A_T_ns_8041" More Information needed
[ "# Dataset Card for \"StanfordCars_test_google_flan_t5_xl_mode_C_A_T_ns_8041\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"StanfordCars_test_google_flan_t5_xl_mode_C_A_T_ns_8041\"\n\nMore Information needed" ]
[ 6, 39 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"StanfordCars_test_google_flan_t5_xl_mode_C_A_T_ns_8041\"\n\nMore Information needed" ]
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5c74d9c6195880eb05ea5ad27098424708c1a639
# AutoTrain Dataset for project: tableros_factibilidad ## Dataset Description This dataset has been automatically processed by AutoTrain for project tableros_factibilidad. ### Languages The BCP-47 code for the dataset's language is unk. ## Dataset Structure ### Data Instances A sample from this dataset looks as follows: ```json [ { "image": "<217x409 RGB PIL image>", "target": 1 }, { "image": "<311x574 RGB PIL image>", "target": 2 } ] ``` ### Dataset Fields The dataset has the following fields (also called "features"): ```json { "image": "Image(decode=True, id=None)", "target": "ClassLabel(names=['sin adecuaciones', 'tablero', 'tablero 2 medidores', 'tablero varios medidores'], id=None)" } ``` ### Dataset Splits This dataset is split into a train and validation split. The split sizes are as follow: | Split name | Num samples | | ------------ | ------------------- | | train | 13 | | valid | 5 |
SebasV/autotrain-data-tableros_factibilidad
[ "task_categories:image-classification", "region:us" ]
2023-03-27T15:25:26+00:00
{"task_categories": ["image-classification"]}
2023-03-27T15:28:56+00:00
[]
[]
TAGS #task_categories-image-classification #region-us
AutoTrain Dataset for project: tableros\_factibilidad ===================================================== Dataset Description ------------------- This dataset has been automatically processed by AutoTrain for project tableros\_factibilidad. ### Languages The BCP-47 code for the dataset's language is unk. Dataset Structure ----------------- ### Data Instances A sample from this dataset looks as follows: ### Dataset Fields The dataset has the following fields (also called "features"): ### Dataset Splits This dataset is split into a train and validation split. The split sizes are as follow:
[ "### Languages\n\n\nThe BCP-47 code for the dataset's language is unk.\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nA sample from this dataset looks as follows:", "### Dataset Fields\n\n\nThe dataset has the following fields (also called \"features\"):", "### Dataset Splits\n\n\nThis dataset is split into a train and validation split. The split sizes are as follow:" ]
[ "TAGS\n#task_categories-image-classification #region-us \n", "### Languages\n\n\nThe BCP-47 code for the dataset's language is unk.\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nA sample from this dataset looks as follows:", "### Dataset Fields\n\n\nThe dataset has the following fields (also called \"features\"):", "### Dataset Splits\n\n\nThis dataset is split into a train and validation split. The split sizes are as follow:" ]
[ 17, 27, 17, 23, 27 ]
[ "passage: TAGS\n#task_categories-image-classification #region-us \n### Languages\n\n\nThe BCP-47 code for the dataset's language is unk.\n\n\nDataset Structure\n-----------------### Data Instances\n\n\nA sample from this dataset looks as follows:### Dataset Fields\n\n\nThe dataset has the following fields (also called \"features\"):### Dataset Splits\n\n\nThis dataset is split into a train and validation split. The split sizes are as follow:" ]
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2455cad551767fd43b27e4fe499b95b76d87f115
# Dataset Card for "test-image" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
lhoestq/test-image
[ "region:us" ]
2023-03-27T15:34:05+00:00
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 173136.0, "num_examples": 1}], "download_size": 174237, "dataset_size": 173136.0}}
2023-03-27T15:34:28+00:00
[]
[]
TAGS #region-us
# Dataset Card for "test-image" More Information needed
[ "# Dataset Card for \"test-image\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"test-image\"\n\nMore Information needed" ]
[ 6, 13 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"test-image\"\n\nMore Information needed" ]
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8555cd118e88c89a4eb3a5cfd4ab9f29f7dae3c1
# Dataset Card for "test-image-list" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
lhoestq/test-image-list
[ "region:us" ]
2023-03-27T15:34:58+00:00
{"dataset_info": {"features": [{"name": "image", "list": "image"}], "splits": [{"name": "train", "num_bytes": 346275.0, "num_examples": 1}], "download_size": 174383, "dataset_size": 346275.0}}
2023-03-27T15:35:34+00:00
[]
[]
TAGS #region-us
# Dataset Card for "test-image-list" More Information needed
[ "# Dataset Card for \"test-image-list\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"test-image-list\"\n\nMore Information needed" ]
[ 6, 15 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"test-image-list\"\n\nMore Information needed" ]
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da57e995e1e056d6b1a87453dd3cc5e7619d2bbe
# Dataset Card for "test-image-nested" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
lhoestq/test-image-nested
[ "region:us" ]
2023-03-27T15:36:17+00:00
{"dataset_info": {"features": [{"name": "image", "struct": [{"name": "nested", "list": "image"}]}], "splits": [{"name": "train", "num_bytes": 346275.0, "num_examples": 1}], "download_size": 174512, "dataset_size": 346275.0}}
2023-03-27T15:36:23+00:00
[]
[]
TAGS #region-us
# Dataset Card for "test-image-nested" More Information needed
[ "# Dataset Card for \"test-image-nested\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"test-image-nested\"\n\nMore Information needed" ]
[ 6, 16 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"test-image-nested\"\n\nMore Information needed" ]
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386b04307706ed61f50beea955ac34515c751faf
# Dataset Card for "somos-clean-alpaca-es-validations" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
dariolopez/somos-clean-alpaca-es-validations
[ "region:us" ]
2023-03-27T16:15:13+00:00
{"dataset_info": {"features": [{"name": "text", "dtype": "null"}, {"name": "inputs", "struct": [{"name": "1-instruction", "dtype": "string"}, {"name": "2-input", "dtype": "string"}, {"name": "3-output", "dtype": "string"}]}, {"name": "prediction", "dtype": "null"}, {"name": "prediction_agent", "dtype": "null"}, {"name": "annotation", "dtype": "string"}, {"name": "annotation_agent", "dtype": "string"}, {"name": "vectors", "struct": [{"name": "input", "sequence": "float64"}, {"name": "instruction", "sequence": "float64"}, {"name": "output", "sequence": "float64"}]}, {"name": "multi_label", "dtype": "bool"}, {"name": "explanation", "dtype": "null"}, {"name": "id", "dtype": "string"}, {"name": "metadata", "dtype": "null"}, {"name": "status", "dtype": "string"}, {"name": "event_timestamp", "dtype": "timestamp[us]"}, {"name": "metrics", "struct": [{"name": "text_length", "dtype": "int64"}]}], "splits": [{"name": "train", "num_bytes": 131073, "num_examples": 7}], "download_size": 0, "dataset_size": 131073}}
2023-05-04T12:41:07+00:00
[]
[]
TAGS #region-us
# Dataset Card for "somos-clean-alpaca-es-validations" More Information needed
[ "# Dataset Card for \"somos-clean-alpaca-es-validations\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"somos-clean-alpaca-es-validations\"\n\nMore Information needed" ]
[ 6, 24 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"somos-clean-alpaca-es-validations\"\n\nMore Information needed" ]
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a7224295466d2370359bc31ebb7b9e185c3eacfc
# Dataset ## Topic-independent split Topics are randomly selected in datasets. For a common purpose, we suggest THESE DATASETS. * test_random.json * training_random.json * validation_random.json # GitHub * https://github.com/declare-lab/WikiDes/ # Citation ## APA Ta, H. T., Rahman, A. B. S., Majumder, N., Hussain, A., Najjar, L., Howard, N., ... & Gelbukh, A. (2022). WikiDes: A Wikipedia-based dataset for generating short descriptions from paragraphs. *Information Fusion*. ## BibTeX ``` @article{Ta_2022, doi = {10.1016/j.inffus.2022.09.022}, url = {https://doi.org/10.1016%2Fj.inffus.2022.09.022}, year = 2022, month = {sep}, publisher = {Elsevier {BV}}, author = {Hoang Thang Ta and Abu Bakar Siddiqur Rahman and Navonil Majumder and Amir Hussain and Lotfollah Najjar and Newton Howard and Soujanya Poria and Alexander Gelbukh}, title = {{WikiDes}: A Wikipedia-based dataset for generating short descriptions from paragraphs}, journal = {Information Fusion}} ``` # Paper links * https://doi.org/10.1016%2Fj.inffus.2022.09.022 * https://arxiv.org/abs/2209.13101 # Contact Hoang Thang Ta, [email protected]
Thang/wikides
[ "license:cc-by-sa-4.0", "arxiv:2209.13101", "doi:10.57967/hf/0480", "region:us" ]
2023-03-27T16:19:44+00:00
{"license": "cc-by-sa-4.0"}
2023-05-16T06:44:14+00:00
[ "2209.13101" ]
[]
TAGS #license-cc-by-sa-4.0 #arxiv-2209.13101 #doi-10.57967/hf/0480 #region-us
# Dataset ## Topic-independent split Topics are randomly selected in datasets. For a common purpose, we suggest THESE DATASETS. * test_random.json * training_random.json * validation_random.json # GitHub * URL ## APA Ta, H. T., Rahman, A. B. S., Majumder, N., Hussain, A., Najjar, L., Howard, N., ... & Gelbukh, A. (2022). WikiDes: A Wikipedia-based dataset for generating short descriptions from paragraphs. *Information Fusion*. ## BibTeX # Paper links * URL * URL # Contact Hoang Thang Ta, tahoangthang@URL
[ "# Dataset", "## Topic-independent split\nTopics are randomly selected in datasets. For a common purpose, we suggest THESE DATASETS.\n* test_random.json\n* training_random.json\n* validation_random.json", "# GitHub\n* URL", "## APA\nTa, H. T., Rahman, A. B. S., Majumder, N., Hussain, A., Najjar, L., Howard, N., ... & Gelbukh, A. (2022). WikiDes: A Wikipedia-based dataset for generating short descriptions from paragraphs. *Information Fusion*.", "## BibTeX", "# Paper links\n* URL\n* URL", "# Contact\nHoang Thang Ta, tahoangthang@URL" ]
[ "TAGS\n#license-cc-by-sa-4.0 #arxiv-2209.13101 #doi-10.57967/hf/0480 #region-us \n", "# Dataset", "## Topic-independent split\nTopics are randomly selected in datasets. For a common purpose, we suggest THESE DATASETS.\n* test_random.json\n* training_random.json\n* validation_random.json", "# GitHub\n* URL", "## APA\nTa, H. T., Rahman, A. B. S., Majumder, N., Hussain, A., Najjar, L., Howard, N., ... & Gelbukh, A. (2022). WikiDes: A Wikipedia-based dataset for generating short descriptions from paragraphs. *Information Fusion*.", "## BibTeX", "# Paper links\n* URL\n* URL", "# Contact\nHoang Thang Ta, tahoangthang@URL" ]
[ 37, 3, 55, 6, 76, 5, 7, 15 ]
[ "passage: TAGS\n#license-cc-by-sa-4.0 #arxiv-2209.13101 #doi-10.57967/hf/0480 #region-us \n# Dataset## Topic-independent split\nTopics are randomly selected in datasets. For a common purpose, we suggest THESE DATASETS.\n* test_random.json\n* training_random.json\n* validation_random.json# GitHub\n* URL## APA\nTa, H. T., Rahman, A. B. S., Majumder, N., Hussain, A., Najjar, L., Howard, N., ... & Gelbukh, A. (2022). WikiDes: A Wikipedia-based dataset for generating short descriptions from paragraphs. *Information Fusion*.## BibTeX# Paper links\n* URL\n* URL# Contact\nHoang Thang Ta, tahoangthang@URL" ]
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d196005ec587b9654ea544b9553b2a142d67165f
# Dataset Card for "UA_speech_noisereduced_test-0.7_train-0.3" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
AravindVadlapudi02/UA_speech_noisereduced_test-0.7_train-0.3
[ "region:us" ]
2023-03-27T16:30:06+00:00
{"dataset_info": {"features": [{"name": "label", "dtype": {"class_label": {"names": {"0": "healthy control", "1": "pathology"}}}}, {"name": "input_features", "sequence": {"sequence": "float32"}}], "splits": [{"name": "train", "num_bytes": 1609516432, "num_examples": 1676}, {"name": "test", "num_bytes": 3757779116, "num_examples": 3913}], "download_size": 619867591, "dataset_size": 5367295548}}
2023-03-27T16:31:15+00:00
[]
[]
TAGS #region-us
# Dataset Card for "UA_speech_noisereduced_test-0.7_train-0.3" More Information needed
[ "# Dataset Card for \"UA_speech_noisereduced_test-0.7_train-0.3\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"UA_speech_noisereduced_test-0.7_train-0.3\"\n\nMore Information needed" ]
[ 6, 28 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"UA_speech_noisereduced_test-0.7_train-0.3\"\n\nMore Information needed" ]
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fe036f735ed32e5b763472f093e934207e88328c
Radar captures of indoor environments
qlairvoyance/indoor_radar
[ "task_categories:object-detection", "task_categories:image-segmentation", "size_categories:10K<n<100K", "region:us" ]
2023-03-27T16:57:03+00:00
{"size_categories": ["10K<n<100K"], "task_categories": ["object-detection", "image-segmentation"]}
2023-03-27T16:58:00+00:00
[]
[]
TAGS #task_categories-object-detection #task_categories-image-segmentation #size_categories-10K<n<100K #region-us
Radar captures of indoor environments
[]
[ "TAGS\n#task_categories-object-detection #task_categories-image-segmentation #size_categories-10K<n<100K #region-us \n" ]
[ 41 ]
[ "passage: TAGS\n#task_categories-object-detection #task_categories-image-segmentation #size_categories-10K<n<100K #region-us \n" ]
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16ffc5a88fb197b99495e9d3f73781dea7674d7e
# Dataset Card for "flores200_eng_input_scaffolding_mix2_mt5" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
hlillemark/flores200_eng_input_scaffolding_mix2_mt5
[ "region:us" ]
2023-03-27T16:57:52+00:00
{"dataset_info": {"features": [{"name": "id", "dtype": "int32"}, {"name": "input_ids", "sequence": "int32"}, {"name": "attention_mask", "sequence": "int8"}, {"name": "labels", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 8041305043, "num_examples": 10240000}, {"name": "val", "num_bytes": 3827042, "num_examples": 5000}, {"name": "test", "num_bytes": 7670994, "num_examples": 10000}], "download_size": 3959567422, "dataset_size": 8052803079}}
2023-03-27T17:07:06+00:00
[]
[]
TAGS #region-us
# Dataset Card for "flores200_eng_input_scaffolding_mix2_mt5" More Information needed
[ "# Dataset Card for \"flores200_eng_input_scaffolding_mix2_mt5\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"flores200_eng_input_scaffolding_mix2_mt5\"\n\nMore Information needed" ]
[ 6, 30 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"flores200_eng_input_scaffolding_mix2_mt5\"\n\nMore Information needed" ]
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da0eaaa5319a11c118d5624e5c955cee90e36788
# Dataset Card for "flores200_eng_input_scaffolding_mix3_mt5" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
hlillemark/flores200_eng_input_scaffolding_mix3_mt5
[ "region:us" ]
2023-03-27T17:25:23+00:00
{"dataset_info": {"features": [{"name": "id", "dtype": "int32"}, {"name": "input_ids", "sequence": "int32"}, {"name": "attention_mask", "sequence": "int8"}, {"name": "labels", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 9290803477, "num_examples": 10240000}, {"name": "val", "num_bytes": 3827042, "num_examples": 5000}, {"name": "test", "num_bytes": 7670994, "num_examples": 10000}], "download_size": 4445111273, "dataset_size": 9302301513}}
2023-03-27T17:35:32+00:00
[]
[]
TAGS #region-us
# Dataset Card for "flores200_eng_input_scaffolding_mix3_mt5" More Information needed
[ "# Dataset Card for \"flores200_eng_input_scaffolding_mix3_mt5\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"flores200_eng_input_scaffolding_mix3_mt5\"\n\nMore Information needed" ]
[ 6, 30 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"flores200_eng_input_scaffolding_mix3_mt5\"\n\nMore Information needed" ]
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1570c3c9d8147467a85cc86c0be6a60930f0044b
# Dataset Card for Dataset Name ## Dataset Description - **Homepage:** https://rucola-benchmark.com - **Repository:** https://github.com/RussianNLP/RuCoLA - **Paper:** https://aclanthology.org/2022.emnlp-main.348/ - **ArXiv:** https://arxiv.org/abs/2210.12814 - **Leaderboard:** https://rucola-benchmark.com/leaderboard - **Point of Contact:** [email protected] - **Language:** Russian ### Dataset Summary ![RuCoLA logo](logo.png) Russian Corpus of Linguistic Acceptability (RuCoLA) is a novel benchmark of 13.4k sentences labeled as acceptable or not. RuCoLA combines in-domain sentences manually collected from linguistic literature and out-of-domain sentences produced by nine machine translation and paraphrase generation models. The motivation behind the out-of-domain set is to facilitate the practical use of acceptability judgments for improving language generation. Each unacceptable sentence is additionally labeled with four standard and machine-specific coarse-grained categories: morphology, syntax, semantics, and hallucinations. ## Dataset Structure ### Supported Tasks and Leaderboards - **Task:** binary classification. - **Metrics:** MCC/Acc. - **Leaderboard:** https://rucola-benchmark.com/leaderboard ### Languages Russian. ### Data Instances ``` { "id": 19, "sentence": "Люк останавливает удачу от этого.", "label": 0, "error_type": "Hallucination", "detailed_source": "WikiMatrix"} } ``` The example in English for illustration purposes: ``` { "id": 19, "sentence": "Luck stops luck from doing this.", "label": 0, "error_type": "Hallucination", "detailed_source": "WikiMatrix"} } ``` ### Data Fields - ```id (int64)```: the sentence's id. - ```sentence (str)```: the sentence. - ```label (str)```: the target class. "1" refers to "acceptable", while "0" corresponds to "unacceptable". - ```error_type (str)```: the coarse-grained violation category (Morphology, Syntax, Semantics, or Hallucination); "0" if the sentence is acceptable. - ```detailed_source```: the data source. ### Data Splits RuCoLA consists of the training, development, and private test sets organised under two subsets: in-domain (linguistic publications) and out-of-domain (texts produced by natural language generation models). - ```train```: 7869 in-domain samples (```"data/in_domain_train.csv"```). - ```validation```: 2787 in-domain and out-of-domain samples. The in-domain (```"data/in_domain_dev.csv"```) and out-of-domain (```"data/out_of_domain_dev.csv"```) validation sets are merged into ```"data/dev.csv"``` for convenience. - ```test```: 2789 in-domain and out-of-domain samples (```"data/test.csv"```). ## Dataset Creation ### Curation Rationale - **In-domain Subset:** The in-domain sentences and the corresponding authors’ acceptability judgments are *manually* drawn from fundamental linguistic textbooks, academic publications, and methodological materials. - **Out-of-domain Subset:** The out-of-domain sentences are produced by nine open-source MT and paraphrase generation models. ### Source Data <details> <summary>Linguistic publications and resources</summary> |Original source |Transliterated source |Source id | |---|---|---| |[Проект корпусного описания русской грамматики](http://rusgram.ru) | [Proekt korpusnogo opisaniya russkoj grammatiki](http://rusgram.ru/)|Rusgram | |Тестелец, Я.Г., 2001. *Введение в общий синтаксис*. Федеральное государственное бюджетное образовательное учреждение высшего образования Российский государственный гуманитарный университет.|Yakov Testelets. 2001. Vvedeniye v obschiy sintaksis. Russian State University for the Humanities. |Testelets | |Лютикова, Е.А., 2010. *К вопросу о категориальном статусе именных групп в русском языке*. Вестник Московского университета. Серия 9. Филология, (6), pp.36-76. |Ekaterina Lutikova. 2010. K voprosu o kategorial’nom statuse imennykh grup v russkom yazyke. Moscow University Philology Bulletin. |Lutikova | |Митренина, О.В., Романова, Е.Е. and Слюсарь, Н.А., 2017. *Введение в генеративную грамматику*. Общество с ограниченной ответственностью "Книжный дом ЛИБРОКОМ". |Olga Mitrenina et al. 2017. Vvedeniye v generativnuyu grammatiku. Limited Liability Company “LIBROCOM”. |Mitrenina | |Падучева, Е.В., 2004. *Динамические модели в семантике лексики*. М.: Языки славянской культуры.| Elena Paducheva. 2004. Dinamicheskiye modeli v semantike leksiki. Languages of Slavonic culture. |Paducheva2004 | |Падучева, Е.В., 2010. *Семантические исследования: Семантика времени и вида в русском языке; Семантика нарратива*. М.: Языки славянской культуры. | Elena Paducheva. 2010. Semanticheskiye issledovaniya: Semantika vremeni i vida v russkom yazyke; Semantika narrativa. Languages of Slavonic culture.|Paducheva2010 | |Падучева, Е.В., 2013. *Русское отрицательное предложение*. М.: Языки славянской культуры |Elena Paducheva. 2013. Russkoye otritsatel’noye predlozheniye. Languages of Slavonic culture. |Paducheva2013 | |Селиверстова, О.Н., 2004. *Труды по семантике*. М.: Языки славянской культуры | Olga Seliverstova. 2004. Trudy po semantike. Languages of Slavonic culture.|Seliverstova | | Набор данных ЕГЭ по русскому языку | Shavrina et al. 2020. [Humans Keep It One Hundred: an Overview of AI Journey](https://aclanthology.org/2020.lrec-1.277/) |USE5, USE7, USE8 | </details> <details> <summary>Machine-generated sentences</summary> <br> **Datasets** |Original source |Source id| |---|---| |Mikel Artetxe and Holger Schwenk. 2019. [Massively Multilingual Sentence Embeddings for Zero-Shot Cross-Lingual Transfer and Beyond](https://direct.mit.edu/tacl/article/doi/10.1162/tacl_a_00288/43523/Massively-Multilingual-Sentence-Embeddings-for)|Tatoeba | |Holger Schwenk et al. 2021. [WikiMatrix: Mining 135M Parallel Sentences in 1620 Language Pairs from Wikipedia](https://aclanthology.org/2021.eacl-main.115/)|WikiMatrix | |Ye Qi et al. 2018. [When and Why Are Pre-Trained Word Embeddings Useful for Neural Machine Translation?](https://aclanthology.org/N18-2084/)|TED | |Alexandra Antonova and Alexey Misyurev. 2011. [Building a Web-Based Parallel Corpus and Filtering Out Machine-Translated Text](https://aclanthology.org/W11-1218/)|YandexCorpus | **Models** [EasyNMT models](https://github.com/UKPLab/EasyNMT): 1. OPUS-MT. Jörg Tiedemann and Santhosh Thottingal. 2020. [OPUS-MT – Building open translation services for the World](https://aclanthology.org/2020.eamt-1.61/) 2. M-BART50. Yuqing Tang et al. 2020. [Multilingual Translation with Extensible Multilingual Pretraining and Finetuning](https://arxiv.org/abs/2008.00401) 3. M2M-100. Angela Fan et al. 2021. [Beyond English-Centric Multilingual Machine Translation](https://jmlr.org/papers/volume22/20-1307/20-1307.pdf) [Paraphrase generation models](https://github.com/RussianNLP/russian_paraphrasers): 1. [ruGPT2-Large](https://huggingface.co/sberbank-ai/rugpt2large) 2. [ruT5](https://huggingface.co/cointegrated/rut5-base-paraphraser) 3. mT5. Linting Xue et al. 2021. [mT5: A Massively Multilingual Pre-trained Text-to-Text Transformer](https://aclanthology.org/2021.naacl-main.41/) </details> ### Annotations #### Annotation process The out-of-domain sentences undergo a two-stage annotation procedure on [Toloka](https://toloka.ai), a crowd-sourcing platform for data labeling. Each stage includes an unpaid training phase with explanations, control tasks for tracking annotation quality, and the main annotation task. Before starting, the worker is given detailed instructions describing the task, explaining the labels, and showing plenty of examples. The instruction is available at any time during both the training and main annotation phases. To get access to the main phase, the worker should first complete the training phase by labeling more than 70% of its examples correctly. Each trained worker receives a page with five sentences, one of which is a control one. We collect the majority vote labels via a dy- namic overlap from three to five workers after filtering them by response time and performance on control tasks. - **Stage 1: Acceptability Judgments** The first annotation stage defines whether a given sentence is acceptable or not. Access to the project is granted to workers certified as native speakers of Russian by Toloka and ranked top-60% workers according to the Toloka rating system. Each worker answers 30 examples in the training phase. Each training example is accompanied by an explanation that appears in an incorrect answer. The main annotation phase counts 3.6k machine-generated sentences. The pay rate is on average $2.55/hr, which is twice the amount of the hourly minimum wage in Russia. Each of 1.3k trained workers get paid, but we keep votes from only 960 workers whose annotation quality rate on the control sentences is more than 50%. - **Stage 2: Violation Categories** The second stage includes validation and annotation of sentences labeled unacceptable on Stage 1 according to five answer options: “Morphology”, “Syntax”, “Semantics”, “Hallucinations” and “Other”. The task is framed as a multi-label classification, i.e., the sentence may contain more than one violation in some rare cases or be re-labeled as acceptable. We create a team of 30 annotators who are undergraduate BA and MA in philology and linguistics from several Russian universities. The students are asked to study the works on CoLA, TGEA, and hallucinations. We also hold an online seminar to discuss the works and clarify the task specifics. Each student undergoes platform-based training on 15 examples before moving onto the main phase of 1.3k sentences. The students are paid on average $5.42/hr and are eligible to get credits for an academic course or an internship. This stage provides direct interaction between authors and students in a group chat. We keep submissions with more than 30 seconds of response time per page and collect the majority vote labels for each answer independently. Sentences having more than one violation category or labeled as “Other” by the majority are filtered out. ### Personal and Sensitive Information The annotators are warned about potentially sensitive topics in data (e.g., politics, culture, and religion). ## Considerations for Using the Data ### Social Impact of Dataset RuCoLA may serve as training data for acceptability classifiers, which may benefit the quality of generated texts. We recognize that such improvements in text generation may lead to misuse of LMs for malicious purposes. However, our corpus can be used to train adversarial defense and artificial text detection models. We introduce a novel dataset for **research and development needs**, and the potential negative uses are not lost on us. ### Discussion of Biases Although we aim to control the number of high-frequency tokens in the RuCoLA’s sentences, we assume that potential word frequency distribution shift between LMs’ pretraining corpora and our corpus can introduce bias in the evaluation. Furthermore, linguistic publications represent a specific domain as the primary source of acceptability judgments. On the one hand, it can lead to a domain shift when using RuCoLA for practical purposes. On the other hand, we observe moderate acceptability classification performance on the out-of-domain test, which spans multiple domains, ranging from subtitles to Wikipedia. ### Other Known Limitations - **Data Collection** Acceptability judgments datasets require a source of unacceptable sentences. Collecting judgments from linguistic literature has become a standard practice replicated in multiple languages. However, this approach has several limitations. First, many studies raise concerns about the reliability and reproducibility of acceptability judgments. Second, the linguists’ judgments may limit data representativeness, as they may not reflect the errors that speakers tend to produce. Third, enriching acceptability judgments datasets is time-consuming, while creating new ones can be challenging due to limited resources, e.g., in low-resource languages. - **Expert vs. Non-expert** One of the open methodological questions on acceptability judgments is whether they should be collected from expert or non-expert speakers. On the one hand, prior linguistic knowledge can introduce bias in reporting judgments. On the other hand, expertise may increase the quality of the linguists’ judgments over the ones of non-linguists. At the same time, the latter tend to be influenced by an individual’s exposure to ungrammatical language use. The objective of involving students with a linguistic background is to maximize the annotation quality. - **Fine-grained Annotation** The coarse-grained annotation scheme of the RuCoLA’s unacceptable sentences relies on four major categories. While the annotation can be helpful for model error analysis, it limits the scope of LMs’ diagnostic evaluation concerning linguistic and machine-specific phenomena. ## Additional Information ### Dataset Curators Correspondence: ```[email protected]``` ### Licensing Information Our baseline code and acceptability labels are available under the Apache 2.0 license. The copyright (where applicable) of texts from the linguistic publications and resources remains with the original authors or publishers. ### Citation Information ``` @inproceedings{mikhailov-etal-2022-rucola, title = "{R}u{C}o{LA}: {R}ussian Corpus of Linguistic Acceptability", author = "Mikhailov, Vladislav and Shamardina, Tatiana and Ryabinin, Max and Pestova, Alena and Smurov, Ivan and Artemova, Ekaterina", booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing", month = dec, year = "2022", address = "Abu Dhabi, United Arab Emirates", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2022.emnlp-main.348", pages = "5207--5227", abstract = "Linguistic acceptability (LA) attracts the attention of the research community due to its many uses, such as testing the grammatical knowledge of language models and filtering implausible texts with acceptability classifiers.However, the application scope of LA in languages other than English is limited due to the lack of high-quality resources.To this end, we introduce the Russian Corpus of Linguistic Acceptability (RuCoLA), built from the ground up under the well-established binary LA approach. RuCoLA consists of 9.8k in-domain sentences from linguistic publications and 3.6k out-of-domain sentences produced by generative models. The out-of-domain set is created to facilitate the practical use of acceptability for improving language generation.Our paper describes the data collection protocol and presents a fine-grained analysis of acceptability classification experiments with a range of baseline approaches.In particular, we demonstrate that the most widely used language models still fall behind humans by a large margin, especially when detecting morphological and semantic errors. We release RuCoLA, the code of experiments, and a public leaderboard to assess the linguistic competence of language models for Russian.", } ``` ### Other Please refer to our [paper](https://aclanthology.org/2022.emnlp-main.348/) for more details.
RussianNLP/rucola
[ "task_categories:text-classification", "size_categories:10K<n<100K", "language:ru", "license:apache-2.0", "arxiv:2210.12814", "arxiv:2008.00401", "region:us" ]
2023-03-27T17:35:06+00:00
{"language": ["ru"], "license": "apache-2.0", "size_categories": ["10K<n<100K"], "task_categories": ["text-classification"]}
2023-03-27T17:47:12+00:00
[ "2210.12814", "2008.00401" ]
[ "ru" ]
TAGS #task_categories-text-classification #size_categories-10K<n<100K #language-Russian #license-apache-2.0 #arxiv-2210.12814 #arxiv-2008.00401 #region-us
Dataset Card for Dataset Name ============================= Dataset Description ------------------- * Homepage: URL * Repository: URL * Paper: URL * ArXiv: URL * Leaderboard: URL * Point of Contact: vmikhailovhse@URL * Language: Russian ### Dataset Summary !RuCoLA logo Russian Corpus of Linguistic Acceptability (RuCoLA) is a novel benchmark of 13.4k sentences labeled as acceptable or not. RuCoLA combines in-domain sentences manually collected from linguistic literature and out-of-domain sentences produced by nine machine translation and paraphrase generation models. The motivation behind the out-of-domain set is to facilitate the practical use of acceptability judgments for improving language generation. Each unacceptable sentence is additionally labeled with four standard and machine-specific coarse-grained categories: morphology, syntax, semantics, and hallucinations. Dataset Structure ----------------- ### Supported Tasks and Leaderboards * Task: binary classification. * Metrics: MCC/Acc. * Leaderboard: URL ### Languages Russian. ### Data Instances The example in English for illustration purposes: ### Data Fields * : the sentence's id. * : the sentence. * : the target class. "1" refers to "acceptable", while "0" corresponds to "unacceptable". * : the coarse-grained violation category (Morphology, Syntax, Semantics, or Hallucination); "0" if the sentence is acceptable. * : the data source. ### Data Splits RuCoLA consists of the training, development, and private test sets organised under two subsets: in-domain (linguistic publications) and out-of-domain (texts produced by natural language generation models). * : 7869 in-domain samples (). * : 2787 in-domain and out-of-domain samples. The in-domain () and out-of-domain () validation sets are merged into for convenience. * : 2789 in-domain and out-of-domain samples (). Dataset Creation ---------------- ### Curation Rationale * In-domain Subset: The in-domain sentences and the corresponding authors’ acceptability judgments are *manually* drawn from fundamental linguistic textbooks, academic publications, and methodological materials. * Out-of-domain Subset: The out-of-domain sentences are produced by nine open-source MT and paraphrase generation models. ### Source Data Linguistic publications and resources Original source: Проект корпусного описания русской грамматики, Transliterated source: Proekt korpusnogo opisaniya russkoj grammatiki, Source id: Rusgram Original source: Тестелец, Я.Г., 2001. *Введение в общий синтаксис*. Федеральное государственное бюджетное образовательное учреждение высшего образования Российский государственный гуманитарный университет., Transliterated source: Yakov Testelets. 2001. Vvedeniye v obschiy sintaksis. Russian State University for the Humanities., Source id: Testelets Original source: Лютикова, Е.А., 2010. *К вопросу о категориальном статусе именных групп в русском языке*. Вестник Московского университета. Серия 9. Филология, (6), pp.36-76., Transliterated source: Ekaterina Lutikova. 2010. K voprosu o kategorial’nom statuse imennykh grup v russkom yazyke. Moscow University Philology Bulletin., Source id: Lutikova Original source: Митренина, О.В., Романова, Е.Е. and Слюсарь, Н.А., 2017. *Введение в генеративную грамматику*. Общество с ограниченной ответственностью "Книжный дом ЛИБРОКОМ"., Transliterated source: Olga Mitrenina et al. 2017. Vvedeniye v generativnuyu grammatiku. Limited Liability Company “LIBROCOM”., Source id: Mitrenina Original source: Падучева, Е.В., 2004. *Динамические модели в семантике лексики*. М.: Языки славянской культуры., Transliterated source: Elena Paducheva. 2004. Dinamicheskiye modeli v semantike leksiki. Languages of Slavonic culture., Source id: Paducheva2004 Original source: Падучева, Е.В., 2010. *Семантические исследования: Семантика времени и вида в русском языке; Семантика нарратива*. М.: Языки славянской культуры., Transliterated source: Elena Paducheva. 2010. Semanticheskiye issledovaniya: Semantika vremeni i vida v russkom yazyke; Semantika narrativa. Languages of Slavonic culture., Source id: Paducheva2010 Original source: Падучева, Е.В., 2013. *Русское отрицательное предложение*. М.: Языки славянской культуры, Transliterated source: Elena Paducheva. 2013. Russkoye otritsatel’noye predlozheniye. Languages of Slavonic culture., Source id: Paducheva2013 Original source: Селиверстова, О.Н., 2004. *Труды по семантике*. М.: Языки славянской культуры, Transliterated source: Olga Seliverstova. 2004. Trudy po semantike. Languages of Slavonic culture., Source id: Seliverstova Original source: Набор данных ЕГЭ по русскому языку, Transliterated source: Shavrina et al. 2020. Humans Keep It One Hundred: an Overview of AI Journey, Source id: USE5, USE7, USE8 Machine-generated sentences Datasets Models EasyNMT models: 1. OPUS-MT. Jörg Tiedemann and Santhosh Thottingal. 2020. OPUS-MT – Building open translation services for the World 2. M-BART50. Yuqing Tang et al. 2020. Multilingual Translation with Extensible Multilingual Pretraining and Finetuning 3. M2M-100. Angela Fan et al. 2021. Beyond English-Centric Multilingual Machine Translation Paraphrase generation models: 1. ruGPT2-Large 2. ruT5 3. mT5. Linting Xue et al. 2021. mT5: A Massively Multilingual Pre-trained Text-to-Text Transformer ### Annotations #### Annotation process The out-of-domain sentences undergo a two-stage annotation procedure on Toloka, a crowd-sourcing platform for data labeling. Each stage includes an unpaid training phase with explanations, control tasks for tracking annotation quality, and the main annotation task. Before starting, the worker is given detailed instructions describing the task, explaining the labels, and showing plenty of examples. The instruction is available at any time during both the training and main annotation phases. To get access to the main phase, the worker should first complete the training phase by labeling more than 70% of its examples correctly. Each trained worker receives a page with five sentences, one of which is a control one. We collect the majority vote labels via a dy- namic overlap from three to five workers after filtering them by response time and performance on control tasks. * Stage 1: Acceptability Judgments The first annotation stage defines whether a given sentence is acceptable or not. Access to the project is granted to workers certified as native speakers of Russian by Toloka and ranked top-60% workers according to the Toloka rating system. Each worker answers 30 examples in the training phase. Each training example is accompanied by an explanation that appears in an incorrect answer. The main annotation phase counts 3.6k machine-generated sentences. The pay rate is on average $2.55/hr, which is twice the amount of the hourly minimum wage in Russia. Each of 1.3k trained workers get paid, but we keep votes from only 960 workers whose annotation quality rate on the control sentences is more than 50%. * Stage 2: Violation Categories The second stage includes validation and annotation of sentences labeled unacceptable on Stage 1 according to five answer options: “Morphology”, “Syntax”, “Semantics”, “Hallucinations” and “Other”. The task is framed as a multi-label classification, i.e., the sentence may contain more than one violation in some rare cases or be re-labeled as acceptable. We create a team of 30 annotators who are undergraduate BA and MA in philology and linguistics from several Russian universities. The students are asked to study the works on CoLA, TGEA, and hallucinations. We also hold an online seminar to discuss the works and clarify the task specifics. Each student undergoes platform-based training on 15 examples before moving onto the main phase of 1.3k sentences. The students are paid on average $5.42/hr and are eligible to get credits for an academic course or an internship. This stage provides direct interaction between authors and students in a group chat. We keep submissions with more than 30 seconds of response time per page and collect the majority vote labels for each answer independently. Sentences having more than one violation category or labeled as “Other” by the majority are filtered out. ### Personal and Sensitive Information The annotators are warned about potentially sensitive topics in data (e.g., politics, culture, and religion). Considerations for Using the Data --------------------------------- ### Social Impact of Dataset RuCoLA may serve as training data for acceptability classifiers, which may benefit the quality of generated texts. We recognize that such improvements in text generation may lead to misuse of LMs for malicious purposes. However, our corpus can be used to train adversarial defense and artificial text detection models. We introduce a novel dataset for research and development needs, and the potential negative uses are not lost on us. ### Discussion of Biases Although we aim to control the number of high-frequency tokens in the RuCoLA’s sentences, we assume that potential word frequency distribution shift between LMs’ pretraining corpora and our corpus can introduce bias in the evaluation. Furthermore, linguistic publications represent a specific domain as the primary source of acceptability judgments. On the one hand, it can lead to a domain shift when using RuCoLA for practical purposes. On the other hand, we observe moderate acceptability classification performance on the out-of-domain test, which spans multiple domains, ranging from subtitles to Wikipedia. ### Other Known Limitations * Data Collection Acceptability judgments datasets require a source of unacceptable sentences. Collecting judgments from linguistic literature has become a standard practice replicated in multiple languages. However, this approach has several limitations. First, many studies raise concerns about the reliability and reproducibility of acceptability judgments. Second, the linguists’ judgments may limit data representativeness, as they may not reflect the errors that speakers tend to produce. Third, enriching acceptability judgments datasets is time-consuming, while creating new ones can be challenging due to limited resources, e.g., in low-resource languages. * Expert vs. Non-expert One of the open methodological questions on acceptability judgments is whether they should be collected from expert or non-expert speakers. On the one hand, prior linguistic knowledge can introduce bias in reporting judgments. On the other hand, expertise may increase the quality of the linguists’ judgments over the ones of non-linguists. At the same time, the latter tend to be influenced by an individual’s exposure to ungrammatical language use. The objective of involving students with a linguistic background is to maximize the annotation quality. * Fine-grained Annotation The coarse-grained annotation scheme of the RuCoLA’s unacceptable sentences relies on four major categories. While the annotation can be helpful for model error analysis, it limits the scope of LMs’ diagnostic evaluation concerning linguistic and machine-specific phenomena. Additional Information ---------------------- ### Dataset Curators Correspondence: ### Licensing Information Our baseline code and acceptability labels are available under the Apache 2.0 license. The copyright (where applicable) of texts from the linguistic publications and resources remains with the original authors or publishers. ### Other Please refer to our paper for more details.
[ "### Dataset Summary\n\n\n!RuCoLA logo\n\n\nRussian Corpus of Linguistic Acceptability (RuCoLA) is a novel benchmark of 13.4k sentences labeled as acceptable or not. RuCoLA combines in-domain sentences manually collected from linguistic literature and out-of-domain sentences produced by nine machine translation and paraphrase generation models.\nThe motivation behind the out-of-domain set is to facilitate the practical use of acceptability judgments for improving language generation.\nEach unacceptable sentence is additionally labeled with four standard and machine-specific coarse-grained categories: morphology, syntax, semantics, and hallucinations.\n\n\nDataset Structure\n-----------------", "### Supported Tasks and Leaderboards\n\n\n* Task: binary classification.\n* Metrics: MCC/Acc.\n* Leaderboard: URL", "### Languages\n\n\nRussian.", "### Data Instances\n\n\nThe example in English for illustration purposes:", "### Data Fields\n\n\n* : the sentence's id.\n* : the sentence.\n* : the target class. \"1\" refers to \"acceptable\", while \"0\" corresponds to \"unacceptable\".\n* : the coarse-grained violation category (Morphology, Syntax, Semantics, or Hallucination); \"0\" if the sentence is acceptable.\n* : the data source.", "### Data Splits\n\n\nRuCoLA consists of the training, development, and private test sets organised under two subsets: in-domain (linguistic publications) and out-of-domain (texts produced by natural language generation models).\n\n\n* : 7869 in-domain samples ().\n* : 2787 in-domain and out-of-domain samples. The in-domain () and out-of-domain () validation sets are merged into for convenience.\n* : 2789 in-domain and out-of-domain samples ().\n\n\nDataset Creation\n----------------", "### Curation Rationale\n\n\n* In-domain Subset: The in-domain sentences and the corresponding authors’ acceptability judgments are *manually* drawn from fundamental linguistic textbooks, academic publications, and methodological materials.\n* Out-of-domain Subset: The out-of-domain sentences are produced by nine open-source MT and paraphrase generation models.", "### Source Data\n\n\n\nLinguistic publications and resources\nOriginal source: Проект корпусного описания русской грамматики, Transliterated source: Proekt korpusnogo opisaniya russkoj grammatiki, Source id: Rusgram\nOriginal source: Тестелец, Я.Г., 2001. *Введение в общий синтаксис*. Федеральное государственное бюджетное образовательное учреждение высшего образования Российский государственный гуманитарный университет., Transliterated source: Yakov Testelets. 2001. Vvedeniye v obschiy sintaksis. Russian State University for the Humanities., Source id: Testelets\nOriginal source: Лютикова, Е.А., 2010. *К вопросу о категориальном статусе именных групп в русском языке*. Вестник Московского университета. Серия 9. Филология, (6), pp.36-76., Transliterated source: Ekaterina Lutikova. 2010. K voprosu o kategorial’nom statuse imennykh grup v russkom yazyke. Moscow University Philology Bulletin., Source id: Lutikova\nOriginal source: Митренина, О.В., Романова, Е.Е. and Слюсарь, Н.А., 2017. *Введение в генеративную грамматику*. Общество с ограниченной ответственностью \"Книжный дом ЛИБРОКОМ\"., Transliterated source: Olga Mitrenina et al. 2017. Vvedeniye v generativnuyu grammatiku. Limited Liability Company “LIBROCOM”., Source id: Mitrenina\nOriginal source: Падучева, Е.В., 2004. *Динамические модели в семантике лексики*. М.: Языки славянской культуры., Transliterated source: Elena Paducheva. 2004. Dinamicheskiye modeli v semantike leksiki. Languages of Slavonic culture., Source id: Paducheva2004\nOriginal source: Падучева, Е.В., 2010. *Семантические исследования: Семантика времени и вида в русском языке; Семантика нарратива*. М.: Языки славянской культуры., Transliterated source: Elena Paducheva. 2010. Semanticheskiye issledovaniya: Semantika vremeni i vida v russkom yazyke; Semantika narrativa. Languages of Slavonic culture., Source id: Paducheva2010\nOriginal source: Падучева, Е.В., 2013. *Русское отрицательное предложение*. М.: Языки славянской культуры, Transliterated source: Elena Paducheva. 2013. Russkoye otritsatel’noye predlozheniye. Languages of Slavonic culture., Source id: Paducheva2013\nOriginal source: Селиверстова, О.Н., 2004. *Труды по семантике*. М.: Языки славянской культуры, Transliterated source: Olga Seliverstova. 2004. Trudy po semantike. Languages of Slavonic culture., Source id: Seliverstova\nOriginal source: Набор данных ЕГЭ по русскому языку, Transliterated source: Shavrina et al. 2020. Humans Keep It One Hundred: an Overview of AI Journey, Source id: USE5, USE7, USE8\n\n\n\n\nMachine-generated sentences\n \n\nDatasets\n\n\n\nModels\n\n\nEasyNMT models:\n\n\n1. OPUS-MT. Jörg Tiedemann and Santhosh Thottingal. 2020. OPUS-MT – Building open translation services for the World\n2. M-BART50. Yuqing Tang et al. 2020. Multilingual Translation with Extensible Multilingual Pretraining and Finetuning\n3. M2M-100. Angela Fan et al. 2021. Beyond English-Centric Multilingual Machine Translation\n\n\nParaphrase generation models:\n\n\n1. ruGPT2-Large\n2. ruT5\n3. mT5. Linting Xue et al. 2021. mT5: A Massively Multilingual Pre-trained Text-to-Text Transformer", "### Annotations", "#### Annotation process\n\n\nThe out-of-domain sentences undergo a two-stage annotation procedure on Toloka, a crowd-sourcing platform for data labeling.\nEach stage includes an unpaid training phase with explanations, control tasks for tracking annotation quality, and the main annotation task. Before starting, the worker is given detailed instructions describing the task, explaining the labels, and showing plenty of examples.\nThe instruction is available at any time during both the training and main annotation phases. To get access to the main phase, the worker should first complete the training phase by labeling more than 70% of its examples correctly. Each trained worker receives a page with five sentences, one of which is a control one.\nWe collect the majority vote labels via a dy- namic overlap from three to five workers after filtering them by response time and performance on control tasks.\n\n\n* Stage 1: Acceptability Judgments\nThe first annotation stage defines whether a given sentence is acceptable or not. Access to the project is granted to workers certified as native speakers of Russian by Toloka and ranked top-60% workers according to the Toloka rating system.\nEach worker answers 30 examples in the training phase. Each training example is accompanied by an explanation that appears in an incorrect answer.\nThe main annotation phase counts 3.6k machine-generated sentences. The pay rate is on average $2.55/hr, which is twice the amount of the hourly minimum wage in Russia. Each of 1.3k trained workers get paid, but we keep votes from only 960 workers whose annotation quality rate on the control sentences is more than 50%.\n* Stage 2: Violation Categories\nThe second stage includes validation and annotation of sentences labeled unacceptable on Stage 1 according to five answer options: “Morphology”, “Syntax”, “Semantics”, “Hallucinations” and “Other”. The task is framed as a multi-label classification, i.e., the sentence may contain more than one violation in some rare cases or be re-labeled as acceptable.\nWe create a team of 30 annotators who are undergraduate BA and MA in philology and linguistics from several Russian universities. The students are asked to study the works on CoLA, TGEA, and hallucinations. We also hold an online seminar to discuss the works and clarify the task specifics. Each student undergoes platform-based training on 15 examples before moving onto the main phase of 1.3k sentences.\nThe students are paid on average $5.42/hr and are eligible to get credits for an academic course or an internship. This stage provides direct interaction between authors and students in a group chat. We keep submissions with more than 30 seconds of response time per page and collect the majority vote labels for each answer independently.\nSentences having more than one violation category or labeled as “Other” by the majority are filtered out.", "### Personal and Sensitive Information\n\n\nThe annotators are warned about potentially sensitive topics in data (e.g., politics, culture, and religion).\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset\n\n\nRuCoLA may serve as training data for acceptability classifiers, which may benefit the quality of generated texts.\nWe recognize that such improvements in text generation may lead to misuse of LMs for malicious purposes. However, our corpus can be used to train adversarial defense and artificial text detection models.\nWe introduce a novel dataset for research and development needs, and the potential negative uses are not lost on us.", "### Discussion of Biases\n\n\nAlthough we aim to control the number of high-frequency tokens in the RuCoLA’s sentences, we assume that potential word frequency distribution shift between LMs’ pretraining corpora and our corpus can introduce bias in the evaluation.\nFurthermore, linguistic publications represent a specific domain as the primary source of acceptability judgments. On the one hand, it can lead to a domain shift when using RuCoLA for practical purposes.\nOn the other hand, we observe moderate acceptability classification performance on the out-of-domain test, which spans multiple domains, ranging from subtitles to Wikipedia.", "### Other Known Limitations\n\n\n* Data Collection\nAcceptability judgments datasets require a source of unacceptable sentences.\nCollecting judgments from linguistic literature has become a standard practice replicated in multiple languages. However, this approach has several limitations. First, many studies raise concerns about the reliability and reproducibility of acceptability judgments. Second, the linguists’ judgments may limit data representativeness, as they may not reflect the errors that speakers tend to produce. Third, enriching acceptability judgments datasets is time-consuming, while creating new ones can be challenging due to limited resources, e.g., in low-resource languages.\n* Expert vs. Non-expert\nOne of the open methodological questions on acceptability judgments is whether they should be collected from expert or non-expert speakers.\nOn the one hand, prior linguistic knowledge can introduce bias in reporting judgments. On the other hand, expertise may increase the quality of the linguists’ judgments over the ones of non-linguists. At the same time, the latter tend to be influenced by an individual’s exposure to ungrammatical language use.\nThe objective of involving students with a linguistic background is to maximize the annotation quality.\n* Fine-grained Annotation\nThe coarse-grained annotation scheme of the RuCoLA’s unacceptable sentences relies on four major categories. While the annotation can be helpful for model error analysis, it limits the scope of LMs’ diagnostic evaluation concerning linguistic and machine-specific phenomena.\n\n\nAdditional Information\n----------------------", "### Dataset Curators\n\n\nCorrespondence:", "### Licensing Information\n\n\nOur baseline code and acceptability labels are available under the Apache 2.0 license. The copyright (where applicable) of texts from the linguistic publications and resources remains with the original authors or publishers.", "### Other\n\n\nPlease refer to our paper for more details." ]
[ "TAGS\n#task_categories-text-classification #size_categories-10K<n<100K #language-Russian #license-apache-2.0 #arxiv-2210.12814 #arxiv-2008.00401 #region-us \n", "### Dataset Summary\n\n\n!RuCoLA logo\n\n\nRussian Corpus of Linguistic Acceptability (RuCoLA) is a novel benchmark of 13.4k sentences labeled as acceptable or not. RuCoLA combines in-domain sentences manually collected from linguistic literature and out-of-domain sentences produced by nine machine translation and paraphrase generation models.\nThe motivation behind the out-of-domain set is to facilitate the practical use of acceptability judgments for improving language generation.\nEach unacceptable sentence is additionally labeled with four standard and machine-specific coarse-grained categories: morphology, syntax, semantics, and hallucinations.\n\n\nDataset Structure\n-----------------", "### Supported Tasks and Leaderboards\n\n\n* Task: binary classification.\n* Metrics: MCC/Acc.\n* Leaderboard: URL", "### Languages\n\n\nRussian.", "### Data Instances\n\n\nThe example in English for illustration purposes:", "### Data Fields\n\n\n* : the sentence's id.\n* : the sentence.\n* : the target class. \"1\" refers to \"acceptable\", while \"0\" corresponds to \"unacceptable\".\n* : the coarse-grained violation category (Morphology, Syntax, Semantics, or Hallucination); \"0\" if the sentence is acceptable.\n* : the data source.", "### Data Splits\n\n\nRuCoLA consists of the training, development, and private test sets organised under two subsets: in-domain (linguistic publications) and out-of-domain (texts produced by natural language generation models).\n\n\n* : 7869 in-domain samples ().\n* : 2787 in-domain and out-of-domain samples. The in-domain () and out-of-domain () validation sets are merged into for convenience.\n* : 2789 in-domain and out-of-domain samples ().\n\n\nDataset Creation\n----------------", "### Curation Rationale\n\n\n* In-domain Subset: The in-domain sentences and the corresponding authors’ acceptability judgments are *manually* drawn from fundamental linguistic textbooks, academic publications, and methodological materials.\n* Out-of-domain Subset: The out-of-domain sentences are produced by nine open-source MT and paraphrase generation models.", "### Source Data\n\n\n\nLinguistic publications and resources\nOriginal source: Проект корпусного описания русской грамматики, Transliterated source: Proekt korpusnogo opisaniya russkoj grammatiki, Source id: Rusgram\nOriginal source: Тестелец, Я.Г., 2001. *Введение в общий синтаксис*. Федеральное государственное бюджетное образовательное учреждение высшего образования Российский государственный гуманитарный университет., Transliterated source: Yakov Testelets. 2001. Vvedeniye v obschiy sintaksis. Russian State University for the Humanities., Source id: Testelets\nOriginal source: Лютикова, Е.А., 2010. *К вопросу о категориальном статусе именных групп в русском языке*. Вестник Московского университета. Серия 9. Филология, (6), pp.36-76., Transliterated source: Ekaterina Lutikova. 2010. K voprosu o kategorial’nom statuse imennykh grup v russkom yazyke. Moscow University Philology Bulletin., Source id: Lutikova\nOriginal source: Митренина, О.В., Романова, Е.Е. and Слюсарь, Н.А., 2017. *Введение в генеративную грамматику*. Общество с ограниченной ответственностью \"Книжный дом ЛИБРОКОМ\"., Transliterated source: Olga Mitrenina et al. 2017. Vvedeniye v generativnuyu grammatiku. Limited Liability Company “LIBROCOM”., Source id: Mitrenina\nOriginal source: Падучева, Е.В., 2004. *Динамические модели в семантике лексики*. М.: Языки славянской культуры., Transliterated source: Elena Paducheva. 2004. Dinamicheskiye modeli v semantike leksiki. Languages of Slavonic culture., Source id: Paducheva2004\nOriginal source: Падучева, Е.В., 2010. *Семантические исследования: Семантика времени и вида в русском языке; Семантика нарратива*. М.: Языки славянской культуры., Transliterated source: Elena Paducheva. 2010. Semanticheskiye issledovaniya: Semantika vremeni i vida v russkom yazyke; Semantika narrativa. Languages of Slavonic culture., Source id: Paducheva2010\nOriginal source: Падучева, Е.В., 2013. *Русское отрицательное предложение*. М.: Языки славянской культуры, Transliterated source: Elena Paducheva. 2013. Russkoye otritsatel’noye predlozheniye. Languages of Slavonic culture., Source id: Paducheva2013\nOriginal source: Селиверстова, О.Н., 2004. *Труды по семантике*. М.: Языки славянской культуры, Transliterated source: Olga Seliverstova. 2004. Trudy po semantike. Languages of Slavonic culture., Source id: Seliverstova\nOriginal source: Набор данных ЕГЭ по русскому языку, Transliterated source: Shavrina et al. 2020. Humans Keep It One Hundred: an Overview of AI Journey, Source id: USE5, USE7, USE8\n\n\n\n\nMachine-generated sentences\n \n\nDatasets\n\n\n\nModels\n\n\nEasyNMT models:\n\n\n1. OPUS-MT. Jörg Tiedemann and Santhosh Thottingal. 2020. OPUS-MT – Building open translation services for the World\n2. M-BART50. Yuqing Tang et al. 2020. Multilingual Translation with Extensible Multilingual Pretraining and Finetuning\n3. M2M-100. Angela Fan et al. 2021. Beyond English-Centric Multilingual Machine Translation\n\n\nParaphrase generation models:\n\n\n1. ruGPT2-Large\n2. ruT5\n3. mT5. Linting Xue et al. 2021. mT5: A Massively Multilingual Pre-trained Text-to-Text Transformer", "### Annotations", "#### Annotation process\n\n\nThe out-of-domain sentences undergo a two-stage annotation procedure on Toloka, a crowd-sourcing platform for data labeling.\nEach stage includes an unpaid training phase with explanations, control tasks for tracking annotation quality, and the main annotation task. Before starting, the worker is given detailed instructions describing the task, explaining the labels, and showing plenty of examples.\nThe instruction is available at any time during both the training and main annotation phases. To get access to the main phase, the worker should first complete the training phase by labeling more than 70% of its examples correctly. Each trained worker receives a page with five sentences, one of which is a control one.\nWe collect the majority vote labels via a dy- namic overlap from three to five workers after filtering them by response time and performance on control tasks.\n\n\n* Stage 1: Acceptability Judgments\nThe first annotation stage defines whether a given sentence is acceptable or not. Access to the project is granted to workers certified as native speakers of Russian by Toloka and ranked top-60% workers according to the Toloka rating system.\nEach worker answers 30 examples in the training phase. Each training example is accompanied by an explanation that appears in an incorrect answer.\nThe main annotation phase counts 3.6k machine-generated sentences. The pay rate is on average $2.55/hr, which is twice the amount of the hourly minimum wage in Russia. Each of 1.3k trained workers get paid, but we keep votes from only 960 workers whose annotation quality rate on the control sentences is more than 50%.\n* Stage 2: Violation Categories\nThe second stage includes validation and annotation of sentences labeled unacceptable on Stage 1 according to five answer options: “Morphology”, “Syntax”, “Semantics”, “Hallucinations” and “Other”. The task is framed as a multi-label classification, i.e., the sentence may contain more than one violation in some rare cases or be re-labeled as acceptable.\nWe create a team of 30 annotators who are undergraduate BA and MA in philology and linguistics from several Russian universities. The students are asked to study the works on CoLA, TGEA, and hallucinations. We also hold an online seminar to discuss the works and clarify the task specifics. Each student undergoes platform-based training on 15 examples before moving onto the main phase of 1.3k sentences.\nThe students are paid on average $5.42/hr and are eligible to get credits for an academic course or an internship. This stage provides direct interaction between authors and students in a group chat. We keep submissions with more than 30 seconds of response time per page and collect the majority vote labels for each answer independently.\nSentences having more than one violation category or labeled as “Other” by the majority are filtered out.", "### Personal and Sensitive Information\n\n\nThe annotators are warned about potentially sensitive topics in data (e.g., politics, culture, and religion).\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset\n\n\nRuCoLA may serve as training data for acceptability classifiers, which may benefit the quality of generated texts.\nWe recognize that such improvements in text generation may lead to misuse of LMs for malicious purposes. However, our corpus can be used to train adversarial defense and artificial text detection models.\nWe introduce a novel dataset for research and development needs, and the potential negative uses are not lost on us.", "### Discussion of Biases\n\n\nAlthough we aim to control the number of high-frequency tokens in the RuCoLA’s sentences, we assume that potential word frequency distribution shift between LMs’ pretraining corpora and our corpus can introduce bias in the evaluation.\nFurthermore, linguistic publications represent a specific domain as the primary source of acceptability judgments. On the one hand, it can lead to a domain shift when using RuCoLA for practical purposes.\nOn the other hand, we observe moderate acceptability classification performance on the out-of-domain test, which spans multiple domains, ranging from subtitles to Wikipedia.", "### Other Known Limitations\n\n\n* Data Collection\nAcceptability judgments datasets require a source of unacceptable sentences.\nCollecting judgments from linguistic literature has become a standard practice replicated in multiple languages. However, this approach has several limitations. First, many studies raise concerns about the reliability and reproducibility of acceptability judgments. Second, the linguists’ judgments may limit data representativeness, as they may not reflect the errors that speakers tend to produce. Third, enriching acceptability judgments datasets is time-consuming, while creating new ones can be challenging due to limited resources, e.g., in low-resource languages.\n* Expert vs. Non-expert\nOne of the open methodological questions on acceptability judgments is whether they should be collected from expert or non-expert speakers.\nOn the one hand, prior linguistic knowledge can introduce bias in reporting judgments. On the other hand, expertise may increase the quality of the linguists’ judgments over the ones of non-linguists. At the same time, the latter tend to be influenced by an individual’s exposure to ungrammatical language use.\nThe objective of involving students with a linguistic background is to maximize the annotation quality.\n* Fine-grained Annotation\nThe coarse-grained annotation scheme of the RuCoLA’s unacceptable sentences relies on four major categories. While the annotation can be helpful for model error analysis, it limits the scope of LMs’ diagnostic evaluation concerning linguistic and machine-specific phenomena.\n\n\nAdditional Information\n----------------------", "### Dataset Curators\n\n\nCorrespondence:", "### Licensing Information\n\n\nOur baseline code and acceptability labels are available under the Apache 2.0 license. The copyright (where applicable) of texts from the linguistic publications and resources remains with the original authors or publishers.", "### Other\n\n\nPlease refer to our paper for more details." ]
[ 59, 158, 35, 6, 15, 88, 139, 92, 865, 5, 653, 47, 100, 147, 359, 11, 52, 12 ]
[ "passage: TAGS\n#task_categories-text-classification #size_categories-10K<n<100K #language-Russian #license-apache-2.0 #arxiv-2210.12814 #arxiv-2008.00401 #region-us \n### Dataset Summary\n\n\n!RuCoLA logo\n\n\nRussian Corpus of Linguistic Acceptability (RuCoLA) is a novel benchmark of 13.4k sentences labeled as acceptable or not. RuCoLA combines in-domain sentences manually collected from linguistic literature and out-of-domain sentences produced by nine machine translation and paraphrase generation models.\nThe motivation behind the out-of-domain set is to facilitate the practical use of acceptability judgments for improving language generation.\nEach unacceptable sentence is additionally labeled with four standard and machine-specific coarse-grained categories: morphology, syntax, semantics, and hallucinations.\n\n\nDataset Structure\n-----------------### Supported Tasks and Leaderboards\n\n\n* Task: binary classification.\n* Metrics: MCC/Acc.\n* Leaderboard: URL### Languages\n\n\nRussian.### Data Instances\n\n\nThe example in English for illustration purposes:### Data Fields\n\n\n* : the sentence's id.\n* : the sentence.\n* : the target class. \"1\" refers to \"acceptable\", while \"0\" corresponds to \"unacceptable\".\n* : the coarse-grained violation category (Morphology, Syntax, Semantics, or Hallucination); \"0\" if the sentence is acceptable.\n* : the data source.### Data Splits\n\n\nRuCoLA consists of the training, development, and private test sets organised under two subsets: in-domain (linguistic publications) and out-of-domain (texts produced by natural language generation models).\n\n\n* : 7869 in-domain samples ().\n* : 2787 in-domain and out-of-domain samples. The in-domain () and out-of-domain () validation sets are merged into for convenience.\n* : 2789 in-domain and out-of-domain samples ().\n\n\nDataset Creation\n----------------", "passage: ### Curation Rationale\n\n\n* In-domain Subset: The in-domain sentences and the corresponding authors’ acceptability judgments are *manually* drawn from fundamental linguistic textbooks, academic publications, and methodological materials.\n* Out-of-domain Subset: The out-of-domain sentences are produced by nine open-source MT and paraphrase generation models.", "passage: ### Source Data\n\n\n\nLinguistic publications and resources\nOriginal source: Проект корпусного описания русской грамматики, Transliterated source: Proekt korpusnogo opisaniya russkoj grammatiki, Source id: Rusgram\nOriginal source: Тестелец, Я.Г., 2001. *Введение в общий синтаксис*. Федеральное государственное бюджетное образовательное учреждение высшего образования Российский государственный гуманитарный университет., Transliterated source: Yakov Testelets. 2001. Vvedeniye v obschiy sintaksis. Russian State University for the Humanities., Source id: Testelets\nOriginal source: Лютикова, Е.А., 2010. *К вопросу о категориальном статусе именных групп в русском языке*. Вестник Московского университета. Серия 9. Филология, (6), pp.36-76., Transliterated source: Ekaterina Lutikova. 2010. K voprosu o kategorial’nom statuse imennykh grup v russkom yazyke. Moscow University Philology Bulletin., Source id: Lutikova\nOriginal source: Митренина, О.В., Романова, Е.Е. and Слюсарь, Н.А., 2017. *Введение в генеративную грамматику*. Общество с ограниченной ответственностью \"Книжный дом ЛИБРОКОМ\"., Transliterated source: Olga Mitrenina et al. 2017. Vvedeniye v generativnuyu grammatiku. Limited Liability Company “LIBROCOM”., Source id: Mitrenina\nOriginal source: Падучева, Е.В., 2004. *Динамические модели в семантике лексики*. М.: Языки славянской культуры., Transliterated source: Elena Paducheva. 2004. Dinamicheskiye modeli v semantike leksiki. Languages of Slavonic culture., Source id: Paducheva2004\nOriginal source: Падучева, Е.В., 2010. *Семантические исследования: Семантика времени и вида в русском языке; Семантика нарратива*. М.: Языки славянской культуры., Transliterated source: Elena Paducheva. 2010. Semanticheskiye issledovaniya: Semantika vremeni i vida v russkom yazyke; Semantika narrativa. Languages of Slavonic culture., Source id: Paducheva2010\nOriginal source: Падучева, Е.В., 2013. *Русское отрицательное предложение*. М.: Языки славянской культуры, Transliterated source: Elena Paducheva. 2013. Russkoye otritsatel’noye predlozheniye. Languages of Slavonic culture., Source id: Paducheva2013\nOriginal source: Селиверстова, О.Н., 2004. *Труды по семантике*. М.: Языки славянской культуры, Transliterated source: Olga Seliverstova. 2004. Trudy po semantike. Languages of Slavonic culture., Source id: Seliverstova\nOriginal source: Набор данных ЕГЭ по русскому языку, Transliterated source: Shavrina et al. 2020. Humans Keep It One Hundred: an Overview of AI Journey, Source id: USE5, USE7, USE8\n\n\n\n\nMachine-generated sentences\n \n\nDatasets\n\n\n\nModels\n\n\nEasyNMT models:\n\n\n1. OPUS-MT. Jörg Tiedemann and Santhosh Thottingal. 2020. OPUS-MT – Building open translation services for the World\n2. M-BART50. Yuqing Tang et al. 2020. Multilingual Translation with Extensible Multilingual Pretraining and Finetuning\n3. M2M-100. Angela Fan et al. 2021. Beyond English-Centric Multilingual Machine Translation\n\n\nParaphrase generation models:\n\n\n1. ruGPT2-Large\n2. ruT5\n3. mT5. Linting Xue et al. 2021. mT5: A Massively Multilingual Pre-trained Text-to-Text Transformer### Annotations", "passage: #### Annotation process\n\n\nThe out-of-domain sentences undergo a two-stage annotation procedure on Toloka, a crowd-sourcing platform for data labeling.\nEach stage includes an unpaid training phase with explanations, control tasks for tracking annotation quality, and the main annotation task. Before starting, the worker is given detailed instructions describing the task, explaining the labels, and showing plenty of examples.\nThe instruction is available at any time during both the training and main annotation phases. To get access to the main phase, the worker should first complete the training phase by labeling more than 70% of its examples correctly. Each trained worker receives a page with five sentences, one of which is a control one.\nWe collect the majority vote labels via a dy- namic overlap from three to five workers after filtering them by response time and performance on control tasks.\n\n\n* Stage 1: Acceptability Judgments\nThe first annotation stage defines whether a given sentence is acceptable or not. Access to the project is granted to workers certified as native speakers of Russian by Toloka and ranked top-60% workers according to the Toloka rating system.\nEach worker answers 30 examples in the training phase. Each training example is accompanied by an explanation that appears in an incorrect answer.\nThe main annotation phase counts 3.6k machine-generated sentences. The pay rate is on average $2.55/hr, which is twice the amount of the hourly minimum wage in Russia. Each of 1.3k trained workers get paid, but we keep votes from only 960 workers whose annotation quality rate on the control sentences is more than 50%.\n* Stage 2: Violation Categories\nThe second stage includes validation and annotation of sentences labeled unacceptable on Stage 1 according to five answer options: “Morphology”, “Syntax”, “Semantics”, “Hallucinations” and “Other”. The task is framed as a multi-label classification, i.e., the sentence may contain more than one violation in some rare cases or be re-labeled as acceptable.\nWe create a team of 30 annotators who are undergraduate BA and MA in philology and linguistics from several Russian universities. The students are asked to study the works on CoLA, TGEA, and hallucinations. We also hold an online seminar to discuss the works and clarify the task specifics. Each student undergoes platform-based training on 15 examples before moving onto the main phase of 1.3k sentences.\nThe students are paid on average $5.42/hr and are eligible to get credits for an academic course or an internship. This stage provides direct interaction between authors and students in a group chat. We keep submissions with more than 30 seconds of response time per page and collect the majority vote labels for each answer independently.\nSentences having more than one violation category or labeled as “Other” by the majority are filtered out.### Personal and Sensitive Information\n\n\nThe annotators are warned about potentially sensitive topics in data (e.g., politics, culture, and religion).\n\n\nConsiderations for Using the Data\n---------------------------------### Social Impact of Dataset\n\n\nRuCoLA may serve as training data for acceptability classifiers, which may benefit the quality of generated texts.\nWe recognize that such improvements in text generation may lead to misuse of LMs for malicious purposes. However, our corpus can be used to train adversarial defense and artificial text detection models.\nWe introduce a novel dataset for research and development needs, and the potential negative uses are not lost on us.### Discussion of Biases\n\n\nAlthough we aim to control the number of high-frequency tokens in the RuCoLA’s sentences, we assume that potential word frequency distribution shift between LMs’ pretraining corpora and our corpus can introduce bias in the evaluation.\nFurthermore, linguistic publications represent a specific domain as the primary source of acceptability judgments. On the one hand, it can lead to a domain shift when using RuCoLA for practical purposes.\nOn the other hand, we observe moderate acceptability classification performance on the out-of-domain test, which spans multiple domains, ranging from subtitles to Wikipedia." ]
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e22605ec5cdcb3fe539bbb45bdc2291ad5a52a7e
# ORB Transformation Applied on diffusiondb Dataset This dataset consists of images, captions and images that are transformed to extract features using [ORB transform](https://docs.opencv.org/4.x/d1/d89/tutorial_py_orb.html). You can find the original dataset [here](https://huggingface.co/datasets/poloclub/diffusiondb). An example sample is below: Caption: "spider - man, cinematic, photography " Image: ![image](https://datasets-server.huggingface.co/assets/merve/diffusion-db-orb/--/merve--diffusion-db-orb/train/0/original_image/image.jpg) Transformation: ![image](https://datasets-server.huggingface.co/assets/merve/diffusion-db-orb/--/merve--diffusion-db-orb/train/0/transformed_image/image.jpg)
jax-diffusers-event/example-dataset
[ "region:us" ]
2023-03-27T17:36:37+00:00
{"dataset_info": {"features": [{"name": "original_image", "dtype": "image"}, {"name": "prompt", "dtype": "string"}, {"name": "transformed_image", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 1226376776.0, "num_examples": 997}], "download_size": 1222888812, "dataset_size": 1226376776.0}}
2023-03-29T09:57:38+00:00
[]
[]
TAGS #region-us
# ORB Transformation Applied on diffusiondb Dataset This dataset consists of images, captions and images that are transformed to extract features using ORB transform. You can find the original dataset here. An example sample is below: Caption: "spider - man, cinematic, photography " Image: !image Transformation: !image
[ "# ORB Transformation Applied on diffusiondb Dataset\n\nThis dataset consists of images, captions and images that are transformed to extract features using ORB transform.\nYou can find the original dataset here.\n\n\nAn example sample is below:\nCaption: \"spider - man, cinematic, photography \"\nImage: !image\nTransformation: !image" ]
[ "TAGS\n#region-us \n", "# ORB Transformation Applied on diffusiondb Dataset\n\nThis dataset consists of images, captions and images that are transformed to extract features using ORB transform.\nYou can find the original dataset here.\n\n\nAn example sample is below:\nCaption: \"spider - man, cinematic, photography \"\nImage: !image\nTransformation: !image" ]
[ 6, 76 ]
[ "passage: TAGS\n#region-us \n# ORB Transformation Applied on diffusiondb Dataset\n\nThis dataset consists of images, captions and images that are transformed to extract features using ORB transform.\nYou can find the original dataset here.\n\n\nAn example sample is below:\nCaption: \"spider - man, cinematic, photography \"\nImage: !image\nTransformation: !image" ]
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5d1329b68b19a510bb09cc1a10d4722b58f3fba2
# Dataset Card for "tokenized_large_corpus_v2" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
ZurabDz/tokenized_large_corpus_v2
[ "region:us" ]
2023-03-27T17:49:01+00:00
{"dataset_info": {"features": [{"name": "input_ids", "sequence": "int32"}, {"name": "token_type_ids", "sequence": "int8"}, {"name": "attention_mask", "sequence": "int8"}, {"name": "special_tokens_mask", "sequence": "int8"}], "splits": [{"name": "train", "num_bytes": 6701093568, "num_examples": 14442012}], "download_size": 2431678404, "dataset_size": 6701093568}}
2023-03-27T18:14:14+00:00
[]
[]
TAGS #region-us
# Dataset Card for "tokenized_large_corpus_v2" More Information needed
[ "# Dataset Card for \"tokenized_large_corpus_v2\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"tokenized_large_corpus_v2\"\n\nMore Information needed" ]
[ 6, 22 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"tokenized_large_corpus_v2\"\n\nMore Information needed" ]
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