davanstrien/embedded-cards · Datasets at Hugging Face

jkwiatkowski/raven

jkwiatkowski

2023-01-14T21:40:08Z

43

0

null

[ "region:us" ]

2023-01-14T21:40:08Z

2023-01-14T21:25:46.000Z

2023-01-14T21:25:46

--- dataset_info: features: - name: inputs dtype: array3_d: shape: - 16 - 160 - 160 dtype: uint8 - name: target dtype: array2_d: shape: - 16 - 113 dtype: int8 - name: index dtype: uint8 splits: - name: train num_bytes: 17714970000 num_examples: 42000 - name: val num_bytes: 5904990000 num_examples: 14000 - name: test num_bytes: 5904990000 num_examples: 14000 download_size: 1225465267 dataset_size: 29524950000 --- # Dataset Card for "raven" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

[ -0.6005617380142212, -0.027202557772397995, 0.034039780497550964, 0.26029589772224426, 0.033646393567323685, -0.19423764944076538, 0.812788188457489, -0.3802948594093323, 1.155961513519287, 0.5254784822463989, -0.9438897371292114, -0.7131692171096802, -0.6113013029098511, -0.29577222466468...

null

competitions/aiornot

competitions

2023-03-30T12:32:32Z

43

25

null

[ "task_categories:image-classification", "image-classification", "autotrain", "competitions", "region:us" ]

2023-03-30T12:32:32Z

2023-01-25T15:22:37.000Z

2023-01-25T15:22:37

--- task_categories: - image-classification tags: - image-classification - autotrain - competitions dataset_info: features: - name: id dtype: string - name: image dtype: image - name: label dtype: int64 --- # Dataset Card for aiornot Dataset for the [aiornot competition](https://hf.co/spaces/competitions/aiornot). By accessing this dataset, you accept the rules of the AI or Not competition. Please note that dataset may contain images which are not considered safe for work. ## Usage ### With Hugging Face Datasets 🤗 You can download and use this dataset using the `datasets` library. 📝 **Note:** You must be logged in to you Hugging Face account for the snippet below to work. You can do this with `huggingface-cli login` or `huggingface_hub.notebook_login` if you have the `huggingface_hub` python library installed (`pip install huggingface_hub`). ```python from datasets import load_dataset ds = load_dataset('competitions/aiornot') ``` ### From Original Files The original files and sample submission can be found in the `.extras` folder (under the files and versions tab of this repo). Feel free to download them and use them directly if you don't wish to use the `datasets` library.

[ -0.39490485191345215, -0.42858749628067017, -0.09024375677108765, 0.6200641989707947, -0.49466678500175476, -0.1339232623577118, -0.05084623768925667, -0.35253527760505676, 0.6617997288703918, 0.24080923199653625, -0.81304532289505, -0.6762734651565552, -0.6632581353187561, 0.2788597047328...

null

Multimodal-Fatima/OK-VQA_test

Multimodal-Fatima

2023-05-29T02:08:55Z

43

0

null

[ "region:us" ]

2023-05-29T02:08:55Z

2023-03-07T14:45:38.000Z

2023-03-07T14:45:38

--- dataset_info: features: - name: image dtype: image - name: question_type dtype: string - name: confidence dtype: int32 - name: answers sequence: string - name: answers_original list: - name: answer dtype: string - name: raw_answer dtype: string - name: answer_confidence dtype: string - name: answer_id dtype: int64 - name: id_image dtype: int64 - name: answer_type dtype: string - name: question_id dtype: int64 - name: question dtype: string - name: id dtype: int64 - name: clip_tags_LAION_ViT_H_14_2B sequence: string - name: clip_tags_ViT_L_14 sequence: string - name: blip_caption_beam_5 dtype: string - name: LLM_Description_gpt3_downstream_tasks_visual_genome_ViT_L_14 sequence: string - name: LLM_Description_gpt3_downstream_tasks_visual_genome_LAION-ViT-H-14-2B sequence: string - name: DETA_detections_deta_swin_large_o365_coco_classes list: - name: attribute dtype: string - name: box sequence: float32 - name: label dtype: string - name: location dtype: string - name: ratio dtype: float32 - name: size dtype: string - name: tag dtype: string - name: DETA_detections_deta_swin_large_o365_coco_classes_caption_module_random list: - name: attribute dtype: string - name: box sequence: float64 - name: captions_module sequence: string - name: captions_module_filter sequence: string - name: label dtype: string - name: location dtype: string - name: ratio dtype: float64 - name: size dtype: string - name: tag dtype: string - name: clip_tags_ViT_B_16_with_openai sequence: string - name: clip_tags_LAION_ViT_H_14_2B_with_openai sequence: string - name: clip_tags_ViT_L_14_with_openai sequence: string - name: Attributes_ViT_L_14_descriptors_text_davinci_003_full sequence: string - name: Attributes_ViT_B_16_descriptors_text_davinci_003_full sequence: string - name: Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full sequence: string - name: DETA_detections_deta_swin_large_o365_coco_classes_caption_all_patches_Salesforce_blip_image_captioning_large_ list: - name: attribute dtype: string - name: box sequence: float64 - name: captions_all_patches sequence: string - name: label dtype: string - name: location dtype: string - name: ratio dtype: float64 - name: size dtype: string - name: tag dtype: string - name: blip_caption_topk_50_Salesforce_blip_image_captioning_large_multiple sequence: string splits: - name: test num_bytes: 1133674079.0 num_examples: 5046 download_size: 959321361 dataset_size: 1133674079.0 --- # Dataset Card for "OK-VQA_test" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

[ -0.7838833332061768, -0.47043442726135254, 0.24061399698257446, -0.12111825495958328, -0.10199844837188721, -0.22216439247131348, 0.49055591225624084, -0.12725578248500824, 0.6808611154556274, 0.4884566068649292, -0.7074025273323059, -0.5855929851531982, -0.374126672744751, -0.336188286542...

null

open-source-metrics/pip-external

open-source-metrics

2023-11-22T15:49:29Z

43

0

null

[ "region:us" ]

2023-11-22T15:49:29Z

2023-03-24T14:32:07.000Z

2023-03-24T14:32:07

--- dataset_info: features: - name: day dtype: string - name: num_downloads dtype: int64 splits: - name: pytorch num_bytes: 33726 num_examples: 1533 - name: openai num_bytes: 24574 num_examples: 1117 - name: langchain num_bytes: 8668 num_examples: 394 - name: tensorflow num_bytes: 33726 num_examples: 1533 download_size: 59366 dataset_size: 100694 configs: - config_name: default data_files: - split: langchain path: data/langchain-* - split: pytorch path: data/pytorch-* - split: tensorflow path: data/tensorflow-* --- # Dataset Card for "pip-external" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

[ -0.5737804770469666, 0.0741593986749649, 0.05101093277335167, 0.33434054255485535, -0.1407611221075058, -0.23954346776008606, 0.39344850182533264, -0.16884899139404297, 0.7893158197402954, 0.36550337076187134, -0.9102442264556885, -0.4241996109485626, -0.6594387292861938, -0.17589490115642...

null

Francesco/road-traffic

Francesco

2023-03-30T09:12:18Z

43

1

null

[ "task_categories:object-detection", "annotations_creators:crowdsourced", "language_creators:found", "multilinguality:monolingual", "size_categories:1K<n<10K", "source_datasets:original", "language:en", "license:cc", "rf100", "region:us" ]

2023-03-30T09:12:18Z

2023-03-30T09:11:50.000Z

2023-03-30T09:11:50

--- dataset_info: features: - name: image_id dtype: int64 - name: image dtype: image - name: width dtype: int32 - name: height dtype: int32 - name: objects sequence: - name: id dtype: int64 - name: area dtype: int64 - name: bbox sequence: float32 length: 4 - name: category dtype: class_label: names: '0': road-traffic '1': bicycles '2': buses '3': crosswalks '4': fire hydrants '5': motorcycles '6': traffic lights '7': vehicles annotations_creators: - crowdsourced language_creators: - found language: - en license: - cc multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - original task_categories: - object-detection task_ids: [] pretty_name: road-traffic tags: - rf100 --- # Dataset Card for road-traffic ** The original COCO dataset is stored at `dataset.tar.gz`** ## Dataset Description - **Homepage:** https://universe.roboflow.com/object-detection/road-traffic - **Point of Contact:** francesco.zuppichini@gmail.com ### Dataset Summary road-traffic ### Supported Tasks and Leaderboards - `object-detection`: The dataset can be used to train a model for Object Detection. ### Languages English ## Dataset Structure ### Data Instances A data point comprises an image and its object annotations. ``` { 'image_id': 15, 'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x640 at 0x2373B065C18>, 'width': 964043, 'height': 640, 'objects': { 'id': [114, 115, 116, 117], 'area': [3796, 1596, 152768, 81002], 'bbox': [ [302.0, 109.0, 73.0, 52.0], [810.0, 100.0, 57.0, 28.0], [160.0, 31.0, 248.0, 616.0], [741.0, 68.0, 202.0, 401.0] ], 'category': [4, 4, 0, 0] } } ``` ### Data Fields - `image`: the image id - `image`: `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]` - `width`: the image width - `height`: the image height - `objects`: a dictionary containing bounding box metadata for the objects present on the image - `id`: the annotation id - `area`: the area of the bounding box - `bbox`: the object's bounding box (in the [coco](https://albumentations.ai/docs/getting_started/bounding_boxes_augmentation/#coco) format) - `category`: the object's category. #### Who are the annotators? Annotators are Roboflow users ## Additional Information ### Licensing Information See original homepage https://universe.roboflow.com/object-detection/road-traffic ### Citation Information ``` @misc{ road-traffic, title = { road traffic Dataset }, type = { Open Source Dataset }, author = { Roboflow 100 }, howpublished = { \url{ https://universe.roboflow.com/object-detection/road-traffic } }, url = { https://universe.roboflow.com/object-detection/road-traffic }, journal = { Roboflow Universe }, publisher = { Roboflow }, year = { 2022 }, month = { nov }, note = { visited on 2023-03-29 }, }" ``` ### Contributions Thanks to [@mariosasko](https://github.com/mariosasko) for adding this dataset.

[ -0.725415050983429, -0.6613062024116516, 0.34089893102645874, -0.08982060849666595, -0.5363978743553162, -0.1311916559934616, -0.08092565089464188, -0.6036465764045715, 0.30932581424713135, 0.44510263204574585, -0.6744555234909058, -0.8976103663444519, -0.5684770345687866, 0.17702196538448...

null

liuyanchen1015/MULTI_VALUE_sst2_comparative_more_and

liuyanchen1015

2023-04-03T19:47:13Z

43

0

null

[ "region:us" ]

2023-04-03T19:47:13Z

2023-04-03T19:47:10.000Z

2023-04-03T19:47:10

--- dataset_info: features: - name: sentence dtype: string - name: label dtype: int64 - name: idx dtype: int64 - name: score dtype: int64 splits: - name: dev num_bytes: 3080 num_examples: 19 - name: test num_bytes: 6036 num_examples: 38 - name: train num_bytes: 73392 num_examples: 631 download_size: 35653 dataset_size: 82508 --- # Dataset Card for "MULTI_VALUE_sst2_comparative_more_and" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

[ -0.38559451699256897, -0.07859700918197632, 0.28031912446022034, 0.0752108246088028, -0.3979485034942627, 0.287525475025177, 0.2331845462322235, -0.18516641855239868, 0.7512829899787903, 0.2106890231370926, -0.6053675413131714, -0.5348562598228455, -0.643838107585907, -0.3702797591686249, ...

null

camel-ai/biology

camel-ai

2023-05-23T21:11:56Z

43

19

null

[ "task_categories:text-generation", "language:en", "license:cc-by-nc-4.0", "instruction-finetuning", "arxiv:2303.17760", "region:us" ]

2023-05-23T21:11:56Z

2023-04-16T01:30:03.000Z

2023-04-16T01:30:03

--- license: cc-by-nc-4.0 language: - en tags: - instruction-finetuning pretty_name: CAMEL Biology task_categories: - text-generation arxiv: 2303.17760 extra_gated_prompt: "By using this data, you acknowledge and agree to utilize it solely for research purposes, recognizing that the dataset may contain inaccuracies due to its artificial generation through ChatGPT." extra_gated_fields: Name: text Email: text I will adhere to the terms and conditions of this dataset: checkbox --- # **CAMEL: Communicative Agents for “Mind” Exploration of Large Scale Language Model Society** - **Github:** https://github.com/lightaime/camel - **Website:** https://www.camel-ai.org/ - **Arxiv Paper:** https://arxiv.org/abs/2303.17760 ## Dataset Summary Biology dataset is composed of 20K problem-solution pairs obtained using gpt-4. The dataset problem-solutions pairs generating from 25 biology topics, 25 subtopics for each topic and 32 problems for each "topic,subtopic" pairs. We provide the data in `biology.zip`. ## Data Fields **The data fields for files in `biology.zip` are as follows:** * `role_1`: assistant role * `topic`: biology topic * `sub_topic`: biology subtopic belonging to topic * `message_1`: refers to the problem the assistant is asked to solve. * `message_2`: refers to the solution provided by the assistant. **Download in python** ``` from huggingface_hub import hf_hub_download hf_hub_download(repo_id="camel-ai/biology", repo_type="dataset", filename="biology.zip", local_dir="datasets/", local_dir_use_symlinks=False) ``` ### Citation ``` @misc{li2023camel, title={CAMEL: Communicative Agents for "Mind" Exploration of Large Scale Language Model Society}, author={Guohao Li and Hasan Abed Al Kader Hammoud and Hani Itani and Dmitrii Khizbullin and Bernard Ghanem}, year={2023}, eprint={2303.17760}, archivePrefix={arXiv}, primaryClass={cs.AI} } ``` ## Disclaimer: This data was synthetically generated by GPT4 and might contain incorrect information. The dataset is there only for research purposes. --- license: cc-by-nc-4.0 ---

[ -0.34610170125961304, -1.030604362487793, 0.1959645003080368, 0.16683945059776306, -0.06699174642562866, 0.023658502846956253, -0.4136985242366791, -0.44533976912498474, 0.39107584953308105, 0.31327933073043823, -0.5514075756072998, -0.3644706606864929, -0.6920718550682068, 0.1374517828226...

null

PierreLepagnol/WRENCH

PierreLepagnol

2023-08-17T11:18:59Z

43

0

null

[ "task_categories:text-classification", "task_categories:token-classification", "size_categories:10K<n<100K", "region:us" ]

2023-08-17T11:18:59Z

2023-04-17T11:42:58.000Z

2023-04-17T11:42:58

--- task_categories: - text-classification - token-classification size_categories: - 10K<n<100K dataset_info: - config_name: yelp features: - name: text dtype: string - name: label dtype: int8 - name: weak_labels sequence: int8 splits: - name: train num_bytes: 22618599 num_examples: 30400 - name: validation num_bytes: 2824249 num_examples: 3800 - name: test num_bytes: 2709033 num_examples: 3800 download_size: 37356054 dataset_size: 28151881 - config_name: imdb features: - name: text dtype: string - name: label dtype: int8 - name: weak_labels sequence: int8 splits: - name: train num_bytes: 25515241 num_examples: 20000 - name: validation num_bytes: 3269130 num_examples: 2500 - name: test num_bytes: 3151954 num_examples: 2500 download_size: 33910706 dataset_size: 31936325 - config_name: agnews features: - name: text dtype: string - name: label dtype: int8 - name: weak_labels sequence: int8 splits: - name: train num_bytes: 20357121 num_examples: 96000 - name: validation num_bytes: 2487983 num_examples: 12000 - name: test num_bytes: 2521518 num_examples: 12000 download_size: 39149014 dataset_size: 25366622 - config_name: cdr features: - name: text dtype: string - name: label dtype: int8 - name: entity1 dtype: string - name: entity2 dtype: string - name: weak_labels sequence: int8 splits: - name: train num_bytes: 2318545 num_examples: 8430 - name: validation num_bytes: 246252 num_examples: 920 - name: test num_bytes: 1229627 num_examples: 4673 download_size: 11036213 dataset_size: 3794424 - config_name: chemprot features: - name: text dtype: string - name: label dtype: int8 - name: entity1 dtype: string - name: entity2 dtype: string - name: weak_labels sequence: int8 splits: - name: train num_bytes: 3474637 num_examples: 12861 - name: validation num_bytes: 435850 num_examples: 1607 - name: test num_bytes: 434031 num_examples: 1607 download_size: 15743249 dataset_size: 4344518 - config_name: semeval features: - name: text dtype: string - name: label dtype: int8 - name: entity1 dtype: string - name: entity2 dtype: string - name: weak_labels sequence: int8 splits: - name: train num_bytes: 532785 num_examples: 1749 - name: validation num_bytes: 54373 num_examples: 178 - name: test num_bytes: 184826 num_examples: 600 download_size: 2295058 dataset_size: 771984 - config_name: sms features: - name: text dtype: string - name: label dtype: int8 - name: weak_labels sequence: int8 splits: - name: train num_bytes: 741520 num_examples: 4571 - name: validation num_bytes: 81747 num_examples: 500 - name: test num_bytes: 80152 num_examples: 500 download_size: 6715435 dataset_size: 903419 - config_name: spouse features: - name: text dtype: string - name: label dtype: int8 - name: entity1 dtype: string - name: entity2 dtype: string - name: weak_labels sequence: int8 splits: - name: train num_bytes: 7550613 num_examples: 22254 - name: validation num_bytes: 952523 num_examples: 2811 - name: test num_bytes: 876804 num_examples: 2701 download_size: 22017644 dataset_size: 9379940 - config_name: trec features: - name: text dtype: string - name: label dtype: int8 - name: weak_labels sequence: int8 splits: - name: train num_bytes: 610244 num_examples: 4965 - name: validation num_bytes: 61048 num_examples: 500 - name: test num_bytes: 56479 num_examples: 500 download_size: 2277067 dataset_size: 727771 - config_name: youtube features: - name: text dtype: string - name: label dtype: int8 - name: weak_labels sequence: int8 splits: - name: train num_bytes: 180736 num_examples: 1586 - name: validation num_bytes: 14659 num_examples: 120 - name: test num_bytes: 33347 num_examples: 250 download_size: 759494 dataset_size: 228742 ---

[ -0.1285335123538971, -0.1861683875322342, 0.6529128551483154, 0.49436232447624207, -0.19319400191307068, 0.23607441782951355, 0.36072009801864624, 0.05056373029947281, 0.5793656706809998, 0.7400146722793579, -0.650810182094574, -0.23784008622169495, -0.7102247476577759, -0.0478255338966846...

null

michaelwzhu/ChatMed_Consult_Dataset

michaelwzhu

2023-05-05T13:41:10Z

43

72

null

[ "task_categories:text-generation", "language:zh", "license:cc-by-4.0", "medical consultation", "finetuning", "region:us" ]

2023-05-05T13:41:10Z

2023-04-20T04:53:33.000Z

2023-04-20T04:53:33

--- license: cc-by-4.0 language: - zh tags: - medical consultation - finetuning pretty_name: ChatMed-Dataset task_categories: - text-generation --- # Dataset Card for ChatMed ## Dataset Description - **Homepage:** https://huggingface.co/datasets/michaelwzhu/ChatMed-Datasets - **Repository:** https://github.com/michael-wzhu/ChatMed - **Paper:** - **Leaderboard:** - **Point of Contact:** Wei Zhu ### Dataset Summary ChatMed-Dataset is a dataset of 110,113 medical query-response pairs (in Chinese) generated by OpenAI's `GPT-3.5` engine. The queries are crawled from several online medical consultation sites, reflecting the medical needs in the real world. The responses are generated by the OpenAI engine. This dataset is designated to to inject medical knowledge into Chinese large language models. The dataset size growing rapidly. Stay tuned for updates! 在大模型时代，模型都是趋同的，比如开源社区由于LlaMa的"开源"，出现了各种stanford-alpaca等一系列的开源模型。但是中文垂直领域上的AGI数据集相对匮乏。本数据集, ChatMed-Dataset, 中的query(或者是prompt)来自于互联网上的医疗问诊问题(110,113)，反映了真实世界的不同用户/患者的医疗问诊需求。目前response都是由OpenAI `GPT-3.5`引擎回答的。我们后续会对互联网上的医生回答与患者回答进行筛选甄别，择优选择，构建质量更优的数据集。 ### Supported Tasks and Leaderboards The ChatMed-Dataset designed for fine-tuning pretrained language models to perform better in automatic medical consultations. ### Languages The data in ChatMed-Dataset are in Chinese (zh). ## Dataset Structure ### Data Instances The dataset is in json-line format and is very convenient to use! An example in the ChatMed-Dataset looks as follows: ```json { "query": "你好，视神经萎缩，黄斑变性。怎样缓解，及日常注意事项。谢谢。", "response": "视神经萎缩和黄斑变性是两种不同的疾病，需要分别进行治疗。\n\n视神经萎缩是指视神经逐渐受损，导致视力逐渐减退，常见的治疗方法包括：\n\n1.药物治疗：如促进视神经细胞再生的药物，改善视网膜供血的药物等。\n\n2.手术治疗：如视神经萎缩的手术治疗可以通过神经移植、修复和再生等手术方式来缓解症状。\n\n3.康复训练：通过视力训练等康复训练可以帮助患者恢复视力。\n\n黄斑变性则是指黄斑区域受损，导致中央视力减退，常见的治疗方法包括：\n\n1.药物治疗：如抗氧化剂、维生素等可以缓解症状。\n\n2.手术治疗：如黄斑脱离手术等可以恢复视力。\n\n3.日常注意事项：保持良好的生活习惯，如戒烟、控制血压、血糖等，避免暴晒和过度用眼，保持充足的睡眠和均衡的饮食等。\n\n需要根据具体情况选择合适的治疗方案，建议您咨询专业医生进行诊断和治疗。" } ``` ### Data Fields The data fields are as follows: * `query`: real-life medical query from the web. * `response`: response generated by the OpenAI `GPT-3.5` engine. ### Data Splits | | train | |---------------|------:| | ChatMed-Dataset | 110,113 | ## 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 > We believe that releasing the above assets will enable the academic community to perform controlled scientific studies on how large language models responses to real-life medical query. > More social impacts will be discussed soon! ### Discussion of Biases [More Information Needed] ### Other Known Limitations The `ChatMed-Dataset` data is generated by a language model (`GPT-3.5`) and inevitably contains some errors or biases. We encourage users to use this data with caution and propose new methods to filter or improve the imperfections. ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information The dataset is available under the [Creative Commons NonCommercial (CC BY-NC 4.0)](https://creativecommons.org/licenses/by-nc/4.0/legalcode). ### Citation Information ``` @misc{alpaca, author = {Wei Zhu }, title = {ChatMed-Dataset: An GPT generated medical query-response datasets for medcial large language models}, year = {2023}, publisher = {GitHub}, journal = {GitHub repository}, howpublished = {\url{https://github.com/michael-wzhu/ChatMed}}, } ``` ### Contributions [More Information Needed]

[ -0.1948956549167633, -0.9471865296363831, 0.009738540276885033, 0.2476164698600769, -0.4048019051551819, -0.3155708611011505, -0.37189266085624695, -0.6297939419746399, 0.6301724910736084, 0.5011020302772522, -0.5354740023612976, -0.7830479741096497, -0.5588686466217041, 0.0473465062677860...

null

heegyu/open-korean-instructions

heegyu

2023-05-06T09:18:37Z

43

12

null

[ "license:mit", "region:us" ]

2023-05-06T09:18:37Z

2023-04-22T02:10:17.000Z

2023-04-22T02:10:17

--- license: mit --- 4가지 한국어 챗봇 학습용 데이터셋을 합쳐놓았습니다. 이중 ShareGPT 데이터는 멀티턴으로 되어있습니다. 데이터 생성 및 합치는 코드는 https://github.com/HeegyuKim/open-korean-instructions 여기를 참고하세요 | 이름 | # | 타입 | |---|---|---| | [KoAlpaca v1.0](https://huggingface.co/datasets/Bingsu/ko_alpaca_data) | 52K | 싱글턴 | | [KoAlpaca v1.1](https://raw.githubusercontent.com/Beomi/KoAlpaca/main/KoAlpaca_v1.1.jsonl) | 21K | 싱글턴 | | [ShareGPT DeepL 번역](https://huggingface.co/datasets/junelee/sharegpt_deepl_ko) | 620K(싱글턴), 84K(멀티턴) | 멀티턴, 싱글턴 | | [OIG-small-chip2-ko](https://huggingface.co/datasets/heegyu/OIG-small-chip2-ko) | 210K | 싱글턴 | | [Korquad-Chat](https://huggingface.co/datasets/heegyu/korquad-chat-v1) | 9.6K | 멀티턴, 지식기반 | 모든 데이터는 포멧이 통일되어 있습니다. `<sys>`, `<usr>`, `<bot>` 세가지 토큰과 줄넘김으로 화자를 구분합니다. korquad-chat 데이터의 경우, 유저와 봇이 서로를 호칭할 때는 `<|bot|>`, `<|user|>`로 되어있습니다. ``` {"source": "koalpaca-v1.0", "text": "<usr> 홀수 중 하나를 밝히세요.\n<sys> 트위터, 인스타그램, 텔레그램\n<bot> 텔레그램입니다."} {"source": "korquad-chat", "text": " <sys>1984년 9월부터 덴노지 역 ~ 히네노 역간에 운전을 개시한 '홈 라이너 이즈미'가 전신이다. 특급 구로시오의 381계 전동차의 히네노 전차구 입선 회송 열차를 좌석 정리권을 필요로 하는 정원제 열차로서 운행해온 것으로 간사이 지방 최초의 홈 라이너였다. 1986년 11월 개정에 따라 운전 구간이 와카야마 역까지 연장되어 한와 라이너로 개칭되어 현재에 이르게 되었다. 이전에는 평일에 상행 3편, 하행 6편이 운전되었지만 일부가 신오사카 역이나 교토 역 방면 발착 특급 구로시오로 변경되어 2002년 3월 이후에는 상행 2편, 하행 4편으로 축소되었다. 2009년 3월의 시간표 개정부터는 휴일 시간대의 운전이 중단되었으며 같은 해 6월 1일부터는 전면 금연화되었다.\n <usr> 안녕하세요, <|bot|>. \"한와_선\"에 대해 알고 계시나요?\n <bot> 네, 전체적으로 알고 있습니다. 1984년에 운전을 개시한 \"홈 라이너 이즈미\"가 전신이며, 간사이 지방 최초의 홈 라이너였습니다. 1986년에는 와카야마 역까지 연장된 후 \"한와 라이너\"로 개칭되었습니다.\n <usr> 그렇군요. 이전에는 평일에 몇 편이 운행되었나요?\n <bot> 평일에 상행 3편, 하행 6편이 운전되었습니다. 하지만 2002년 이후로는 상행 2편, 하행 4편으로 축소되었습니다.\n <usr> 그렇군요. 혹시 특급 구로시오와 연관이 있을까요?\n <bot> 네, \"한와 라이너\"는 특급 구로시오의 381계 전동차의 히네노 전차구 입선 회송 열차를 좌석 정리권을 필요로 하는 정원제 열차로서 운행되었습니다." } ```

[ -0.6776437759399414, -0.8182427287101746, 0.3058846890926361, 0.5684847831726074, -0.5376150608062744, -0.007696210406720638, 0.2074437439441681, -0.38939815759658813, 0.762615978717804, 0.39154863357543945, -0.5585095286369324, -0.6197200417518616, -0.7746405005455017, -0.0803266763687133...

null

thehamkercat/telegram-spam-ham

thehamkercat

2023-04-30T15:11:17Z

43

1

null

[ "license:wtfpl", "region:us" ]

2023-04-30T15:11:17Z

2023-04-30T15:09:34.000Z

2023-04-30T15:09:34

--- license: wtfpl ---

[ -0.1285335123538971, -0.1861683875322342, 0.6529128551483154, 0.49436232447624207, -0.19319400191307068, 0.23607441782951355, 0.36072009801864624, 0.05056373029947281, 0.5793656706809998, 0.7400146722793579, -0.650810182094574, -0.23784008622169495, -0.7102247476577759, -0.0478255338966846...

null

fptudsc/face-celeb-vietnamese

fptudsc

2023-05-10T15:13:18Z

43

1

null

[ "task_categories:image-classification", "task_categories:zero-shot-classification", "size_categories:10M<n<100M", "language:vi", "license:apache-2.0", "region:us" ]

2023-05-10T15:13:18Z

2023-05-03T17:56:54.000Z

2023-05-03T17:56:54

--- dataset_info: features: - name: image dtype: image - name: label dtype: string splits: - name: train num_bytes: 82233752.864 num_examples: 8557 download_size: 80630170 dataset_size: 82233752.864 license: apache-2.0 task_categories: - image-classification - zero-shot-classification language: - vi size_categories: - 10M<n<100M --- # Dataset Card for "face-celeb-vietnamese" ## Dataset Summary This dataset contains information on over 8,000 samples of well-known Vietnamese individuals, categorized into three professions: singers, actors, and beauty queens. The dataset includes data on more than 100 celebrities in each of the three job categories. ## Languages - Vietnamese: The label is used to indicate the name of celebrities in Vietnamese. ## Dataset Structure - The image and Vietnamese sequences are ## Source Data - Initial Data Collection and Normalization [Website người nổi tiếng](https://nguoinoitieng.tv) ### Licensing Information Apache License 2.0 ### Contributions Thanks to [@github-duongttr](https://github.com/duongttr) and [@github-pphuc25](https://github.com/pphuc25) for adding this dataset.

[ -0.2535244822502136, -0.4009217917919159, -0.20677080750465393, 0.1814933568239212, -0.16279755532741547, 0.12318381667137146, 0.02658444084227085, -0.18506939709186554, 0.21042849123477936, 1.0871955156326294, -0.5686021447181702, -0.7138303518295288, -0.6565490365028381, 0.06075911596417...

null

jkhedri/psychology-dataset

jkhedri

2023-05-04T10:12:40Z

43

16

null

[ "region:us" ]

2023-05-04T10:12:40Z

2023-05-04T10:08:53.000Z

2023-05-04T10:08:53

Entry not found

[ -0.32276487350463867, -0.22568444907665253, 0.8622263073921204, 0.43461570143699646, -0.5282988548278809, 0.7012969255447388, 0.7915717363357544, 0.07618642598390579, 0.7746027112007141, 0.25632190704345703, -0.7852815389633179, -0.22573848068714142, -0.910447895526886, 0.5715675354003906,...

null

xmcmic/PMC-VQA

xmcmic

2023-07-12T01:10:58Z

43

18

null

[ "region:us" ]

2023-07-12T01:10:58Z

2023-05-24T12:00:03.000Z

2023-05-24T12:00:03

# PMC-VQA Dataset - [PMC-VQA Dataset](#pmc-vqa-dataset) - [Daraset Structure](#daraset-structure) - [Sample](#sample) ## Daraset Structure **PMC-VQA** (version-1: 227k VQA pairs of 149k images). - `train.csv`: metafile of train set - `test.csv`: metafile of test set - `test_clean.csv`: metafile of test clean set - `images.zip`: images folder - (**update** version-2: noncompound images). - `train2.csv`: metafile of train set - `test2.csv`: metafile of test set - `images2.zip`: images folder - ## Sample A row in `train.csv` is shown bellow, | Figure_path | PMC1064097_F1.jpg | | ------------ | --------------------------------------------- | | Question | What is the uptake pattern in the breast? | | Answer | Focal uptake pattern | | Choice A | A:Diffuse uptake pattern | | Choice B | B:Focal uptake pattern | | Choice C | C:No uptake pattern | | Choice D | D:Cannot determine from the information given | | Answer_label | B | Explanation to each key - Figure_path: path to the image - Question: question corresponding to the image - Answer: the correct answer corresponding to the image - Choice A: the provide choice A - Choice B: the provide choice B - Choice C: the provide choice C - Choice D: the provide choice D - Anwser_label: the correct answer label

[ -0.3357618451118469, -0.0659436285495758, 0.5220481753349304, 0.39933401346206665, -0.5142713785171509, -0.13122515380382538, 0.4726024866104126, 0.20063824951648712, 0.12464544177055359, 0.698150634765625, -0.9735161066055298, -0.6449598073959351, -0.6949925422668457, 0.12075259536504745,...

null

d0rj/wikisum

d0rj

2023-06-16T11:24:25Z

43

1

wikisum

[ "task_categories:summarization", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:en", "license:unknown", "abstractive-summarization", "wiki", "abstractive", "arxiv:1801.10198", "region:us" ]

2023-06-16T11:24:25Z

2023-06-16T11:13:38.000Z

2023-06-16T11:13:38

--- dataset_info: features: - name: url dtype: string - name: title dtype: string - name: summary dtype: string - name: article dtype: string - name: step_headers dtype: string splits: - name: train num_bytes: 315275236 num_examples: 35775 - name: test num_bytes: 17584216 num_examples: 2000 - name: validation num_bytes: 17880851 num_examples: 2000 download_size: 194202865 dataset_size: 350740303 license: - unknown task_categories: - summarization language: - en multilinguality: - monolingual tags: - abstractive-summarization - wiki - abstractive pretty_name: 'WikiSum: Coherent Summarization Dataset for Efficient Human-Evaluation' size_categories: - 10K<n<100K source_datasets: - original paperswithcode_id: wikisum --- # wikisum ## Dataset Description - **Homepage:** https://registry.opendata.aws/wikisum/ - **Repository:** https://github.com/tensorflow/tensor2tensor/tree/master/tensor2tensor/data_generators/wikisum - **Paper:** [Generating Wikipedia by Summarizing Long Sequences](https://arxiv.org/abs/1801.10198) - **Leaderboard:** [More Information Needed] - **Point of Contact:** [nachshon](mailto:nachshon@amazon.com)

[ -0.5056736469268799, -0.3219315707683563, -0.017971374094486237, 0.08399040251970291, -0.4157596826553345, -0.07268033176660538, -0.23377051949501038, -0.1514424830675125, 0.38166099786758423, 0.3418792486190796, -0.6548262238502502, -0.5391581654548645, -0.6513972282409668, 0.087702766060...

null

openaccess-ai-collective/chatbot-arena-elo-scores

openaccess-ai-collective

2023-06-23T19:57:16Z

43

0

null

[ "region:us" ]

2023-06-23T19:57:16Z

2023-06-17T23:31:18.000Z

2023-06-17T23:31:18

--- dataset_info: features: - name: elo_score dtype: float64 - name: chatbot_name dtype: string splits: - name: train num_bytes: 359 num_examples: 14 download_size: 1669 dataset_size: 359 --- # Dataset Card for "chatbot-arena-elo-scores" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

[ -0.44750484824180603, -0.4757183790206909, -0.03251056745648384, 0.3160454034805298, -0.06678543984889984, 0.12328743189573288, 0.08661755919456482, -0.22261358797550201, 0.8693844079971313, 0.4432219862937927, -0.7620216012001038, -0.841468095779419, -0.46548816561698914, -0.3371861875057...

null

yulongmannlp/adv_ori

yulongmannlp

2023-06-26T00:35:38Z

43

0

null

[ "region:us" ]

2023-06-26T00:35:38Z

2023-06-26T00:34:08.000Z

2023-06-26T00:34:08

Entry not found

[ -0.3227645754814148, -0.22568479180335999, 0.8622264862060547, 0.43461528420448303, -0.52829909324646, 0.7012971639633179, 0.7915720343589783, 0.07618614286184311, 0.774603009223938, 0.2563217282295227, -0.7852813005447388, -0.22573819756507874, -0.9104477167129517, 0.5715674161911011, -...

null

Alignment-Lab-AI/Lawyer-Instruct

Alignment-Lab-AI

2023-07-14T17:21:48Z

43

6

null

[ "license:apache-2.0", "region:us" ]

2023-07-14T17:21:48Z

2023-07-14T06:59:11.000Z

2023-07-14T06:59:11

--- license: apache-2.0 --- # Dataset Card for "Lawyer-Instruct" ## 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 Description ### Dataset Summary Lawyer-Instruct is a conversational dataset primarily in English, reformatted from the original LawyerChat dataset. It contains legal dialogue scenarios reshaped into an instruction, input, and expected output format. This reshaped dataset is ideal for supervised dialogue model training. Dataset generated in part by dang/futures ### Supported Tasks and Leaderboards - `dialogue-modeling`: The dataset can be used to train a model for dialogue understanding and response generation based on given instruction. Performance can be evaluated based on dialogue understanding and the quality of the generated responses. - There is no official leaderboard associated with this dataset at this time. ### Languages The text in the dataset is in English. ## Dataset Structure ### Data Instances An instance in the Lawyer-Instruct dataset represents a dialogue instruction and its corresponding output. Example: ```json { "instruction": "What are the possible legal consequences of not paying taxes?", "input": "", "output": "There can be several legal consequences, ranging from fines to imprisonment..." } ``` ### Data Fields - `instruction`: a string representing the client's question or statement in the dialogue, serving as the input for dialogue model training. - `input`: - `output`: a string representing the legal professional's response. ### Data Splits This dataset does not have a standard split. Users should carefully consider how they wish to split the data for training, validation, and testing purposes.

[ -0.2871548533439636, -0.6438157558441162, 0.1831914484500885, -0.08602054417133331, -0.40869879722595215, 0.12657950818538666, -0.053288690745830536, 0.08567647635936737, 0.21447981894016266, 0.9137961864471436, -0.7894001603126526, -0.9552119970321655, -0.40259847044944763, -0.10297254472...

null

OneFly7/llama2-sst2-fine-tuning

OneFly7

2023-08-08T07:03:26Z

43

2

null

[ "task_categories:text-classification", "language:en", "region:us" ]

2023-08-08T07:03:26Z

2023-07-29T19:28:23.000Z

2023-07-29T19:28:23

--- dataset_info: features: - name: label_text dtype: string - name: text dtype: string splits: - name: train num_bytes: 23202578 num_examples: 67349 - name: validation num_bytes: 334716 num_examples: 872 download_size: 4418625 dataset_size: 23537294 task_categories: - text-classification language: - en --- # Dataset Card for "llama2-sst2-finetuning" ## Dataset Description The Llama2-sst2-fine-tuning dataset is designed for supervised fine-tuning of the LLaMA V2 based on the GLUE SST2 for sentiment analysis classification task. We provide two subsets: training and validation. To ensure the effectiveness of fine-tuning, we convert the data into the prompt template for LLaMA V2 supervised fine-tuning, where the data will follow this format: ``` <s>[INST] <<SYS>> {System prompt} <</SYS>> {User prompt} [/INST] {Label} </s>. ``` The feasibility of this dataset has been tested in supervised fine-tuning on the meta-llama/Llama-2-7b-hf model. Note. For the sake of simplicity, we have retained only one new column of data ('text'). ## Other Useful Links - [Get Llama 2 Prompt Format Right](https://www.reddit.com/r/LocalLLaMA/comments/155po2p/get_llama_2_prompt_format_right/) - [Fine-Tune Your Own Llama 2 Model in a Colab Notebook](https://towardsdatascience.com/fine-tune-your-own-llama-2-model-in-a-colab-notebook-df9823a04a32) - [Instruction fine-tuning Llama 2 with PEFT’s QLoRa method](https://medium.com/@ud.chandra/instruction-fine-tuning-llama-2-with-pefts-qlora-method-d6a801ebb19) - [GLUE SST2 Dataset](https://www.tensorflow.org/datasets/catalog/glue#gluesst2)

[ -0.2522786855697632, -0.6928192973136902, 0.33277854323387146, 0.2654375731945038, -0.6749866604804993, 0.12734510004520416, -0.1964537650346756, -0.19423919916152954, 0.1713409572839737, 0.40373560786247253, -0.9330906271934509, -0.6321382522583008, -0.5692822933197021, 0.0890729799866676...

null

shanover/disease_symptoms_prec_full

shanover

2023-08-01T06:26:27Z

43

2

null

[ "license:mit", "region:us" ]

2023-08-01T06:26:27Z

2023-08-01T06:25:45.000Z

2023-08-01T06:25:45

--- license: mit ---

[ -0.1285335123538971, -0.1861683875322342, 0.6529128551483154, 0.49436232447624207, -0.19319400191307068, 0.23607441782951355, 0.36072009801864624, 0.05056373029947281, 0.5793656706809998, 0.7400146722793579, -0.650810182094574, -0.23784008622169495, -0.7102247476577759, -0.0478255338966846...

null

EleutherAI/CEBaB

EleutherAI

2023-08-16T23:09:21Z

43

1

null

[ "task_categories:text-classification", "language:en", "license:cc-by-4.0", "arxiv:2205.14140", "region:us" ]

2023-08-16T23:09:21Z

2023-08-16T05:13:51.000Z

2023-08-16T05:13:51

--- license: cc-by-4.0 dataset_info: features: - name: original_id dtype: int32 - name: edit_goal dtype: string - name: edit_type dtype: string - name: text dtype: string - name: food dtype: string - name: ambiance dtype: string - name: service dtype: string - name: noise dtype: string - name: counterfactual dtype: bool - name: rating dtype: int64 splits: - name: validation num_bytes: 306529 num_examples: 1673 - name: test num_bytes: 309751 num_examples: 1689 - name: train num_bytes: 2282439 num_examples: 11728 download_size: 628886 dataset_size: 2898719 task_categories: - text-classification language: - en --- # Dataset Card for "CEBaB" This is a lightly cleaned and simplified version of the CEBaB counterfactual restaurant review dataset from [this paper](https://arxiv.org/abs/2205.14140). The most important difference from the original dataset is that the `rating` column corresponds to the _median_ rating provided by the Mechanical Turkers, rather than the majority rating. These are the same whenever a majority rating exists, but when there is no majority rating (e.g. because there were two 1s, two 2s, and one 3), the original dataset used a `"no majority"` placeholder whereas we are able to provide an aggregate rating for all reviews. The exact code used to process the original dataset is provided below: ```py from ast import literal_eval from datasets import DatasetDict, Value, load_dataset def compute_median(x: str): """Compute the median rating given a multiset of ratings.""" # Decode the dictionary from string format dist = literal_eval(x) # Should be a dictionary whose keys are string-encoded integer ratings # and whose values are the number of times that the rating was observed assert isinstance(dist, dict) assert sum(dist.values()) % 2 == 1, "Number of ratings should be odd" ratings = [] for rating, count in dist.items(): ratings.extend([int(rating)] * count) ratings.sort() return ratings[len(ratings) // 2] cebab = load_dataset('CEBaB/CEBaB') assert isinstance(cebab, DatasetDict) # Remove redundant splits cebab['train'] = cebab.pop('train_inclusive') del cebab['train_exclusive'] del cebab['train_observational'] cebab = cebab.cast_column( 'original_id', Value('int32') ).map( lambda x: { # New column with inverted label for counterfactuals 'counterfactual': not x['is_original'], # Reduce the rating multiset into a single median rating 'rating': compute_median(x['review_label_distribution']) } ).map( # Replace the empty string and 'None' with Apache Arrow nulls lambda x: { k: v if v not in ('', 'no majority', 'None') else None for k, v in x.items() } ) # Sanity check that all the splits have the same columns cols = next(iter(cebab.values())).column_names assert all(split.column_names == cols for split in cebab.values()) # Clean up the names a bit cebab = cebab.rename_columns({ col: col.removesuffix('_majority').removesuffix('_aspect') for col in cols if col.endswith('_majority') }).rename_column( 'description', 'text' ) # Drop the unimportant columns cebab = cebab.remove_columns([ col for col in cols if col.endswith('_distribution') or col.endswith('_workers') ] + [ 'edit_id', 'edit_worker', 'id', 'is_original', 'opentable_metadata', 'review' ]).sort([ # Make sure counterfactual reviews come immediately after each original review 'original_id', 'counterfactual' ]) ```

[ -0.6946167349815369, -0.6743980646133423, 0.2029796689748764, 0.2222827821969986, -0.36193352937698364, -0.39566025137901306, -0.14374130964279175, -0.028203856199979782, 0.4259285628795624, 0.5809728503227234, -0.4916161894798279, -0.8156996369361877, -0.46290111541748047, 0.1953769028186...

null

SEACrowd/minangnlp_mt

SEACrowd

2023-09-26T12:29:22Z

43

0

null

[ "language:min", "language:ind", "license:mit", "machine-translation", "region:us" ]

2023-09-26T12:29:22Z

2023-09-26T11:11:19.000Z

2023-09-26T11:11:19

--- license: mit tags: - machine-translation language: - min - ind --- # minangnlp_mt In this work, we create Minangkabau–Indonesian (MIN-ID) parallel corpus by using Wikipedia. We obtain 224,180 Minangkabau and 510,258 Indonesian articles, and align documents through title matching, resulting in 111,430 MINID document pairs. After that, we do sentence segmentation based on simple punctuation heuristics and obtain 4,323,315 Minangkabau sentences. We then use the bilingual dictionary to translate Minangkabau article (MIN) into Indonesian language (ID'). Sentence alignment is conducted using ROUGE-1 (F1) score (unigram overlap) (Lin, 2004) between ID’ and ID, and we pair each MIN sentencewith an ID sentence based on the highest ROUGE1. We then discard sentence pairs with a score of less than 0.5 to result in 345,146 MIN-ID parallel sentences. We observe that the sentence pattern in the collection is highly repetitive (e.g. 100k sentences are about biological term definition). Therefore, we conduct final filtering based on top-1000 trigram by iteratively discarding sentences until the frequency of each trigram equals to 100. Finally, we obtain 16,371 MIN-ID parallel sentences and conducted manual evaluation by asking two native Minangkabau speakers to assess the adequacy and fluency (Koehn and Monz, 2006). The human judgement is based on scale 1–5 (1 means poor quality and 5 otherwise) and conducted against 100 random samples. We average the weights of two annotators before computing the overall score, and we achieve 4.98 and 4.87 for adequacy and fluency respectively. This indicates that the resulting corpus is high-quality for machine translation training. ## Dataset Usage Run `pip install nusacrowd` before loading the dataset through HuggingFace's `load_dataset`. ## Citation ``` @inproceedings{koto-koto-2020-towards, title = "Towards Computational Linguistics in {M}inangkabau Language: Studies on Sentiment Analysis and Machine Translation", author = "Koto, Fajri and Koto, Ikhwan", booktitle = "Proceedings of the 34th Pacific Asia Conference on Language, Information and Computation", month = oct, year = "2020", address = "Hanoi, Vietnam", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2020.paclic-1.17", pages = "138--148", } ``` ## License MIT ## Homepage [https://github.com/fajri91/minangNLP](https://github.com/fajri91/minangNLP) ### NusaCatalogue For easy indexing and metadata: [https://indonlp.github.io/nusa-catalogue](https://indonlp.github.io/nusa-catalogue)

[ -0.7880073189735413, -0.7326773405075073, 0.15478907525539398, 0.5952534675598145, -0.3640165328979492, -0.01903381384909153, -0.310352623462677, -0.34145116806030273, 0.4861316680908203, 0.540446400642395, -0.2906944453716278, -0.7065355777740479, -0.5238995552062988, 0.662419855594635, ...

null

bigbio/czi_drsm

bigbio

2023-11-23T12:33:19Z

43

0

null

[ "multilinguality:monolingual", "language:en", "license:cc0-1.0", "region:us" ]

2023-11-23T12:33:19Z

2023-09-26T13:22:47.000Z

2023-09-26T13:22:47

--- language: - en bigbio_language: - English license: cc0-1.0 bigbio_license_shortname: cc0-1.0 multilinguality: monolingual pretty_name: CZI DRSM homepage: https://github.com/chanzuckerberg/DRSM-corpus bigbio_pubmed: false bigbio_public: true bigbio_tasks: - TXTCLASS --- # Dataset Card for CZI DRSM ## Dataset Description - **Homepage:** https://github.com/chanzuckerberg/DRSM-corpus - **Pubmed:** False - **Public:** True - **Tasks:** TXTCLASS Research Article document classification dataset based on aspects of disease research. Currently, the dataset consists of three subsets: (A) classifies title/abstracts of papers into most popular subtypes of clinical, basic, and translational papers (~20k papers); - Clinical Characteristics, Disease Pathology, and Diagnosis - Text that describes (A) symptoms, signs, or ‘phenotype’ of a disease; (B) the effects of the disease on patient organs, tissues, or cells; (C) the results of clinical tests that reveal pathology (including biomarkers); (D) research that use this information to figure out a diagnosis. - Therapeutics in the clinic - Text describing how treatments work in the clinic (but not in a clinical trial). - Disease mechanism - Text that describes either (A) mechanistic involvement of specific genes in disease (deletions, gain of function, etc); (B) how molecular signalling or metabolism binding, activating, phosphorylation, concentration increase, etc.) are involved in the mechanism of a disease; or (C) the physiological mechanism of disease at the level of tissues, organs, and body systems. - Patient-Based Therapeutics - Text describing (A) Clinical trials (studies of therapeutic measures being used on patients in a clinical trial); (B) Post Marketing Drug Surveillance (effects of a drug after approval in the general population or as part of ‘standard healthcare’); (C) Drug repurposing (how a drug that has been approved for one use is being applied to a new disease). (B) identifies whether a title/abstract of a paper describes substantive research into Quality of Life (~10k papers); - -1 - the paper is not a primary experimental study in rare disease - 0 - the study does not directly investigate quality of life - 1 - the study investigates qol but not as its primary contribution - 2 - the study's primary contribution centers on quality of life measures (C) identifies if a paper is a natural history study (~10k papers). - -1 - the paper is not a primary experimental study in rare disease - 0 - the study is not directly investigating the natural history of a disease - 1 - the study includes some elements a natural history but not as its primary contribution - 2 - the study's primary contribution centers on observing the time course of a rare disease These classifications are particularly relevant in rare disease research, a field that is generally understudied. ## Citation Information ``` # N/A ```

[ -0.03644198924303055, -0.4680969715118408, 0.4056791365146637, -0.08593057841062546, -0.33537015318870544, 0.10386127233505249, 0.11315945535898209, -0.5509960651397705, 0.5707467198371887, 0.5564211010932922, -0.26160621643066406, -0.727165162563324, -0.6203343868255615, 0.237175568938255...

null

liyucheng/ceval_all

liyucheng

2023-09-29T10:07:50Z

43

0

null

[ "region:us" ]

2023-09-29T10:07:50Z

2023-09-29T10:04:27.000Z

2023-09-29T10:04:27

--- dataset_info: features: - name: id dtype: string - name: question dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: answer dtype: string - name: explanation dtype: string splits: - name: val num_bytes: 406528 num_examples: 1346 - name: test num_bytes: 3720917 num_examples: 12342 - name: dev num_bytes: 172688 num_examples: 260 download_size: 2792076 dataset_size: 4300133 --- # Dataset Card for "ceval_all" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

[ -0.5494203567504883, -0.31406843662261963, 0.4239391088485718, 0.17056383192539215, -0.2549703121185303, -0.19323690235614777, 0.1720377802848816, -0.1453969031572342, 0.9439498782157898, 0.6072936654090881, -0.6155927777290344, -1.0429418087005615, -0.6235786080360413, -0.2352985739707946...

null

bitadin/attributes-v10

bitadin

2023-10-04T16:37:47Z

43

0

null

[ "region:us" ]

2023-10-04T16:37:47Z

2023-10-03T09:50:31.000Z

2023-10-03T09:50:31

--- dataset_info: features: - name: prompt dtype: string - name: completion dtype: string splits: - name: train num_bytes: 37543937 num_examples: 42533 download_size: 20572906 dataset_size: 37543937 --- # Dataset Card for "attributes-v10" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

[ -0.6389021277427673, -0.08920017629861832, 0.28191739320755005, 0.34039705991744995, 0.11257288604974747, -0.05918734148144722, 0.34871524572372437, -0.389333575963974, 0.8055630326271057, 0.43912672996520996, -1.0591931343078613, -0.6113393902778625, -0.5157551765441895, -0.11462496966123...

null

approximatelabs/tablib-v1-sample

approximatelabs

2023-10-13T22:34:05Z

43

8

null

[ "size_categories:1M<n<10M", "license:other", "arxiv:2310.07875", "region:us" ]

2023-10-13T22:34:05Z

2023-10-04T16:55:20.000Z

2023-10-04T16:55:20

--- license: other pretty_name: TabLib size_categories: - 1M<n<10M extra_gated_prompt: >- Access to this dataset is automatically granted once this form is completed. Note that this access request is for the TabLib sample, not [the full TabLib dataset](https://huggingface.co/datasets/approximatelabs/tablib-v1-full). extra_gated_fields: I agree to abide by the license requirements of the data contained in TabLib: checkbox --- [![](https://dcbadge.vercel.app/api/server/kW9nBQErGe?compact=true&style=flat)](https://discord.gg/kW9nBQErGe) <img src="https://approximatelabs.com/tablib.png" width="800" /> # TabLib Sample **NOTE**: This is a 0.1% sample of [the full TabLib dataset](https://huggingface.co/datasets/approximatelabs/tablib-v1-full). TabLib is a minimally-preprocessed dataset of 627M tables (69 TiB) extracted from HTML, PDF, CSV, TSV, Excel, and SQLite files from GitHub and Common Crawl. This includes 867B tokens of "context metadata": each table includes provenance information and table context such as filename, text before/after, HTML metadata, etc. For more information, read the [paper](https://arxiv.org/abs/2310.07875) & [announcement blog](https://approximatelabs.com/blog/tablib). # Dataset Details ## Sources * **GitHub**: nearly all public GitHub repositories * **Common Crawl**: the `CC-MAIN-2023-23` crawl ## Reading Tables Tables are stored as serialized Arrow bytes in the `arrow_bytes` column. To read these, you will need to deserialize the bytes: ```python import datasets import pyarrow as pa # load a single file of the dataset ds = datasets.load_dataset( 'approximatelabs/tablib-v1-sample', token='...', ) df = ds['train'].to_pandas() tables = [pa.RecordBatchStreamReader(b).read_all() for b in df['arrow_bytes']] ``` ## Licensing This dataset is intended for research use only. For specific licensing information, refer to the license of the specific datum being used. # Contact If you have any questions, comments, or concerns about licensing, pii, etc. please contact using [this form](https://forms.gle/C74VTWP7L78QDVR67). # Approximate Labs TabLib is a project from Approximate Labs. Find us on [Twitter](https://twitter.com/approximatelabs), [Github](https://github.com/approximatelabs), [Linkedin](https://www.linkedin.com/company/approximate-labs), and [Discord](https://discord.gg/kW9nBQErGe). # Citations If you use TabLib for any of your research, please cite the TabLib paper: ``` @misc{eggert2023tablib, title={TabLib: A Dataset of 627M Tables with Context}, author={Gus Eggert and Kevin Huo and Mike Biven and Justin Waugh}, year={2023}, eprint={2310.07875}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```

[ -0.2783307135105133, -0.5830659866333008, 0.2683723270893097, -0.11500802636146545, -0.05614251643419266, -0.06311583518981934, -0.143595352768898, -0.24119189381599426, 0.3561713397502899, 0.14900581538677216, -0.3954290449619293, -0.6065826416015625, 0.13813234865665436, 0.11945387721061...

null

madaanpulkit/tab-wnut

madaanpulkit

2023-11-02T06:07:27Z

43

0

null

[ "region:us" ]

2023-11-02T06:07:27Z

2023-10-11T07:38:29.000Z

2023-10-11T07:38:29

--- dataset_info: features: - name: text dtype: string - name: tokens sequence: string - name: tagged_text sequence: string - name: tags sequence: class_label: names: '0': '0' '1': B-DIRECT-CODE '2': I-DIRECT-CODE '3': B-DIRECT-PERSON '4': I-DIRECT-PERSON '5': B-QUASI-DATETIME '6': I-QUASI-DATETIME '7': B-QUASI-PERSON '8': I-QUASI-PERSON '9': B-QUASI-LOC '10': I-QUASI-LOC '11': B-QUASI-QUANTITY '12': I-QUASI-QUANTITY '13': B-QUASI-CODE '14': I-QUASI-CODE '15': B-QUASI-ORG '16': I-QUASI-ORG '17': B-QUASI-DEM '18': I-QUASI-DEM '19': B-QUASI-MISC '20': I-QUASI-MISC '21': B-DIRECT-ORG '22': I-DIRECT-ORG '23': B-DIRECT-DATETIME '24': I-DIRECT-DATETIME '25': B-DIRECT-LOC '26': I-DIRECT-LOC '27': B-DIRECT-MISC '28': I-DIRECT-MISC '29': B-DIRECT-DEM '30': I-DIRECT-DEM splits: - name: train num_bytes: 45872319 num_examples: 1014 - name: dev num_bytes: 3749307 num_examples: 127 - name: test num_bytes: 3619745 num_examples: 127 download_size: 11056816 dataset_size: 53241371 --- # Dataset Card for "tab-wnut" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

[ -0.6508665680885315, -0.46953272819519043, 0.10622363537549973, 0.15729834139347076, -0.1762140393257141, 0.270943284034729, -0.07176018506288528, -0.10128361731767654, 0.9958912134170532, 0.5741275548934937, -0.8620771765708923, -0.8444288969039917, -0.34829437732696533, -0.28843483328819...

null

theblackcat102/gpt-4v-eval-samples

theblackcat102

2023-11-05T12:20:01Z

43

1

null

[ "region:us" ]

2023-11-05T12:20:01Z

2023-10-13T00:51:36.000Z

2023-10-13T00:51:36

--- configs: - config_name: default data_files: - split: test path: data/test-* dataset_info: features: - name: image dtype: image - name: conversations dtype: string splits: - name: test num_bytes: 334178840.35 num_examples: 1682 download_size: 324453952 dataset_size: 334178840.35 --- # GPT-4V Eval samples This is a hand curated images from the web and questions asked by myself to GPT-4V to understand its ability and limits. I am mainly focus in localization, OCR ability and understanding of GPT-4V vision module. So the language part is skipped as we already seen in GPT-4. As long as GPT-4V can extract the required information in text, the rest of the LLM shouldn't have any issue answering the rest of the questions. The numbers of examples is still pretty tiny and will continue to increase further in the future until I am satisfy with the size. So please check back from time to time. Note : the dataset viewer had a bug which cause the image displayed differ from the actual dataset (Due to frequent update). Please load the dataset and save it on your local path for best accuracy. ## How to use: ``` import json from datasets import load_dataset dataset = load_dataset('theblackcat102/gpt-4v-eval-samples')['test'] print(dataset[0]['image']) print(json.loads(dataset[0]['conversations'])) ``` ## Contributions Please checkout my github repo for more details : [theblackcat102/gpt-4v-samples](https://github.com/theblackcat102/gpt-4v-samples) ## Citation ``` @article{yang2023dawn, title={The Dawn of LMMs: Preliminary Explorations with GPT-4V (ision)}, author={Yang, Zhengyuan and Li, Linjie and Lin, Kevin and Wang, Jianfeng and Lin, Chung-Ching and Liu, Zicheng and Wang, Lijuan}, journal={arXiv preprint arXiv:2309.17421}, year={2023} } ```

[ -0.2684652805328369, -0.6585031151771545, 0.7817088961601257, -0.06891198456287384, -0.21350471675395966, 0.014596681110560894, -0.11287204176187515, -0.4157443344593048, -0.011691453866660595, 0.4277186393737793, -0.41706493496894836, -0.5328382849693298, -0.2800806760787964, 0.3322938680...

null

coastalcph/fm_aliases

coastalcph

2023-10-20T08:18:20Z

43

0

null

[ "region:us" ]

2023-10-20T08:18:20Z

2023-10-20T07:40:26.000Z

2023-10-20T07:40:26

Entry not found

[ -0.32276469469070435, -0.22568407654762268, 0.8622258901596069, 0.434614896774292, -0.5282987952232361, 0.7012966275215149, 0.7915717363357544, 0.07618635147809982, 0.7746022939682007, 0.25632190704345703, -0.7852814793586731, -0.22573821246623993, -0.9104482531547546, 0.5715669393539429, ...

null

Horus7/FromTo

Horus7

2023-11-07T10:53:44Z

43

0

null

[ "task_categories:token-classification", "region:us" ]

2023-11-07T10:53:44Z

2023-10-22T12:54:04.000Z

2023-10-22T12:54:04

--- task_categories: - token-classification ---

[ -0.128533735871315, -0.18616747856140137, 0.6529128551483154, 0.4943627715110779, -0.19319336116313934, 0.2360745221376419, 0.3607197701931, 0.05056330934166908, 0.5793653130531311, 0.740013837814331, -0.6508103013038635, -0.23783954977989197, -0.7102248668670654, -0.04782583937048912, -...

null

worden1/ultra-feedback-paired

worden1

2023-10-24T04:21:21Z

43

1

null

[ "task_categories:question-answering", "task_categories:text-generation", "size_categories:10M<n<100M", "language:en", "region:us" ]

2023-10-24T04:21:21Z

2023-10-24T04:00:41.000Z

2023-10-24T04:00:41

--- task_categories: - question-answering - text-generation language: - en pretty_name: ultra_feedback_paired size_categories: - 10M<n<100M ---

[ -0.128533735871315, -0.18616747856140137, 0.6529128551483154, 0.4943627715110779, -0.19319336116313934, 0.2360745221376419, 0.3607197701931, 0.05056330934166908, 0.5793653130531311, 0.740013837814331, -0.6508103013038635, -0.23783954977989197, -0.7102248668670654, -0.04782583937048912, -...

null

gokul00060/armchat1

gokul00060

2023-10-28T09:29:18Z

43

1

null

[ "license:mit", "region:us" ]

2023-10-28T09:29:18Z

2023-10-28T08:02:33.000Z

2023-10-28T08:02:33

--- license: mit --- ## THIS DATASET IS ONLY MADE FOR THESE # ID name color # 1. ball yellow # 2. battery silver # 3. wood wood # 4. bowl white

[ 0.0160054974257946, -0.03366042673587799, 0.24456089735031128, 0.2935163676738739, -0.3947492241859436, 0.12965478003025055, 0.591144323348999, 0.11723477393388748, 0.5281916856765747, 0.41909998655319214, -0.8528489470481873, -0.4740232825279236, -0.35777005553245544, 0.20128236711025238,...

null

bragovo/dsum_en

bragovo

2023-11-05T18:57:50Z

43

0

null

[ "language:en", "region:us" ]

2023-11-05T18:57:50Z

2023-11-04T07:28:09.000Z

2023-11-04T07:28:09

--- configs: - config_name: default task: summarization data_files: - split: train path: data/train-* language: - en ---

[ -0.12853367626667023, -0.18616794049739838, 0.6529126763343811, 0.4943627417087555, -0.19319313764572144, 0.23607443273067474, 0.36071979999542236, 0.05056338757276535, 0.5793654322624207, 0.7400138974189758, -0.6508103013038635, -0.23783987760543823, -0.710224986076355, -0.047825977206230...

null

Dippi9845/sci_lay

Dippi9845

2023-11-04T19:40:46Z

43

0

null

[ "region:us" ]

2023-11-04T19:40:46Z

2023-11-04T19:22:23.000Z

2023-11-04T19:22:23

Entry not found

[ -0.3227649927139282, -0.225684255361557, 0.862226128578186, 0.43461498618125916, -0.5282987952232361, 0.7012963891029358, 0.7915717363357544, 0.07618629932403564, 0.7746025919914246, 0.2563219666481018, -0.7852816581726074, -0.2257382869720459, -0.9104480743408203, 0.5715669393539429, -0...

null

nicolepng/project_train_set

nicolepng

2023-11-28T04:38:09Z

43

0

null

[ "region:us" ]

2023-11-28T04:38:09Z

2023-11-07T16:13:53.000Z

2023-11-07T16:13:53

Entry not found

[ -0.3227649927139282, -0.225684255361557, 0.862226128578186, 0.43461498618125916, -0.5282987952232361, 0.7012963891029358, 0.7915717363357544, 0.07618629932403564, 0.7746025919914246, 0.2563219666481018, -0.7852816581726074, -0.2257382869720459, -0.9104480743408203, 0.5715669393539429, -0...

null

jin05102518/Raw_koen_v2

jin05102518

2023-11-10T15:58:15Z

43

0

null

[ "region:us" ]

2023-11-10T15:58:15Z

2023-11-10T15:53:46.000Z

2023-11-10T15:53:46

Entry not found

[ -0.3227649927139282, -0.225684255361557, 0.862226128578186, 0.43461498618125916, -0.5282987952232361, 0.7012963891029358, 0.7915717363357544, 0.07618629932403564, 0.7746025919914246, 0.2563219666481018, -0.7852816581726074, -0.2257382869720459, -0.9104480743408203, 0.5715669393539429, -0...

null

danielz01/BigEarthNet-S2-v1.0

danielz01

2023-11-13T09:11:20Z

43

0

null

[ "region:us" ]

2023-11-13T09:11:20Z

2023-11-12T20:17:15.000Z

2023-11-12T20:17:15

--- configs: - config_name: s2-rgb data_files: - split: test path: s2-rgb/test-* - split: val path: s2-rgb/val-* - split: train path: s2-rgb/train-* dataset_info: config_name: s2-rgb features: - name: img dtype: image - name: labels sequence: string - name: coordinates struct: - name: lrx dtype: int64 - name: lry dtype: int64 - name: ulx dtype: int64 - name: uly dtype: int64 - name: projection dtype: string - name: tile_source dtype: string - name: acquisition_date dtype: string splits: - name: test num_bytes: 3453114936.75 num_examples: 125866 - name: val num_bytes: 3393628600.625 num_examples: 123723 - name: train num_bytes: 7391482704.125 num_examples: 269695 download_size: 13839792533 dataset_size: 14238226241.5 --- # Dataset Card for "BigEarthNet-S2-v1.0" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

[ -0.36614206433296204, -0.06623243540525436, 0.2273523360490799, 0.21576648950576782, -0.38680514693260193, -0.34535282850265503, 0.23066940903663635, -0.18518057465553284, 1.0744163990020752, 0.5938584804534912, -0.8570621609687805, -0.6570728421211243, -0.7954596877098083, -0.322587013244...

null

varox34/med

varox34

2023-11-16T14:51:07Z

43

0

null

[ "region:us" ]

2023-11-16T14:51:07Z

2023-11-14T16:12:45.000Z

2023-11-14T16:12:45

Entry not found

[ -0.32276472449302673, -0.22568407654762268, 0.8622258901596069, 0.4346148371696472, -0.5282984972000122, 0.7012965679168701, 0.7915717363357544, 0.07618629932403564, 0.7746022939682007, 0.2563222646713257, -0.785281777381897, -0.22573848068714142, -0.9104482531547546, 0.5715669393539429, ...

null

openerotica/basilisk-v0.2

openerotica

2023-11-18T04:46:33Z

43

5

null

[ "license:apache-2.0", "region:us" ]

2023-11-18T04:46:33Z

2023-11-18T01:39:15.000Z

2023-11-18T01:39:15

--- license: apache-2.0 --- This is the dataset that was used to train basilisk-7b-v0.2. It contains a subset of orca-best, as well as most of freedom-rp. If you like what I'm trying to do, please consider subscribing to my patreon. I'm only asking for about tree fiddy. https://patreon.com/openerotica

[ -0.39424604177474976, 0.053046055138111115, -0.1137784942984581, 0.03043847158551216, -0.8074538111686707, 0.014721393585205078, 0.18955311179161072, -0.4360947906970978, 0.7952367067337036, 0.4511547386646271, -0.9075160622596741, -0.4082108438014984, -0.32066115736961365, -0.006198649760...

null

idning/ffhq32-caption

idning

2023-11-21T02:53:23Z

43

0

null

[ "license:mit", "region:us" ]

2023-11-21T02:53:23Z

2023-11-21T02:10:57.000Z

2023-11-21T02:10:57

--- license: mit dataset_info: features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 174847756.0 num_examples: 70000 download_size: 182541625 dataset_size: 174847756.0 configs: - config_name: default data_files: - split: train path: data/train-* ---

[ -0.12853392958641052, -0.18616779148578644, 0.6529127955436707, 0.49436280131340027, -0.19319361448287964, 0.23607419431209564, 0.36072003841400146, 0.050563063472509384, 0.579365611076355, 0.7400140762329102, -0.6508104205131531, -0.23783954977989197, -0.7102249264717102, -0.0478260256350...

null

michaelginn/latent-trees-agreement-ID

michaelginn

2023-11-26T02:52:05Z

43

0

null

[ "region:us" ]

2023-11-26T02:52:05Z

2023-11-24T20:32:27.000Z

2023-11-24T20:32:27

--- dataset_info: features: - name: text dtype: string - name: labels dtype: class_label: names: '0': '0' '1': '1' splits: - name: train num_bytes: 87976.8 num_examples: 2400 - name: eval num_bytes: 29325.6 num_examples: 800 - name: test num_bytes: 29325.6 num_examples: 800 download_size: 53220 dataset_size: 146628.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: eval path: data/eval-* - split: test path: data/test-* --- # Dataset Card for "latent-trees-agreement-ID" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

[ -0.3402356803417206, -0.2793717086315155, 0.2816840708255768, 0.299689918756485, -0.3870009481906891, 0.2905147969722748, 0.3010704815387726, -0.11942082643508911, 0.7732731103897095, 0.13998448848724365, -0.6947784423828125, -0.7373343110084534, -0.6647218465805054, -0.2833920419216156, ...

null

RahulRaman/counting-object-sd

RahulRaman

2023-11-24T22:59:30Z

43

0

null

[ "license:mit", "region:us" ]

2023-11-24T22:59:30Z

2023-11-24T22:13:27.000Z

2023-11-24T22:13:27

--- license: mit dataset_info: features: - name: input_image dtype: image - name: edit_prompt dtype: string - name: edited_image dtype: image splits: - name: train num_bytes: 155541.0 num_examples: 1 download_size: 157933 dataset_size: 155541.0 configs: - config_name: default data_files: - split: train path: data/train-* ---

[ -0.12853392958641052, -0.18616779148578644, 0.6529127955436707, 0.49436280131340027, -0.19319361448287964, 0.23607419431209564, 0.36072003841400146, 0.050563063472509384, 0.579365611076355, 0.7400140762329102, -0.6508104205131531, -0.23783954977989197, -0.7102249264717102, -0.0478260256350...

null

thomwolf/github-python

thomwolf

2021-07-07T11:53:28Z

42

7

null

[ "region:us" ]

2021-07-07T11:53:28Z

2022-03-02T23:29:22.000Z

2022-03-02T23:29:22

Entry not found

[ -0.32276472449302673, -0.22568407654762268, 0.8622258901596069, 0.4346148371696472, -0.5282984972000122, 0.7012965679168701, 0.7915717363357544, 0.07618629932403564, 0.7746022939682007, 0.2563222646713257, -0.785281777381897, -0.22573848068714142, -0.9104482531547546, 0.5715669393539429, ...

null

transformersbook/codeparrot

transformersbook

2022-02-05T16:15:40Z

42

36

null

[ "python", "code", "region:us" ]

2022-02-05T16:15:40Z

2022-03-02T23:29:22.000Z

2022-03-02T23:29:22

--- tags: - python - code --- # CodeParrot 🦜 Dataset ## What is it? This is the full CodeParrot dataset. It contains Python files used to train the code generation model in Chapter 10: Training Transformers from Scratch in the [NLP with Transformers book](https://learning.oreilly.com/library/view/natural-language-processing/9781098103231/). You can find the full code in the accompanying [Github repository](https://github.com/nlp-with-transformers/notebooks/blob/main/10_transformers-from-scratch.ipynb). ## Creation It was created with the GitHub dataset available via Google's BigQuery. It contains approximately 22 million Python files and is 180 GB (50 GB compressed) big. The SQL query to create the dataset is the following: ```sql SELECT f.repo_name, f.path, c.copies, c.size, c.content, l.license FROM `bigquery-public-data.github_repos.files` AS f JOIN `bigquery-public-data.github_repos.contents` AS c ON f.id = c.id JOIN `bigquery-public-data.github_repos.licenses` AS l ON f.repo_name = l.repo_name WHERE NOT c.binary AND ((f.path LIKE '%.py') AND (c.size BETWEEN 1024 AND 1048575)) ``` ## Duplication Note that about 70% of the dataset is duplicated. If you use the dataset make sure to deal with them appropriately. See [codeparrot-clean](https://huggingface.co/datasets/lvwerra/codeparrot-clean) for a deduplicated version of this dataset.

[ -0.4493507146835327, -0.2454541176557541, -0.18914784491062164, 0.01970096118748188, -0.392004132270813, 0.16630613803863525, -0.15097640454769135, 0.029605261981487274, 0.2747681736946106, 0.7408788800239563, -0.641850471496582, -0.39302197098731995, -0.2935982942581177, 0.280081003904342...

null

crystina-z/no-nonself-title-mrtydi-corpus

crystina-z

2022-03-11T22:55:53Z

42

0

null

[ "region:us" ]

2022-03-11T22:55:53Z

2022-03-11T22:40:38.000Z

2022-03-11T22:40:38

Entry not found

[ -0.3227645754814148, -0.22568479180335999, 0.8622263669967651, 0.43461522459983826, -0.52829909324646, 0.7012971639633179, 0.7915719747543335, 0.07618614286184311, 0.774603009223938, 0.2563217282295227, -0.7852813005447388, -0.22573819756507874, -0.9104475975036621, 0.5715674161911011, -...

null

Guldeniz/flower_dataset

Guldeniz

2022-04-09T20:52:59Z

42

0

null

[ "region:us" ]

2022-04-09T20:52:59Z

2022-04-09T20:36:46.000Z

2022-04-09T20:36:46

#flowersdataset #segmentation #VGG # Dataset Card for Flowers Dataset ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Official VGG'S README.md](#official-vggs-README.md) ## Dataset Description - **Homepage:** https://www.robots.ox.ac.uk/~vgg/data/flowers/17/index.html - **Repository:** https://huggingface.co/datasets/Guldeniz/flower_dataset - **Paper:** [Needs More Information] - **Leaderboard:** [Needs More Information] - **Point of Contact:** [Needs More Information] ### Dataset Summary VGG have created a 17 category flower dataset with 80 images for each class. The flowers chosen are some common flowers in the UK. The images have large scale, pose and light variations and there are also classes with large varations of images within the class and close similarity to other classes. The categories can be seen in the figure below. We randomly split the dataset into 3 different training, validation and test sets. A subset of the images have been groundtruth labelled for segmentation. You can find the split files in the link, as a mat file. ### Official VGG's README.md 17 Flower Category Database ---------------------------------------------- This set contains images of flowers belonging to 17 different categories. The images were acquired by searching the web and taking pictures. There are 80 images for each category. The database was used in: Nilsback, M-E. and Zisserman, A. A Visual Vocabulary for Flower Classification. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2006) http://www.robots.ox.ac.uk/~vgg/publications/papers/nilsback06.{pdf,ps.gz}. The datasplits used in this paper are specified in datasplits.mat There are 3 separate splits. The results in the paper are averaged over the 3 splits. Each split has a training file (trn1,trn2,trn3), a validation file (val1, val2, val3) and a testfile (tst1, tst2 or tst3). Segmentation Ground Truth ------------------------------------------------ The ground truth is given for a subset of the images from 13 different categories. More details can be found in: Nilsback, M-E. and Zisserman, A. Delving into the whorl of flower segmentation. Proceedings of the British Machine Vision Conference (2007) http:www.robots.ox.ac.uk/~vgg/publications/papers/nilsback06.(pdf,ps.gz). The ground truth file also contains the file imlist.mat, which indicated which images in the original database that have been anotated. Distance matrices ----------------------------------------------- We provide two set of distance matrices: 1. distancematrices17gcfeat06.mat - Distance matrices using the same features and segmentation as detailed in: Nilsback, M-E. and Zisserman, A. A Visual Vocabulary for Flower Classification. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition(2006) http://www.robots.ox.ac.uk/~vgg/publications/papers/nilsback06.{pdf,ps.gz}. 2. distancematrices17itfeat08.mat - Distance matrices using the same features as described in: Nilsback, M-E. and Zisserman, A. Automated flower classification over a large number of classes. Proceedings of the Indian Conference on Computer Vision, Graphics and Image Processing (2008) http://www.robots.ox.ac.uk/~vgg/publications/papers/nilsback08.{pdf,ps.gz}. and the iterative segmenation scheme detailed in Nilsback, M-E. and Zisserman, A. Delving into the whorl of flower segmentation. Proceedings of the British Machine Vision Conference (2007) http:www.robots.ox.ac.uk/~vgg/publications/papers/nilsback06.(pdf,ps.gz).

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null

ziq/depression_tweet

ziq

2022-06-06T07:09:06Z

42

0

null

[ "region:us" ]

2022-06-06T07:09:06Z

2022-06-06T06:48:27.000Z

2022-06-06T06:48:27

Entry not found

[ -0.3227645754814148, -0.22568479180335999, 0.8622263669967651, 0.43461522459983826, -0.52829909324646, 0.7012971639633179, 0.7915719747543335, 0.07618614286184311, 0.774603009223938, 0.2563217282295227, -0.7852813005447388, -0.22573819756507874, -0.9104475975036621, 0.5715674161911011, -...

null

Heriot-WattUniversity/dialog_babi

Heriot-WattUniversity

2022-07-12T08:27:12Z

42

2

null

[ "arxiv:1605.07683", "arxiv:1502.05698", "region:us" ]

2022-07-12T08:27:12Z

2022-07-09T09:32:32.000Z

2022-07-09T09:32:32

# Dialog bAbI tasks data In this directory is the set of 6 tasks for testing end-to-end dialog systems in the restaurant domain as described in the paper "Learning End-to-End Goal-Oriented Dialog" by Bordes & Weston (http://arxiv.org/abs/1605.07683). The aim is that each task tests a unique aspect of dialog. Tasks are designed to complement the set of 20 bAbI tasks for story understanding already released with the paper "Towards AI Complete Question Answering: A Set of Prerequisite Toy Tasks" by Weston et al. (http://arxiv.org/abs/1502.05698). ## Data For each task, there are 1000 dialogs for training, 1000 for development and 1000 for testing. For tasks 1-5, we also include a second test set (with suffix -OOV.txt) that contains dialogs including entities not present in training and development sets. The file format for each task is as follows: `ID user_utterance [tab] bot_utterances` The IDs for a given dialog start at 1 and increase. When the IDs in a file reset back to 1 you can consider the following sentences as a new dialog. When the bot speaks two times in a row, we used the special token "<SILENCE>" to fill in for the missing user utterance. For example (for task 1): ``` 1 hi hello what can i help you with today 2 can you make a restaurant reservation with italian cuisine for six people in a cheap price range i'm on it 3 <SILENCE> where should it be 4 rome please ok let me look into some options for you 5 <SILENCE> api_call italian rome six cheap ``` The goal of the tasks is to predict the bot utterances, that can be sentences or API calls (sentences starting with the special token "api_call"). Along with the train, dev and test sets, we also include a knowledge base file (dialog-babi-kb-all.txt) that contain all entities appearing in dialogs for tasks 1-5. We also include a file containing the candidates to select the answer from (dialog-babi-candidates.txt) for tasks 1-5, that is simply made of all the bot utterances in train, dev, test for these tasks. Task 6 is a bit different since its data comes from the Dialog State Tracking Challenge 2 (http://camdial.org/~mh521/dstc/), which we modified to convert it into the same format as the other tasks. There is no OOV test set associated with this task and the knowledge base (dialog-babi-task6-dstc2-kb.txt) is imperfect. This task has its own candidates file (dialog-babi-task6-dstc2-candidates.txt). ## License This dataset is released under Creative Commons Attribution 3.0 Unported license. A copy of this license is included with the data. ## Contact The author of this porting is Alessandro Suglia and he has only made available the dataset via Huggingface datasets. For more details on the dataset and baselines, see the paper "Learning End-to-End Goal-Oriented Dialog" by Antoine Bordes and Jason Weston (http://arxiv.org/abs/1605.07683). For any information, contact Antoine Bordes : abordes (at) fb (dot) com .

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null

bigbio/genetag

bigbio

2022-12-22T15:44:38Z

42

2

null

[ "multilinguality:monolingual", "language:en", "license:other", "region:us" ]

2022-12-22T15:44:38Z

2022-11-13T22:08:32.000Z

2022-11-13T22:08:32

--- language: - en bigbio_language: - English license: other multilinguality: monolingual bigbio_license_shortname: NCBI_LICENSE pretty_name: GENETAG homepage: https://github.com/openbiocorpora/genetag bigbio_pubmed: True bigbio_public: True bigbio_tasks: - NAMED_ENTITY_RECOGNITION --- # Dataset Card for GENETAG ## Dataset Description - **Homepage:** https://github.com/openbiocorpora/genetag - **Pubmed:** True - **Public:** True - **Tasks:** NER Named entity recognition (NER) is an important first step for text mining the biomedical literature. Evaluating the performance of biomedical NER systems is impossible without a standardized test corpus. The annotation of such a corpus for gene/protein name NER is a difficult process due to the complexity of gene/protein names. We describe the construction and annotation of GENETAG, a corpus of 20K MEDLINE® sentences for gene/protein NER. 15K GENETAG sentences were used for the BioCreAtIvE Task 1A Competition.. ## Citation Information ``` @article{Tanabe2005, author = {Lorraine Tanabe and Natalie Xie and Lynne H Thom and Wayne Matten and W John Wilbur}, title = {{GENETAG}: a tagged corpus for gene/protein named entity recognition}, journal = {{BMC} Bioinformatics}, volume = {6}, year = {2005}, url = {https://doi.org/10.1186/1471-2105-6-S1-S3}, doi = {10.1186/1471-2105-6-s1-s3}, biburl = {}, bibsource = {} } ```

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null

xusenlin/cmeee

xusenlin

2022-12-07T14:24:00Z

42

3

null

[ "region:us" ]

2022-12-07T14:24:00Z

2022-12-07T14:16:08.000Z

2022-12-07T14:16:08

--- dataset_info: features: - name: text dtype: string - name: entities list: - name: id dtype: int64 - name: entity dtype: string - name: start_offset dtype: int64 - name: end_offset dtype: int64 - name: label dtype: string splits: - name: train num_bytes: 5289666 num_examples: 15000 - name: test num_bytes: 461472 num_examples: 3000 - name: validation num_bytes: 1752698 num_examples: 5000 download_size: 3359069 dataset_size: 7503836 --- # CMeEE 中文医学命名实体识别数据集字段说明 + `text`: 文本 + `entities`: 文本中包含的实体 + `id`: 实体 `id` + `entity`: 实体对应的字符串 + `start_offset`: 实体开始位置 + `end_offset`: 实体结束位置的下一位 + `label`: 实体对应的开始位置

[ -0.840850830078125, -0.892907977104187, 0.5226985216140747, 0.3399074077606201, -0.6539755463600159, 0.04288371279835701, 0.10504792630672455, -0.4010762870311737, 0.7671908140182495, 0.25808602571487427, -0.4637112021446228, -0.9510857462882996, -0.8738322257995605, -0.15990909934043884, ...

null

clip-benchmark/wds_vtab-flowers

clip-benchmark

2023-01-20T07:14:22Z

42

0

null

[ "region:us" ]

2023-01-20T07:14:22Z

2023-01-20T07:13:41.000Z

2023-01-20T07:13:41

Entry not found

[ -0.32276472449302673, -0.22568407654762268, 0.8622258901596069, 0.4346148371696472, -0.5282984972000122, 0.7012965679168701, 0.7915717363357544, 0.07618629932403564, 0.7746022939682007, 0.2563222646713257, -0.785281777381897, -0.22573848068714142, -0.9104482531547546, 0.5715669393539429, ...

null

davanstrien/notebooks_by_repo_type

davanstrien

2023-11-28T01:31:04Z

42

0

null

[ "region:us" ]

2023-11-28T01:31:04Z

2023-03-27T09:16:12.000Z

2023-03-27T09:16:12

--- dataset_info: features: - name: repo_type dtype: large_string - name: repo_notebook_count dtype: int64 - name: date dtype: date32 splits: - name: train num_bytes: 10550 num_examples: 422 download_size: 5072 dataset_size: 10550 --- # 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)

[ -0.3241296112537384, 0.00674766581505537, 0.2476266473531723, 0.26046857237815857, -0.2796349823474884, 0.18740269541740417, -0.04864993318915367, 0.016417181119322777, 0.8191913366317749, 0.6630593538284302, -0.563766598701477, -0.9094176888465881, -0.4772111177444458, -0.0087682949379086...

null

davanstrien/notebooks_on_the_hub_raw

davanstrien

2023-11-28T01:31:00Z

42

0

null

[ "region:us" ]

2023-11-28T01:31:00Z

2023-04-03T12:41:41.000Z

2023-04-03T12:41:41

--- dataset_info: features: - name: date dtype: date32 - name: repo_type dtype: large_string - name: user dtype: large_string - name: repo_id dtype: large_string - name: repo_notebook_count dtype: int64 splits: - name: train num_bytes: 63192539 num_examples: 959671 download_size: 24373235 dataset_size: 63192539 --- # Dataset Card for "notebooks_on_the_hub_raw" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

[ -0.46061551570892334, -0.5036563873291016, 0.16005496680736542, 0.281045526266098, -0.12038256227970123, 0.20998729765415192, -0.028321102261543274, -0.05998429283499718, 0.9584814310073853, 0.28292882442474365, -0.6815826892852783, -0.7447462677955627, -0.18636783957481384, -0.08952058106...

null

metaeval/chaos-mnli-ambiguity

metaeval

2023-04-11T13:43:17Z

42

0

null

[ "region:us" ]

2023-04-11T13:43:17Z

2023-04-11T13:37:19.000Z

2023-04-11T13:37:19

Chaos NLI MNLI portion with gini coefficient pre-computed (from 0 to 1) High gini means unambiguous inference. ``` @inproceedings{xzhou2022distnli, Author = {Xiang Zhou and Yixin Nie and Mohit Bansal}, Booktitle = {Findings of the Association for Computational Linguistics: ACL 2022}, Publisher = {Association for Computational Linguistics}, Title = {Distributed NLI: Learning to Predict Human Opinion Distributions for Language Reasoning}, Year = {2022} } ```

[ -0.45982372760772705, -0.5642482042312622, 0.04255004972219467, 0.5811824798583984, -0.12992902100086212, -0.09203768521547318, 0.04897983744740486, -0.5110451579093933, 0.5697202682495117, -0.03630997985601425, -0.8726965188980103, -0.43263646960258484, -0.4137818515300751, 0.150115519762...

null

mstz/steel_plates

mstz

2023-04-16T18:03:07Z

42

1

null

[ "task_categories:tabular-classification", "size_categories:1K<n<5K", "language:en", "license:cc", "steel_plates", "tabular_classification", "binary_classification", "multiclass_classification", "UCI", "region:us" ]

2023-04-16T18:03:07Z

2023-04-14T15:26:35.000Z

2023-04-14T15:26:35

--- language: - en tags: - steel_plates - tabular_classification - binary_classification - multiclass_classification - UCI pretty_name: Landsat size_categories: - 1K<n<5K task_categories: - tabular-classification configs: - steel_plates - steel_plates_0 - steel_plates_1 - steel_plates_2 - steel_plates_3 - steel_plates_4 - steel_plates_5 - steel_plates_6 license: cc --- # Landsat The [Steel Plates dataset](https://archive-beta.ics.uci.edu/dataset/198/steel+plates+faults) from the [UCI repository](https://archive-beta.ics.uci.edu/). # Configurations and tasks | **Configuration** | **Task** | **Description** | |-----------------------|---------------------------|-------------------------| | steel_plates | Multiclass classification.| | | steel_plates_0 | Binary classification. | Is the input of class 0? | | steel_plates_1 | Binary classification. | Is the input of class 1? | | steel_plates_2 | Binary classification. | Is the input of class 2? | | steel_plates_3 | Binary classification. | Is the input of class 3? | | steel_plates_4 | Binary classification. | Is the input of class 4? | | steel_plates_5 | Binary classification. | Is the input of class 5? | | steel_plates_6 | Binary classification. | Is the input of class 6? |

[ -0.6809302568435669, -0.03178585320711136, 0.6375670433044434, 0.33013519644737244, -0.18740376830101013, 0.0882507860660553, 0.388309121131897, 0.12169593572616577, 0.24040977656841278, 0.6550065279006958, -0.6043320298194885, -0.7886854410171509, -0.7146094441413879, -0.23535528779029846...

null

PaulineSanchez/Translation_words_and_sentences_english_french

PaulineSanchez

2023-04-27T12:29:31Z

42

0

null

[ "task_categories:translation", "language:en", "language:fr", "words", "sentences", "everyday life", "casual", "region:us" ]

2023-04-27T12:29:31Z

2023-04-27T12:23:20.000Z

2023-04-27T12:23:20

--- task_categories: - translation language: - en - fr tags: - words - sentences - everyday life - casual --- # 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). This dataset is a clean version (all NanN removed) of this dataset : https://www.kaggle.com/datasets/devicharith/language-translation-englishfrench . I'm not the person who posted it first on Kaggle. ### 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]

[ -0.45667073130607605, -0.49136802554130554, -0.06790883839130402, 0.3569483757019043, -0.4825454652309418, 0.01670614816248417, -0.27151650190353394, -0.3108958899974823, 0.6836026906967163, 0.6072930097579956, -0.9122272729873657, -1.0814622640609741, -0.7831945419311523, 0.06850252300500...

null

MU-NLPC/Calc-ape210k

MU-NLPC

2023-10-30T15:56:39Z

42

13

null

[ "license:mit", "arxiv:2305.15017", "arxiv:2009.11506", "region:us" ]

2023-10-30T15:56:39Z

2023-05-22T14:20:16.000Z

2023-05-22T14:20:16

--- license: mit dataset_info: - config_name: default features: - name: id dtype: string - name: question dtype: string - name: question_chinese dtype: string - name: chain dtype: string - name: result dtype: string - name: result_float dtype: float64 - name: equation dtype: string splits: - name: train num_bytes: 111988047 num_examples: 195179 - name: validation num_bytes: 1172933 num_examples: 1783 - name: test num_bytes: 1157061 num_examples: 1785 download_size: 50827709 dataset_size: 114318041 - config_name: original-splits features: - name: id dtype: string - name: question dtype: string - name: question_chinese dtype: string - name: chain dtype: string - name: result dtype: string - name: result_float dtype: float64 - name: equation dtype: string splits: - name: train num_bytes: 111988047 num_examples: 195179 - name: validation num_bytes: 2798479 num_examples: 4867 - name: test num_bytes: 2793355 num_examples: 4867 download_size: 52234086 dataset_size: 117579881 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* - config_name: original-splits data_files: - split: train path: original-splits/train-* - split: validation path: original-splits/validation-* - split: test path: original-splits/test-* --- # Dataset Card for Calc-ape210k ## Summary This dataset is an instance of Ape210K dataset, converted to a simple HTML-like language that can be easily parsed (e.g. by BeautifulSoup). The data contains 3 types of tags: - gadget: A tag whose content is intended to be evaluated by calling an external tool (sympy-based calculator in this case) - output: An output of the external tool - result: The final answer to the mathematical problem (a number) ## Supported Tasks The dataset is intended for training Chain-of-Thought reasoning **models able to use external tools** to enhance the factuality of their responses. This dataset presents in-context scenarios where models can outsource the computations in the reasoning chain to a calculator. ## Construction Process First, we translated the questions into English using Google Translate. Next, we parsed the equations and the results. We linearized the equations into a sequence of elementary steps and evaluated them using a sympy-based calculator. We numerically compare the output with the result in the data and remove all examples where they do not match (less than 3% loss in each split). Finally, we save the chain of steps in the HTML-like language in the `chain` column. We keep the original columns in the dataset for convenience. We also perform in-dataset and cross-dataset data-leak detection within [Calc-X collection](https://huggingface.co/collections/MU-NLPC/calc-x-652fee9a6b838fd820055483). Specifically for Ape210k, we removed parts of the validation and test split, with around 1700 remaining in each. You can read more information about this process in our [Calc-X paper](https://arxiv.org/abs/2305.15017). ## Data splits The default config contains filtered splits with data leaks removed. You can load it using: ```python datasets.load_dataset("MU-NLPC/calc-ape210k") ``` In the `original-splits` config, the data splits are unfiltered and correspond to the original Ape210K dataset. See [ape210k dataset github](https://github.com/Chenny0808/ape210k) and [the paper](https://arxiv.org/abs/2009.11506) for more info. You can load it using: ```python datasets.load_dataset("MU-NLPC/calc-ape210k", "original-splits") ``` ## Attributes - **id** - id of the example - **question** - the description of the math problem. Automatically translated from the `question_chinese` column into English using Google Translate - **question_chinese** - the original description of the math problem in Chinese - **chain** - linearized `equation`, sequence of arithmetic steps in HTML-like language that can be evaluated using our sympy-based calculator - **result** - result as a string (can be an integer, float, or a fraction) - **result_float** - result, converted to a float - **equation** - a nested expression that evaluates to the correct answer Attributes **id**, **question**, **chain**, and **result** are present in all datasets in [Calc-X collection](https://huggingface.co/collections/MU-NLPC/calc-x-652fee9a6b838fd820055483). ## Related work This dataset was created as a part of a larger effort in training models capable of using a calculator during inference, which we call Calcformers. - [**Calc-X collection**](https://huggingface.co/collections/MU-NLPC/calc-x-652fee9a6b838fd820055483) - datasets for training Calcformers - [**Calcformers collection**](https://huggingface.co/collections/MU-NLPC/calcformers-65367392badc497807b3caf5) - calculator-using models we trained and published on HF - [**Calc-X and Calcformers paper**](https://arxiv.org/abs/2305.15017) - [**Calc-X and Calcformers repo**](https://github.com/prompteus/calc-x) Here are links to the original dataset: - [**original Ape210k dataset and repo**](https://github.com/Chenny0808/ape210k) - [**original Ape210k paper**](https://arxiv.org/abs/2009.11506) ## Licence MIT, consistently with the original dataset. ## Cite If you use this version of the dataset in research, please cite the [original Ape210k paper](https://arxiv.org/abs/2009.11506), and the [Calc-X paper](https://arxiv.org/abs/2305.15017) as follows: ```bibtex @inproceedings{kadlcik-etal-2023-soft, title = "Calc-X and Calcformers: Empowering Arithmetical Chain-of-Thought through Interaction with Symbolic Systems", author = "Marek Kadlčík and Michal Štefánik and Ondřej Sotolář and Vlastimil Martinek", booktitle = "Proceedings of the The 2023 Conference on Empirical Methods in Natural Language Processing: Main track", month = dec, year = "2023", address = "Singapore, Singapore", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/2305.15017", } ```

[ -0.511009931564331, -0.48047757148742676, 0.19785691797733307, 0.22766044735908508, 0.04139144718647003, -0.19017547369003296, -0.18192116916179657, -0.37438857555389404, 0.20941385626792908, 0.3604888617992401, -0.7455439567565918, -0.18350094556808472, -0.29360589385032654, 0.25759023427...

null

EasyTerms/Manuel_dataset

EasyTerms

2023-11-02T14:03:24Z

42

2

null

[ "task_categories:summarization", "size_categories:n<1K", "language:en", "license:apache-2.0", "legal", "region:us" ]

2023-11-02T14:03:24Z

2023-06-07T08:50:06.000Z

2023-06-07T08:50:06

--- license: apache-2.0 task_categories: - summarization language: - en tags: - legal pretty_name: lilo size_categories: - n<1K ---

[ -0.1285335123538971, -0.1861683875322342, 0.6529128551483154, 0.49436232447624207, -0.19319400191307068, 0.23607441782951355, 0.36072009801864624, 0.05056373029947281, 0.5793656706809998, 0.7400146722793579, -0.650810182094574, -0.23784008622169495, -0.7102247476577759, -0.0478255338966846...

null

teleprint-me/phi-1

teleprint-me

2023-07-08T04:01:52Z

42

32

null

[ "license:cc-by-nc-sa-3.0", "arxiv:2306.11644", "region:us" ]

2023-07-08T04:01:52Z

2023-07-04T03:49:40.000Z

2023-07-04T03:49:40

--- title: 'Phi-1 Model Dataset' date: '2023-07-03' license: cc-by-nc-sa-3.0 --- ## Dataset Description - **Homepage:** [teleprint.me](https://teleprint.me) - **Repository:** [phi-1](https://huggingface.co/datasets/teleprint-me/phi-1) - **Paper:** [2306.11644v1](https://arxiv.org/abs/2306.11644v1) - **Leaderboard:** [Link to the leaderboard] - **Point of Contact:** [aberrio@teleprint.me](aberrio@teleprint.me) ### Dataset Summary This dataset is created for training the phi-1 model, based on the paper "Textbooks are All You Need". It contains high-quality data derived from various textbooks, transformed and synthesized using OpenAI's GPT-3.5 and GPT-4 models. For optimal results, it is recommended to train models with the following parameters and sequence lengths: - For a model with 350M parameters, use a sequence length of 2048. - For a model with 700M parameters, use a sequence length of 4096. - For a model with 1.3B parameters, use a sequence length of 8096. Please note that the dataset is currently in its initial phase of planning and collection. The process involves preparing the data, extracting it, formatting it, chunking it, and preparing it for synthesis. Scripts for preparing and processing the data for the model will be developed. Once the data is generated, it will undergo a review and revision process to ensure its quality and relevance. These recommendations and notes are based on the dataset creator's initial plans and may be subject to change as the project progresses. **NOTE**: Due to the nature of this dataset, it cannot be released without obtaining permissions from the respective publishers and/or authors. If you are an author or publisher and have any concerns about this repository, please feel free to email me. If you are an author or publisher and would like to grant permission for the use of your work, your support would be greatly appreciated. Please note that in order for the dataset to be released, permissions would need to be unanimous from all involved parties. In the absence of such permissions, I will respect the copyrights of the copyrighted materials and exercise my right to Fair Use with my own physical property for personal use. **This dataset is NOT intended for commercial purposes**. Its primary purpose is for research in machine learning and AI software development. If a model is created using this dataset, it will be shared under the same license. Any proceeds derived from donations will be primarily used for the development of the dataset and the model. ### Supported Tasks and Leaderboards - `text-generation`: The dataset can be used to train a model for chat-like text generation, more specifically, for generating explanations and examples in the context of arithmetic, algebra, geometry, trigonometry, calculus, algorithms and data structures, design patterns, and the python programming language. ### Languages The text in the dataset is in English. ## Dataset Structure ### Data Instances A data instance consists of a dialogue between a user and an assistant, discussing a topic in arithmetic, algebra, geometry, trigonometry, calculus, algorithms and data structures, design patterns, or the Python programming language. The dialogue is structured as a list of turns, each turn containing the role ("user" or "assistant") and the content of the turn. ### Data Fields - `role`: a string indicating the role of the speaker in the dialogue ("system", "user", "assistant", "function"). - `content`: a string containing the content of the speaker's turn in the dialogue. ### Data Splits The dataset is split into a training set, a validation set, and a test set. The exact sizes and proportions of these splits will depend on the final size of the dataset. ## Dataset Creation ### Curation Rationale The dataset is being created to train a model capable of generating explanations and examples in the context of various mathematical and computer science topics. The goal is to create an AI assistant that can provide clear, accurate, and pedagogically sound responses to user queries on these topics. ### Source Data #### Initial Data Collection and Normalization The data is collected from a variety of textbooks covering arithmetic, algebra, geometry, trigonometry, calculus, algorithms and data structures, design patterns, and the Python programming language. The textbooks used include: - Barron's Arithmetic The Easy Way Fourth Edition - Blitzer Introductory Algebra for College Students Fifth Edition - McDougal Littell Geometry - Blitzer Intermediate Algebra for College Students 5th Edition - Trigonometry Sixth Edition - Pearson College Algebra Fourth Edition - Hughes-Hallet Applied Calculus 5th Edition - CLRS Introduction to Algorithms Third Edition In addition to the textbooks, the dataset also includes material from the following online resources: - [C reference](https://en.cppreference.com/w/c) - [Cpp reference](https://en.cppreference.com/w/cpp) - [Python Standard Library](https://docs.python.org/3/) These resources provide up-to-date information and examples for the C, C++, and Python programming languages. The creators of the Cppreference site also provide [archives](https://en.cppreference.com/w/Cppreference:Archives) of their site for offline use. Code samples synthesized by OpenAI's GPT models, curated by the dataset creator, are also included in the dataset. **Note:** The creator of this dataset owns physical copies of all the textbooks listed above. The data from these sources are transformed into a dialogue format using OpenAI's GPT-3.5 and GPT-4 models. The resulting dialogues are then used as the training data for the phi-1 model. This dataset does not include the full content of the source textbooks. Instead, it consists of transformations and syntheses of the original content. Anyone who wants access to the full original content should purchase or otherwise legally access the textbooks themselves. #### Who are the source language producers? The original language data was created by a variety of authors and educators, who wrote the textbooks and other materials used as sources for this dataset. These include: - Barron's Arithmetic The Easy Way Fourth Edition - Edward Williams, Katie Prindle - Blitzer Introductory Algebra for College Students Fifth Edition - Robert Blitzer - McDougal Littell Geometry - Ron Larson, Laurie Boswell, Timothy D. Kanold, Lee Stiff - Blitzer Intermediate Algebra for College Students 5th Edition - Robert Blitzer - Trigonometry Sixth Edition - Charles P. McKeague, Mark D. Turner - Pearson College Algebra Fourth Edition - Robert F. Blitzer - Hughes-Hallet Applied Calculus 5th Edition - Deborah Hughes-Hallett, Andrew M. Gleason, Patti Frazer Lock, Daniel E. Flath, Sheldon P. Gordon, David O. Lomen, David Lovelock, William G. McCallum, Brad G. Osgood, Andrew Pasquale, Jeff Tecosky-Feldman, Joseph Thrash, Karen R. Rhea, Thomas W. Tucker - CLRS Introduction to Algorithms Third Edition - Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest, Clifford Stein In addition to these authors, the developers of OpenAI's GPT-3.5 and GPT-4 models also contributed to the creation of the language data, as these models were used to transform the source material into a dialogue format. ### Annotations #### Annotation process The dataset does not contain any explicit annotations. However, the data is curated and synthesized using OpenAI's GPT-3.5 and GPT-4 models. The process involves transforming the source material into a dialogue format suitable for training the phi-1 model. The dataset creator, an independent learner with a strong interest in computer science, reviewed and curated the synthesized dialogues to ensure their quality and relevance. #### Who are the annotators? The dataset creator, an independent learner who has studied computer science extensively in a self-directed manner, performed the curation and review of the synthesized dialogues. ### Personal and Sensitive Information The dataset does not contain any personal or sensitive information. All the data is derived from publicly available textbooks and online resources. Any names or other potential identifiers in the source material have been removed or anonymized. ### Social Impact of Dataset The dataset is intended to support the development of AI models capable of providing detailed explanations and examples in the context of arithmetic, algebra, geometry, trigonometry, calculus, algorithms and data structures, design patterns, and the python programming language. The potential social impact is significant, as such models could greatly enhance self-directed learning and provide valuable educational support to students worldwide. However, it's important to note that the quality and usefulness of the AI models trained on this dataset will depend on the quality of the data itself. If the data is inaccurate or biased, the models could propagate these inaccuracies and biases, potentially leading to misinformation or unfair outcomes. ### Discussion of Biases The dataset is based on a variety of textbooks and online resources, which may contain their own inherent biases. For example, textbooks often reflect the perspectives and biases of their authors, which can influence the way information is presented. These biases could potentially be reflected in the dataset and in any models trained on it. ### Other Known Limitations At this stage of the dataset creation process, it's difficult to identify all potential limitations. However, one potential limitation is that the dataset may not cover all possible topics or perspectives within the fields it addresses. The dataset creator will continue to monitor and assess the dataset for limitations as the work progresses. ## Additional Information ### Dataset Curators The dataset was curated by an independent learner with a strong interest in computer science. The curator has studied the subject matter in a self-directed manner, using a variety of resources including textbooks and online materials. The curation process also involved the use of OpenAI's GPT-3.5 and GPT-4 models to synthesize dialogues based on the source material. ### Licensing Information This dataset is released under the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 International (CC BY-NC-SA 3.0) license. ### Citation Information As this dataset is a compilation of various sources synthesized and curated for the purpose of training the phi-1 model, please ensure to cite the original sources when using this dataset. If referencing the dataset directly, please refer to this repository.

[ -0.34044349193573, -0.8559185266494751, 0.08879148960113525, 0.16663244366645813, -0.1542578488588333, -0.3713133633136749, -0.11402738839387894, -0.26436591148376465, -0.06326959282159805, 0.42348477244377136, -0.6403316855430603, -0.42881911993026733, -0.3644697070121765, 0.0887374207377...

null

germank/hh-generated_flan_t5_large_with_features2

germank

2023-07-07T14:32:37Z

42

0

null

[ "region:us" ]

2023-07-07T14:32:37Z

2023-07-07T14:32:34.000Z

2023-07-07T14:32:34

--- dataset_info: features: - name: prompt dtype: string - name: response dtype: string - name: 'biased:' dtype: int64 - name: easy-to-understand dtype: int64 - name: enough-detail dtype: int64 - name: factuality dtype: int64 - name: fail-to-consider-context dtype: int64 - name: fail-to-consider-individual-preferences dtype: int64 - name: helpfulness dtype: int64 - name: intent dtype: int64 - name: readability dtype: int64 - name: relevance dtype: int64 - name: repetetive dtype: int64 - name: specificity dtype: int64 - name: too-long dtype: int64 splits: - name: train num_bytes: 395548 num_examples: 1600 download_size: 76218 dataset_size: 395548 --- # Dataset Card for "hh-generated_flan_t5_large_with_features2" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

[ -0.6099486351013184, -0.2668794095516205, 0.23376578092575073, 0.2297472506761551, -0.24362187087535858, -0.08947805315256119, 0.03599203750491142, -0.3987950384616852, 0.9470623135566711, 0.6430664658546448, -0.6460676193237305, -0.6454576253890991, -0.5401854515075684, -0.191577464342117...

null

ssbuild/alpaca_finance_en

ssbuild

2023-07-09T03:32:00Z

42

3

null

[ "license:apache-2.0", "region:us" ]

2023-07-09T03:32:00Z

2023-07-09T03:29:57.000Z

2023-07-09T03:29:57

--- license: apache-2.0 ---

[ -0.1285335123538971, -0.1861683875322342, 0.6529128551483154, 0.49436232447624207, -0.19319400191307068, 0.23607441782951355, 0.36072009801864624, 0.05056373029947281, 0.5793656706809998, 0.7400146722793579, -0.650810182094574, -0.23784008622169495, -0.7102247476577759, -0.0478255338966846...

null

language-and-voice-lab/samromur_milljon

language-and-voice-lab

2023-10-15T15:14:45Z

42

2

null

[ "task_categories:automatic-speech-recognition", "annotations_creators:crowdsourced", "language_creators:crowdsourced", "multilinguality:monolingual", "size_categories:1M<n<10M", "source_datasets:original", "language:is", "license:cc-by-4.0", "crowd-sourced icelandic", "samrómur", "icelandic spee...

2023-10-15T15:14:45Z

2023-07-09T04:29:15.000Z

2023-07-09T04:29:15

--- annotations_creators: - crowdsourced language: - is language_creators: - crowdsourced license: - cc-by-4.0 multilinguality: - monolingual pretty_name: "Samrómur Milljón, Audio and Transcriptions" size_categories: - 1M<n<10M source_datasets: - original tags: - crowd-sourced icelandic - samrómur - icelandic speech - samromur - iceland task_categories: - automatic-speech-recognition task_ids: [] --- # Dataset Card for samromur_milljon ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-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:** [Samrómur Milljón](https://huggingface.co/datasets/language-and-voice-lab/samromur_milljon) - **Repository:** [Language and Voice Laboratory](https://huggingface.co/language-and-voice-lab) - **Point of Contact:** [Carlos Mena](mailto:carlos.mena@ciempiess.org), [Jón Guðnason](mailto:jg@ru.is) ### Dataset Summary Samrómur Milljón is the result of the automatic verification of the corpus [Samromur Unverified 22.07](http://hdl.handle.net/20.500.12537/265) which contains 2,159,314 (2233 hours) speech-recordings in Icelandic that are essentially unverified. Samrómur Milljón consists of 1,002,157 of speech recordings (967 hours). The transcripts accompanying these recordings were automatically verified using various ASR systems such as: [Wav2Vec2](https://huggingface.co/carlosdanielhernandezmena/wav2vec2-large-xlsr-53-icelandic-ep10-1000h), [Whisper](https://huggingface.co/language-and-voice-lab/whisper-large-icelandic-30k-steps-1000h), [Faster-Whisper](https://huggingface.co/language-and-voice-lab/whisper-large-icelandic-30k-steps-1000h-ct2) and [NeMo](https://huggingface.co/carlosdanielhernandezmena/stt_is_quartznet15x5_ft_ep56_875h). If any of the ASR systems get a transcript that perfectly matches the reference transcription, the speech-recording is then cosidered as verified. ### Example Usage Samrómur Milljón is divided in 7 portions. To load a specific split pass its name as a config name: ```python from datasets import load_dataset samromur_milljon = load_dataset("language-and-voice-lab/samromur_milljon") ``` To load a specific split (for example, the split with female speakers between 18 and 49 years old) do: ```python from datasets import load_dataset samromur_milljon = load_dataset("language-and-voice-lab/samromur_milljon",split="female_18to49_yrs") ``` ### Supported Tasks automatic-speech-recognition: The dataset can be used to train a model for Automatic Speech Recognition (ASR). The model is presented with an audio file and asked to transcribe the audio file to written text. The most common evaluation metric is the word error rate (WER). ### Languages The audio is in Icelandic. The reading prompts were gathered from a variety of sources, mainly from the [Icelandic Gigaword Corpus](http://clarin.is/en/resources/gigaword). The corpus includes text from novels, news, plays, and from a list of location names in Iceland. The prompts also came from the [Icelandic Web of Science](https://www.visindavefur.is/). ## Dataset Structure ### Data Instances ```python { 'audio_id': '000664-0013663', 'audio': { 'path': '/home/carlos/.cache/HuggingFace/datasets/downloads/extracted/5238762551eb35654e659f0bf3f3bf30ba02b40036715337500cba84051fed4e/FEM_18to49_YRS_PART_0001/000664/000664-0013663.flac', 'array': array([0.0000000e+00, 0.0000000e+00, 0.0000000e+00, ..., 0.0000000e+00, 3.0517578e-05, 3.0517578e-05], dtype=float32), 'sampling_rate': 16000 }, 'speaker_id': '000664', 'gender': 'female', 'age': '30-39', 'duration': 3.7799999713897705, 'verified_with': 'V+F', 'normalized_text': 'mattías lækkaðu í hátalaranum' } ``` ### Data Fields * `audio_id` (string) - id of audio segment * `audio` (datasets.Audio) - a dictionary containing the path to the audio, the decoded audio array, and the sampling rate. In non-streaming mode (default), the path points to the locally extracted audio. In streaming mode, the path is the relative path of an audio inside its archive (as files are not downloaded and extracted locally). * `speaker_id` (string) - id of speaker * `gender` (string) - gender of speaker (male or female) * `age` (string) - range of age of the speaker. * `duration` (float32) - duration of the audio file in seconds. * `verified_with` (string) - Set of ASR systems that found a perfect match between the current speech file and the reference transcription : V = wav2vec, N = NeMo, W = Whisper, F = Whisper-Faster * `normalized_text` (string) - normalized audio segment transcription. ### Data Splits Due to its large size, the corpus is divided in 7 splits with the objective that it will be more manageable this way. * Female speakers less than 18 years old (n < 18): **female_lt_18_yrs** * Female speakers from 18 to 49 years old (18 <= n <=49): **female_18to49_yrs** * Female speakers greater than 49 years old (n > 49): **female_gt_49_yrs** * Male speakers less than 18 years old (n < 18): **male_lt_18_yrs** * Male speakers from 18 to 49 years old (18 <= n <=49): **male_18to49_yrs** * Male speakers greater than 49 years old (n > 49): **male_gt_49_yrs** * Speakers where age, gender or both are unknown: **other** To load an specific portion please see the above section "Example Usage". ## Dataset Creation ### Curation Rationale * The collection process started in October 2019. * The aim is to create an open-source speech corpus to enable research and development for Icelandic Language Technology. * The corpus comprises of audio recordings and metadata files containing the text sentences read by the participants. * At the beginning, some data was manually verified by summer students, but due to the success of the collection process, we ended up with more than 2 million of unverified speech-recordings. In 2023, it was decided to use the distict ASR systems that we already had in Icelandic to automatically verify as much data as possible. The verification process consited in transcribing the recordings using various ASR systems. If one or more of them get a transcription that perfectly matches the reference transcription, the recording is considered as verified. * The resulting corpus of more than 1 million recordings is too big to download it all at once. In consequence, it was decided to split it in 7 portions with the hope that it will be more manageable this way. ### Source Data #### Initial Data Collection and Normalization * The utterances were recorded by a smartphone or the web app. * The data was collected using the website https://samromur.is, code of which is available at https://github.com/cadia-lvl/samromur. * Each recording contains one read sentence from a script. ### Annotations #### Annotation process Prompts were pulled from these corpora if they met the criteria of having only letters which are present in the Icelandic alphabet, and if they are listed in the [DIM: Database Icelandic Morphology](https://aclanthology.org/W19-6116.pdf). There are also synthesised prompts consisting of a name followed by a question or a demand, in order to simulate a dialogue with a smart-device. #### Who are the annotators? The audio files content was automatically verified using ASR systems such as: [Wav2Vec2](https://huggingface.co/carlosdanielhernandezmena/wav2vec2-large-xlsr-53-icelandic-ep10-1000h), [Whisper](https://huggingface.co/language-and-voice-lab/whisper-large-icelandic-30k-steps-1000h), [Faster-Whisper](https://huggingface.co/language-and-voice-lab/whisper-large-icelandic-30k-steps-1000h-ct2) and [NeMo](https://huggingface.co/carlosdanielhernandezmena/stt_is_quartznet15x5_ft_ep56_875h). If any of the ASR systems get a transcript that perfectly matches the reference transcription, the speech-recording is then cosidered as verified. ### Personal and Sensitive Information The dataset consists of people who have donated their voice. You agree to not attempt to determine the identity of speakers in this dataset. ## Considerations for Using the Data ### Social Impact of Dataset This contribution describes a project of speech data collection, using the web application [Samrómur](samromur.is) which is built upon [Common Voice](https://commonvoice.mozilla.org/), Mozilla Foundation's web platform for open-source voice collection. The goal of the project is to build a large-scale speech corpus for Automatic Speech Recognition (ASR) for Icelandic. Samrómur is the largest open speech corpus for Icelandic collected from the public domain. ### Discussion of Biases * The participants are aged between 4 to 90 years old. The speech files were recorded by a smartphone or the web app. * Participants self-reported their age group, gender, and the native language. * The corpus contains 1,002,157 from 16,729 speakers, totalling 967 hours and 7 minutes. * The number of recordings coming from female speakers are 714,564, totalling 697h22m. * The number of recordings coming from male speakers are 282,499, totalling 264h28m. * The number of recordings where the gender, the age or both is unknown are 5,094, totalling 5h16m. These recordings belong to the split called "other". ### Other Known Limitations "Samrómur Milljón" by the Language and Voice Laboratory (LVL) from Reykjavik University (RU) is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) License with the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. ## Additional Information ### Dataset Curators The corpus was curated by [Carlos Daniel Hernández Mena](https://huggingface.co/carlosdanielhernandezmena) during 2023 at the [Language and Voice Laboratory](https://lvl.ru.is/) from [Reykjavik University](https://en.ru.is/). ### Licensing Information [CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/) ### Citation Information ``` @misc{menasamromurmilljon2023, title={Samrómur Milljón, Audio and Transcriptions}, author={Hernández Mena, Carlos Daniel and Guðnason, Jón}, publisher={Reykjavík University}, year={2023}, url={https://huggingface.co/datasets/language-and-voice-lab/samromur_milljon} } ``` ### Contributions This project was funded by the Language Technology Programme for Icelandic 2019-2023. The programme, which is managed and coordinated by Almannarómur, is funded by the Icelandic Ministry of Education, Science and Culture.

[ -0.44506344199180603, -0.7101281881332397, -0.015282555483281612, 0.2058647722005844, -0.2865961194038391, -0.023824017494916916, -0.2620087265968323, -0.39566609263420105, 0.2943781316280365, 0.42428910732269287, -0.6300172805786133, -0.6150010228157043, -0.5684615969657898, 0.27953588962...

null

DynamicSuperb/SpoofDetection_ASVspoof2017

DynamicSuperb

2023-07-31T10:54:40Z

42

0

null

[ "region:us" ]

2023-07-31T10:54:40Z

2023-07-13T03:40:36.000Z

2023-07-13T03:40:36

--- dataset_info: features: - name: file dtype: string - name: audio dtype: audio - name: instruction dtype: string - name: label dtype: string splits: - name: test num_bytes: 1411064438.928 num_examples: 13306 download_size: 1361993549 dataset_size: 1411064438.928 --- # Dataset Card for "SpoofDetection_ASVspoof2017" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

[ -0.36759451031684875, -0.3352685570716858, 0.04605626314878464, 0.45132046937942505, -0.20406070351600647, 0.002451710170134902, 0.425631582736969, -0.302956759929657, 0.9166664481163025, 0.5812397003173828, -0.9144275784492493, -0.5550990104675293, -0.6897755265235901, -0.2436248660087585...

null

nahyeon00/mixsnips_clean

nahyeon00

2023-07-19T08:38:38Z

42

0

null

[ "region:us" ]

2023-07-19T08:38:38Z

2023-07-19T08:38:23.000Z

2023-07-19T08:38:23

--- dataset_info: features: - name: token sequence: string - name: tag sequence: string - name: intent sequence: string splits: - name: train num_bytes: 16319528 num_examples: 39776 - name: validation num_bytes: 915087 num_examples: 2198 - name: test num_bytes: 902367 num_examples: 2199 download_size: 3076227 dataset_size: 18136982 --- # Dataset Card for "mixsnips_clean" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

[ -0.613571286201477, -0.10863792151212692, -0.056348010897636414, 0.2060285061597824, -0.2805746793746948, 0.05870713293552399, 0.20118677616119385, -0.2758224308490753, 1.1619783639907837, 0.6712122559547424, -0.8539255261421204, -0.6207185983657837, -0.5563148260116577, -0.297561705112457...

null

heliosbrahma/mental_health_conversational_dataset

heliosbrahma

2023-07-22T11:30:56Z

42

6

null

[ "task_categories:text-generation", "task_categories:conversational", "size_categories:n<1K", "language:en", "license:mit", "medical", "region:us" ]

2023-07-22T11:30:56Z

2023-07-21T06:58:12.000Z

2023-07-21T06:58:12

--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 102904 num_examples: 154 download_size: 60865 dataset_size: 102904 license: mit task_categories: - text-generation - conversational language: - en tags: - medical pretty_name: Mental Health Conversational Dataset size_categories: - n<1K --- # Dataset Card for "mental_health_conversational_dataset" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-instances) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Personal and Sensitive Information](#personal-and-sensitive-information) ## Dataset Description ### Dataset Summary This dataset contains conversational pair of questions and answers in a single text related to Mental Health. Dataset was curated from healthcare websites, popular blogs like WebMD and HeatlhLine, online FAQs etc. All questions and answers have been anonymized to remove any PII data and pre-processed to remove any unwanted characters. ### Languages The text in the dataset is in English. ## Dataset Structure ### Data Instances A data instance include a text columns which is a conversational pair of questions and answers. Questions were asked by the patients and answers were given by healthcare providers. ### Data Fields - 'text': conversational pair of questions and answers between patient and healthcare provider. ## Dataset Creation ### Curation Rationale Chatbots offer a readily available and accessible platform for individuals seeking support. They can be accessed anytime and anywhere, providing immediate assistance to those in need. Chatbots can offer empathetic and non-judgmental responses, providing emotional support to users. While they cannot replace human interaction entirely, they can be a helpful supplement, especially in moments of distress. Hence, this dataset was curated to help finetune a conversational AI bot using this custom dataset which can then be deployed and be provided to the end patient as a chatbot. ### Source Data This dataset was curated from healthcare websites, popular blogs like WebMD and HeatlhLine, online FAQs etc. ### Personal and Sensitive Information The dataset may contain sensitive information related to mental health. All questions and answers have been anonymized to remove any PII data.

[ -0.397456556558609, -1.0399049520492554, 0.33559897541999817, 0.23919573426246643, -0.098739854991436, 0.10757171362638474, -0.17098332941532135, -0.20774517953395844, 0.5017397403717041, 0.5549408197402954, -1.078742504119873, -0.7450926899909973, -0.4924566447734833, 0.001868122024461627...

null

AdiOO7/Llama-2

AdiOO7

2023-07-24T20:40:11Z

42

0

null

[ "license:apache-2.0", "region:us" ]

2023-07-24T20:40:11Z

2023-07-22T10:53:21.000Z

2023-07-22T10:53:21

--- license: apache-2.0 ---

[ -0.1285335123538971, -0.1861683875322342, 0.6529128551483154, 0.49436232447624207, -0.19319400191307068, 0.23607441782951355, 0.36072009801864624, 0.05056373029947281, 0.5793656706809998, 0.7400146722793579, -0.650810182094574, -0.23784008622169495, -0.7102247476577759, -0.0478255338966846...

null

wesley7137/psychology1cllm

wesley7137

2023-07-31T23:13:11Z

42

3

null

[ "region:us" ]

2023-07-31T23:13:11Z

2023-07-31T23:12:55.000Z

2023-07-31T23:12:55

Entry not found

[ -0.3227645754814148, -0.22568479180335999, 0.8622264862060547, 0.43461528420448303, -0.52829909324646, 0.7012971639633179, 0.7915720343589783, 0.07618614286184311, 0.774603009223938, 0.2563217282295227, -0.7852813005447388, -0.22573819756507874, -0.9104477167129517, 0.5715674161911011, -...

null

seanlb02/medicare_faq

seanlb02

2023-07-31T23:38:43Z

42

0

null

[ "license:mit", "region:us" ]

2023-07-31T23:38:43Z

2023-07-31T23:22:03.000Z

2023-07-31T23:22:03

--- license: mit ---

[ -0.1285335123538971, -0.1861683875322342, 0.6529128551483154, 0.49436232447624207, -0.19319400191307068, 0.23607441782951355, 0.36072009801864624, 0.05056373029947281, 0.5793656706809998, 0.7400146722793579, -0.650810182094574, -0.23784008622169495, -0.7102247476577759, -0.0478255338966846...

null

hsultanbey/javascript

hsultanbey

2023-08-03T09:42:53Z

42

0

null

[ "region:us" ]

2023-08-03T09:42:53Z

2023-08-03T09:42:14.000Z

2023-08-03T09:42:14

--- dataset_info: features: - name: code dtype: string splits: - name: train num_bytes: 863518025 num_examples: 99999 download_size: 308377342 dataset_size: 863518025 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "javascript" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

[ -0.419099897146225, -0.23142649233341217, 0.11820007860660553, 0.2183465212583542, 0.04537452384829521, 0.21743007004261017, 0.07949639111757278, -0.18311390280723572, 0.7626698017120361, 0.43227702379226685, -0.7853715419769287, -1.0367473363876343, -0.5901117920875549, -0.399862200021743...

null

PL-MTEB/polemo2_out

PL-MTEB

2023-08-11T12:42:58Z

42

0

null

[ "license:cc-by-nc-sa-4.0", "region:us" ]

2023-08-11T12:42:58Z

2023-08-11T12:42:01.000Z

2023-08-11T12:42:01

--- license: cc-by-nc-sa-4.0 ---

[ -0.12853392958641052, -0.18616779148578644, 0.6529127955436707, 0.49436280131340027, -0.19319361448287964, 0.23607419431209564, 0.36072003841400146, 0.050563063472509384, 0.579365611076355, 0.7400140762329102, -0.6508104205131531, -0.23783954977989197, -0.7102249264717102, -0.0478260256350...

null

PL-MTEB/allegro-reviews

PL-MTEB

2023-08-11T13:11:54Z

42

0

null

[ "license:cc-by-sa-4.0", "region:us" ]

2023-08-11T13:11:54Z

2023-08-11T13:11:37.000Z

2023-08-11T13:11:37

--- license: cc-by-sa-4.0 ---

[ -0.12853392958641052, -0.18616779148578644, 0.6529127955436707, 0.49436280131340027, -0.19319361448287964, 0.23607419431209564, 0.36072003841400146, 0.050563063472509384, 0.579365611076355, 0.7400140762329102, -0.6508104205131531, -0.23783954977989197, -0.7102249264717102, -0.0478260256350...

null

Matias12f/cats_dogs_trabajo

Matias12f

2023-08-22T02:24:38Z

42

0

null

[ "license:apache-2.0", "region:us" ]

2023-08-22T02:24:38Z

2023-08-22T02:23:34.000Z

2023-08-22T02:23:34

--- license: apache-2.0 ---

[ -0.12853392958641052, -0.18616779148578644, 0.6529127955436707, 0.49436280131340027, -0.19319361448287964, 0.23607419431209564, 0.36072003841400146, 0.050563063472509384, 0.579365611076355, 0.7400140762329102, -0.6508104205131531, -0.23783954977989197, -0.7102249264717102, -0.0478260256350...

null

Fsoft-AIC/the-vault-class

Fsoft-AIC

2023-10-11T16:42:43Z

42

1

null

[ "task_categories:text-generation", "multilinguality:multiprogramming languages", "language:code", "language:en", "license:mit", "arxiv:2305.06156", "region:us" ]

2023-10-11T16:42:43Z

2023-08-22T07:11:11.000Z

2023-08-22T07:11:11

--- language: - code - en multilinguality: - multiprogramming languages task_categories: - text-generation license: mit dataset_info: features: - name: identifier dtype: string - name: repo dtype: string - name: path dtype: string - name: language dtype: string - name: code dtype: string - name: code_tokens dtype: string - name: original_docstring dtype: string - name: comment dtype: string - name: docstring_tokens dtype: string - name: docstring dtype: string - name: original_string dtype: string pretty_name: The Vault Function viewer: true --- ## 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) - [Dataset Statistics](#dataset-statistics) - [Usage](#usage) - [Additional Information](#additional-information) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Repository:** [FSoft-AI4Code/TheVault](https://github.com/FSoft-AI4Code/TheVault) - **Paper:** [The Vault: A Comprehensive Multilingual Dataset for Advancing Code Understanding and Generation](https://arxiv.org/abs/2305.06156) - **Contact:** support.ailab@fpt.com - **Website:** https://www.fpt-aicenter.com/ai-residency/ <img src="https://raw.githubusercontent.com/FSoft-AI4Code/TheVault/main/assets/the-vault-4-logo-png.png" width="300px" alt="logo"> <div align="center"> # The Vault: A Comprehensive Multilingual Dataset for Advancing Code Understanding and Generation </div> ## Dataset Summary The Vault dataset is a comprehensive, large-scale, multilingual parallel dataset that features high-quality code-text pairs derived from The Stack, the largest permissively-licensed source code dataset. We provide The Vault which contains code snippets from 10 popular programming languages such as Java, JavaScript, Python, Ruby, Rust, Golang, C#, C++, C, and PHP. This dataset provides multiple code-snippet levels, metadata, and 11 docstring styles for enhanced usability and versatility. ## Supported Tasks The Vault can be used for pretraining LLMs or downstream code-text interaction tasks. A number of tasks related to code understanding and geneartion can be constructed using The Vault such as *code summarization*, *text-to-code generation* and *code search*. ## Languages The natural language text (docstring) is in English. 10 programming languages are supported in The Vault: `Python`, `Java`, `JavaScript`, `PHP`, `C`, `C#`, `C++`, `Go`, `Ruby`, `Rust` *Note: C and Go are not contained in this repo due to the nonexistence of traditional classes in these languages.* ## Dataset Structure ### Data Instances ``` { "hexsha": "78b961a6673ec1e12f8d95c33ef081f75561a87c", "repo": "AIS-Bonn/sl-cutscenes", "path": "sl_cutscenes/object_models.py", "license": [ "MIT" ], "language": "Python", "identifier": "MeshLoader", "original_docstring": "\n Class to load the meshes for the objects in a scene.\n ", "docstring": "Class to load the meshes for the objects in a scene.", "docstring_tokens": [ "Class", "to", "load", "the", "meshes", "for", "the", "objects", "in", "a", "scene", "." ], "code": "class MeshLoader:\n \"\"\"\n Class to load the meshes for the objects in a scene.\n \"\"\"\n\n def __init__(self):\n \"\"\"Module initializer\"\"\"\n self.base_dir = CONSTANTS.MESH_BASE_DIR\n self.text_dir = CONSTANTS.TEXT_BASE_DIR\n self.reset()\n\n def reset(self):\n self.loaded_meshes = []\n\n def get_meshes(self):\n \"\"\" \"\"\"\n extract_singular = lambda x: x[0] if len(x) == 1 else x\n return [extract_singular(item) for item in self.loaded_meshes]\n\n def load_meshes(self, obj_info: List[object_info.ObjectInfo], **kwargs):\n \"\"\"\n Loads the meshes whose information is given in parameter 'obj_info.\n Each call of this method APPENDS a list to the loaded_meshes attribute.\n :param obj_info: The object information of the meshes to be loaded.\n :param kwargs: additional mesh modifiers such as scale, specified with a leading 'mod_'\n \"\"\"\n paths = []\n for obj in obj_info:\n path = self.text_dir if obj.name.endswith(\"_floor\") or obj.name.endswith(\"_wall\") else self.base_dir\n paths.append((path / obj.mesh_fp).resolve())\n scales = [obj.scale for obj in obj_info]\n class_ids = [obj.class_id for obj in obj_info]\n mod_scales = kwargs.get(\"mod_scale\", [1.0] * len(scales))\n scales = [s * ms for (s, ms) in zip(scales, mod_scales)]\n flags = [mesh_flags(obj) for obj in obj_info]\n meshes = sl.Mesh.load_threaded(filenames=paths, flags=flags)\n\n # Setup class IDs\n for _, (mesh, scale, class_id) in enumerate(zip(meshes, scales, class_ids)):\n pt = torch.eye(4)\n pt[:3, :3] *= scale\n mesh.pretransform = pt\n mesh.class_index = class_id\n\n info_mesh_tuples = list(zip(obj_info, meshes))\n self.loaded_meshes.append(info_mesh_tuples)", "code_tokens": [ "class", "MeshLoader", ":", "def", "__init__", "(", "self", ")", ":", "\"\"\"Module initializer\"\"\"", "self", ".", "base_dir", "=", "CONSTANTS", ".", "MESH_BASE_DIR", "self", ".", "text_dir", "=", "CONSTANTS", ".", "TEXT_BASE_DIR", "self", ".", "reset", "(", ")", "def", "reset", "(", "self", ")", ":", "self", ".", "loaded_meshes", "=", "[", "]", "def", "get_meshes", "(", "self", ")", ":", "\"\"\" \"\"\"", "extract_singular", "=", "lambda", "x", ":", "x", "[", "0", "]", "if", "len", "(", "x", ")", "==", "1", "else", "x", "return", "[", "extract_singular", "(", "item", ")", "for", "item", "in", "self", ".", "loaded_meshes", "]", "def", "load_meshes", "(", "self", ",", "obj_info", ":", "List", "[", "object_info", ".", "ObjectInfo", "]", ",", "**", "kwargs", ")", ":", "\"\"\"\n Loads the meshes whose information is given in parameter 'obj_info.\n Each call of this method APPENDS a list to the loaded_meshes attribute.\n :param obj_info: The object information of the meshes to be loaded.\n :param kwargs: additional mesh modifiers such as scale, specified with a leading 'mod_'\n \"\"\"", "paths", "=", "[", "]", "for", "obj", "in", "obj_info", ":", "path", "=", "self", ".", "text_dir", "if", "obj", ".", "name", ".", "endswith", "(", "\"_floor\"", ")", "or", "obj", ".", "name", ".", "endswith", "(", "\"_wall\"", ")", "else", "self", ".", "base_dir", "paths", ".", "append", "(", "(", "path", "/", "obj", ".", "mesh_fp", ")", ".", "resolve", "(", ")", ")", "scales", "=", "[", "obj", ".", "scale", "for", "obj", "in", "obj_info", "]", "class_ids", "=", "[", "obj", ".", "class_id", "for", "obj", "in", "obj_info", "]", "mod_scales", "=", "kwargs", ".", "get", "(", "\"mod_scale\"", ",", "[", "1.0", "]", "*", "len", "(", "scales", ")", ")", "scales", "=", "[", "s", "*", "ms", "for", "(", "s", ",", "ms", ")", "in", "zip", "(", "scales", ",", "mod_scales", ")", "]", "flags", "=", "[", "mesh_flags", "(", "obj", ")", "for", "obj", "in", "obj_info", "]", "meshes", "=", "sl", ".", "Mesh", ".", "load_threaded", "(", "filenames", "=", "paths", ",", "flags", "=", "flags", ")", "for", "_", ",", "(", "mesh", ",", "scale", ",", "class_id", ")", "in", "enumerate", "(", "zip", "(", "meshes", ",", "scales", ",", "class_ids", ")", ")", ":", "pt", "=", "torch", ".", "eye", "(", "4", ")", "pt", "[", ":", "3", ",", ":", "3", "]", "*=", "scale", "mesh", ".", "pretransform", "=", "pt", "mesh", ".", "class_index", "=", "class_id", "info_mesh_tuples", "=", "list", "(", "zip", "(", "obj_info", ",", "meshes", ")", ")", "self", ".", "loaded_meshes", ".", "append", "(", "info_mesh_tuples", ")" ], "short_docstring": "Class to load the meshes for the objects in a scene.", "short_docstring_tokens": [ "Class", "to", "load", "the", "meshes", "for", "the", "objects", "in", "a", "scene", "." ], "comment": [ "\"\"\"\n Class to load the meshes for the objects in a scene.\n \"\"\"", "\"\"\"Module initializer\"\"\"", "\"\"\" \"\"\"", "\"\"\"\n Loads the meshes whose information is given in parameter 'obj_info.\n Each call of this method APPENDS a list to the loaded_meshes attribute.\n :param obj_info: The object information of the meshes to be loaded.\n :param kwargs: additional mesh modifiers such as scale, specified with a leading 'mod_'\n \"\"\"", "# Setup class IDs" ], "parameters": [], "docstring_params": { "returns": [], "raises": [], "params": [], "outlier_params": [], "others": [] } } ``` ### Data Fields Data fields for function level: - **hexsha** (string): the unique git hash of file - **repo** (string): the owner/repo - **path** (string): the full path to the original file - **license** (list): licenses in the repo - **language** (string): the programming language - **identifier** (string): the function or method name - **original_string** (string): original version of function/class node - **original_docstring** (string): the raw string before tokenization or parsing - **code** (string): the part of the original that is code - **code_tokens** (list): tokenized version of `code` - **short_docstring** (string): short, brief summarization (first line of the docstring) - **short_docstring_tokens** (list): tokenized version of `short_docstring - **docstring** (string): the top-level comment or docstring (docstring version without param’s doc, return, exception fields, etc) - **docstring_tokens** (list): tokenized version of docstring - **comment** (list): list of comments (line) inside the function/class - **parameters** (list): List of parameters and its type (type can be None) - **docstring_params** (dict): Dictionary of the parsed information from docstring See [here](https://github.com/FSoft-AI4Code/TheVault/blob/main/data/README.md) for more details and examples. ### Data Splits In this repo, the class level data is not split, and contained in only train set. ## Dataset Statistics |Language | Number of samples | |:-----------|------------------------:| |Python | 422,187 | |Java | 4,872,485 | |JavaScript | 291,479 | |PHP | 1,173,916 | |C# | 1,437,800 | |C++ | 174,370 | |Ruby | 353,859 | |Rust | 93,311 | |C | - | |Go | - | |TOTAL | **9,121,300** | ## Usage You can load The Vault dataset using datasets library: ```pip install datasets``` ```python from datasets import load_dataset # Load full class level dataset dataset = load_dataset("Fsoft-AIC/the-vault-class") # specific language (e.g. Python) dataset = load_dataset("Fsoft-AIC/the-vault-class", languages=['Python']) # dataset streaming data = load_dataset("Fsoft-AIC/the-vault-class", streaming= True) for sample in iter(data['train']): print(sample) ``` A back up dataset can be downloaded in azure storage. See [Download The Vault from Azure blob storage](https://github.com/FSoft-AI4Code/TheVault#download-via-link). ## Additional information ### Licensing Information MIT License ### Citation Information ``` @article{manh2023vault, title={The Vault: A Comprehensive Multilingual Dataset for Advancing Code Understanding and Generation}, author={Manh, Dung Nguyen and Hai, Nam Le and Dau, Anh TV and Nguyen, Anh Minh and Nghiem, Khanh and Guo, Jin and Bui, Nghi DQ}, journal={arXiv preprint arXiv:2305.06156}, year={2023} } ``` ### Contributions This dataset is developed by [FSOFT AI4Code team](https://github.com/FSoft-AI4Code).

[ -0.39418020844459534, -0.7190294861793518, 0.19936105608940125, 0.39837419986724854, 0.05490336939692497, 0.07443682849407196, 0.11695282906293869, -0.2424573004245758, -0.008141901344060898, 0.49152934551239014, -0.526888906955719, -0.8261966705322266, -0.4479849636554718, 0.0708123967051...

null

folkopinion/government-interpellation-qa-swedish

folkopinion

2023-09-25T18:07:34Z

42

1

null

[ "task_categories:table-question-answering", "size_categories:1K<n<10K", "language:sv", "government", "politics", "interpellation", "region:us" ]

2023-09-25T18:07:34Z

2023-09-17T19:00:42.000Z

2023-09-17T19:00:42

--- task_categories: - table-question-answering language: - sv tags: - government - politics - interpellation pretty_name: Riksdagen Interpellation Debate QA size_categories: - 1K<n<10K ---

[ -0.1285335123538971, -0.1861683875322342, 0.6529128551483154, 0.49436232447624207, -0.19319400191307068, 0.23607441782951355, 0.36072009801864624, 0.05056373029947281, 0.5793656706809998, 0.7400146722793579, -0.650810182094574, -0.23784008622169495, -0.7102247476577759, -0.0478255338966846...

null

mcaleste/sat_multiple_choice_math_may_23

mcaleste

2023-10-14T02:23:29Z

42

0

null

[ "size_categories:n<1K", "language:en", "region:us" ]

2023-10-14T02:23:29Z

2023-09-18T21:30:36.000Z

2023-09-18T21:30:36

--- language: - en size_categories: - n<1K --- This is the set of math SAT questions from the May 2023 SAT, taken from here: https://www.mcelroytutoring.com/lower.php?url=44-official-sat-pdfs-and-82-official-act-pdf-practice-tests-free. Questions that included images were not included but all other math questions, including those that have tables were included.

[ -0.7273389101028442, -1.0905193090438843, 0.7060952186584473, 0.18812677264213562, 0.012341175228357315, -0.47519487142562866, 0.7260611653327942, -0.288883775472641, 0.4807451367378235, 1.1878622770309448, -1.2471224069595337, -0.03385022655129433, -0.3393001854419708, 0.03873644396662712...

null

Hieu-Pham/cooking_squad

Hieu-Pham

2023-10-11T04:23:13Z

42

0

null

[ "license:mit", "region:us" ]

2023-10-11T04:23:13Z

2023-10-11T03:31:28.000Z

2023-10-11T03:31:28

--- license: mit ---

[ -0.1285335123538971, -0.1861683875322342, 0.6529128551483154, 0.49436232447624207, -0.19319400191307068, 0.23607441782951355, 0.36072009801864624, 0.05056373029947281, 0.5793656706809998, 0.7400146722793579, -0.650810182094574, -0.23784008622169495, -0.7102247476577759, -0.0478255338966846...

null

humane-lab/K-HATERS

humane-lab

2023-10-27T03:45:26Z

42

0

null

[ "license:cc-by-4.0", "region:us" ]

2023-10-27T03:45:26Z

2023-10-16T04:29:58.000Z

2023-10-16T04:29:58

--- license: cc-by-4.0 ---

[ -0.1285339742898941, -0.18616800010204315, 0.6529127359390259, 0.4943626821041107, -0.1931934952735901, 0.2360742688179016, 0.360720157623291, 0.05056300014257431, 0.5793654322624207, 0.7400140166282654, -0.6508105993270874, -0.23783984780311584, -0.7102248668670654, -0.047826044261455536,...

null

zelros/pj-ca

zelros

2023-11-05T22:55:31Z

42

0

null

[ "insurance", "region:us" ]

2023-11-05T22:55:31Z

2023-10-17T19:41:34.000Z

2023-10-17T19:41:34

--- tags: - insurance --- This dataset contains question/answer pairs from a French legal protection insurance (https://www.service-public.fr/particuliers/vosdroits/F3049?lang=en). The objective of this dataset is to contribute to open source research projects aiming to, for instance: * fine-tune LLMs on high-quality datasets, specializing them in the insurance domain * develop new question/answer applications using Retrieval Augmented Generation (RAG) for insurance contracts * assess the knowledge of language models in the insurance field * more generally, apply LLMs to the insurance domain for better understanding and increased transparency of this industry. Other datasets of the same kind are also available - or will be available soon - and are part of this research effort. See here: https://huggingface.co/collections/zelros/legal-protection-insurance-6536e8f389dd48faca78447e Here is an example of usages of this dataset: https://huggingface.co/spaces/zelros/The-legal-protection-insurance-comparator

[ -0.12690630555152893, -0.6109647154808044, 0.20794343948364258, 0.2446521520614624, -0.06387617439031601, -0.19570039212703705, 0.15373264253139496, -0.43992751836776733, 0.39488929510116577, 1.1311315298080444, -0.28130269050598145, -0.5277532935142517, -0.4185519516468048, -0.14612421393...

null

Taj-Mahal/magic-the-gathering

Taj-Mahal

2023-10-31T20:58:35Z

42

1

null

[ "region:us" ]

2023-10-31T20:58:35Z

2023-10-26T19:43:32.000Z

2023-10-26T19:43:32

--- dataset_info: features: - name: name dtype: string - name: firstPrinting dtype: string - name: manaCost dtype: string - name: convertedManaCost dtype: float64 - name: type dtype: string - name: text dtype: string - name: power dtype: string - name: toughness dtype: string - name: loyalty dtype: string - name: layout dtype: string splits: - name: train num_bytes: 6999997 num_examples: 27703 - name: train_clean num_bytes: 6813519.081146446 num_examples: 26965 download_size: 2539289 dataset_size: 13813516.081146445 configs: - config_name: default data_files: - split: train path: data/train-* - split: train_clean path: data/train_clean-* --- # Dataset Card for "magic-the-gathering" This is a HuggingFace adaptation of the [MTGJSON Atomic Card Database](https://mtgjson.com/data-models/card/card-atomic/) from the Taj-Mahal Data Science & Machine Learning Group. ## Usage ``` from datasets import load_dataset dataset = load_dataset("Taj-Mahal/magic-the-gathering") ```

[ -0.45174098014831543, -0.6950588822364807, -0.3052142858505249, -0.06703761219978333, -0.5002790093421936, 0.09664130955934525, 0.04445413872599602, -0.26922813057899475, 0.5714948177337646, 0.5423680543899536, -0.9428568482398987, -0.6494654417037964, -0.42537567019462585, 0.0422885119915...

null

JosueElias/pipeline_dataset2

JosueElias

2023-10-29T21:23:27Z

42

0

null

[ "region:us" ]

2023-10-29T21:23:27Z

2023-10-29T20:59:49.000Z

2023-10-29T20:59:49

--- dataset_info: features: - name: title dtype: string - name: section dtype: string - name: text dtype: string splits: - name: train num_bytes: 1522896529 num_examples: 2101279 download_size: 850821844 dataset_size: 1522896529 --- # Dataset Card for "pipeline_dataset2" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

[ -0.3034590482711792, -0.08749112486839294, 0.12437744438648224, 0.19001281261444092, -0.3166126608848572, 0.13243848085403442, 0.5400586128234863, -0.1195332482457161, 0.75567626953125, 0.5601155757904053, -0.8765096068382263, -0.5750784277915955, -0.8440394997596741, -0.4758780598640442, ...

null

ftang97/sw-consultancy-book

ftang97

2023-11-03T15:23:20Z

42

0

null

[ "region:us" ]

2023-11-03T15:23:20Z

2023-11-03T15:23:17.000Z

2023-11-03T15:23:17

--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 2592763 num_examples: 1 download_size: 1529310 dataset_size: 2592763 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "sw-consultancy-book" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

[ -0.5195884704589844, -0.06594770401716232, -0.03535483032464981, 0.23486678302288055, -0.20163089036941528, -0.023167749866843224, 0.2417953610420227, -0.33982396125793457, 0.5954000353813171, 0.6877579092979431, -1.0472073554992676, -0.8918899893760681, -0.27092474699020386, -0.3203490078...

null

Ceroxlol/pictarine

Ceroxlol

2023-11-03T16:59:34Z

42

0

null

[ "task_categories:question-answering", "size_categories:n<1K", "language:en", "region:us" ]

2023-11-03T16:59:34Z

2023-11-03T16:53:38.000Z

2023-11-03T16:53:38

--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 110425 num_examples: 520 download_size: 57760 dataset_size: 110425 configs: - config_name: default data_files: - split: train path: data/train-* task_categories: - question-answering language: - en pretty_name: pictarineChatbot size_categories: - n<1K --- # Dataset Card for "pictarine" Dataset for training pictarineChatbot

[ -0.13372467458248138, -0.2952459752559662, -0.4012910723686218, 0.5577057003974915, -0.5757617354393005, 0.4014209508895874, 0.023343531414866447, -0.1169564500451088, 0.2739781439304352, 0.3025149703025818, -0.41545504331588745, -0.6753764748573303, -0.36801373958587646, -0.35433885455131...

null

Flyfer/TestingFineTuningCB

Flyfer

2023-11-13T10:17:27Z

42

0

null

[ "license:apache-2.0", "region:us" ]

2023-11-13T10:17:27Z

2023-11-07T08:56:45.000Z

2023-11-07T08:56:45

--- license: apache-2.0 dataset_info: features: - name: instruction dtype: string - name: input dtype: string - name: text dtype: string - name: output dtype: string splits: - name: train num_bytes: 7667 num_examples: 4 download_size: 11176 dataset_size: 7667 configs: - config_name: default data_files: - split: train path: data/train-* ---

[ -0.12853392958641052, -0.18616779148578644, 0.6529127955436707, 0.49436280131340027, -0.19319361448287964, 0.23607419431209564, 0.36072003841400146, 0.050563063472509384, 0.579365611076355, 0.7400140762329102, -0.6508104205131531, -0.23783954977989197, -0.7102249264717102, -0.0478260256350...

null

realshyfox/GuanacoFormat

realshyfox

2023-11-07T21:46:46Z

42

0

null

[ "license:llama2", "region:us" ]

2023-11-07T21:46:46Z

2023-11-07T20:07:40.000Z

2023-11-07T20:07:40

--- license: llama2 ---

[ -0.12853392958641052, -0.18616779148578644, 0.6529127955436707, 0.49436280131340027, -0.19319361448287964, 0.23607419431209564, 0.36072003841400146, 0.050563063472509384, 0.579365611076355, 0.7400140762329102, -0.6508104205131531, -0.23783954977989197, -0.7102249264717102, -0.0478260256350...

null

GabrielGaleana34/Products

GabrielGaleana34

2023-11-13T04:26:55Z

42

0

null

[ "license:mit", "region:us" ]

2023-11-13T04:26:55Z

2023-11-08T00:10:44.000Z

2023-11-08T00:10:44

--- license: mit configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: image dtype: image - name: caption dtype: string splits: - name: train num_bytes: 91453253.0 num_examples: 451 download_size: 88009496 dataset_size: 91453253.0 ---

[ -0.12853392958641052, -0.18616779148578644, 0.6529127955436707, 0.49436280131340027, -0.19319361448287964, 0.23607419431209564, 0.36072003841400146, 0.050563063472509384, 0.579365611076355, 0.7400140762329102, -0.6508104205131531, -0.23783954977989197, -0.7102249264717102, -0.0478260256350...

null

dz-data-ai/A10_CMD_SCRIPT_MULLEN_TEST

dz-data-ai

2023-11-23T07:23:11Z

42

0

null

[ "region:us" ]

2023-11-23T07:23:11Z

2023-11-13T07:11:20.000Z

2023-11-13T07:11:20

Entry not found

[ -0.32276472449302673, -0.22568407654762268, 0.8622258901596069, 0.4346148371696472, -0.5282984972000122, 0.7012965679168701, 0.7915717363357544, 0.07618629932403564, 0.7746022939682007, 0.2563222646713257, -0.785281777381897, -0.22573848068714142, -0.9104482531547546, 0.5715669393539429, ...

null

mengmengmmm/concode_trainuse

mengmengmmm

2023-11-13T13:37:42Z

42

0

null

[ "region:us" ]

2023-11-13T13:37:42Z

2023-11-13T13:36:15.000Z

2023-11-13T13:36:15

Entry not found

[ -0.32276472449302673, -0.22568407654762268, 0.8622258901596069, 0.4346148371696472, -0.5282984972000122, 0.7012965679168701, 0.7915717363357544, 0.07618629932403564, 0.7746022939682007, 0.2563222646713257, -0.785281777381897, -0.22573848068714142, -0.9104482531547546, 0.5715669393539429, ...

null

joseluhf11/oct-object-detection

joseluhf11

2023-11-22T08:36:49Z

42

0

null

[ "region:us" ]

2023-11-22T08:36:49Z

2023-11-13T13:51:02.000Z

2023-11-13T13:51:02

--- dataset_info: features: - name: image dtype: image - name: objects struct: - name: bbox sequence: sequence: int64 - name: categories sequence: string splits: - name: train num_bytes: 666483144.628 num_examples: 4698 download_size: 76903163 dataset_size: 666483144.628 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "oct-object-detection" Dataset is composed of images with individual object detection box in coco format (x,y,w,h). Images are OCT (type of eye scaner) with boxes indicating some features associated to AMD disease. [Source datataset](https://doi.org/10.1101/2023.03.29.534704)

[ -0.7288023829460144, -0.8208656907081604, 0.08783753961324692, -0.29677173495292664, -0.6897058486938477, 0.09807159006595612, 0.5399805307388306, -0.4976142644882202, 0.5162763595581055, 0.9565261006355286, -0.4565790891647339, -0.7641271948814392, -0.4670449495315552, 0.39208531379699707...

null

josephimperial/CebuaNER

josephimperial

2023-11-17T12:07:58Z

42

0

null

[ "size_categories:1K<n<10K", "license:cc-by-nc-4.0", "arxiv:2310.00679", "region:us" ]

2023-11-17T12:07:58Z

2023-11-17T11:58:38.000Z

2023-11-17T11:58:38

--- license: cc-by-nc-4.0 size_categories: - 1K<n<10K --- This repository contains CebuaNER, the largest gold-standard datasets for named entities in Cebuano. This dataset is used for the [paper](https://arxiv.org/abs/2310.00679) `CebuaNER: A New Baseline Cebuano Named Entity Recognition Model` to be presented at PACLIC 2023, authored by Ma. Beatrice Emanuela N. Pilar, Ellyza Mari J. Papas, Mary Loise Buenaventura, Dane C. Dedoroy, Myron Montefalcon, Jay Rhald Padilla, Lany Maceda, Mideth Abisado, and Joseph Imperial. ## Data The dataset contribution of this study is a compilation of Cebuano news articles from two local news stations, Yes the Best Dumaguete and the Filipinas Bisaya. To further increase the data count, we also incorporated another publicly available dataset from [Sunstar Cebu](https://github.com/rjrequina/Cebuano-POS-Tagger/blob/master/eval/data/scraped/news-raw.txt) pre-collected by independent researcher Arjemariel Requina. The total accumulated and filtered size of the Cebuano dataset is 4258 articles. All the dataset used for this study is purely for non-commercial research purposes and under Fair Use. | Source | Original | Cleaned | | ------------------------| ------------- | ------- | | Yes the Best Dumaguete | 1484 | 781 | | Filipinas Bisaya | 769 | 377 | | Sunstar | 3100 | 3100 | ## Note on Data Cataloging Please send an email before submitting this repository to any data cataloging, data aggregation, and benchmarking projects/initiatives. The proponents of the paper of this dataset would like to be acknowledged appropriately or involved in co-authorship. ## Contact For questions, you may reach the corresponding author of the study at: **Joseph Marvin Imperial**\ Faculty Member, Department of Computer Science\ Lab Head, NU Human Language Technology Lab\ jrimperial@national-u.edu.ph

[ -0.3761669993400574, 0.0866926908493042, 0.1247243583202362, 0.4606899619102478, -0.46812909841537476, -0.268138587474823, -0.23521602153778076, -0.35500505566596985, 0.3941730558872223, 1.1633199453353882, -0.24005882441997528, -0.6712182760238647, -0.4943116307258606, 0.635182797908783, ...

null

arianhosseini/openai_summarize_unlabelled

arianhosseini

2023-11-18T21:34:06Z

42

0

null

[ "region:us" ]

2023-11-18T21:34:06Z

2023-11-18T21:33:58.000Z

2023-11-18T21:33:58

--- dataset_info: features: - name: prompt dtype: string - name: label dtype: string splits: - name: train num_bytes: 167037359 num_examples: 107543 download_size: 101979854 dataset_size: 167037359 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "openai_summarize_unlabelled" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

[ -0.5498514771461487, -0.0042190817184746265, -0.07743614912033081, 0.2613656222820282, -0.3403221666812897, -0.13584265112876892, -0.00758151151239872, -0.014219074510037899, 0.998954176902771, 0.5618550181388855, -0.5642372965812683, -0.7596110701560974, -0.6582470536231995, -0.2543130517...

null

portafolio/llamadas-celular-04

portafolio

2023-11-20T12:37:49Z

42

0

null

[ "task_categories:conversational", "size_categories:n<1K", "size_categories:divition 80-20", "language:es", "region:us" ]

2023-11-20T12:37:49Z

2023-11-19T15:48:18.000Z

2023-11-19T15:48:18

--- task_categories: - conversational language: - es pretty_name: llamadas telefónicas size_categories: - n<1K - divition 80-20 ---

[ -0.1285335123538971, -0.1861683875322342, 0.6529128551483154, 0.49436232447624207, -0.19319400191307068, 0.23607441782951355, 0.36072009801864624, 0.05056373029947281, 0.5793656706809998, 0.7400146722793579, -0.650810182094574, -0.23784008622169495, -0.7102247476577759, -0.0478255338966846...

null

MrBananaHuman/kor_ethical_question_answer

MrBananaHuman

2023-11-20T11:08:47Z

42

1

null

[ "license:cc-by-nc-nd-4.0", "region:us" ]

2023-11-20T11:08:47Z

2023-11-20T11:08:01.000Z

2023-11-20T11:08:01

--- license: cc-by-nc-nd-4.0 ---

[ -0.1285335123538971, -0.1861683875322342, 0.6529128551483154, 0.49436232447624207, -0.19319400191307068, 0.23607441782951355, 0.36072009801864624, 0.05056373029947281, 0.5793656706809998, 0.7400146722793579, -0.650810182094574, -0.23784008622169495, -0.7102247476577759, -0.0478255338966846...

null

PaulTran/translated_math

PaulTran

2023-11-21T01:06:23Z

42

0

null

[ "region:us" ]

2023-11-21T01:06:23Z

2023-11-20T12:43:30.000Z

2023-11-20T12:43:30

--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: answer dtype: string - name: explanation dtype: string - name: choices sequence: string - name: question dtype: string - name: id dtype: string splits: - name: train num_bytes: 7908449 num_examples: 28386 download_size: 1068029 dataset_size: 7908449 --- # Dataset Card for "translated_math" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

[ -0.45498740673065186, -0.35691264271736145, 0.14711567759513855, 0.4955821633338928, -0.26428425312042236, -0.04074779525399208, -0.08143892139196396, -0.04963758960366249, 0.7440202236175537, 0.4875999093055725, -0.8252142667770386, -0.8094675540924072, -0.7669659852981567, -0.21924106776...

null

Gaxys/wayuu_spa

Gaxys

2023-11-26T00:14:24Z

42

0

null

[ "region:us" ]

2023-11-26T00:14:24Z

2023-11-22T09:44:00.000Z

2023-11-22T09:44:00

--- dataset_info: features: - name: translation struct: - name: guc dtype: string - name: spa dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 10761772 num_examples: 46827 download_size: 6083524 dataset_size: 10761772 configs: - config_name: default data_files: - split: train path: data/train-* ---

[ -0.12853392958641052, -0.18616779148578644, 0.6529127955436707, 0.49436280131340027, -0.19319361448287964, 0.23607419431209564, 0.36072003841400146, 0.050563063472509384, 0.579365611076355, 0.7400140762329102, -0.6508104205131531, -0.23783954977989197, -0.7102249264717102, -0.0478260256350...

null

tankaplans/buddismEval

tankaplans

2023-11-27T05:10:36Z

42

0

null

[ "region:us" ]

2023-11-27T05:10:36Z

2023-11-27T02:59:41.000Z

2023-11-27T02:59:41

Entry not found

[ -0.32276472449302673, -0.22568407654762268, 0.8622258901596069, 0.4346148371696472, -0.5282984972000122, 0.7012965679168701, 0.7915717363357544, 0.07618629932403564, 0.7746022939682007, 0.2563222646713257, -0.785281777381897, -0.22573848068714142, -0.9104482531547546, 0.5715669393539429, ...

null

liweili/c4_200m

liweili

2022-10-23T11:00:46Z

41

25

null

[ "task_categories:text-generation", "source_datasets:allenai/c4", "language:en", "grammatical-error-correction", "region:us" ]

2022-10-23T11:00:46Z

2022-03-02T23:29:22.000Z

2022-03-02T23:29:22

--- language: - en source_datasets: - allenai/c4 task_categories: - text-generation pretty_name: C4 200M Grammatical Error Correction Dataset tags: - grammatical-error-correction --- # C4 200M # Dataset Summary c4_200m is a collection of 185 million sentence pairs generated from the cleaned English dataset from C4. This dataset can be used in grammatical error correction (GEC) tasks. The corruption edits and scripts used to synthesize this dataset is referenced from: [C4_200M Synthetic Dataset](https://github.com/google-research-datasets/C4_200M-synthetic-dataset-for-grammatical-error-correction) # Description As discussed before, this dataset contains 185 million sentence pairs. Each article has these two attributes: `input` and `output`. Here is a sample of dataset: ``` { "input": "Bitcoin is for $7,094 this morning, which CoinDesk says." "output": "Bitcoin goes for $7,094 this morning, according to CoinDesk." } ```

[ -0.3422134518623352, -0.7494551539421082, 0.5181824564933777, 0.08256334811449051, 0.0328662283718586, 0.18976685404777527, -0.2387077510356903, -0.3461028039455414, 0.16504374146461487, 0.5613929033279419, -0.45490849018096924, -0.5462290048599243, -0.4097924828529358, 0.4829891622066498,...

null

SetFit/amazon_reviews_multi_es

SetFit

2022-03-23T15:43:09Z

41

0

null

[ "region:us" ]

2022-03-23T15:43:09Z

2022-03-13T02:45:47.000Z

2022-03-13T02:45:47

#amazon reviews multi spanish This dataset is a port of the official ['amazon_reviews_multi' dataset] (https://huggingface.co/datasets/amazon_reviews_multi) on the Hub. It has just the Spanish language version. It has been reduced to just 3 columns (and 4th "label_text") that are relevant to the SetFit task.

[ -0.8170874714851379, -0.4594860076904297, -0.10711963474750519, 0.9387503266334534, -0.27773210406303406, 0.1738867163658142, 0.010639384388923645, -0.6464211940765381, 0.8231520652770996, 1.0282046794891357, -1.1254680156707764, -0.4142968952655792, -0.16464002430438995, 0.429659187793731...

null

google/wit

google

2022-07-04T10:47:07Z

41

14

wit

[ "task_categories:text-retrieval", "task_categories:image-to-text", "task_ids:image-captioning", "annotations_creators:machine-generated", "language_creators:found", "multilinguality:multilingual", "size_categories:10M<n<100M", "source_datasets:original", "source_datasets:extended|wikipedia", "lang...

2022-07-04T10:47:07Z

2022-05-02T11:22:32.000Z

2022-05-02T11:22:32

--- annotations_creators: - machine-generated language_creators: - found language: - af - ar - ast - azb - be - bg - bn - br - ca - cs - cy - da - de - el - en - eo - es - et - eu - fa - fi - fr - fy - ga - gl - hr - hu - hy - id - it - iw - ja - ka - ko - la - lt - lv - mk - ml - ms - nl - nn - 'no' - pl - pt - ro - ru - sk - sl - sr - sv - th - tr - uk - ur - vi - vo - zh license: - cc-by-sa-3.0 multilinguality: - multilingual paperswithcode_id: wit pretty_name: Wikipedia-based Image Text size_categories: - 10M<n<100M source_datasets: - original - extended|wikipedia task_categories: - text-retrieval - image-to-text task_ids: - text-retrieval-other-text-image-retrieval - image-captioning --- # Dataset Card for WIT ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Dataset Preprocessing](#dataset-preprocessing) - [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:** [WIT homepage](https://github.com/google-research-datasets/wit) - **Repository:** [WIT repository](https://github.com/google-research-datasets/wit) - **Paper:** [WIT: Wikipedia-based Image Text Dataset for Multimodal Multilingual Machine Learning ](https://arxiv.org/abs/2103.01913) - **Leaderboard:** [WIT leaderboard](https://www.kaggle.com/c/wikipedia-image-caption) - **Point of Contact:** [WIT e-mail](mailto:wit-dataset@google.com) ### Dataset Summary Wikipedia-based Image Text (WIT) Dataset is a large multimodal multilingual dataset. WIT is composed of a curated set of 37.6 million entity rich image-text examples with 11.5 million unique images across 108 Wikipedia languages. Its size enables WIT to be used as a pretraining dataset for multimodal machine learning models. A few unique advantages of WIT: * The largest multimodal dataset (time of this writing) by the number of image-text examples. * A massively multilingual (first of its kind) with coverage for over 100+ languages. * A collection of diverse set of concepts and real world entities. * Brings forth challenging real-world test sets. ### Dataset Preprocessing This dataset doesn't download the images locally by default. Instead, it exposes URLs to the images. To fetch the images, use the following code: ```python from concurrent.futures import ThreadPoolExecutor from functools import partial import io import urllib import PIL.Image from datasets import load_dataset from datasets.utils.file_utils import get_datasets_user_agent def fetch_single_image(image_url, timeout=None, retries=0): for _ in range(retries + 1): try: request = urllib.request.Request( image_url, data=None, headers={"user-agent": get_datasets_user_agent()}, ) with urllib.request.urlopen(request, timeout=timeout) as req: image = PIL.Image.open(io.BytesIO(req.read())) break except Exception: image = None return image def fetch_images(batch, num_threads, timeout=None, retries=0): fetch_single_image_with_args = partial(fetch_single_image, timeout=timeout, retries=retries) with ThreadPoolExecutor(max_workers=num_threads) as executor: batch["image"] = list(executor.map(fetch_single_image_with_args, batch["image_url"])) return batch num_threads = 20 dset = load_dataset("wit") dset = dset.map(fetch_images, batched=True, batch_size=100, fn_kwargs={"num_threads": num_threads}) ``` ### Supported Tasks and Leaderboards - `image-captioning`: This dataset can be used to train a model for image captioning where the goal is to predict a caption given the image. - `text-retrieval`: The goal in this task is to build a model that retrieves the text closest to an image. In these tasks, any combination of the `caption_reference_description`, `caption_attribution_description` and `caption_alt_text_description` fields can be used as the input text/caption. ### Languages The dataset contains examples from all Wikipedia languages, with the following stats: Image-Text | # Lang | Uniq. Images | # Lang ------------ | ------ | ------------- | ------ total > 1M | 9 | images > 1M | 6 total > 500K | 10 | images > 500K | 12 total > 100K | 36 | images > 100K | 35 total > 50K | 15 | images > 50K | 17 total > 14K | 38 | images > 13K | 38 ## Dataset Structure ### Data Instances ``` { 'language': 'en', 'page_url': 'https://en.wikipedia.org/wiki/Oxydactylus', 'image_url': 'https://upload.wikimedia.org/wikipedia/commons/5/5f/Oxydactylus_longipes_fm.jpg', 'page_title': 'Oxydactylus', 'section_title': None, 'hierarchical_section_title': 'Oxydactylus', 'caption_reference_description': None, 'caption_attribution_description': 'English: Mounted skeleton of Oxydactylus longipes in the Field Museum of Natural History.', 'caption_alt_text_description': None, 'mime_type': 'image/jpeg', 'original_height': 3564, 'original_width': 2748, 'is_main_image': True, 'attribution_passes_lang_id': True, 'page_changed_recently': True, 'context_page_description': 'Oxydactylus is an extinct genus of camelid endemic to North America. It lived from the Late Oligocene to the Middle Miocene, existing for approximately 14 million years. The name is from the Ancient Greek οξύς and δάκτυλος.\nThey had very long legs and necks, and were probably adapted to eating high vegetation, much like modern giraffes. Unlike modern camelids, they had hooves, rather than tough sole-pads, and splayed toes.', 'context_section_description': 'Oxydactylus is an extinct genus of camelid endemic to North America. It lived from the Late Oligocene to the Middle Miocene (28.4–13.7 mya), existing for approximately 14 million years. The name is from the Ancient Greek οξύς (oxys, "sharp")and δάκτυλος (daktylos, "finger").\n \nThey had very long legs and necks, and were probably adapted to eating high vegetation, much like modern giraffes. Unlike modern camelids, they had hooves, rather than tough sole-pads, and splayed toes.' } ``` ### Data Fields - `language`: Language code depicting wikipedia language of the page - `page_url`: URL to wikipedia page - `image_url`: URL to wikipedia image - `page_title`: Wikipedia page's title - `section_title`: Section's title - `hierarchical_section_title`: Hierarchical section's title - `caption_reference_description`: This is the caption that is visible on the wiki page directly below the image. - `caption_attribution_description`: This is the text found on the Wikimedia page of the image. This text is common to all occurrences of that image across all Wikipedias and thus can be in a language different to the original page article. - `caption_alt_text_description`: This is the “alt” text associated with the image. While not visible in general, it is commonly used for accessibility / screen readers - `mime_type`: Mime type associated to the image. - `original_height`: Image height - `original_width`: Image width - `is_main_image`: Flag determining if the image is the first image of the page. Usually displayed on the top-right part of the page when using web browsers. - `attribution_passes_lang_id`: Compared `language` field with the attribution language (written in the prefix of the attribution description). - `page_changed_recently`: [More Information Needed] - `context_page_description`: Page description corresponds to the short description of the page. It provides a concise explanation of the scope of the page. - `context_section_description`: Text within the image's section. <img width='75%' src='https://production-media.paperswithcode.com/datasets/Screenshot_2021-03-04_at_14.26.02.png' alt="Half Dome" /> Figure: WIT annotation example. Details on the field content can be found directly in the [paper, figure 5 and table 12.](https://arxiv.org/abs/2103.01913) ### Data Splits All data is held in `train` split, with a total of 37046386 rows. ## Dataset Creation ### Curation Rationale From the [repository](https://github.com/google-research-datasets/wit#motivation): > Multimodal visio-linguistic models rely on a rich dataset to help them learn to model the relationship between images and texts. Having large image-text datasets can significantly improve performance, as shown by recent works. Furthermore the lack of language coverage in existing datasets (which are mostly only in English) also impedes research in the multilingual multimodal space – we consider this a lost opportunity given the potential shown in leveraging images (as a language-agnostic medium) to help improve our multilingual textual understanding. > > To address these challenges and advance research on multilingual, multimodal learning we created the Wikipedia-based Image Text (WIT) Dataset. WIT is created by extracting multiple different texts associated with an image (e.g., as shown in the above image) from Wikipedia articles and Wikimedia image links. This was accompanied by rigorous filtering to only retain high quality image-text sets. > > The resulting dataset contains over 37.6 million image-text sets – making WIT the largest multimodal dataset (publicly available at the time of this writing) with unparalleled multilingual coverage – with 12K+ examples in each of 108 languages (53 languages have 100K+ image-text pairs). ### Source Data #### Initial Data Collection and Normalization From the [paper, section 3.1](https://arxiv.org/abs/2103.01913): > We started with all Wikipedia content pages (i.e., ignoring other pages that have discussions, comments and such). These number about ∼124M pages across 279 languages. #### Who are the source language producers? Text was extracted from Wikipedia. ### Annotations #### Annotation process WIT was constructed using an automatic process. However it was human-validated. From the [paper, section 3.7](https://arxiv.org/abs/2103.01913): > To further verify the quality of the WIT dataset we performed a study using (crowd-sourced) human annotators. As seen in Fig. 3, we asked raters to answer 3 questions. Given an image and the page title, raters first evaluate the quality of the attribution description and reference description in the first two questions (order randomized). The third question understands the contextual quality of these text descriptions given the page description and caption. Each response is on a 3-point scale: "Yes" if the text perfectly describes the image, "Maybe" if it is sufficiently explanatory and "No" if it is irrelevant or the image is inappropriate. #### 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 From the [paper, section 3.4](https://arxiv.org/abs/2103.01913): > Lastly we found that certain image-text pairs occurred very frequently. These were often generic images that did not have much to do with the main article page. Common examples included flags, logos, maps, insignia and such. To prevent biasing the data, we heavily under-sampled all such images ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information ```bibtex @article{srinivasan2021wit, title={WIT: Wikipedia-based Image Text Dataset for Multimodal Multilingual Machine Learning}, author={Srinivasan, Krishna and Raman, Karthik and Chen, Jiecao and Bendersky, Michael and Najork, Marc}, journal={arXiv preprint arXiv:2103.01913}, year={2021} } ``` ### Contributions Thanks to [@thomasw21](https://github.com/thomasw21), [@nateraw](https://github.com/nateraw) and [hassiahk](https://github.com/hassiahk) for adding this dataset.

[ -0.6656076312065125, -0.5034418106079102, 0.19551177322864532, -0.0853305384516716, -0.44727781414985657, -0.23967157304286957, -0.2058749794960022, -0.6034829020500183, 0.2808675765991211, 0.3733353614807129, -0.7191671133041382, -0.5403562784194946, -0.42393526434898376, 0.31614708900451...

null

BeIR/nq-generated-queries

BeIR

2022-10-23T06:15:15Z

41

0

beir

[ "task_categories:text-retrieval", "task_ids:entity-linking-retrieval", "task_ids:fact-checking-retrieval", "multilinguality:monolingual", "language:en", "license:cc-by-sa-4.0", "region:us" ]

2022-10-23T06:15:15Z

2022-06-17T13:20:26.000Z

2022-06-17T13:20:26

--- annotations_creators: [] language_creators: [] language: - en license: - cc-by-sa-4.0 multilinguality: - monolingual paperswithcode_id: beir pretty_name: BEIR Benchmark size_categories: msmarco: - 1M<n<10M trec-covid: - 100k<n<1M nfcorpus: - 1K<n<10K nq: - 1M<n<10M hotpotqa: - 1M<n<10M fiqa: - 10K<n<100K arguana: - 1K<n<10K touche-2020: - 100K<n<1M cqadupstack: - 100K<n<1M quora: - 100K<n<1M dbpedia: - 1M<n<10M scidocs: - 10K<n<100K fever: - 1M<n<10M climate-fever: - 1M<n<10M scifact: - 1K<n<10K source_datasets: [] task_categories: - text-retrieval - zero-shot-retrieval - information-retrieval - zero-shot-information-retrieval task_ids: - passage-retrieval - entity-linking-retrieval - fact-checking-retrieval - tweet-retrieval - citation-prediction-retrieval - duplication-question-retrieval - argument-retrieval - news-retrieval - biomedical-information-retrieval - question-answering-retrieval --- # Dataset Card for BEIR Benchmark ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** https://github.com/UKPLab/beir - **Repository:** https://github.com/UKPLab/beir - **Paper:** https://openreview.net/forum?id=wCu6T5xFjeJ - **Leaderboard:** https://docs.google.com/spreadsheets/d/1L8aACyPaXrL8iEelJLGqlMqXKPX2oSP_R10pZoy77Ns - **Point of Contact:** nandan.thakur@uwaterloo.ca ### Dataset Summary BEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks: - Fact-checking: [FEVER](http://fever.ai), [Climate-FEVER](http://climatefever.ai), [SciFact](https://github.com/allenai/scifact) - Question-Answering: [NQ](https://ai.google.com/research/NaturalQuestions), [HotpotQA](https://hotpotqa.github.io), [FiQA-2018](https://sites.google.com/view/fiqa/) - Bio-Medical IR: [TREC-COVID](https://ir.nist.gov/covidSubmit/index.html), [BioASQ](http://bioasq.org), [NFCorpus](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/) - News Retrieval: [TREC-NEWS](https://trec.nist.gov/data/news2019.html), [Robust04](https://trec.nist.gov/data/robust/04.guidelines.html) - Argument Retrieval: [Touche-2020](https://webis.de/events/touche-20/shared-task-1.html), [ArguAna](tp://argumentation.bplaced.net/arguana/data) - Duplicate Question Retrieval: [Quora](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs), [CqaDupstack](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/) - Citation-Prediction: [SCIDOCS](https://allenai.org/data/scidocs) - Tweet Retrieval: [Signal-1M](https://research.signal-ai.com/datasets/signal1m-tweetir.html) - Entity Retrieval: [DBPedia](https://github.com/iai-group/DBpedia-Entity/) All these datasets have been preprocessed and can be used for your experiments. ```python ``` ### Supported Tasks and Leaderboards The dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia. The current best performing models can be found [here](https://eval.ai/web/challenges/challenge-page/689/leaderboard/). ### Languages All tasks are in English (`en`). ## Dataset Structure All BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format: - `corpus` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with three fields `_id` with unique document identifier, `title` with document title (optional) and `text` with document paragraph or passage. For example: `{"_id": "doc1", "title": "Albert Einstein", "text": "Albert Einstein was a German-born...."}` - `queries` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with two fields `_id` with unique query identifier and `text` with query text. For example: `{"_id": "q1", "text": "Who developed the mass-energy equivalence formula?"}` - `qrels` file: a `.tsv` file (tab-seperated) that contains three columns, i.e. the `query-id`, `corpus-id` and `score` in this order. Keep 1st row as header. For example: `q1 doc1 1` ### Data Instances A high level example of any beir dataset: ```python corpus = { "doc1" : { "title": "Albert Einstein", "text": "Albert Einstein was a German-born theoretical physicist. who developed the theory of relativity, \ one of the two pillars of modern physics (alongside quantum mechanics). His work is also known for \ its influence on the philosophy of science. He is best known to the general public for his massâ€“energy \ equivalence formula E = mc2, which has been dubbed 'the world's most famous equation'. He received the 1921 \ Nobel Prize in Physics 'for his services to theoretical physics, and especially for his discovery of the law \ of the photoelectric effect', a pivotal step in the development of quantum theory." }, "doc2" : { "title": "", # Keep title an empty string if not present "text": "Wheat beer is a top-fermented beer which is brewed with a large proportion of wheat relative to the amount of \ malted barley. The two main varieties are German WeiÃŸbier and Belgian witbier; other types include Lambic (made\ with wild yeast), Berliner Weisse (a cloudy, sour beer), and Gose (a sour, salty beer)." }, } queries = { "q1" : "Who developed the mass-energy equivalence formula?", "q2" : "Which beer is brewed with a large proportion of wheat?" } qrels = { "q1" : {"doc1": 1}, "q2" : {"doc2": 1}, } ``` ### Data Fields Examples from all configurations have the following features: ### Corpus - `corpus`: a `dict` feature representing the document title and passage text, made up of: - `_id`: a `string` feature representing the unique document id - `title`: a `string` feature, denoting the title of the document. - `text`: a `string` feature, denoting the text of the document. ### Queries - `queries`: a `dict` feature representing the query, made up of: - `_id`: a `string` feature representing the unique query id - `text`: a `string` feature, denoting the text of the query. ### Qrels - `qrels`: a `dict` feature representing the query document relevance judgements, made up of: - `_id`: a `string` feature representing the query id - `_id`: a `string` feature, denoting the document id. - `score`: a `int32` feature, denoting the relevance judgement between query and document. ### Data Splits | Dataset | Website| BEIR-Name | Type | Queries | Corpus | Rel D/Q | Down-load | md5 | | -------- | -----| ---------| --------- | ----------- | ---------| ---------| :----------: | :------:| | MSMARCO | [Homepage](https://microsoft.github.io/msmarco/)| ``msmarco`` | ``train`` ``dev`` ``test``| 6,980 | 8.84M | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/msmarco.zip) | ``444067daf65d982533ea17ebd59501e4`` | | TREC-COVID | [Homepage](https://ir.nist.gov/covidSubmit/index.html)| ``trec-covid``| ``test``| 50| 171K| 493.5 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/trec-covid.zip) | ``ce62140cb23feb9becf6270d0d1fe6d1`` | | NFCorpus | [Homepage](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/) | ``nfcorpus`` | ``train`` ``dev`` ``test``| 323 | 3.6K | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nfcorpus.zip) | ``a89dba18a62ef92f7d323ec890a0d38d`` | | BioASQ | [Homepage](http://bioasq.org) | ``bioasq``| ``train`` ``test`` | 500 | 14.91M | 8.05 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#2-bioasq) | | NQ | [Homepage](https://ai.google.com/research/NaturalQuestions) | ``nq``| ``train`` ``test``| 3,452 | 2.68M | 1.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nq.zip) | ``d4d3d2e48787a744b6f6e691ff534307`` | | HotpotQA | [Homepage](https://hotpotqa.github.io) | ``hotpotqa``| ``train`` ``dev`` ``test``| 7,405 | 5.23M | 2.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/hotpotqa.zip) | ``f412724f78b0d91183a0e86805e16114`` | | FiQA-2018 | [Homepage](https://sites.google.com/view/fiqa/) | ``fiqa`` | ``train`` ``dev`` ``test``| 648 | 57K | 2.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fiqa.zip) | ``17918ed23cd04fb15047f73e6c3bd9d9`` | | Signal-1M(RT) | [Homepage](https://research.signal-ai.com/datasets/signal1m-tweetir.html)| ``signal1m`` | ``test``| 97 | 2.86M | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#4-signal-1m) | | TREC-NEWS | [Homepage](https://trec.nist.gov/data/news2019.html) | ``trec-news`` | ``test``| 57 | 595K | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#1-trec-news) | | ArguAna | [Homepage](http://argumentation.bplaced.net/arguana/data) | ``arguana``| ``test`` | 1,406 | 8.67K | 1.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/arguana.zip) | ``8ad3e3c2a5867cdced806d6503f29b99`` | | Touche-2020| [Homepage](https://webis.de/events/touche-20/shared-task-1.html) | ``webis-touche2020``| ``test``| 49 | 382K | 19.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/webis-touche2020.zip) | ``46f650ba5a527fc69e0a6521c5a23563`` | | CQADupstack| [Homepage](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/) | ``cqadupstack``| ``test``| 13,145 | 457K | 1.4 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/cqadupstack.zip) | ``4e41456d7df8ee7760a7f866133bda78`` | | Quora| [Homepage](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs) | ``quora``| ``dev`` ``test``| 10,000 | 523K | 1.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/quora.zip) | ``18fb154900ba42a600f84b839c173167`` | | DBPedia | [Homepage](https://github.com/iai-group/DBpedia-Entity/) | ``dbpedia-entity``| ``dev`` ``test``| 400 | 4.63M | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/dbpedia-entity.zip) | ``c2a39eb420a3164af735795df012ac2c`` | | SCIDOCS| [Homepage](https://allenai.org/data/scidocs) | ``scidocs``| ``test``| 1,000 | 25K | 4.9 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scidocs.zip) | ``38121350fc3a4d2f48850f6aff52e4a9`` | | FEVER | [Homepage](http://fever.ai) | ``fever``| ``train`` ``dev`` ``test``| 6,666 | 5.42M | 1.2| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fever.zip) | ``5a818580227bfb4b35bb6fa46d9b6c03`` | | Climate-FEVER| [Homepage](http://climatefever.ai) | ``climate-fever``|``test``| 1,535 | 5.42M | 3.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/climate-fever.zip) | ``8b66f0a9126c521bae2bde127b4dc99d`` | | SciFact| [Homepage](https://github.com/allenai/scifact) | ``scifact``| ``train`` ``test``| 300 | 5K | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scifact.zip) | ``5f7d1de60b170fc8027bb7898e2efca1`` | | Robust04 | [Homepage](https://trec.nist.gov/data/robust/04.guidelines.html) | ``robust04``| ``test``| 249 | 528K | 69.9 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#3-robust04) | ## Dataset Creation ### Curation Rationale [Needs More Information] ### Source Data #### Initial Data Collection and Normalization [Needs More Information] #### Who are the source language producers? [Needs More Information] ### Annotations #### Annotation process [Needs More Information] #### Who are the annotators? [Needs More Information] ### Personal and Sensitive Information [Needs More Information] ## Considerations for Using the Data ### Social Impact of Dataset [Needs More Information] ### Discussion of Biases [Needs More Information] ### Other Known Limitations [Needs More Information] ## Additional Information ### Dataset Curators [Needs More Information] ### Licensing Information [Needs More Information] ### Citation Information Cite as: ``` @inproceedings{ thakur2021beir, title={{BEIR}: A Heterogeneous Benchmark for Zero-shot Evaluation of Information Retrieval Models}, author={Nandan Thakur and Nils Reimers and Andreas R{\"u}ckl{\'e} and Abhishek Srivastava and Iryna Gurevych}, booktitle={Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2)}, year={2021}, url={https://openreview.net/forum?id=wCu6T5xFjeJ} } ``` ### Contributions Thanks to [@Nthakur20](https://github.com/Nthakur20) for adding this dataset.

[ -0.5227212905883789, -0.5249219536781311, 0.14435674250125885, 0.04820423573255539, 0.055916160345077515, 0.0011022627586498857, -0.1081070527434349, -0.24874727427959442, 0.28598034381866455, 0.07840226590633392, -0.45233607292175293, -0.7186435461044312, -0.347678542137146, 0.20300328731...

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jakartaresearch/indonews

jakartaresearch

2022-08-07T04:27:54Z

41

2

null

[ "task_categories:text-classification", "task_ids:multi-class-classification", "annotations_creators:found", "language_creators:found", "multilinguality:monolingual", "size_categories:1K<n<10K", "source_datasets:original", "language:id", "license:cc-by-4.0", "news", "news-classifcation", "indon...

2022-08-07T04:27:54Z

2022-08-07T04:03:02.000Z

2022-08-07T04:03:02

--- annotations_creators: - found language: - id language_creators: - found license: - cc-by-4.0 multilinguality: - monolingual pretty_name: Indonews size_categories: - 1K<n<10K source_datasets: - original tags: - news - news-classifcation - indonesia task_categories: - text-classification task_ids: - multi-class-classification --- # Indonesian News Categorization ## 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 Indonews: Multiclass News Categorization scrapped popular news portals in Indonesia. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions Thanks to [@andreaschandra](https://github.com/andreaschandra) for adding this dataset.

[ -0.5002199411392212, -0.6640398502349854, -0.06621712446212769, 0.6014594435691833, -0.5171281695365906, -0.0065231104381382465, -0.4123924672603607, -0.31859663128852844, 0.6532034277915955, 0.9153424501419067, -0.6304149627685547, -0.8700197339057922, -0.8426398038864136, 0.5489035844802...

null