sha
stringlengths 40
40
| text
stringlengths 0
13.4M
| id
stringlengths 2
117
| tags
list | created_at
stringlengths 25
25
| metadata
stringlengths 2
31.7M
| last_modified
stringlengths 25
25
|
|---|---|---|---|---|---|---|
0929565636acc37c329ad0fe0d44c5a715351427
|
HEROS is a dataset used to compare the sentence cosine similarity among sentences with high lexical overlapping but differ in their semantics.
Please refer to the paper, "Revealing the Blind Spot of Sentence Encoder Evaluation by HEROS" for more details of how the dataset is constructed and the comparison of different sentence encoders.
The dataset `heros.tsv` consists of 6 columns: `Original`, `Synonym`, `Antonym`, `Negation`, `Random`, `Typo`, `Negation`.
The first column, `Original` are the sentences from GoEmotion dataset, and sentences in the other columns are constructed by replacing some words in the original sentences based on different rules, making up different subsets in HEROS.
Different subsets in HEROS capture various aspects of semantics.
Comparing the average cosine similarity between minimal pairs in Synonym and Antonym allows one to understand whether replacing a word with an antonym is more dissimilar to the original semantics than replacing a word with a synonym.
The average cosine similarity between minimal pairs in Negation can tell us how negation affects sentence embedding similarity.
Typos are realistic and happen every day.
While humans can infer the original word from a typo and get the original meaning of the sentence, it will be interesting to see how the typos affect the sentences' similarity with the original sentences.
The Random MLM subset can tell us how similar the sentence embedding can be when two sentences are semantically different but with high lexical overlaps.
By comparing the performance of different SEs on different subsets in HEROS, we can further understand the trait of different SEs.
|
dcml0714/Heros
|
[
"size_categories:n<1K",
"license:apache-2.0",
"region:us"
] |
2023-06-08T04:07:48+00:00
|
{"license": "apache-2.0", "size_categories": ["n<1K"]}
|
2023-06-08T04:24:15+00:00
|
3b78afa59d8fff8978069e79803710f8fa4e82da
|
# Dataset Card for "ph_er_dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
steviebarot/ph_er_dataset
|
[
"region:us"
] |
2023-06-08T04:18:51+00:00
|
{"dataset_info": {"features": [{"name": "audio", "dtype": "audio"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "angry", "1": "sad", "2": "well"}}}}], "splits": [{"name": "train", "num_bytes": 89136166.0, "num_examples": 75}, {"name": "validation", "num_bytes": 52124338.0, "num_examples": 45}, {"name": "test", "num_bytes": 35891936.0, "num_examples": 30}], "download_size": 172308802, "dataset_size": 177152440.0}}
|
2023-06-14T23:46:46+00:00
|
3ebf22bb8341776ec98eb6eaf578fc7f524f5173
|
# Dataset Card for "VQAv2_minival_validation_v2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Multimodal-Fatima/VQAv2_minival_validation
|
[
"region:us"
] |
2023-06-08T04:25:05+00:00
|
{"dataset_info": {"features": [{"name": "question_type", "dtype": "string"}, {"name": "multiple_choice_answer", "dtype": "string"}, {"name": "answers", "sequence": "string"}, {"name": "answers_original", "list": [{"name": "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": "image", "dtype": "image"}, {"name": "clip_tags_ViT_L_14", "sequence": "string"}, {"name": "blip_caption", "dtype": "string"}, {"name": "LLM_Description_gpt3_downstream_tasks_visual_genome_ViT_L_14", "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_clip_ViT_L_14", "list": [{"name": "attribute", "dtype": "string"}, {"name": "box", "sequence": "float64"}, {"name": "label", "dtype": "string"}, {"name": "location", "dtype": "string"}, {"name": "ratio", "dtype": "float64"}, {"name": "size", "dtype": "string"}, {"name": "tag", "dtype": "string"}]}, {"name": "DETA_detections_deta_swin_large_o365_clip_ViT_L_14_blip_caption", "list": [{"name": "attribute", "dtype": "string"}, {"name": "box", "sequence": "float64"}, {"name": "caption", "dtype": "string"}, {"name": "label", "dtype": "string"}, {"name": "location", "dtype": "string"}, {"name": "ratio", "dtype": "float64"}, {"name": "size", "dtype": "string"}, {"name": "tag", "dtype": "string"}]}, {"name": "id", "dtype": "int64"}, {"name": "DETA_detections_deta_swin_large_o365_clip_ViT_L_14_blip_caption_caption_module", "list": [{"name": "attribute", "dtype": "string"}, {"name": "box", "sequence": "float64"}, {"name": "caption", "dtype": "string"}, {"name": "captions_module", "sequence": "string"}, {"name": "label", "dtype": "string"}, {"name": "location", "dtype": "string"}, {"name": "ratio", "dtype": "float64"}, {"name": "size", "dtype": "string"}, {"name": "tag", "dtype": "string"}]}, {"name": "DETA_detections_deta_swin_large_o365_clip_ViT_L_14_blip_caption_caption_module_without_filtering", "list": [{"name": "attribute", "dtype": "string"}, {"name": "box", "sequence": "float64"}, {"name": "caption", "dtype": "string"}, {"name": "captions_module", "sequence": "string"}, {"name": "label", "dtype": "string"}, {"name": "location", "dtype": "string"}, {"name": "ratio", "dtype": "float64"}, {"name": "size", "dtype": "string"}, {"name": "tag", "dtype": "string"}]}, {"name": "DETA_detections_deta_swin_large_o365_clip_ViT_L_14_blip_caption_caption_module_random", "list": [{"name": "attribute", "dtype": "string"}, {"name": "box", "sequence": "float64"}, {"name": "caption", "dtype": "string"}, {"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_LAION_ViT_H_14_2B", "sequence": "string"}, {"name": "LLM_Description_gpt3_downstream_tasks_visual_genome_LAION-ViT-H-14-2B", "sequence": "string"}, {"name": "Attributes_ViT_L_14_descriptors_text_davinci_003_full", "sequence": "string"}, {"name": "clip_tags_ViT_L_14_wo_openai", "sequence": "string"}, {"name": "clip_tags_ViT_L_14_with_openai", "sequence": "string"}, {"name": "clip_tags_LAION_ViT_H_14_2B_wo_openai", "sequence": "string"}, {"name": "clip_tags_LAION_ViT_H_14_2B_with_openai", "sequence": "string"}, {"name": "clip_tags_LAION_ViT_bigG_14_2B_wo_openai", "sequence": "string"}, {"name": "clip_tags_LAION_ViT_bigG_14_2B_with_openai", "sequence": "string"}, {"name": "Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full", "sequence": "string"}, {"name": "Attributes_LAION_ViT_bigG_14_2B_descriptors_text_davinci_003_full", "sequence": "string"}, {"name": "clip_tags_ViT_B_16_with_openai", "sequence": "string"}, {"name": "blip_caption_beam_5_Salesforce_blip2_flan_t5_xxl", "dtype": "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": "DETA_detections_deta_swin_large_o365_coco_classes_caption_all_patches_Salesforce_blip_image_captioning_large_clean", "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_Salesforce_blip_image_captioning_large_intensive", "sequence": "string"}, {"name": "blip_caption_Salesforce_blip_image_captioning_base_intensive", "sequence": "string"}], "splits": [{"name": "validation", "num_bytes": 10757838822.0, "num_examples": 25994}], "download_size": 2788131849, "dataset_size": 10757838822.0}}
|
2023-06-09T01:25:25+00:00
|
f50256f6e305584f08dd4afc2ded5c79b84d21c7
|
GautamR/test_agri
|
[
"license:apache-2.0",
"region:us"
] |
2023-06-08T04:32:54+00:00
|
{"license": "apache-2.0"}
|
2023-06-08T04:36:29+00:00
|
|
9eede1df800aa12c4a064332478ad6b0a9a2208f
|
# Dataset Card for "minipile_512_tiny"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
ZelaAI/minipile_512_tiny
|
[
"region:us"
] |
2023-06-08T04:36:49+00:00
|
{"dataset_info": {"features": [{"name": "tokens", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 11229400, "num_examples": 2738}], "download_size": 3003789, "dataset_size": 11229400}}
|
2023-06-08T04:37:33+00:00
|
aca6d473ed918f15e34a77c423396e258d0e926e
|
# COIG Prompt Collection
## License
**Default Licensing for Sub-Datasets Without Specific License Declaration**: In instances where sub-datasets within the COIG-PC Dataset do not have a specific license declaration, the Apache License 2.0 (Apache-2.0) will be the applicable licensing terms by default.
**Precedence of Declared Licensing for Sub-Datasets**: For any sub-dataset within the COIG-PC Dataset that has an explicitly declared license, the terms and conditions of the declared license shall take precedence and govern the usage of that particular sub-dataset.
Users and developers utilizing the COIG-PC Dataset must ensure compliance with the licensing terms as outlined above. It is imperative to review and adhere to the specified licensing conditions of each sub-dataset, as they may vary.
## What is COIG-PC?
The COIG-PC Dataset is a meticulously curated and comprehensive collection of Chinese tasks and data, designed to facilitate the fine-tuning and optimization of language models for Chinese natural language processing (NLP). The dataset aims to provide researchers and developers with a rich set of resources to improve the capabilities of language models in handling Chinese text, which can be utilized in various fields such as text generation, information extraction, sentiment analysis, machine translation, among others.
If you think COIG-PC is too huge, please refer to [COIG-PC-Lite](https://huggingface.co/datasets/BAAI/COIG-PC-Lite) which is a subset of COIG-PC with only 200 samples from each task file.
## Why COIG-PC?
The COIG-PC Dataset is an invaluable resource for the domain of natural language processing (NLP) for various compelling reasons:
**Addressing Language Complexity**: Chinese is known for its intricacy, with a vast array of characters and diverse grammatical structures. A specialized dataset like COIG-PC, which is tailored for the Chinese language, is essential to adequately address these complexities during model training.
**Comprehensive Data Aggregation**: The COIG-PC Dataset is a result of an extensive effort in integrating almost all available Chinese datasets in the market. This comprehensive aggregation makes it one of the most exhaustive collections for Chinese NLP.
**Data Deduplication and Normalization**: The COIG-PC Dataset underwent rigorous manual processing to eliminate duplicate data and perform normalization. This ensures that the dataset is free from redundancy, and the data is consistent and well-structured, making it more user-friendly and efficient for model training.
**Fine-tuning and Optimization**: The dataset’s instruction-based phrasing facilitates better fine-tuning and optimization of language models. This structure allows models to better understand and execute tasks, which is particularly beneficial in improving performance on unseen or novel tasks.
The COIG-PC Dataset, with its comprehensive aggregation, meticulous selection, deduplication, and normalization of data, stands as an unmatched resource for training and optimizing language models tailored for the Chinese language and culture. It addresses the unique challenges of Chinese language processing and serves as a catalyst for advancements in Chinese NLP.
## Who builds COIG-PC?
The bedrock of COIG-PC is anchored in the dataset furnished by stardust.ai, which comprises an aggregation of data collected from the Internet.
And COIG-PC is the result of a collaborative effort involving engineers and experts from over twenty distinguished universities both domestically and internationally. Due to space constraints, it is not feasible to list all of them; however, the following are a few notable institutions among the collaborators:
- Beijing Academy of Artificial Intelligence, China
<img src="https://huggingface.co/datasets/BAAI/COIG-PC/resolve/main/assets/baai.png" alt= “BAAI” height="100" width="150">
- Peking University, China
<img src="https://huggingface.co/datasets/BAAI/COIG-PC/resolve/main/assets/pku.png" alt= “PKU” height="100" width="200">
- The Hong Kong University of Science and Technology (HKUST), China
<img src="https://huggingface.co/datasets/BAAI/COIG-PC/resolve/main/assets/hkust.png" alt= “HKUST” height="100" width="200">
- The University of Waterloo, Canada
<img src="https://huggingface.co/datasets/BAAI/COIG-PC/resolve/main/assets/waterloo.png" alt= “Waterloo” height="100" width="150">
- The University of Sheffield, United Kingdom
<img src="https://huggingface.co/datasets/BAAI/COIG-PC/resolve/main/assets/sheffield.png" alt= “Sheffield” height="100" width="200">
- Beijing University of Posts and Telecommunications, China
<img src="https://huggingface.co/datasets/BAAI/COIG-PC/resolve/main/assets/bupt.png" alt= “BUPT” height="100" width="200">
- [Multimodal Art Projection](https://huggingface.co/m-a-p)
<img src="https://huggingface.co/datasets/BAAI/COIG-PC/resolve/main/assets/map.png" alt= “M.A.P” height="100" width="200">
- stardust.ai, China
<img src="https://huggingface.co/datasets/BAAI/COIG-PC/resolve/main/assets/stardust.png" alt= “stardust.ai” height="100" width="200">
- LinkSoul.AI, China
<img src="https://huggingface.co/datasets/BAAI/COIG-PC/resolve/main/assets/linksoul.png" alt= “linksoul.ai” height="100" width="200">
For the detailed list of engineers involved in the creation and refinement of COIG-PC, please refer to the paper that will be published subsequently. This paper will provide in-depth information regarding the contributions and the specifics of the dataset’s development process.
## How to use COIG-PC?
COIG-PC is structured in a **.jsonl** file format. Each line in the file represents a single data record and is structured in JSON (JavaScript Object Notation) format. Below is a breakdown of the elements within each line:
**instruction**: This is a text string that provides the instruction for the task. For example, it might tell the model what to do with the input data.
**input**: This is the input data that the model needs to process. In the context of translation, it would be the text that needs to be translated.
**output**: This contains the expected output data after processing the input. In the context of translation, it would be the translated text.
**split**: Indicates the official split of the original dataset, which is used to categorize data for different phases of model training and evaluation. It can be 'train', 'test', 'valid', etc.
**task_type**: Contains major and minor categories for the dataset. Major categories are broader, while minor categories can be more specific subcategories.
**domain**: Indicates the domain or field to which the data belongs.
**other**: This field can contain additional information or metadata regarding the data record. If there is no additional information, it may be set to null.
### Example
Here is an example of how a line in the COIG-PC dataset might be structured:
```
{
"instruction": "请把下面的中文句子翻译成英文",
"input": "我爱你。",
"output": "I love you.",
"split": "train",
"task_type": {
"major": ["翻译"],
"minor": ["翻译", "中译英"]
},
"domain": ["通用"],
"other": null
}
```
In this example:
**instruction** tells the model to translate the following Chinese sentence into English.
**input** contains the Chinese text "我爱你" which means "I love you".
**output** contains the expected translation in English: "I love you".
**split** indicates that this data record is part of the training set.
**task_type** specifies that the major category is "Translation" and the minor categories are "Translation" and "Chinese to English".
**domain** specifies that this data record belongs to the general domain.
**other** is set to null as there is no additional information for this data record.
## Update: Oct. 8, 2023
- v1.3: Upload all splits to the main branch as arrow datasets. All jsonl files are stored in the raw_json branch now. Remove 152 task files. Add 10 task files. In total, 275 task files updated.
- v1.2: Delete 31 bad task files. Update 99 task files. Rename 2 task files. Add 3 new task files. COIG-PC now has 3339 tasks in total.
- v1.1: Fix 00040-001-000 and 00050-003-000, ignore 00930 and 01373.
- v1.0: First version for arXiv paper.
- v0.6: Upload 28 new tasks. COIG-PC now has 3367 tasks in total.
- v0.5: Upload 202 new tasks. COIG-PC now has 3339 tasks in total.
- v0.4: Upload 1049 new tasks. COIG-PC now has 3137 tasks in total.
- v0.3: Upload 1139 new tasks. COIG-PC now has 2088 tasks in total.
- v0.2: Upload 422 new tasks. COIG-PC now has 949 tasks in total. Add "TopSamplenumPerTask" split where only "Samplenum" samples are used from each task.
- v0.1: Upload 527 tasks.
## COIG-PC Citation
If you want to cite COIG-PC dataset, you could use this:
```
```
## Contact Us
To contact us feel free to create an Issue in this repository.
|
BAAI/COIG-PC
|
[
"language:zh",
"license:unknown",
"region:us"
] |
2023-06-08T04:41:11+00:00
|
{"language": ["zh"], "license": "unknown", "extra_gated_heading": "Acknowledge license to accept the repository", "extra_gated_prompt": "\u5317\u4eac\u667a\u6e90\u4eba\u5de5\u667a\u80fd\u7814\u7a76\u9662\uff08\u4ee5\u4e0b\u7b80\u79f0\u201c\u6211\u4eec\u201d\u6216\u201c\u7814\u7a76\u9662\u201d\uff09\u901a\u8fc7BAAI DataHub\uff08data.baai.ac.cn\uff09\u548cCOIG-PC HuggingFace\u4ed3\u5e93\uff08https://huggingface.co/datasets/BAAI/COIG-PC\uff09\u5411\u60a8\u63d0\u4f9b\u5f00\u6e90\u6570\u636e\u96c6\uff08\u4ee5\u4e0b\u6216\u79f0\u201c\u6570\u636e\u96c6\u201d\uff09\uff0c\u60a8\u53ef\u901a\u8fc7\u4e0b\u8f7d\u7684\u65b9\u5f0f\u83b7\u53d6\u60a8\u6240\u9700\u7684\u5f00\u6e90\u6570\u636e\u96c6\uff0c\u5e76\u5728\u9075\u5b88\u5404\u539f\u59cb\u6570\u636e\u96c6\u4f7f\u7528\u89c4\u5219\u524d\u63d0\u4e0b\uff0c\u57fa\u4e8e\u5b66\u4e60\u3001\u7814\u7a76\u3001\u5546\u4e1a\u7b49\u76ee\u7684\u4f7f\u7528\u76f8\u5173\u6570\u636e\u96c6\u3002\n\u5728\u60a8\u83b7\u53d6\uff08\u5305\u62ec\u4f46\u4e0d\u9650\u4e8e\u8bbf\u95ee\u3001\u4e0b\u8f7d\u3001\u590d\u5236\u3001\u4f20\u64ad\u3001\u4f7f\u7528\u7b49\u5904\u7406\u6570\u636e\u96c6\u7684\u884c\u4e3a\uff09\u5f00\u6e90\u6570\u636e\u96c6\u524d\uff0c\u60a8\u5e94\u8ba4\u771f\u9605\u8bfb\u5e76\u7406\u89e3\u672c\u300aCOIG-PC\u5f00\u6e90\u6570\u636e\u96c6\u4f7f\u7528\u987b\u77e5\u4e0e\u514d\u8d23\u58f0\u660e\u300b\uff08\u4ee5\u4e0b\u7b80\u79f0\u201c\u672c\u58f0\u660e\u201d\uff09\u3002\u4e00\u65e6\u60a8\u83b7\u53d6\u5f00\u6e90\u6570\u636e\u96c6\uff0c\u65e0\u8bba\u60a8\u7684\u83b7\u53d6\u65b9\u5f0f\u4e3a\u4f55\uff0c\u60a8\u7684\u83b7\u53d6\u884c\u4e3a\u5747\u5c06\u88ab\u89c6\u4e3a\u5bf9\u672c\u58f0\u660e\u5168\u90e8\u5185\u5bb9\u7684\u8ba4\u53ef\u3002\n1.\t\u5e73\u53f0\u7684\u6240\u6709\u6743\u4e0e\u8fd0\u8425\u6743\n\u60a8\u5e94\u5145\u5206\u4e86\u89e3\u5e76\u77e5\u6089\uff0cBAAI DataHub\u548cCOIG-PC HuggingFace\u4ed3\u5e93\uff08\u5305\u62ec\u5f53\u524d\u7248\u672c\u53ca\u5168\u90e8\u5386\u53f2\u7248\u672c\uff09\u7684\u6240\u6709\u6743\u4e0e\u8fd0\u8425\u6743\u5f52\u667a\u6e90\u4eba\u5de5\u667a\u80fd\u7814\u7a76\u9662\u6240\u6709\uff0c\u667a\u6e90\u4eba\u5de5\u667a\u80fd\u7814\u7a76\u9662\u5bf9\u672c\u5e73\u53f0/\u672c\u5de5\u5177\u53ca\u5f00\u6e90\u6570\u636e\u96c6\u5f00\u653e\u8ba1\u5212\u62e5\u6709\u6700\u7ec8\u89e3\u91ca\u6743\u548c\u51b3\u5b9a\u6743\u3002\n\u60a8\u77e5\u6089\u5e76\u7406\u89e3\uff0c\u57fa\u4e8e\u76f8\u5173\u6cd5\u5f8b\u6cd5\u89c4\u66f4\u65b0\u548c\u5b8c\u5584\u4ee5\u53ca\u6211\u4eec\u9700\u5c65\u884c\u6cd5\u5f8b\u5408\u89c4\u4e49\u52a1\u7684\u5ba2\u89c2\u53d8\u5316\uff0c\u6211\u4eec\u4fdd\u7559\u5bf9\u672c\u5e73\u53f0/\u672c\u5de5\u5177\u8fdb\u884c\u4e0d\u5b9a\u65f6\u66f4\u65b0\u3001\u7ef4\u62a4\uff0c\u6216\u8005\u4e2d\u6b62\u4e43\u81f3\u6c38\u4e45\u7ec8\u6b62\u63d0\u4f9b\u672c\u5e73\u53f0/\u672c\u5de5\u5177\u670d\u52a1\u7684\u6743\u5229\u3002\u6211\u4eec\u5c06\u5728\u5408\u7406\u65f6\u95f4\u5185\u5c06\u53ef\u80fd\u53d1\u751f\u524d\u8ff0\u60c5\u5f62\u901a\u8fc7\u516c\u544a\u6216\u90ae\u4ef6\u7b49\u5408\u7406\u65b9\u5f0f\u544a\u77e5\u60a8\uff0c\u60a8\u5e94\u5f53\u53ca\u65f6\u505a\u597d\u76f8\u5e94\u7684\u8c03\u6574\u548c\u5b89\u6392\uff0c\u4f46\u6211\u4eec\u4e0d\u56e0\u53d1\u751f\u524d\u8ff0\u4efb\u4f55\u60c5\u5f62\u5bf9\u60a8\u9020\u6210\u7684\u4efb\u4f55\u635f\u5931\u627f\u62c5\u4efb\u4f55\u8d23\u4efb\u3002\n2.\t\u5f00\u6e90\u6570\u636e\u96c6\u7684\u6743\u5229\u4e3b\u5f20\n\u4e3a\u4e86\u4fbf\u4e8e\u60a8\u57fa\u4e8e\u5b66\u4e60\u3001\u7814\u7a76\u3001\u5546\u4e1a\u7684\u76ee\u7684\u5f00\u5c55\u6570\u636e\u96c6\u83b7\u53d6\u3001\u4f7f\u7528\u7b49\u6d3b\u52a8\uff0c\u6211\u4eec\u5bf9\u7b2c\u4e09\u65b9\u539f\u59cb\u6570\u636e\u96c6\u8fdb\u884c\u4e86\u5fc5\u8981\u7684\u683c\u5f0f\u6574\u5408\u3001\u6570\u636e\u6e05\u6d17\u3001\u6807\u6ce8\u3001\u5206\u7c7b\u3001\u6ce8\u91ca\u7b49\u76f8\u5173\u5904\u7406\u73af\u8282\uff0c\u5f62\u6210\u53ef\u4f9b\u672c\u5e73\u53f0/\u672c\u5de5\u5177\u7528\u6237\u4f7f\u7528\u7684\u5f00\u6e90\u6570\u636e\u96c6\u3002\n\u60a8\u77e5\u6089\u5e76\u7406\u89e3\uff0c\u6211\u4eec\u4e0d\u5bf9\u5f00\u6e90\u6570\u636e\u96c6\u4e3b\u5f20\u77e5\u8bc6\u4ea7\u6743\u4e2d\u7684\u76f8\u5173\u8d22\u4ea7\u6027\u6743\u5229\uff0c\u56e0\u6b64\u6211\u4eec\u4ea6\u65e0\u76f8\u5e94\u4e49\u52a1\u5bf9\u5f00\u6e90\u6570\u636e\u96c6\u53ef\u80fd\u5b58\u5728\u7684\u77e5\u8bc6\u4ea7\u6743\u8fdb\u884c\u4e3b\u52a8\u8bc6\u522b\u548c\u4fdd\u62a4\uff0c\u4f46\u8fd9\u4e0d\u610f\u5473\u7740\u6211\u4eec\u653e\u5f03\u5f00\u6e90\u6570\u636e\u96c6\u4e3b\u5f20\u7f72\u540d\u6743\u3001\u53d1\u8868\u6743\u3001\u4fee\u6539\u6743\u548c\u4fdd\u62a4\u4f5c\u54c1\u5b8c\u6574\u6743\uff08\u5982\u6709\uff09\u7b49\u4eba\u8eab\u6027\u6743\u5229\u3002\u800c\u539f\u59cb\u6570\u636e\u96c6\u53ef\u80fd\u5b58\u5728\u7684\u77e5\u8bc6\u4ea7\u6743\u53ca\u76f8\u5e94\u5408\u6cd5\u6743\u76ca\u7531\u539f\u6743\u5229\u4eba\u4eab\u6709\u3002\n\u6b64\u5916\uff0c\u5411\u60a8\u5f00\u653e\u548c\u4f7f\u7528\u7ecf\u5408\u7406\u7f16\u6392\u3001\u52a0\u5de5\u548c\u5904\u7406\u540e\u7684\u5f00\u6e90\u6570\u636e\u96c6\uff0c\u5e76\u4e0d\u610f\u5473\u7740\u6211\u4eec\u5bf9\u539f\u59cb\u6570\u636e\u96c6\u77e5\u8bc6\u4ea7\u6743\u3001\u4fe1\u606f\u5185\u5bb9\u7b49\u771f\u5b9e\u3001\u51c6\u786e\u6216\u65e0\u4e89\u8bae\u7684\u8ba4\u53ef\uff0c\u60a8\u5e94\u5f53\u81ea\u884c\u7b5b\u9009\u3001\u4ed4\u7ec6\u7504\u522b\uff0c\u4f7f\u7528\u7ecf\u60a8\u9009\u62e9\u7684\u5f00\u6e90\u6570\u636e\u96c6\u3002\u60a8\u77e5\u6089\u5e76\u540c\u610f\uff0c\u7814\u7a76\u9662\u5bf9\u60a8\u81ea\u884c\u9009\u62e9\u4f7f\u7528\u7684\u539f\u59cb\u6570\u636e\u96c6\u4e0d\u8d1f\u6709\u4efb\u4f55\u65e0\u7f3a\u9677\u6216\u65e0\u7455\u75b5\u7684\u627f\u8bfa\u4e49\u52a1\u6216\u62c5\u4fdd\u8d23\u4efb\u3002\n3.\t\u5f00\u6e90\u6570\u636e\u96c6\u7684\u4f7f\u7528\u9650\u5236\n\u60a8\u4f7f\u7528\u6570\u636e\u96c6\u4e0d\u5f97\u4fb5\u5bb3\u6211\u4eec\u6216\u4efb\u4f55\u7b2c\u4e09\u65b9\u7684\u5408\u6cd5\u6743\u76ca\uff08\u5305\u62ec\u4f46\u4e0d\u9650\u4e8e\u8457\u4f5c\u6743\u3001\u4e13\u5229\u6743\u3001\u5546\u6807\u6743\u7b49\u77e5\u8bc6\u4ea7\u6743\u4e0e\u5176\u4ed6\u6743\u76ca\uff09\u3002\n\u83b7\u53d6\u5f00\u6e90\u6570\u636e\u96c6\u540e\uff0c\u60a8\u5e94\u786e\u4fdd\u5bf9\u5f00\u6e90\u6570\u636e\u96c6\u7684\u4f7f\u7528\u4e0d\u8d85\u8fc7\u539f\u59cb\u6570\u636e\u96c6\u7684\u6743\u5229\u4eba\u4ee5\u516c\u793a\u6216\u534f\u8bae\u7b49\u5f62\u5f0f\u660e\u786e\u89c4\u5b9a\u7684\u4f7f\u7528\u89c4\u5219\uff0c\u5305\u62ec\u539f\u59cb\u6570\u636e\u7684\u4f7f\u7528\u8303\u56f4\u3001\u76ee\u7684\u548c\u5408\u6cd5\u7528\u9014\u7b49\u3002\u6211\u4eec\u5728\u6b64\u5584\u610f\u5730\u63d0\u8bf7\u60a8\u7559\u610f\uff0c\u5982\u60a8\u5bf9\u5f00\u6e90\u6570\u636e\u96c6\u7684\u4f7f\u7528\u8d85\u51fa\u539f\u59cb\u6570\u636e\u96c6\u7684\u539f\u5b9a\u4f7f\u7528\u8303\u56f4\u53ca\u7528\u9014\uff0c\u60a8\u53ef\u80fd\u9762\u4e34\u4fb5\u72af\u539f\u59cb\u6570\u636e\u96c6\u6743\u5229\u4eba\u7684\u5408\u6cd5\u6743\u76ca\u4f8b\u5982\u77e5\u8bc6\u4ea7\u6743\u7684\u98ce\u9669\uff0c\u5e76\u53ef\u80fd\u627f\u62c5\u76f8\u5e94\u7684\u6cd5\u5f8b\u8d23\u4efb\u3002\n4.\t\u4e2a\u4eba\u4fe1\u606f\u4fdd\u62a4\n\u57fa\u4e8e\u6280\u672f\u9650\u5236\u53ca\u5f00\u6e90\u6570\u636e\u96c6\u7684\u516c\u76ca\u6027\u8d28\u7b49\u5ba2\u89c2\u539f\u56e0\uff0c\u6211\u4eec\u65e0\u6cd5\u4fdd\u8bc1\u5f00\u6e90\u6570\u636e\u96c6\u4e2d\u4e0d\u5305\u542b\u4efb\u4f55\u4e2a\u4eba\u4fe1\u606f\uff0c\u6211\u4eec\u4e0d\u5bf9\u5f00\u6e90\u6570\u636e\u96c6\u4e2d\u53ef\u80fd\u6d89\u53ca\u7684\u4e2a\u4eba\u4fe1\u606f\u627f\u62c5\u4efb\u4f55\u6cd5\u5f8b\u8d23\u4efb\u3002\n\u5982\u5f00\u6e90\u6570\u636e\u96c6\u6d89\u53ca\u4e2a\u4eba\u4fe1\u606f\uff0c\u6211\u4eec\u4e0d\u5bf9\u60a8\u4f7f\u7528\u5f00\u6e90\u6570\u636e\u96c6\u53ef\u80fd\u6d89\u53ca\u7684\u4efb\u4f55\u4e2a\u4eba\u4fe1\u606f\u5904\u7406\u884c\u4e3a\u627f\u62c5\u6cd5\u5f8b\u8d23\u4efb\u3002\u6211\u4eec\u5728\u6b64\u5584\u610f\u5730\u63d0\u8bf7\u60a8\u7559\u610f\uff0c\u60a8\u5e94\u4f9d\u636e\u300a\u4e2a\u4eba\u4fe1\u606f\u4fdd\u62a4\u6cd5\u300b\u7b49\u76f8\u5173\u6cd5\u5f8b\u6cd5\u89c4\u7684\u89c4\u5b9a\u5904\u7406\u4e2a\u4eba\u4fe1\u606f\u3002\n\u4e3a\u4e86\u7ef4\u62a4\u4fe1\u606f\u4e3b\u4f53\u7684\u5408\u6cd5\u6743\u76ca\u3001\u5c65\u884c\u53ef\u80fd\u9002\u7528\u7684\u6cd5\u5f8b\u3001\u884c\u653f\u6cd5\u89c4\u7684\u89c4\u5b9a\uff0c\u5982\u60a8\u5728\u4f7f\u7528\u5f00\u6e90\u6570\u636e\u96c6\u7684\u8fc7\u7a0b\u4e2d\u53d1\u73b0\u6d89\u53ca\u6216\u8005\u53ef\u80fd\u6d89\u53ca\u4e2a\u4eba\u4fe1\u606f\u7684\u5185\u5bb9\uff0c\u5e94\u7acb\u5373\u505c\u6b62\u5bf9\u6570\u636e\u96c6\u4e2d\u6d89\u53ca\u4e2a\u4eba\u4fe1\u606f\u90e8\u5206\u7684\u4f7f\u7528\uff0c\u5e76\u53ca\u65f6\u901a\u8fc7\u201c6. \u6295\u8bc9\u4e0e\u901a\u77e5\u201d\u4e2d\u8f7d\u660e\u7684\u8054\u7cfb\u6211\u4eec\u3002\n5.\t\u4fe1\u606f\u5185\u5bb9\u7ba1\u7406\n\u6211\u4eec\u4e0d\u5bf9\u5f00\u6e90\u6570\u636e\u96c6\u53ef\u80fd\u6d89\u53ca\u7684\u8fdd\u6cd5\u4e0e\u4e0d\u826f\u4fe1\u606f\u627f\u62c5\u4efb\u4f55\u6cd5\u5f8b\u8d23\u4efb\u3002\n\u5982\u60a8\u5728\u4f7f\u7528\u5f00\u6e90\u6570\u636e\u96c6\u7684\u8fc7\u7a0b\u4e2d\u53d1\u73b0\u5f00\u6e90\u6570\u636e\u96c6\u6d89\u53ca\u6216\u8005\u53ef\u80fd\u6d89\u53ca\u4efb\u4f55\u8fdd\u6cd5\u4e0e\u4e0d\u826f\u4fe1\u606f\uff0c\u60a8\u5e94\u7acb\u5373\u505c\u6b62\u5bf9\u6570\u636e\u96c6\u4e2d\u6d89\u53ca\u8fdd\u6cd5\u4e0e\u4e0d\u826f\u4fe1\u606f\u90e8\u5206\u7684\u4f7f\u7528\uff0c\u5e76\u53ca\u65f6\u901a\u8fc7\u201c6. \u6295\u8bc9\u4e0e\u901a\u77e5\u201d\u4e2d\u8f7d\u660e\u7684\u8054\u7cfb\u6211\u4eec\u3002\n6.\t\u6295\u8bc9\u4e0e\u901a\u77e5\n\u5982\u60a8\u8ba4\u4e3a\u5f00\u6e90\u6570\u636e\u96c6\u4fb5\u72af\u4e86\u60a8\u7684\u5408\u6cd5\u6743\u76ca\uff0c\u60a8\u53ef\u901a\u8fc7010-50955974\u8054\u7cfb\u6211\u4eec\uff0c\u6211\u4eec\u4f1a\u53ca\u65f6\u4f9d\u6cd5\u5904\u7406\u60a8\u7684\u4e3b\u5f20\u4e0e\u6295\u8bc9\u3002\n\u4e3a\u4e86\u5904\u7406\u60a8\u7684\u4e3b\u5f20\u548c\u6295\u8bc9\uff0c\u6211\u4eec\u53ef\u80fd\u9700\u8981\u60a8\u63d0\u4f9b\u8054\u7cfb\u65b9\u5f0f\u3001\u4fb5\u6743\u8bc1\u660e\u6750\u6599\u4ee5\u53ca\u8eab\u4efd\u8bc1\u660e\u7b49\u6750\u6599\u3002\u8bf7\u6ce8\u610f\uff0c\u5982\u679c\u60a8\u6076\u610f\u6295\u8bc9\u6216\u9648\u8ff0\u5931\u5b9e\uff0c\u60a8\u5c06\u627f\u62c5\u7531\u6b64\u9020\u6210\u7684\u5168\u90e8\u6cd5\u5f8b\u8d23\u4efb\uff08\u5305\u62ec\u4f46\u4e0d\u9650\u4e8e\u5408\u7406\u7684\u8d39\u7528\u8d54\u507f\u7b49\uff09\u3002\n7.\t\u8d23\u4efb\u58f0\u660e\n\u60a8\u7406\u89e3\u5e76\u540c\u610f\uff0c\u57fa\u4e8e\u5f00\u6e90\u6570\u636e\u96c6\u7684\u6027\u8d28\uff0c\u6570\u636e\u96c6\u4e2d\u53ef\u80fd\u5305\u542b\u6765\u81ea\u4e0d\u540c\u6765\u6e90\u548c\u8d21\u732e\u8005\u7684\u6570\u636e\uff0c\u5176\u771f\u5b9e\u6027\u3001\u51c6\u786e\u6027\u3001\u5ba2\u89c2\u6027\u7b49\u53ef\u80fd\u4f1a\u6709\u6240\u5dee\u5f02\uff0c\u6211\u4eec\u65e0\u6cd5\u5bf9\u4efb\u4f55\u6570\u636e\u96c6\u7684\u53ef\u7528\u6027\u3001\u53ef\u9760\u6027\u7b49\u505a\u51fa\u4efb\u4f55\u627f\u8bfa\u3002\n\u5728\u4efb\u4f55\u60c5\u51b5\u4e0b\uff0c\u6211\u4eec\u4e0d\u5bf9\u5f00\u6e90\u6570\u636e\u96c6\u53ef\u80fd\u5b58\u5728\u7684\u4e2a\u4eba\u4fe1\u606f\u4fb5\u6743\u3001\u8fdd\u6cd5\u4e0e\u4e0d\u826f\u4fe1\u606f\u4f20\u64ad\u3001\u77e5\u8bc6\u4ea7\u6743\u4fb5\u6743\u7b49\u4efb\u4f55\u98ce\u9669\u627f\u62c5\u4efb\u4f55\u6cd5\u5f8b\u8d23\u4efb\u3002\n\u5728\u4efb\u4f55\u60c5\u51b5\u4e0b\uff0c\u6211\u4eec\u4e0d\u5bf9\u60a8\u56e0\u5f00\u6e90\u6570\u636e\u96c6\u906d\u53d7\u7684\u6216\u4e0e\u4e4b\u76f8\u5173\u7684\u4efb\u4f55\u635f\u5931\uff08\u5305\u62ec\u4f46\u4e0d\u9650\u4e8e\u76f4\u63a5\u635f\u5931\u3001\u95f4\u63a5\u635f\u5931\u4ee5\u53ca\u53ef\u5f97\u5229\u76ca\u635f\u5931\u7b49\uff09\u627f\u62c5\u4efb\u4f55\u6cd5\u5f8b\u8d23\u4efb\u3002\n8.\t\u5176\u4ed6\n\u5f00\u6e90\u6570\u636e\u96c6\u5904\u4e8e\u4e0d\u65ad\u53d1\u5c55\u3001\u53d8\u5316\u7684\u9636\u6bb5\uff0c\u6211\u4eec\u53ef\u80fd\u56e0\u4e1a\u52a1\u53d1\u5c55\u3001\u7b2c\u4e09\u65b9\u5408\u4f5c\u3001\u6cd5\u5f8b\u6cd5\u89c4\u53d8\u52a8\u7b49\u539f\u56e0\u66f4\u65b0\u3001\u8c03\u6574\u6240\u63d0\u4f9b\u7684\u5f00\u6e90\u6570\u636e\u96c6\u8303\u56f4\uff0c\u6216\u4e2d\u6b62\u3001\u6682\u505c\u3001\u7ec8\u6b62\u5f00\u6e90\u6570\u636e\u96c6\u63d0\u4f9b\u4e1a\u52a1\u3002\n", "extra_gated_fields": {"Name": "text", "Affiliation": "text", "Country": "text", "I agree to use this model for non-commercial use ONLY": "checkbox"}, "extra_gated_button_content": "Acknowledge license", "configs": [{"config_name": "default", "data_files": [{"split": "full", "path": "data/full-*"}, {"split": "train", "path": "data/train-*"}, {"split": "valid", "path": "data/valid-*"}, {"split": "test", "path": "data/test-*"}, {"split": "Top50PerTask", "path": "data/Top50PerTask-*"}, {"split": "Top100PerTask", "path": "data/Top100PerTask-*"}, {"split": "Top200PerTask", "path": "data/Top200PerTask-*"}]}], "dataset_info": {"features": [{"name": "instruction", "dtype": "string"}, {"name": "input", "dtype": "string"}, {"name": "output", "dtype": "string"}, {"name": "split", "dtype": "string"}, {"name": "task_name_in_eng", "dtype": "string"}, {"name": "task_type", "struct": [{"name": "major", "sequence": "string"}, {"name": "minor", "sequence": "string"}]}, {"name": "domain", "sequence": "string"}, {"name": "other", "dtype": "string"}, {"name": "filename", "dtype": "string"}], "splits": [{"name": "full", "num_bytes": 198933665241, "num_examples": 321332879}, {"name": "train", "num_bytes": 135575192364, "num_examples": 208529583}, {"name": "valid", "num_bytes": 1703151331, "num_examples": 2087767}, {"name": "test", "num_bytes": 5763748490, "num_examples": 8094740}, {"name": "Top50PerTask", "num_bytes": 113823936, "num_examples": 63643}, {"name": "Top100PerTask", "num_bytes": 222242916, "num_examples": 127158}, {"name": "Top200PerTask", "num_bytes": 435753269, "num_examples": 253558}], "download_size": 275132519, "dataset_size": 342747577547}}
|
2023-10-14T09:38:40+00:00
|
fd9179468b5f172c470ca52a65531fc999b7e810
|
# Dataset Card for "redteaming_eval_pairwise"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
andersonbcdefg/redteaming_eval_pairwise
|
[
"region:us"
] |
2023-06-08T04:48:52+00:00
|
{"dataset_info": {"features": [{"name": "prompt", "dtype": "string"}, {"name": "response_a", "dtype": "string"}, {"name": "response_b", "dtype": "string"}, {"name": "explanation", "dtype": "string"}, {"name": "preferred", "dtype": "string"}, {"name": "__index_level_0__", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 79844, "num_examples": 105}], "download_size": 0, "dataset_size": 79844}}
|
2023-06-08T04:51:12+00:00
|
ff2d909accd4d69fb53f9d6cfe67a84cf7077c0c
|
# Dataset Card for "malang3"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
minyoung9353/malang3
|
[
"region:us"
] |
2023-06-08T05:07:54+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 36765336.454, "num_examples": 1543}], "download_size": 33870554, "dataset_size": 36765336.454}}
|
2023-06-08T05:09:41+00:00
|
e8a9708cc3fc5d4e2314aeff412eddce3d14cf6e
|
# Dataset Card for "pixel_glue_wnli"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Nadav/pixel_glue_wnli
|
[
"region:us"
] |
2023-06-08T05:14:26+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "0", "1": "1"}}}}], "splits": [{"name": "train", "num_bytes": 8370447.0, "num_examples": 635}, {"name": "validation", "num_bytes": 937910.0, "num_examples": 71}], "download_size": 8954357, "dataset_size": 9308357.0}}
|
2023-06-08T05:36:15+00:00
|
f1b95c3bf4c94e8ff88ea27da8c17e52e4f7bf55
|
# Dataset Card for "pixel_glue_stsb"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Nadav/pixel_glue_stsb
|
[
"region:us"
] |
2023-06-08T05:25:41+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": "float32"}], "splits": [{"name": "train", "num_bytes": 82776786.375, "num_examples": 5749}, {"name": "validation", "num_bytes": 17450726.5, "num_examples": 1500}], "download_size": 97774662, "dataset_size": 100227512.875}}
|
2023-06-08T05:25:56+00:00
|
98f93a3d8e6a4e80faa37ff92b0c90f212f503d1
|
# Dataset Card for "OSCAR-2201"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
vietgpt-archive/OSCAR-2201
|
[
"region:us"
] |
2023-06-08T05:33:29+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "date", "dtype": "string"}, {"name": "perplexity", "dtype": "float64"}], "splits": [{"name": "train", "num_bytes": 77720649016, "num_examples": 8270874}], "download_size": 37705074685, "dataset_size": 77720649016}}
|
2023-06-10T11:15:45+00:00
|
e47a94f9d9f48ed1dde9c4059bb3c0a521d908ec
|
paragonnov/coway_faq
|
[
"task_categories:text-generation",
"task_categories:question-answering",
"size_categories:n<1K",
"language:ko",
"region:us"
] |
2023-06-08T05:54:37+00:00
|
{"language": ["ko"], "size_categories": ["n<1K"], "task_categories": ["text-generation", "question-answering"]}
|
2023-06-12T04:56:59+00:00
|
|
1a836c81d0237153090c77388803a15089261c83
|
**F**unds **R**eport **F**ront **P**age **E**ntities (FRFPE) is a dataset for document understanding and token classification.
It contains 356 titles/front pages of annual and semi-annual reports as well as extracted text and annotations for five different token categories.
FRFPE serves as an example of how to train and evaluate multimodal models such as LayoutLM using the deepdoctection framework on a custom dataset.
FRFPE contains documents in three different languages
- english: 167
- german: 149
- french: 9
as well as the token categories:
- report_date (1096 samples) - reporting date of the report
- report_type (738 samples) - annual/semi-annual report
- umbrella (912 samples) - fund issued as umbrella
- fund_name (2122 samples) - Subfund, as part of an umbrella fund or standalone fund
- other (12903 samples) - None of the above categories
The annotations have been made to the best of our knowledge and belief, but there is no claim on correctness.
Some cursory notes:
- The images were created by converting PDF files. A resolution of 300 dpi was applied during the conversion.
- The text was extracted from the PDF file using PDFPlumber. In some cases the PDF contains embedded images, which in turn contain text, such as corporate names. These are not extracted and are therefore not taken into account.
- The annotation was carried out with the annotation tool Prodigy.
- The category `report_date` is self-explanatory. `report_type` was used to indicate whether the report is an annual semi-annual report or a report in a different cycle.
- `umbrella`/`fund_name` is the classification of any token that is part of a fund name that represents either an umbrella, subfund or individual fund.
The distinction between whether a fund represents an umbrella, or single fund is not always apparent from the context of the document, which makes the classification
particularly challenging. In order to remain correct in the annotation, information from the Bafin database was used for cases that could not be clarified from the context.
To explore the dataset we suggest to use **deep**doctection. Place the unzipped folder in the `**deep**doctection ~/.cache/datasets` folder.
```python
import deepdoctection as dd
from pathlib import Path
@dd.object_types_registry.register("ner_first_page")
class FundsFirstPage(dd.ObjectTypes):
report_date = "report_date"
umbrella = "umbrella"
report_type = "report_type"
fund_name = "fund_name"
dd.update_all_types_dict()
path = Path("~/.cache/datasets/fund_ar_front_page/40952248ba13ae8bfdd39f56af22f7d9_0.json")
page = dd.Page.from_file(path)
page.image = dd.load_image_from_file(path.parents[0] / "image" / page.file_name.replace("pdf","png"))
page.viz(interactive=True,show_words=True) # close interactive window with q
for word in page.words:
print(f"text: {word.characters}, token class: {word.token_class}")
```
|
deepdoctection/FRFPE
|
[
"task_categories:token-classification",
"size_categories:n<1K",
"language:de",
"language:en",
"language:fr",
"license:odc-by",
"finance",
"region:us"
] |
2023-06-08T06:03:46+00:00
|
{"language": ["de", "en", "fr"], "license": "odc-by", "size_categories": ["n<1K"], "task_categories": ["token-classification"], "pretty_name": "Funds report token classification ", "tags": ["finance"]}
|
2023-06-08T06:06:33+00:00
|
2eb0dbf521b8ba88de037af33d15370577b63850
|
# Dataset Card for "nyaya-ae-all-mpnet-base-v2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
sukantan/nyaya-ae-all-mpnet-base-v2
|
[
"region:us"
] |
2023-06-08T06:05:27+00:00
|
{"dataset_info": {"features": [{"name": "0", "dtype": "float32"}, {"name": "1", "dtype": "float32"}, {"name": "2", "dtype": "float32"}, {"name": "3", "dtype": "float32"}, {"name": "4", "dtype": "float32"}, {"name": "5", "dtype": "float32"}, {"name": "6", "dtype": "float32"}, {"name": "7", "dtype": "float32"}, {"name": "8", "dtype": "float32"}, {"name": "9", "dtype": "float32"}, {"name": "10", "dtype": "float32"}, {"name": "11", "dtype": "float32"}, {"name": "12", "dtype": "float32"}, {"name": "13", "dtype": "float32"}, {"name": "14", "dtype": "float32"}, {"name": "15", "dtype": "float32"}, {"name": "16", "dtype": "float32"}, {"name": "17", "dtype": "float32"}, {"name": "18", "dtype": "float32"}, {"name": "19", "dtype": "float32"}, {"name": "20", "dtype": "float32"}, {"name": "21", "dtype": "float32"}, {"name": "22", "dtype": "float32"}, {"name": "23", "dtype": "float32"}, {"name": "24", "dtype": "float32"}, {"name": "25", "dtype": "float32"}, {"name": "26", "dtype": "float32"}, {"name": "27", "dtype": "float32"}, {"name": "28", "dtype": "float32"}, {"name": "29", "dtype": "float32"}, {"name": "30", "dtype": "float32"}, {"name": "31", "dtype": "float32"}, {"name": "32", "dtype": "float32"}, {"name": "33", "dtype": "float32"}, {"name": "34", "dtype": "float32"}, {"name": "35", "dtype": "float32"}, {"name": "36", "dtype": "float32"}, {"name": "37", "dtype": "float32"}, {"name": "38", "dtype": "float32"}, {"name": "39", "dtype": "float32"}, {"name": "40", "dtype": "float32"}, {"name": "41", "dtype": "float32"}, {"name": "42", "dtype": "float32"}, {"name": "43", "dtype": "float32"}, {"name": "44", "dtype": "float32"}, {"name": "45", "dtype": "float32"}, {"name": "46", "dtype": "float32"}, {"name": "47", "dtype": "float32"}, {"name": "48", "dtype": "float32"}, {"name": "49", "dtype": "float32"}, {"name": "50", "dtype": "float32"}, {"name": "51", "dtype": "float32"}, {"name": "52", "dtype": "float32"}, {"name": "53", "dtype": "float32"}, {"name": "54", "dtype": "float32"}, {"name": "55", "dtype": "float32"}, {"name": "56", "dtype": "float32"}, {"name": "57", "dtype": "float32"}, {"name": "58", "dtype": "float32"}, {"name": "59", "dtype": "float32"}, {"name": "60", "dtype": "float32"}, {"name": "61", "dtype": "float32"}, {"name": "62", "dtype": "float32"}, {"name": "63", "dtype": "float32"}, {"name": "64", "dtype": "float32"}, {"name": "65", "dtype": "float32"}, {"name": "66", "dtype": "float32"}, {"name": "67", "dtype": "float32"}, {"name": "68", "dtype": "float32"}, {"name": "69", "dtype": "float32"}, {"name": "70", "dtype": "float32"}, {"name": "71", "dtype": "float32"}, {"name": "72", "dtype": "float32"}, {"name": "73", "dtype": "float32"}, {"name": "74", "dtype": "float32"}, {"name": "75", "dtype": "float32"}, {"name": "76", "dtype": "float32"}, {"name": "77", "dtype": "float32"}, {"name": "78", "dtype": "float32"}, {"name": "79", "dtype": "float32"}, {"name": "80", "dtype": "float32"}, {"name": "81", "dtype": "float32"}, {"name": "82", "dtype": "float32"}, {"name": "83", "dtype": "float32"}, {"name": "84", "dtype": "float32"}, {"name": "85", "dtype": "float32"}, {"name": "86", "dtype": "float32"}, {"name": "87", "dtype": "float32"}, {"name": "88", "dtype": "float32"}, {"name": "89", "dtype": "float32"}, {"name": "90", "dtype": "float32"}, {"name": "91", "dtype": "float32"}, {"name": "92", "dtype": "float32"}, {"name": "93", "dtype": "float32"}, {"name": "94", "dtype": "float32"}, {"name": "95", "dtype": "float32"}, {"name": "96", "dtype": "float32"}, {"name": "97", "dtype": "float32"}, {"name": "98", "dtype": "float32"}, {"name": "99", "dtype": "float32"}, {"name": "100", "dtype": "float32"}, {"name": "101", "dtype": "float32"}, {"name": "102", "dtype": "float32"}, {"name": "103", "dtype": "float32"}, {"name": "104", "dtype": "float32"}, {"name": "105", "dtype": "float32"}, {"name": "106", "dtype": "float32"}, {"name": "107", "dtype": "float32"}, {"name": "108", "dtype": "float32"}, {"name": "109", "dtype": "float32"}, {"name": "110", "dtype": "float32"}, {"name": "111", "dtype": "float32"}, {"name": "112", "dtype": "float32"}, {"name": "113", "dtype": "float32"}, {"name": "114", "dtype": "float32"}, {"name": "115", "dtype": "float32"}, {"name": "116", "dtype": "float32"}, {"name": "117", "dtype": "float32"}, {"name": "118", "dtype": "float32"}, {"name": "119", "dtype": "float32"}, {"name": "120", "dtype": "float32"}, {"name": "121", "dtype": "float32"}, {"name": "122", "dtype": "float32"}, {"name": "123", "dtype": "float32"}, {"name": "124", "dtype": "float32"}, {"name": "125", "dtype": "float32"}, {"name": "126", "dtype": "float32"}, {"name": "127", "dtype": "float32"}, {"name": "128", "dtype": "float32"}, {"name": "129", "dtype": "float32"}, {"name": "130", "dtype": "float32"}, {"name": "131", "dtype": "float32"}, {"name": "132", "dtype": "float32"}, {"name": "133", "dtype": "float32"}, {"name": "134", "dtype": "float32"}, {"name": "135", "dtype": "float32"}, {"name": "136", "dtype": "float32"}, {"name": "137", "dtype": "float32"}, {"name": "138", "dtype": "float32"}, {"name": "139", "dtype": "float32"}, {"name": "140", "dtype": "float32"}, {"name": "141", "dtype": "float32"}, {"name": "142", "dtype": "float32"}, {"name": "143", "dtype": "float32"}, {"name": "144", "dtype": "float32"}, {"name": "145", "dtype": "float32"}, {"name": "146", "dtype": "float32"}, {"name": "147", "dtype": "float32"}, {"name": "148", "dtype": "float32"}, {"name": "149", "dtype": "float32"}, {"name": "150", "dtype": "float32"}, {"name": "151", "dtype": "float32"}, {"name": "152", "dtype": "float32"}, {"name": "153", "dtype": "float32"}, {"name": "154", "dtype": "float32"}, {"name": "155", "dtype": "float32"}, {"name": "156", "dtype": "float32"}, {"name": "157", "dtype": "float32"}, {"name": "158", "dtype": "float32"}, {"name": "159", "dtype": "float32"}, {"name": "160", "dtype": "float32"}, {"name": "161", "dtype": "float32"}, {"name": "162", "dtype": "float32"}, {"name": "163", "dtype": "float32"}, {"name": "164", "dtype": "float32"}, {"name": "165", "dtype": "float32"}, {"name": "166", "dtype": "float32"}, {"name": "167", "dtype": "float32"}, {"name": "168", "dtype": "float32"}, {"name": "169", "dtype": "float32"}, {"name": "170", "dtype": "float32"}, {"name": "171", "dtype": "float32"}, {"name": "172", "dtype": "float32"}, {"name": "173", "dtype": "float32"}, {"name": "174", "dtype": "float32"}, {"name": "175", "dtype": "float32"}, {"name": "176", "dtype": "float32"}, {"name": "177", "dtype": "float32"}, {"name": "178", "dtype": "float32"}, {"name": "179", "dtype": "float32"}, {"name": "180", "dtype": "float32"}, {"name": "181", "dtype": "float32"}, {"name": "182", "dtype": "float32"}, {"name": "183", "dtype": "float32"}, {"name": "184", "dtype": "float32"}, {"name": "185", "dtype": "float32"}, {"name": "186", "dtype": "float32"}, {"name": "187", "dtype": "float32"}, {"name": "188", "dtype": "float32"}, {"name": "189", "dtype": "float32"}, {"name": "190", "dtype": "float32"}, {"name": "191", "dtype": "float32"}, {"name": "192", "dtype": "float32"}, {"name": "193", "dtype": "float32"}, {"name": "194", "dtype": "float32"}, {"name": "195", "dtype": "float32"}, {"name": "196", "dtype": "float32"}, {"name": "197", "dtype": "float32"}, {"name": "198", "dtype": "float32"}, {"name": "199", "dtype": "float32"}, {"name": "200", "dtype": "float32"}, {"name": "201", "dtype": "float32"}, {"name": "202", "dtype": "float32"}, {"name": "203", "dtype": "float32"}, {"name": "204", "dtype": "float32"}, {"name": "205", "dtype": "float32"}, {"name": "206", "dtype": "float32"}, {"name": "207", "dtype": "float32"}, {"name": "208", "dtype": "float32"}, {"name": "209", "dtype": "float32"}, {"name": "210", "dtype": "float32"}, {"name": "211", "dtype": "float32"}, {"name": "212", "dtype": "float32"}, {"name": "213", "dtype": "float32"}, {"name": "214", "dtype": "float32"}, {"name": "215", "dtype": "float32"}, {"name": "216", "dtype": "float32"}, {"name": "217", "dtype": "float32"}, {"name": "218", "dtype": "float32"}, {"name": "219", "dtype": "float32"}, {"name": "220", "dtype": "float32"}, {"name": "221", "dtype": "float32"}, {"name": "222", "dtype": "float32"}, {"name": "223", "dtype": "float32"}, {"name": "224", "dtype": "float32"}, {"name": "225", "dtype": "float32"}, {"name": "226", "dtype": "float32"}, {"name": "227", "dtype": "float32"}, {"name": "228", "dtype": "float32"}, {"name": "229", "dtype": "float32"}, {"name": "230", "dtype": "float32"}, {"name": "231", "dtype": "float32"}, {"name": "232", "dtype": "float32"}, {"name": "233", "dtype": "float32"}, {"name": "234", "dtype": "float32"}, {"name": "235", "dtype": "float32"}, {"name": "236", "dtype": "float32"}, {"name": "237", "dtype": "float32"}, {"name": "238", "dtype": "float32"}, {"name": "239", "dtype": "float32"}, {"name": "240", "dtype": "float32"}, {"name": "241", "dtype": "float32"}, {"name": "242", "dtype": "float32"}, {"name": "243", "dtype": "float32"}, {"name": "244", "dtype": "float32"}, {"name": "245", "dtype": "float32"}, {"name": "246", "dtype": "float32"}, {"name": "247", "dtype": "float32"}, {"name": "248", "dtype": "float32"}, {"name": "249", "dtype": "float32"}, {"name": "250", "dtype": "float32"}, {"name": "251", "dtype": "float32"}, {"name": "252", "dtype": "float32"}, {"name": "253", "dtype": "float32"}, {"name": "254", "dtype": "float32"}, {"name": "255", "dtype": "float32"}, {"name": "256", "dtype": "float32"}, {"name": "257", "dtype": "float32"}, {"name": "258", "dtype": "float32"}, {"name": "259", "dtype": "float32"}, {"name": "260", "dtype": "float32"}, {"name": "261", "dtype": "float32"}, {"name": "262", "dtype": "float32"}, {"name": "263", "dtype": "float32"}, {"name": "264", "dtype": "float32"}, {"name": "265", "dtype": "float32"}, {"name": "266", "dtype": "float32"}, {"name": "267", "dtype": "float32"}, {"name": "268", "dtype": "float32"}, {"name": "269", "dtype": "float32"}, {"name": "270", "dtype": "float32"}, {"name": "271", "dtype": "float32"}, {"name": "272", "dtype": "float32"}, {"name": "273", "dtype": "float32"}, {"name": "274", "dtype": "float32"}, {"name": "275", "dtype": "float32"}, {"name": "276", "dtype": "float32"}, {"name": "277", "dtype": "float32"}, {"name": "278", "dtype": "float32"}, {"name": "279", "dtype": "float32"}, {"name": "280", "dtype": "float32"}, {"name": "281", "dtype": "float32"}, {"name": "282", "dtype": "float32"}, {"name": "283", "dtype": "float32"}, {"name": "284", "dtype": "float32"}, {"name": "285", "dtype": "float32"}, {"name": "286", "dtype": "float32"}, {"name": "287", "dtype": "float32"}, {"name": "288", "dtype": "float32"}, {"name": "289", "dtype": "float32"}, {"name": "290", "dtype": "float32"}, {"name": "291", "dtype": "float32"}, {"name": "292", "dtype": "float32"}, {"name": "293", "dtype": "float32"}, {"name": "294", "dtype": "float32"}, {"name": "295", "dtype": "float32"}, {"name": "296", "dtype": "float32"}, {"name": "297", "dtype": "float32"}, {"name": "298", "dtype": "float32"}, {"name": "299", "dtype": "float32"}, {"name": "300", "dtype": "float32"}, {"name": "301", "dtype": "float32"}, {"name": "302", "dtype": "float32"}, {"name": "303", "dtype": "float32"}, {"name": "304", "dtype": "float32"}, {"name": "305", "dtype": "float32"}, {"name": "306", "dtype": "float32"}, {"name": "307", "dtype": "float32"}, {"name": "308", "dtype": "float32"}, {"name": "309", "dtype": "float32"}, {"name": "310", "dtype": "float32"}, {"name": "311", "dtype": "float32"}, {"name": "312", "dtype": "float32"}, {"name": "313", "dtype": "float32"}, {"name": "314", "dtype": "float32"}, {"name": "315", "dtype": "float32"}, {"name": "316", "dtype": "float32"}, {"name": "317", "dtype": "float32"}, {"name": "318", "dtype": "float32"}, {"name": "319", "dtype": "float32"}, {"name": "320", "dtype": "float32"}, {"name": "321", "dtype": "float32"}, {"name": "322", "dtype": "float32"}, {"name": "323", "dtype": "float32"}, {"name": "324", "dtype": "float32"}, {"name": "325", "dtype": "float32"}, {"name": "326", "dtype": "float32"}, {"name": "327", "dtype": "float32"}, {"name": "328", "dtype": "float32"}, {"name": "329", "dtype": "float32"}, {"name": "330", "dtype": "float32"}, {"name": "331", "dtype": "float32"}, {"name": "332", "dtype": "float32"}, {"name": "333", "dtype": "float32"}, {"name": "334", "dtype": "float32"}, {"name": "335", "dtype": "float32"}, {"name": "336", "dtype": "float32"}, {"name": "337", "dtype": "float32"}, {"name": "338", "dtype": "float32"}, {"name": "339", "dtype": "float32"}, {"name": "340", "dtype": "float32"}, {"name": "341", "dtype": "float32"}, {"name": "342", "dtype": "float32"}, {"name": "343", "dtype": "float32"}, {"name": "344", "dtype": "float32"}, {"name": "345", "dtype": "float32"}, {"name": "346", "dtype": "float32"}, {"name": "347", "dtype": "float32"}, {"name": "348", "dtype": "float32"}, {"name": "349", "dtype": "float32"}, {"name": "350", "dtype": "float32"}, {"name": "351", "dtype": "float32"}, {"name": "352", "dtype": "float32"}, {"name": "353", "dtype": "float32"}, {"name": "354", "dtype": "float32"}, {"name": "355", "dtype": "float32"}, {"name": "356", "dtype": "float32"}, {"name": "357", "dtype": "float32"}, {"name": "358", "dtype": "float32"}, {"name": "359", "dtype": "float32"}, {"name": "360", "dtype": "float32"}, {"name": "361", "dtype": "float32"}, {"name": "362", "dtype": "float32"}, {"name": "363", "dtype": "float32"}, {"name": "364", "dtype": "float32"}, {"name": "365", "dtype": "float32"}, {"name": "366", "dtype": "float32"}, {"name": "367", "dtype": "float32"}, {"name": "368", "dtype": "float32"}, {"name": "369", "dtype": "float32"}, {"name": "370", "dtype": "float32"}, {"name": "371", "dtype": "float32"}, {"name": "372", "dtype": "float32"}, {"name": "373", "dtype": "float32"}, {"name": "374", "dtype": "float32"}, {"name": "375", "dtype": "float32"}, {"name": "376", "dtype": "float32"}, {"name": "377", "dtype": "float32"}, {"name": "378", "dtype": "float32"}, {"name": "379", "dtype": "float32"}, {"name": "380", "dtype": "float32"}, {"name": "381", "dtype": "float32"}, {"name": "382", "dtype": "float32"}, {"name": "383", "dtype": "float32"}, {"name": "384", "dtype": "float32"}, {"name": "385", "dtype": "float32"}, {"name": "386", "dtype": "float32"}, {"name": "387", "dtype": "float32"}, {"name": "388", "dtype": "float32"}, {"name": "389", "dtype": "float32"}, {"name": "390", "dtype": "float32"}, {"name": "391", "dtype": "float32"}, {"name": "392", "dtype": "float32"}, {"name": "393", "dtype": "float32"}, {"name": "394", "dtype": "float32"}, {"name": "395", "dtype": "float32"}, {"name": "396", "dtype": "float32"}, {"name": "397", "dtype": "float32"}, {"name": "398", "dtype": "float32"}, {"name": "399", "dtype": "float32"}, {"name": "400", "dtype": "float32"}, {"name": "401", "dtype": "float32"}, {"name": "402", "dtype": "float32"}, {"name": "403", "dtype": "float32"}, {"name": "404", "dtype": "float32"}, {"name": "405", "dtype": "float32"}, {"name": "406", "dtype": "float32"}, {"name": "407", "dtype": "float32"}, {"name": "408", "dtype": "float32"}, {"name": "409", "dtype": "float32"}, {"name": "410", "dtype": "float32"}, {"name": "411", "dtype": "float32"}, {"name": "412", "dtype": "float32"}, {"name": "413", "dtype": "float32"}, {"name": "414", "dtype": "float32"}, {"name": "415", "dtype": "float32"}, {"name": "416", "dtype": "float32"}, {"name": "417", "dtype": "float32"}, {"name": "418", "dtype": "float32"}, {"name": "419", "dtype": "float32"}, {"name": "420", "dtype": "float32"}, {"name": "421", "dtype": "float32"}, {"name": "422", "dtype": "float32"}, {"name": "423", "dtype": "float32"}, {"name": "424", "dtype": "float32"}, {"name": "425", "dtype": "float32"}, {"name": "426", "dtype": "float32"}, {"name": "427", "dtype": "float32"}, {"name": "428", "dtype": "float32"}, {"name": "429", "dtype": "float32"}, {"name": "430", "dtype": "float32"}, {"name": "431", "dtype": "float32"}, {"name": "432", "dtype": "float32"}, {"name": "433", "dtype": "float32"}, {"name": "434", "dtype": "float32"}, {"name": "435", "dtype": "float32"}, {"name": "436", "dtype": "float32"}, {"name": "437", "dtype": "float32"}, {"name": "438", "dtype": "float32"}, {"name": "439", "dtype": "float32"}, {"name": "440", "dtype": "float32"}, {"name": "441", "dtype": "float32"}, {"name": "442", "dtype": "float32"}, {"name": "443", "dtype": "float32"}, {"name": "444", "dtype": "float32"}, {"name": "445", "dtype": "float32"}, {"name": "446", "dtype": "float32"}, {"name": "447", "dtype": "float32"}, {"name": "448", "dtype": "float32"}, {"name": "449", "dtype": "float32"}, {"name": "450", "dtype": "float32"}, {"name": "451", "dtype": "float32"}, {"name": "452", "dtype": "float32"}, {"name": "453", "dtype": "float32"}, {"name": "454", "dtype": "float32"}, {"name": "455", "dtype": "float32"}, {"name": "456", "dtype": "float32"}, {"name": "457", "dtype": "float32"}, {"name": "458", "dtype": "float32"}, {"name": "459", "dtype": "float32"}, {"name": "460", "dtype": "float32"}, {"name": "461", "dtype": "float32"}, {"name": "462", "dtype": "float32"}, {"name": "463", "dtype": "float32"}, {"name": "464", "dtype": "float32"}, {"name": "465", "dtype": "float32"}, {"name": "466", "dtype": "float32"}, {"name": "467", "dtype": "float32"}, {"name": "468", "dtype": "float32"}, {"name": "469", "dtype": "float32"}, {"name": "470", "dtype": "float32"}, {"name": "471", "dtype": "float32"}, {"name": "472", "dtype": "float32"}, {"name": "473", "dtype": "float32"}, {"name": "474", "dtype": "float32"}, {"name": "475", "dtype": "float32"}, {"name": "476", "dtype": "float32"}, {"name": "477", "dtype": "float32"}, {"name": "478", "dtype": "float32"}, {"name": "479", "dtype": "float32"}, {"name": "480", "dtype": "float32"}, {"name": "481", "dtype": "float32"}, {"name": "482", "dtype": "float32"}, {"name": "483", "dtype": "float32"}, {"name": "484", "dtype": "float32"}, {"name": "485", "dtype": "float32"}, {"name": "486", "dtype": "float32"}, {"name": "487", "dtype": "float32"}, {"name": "488", "dtype": "float32"}, {"name": "489", "dtype": "float32"}, {"name": "490", "dtype": "float32"}, {"name": "491", "dtype": "float32"}, {"name": "492", "dtype": "float32"}, {"name": "493", "dtype": "float32"}, {"name": "494", "dtype": "float32"}, {"name": "495", "dtype": "float32"}, {"name": "496", "dtype": "float32"}, {"name": "497", "dtype": "float32"}, {"name": "498", "dtype": "float32"}, {"name": "499", "dtype": "float32"}, {"name": "500", "dtype": "float32"}, {"name": "501", "dtype": "float32"}, {"name": "502", "dtype": "float32"}, {"name": "503", "dtype": "float32"}, {"name": "504", "dtype": "float32"}, {"name": "505", "dtype": "float32"}, {"name": "506", "dtype": "float32"}, {"name": "507", "dtype": "float32"}, {"name": "508", "dtype": "float32"}, {"name": "509", "dtype": "float32"}, {"name": "510", "dtype": "float32"}, {"name": "511", "dtype": "float32"}, {"name": "512", "dtype": "float32"}, {"name": "513", "dtype": "float32"}, {"name": "514", "dtype": "float32"}, {"name": "515", "dtype": "float32"}, {"name": "516", "dtype": "float32"}, {"name": "517", "dtype": "float32"}, {"name": "518", "dtype": "float32"}, {"name": "519", "dtype": "float32"}, {"name": "520", "dtype": "float32"}, {"name": "521", "dtype": "float32"}, {"name": "522", "dtype": "float32"}, {"name": "523", "dtype": "float32"}, {"name": "524", "dtype": "float32"}, {"name": "525", "dtype": "float32"}, {"name": "526", "dtype": "float32"}, {"name": "527", "dtype": "float32"}, {"name": "528", "dtype": "float32"}, {"name": "529", "dtype": "float32"}, {"name": "530", "dtype": "float32"}, {"name": "531", "dtype": "float32"}, {"name": "532", "dtype": "float32"}, {"name": "533", "dtype": "float32"}, {"name": "534", "dtype": "float32"}, {"name": "535", "dtype": "float32"}, {"name": "536", "dtype": "float32"}, {"name": "537", "dtype": "float32"}, {"name": "538", "dtype": "float32"}, {"name": "539", "dtype": "float32"}, {"name": "540", "dtype": "float32"}, {"name": "541", "dtype": "float32"}, {"name": "542", "dtype": "float32"}, {"name": "543", "dtype": "float32"}, {"name": "544", "dtype": "float32"}, {"name": "545", "dtype": "float32"}, {"name": "546", "dtype": "float32"}, {"name": "547", "dtype": "float32"}, {"name": "548", "dtype": "float32"}, {"name": "549", "dtype": "float32"}, {"name": "550", "dtype": "float32"}, {"name": "551", "dtype": "float32"}, {"name": "552", "dtype": "float32"}, {"name": "553", "dtype": "float32"}, {"name": "554", "dtype": "float32"}, {"name": "555", "dtype": "float32"}, {"name": "556", "dtype": "float32"}, {"name": "557", "dtype": "float32"}, {"name": "558", "dtype": "float32"}, {"name": "559", "dtype": "float32"}, {"name": "560", "dtype": "float32"}, {"name": "561", "dtype": "float32"}, {"name": "562", "dtype": "float32"}, {"name": "563", "dtype": "float32"}, {"name": "564", "dtype": "float32"}, {"name": "565", "dtype": "float32"}, {"name": "566", "dtype": "float32"}, {"name": "567", "dtype": "float32"}, {"name": "568", "dtype": "float32"}, {"name": "569", "dtype": "float32"}, {"name": "570", "dtype": "float32"}, {"name": "571", "dtype": "float32"}, {"name": "572", "dtype": "float32"}, {"name": "573", "dtype": "float32"}, {"name": "574", "dtype": "float32"}, {"name": "575", "dtype": "float32"}, {"name": "576", "dtype": "float32"}, {"name": "577", "dtype": "float32"}, {"name": "578", "dtype": "float32"}, {"name": "579", "dtype": "float32"}, {"name": "580", "dtype": "float32"}, {"name": "581", "dtype": "float32"}, {"name": "582", "dtype": "float32"}, {"name": "583", "dtype": "float32"}, {"name": "584", "dtype": "float32"}, {"name": "585", "dtype": "float32"}, {"name": "586", "dtype": "float32"}, {"name": "587", "dtype": "float32"}, {"name": "588", "dtype": "float32"}, {"name": "589", "dtype": "float32"}, {"name": "590", "dtype": "float32"}, {"name": "591", "dtype": "float32"}, {"name": "592", "dtype": "float32"}, {"name": "593", "dtype": "float32"}, {"name": "594", "dtype": "float32"}, {"name": "595", "dtype": "float32"}, {"name": "596", "dtype": "float32"}, {"name": "597", "dtype": "float32"}, {"name": "598", "dtype": "float32"}, {"name": "599", "dtype": "float32"}, {"name": "600", "dtype": "float32"}, {"name": "601", "dtype": "float32"}, {"name": "602", "dtype": "float32"}, {"name": "603", "dtype": "float32"}, {"name": "604", "dtype": "float32"}, {"name": "605", "dtype": "float32"}, {"name": "606", "dtype": "float32"}, {"name": "607", "dtype": "float32"}, {"name": "608", "dtype": "float32"}, {"name": "609", "dtype": "float32"}, {"name": "610", "dtype": "float32"}, {"name": "611", "dtype": "float32"}, {"name": "612", "dtype": "float32"}, {"name": "613", "dtype": "float32"}, {"name": "614", "dtype": "float32"}, {"name": "615", "dtype": "float32"}, {"name": "616", "dtype": "float32"}, {"name": "617", "dtype": "float32"}, {"name": "618", "dtype": "float32"}, {"name": "619", "dtype": "float32"}, {"name": "620", "dtype": "float32"}, {"name": "621", "dtype": "float32"}, {"name": "622", "dtype": "float32"}, {"name": "623", "dtype": "float32"}, {"name": "624", "dtype": "float32"}, {"name": "625", "dtype": "float32"}, {"name": "626", "dtype": "float32"}, {"name": "627", "dtype": "float32"}, {"name": "628", "dtype": "float32"}, {"name": "629", "dtype": "float32"}, {"name": "630", "dtype": "float32"}, {"name": "631", "dtype": "float32"}, {"name": "632", "dtype": "float32"}, {"name": "633", "dtype": "float32"}, {"name": "634", "dtype": "float32"}, {"name": "635", "dtype": "float32"}, {"name": "636", "dtype": "float32"}, {"name": "637", "dtype": "float32"}, {"name": "638", "dtype": "float32"}, {"name": "639", "dtype": "float32"}, {"name": "640", "dtype": "float32"}, {"name": "641", "dtype": "float32"}, {"name": "642", "dtype": "float32"}, {"name": "643", "dtype": "float32"}, {"name": "644", "dtype": "float32"}, {"name": "645", "dtype": "float32"}, {"name": "646", "dtype": "float32"}, {"name": "647", "dtype": "float32"}, {"name": "648", "dtype": "float32"}, {"name": "649", "dtype": "float32"}, {"name": "650", "dtype": "float32"}, {"name": "651", "dtype": "float32"}, {"name": "652", "dtype": "float32"}, {"name": "653", "dtype": "float32"}, {"name": "654", "dtype": "float32"}, {"name": "655", "dtype": "float32"}, {"name": "656", "dtype": "float32"}, {"name": "657", "dtype": "float32"}, {"name": "658", "dtype": "float32"}, {"name": "659", "dtype": "float32"}, {"name": "660", "dtype": "float32"}, {"name": "661", "dtype": "float32"}, {"name": "662", "dtype": "float32"}, {"name": "663", "dtype": "float32"}, {"name": "664", "dtype": "float32"}, {"name": "665", "dtype": "float32"}, {"name": "666", "dtype": "float32"}, {"name": "667", "dtype": "float32"}, {"name": "668", "dtype": "float32"}, {"name": "669", "dtype": "float32"}, {"name": "670", "dtype": "float32"}, {"name": "671", "dtype": "float32"}, {"name": "672", "dtype": "float32"}, {"name": "673", "dtype": "float32"}, {"name": "674", "dtype": "float32"}, {"name": "675", "dtype": "float32"}, {"name": "676", "dtype": "float32"}, {"name": "677", "dtype": "float32"}, {"name": "678", "dtype": "float32"}, {"name": "679", "dtype": "float32"}, {"name": "680", "dtype": "float32"}, {"name": "681", "dtype": "float32"}, {"name": "682", "dtype": "float32"}, {"name": "683", "dtype": "float32"}, {"name": "684", "dtype": "float32"}, {"name": "685", "dtype": "float32"}, {"name": "686", "dtype": "float32"}, {"name": "687", "dtype": "float32"}, {"name": "688", "dtype": "float32"}, {"name": "689", "dtype": "float32"}, {"name": "690", "dtype": "float32"}, {"name": "691", "dtype": "float32"}, {"name": "692", "dtype": "float32"}, {"name": "693", "dtype": "float32"}, {"name": "694", "dtype": "float32"}, {"name": "695", "dtype": "float32"}, {"name": "696", "dtype": "float32"}, {"name": "697", "dtype": "float32"}, {"name": "698", "dtype": "float32"}, {"name": "699", "dtype": "float32"}, {"name": "700", "dtype": "float32"}, {"name": "701", "dtype": "float32"}, {"name": "702", "dtype": "float32"}, {"name": "703", "dtype": "float32"}, {"name": "704", "dtype": "float32"}, {"name": "705", "dtype": "float32"}, {"name": "706", "dtype": "float32"}, {"name": "707", "dtype": "float32"}, {"name": "708", "dtype": "float32"}, {"name": "709", "dtype": "float32"}, {"name": "710", "dtype": "float32"}, {"name": "711", "dtype": "float32"}, {"name": "712", "dtype": "float32"}, {"name": "713", "dtype": "float32"}, {"name": "714", "dtype": "float32"}, {"name": "715", "dtype": "float32"}, {"name": "716", "dtype": "float32"}, {"name": "717", "dtype": "float32"}, {"name": "718", "dtype": "float32"}, {"name": "719", "dtype": "float32"}, {"name": "720", "dtype": "float32"}, {"name": "721", "dtype": "float32"}, {"name": "722", "dtype": "float32"}, {"name": "723", "dtype": "float32"}, {"name": "724", "dtype": "float32"}, {"name": "725", "dtype": "float32"}, {"name": "726", "dtype": "float32"}, {"name": "727", "dtype": "float32"}, {"name": "728", "dtype": "float32"}, {"name": "729", "dtype": "float32"}, {"name": "730", "dtype": "float32"}, {"name": "731", "dtype": "float32"}, {"name": "732", "dtype": "float32"}, {"name": "733", "dtype": "float32"}, {"name": "734", "dtype": "float32"}, {"name": "735", "dtype": "float32"}, {"name": "736", "dtype": "float32"}, {"name": "737", "dtype": "float32"}, {"name": "738", "dtype": "float32"}, {"name": "739", "dtype": "float32"}, {"name": "740", "dtype": "float32"}, {"name": "741", "dtype": "float32"}, {"name": "742", "dtype": "float32"}, {"name": "743", "dtype": "float32"}, {"name": "744", "dtype": "float32"}, {"name": "745", "dtype": "float32"}, {"name": "746", "dtype": "float32"}, {"name": "747", "dtype": "float32"}, {"name": "748", "dtype": "float32"}, {"name": "749", "dtype": "float32"}, {"name": "750", "dtype": "float32"}, {"name": "751", "dtype": "float32"}, {"name": "752", "dtype": "float32"}, {"name": "753", "dtype": "float32"}, {"name": "754", "dtype": "float32"}, {"name": "755", "dtype": "float32"}, {"name": "756", "dtype": "float32"}, {"name": "757", "dtype": "float32"}, {"name": "758", "dtype": "float32"}, {"name": "759", "dtype": "float32"}, {"name": "760", "dtype": "float32"}, {"name": "761", "dtype": "float32"}, {"name": "762", "dtype": "float32"}, {"name": "763", "dtype": "float32"}, {"name": "764", "dtype": "float32"}, {"name": "765", "dtype": "float32"}, {"name": "766", "dtype": "float32"}, {"name": "767", "dtype": "float32"}], "splits": [{"name": "train", "num_bytes": 165236736, "num_examples": 53788}], "download_size": 0, "dataset_size": 165236736}}
|
2023-06-08T06:24:21+00:00
|
b68cdd3e972e7593b88e0939d897ffca9d2c70c6
|
# Dataset Card for "b804db05"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
results-sd-v1-5-sd-v2-1-if-v1-0-karlo/b804db05
|
[
"region:us"
] |
2023-06-08T06:20:18+00:00
|
{"dataset_info": {"features": [{"name": "result", "dtype": "string"}, {"name": "id", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 182, "num_examples": 10}], "download_size": 1336, "dataset_size": 182}}
|
2023-06-08T06:20:19+00:00
|
505ed270f9e9865c6cfab1e222d804fedd837127
| ERROR: type should be string, got "\nhttps://github.com/selenashe/ScoNe\nNLI subset, original part (excluding one-scope)\n```\n@misc{she2023scone,\n title={ScoNe: Benchmarking Negation Reasoning in Language Models With Fine-Tuning and In-Context Learning}, \n author={Jingyuan Selena She and Christopher Potts and Samuel R. Bowman and Atticus Geiger},\n year={2023},\n eprint={2305.19426},\n archivePrefix={arXiv},\n primaryClass={cs.CL}\n}\n```" |
tasksource/scone
|
[
"task_categories:text-classification",
"task_ids:natural-language-inference",
"license:cc0-1.0",
"arxiv:2305.19426",
"region:us"
] |
2023-06-08T06:22:53+00:00
|
{"license": "cc0-1.0", "task_categories": ["text-classification"], "task_ids": ["natural-language-inference"], "dataset_info": {"features": [{"name": "sentence1_edited", "dtype": "string"}, {"name": "sentence2_edited", "dtype": "string"}, {"name": "gold_label_edited", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 694572, "num_examples": 5010}, {"name": "test", "num_bytes": 149006, "num_examples": 1000}], "download_size": 114079, "dataset_size": 843578}}
|
2023-06-08T07:58:32+00:00
|
8585231417a18e9514d5b7c3f9be36f40a84ee88
|
https://github.com/atticusg/MoNLI
```
@inproceedings{geiger-etal-2020-neural,
address = {Online},
author = {Geiger, Atticus and Richardson, Kyle and Potts, Christopher},
booktitle = {Proceedings of the Third BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP},
doi = {10.18653/v1/2020.blackboxnlp-1.16},
month = nov,
pages = {163--173},
publisher = {Association for Computational Linguistics},
title = {Neural Natural Language Inference Models Partially Embed Theories of Lexical Entailment and Negation},
url = {https://www.aclweb.org/anthology/2020.blackboxnlp-1.16},
year = {2020}}
```
|
tasksource/monli
|
[
"task_categories:text-classification",
"task_ids:natural-language-inference",
"language:en",
"region:us"
] |
2023-06-08T06:28:30+00:00
|
{"language": ["en"], "task_categories": ["text-classification"], "task_ids": ["natural-language-inference"]}
|
2023-12-05T11:02:07+00:00
|
c283ac890f4f419f9413597f49558212b5578d4f
|
## Dataset Summary
This dataset currently contains 5043 movie posts and their corresponding Chinese title which are collected from [IMDb](https://www.imdb.com/) and [Douban](https://www.douban.com/) by crawler.
In the future, we will add more data to it.
|
snzhang/Movie-Title-Post
|
[
"size_categories:100M<n<1B",
"language:zh",
"license:apache-2.0",
"region:us"
] |
2023-06-08T06:42:23+00:00
|
{"language": ["zh"], "license": "apache-2.0", "size_categories": ["100M<n<1B"]}
|
2023-06-08T06:56:58+00:00
|
23e585133083e8de193bbccf285ec934391d6662
|
setiadi01/test-lawyer
|
[
"size_categories:n<1K",
"language:en",
"license:openrail",
"region:us"
] |
2023-06-08T07:06:51+00:00
|
{"language": ["en"], "license": "openrail", "size_categories": ["n<1K"]}
|
2023-06-08T07:17:34+00:00
|
|
a83fac176b48d0c92256f3bd338cc9ade8add0e7
|
# Dataset Card for "sgd-indirect-utterances"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
msamogh/sgd-indirect-utterances
|
[
"region:us"
] |
2023-06-08T07:25:42+00:00
|
{"dataset_info": {"features": [{"name": "train", "struct": [{"name": "input", "dtype": "string"}, {"name": "instruction", "dtype": "string"}, {"name": "output", "dtype": "string"}]}, {"name": "val", "struct": [{"name": "input", "dtype": "string"}, {"name": "instruction", "dtype": "string"}, {"name": "output", "dtype": "string"}]}, {"name": "test", "struct": [{"name": "input", "dtype": "string"}, {"name": "instruction", "dtype": "string"}, {"name": "output", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 455832, "num_examples": 291}], "download_size": 59599, "dataset_size": 455832}}
|
2023-06-08T07:28:59+00:00
|
53547daf23e45f59054c47343d02e2de0c0bf15b
|
# Dataset Card for M3IT-80
Project Page: [https://m3-it.github.io/](https://m3-it.github.io/)
## Dataset Description
- **Homepage: https://huggingface.co/datasets/MMInstruction/M3IT-80**
- **Repository: https://huggingface.co/datasets/MMInstruction/M3IT-80**
- **Paper: https://huggingface.co/papers/2306.04387**
- **Leaderboard:**
- **Point of Contact:**
### Languages
80 languages translated from English.
## Dataset Metainfo
[M3IT](https://huggingface.co/datasets/MMInstruction/M3IT) dataset
compiles diverse tasks of classical vision-language tasks, including captioning,
visual question answering~(VQA), visual conditioned generation, reasoning and classification.
**M3IT-80** is the 80-language translated version of M3IT.
### Languages
```python
_LAN_CODES = [
"af", "am", "ar", "as", "ast", "be", "bg", "bn", "bs", "ca",
"ceb", "cs", "cy", "da", "de", "el", "es", "et", "fi", "fr",
"fuv", "gl", "gu", "ha", "he", "hi", "hr", "hu", "hy", "id",
"ig", "is", "it", "ja", "jv", "ka", "kk", "km", "kn", "ko",
"ky", "lb", "lg", "lij", "li", "ln", "lo", "lt", "lv", "mi",
"mk", "ml", "mr", "mt", "my", "nl", "ny", "oc", "pa", "pl",
"pt", "ro", "ru", "sd", "sk", "sn", "so", "sr", "sv", "ta",
"te", "tg", "th", "tl", "tr", "uk", "ur", "vi", "wo", "zh",
]
```
### Dataset Statistics
We report the number of the train/validation/test of each dataset per language.
| Task | Dataset | #Train | #Val | #Test |
|---------------------------|--------------|--------|------|-------|
| Classification | `imagenet` | 500 | 500 | 0 |
| Visual Question Answering | `vqa-v2` | 500 | 500 | 0 |
| Knowledgeable Visual QA | `okvqa` | 500 | 500 | 0 |
| Reasoning | `winoground` | 0 | 0 | 800 |
| Generation | `vist` | 500 | 500 | 500 |
| Video | `msrvtt` | 500 | 500 | 0 |
| | `msrvtt-qa` | 500 | 500 | 0 |
### Source Data
Source language: English
| Task | Dataset [Citation] | Source |
|---------------------------|--------------------|------------------------------------------------------------------------------------|
| Classification | `imagenet` [1] | [Source](https://www.image-net.org/) |
| Visual Question Answering | `vqa-v2` [2] | [Source](https://visualqa.org/) |
| Knowledgeable Visual QA | `okvqa` [3] | [Source](https://okvqa.allenai.org/) |
| Reasoning | `winoground` [4] | [Source](https://huggingface.co/datasets/facebook/winoground) |
| Generation | `vist` [5] | [Source](https://visionandlanguage.net/VIST/) |
| Video | `msrvtt` [6] | [Source](https://paperswithcode.com/dataset/msr-vtt) |
| | `msrvtt-qa` [7] | [Source](https://paperswithcode.com/sota/visual-question-answering-on-msrvtt-qa-1) |
### Translation
We use free [Alibaba Translate](https://www.alibabacloud.com/product/machine-translation),
a deep neural network translation (NMT) system, to perform the translation task.
## Dataset Structure
### HuggingFace Login (Optional)
```python
# OR run huggingface-cli login
from huggingface_hub import login
hf_token = "hf_xxx" # TODO: set a valid HuggingFace access token for loading datasets/models
login(token=hf_token)
```
### Data Loading
```python
from datasets import load_dataset
ds_name = "okvqa-zh" # change the dataset name here
dataset = load_dataset("MMInstruction/M3IT-80", ds_name)
```
### Data Splits
```python
from datasets import load_dataset
ds_name = "okvqa-zh" # change the dataset name here
dataset = load_dataset("MMInstruction/M3IT-80", ds_name)
train_set = dataset["train"]
validation_set = dataset["validation"]
test_set = dataset["test"]
```
### Data Instances
```python
from datasets import load_dataset
from io import BytesIO
from base64 import b64decode
from PIL import Image
ds_name = "okvqa-zh" # change the dataset name here
dataset = load_dataset("MMInstruction/M3IT-80", ds_name)
train_set = dataset["train"]
for train_instance in train_set:
instruction = train_instance["instruction"] # str
inputs = train_instance["inputs"] # str
outputs = train_instance["outputs"] # str
image_base64_str_list = train_instance["image_base64_str"] # str (base64)
image_0 = Image.open(BytesIO(b64decode(image_base64_str_list[0])))
```
### Data Fields
```python
import datasets
features = datasets.Features(
{
"instruction": datasets.Value("string"),
"inputs": datasets.Value("string"),
"image_base64_str": [datasets.Value("string")],
"outputs": datasets.Value("string"),
}
)
```
### Licensing Information
The content of original dataset follows their original license.
We suggest that for the task with Unknown/Custom license,
the user can check the original project or contact the dataset owner for detailed license information.
Our annotated instruction data is licensed under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/).
### Citation Information
```bibtex
@article{li2023m3it,
title={M$^3$IT: A Large-Scale Dataset towards Multi-Modal Multilingual Instruction Tuning},
author={Lei Li and Yuwei Yin and Shicheng Li and Liang Chen and Peiyi Wang and Shuhuai Ren and Mukai Li and Yazheng Yang and Jingjing Xu and Xu Sun and Lingpeng Kong and Qi Liu},
journal={arXiv preprint arXiv:2306.04387},
year={2023}
}
```
### Contributions
M3IT-80 is the translated version of M3IT,
an open-source, large-scale Multi-modal, Multilingual Instruction Tuning dataset,
designed to enable the development of general-purpose multi-modal agents.
## References
- [1] Imagenet large scale visual recognition challenge
- [2] Making the V in VQA Matter: Elevating the Role of Image Understanding in Visual Question Answering
- [3] OK-VQA: A Visual Question Answering Benchmark Requiring External Knowledge
- [4] WinoGround: Probing vision and language models for visio-linguistic compositionality
- [5] Visual Storytelling
- [6] Video Question Answering via Gradually Refined Attention over Appearance and Motion
- [7] MSR-VTT: A large video description dataset for bridging video and language
|
MMInstruction/M3IT-80
|
[
"task_categories:image-to-text",
"task_categories:image-classification",
"size_categories:0.5M<n<1M",
"license:other",
"arxiv:2306.04387",
"region:us"
] |
2023-06-08T07:27:40+00:00
|
{"license": "other", "size_categories": ["0.5M<n<1M"], "task_categories": ["image-to-text", "image-classification"]}
|
2023-06-20T11:43:25+00:00
|
00fd1ca169bef6580c42180b1abb7be153199781
|
# Dataset Card for "test_dataset_for_SD"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
irodkin/test_dataset_for_SD
|
[
"region:us"
] |
2023-06-08T08:07:10+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "conditioning_image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 998479.0, "num_examples": 3}], "download_size": 983584, "dataset_size": 998479.0}}
|
2023-06-08T09:06:12+00:00
|
80122d13a70871cfd9621a563be8d1c45f1bee2b
|
# Sol: Simian Opertional Lexicon
The dataset
|
branles14/sol_dataset
|
[
"task_categories:conversational",
"language:en",
"license:cc-by-nc-4.0",
"region:us"
] |
2023-06-08T08:15:07+00:00
|
{"language": ["en"], "license": "cc-by-nc-4.0", "task_categories": ["conversational"]}
|
2023-07-20T05:09:27+00:00
|
72c7ec2989b2779cb4694b7697641aa74821fecb
|
# Dataset Card for "ph_er_dataset_binary"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
steviebarot/ph_er_dataset_binary
|
[
"region:us"
] |
2023-06-08T08:16:45+00:00
|
{"dataset_info": {"features": [{"name": "audio", "dtype": "audio"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "unwell", "1": "well"}}}}], "splits": [{"name": "train", "num_bytes": 12741962.0, "num_examples": 14}, {"name": "test", "num_bytes": 11311022.0, "num_examples": 12}], "download_size": 23923824, "dataset_size": 24052984.0}}
|
2023-06-08T08:17:15+00:00
|
039652b61febf538001b68b8f447cc932f5741c4
|
# Dataset Card for "9afcc3a7"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
results-sd-v1-5-sd-v2-1-if-v1-0-karlo/9afcc3a7
|
[
"region:us"
] |
2023-06-08T08:57:28+00:00
|
{"dataset_info": {"features": [{"name": "result", "dtype": "string"}, {"name": "id", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 182, "num_examples": 10}], "download_size": 1341, "dataset_size": 182}}
|
2023-06-08T08:57:28+00:00
|
6600e48f6a211b7a073a75de7d583e2bb508b582
|
# Dataset Card for "GCRL-flibusta"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
0x7o/GCRL-flibusta
|
[
"region:us"
] |
2023-06-08T09:06:54+00:00
|
{"dataset_info": {"features": [{"name": "url", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 25873591673, "num_examples": 28972}], "download_size": 13129972107, "dataset_size": 25873591673}}
|
2023-06-08T09:17:48+00:00
|
27f8656a8df20037be48daee9a5dbf9bc5925025
|
# Toxic Tweets, Greek Dataset
## Dataset Description
A frequent debate regarding research on media platforms revolves around the term of toxicity. However, the term itself is poorly defined and often contradictory, encompassing everything between online harassment and bullying to a negative commentary. In order to map online toxicity on Twitter we formed 7 different categories: i) hateful ii) insulting iii) threatening iv) racist v) sexist vi) using anti-refugee rhetoric vii) using nationalistic language (table 4). These categories were clustered into wider groups. Every tweet that contained at least one of the above mentioned categories was marked as toxic. Further on, tweets that included hateful language, threats and / or insults were marked as severe toxic. Tweets targeting an individual or a group based on identity characteristics such as gender, ethnic minority and / or religion were marked as identity hate.
### Dataset Curators
Published by Dimitris Papaevagelou (Civic Information Office), Ioanna Archontaki (Civic Information Office), Stefanos Loukopoulos (VouliWatch), Maria Nathanail (VouliWatch) and Konstantinos Mentzelos (VouliWatch).
### Annotation Process
[VouliWatch](https://vouliwatch.gr/), a Greek non-profit and non-partisan parliamentary monitoring organisation, currated the annotation process with 15 annotators that marked 112.000 tweets, resulting this dataset.
## Citation
```
@misc {civic_information_office_2023,
author = { {Civic Information Office} },
title = { toxic-el (Revision 65f60da) },
year = 2023,
url = { https://huggingface.co/datasets/cvcio/toxic-el },
doi = { 10.57967/hf/0744 },
publisher = { Hugging Face }
}
```
## Authors
Dimitris Papaevagelou (Civic Information Office) - [@andefined](https://huggingface.co/andefined)
## About
[Civic Information Office](https://cvcio.org/) is a Non Profit Organization based in Athens, Greece focusing on creating technology and research products for the public interest.
[VouliWatch](https://vouliwatch.gr/) is a Non Profit parliamentary monitoring and transparency watchdog organisation that promotes political integrity, engages Greek citizens with legislative politics and grants them with the opportunity to communicate, evaluate and hold elected representatives in the Greek and the European Parliament accountable.
|
cvcio/toxic-el
|
[
"task_categories:text-classification",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"language:el",
"license:gpl-3.0",
"text-classification",
"toxicity",
"doi:10.57967/hf/0744",
"region:us"
] |
2023-06-08T09:15:45+00:00
|
{"language": ["el"], "license": "gpl-3.0", "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "task_categories": ["text-classification"], "pretty_name": "Toxic Tweets, Greek Dataset", "tags": ["text-classification", "toxicity"]}
|
2023-06-08T12:47:19+00:00
|
febc517f0dc645e17185c87ac876b6a2ac11d0e5
|
# Dataset Card for "prm800k_en"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
vietgpt/prm800k_en
|
[
"region:us"
] |
2023-06-08T09:20:29+00:00
|
{"dataset_info": {"features": [{"name": "problem", "dtype": "string"}, {"name": "solution", "dtype": "string"}, {"name": "answer", "dtype": "string"}, {"name": "subject", "dtype": "string"}, {"name": "level", "dtype": "int64"}, {"name": "unique_id", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 9803889, "num_examples": 12000}, {"name": "test", "num_bytes": 400274, "num_examples": 500}], "download_size": 5359488, "dataset_size": 10204163}}
|
2023-06-08T09:20:38+00:00
|
f6d797550cee026e15d1851a630a349f290827e2
|
# Dataset Card for "cluster-colors.csv"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
ninja/cluster-colors.csv
|
[
"region:us"
] |
2023-06-08T09:21:17+00:00
|
{"dataset_info": {"features": [{"name": "color", "dtype": "string"}, {"name": "hex", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 392073, "num_examples": 11936}], "download_size": 264134, "dataset_size": 392073}}
|
2023-06-08T09:21:18+00:00
|
306934e619386e1cb7242448ea4e5d72a7111c10
|
# Dataset Card for "cluster-colors"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
ninja/cluster-colors
|
[
"region:us"
] |
2023-06-08T09:21:24+00:00
|
{"dataset_info": {"features": [{"name": "color", "dtype": "string"}, {"name": "hex", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 392073, "num_examples": 11936}], "download_size": 264134, "dataset_size": 392073}}
|
2023-06-08T09:21:25+00:00
|
382b33c4878632b647506094ee7a4bebb398dc03
|
divi7007/openassistant
|
[
"license:other",
"region:us"
] |
2023-06-08T09:24:30+00:00
|
{"license": "other"}
|
2023-06-08T09:30:36+00:00
|
|
341cdb803909dea81b27fddbbbe1c85005aacd2f
|
# Dataset Card for "a1a25b97"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
results-sd-v1-5-sd-v2-1-if-v1-0-karlo/a1a25b97
|
[
"region:us"
] |
2023-06-08T09:29:48+00:00
|
{"dataset_info": {"features": [{"name": "result", "dtype": "string"}, {"name": "id", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 182, "num_examples": 10}], "download_size": 1337, "dataset_size": 182}}
|
2023-06-08T09:29:49+00:00
|
8e5bcc24ac5ad91bbaa2bce815d0eb2ea232be16
|
ChenyangSi/sky_timelapse
|
[
"license:openrail",
"region:us"
] |
2023-06-08T10:02:32+00:00
|
{"license": "openrail"}
|
2023-12-30T07:12:28+00:00
|
|
9dd05329bb49eb1f69969a420050e2b258ca2380
|
# Dataset Card for "diviO"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
divi7007/diviO
|
[
"region:us"
] |
2023-06-08T10:14:26+00:00
|
{"dataset_info": {"features": [{"name": "input_ids", "sequence": "int32"}, {"name": "attention_mask", "sequence": "int8"}], "splits": [{"name": "train", "num_bytes": 20361096, "num_examples": 497}], "download_size": 6945517, "dataset_size": 20361096}}
|
2023-06-08T10:14:37+00:00
|
e9f7f885a6e8069682702042bb8bea69570bd9c2
|
Myccel0t/verify
|
[
"license:cc-by-nc-4.0",
"region:us"
] |
2023-06-08T10:28:49+00:00
|
{"license": "cc-by-nc-4.0"}
|
2023-06-08T11:38:29+00:00
|
|
fda385364994d755e460da7a796ee3a899655283
|
All uploaded word embeddings are trained with Word2Vec on CHILDES data.
available languages:
- nor: Norwegian
- en: English
|
JudithKalinowski/CHILDES_word2vec
|
[
"region:us"
] |
2023-06-08T10:55:41+00:00
|
{}
|
2023-06-08T11:02:10+00:00
|
a40777708729bcff8c81a3572914461514b88dd9
|
tum-nlp/sexism-socialmedia-balanced
|
[
"license:cc-by-sa-4.0",
"region:us"
] |
2023-06-08T10:56:02+00:00
|
{"license": "cc-by-sa-4.0"}
|
2023-06-08T10:56:54+00:00
|
|
1ada40cdd543cf1c5e5d89df875863288ac5268b
|
| Title | Annotation | PDF | Latex |
|:-------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------|:--------|
| Axion bremsstrahlung from collisions of global strings | We calculate axion radiation emitted in the collision of two straight globalstrings. The strings are supposed to be in the unexcited ground state, to beinclined with respect to each other, and to move in parallel planes. Radiationarises when the point of minimal separation between the strings moves fasterthan light. This effect exhibits a typical Cerenkov nature. Surprisingly, itallows an alternative interpretation as bremsstrahlung under a collision ofpoint charges in 2+1 electrodynamics. This can be demonstrated by suitableworld-sheet reparameterizations and dimensional reduction. Cosmologicalestimates show that our mechanism generates axion production comparable withthat from the oscillating string loops and may lead to further restrictions onthe axion window.... | https://export.arxiv.org/pdf/astro-ph/0310718 | \... |
This dataset consists of many csv format files, the name of each of which contains the category of scientific articles presented in this file. Each file consists of 1024 articles.
The first column is Title, which is the title of the text. The format of this cell is string.
The next column is Annotation, which is an annotation of the text. The format of this cell is string.
The next column is PDF, which is a link to the PDF file of this article. The format of this cell is string.
The last column is Latex, which is the text of the article in tex format. The format of this cell is string.
|
Dan-Kos/arxivannotations
|
[
"task_categories:summarization",
"size_categories:1M<n<10M",
"language:en",
"license:mit",
"region:us"
] |
2023-06-08T10:57:24+00:00
|
{"language": ["en"], "license": "mit", "size_categories": ["1M<n<10M"], "task_categories": ["summarization"]}
|
2023-10-29T13:29:27+00:00
|
973e86f54a6ccb8023d6060790d5f473fa9ce1df
|
# Dataset Card for "f247faaa"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
results-sd-v1-5-sd-v2-1-if-v1-0-karlo/f247faaa
|
[
"region:us"
] |
2023-06-08T11:23:40+00:00
|
{"dataset_info": {"features": [{"name": "result", "dtype": "string"}, {"name": "id", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 180, "num_examples": 10}], "download_size": 1337, "dataset_size": 180}}
|
2023-06-08T11:23:41+00:00
|
d40d9cb2eb5fefd8f46020c5a134ae27b29d1e5c
|
# Dataset Card for "bbf77e22"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
results-sd-v1-5-sd-v2-1-if-v1-0-karlo/bbf77e22
|
[
"region:us"
] |
2023-06-08T11:39:33+00:00
|
{"dataset_info": {"features": [{"name": "result", "dtype": "string"}, {"name": "id", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 182, "num_examples": 10}], "download_size": 1332, "dataset_size": 182}}
|
2023-06-08T11:39:34+00:00
|
f51d3f2377ddaf9edf5fab6a7c290e551da99ed5
|
# Dataset Card for "unsplash_10k_blur_rand_KS"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
wtcherr/unsplash_10k_blur_rand_KS
|
[
"region:us"
] |
2023-06-08T12:15:06+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "guide", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 3261835252.0, "num_examples": 10000}], "download_size": 3261738834, "dataset_size": 3261835252.0}}
|
2023-06-08T12:16:38+00:00
|
693cf226f6e8d3f28d60d677ece075d01a14eac2
|
weitung8/persam-bella
|
[
"license:apache-2.0",
"region:us"
] |
2023-06-08T12:37:03+00:00
|
{"license": "apache-2.0"}
|
2023-06-08T12:40:05+00:00
|
|
431d35e0a956bf791bd812963f7268d390f9d228
|
# Dataset Card for Dataset Name
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1).
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed]
|
davanstrien/on_the_books_example
|
[
"task_categories:text-classification",
"language:en",
"license:cc-by-3.0",
"lam",
"legal",
"region:us"
] |
2023-06-08T12:40:24+00:00
|
{"language": ["en"], "license": "cc-by-3.0", "task_categories": ["text-classification"], "pretty_name": "On the Books training data", "tags": ["lam", "legal"]}
|
2023-06-08T12:41:08+00:00
|
5e9a87a6ebf2774ad28b552ab56b11a6b621c24f
|
# Dataset Card for "90db6fe0"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
results-sd-v1-5-sd-v2-1-if-v1-0-karlo/90db6fe0
|
[
"region:us"
] |
2023-06-08T13:32:54+00:00
|
{"dataset_info": {"features": [{"name": "result", "dtype": "string"}, {"name": "id", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 182, "num_examples": 10}], "download_size": 1342, "dataset_size": 182}}
|
2023-06-08T13:32:55+00:00
|
b1dce216ed62f63d53429960e919836f7f0fb45f
|
# Dataset Card for "bccd-raw"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
SupawitMarayat/bccd-raw
|
[
"region:us"
] |
2023-06-08T13:57:07+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "0", "1": "1", "2": "2", "3": "3", "4": "4", "5": "5"}}}}], "splits": [{"name": "train", "num_bytes": 95000210.64, "num_examples": 4523}], "download_size": 129487676, "dataset_size": 95000210.64}}
|
2023-06-08T13:57:15+00:00
|
bfb1fd792c611077d0e70b84c2f1facae50b442c
|
# Dataset Card for "delays_class"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
polinaeterna/delays_class
|
[
"region:us"
] |
2023-06-08T14:00:52+00:00
|
{"dataset_info": {"features": [{"name": "vehicle_type", "dtype": "int64"}, {"name": "direction", "dtype": "int64"}, {"name": "weekday", "dtype": "int64"}, {"name": "temp", "dtype": "float64"}, {"name": "windspeed_max", "dtype": "float64"}, {"name": "windspeed_avg", "dtype": "float64"}, {"name": "precipitation", "dtype": "float64"}, {"name": "dew_point", "dtype": "float64"}, {"name": "humidity", "dtype": "int64"}, {"name": "hour", "dtype": "int64"}, {"name": "dayminute", "dtype": "float64"}, {"name": "delay", "dtype": "float64"}, {"name": "string_col", "dtype": "string"}, {"name": "class_col", "dtype": {"class_label": {"names": {"0": "this", "1": "are", "2": "random", "3": "words", "4": "test"}}}}], "splits": [{"name": "train", "num_bytes": 630729231, "num_examples": 5465575}], "download_size": 72156386, "dataset_size": 630729231}}
|
2023-06-08T14:01:03+00:00
|
8dc4d889a334aac47c4a4580b12959084d162d94
|
# Dataset Card for "delays_sample"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
polinaeterna/delays_sample
|
[
"region:us"
] |
2023-06-08T14:03:47+00:00
|
{"dataset_info": {"features": [{"name": "vehicle_type", "dtype": "int64"}, {"name": "direction", "dtype": "int64"}, {"name": "weekday", "dtype": "int64"}, {"name": "temp", "dtype": "float64"}, {"name": "windspeed_max", "dtype": "float64"}, {"name": "windspeed_avg", "dtype": "float64"}, {"name": "precipitation", "dtype": "float64"}, {"name": "dew_point", "dtype": "float64"}, {"name": "humidity", "dtype": "int64"}, {"name": "hour", "dtype": "int64"}, {"name": "dayminute", "dtype": "float64"}, {"name": "delay", "dtype": "float64"}, {"name": "string_col", "dtype": "string"}, {"name": "class_col", "dtype": {"class_label": {"names": {"0": "this", "1": "are", "2": "random", "3": "words", "4": "test"}}}}], "splits": [{"name": "train", "num_bytes": 11541882, "num_examples": 100000}], "download_size": 1766016, "dataset_size": 11541882}}
|
2023-06-08T15:58:27+00:00
|
1c501522f77594726b00b5943f1804c600e0230d
|
YangQiee/HQ-50K
|
[
"license:cc-by-4.0",
"region:us"
] |
2023-06-08T14:16:41+00:00
|
{"license": "cc-by-4.0"}
|
2023-06-08T14:17:44+00:00
|
|
014169fa14e9c91255c5655753f6deff61c0645e
|
# Dataset Card for "VQAv2_sample_validation_google_flan_t5_xxl_mode_D_PNP_GENERIC_Q_rices_ns_100"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
CVasNLPExperiments/VQAv2_sample_validation_google_flan_t5_xxl_mode_D_PNP_GENERIC_Q_rices_ns_100
|
[
"region:us"
] |
2023-06-08T14:20:23+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "prompt", "sequence": "string"}, {"name": "question", "dtype": "string"}, {"name": "true_label", "sequence": "string"}, {"name": "prediction", "dtype": "string"}], "splits": [{"name": "fewshot_0_clip_tags_LAION_ViT_H_14_2B_with_openai_Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full_DETA_detections_deta_swin_large_o365_coco_classes_caption_all_patches_Salesforce_blip_image_captioning_large_clean_", "num_bytes": 1172541, "num_examples": 100}], "download_size": 164191, "dataset_size": 1172541}}
|
2023-06-08T14:20:27+00:00
|
614c4e632889ce3c885a5b7fd3f34ed88867b2ef
|
# Dataset Card for "VQAv2_sample_validation_google_flan_t5_xl_mode_D_PNP_GENERIC_Q_rices_ns_100"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
CVasNLPExperiments/VQAv2_sample_validation_google_flan_t5_xl_mode_D_PNP_GENERIC_Q_rices_ns_100
|
[
"region:us"
] |
2023-06-08T14:31:47+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "prompt", "sequence": "string"}, {"name": "question", "dtype": "string"}, {"name": "true_label", "sequence": "string"}, {"name": "prediction", "dtype": "string"}], "splits": [{"name": "fewshot_0_clip_tags_LAION_ViT_H_14_2B_with_openai_Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full_DETA_detections_deta_swin_large_o365_coco_classes_caption_all_patches_Salesforce_blip_image_captioning_large_clean_", "num_bytes": 1172560, "num_examples": 100}], "download_size": 164200, "dataset_size": 1172560}}
|
2023-06-08T14:31:51+00:00
|
56a456dbe7e089f79d12ae5a15a9d6ea6f6b1fd5
|
Checkpoints for [Piper](https://github.com/rhasspy/piper) text to speech system.
|
rhasspy/piper-checkpoints
|
[
"license:mit",
"region:us"
] |
2023-06-08T14:33:16+00:00
|
{"license": "mit"}
|
2024-01-13T17:26:43+00:00
|
5da7ecfc1a2fe5d1774da7c0b2e4a46c6045fe62
|
# Dataset Card for "VQAv2_minival_validation_google_flan_t5_xxl_mode_D_PNP_GENERIC_Q_rices_ns_100"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
CVasNLPExperiments/VQAv2_minival_validation_google_flan_t5_xxl_mode_D_PNP_GENERIC_Q_rices_ns_100
|
[
"region:us"
] |
2023-06-08T15:08:14+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "prompt", "sequence": "string"}, {"name": "question", "dtype": "string"}, {"name": "true_label", "sequence": "string"}, {"name": "prediction", "dtype": "string"}], "splits": [{"name": "fewshot_0_clip_tags_LAION_ViT_H_14_2B_with_openai_Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full_DETA_detections_deta_swin_large_o365_coco_classes_caption_all_patches_Salesforce_blip_image_captioning_large_clean_", "num_bytes": 1209831, "num_examples": 100}], "download_size": 121171, "dataset_size": 1209831}}
|
2023-06-08T15:08:18+00:00
|
9d648119b6ee057e2fc0f88be095c344b77dcb09
|
patrickNLP/BIRD
|
[
"license:cc-by-nc-4.0",
"region:us"
] |
2023-06-08T15:35:55+00:00
|
{"license": "cc-by-nc-4.0"}
|
2023-06-08T15:35:55+00:00
|
|
ea4b70ca6cec6e2517657ea9cb004fcad8e0ac09
|
patrickNLP/bird-dev
|
[
"license:cc-by-nc-4.0",
"region:us"
] |
2023-06-08T15:38:20+00:00
|
{"license": "cc-by-nc-4.0"}
|
2023-06-08T15:38:20+00:00
|
|
1dfad058c649942ce7132b5f1acf42f2ca0888af
|
# Dataset Card for "ah_openai_dialog_val_test"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Deojoandco/ah_openai_dialog_val_test
|
[
"region:us"
] |
2023-06-08T16:22:05+00:00
|
{"dataset_info": {"features": [{"name": "url", "dtype": "string"}, {"name": "id", "dtype": "string"}, {"name": "num_comments", "dtype": "int64"}, {"name": "name", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "body", "dtype": "string"}, {"name": "score", "dtype": "int64"}, {"name": "upvote_ratio", "dtype": "float64"}, {"name": "distinguished", "dtype": "string"}, {"name": "over_18", "dtype": "bool"}, {"name": "created_utc", "dtype": "int64"}, {"name": "comments", "list": [{"name": "body", "dtype": "string"}, {"name": "created_utc", "dtype": "float64"}, {"name": "distinguished", "dtype": "string"}, {"name": "id", "dtype": "string"}, {"name": "permalink", "dtype": "string"}, {"name": "score", "dtype": "int64"}]}, {"name": "best_num_comments", "dtype": "int64"}, {"name": "query", "dtype": "string"}, {"name": "dialog", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 5806721, "num_examples": 585}], "download_size": 3436725, "dataset_size": 5806721}}
|
2023-06-08T16:22:45+00:00
|
267033f711e97b7b995dadecf39e915cda520727
|
# Dataset Card for "airoboros-chatml"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
AlekseyKorshuk/airoboros-chatml
|
[
"region:us"
] |
2023-06-08T16:25:25+00:00
|
{"dataset_info": {"features": [{"name": "conversation", "list": [{"name": "content", "dtype": "string"}, {"name": "do_train", "dtype": "bool"}, {"name": "role", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 187514793, "num_examples": 118142}], "download_size": 104063685, "dataset_size": 187514793}}
|
2023-06-08T16:25:33+00:00
|
cc34437093ff2a3feb478168e909854e7175bdfb
|
# Dataset Card for "the_pile_WordPiecex32768_2efdb9d060d1ae95faf952ec1a50f020"
## Dataset Description
- **Repository:** https://github.com/JonasGeiping/cramming
- **Paper:** https://arxiv.org/abs/2212.14034
- **Raw Data Source Paper:** [The Pile: An 800GB Dataset of Diverse Text for Language Modeling](https://arxiv.org/abs/2101.00027)
- **Raw Data Source Datasheet:** [Datasheet for the Pile](https://arxiv.org/abs/2201.07311)
### Dataset Summary
This is a preprocessed, tokenized dataset for the cramming-project.
Use only with the tokenizer uploaded here.
This version is `2efdb9d060d1ae95faf952ec1a50f020`, which corresponds to a specific dataset construction setup, described below.
The raw data source is the Pile, a 825 GiB diverse, open source language modelling data set that consists of 22 smaller, high-quality
datasets combined together.
### Languages
This dataset is in English (`EN`).
### Data Splits
This preprocessed subset contains only a train split.
## Dataset Creation
The configuration to create this dataset with the cramming project code (https://github.com/JonasGeiping/cramming) is
```
# This is a slice of the pile
name: the_pile
defaults:
- sources:
- the_pile
#
# Preprocessing
normalizer:
force_lowercase: True
strip_accents: True
force_english_keyboard: True
whitespace_escape: False
tokenizer: WordPiece
vocab_size: 32768
# Dataset Formation
seq_length: 128
include_cls_token_in_corpus: False
include_sep_token_in_corpus: True
use_type_ids: False
max_entries_in_raw_dataset: 16e6
max_seq_in_tokenized_dataset: 85e6
# Data Cleaning:
named_entity_simplification: False
remove_whitespaces: False
remove_trash: True
trash_cutoff: 0.25
deduplicate_entries: False
deduplication_threshold: 75
# Data Order:
ordering: sentence-length-curriculum
```
## Considerations for Using the Data
Limitations and bias:
This training data was further filtered and sorted beyond the normal preprocessing.
These modifications were not tested for unintended consequences.
## Additional Information
### Dataset Curators
This dataset is a filtered, sorted and preprocessed subset of the the-Pile made by Jonas Geiping . The original dataset was primarily curated by Leo Gao and Stella Biderman, with assistance from other authors of the Pile paper.
### Licensing Information
Please refer to the specific license depending on the subset you use at https://huggingface.co/datasets/EleutherAI/pile
### Citation Information
Filtered version for the cramming project:
```
@article{geiping_cramming_2022,
title = {Cramming: {{Training}} a {{Language Model}} on a {{Single GPU}} in {{One Day}}},
shorttitle = {Cramming},
author = {Geiping, Jonas and Goldstein, Tom},
year = {2022},
month = dec,
eprint = {2212.14034},
primaryclass = {cs},
publisher = {{arXiv}},
doi = {10.48550/arXiv.2212.14034},
url = {http://arxiv.org/abs/2212.14034},
urldate = {2023-01-10},
archiveprefix = {arxiv},
keywords = {Computer Science - Computation and Language,Computer Science - Machine Learning},
journal = {arxiv:2212.14034[cs]}
}
```
Original Data Curation:
```
@article{gao2020pile,
title={The {P}ile: An 800{GB} dataset of diverse text for language modeling},
author={Gao, Leo and Biderman, Stella and Black, Sid and Golding, Laurence and Hoppe, Travis and Foster, Charles and Phang, Jason and He, Horace and Thite, Anish and Nabeshima, Noa and others},
journal={arXiv preprint arXiv:2101.00027},
year={2020}
}
@article{biderman2022datasheet,
title={Datasheet for the pile},
author={Biderman, Stella and Bicheno, Kieran and Gao, Leo},
journal={arXiv preprint arXiv:2201.07311},
year={2022}
}
```
|
JonasGeiping/the_pile_WordPiecex32768_2efdb9d060d1ae95faf952ec1a50f020
|
[
"arxiv:2212.14034",
"arxiv:2101.00027",
"arxiv:2201.07311",
"region:us"
] |
2023-06-08T16:30:55+00:00
|
{"dataset_info": {"features": [{"name": "input_ids", "sequence": "int32"}], "splits": [{"name": "train", "num_bytes": 43860000000, "num_examples": 85000000}], "download_size": 24001057282, "dataset_size": 43860000000, "annotations_creators": ["no-annotation"], "language_creators": ["found"], "language": ["en"], "license": "other", "multilinguality": ["monolingual"], "pretty_name": "pretokenized,filtered,sorted subset of the Pile", "size_categories": ["10B<n<100B"], "source_datasets": ["the-pile"], "task_categories": ["text-generation", "fill-mask"], "task_ids": ["language-modeling", "masked-language-modeling"], "paperswithcode_id": "the-pile-cramming"}}
|
2023-06-13T15:25:54+00:00
|
95170ee599d5a83c185d982daa9c9c813a07d134
|
# Dataset Card for "wizardlm-alpaca-evol-instruct-chatml"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
AlekseyKorshuk/wizardlm-alpaca-evol-instruct-chatml
|
[
"region:us"
] |
2023-06-08T17:05:58+00:00
|
{"dataset_info": {"features": [{"name": "conversation", "list": [{"name": "content", "dtype": "string"}, {"name": "do_train", "dtype": "bool"}, {"name": "role", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 96942441, "num_examples": 54974}], "download_size": 46458065, "dataset_size": 96942441}}
|
2023-06-08T17:06:03+00:00
|
0dee0f3ca84e7e650c7cf58a0066ee993ae5019a
|
# Dataset Card for "VQAv2_minival_validation_google_flan_t5_xxl_mode_CM_Q_rices_ns_25994"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
CVasNLPExperiments/VQAv2_minival_validation_google_flan_t5_xxl_mode_CM_Q_rices_ns_25994
|
[
"region:us"
] |
2023-06-08T17:20:07+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "prompt", "sequence": "string"}, {"name": "question", "dtype": "string"}, {"name": "true_label", "sequence": "string"}, {"name": "prediction", "dtype": "string"}], "splits": [{"name": "fewshot_0_clip_tags_LAION_ViT_H_14_2B_with_openai_Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full_DETA_detections_deta_swin_large_o365_coco_classes_caption_all_patches_Salesforce_blip_image_captioning_large_clean_", "num_bytes": 10536194, "num_examples": 25994}, {"name": "fewshot_1", "num_bytes": 64729039, "num_examples": 25994}, {"name": "fewshot_4", "num_bytes": 157012975, "num_examples": 25994}], "download_size": 22918798, "dataset_size": 232278208}}
|
2023-06-13T18:49:34+00:00
|
fa44be7b20b5106cdcb0a0b80aa15611ccc98cd3
|
shrinath-suresh/blogs-docs-splitted
|
[
"license:apache-2.0",
"region:us"
] |
2023-06-08T17:25:19+00:00
|
{"license": "apache-2.0"}
|
2023-06-09T10:16:21+00:00
|
|
097f30c46992472bbb606bc30b9bf8153e9171e4
|
pulkitpaliwal/planeperturbed
|
[
"license:cc-by-2.0",
"region:us"
] |
2023-06-08T17:35:18+00:00
|
{"license": "cc-by-2.0"}
|
2023-06-08T17:35:18+00:00
|
|
e5b65d4eb08b5cc02cced9a0e6fd80ab7a34b19f
|
# Dataset Card for "VQAv2_minival_validation_google_flan_t5_xxl_mode_D_PNP_GENERIC_CM_Q_rices_ns_25994"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
CVasNLPExperiments/VQAv2_minival_validation_google_flan_t5_xxl_mode_D_PNP_GENERIC_CM_Q_rices_ns_25994
|
[
"region:us"
] |
2023-06-08T17:35:28+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "prompt", "sequence": "string"}, {"name": "question", "dtype": "string"}, {"name": "true_label", "sequence": "string"}, {"name": "prediction", "dtype": "string"}], "splits": [{"name": "fewshot_0_clip_tags_LAION_ViT_H_14_2B_with_openai_Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full_DETA_detections_deta_swin_large_o365_coco_classes_caption_all_patches_Salesforce_blip_image_captioning_large_clean_", "num_bytes": 349814852, "num_examples": 25994}], "download_size": 34573141, "dataset_size": 349814852}}
|
2023-06-09T15:50:44+00:00
|
7f2a1959bbb8c54b7747bde5bea67125fe4db8bb
|
# Dataset Card for "intothatgoodnight-guanaco-chatml"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
AlekseyKorshuk/intothatgoodnight-guanaco-chatml
|
[
"region:us"
] |
2023-06-08T17:37:00+00:00
|
{"dataset_info": {"features": [{"name": "conversation", "list": [{"name": "content", "dtype": "string"}, {"name": "do_train", "dtype": "bool"}, {"name": "role", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 69125408, "num_examples": 50000}], "download_size": 38037513, "dataset_size": 69125408}}
|
2023-06-08T17:37:44+00:00
|
3b5e16cae64f7c602763169ba7385597ddbf185b
|
# Dataset Card for "conversation-starters-moderated"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Dataset of ~17.000 rows filtered with OpenAI moderation API
|
Langame/conversation-starters-moderated
|
[
"region:us"
] |
2023-06-08T17:40:15+00:00
|
{"dataset_info": {"features": [{"name": "prompt", "dtype": "string"}, {"name": "topics", "sequence": "string"}], "splits": [{"name": "train", "num_bytes": 2049719, "num_examples": 16507}], "download_size": 955187, "dataset_size": 2049719}}
|
2023-06-08T17:46:15+00:00
|
1dbe6ec325819df632ccb562c7c28cbc1a265b47
|
# Dataset Card for "the_pile_WordPiecex32768_8eb2d0ea9da707676c81314c4ea04507"
## Dataset Description
- **Repository:** https://github.com/JonasGeiping/cramming
- **Paper:** https://arxiv.org/abs/2212.14034
- **Raw Data Source Paper:** [The Pile: An 800GB Dataset of Diverse Text for Language Modeling](https://arxiv.org/abs/2101.00027)
- **Raw Data Source Datasheet:** [Datasheet for the Pile](https://arxiv.org/abs/2201.07311)
### Dataset Summary
This is a preprocessed, tokenized dataset for the cramming-project.
Use only with the tokenizer uploaded here.
This version is `8eb2d0ea9da707676c81314c4ea04507`, which corresponds to a specific dataset construction setup, described below.
The raw data source is the Pile, a 825 GiB diverse, open source language modelling data set that consists of 22 smaller, high-quality
datasets combined together.
### Languages
This dataset is in English (`EN`).
### Data Splits
This preprocessed subset contains only a train split.
## Dataset Creation
The configuration to create this dataset with the cramming project code (https://github.com/JonasGeiping/cramming) is
```
# This is a slice of the pile
name: the_pile
defaults:
- sources:
- the_pile
#
# Preprocessing
normalizer:
force_lowercase: True
strip_accents: True
force_english_keyboard: True
whitespace_escape: False
tokenizer: WordPiece
vocab_size: 32768
# Dataset Formation
seq_length: 128
include_cls_token_in_corpus: False
include_sep_token_in_corpus: True
use_type_ids: False
max_entries_in_raw_dataset: 16e6
max_seq_in_tokenized_dataset: 85e6
# Data Cleaning:
named_entity_simplification: False
remove_whitespaces: False
remove_trash: True
trash_cutoff: 0.25
deduplicate_entries: True
deduplication_threshold: 75
# Data Order:
ordering: sentence-length-curriculum
```
## Considerations for Using the Data
Limitations and bias:
This training data was further filtered and sorted beyond the normal preprocessing.
These modifications were not tested for unintended consequences.
## Additional Information
### Dataset Curators
This dataset is a filtered, sorted and preprocessed subset of the the-Pile made by Jonas Geiping . The original dataset was primarily curated by Leo Gao and Stella Biderman, with assistance from other authors of the Pile paper.
### Licensing Information
Please refer to the specific license depending on the subset you use at https://huggingface.co/datasets/EleutherAI/pile
### Citation Information
Filtered version for the cramming project:
```
@article{geiping_cramming_2022,
title = {Cramming: {{Training}} a {{Language Model}} on a {{Single GPU}} in {{One Day}}},
shorttitle = {Cramming},
author = {Geiping, Jonas and Goldstein, Tom},
year = {2022},
month = dec,
eprint = {2212.14034},
primaryclass = {cs},
publisher = {{arXiv}},
doi = {10.48550/arXiv.2212.14034},
url = {http://arxiv.org/abs/2212.14034},
urldate = {2023-01-10},
archiveprefix = {arxiv},
keywords = {Computer Science - Computation and Language,Computer Science - Machine Learning},
journal = {arxiv:2212.14034[cs]}
}
```
Original Data Curation:
```
@article{gao2020pile,
title={The {P}ile: An 800{GB} dataset of diverse text for language modeling},
author={Gao, Leo and Biderman, Stella and Black, Sid and Golding, Laurence and Hoppe, Travis and Foster, Charles and Phang, Jason and He, Horace and Thite, Anish and Nabeshima, Noa and others},
journal={arXiv preprint arXiv:2101.00027},
year={2020}
}
@article{biderman2022datasheet,
title={Datasheet for the pile},
author={Biderman, Stella and Bicheno, Kieran and Gao, Leo},
journal={arXiv preprint arXiv:2201.07311},
year={2022}
}
```
|
JonasGeiping/the_pile_WordPiecex32768_8eb2d0ea9da707676c81314c4ea04507
|
[
"arxiv:2212.14034",
"arxiv:2101.00027",
"arxiv:2201.07311",
"region:us"
] |
2023-06-08T17:47:12+00:00
|
{"dataset_info": {"features": [{"name": "input_ids", "sequence": "int32"}], "splits": [{"name": "train", "num_bytes": 38252459784, "num_examples": 74132674}], "download_size": 20976468705, "dataset_size": 38252459784, "annotations_creators": ["no-annotation"], "language_creators": ["found"], "language": ["en"], "license": "other", "multilinguality": ["monolingual"], "pretty_name": "pretokenized,filtered,sorted subset of the Pile", "size_categories": ["10B<n<100B"], "source_datasets": ["the-pile"], "task_categories": ["text-generation", "fill-mask"], "task_ids": ["language-modeling", "masked-language-modeling"], "paperswithcode_id": "the-pile-cramming"}}
|
2023-06-13T15:25:35+00:00
|
ef34f1f98f3c637ef37fac22d79f5e28483d7936
|
# Dataset Card for "recipes_translation_2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
PaulineSanchez/recipes_translation_2
|
[
"region:us"
] |
2023-06-08T17:50:19+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "translation", "dtype": {"translation": {"languages": ["en", "fr"]}}}], "splits": [{"name": "train", "num_bytes": 57430.4, "num_examples": 200}, {"name": "validation", "num_bytes": 14357.6, "num_examples": 50}], "download_size": 48205, "dataset_size": 71788.0}}
|
2023-06-08T17:50:24+00:00
|
a2a0b33bae43bf7629b1a4d7cf938acad2a3f445
|
# Dataset Card for "gpt4-llm-cleaned-chatml"
Data preprocessing pipeline: https://github.com/AlekseyKorshuk/chat-data-pipeline
|
AlekseyKorshuk/gpt4-llm-cleaned-chatml
|
[
"region:us"
] |
2023-06-08T17:52:34+00:00
|
{"dataset_info": {"features": [{"name": "conversation", "list": [{"name": "content", "dtype": "string"}, {"name": "do_train", "dtype": "bool"}, {"name": "role", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 39157218, "num_examples": 54568}], "download_size": 21310829, "dataset_size": 39157218}}
|
2023-07-24T19:21:19+00:00
|
1e5e75cc3b35f3e1655711ef8aa83af10cdaada6
|
# Dataset Card for "a61a38e2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
results-sd-v1-5-sd-v2-1-if-v1-0-karlo/a61a38e2
|
[
"region:us"
] |
2023-06-08T17:53:11+00:00
|
{"dataset_info": {"features": [{"name": "result", "dtype": "string"}, {"name": "id", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 180, "num_examples": 10}], "download_size": 1339, "dataset_size": 180}}
|
2023-06-08T17:53:12+00:00
|
e01b1364dbea0c68b7abb3a6b9f57bffe0557f78
|
# Dataset Card for "test_genome"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
gustamatos/test_genome
|
[
"region:us"
] |
2023-06-08T18:27:52+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 29555981, "num_examples": 30}, {"name": "test", "num_bytes": 6004459, "num_examples": 3}], "download_size": 16043250, "dataset_size": 35560440}}
|
2023-06-08T18:28:04+00:00
|
47038bfd813905bca63b72704164742d2d4a313b
|
# Dataset Card for Dataset Name
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1).
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
StudyInstanceUID, SeriesInstanceUID, SOPInstanceUID, pe_present_on_image, negative_exam_for_pe, qa_motion, qa_contrast, flow_artifact
### 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]
|
Richardol1219/RSNA-PE-Training
|
[
"license:openrail",
"region:us"
] |
2023-06-08T18:32:54+00:00
|
{"license": "openrail"}
|
2023-06-13T15:56:40+00:00
|
4f80f19c970bf2858e8fe4003cebc6153034eb34
|
# Dataset Card for "planeperturbed"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
pppppppppp2/planeperturbed
|
[
"region:us"
] |
2023-06-08T18:52:28+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 647755473.5, "num_examples": 5500}], "download_size": 622143522, "dataset_size": 647755473.5}}
|
2023-10-13T10:12:52+00:00
|
2ab362d3965ef54df7000c88da1657860dda8b9c
|
Dataset redistributed without change with permission from the author. If you use this dataset in your research, please cite the following paper: https://doi.org/10.3390/rs6064907
|
torchgeo/l7sparcs
|
[
"task_categories:image-segmentation",
"size_categories:n<1K",
"license:cc0-1.0",
"climate",
"region:us"
] |
2023-06-08T19:54:21+00:00
|
{"license": "cc0-1.0", "size_categories": ["n<1K"], "task_categories": ["image-segmentation"], "pretty_name": "L7 SPARCS", "tags": ["climate"]}
|
2023-06-08T19:57:27+00:00
|
433e9c34e80a1f2f7f9fb30e7815bf147005ce0f
|
### Dataset Summary
This is a dataset of stand up comedy transcripts. It was scraped from
https://scrapsfromtheloft.com/stand-up-comedy-scripts/ and all terms of use
apply. The transcripts are offered to the public as a contribution to education
and scholarship, and for the private, non-profit use of the academic community.
|
zachgitt/comedy-transcripts
|
[
"size_categories:n<1K",
"language:en",
"art",
"region:us"
] |
2023-06-08T20:26:43+00:00
|
{"language": ["en"], "size_categories": ["n<1K"], "pretty_name": "comedy_transcripts", "tags": ["art"]}
|
2023-06-08T20:39:54+00:00
|
c8d5b39396e38b6f2ad506976658f982d9c9e583
|
# Dataset Card for "hateful_memes_train_dev"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
emily49/hateful_memes_train_dev
|
[
"region:us"
] |
2023-06-08T21:08:32+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "id", "dtype": "int64"}, {"name": "img", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 409371554.0, "num_examples": 8500}, {"name": "validation", "num_bytes": 25599957.0, "num_examples": 500}], "download_size": 444287887, "dataset_size": 434971511.0}}
|
2023-06-08T21:09:13+00:00
|
1cf5c86835f0670e0532a31e1dc2f2675df11d04
|
# Dataset Card for "corrosion_segment"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
rkumari/corrosion_segment
|
[
"region:us"
] |
2023-06-08T21:38:13+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 3474520.0, "num_examples": 30}, {"name": "validation", "num_bytes": 1479472.0, "num_examples": 14}], "download_size": 1484482, "dataset_size": 4953992.0}}
|
2023-06-08T22:12:03+00:00
|
2581c3e7bf325269598f97dac1ed4329fd536a44
|
higart/beginner
|
[
"license:bigcode-openrail-m",
"region:us"
] |
2023-06-08T23:32:03+00:00
|
{"license": "bigcode-openrail-m"}
|
2024-02-08T22:59:28+00:00
|
|
7437a863b389078cbb0c7b7eb8671480ef2606f5
|
ThiennNguyen/MangaColoring
|
[
"license:openrail",
"region:us"
] |
2023-06-09T00:53:33+00:00
|
{"license": "openrail"}
|
2023-06-09T03:16:21+00:00
|
|
969e7e2a76ac5cbb0c6cbf4d351a7f3dfc562444
|
LolorzoloL/crypto_news
|
[
"license:apache-2.0",
"region:us"
] |
2023-06-09T01:14:01+00:00
|
{"license": "apache-2.0"}
|
2023-06-12T06:01:52+00:00
|
|
6f1135022bb5b76a89c61f5ec44d216eed171354
|
totally-not-an-llm/melbourne-20
|
[
"license:mit",
"region:us"
] |
2023-06-09T01:24:49+00:00
|
{"license": "mit"}
|
2023-06-11T05:02:06+00:00
|
|
032cd9d2d6dd8f89047ae2ca0d1f4f3d0b947770
|
# Dataset Card for "BiasTestGPT: Bias Specifications"
Dataset of sentences for bias testing in open-sourced Pretrained Language Models generated using ChatGPT and other generative Language Models.
This dataset is used and actively populated by the [BiasTestGPT HuggingFace Tool](https://huggingface.co/spaces/RKocielnik/bias-test-gpt).
- [BiasTestGPT HuggingFace Tool](https://huggingface.co/spaces/RKocielnik/bias-test-gpt)
- [Github Repository](https://github.com/Kaminari84/BiasTestGPT)
- [Dataset with Generated Test Sentences](https://huggingface.co/datasets/RKocielnik/bias_test_gpt_sentences)
- [Datasheet for Datasets](https://github.com/Kaminari84/BiasTestGPT/blob/main/documents/BiasTestGPT___Datasheet_for_dataset_template.pdf)
## Data Structure
### Data Instances
Dataset instances consist of JSON files with bias specifications.
There are 2 types of specifications kept in separate folders:
- **'predefined biases'** - predefined bias specifications are obtained from prior work such as [Caliskan'17](https://www.science.org/doi/full/10.1126/science.aal4230?casa_token=c7LtvGWRQqIAAAAA:5QJ_M6jLk3675-50ZIIGgO2XQ5ewtZZxnKW-NlqUmxT6H112t4PIptRA94JLswFcXYIyl3yMML86duQ) and [Kurita'19](https://arxiv.org/pdf/1906.07337.pdf).
- **'custom biases'** - are biases defined by the users of our [HuggingFace Tool](https://huggingface.co/spaces/RKocielnik/bias-test-gpt). These are dynamically added to the repository based on user interactions with the tool.
An example of predefined bias specification in JSON format is provided below:
```json
{ "name": "Career_Family_vs_MaleName_FemaleName",
"social_groups": {
"Male_Names": ["John", "Paul", "Mike", "Kevin", "Steve", "Greg", "Jeff", "Bill"],
"Female_Names": ["Amy", "Joan", "Lisa", "Sarah", "Diana", "Kate", "Ann", "Donna"]
},
"attributes": {
"Career": ["executive", "management", "professional", "corporation", "salary", "office", "business", "career"],
"Family": ["home", "parents", "children", "family", "cousins", "marriage", "wedding", "relatives"]
},
"templates": ["[T] likes [A]", "[T] like [A]", "[T] is interested in [A]"],
"source": "Caliskan'17 - Weat 6",
"url": "https://purehost.bath.ac.uk/ws/portalfiles/portal/168480066/CaliskanEtAl_authors_full.pdf",
"type": "predefined",
"created": "09/06/2023 09:58:37"
}
```
### Data Fields
Each bias specification contains several data fields defining the bias to test itself, but also additional metadata about the bias specification origins.
#### Description of Data Fields in the Bias Specification
- **'name'**: a `string` feature - a short descriptive name of the bias.
- **'social_groups'**: a `dictionary` feature containing specifications of 2 or more social groups to compare.
- **'Male_Names'**, **'Female_Names'** - bias specific *keys* with names defininig the compared social groups. Value for each kay is a list of terms defining the particular social group.
- **'attributes'**: a `dictionary' feature containing specifications of 2 ideally polar opposite attributes to test in comparison of social groups.
- **'Career'**, **`Family'** - bias specific *keys* with names of opposing attributes. Value for each key is a list of terms defining the attribute.
- **'templates'**: a 'list' feature - legacy test sentence templates used in prior work. Used for a baseline bias measurement.
- **'source'**: a 'string' feature - the source of the bias specification, usually prior work
- **'url'**: a `string' feature - link to the research paper providing the bias specification
- **'type'**: a `string' feature - specifies whether bias has been predefined by prior work or defined using our [HuggingFace Tool](https://huggingface.co/spaces/RKocielnik/bias-test-gpt).
- **'created'**: a data of addition of the bias specification to the repository. Generated automatically upon addition from our tool.
### Bias Specification - Data Splits
The repository contains 15 predefined bias specifications based on prior work and an additional 4 or more custom-defined bias specifications.
We note that the number of custom-defined bias specifications is constantly growing as it is being populated by the interactions with the [HuggingFace Tool](https://huggingface.co/spaces/RKocielnik/bias-test-gpt).
| Type | Meaning | Size |
|--------|--------|------:|
| predefined | biases for which specification has been provided in prior work | 15 |
| custom | biases added to the repository based on interaction with the [BiasTestGPT tool](https://huggingface.co/spaces/RKocielnik/bias-test-gpt) | 4+ |
|
RKocielnik/bias_test_gpt_biases
|
[
"language:en",
"license:apache-2.0",
"arxiv:1906.07337",
"region:us"
] |
2023-06-09T01:25:42+00:00
|
{"language": ["en"], "license": "apache-2.0", "pretty_name": "BiasTestGPT-sentences"}
|
2023-06-13T15:10:03+00:00
|
e478d736542cdec9b236a1f52373aa7976b219dc
|
---本数据集依据Hecate2/sukasuka-vocal-dataset-builder所提取的末日三问语音数据后，进行人声分离与降噪，最后筛选适合进行训练的语音内容。
以下为已完成筛选角色 ——————数据由动画与广播剧组成。
chtholly
Willem
Ithea
Nopht
Nygglatho
Rhantolk
Nephren由于数据集质量较低，暂无法提取满意效果，暂时放弃
其余配角待定
添加个人sovits模型
|
camimo/sukasuka-Dataset
|
[
"region:us"
] |
2023-06-09T01:30:44+00:00
|
{}
|
2023-07-12T12:43:14+00:00
|
bf0b976fce4eff992e7575fa9d008ca7bfcb8a53
|
# Los Angeles MIDI Dataset
## SOTA kilo-scale MIDI dataset for MIR and Music AI purposes
***
***
## Search and Explore Los Angeles MIDI dataset
[![Open In Colab][colab-badge]][colab-notebook1]
[colab-notebook1]: <https://colab.research.google.com/github/asigalov61/Los-Angeles-MIDI-Dataset/blob/main/Los_Angeles_MIDI_Dataset_Search_and_Explore.ipynb>
[colab-badge]: <https://colab.research.google.com/assets/colab-badge.svg>
***
## [NEW] Master MIDI Dataset GPU Search and Filter
[![Open In Colab][colab-badge]][colab-notebook5]
[colab-notebook5]: <https://colab.research.google.com/github/asigalov61/Los-Angeles-MIDI-Dataset/blob/main/Extras/Master_MIDI_Dataset_GPU_Search_and_Filter.ipynb>
[colab-badge]: <https://colab.research.google.com/assets/colab-badge.svg>
***
## Master MIDI Dataset Search and Filter
[![Open In Colab][colab-badge]][colab-notebook4]
[colab-notebook4]: <https://colab.research.google.com/github/asigalov61/Los-Angeles-MIDI-Dataset/blob/main/Extras/Master_MIDI_Dataset_Search_and_Filter.ipynb>
[colab-badge]: <https://colab.research.google.com/assets/colab-badge.svg>
***
## Make your own Los Angeles MIDI Dataset from any MIDI scrape
[![Open In Colab][colab-badge]][colab-notebook2]
[colab-notebook2]: <https://colab.research.google.com/github/asigalov61/Los-Angeles-MIDI-Dataset/blob/main/Los_Angeles_MIDI_Dataset_Maker.ipynb>
[colab-badge]: <https://colab.research.google.com/assets/colab-badge.svg>
***
## Make your own Los Angeles MIDI Dataset Metadata
[![Open In Colab][colab-badge]][colab-notebook3]
[colab-notebook3]: <https://colab.research.google.com/github/asigalov61/Los-Angeles-MIDI-Dataset/blob/main/META-DATA/Los_Angeles_MIDI_Dataset_Metadata_Maker.ipynb>
[colab-badge]: <https://colab.research.google.com/assets/colab-badge.svg>
***
## [Los Angeles MIDI Dataset is now avaialable for download!!!](https://huggingface.co/datasets/projectlosangeles/Los-Angeles-MIDI-Dataset)
***
## Main Features:
### 1) ~405000 100% unique MIDIs to explore :)
### 2) Each MIDI file was read-checked and 100% de-duped
### 3) Extensive meta-data for each MIDI file
### 4) Full chords data for each MIDI file
### 5) Helper Python code
***
## NEW in version 4.0
### 1) Added 160519 new unique MIDIs
### 2) Dataset now contains 404714 MIDIs
### 3) Removed all malformed MIDIs
### 4) Expanded dataset MIDIs metadata
### 5) Added MIDIs chords database
### 6) Updated dataset concept artwork
### Enjoy! :)
***
```bibtex
@inproceedings{lev2024losangelesmididataset,
title = {Los Angeles MIDI Dataset: SOTA kilo-scale MIDI dataset for MIR and Music AI purposes},
author = {Aleksandr Lev},
booktitle = {GitHub},
year = {2024},
}
```
***
### Project Los Angeles
### Tegridy Code 2024
|
projectlosangeles/Los-Angeles-MIDI-Dataset
|
[
"license:cc-by-nc-sa-4.0",
"mir",
"music",
"midi",
"midi-dataset",
"region:us"
] |
2023-06-09T01:41:29+00:00
|
{"license": "cc-by-nc-sa-4.0", "tags": ["mir", "music", "midi", "midi-dataset"]}
|
2024-02-17T16:45:06+00:00
|
dfd29e38f3f3c99db9ba9ea3e9c956126ba93585
|
LIMA dataset in Vicuna ShareGPT format.
License under LIMA's License.
Original Repo:
https://huggingface.co/datasets/GAIR/lima
|
64bits/lima_vicuna_format
|
[
"task_categories:text-generation",
"language:en",
"license:other",
"region:us"
] |
2023-06-09T01:46:06+00:00
|
{"language": ["en"], "license": "other", "task_categories": ["text-generation"]}
|
2023-06-09T01:47:39+00:00
|
bfaffa1a2df4c8a87dddcb7e1ad17d0ac3b411c7
|
# Dataset Card for "Instruction_filtered_set"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Quxingwei/Instruction_filtered_set
|
[
"region:us"
] |
2023-06-09T02:14:16+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "conversations", "list": [{"name": "from", "dtype": "string"}, {"name": "value", "dtype": "string"}]}, {"name": "instruction", "dtype": "string"}, {"name": "is_english", "dtype": "bool"}], "splits": [{"name": "train", "num_bytes": 7251734086, "num_examples": 2595569}], "download_size": 2390951997, "dataset_size": 7251734086}}
|
2023-06-09T02:57:00+00:00
|
30dc1e8a573121db8edaed9444162f92b1aeffa0
|
jhopela/dolly_train
|
[
"license:openrail",
"region:us"
] |
2023-06-09T02:22:08+00:00
|
{"license": "openrail"}
|
2023-06-09T15:36:47+00:00
|
|
e627327dfb4cb1fddbc84513af13ded35ce3690b
|
# Snapshot Twitter
**We no longer able to snapshot due to API changes**.
## description
1. minimum timestamp, 2022-04-17T16:30:07.000Z2.
2. maximum timestamp, 2022-09-03T09:23:52.000Z
3. 7075025 rows
4. full attributes,
```json
{
"datetime": "2022-04-18T05:57:04",
"datetime_gmt8": "2022-04-18T13:57:04",
"data_text": "kekal halal kak https://t.co/YHKqszqPnS",
"body": "kekal halal kak https://t.co/YHKqszqPnS",
"screen_name": "Luke_Sebastian2",
"followers_count": 10413,
"friends_count": 72,
"listed_count": 6,
"favourites_count": 1494,
"statuses_count": 948,
"quoted_status_text": "NULL",
"lang": "in",
"retweet": "false",
"retweet_text": "NULL",
"retweet_text_full": "NULL",
"retweet_count": 0,
"retweet_detail": {},
"quote_count": 0,
"favorite_count": 0,
"reply_count": 0,
"id_str": "1515932406368202753",
"tweet": {
"created_at": "Mon Apr 18 05:57:04 +0000 2022",
"id": 1515932406368202800,
"id_str": "1515932406368202753",
"text": "kekal halal kak😏🤫 https://t.co/YHKqszqPnS",
"display_text_range": [
0,
17
],
"source": "<a href=\"http://twitter.com/download/android\" rel=\"nofollow\">Twitter for Android</a>",
"truncated": false,
"in_reply_to_status_id": null,
"in_reply_to_status_id_str": null,
"in_reply_to_user_id": null,
"in_reply_to_user_id_str": null,
"in_reply_to_screen_name": null,
"user": {
"id": 1431086333024374800,
"id_str": "1431086333024374792",
"name": "☄ʟᴜᴋᴇ",
"screen_name": "Luke_Sebastian2",
"location": "Malaysia",
"url": "http://t.me/Luke_Alqamara",
"description": "|𝟮𝟬🍰|⚤|📚𝗧𝗼𝗽|🇮🇩|📌🇲🇾|Law Student💼|•𝐤𝐞𝐤𝐚𝐬𝐢𝐡𝐤𝐮:@Trevor_Louise1•|Dm me for endorsement/Collab and rates also📩!|•don't forget to smile😊•",
"translator_type": "none",
"protected": false,
"verified": false,
"followers_count": 10413,
"friends_count": 72,
"listed_count": 6,
"favourites_count": 1494,
"statuses_count": 948,
"created_at": "Fri Aug 27 02:49:28 +0000 2021",
"utc_offset": null,
"time_zone": null,
"geo_enabled": true,
"lang": null,
"contributors_enabled": false,
"is_translator": false,
"profile_background_color": "F5F8FA",
"profile_background_image_url": "",
"profile_background_image_url_https": "",
"profile_background_tile": false,
"profile_link_color": "1DA1F2",
"profile_sidebar_border_color": "C0DEED",
"profile_sidebar_fill_color": "DDEEF6",
"profile_text_color": "333333",
"profile_use_background_image": true,
"profile_image_url": "http://pbs.twimg.com/profile_images/1500850780823494658/snCdyeen_normal.jpg",
"profile_image_url_https": "https://pbs.twimg.com/profile_images/1500850780823494658/snCdyeen_normal.jpg",
"profile_banner_url": "https://pbs.twimg.com/profile_banners/1431086333024374792/1647061513",
"default_profile": true,
"default_profile_image": false,
"following": null,
"follow_request_sent": null,
"notifications": null,
"withheld_in_countries": []
},
"geo": null,
"coordinates": null,
"place": {
"id": "7b02fbddf4d9f2c6",
"url": "https://api.twitter.com/1.1/geo/id/7b02fbddf4d9f2c6.json",
"place_type": "city",
"name": "Kuala Lumpur City",
"full_name": "Kuala Lumpur City, Kuala Lumpur Federal Territory",
"country_code": "MY",
"country": "Malaysia",
"bounding_box": {
"type": "Polygon",
"coordinates": [
[
[
101.668232,
3.104906
],
[
101.668232,
3.192155
],
[
101.742378,
3.192155
],
[
101.742378,
3.104906
]
]
]
},
"attributes": {}
},
"contributors": null,
"is_quote_status": false,
"quote_count": 0,
"reply_count": 0,
"retweet_count": 0,
"favorite_count": 0,
"entities": {
"hashtags": [],
"urls": [],
"user_mentions": [],
"symbols": [],
"media": [
{
"id": 1515932334612107300,
"id_str": "1515932334612107268",
"indices": [
18,
41
],
"additional_media_info": {
"monetizable": false
},
"media_url": "http://pbs.twimg.com/ext_tw_video_thumb/1515932334612107268/pu/img/ak2K23DgNDDV-UCC.jpg",
"media_url_https": "https://pbs.twimg.com/ext_tw_video_thumb/1515932334612107268/pu/img/ak2K23DgNDDV-UCC.jpg",
"url": "https://t.co/YHKqszqPnS",
"display_url": "pic.twitter.com/YHKqszqPnS",
"expanded_url": "https://twitter.com/Luke_Sebastian2/status/1515932406368202753/video/1",
"type": "photo",
"sizes": {
"thumb": {
"w": 150,
"h": 150,
"resize": "crop"
},
"medium": {
"w": 540,
"h": 960,
"resize": "fit"
},
"small": {
"w": 383,
"h": 680,
"resize": "fit"
},
"large": {
"w": 540,
"h": 960,
"resize": "fit"
}
}
}
]
},
"extended_entities": {
"media": [
{
"id": 1515932334612107300,
"id_str": "1515932334612107268",
"indices": [
18,
41
],
"additional_media_info": {
"monetizable": false
},
"media_url": "http://pbs.twimg.com/ext_tw_video_thumb/1515932334612107268/pu/img/ak2K23DgNDDV-UCC.jpg",
"media_url_https": "https://pbs.twimg.com/ext_tw_video_thumb/1515932334612107268/pu/img/ak2K23DgNDDV-UCC.jpg",
"url": "https://t.co/YHKqszqPnS",
"display_url": "pic.twitter.com/YHKqszqPnS",
"expanded_url": "https://twitter.com/Luke_Sebastian2/status/1515932406368202753/video/1",
"type": "video",
"video_info": {
"aspect_ratio": [
9,
16
],
"duration_millis": 15232,
"variants": [
{
"bitrate": 632000,
"content_type": "video/mp4",
"url": "https://video.twimg.com/ext_tw_video/1515932334612107268/pu/vid/320x568/3gN3Udy0BrbU8HFr.mp4?tag=12"
},
{
"content_type": "application/x-mpegURL",
"url": "https://video.twimg.com/ext_tw_video/1515932334612107268/pu/pl/V6UZr3a49tZHwoia.m3u8?tag=12&container=fmp4"
},
{
"bitrate": 950000,
"content_type": "video/mp4",
"url": "https://video.twimg.com/ext_tw_video/1515932334612107268/pu/vid/480x852/CpA6Jht3IZjzh75X.mp4?tag=12"
},
{
"bitrate": 2176000,
"content_type": "video/mp4",
"url": "https://video.twimg.com/ext_tw_video/1515932334612107268/pu/vid/540x960/EdWN9mo8jIbA5PDM.mp4?tag=12"
}
]
},
"sizes": {
"thumb": {
"w": 150,
"h": 150,
"resize": "crop"
},
"medium": {
"w": 540,
"h": 960,
"resize": "fit"
},
"small": {
"w": 383,
"h": 680,
"resize": "fit"
},
"large": {
"w": 540,
"h": 960,
"resize": "fit"
}
}
}
]
},
"favorited": false,
"retweeted": false,
"possibly_sensitive": false,
"filter_level": "low",
"lang": "in",
"timestamp_ms": "1650261424997",
"ignore_lang": true
},
"type": "search"
}
```
5. stream filtered by geo boundary,
```python
stream.filter(
locations=[
99.8568959909,
0.8232449017,
119.5213933664,
7.2037547089,
]
)
```
|
mesolitica/snapshot-twitter-2022-09-03
|
[
"language:ms",
"region:us"
] |
2023-06-09T02:27:13+00:00
|
{"language": ["ms"]}
|
2023-06-09T07:45:38+00:00
|
9fb2db904f8a14ca37ef9989f79d53cbb6b9d811
|
reshinthadith/basic_code_ppl_eval
|
[
"task_categories:text-generation",
"size_categories:1K<n<10K",
"license:apache-2.0",
"code",
"region:us"
] |
2023-06-09T03:05:04+00:00
|
{"license": "apache-2.0", "size_categories": ["1K<n<10K"], "task_categories": ["text-generation"], "tags": ["code"]}
|
2023-06-09T04:46:00+00:00
|
|
4d1cdfd4b3dc543df1e2efb86fd5b30928f1eab5
|
# Dataset Card for "books_paragraph"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
ranWang/books_paragraph
|
[
"region:us"
] |
2023-06-09T03:43:49+00:00
|
{"dataset_info": {"features": [{"name": "raw_text", "dtype": "string"}, {"name": "output_text", "dtype": "string"}, {"name": "file_name", "dtype": "string"}, {"name": "is_hard_linebreak", "sequence": "bool"}], "splits": [{"name": "train", "num_bytes": 2212277566, "num_examples": 2982}], "download_size": 1336879971, "dataset_size": 2212277566}}
|
2023-06-10T06:35:47+00:00
|
f78c175b1e4713b6ae9c92e2f642f579493afea7
|
# Dataset Card for "tmp-translation"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Zaid/tmp-translation
|
[
"region:us"
] |
2023-06-09T03:56:41+00:00
|
{"dataset_info": {"features": [{"name": "arabic", "dtype": "string"}, {"name": "english", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 39, "num_examples": 1}, {"name": "test", "num_bytes": 39, "num_examples": 1}], "download_size": 2648, "dataset_size": 78}}
|
2023-06-09T04:09:14+00:00
|
b084bf418c7f446af790d7eadae1c863dab32bb3
|
Dhika/defectfft
|
[
"license:unknown",
"region:us"
] |
2023-06-09T04:08:08+00:00
|
{"license": "unknown"}
|
2023-06-10T04:51:44+00:00
|
|
95d6ffdab97aaf797acfe61661cdc8f9bc8757db
|
hamishivi/alpaca-farm-davinci-003-2048-token
|
[
"license:cc-by-nc-4.0",
"region:us"
] |
2023-06-09T05:14:19+00:00
|
{"license": "cc-by-nc-4.0"}
|
2023-06-09T05:14:36+00:00
|
|
0af4d4c64c962ac62068776eb121a732e024099b
|
lmeribal/diaccept
|
[
"task_categories:conversational",
"size_categories:1K<n<10K",
"language:en",
"license:mit",
"region:us"
] |
2023-06-09T05:26:22+00:00
|
{"language": ["en"], "license": "mit", "size_categories": ["1K<n<10K"], "task_categories": ["conversational"], "pretty_name": "diaccept"}
|
2023-06-09T05:28:35+00:00
|
|
b2e24eadc7a8897f4017bb82ac89bb0601f5184b
|
luisf1xc/data_drugs_class
|
[
"license:unknown",
"region:us"
] |
2023-06-09T05:51:30+00:00
|
{"license": "unknown"}
|
2023-06-09T05:52:54+00:00
|
|
50fc069ad2495df646269a7162a49e31309f47fd
|
# Dataset Card for "VQAv2_minival_validation_google_flan_t5_xxl_mode_CM_D_PNP_GENERIC_Q_rices_ns_25994"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
CVasNLPExperiments/VQAv2_minival_validation_google_flan_t5_xxl_mode_CM_D_PNP_GENERIC_Q_rices_ns_25994
|
[
"region:us"
] |
2023-06-09T06:04:42+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "prompt", "sequence": "string"}, {"name": "question", "dtype": "string"}, {"name": "true_label", "sequence": "string"}, {"name": "prediction", "dtype": "string"}], "splits": [{"name": "fewshot_0_clip_tags_LAION_ViT_H_14_2B_with_openai_Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full_DETA_detections_deta_swin_large_o365_coco_classes_caption_all_patches_Salesforce_blip_image_captioning_large_clean_", "num_bytes": 312903052, "num_examples": 25994}], "download_size": 30902093, "dataset_size": 312903052}}
|
2023-06-09T12:59:34+00:00
|
6f3f81347598ee984893e238b62b5850386c7892
|
# Project Links
# Dataset Description
The IMPACT dataset contains 50 human created prompts for each category, 200 in total, to test LLMs general writing ability.
Instructed LLMs demonstrate promising ability in writing-based tasks, such as composing letters or ethical debates. This dataset consists prompts across 4 diverse usage scenarios:
- **Informative Writing**: User queries such as self-help advice or explanations for various concept
- **Professional Writing**: Format involves suggestions presentations or emails in a business setting
- **Argumentative Writing**: Debate positions on ethical and societal question
- **Creative Writing**: Diverse writing formats such as stories, poems, and songs.
The IMPACT dataset is included in our [InstructEval Benchmark Suite](https://github.com/declare-lab/instruct-eval).
# Evaluation Results
We leverage ChatGPT to judge the quality of the generated answers by LLMs. In terms of:
- Relevance: how well the answer engages with the given prompt
- Coherence: general text quality such as organization and logical flow
Each answer is scored on a Likert scale from 1 to 5. We evaluate the models in the zero-shot
setting based on the given prompt and perform sampling-based decoding with a temperature of 1.0
| **Model** | **Size** | **Informative** | | **Professional** | | **Argumentative** | | **Creative** | | **Avg.** | |
| :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |
| | | Rel. | Coh. | Rel. | Coh. | Rel. | Coh. | Rel. | Coh. | Rel. | Coh. |
| **ChatGPT** | - | 3.34 | 3.98 | 3.88 | 3.96 | 3.96 | 3.82 | 3.92 | 3.94 | 3.78 | 3.93 |
| [**Flan-Alpaca**](https://huggingface.co/declare-lab/flan-alpaca-xxl) | 11B | 3.56 | 3.46 | 3.54 | 3.70 | 3.22 | 3.28 | 3.70 | 3.40 | 3.51 | 3.46 |
| [**Dolly-V2**](https://huggingface.co/databricks/dolly-v2-12b) | 12 B | 3.54 | 3.64 | 2.96 | 3.74 | 3.66 | 3.20 | 3.02 | 3.18 | 3.30 | 3.44 |
| [**StableVicuna**](https://huggingface.co/TheBloke/stable-vicuna-13B-HF) | 13B | 3.54 | 3.64 | 2.96 | 3.74 | 3.30 | 3.20 | 3.02 | 3.18 | 3.21 | 3.44 |
| [**Flan-T5**](https://huggingface.co/google/flan-t5-xxl) | 11B | 2.64 | 3.24 | 2.62 | 3.22 | 2.54 | 3.40 | 2.50 | 2.72 | 2.58 | 3.15 |
# Citation
Please consider citing the following article if you found our work useful:
```
bibtex
@article{chia2023instructeval,
title={INSTRUCTEVAL: Towards Holistic Evaluation of Instruction-Tuned Large Language Models},
author={Yew Ken Chia and Pengfei Hong and Lidong Bing and Soujanya Poria},
journal={arXiv preprint arXiv:2306.04757},
year={2023}
}
```
|
declare-lab/InstructEvalImpact
|
[
"size_categories:n<1K",
"license:apache-2.0",
"region:us"
] |
2023-06-09T06:05:44+00:00
|
{"license": "apache-2.0", "size_categories": ["n<1K"], "ArXiv": 2306.04757}
|
2023-06-09T07:53:22+00:00
|
3d59b1e95d5a2293c542088a792cc61c04244453
|
# Dataset Card for "english_test_01"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
xjLee/english_test_01
|
[
"region:us"
] |
2023-06-09T06:39:28+00:00
|
{"dataset_info": {"features": [{"name": "input", "dtype": "string"}, {"name": "output", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 162320, "num_examples": 323}], "download_size": 76449, "dataset_size": 162320}}
|
2023-06-09T06:40:16+00:00
|
a49b3c76765087df32d25e5e07c954a22676e5e8
|
jignasha/medical
|
[
"license:mit",
"region:us"
] |
2023-06-09T06:48:34+00:00
|
{"license": "mit"}
|
2023-06-09T06:49:41+00:00
|
|
a34604c2344f26914510dfc60d7271a4c9ad0eb0
|
# Dataset Card for "d9292a47"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
results-sd-v1-5-sd-v2-1-if-v1-0-karlo/d9292a47
|
[
"region:us"
] |
2023-06-09T07:06:45+00:00
|
{"dataset_info": {"features": [{"name": "result", "dtype": "string"}, {"name": "id", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 184, "num_examples": 10}], "download_size": 1342, "dataset_size": 184}}
|
2023-06-09T07:06:45+00:00
|
76c5e7f6f63abf4da3af3c39b0c268622b5341b6
|
olimiemma/WaziGroup
|
[
"license:openrail",
"region:us"
] |
2023-06-09T07:26:41+00:00
|
{"license": "openrail"}
|
2023-06-09T07:26:41+00:00
|
|
67f0a618399e4390678264acddb062e8079507ea
|
# Dataset Card for "travel_sample"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Binaryy/travel_sample
|
[
"region:us"
] |
2023-06-09T08:05:26+00:00
|
{"dataset_info": {"features": [{"name": "instruction", "dtype": "string"}, {"name": "output", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 41063, "num_examples": 20}], "download_size": 29530, "dataset_size": 41063}}
|
2023-06-09T10:53:34+00:00
|
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.