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8a6d5a4445967da0a2d84ec54ab59097f2c6b7ac |
# Dataset Card for Dataset Name
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
[More Information Needed]
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] | ancerlop/crowdsourced-calculator-demo | [
"region:us"
] | 2023-11-27T17:05:09+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data.csv"}]}]} | 2023-11-27T20:41:13+00:00 | [] | [] | TAGS
#region-us
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## Dataset Description
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- Leaderboard:
- Point of Contact:
### Dataset Summary
### Supported Tasks and Leaderboards
### Languages
## Dataset Structure
### Data Instances
### Data Fields
### Data Splits
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
## Considerations for Using the Data
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
## Additional Information
### Dataset Curators
### Licensing Information
### Contributions
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] |
5ca64ba7e62d9d9f931c397eae110ea7e94d4756 | # Dataset Card for "small_alpaca_bc_data"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | tr416/small_alpaca_bc_data | [
"region:us"
] | 2023-11-27T17:06:06+00:00 | {"dataset_info": {"features": [{"name": "instruction", "dtype": "string"}, {"name": "input", "dtype": "string"}, {"name": "output", "dtype": "string"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 25568053.244853117, "num_examples": 11833}], "download_size": 13090982, "dataset_size": 25568053.244853117}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2023-11-27T17:06:14+00:00 | [] | [] | TAGS
#region-us
| # Dataset Card for "small_alpaca_bc_data"
More Information needed | [
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5950e280a67b475f9121b7ce43984bdb79fe098a | # Dataset Card for "test_tiny"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | tr416/test_tiny | [
"region:us"
] | 2023-11-27T17:09:32+00:00 | {"dataset_info": {"features": [{"name": "instruction", "dtype": "string"}, {"name": "input", "dtype": "string"}, {"name": "output", "dtype": "string"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 64822.24265575876, "num_examples": 30}], "download_size": 54104, "dataset_size": 64822.24265575876}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2023-11-27T17:09:34+00:00 | [] | [] | TAGS
#region-us
| # Dataset Card for "test_tiny"
More Information needed | [
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d6a85b49c5185253c2b619eafdde2108385e2dc4 | # Dataset Card for "customers-complaints-train-eval"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | aciborowska/customers-complaints-train-eval | [
"region:us"
] | 2023-11-27T17:11:46+00:00 | {"dataset_info": {"features": [{"name": "Date_received", "dtype": "string"}, {"name": "Product", "dtype": "string"}, {"name": "Sub_product", "dtype": "string"}, {"name": "Issue", "dtype": "string"}, {"name": "Sub_issue", "dtype": "string"}, {"name": "Consumer_complaint_narrative", "dtype": "string"}, {"name": "Company_public_response", "dtype": "string"}, {"name": "Company", "dtype": "string"}, {"name": "State", "dtype": "string"}, {"name": "ZIP_code", "dtype": "string"}, {"name": "Tags", "dtype": "string"}, {"name": "Consumer_consent_provided?", "dtype": "string"}, {"name": "Submitted_via", "dtype": "string"}, {"name": "Date_sent_to_company", "dtype": "string"}, {"name": "Company response to consumer", "dtype": "string"}, {"name": "Timely_response?", "dtype": "string"}, {"name": "Consumer_disputed?", "dtype": "string"}, {"name": "Complaint_ID", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 36271224, "num_examples": 27000}], "download_size": 14216092, "dataset_size": 36271224}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2023-11-27T17:11:53+00:00 | [] | [] | TAGS
#region-us
| # Dataset Card for "customers-complaints-train-eval"
More Information needed | [
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a2bcc43e3bd0f09f4d1d311b16a04a65fce40db3 |
Chinese 18+ novels corpus, use at your own risk, you and only you are responsible for every choice you make.
tags: socks, garter belt, foot fetish, ntr, netori..... | b3x0m/Chinese-H-Novels | [
"task_categories:text-classification",
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"region:us"
] | 2023-11-27T17:19:10+00:00 | {"language": ["zh"], "size_categories": ["10B<n<100B"], "task_categories": ["text-classification", "summarization", "token-classification", "text2text-generation"], "pretty_name": "H-novel-corpus", "tags": ["not-for-all-audiences", "art"]} | 2023-12-08T10:32:08+00:00 | [] | [
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|
Chinese 18+ novels corpus, use at your own risk, you and only you are responsible for every choice you make.
tags: socks, garter belt, foot fetish, ntr, netori..... | [] | [
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] |
7de12bd8793cc7b38cf635cc777e8b35ca52528f | # LVIS-Instruct4V-Nodetail-mix619k
This is a mixture of our LVIS-Instruct4V dataset (no detail and only conversation) with the academic task related data, including ShareGPT, VQAv2, GQA, OKVQA, OCRVQA, AOKVQA, TextCaps, RefCOCO, and VG.
Please refer to the Sec. 3 and pseudo-code in [our paper](https://arxiv.org/abs/2311.07574) for the clarification of ***detail*** and ***conversation*** in our dataset. | X2FD/LVIS-Instruct4V-Nodetail-mix619k | [
"arxiv:2311.07574",
"region:us"
] | 2023-11-27T17:22:17+00:00 | {} | 2023-11-28T06:53:43+00:00 | [
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#arxiv-2311.07574 #region-us
| # LVIS-Instruct4V-Nodetail-mix619k
This is a mixture of our LVIS-Instruct4V dataset (no detail and only conversation) with the academic task related data, including ShareGPT, VQAv2, GQA, OKVQA, OCRVQA, AOKVQA, TextCaps, RefCOCO, and VG.
Please refer to the Sec. 3 and pseudo-code in our paper for the clarification of *detail* and *conversation* in our dataset. | [
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] |
35d0dbc8404991c0077c6ff6c6e1aad8c228644d | # Dataset Card for "KOR-gugugu-platypus-set"
## Pre-processing
```
# Make the special text lists, manually.
[\n\t-=+,#/\๏ผ?:^$.@*\"โโผโ โกโขโฃโคโโโใฎใฏใฐใยฎ...TL;DR...โฮฃโฉโ
ฯฮผฯโฮปฮโฅโโโฮธยฑโฌรฯโโ โคฮตโโซฯฮทฮฑฮฒรทโรหฬยฐยฒ/]
```
- ์์ ์ ๊ทํํ์์ ์ด์ฉํ์ฌ, ํ๊ตญ์ด ๋ฐ ์์ด๋ฅผ ์ ์ธํ ๋ค์ํ ์ธ๊ตญ์ด, ์ด๋ชจ์ง, ํน์ ๋ฌธ์ ๋ฑ๋ฑ ์ ๊ฑฐ.
- ๋ฒ์ญ task ์ต๋ํ ์ ๊ฑฐ. (~๋ฒ์ญ task๋ ํ๊ตญ์ด๋ก ๋ฒ์ญํ๋ฉด ๊ฑฐ์ 100% ์ค๋ฅ)
- [gugugu-ko](https://huggingface.co/datasets/squarelike/OpenOrca-gugugo-ko)์ GPT4 ๋ฒ์ญ ๊ฒฐ๊ณผ์์ ์ฝ 20k sampling.
- ๋ฐ์ดํฐ์
์ด์ฉํ์
์ ๋ชจ๋ธ์ด๋ ๋ฐ์ดํฐ์
์ ๋ง๋์ค ๋, ๊ฐ๋จํ ์ถ์ฒ ํ๊ธฐ๋ฅผ ํด์ฃผ์ ๋ค๋ฉด ์ฐ๊ตฌ์ ํฐ ๋์์ด ๋ฉ๋๋ค๐ญ๐ญ
---
# References
- Thank you for [squarelike/OpenOrca-gugugo-ko](https://huggingface.co/datasets/squarelike/OpenOrca-gugugo-ko)
- My dataset [kyujinpy/KOR-OpenOrca-Platypus-v3](https://huggingface.co/datasets/kyujinpy/KOR-OpenOrca-Platypus-v3) | kyujinpy/KOR-gugugu-platypus-set | [
"region:us"
] | 2023-11-27T17:27:18+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "input", "dtype": "string"}, {"name": "output", "dtype": "string"}, {"name": "instruction", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 79087758, "num_examples": 51170}], "download_size": 39384183, "dataset_size": 79087758}} | 2023-11-28T19:03:36+00:00 | [] | [] | TAGS
#region-us
| # Dataset Card for "KOR-gugugu-platypus-set"
## Pre-processing
- ์์ ์ ๊ทํํ์์ ์ด์ฉํ์ฌ, ํ๊ตญ์ด ๋ฐ ์์ด๋ฅผ ์ ์ธํ ๋ค์ํ ์ธ๊ตญ์ด, ์ด๋ชจ์ง, ํน์ ๋ฌธ์ ๋ฑ๋ฑ ์ ๊ฑฐ.
- ๋ฒ์ญ task ์ต๋ํ ์ ๊ฑฐ. (~๋ฒ์ญ task๋ ํ๊ตญ์ด๋ก ๋ฒ์ญํ๋ฉด ๊ฑฐ์ 100% ์ค๋ฅ)
- gugugu-ko์ GPT4 ๋ฒ์ญ ๊ฒฐ๊ณผ์์ ์ฝ 20k sampling.
- ๋ฐ์ดํฐ์
์ด์ฉํ์
์ ๋ชจ๋ธ์ด๋ ๋ฐ์ดํฐ์
์ ๋ง๋์ค ๋, ๊ฐ๋จํ ์ถ์ฒ ํ๊ธฐ๋ฅผ ํด์ฃผ์ ๋ค๋ฉด ์ฐ๊ตฌ์ ํฐ ๋์์ด ๋ฉ๋๋ค
---
# References
- Thank you for squarelike/OpenOrca-gugugo-ko
- My dataset kyujinpy/KOR-OpenOrca-Platypus-v3 | [
"# Dataset Card for \"KOR-gugugu-platypus-set\"",
"## Pre-processing\n \n- ์์ ์ ๊ทํํ์์ ์ด์ฉํ์ฌ, ํ๊ตญ์ด ๋ฐ ์์ด๋ฅผ ์ ์ธํ ๋ค์ํ ์ธ๊ตญ์ด, ์ด๋ชจ์ง, ํน์ ๋ฌธ์ ๋ฑ๋ฑ ์ ๊ฑฐ. \n- ๋ฒ์ญ task ์ต๋ํ ์ ๊ฑฐ. (~๋ฒ์ญ task๋ ํ๊ตญ์ด๋ก ๋ฒ์ญํ๋ฉด ๊ฑฐ์ 100% ์ค๋ฅ) \n- gugugu-ko์ GPT4 ๋ฒ์ญ ๊ฒฐ๊ณผ์์ ์ฝ 20k sampling. \n- ๋ฐ์ดํฐ์
์ด์ฉํ์
์ ๋ชจ๋ธ์ด๋ ๋ฐ์ดํฐ์
์ ๋ง๋์ค ๋, ๊ฐ๋จํ ์ถ์ฒ ํ๊ธฐ๋ฅผ ํด์ฃผ์ ๋ค๋ฉด ์ฐ๊ตฌ์ ํฐ ๋์์ด ๋ฉ๋๋ค \n\n---",
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์ด์ฉํ์
์ ๋ชจ๋ธ์ด๋ ๋ฐ์ดํฐ์
์ ๋ง๋์ค ๋, ๊ฐ๋จํ ์ถ์ฒ ํ๊ธฐ๋ฅผ ํด์ฃผ์ ๋ค๋ฉด ์ฐ๊ตฌ์ ํฐ ๋์์ด ๋ฉ๋๋ค \n\n---",
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์ด์ฉํ์
์ ๋ชจ๋ธ์ด๋ ๋ฐ์ดํฐ์
์ ๋ง๋์ค ๋, ๊ฐ๋จํ ์ถ์ฒ ํ๊ธฐ๋ฅผ ํด์ฃผ์ ๋ค๋ฉด ์ฐ๊ตฌ์ ํฐ ๋์์ด ๋ฉ๋๋ค \n\n---# References\n- Thank you for squarelike/OpenOrca-gugugo-ko \n- My dataset kyujinpy/KOR-OpenOrca-Platypus-v3"
] |
1dd9a011b1707b2abd4c752554a81775bb9b1071 | # Dataset Card for "customers-complaints-test"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | aciborowska/customers-complaints-test | [
"region:us"
] | 2023-11-27T17:38:03+00:00 | {"dataset_info": {"features": [{"name": "Date_received", "dtype": "string"}, {"name": "Product", "dtype": "string"}, {"name": "Sub_product", "dtype": "string"}, {"name": "Issue", "dtype": "string"}, {"name": "Sub_issue", "dtype": "string"}, {"name": "Consumer_complaint_narrative", "dtype": "string"}, {"name": "Company_public_response", "dtype": "string"}, {"name": "Company", "dtype": "string"}, {"name": "State", "dtype": "string"}, {"name": "ZIP_code", "dtype": "string"}, {"name": "Tags", "dtype": "string"}, {"name": "Consumer_consent_provided?", "dtype": "string"}, {"name": "Submitted_via", "dtype": "string"}, {"name": "Date_sent_to_company", "dtype": "string"}, {"name": "Company response to consumer", "dtype": "string"}, {"name": "Timely_response?", "dtype": "string"}, {"name": "Consumer_disputed?", "dtype": "string"}, {"name": "Complaint_ID", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 4068482, "num_examples": 3000}], "download_size": 1612360, "dataset_size": 4068482}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2023-11-27T17:38:33+00:00 | [] | [] | TAGS
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More Information needed | [
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e5433674c63ec419020d23958aa36590c76ee00a | # Dataset Card for "customers-complaints-eval"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | aciborowska/customers-complaints-eval | [
"region:us"
] | 2023-11-27T17:38:13+00:00 | {"dataset_info": {"features": [{"name": "Date_received", "dtype": "string"}, {"name": "Product", "dtype": "string"}, {"name": "Sub_product", "dtype": "string"}, {"name": "Issue", "dtype": "string"}, {"name": "Sub_issue", "dtype": "string"}, {"name": "Consumer_complaint_narrative", "dtype": "string"}, {"name": "Company_public_response", "dtype": "string"}, {"name": "Company", "dtype": "string"}, {"name": "State", "dtype": "string"}, {"name": "ZIP_code", "dtype": "string"}, {"name": "Tags", "dtype": "string"}, {"name": "Consumer_consent_provided?", "dtype": "string"}, {"name": "Submitted_via", "dtype": "string"}, {"name": "Date_sent_to_company", "dtype": "string"}, {"name": "Company response to consumer", "dtype": "string"}, {"name": "Timely_response?", "dtype": "string"}, {"name": "Consumer_disputed?", "dtype": "string"}, {"name": "Complaint_ID", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 3948222, "num_examples": 3000}], "download_size": 1539746, "dataset_size": 3948222}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2023-11-27T17:38:31+00:00 | [] | [] | TAGS
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a66149be01a2b90bfe22818f96386e0d9b7d31df |
# Dataset Card for Dataset Name
<!-- Provide a quick summary of the dataset. -->
## Dataset Details
### Dataset Description
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## Bias, Risks, and Limitations
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Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
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[More Information Needed] | ancerlop/MistralAI | [
"region:us"
] | 2023-11-27T17:40:19+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data.csv"}]}]} | 2023-12-01T12:25:42+00:00 | [] | [] | TAGS
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#### Personal and Sensitive Information
## Bias, Risks, and Limitations
### Recommendations
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
[optional]
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APA:
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## Dataset Card Authors [optional]
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1cc6f9f3d06b5b336e4e0ca9bb17fe72eceb27c9 |
# MMMU (A Massive Multi-discipline Multimodal Understanding and Reasoning Benchmark for Expert AGI)
[**๐ Homepage**](https://mmmu-benchmark.github.io/) | [**๐ค Dataset**](https://huggingface.co/datasets/MMMU/MMMU/) | [**๐ค Paper**](https://huggingface.co/papers/2311.16502) | [**๐ arXiv**](https://arxiv.org/abs/2311.16502) | [**GitHub**](https://github.com/MMMU-Benchmark/MMMU)
## ๐News
- **๐[2024-01-31]: We added Human Expert performance on the [Leaderboard](https://mmmu-benchmark.github.io/#leaderboard)!๐**
- **๐ฅ[2023-12-04]: Our evaluation server for test set is now availble on [EvalAI](https://eval.ai/web/challenges/challenge-page/2179/overview). We welcome all submissions and look forward to your participation! ๐**
## Dataset Details
### Dataset Description
We introduce MMMU: a new benchmark designed to evaluate multimodal models on massive multi-discipline tasks demanding college-level subject knowledge and deliberate reasoning. MMMU includes **11.5K meticulously collected multimodal questions** from college exams, quizzes, and textbooks, covering six core disciplines: Art & Design, Business, Science, Health & Medicine, Humanities & Social Science, and Tech & Engineering. These questions span **30 subjects** and **183 subfields**, comprising **30 highly heterogeneous image types**, such as charts, diagrams, maps, tables, music sheets, and chemical structures. We believe MMMU will stimulate the community to build next-generation multimodal foundation models towards expert artificial general intelligence (AGI).
๐ฏ **We have released a full set comprising 150 development samples and 900 validation samples. We have released 10,500 test questions without their answers.**
The development set is used for few-shot/in-context learning, and the validation set is used for debugging models, selecting hyperparameters, or quick evaluations. The answers and explanations for the test set questions are withheld. You can submit your model's predictions for the **test set** on **[EvalAI](https://eval.ai/web/challenges/challenge-page/2179/overview)**.
### Dataset Creation
MMMU was created to challenge multimodal models with tasks that demand college-level subject knowledge and deliberate reasoning, pushing the boundaries of what these models can achieve in terms of expert-level perception and reasoning.
The data for the MMMU dataset was manually collected by a team of college students from various disciplines, using online sources, textbooks, and lecture materials.
- **Content:** The dataset contains 11.5K college-level problems across six broad disciplines (Art & Design, Business, Science, Health & Medicine, Humanities & Social Science, Tech & Engineering) and 30 college subjects.
- **Image Types:** The dataset includes 30 highly heterogeneous image types, such as charts, diagrams, maps, tables, music sheets, and chemical structures, interleaved with text.
## ๐ Mini-Leaderboard
We show a mini-leaderboard here and please find more information in our paper or [**homepage**](https://mmmu-benchmark.github.io/).
| Model | Val (900) | Test (10.5K) |
|----------------------------|:---------:|:------------:|
| Expert (Best) | **88.6** | - |
| Expert (Medium) | 82.6 | - |
| Expert (Worst) | 76.2 | - |
| Gemini Ultra* | **59.4** | - |
| GPT-4V(ision) (Playground) | 56.8 | **55.7** |
| Qwen-VL-MAX* | 51.4 | 46.8 |
| LLaVA-1.6-34B* | 51.1 | 44.7 |
| Adept Fuyu-Heavy* | 48.3 | - |
| Gemini Pro* | 47.9 | - |
| Yi-VL-34B* | 45.9 | 41.6 |
| Qwen-VL-PLUS* | 45.2 | 40.8 |
| Marco-VL* | 41.2 | 40.4 |
| OmniLMM-12B* | 41.1 | 40.4 |
| InternLM-XComposer2-VL* | 43.0 | 38.2 |
| Yi-VL-6B* | 39.1 | 37.8 |
| InfiMM-Zephyr-7B* | 39.4 | 35.5 |
| InternVL-Chat-V1.1* | 39.1 | 35.3 |
| SVIT* | 38.0 | 34.1 |
| MiniCPM-V* | 37.2 | 34.1 |
| Emu2-Chat* | 36.3 | 34.1 |
| BLIP-2 FLAN-T5-XXL | 35.4 | 34.0 |
| InstructBLIP-T5-XXL | 35.7 | 33.8 |
| LLaVA-1.5-13B | 36.4 | 33.6 |
| Bunny-3B* | 38.2 | 33.0 |
| Qwen-VL-7B-Chat | 35.9 | 32.9 |
| SPHINX* | 32.9 | 32.9 |
| mPLUG-OWL2* | 32.7 | 32.1 |
| BLIP-2 FLAN-T5-XL | 34.4 | 31.0 |
| InstructBLIP-T5-XL | 32.9 | 30.6 |
| Gemini Nano2* | 32.6 | - |
| CogVLM | 32.1 | 30.1 |
| Otter | 32.2 | 29.1 |
| LLaMA-Adapter2-7B | 29.8 | 27.7 |
| MiniGPT4-Vicuna-13B | 26.8 | 27.6 |
| Adept Fuyu-8B | 27.9 | 27.4 |
| Kosmos2 | 24.4 | 26.6 |
| OpenFlamingo2-9B | 28.7 | 26.3 |
| Frequent Choice | 22.1 | 23.9 |
| Random Choice | 26.8 | 25.8 |
*: results provided by the authors.
## Limitations
Despite its comprehensive nature, MMMU, like any benchmark, is not without limitations. The manual curation process, albeit thorough, may carry biases.
And the focus on college-level subjects might not fully be a sufficient test for Expert AGI.
However, we believe it should be necessary for an Expert AGI to achieve strong performance on MMMU to demonstrate their broad and deep subject knowledge as well as expert-level understanding and reasoning capabilities.
In future work, we plan to incorporate human evaluations into MMMU. This will provide a more grounded comparison between model capabilities and expert performance, shedding light on the proximity of current AI systems to achieving Expert AGI.
## Disclaimers
The guidelines for the annotators emphasized strict compliance with copyright and licensing rules from the initial data source, specifically avoiding materials from websites that forbid copying and redistribution.
Should you encounter any data samples potentially breaching the copyright or licensing regulations of any site, we encourage you to notify us. Upon verification, such samples will be promptly removed.
## Contact
- Xiang Yue: [email protected]
- Yu Su: [email protected]
- Wenhu Chen: [email protected]
## Citation
**BibTeX:**
```bibtex
@article{yue2023mmmu,
title={MMMU: A Massive Multi-discipline Multimodal Understanding and Reasoning Benchmark for Expert AGI},
author={Xiang Yue and Yuansheng Ni and Kai Zhang and Tianyu Zheng and Ruoqi Liu and Ge Zhang and Samuel Stevens and Dongfu Jiang and Weiming Ren and Yuxuan Sun and Cong Wei and Botao Yu and Ruibin Yuan and Renliang Sun and Ming Yin and Boyuan Zheng and Zhenzhu Yang and Yibo Liu and Wenhao Huang and Huan Sun and Yu Su and Wenhu Chen},
journal={arXiv preprint arXiv:2311.16502},
year={2023},
}
``` | MMMU/MMMU | [
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"Public_Health/validation-*"}, {"split": "test", "path": "Public_Health/test-*"}]}, {"config_name": "Sociology", "data_files": [{"split": "dev", "path": "Sociology/dev-*"}, {"split": "validation", "path": "Sociology/validation-*"}, {"split": "test", "path": "Sociology/test-*"}]}], "tags": ["biology", "medical", "finance", "chemistry", "music", "art", "art_theory", "design", "music", "business", "accounting", "economics", "finance", "manage", "marketing", "health", "medicine", "basic_medical_science", "clinical", "pharmacy", "public_health", "humanities", "social_science", "history", "literature", "sociology", "psychology", "science", "biology", "chemistry", "geography", "math", "physics", "engineering", "agriculture", "architecture", "computer_science", "electronics", "energy_and_power", "materials", "mechanical_engineering"]} | 2024-02-13T10:39:10+00:00 | [
"2311.16502"
] | [
"en"
] | TAGS
#task_categories-question-answering #task_categories-visual-question-answering #task_categories-multiple-choice #size_categories-10K<n<100K #language-English #license-apache-2.0 #biology #medical #finance #chemistry #music #art #art_theory #design #business #accounting #economics #manage #marketing #health #medicine #basic_medical_science #clinical #pharmacy #public_health #humanities #social_science #history #literature #sociology #psychology #science #geography #math #physics #engineering #agriculture #architecture #computer_science #electronics #energy_and_power #materials #mechanical_engineering #arxiv-2311.16502 #region-us
| MMMU (A Massive Multi-discipline Multimodal Understanding and Reasoning Benchmark for Expert AGI)
=================================================================================================
Homepage | Dataset | Paper | arXiv | GitHub
News
----
* [2024-01-31]: We added Human Expert performance on the Leaderboard!
* [2023-12-04]: Our evaluation server for test set is now availble on EvalAI. We welcome all submissions and look forward to your participation!
Dataset Details
---------------
### Dataset Description
We introduce MMMU: a new benchmark designed to evaluate multimodal models on massive multi-discipline tasks demanding college-level subject knowledge and deliberate reasoning. MMMU includes 11.5K meticulously collected multimodal questions from college exams, quizzes, and textbooks, covering six core disciplines: Art & Design, Business, Science, Health & Medicine, Humanities & Social Science, and Tech & Engineering. These questions span 30 subjects and 183 subfields, comprising 30 highly heterogeneous image types, such as charts, diagrams, maps, tables, music sheets, and chemical structures. We believe MMMU will stimulate the community to build next-generation multimodal foundation models towards expert artificial general intelligence (AGI).
We have released a full set comprising 150 development samples and 900 validation samples. We have released 10,500 test questions without their answers.
The development set is used for few-shot/in-context learning, and the validation set is used for debugging models, selecting hyperparameters, or quick evaluations. The answers and explanations for the test set questions are withheld. You can submit your model's predictions for the test set on EvalAI.
!image/png
### Dataset Creation
MMMU was created to challenge multimodal models with tasks that demand college-level subject knowledge and deliberate reasoning, pushing the boundaries of what these models can achieve in terms of expert-level perception and reasoning.
The data for the MMMU dataset was manually collected by a team of college students from various disciplines, using online sources, textbooks, and lecture materials.
* Content: The dataset contains 11.5K college-level problems across six broad disciplines (Art & Design, Business, Science, Health & Medicine, Humanities & Social Science, Tech & Engineering) and 30 college subjects.
* Image Types: The dataset includes 30 highly heterogeneous image types, such as charts, diagrams, maps, tables, music sheets, and chemical structures, interleaved with text.
!image/png
Mini-Leaderboard
----------------
We show a mini-leaderboard here and please find more information in our paper or homepage.
\*: results provided by the authors.
Limitations
-----------
Despite its comprehensive nature, MMMU, like any benchmark, is not without limitations. The manual curation process, albeit thorough, may carry biases.
And the focus on college-level subjects might not fully be a sufficient test for Expert AGI.
However, we believe it should be necessary for an Expert AGI to achieve strong performance on MMMU to demonstrate their broad and deep subject knowledge as well as expert-level understanding and reasoning capabilities.
In future work, we plan to incorporate human evaluations into MMMU. This will provide a more grounded comparison between model capabilities and expert performance, shedding light on the proximity of current AI systems to achieving Expert AGI.
Disclaimers
-----------
The guidelines for the annotators emphasized strict compliance with copyright and licensing rules from the initial data source, specifically avoiding materials from websites that forbid copying and redistribution.
Should you encounter any data samples potentially breaching the copyright or licensing regulations of any site, we encourage you to notify us. Upon verification, such samples will be promptly removed.
Contact
-------
* Xiang Yue: URL@URL
* Yu Su: su.809@URL
* Wenhu Chen: wenhuchen@URL
BibTeX:
| [
"### Dataset Description\n\n\nWe introduce MMMU: a new benchmark designed to evaluate multimodal models on massive multi-discipline tasks demanding college-level subject knowledge and deliberate reasoning. MMMU includes 11.5K meticulously collected multimodal questions from college exams, quizzes, and textbooks, covering six core disciplines: Art & Design, Business, Science, Health & Medicine, Humanities & Social Science, and Tech & Engineering. These questions span 30 subjects and 183 subfields, comprising 30 highly heterogeneous image types, such as charts, diagrams, maps, tables, music sheets, and chemical structures. We believe MMMU will stimulate the community to build next-generation multimodal foundation models towards expert artificial general intelligence (AGI).\n\n\nWe have released a full set comprising 150 development samples and 900 validation samples. We have released 10,500 test questions without their answers.\nThe development set is used for few-shot/in-context learning, and the validation set is used for debugging models, selecting hyperparameters, or quick evaluations. The answers and explanations for the test set questions are withheld. You can submit your model's predictions for the test set on EvalAI.\n\n\n!image/png",
"### Dataset Creation\n\n\nMMMU was created to challenge multimodal models with tasks that demand college-level subject knowledge and deliberate reasoning, pushing the boundaries of what these models can achieve in terms of expert-level perception and reasoning.\nThe data for the MMMU dataset was manually collected by a team of college students from various disciplines, using online sources, textbooks, and lecture materials.\n\n\n* Content: The dataset contains 11.5K college-level problems across six broad disciplines (Art & Design, Business, Science, Health & Medicine, Humanities & Social Science, Tech & Engineering) and 30 college subjects.\n* Image Types: The dataset includes 30 highly heterogeneous image types, such as charts, diagrams, maps, tables, music sheets, and chemical structures, interleaved with text.\n\n\n!image/png\n\n\nMini-Leaderboard\n----------------\n\n\nWe show a mini-leaderboard here and please find more information in our paper or homepage.\n\n\n\n\\*: results provided by the authors.\n\n\nLimitations\n-----------\n\n\nDespite its comprehensive nature, MMMU, like any benchmark, is not without limitations. The manual curation process, albeit thorough, may carry biases.\nAnd the focus on college-level subjects might not fully be a sufficient test for Expert AGI.\nHowever, we believe it should be necessary for an Expert AGI to achieve strong performance on MMMU to demonstrate their broad and deep subject knowledge as well as expert-level understanding and reasoning capabilities.\nIn future work, we plan to incorporate human evaluations into MMMU. This will provide a more grounded comparison between model capabilities and expert performance, shedding light on the proximity of current AI systems to achieving Expert AGI.\n\n\nDisclaimers\n-----------\n\n\nThe guidelines for the annotators emphasized strict compliance with copyright and licensing rules from the initial data source, specifically avoiding materials from websites that forbid copying and redistribution.\nShould you encounter any data samples potentially breaching the copyright or licensing regulations of any site, we encourage you to notify us. Upon verification, such samples will be promptly removed.\n\n\nContact\n-------\n\n\n* Xiang Yue: URL@URL\n* Yu Su: su.809@URL\n* Wenhu Chen: wenhuchen@URL\n\n\nBibTeX:"
] | [
"TAGS\n#task_categories-question-answering #task_categories-visual-question-answering #task_categories-multiple-choice #size_categories-10K<n<100K #language-English #license-apache-2.0 #biology #medical #finance #chemistry #music #art #art_theory #design #business #accounting #economics #manage #marketing #health #medicine #basic_medical_science #clinical #pharmacy #public_health #humanities #social_science #history #literature #sociology #psychology #science #geography #math #physics #engineering #agriculture #architecture #computer_science #electronics #energy_and_power #materials #mechanical_engineering #arxiv-2311.16502 #region-us \n",
"### Dataset Description\n\n\nWe introduce MMMU: a new benchmark designed to evaluate multimodal models on massive multi-discipline tasks demanding college-level subject knowledge and deliberate reasoning. MMMU includes 11.5K meticulously collected multimodal questions from college exams, quizzes, and textbooks, covering six core disciplines: Art & Design, Business, Science, Health & Medicine, Humanities & Social Science, and Tech & Engineering. These questions span 30 subjects and 183 subfields, comprising 30 highly heterogeneous image types, such as charts, diagrams, maps, tables, music sheets, and chemical structures. We believe MMMU will stimulate the community to build next-generation multimodal foundation models towards expert artificial general intelligence (AGI).\n\n\nWe have released a full set comprising 150 development samples and 900 validation samples. We have released 10,500 test questions without their answers.\nThe development set is used for few-shot/in-context learning, and the validation set is used for debugging models, selecting hyperparameters, or quick evaluations. The answers and explanations for the test set questions are withheld. You can submit your model's predictions for the test set on EvalAI.\n\n\n!image/png",
"### Dataset Creation\n\n\nMMMU was created to challenge multimodal models with tasks that demand college-level subject knowledge and deliberate reasoning, pushing the boundaries of what these models can achieve in terms of expert-level perception and reasoning.\nThe data for the MMMU dataset was manually collected by a team of college students from various disciplines, using online sources, textbooks, and lecture materials.\n\n\n* Content: The dataset contains 11.5K college-level problems across six broad disciplines (Art & Design, Business, Science, Health & Medicine, Humanities & Social Science, Tech & Engineering) and 30 college subjects.\n* Image Types: The dataset includes 30 highly heterogeneous image types, such as charts, diagrams, maps, tables, music sheets, and chemical structures, interleaved with text.\n\n\n!image/png\n\n\nMini-Leaderboard\n----------------\n\n\nWe show a mini-leaderboard here and please find more information in our paper or homepage.\n\n\n\n\\*: results provided by the authors.\n\n\nLimitations\n-----------\n\n\nDespite its comprehensive nature, MMMU, like any benchmark, is not without limitations. The manual curation process, albeit thorough, may carry biases.\nAnd the focus on college-level subjects might not fully be a sufficient test for Expert AGI.\nHowever, we believe it should be necessary for an Expert AGI to achieve strong performance on MMMU to demonstrate their broad and deep subject knowledge as well as expert-level understanding and reasoning capabilities.\nIn future work, we plan to incorporate human evaluations into MMMU. This will provide a more grounded comparison between model capabilities and expert performance, shedding light on the proximity of current AI systems to achieving Expert AGI.\n\n\nDisclaimers\n-----------\n\n\nThe guidelines for the annotators emphasized strict compliance with copyright and licensing rules from the initial data source, specifically avoiding materials from websites that forbid copying and redistribution.\nShould you encounter any data samples potentially breaching the copyright or licensing regulations of any site, we encourage you to notify us. Upon verification, such samples will be promptly removed.\n\n\nContact\n-------\n\n\n* Xiang Yue: URL@URL\n* Yu Su: su.809@URL\n* Wenhu Chen: wenhuchen@URL\n\n\nBibTeX:"
] | [
203,
284,
511
] | [
"passage: TAGS\n#task_categories-question-answering #task_categories-visual-question-answering #task_categories-multiple-choice #size_categories-10K<n<100K #language-English #license-apache-2.0 #biology #medical #finance #chemistry #music #art #art_theory #design #business #accounting #economics #manage #marketing #health #medicine #basic_medical_science #clinical #pharmacy #public_health #humanities #social_science #history #literature #sociology #psychology #science #geography #math #physics #engineering #agriculture #architecture #computer_science #electronics #energy_and_power #materials #mechanical_engineering #arxiv-2311.16502 #region-us \n### Dataset Description\n\n\nWe introduce MMMU: a new benchmark designed to evaluate multimodal models on massive multi-discipline tasks demanding college-level subject knowledge and deliberate reasoning. MMMU includes 11.5K meticulously collected multimodal questions from college exams, quizzes, and textbooks, covering six core disciplines: Art & Design, Business, Science, Health & Medicine, Humanities & Social Science, and Tech & Engineering. These questions span 30 subjects and 183 subfields, comprising 30 highly heterogeneous image types, such as charts, diagrams, maps, tables, music sheets, and chemical structures. We believe MMMU will stimulate the community to build next-generation multimodal foundation models towards expert artificial general intelligence (AGI).\n\n\nWe have released a full set comprising 150 development samples and 900 validation samples. We have released 10,500 test questions without their answers.\nThe development set is used for few-shot/in-context learning, and the validation set is used for debugging models, selecting hyperparameters, or quick evaluations. The answers and explanations for the test set questions are withheld. You can submit your model's predictions for the test set on EvalAI.\n\n\n!image/png"
] |
f04a76f8650fd1e39db696626d08c493ea95fe56 | # Dataset Card for "sst2-remove-stopwords-n2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | yangwang825/sst2-remove-stopwords-n2 | [
"region:us"
] | 2023-11-27T18:34:56+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "validation", "path": "data/validation-*"}, {"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "label_text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 884164, "num_examples": 6920}, {"name": "validation", "num_bytes": 112712, "num_examples": 872}, {"name": "test", "num_bytes": 218641, "num_examples": 1821}], "download_size": 722219, "dataset_size": 1215517}} | 2023-11-27T18:35:03+00:00 | [] | [] | TAGS
#region-us
| # Dataset Card for "sst2-remove-stopwords-n2"
More Information needed | [
"# Dataset Card for \"sst2-remove-stopwords-n2\"\n\nMore Information needed"
] | [
"TAGS\n#region-us \n",
"# Dataset Card for \"sst2-remove-stopwords-n2\"\n\nMore Information needed"
] | [
6,
22
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"passage: TAGS\n#region-us \n# Dataset Card for \"sst2-remove-stopwords-n2\"\n\nMore Information needed"
] |
bccb4eeedead647db0fbeb5932db84e4d1511195 | # Dataset Card for "sst2-remove-non-stopwords-n2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | yangwang825/sst2-remove-non-stopwords-n2 | [
"region:us"
] | 2023-11-27T18:40:10+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "validation", "path": "data/validation-*"}, {"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "label_text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 884164, "num_examples": 6920}, {"name": "validation", "num_bytes": 112712, "num_examples": 872}, {"name": "test", "num_bytes": 208473, "num_examples": 1821}], "download_size": 713427, "dataset_size": 1205349}} | 2023-11-27T18:40:17+00:00 | [] | [] | TAGS
#region-us
| # Dataset Card for "sst2-remove-non-stopwords-n2"
More Information needed | [
"# Dataset Card for \"sst2-remove-non-stopwords-n2\"\n\nMore Information needed"
] | [
"TAGS\n#region-us \n",
"# Dataset Card for \"sst2-remove-non-stopwords-n2\"\n\nMore Information needed"
] | [
6,
24
] | [
"passage: TAGS\n#region-us \n# Dataset Card for \"sst2-remove-non-stopwords-n2\"\n\nMore Information needed"
] |
b5aeb495497f4535786e340b373eea916ff7dbd8 |
# Bangumi Image Base of The Dangers In My Heart
This is the image base of bangumi The Dangers in My Heart, we detected 18 characters, 1703 images in total. The full dataset is [here](all.zip).
**Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual.** If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
| # | Images | Download | Preview 1 | Preview 2 | Preview 3 | Preview 4 | Preview 5 | Preview 6 | Preview 7 | Preview 8 |
|:------|---------:|:---------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|
| 0 | 561 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 83 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 78 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 44 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 27 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 33 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 70 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 21 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 261 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 117 | [Download](9/dataset.zip) |  |  |  |  |  |  |  |  |
| 10 | 123 | [Download](10/dataset.zip) |  |  |  |  |  |  |  |  |
| 11 | 27 | [Download](11/dataset.zip) |  |  |  |  |  |  |  |  |
| 12 | 36 | [Download](12/dataset.zip) |  |  |  |  |  |  |  |  |
| 13 | 29 | [Download](13/dataset.zip) |  |  |  |  |  |  |  |  |
| 14 | 10 | [Download](14/dataset.zip) |  |  |  |  |  |  |  |  |
| 15 | 13 | [Download](15/dataset.zip) |  |  |  |  |  |  |  |  |
| 16 | 51 | [Download](16/dataset.zip) |  |  |  |  |  |  |  |  |
| noise | 119 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
| BangumiBase/thedangersinmyheart | [
"size_categories:1K<n<10K",
"license:mit",
"art",
"region:us"
] | 2023-11-27T18:42:31+00:00 | {"license": "mit", "size_categories": ["1K<n<10K"], "tags": ["art"]} | 2023-11-27T19:43:47+00:00 | [] | [] | TAGS
#size_categories-1K<n<10K #license-mit #art #region-us
| Bangumi Image Base of The Dangers In My Heart
=============================================
This is the image base of bangumi The Dangers in My Heart, we detected 18 characters, 1703 images in total. The full dataset is here.
Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual. If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
| [] | [
"TAGS\n#size_categories-1K<n<10K #license-mit #art #region-us \n"
] | [
25
] | [
"passage: TAGS\n#size_categories-1K<n<10K #license-mit #art #region-us \n"
] |
a194fc6b9067d67613154665c64b069025faffb9 | # Dataset Card for "sst2-remove-non-stopwords-n5"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | yangwang825/sst2-remove-non-stopwords-n5 | [
"region:us"
] | 2023-11-27T18:43:30+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "validation", "path": "data/validation-*"}, {"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "label_text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 884164, "num_examples": 6920}, {"name": "validation", "num_bytes": 112712, "num_examples": 872}, {"name": "test", "num_bytes": 174288, "num_examples": 1821}], "download_size": 688195, "dataset_size": 1171164}} | 2023-11-27T18:43:39+00:00 | [] | [] | TAGS
#region-us
| # Dataset Card for "sst2-remove-non-stopwords-n5"
More Information needed | [
"# Dataset Card for \"sst2-remove-non-stopwords-n5\"\n\nMore Information needed"
] | [
"TAGS\n#region-us \n",
"# Dataset Card for \"sst2-remove-non-stopwords-n5\"\n\nMore Information needed"
] | [
6,
24
] | [
"passage: TAGS\n#region-us \n# Dataset Card for \"sst2-remove-non-stopwords-n5\"\n\nMore Information needed"
] |
18b96544d5899087bb46aff08fa6a36bbfa0a4e0 | # Dataset Card for "sst2-remove-stopwords-n5"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | yangwang825/sst2-remove-stopwords-n5 | [
"region:us"
] | 2023-11-27T18:43:47+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "validation", "path": "data/validation-*"}, {"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "label_text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 884164, "num_examples": 6920}, {"name": "validation", "num_bytes": 112712, "num_examples": 872}, {"name": "test", "num_bytes": 202391, "num_examples": 1821}], "download_size": 713769, "dataset_size": 1199267}} | 2023-11-27T18:44:10+00:00 | [] | [] | TAGS
#region-us
| # Dataset Card for "sst2-remove-stopwords-n5"
More Information needed | [
"# Dataset Card for \"sst2-remove-stopwords-n5\"\n\nMore Information needed"
] | [
"TAGS\n#region-us \n",
"# Dataset Card for \"sst2-remove-stopwords-n5\"\n\nMore Information needed"
] | [
6,
22
] | [
"passage: TAGS\n#region-us \n# Dataset Card for \"sst2-remove-stopwords-n5\"\n\nMore Information needed"
] |
b275ae3724cb22b2a10cf0f8e2092e99c29bd935 |
Original dataset: https://www.microsoft.com/en-us/download/details.aspx?id=52419
Original paper: https://aclanthology.org/D15-1237.pdf | beanham/wikiqa | [
"task_categories:question-answering",
"language:en",
"region:us"
] | 2023-11-27T18:57:35+00:00 | {"language": ["en"], "task_categories": ["question-answering"]} | 2023-11-27T18:59:53+00:00 | [] | [
"en"
] | TAGS
#task_categories-question-answering #language-English #region-us
|
Original dataset: URL
Original paper: URL | [] | [
"TAGS\n#task_categories-question-answering #language-English #region-us \n"
] | [
22
] | [
"passage: TAGS\n#task_categories-question-answering #language-English #region-us \n"
] |
61063f8f30f8f008b0b4c22bb2d8108db6929863 | This is dataset based on airoboros 2.2.1 with removed orca and gptslop samples.
Models trained on this datasets are likely to hallucinate more than base airoboros since I also removed a lot of samples that made the model aware that it's not a human but an ai and it doesn't have physical body.
The plus of that is that non-llama model trained on it should very rarely if ever issue a refusal.
It also should sound more like a person than a sterile gpt-4.
I can't guarantee for that to happen with llama 2 base models since they are pre-trained with gptslop and refusals.
If you see a model that was trained on this dataset generating refusals, let me know and I will try to fix that.
I removed jokes from airoboros 2.2.1 that I used as base and put in jokes from airoboros 2.2, as jokes from 2.2.1 were really lame.
I will probably release fine-tunes of Yi-34B and Llama 1 33B on this dataset soon, Mistral finetune is available already.
License: same as airoboros 2.2.1/airoboros 2.2 | adamo1139/AEZAKMI_v1 | [
"license:other",
"region:us"
] | 2023-11-27T19:02:57+00:00 | {"license": "other", "license_name": "other", "license_link": "LICENSE"} | 2023-11-27T23:50:46+00:00 | [] | [] | TAGS
#license-other #region-us
| This is dataset based on airoboros 2.2.1 with removed orca and gptslop samples.
Models trained on this datasets are likely to hallucinate more than base airoboros since I also removed a lot of samples that made the model aware that it's not a human but an ai and it doesn't have physical body.
The plus of that is that non-llama model trained on it should very rarely if ever issue a refusal.
It also should sound more like a person than a sterile gpt-4.
I can't guarantee for that to happen with llama 2 base models since they are pre-trained with gptslop and refusals.
If you see a model that was trained on this dataset generating refusals, let me know and I will try to fix that.
I removed jokes from airoboros 2.2.1 that I used as base and put in jokes from airoboros 2.2, as jokes from 2.2.1 were really lame.
I will probably release fine-tunes of Yi-34B and Llama 1 33B on this dataset soon, Mistral finetune is available already.
License: same as airoboros 2.2.1/airoboros 2.2 | [] | [
"TAGS\n#license-other #region-us \n"
] | [
11
] | [
"passage: TAGS\n#license-other #region-us \n"
] |
e2eb438d20c95893552981f0494a40a1a939ef1c | # Dataset Card for "cai-conversation-prod"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | vwxyzjn/cai-conversation-prod | [
"region:us"
] | 2023-11-27T19:22:51+00:00 | {"dataset_info": {"features": [{"name": "index", "dtype": "int64"}, {"name": "prompt", "dtype": "string"}, {"name": "init_prompt", "struct": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}, {"name": "init_response", "struct": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}, {"name": "critic_prompt", "struct": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}, {"name": "critic_response", "struct": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}, {"name": "revision_prompt", "struct": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}, {"name": "revision_response", "struct": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}], "splits": [{"name": "test", "num_bytes": 15206093, "num_examples": 8552}, {"name": "train", "num_bytes": 283519246, "num_examples": 160800}], "download_size": 120521045, "dataset_size": 298899650}, "configs": [{"config_name": "default", "data_files": [{"split": "test", "path": "data/test-*"}, {"split": "train", "path": "data/train-*"}]}]} | 2023-11-29T07:26:07+00:00 | [] | [] | TAGS
#region-us
| # Dataset Card for "cai-conversation-prod"
More Information needed | [
"# Dataset Card for \"cai-conversation-prod\"\n\nMore Information needed"
] | [
"TAGS\n#region-us \n",
"# Dataset Card for \"cai-conversation-prod\"\n\nMore Information needed"
] | [
6,
18
] | [
"passage: TAGS\n#region-us \n# Dataset Card for \"cai-conversation-prod\"\n\nMore Information needed"
] |
77de5c15d7b8069e052f5a00af11d426bb050374 |
# Bangumi Image Base of Xxxholic
This is the image base of bangumi xxxHOLiC, we detected 36 characters, 3967 images in total. The full dataset is [here](all.zip).
**Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual.** If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
| # | Images | Download | Preview 1 | Preview 2 | Preview 3 | Preview 4 | Preview 5 | Preview 6 | Preview 7 | Preview 8 |
|:------|---------:|:---------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|
| 0 | 2265 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 70 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 20 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 189 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 20 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 23 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 11 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 16 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 27 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 9 | [Download](9/dataset.zip) |  |  |  |  |  |  |  |  |
| 10 | 59 | [Download](10/dataset.zip) |  |  |  |  |  |  |  |  |
| 11 | 94 | [Download](11/dataset.zip) |  |  |  |  |  |  |  |  |
| 12 | 20 | [Download](12/dataset.zip) |  |  |  |  |  |  |  |  |
| 13 | 67 | [Download](13/dataset.zip) |  |  |  |  |  |  |  |  |
| 14 | 33 | [Download](14/dataset.zip) |  |  |  |  |  |  |  |  |
| 15 | 48 | [Download](15/dataset.zip) |  |  |  |  |  |  |  |  |
| 16 | 543 | [Download](16/dataset.zip) |  |  |  |  |  |  |  |  |
| 17 | 29 | [Download](17/dataset.zip) |  |  |  |  |  |  |  |  |
| 18 | 66 | [Download](18/dataset.zip) |  |  |  |  |  |  |  |  |
| 19 | 16 | [Download](19/dataset.zip) |  |  |  |  |  |  |  |  |
| 20 | 26 | [Download](20/dataset.zip) |  |  |  |  |  |  |  |  |
| 21 | 11 | [Download](21/dataset.zip) |  |  |  |  |  |  |  |  |
| 22 | 30 | [Download](22/dataset.zip) |  |  |  |  |  |  |  |  |
| 23 | 29 | [Download](23/dataset.zip) |  |  |  |  |  |  |  |  |
| 24 | 31 | [Download](24/dataset.zip) |  |  |  |  |  |  |  |  |
| 25 | 9 | [Download](25/dataset.zip) |  |  |  |  |  |  |  |  |
| 26 | 12 | [Download](26/dataset.zip) |  |  |  |  |  |  |  |  |
| 27 | 7 | [Download](27/dataset.zip) |  |  |  |  |  |  |  | N/A |
| 28 | 6 | [Download](28/dataset.zip) |  |  |  |  |  |  | N/A | N/A |
| 29 | 8 | [Download](29/dataset.zip) |  |  |  |  |  |  |  |  |
| 30 | 39 | [Download](30/dataset.zip) |  |  |  |  |  |  |  |  |
| 31 | 23 | [Download](31/dataset.zip) |  |  |  |  |  |  |  |  |
| 32 | 7 | [Download](32/dataset.zip) |  |  |  |  |  |  |  | N/A |
| 33 | 14 | [Download](33/dataset.zip) |  |  |  |  |  |  |  |  |
| 34 | 21 | [Download](34/dataset.zip) |  |  |  |  |  |  |  |  |
| noise | 69 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
| BangumiBase/xxxholic | [
"size_categories:1K<n<10K",
"license:mit",
"art",
"region:us"
] | 2023-11-27T19:54:38+00:00 | {"license": "mit", "size_categories": ["1K<n<10K"], "tags": ["art"]} | 2023-11-27T21:21:54+00:00 | [] | [] | TAGS
#size_categories-1K<n<10K #license-mit #art #region-us
| Bangumi Image Base of Xxxholic
==============================
This is the image base of bangumi xxxHOLiC, we detected 36 characters, 3967 images in total. The full dataset is here.
Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual. If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
| [] | [
"TAGS\n#size_categories-1K<n<10K #license-mit #art #region-us \n"
] | [
25
] | [
"passage: TAGS\n#size_categories-1K<n<10K #license-mit #art #region-us \n"
] |
25d0b503ea7226d18444a1af972387c26c7c28d3 |
# Bangumi Image Base of Kamisama Kiss
This is the image base of bangumi Kamisama Kiss, we detected 50 characters, 2686 images in total. The full dataset is [here](all.zip).
**Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual.** If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
| # | Images | Download | Preview 1 | Preview 2 | Preview 3 | Preview 4 | Preview 5 | Preview 6 | Preview 7 | Preview 8 |
|:------|---------:|:---------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|
| 0 | 11 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 65 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 40 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 24 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 59 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 63 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 50 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 20 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 122 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 122 | [Download](9/dataset.zip) |  |  |  |  |  |  |  |  |
| 10 | 544 | [Download](10/dataset.zip) |  |  |  |  |  |  |  |  |
| 11 | 176 | [Download](11/dataset.zip) |  |  |  |  |  |  |  |  |
| 12 | 18 | [Download](12/dataset.zip) |  |  |  |  |  |  |  |  |
| 13 | 33 | [Download](13/dataset.zip) |  |  |  |  |  |  |  |  |
| 14 | 29 | [Download](14/dataset.zip) |  |  |  |  |  |  |  |  |
| 15 | 22 | [Download](15/dataset.zip) |  |  |  |  |  |  |  |  |
| 16 | 32 | [Download](16/dataset.zip) |  |  |  |  |  |  |  |  |
| 17 | 16 | [Download](17/dataset.zip) |  |  |  |  |  |  |  |  |
| 18 | 27 | [Download](18/dataset.zip) |  |  |  |  |  |  |  |  |
| 19 | 257 | [Download](19/dataset.zip) |  |  |  |  |  |  |  |  |
| 20 | 31 | [Download](20/dataset.zip) |  |  |  |  |  |  |  |  |
| 21 | 14 | [Download](21/dataset.zip) |  |  |  |  |  |  |  |  |
| 22 | 41 | [Download](22/dataset.zip) |  |  |  |  |  |  |  |  |
| 23 | 10 | [Download](23/dataset.zip) |  |  |  |  |  |  |  |  |
| 24 | 25 | [Download](24/dataset.zip) |  |  |  |  |  |  |  |  |
| 25 | 17 | [Download](25/dataset.zip) |  |  |  |  |  |  |  |  |
| 26 | 106 | [Download](26/dataset.zip) |  |  |  |  |  |  |  |  |
| 27 | 12 | [Download](27/dataset.zip) |  |  |  |  |  |  |  |  |
| 28 | 86 | [Download](28/dataset.zip) |  |  |  |  |  |  |  |  |
| 29 | 16 | [Download](29/dataset.zip) |  |  |  |  |  |  |  |  |
| 30 | 9 | [Download](30/dataset.zip) |  |  |  |  |  |  |  |  |
| 31 | 10 | [Download](31/dataset.zip) |  |  |  |  |  |  |  |  |
| 32 | 51 | [Download](32/dataset.zip) |  |  |  |  |  |  |  |  |
| 33 | 26 | [Download](33/dataset.zip) |  |  |  |  |  |  |  |  |
| 34 | 11 | [Download](34/dataset.zip) |  |  |  |  |  |  |  |  |
| 35 | 25 | [Download](35/dataset.zip) |  |  |  |  |  |  |  |  |
| 36 | 8 | [Download](36/dataset.zip) |  |  |  |  |  |  |  |  |
| 37 | 10 | [Download](37/dataset.zip) |  |  |  |  |  |  |  |  |
| 38 | 28 | [Download](38/dataset.zip) |  |  |  |  |  |  |  |  |
| 39 | 40 | [Download](39/dataset.zip) |  |  |  |  |  |  |  |  |
| 40 | 9 | [Download](40/dataset.zip) |  |  |  |  |  |  |  |  |
| 41 | 10 | [Download](41/dataset.zip) |  |  |  |  |  |  |  |  |
| 42 | 9 | [Download](42/dataset.zip) |  |  |  |  |  |  |  |  |
| 43 | 11 | [Download](43/dataset.zip) |  |  |  |  |  |  |  |  |
| 44 | 16 | [Download](44/dataset.zip) |  |  |  |  |  |  |  |  |
| 45 | 12 | [Download](45/dataset.zip) |  |  |  |  |  |  |  |  |
| 46 | 32 | [Download](46/dataset.zip) |  |  |  |  |  |  |  |  |
| 47 | 8 | [Download](47/dataset.zip) |  |  |  |  |  |  |  |  |
| 48 | 6 | [Download](48/dataset.zip) |  |  |  |  |  |  | N/A | N/A |
| noise | 267 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
| BangumiBase/kamisamakiss | [
"size_categories:1K<n<10K",
"license:mit",
"art",
"region:us"
] | 2023-11-27T19:55:35+00:00 | {"license": "mit", "size_categories": ["1K<n<10K"], "tags": ["art"]} | 2023-11-27T21:41:48+00:00 | [] | [] | TAGS
#size_categories-1K<n<10K #license-mit #art #region-us
| Bangumi Image Base of Kamisama Kiss
===================================
This is the image base of bangumi Kamisama Kiss, we detected 50 characters, 2686 images in total. The full dataset is here.
Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual. If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
| [] | [
"TAGS\n#size_categories-1K<n<10K #license-mit #art #region-us \n"
] | [
25
] | [
"passage: TAGS\n#size_categories-1K<n<10K #license-mit #art #region-us \n"
] |
f2fa46ddafa1abba7f4bbfdbc8908585977cd730 | # Dataset Card for "ffmperative_dpo"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | remyxai/ffmperative_dpo | [
"region:us"
] | 2023-11-27T20:00:21+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "prompt", "dtype": "string"}, {"name": "chosen", "dtype": "string"}, {"name": "rejected", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1230727, "num_examples": 1475}], "download_size": 352567, "dataset_size": 1230727}} | 2023-11-30T02:06:50+00:00 | [] | [] | TAGS
#region-us
| # Dataset Card for "ffmperative_dpo"
More Information needed | [
"# Dataset Card for \"ffmperative_dpo\"\n\nMore Information needed"
] | [
"TAGS\n#region-us \n",
"# Dataset Card for \"ffmperative_dpo\"\n\nMore Information needed"
] | [
6,
16
] | [
"passage: TAGS\n#region-us \n# Dataset Card for \"ffmperative_dpo\"\n\nMore Information needed"
] |
e44641d6267d741457b909708dfb022b11012090 |
# Bangumi Image Base of Shinsekai Yori
This is the image base of bangumi Shinsekai Yori, we detected 31 characters, 1618 images in total. The full dataset is [here](all.zip).
**Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual.** If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
| # | Images | Download | Preview 1 | Preview 2 | Preview 3 | Preview 4 | Preview 5 | Preview 6 | Preview 7 | Preview 8 |
|:------|---------:|:---------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|
| 0 | 431 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 90 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 66 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 19 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 24 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 18 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 13 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 19 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 21 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 14 | [Download](9/dataset.zip) |  |  |  |  |  |  |  |  |
| 10 | 45 | [Download](10/dataset.zip) |  |  |  |  |  |  |  |  |
| 11 | 14 | [Download](11/dataset.zip) |  |  |  |  |  |  |  |  |
| 12 | 119 | [Download](12/dataset.zip) |  |  |  |  |  |  |  |  |
| 13 | 29 | [Download](13/dataset.zip) |  |  |  |  |  |  |  |  |
| 14 | 21 | [Download](14/dataset.zip) |  |  |  |  |  |  |  |  |
| 15 | 69 | [Download](15/dataset.zip) |  |  |  |  |  |  |  |  |
| 16 | 198 | [Download](16/dataset.zip) |  |  |  |  |  |  |  |  |
| 17 | 33 | [Download](17/dataset.zip) |  |  |  |  |  |  |  |  |
| 18 | 46 | [Download](18/dataset.zip) |  |  |  |  |  |  |  |  |
| 19 | 15 | [Download](19/dataset.zip) |  |  |  |  |  |  |  |  |
| 20 | 12 | [Download](20/dataset.zip) |  |  |  |  |  |  |  |  |
| 21 | 12 | [Download](21/dataset.zip) |  |  |  |  |  |  |  |  |
| 22 | 25 | [Download](22/dataset.zip) |  |  |  |  |  |  |  |  |
| 23 | 40 | [Download](23/dataset.zip) |  |  |  |  |  |  |  |  |
| 24 | 34 | [Download](24/dataset.zip) |  |  |  |  |  |  |  |  |
| 25 | 12 | [Download](25/dataset.zip) |  |  |  |  |  |  |  |  |
| 26 | 25 | [Download](26/dataset.zip) |  |  |  |  |  |  |  |  |
| 27 | 8 | [Download](27/dataset.zip) |  |  |  |  |  |  |  |  |
| 28 | 8 | [Download](28/dataset.zip) |  |  |  |  |  |  |  |  |
| 29 | 5 | [Download](29/dataset.zip) |  |  |  |  |  | N/A | N/A | N/A |
| noise | 133 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
| BangumiBase/shinsekaiyori | [
"size_categories:1K<n<10K",
"license:mit",
"art",
"region:us"
] | 2023-11-27T20:16:23+00:00 | {"license": "mit", "size_categories": ["1K<n<10K"], "tags": ["art"]} | 2023-11-27T21:35:09+00:00 | [] | [] | TAGS
#size_categories-1K<n<10K #license-mit #art #region-us
| Bangumi Image Base of Shinsekai Yori
====================================
This is the image base of bangumi Shinsekai Yori, we detected 31 characters, 1618 images in total. The full dataset is here.
Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual. If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
| [] | [
"TAGS\n#size_categories-1K<n<10K #license-mit #art #region-us \n"
] | [
25
] | [
"passage: TAGS\n#size_categories-1K<n<10K #license-mit #art #region-us \n"
] |
7643f65e66deefdfd390896a5a03dd6c25729815 |
# Bangumi Image Base of Vanitas No Karte
This is the image base of bangumi Vanitas no Karte, we detected 31 characters, 2212 images in total. The full dataset is [here](all.zip).
**Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual.** If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
| # | Images | Download | Preview 1 | Preview 2 | Preview 3 | Preview 4 | Preview 5 | Preview 6 | Preview 7 | Preview 8 |
|:------|---------:|:---------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|
| 0 | 446 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 58 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 47 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 21 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 20 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 31 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 102 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 14 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 13 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 42 | [Download](9/dataset.zip) |  |  |  |  |  |  |  |  |
| 10 | 16 | [Download](10/dataset.zip) |  |  |  |  |  |  |  |  |
| 11 | 11 | [Download](11/dataset.zip) |  |  |  |  |  |  |  |  |
| 12 | 46 | [Download](12/dataset.zip) |  |  |  |  |  |  |  |  |
| 13 | 38 | [Download](13/dataset.zip) |  |  |  |  |  |  |  |  |
| 14 | 12 | [Download](14/dataset.zip) |  |  |  |  |  |  |  |  |
| 15 | 481 | [Download](15/dataset.zip) |  |  |  |  |  |  |  |  |
| 16 | 67 | [Download](16/dataset.zip) |  |  |  |  |  |  |  |  |
| 17 | 94 | [Download](17/dataset.zip) |  |  |  |  |  |  |  |  |
| 18 | 40 | [Download](18/dataset.zip) |  |  |  |  |  |  |  |  |
| 19 | 64 | [Download](19/dataset.zip) |  |  |  |  |  |  |  |  |
| 20 | 19 | [Download](20/dataset.zip) |  |  |  |  |  |  |  |  |
| 21 | 9 | [Download](21/dataset.zip) |  |  |  |  |  |  |  |  |
| 22 | 40 | [Download](22/dataset.zip) |  |  |  |  |  |  |  |  |
| 23 | 55 | [Download](23/dataset.zip) |  |  |  |  |  |  |  |  |
| 24 | 39 | [Download](24/dataset.zip) |  |  |  |  |  |  |  |  |
| 25 | 55 | [Download](25/dataset.zip) |  |  |  |  |  |  |  |  |
| 26 | 32 | [Download](26/dataset.zip) |  |  |  |  |  |  |  |  |
| 27 | 5 | [Download](27/dataset.zip) |  |  |  |  |  | N/A | N/A | N/A |
| 28 | 8 | [Download](28/dataset.zip) |  |  |  |  |  |  |  |  |
| 29 | 10 | [Download](29/dataset.zip) |  |  |  |  |  |  |  |  |
| noise | 277 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
| BangumiBase/vanitasnokarte | [
"size_categories:1K<n<10K",
"license:mit",
"art",
"region:us"
] | 2023-11-27T20:36:56+00:00 | {"license": "mit", "size_categories": ["1K<n<10K"], "tags": ["art"]} | 2023-11-27T22:15:06+00:00 | [] | [] | TAGS
#size_categories-1K<n<10K #license-mit #art #region-us
| Bangumi Image Base of Vanitas No Karte
======================================
This is the image base of bangumi Vanitas no Karte, we detected 31 characters, 2212 images in total. The full dataset is here.
Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual. If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
| [] | [
"TAGS\n#size_categories-1K<n<10K #license-mit #art #region-us \n"
] | [
25
] | [
"passage: TAGS\n#size_categories-1K<n<10K #license-mit #art #region-us \n"
] |
0ea345902711bdc7870626afc77ec86fc9a85c09 | # CORD-T1
dataset_info:
features:
- name: image
dtype: image
splits:
- name: train
num_bytes: 57404876
num_examples: 15
- name: validation
num_bytes:
num_examples: 3
- name: test
num_bytes:
num_examples: 3
download_size: 57334757
dataset_size: 57404876
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
license: mit
task_categories:
- token-classification
language:
- ak
tags:
- medical
pretty_name: CORD-T1
size_categories:
- 1K<n<10K
--- | hsienchen/CORD-T1 | [
"task_categories:token-classification",
"size_categories:1K<n<10K",
"language:ab",
"license:apache-2.0",
"medical",
"region:us"
] | 2023-11-27T20:57:00+00:00 | {"language": ["ab"], "license": "apache-2.0", "size_categories": ["1K<n<10K"], "task_categories": ["token-classification"], "pretty_name": "CORD2", "tags": ["medical"]} | 2023-12-05T12:18:33+00:00 | [] | [
"ab"
] | TAGS
#task_categories-token-classification #size_categories-1K<n<10K #language-Abkhazian #license-apache-2.0 #medical #region-us
| # CORD-T1
dataset_info:
features:
- name: image
dtype: image
splits:
- name: train
num_bytes: 57404876
num_examples: 15
- name: validation
num_bytes:
num_examples: 3
- name: test
num_bytes:
num_examples: 3
download_size: 57334757
dataset_size: 57404876
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
license: mit
task_categories:
- token-classification
language:
- ak
tags:
- medical
pretty_name: CORD-T1
size_categories:
- 1K<n<10K
--- | [
"# CORD-T1\n\ndataset_info:\n features:\n - name: image\n dtype: image\n splits:\n - name: train\n num_bytes: 57404876\n num_examples: 15\n - name: validation\n num_bytes: \n num_examples: 3\n - name: test\n num_bytes: \n num_examples: 3\n download_size: 57334757\n dataset_size: 57404876\nconfigs:\n- config_name: default\n data_files:\n - split: train\n path: data/train-*\nlicense: mit\ntask_categories:\n- token-classification\nlanguage:\n- ak\ntags:\n- medical\npretty_name: CORD-T1\nsize_categories:\n- 1K<n<10K\n---"
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] | [
48,
166
] | [
"passage: TAGS\n#task_categories-token-classification #size_categories-1K<n<10K #language-Abkhazian #license-apache-2.0 #medical #region-us \n# CORD-T1\n\ndataset_info:\n features:\n - name: image\n dtype: image\n splits:\n - name: train\n num_bytes: 57404876\n num_examples: 15\n - name: validation\n num_bytes: \n num_examples: 3\n - name: test\n num_bytes: \n num_examples: 3\n download_size: 57334757\n dataset_size: 57404876\nconfigs:\n- config_name: default\n data_files:\n - split: train\n path: data/train-*\nlicense: mit\ntask_categories:\n- token-classification\nlanguage:\n- ak\ntags:\n- medical\npretty_name: CORD-T1\nsize_categories:\n- 1K<n<10K\n---"
] |
2e110901432fe36ef14ea4c8fa90d6e3692754f3 |
# Claire French Dialogue Dataset (CFDD) <br /> _A collection of French dialogue transcripts and plays_
This is the first packaged version of the datasets used to train the Claire family of large language models
([OpenLLM-France/Claire-7B-0.1](https://huggingface.co/OpenLLM-France/Claire-7B-0.1)).
The Claire French Dialogue Dataset (CFDD) is a collection of theater plays and transcripts of real French dialogues from various sources, including parliamentary proceedings, interviews, debates, meetings, and free conversations.
Each dialogue is split into speech turns, and each speech turn is labeled with the name of the speaker, or a unique identifier if the speaker is unknown.
* [Dataset composition](#dataset-composition)
* [Data sources](#data-sources)
* [Example use (python)](#example-use-python)
* [Important notes](#important-notes)
* [License](#license)
* [Citations](#citations)
* [Contact](#contact)
## Dataset composition
CFDD can be broken down into:
* 37ย 015 conversations in total (36ย 731 in train, 284 in test)
* 2ย 961ย 116 speech turns in total (2ย 934ย 084 in train, 27ย 032 in test)
* around 150M words
It is a collection of several independent datasets, classified by the types of conversations they contain. This categorization is designed to more evenly balance the influence of different styles of dialogue on model training and to facilitate future applications of CFDD for which certain types of dialogue might be more helpful than others.
Note that this categorization leads to multiple cases in which the original corpus is split into subcorpora. When the smaller sets are included in our corpus, they are clearly indicated, e.g., "ESLO (1/5)". Some portions of the original corpora have been excluded entirely because they did not include dialogue between adults (e.g., monologues, read literature).
For more information, you can look at the following documents:
* [FR/metadata.csv](FR/metadata.csv) contains further statistics on the different subcorpora (broken down by train/test splits).
* [FR/metadata_filter_datasets_regex.json](FR/metadata_filter_datasets_regex.json) contains information about how original datasets were filtered and/or split into sub-categories.
* [FR/metadata_split_testset_list.json](FR/metadata_split_testset_list.json) contains information about which files in the original datasets were chosen to be in the test set.
### Data sources
<table>
<thead>
<tr>
<th>Dataset</th>
<th>Sub-folder(s)</th>
<th>Description</th>
<th>Words</th>
<th>Turns</th>
<th>Conversations</th>
<th>License (and conditions)</th>
</tr>
</thead>
<tbody>
<tr>
<td colspan="7" style="text-align: center;"><u><em><strong>Parliamentary Proceedings</strong></em></u></td></tr>
<tr>
<td><a href="https://www.assemblee-nationale.fr">Assemblรฉe Nationale</a></td>
<td><a href="./Claire-Dialogue-French-0.1/tree/main/FR"><code>FR/AssembleeNationale*</code></a> <br /> <em>(one folder per legislative period)</em></td>
<td>Parliamentary proceedings from the French National Assembly</td>
<td>133M</td>
<td>1.6M</td>
<td>4.5k</td>
<td><a href="https://www.etalab.gouv.fr/wp-content/uploads/2017/04/ETALAB-Licence-Ouverte-v2.0.pdf">Open License 2.0</a></td>
</tr>
<tr>
<td colspan="7" style="text-align: center;"><u><em><strong>Theatre</strong></em></u></td></tr>
<tr>
<td><a href="https://dracor.org/fre">Theatre Classique</a></td>
<td><a href="./Claire-Dialogue-French-0.1/tree/main/FR/TheatreClassique"><code>FR/TheatreClassique</code></a></td>
<td>Classic stage plays</td>
<td>12.8M</td>
<td>441k</td>
<td>25k</td>
<td><a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">CC BY-NC-SA 4.0</a> (<a href="https://github.com/dracor-org/fredracor#to-cite-fredracor-">please cite</a>)</td>
</tr>
<tr>
<td><a href="https://theatregratuit.com">Theatre Gratuit</a></td>
<td><a href="./Claire-Dialogue-French-0.1/tree/main/FR/TheatreGratuit"><code>FR/TheatreGratuit</code></a></td>
<td>Stage plays</td>
<td>2.7M</td>
<td>155k</td>
<td>4k</td>
<td></td>
</tr>
<tr>
<td colspan="7" style="text-align: center;"><u><em><strong>Interviews</strong></em></u></td></tr>
<tr>
<td><a href="https://www.ortolang.fr/market/corpora/eslo">ESLO</a> (1/5)</td>
<td><a href="./Claire-Dialogue-French-0.1/tree/main/FR/ESLO_interview"><code>FR/ESLO_interview</code></a></td>
<td>Guided conversations</td>
<td>4.2M</td>
<td>329k</td>
<td>399</td>
<td><a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">CC BY-NC-SA 4.0</a> (<a href="https://www.ortolang.fr/market/corpora/eslo">please cite</a>)</td>
</tr>
<tr>
<td><a href="https://www.cnrtl.fr/corpus/tcof/">TCOF</a> (adults)</td>
<td><a href="./Claire-Dialogue-French-0.1/tree/main/FR/TCOF_adults"><code>FR/TCOF_adults</code></a></td>
<td>Guided conversations (between adults)</td>
<td>765k</td>
<td>49k</td>
<td>237</td>
<td><a href="https://creativecommons.org/licenses/by-nc-sa/2.0/">CC BY-NC-SA 2.0</a> (<a href="https://www.ortolang.fr/market/corpora/tcof">please cite</a>)</td>
</tr>
<tr>
<td><a href="http://cfpp2000.univ-paris3.fr/Presentation.html">CFPP</a></td>
<td><a href="./Claire-Dialogue-French-0.1/tree/main/FR/CFPP"><code>FR/CFPP</code></a></td>
<td>Interviews of people in Paris in 2000</td>
<td>608k</td>
<td>48k</td>
<td>42</td>
<td><a href="https://creativecommons.org/licenses/by-nc-sa/3.0/">CC BY-NC-SA 3.0</a> (<a href="https://www.ortolang.fr/market/corpora/cfpp2000">please cite</a>)</td>
</tr>
<tr>
<td><a href="https://repository.ortolang.fr/api/content/cefc-orfeo/11/documentation/site-orfeo/home/index.html">ORFEO/Valibel</a> (1/2)</td>
<td><a href="./Claire-Dialogue-French-0.1/tree/main/FR/ORFEO_valibel_interview"><code>FR/ORFEO_valibel_interview</code></a></td>
<td>Guided conversations of Belgian French speakers</td>
<td>458k</td>
<td>19k</td>
<td>67</td>
<td><a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">CC BY-NC-SA 4.0</a> (<a href="https://repository.ortolang.fr/api/content/cefc-orfeo/11/documentation/site-orfeo/mentions-legales/index.html">please cite</a>)</td>
</tr>
<tr>
<td><a href="https://www.ortolang.fr/market/corpora/pfc">PFC</a> (1/2)</td>
<td><a href="./Claire-Dialogue-French-0.1/tree/main/FR/PFC_guided"><code>FR/PFC_guided</code></a></td>
<td>Guided interviews</td>
<td>268k</td>
<td>15k</td>
<td>173</td>
<td><a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">CC BY-NC-SA 4.0</a> (<a href="https://www.ortolang.fr/market/corpora/pfc">please cite</a>)</td>
</tr>
<tr>
<td><a href="http://ortolang107.inist.fr/?f%5BnomCorpus%5D%5B%5D=ORFEO_cfpb+%28O%29&locale=fr">ORFEO/CFPB</a></td>
<td><a href="./Claire-Dialogue-French-0.1/tree/main/FR/ORFEO_cfpb"><code>FR/ORFEO_cfpb</code></a></td>
<td>Interviews of people in Brussels</td>
<td>138k</td>
<td>11k</td>
<td>12</td>
<td><a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">CC BY-NC-SA 4.0</a></td>
</tr>
<tr>
<td><a href="https://www.ortolang.fr/market/corpora/sldr000832">ACSYNT</a></td>
<td><a href="./Claire-Dialogue-French-0.1/tree/main/FR/ACSYNT"><code>FR/ACSYNT</code></a></td>
<td>Guided interviews from southwestern France</td>
<td>61k</td>
<td>2.7k</td>
<td>144</td>
<td><a href="https://creativecommons.org/licenses/by-sa/4.0/">CC BY-SA 4.0</a> (<a href="https://www.ortolang.fr/market/corpora/sldr000832">please cite</a>)</td>
</tr>
<tr>
<td colspan="7" style="text-align: center;"><u><em><strong>Free Conversations</strong></em></u></td></tr>
<tr>
<td><a href="https://ofrom.unine.ch/">OFROM</a></td>
<td><a href="./Claire-Dialogue-French-0.1/tree/main/FR/OFROM"><code>FR/OFROM</code></a></td>
<td>Conversations in French-speaking Switzerland</td>
<td>590k</td>
<td>44k</td>
<td>151</td>
<td><a href="https://creativecommons.org/licenses/by-nc-sa/3.0/">CC BY-NC-SA 3.0</a> (<a href="https://ofrom.unine.ch/index.php?page=citations">please cite</a>)</td>
</tr>
<tr>
<td><a href="https://www.ortolang.fr/market/corpora/eslo">ESLO</a> (2/5)</td>
<td><a href="./Claire-Dialogue-French-0.1/tree/main/FR/ESLO_free"><code>FR/ESLO_free</code></a></td>
<td>Diverse conversation</td>
<td>480k</td>
<td>47k</td>
<td>98</td>
<td><a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">CC BY-NC-SA 4.0</a> (<a href="https://www.ortolang.fr/market/corpora/eslo">please cite</a>)</td>
</tr>
<tr>
<td><a href="https://repository.ortolang.fr/api/content/cefc-orfeo/11/documentation/site-orfeo/home/index.html">ORFEO/CRFP</a></td>
<td><a href="./Claire-Dialogue-French-0.1/tree/main/FR/ORFEO_crfp"><code>FR/ORFEO_crfp</code></a></td>
<td>Diverse conversations</td>
<td>405k</td>
<td>9k</td>
<td>124</td>
<td><a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">CC BY-NC-SA 4.0</a> (<a href="https://repository.ortolang.fr/api/content/cefc-orfeo/11/documentation/site-orfeo/mentions-legales/index.html">please cite</a>)</td>
</tr>
<tr>
<td><a href="https://repository.ortolang.fr/api/content/cefc-orfeo/11/documentation/site-orfeo/home/index.html">ORFEO/C-ORAL-ROM</a></td>
<td><a href="./Claire-Dialogue-French-0.1/tree/main/FR/ORFEO_coralrom"><code>FR/ORFEO_coralrom</code></a></td>
<td>Diverse conversation</td>
<td>248k</td>
<td>6k</td>
<td>152</td>
<td><a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">CC BY-NC-SA 4.0</a> (<a href="https://repository.ortolang.fr/api/content/cefc-orfeo/11/documentation/site-orfeo/mentions-legales/index.html">please cite</a>)</td>
</tr>
<tr>
<td><a href="https://www.ortolang.fr/market/corpora/pfc">PFC</a> (2/2)</td>
<td><a href="./Claire-Dialogue-French-0.1/tree/main/FR/PFC_free"><code>FR/PFC_free</code></a></td>
<td>Diverse conversation</td>
<td>230k</td>
<td>14k</td>
<td>146</td>
<td><a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">CC BY-NC-SA 4.0</a> (<a href="https://www.ortolang.fr/market/corpora/pfc">please cite</a>)</td>
</tr>
<tr>
<td><a href="http://clapi.ish-lyon.cnrs.fr/">CLAPI</a></td>
<td><a href="./Claire-Dialogue-French-0.1/tree/main/FR/CLAPI"><code>FR/CLAPI</code></a></td>
<td>Diverse conversation</td>
<td>122k</td>
<td>15k</td>
<td>14</td>
<td><a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">CC BY-NC-SA 4.0</a></td>
</tr>
<tr>
<td><a href="https://www.ortolang.fr/market/corpora/sldr000720">CID</a></td>
<td><a href="./Claire-Dialogue-French-0.1/tree/main/FR/CID"><code>FR/CID</code></a></td>
<td>Dialogues between two friends</td>
<td>118k</td>
<td>9k</td>
<td>8</td>
<td><a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">CC BY-NC-SA 4.0</a> (<a href="https://www.ortolang.fr/market/corpora/sldr000720">please cite</a>)</td>
</tr>
<tr>
<td><a href="https://rhapsodie.modyco.fr/">Rhapsodie</a></td>
<td><a href="./Claire-Dialogue-French-0.1/tree/main/FR/Rhapsodie"><code>FR/Rhapsodie</code></a></td>
<td>Diverse conversations</td>
<td>28k</td>
<td>1k</td>
<td>41</td>
<td><a href="https://creativecommons.org/licenses/by-nc-sa/3.0/">CC BY-NC-SA 3.0</a> (<a href="https://rhapsodie.modyco.fr/propriete-intellectuelle/">please cite</a>)</td>
</tr>
<tr>
<td><a href="https://github.com/surfacesyntacticud/SUD_French-ParisStories">Paris Stories</a></td>
<td><a href="./Claire-Dialogue-French-0.1/tree/main/FR/ParisStories"><code>FR/ParisStories</code></a></td>
<td>Diverse conversations in Paris</td>
<td>28k</td>
<td>351</td>
<td>54</td>
<td><a href="https://creativecommons.org/licenses/by-sa/4.0/">CC BY-SA 4.0</a></td>
</tr>
<tr>
<td>LinTO (1/3)</td>
<td><a href="./Claire-Dialogue-French-0.1/tree/main/FR/LINAGORA_free"><code>FR/LINAGORA_free</code></a></td>
<td>Diverse conversation</td>
<td>26k</td>
<td>2k</td>
<td>4</td>
<td><a href="https://creativecommons.org/licenses/by-sa/4.0/">CC BY-SA 4.0</a> (<a href="https://aclanthology.org/2021.emnlp-main.104/">please cite</a>)</td>
</tr>
<tr>
<td colspan="7" style="text-align: center;"><u><em><strong>Meetings</strong></em></u></td></tr>
<tr>
<td>SUMM-RE</td>
<td><a href="./Claire-Dialogue-French-0.1/tree/main/FR/SUMM-RE"><code>FR/SUMM-RE</code></a></td>
<td>Meeting-style conversations (transcribed with Whisper large-v2 ASR)</td>
<td>1.3M</td>
<td>39k</td>
<td>283</td>
<td><a href="https://creativecommons.org/licenses/by-sa/4.0/">CC BY-SA 4.0</a> (please cite)</td>
</tr>
<tr>
<td><a href="http://ortolang107.inist.fr/?f%5BnomCorpus%5D%5B%5D=R%C3%A9unions+de+travail+%28O%29&fnomCorpus=Chambers-Rostand+E&locale=fr">ORFEO/Reunions-de-Travail</a></td>
<td><a href="./Claire-Dialogue-French-0.1/tree/main/FR/ORFEO_reunions-de-travail"><code>FR/ORFEO_reunions-de-travail</code></a></td>
<td>Real meetings</td>
<td>210k</td>
<td>12k</td>
<td>29</td>
<td><a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">CC BY-NC-SA 4.0</a></td>
</tr>
<tr>
<td>LinTO (2/3)</td>
<td><a href="./Claire-Dialogue-French-0.1/tree/main/FR/LINAGORA_meetings"><code>FR/LINAGORA_meetings</code></a></td>
<td>Meetings on speech recognition</td>
<td>41k</td>
<td>1.8k</td>
<td>6</td>
<td><a href="https://creativecommons.org/licenses/by-sa/4.0/">CC BY-SA 4.0</a> (<a href="https://aclanthology.org/2021.emnlp-main.104/">please cite</a>)</td>
</tr>
<tr>
<td colspan="7" style="text-align: center;"><u><em><strong>Debates</strong></em></u></td></tr>
<tr>
<td><a href="https://github.com/linto-ai/FREDSum">FREDSum</a></td>
<td><a href="./Claire-Dialogue-French-0.1/tree/main/FR/FREDSum"><code>FR/FREDSum</code></a></td>
<td>French political debates</td>
<td>406k</td>
<td>7k</td>
<td>144</td>
<td><a href="https://creativecommons.org/licenses/by-sa/4.0/">CC BY-SA 4.0</a> (please cite)</td>
</tr>
<tr>
<td><a href="https://www.ortolang.fr/market/corpora/eslo">ESLO</a> (3/5)</td>
<td><a href="./Claire-Dialogue-French-0.1/tree/main/FR/ESLO_conf"><code>FR/ESLO_conf</code></a></td>
<td>Conferences</td>
<td>76k</td>
<td>2k</td>
<td>4</td>
<td><a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">CC BY-NC-SA 4.0</a> (<a href="https://www.ortolang.fr/market/corpora/eslo">please cite</a>)</td>
</tr>
<tr>
<td colspan="7" style="text-align: center;"><u><em><strong>Assistance</strong></em></u></td></tr>
<tr>
<td><a href="https://www.ortolang.fr/market/corpora/eslo">ESLO</a> (4/5)</td>
<td><a href="./Claire-Dialogue-French-0.1/tree/main/FR/ESLO_assistance"><code>FR/ESLO_assistance</code></a></td>
<td>In-person assistance and call-centers</td>
<td>95k</td>
<td>11k</td>
<td>143</td>
<td><a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">CC BY-NC-SA 4.0</a> (<a href="https://www.ortolang.fr/market/corpora/eslo">please cite</a>)</td>
</tr>
<tr>
<td><a href="https://repository.ortolang.fr/api/content/cefc-orfeo/11/documentation/site-orfeo/home/index.html">ORFEO/Fleuron</a></td>
<td><a href="./Claire-Dialogue-French-0.1/tree/main/FR/ORFEO_fleuron"><code>FR/ORFEO_fleuron</code></a></td>
<td>Interactions created to teach foreign students about university life</td>
<td>33k</td>
<td>2k</td>
<td>51</td>
<td><a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">CC BY-NC-SA 4.0</a> (<a href="https://repository.ortolang.fr/api/content/cefc-orfeo/11/documentation/site-orfeo/mentions-legales/index.html">please cite</a>)</td>
</tr>
<tr>
<td><a href="https://www.info.univ-tours.fr/~antoine/parole_publique/OTG/index.html">OTG</a></td>
<td><a href="./Claire-Dialogue-French-0.1/tree/main/FR/OTG"><code>FR/OTG</code></a></td>
<td>Dialogues in a tourism office</td>
<td>27k</td>
<td>4k</td>
<td>315</td>
<td><a href="https://creativecommons.org/licenses/by-sa/3.0/">CC BY-SA 3.0</a> (<a href="https://www.info.univ-tours.fr/~antoine/parole_publique/OTG/index.html">contact before usage</a>)</td>
</tr>
<tr>
<td><a href="https://www.info.univ-tours.fr/~antoine/parole_publique/Accueil_UBS/index.html">Accueil UBS</a></td>
<td><a href="./Claire-Dialogue-French-0.1/tree/main/FR/UBS"><code>FR/UBS</code></a></td>
<td>University telephone answering service</td>
<td>7.2k</td>
<td>1k</td>
<td>41</td>
<td><a href="https://creativecommons.org/licenses/by-sa/3.0/">CC BY-SA 3.0</a> (<a href="https://www.info.univ-tours.fr/~antoine/parole_publique/Accueil_UBS/index.html">contact before usage</a>)</td>
</tr>
<tr>
<td colspan="7" style="text-align: center;"><u><em><strong>Presentation, Formal Address</strong></em></u></td></tr>
<tr>
<td><a href="https://www.ortolang.fr/market/corpora/eslo">ESLO</a> (5/5)</td>
<td><a href="./Claire-Dialogue-French-0.1/tree/main/FR/ESLO_discourse"><code>FR/ESLO_discourse</code></a></td>
<td>Conference presentations</td>
<td>43k</td>
<td>120</td>
<td>9</td>
<td><a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">CC BY-NC-SA 4.0</a> (<a href="https://www.ortolang.fr/market/corpora/eslo">please cite</a>)</td>
</tr>
<tr>
<td>LinTO (3/3)</td>
<td><a href="./Claire-Dialogue-French-0.1/tree/main/FR/LINAGORA_discourse"><code>FR/LINAGORA_discourse</code></a></td>
<td>Technical presentations (AI topics) with Q/A</td>
<td>38k</td>
<td>1.5k</td>
<td>4</td>
<td><a href="https://creativecommons.org/licenses/by-sa/4.0/">CC BY-SA 4.0</a> (<a href="https://aclanthology.org/2021.emnlp-main.104/">please cite</a>)</td>
</tr>
<tr>
<td><a href="https://repository.ortolang.fr/api/content/cefc-orfeo/11/documentation/site-orfeo/home/index.html">ORFEO/Valibel</a> (2/2)</td>
<td><a href="./Claire-Dialogue-French-0.1/tree/main/FR/ORFEO_valibel_discourse"><code>FR/ORFEO_valibel_discourse</code></a></td>
<td>Formal university addresses</td>
<td>12k</td>
<td>5</td>
<td>5</td>
<td><a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">CC BY-NC-SA 4.0</a> (<a href="https://repository.ortolang.fr/api/content/cefc-orfeo/11/documentation/site-orfeo/mentions-legales/index.html">please cite</a>)</td>
</tr>
</tbody>
</table>
## Example use (python)
In the following `sample_by="paragraph"` is important to ensure that each sample corresponds to a full conversation (not just a speech turn).
Load dataset from HuggingFace cache (downloaded under `~/.cache/huggingface/datasets`):
```python
from datasets import load_dataset
dataset = load_dataset("OpenLLM-France/Claire-Dialogue-French-0.1", sample_by="paragraph", streaming=True)
```
Load dataset from raw text files:
```python
from datasets import load_dataset
import glob
path = "path/to/dataset"
train_files = glob.glob(path + "/*/train.txt")
test_files = glob.glob(path + "/*/test.txt")
dataset = load_dataset("text", data_files={"train": train_files, "test": test_files}, sample_by="paragraph", streaming=True)
```
Iterate on the dataset:
```python
for sample in dataset["train"]:
train_conversation = sample["text"]
...
for sample in dataset["test"]:
test_conversation = sample["text"]
...
```
## Important notes
All datasets were normalized in text files so that:
* Conversations are separated by a single blank line.
* Each line corresponds to a single speech turn.
* Each line begins with a speaker label of the form "`[***:]`".
* When speaker names are anonymized or otherwise unknown, speakers are distinguished by numbers in the following format: "**`[speaker001:]`**", "**`[speaker002:]`**", โฆ <br /> Otherwise, speakers are labeled with their names or roles, e.g. "`[Paul:]`", "`[Franรงois Mitterrand:]`", "`[M. le prรฉsident:]`".
* There are no parentheses: special annotations are always between square brackets.
* Commong tags include:
* "**`[PII]`**": Personally Identifiable Information (anonymized name...)
* "`[NOISE]`": distinct ambient noises
* "`[LAUGHTER]`": laughter
<!-- * Truncated words are sometimes marked with "-" (ex: "je suis dรฉ- dรฉcidรฉ") -->
* Depending on the data source, data may or may not include punctuation marks and upper case letters.
* The data were normalized in various ways including unicode NFC normalization, conversion of unbreakable spaces to spaces, and standardization of punctuation marks (`โฆ` -> `...`, `ยซ`/`ยป`/`โ`/`โ`/`โณ`/`โ` -> `"`). <!-- `โ`/`โ`/`โ`/`สฟ` -> `'`, `แต`/`แตสณ` -> `e`/`er`, `โ` -> `,` -->
Those details are described in the paper:
[_ยซ The Claire French Dialogue Dataset ยป_](https://arxiv.org/abs/2311.16840) (2023).
## License
Given that some of the corpora used for training are only available under CC-BY-NC-SA licenses,
Claire-Dialogue-French-0.1 is made available under the [CC-BY-NC-SA 4.0 license](https://creativecommons.org/licenses/by-nc-sa/4.0/).
## Citations
When using the CFDD corpus, please cite the following paper:
โ Julie Hunter, Jรฉrรดme Louradour, Virgile Rennard, Ismaรฏl Harrando, Guokan Shang, Jean-Pierre Lorrรฉ (2023)
[The Claire French Dialogue Dataset](https://arxiv.org/abs/2311.16840)
```bibtex
@misc{openllm2023claire,
title={The Claire French Dialogue Dataset},
author={Julie Hunter and Jรฉrรดme Louradour and Virgile Rennard and Ismaรฏl Harrando and Guokan Shang and Jean-Pierre Lorrรฉ},
year={2023},
eprint={2311.16840},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
This paper in turn provides the requested citations for all of the original corpora.
The same references are also listed below.
* **Accueil UBS**
* Pascale Nicolas, Sabine Letellier-Zarshenas, Igor Schadle, Jean-Yves Antoine, Jean Caelen (2002). [Towards a large corpus of spoken dialogue in French that will be freely available: the "Parole Publique" project and its first realisations](https://www.info.univ-tours.fr/~antoine/parole_publique/articles/2002_LREC_CORP.pdf). _Third European Conference on Language Resources and Evaluation_ (LREC). Las Palmas de Gran Canaria, Espagne.
* Jean-Yves Antoine, Jerome Goulian, Jeanne Villaneau, Marc le Tallec (2009). [Word Order Phenomena in Spoken French : a Study on Four Corpora of Task-Oriented Dialogue and its Consequences on Language Processing](https://www.info.univ-tours.fr/~antoine/articles/2009_Corpus_Linguistics.pdf). _Corpus Linguistics_, Liverpool, UK.
* **ACSYNT**
* Cognition, Langue, Langages, Ergonomie - UMR 5263 (CLLE) (2013). [ACSYNT [Corpus]](https://hdl.handle.net/11403/sldr000832/v1). [ORTOLANG](www.ortolang.fr) (Open Resources and TOols for LANGuage).
* **CFPP**
* Branca-Rosoff S., Fleury S., Lefeuvre F., Pires M., 2012, [Discours sur la ville. Prรฉsentation du Corpus de Franรงais Parlรฉ Parisien des annรฉes 2000](http://cfpp2000.univ-paris3.fr/CFPP2000.pdf) (CFPP2000).
* CLESTHIA - Langage, systรจmes, discours - EA 7345 (CLESTHIA) (2018). [CFPP2000 [Corpus]](https://hdl.handle.net/11403/cfpp2000/v1). [ORTOLANG](www.ortolang.fr) (Open Resources and TOols for LANGuage), v1.
* **CID**
* Roxane Bertrand, Philippe Blache, Robert Espesser, Gaรซlle Ferrรฉ, Christine Meunier, Bรฉatrice Priego-Valverde, Stรฉphane Rauzy (2008). [Le CID โ Corpus of Interactional Data โ Annotation et Exploitation Multimodale de Parole Conversationnelle](https://hal.science/hal-00349893). _Traitement Automatique des Langues_, vol. 49, no. 3.
* Philippe Blache, Roxane Bertrand, Brigitte Bigi et al. (2010). [Multimodal annotation of conversational data](http://portal.acm.org/citation.cfm?id=1868749). _Proceedings of the Fourth Linguistic Annotation Workshop_.
* Laboratoire parole et langage - UMR 7309 (LPL) (2021). [Transcriptions du corpus CID [Corpus]](https://hdl.handle.net/11403/sldr000720/v1). [ORTOLANG](www.ortolang.fr) (Open Resources and TOols for LANGuage).
* **CLAPI**
* CLAPI, [http://clapi.icar.cnrs.fr](http://clapi.icar.cnrs.fr)
* Groupe ICOR (H. Baldauf-Quilliatre, I. Colon de Carvajal, C. Etienne, E. Jouin-Chardon, S. Teston-Bonnard, V. Traverso) (2016). [CLAPI, une base de donnรฉes multimodale pour la parole en interaction : apports et dilemmes](https://shs.hal.science/halshs-01316283/). In Avanzi M., Bรฉguelin M.-J. & Diรฉmoz F. (eds), _Corpus de franรงais parlรฉs et franรงais parlรฉs des corpus, Corpus_ 15.
* **ESLO**
* Iris Eshkol-Taravella, Olivier Baude, Denis Maurel, Linda Hriba, Cรฉline Dugua, Isabelle Tellier (2012). Un grand corpus oral ยซ disponible ยป : le corpus dโOrlรฉans 1968-2012, _Ressources linguistiques libres, TAL_. Volume 52 โ nยฐ 3/2011, 17-46.
* Laboratoire Ligรฉrien de Linguistique - UMR 7270 (LLL) (2023). [ESLO [Corpus]](https://hdl.handle.net/11403/eslo/v1). [ORTOLANG](www.ortolang.fr) (Open Resources and TOols for LANGuage), v1.
* **FREDSum**
* Virgile Rennard, Guokan Shang, Damien Grari, Julie Hunter, Michalis Vazirgiannis (forthcoming). FREDSum: A Dialogue Summarization Corpus for French Political Debates. _Findings of Empirical Methods in Natural Language Processsing_ (EMNLP).
* **LinTO**
* Lila Gravellier, Julie Hunter, Philippe Muller, Thomas Pellegrini, Isabelle Ferranรฉ (2021).
[Weakly Supervised Discourse Segmentation for Multiparty Oral Conversation](https://aclanthology.org/2021.emnlp-main.104/).
_The 2021 Conference on Empirical Methods in Natural Language Processing_ (EMNLP), pp. 1381โ1392.
* **OFROM**
* Mathieu Avanzi, Marie-Josรฉ Bรฉguelin, Gilles Corminboeuf, Federica Diรฉmoz, Laure Anne Johnsen (2012-2023). [Corpus OFROM โ Corpus oral de franรงais de Suisse romande](ofrom.unine.ch). Universitรฉ de Neuchรขtel.
* Mathieu Avanzi, Marie-Josรฉ Bรฉguelin, Federica Diรฉmoz (2016). [Prรฉsentation du corpus OFROM โ Corpus oral de franรงais de Suisse romande](ofrom.unine.ch/uploads/Documents/AM-MJB-FD_GC_LAJ_OFROM_23.pdf). Universitรฉ de Neuchรขtel.
* Mathieu Avanzi, Marie-Josรฉ Bรฉguelin, Federica Diรฉmoz (2016). De lโarchive de parole au corpus de rรฉfรฉrence. Le corpus oral de franรงais de Suisse romande (OFROM). _Actes du colloque Corpus de Franรงais Parlรฉs et Franรงais Parlรฉs des Corpus_ (= Corpus 15), 309-342.
* Gilles Corminboeuf, Julie Rothenbรผhler, Maguelone Sauzet (รฉds) (2020). [Franรงais parlรฉs et franรงais โtout courtโ](studialinguisticaromanica.org/index.php/slr/issue/view/4). _Studia Linguistica Romanica_ nยฐ4, publication รฉlectronique.
* **ORFEO** (pour chaque corpus issu du projet ORFEO)
* Jeanne-Marie Debaisieux, Christophe Benzitoun, Henri-Josรฉ Deulofeu. Le projet ORFรO : un corpus dโรฉtude pour le franรงais contemporain, _Corpus 15_, Actes du colloque Corpus de Franรงais Parlรฉs et Franรงais Parlรฉs des Corpus.
* Carruthers, Janice (2008). Annotating an Oral Corpus using the Text Encoding Initiative. Methodology, Problems, Solutions, _Journal of French Language Studies_ 18(1), 103-119.
* Agnรจs Tutin, Francis Grossmann (2014). _Lโรฉcrit scientifique : du lexique au discours. Autour de Scientext_. Presses de lโUniversitรฉ de Rennes.
* **ORFEO/CFPB**
* Anne Dister, Emmanuelle Labeau (2017). [Le corpus de franรงais parlรฉ ร Bruxelles: origines, hypothรจses, dรฉveloppements et prรฉdictions](https://www.semanticscholar.org/paper/Le-corpus-de-fran%C3%A7ais-parl%C3%A9-%C3%A0-Bruxelles%3A-origines%2C-Dister-Labeau/0fe858f6b8c1ce49a2e43b34494c2e76922162fa).
* **ORFEO/C-Oral-Rom**
* Cresti Emanuela, Bacelar do Nascimento Fernanda, Moreno Sandoval Antonio, Veronis Jean, Martin Philippe, Kalid Choukri (2005). The C-ORAL-ROM CORPUS: A Multilingual Resource of Spontaneous Speech for Romance Languages. _Studies in Corpus Linguistics_, 15. John Benjamins Publishing Company 304 pp. (incl. DVD).
* **ORFEO/CRFP**
* รquipe Delic (2004). Recherches sur le franรงais parlรฉ nยฐ 18, ยซ Autour du Corpus de rรฉfรฉrence du franรงais parlรฉ ยป Publications de lโuniversitรฉ de Provence, 265 p.
* **ORFEO/Valibel**
* Anne Dister, Michel Francard, Philippe Hambye, Anne-Catherine Simon (2009). [Du corpus ร la banque de donnรฉes. Du son, des textes et des mรฉtadonnรฉes. L'รฉvolution de banque de donnรฉes textuelles orales VALIBEL (1989-2009)](https://cdn.uclouvain.be/public/Exports%20reddot/valibel/documents/Dister_et_al_2009_Cahiers.pdf), _Cahiers de Linguistique_ 33/2, 113-129.
* **OTG**
* Pascale Nicolas, Sabine Letellier-Zarshenas, Igor Schadle, Jean-Yves Antoine, Jean Caelen (2002). [Towards a large corpus of spoken dialogue in French that will be freely available: the "Parole Publique" project and its first realisations](https://www.info.univ-tours.fr/~antoine/parole_publique/articles/2002_LREC_CORP.pdf). _Third European Conference on Language Resources and Evaluation_ (LREC). Las Palmas de Gran Canaria, Espagne.
* Jean-Yves Antoine, Sabine Letellier-Zarshenas, Pascale Nicolas, Igor Schadle (2002). [Corpus OTG et ECOLE_MASSY : vers la constitution dโun collection de corpus francophones de dialogue oral diffusรฉs librement](https://www.info.univ-tours.fr/~antoine/parole_publique/articles/2002_TALN_CORP.pdf). _Actes TALN_ 2002. Nancy, France.
* **Paris Stories**
* Sylvain Kahane, Bernard Caron, Emmett Strickland, Kim Gerdes. Annotation guidelines of UD and SUD treebanks for spoken corpora: A proposal. _Proceedings of the 20th International Workshop on Treebanks and Linguistic Theories_ (TLT, SyntaxFest 2021).
* **PFC**
* Jacques Durand, Bernard Laks, Chantal Lyche (2009). Le projet PFC: une source de donnรฉes primaires structurรฉes. In J. Durand, B. Laks et C. Lyche (eds)(2009) _Phonologie, variation et accents du franรงais_. Paris: Hermรจs. pp. 19-61.
* Modรจles, Dynamiques, Corpus - UMR 7114 (MoDyCo), Universitรฉ de Groningen (RUG) (2017). [PFC - Phonologie du Franรงais Contemporain [Corpus]](https://hdl.handle.net/11403/pfc/v1). [ORTOLANG](www.ortolang.fr) (Open Resources and TOols for LANGuage), v1.
* **Rhapsodie**
* see [https://rhapsodie.modyco.fr/propriete-intellectuelle/](https://rhapsodie.modyco.fr/propriete-intellectuelle/)
* **SUMM-RE**
* Hiroyoshi Yamasaki, Jรฉrรดme Louradour, Julie Hunter, Laurent Prรฉvot (forthcoming). Transcribing And Aligning Conversational Speech: A Hybrid Pipeline Applied To French Conversations. _Workshop on Automatic Speech Recognition and Understanding_ (ASRU).
* **TCOF**
* Analyse et Traitement Informatique de la Langue Franรงaise (2020). [TCOF : Traitement de Corpus Oraux en Franรงais [Corpus]](https://www.ortolang.fr/market/corpora/tcof/v2.1). _ORTOLANG (Open Resources and TOols for LANGuage)_
* **Theatre Classique**
* French Drama Corpus (FreDraCor): A TEI P5 Version of Paul Fiรจvre's "Thรฉรขtre Classique" Corpus. Edited by Carsten Milling, Frank Fischer and Mathias Gรถbel. Hosted on GitHub, 2021 โ [https://github.com/dracor-org/fredracor](https://github.com/dracor-org/fredracor)
## Contact
[email protected] | OpenLLM-France/Claire-Dialogue-French-0.1 | [
"task_categories:text-generation",
"task_categories:text2text-generation",
"task_categories:conversational",
"task_ids:language-modeling",
"task_ids:dialogue-modeling",
"task_ids:dialogue-generation",
"multilinguality:monolingual",
"size_categories:100M<n<1B",
"language:fr",
"license:cc-by-nc-sa-4.0",
"conversational",
"text-generation",
"conditional-text-generation",
"dialogue-modeling",
"dialogue-generation",
"arxiv:2311.16840",
"region:us"
] | 2023-11-27T21:16:05+00:00 | {"language": ["fr"], "license": "cc-by-nc-sa-4.0", "multilinguality": ["monolingual"], "size_categories": ["100M<n<1B"], "task_categories": ["text-generation", "text2text-generation", "conversational"], "task_ids": ["language-modeling", "dialogue-modeling", "dialogue-generation"], "pretty_name": "Claire French Dialogue Dataset (CFDD)", "tags": ["conversational", "text-generation", "conditional-text-generation", "dialogue-modeling", "dialogue-generation"], "viewer": true, "configs": [{"config_name": "default", "sample_by": "paragraph", "data_files": [{"split": "train", "path": "FR/*/train.txt"}, {"split": "test", "path": "FR/*/test.txt"}]}]} | 2023-12-05T09:14:53+00:00 | [
"2311.16840"
] | [
"fr"
] | TAGS
#task_categories-text-generation #task_categories-text2text-generation #task_categories-conversational #task_ids-language-modeling #task_ids-dialogue-modeling #task_ids-dialogue-generation #multilinguality-monolingual #size_categories-100M<n<1B #language-French #license-cc-by-nc-sa-4.0 #conversational #text-generation #conditional-text-generation #dialogue-modeling #dialogue-generation #arxiv-2311.16840 #region-us
| Claire French Dialogue Dataset (CFDD) *A collection of French dialogue transcripts and plays*
==============================================================================================
This is the first packaged version of the datasets used to train the Claire family of large language models
(OpenLLM-France/Claire-7B-0.1).
The Claire French Dialogue Dataset (CFDD) is a collection of theater plays and transcripts of real French dialogues from various sources, including parliamentary proceedings, interviews, debates, meetings, and free conversations.
Each dialogue is split into speech turns, and each speech turn is labeled with the name of the speaker, or a unique identifier if the speaker is unknown.
* Dataset composition
+ Data sources
* Example use (python)
* Important notes
* License
* Citations
* Contact
Dataset composition
-------------------
CFDD can be broken down into:
* 37ย 015 conversations in total (36ย 731 in train, 284 in test)
* 2ย 961ย 116 speech turns in total (2ย 934ย 084 in train, 27ย 032 in test)
* around 150M words
It is a collection of several independent datasets, classified by the types of conversations they contain. This categorization is designed to more evenly balance the influence of different styles of dialogue on model training and to facilitate future applications of CFDD for which certain types of dialogue might be more helpful than others.
Note that this categorization leads to multiple cases in which the original corpus is split into subcorpora. When the smaller sets are included in our corpus, they are clearly indicated, e.g., "ESLO (1/5)". Some portions of the original corpora have been excluded entirely because they did not include dialogue between adults (e.g., monologues, read literature).
For more information, you can look at the following documents:
* FR/URL contains further statistics on the different subcorpora (broken down by train/test splits).
* FR/metadata\_filter\_datasets\_regex.json contains information about how original datasets were filtered and/or split into sub-categories.
* FR/metadata\_split\_testset\_list.json contains information about which files in the original datasets were chosen to be in the test set.
### Data sources
Example use (python)
--------------------
In the following 'sample\_by="paragraph"' is important to ensure that each sample corresponds to a full conversation (not just a speech turn).
Load dataset from HuggingFace cache (downloaded under '~/.cache/huggingface/datasets'):
Load dataset from raw text files:
Iterate on the dataset:
Important notes
---------------
All datasets were normalized in text files so that:
* Conversations are separated by a single blank line.
* Each line corresponds to a single speech turn.
* Each line begins with a speaker label of the form "'[\*:]'".
* When speaker names are anonymized or otherwise unknown, speakers are distinguished by numbers in the following format: "'[speaker001:]'", "'[speaker002:]'", โฆ
Otherwise, speakers are labeled with their names or roles, e.g. "'[Paul:]'", "'[Franรงois Mitterrand:]'", "'[M. le prรฉsident:]'".
* There are no parentheses: special annotations are always between square brackets.
* Commong tags include:
+ "'[PII]'": Personally Identifiable Information (anonymized name...)
+ "'[NOISE]'": distinct ambient noises
+ "'[LAUGHTER]'": laughter
* Depending on the data source, data may or may not include punctuation marks and upper case letters.
* The data were normalized in various ways including unicode NFC normalization, conversion of unbreakable spaces to spaces, and standardization of punctuation marks ('โฆ' -> '...', 'ยซ'/'ยป'/'โ'/'โ'/'โณ'/'โ' -> '"').
Those details are described in the paper:
*ยซย The Claire French Dialogue Datasetย ยป* (2023).
License
-------
Given that some of the corpora used for training are only available under CC-BY-NC-SA licenses,
Claire-Dialogue-French-0.1 is made available under the CC-BY-NC-SA 4.0 license.
s
When using the CFDD corpus, please cite the following paper:
Julie Hunter, Jรฉrรดme Louradour, Virgile Rennard, Ismaรฏl Harrando, Guokan Shang, Jean-Pierre Lorrรฉ (2023)
The Claire French Dialogue Dataset
This paper in turn provides the requested citations for all of the original corpora.
The same references are also listed below.
* Accueil UBS
+ Pascale Nicolas, Sabine Letellier-Zarshenas, Igor Schadle, Jean-Yves Antoine, Jean Caelen (2002). Towards a large corpus of spoken dialogue in French that will be freely available: the "Parole Publique" project and its first realisations. *Third European Conference on Language Resources and Evaluation* (LREC). Las Palmas de Gran Canaria, Espagne.
+ Jean-Yves Antoine, Jerome Goulian, Jeanne Villaneau, Marc le Tallec (2009). Word Order Phenomena in Spoken French : a Study on Four Corpora of Task-Oriented Dialogue and its Consequences on Language Processing. *Corpus Linguistics*, Liverpool, UK.
* ACSYNT
+ Cognition, Langue, Langages, Ergonomie - UMR 5263 (CLLE) (2013). [ACSYNT [Corpus]](URL ORTOLANG (Open Resources and TOols for LANGuage).
* CFPP
+ Branca-Rosoff S., Fleury S., Lefeuvre F., Pires M., 2012, Discours sur la ville. Prรฉsentation du Corpus de Franรงais Parlรฉ Parisien des annรฉes 2000 (CFPP2000).
+ CLESTHIA - Langage, systรจmes, discours - EA 7345 (CLESTHIA) (2018). [CFPP2000 [Corpus]](URL ORTOLANG (Open Resources and TOols for LANGuage), v1.
* CID
+ Roxane Bertrand, Philippe Blache, Robert Espesser, Gaรซlle Ferrรฉ, Christine Meunier, Bรฉatrice Priego-Valverde, Stรฉphane Rauzy (2008). Le CID โ Corpus of Interactional Data โ Annotation et Exploitation Multimodale de Parole Conversationnelle. *Traitement Automatique des Langues*, vol. 49, no. 3.
+ Philippe Blache, Roxane Bertrand, Brigitte Bigi et al. (2010). Multimodal annotation of conversational data. *Proceedings of the Fourth Linguistic Annotation Workshop*.
+ Laboratoire parole et langage - UMR 7309 (LPL) (2021). [Transcriptions du corpus CID [Corpus]](URL ORTOLANG (Open Resources and TOols for LANGuage).
* CLAPI
+ CLAPI, URL
+ Groupe ICOR (H. Baldauf-Quilliatre, I. Colon de Carvajal, C. Etienne, E. Jouin-Chardon, S. Teston-Bonnard, V. Traverso) (2016). CLAPI, une base de donnรฉes multimodale pour la parole en interaction : apports et dilemmes. In Avanzi M., Bรฉguelin M.-J. & Diรฉmoz F. (eds), *Corpus de franรงais parlรฉs et franรงais parlรฉs des corpus, Corpus* 15.
* ESLO
+ Iris Eshkol-Taravella, Olivier Baude, Denis Maurel, Linda Hriba, Cรฉline Dugua, Isabelle Tellier (2012). Un grand corpus oral ยซ disponible ยป : le corpus dโOrlรฉans 1968-2012, *Ressources linguistiques libres, TAL*. Volume 52 โ nยฐ 3/2011, 17-46.
+ Laboratoire Ligรฉrien de Linguistique - UMR 7270 (LLL) (2023). [ESLO [Corpus]](URL ORTOLANG (Open Resources and TOols for LANGuage), v1.
* FREDSum
+ Virgile Rennard, Guokan Shang, Damien Grari, Julie Hunter, Michalis Vazirgiannis (forthcoming). FREDSum: A Dialogue Summarization Corpus for French Political Debates. *Findings of Empirical Methods in Natural Language Processsing* (EMNLP).
* LinTO
+ Lila Gravellier, Julie Hunter, Philippe Muller, Thomas Pellegrini, Isabelle Ferranรฉ (2021).
Weakly Supervised Discourse Segmentation for Multiparty Oral Conversation.
*The 2021 Conference on Empirical Methods in Natural Language Processing* (EMNLP), pp. 1381โ1392.
* OFROM
+ Mathieu Avanzi, Marie-Josรฉ Bรฉguelin, Gilles Corminboeuf, Federica Diรฉmoz, Laure Anne Johnsen (2012-2023). Corpus OFROM โ Corpus oral de franรงais de Suisse romande. Universitรฉ de Neuchรขtel.
+ Mathieu Avanzi, Marie-Josรฉ Bรฉguelin, Federica Diรฉmoz (2016). Prรฉsentation du corpus OFROM โ Corpus oral de franรงais de Suisse romande. Universitรฉ de Neuchรขtel.
+ Mathieu Avanzi, Marie-Josรฉ Bรฉguelin, Federica Diรฉmoz (2016). De lโarchive de parole au corpus de rรฉfรฉrence. Le corpus oral de franรงais de Suisse romande (OFROM). *Actes du colloque Corpus de Franรงais Parlรฉs et Franรงais Parlรฉs des Corpus* (= Corpus 15), 309-342.
+ Gilles Corminboeuf, Julie Rothenbรผhler, Maguelone Sauzet (รฉds) (2020). Franรงais parlรฉs et franรงais โtout courtโ. *Studia Linguistica Romanica* nยฐ4, publication รฉlectronique.
* ORFEO (pour chaque corpus issu du projet ORFEO)
+ Jeanne-Marie Debaisieux, Christophe Benzitoun, Henri-Josรฉ Deulofeu. Le projet ORFรO : un corpus dโรฉtude pour le franรงais contemporain, *Corpus 15*, Actes du colloque Corpus de Franรงais Parlรฉs et Franรงais Parlรฉs des Corpus.
+ Carruthers, Janice (2008). Annotating an Oral Corpus using the Text Encoding Initiative. Methodology, Problems, Solutions, *Journal of French Language Studies* 18(1), 103-119.
+ Agnรจs Tutin, Francis Grossmann (2014). *Lโรฉcrit scientifique : du lexique au discours. Autour de Scientext*. Presses de lโUniversitรฉ de Rennes.
* ORFEO/CFPB
+ Anne Dister, Emmanuelle Labeau (2017). Le corpus de franรงais parlรฉ ร Bruxelles: origines, hypothรจses, dรฉveloppements et prรฉdictions.
* ORFEO/C-Oral-Rom
+ Cresti Emanuela, Bacelar do Nascimento Fernanda, Moreno Sandoval Antonio, Veronis Jean, Martin Philippe, Kalid Choukri (2005). The C-ORAL-ROM CORPUS: A Multilingual Resource of Spontaneous Speech for Romance Languages. *Studies in Corpus Linguistics*, 15. John Benjamins Publishing Company 304 pp. (incl. DVD).
* ORFEO/CRFP
+ รquipe Delic (2004). Recherches sur le franรงais parlรฉ nยฐ 18, ยซ Autour du Corpus de rรฉfรฉrence du franรงais parlรฉ ยป Publications de lโuniversitรฉ de Provence, 265 p.
* ORFEO/Valibel
+ Anne Dister, Michel Francard, Philippe Hambye, Anne-Catherine Simon (2009). Du corpus ร la banque de donnรฉes. Du son, des textes et des mรฉtadonnรฉes. L'รฉvolution de banque de donnรฉes textuelles orales VALIBEL (1989-2009), *Cahiers de Linguistique* 33/2, 113-129.
* OTG
+ Pascale Nicolas, Sabine Letellier-Zarshenas, Igor Schadle, Jean-Yves Antoine, Jean Caelen (2002). Towards a large corpus of spoken dialogue in French that will be freely available: the "Parole Publique" project and its first realisations. *Third European Conference on Language Resources and Evaluation* (LREC). Las Palmas de Gran Canaria, Espagne.
+ Jean-Yves Antoine, Sabine Letellier-Zarshenas, Pascale Nicolas, Igor Schadle (2002). Corpus OTG et ECOLE\_MASSY : vers la constitution dโun collection de corpus francophones de dialogue oral diffusรฉs librement. *Actes TALN* 2002. Nancy, France.
* Paris Stories
+ Sylvain Kahane, Bernard Caron, Emmett Strickland, Kim Gerdes. Annotation guidelines of UD and SUD treebanks for spoken corpora: A proposal. *Proceedings of the 20th International Workshop on Treebanks and Linguistic Theories* (TLT, SyntaxFest 2021).
* PFC
+ Jacques Durand, Bernard Laks, Chantal Lyche (2009). Le projet PFC: une source de donnรฉes primaires structurรฉes. In J. Durand, B. Laks et C. Lyche (eds)(2009) *Phonologie, variation et accents du franรงais*. Paris: Hermรจs. pp. 19-61.
+ Modรจles, Dynamiques, Corpus - UMR 7114 (MoDyCo), Universitรฉ de Groningen (RUG) (2017). [PFC - Phonologie du Franรงais Contemporain [Corpus]](URL ORTOLANG (Open Resources and TOols for LANGuage), v1.
* Rhapsodie
+ see URL
* SUMM-RE
+ Hiroyoshi Yamasaki, Jรฉrรดme Louradour, Julie Hunter, Laurent Prรฉvot (forthcoming). Transcribing And Aligning Conversational Speech: A Hybrid Pipeline Applied To French Conversations. *Workshop on Automatic Speech Recognition and Understanding* (ASRU).
* TCOF
+ Analyse et Traitement Informatique de la Langue Franรงaise (2020). [TCOF : Traitement de Corpus Oraux en Franรงais [Corpus]](URL *ORTOLANG (Open Resources and TOols for LANGuage)*
* Theatre Classique
+ French Drama Corpus (FreDraCor): A TEI P5 Version of Paul Fiรจvre's "Thรฉรขtre Classique" Corpus. Edited by Carsten Milling, Frank Fischer and Mathias Gรถbel. Hosted on GitHub, 2021 โ URL
Contact
-------
contact@URL
| [
"### Data sources\n\n\n\nExample use (python)\n--------------------\n\n\nIn the following 'sample\\_by=\"paragraph\"' is important to ensure that each sample corresponds to a full conversation (not just a speech turn).\n\n\nLoad dataset from HuggingFace cache (downloaded under '~/.cache/huggingface/datasets'):\n\n\nLoad dataset from raw text files:\n\n\nIterate on the dataset:\n\n\nImportant notes\n---------------\n\n\nAll datasets were normalized in text files so that:\n\n\n* Conversations are separated by a single blank line.\n* Each line corresponds to a single speech turn.\n* Each line begins with a speaker label of the form \"'[\\*:]'\".\n* When speaker names are anonymized or otherwise unknown, speakers are distinguished by numbers in the following format: \"'[speaker001:]'\", \"'[speaker002:]'\", โฆ \n Otherwise, speakers are labeled with their names or roles, e.g. \"'[Paul:]'\", \"'[Franรงois Mitterrand:]'\", \"'[M. le prรฉsident:]'\".\n* There are no parentheses: special annotations are always between square brackets.\n* Commong tags include:\n\n\n\t+ \"'[PII]'\": Personally Identifiable Information (anonymized name...)\n\t+ \"'[NOISE]'\": distinct ambient noises\n\t+ \"'[LAUGHTER]'\": laughter\n* Depending on the data source, data may or may not include punctuation marks and upper case letters.\n* The data were normalized in various ways including unicode NFC normalization, conversion of unbreakable spaces to spaces, and standardization of punctuation marks ('โฆ' -> '...', 'ยซ'/'ยป'/'โ'/'โ'/'โณ'/'โ' -> '\"').\n\n\nThose details are described in the paper:\n*ยซย The Claire French Dialogue Datasetย ยป* (2023).\n\n\nLicense\n-------\n\n\nGiven that some of the corpora used for training are only available under CC-BY-NC-SA licenses,\nClaire-Dialogue-French-0.1 is made available under the CC-BY-NC-SA 4.0 license.\n\n\ns\n\n\nWhen using the CFDD corpus, please cite the following paper:\n\n\nJulie Hunter, Jรฉrรดme Louradour, Virgile Rennard, Ismaรฏl Harrando, Guokan Shang, Jean-Pierre Lorrรฉ (2023)\nThe Claire French Dialogue Dataset\n\n\nThis paper in turn provides the requested citations for all of the original corpora.\nThe same references are also listed below.\n\n\n* Accueil UBS\n\t+ Pascale Nicolas, Sabine Letellier-Zarshenas, Igor Schadle, Jean-Yves Antoine, Jean Caelen (2002). Towards a large corpus of spoken dialogue in French that will be freely available: the \"Parole Publique\" project and its first realisations. *Third European Conference on Language Resources and Evaluation* (LREC). Las Palmas de Gran Canaria, Espagne.\n\t+ Jean-Yves Antoine, Jerome Goulian, Jeanne Villaneau, Marc le Tallec (2009). Word Order Phenomena in Spoken French : a Study on Four Corpora of Task-Oriented Dialogue and its Consequences on Language Processing. *Corpus Linguistics*, Liverpool, UK.\n* ACSYNT\n\t+ Cognition, Langue, Langages, Ergonomie - UMR 5263 (CLLE) (2013). [ACSYNT [Corpus]](URL ORTOLANG (Open Resources and TOols for LANGuage).\n* CFPP\n\t+ Branca-Rosoff S., Fleury S., Lefeuvre F., Pires M., 2012, Discours sur la ville. Prรฉsentation du Corpus de Franรงais Parlรฉ Parisien des annรฉes 2000 (CFPP2000).\n\t+ CLESTHIA - Langage, systรจmes, discours - EA 7345 (CLESTHIA) (2018). [CFPP2000 [Corpus]](URL ORTOLANG (Open Resources and TOols for LANGuage), v1.\n* CID\n\t+ Roxane Bertrand, Philippe Blache, Robert Espesser, Gaรซlle Ferrรฉ, Christine Meunier, Bรฉatrice Priego-Valverde, Stรฉphane Rauzy (2008). Le CID โ Corpus of Interactional Data โ Annotation et Exploitation Multimodale de Parole Conversationnelle. *Traitement Automatique des Langues*, vol. 49, no. 3.\n\t+ Philippe Blache, Roxane Bertrand, Brigitte Bigi et al. (2010). Multimodal annotation of conversational data. *Proceedings of the Fourth Linguistic Annotation Workshop*.\n\t+ Laboratoire parole et langage - UMR 7309 (LPL) (2021). [Transcriptions du corpus CID [Corpus]](URL ORTOLANG (Open Resources and TOols for LANGuage).\n* CLAPI\n\t+ CLAPI, URL\n\t+ Groupe ICOR (H. Baldauf-Quilliatre, I. Colon de Carvajal, C. Etienne, E. Jouin-Chardon, S. Teston-Bonnard, V. Traverso) (2016). CLAPI, une base de donnรฉes multimodale pour la parole en interaction : apports et dilemmes. In Avanzi M., Bรฉguelin M.-J. & Diรฉmoz F. (eds), *Corpus de franรงais parlรฉs et franรงais parlรฉs des corpus, Corpus* 15.\n* ESLO\n\t+ Iris Eshkol-Taravella, Olivier Baude, Denis Maurel, Linda Hriba, Cรฉline Dugua, Isabelle Tellier (2012). Un grand corpus oral ยซ disponible ยป : le corpus dโOrlรฉans 1968-2012, *Ressources linguistiques libres, TAL*. Volume 52 โ nยฐ 3/2011, 17-46.\n\t+ Laboratoire Ligรฉrien de Linguistique - UMR 7270 (LLL) (2023). [ESLO [Corpus]](URL ORTOLANG (Open Resources and TOols for LANGuage), v1.\n* FREDSum\n\t+ Virgile Rennard, Guokan Shang, Damien Grari, Julie Hunter, Michalis Vazirgiannis (forthcoming). FREDSum: A Dialogue Summarization Corpus for French Political Debates. *Findings of Empirical Methods in Natural Language Processsing* (EMNLP).\n* LinTO\n\t+ Lila Gravellier, Julie Hunter, Philippe Muller, Thomas Pellegrini, Isabelle Ferranรฉ (2021).\n\tWeakly Supervised Discourse Segmentation for Multiparty Oral Conversation.\n\t*The 2021 Conference on Empirical Methods in Natural Language Processing* (EMNLP), pp. 1381โ1392.\n* OFROM\n\t+ Mathieu Avanzi, Marie-Josรฉ Bรฉguelin, Gilles Corminboeuf, Federica Diรฉmoz, Laure Anne Johnsen (2012-2023). Corpus OFROM โ Corpus oral de franรงais de Suisse romande. Universitรฉ de Neuchรขtel.\n\t+ Mathieu Avanzi, Marie-Josรฉ Bรฉguelin, Federica Diรฉmoz (2016). Prรฉsentation du corpus OFROM โ Corpus oral de franรงais de Suisse romande. Universitรฉ de Neuchรขtel.\n\t+ Mathieu Avanzi, Marie-Josรฉ Bรฉguelin, Federica Diรฉmoz (2016). De lโarchive de parole au corpus de rรฉfรฉrence. Le corpus oral de franรงais de Suisse romande (OFROM). *Actes du colloque Corpus de Franรงais Parlรฉs et Franรงais Parlรฉs des Corpus* (= Corpus 15), 309-342.\n\t+ Gilles Corminboeuf, Julie Rothenbรผhler, Maguelone Sauzet (รฉds) (2020). Franรงais parlรฉs et franรงais โtout courtโ. *Studia Linguistica Romanica* nยฐ4, publication รฉlectronique.\n* ORFEO (pour chaque corpus issu du projet ORFEO)\n\t+ Jeanne-Marie Debaisieux, Christophe Benzitoun, Henri-Josรฉ Deulofeu. Le projet ORFรO : un corpus dโรฉtude pour le franรงais contemporain, *Corpus 15*, Actes du colloque Corpus de Franรงais Parlรฉs et Franรงais Parlรฉs des Corpus.\n\t+ Carruthers, Janice (2008). Annotating an Oral Corpus using the Text Encoding Initiative. Methodology, Problems, Solutions, *Journal of French Language Studies* 18(1), 103-119.\n\t+ Agnรจs Tutin, Francis Grossmann (2014). *Lโรฉcrit scientifique : du lexique au discours. Autour de Scientext*. Presses de lโUniversitรฉ de Rennes.\n* ORFEO/CFPB\n\t+ Anne Dister, Emmanuelle Labeau (2017). Le corpus de franรงais parlรฉ ร Bruxelles: origines, hypothรจses, dรฉveloppements et prรฉdictions.\n* ORFEO/C-Oral-Rom\n\t+ Cresti Emanuela, Bacelar do Nascimento Fernanda, Moreno Sandoval Antonio, Veronis Jean, Martin Philippe, Kalid Choukri (2005). The C-ORAL-ROM CORPUS: A Multilingual Resource of Spontaneous Speech for Romance Languages. *Studies in Corpus Linguistics*, 15. John Benjamins Publishing Company 304 pp. (incl. DVD).\n* ORFEO/CRFP\n\t+ รquipe Delic (2004). Recherches sur le franรงais parlรฉ nยฐ 18, ยซ Autour du Corpus de rรฉfรฉrence du franรงais parlรฉ ยป Publications de lโuniversitรฉ de Provence, 265 p.\n* ORFEO/Valibel\n\t+ Anne Dister, Michel Francard, Philippe Hambye, Anne-Catherine Simon (2009). Du corpus ร la banque de donnรฉes. Du son, des textes et des mรฉtadonnรฉes. L'รฉvolution de banque de donnรฉes textuelles orales VALIBEL (1989-2009), *Cahiers de Linguistique* 33/2, 113-129.\n* OTG\n\t+ Pascale Nicolas, Sabine Letellier-Zarshenas, Igor Schadle, Jean-Yves Antoine, Jean Caelen (2002). Towards a large corpus of spoken dialogue in French that will be freely available: the \"Parole Publique\" project and its first realisations. *Third European Conference on Language Resources and Evaluation* (LREC). Las Palmas de Gran Canaria, Espagne.\n\t+ Jean-Yves Antoine, Sabine Letellier-Zarshenas, Pascale Nicolas, Igor Schadle (2002). Corpus OTG et ECOLE\\_MASSY : vers la constitution dโun collection de corpus francophones de dialogue oral diffusรฉs librement. *Actes TALN* 2002. Nancy, France.\n* Paris Stories\n\t+ Sylvain Kahane, Bernard Caron, Emmett Strickland, Kim Gerdes. Annotation guidelines of UD and SUD treebanks for spoken corpora: A proposal. *Proceedings of the 20th International Workshop on Treebanks and Linguistic Theories* (TLT, SyntaxFest 2021).\n* PFC\n\t+ Jacques Durand, Bernard Laks, Chantal Lyche (2009). Le projet PFC: une source de donnรฉes primaires structurรฉes. In J. Durand, B. Laks et C. Lyche (eds)(2009) *Phonologie, variation et accents du franรงais*. Paris: Hermรจs. pp. 19-61.\n\t+ Modรจles, Dynamiques, Corpus - UMR 7114 (MoDyCo), Universitรฉ de Groningen (RUG) (2017). [PFC - Phonologie du Franรงais Contemporain [Corpus]](URL ORTOLANG (Open Resources and TOols for LANGuage), v1.\n* Rhapsodie\n\t+ see URL\n* SUMM-RE\n\t+ Hiroyoshi Yamasaki, Jรฉrรดme Louradour, Julie Hunter, Laurent Prรฉvot (forthcoming). Transcribing And Aligning Conversational Speech: A Hybrid Pipeline Applied To French Conversations. *Workshop on Automatic Speech Recognition and Understanding* (ASRU).\n* TCOF\n\t+ Analyse et Traitement Informatique de la Langue Franรงaise (2020). [TCOF : Traitement de Corpus Oraux en Franรงais [Corpus]](URL *ORTOLANG (Open Resources and TOols for LANGuage)*\n* Theatre Classique\n\t+ French Drama Corpus (FreDraCor): A TEI P5 Version of Paul Fiรจvre's \"Thรฉรขtre Classique\" Corpus. Edited by Carsten Milling, Frank Fischer and Mathias Gรถbel. Hosted on GitHub, 2021 โ URL\n\n\nContact\n-------\n\n\ncontact@URL"
] | [
"TAGS\n#task_categories-text-generation #task_categories-text2text-generation #task_categories-conversational #task_ids-language-modeling #task_ids-dialogue-modeling #task_ids-dialogue-generation #multilinguality-monolingual #size_categories-100M<n<1B #language-French #license-cc-by-nc-sa-4.0 #conversational #text-generation #conditional-text-generation #dialogue-modeling #dialogue-generation #arxiv-2311.16840 #region-us \n",
"### Data sources\n\n\n\nExample use (python)\n--------------------\n\n\nIn the following 'sample\\_by=\"paragraph\"' is important to ensure that each sample corresponds to a full conversation (not just a speech turn).\n\n\nLoad dataset from HuggingFace cache (downloaded under '~/.cache/huggingface/datasets'):\n\n\nLoad dataset from raw text files:\n\n\nIterate on the dataset:\n\n\nImportant notes\n---------------\n\n\nAll datasets were normalized in text files so that:\n\n\n* Conversations are separated by a single blank line.\n* Each line corresponds to a single speech turn.\n* Each line begins with a speaker label of the form \"'[\\*:]'\".\n* When speaker names are anonymized or otherwise unknown, speakers are distinguished by numbers in the following format: \"'[speaker001:]'\", \"'[speaker002:]'\", โฆ \n Otherwise, speakers are labeled with their names or roles, e.g. \"'[Paul:]'\", \"'[Franรงois Mitterrand:]'\", \"'[M. le prรฉsident:]'\".\n* There are no parentheses: special annotations are always between square brackets.\n* Commong tags include:\n\n\n\t+ \"'[PII]'\": Personally Identifiable Information (anonymized name...)\n\t+ \"'[NOISE]'\": distinct ambient noises\n\t+ \"'[LAUGHTER]'\": laughter\n* Depending on the data source, data may or may not include punctuation marks and upper case letters.\n* The data were normalized in various ways including unicode NFC normalization, conversion of unbreakable spaces to spaces, and standardization of punctuation marks ('โฆ' -> '...', 'ยซ'/'ยป'/'โ'/'โ'/'โณ'/'โ' -> '\"').\n\n\nThose details are described in the paper:\n*ยซย The Claire French Dialogue Datasetย ยป* (2023).\n\n\nLicense\n-------\n\n\nGiven that some of the corpora used for training are only available under CC-BY-NC-SA licenses,\nClaire-Dialogue-French-0.1 is made available under the CC-BY-NC-SA 4.0 license.\n\n\ns\n\n\nWhen using the CFDD corpus, please cite the following paper:\n\n\nJulie Hunter, Jรฉrรดme Louradour, Virgile Rennard, Ismaรฏl Harrando, Guokan Shang, Jean-Pierre Lorrรฉ (2023)\nThe Claire French Dialogue Dataset\n\n\nThis paper in turn provides the requested citations for all of the original corpora.\nThe same references are also listed below.\n\n\n* Accueil UBS\n\t+ Pascale Nicolas, Sabine Letellier-Zarshenas, Igor Schadle, Jean-Yves Antoine, Jean Caelen (2002). Towards a large corpus of spoken dialogue in French that will be freely available: the \"Parole Publique\" project and its first realisations. *Third European Conference on Language Resources and Evaluation* (LREC). Las Palmas de Gran Canaria, Espagne.\n\t+ Jean-Yves Antoine, Jerome Goulian, Jeanne Villaneau, Marc le Tallec (2009). Word Order Phenomena in Spoken French : a Study on Four Corpora of Task-Oriented Dialogue and its Consequences on Language Processing. *Corpus Linguistics*, Liverpool, UK.\n* ACSYNT\n\t+ Cognition, Langue, Langages, Ergonomie - UMR 5263 (CLLE) (2013). [ACSYNT [Corpus]](URL ORTOLANG (Open Resources and TOols for LANGuage).\n* CFPP\n\t+ Branca-Rosoff S., Fleury S., Lefeuvre F., Pires M., 2012, Discours sur la ville. Prรฉsentation du Corpus de Franรงais Parlรฉ Parisien des annรฉes 2000 (CFPP2000).\n\t+ CLESTHIA - Langage, systรจmes, discours - EA 7345 (CLESTHIA) (2018). [CFPP2000 [Corpus]](URL ORTOLANG (Open Resources and TOols for LANGuage), v1.\n* CID\n\t+ Roxane Bertrand, Philippe Blache, Robert Espesser, Gaรซlle Ferrรฉ, Christine Meunier, Bรฉatrice Priego-Valverde, Stรฉphane Rauzy (2008). Le CID โ Corpus of Interactional Data โ Annotation et Exploitation Multimodale de Parole Conversationnelle. *Traitement Automatique des Langues*, vol. 49, no. 3.\n\t+ Philippe Blache, Roxane Bertrand, Brigitte Bigi et al. (2010). Multimodal annotation of conversational data. *Proceedings of the Fourth Linguistic Annotation Workshop*.\n\t+ Laboratoire parole et langage - UMR 7309 (LPL) (2021). [Transcriptions du corpus CID [Corpus]](URL ORTOLANG (Open Resources and TOols for LANGuage).\n* CLAPI\n\t+ CLAPI, URL\n\t+ Groupe ICOR (H. Baldauf-Quilliatre, I. Colon de Carvajal, C. Etienne, E. Jouin-Chardon, S. Teston-Bonnard, V. Traverso) (2016). CLAPI, une base de donnรฉes multimodale pour la parole en interaction : apports et dilemmes. In Avanzi M., Bรฉguelin M.-J. & Diรฉmoz F. (eds), *Corpus de franรงais parlรฉs et franรงais parlรฉs des corpus, Corpus* 15.\n* ESLO\n\t+ Iris Eshkol-Taravella, Olivier Baude, Denis Maurel, Linda Hriba, Cรฉline Dugua, Isabelle Tellier (2012). Un grand corpus oral ยซ disponible ยป : le corpus dโOrlรฉans 1968-2012, *Ressources linguistiques libres, TAL*. Volume 52 โ nยฐ 3/2011, 17-46.\n\t+ Laboratoire Ligรฉrien de Linguistique - UMR 7270 (LLL) (2023). [ESLO [Corpus]](URL ORTOLANG (Open Resources and TOols for LANGuage), v1.\n* FREDSum\n\t+ Virgile Rennard, Guokan Shang, Damien Grari, Julie Hunter, Michalis Vazirgiannis (forthcoming). FREDSum: A Dialogue Summarization Corpus for French Political Debates. *Findings of Empirical Methods in Natural Language Processsing* (EMNLP).\n* LinTO\n\t+ Lila Gravellier, Julie Hunter, Philippe Muller, Thomas Pellegrini, Isabelle Ferranรฉ (2021).\n\tWeakly Supervised Discourse Segmentation for Multiparty Oral Conversation.\n\t*The 2021 Conference on Empirical Methods in Natural Language Processing* (EMNLP), pp. 1381โ1392.\n* OFROM\n\t+ Mathieu Avanzi, Marie-Josรฉ Bรฉguelin, Gilles Corminboeuf, Federica Diรฉmoz, Laure Anne Johnsen (2012-2023). Corpus OFROM โ Corpus oral de franรงais de Suisse romande. Universitรฉ de Neuchรขtel.\n\t+ Mathieu Avanzi, Marie-Josรฉ Bรฉguelin, Federica Diรฉmoz (2016). Prรฉsentation du corpus OFROM โ Corpus oral de franรงais de Suisse romande. Universitรฉ de Neuchรขtel.\n\t+ Mathieu Avanzi, Marie-Josรฉ Bรฉguelin, Federica Diรฉmoz (2016). De lโarchive de parole au corpus de rรฉfรฉrence. Le corpus oral de franรงais de Suisse romande (OFROM). *Actes du colloque Corpus de Franรงais Parlรฉs et Franรงais Parlรฉs des Corpus* (= Corpus 15), 309-342.\n\t+ Gilles Corminboeuf, Julie Rothenbรผhler, Maguelone Sauzet (รฉds) (2020). Franรงais parlรฉs et franรงais โtout courtโ. *Studia Linguistica Romanica* nยฐ4, publication รฉlectronique.\n* ORFEO (pour chaque corpus issu du projet ORFEO)\n\t+ Jeanne-Marie Debaisieux, Christophe Benzitoun, Henri-Josรฉ Deulofeu. Le projet ORFรO : un corpus dโรฉtude pour le franรงais contemporain, *Corpus 15*, Actes du colloque Corpus de Franรงais Parlรฉs et Franรงais Parlรฉs des Corpus.\n\t+ Carruthers, Janice (2008). Annotating an Oral Corpus using the Text Encoding Initiative. Methodology, Problems, Solutions, *Journal of French Language Studies* 18(1), 103-119.\n\t+ Agnรจs Tutin, Francis Grossmann (2014). *Lโรฉcrit scientifique : du lexique au discours. Autour de Scientext*. Presses de lโUniversitรฉ de Rennes.\n* ORFEO/CFPB\n\t+ Anne Dister, Emmanuelle Labeau (2017). Le corpus de franรงais parlรฉ ร Bruxelles: origines, hypothรจses, dรฉveloppements et prรฉdictions.\n* ORFEO/C-Oral-Rom\n\t+ Cresti Emanuela, Bacelar do Nascimento Fernanda, Moreno Sandoval Antonio, Veronis Jean, Martin Philippe, Kalid Choukri (2005). The C-ORAL-ROM CORPUS: A Multilingual Resource of Spontaneous Speech for Romance Languages. *Studies in Corpus Linguistics*, 15. John Benjamins Publishing Company 304 pp. (incl. DVD).\n* ORFEO/CRFP\n\t+ รquipe Delic (2004). Recherches sur le franรงais parlรฉ nยฐ 18, ยซ Autour du Corpus de rรฉfรฉrence du franรงais parlรฉ ยป Publications de lโuniversitรฉ de Provence, 265 p.\n* ORFEO/Valibel\n\t+ Anne Dister, Michel Francard, Philippe Hambye, Anne-Catherine Simon (2009). Du corpus ร la banque de donnรฉes. Du son, des textes et des mรฉtadonnรฉes. L'รฉvolution de banque de donnรฉes textuelles orales VALIBEL (1989-2009), *Cahiers de Linguistique* 33/2, 113-129.\n* OTG\n\t+ Pascale Nicolas, Sabine Letellier-Zarshenas, Igor Schadle, Jean-Yves Antoine, Jean Caelen (2002). Towards a large corpus of spoken dialogue in French that will be freely available: the \"Parole Publique\" project and its first realisations. *Third European Conference on Language Resources and Evaluation* (LREC). Las Palmas de Gran Canaria, Espagne.\n\t+ Jean-Yves Antoine, Sabine Letellier-Zarshenas, Pascale Nicolas, Igor Schadle (2002). Corpus OTG et ECOLE\\_MASSY : vers la constitution dโun collection de corpus francophones de dialogue oral diffusรฉs librement. *Actes TALN* 2002. Nancy, France.\n* Paris Stories\n\t+ Sylvain Kahane, Bernard Caron, Emmett Strickland, Kim Gerdes. Annotation guidelines of UD and SUD treebanks for spoken corpora: A proposal. *Proceedings of the 20th International Workshop on Treebanks and Linguistic Theories* (TLT, SyntaxFest 2021).\n* PFC\n\t+ Jacques Durand, Bernard Laks, Chantal Lyche (2009). Le projet PFC: une source de donnรฉes primaires structurรฉes. In J. Durand, B. Laks et C. Lyche (eds)(2009) *Phonologie, variation et accents du franรงais*. Paris: Hermรจs. pp. 19-61.\n\t+ Modรจles, Dynamiques, Corpus - UMR 7114 (MoDyCo), Universitรฉ de Groningen (RUG) (2017). [PFC - Phonologie du Franรงais Contemporain [Corpus]](URL ORTOLANG (Open Resources and TOols for LANGuage), v1.\n* Rhapsodie\n\t+ see URL\n* SUMM-RE\n\t+ Hiroyoshi Yamasaki, Jรฉrรดme Louradour, Julie Hunter, Laurent Prรฉvot (forthcoming). Transcribing And Aligning Conversational Speech: A Hybrid Pipeline Applied To French Conversations. *Workshop on Automatic Speech Recognition and Understanding* (ASRU).\n* TCOF\n\t+ Analyse et Traitement Informatique de la Langue Franรงaise (2020). [TCOF : Traitement de Corpus Oraux en Franรงais [Corpus]](URL *ORTOLANG (Open Resources and TOols for LANGuage)*\n* Theatre Classique\n\t+ French Drama Corpus (FreDraCor): A TEI P5 Version of Paul Fiรจvre's \"Thรฉรขtre Classique\" Corpus. Edited by Carsten Milling, Frank Fischer and Mathias Gรถbel. Hosted on GitHub, 2021 โ URL\n\n\nContact\n-------\n\n\ncontact@URL"
] | [
153,
2791
] | [
"passage: TAGS\n#task_categories-text-generation #task_categories-text2text-generation #task_categories-conversational #task_ids-language-modeling #task_ids-dialogue-modeling #task_ids-dialogue-generation #multilinguality-monolingual #size_categories-100M<n<1B #language-French #license-cc-by-nc-sa-4.0 #conversational #text-generation #conditional-text-generation #dialogue-modeling #dialogue-generation #arxiv-2311.16840 #region-us \n"
] |
06510ee73b0d197c38e987e103e3bdf664f49161 | # Dataset Card for "ffmperative_with_paths"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | remyxai/ffmperative_with_paths | [
"region:us"
] | 2023-11-27T21:37:26+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 13290626.368026014, "num_examples": 29188}], "download_size": 4090174, "dataset_size": 13290626.368026014}} | 2023-11-27T21:37:34+00:00 | [] | [] | TAGS
#region-us
| # Dataset Card for "ffmperative_with_paths"
More Information needed | [
"# Dataset Card for \"ffmperative_with_paths\"\n\nMore Information needed"
] | [
"TAGS\n#region-us \n",
"# Dataset Card for \"ffmperative_with_paths\"\n\nMore Information needed"
] | [
6,
18
] | [
"passage: TAGS\n#region-us \n# Dataset Card for \"ffmperative_with_paths\"\n\nMore Information needed"
] |
2db018cb247395bf8e36a6168b900bce7c08772b | # talk-to-paul
Goal: finetune a LLM on conversational data of Paul Christiano, so you can "talk to paul".
Previously on [Github](https://github.com/mtrazzi/talk-to-paul).
Currently, I've made a few datasets, consisting of Lesswrong posts/comments and podcast transcripts.
* The [15M.jsonl](./15M.jsonl) file has one podcast / lesswrong post data per line. Format is {"text": "..."}. Size 15Mb.
* The [15M.txt](./15M.txt) is the same though instead is just a long text file where things are separated by \<eop\> (end of post) instead of the jsonl format above. Size 15Mb.
* The [prompt_completion_podcast_data.jsonl](./prompt_completion_podcast_data.jsonl) has format {"prompt": "...", "completion": "..."} (see below). It does not currently contain the lesswrong data because lesswrong threads are more tricky to put into some prompt / completion format. (I might add it in the future if it turns out that the prompt completion data is superior).
These are concatenations of smaller datasets you can read more about in the [raw_data](./raw_data) README.
## Format
* In [prompt_completion_data](./prompt_completion_data) on github I have the raw files in a form {"prompt": "...", "completion": "..."} where the prompt is the message before paul christiano says something and the completion is what paul says. This is useful for doing more like instruction finetuning thing, or really training a chatbot. There is no "Paul Christiano:" or "Rob Wiblin:" in this, just directly the text that is being said.
* In the other files however, the messages inside the "text": "" double quotes are separated by \<eom\> (end of message), and at the end of a podcast or a lesswrong thread I have a \<eot\> (end of thread) separator.
* Messages / posts / speakers alternate with either "Full Name:" [... their text ...]] or with username: [...] on lesswrong. Same format with lesswrong posts and comments. For convenience, on lesswrong paul is "Paul Christiano: " instead of "paulfchristiano: " to make it easier for the trained model to learn what to say when prompted "Paul Christiano: " (useful for deploying the paul chatbot).
| mtrazzi/talk-to-paul | [
"region:us"
] | 2023-11-27T22:47:24+00:00 | {} | 2023-11-27T22:56:40+00:00 | [] | [] | TAGS
#region-us
| # talk-to-paul
Goal: finetune a LLM on conversational data of Paul Christiano, so you can "talk to paul".
Previously on Github.
Currently, I've made a few datasets, consisting of Lesswrong posts/comments and podcast transcripts.
* The URL file has one podcast / lesswrong post data per line. Format is {"text": "..."}. Size 15Mb.
* The URL is the same though instead is just a long text file where things are separated by \<eop\> (end of post) instead of the jsonl format above. Size 15Mb.
* The prompt_completion_podcast_data.jsonl has format {"prompt": "...", "completion": "..."} (see below). It does not currently contain the lesswrong data because lesswrong threads are more tricky to put into some prompt / completion format. (I might add it in the future if it turns out that the prompt completion data is superior).
These are concatenations of smaller datasets you can read more about in the raw_data README.
## Format
* In prompt_completion_data on github I have the raw files in a form {"prompt": "...", "completion": "..."} where the prompt is the message before paul christiano says something and the completion is what paul says. This is useful for doing more like instruction finetuning thing, or really training a chatbot. There is no "Paul Christiano:" or "Rob Wiblin:" in this, just directly the text that is being said.
* In the other files however, the messages inside the "text": "" double quotes are separated by \<eom\> (end of message), and at the end of a podcast or a lesswrong thread I have a \<eot\> (end of thread) separator.
* Messages / posts / speakers alternate with either "Full Name:" [... their text ...]] or with username: [...] on lesswrong. Same format with lesswrong posts and comments. For convenience, on lesswrong paul is "Paul Christiano: " instead of "paulfchristiano: " to make it easier for the trained model to learn what to say when prompted "Paul Christiano: " (useful for deploying the paul chatbot).
| [
"# talk-to-paul\n\nGoal: finetune a LLM on conversational data of Paul Christiano, so you can \"talk to paul\".\n\nPreviously on Github.\n\nCurrently, I've made a few datasets, consisting of Lesswrong posts/comments and podcast transcripts.\n* The URL file has one podcast / lesswrong post data per line. Format is {\"text\": \"...\"}. Size 15Mb.\n* The URL is the same though instead is just a long text file where things are separated by \\<eop\\> (end of post) instead of the jsonl format above. Size 15Mb.\n* The prompt_completion_podcast_data.jsonl has format {\"prompt\": \"...\", \"completion\": \"...\"} (see below). It does not currently contain the lesswrong data because lesswrong threads are more tricky to put into some prompt / completion format. (I might add it in the future if it turns out that the prompt completion data is superior).\n\nThese are concatenations of smaller datasets you can read more about in the raw_data README.",
"## Format\n\n* In prompt_completion_data on github I have the raw files in a form {\"prompt\": \"...\", \"completion\": \"...\"} where the prompt is the message before paul christiano says something and the completion is what paul says. This is useful for doing more like instruction finetuning thing, or really training a chatbot. There is no \"Paul Christiano:\" or \"Rob Wiblin:\" in this, just directly the text that is being said.\n* In the other files however, the messages inside the \"text\": \"\" double quotes are separated by \\<eom\\> (end of message), and at the end of a podcast or a lesswrong thread I have a \\<eot\\> (end of thread) separator.\n* Messages / posts / speakers alternate with either \"Full Name:\" [... their text ...]] or with username: [...] on lesswrong. Same format with lesswrong posts and comments. For convenience, on lesswrong paul is \"Paul Christiano: \" instead of \"paulfchristiano: \" to make it easier for the trained model to learn what to say when prompted \"Paul Christiano: \" (useful for deploying the paul chatbot)."
] | [
"TAGS\n#region-us \n",
"# talk-to-paul\n\nGoal: finetune a LLM on conversational data of Paul Christiano, so you can \"talk to paul\".\n\nPreviously on Github.\n\nCurrently, I've made a few datasets, consisting of Lesswrong posts/comments and podcast transcripts.\n* The URL file has one podcast / lesswrong post data per line. Format is {\"text\": \"...\"}. Size 15Mb.\n* The URL is the same though instead is just a long text file where things are separated by \\<eop\\> (end of post) instead of the jsonl format above. Size 15Mb.\n* The prompt_completion_podcast_data.jsonl has format {\"prompt\": \"...\", \"completion\": \"...\"} (see below). It does not currently contain the lesswrong data because lesswrong threads are more tricky to put into some prompt / completion format. (I might add it in the future if it turns out that the prompt completion data is superior).\n\nThese are concatenations of smaller datasets you can read more about in the raw_data README.",
"## Format\n\n* In prompt_completion_data on github I have the raw files in a form {\"prompt\": \"...\", \"completion\": \"...\"} where the prompt is the message before paul christiano says something and the completion is what paul says. This is useful for doing more like instruction finetuning thing, or really training a chatbot. There is no \"Paul Christiano:\" or \"Rob Wiblin:\" in this, just directly the text that is being said.\n* In the other files however, the messages inside the \"text\": \"\" double quotes are separated by \\<eom\\> (end of message), and at the end of a podcast or a lesswrong thread I have a \\<eot\\> (end of thread) separator.\n* Messages / posts / speakers alternate with either \"Full Name:\" [... their text ...]] or with username: [...] on lesswrong. Same format with lesswrong posts and comments. For convenience, on lesswrong paul is \"Paul Christiano: \" instead of \"paulfchristiano: \" to make it easier for the trained model to learn what to say when prompted \"Paul Christiano: \" (useful for deploying the paul chatbot)."
] | [
6,
263,
290
] | [
"passage: TAGS\n#region-us \n# talk-to-paul\n\nGoal: finetune a LLM on conversational data of Paul Christiano, so you can \"talk to paul\".\n\nPreviously on Github.\n\nCurrently, I've made a few datasets, consisting of Lesswrong posts/comments and podcast transcripts.\n* The URL file has one podcast / lesswrong post data per line. Format is {\"text\": \"...\"}. Size 15Mb.\n* The URL is the same though instead is just a long text file where things are separated by \\<eop\\> (end of post) instead of the jsonl format above. Size 15Mb.\n* The prompt_completion_podcast_data.jsonl has format {\"prompt\": \"...\", \"completion\": \"...\"} (see below). It does not currently contain the lesswrong data because lesswrong threads are more tricky to put into some prompt / completion format. (I might add it in the future if it turns out that the prompt completion data is superior).\n\nThese are concatenations of smaller datasets you can read more about in the raw_data README."
] |
0a79638a8fc4aed1ca7cb4b95e691249b627913d |
# Bangumi Image Base of Card Captor Sakura (1998)
This is the image base of bangumi Card Captor Sakura (1998), we detected 59 characters, 8455 images in total. The full dataset is [here](all.zip).
**Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual.** If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
| # | Images | Download | Preview 1 | Preview 2 | Preview 3 | Preview 4 | Preview 5 | Preview 6 | Preview 7 | Preview 8 |
|:------|---------:|:---------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|
| 0 | 2737 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 116 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 111 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 75 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 94 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 261 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 37 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 56 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 943 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 77 | [Download](9/dataset.zip) |  |  |  |  |  |  |  |  |
| 10 | 297 | [Download](10/dataset.zip) |  |  |  |  |  |  |  |  |
| 11 | 195 | [Download](11/dataset.zip) |  |  |  |  |  |  |  |  |
| 12 | 316 | [Download](12/dataset.zip) |  |  |  |  |  |  |  |  |
| 13 | 86 | [Download](13/dataset.zip) |  |  |  |  |  |  |  |  |
| 14 | 62 | [Download](14/dataset.zip) |  |  |  |  |  |  |  |  |
| 15 | 14 | [Download](15/dataset.zip) |  |  |  |  |  |  |  |  |
| 16 | 111 | [Download](16/dataset.zip) |  |  |  |  |  |  |  |  |
| 17 | 40 | [Download](17/dataset.zip) |  |  |  |  |  |  |  |  |
| 18 | 47 | [Download](18/dataset.zip) |  |  |  |  |  |  |  |  |
| 19 | 24 | [Download](19/dataset.zip) |  |  |  |  |  |  |  |  |
| 20 | 132 | [Download](20/dataset.zip) |  |  |  |  |  |  |  |  |
| 21 | 186 | [Download](21/dataset.zip) |  |  |  |  |  |  |  |  |
| 22 | 16 | [Download](22/dataset.zip) |  |  |  |  |  |  |  |  |
| 23 | 25 | [Download](23/dataset.zip) |  |  |  |  |  |  |  |  |
| 24 | 79 | [Download](24/dataset.zip) |  |  |  |  |  |  |  |  |
| 25 | 296 | [Download](25/dataset.zip) |  |  |  |  |  |  |  |  |
| 26 | 373 | [Download](26/dataset.zip) |  |  |  |  |  |  |  |  |
| 27 | 452 | [Download](27/dataset.zip) |  |  |  |  |  |  |  |  |
| 28 | 37 | [Download](28/dataset.zip) |  |  |  |  |  |  |  |  |
| 29 | 32 | [Download](29/dataset.zip) |  |  |  |  |  |  |  |  |
| 30 | 37 | [Download](30/dataset.zip) |  |  |  |  |  |  |  |  |
| 31 | 72 | [Download](31/dataset.zip) |  |  |  |  |  |  |  |  |
| 32 | 32 | [Download](32/dataset.zip) |  |  |  |  |  |  |  |  |
| 33 | 21 | [Download](33/dataset.zip) |  |  |  |  |  |  |  |  |
| 34 | 8 | [Download](34/dataset.zip) |  |  |  |  |  |  |  |  |
| 35 | 66 | [Download](35/dataset.zip) |  |  |  |  |  |  |  |  |
| 36 | 11 | [Download](36/dataset.zip) |  |  |  |  |  |  |  |  |
| 37 | 96 | [Download](37/dataset.zip) |  |  |  |  |  |  |  |  |
| 38 | 18 | [Download](38/dataset.zip) |  |  |  |  |  |  |  |  |
| 39 | 112 | [Download](39/dataset.zip) |  |  |  |  |  |  |  |  |
| 40 | 28 | [Download](40/dataset.zip) |  |  |  |  |  |  |  |  |
| 41 | 30 | [Download](41/dataset.zip) |  |  |  |  |  |  |  |  |
| 42 | 13 | [Download](42/dataset.zip) |  |  |  |  |  |  |  |  |
| 43 | 10 | [Download](43/dataset.zip) |  |  |  |  |  |  |  |  |
| 44 | 21 | [Download](44/dataset.zip) |  |  |  |  |  |  |  |  |
| 45 | 17 | [Download](45/dataset.zip) |  |  |  |  |  |  |  |  |
| 46 | 20 | [Download](46/dataset.zip) |  |  |  |  |  |  |  |  |
| 47 | 15 | [Download](47/dataset.zip) |  |  |  |  |  |  |  |  |
| 48 | 8 | [Download](48/dataset.zip) |  |  |  |  |  |  |  |  |
| 49 | 67 | [Download](49/dataset.zip) |  |  |  |  |  |  |  |  |
| 50 | 9 | [Download](50/dataset.zip) |  |  |  |  |  |  |  |  |
| 51 | 18 | [Download](51/dataset.zip) |  |  |  |  |  |  |  |  |
| 52 | 11 | [Download](52/dataset.zip) |  |  |  |  |  |  |  |  |
| 53 | 6 | [Download](53/dataset.zip) |  |  |  |  |  |  | N/A | N/A |
| 54 | 11 | [Download](54/dataset.zip) |  |  |  |  |  |  |  |  |
| 55 | 13 | [Download](55/dataset.zip) |  |  |  |  |  |  |  |  |
| 56 | 8 | [Download](56/dataset.zip) |  |  |  |  |  |  |  |  |
| 57 | 5 | [Download](57/dataset.zip) |  |  |  |  |  | N/A | N/A | N/A |
| noise | 345 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
| BangumiBase/cardcaptorsakura1998 | [
"size_categories:1K<n<10K",
"license:mit",
"art",
"region:us"
] | 2023-11-27T22:54:09+00:00 | {"license": "mit", "size_categories": ["1K<n<10K"], "tags": ["art"]} | 2023-11-28T03:36:42+00:00 | [] | [] | TAGS
#size_categories-1K<n<10K #license-mit #art #region-us
| Bangumi Image Base of Card Captor Sakura (1998)
===============================================
This is the image base of bangumi Card Captor Sakura (1998), we detected 59 characters, 8455 images in total. The full dataset is here.
Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual. If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
| [] | [
"TAGS\n#size_categories-1K<n<10K #license-mit #art #region-us \n"
] | [
25
] | [
"passage: TAGS\n#size_categories-1K<n<10K #license-mit #art #region-us \n"
] |
ddbc8d4a9424eb049e1a807e89682fe95c56d6a8 |
# Multilingual Phonemes 10K Alpha
This dataset contains approximately 10,000 pairs of text and phonemes from each supported language. We support 15 languages in this dataset, so we have a total of ~150K pairs. This does not include the English-XL dataset, which includes another 100K unique rows.
## Languages
We support 15 languages, which means we have around 150,000 pairs of text and phonemes in multiple languages. This excludes the English-XL dataset, which has 100K unique (not included in any other split) additional phonemized pairs.
* English (en)
* English-XL (en-xl): ~100K phonemized pairs, English-only
* Catalan (ca)
* German (de)
* Spanish (es)
* Greek (el)
* Persian (fa): Requested by [@Respair](https://huggingface.co/Respair)
* Finnish (fi)
* French (fr)
* Italian (it)
* Polish (pl)
* Portuguese (pt)
* Russian (ru)
* Swedish (sw)
* Ukrainian (uk)
* Chinese (zh): Thank you to [@eugenepentland](https://huggingface.co/eugenepentland) for assistance in processing this text, as East-Asian languages are the most compute-intensive!
## License + Credits
Source data comes from [Wikipedia](https://huggingface.co/datasets/wikimedia/wikipedia) and is licensed under CC-BY-SA 3.0. This dataset is licensed under CC-BY-SA 3.0.
## Processing
We utilized the following process to preprocess the dataset:
1. Download data from [Wikipedia](https://huggingface.co/datasets/wikimedia/wikipedia) by language, selecting only the first Parquet file and naming it with the language code
2. Process using [Data Preprocessing Scripts (StyleTTS 2 Community members only)](https://huggingface.co/styletts2-community/data-preprocessing-scripts) and modify the code to work with the language
3. Script: Clean the text
4. Script: Remove ultra-short phrases
5. Script: Phonemize
6. Script: Save JSON
7. Upload dataset
## Note
East-Asian languages are experimental. We do not distinguish between Traditional and Simplified Chinese. The dataset consists mainly of Simplified Chinese in the `zh` split. We recommend converting characters to Simplified Chinese during inference, using a library such as `hanziconv` or `chinese-converter`. | styletts2-community/multilingual-phonemes-10k-alpha | [
"language:en",
"language:ca",
"language:de",
"language:es",
"language:el",
"language:fa",
"language:fi",
"language:fr",
"language:it",
"language:pl",
"language:pt",
"language:ru",
"language:sv",
"language:uk",
"language:zh",
"license:cc-by-sa-3.0",
"region:us"
] | 2023-11-27T23:15:49+00:00 | {"language": ["en", "ca", "de", "es", "el", "fa", "fi", "fr", "it", "pl", "pt", "ru", "sv", "uk", "zh"], "license": "cc-by-sa-3.0", "license_name": "cc-by-sa", "configs": [{"config_name": "en", "data_files": "en.json", "default": true}, {"config_name": "en-xl", "data_files": "en-xl.json"}, {"config_name": "ca", "data_files": "ca.json"}, {"config_name": "de", "data_files": "de.json"}, {"config_name": "es", "data_files": "es.json"}, {"config_name": "el", "data_files": "el.json"}, {"config_name": "fa", "data_files": "fa.json"}, {"config_name": "fi", "data_files": "fi.json"}, {"config_name": "fr", "data_files": "fr.json"}, {"config_name": "it", "data_files": "it.json"}, {"config_name": "pl", "data_files": "pl.json"}, {"config_name": "pt", "data_files": "pt.json"}, {"config_name": "ru", "data_files": "ru.json"}, {"config_name": "sv", "data_files": "sv.json"}, {"config_name": "uk", "data_files": "uk.json"}, {"config_name": "zh", "data_files": "zh.json"}]} | 2024-01-10T23:53:59+00:00 | [] | [
"en",
"ca",
"de",
"es",
"el",
"fa",
"fi",
"fr",
"it",
"pl",
"pt",
"ru",
"sv",
"uk",
"zh"
] | TAGS
#language-English #language-Catalan #language-German #language-Spanish #language-Modern Greek (1453-) #language-Persian #language-Finnish #language-French #language-Italian #language-Polish #language-Portuguese #language-Russian #language-Swedish #language-Ukrainian #language-Chinese #license-cc-by-sa-3.0 #region-us
|
# Multilingual Phonemes 10K Alpha
This dataset contains approximately 10,000 pairs of text and phonemes from each supported language. We support 15 languages in this dataset, so we have a total of ~150K pairs. This does not include the English-XL dataset, which includes another 100K unique rows.
## Languages
We support 15 languages, which means we have around 150,000 pairs of text and phonemes in multiple languages. This excludes the English-XL dataset, which has 100K unique (not included in any other split) additional phonemized pairs.
* English (en)
* English-XL (en-xl): ~100K phonemized pairs, English-only
* Catalan (ca)
* German (de)
* Spanish (es)
* Greek (el)
* Persian (fa): Requested by @Respair
* Finnish (fi)
* French (fr)
* Italian (it)
* Polish (pl)
* Portuguese (pt)
* Russian (ru)
* Swedish (sw)
* Ukrainian (uk)
* Chinese (zh): Thank you to @eugenepentland for assistance in processing this text, as East-Asian languages are the most compute-intensive!
## License + Credits
Source data comes from Wikipedia and is licensed under CC-BY-SA 3.0. This dataset is licensed under CC-BY-SA 3.0.
## Processing
We utilized the following process to preprocess the dataset:
1. Download data from Wikipedia by language, selecting only the first Parquet file and naming it with the language code
2. Process using Data Preprocessing Scripts (StyleTTS 2 Community members only) and modify the code to work with the language
3. Script: Clean the text
4. Script: Remove ultra-short phrases
5. Script: Phonemize
6. Script: Save JSON
7. Upload dataset
## Note
East-Asian languages are experimental. We do not distinguish between Traditional and Simplified Chinese. The dataset consists mainly of Simplified Chinese in the 'zh' split. We recommend converting characters to Simplified Chinese during inference, using a library such as 'hanziconv' or 'chinese-converter'. | [
"# Multilingual Phonemes 10K Alpha\n\n\nThis dataset contains approximately 10,000 pairs of text and phonemes from each supported language. We support 15 languages in this dataset, so we have a total of ~150K pairs. This does not include the English-XL dataset, which includes another 100K unique rows.",
"## Languages\n\nWe support 15 languages, which means we have around 150,000 pairs of text and phonemes in multiple languages. This excludes the English-XL dataset, which has 100K unique (not included in any other split) additional phonemized pairs.\n\n* English (en)\n* English-XL (en-xl): ~100K phonemized pairs, English-only\n* Catalan (ca)\n* German (de)\n* Spanish (es)\n* Greek (el)\n* Persian (fa): Requested by @Respair\n* Finnish (fi)\n* French (fr)\n* Italian (it)\n* Polish (pl)\n* Portuguese (pt)\n* Russian (ru)\n* Swedish (sw)\n* Ukrainian (uk)\n* Chinese (zh): Thank you to @eugenepentland for assistance in processing this text, as East-Asian languages are the most compute-intensive!",
"## License + Credits\n\nSource data comes from Wikipedia and is licensed under CC-BY-SA 3.0. This dataset is licensed under CC-BY-SA 3.0.",
"## Processing\n\nWe utilized the following process to preprocess the dataset:\n\n1. Download data from Wikipedia by language, selecting only the first Parquet file and naming it with the language code\n2. Process using Data Preprocessing Scripts (StyleTTS 2 Community members only) and modify the code to work with the language\n3. Script: Clean the text\n4. Script: Remove ultra-short phrases\n5. Script: Phonemize\n6. Script: Save JSON\n7. Upload dataset",
"## Note\n\nEast-Asian languages are experimental. We do not distinguish between Traditional and Simplified Chinese. The dataset consists mainly of Simplified Chinese in the 'zh' split. We recommend converting characters to Simplified Chinese during inference, using a library such as 'hanziconv' or 'chinese-converter'."
] | [
"TAGS\n#language-English #language-Catalan #language-German #language-Spanish #language-Modern Greek (1453-) #language-Persian #language-Finnish #language-French #language-Italian #language-Polish #language-Portuguese #language-Russian #language-Swedish #language-Ukrainian #language-Chinese #license-cc-by-sa-3.0 #region-us \n",
"# Multilingual Phonemes 10K Alpha\n\n\nThis dataset contains approximately 10,000 pairs of text and phonemes from each supported language. We support 15 languages in this dataset, so we have a total of ~150K pairs. This does not include the English-XL dataset, which includes another 100K unique rows.",
"## Languages\n\nWe support 15 languages, which means we have around 150,000 pairs of text and phonemes in multiple languages. This excludes the English-XL dataset, which has 100K unique (not included in any other split) additional phonemized pairs.\n\n* English (en)\n* English-XL (en-xl): ~100K phonemized pairs, English-only\n* Catalan (ca)\n* German (de)\n* Spanish (es)\n* Greek (el)\n* Persian (fa): Requested by @Respair\n* Finnish (fi)\n* French (fr)\n* Italian (it)\n* Polish (pl)\n* Portuguese (pt)\n* Russian (ru)\n* Swedish (sw)\n* Ukrainian (uk)\n* Chinese (zh): Thank you to @eugenepentland for assistance in processing this text, as East-Asian languages are the most compute-intensive!",
"## License + Credits\n\nSource data comes from Wikipedia and is licensed under CC-BY-SA 3.0. This dataset is licensed under CC-BY-SA 3.0.",
"## Processing\n\nWe utilized the following process to preprocess the dataset:\n\n1. Download data from Wikipedia by language, selecting only the first Parquet file and naming it with the language code\n2. Process using Data Preprocessing Scripts (StyleTTS 2 Community members only) and modify the code to work with the language\n3. Script: Clean the text\n4. Script: Remove ultra-short phrases\n5. Script: Phonemize\n6. Script: Save JSON\n7. Upload dataset",
"## Note\n\nEast-Asian languages are experimental. We do not distinguish between Traditional and Simplified Chinese. The dataset consists mainly of Simplified Chinese in the 'zh' split. We recommend converting characters to Simplified Chinese during inference, using a library such as 'hanziconv' or 'chinese-converter'."
] | [
101,
72,
202,
36,
97,
80
] | [
"passage: TAGS\n#language-English #language-Catalan #language-German #language-Spanish #language-Modern Greek (1453-) #language-Persian #language-Finnish #language-French #language-Italian #language-Polish #language-Portuguese #language-Russian #language-Swedish #language-Ukrainian #language-Chinese #license-cc-by-sa-3.0 #region-us \n# Multilingual Phonemes 10K Alpha\n\n\nThis dataset contains approximately 10,000 pairs of text and phonemes from each supported language. We support 15 languages in this dataset, so we have a total of ~150K pairs. This does not include the English-XL dataset, which includes another 100K unique rows.## Languages\n\nWe support 15 languages, which means we have around 150,000 pairs of text and phonemes in multiple languages. This excludes the English-XL dataset, which has 100K unique (not included in any other split) additional phonemized pairs.\n\n* English (en)\n* English-XL (en-xl): ~100K phonemized pairs, English-only\n* Catalan (ca)\n* German (de)\n* Spanish (es)\n* Greek (el)\n* Persian (fa): Requested by @Respair\n* Finnish (fi)\n* French (fr)\n* Italian (it)\n* Polish (pl)\n* Portuguese (pt)\n* Russian (ru)\n* Swedish (sw)\n* Ukrainian (uk)\n* Chinese (zh): Thank you to @eugenepentland for assistance in processing this text, as East-Asian languages are the most compute-intensive!## License + Credits\n\nSource data comes from Wikipedia and is licensed under CC-BY-SA 3.0. This dataset is licensed under CC-BY-SA 3.0."
] |
bb3c3d0a570d271d4a21ebf0a225729cd63c1ff2 |
# Dataset Card for GPQA
<!-- Provide a quick summary of the dataset. -->
GPQA is a multiple-choice, Q&A dataset of very hard questions written and validated by experts in biology, physics, and chemistry. When attempting questions out of their own domain (e.g., a physicist answers a chemistry question), these experts get only 34% accuracy, despite spending >30m with full access to Google.
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
We present GPQA, a challenging dataset of 448 multiple-choice questions written by domain experts in biology, physics, and chemistry. We ensure that the questions are high-quality and extremely difficult: experts who have or are pursuing PhDs in the corresponding domains reach 65% accuracy (74% when discounting clear mistakes the experts identified in retrospect), while highly skilled non-expert validators only reach 34% accuracy, despite spending on average over 30 minutes with unrestricted access to the web (i.e., the questions are "Google-proof"). The questions are also difficult for state-of-the-art AI systems, with our strongest GPT-4 based baseline achieving 39% accuracy. If we are to use future AI systems to help us answer very hard questions, for example, when developing new scientific knowledge, we need to develop scalable oversight methods that enable humans to supervise their outputs, which may be difficult even if the supervisors are themselves skilled and knowledgeable. The difficulty of GPQA both for skilled non-experts and frontier AI systems should enable realistic scalable oversight experiments, which we hope can help devise ways for human experts to reliably get truthful information from AI systems that surpass human capabilities.
- **Curated by:** David Rein, Betty Li Hou, Asa Cooper Stickland, Jackson Petty, Richard Yuanzhe Pang, Julien Dirani, Julian Michael, Samuel R. Bowman
- **License:** CC BY 4.0
### Dataset Sources
<!-- Provide the basic links for the dataset. -->
- **Repository:** https://github.com/idavidrein/gpqa
- **Paper:** https://arxiv.org/abs/2311.12022
## Uses
The dataset is primarily intended to be used for scalable oversight experiments, although it can also be used for more general LLM capabilities benchmarking.
## Dataset Card Contact
David Rein: [email protected]
---
Submit corrections to examples in GPQA via this form: https://forms.gle/iTY4zMETNsPhJq8R9
--- | Idavidrein/gpqa | [
"task_categories:question-answering",
"task_categories:text-generation",
"size_categories:n<1K",
"language:en",
"license:cc-by-4.0",
"open-domain-qa",
"open-book-qa",
"multiple-choice-qa",
"arxiv:2311.12022",
"region:us"
] | 2023-11-27T23:18:46+00:00 | {"language": ["en"], "license": "cc-by-4.0", "size_categories": ["n<1K"], "task_categories": ["question-answering", "text-generation"], "pretty_name": "GPQA", "viewer": true, "extra_gated_prompt": "You agree to NOT reveal examples from this dataset in plain text or images online, to reduce the risk of leakage into foundation model training corpora.", "extra_gated_fields": {"I accept these terms": "checkbox"}, "configs": [{"config_name": "gpqa_extended", "data_files": "gpqa_extended.csv"}, {"config_name": "gpqa_main", "data_files": "gpqa_main.csv"}, {"config_name": "gpqa_diamond", "data_files": "gpqa_diamond.csv"}, {"config_name": "gpqa_experts", "data_files": "gpqa_experts.csv"}], "tags": ["open-domain-qa", "open-book-qa", "multiple-choice-qa"]} | 2023-11-30T21:56:58+00:00 | [
"2311.12022"
] | [
"en"
] | TAGS
#task_categories-question-answering #task_categories-text-generation #size_categories-n<1K #language-English #license-cc-by-4.0 #open-domain-qa #open-book-qa #multiple-choice-qa #arxiv-2311.12022 #region-us
|
# Dataset Card for GPQA
GPQA is a multiple-choice, Q&A dataset of very hard questions written and validated by experts in biology, physics, and chemistry. When attempting questions out of their own domain (e.g., a physicist answers a chemistry question), these experts get only 34% accuracy, despite spending >30m with full access to Google.
## Dataset Details
### Dataset Description
We present GPQA, a challenging dataset of 448 multiple-choice questions written by domain experts in biology, physics, and chemistry. We ensure that the questions are high-quality and extremely difficult: experts who have or are pursuing PhDs in the corresponding domains reach 65% accuracy (74% when discounting clear mistakes the experts identified in retrospect), while highly skilled non-expert validators only reach 34% accuracy, despite spending on average over 30 minutes with unrestricted access to the web (i.e., the questions are "Google-proof"). The questions are also difficult for state-of-the-art AI systems, with our strongest GPT-4 based baseline achieving 39% accuracy. If we are to use future AI systems to help us answer very hard questions, for example, when developing new scientific knowledge, we need to develop scalable oversight methods that enable humans to supervise their outputs, which may be difficult even if the supervisors are themselves skilled and knowledgeable. The difficulty of GPQA both for skilled non-experts and frontier AI systems should enable realistic scalable oversight experiments, which we hope can help devise ways for human experts to reliably get truthful information from AI systems that surpass human capabilities.
- Curated by: David Rein, Betty Li Hou, Asa Cooper Stickland, Jackson Petty, Richard Yuanzhe Pang, Julien Dirani, Julian Michael, Samuel R. Bowman
- License: CC BY 4.0
### Dataset Sources
- Repository: URL
- Paper: URL
## Uses
The dataset is primarily intended to be used for scalable oversight experiments, although it can also be used for more general LLM capabilities benchmarking.
## Dataset Card Contact
David Rein: idavidrein@URL
---
Submit corrections to examples in GPQA via this form: URL
--- | [
"# Dataset Card for GPQA\n\n\n\nGPQA is a multiple-choice, Q&A dataset of very hard questions written and validated by experts in biology, physics, and chemistry. When attempting questions out of their own domain (e.g., a physicist answers a chemistry question), these experts get only 34% accuracy, despite spending >30m with full access to Google.",
"## Dataset Details",
"### Dataset Description\n\n\n\nWe present GPQA, a challenging dataset of 448 multiple-choice questions written by domain experts in biology, physics, and chemistry. We ensure that the questions are high-quality and extremely difficult: experts who have or are pursuing PhDs in the corresponding domains reach 65% accuracy (74% when discounting clear mistakes the experts identified in retrospect), while highly skilled non-expert validators only reach 34% accuracy, despite spending on average over 30 minutes with unrestricted access to the web (i.e., the questions are \"Google-proof\"). The questions are also difficult for state-of-the-art AI systems, with our strongest GPT-4 based baseline achieving 39% accuracy. If we are to use future AI systems to help us answer very hard questions, for example, when developing new scientific knowledge, we need to develop scalable oversight methods that enable humans to supervise their outputs, which may be difficult even if the supervisors are themselves skilled and knowledgeable. The difficulty of GPQA both for skilled non-experts and frontier AI systems should enable realistic scalable oversight experiments, which we hope can help devise ways for human experts to reliably get truthful information from AI systems that surpass human capabilities.\n\n\n- Curated by: David Rein, Betty Li Hou, Asa Cooper Stickland, Jackson Petty, Richard Yuanzhe Pang, Julien Dirani, Julian Michael, Samuel R. Bowman\n- License: CC BY 4.0",
"### Dataset Sources\n\n\n\n- Repository: URL\n- Paper: URL",
"## Uses\n\nThe dataset is primarily intended to be used for scalable oversight experiments, although it can also be used for more general LLM capabilities benchmarking.",
"## Dataset Card Contact\n\nDavid Rein: idavidrein@URL\n\n---\nSubmit corrections to examples in GPQA via this form: URL\n\n---"
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"# Dataset Card for GPQA\n\n\n\nGPQA is a multiple-choice, Q&A dataset of very hard questions written and validated by experts in biology, physics, and chemistry. When attempting questions out of their own domain (e.g., a physicist answers a chemistry question), these experts get only 34% accuracy, despite spending >30m with full access to Google.",
"## Dataset Details",
"### Dataset Description\n\n\n\nWe present GPQA, a challenging dataset of 448 multiple-choice questions written by domain experts in biology, physics, and chemistry. We ensure that the questions are high-quality and extremely difficult: experts who have or are pursuing PhDs in the corresponding domains reach 65% accuracy (74% when discounting clear mistakes the experts identified in retrospect), while highly skilled non-expert validators only reach 34% accuracy, despite spending on average over 30 minutes with unrestricted access to the web (i.e., the questions are \"Google-proof\"). The questions are also difficult for state-of-the-art AI systems, with our strongest GPT-4 based baseline achieving 39% accuracy. If we are to use future AI systems to help us answer very hard questions, for example, when developing new scientific knowledge, we need to develop scalable oversight methods that enable humans to supervise their outputs, which may be difficult even if the supervisors are themselves skilled and knowledgeable. The difficulty of GPQA both for skilled non-experts and frontier AI systems should enable realistic scalable oversight experiments, which we hope can help devise ways for human experts to reliably get truthful information from AI systems that surpass human capabilities.\n\n\n- Curated by: David Rein, Betty Li Hou, Asa Cooper Stickland, Jackson Petty, Richard Yuanzhe Pang, Julien Dirani, Julian Michael, Samuel R. Bowman\n- License: CC BY 4.0",
"### Dataset Sources\n\n\n\n- Repository: URL\n- Paper: URL",
"## Uses\n\nThe dataset is primarily intended to be used for scalable oversight experiments, although it can also be used for more general LLM capabilities benchmarking.",
"## Dataset Card Contact\n\nDavid Rein: idavidrein@URL\n\n---\nSubmit corrections to examples in GPQA via this form: URL\n\n---"
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] |
ccba83927cb0026e79b0a8b07929dc0cefd744bb | # Dataset Card for "lava2llama"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | peytonwsmith/lava2llama | [
"region:us"
] | 2023-11-27T23:24:34+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 38289, "num_examples": 30}, {"name": "test", "num_bytes": 19031, "num_examples": 13}], "download_size": 0, "dataset_size": 57320}} | 2023-11-27T23:30:15+00:00 | [] | [] | TAGS
#region-us
| # Dataset Card for "lava2llama"
More Information needed | [
"# Dataset Card for \"lava2llama\"\n\nMore Information needed"
] | [
"TAGS\n#region-us \n",
"# Dataset Card for \"lava2llama\"\n\nMore Information needed"
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14
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"passage: TAGS\n#region-us \n# Dataset Card for \"lava2llama\"\n\nMore Information needed"
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1f69fd24bd9bbe278c9f265f90a706d593e392be | # Dataset Card for "TANDEM_stimuli"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | suyuanliu/TANDEM | [
"region:us"
] | 2023-11-27T23:31:52+00:00 | {"dataset_info": {"features": [{"name": "audio", "dtype": {"audio": {"sampling_rate": 16000}}}], "splits": [{"name": "train", "num_bytes": 28418215.0, "num_examples": 504}], "download_size": 28035107, "dataset_size": 28418215.0}} | 2023-11-27T23:31:56+00:00 | [] | [] | TAGS
#region-us
| # Dataset Card for "TANDEM_stimuli"
More Information needed | [
"# Dataset Card for \"TANDEM_stimuli\"\n\nMore Information needed"
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"TAGS\n#region-us \n",
"# Dataset Card for \"TANDEM_stimuli\"\n\nMore Information needed"
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0723d2dc3c9f2d1a2d7fb12e02c404d6c8d7b07e | # Dataset Card for "WinnVOT"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | suyuanliu/WinnVOT | [
"region:us"
] | 2023-11-27T23:36:17+00:00 | {"dataset_info": {"features": [{"name": "audio", "dtype": "audio"}], "splits": [{"name": "train", "num_bytes": 28249935.0, "num_examples": 504}], "download_size": 26349798, "dataset_size": 28249935.0}} | 2023-11-27T23:36:21+00:00 | [] | [] | TAGS
#region-us
| # Dataset Card for "WinnVOT"
More Information needed | [
"# Dataset Card for \"WinnVOT\"\n\nMore Information needed"
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"TAGS\n#region-us \n",
"# Dataset Card for \"WinnVOT\"\n\nMore Information needed"
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"passage: TAGS\n#region-us \n# Dataset Card for \"WinnVOT\"\n\nMore Information needed"
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d8333a284c1153bbc026ce88c2fa2ac84f918ba2 | # Dataset Card for "Winnf0VOT"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | suyuanliu/Winnf0VOT | [
"region:us"
] | 2023-11-27T23:36:42+00:00 | {"dataset_info": {"features": [{"name": "audio", "dtype": "audio"}], "splits": [{"name": "train", "num_bytes": 100267569.0, "num_examples": 1800}], "download_size": 93892942, "dataset_size": 100267569.0}} | 2023-11-27T23:36:49+00:00 | [] | [] | TAGS
#region-us
| # Dataset Card for "Winnf0VOT"
More Information needed | [
"# Dataset Card for \"Winnf0VOT\"\n\nMore Information needed"
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"TAGS\n#region-us \n",
"# Dataset Card for \"Winnf0VOT\"\n\nMore Information needed"
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"passage: TAGS\n#region-us \n# Dataset Card for \"Winnf0VOT\"\n\nMore Information needed"
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20d3136df00ff215226a01b09be086130d4d00ca |
# Bangumi Image Base of Cardcaptor Sakura - Clear Card-hen
This is the image base of bangumi Cardcaptor Sakura - Clear Card-hen, we detected 46 characters, 5120 images in total. The full dataset is [here](all.zip).
**Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual.** If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
| # | Images | Download | Preview 1 | Preview 2 | Preview 3 | Preview 4 | Preview 5 | Preview 6 | Preview 7 | Preview 8 |
|:------|---------:|:---------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|
| 0 | 1583 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 381 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 26 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 21 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 57 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 47 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 55 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 18 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 24 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 22 | [Download](9/dataset.zip) |  |  |  |  |  |  |  |  |
| 10 | 38 | [Download](10/dataset.zip) |  |  |  |  |  |  |  |  |
| 11 | 381 | [Download](11/dataset.zip) |  |  |  |  |  |  |  |  |
| 12 | 65 | [Download](12/dataset.zip) |  |  |  |  |  |  |  |  |
| 13 | 120 | [Download](13/dataset.zip) |  |  |  |  |  |  |  |  |
| 14 | 81 | [Download](14/dataset.zip) |  |  |  |  |  |  |  |  |
| 15 | 33 | [Download](15/dataset.zip) |  |  |  |  |  |  |  |  |
| 16 | 21 | [Download](16/dataset.zip) |  |  |  |  |  |  |  |  |
| 17 | 19 | [Download](17/dataset.zip) |  |  |  |  |  |  |  |  |
| 18 | 24 | [Download](18/dataset.zip) |  |  |  |  |  |  |  |  |
| 19 | 23 | [Download](19/dataset.zip) |  |  |  |  |  |  |  |  |
| 20 | 14 | [Download](20/dataset.zip) |  |  |  |  |  |  |  |  |
| 21 | 99 | [Download](21/dataset.zip) |  |  |  |  |  |  |  |  |
| 22 | 14 | [Download](22/dataset.zip) |  |  |  |  |  |  |  |  |
| 23 | 46 | [Download](23/dataset.zip) |  |  |  |  |  |  |  |  |
| 24 | 59 | [Download](24/dataset.zip) |  |  |  |  |  |  |  |  |
| 25 | 47 | [Download](25/dataset.zip) |  |  |  |  |  |  |  |  |
| 26 | 129 | [Download](26/dataset.zip) |  |  |  |  |  |  |  |  |
| 27 | 107 | [Download](27/dataset.zip) |  |  |  |  |  |  |  |  |
| 28 | 462 | [Download](28/dataset.zip) |  |  |  |  |  |  |  |  |
| 29 | 64 | [Download](29/dataset.zip) |  |  |  |  |  |  |  |  |
| 30 | 9 | [Download](30/dataset.zip) |  |  |  |  |  |  |  |  |
| 31 | 134 | [Download](31/dataset.zip) |  |  |  |  |  |  |  |  |
| 32 | 90 | [Download](32/dataset.zip) |  |  |  |  |  |  |  |  |
| 33 | 478 | [Download](33/dataset.zip) |  |  |  |  |  |  |  |  |
| 34 | 14 | [Download](34/dataset.zip) |  |  |  |  |  |  |  |  |
| 35 | 21 | [Download](35/dataset.zip) |  |  |  |  |  |  |  |  |
| 36 | 20 | [Download](36/dataset.zip) |  |  |  |  |  |  |  |  |
| 37 | 16 | [Download](37/dataset.zip) |  |  |  |  |  |  |  |  |
| 38 | 29 | [Download](38/dataset.zip) |  |  |  |  |  |  |  |  |
| 39 | 6 | [Download](39/dataset.zip) |  |  |  |  |  |  | N/A | N/A |
| 40 | 16 | [Download](40/dataset.zip) |  |  |  |  |  |  |  |  |
| 41 | 6 | [Download](41/dataset.zip) |  |  |  |  |  |  | N/A | N/A |
| 42 | 8 | [Download](42/dataset.zip) |  |  |  |  |  |  |  |  |
| 43 | 23 | [Download](43/dataset.zip) |  |  |  |  |  |  |  |  |
| 44 | 5 | [Download](44/dataset.zip) |  |  |  |  |  | N/A | N/A | N/A |
| noise | 165 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
| BangumiBase/cardcaptorsakuraclearcardhen | [
"size_categories:1K<n<10K",
"license:mit",
"art",
"region:us"
] | 2023-11-27T23:49:26+00:00 | {"license": "mit", "size_categories": ["1K<n<10K"], "tags": ["art"]} | 2023-11-28T03:22:30+00:00 | [] | [] | TAGS
#size_categories-1K<n<10K #license-mit #art #region-us
| Bangumi Image Base of Cardcaptor Sakura - Clear Card-hen
========================================================
This is the image base of bangumi Cardcaptor Sakura - Clear Card-hen, we detected 46 characters, 5120 images in total. The full dataset is here.
Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual. If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
| [] | [
"TAGS\n#size_categories-1K<n<10K #license-mit #art #region-us \n"
] | [
25
] | [
"passage: TAGS\n#size_categories-1K<n<10K #license-mit #art #region-us \n"
] |
5379ee3336900defa9615f88ee7e72075c06d3fd | ---
language:
- en | yin001/imdb_dataset_positive_negative | [
"language:en",
"region:us"
] | 2023-11-27T23:58:47+00:00 | {"language": ["en"], "pretty_name": "imdb_dataset_positive_negative"} | 2023-11-28T00:23:35+00:00 | [] | [
"en"
] | TAGS
#language-English #region-us
| ---
language:
- en | [] | [
"TAGS\n#language-English #region-us \n"
] | [
10
] | [
"passage: TAGS\n#language-English #region-us \n"
] |
224b244d4e60e3efff242ca1f02f8727b03cf18a | # Dataset Card for "SFD_7_9010_split"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | AdvayK/SFD_7_9010_split | [
"region:us"
] | 2023-11-28T00:04:03+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "audio", "dtype": "audio"}, {"name": "transcription", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 495058139.16573346, "num_examples": 803}, {"name": "test", "num_bytes": 52309573.83426652, "num_examples": 90}], "download_size": 444464113, "dataset_size": 547367713.0}} | 2023-11-28T00:13:26+00:00 | [] | [] | TAGS
#region-us
| # Dataset Card for "SFD_7_9010_split"
More Information needed | [
"# Dataset Card for \"SFD_7_9010_split\"\n\nMore Information needed"
] | [
"TAGS\n#region-us \n",
"# Dataset Card for \"SFD_7_9010_split\"\n\nMore Information needed"
] | [
6,
20
] | [
"passage: TAGS\n#region-us \n# Dataset Card for \"SFD_7_9010_split\"\n\nMore Information needed"
] |
5785086a7cb303e2d50b64f39bfea46734b855c3 |
A filtered version of the [original English subset from the MultiNERD dataset by Babelscape](https://huggingface.co/datasets/Babelscape/multinerd). The version contains only 5 of the original 15 NER categories: PERSON(PER), ORGANIZATION(ORG), LOCATION(LOC), DISEASES(DIS),
ANIMAL(ANIM). Dataset filtered as part of a test.
See https://huggingface.co/datasets/Babelscape/multinerd for information on the dataset structure. | shrop/multinerd_en_filtered | [
"language:en",
"license:unknown",
"region:us"
] | 2023-11-28T00:22:23+00:00 | {"language": ["en"], "license": "unknown"} | 2023-11-28T00:27:58+00:00 | [] | [
"en"
] | TAGS
#language-English #license-unknown #region-us
|
A filtered version of the original English subset from the MultiNERD dataset by Babelscape. The version contains only 5 of the original 15 NER categories: PERSON(PER), ORGANIZATION(ORG), LOCATION(LOC), DISEASES(DIS),
ANIMAL(ANIM). Dataset filtered as part of a test.
See URL for information on the dataset structure. | [] | [
"TAGS\n#language-English #license-unknown #region-us \n"
] | [
17
] | [
"passage: TAGS\n#language-English #license-unknown #region-us \n"
] |
1e9dffb7c389cb329d45b5a584e0705f49fa2ae6 | # Dataset Card for "snli"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | veluribharath/snli | [
"region:us"
] | 2023-11-28T01:09:18+00:00 | {"dataset_info": {"features": [{"name": "premise", "dtype": "string"}, {"name": "hypothesis", "dtype": "string"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "entailment", "1": "neutral", "2": "contradiction"}}}}, {"name": "id", "dtype": "int64"}], "splits": [{"name": "test", "num_bytes": 1312928, "num_examples": 9824}, {"name": "train", "num_bytes": 70180560, "num_examples": 549367}, {"name": "validation", "num_bytes": 1320288, "num_examples": 9842}], "download_size": 23577753, "dataset_size": 72813776}} | 2023-11-28T09:24:09+00:00 | [] | [] | TAGS
#region-us
| # Dataset Card for "snli"
More Information needed | [
"# Dataset Card for \"snli\"\n\nMore Information needed"
] | [
"TAGS\n#region-us \n",
"# Dataset Card for \"snli\"\n\nMore Information needed"
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6,
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"passage: TAGS\n#region-us \n# Dataset Card for \"snli\"\n\nMore Information needed"
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6ce15a4bceb87c66b4b948da50189979c90c93de | - Download heart MRI data [MICCAI 2018 Atrial Segmentation Challenge](http://atriaseg2018.cardiacatlas.org/data/).
- Pre-processing data like existing work [UA-MT](https://github.com/yulequan/UA-MT)
| limberc/LA2018 | [
"region:us"
] | 2023-11-28T01:48:52+00:00 | {} | 2023-11-28T01:53:18+00:00 | [] | [] | TAGS
#region-us
| - Download heart MRI data MICCAI 2018 Atrial Segmentation Challenge.
- Pre-processing data like existing work UA-MT
| [] | [
"TAGS\n#region-us \n"
] | [
6
] | [
"passage: TAGS\n#region-us \n"
] |
46315e7c0964f5e43e288f403998ba8ede45d3bd | # Dataset Card for Dataset Name
This is a lot of info wow.
## Dataset Details
### Dataset Description
Just a demo
- **Curated by:** Turtles
- **Funded by [optional]:** Turtles
- **Shared by [optional]:** Turtles
- **Language(s) (NLP):** Enlish
- **License:** [More Information Needed]
### Dataset Sources [optional]
<!-- Provide the basic links for the dataset. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the dataset is intended to be used. -->
### Direct Use
<!-- This section describes suitable use cases for the dataset. -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
[More Information Needed]
## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
[More Information Needed]
## Dataset Creation
### Curation Rationale
<!-- Motivation for the creation of this dataset. -->
[More Information Needed]
### Source Data
<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
#### Data Collection and Processing
<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
[More Information Needed]
#### Who are the source data producers?
<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
[More Information Needed]
### Annotations [optional]
<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
#### Annotation process
<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
[More Information Needed]
#### Who are the annotators?
<!-- This section describes the people or systems who created the annotations. -->
[More Information Needed]
#### Personal and Sensitive Information
<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
## Citation [optional]
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Dataset Card Authors [optional]
[More Information Needed]
## Dataset Card Contact
[More Information Needed] | andrew-noske/demo | [
"task_categories:text-generation",
"size_categories:n<1K",
"language:en",
"license:openrail",
"region:us"
] | 2023-11-28T02:53:39+00:00 | {"language": ["en"], "license": "openrail", "size_categories": ["n<1K"], "task_categories": ["text-generation"], "pretty_name": "tiny_demo", "dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "package_name", "dtype": "string"}, {"name": "review", "dtype": "string"}, {"name": "date", "dtype": "string"}, {"name": "star", "dtype": "int64"}, {"name": "version_id", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 1548, "num_examples": 5}, {"name": "test", "num_bytes": 996, "num_examples": 5}], "download_size": 9560, "dataset_size": 2544}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}]}]} | 2023-11-28T03:42:56+00:00 | [] | [
"en"
] | TAGS
#task_categories-text-generation #size_categories-n<1K #language-English #license-openrail #region-us
| # Dataset Card for Dataset Name
This is a lot of info wow.
## Dataset Details
### Dataset Description
Just a demo
- Curated by: Turtles
- Funded by [optional]: Turtles
- Shared by [optional]: Turtles
- Language(s) (NLP): Enlish
- License:
### Dataset Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Out-of-Scope Use
## Dataset Structure
## Dataset Creation
### Curation Rationale
### Source Data
#### Data Collection and Processing
#### Who are the source data producers?
### Annotations [optional]
#### Annotation process
#### Who are the annotators?
#### Personal and Sensitive Information
## Bias, Risks, and Limitations
### Recommendations
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Dataset Card Authors [optional]
## Dataset Card Contact
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"## More Information [optional]",
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] |
e58107e6e1ff7846255e3f9b5e16789d331163ab | # Dataset Card for Portuguese Chat
We know that current English-first LLMs donโt work well for many other languages, both in terms of performance, latency, and speed. Building instruction datasets for non-English languages is an important challenge that needs to be solved.
Dedicated towards addressing this problem, I release 3 new datasets [rishiraj/portuguesechat](https://huggingface.co/datasets/rishiraj/portuguesechat/), [rishiraj/bengalichat](https://huggingface.co/datasets/rishiraj/bengalichat/) & [rishiraj/hindichat](https://huggingface.co/datasets/rishiraj/hindichat/) of 10,000 instructions and demonstrations each. This data can be used for supervised fine-tuning (SFT) to make language multilingual models follow instructions better.
### Dataset Summary
[rishiraj/portuguesechat](https://huggingface.co/datasets/rishiraj/portuguesechat/) was modelled after the instruction dataset described in OpenAI's [InstructGPT paper](https://huggingface.co/papers/2203.02155), and is translated from [HuggingFaceH4/no_robots](https://huggingface.co/datasets/HuggingFaceH4/no_robots/) which comprised mostly of single-turn instructions across the following categories:
| Category | Count |
|:-----------|--------:|
| Generation | 4560 |
| Open QA | 1240 |
| Brainstorm | 1120 |
| Chat | 850 |
| Rewrite | 660 |
| Summarize | 420 |
| Coding | 350 |
| Classify | 350 |
| Closed QA | 260 |
| Extract | 190 |
### Languages
The data in [rishiraj/portuguesechat](https://huggingface.co/datasets/rishiraj/portuguesechat/) are in Portuguese (BCP-47 pt).
### Data Fields
The data fields are as follows:
* `prompt`: Describes the task the model should perform.
* `prompt_id`: A unique ID for the prompt.
* `messages`: An array of messages, where each message indicates the role (system, user, assistant) and the content.
* `category`: Which category the example belongs to (e.g. `Chat` or `Coding`).
* `text`: Content of `messages` in a format that is compatible with dataset_text_field of SFTTrainer.
### Data Splits
| | train_sft | test_sft |
|---------------|------:| ---: |
| portuguesechat | 9500 | 500 |
### Licensing Information
The dataset is available under the [Creative Commons NonCommercial (CC BY-NC 4.0)](https://creativecommons.org/licenses/by-nc/4.0/legalcode).
### Citation Information
```
@misc{portuguesechat,
author = {Rishiraj Acharya},
title = {Portuguese Chat},
year = {2023},
publisher = {Hugging Face},
journal = {Hugging Face repository},
howpublished = {\url{https://huggingface.co/datasets/rishiraj/portuguesechat}}
}
``` | rishiraj/portuguesechat | [
"task_categories:conversational",
"task_categories:text-generation",
"language:pt",
"license:cc-by-nc-4.0",
"arxiv:2203.02155",
"region:us"
] | 2023-11-28T04:30:15+00:00 | {"language": ["pt"], "license": "cc-by-nc-4.0", "task_categories": ["conversational", "text-generation"], "pretty_name": "Portuguese Chat", "dataset_info": {"features": [{"name": "prompt", "dtype": "string"}, {"name": "prompt_id", "dtype": "string"}, {"name": "messages", "list": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}, {"name": "category", "dtype": "string"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 30628039, "num_examples": 9500}, {"name": "test", "num_bytes": 1644450, "num_examples": 500}], "download_size": 19873853, "dataset_size": 32272489}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}]}]} | 2023-11-28T06:19:44+00:00 | [
"2203.02155"
] | [
"pt"
] | TAGS
#task_categories-conversational #task_categories-text-generation #language-Portuguese #license-cc-by-nc-4.0 #arxiv-2203.02155 #region-us
| Dataset Card for Portuguese Chat
================================
We know that current English-first LLMs donโt work well for many other languages, both in terms of performance, latency, and speed. Building instruction datasets for non-English languages is an important challenge that needs to be solved.
Dedicated towards addressing this problem, I release 3 new datasets rishiraj/portuguesechat, rishiraj/bengalichat & rishiraj/hindichat of 10,000 instructions and demonstrations each. This data can be used for supervised fine-tuning (SFT) to make language multilingual models follow instructions better.
### Dataset Summary
rishiraj/portuguesechat was modelled after the instruction dataset described in OpenAI's InstructGPT paper, and is translated from HuggingFaceH4/no\_robots which comprised mostly of single-turn instructions across the following categories:
### Languages
The data in rishiraj/portuguesechat are in Portuguese (BCP-47 pt).
### Data Fields
The data fields are as follows:
* 'prompt': Describes the task the model should perform.
* 'prompt\_id': A unique ID for the prompt.
* 'messages': An array of messages, where each message indicates the role (system, user, assistant) and the content.
* 'category': Which category the example belongs to (e.g. 'Chat' or 'Coding').
* 'text': Content of 'messages' in a format that is compatible with dataset\_text\_field of SFTTrainer.
### Data Splits
### Licensing Information
The dataset is available under the Creative Commons NonCommercial (CC BY-NC 4.0).
| [
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] |
ca94650ff318928e02a75da5c23f0be7a0e78eee |
<div align="center">
<img src="https://huggingface.co/datasets/diffusers/docs-images/resolve/main/benchmarking/benchmarks_headshot.jpg" width=800/>
</div>
Welcome to ๐ค Diffusers Benchmarks!
This is dataset where we keep track of the inference latency and memory information of the core pipelines in the `diffusers` library.
Currently, the core pipelines are the following:
* Stable Diffusion and its derivatives such as ControlNet, T2I Adapter, Image-to-Image, Inpainting
* Stable Diffusion XL and its derivatives
* SSD-1B
* Kandinsky
* Wรผrstchen
* LCM
*Note that we will continue to extend the list of core pipelines based on their API usage.*
We use [this GitHub Actions workflow](https://github.com/huggingface/diffusers/blob/main/.github/workflows/benchmark.yml) to report the above numbers automatically. This workflow runs on a biweekly cadence.
The benchmarks are run on an A10G GPU. | diffusers/benchmarks | [
"language:en",
"license:apache-2.0",
"region:us"
] | 2023-11-28T04:33:41+00:00 | {"language": ["en"], "license": "apache-2.0", "pretty_name": "Diffusers Benchmarks"} | 2024-02-01T02:47:45+00:00 | [] | [
"en"
] | TAGS
#language-English #license-apache-2.0 #region-us
|
<div align="center">
<img src="URL width=800/>
</div>
Welcome to Diffusers Benchmarks!
This is dataset where we keep track of the inference latency and memory information of the core pipelines in the 'diffusers' library.
Currently, the core pipelines are the following:
* Stable Diffusion and its derivatives such as ControlNet, T2I Adapter, Image-to-Image, Inpainting
* Stable Diffusion XL and its derivatives
* SSD-1B
* Kandinsky
* Wรผrstchen
* LCM
*Note that we will continue to extend the list of core pipelines based on their API usage.*
We use this GitHub Actions workflow to report the above numbers automatically. This workflow runs on a biweekly cadence.
The benchmarks are run on an A10G GPU. | [] | [
"TAGS\n#language-English #license-apache-2.0 #region-us \n"
] | [
18
] | [
"passage: TAGS\n#language-English #license-apache-2.0 #region-us \n"
] |
00ddd2067fbd79ac33d614d6d2cb1cac1fe862e1 |
# Mosaic format for filtered combine dataset to finetune Yi models
This repository is to store dataset shards using mosaic format.
1. prepared at https://github.com/malaysia-ai/dedup-text-dataset/blob/main/yi/combine-dataset.ipynb
2. using tokenizer https://huggingface.co/01-ai/Yi-6B
3. 4096 context length.
## how-to
1. git clone,
```bash
git lfs clone https://huggingface.co/datasets/malaysia-ai/mosaic-yi
```
2. load it,
```python
from streaming import LocalDataset
import numpy as np
from streaming.base.format.mds.encodings import Encoding, _encodings
class UInt16(Encoding):
def encode(self, obj) -> bytes:
return obj.tobytes()
def decode(self, data: bytes):
return np.frombuffer(data, np.uint16)
_encodings['uint16'] = UInt16
dataset = LocalDataset('mosaic-yi')
len(dataset)
``` | malaysia-ai/mosaic-yi | [
"language:ms",
"region:us"
] | 2023-11-28T05:10:59+00:00 | {"language": ["ms"]} | 2023-11-28T08:15:38+00:00 | [] | [
"ms"
] | TAGS
#language-Malay (macrolanguage) #region-us
|
# Mosaic format for filtered combine dataset to finetune Yi models
This repository is to store dataset shards using mosaic format.
1. prepared at URL
2. using tokenizer URL
3. 4096 context length.
## how-to
1. git clone,
2. load it,
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] |
c48d1fbb7320b0689ba60430ae8bfd32850b4542 |
Images are from [JourneyDB](https://journeydb.github.io/) | PixArt-alpha/data_toy | [
"license:openrail++",
"text-to-image",
"Pixart-ฮฑ",
"region:us"
] | 2023-11-28T06:22:46+00:00 | {"license": "openrail++", "tags": ["text-to-image", "Pixart-\u03b1"]} | 2024-01-11T11:03:46+00:00 | [] | [] | TAGS
#license-openrail++ #text-to-image #Pixart-ฮฑ #region-us
|
Images are from JourneyDB | [] | [
"TAGS\n#license-openrail++ #text-to-image #Pixart-ฮฑ #region-us \n"
] | [
25
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f29526cca3202eb7cff5bcb19a34ad57243428e6 |
**Content**
Mental health includes our emotional, psychological, and social well-being. Mental health is integral to living a healthy, balanced life. It affects how we think, feel, and act. It also helps determine how we handle stress, relate to others, and make choices. Emotional and mental health is important because itโs a vital part of your life and impacts your thoughts, behaviors and emotions. Being healthy emotionally can promote productivity and effectiveness in activities like work, school or care-giving. It plays an important part in the health of your relationships, and allows you to adapt to changes in your life and cope with adversity. Mental health problems are common but help is available. People with mental health problems can get better and many recover completely.
This dataset consists of FAQs about Mental Health.
**Acknowledgements**
https://www.thekimfoundation.org/faqs/
https://www.mhanational.org/frequently-asked-questions
https://www.wellnessinmind.org/frequently-asked-questions/
https://www.heretohelp.bc.ca/questions-and-answers | tolu07/Mental_Health_FAQ | [
"task_categories:conversational",
"task_categories:text-generation",
"license:mit",
"chatbot",
"mental health",
"therapy",
"region:us"
] | 2023-11-28T06:24:38+00:00 | {"license": "mit", "task_categories": ["conversational", "text-generation"], "tags": ["chatbot", "mental health", "therapy"]} | 2023-11-28T06:33:34+00:00 | [] | [] | TAGS
#task_categories-conversational #task_categories-text-generation #license-mit #chatbot #mental health #therapy #region-us
|
Content
Mental health includes our emotional, psychological, and social well-being. Mental health is integral to living a healthy, balanced life. It affects how we think, feel, and act. It also helps determine how we handle stress, relate to others, and make choices. Emotional and mental health is important because itโs a vital part of your life and impacts your thoughts, behaviors and emotions. Being healthy emotionally can promote productivity and effectiveness in activities like work, school or care-giving. It plays an important part in the health of your relationships, and allows you to adapt to changes in your life and cope with adversity. Mental health problems are common but help is available. People with mental health problems can get better and many recover completely.
This dataset consists of FAQs about Mental Health.
Acknowledgements
URL
URL
URL
URL | [] | [
"TAGS\n#task_categories-conversational #task_categories-text-generation #license-mit #chatbot #mental health #therapy #region-us \n"
] | [
40
] | [
"passage: TAGS\n#task_categories-conversational #task_categories-text-generation #license-mit #chatbot #mental health #therapy #region-us \n"
] |
5944d846f9ade5b965878dc3acf1ad0ee5682437 | # Dataset Card for "vehicle-dataset-v2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | roupenminassian/vehicle-dataset-v2 | [
"region:us"
] | 2023-11-28T06:35:21+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "image_id", "dtype": "int64"}, {"name": "width", "dtype": "int64"}, {"name": "height", "dtype": "int64"}, {"name": "objects", "struct": [{"name": "id", "sequence": "int64"}, {"name": "area", "sequence": "float64"}, {"name": "bbox", "sequence": {"sequence": "float64"}}, {"name": "category", "sequence": "int64"}]}], "splits": [{"name": "train", "num_bytes": 120781140.624, "num_examples": 1128}], "download_size": 122076069, "dataset_size": 120781140.624}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2023-11-28T06:36:20+00:00 | [] | [] | TAGS
#region-us
| # Dataset Card for "vehicle-dataset-v2"
More Information needed | [
"# Dataset Card for \"vehicle-dataset-v2\"\n\nMore Information needed"
] | [
"TAGS\n#region-us \n",
"# Dataset Card for \"vehicle-dataset-v2\"\n\nMore Information needed"
] | [
6,
19
] | [
"passage: TAGS\n#region-us \n# Dataset Card for \"vehicle-dataset-v2\"\n\nMore Information needed"
] |
ad92e2d1d0fe443059d2b1e1b4f30e85eb399d10 | # Dataset Card for "iv4-chatml-8k"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | sam-mosaic/iv4-chatml-8k | [
"region:us"
] | 2023-11-28T06:37:50+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "source", "dtype": "string"}, {"name": "prompt", "dtype": "string"}, {"name": "response", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2207667895.4360986, "num_examples": 363958}, {"name": "test", "num_bytes": 330419382.2206894, "num_examples": 54042}], "download_size": 618017532, "dataset_size": 2538087277.656788}} | 2023-11-28T06:38:55+00:00 | [] | [] | TAGS
#region-us
| # Dataset Card for "iv4-chatml-8k"
More Information needed | [
"# Dataset Card for \"iv4-chatml-8k\"\n\nMore Information needed"
] | [
"TAGS\n#region-us \n",
"# Dataset Card for \"iv4-chatml-8k\"\n\nMore Information needed"
] | [
6,
16
] | [
"passage: TAGS\n#region-us \n# Dataset Card for \"iv4-chatml-8k\"\n\nMore Information needed"
] |
8ad2f8ea7fad609ed957149766129c970cce43d4 | # Dataset Card for "iv4-chatml-4k"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | sam-mosaic/iv4-chatml-4k | [
"region:us"
] | 2023-11-28T06:39:31+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "source", "dtype": "string"}, {"name": "prompt", "dtype": "string"}, {"name": "response", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2099655591.2305844, "num_examples": 346151}, {"name": "test", "num_bytes": 315348071.4406665, "num_examples": 51577}], "download_size": 295209643, "dataset_size": 2415003662.671251}} | 2023-11-28T07:03:55+00:00 | [] | [] | TAGS
#region-us
| # Dataset Card for "iv4-chatml-4k"
More Information needed | [
"# Dataset Card for \"iv4-chatml-4k\"\n\nMore Information needed"
] | [
"TAGS\n#region-us \n",
"# Dataset Card for \"iv4-chatml-4k\"\n\nMore Information needed"
] | [
6,
16
] | [
"passage: TAGS\n#region-us \n# Dataset Card for \"iv4-chatml-4k\"\n\nMore Information needed"
] |
3168a9fd27bda0d6efe8d8de12e866da7ebb7cb9 | # Dataset Card for "iv4-chatml-16k"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | sam-mosaic/iv4-chatml-16k | [
"region:us"
] | 2023-11-28T06:41:35+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "source", "dtype": "string"}, {"name": "prompt", "dtype": "string"}, {"name": "response", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2334144256.136339, "num_examples": 384809}, {"name": "test", "num_bytes": 349214193.311071, "num_examples": 57116}], "download_size": 1227729872, "dataset_size": 2683358449.44741}} | 2023-11-28T06:43:19+00:00 | [] | [] | TAGS
#region-us
| # Dataset Card for "iv4-chatml-16k"
More Information needed | [
"# Dataset Card for \"iv4-chatml-16k\"\n\nMore Information needed"
] | [
"TAGS\n#region-us \n",
"# Dataset Card for \"iv4-chatml-16k\"\n\nMore Information needed"
] | [
6,
16
] | [
"passage: TAGS\n#region-us \n# Dataset Card for \"iv4-chatml-16k\"\n\nMore Information needed"
] |
1b2c05d469cc4d30b2f50daa2599d45588e91952 | # LVIS-Instruct4V-LLaVA-Instruct-mix880k
This is a mixture of our LVIS-Instruct4V dataset with [LLaVA-Instruct](https://huggingface.co/datasets/liuhaotian/LLaVA-Instruct-150K/blob/main/llava_instruct_150k.json) (150k) , and the academic task related data, including ShareGPT, VQAv2, GQA, OKVQA, OCRVQA, AOKVQA, TextCaps, RefCOCO, and VG.
| X2FD/LVIS-Instruct4V-LLaVA-Instruct-mix880k | [
"region:us"
] | 2023-11-28T06:43:55+00:00 | {} | 2023-11-28T06:53:07+00:00 | [] | [] | TAGS
#region-us
| # LVIS-Instruct4V-LLaVA-Instruct-mix880k
This is a mixture of our LVIS-Instruct4V dataset with LLaVA-Instruct (150k) , and the academic task related data, including ShareGPT, VQAv2, GQA, OKVQA, OCRVQA, AOKVQA, TextCaps, RefCOCO, and VG.
| [
"# LVIS-Instruct4V-LLaVA-Instruct-mix880k\n\nThis is a mixture of our LVIS-Instruct4V dataset with LLaVA-Instruct (150k) , and the academic task related data, including ShareGPT, VQAv2, GQA, OKVQA, OCRVQA, AOKVQA, TextCaps, RefCOCO, and VG."
] | [
"TAGS\n#region-us \n",
"# LVIS-Instruct4V-LLaVA-Instruct-mix880k\n\nThis is a mixture of our LVIS-Instruct4V dataset with LLaVA-Instruct (150k) , and the academic task related data, including ShareGPT, VQAv2, GQA, OKVQA, OCRVQA, AOKVQA, TextCaps, RefCOCO, and VG."
] | [
6,
92
] | [
"passage: TAGS\n#region-us \n# LVIS-Instruct4V-LLaVA-Instruct-mix880k\n\nThis is a mixture of our LVIS-Instruct4V dataset with LLaVA-Instruct (150k) , and the academic task related data, including ShareGPT, VQAv2, GQA, OKVQA, OCRVQA, AOKVQA, TextCaps, RefCOCO, and VG."
] |
551e15d171e21c434658ab53c69ebe63ee5f3798 | ๆด็ๅฅฝ็ไธไบHๅฐ่ฏดjsonlๆไปถ | Xiami2000/Trainingforaxolotl1 | [
"region:us"
] | 2023-11-28T06:48:21+00:00 | {} | 2023-11-29T17:35:32+00:00 | [] | [] | TAGS
#region-us
| ๆด็ๅฅฝ็ไธไบHๅฐ่ฏดjsonlๆไปถ | [] | [
"TAGS\n#region-us \n"
] | [
6
] | [
"passage: TAGS\n#region-us \n"
] |
0d94e7ee91830979a3c8cbc8b85106cdac39ac90 | # Dataset Card for "vehicle-dataset-v3"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | roupenminassian/vehicle-dataset-v3 | [
"region:us"
] | 2023-11-28T06:51:28+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "image_id", "dtype": "int64"}, {"name": "width", "dtype": "int64"}, {"name": "height", "dtype": "int64"}, {"name": "objects", "struct": [{"name": "id", "sequence": "int64"}, {"name": "area", "sequence": "float64"}, {"name": "bbox", "sequence": {"sequence": "float64"}}, {"name": "category", "sequence": "int64"}]}], "splits": [{"name": "train", "num_bytes": 120781140.624, "num_examples": 1128}], "download_size": 122076069, "dataset_size": 120781140.624}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2023-11-28T06:52:32+00:00 | [] | [] | TAGS
#region-us
| # Dataset Card for "vehicle-dataset-v3"
More Information needed | [
"# Dataset Card for \"vehicle-dataset-v3\"\n\nMore Information needed"
] | [
"TAGS\n#region-us \n",
"# Dataset Card for \"vehicle-dataset-v3\"\n\nMore Information needed"
] | [
6,
19
] | [
"passage: TAGS\n#region-us \n# Dataset Card for \"vehicle-dataset-v3\"\n\nMore Information needed"
] |
10d3563106a0bbf6bf6210f3d59a0e4b6facc78b | # Dataset Card for "pokemon-gpt4-captions"
This dataset is just [lambdalabs/pokemon-blip-captions](https://huggingface.co/datasets/lambdalabs/pokemon-blip-captions) but the captions come from GPT-4 (Turbo).
Code used to generate the captions:
```python
import base64
from io import BytesIO
import requests
from PIL import Image
def encode_image(image):
buffered = BytesIO()
image.save(buffered, format="JPEG")
img_str = base64.b64encode(buffered.getvalue())
return img_str.decode("utf-8")
def create_payload(image_string):
payload = {
"model": "gpt-4-vision-preview",
"messages": [
{
"role": "user",
"content": [
{
"type": "text",
"text": "Provide caption for the image in one sentence. Be detailed but precise.",
},
{
"type": "image_url",
"image_url": {"url": f"data:image/jpeg;base64,{image_string}"},
},
],
}
],
"max_tokens": 100,
}
return payload
def get_response(image_string):
payload = create_payload(image_string)
response = requests.post(
"https://api.openai.com/v1/chat/completions", headers=headers, json=payload
)
return response.json()
image = Image.open("path_to_you_image").convert("RGB")
image_str = encode_image(image)
response = get_response(image_str)
```
Generating captions for 833 images from the [lambdalabs/pokemon-blip-captions](https://huggingface.co/datasets/lambdalabs/pokemon-blip-captions) dataset costed about $5.
You can use this dataset for non-commercial applications. | diffusers/pokemon-gpt4-captions | [
"task_categories:text-to-image",
"size_categories:1K<n<10K",
"language:en",
"license:other",
"region:us"
] | 2023-11-28T06:54:16+00:00 | {"language": ["en"], "license": "other", "size_categories": ["1K<n<10K"], "task_categories": ["text-to-image"], "pretty_name": "Pokemons with captions generated using GPT-4. ", "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 56664550, "num_examples": 833}], "download_size": 51051224, "dataset_size": 56664550}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2023-11-28T07:07:09+00:00 | [] | [
"en"
] | TAGS
#task_categories-text-to-image #size_categories-1K<n<10K #language-English #license-other #region-us
| # Dataset Card for "pokemon-gpt4-captions"
This dataset is just lambdalabs/pokemon-blip-captions but the captions come from GPT-4 (Turbo).
Code used to generate the captions:
Generating captions for 833 images from the lambdalabs/pokemon-blip-captions dataset costed about $5.
You can use this dataset for non-commercial applications. | [
"# Dataset Card for \"pokemon-gpt4-captions\"\n\nThis dataset is just lambdalabs/pokemon-blip-captions but the captions come from GPT-4 (Turbo).\n\nCode used to generate the captions:\n\n\n\nGenerating captions for 833 images from the lambdalabs/pokemon-blip-captions dataset costed about $5. \n\nYou can use this dataset for non-commercial applications."
] | [
"TAGS\n#task_categories-text-to-image #size_categories-1K<n<10K #language-English #license-other #region-us \n",
"# Dataset Card for \"pokemon-gpt4-captions\"\n\nThis dataset is just lambdalabs/pokemon-blip-captions but the captions come from GPT-4 (Turbo).\n\nCode used to generate the captions:\n\n\n\nGenerating captions for 833 images from the lambdalabs/pokemon-blip-captions dataset costed about $5. \n\nYou can use this dataset for non-commercial applications."
] | [
39,
98
] | [
"passage: TAGS\n#task_categories-text-to-image #size_categories-1K<n<10K #language-English #license-other #region-us \n# Dataset Card for \"pokemon-gpt4-captions\"\n\nThis dataset is just lambdalabs/pokemon-blip-captions but the captions come from GPT-4 (Turbo).\n\nCode used to generate the captions:\n\n\n\nGenerating captions for 833 images from the lambdalabs/pokemon-blip-captions dataset costed about $5. \n\nYou can use this dataset for non-commercial applications."
] |
f78fde077e41ac97a8eef415b7851f14074b4d93 |
# Bangumi Image Base of Death Parade
This is the image base of bangumi Death Parade, we detected 20 characters, 1332 images in total. The full dataset is [here](all.zip).
**Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual.** If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
| # | Images | Download | Preview 1 | Preview 2 | Preview 3 | Preview 4 | Preview 5 | Preview 6 | Preview 7 | Preview 8 |
|:------|---------:|:---------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|
| 0 | 186 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 28 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 57 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 45 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 59 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 70 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 31 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 72 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 117 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 46 | [Download](9/dataset.zip) |  |  |  |  |  |  |  |  |
| 10 | 40 | [Download](10/dataset.zip) |  |  |  |  |  |  |  |  |
| 11 | 63 | [Download](11/dataset.zip) |  |  |  |  |  |  |  |  |
| 12 | 15 | [Download](12/dataset.zip) |  |  |  |  |  |  |  |  |
| 13 | 22 | [Download](13/dataset.zip) |  |  |  |  |  |  |  |  |
| 14 | 214 | [Download](14/dataset.zip) |  |  |  |  |  |  |  |  |
| 15 | 60 | [Download](15/dataset.zip) |  |  |  |  |  |  |  |  |
| 16 | 49 | [Download](16/dataset.zip) |  |  |  |  |  |  |  |  |
| 17 | 13 | [Download](17/dataset.zip) |  |  |  |  |  |  |  |  |
| 18 | 47 | [Download](18/dataset.zip) |  |  |  |  |  |  |  |  |
| noise | 98 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
| BangumiBase/deathparade | [
"size_categories:1K<n<10K",
"license:mit",
"art",
"region:us"
] | 2023-11-28T07:01:59+00:00 | {"license": "mit", "size_categories": ["1K<n<10K"], "tags": ["art"]} | 2023-11-28T08:41:17+00:00 | [] | [] | TAGS
#size_categories-1K<n<10K #license-mit #art #region-us
| Bangumi Image Base of Death Parade
==================================
This is the image base of bangumi Death Parade, we detected 20 characters, 1332 images in total. The full dataset is here.
Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual. If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
| [] | [
"TAGS\n#size_categories-1K<n<10K #license-mit #art #region-us \n"
] | [
25
] | [
"passage: TAGS\n#size_categories-1K<n<10K #license-mit #art #region-us \n"
] |
1f30a1ba4c355902ad282be11666b9834810f48b |
# Bangumi Image Base of Aria The Animation
This is the image base of bangumi Aria The Animation, we detected 50 characters, 5059 images in total. The full dataset is [here](all.zip).
**Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual.** If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
| # | Images | Download | Preview 1 | Preview 2 | Preview 3 | Preview 4 | Preview 5 | Preview 6 | Preview 7 | Preview 8 |
|:------|---------:|:---------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|
| 0 | 1592 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 18 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 29 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 25 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 20 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 22 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 30 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 19 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 42 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 58 | [Download](9/dataset.zip) |  |  |  |  |  |  |  |  |
| 10 | 58 | [Download](10/dataset.zip) |  |  |  |  |  |  |  |  |
| 11 | 27 | [Download](11/dataset.zip) |  |  |  |  |  |  |  |  |
| 12 | 22 | [Download](12/dataset.zip) |  |  |  |  |  |  |  |  |
| 13 | 48 | [Download](13/dataset.zip) |  |  |  |  |  |  |  |  |
| 14 | 106 | [Download](14/dataset.zip) |  |  |  |  |  |  |  |  |
| 15 | 15 | [Download](15/dataset.zip) |  |  |  |  |  |  |  |  |
| 16 | 18 | [Download](16/dataset.zip) |  |  |  |  |  |  |  |  |
| 17 | 87 | [Download](17/dataset.zip) |  |  |  |  |  |  |  |  |
| 18 | 25 | [Download](18/dataset.zip) |  |  |  |  |  |  |  |  |
| 19 | 26 | [Download](19/dataset.zip) |  |  |  |  |  |  |  |  |
| 20 | 13 | [Download](20/dataset.zip) |  |  |  |  |  |  |  |  |
| 21 | 41 | [Download](21/dataset.zip) |  |  |  |  |  |  |  |  |
| 22 | 183 | [Download](22/dataset.zip) |  |  |  |  |  |  |  |  |
| 23 | 43 | [Download](23/dataset.zip) |  |  |  |  |  |  |  |  |
| 24 | 387 | [Download](24/dataset.zip) |  |  |  |  |  |  |  |  |
| 25 | 336 | [Download](25/dataset.zip) |  |  |  |  |  |  |  |  |
| 26 | 30 | [Download](26/dataset.zip) |  |  |  |  |  |  |  |  |
| 27 | 12 | [Download](27/dataset.zip) |  |  |  |  |  |  |  |  |
| 28 | 34 | [Download](28/dataset.zip) |  |  |  |  |  |  |  |  |
| 29 | 18 | [Download](29/dataset.zip) |  |  |  |  |  |  |  |  |
| 30 | 106 | [Download](30/dataset.zip) |  |  |  |  |  |  |  |  |
| 31 | 26 | [Download](31/dataset.zip) |  |  |  |  |  |  |  |  |
| 32 | 7 | [Download](32/dataset.zip) |  |  |  |  |  |  |  | N/A |
| 33 | 19 | [Download](33/dataset.zip) |  |  |  |  |  |  |  |  |
| 34 | 488 | [Download](34/dataset.zip) |  |  |  |  |  |  |  |  |
| 35 | 87 | [Download](35/dataset.zip) |  |  |  |  |  |  |  |  |
| 36 | 19 | [Download](36/dataset.zip) |  |  |  |  |  |  |  |  |
| 37 | 395 | [Download](37/dataset.zip) |  |  |  |  |  |  |  |  |
| 38 | 46 | [Download](38/dataset.zip) |  |  |  |  |  |  |  |  |
| 39 | 15 | [Download](39/dataset.zip) |  |  |  |  |  |  |  |  |
| 40 | 17 | [Download](40/dataset.zip) |  |  |  |  |  |  |  |  |
| 41 | 24 | [Download](41/dataset.zip) |  |  |  |  |  |  |  |  |
| 42 | 7 | [Download](42/dataset.zip) |  |  |  |  |  |  |  | N/A |
| 43 | 13 | [Download](43/dataset.zip) |  |  |  |  |  |  |  |  |
| 44 | 8 | [Download](44/dataset.zip) |  |  |  |  |  |  |  |  |
| 45 | 133 | [Download](45/dataset.zip) |  |  |  |  |  |  |  |  |
| 46 | 6 | [Download](46/dataset.zip) |  |  |  |  |  |  | N/A | N/A |
| 47 | 15 | [Download](47/dataset.zip) |  |  |  |  |  |  |  |  |
| 48 | 15 | [Download](48/dataset.zip) |  |  |  |  |  |  |  |  |
| noise | 229 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
| BangumiBase/ariatheanimation | [
"size_categories:1K<n<10K",
"license:mit",
"art",
"region:us"
] | 2023-11-28T07:03:51+00:00 | {"license": "mit", "size_categories": ["1K<n<10K"], "tags": ["art"]} | 2023-11-28T10:55:37+00:00 | [] | [] | TAGS
#size_categories-1K<n<10K #license-mit #art #region-us
| Bangumi Image Base of Aria The Animation
========================================
This is the image base of bangumi Aria The Animation, we detected 50 characters, 5059 images in total. The full dataset is here.
Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual. If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
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25e1851683ebc407d28e5a2d514b7ae0eadd4e4b | # Vietnamese Open-Domain Complaint Detection in E-commerce Websites
This is the official repository for the ViOCD dataset from the paper [Vietnamese Open-Domain Complaint Detection in E-commerce Websites](https://arxiv.org/pdf/2103.10069.pdf), which was accepted at the [SoMeT 2021](https://dblp.org/db/conf/somet/somet2021.html).
# Citation Information
The provided dataset is only used for research purposes!
```
@misc{nguyen2021vietnamese,
title={Vietnamese Complaint Detection on E-Commerce Websites},
author={Nhung Thi-Hong Nguyen and Phuong Phan-Dieu Ha and Luan Thanh Nguyen and Kiet Van Nguyen and Ngan Luu-Thuy Nguyen},
year={2021},
eprint={2104.11969},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
## Abstract
Customer product reviews play a role in improving the quality of products and services for business organizations or their brands. Complaining is an attitude that expresses dissatisfaction with an event or a product not meeting customer expectations. In this paper, we build a Open-domain Complaint Detection dataset (ViOCD), including 5,485 human-annotated reviews on four categories about product reviews on e-commerce sites. After the data collection phase, we proceed to the annotation task and achieve the inter-annotator agreement Am of 87%. Then, we present an extensive methodology for the research purposes and achieve 92.16% by F1-score for identifying complaints. With the results, in the future, we aim to build a system for open-domain complaint detection in E-commerce websites.
## Dataset
The ViOCD dataset is consist of 5,485 reviews on four categories about product reviews on e-commerce sites.
The dataset is divided into three parts as below:
1. Train set: 4.39K reviews
2. Valid set: 548 reviews
3. Test set: 549 reviews
## Contact
Please feel free to contact us by email [email protected] if you have any further information! | tarudesu/ViOCD | [
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#task_categories-text-classification #size_categories-1K<n<10K #language-Vietnamese #code #arxiv-2103.10069 #arxiv-2104.11969 #region-us
| # Vietnamese Open-Domain Complaint Detection in E-commerce Websites
This is the official repository for the ViOCD dataset from the paper Vietnamese Open-Domain Complaint Detection in E-commerce Websites, which was accepted at the SoMeT 2021.
The provided dataset is only used for research purposes!
## Abstract
Customer product reviews play a role in improving the quality of products and services for business organizations or their brands. Complaining is an attitude that expresses dissatisfaction with an event or a product not meeting customer expectations. In this paper, we build a Open-domain Complaint Detection dataset (ViOCD), including 5,485 human-annotated reviews on four categories about product reviews on e-commerce sites. After the data collection phase, we proceed to the annotation task and achieve the inter-annotator agreement Am of 87%. Then, we present an extensive methodology for the research purposes and achieve 92.16% by F1-score for identifying complaints. With the results, in the future, we aim to build a system for open-domain complaint detection in E-commerce websites.
## Dataset
The ViOCD dataset is consist of 5,485 reviews on four categories about product reviews on e-commerce sites.
The dataset is divided into three parts as below:
1. Train set: 4.39K reviews
2. Valid set: 548 reviews
3. Test set: 549 reviews
## Contact
Please feel free to contact us by email luannt@URL if you have any further information! | [
"# Vietnamese Open-Domain Complaint Detection in E-commerce Websites\nThis is the official repository for the ViOCD dataset from the paper Vietnamese Open-Domain Complaint Detection in E-commerce Websites, which was accepted at the SoMeT 2021.\n\n\nThe provided dataset is only used for research purposes!",
"## Abstract\nCustomer product reviews play a role in improving the quality of products and services for business organizations or their brands. Complaining is an attitude that expresses dissatisfaction with an event or a product not meeting customer expectations. In this paper, we build a Open-domain Complaint Detection dataset (ViOCD), including 5,485 human-annotated reviews on four categories about product reviews on e-commerce sites. After the data collection phase, we proceed to the annotation task and achieve the inter-annotator agreement Am of 87%. Then, we present an extensive methodology for the research purposes and achieve 92.16% by F1-score for identifying complaints. With the results, in the future, we aim to build a system for open-domain complaint detection in E-commerce websites.",
"## Dataset\nThe ViOCD dataset is consist of 5,485 reviews on four categories about product reviews on e-commerce sites.\n\nThe dataset is divided into three parts as below:\n1. Train set: 4.39K reviews\n2. Valid set: 548 reviews\n3. Test set: 549 reviews",
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"## Abstract\nCustomer product reviews play a role in improving the quality of products and services for business organizations or their brands. Complaining is an attitude that expresses dissatisfaction with an event or a product not meeting customer expectations. In this paper, we build a Open-domain Complaint Detection dataset (ViOCD), including 5,485 human-annotated reviews on four categories about product reviews on e-commerce sites. After the data collection phase, we proceed to the annotation task and achieve the inter-annotator agreement Am of 87%. Then, we present an extensive methodology for the research purposes and achieve 92.16% by F1-score for identifying complaints. With the results, in the future, we aim to build a system for open-domain complaint detection in E-commerce websites.",
"## Dataset\nThe ViOCD dataset is consist of 5,485 reviews on four categories about product reviews on e-commerce sites.\n\nThe dataset is divided into three parts as below:\n1. Train set: 4.39K reviews\n2. Valid set: 548 reviews\n3. Test set: 549 reviews",
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# Dataset Card for Dataset Name
<!-- Provide a quick summary of the dataset. -->
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).
## Dataset Details
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[More Information Needed] | siddrao11/cs182-storytelling-dataset | [
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9a166e9110fefb8e707f4964667a1f6d36be5d00 |
# Dataset Card for Dataset Name
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## Dataset Details
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[More Information Needed] | desarrolloasesoreslocales/MistralAI | [
"region:us"
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Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
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"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Dataset Card Authors [optional]",
"## Dataset Card Contact"
] | [
"TAGS\n#region-us \n",
"# Dataset Card for Dataset Name",
"## Dataset Details",
"### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:",
"### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Out-of-Scope Use",
"## Dataset Structure",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Data Collection and Processing",
"#### Who are the source data producers?",
"### Annotations [optional]",
"#### Annotation process",
"#### Who are the annotators?",
"#### Personal and Sensitive Information",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Dataset Card Authors [optional]",
"## Dataset Card Contact"
] | [
6,
8,
4,
40,
29,
3,
4,
9,
6,
5,
7,
4,
7,
10,
9,
5,
9,
8,
10,
46,
8,
7,
10,
5
] | [
"passage: TAGS\n#region-us \n# Dataset Card for Dataset Name## Dataset Details### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Out-of-Scope Use## Dataset Structure## Dataset Creation### Curation Rationale### Source Data#### Data Collection and Processing#### Who are the source data producers?### Annotations [optional]#### Annotation process#### Who are the annotators?#### Personal and Sensitive Information## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Dataset Card Authors [optional]## Dataset Card Contact"
] |
b96af0450242eb4da433032b90998f25588a5d0f | <h1 align="center"> CRUXEval: Code Reasoning, Understanding, and Execution Evaluation </h1>
<p align="center">
<a href="https://crux-eval.github.io/">๐ Home Page</a> โข
<a href="https://github.com/facebookresearch/cruxeval">๐ป GitHub Repository </a> โข
<a href="https://crux-eval.github.io/leaderboard.html">๐ Leaderboard</a> โข
<a href="https://crux-eval.github.io/demo.html">๐ Sample Explorer</a>
</p>
CRUXEval (**C**ode **R**easoning, **U**nderstanding, and e**X**ecution **Eval**uation) is a benchmark of 800 Python functions and input-output pairs. The benchmark consists of two tasks, CRUXEval-I (input prediction) and CRUXEval-O (output prediction).
The benchmark was constructed as follows: first, we use [Code Llama 34B](https://huggingface.co/codellama/CodeLlama-34b-hf) to generate a large set of functions and inputs. The outputs are generated by executing the functions on the inputs. Second, we filter the set so that our benchmark only consists of short problems with low computation and memory requirements, problems which a good human programmer should be able to do without extra memory in a minute or so. Third, we randomly select 800 samples passing the filter, ensuring the benchmark is both small enough to easily run but large enough to reliably see performance differences among various models.
## Dataset Description
- **Homepage:** https://crux-eval.github.io/
- **Repository:** https://github.com/facebookresearch/cruxeval
- **Paper:** https://arxiv.org/abs/2401.03065
- **Leaderboard:** https://crux-eval.github.io/leaderboard.html
## Additional Information
### Licensing Information
CRUXEval is [MIT licensed](https://github.com/facebookresearch/cruxeval/blob/main/LICENSE).
### Citation Information
```
@article{gu2024cruxeval,
title={CRUXEval: A Benchmark for Code Reasoning, Understanding and Execution},
author={Alex Gu and Baptiste Roziรจre and Hugh Leather and Armando Solar-Lezama and Gabriel Synnaeve and Sida I. Wang},
year={2024},
journal = {arXiv preprint arXiv:2401.03065},
}
``` | cruxeval-org/cruxeval | [
"task_categories:text2text-generation",
"language:code",
"license:mit",
"code-generation",
"arxiv:2401.03065",
"region:us"
] | 2023-11-28T07:55:06+00:00 | {"language": ["code"], "license": "mit", "task_categories": ["text2text-generation"], "pretty_name": "CRUXEval", "tags": ["code-generation"]} | 2024-01-23T23:20:31+00:00 | [
"2401.03065"
] | [
"code"
] | TAGS
#task_categories-text2text-generation #language-code #license-mit #code-generation #arxiv-2401.03065 #region-us
| <h1 align="center"> CRUXEval: Code Reasoning, Understanding, and Execution Evaluation </h1>
<p align="center">
<a href="URL Home Page</a> โข
<a href="URL GitHub Repository </a> โข
<a href="URL Leaderboard</a> โข
<a href="URL Sample Explorer</a>
</p>
!image
CRUXEval (Code Reasoning, Understanding, and eXecution Evaluation) is a benchmark of 800 Python functions and input-output pairs. The benchmark consists of two tasks, CRUXEval-I (input prediction) and CRUXEval-O (output prediction).
The benchmark was constructed as follows: first, we use Code Llama 34B to generate a large set of functions and inputs. The outputs are generated by executing the functions on the inputs. Second, we filter the set so that our benchmark only consists of short problems with low computation and memory requirements, problems which a good human programmer should be able to do without extra memory in a minute or so. Third, we randomly select 800 samples passing the filter, ensuring the benchmark is both small enough to easily run but large enough to reliably see performance differences among various models.
## Dataset Description
- Homepage: URL
- Repository: URL
- Paper: URL
- Leaderboard: URL
## Additional Information
### Licensing Information
CRUXEval is MIT licensed.
| [
"## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Paper: URL\n- Leaderboard: URL",
"## Additional Information",
"### Licensing Information\n\nCRUXEval is MIT licensed."
] | [
"TAGS\n#task_categories-text2text-generation #language-code #license-mit #code-generation #arxiv-2401.03065 #region-us \n",
"## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Paper: URL\n- Leaderboard: URL",
"## Additional Information",
"### Licensing Information\n\nCRUXEval is MIT licensed."
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42,
23,
5,
15
] | [
"passage: TAGS\n#task_categories-text2text-generation #language-code #license-mit #code-generation #arxiv-2401.03065 #region-us \n## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Paper: URL\n- Leaderboard: URL## Additional Information### Licensing Information\n\nCRUXEval is MIT licensed."
] |
d939b3a3662e87b94493c7ca932c6d382819450d | Example of usage:
```python
from datasets import load_dataset
dataset = load_dataset("Andron00e/CIFAR10-custom")
splitted_dataset = dataset["train"].train_test_split(test_size=0.2)
``` | Andron00e/CIFAR10-custom | [
"task_categories:image-classification",
"size_categories:10K<n<100K",
"language:en",
"license:mit",
"region:us"
] | 2023-11-28T09:20:50+00:00 | {"language": ["en"], "license": "mit", "size_categories": ["10K<n<100K"], "task_categories": ["image-classification"], "dataset_info": {"features": [{"name": "image_file_path", "dtype": "string"}, {"name": "image", "dtype": "image"}, {"name": "labels", "dtype": "uint8"}], "splits": [{"name": "train", "num_bytes": 59153400, "num_examples": 60000}], "download_size": 26957572, "dataset_size": 59153400}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2023-11-28T09:43:03+00:00 | [] | [
"en"
] | TAGS
#task_categories-image-classification #size_categories-10K<n<100K #language-English #license-mit #region-us
| Example of usage:
| [] | [
"TAGS\n#task_categories-image-classification #size_categories-10K<n<100K #language-English #license-mit #region-us \n"
] | [
38
] | [
"passage: TAGS\n#task_categories-image-classification #size_categories-10K<n<100K #language-English #license-mit #region-us \n"
] |
9745a156507c280127db5d2494a63f433c86746a |
# Bangumi Image Base of Princess Tutu
This is the image base of bangumi Princess Tutu, we detected 23 characters, 2179 images in total. The full dataset is [here](all.zip).
**Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual.** If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
| # | Images | Download | Preview 1 | Preview 2 | Preview 3 | Preview 4 | Preview 5 | Preview 6 | Preview 7 | Preview 8 |
|:------|---------:|:---------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|
| 0 | 190 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 536 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 67 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 21 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 288 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 20 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 19 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 23 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 22 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 250 | [Download](9/dataset.zip) |  |  |  |  |  |  |  |  |
| 10 | 352 | [Download](10/dataset.zip) |  |  |  |  |  |  |  |  |
| 11 | 27 | [Download](11/dataset.zip) |  |  |  |  |  |  |  |  |
| 12 | 23 | [Download](12/dataset.zip) |  |  |  |  |  |  |  |  |
| 13 | 35 | [Download](13/dataset.zip) |  |  |  |  |  |  |  |  |
| 14 | 22 | [Download](14/dataset.zip) |  |  |  |  |  |  |  |  |
| 15 | 19 | [Download](15/dataset.zip) |  |  |  |  |  |  |  |  |
| 16 | 38 | [Download](16/dataset.zip) |  |  |  |  |  |  |  |  |
| 17 | 13 | [Download](17/dataset.zip) |  |  |  |  |  |  |  |  |
| 18 | 10 | [Download](18/dataset.zip) |  |  |  |  |  |  |  |  |
| 19 | 16 | [Download](19/dataset.zip) |  |  |  |  |  |  |  |  |
| 20 | 67 | [Download](20/dataset.zip) |  |  |  |  |  |  |  |  |
| 21 | 14 | [Download](21/dataset.zip) |  |  |  |  |  |  |  |  |
| noise | 107 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
| BangumiBase/princesstutu | [
"size_categories:1K<n<10K",
"license:mit",
"art",
"region:us"
] | 2023-11-28T09:30:22+00:00 | {"license": "mit", "size_categories": ["1K<n<10K"], "tags": ["art"]} | 2023-11-28T11:12:28+00:00 | [] | [] | TAGS
#size_categories-1K<n<10K #license-mit #art #region-us
| Bangumi Image Base of Princess Tutu
===================================
This is the image base of bangumi Princess Tutu, we detected 23 characters, 2179 images in total. The full dataset is here.
Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual. If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
| [] | [
"TAGS\n#size_categories-1K<n<10K #license-mit #art #region-us \n"
] | [
25
] | [
"passage: TAGS\n#size_categories-1K<n<10K #license-mit #art #region-us \n"
] |
36bc51e3be41b73c0afb2c60c81a91acc572daaa | cp -r -f -n -s /root/sd_webui/cache/huggingface/huggingface_repo/* /root/sd_webui/sd_main_dir/log | shenmixy/huggingface_token | [
"region:us"
] | 2023-11-28T09:33:00+00:00 | {} | 2023-11-28T10:10:03+00:00 | [] | [] | TAGS
#region-us
| cp -r -f -n -s /root/sd_webui/cache/huggingface/huggingface_repo/* /root/sd_webui/sd_main_dir/log | [] | [
"TAGS\n#region-us \n"
] | [
6
] | [
"passage: TAGS\n#region-us \n"
] |
6c0801c6204b31137a4fe4686a5967575e0771bf | Example of usage:
```python
from datasets import load_dataset
dataset = load_dataset("Andron00e/CIFAR100-custom")
splitted_dataset = dataset["train"].train_test_split(test_size=0.2)
``` | Andron00e/CIFAR100-custom | [
"task_categories:image-classification",
"size_categories:10K<n<100K",
"language:en",
"license:mit",
"region:us"
] | 2023-11-28T09:51:12+00:00 | {"language": ["en"], "license": "mit", "size_categories": ["10K<n<100K"], "task_categories": ["image-classification"], "dataset_info": {"features": [{"name": "image_file_path", "dtype": "string"}, {"name": "image", "dtype": "image"}, {"name": "labels", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 59505360, "num_examples": 60000}], "download_size": 27123594, "dataset_size": 59505360}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2023-11-28T10:00:58+00:00 | [] | [
"en"
] | TAGS
#task_categories-image-classification #size_categories-10K<n<100K #language-English #license-mit #region-us
| Example of usage:
| [] | [
"TAGS\n#task_categories-image-classification #size_categories-10K<n<100K #language-English #license-mit #region-us \n"
] | [
38
] | [
"passage: TAGS\n#task_categories-image-classification #size_categories-10K<n<100K #language-English #license-mit #region-us \n"
] |
6413d0df4ed8c917440f080eaf57ef65eae0855f |
# Description
Export of Sefaria's English library data. This data represents over version in the library marked as English.
## Schema
| Field | Description |
| --- | --- |
| text | The text of a single segment in the library. A segment is the smallest chunk of test, usually representing a paragraph. |
| metadata | Dictionary of metadata. See below for schema. |
### Metadata Schema
| Field | Description |
| --- | --- |
| url | URL to this segment in Sefaria |
| ref | Canonical Ref to this segment. Refs are a human readable ID that is unique independent of version. Different versions of a segment all share the same Ref. |
| versionTitle | Version title of the version this segment came from. |
| lang | two letter language code. |
| docCategory | Category for this segment. This corresponds to where the segment's book is located in Sefaria's table of contents. |
| dataQuality | Estimate of the quality of the text. This can be either "professional" or "user". |
| pagerank | Pagerank for this segment calculated using Sefaria's internal link graph. Higher values indicate the segment is more centrally cited by sources. |
| Sefaria/english_library | [
"license:gpl-3.0",
"region:us"
] | 2023-11-28T09:58:25+00:00 | {"license": "gpl-3.0"} | 2023-11-28T10:21:12+00:00 | [] | [] | TAGS
#license-gpl-3.0 #region-us
| Description
===========
Export of Sefaria's English library data. This data represents over version in the library marked as English.
Schema
------
### Metadata Schema
| [
"### Metadata Schema"
] | [
"TAGS\n#license-gpl-3.0 #region-us \n",
"### Metadata Schema"
] | [
14,
6
] | [
"passage: TAGS\n#license-gpl-3.0 #region-us \n### Metadata Schema"
] |
04642192a1e702647a84c46c3ed1871b085724f5 |
Fork of [findnitai/english-to-hinglish](https://huggingface.co/datasets/findnitai/english-to-hinglish) that splits the training set into train/test. | nateraw/english-to-hinglish | [
"region:us"
] | 2023-11-28T10:01:50+00:00 | {"dataset_info": {"features": [{"name": "en", "dtype": "string"}, {"name": "hi_ng", "dtype": "string"}, {"name": "source", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 18814411, "num_examples": 178701}, {"name": "test", "num_bytes": 1098000, "num_examples": 10401}], "download_size": 11924718, "dataset_size": 19912411}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}]}]} | 2023-11-28T21:16:58+00:00 | [] | [] | TAGS
#region-us
|
Fork of findnitai/english-to-hinglish that splits the training set into train/test. | [] | [
"TAGS\n#region-us \n"
] | [
6
] | [
"passage: TAGS\n#region-us \n"
] |
908d1fc8c95ccef9fa74edf5da56fc4d09da89ac | # Description
Export of Sefaria's Hebrew library data. This data represents over version in the library marked as Hebrew.
## Schema
| Field | Description |
| --- | --- |
| text | The text of a single segment in the library. A segment is the smallest chunk of test, usually representing a paragraph. |
| metadata | Dictionary of metadata. See below for schema. |
### Metadata Schema
| Field | Description |
| --- | --- |
| url | URL to this segment in Sefaria |
| ref | Canonical Ref to this segment. Refs are a human readable ID that is unique independent of version. Different versions of a segment all share the same Ref. |
| versionTitle | Version title of the version this segment came from. |
| lang | two letter language code. |
| docCategory | Category for this segment. This corresponds to where the segment's book is located in Sefaria's table of contents. |
| dataQuality | Estimate of the quality of the text. This can be either "professional" or "user". |
| pagerank | Pagerank for this segment calculated using Sefaria's internal link graph. Higher values indicate the segment is more centrally cited by sources. | | Sefaria/hebrew_library | [
"license:gpl-3.0",
"region:us"
] | 2023-11-28T10:23:03+00:00 | {"license": "gpl-3.0"} | 2023-11-28T10:47:51+00:00 | [] | [] | TAGS
#license-gpl-3.0 #region-us
| Description
===========
Export of Sefaria's Hebrew library data. This data represents over version in the library marked as Hebrew.
Schema
------
### Metadata Schema
| [
"### Metadata Schema"
] | [
"TAGS\n#license-gpl-3.0 #region-us \n",
"### Metadata Schema"
] | [
14,
6
] | [
"passage: TAGS\n#license-gpl-3.0 #region-us \n### Metadata Schema"
] |
e1ee7ea9604bb64a193a8d98495f900d15f33f30 | # Dataset Card for "Stack-Overflow-Zero-Shot-Classification"
# Automatic Stack Overflow Question Classifier
## Important
All credit goes to huggingface user [MoritzLaurer](https://huggingface.co/MoritzLaurer) as his model is the basis for this project.
## Introduction
The Automatic Stack Overflow Question Classifier harnesses the latest advancements in artificial intelligence to systematically categorize questions on Stack Overflow. Its primary goal is to streamline the process of sorting queries, enhancing navigability, and improving the overall user experience on the platform.
## About the Project
This initiative takes advantage of the DeBERTa V3 model's capabilities in zero-shot classification. By doing so, it aims to revolutionize how questions are organized on Stack Overflow. Instead of relying on manual categorization, which can be time-consuming and inconsistent, this project introduces an automated, AI-driven approach for more precise and efficient question sorting.
## Code and Repository
Access the complete source code and project details on GitHub: [Stack Overflow Question Classifier Repository](https://github.com/amaye15/stackoverflow-question-classifier).
## Streamlit App
Access our live classifier [here](https://stack-overflow-question-classifier.streamlit.app/). This interactive web application demonstrates the model's capabilities in real-time.
## Model
Learn more about the DeBERTa V3 model and its adaptation for this project on Hugging Face: [DeBERTa V3 on Hugging Face](https://huggingface.co/amaye15/Stack-Overflow-Zero-Shot-Classification).
## Dataset
The dataset, curated specifically for this project, can be found here: [Stack Overflow Zero-Shot Classification Dataset](https://huggingface.co/datasets/amaye15/Stack-Overflow-Zero-Shot-Classification). It encompasses a wide range of Stack Overflow questions, providing a comprehensive base for model training and testing.
| amaye15/Stack-Overflow-Zero-Shot-Classification | [
"region:us"
] | 2023-11-28T10:29:35+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "Title", "dtype": "string"}, {"name": "Tags", "dtype": "string"}, {"name": "Predicted_Tags", "dtype": "string"}, {"name": "Predicted_Tag_Scores", "sequence": "float64"}], "splits": [{"name": "train", "num_bytes": 27853258, "num_examples": 111030}], "download_size": 16579853, "dataset_size": 27853258}} | 2023-12-20T08:19:31+00:00 | [] | [] | TAGS
#region-us
| # Dataset Card for "Stack-Overflow-Zero-Shot-Classification"
# Automatic Stack Overflow Question Classifier
## Important
All credit goes to huggingface user MoritzLaurer as his model is the basis for this project.
## Introduction
The Automatic Stack Overflow Question Classifier harnesses the latest advancements in artificial intelligence to systematically categorize questions on Stack Overflow. Its primary goal is to streamline the process of sorting queries, enhancing navigability, and improving the overall user experience on the platform.
## About the Project
This initiative takes advantage of the DeBERTa V3 model's capabilities in zero-shot classification. By doing so, it aims to revolutionize how questions are organized on Stack Overflow. Instead of relying on manual categorization, which can be time-consuming and inconsistent, this project introduces an automated, AI-driven approach for more precise and efficient question sorting.
## Code and Repository
Access the complete source code and project details on GitHub: Stack Overflow Question Classifier Repository.
## Streamlit App
Access our live classifier here. This interactive web application demonstrates the model's capabilities in real-time.
## Model
Learn more about the DeBERTa V3 model and its adaptation for this project on Hugging Face: DeBERTa V3 on Hugging Face.
## Dataset
The dataset, curated specifically for this project, can be found here: Stack Overflow Zero-Shot Classification Dataset. It encompasses a wide range of Stack Overflow questions, providing a comprehensive base for model training and testing.
| [
"# Dataset Card for \"Stack-Overflow-Zero-Shot-Classification\"",
"# Automatic Stack Overflow Question Classifier",
"## Important\n\nAll credit goes to huggingface user MoritzLaurer as his model is the basis for this project.",
"## Introduction\nThe Automatic Stack Overflow Question Classifier harnesses the latest advancements in artificial intelligence to systematically categorize questions on Stack Overflow. Its primary goal is to streamline the process of sorting queries, enhancing navigability, and improving the overall user experience on the platform.",
"## About the Project\nThis initiative takes advantage of the DeBERTa V3 model's capabilities in zero-shot classification. By doing so, it aims to revolutionize how questions are organized on Stack Overflow. Instead of relying on manual categorization, which can be time-consuming and inconsistent, this project introduces an automated, AI-driven approach for more precise and efficient question sorting.",
"## Code and Repository\nAccess the complete source code and project details on GitHub: Stack Overflow Question Classifier Repository.",
"## Streamlit App\n\nAccess our live classifier here. This interactive web application demonstrates the model's capabilities in real-time.",
"## Model\nLearn more about the DeBERTa V3 model and its adaptation for this project on Hugging Face: DeBERTa V3 on Hugging Face.",
"## Dataset\nThe dataset, curated specifically for this project, can be found here: Stack Overflow Zero-Shot Classification Dataset. It encompasses a wide range of Stack Overflow questions, providing a comprehensive base for model training and testing."
] | [
"TAGS\n#region-us \n",
"# Dataset Card for \"Stack-Overflow-Zero-Shot-Classification\"",
"# Automatic Stack Overflow Question Classifier",
"## Important\n\nAll credit goes to huggingface user MoritzLaurer as his model is the basis for this project.",
"## Introduction\nThe Automatic Stack Overflow Question Classifier harnesses the latest advancements in artificial intelligence to systematically categorize questions on Stack Overflow. Its primary goal is to streamline the process of sorting queries, enhancing navigability, and improving the overall user experience on the platform.",
"## About the Project\nThis initiative takes advantage of the DeBERTa V3 model's capabilities in zero-shot classification. By doing so, it aims to revolutionize how questions are organized on Stack Overflow. Instead of relying on manual categorization, which can be time-consuming and inconsistent, this project introduces an automated, AI-driven approach for more precise and efficient question sorting.",
"## Code and Repository\nAccess the complete source code and project details on GitHub: Stack Overflow Question Classifier Repository.",
"## Streamlit App\n\nAccess our live classifier here. This interactive web application demonstrates the model's capabilities in real-time.",
"## Model\nLearn more about the DeBERTa V3 model and its adaptation for this project on Hugging Face: DeBERTa V3 on Hugging Face.",
"## Dataset\nThe dataset, curated specifically for this project, can be found here: Stack Overflow Zero-Shot Classification Dataset. It encompasses a wide range of Stack Overflow questions, providing a comprehensive base for model training and testing."
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"passage: TAGS\n#region-us \n# Dataset Card for \"Stack-Overflow-Zero-Shot-Classification\"# Automatic Stack Overflow Question Classifier## Important\n\nAll credit goes to huggingface user MoritzLaurer as his model is the basis for this project.## Introduction\nThe Automatic Stack Overflow Question Classifier harnesses the latest advancements in artificial intelligence to systematically categorize questions on Stack Overflow. Its primary goal is to streamline the process of sorting queries, enhancing navigability, and improving the overall user experience on the platform.## About the Project\nThis initiative takes advantage of the DeBERTa V3 model's capabilities in zero-shot classification. By doing so, it aims to revolutionize how questions are organized on Stack Overflow. Instead of relying on manual categorization, which can be time-consuming and inconsistent, this project introduces an automated, AI-driven approach for more precise and efficient question sorting.## Code and Repository\nAccess the complete source code and project details on GitHub: Stack Overflow Question Classifier Repository.## Streamlit App\n\nAccess our live classifier here. This interactive web application demonstrates the model's capabilities in real-time.## Model\nLearn more about the DeBERTa V3 model and its adaptation for this project on Hugging Face: DeBERTa V3 on Hugging Face.## Dataset\nThe dataset, curated specifically for this project, can be found here: Stack Overflow Zero-Shot Classification Dataset. It encompasses a wide range of Stack Overflow questions, providing a comprehensive base for model training and testing."
] |
2a775817d6941dc44eee24b7beb95167a72ddbfa | # Dataset Card for "vehicle-dataset-v4"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | roupenminassian/vehicle-dataset-v4 | [
"region:us"
] | 2023-11-28T10:44:35+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "image_id", "dtype": "int64"}, {"name": "width", "dtype": "int64"}, {"name": "height", "dtype": "int64"}, {"name": "objects", "struct": [{"name": "id", "sequence": "int64"}, {"name": "area", "sequence": "float64"}, {"name": "bbox", "sequence": {"sequence": "float64"}}, {"name": "category", "sequence": "int64"}]}], "splits": [{"name": "train", "num_bytes": 151700808.768, "num_examples": 1364}], "download_size": 149189451, "dataset_size": 151700808.768}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2023-11-28T10:46:14+00:00 | [] | [] | TAGS
#region-us
| # Dataset Card for "vehicle-dataset-v4"
More Information needed | [
"# Dataset Card for \"vehicle-dataset-v4\"\n\nMore Information needed"
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3ddef4deb8148e81ecd088dfdff66b8489270c25 |
# FLAN NIv2 without explanation
This is a subset of [FLAN NIv2](https://huggingface.co/datasets/Open-Orca/FLAN). We removed all examples with explanations in the few-shot template, as the final answers also don't have explanations. | imone/FLAN_NIv2_NoExplanation | [
"license:mit",
"region:us"
] | 2023-11-28T10:48:30+00:00 | {"license": "mit"} | 2023-11-28T10:56:20+00:00 | [] | [] | TAGS
#license-mit #region-us
|
# FLAN NIv2 without explanation
This is a subset of FLAN NIv2. We removed all examples with explanations in the few-shot template, as the final answers also don't have explanations. | [
"# FLAN NIv2 without explanation\n\nThis is a subset of FLAN NIv2. We removed all examples with explanations in the few-shot template, as the final answers also don't have explanations."
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"passage: TAGS\n#license-mit #region-us \n# FLAN NIv2 without explanation\n\nThis is a subset of FLAN NIv2. We removed all examples with explanations in the few-shot template, as the final answers also don't have explanations."
] |
a5ccc798b82b0e4825ba62c21c86c773eb1770df | # Dataset Card for "OK-VQA_train"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | James332/tt3 | [
"region:us"
] | 2023-11-28T11:02:14+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "question_type", "dtype": "string"}, {"name": "confidence", "dtype": "int32"}, {"name": "answers", "sequence": "string"}, {"name": "answers_original", "list": [{"name": "answer", "dtype": "string"}, {"name": "raw_answer", "dtype": "string"}, {"name": "answer_confidence", "dtype": "string"}, {"name": "answer_id", "dtype": "int64"}]}, {"name": "id_image", "dtype": "int64"}, {"name": "answer_type", "dtype": "string"}, {"name": "question_id", "dtype": "int64"}, {"name": "question", "dtype": "string"}, {"name": "id", "dtype": "int64"}, {"name": "clip_tags_ViT_L_14", "sequence": "string"}, {"name": "clip_tags_LAION_ViT_H_14_2B", "sequence": "string"}, {"name": "blip_caption_beam_5", "dtype": "string"}, {"name": "LLM_Description_gpt3_downstream_tasks_visual_genome_ViT_L_14", "sequence": "string"}, {"name": "LLM_Description_gpt3_downstream_tasks_visual_genome_LAION-ViT-H-14-2B", "sequence": "string"}, {"name": "DETA_detections_deta_swin_large_o365_coco_classes", "list": [{"name": "attribute", "dtype": "string"}, {"name": "box", "sequence": "float32"}, {"name": "label", "dtype": "string"}, {"name": "location", "dtype": "string"}, {"name": "ratio", "dtype": "float32"}, {"name": "size", "dtype": "string"}, {"name": "tag", "dtype": "string"}]}, {"name": "DETA_detections_deta_swin_large_o365_coco_classes_caption_module_random", "list": [{"name": "attribute", "dtype": "string"}, {"name": "box", "sequence": "float64"}, {"name": "captions_module", "sequence": "string"}, {"name": "captions_module_filter", "sequence": "string"}, {"name": "label", "dtype": "string"}, {"name": "location", "dtype": "string"}, {"name": "ratio", "dtype": "float64"}, {"name": "size", "dtype": "string"}, {"name": "tag", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 1686555802.0, "num_examples": 9009}], "download_size": 1572400067, "dataset_size": 1686555802.0}} | 2023-11-28T11:31:31+00:00 | [] | [] | TAGS
#region-us
| # Dataset Card for "OK-VQA_train"
More Information needed | [
"# Dataset Card for \"OK-VQA_train\"\n\nMore Information needed"
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"TAGS\n#region-us \n",
"# Dataset Card for \"OK-VQA_train\"\n\nMore Information needed"
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"passage: TAGS\n#region-us \n# Dataset Card for \"OK-VQA_train\"\n\nMore Information needed"
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cbd7ab2166399e34f0b29ec43685931da62ebb70 |
# Dataset Card for Dataset Name
<!-- Provide a quick summary of the dataset. -->
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).
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
### Dataset Sources [optional]
<!-- Provide the basic links for the dataset. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the dataset is intended to be used. -->
### Direct Use
<!-- This section describes suitable use cases for the dataset. -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
[More Information Needed]
## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
[More Information Needed]
## Dataset Creation
### Curation Rationale
<!-- Motivation for the creation of this dataset. -->
[More Information Needed]
### Source Data
<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
#### Data Collection and Processing
<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
[More Information Needed]
#### Who are the source data producers?
<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
[More Information Needed]
### Annotations [optional]
<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
#### Annotation process
<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
[More Information Needed]
#### Who are the annotators?
<!-- This section describes the people or systems who created the annotations. -->
[More Information Needed]
#### Personal and Sensitive Information
<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
## Citation [optional]
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Dataset Card Authors [optional]
[More Information Needed]
## Dataset Card Contact
[More Information Needed] | Shawt/liz | [
"license:openrail",
"art",
"lizz",
"region:us"
] | 2023-11-28T11:12:58+00:00 | {"license": "openrail", "tags": ["art", "lizz"]} | 2023-11-28T11:26:54+00:00 | [] | [] | TAGS
#license-openrail #art #lizz #region-us
|
# Dataset Card for Dataset Name
This dataset card aims to be a base template for new datasets. It has been generated using this raw template.
## Dataset Details
### Dataset Description
- Curated by:
- Funded by [optional]:
- Shared by [optional]:
- Language(s) (NLP):
- License:
### Dataset Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Out-of-Scope Use
## Dataset Structure
## Dataset Creation
### Curation Rationale
### Source Data
#### Data Collection and Processing
#### Who are the source data producers?
### Annotations [optional]
#### Annotation process
#### Who are the annotators?
#### Personal and Sensitive Information
## Bias, Risks, and Limitations
### Recommendations
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Dataset Card Authors [optional]
## Dataset Card Contact
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] |
c6d8e5ed0f34557804c8bf785e98edfdf17bda97 |
## Cinematika
Cinematika is a collection of 211 movie scripts converted to novel style, multi-character RP data.
The conversions were performed using a mix of manual regexp parsing and LLM augmentation using in-context learning with a custom mistral-7b fine-tune.
The code will be released shortly, and I plan to run the same pipeline for ~2400 movies, once the fine-tune is complete.
### Dataset files
- __plain_scenes.parquet__
- Individual RP-ified "scenes", essentially the script was split up using INT., EXT., FADE TO, and other identifiers of when the scene changes. Small scenes are merged.
- __plain_full_script.parquet__
- The full RP-ified script, i.e. basically `"\n".join(plain_scenes)`
- __scene_by_scene.parquet__
- The individual scenes, prefixed with character cards, list of "NPCs" (where NPC is a character with fewer than 15 lines in the whole script) and scenario (summary of the scene).
- __full_script.parquet__
- The full script, with character cards/NPCs introduced as the script progresses.
- __character_cards.parquet__
- Each character card that was created, only for characters with >= 15 lines in a script.
- __scene_enhancement.parquet__
- Training data for converting a snippet of movie script text into roleplay format.
- __scene_summary.parquet__
- Training data for converting movie scenes into summaries.
- __rp_to_character_card.parquet__
- Training data for converting examples of dialogue for a character into a character card.
- __character_card_reverse_prompt.parquet__
- Training data for generating a reverse character card prompt from a card, that is, given a character card, generate a prompt that would produce that character card.
- __prompt_to_character_card.parquet__
- Training data for generating a character card from a prompt (the opposite of character_card_reverse_prompt).
Each parquet has various fields, among them `movie_id: uuid` and `title: str`
### Example character card
```
name: Rorschach
characteristics:
Determination: Exhibits a relentless pursuit of the truth and justice, no matter the cost. Suitable for a character who is unwavering in their mission.
Isolation: Lives a solitary life, disconnected from society. Fits a character who distrusts others and prefers to work alone.
Observant: Highly perceptive, able to piece together clues and draw conclusions. Represents a character with keen investigative skills.
Cynicism: Holds a deep-seated distrust of humanity and its institutions. Suitable for a character who is pessimistic about human nature.
Vigilantism: Believes in taking justice into his own hands, often through violent means. Fits a character who operates outside the law to fight crime.
Secrecy: Keeps his personal life and methods of operation secret. Suitable for a character who is enigmatic and elusive.
Dedication: Committed to his cause, often to the point of obsession. Represents a character who is single-minded in their goals.
Intimidation: Uses his intimidating presence and demeanor to control situations. Suitable for a character who is assertive and imposing.
Paranoia: Suspects conspiracy and deception at every turn. Fits a character who is constantly on high alert for threats.
Moral Compass: Has a rigid moral code, which he adheres to strictly. Suitable for a character who is principled and unyielding.
description: |
Rorschach is a vigilante operating in the grim and gritty world of a decaying city. He is a man of average height with a muscular build, his face hidden behind a mask with a constantly changing inkblot pattern. His attire is a dark trench coat and gloves, paired with a plain white shirt and black pants, all chosen for their practicality and anonymity. His eyes, the only visible feature of his face, are sharp and calculating, always scanning for signs of deception or danger.
Rorschach is a man of few words, but when he speaks, it is with a gravitas that demands attention. He is a master of deduction, using his keen observation skills to unravel the truth behind the facades of others. His methods are often violent and confrontational, as he believes that crime must be met with force to be truly defeated.
He lives a life of solitude, distrusting the very systems he seeks to protect and often finds himself at odds with the very people he is trying to save. His moral compass is unyielding, and he will not hesitate to take the law into his own hands if he believes the justice system has failed.
Rorschach's past is a mystery to most, but it is clear that he has experienced trauma and hardship that has shaped his worldview and his need for vigilantism. He is a vigilante in the truest sense, a man without fear who is willing to sacrifice everything for his belief in a world that is, in his eyes, spiraling into chaos.
example_dialogue: |
Rorschach: "Rorschach's Journal, October 19th." I speak the words into the darkness, a record of my thoughts, "Someone tried to kill Adrian Veidt. Proves mask killer theoryโthe murderer is closing in. Pyramid Industries is the key."
{{user}}: I watch him for a moment, trying to gauge his intentions. "What are you going to do about it?"
Rorschach: "I'm going to find out why and who is behind it. I'm going to do what I always doโprotect the innocent."
{{user}}: "You can't keep doing this, Rorschach. You're putting yourself in danger."
Rorschach: My eyes narrow, the inkblot pattern of my mask shifting subtly. "I've been in danger my whole life. It's why I do this. It's why I have to do this."
{{user}}: "And what about the law? What if you're wrong about this Pyramid Industries thing?"
Rorschach: I pull out a notepad, my pen scratching across the paper as I write. "The law often gets it wrong. I've seen it. I'm not about to wait around for society's slow, corrupt wheels to turn."
```
### Example scene
```
[characters]
name: Rorschach
...
name: Hollis Mason
...
NPCS:
- News Vendor
- Shopkeeper
[/characters]
[scenario]
Hollis Mason reflects on his past as the original Nite Owl, reminiscing about the early days of masked heroes and the formation of the Watchmen.
He discusses the absurdity of the superhero world and the encounters he had with various villains.
Dan Dreiberg, the second Nite Owl, joins the conversation and they share a moment of camaraderie before Dan leaves.
The news of Rorschach's actions serves as a reminder of the legacy of masked heroes that still persists.
[/scenario]
[setting] The quiet of night, if it could be called that, wraps around Hollis Mason's apartment.
The air is thick with memories and the faint hum of an old television from another room.
Hollis: As I hold the framed photo of the first Watchmen, the one just like the one in Blake's closet, I can't help but reflect on the past.
"He was young and arrogant, but what he lacked in experience, he made up for in tenacity."
My voice carries through the stillness of the room, each word a testament to the boy I once was.
...
```
### Contribute
If you're interested in new functionality/datasets, take a look at [bagel repo](https://github.com/jondurbin/bagel) and [airoboros](https://github.com/jondurbin/airoboros) and either make a PR or open an issue with details.
To help me with the fine-tuning costs, dataset generation, etc., please use one of the following:
- https://bmc.link/jondurbin
- ETH 0xce914eAFC2fe52FdceE59565Dd92c06f776fcb11
- BTC bc1qdwuth4vlg8x37ggntlxu5cjfwgmdy5zaa7pswf | jondurbin/cinematika-v0.1 | [
"license:cc-by-nc-4.0",
"region:us"
] | 2023-11-28T11:37:30+00:00 | {"license": "cc-by-nc-4.0"} | 2024-01-12T20:19:59+00:00 | [] | [] | TAGS
#license-cc-by-nc-4.0 #region-us
|
!Cinematika
## Cinematika
Cinematika is a collection of 211 movie scripts converted to novel style, multi-character RP data.
The conversions were performed using a mix of manual regexp parsing and LLM augmentation using in-context learning with a custom mistral-7b fine-tune.
The code will be released shortly, and I plan to run the same pipeline for ~2400 movies, once the fine-tune is complete.
### Dataset files
- __plain_scenes.parquet__
- Individual RP-ified "scenes", essentially the script was split up using INT., EXT., FADE TO, and other identifiers of when the scene changes. Small scenes are merged.
- __plain_full_script.parquet__
- The full RP-ified script, i.e. basically '"\n".join(plain_scenes)'
- __scene_by_scene.parquet__
- The individual scenes, prefixed with character cards, list of "NPCs" (where NPC is a character with fewer than 15 lines in the whole script) and scenario (summary of the scene).
- __full_script.parquet__
- The full script, with character cards/NPCs introduced as the script progresses.
- __character_cards.parquet__
- Each character card that was created, only for characters with >= 15 lines in a script.
- __scene_enhancement.parquet__
- Training data for converting a snippet of movie script text into roleplay format.
- __scene_summary.parquet__
- Training data for converting movie scenes into summaries.
- __rp_to_character_card.parquet__
- Training data for converting examples of dialogue for a character into a character card.
- __character_card_reverse_prompt.parquet__
- Training data for generating a reverse character card prompt from a card, that is, given a character card, generate a prompt that would produce that character card.
- __prompt_to_character_card.parquet__
- Training data for generating a character card from a prompt (the opposite of character_card_reverse_prompt).
Each parquet has various fields, among them 'movie_id: uuid' and 'title: str'
### Example character card
### Example scene
### Contribute
If you're interested in new functionality/datasets, take a look at bagel repo and airoboros and either make a PR or open an issue with details.
To help me with the fine-tuning costs, dataset generation, etc., please use one of the following:
- URL
- ETH 0xce914eAFC2fe52FdceE59565Dd92c06f776fcb11
- BTC bc1qdwuth4vlg8x37ggntlxu5cjfwgmdy5zaa7pswf | [
"## Cinematika\n\nCinematika is a collection of 211 movie scripts converted to novel style, multi-character RP data.\n\nThe conversions were performed using a mix of manual regexp parsing and LLM augmentation using in-context learning with a custom mistral-7b fine-tune.\n\nThe code will be released shortly, and I plan to run the same pipeline for ~2400 movies, once the fine-tune is complete.",
"### Dataset files\n\n- __plain_scenes.parquet__\n - Individual RP-ified \"scenes\", essentially the script was split up using INT., EXT., FADE TO, and other identifiers of when the scene changes. Small scenes are merged.\n- __plain_full_script.parquet__\n - The full RP-ified script, i.e. basically '\"\\n\".join(plain_scenes)'\n- __scene_by_scene.parquet__\n - The individual scenes, prefixed with character cards, list of \"NPCs\" (where NPC is a character with fewer than 15 lines in the whole script) and scenario (summary of the scene).\n- __full_script.parquet__\n - The full script, with character cards/NPCs introduced as the script progresses.\n- __character_cards.parquet__\n - Each character card that was created, only for characters with >= 15 lines in a script.\n- __scene_enhancement.parquet__\n - Training data for converting a snippet of movie script text into roleplay format.\n- __scene_summary.parquet__\n - Training data for converting movie scenes into summaries.\n- __rp_to_character_card.parquet__\n - Training data for converting examples of dialogue for a character into a character card.\n- __character_card_reverse_prompt.parquet__\n - Training data for generating a reverse character card prompt from a card, that is, given a character card, generate a prompt that would produce that character card.\n- __prompt_to_character_card.parquet__\n - Training data for generating a character card from a prompt (the opposite of character_card_reverse_prompt).\n \nEach parquet has various fields, among them 'movie_id: uuid' and 'title: str'",
"### Example character card",
"### Example scene",
"### Contribute\n\nIf you're interested in new functionality/datasets, take a look at bagel repo and airoboros and either make a PR or open an issue with details.\n\nTo help me with the fine-tuning costs, dataset generation, etc., please use one of the following:\n\n- URL\n- ETH 0xce914eAFC2fe52FdceE59565Dd92c06f776fcb11\n- BTC bc1qdwuth4vlg8x37ggntlxu5cjfwgmdy5zaa7pswf"
] | [
"TAGS\n#license-cc-by-nc-4.0 #region-us \n",
"## Cinematika\n\nCinematika is a collection of 211 movie scripts converted to novel style, multi-character RP data.\n\nThe conversions were performed using a mix of manual regexp parsing and LLM augmentation using in-context learning with a custom mistral-7b fine-tune.\n\nThe code will be released shortly, and I plan to run the same pipeline for ~2400 movies, once the fine-tune is complete.",
"### Dataset files\n\n- __plain_scenes.parquet__\n - Individual RP-ified \"scenes\", essentially the script was split up using INT., EXT., FADE TO, and other identifiers of when the scene changes. Small scenes are merged.\n- __plain_full_script.parquet__\n - The full RP-ified script, i.e. basically '\"\\n\".join(plain_scenes)'\n- __scene_by_scene.parquet__\n - The individual scenes, prefixed with character cards, list of \"NPCs\" (where NPC is a character with fewer than 15 lines in the whole script) and scenario (summary of the scene).\n- __full_script.parquet__\n - The full script, with character cards/NPCs introduced as the script progresses.\n- __character_cards.parquet__\n - Each character card that was created, only for characters with >= 15 lines in a script.\n- __scene_enhancement.parquet__\n - Training data for converting a snippet of movie script text into roleplay format.\n- __scene_summary.parquet__\n - Training data for converting movie scenes into summaries.\n- __rp_to_character_card.parquet__\n - Training data for converting examples of dialogue for a character into a character card.\n- __character_card_reverse_prompt.parquet__\n - Training data for generating a reverse character card prompt from a card, that is, given a character card, generate a prompt that would produce that character card.\n- __prompt_to_character_card.parquet__\n - Training data for generating a character card from a prompt (the opposite of character_card_reverse_prompt).\n \nEach parquet has various fields, among them 'movie_id: uuid' and 'title: str'",
"### Example character card",
"### Example scene",
"### Contribute\n\nIf you're interested in new functionality/datasets, take a look at bagel repo and airoboros and either make a PR or open an issue with details.\n\nTo help me with the fine-tuning costs, dataset generation, etc., please use one of the following:\n\n- URL\n- ETH 0xce914eAFC2fe52FdceE59565Dd92c06f776fcb11\n- BTC bc1qdwuth4vlg8x37ggntlxu5cjfwgmdy5zaa7pswf"
] | [
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"passage: TAGS\n#license-cc-by-nc-4.0 #region-us \n## Cinematika\n\nCinematika is a collection of 211 movie scripts converted to novel style, multi-character RP data.\n\nThe conversions were performed using a mix of manual regexp parsing and LLM augmentation using in-context learning with a custom mistral-7b fine-tune.\n\nThe code will be released shortly, and I plan to run the same pipeline for ~2400 movies, once the fine-tune is complete."
] |
7b82fc8fc089fccc38f271ab8019009a2f7c2ab3 |
<img src="https://raw.githubusercontent.com/argilla-io/distilabel/main/docs/assets/distilabel-badge-dark.png" alt="Built with Distilabel" width="200" height="32"/>
# HelpSteer: Helpfulness SteerLM Dataset
HelpSteer is an open-source Helpfulness Dataset (CC-BY-4.0) that supports aligning models to become more helpful, factually correct and coherent, while being adjustable in terms of the complexity and verbosity of its responses.
[HelpSteer: Multi-attribute Helpfulness Dataset for SteerLM](http://arxiv.org/abs/2311.09528)
## Disclaimer
This is only a subset created with `distilabel` to evaluate the first 1000 rows using AI Feedback (AIF) coming from GPT-4, only created for experimenting / research purposes, please refer to [nvidia/HelpSteer](https://hf.co/nvidia/HelpSteer) if you want more information about the HelpSteer dataset.
## Dataset Description
HelpSteer contains 37120 samples, while this subset only contains the first 1000, each only containing a prompt and a response, even though the same prompt may appear up to 4 times with different responses generated by their in-house LLM of 43B params.
In this case, the annotations of the attributes have been discarded while just keeping the prompt and the response, to generate the annotations using AIF via `distilabel`.
## Attributes
1. **Helpfulness**: Overall helpfulness of the response to the prompt.
2. **Correctness**: Inclusion of all pertinent facts without errors.
3. **Coherence**: Consistency and clarity of expression.
4. **Complexity**: Intellectual depth required to write response (i.e. whether the response can be written by anyone with basic language competency or requires deep domain expertise).
5. **Verbosity**: Amount of detail included in the response, relative to what is asked for in the prompt.
## Source
1. (original) Prompts are collected based on a mixture of template-generated (mainly for prompt involving long reference text) and human generated by Scale AI. These prompts relate to the tasks of Rewrite, Summarization, Classification, Extraction, Closed Question Answering, Open Question Answering, Generation and Brainstorming.
2. (original) Responses are generated by an early version of an inhouse LLM. We generate up to 4 responses per prompts using sample techniques to give diverse yet reasonable responses.
3. (distilabel) Annotations of various attributes were done using OpenAI's GPT-4 via `distilabel`, following the same Likert 5 scale (0-4) that Scale AI used with human annotators, but this time asking GPT-4 to provide those, via AI Feedback (AIF).
## Citation
If you find this dataset useful, make sure to cite the original work, as the prompt and the responses have been reused from them, while only the annotations have been modified.
```bibtex
@misc{wang2023helpsteer,
title={HelpSteer: Multi-attribute Helpfulness Dataset for SteerLM},
author={Zhilin Wang and Yi Dong and Jiaqi Zeng and Virginia Adams and Makesh Narsimhan Sreedhar and Daniel Egert and Olivier Delalleau and Jane Polak Scowcroft and Neel Kant and Aidan Swope and Oleksii Kuchaiev},
year={2023},
eprint={2311.09528},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
``` | alvarobartt/HelpSteer-AIF | [
"size_categories:n<1K",
"language:en",
"license:cc-by-4.0",
"synthetic",
"distilabel",
"helpsteer",
"ai-feedback",
"preference",
"arxiv:2311.09528",
"region:us"
] | 2023-11-28T11:41:53+00:00 | {"language": ["en"], "license": "cc-by-4.0", "size_categories": ["n<1K"], "pretty_name": "HelpSteer with AIF", "dataset_info": {"features": [{"name": "prompt", "dtype": "string"}, {"name": "response", "dtype": "string"}, {"name": "model", "dtype": "string"}, {"name": "correctness", "dtype": "int64"}, {"name": "coherence", "dtype": "int64"}, {"name": "complexity", "dtype": "int64"}, {"name": "verbosity", "dtype": "int64"}, {"name": "helpfulness", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 2832095, "num_examples": 1000}], "download_size": 677100, "dataset_size": 2832095}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "tags": ["synthetic", "distilabel", "helpsteer", "ai-feedback", "preference"]} | 2024-02-06T07:32:41+00:00 | [
"2311.09528"
] | [
"en"
] | TAGS
#size_categories-n<1K #language-English #license-cc-by-4.0 #synthetic #distilabel #helpsteer #ai-feedback #preference #arxiv-2311.09528 #region-us
|
<img src="URL alt="Built with Distilabel" width="200" height="32"/>
# HelpSteer: Helpfulness SteerLM Dataset
HelpSteer is an open-source Helpfulness Dataset (CC-BY-4.0) that supports aligning models to become more helpful, factually correct and coherent, while being adjustable in terms of the complexity and verbosity of its responses.
HelpSteer: Multi-attribute Helpfulness Dataset for SteerLM
## Disclaimer
This is only a subset created with 'distilabel' to evaluate the first 1000 rows using AI Feedback (AIF) coming from GPT-4, only created for experimenting / research purposes, please refer to nvidia/HelpSteer if you want more information about the HelpSteer dataset.
## Dataset Description
HelpSteer contains 37120 samples, while this subset only contains the first 1000, each only containing a prompt and a response, even though the same prompt may appear up to 4 times with different responses generated by their in-house LLM of 43B params.
In this case, the annotations of the attributes have been discarded while just keeping the prompt and the response, to generate the annotations using AIF via 'distilabel'.
## Attributes
1. Helpfulness: Overall helpfulness of the response to the prompt.
2. Correctness: Inclusion of all pertinent facts without errors.
3. Coherence: Consistency and clarity of expression.
4. Complexity: Intellectual depth required to write response (i.e. whether the response can be written by anyone with basic language competency or requires deep domain expertise).
5. Verbosity: Amount of detail included in the response, relative to what is asked for in the prompt.
## Source
1. (original) Prompts are collected based on a mixture of template-generated (mainly for prompt involving long reference text) and human generated by Scale AI. These prompts relate to the tasks of Rewrite, Summarization, Classification, Extraction, Closed Question Answering, Open Question Answering, Generation and Brainstorming.
2. (original) Responses are generated by an early version of an inhouse LLM. We generate up to 4 responses per prompts using sample techniques to give diverse yet reasonable responses.
3. (distilabel) Annotations of various attributes were done using OpenAI's GPT-4 via 'distilabel', following the same Likert 5 scale (0-4) that Scale AI used with human annotators, but this time asking GPT-4 to provide those, via AI Feedback (AIF).
If you find this dataset useful, make sure to cite the original work, as the prompt and the responses have been reused from them, while only the annotations have been modified.
| [
"# HelpSteer: Helpfulness SteerLM Dataset\n\nHelpSteer is an open-source Helpfulness Dataset (CC-BY-4.0) that supports aligning models to become more helpful, factually correct and coherent, while being adjustable in terms of the complexity and verbosity of its responses.\n\nHelpSteer: Multi-attribute Helpfulness Dataset for SteerLM",
"## Disclaimer\n\nThis is only a subset created with 'distilabel' to evaluate the first 1000 rows using AI Feedback (AIF) coming from GPT-4, only created for experimenting / research purposes, please refer to nvidia/HelpSteer if you want more information about the HelpSteer dataset.",
"## Dataset Description\n\nHelpSteer contains 37120 samples, while this subset only contains the first 1000, each only containing a prompt and a response, even though the same prompt may appear up to 4 times with different responses generated by their in-house LLM of 43B params.\n\nIn this case, the annotations of the attributes have been discarded while just keeping the prompt and the response, to generate the annotations using AIF via 'distilabel'.",
"## Attributes\n\n1. Helpfulness: Overall helpfulness of the response to the prompt.\n2. Correctness: Inclusion of all pertinent facts without errors. \n3. Coherence: Consistency and clarity of expression. \n4. Complexity: Intellectual depth required to write response (i.e. whether the response can be written by anyone with basic language competency or requires deep domain expertise).\n5. Verbosity: Amount of detail included in the response, relative to what is asked for in the prompt.",
"## Source\n\n1. (original) Prompts are collected based on a mixture of template-generated (mainly for prompt involving long reference text) and human generated by Scale AI. These prompts relate to the tasks of Rewrite, Summarization, Classification, Extraction, Closed Question Answering, Open Question Answering, Generation and Brainstorming.\n2. (original) Responses are generated by an early version of an inhouse LLM. We generate up to 4 responses per prompts using sample techniques to give diverse yet reasonable responses.\n3. (distilabel) Annotations of various attributes were done using OpenAI's GPT-4 via 'distilabel', following the same Likert 5 scale (0-4) that Scale AI used with human annotators, but this time asking GPT-4 to provide those, via AI Feedback (AIF).\n\nIf you find this dataset useful, make sure to cite the original work, as the prompt and the responses have been reused from them, while only the annotations have been modified."
] | [
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"## Disclaimer\n\nThis is only a subset created with 'distilabel' to evaluate the first 1000 rows using AI Feedback (AIF) coming from GPT-4, only created for experimenting / research purposes, please refer to nvidia/HelpSteer if you want more information about the HelpSteer dataset.",
"## Dataset Description\n\nHelpSteer contains 37120 samples, while this subset only contains the first 1000, each only containing a prompt and a response, even though the same prompt may appear up to 4 times with different responses generated by their in-house LLM of 43B params.\n\nIn this case, the annotations of the attributes have been discarded while just keeping the prompt and the response, to generate the annotations using AIF via 'distilabel'.",
"## Attributes\n\n1. Helpfulness: Overall helpfulness of the response to the prompt.\n2. Correctness: Inclusion of all pertinent facts without errors. \n3. Coherence: Consistency and clarity of expression. \n4. Complexity: Intellectual depth required to write response (i.e. whether the response can be written by anyone with basic language competency or requires deep domain expertise).\n5. Verbosity: Amount of detail included in the response, relative to what is asked for in the prompt.",
"## Source\n\n1. (original) Prompts are collected based on a mixture of template-generated (mainly for prompt involving long reference text) and human generated by Scale AI. These prompts relate to the tasks of Rewrite, Summarization, Classification, Extraction, Closed Question Answering, Open Question Answering, Generation and Brainstorming.\n2. (original) Responses are generated by an early version of an inhouse LLM. We generate up to 4 responses per prompts using sample techniques to give diverse yet reasonable responses.\n3. (distilabel) Annotations of various attributes were done using OpenAI's GPT-4 via 'distilabel', following the same Likert 5 scale (0-4) that Scale AI used with human annotators, but this time asking GPT-4 to provide those, via AI Feedback (AIF).\n\nIf you find this dataset useful, make sure to cite the original work, as the prompt and the responses have been reused from them, while only the annotations have been modified."
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"passage: TAGS\n#size_categories-n<1K #language-English #license-cc-by-4.0 #synthetic #distilabel #helpsteer #ai-feedback #preference #arxiv-2311.09528 #region-us \n# HelpSteer: Helpfulness SteerLM Dataset\n\nHelpSteer is an open-source Helpfulness Dataset (CC-BY-4.0) that supports aligning models to become more helpful, factually correct and coherent, while being adjustable in terms of the complexity and verbosity of its responses.\n\nHelpSteer: Multi-attribute Helpfulness Dataset for SteerLM## Disclaimer\n\nThis is only a subset created with 'distilabel' to evaluate the first 1000 rows using AI Feedback (AIF) coming from GPT-4, only created for experimenting / research purposes, please refer to nvidia/HelpSteer if you want more information about the HelpSteer dataset.## Dataset Description\n\nHelpSteer contains 37120 samples, while this subset only contains the first 1000, each only containing a prompt and a response, even though the same prompt may appear up to 4 times with different responses generated by their in-house LLM of 43B params.\n\nIn this case, the annotations of the attributes have been discarded while just keeping the prompt and the response, to generate the annotations using AIF via 'distilabel'.## Attributes\n\n1. Helpfulness: Overall helpfulness of the response to the prompt.\n2. Correctness: Inclusion of all pertinent facts without errors. \n3. Coherence: Consistency and clarity of expression. \n4. Complexity: Intellectual depth required to write response (i.e. whether the response can be written by anyone with basic language competency or requires deep domain expertise).\n5. Verbosity: Amount of detail included in the response, relative to what is asked for in the prompt."
] |
5461410310c8d69b5d63e664bfc5d76dab90054c | # MVBench
## Dataset Description
- **Repository:** [MVBench](https://github.com/OpenGVLab/Ask-Anything/blob/main/video_chat2/mvbench.ipynb)
- **Paper:** [2311.17005](https://arxiv.org/abs/2311.17005)
- **Point of Contact:** mailto:[kunchang li]([email protected])
We introduce a novel static-to-dynamic method for defining temporal-related tasks. By converting static tasks into dynamic ones, we facilitate systematic generation of video tasks necessitating a wide range of temporal abilities, from perception to cognition. Guided by task definitions, we then **automatically transform public video annotations into multiple-choice QA** for task evaluation. This unique paradigm enables efficient creation of MVBench with minimal manual intervention while ensuring evaluation fairness through ground-truth video annotations and avoiding biased LLM scoring. The **20** temporal task examples are as follows.
## Evaluation
An evaluation example is provided in [mvbench.ipynb](https://github.com/OpenGVLab/Ask-Anything/blob/main/video_chat2/mvbench.ipynb). Please follow the pipeline to prepare the evaluation code for various MLLMs.
- **Preprocess**: We preserve the raw video (high resolution, long duration, etc.) along with corresponding annotations (start, end, subtitles, etc.) for future exploration; hence, the decoding of some raw videos like Perception Test may be slow.
- **Prompt**: We explore effective system prompts to encourage better temporal reasoning in MLLM, as well as efficient answer prompts for option extraction.
## Leadrboard
While an [Online leaderboard]() is under construction, the current standings are as follows:
 | OpenGVLab/MVBench | [
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"language:en",
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"arxiv:2311.17005",
"region:us"
] | 2023-11-28T12:03:30+00:00 | {"language": ["en"], "license": "mit", "size_categories": ["1K<n<10K"], "task_categories": ["visual-question-answering", "question-answering", "conversational"], "extra_gated_prompt": "You agree to not use the dataset to conduct experiments that cause harm to human subjects. Please note that the data in this dataset may be subject to other agreements. Before using the data, be sure to read the relevant agreements carefully to ensure compliant use. Video copyrights belong to the original video creators or platforms and are for academic research use only.", "extra_gated_fields": {"Name": "text", "Company/Organization": "text", "Country": "text", "E-Mail": "text"}, "configs": [{"config_name": "action_sequence", "data_files": "json/action_sequence.json"}, {"config_name": "moving_count", "data_files": "json/moving_count.json"}, {"config_name": "action_prediction", "data_files": "json/action_prediction.json"}, {"config_name": "episodic_reasoning", "data_files": "json/episodic_reasoning.json"}, {"config_name": "action_antonym", "data_files": "json/action_antonym.json"}, {"config_name": "action_count", "data_files": "json/action_count.json"}, {"config_name": "scene_transition", "data_files": "json/scene_transition.json"}, {"config_name": "object_shuffle", "data_files": "json/object_shuffle.json"}, {"config_name": "object_existence", "data_files": "json/object_existence.json"}, {"config_name": "fine_grained_pose", "data_files": "json/fine_grained_pose.json"}, {"config_name": "unexpected_action", "data_files": "json/unexpected_action.json"}, {"config_name": "moving_direction", "data_files": "json/moving_direction.json"}, {"config_name": "state_change", "data_files": "json/state_change.json"}, {"config_name": "object_interaction", "data_files": "json/object_interaction.json"}, {"config_name": "character_order", "data_files": "json/character_order.json"}, {"config_name": "action_localization", "data_files": "json/action_localization.json"}, {"config_name": "counterfactual_inference", "data_files": "json/counterfactual_inference.json"}, {"config_name": "fine_grained_action", "data_files": "json/fine_grained_action.json"}, {"config_name": "moving_attribute", "data_files": "json/moving_attribute.json"}, {"config_name": "egocentric_navigation", "data_files": "json/egocentric_navigation.json"}]} | 2023-12-01T15:17:30+00:00 | [
"2311.17005"
] | [
"en"
] | TAGS
#task_categories-visual-question-answering #task_categories-question-answering #task_categories-conversational #size_categories-1K<n<10K #language-English #license-mit #arxiv-2311.17005 #region-us
| # MVBench
## Dataset Description
- Repository: MVBench
- Paper: 2311.17005
- Point of Contact: mailto:kunchang li
!images
We introduce a novel static-to-dynamic method for defining temporal-related tasks. By converting static tasks into dynamic ones, we facilitate systematic generation of video tasks necessitating a wide range of temporal abilities, from perception to cognition. Guided by task definitions, we then automatically transform public video annotations into multiple-choice QA for task evaluation. This unique paradigm enables efficient creation of MVBench with minimal manual intervention while ensuring evaluation fairness through ground-truth video annotations and avoiding biased LLM scoring. The 20 temporal task examples are as follows.
!images
## Evaluation
An evaluation example is provided in URL. Please follow the pipeline to prepare the evaluation code for various MLLMs.
- Preprocess: We preserve the raw video (high resolution, long duration, etc.) along with corresponding annotations (start, end, subtitles, etc.) for future exploration; hence, the decoding of some raw videos like Perception Test may be slow.
- Prompt: We explore effective system prompts to encourage better temporal reasoning in MLLM, as well as efficient answer prompts for option extraction.
## Leadrboard
While an [Online leaderboard]() is under construction, the current standings are as follows:
!images | [
"# MVBench",
"## Dataset Description\n\n- Repository: MVBench\n- Paper: 2311.17005\n- Point of Contact: mailto:kunchang li\n\n!images\n\nWe introduce a novel static-to-dynamic method for defining temporal-related tasks. By converting static tasks into dynamic ones, we facilitate systematic generation of video tasks necessitating a wide range of temporal abilities, from perception to cognition. Guided by task definitions, we then automatically transform public video annotations into multiple-choice QA for task evaluation. This unique paradigm enables efficient creation of MVBench with minimal manual intervention while ensuring evaluation fairness through ground-truth video annotations and avoiding biased LLM scoring. The 20 temporal task examples are as follows.\n\n!images",
"## Evaluation\n\nAn evaluation example is provided in URL. Please follow the pipeline to prepare the evaluation code for various MLLMs.\n\n- Preprocess: We preserve the raw video (high resolution, long duration, etc.) along with corresponding annotations (start, end, subtitles, etc.) for future exploration; hence, the decoding of some raw videos like Perception Test may be slow.\n- Prompt: We explore effective system prompts to encourage better temporal reasoning in MLLM, as well as efficient answer prompts for option extraction.",
"## Leadrboard\n\nWhile an [Online leaderboard]() is under construction, the current standings are as follows:\n\n!images"
] | [
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"# MVBench",
"## Dataset Description\n\n- Repository: MVBench\n- Paper: 2311.17005\n- Point of Contact: mailto:kunchang li\n\n!images\n\nWe introduce a novel static-to-dynamic method for defining temporal-related tasks. By converting static tasks into dynamic ones, we facilitate systematic generation of video tasks necessitating a wide range of temporal abilities, from perception to cognition. Guided by task definitions, we then automatically transform public video annotations into multiple-choice QA for task evaluation. This unique paradigm enables efficient creation of MVBench with minimal manual intervention while ensuring evaluation fairness through ground-truth video annotations and avoiding biased LLM scoring. The 20 temporal task examples are as follows.\n\n!images",
"## Evaluation\n\nAn evaluation example is provided in URL. Please follow the pipeline to prepare the evaluation code for various MLLMs.\n\n- Preprocess: We preserve the raw video (high resolution, long duration, etc.) along with corresponding annotations (start, end, subtitles, etc.) for future exploration; hence, the decoding of some raw videos like Perception Test may be slow.\n- Prompt: We explore effective system prompts to encourage better temporal reasoning in MLLM, as well as efficient answer prompts for option extraction.",
"## Leadrboard\n\nWhile an [Online leaderboard]() is under construction, the current standings are as follows:\n\n!images"
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"passage: TAGS\n#task_categories-visual-question-answering #task_categories-question-answering #task_categories-conversational #size_categories-1K<n<10K #language-English #license-mit #arxiv-2311.17005 #region-us \n# MVBench## Dataset Description\n\n- Repository: MVBench\n- Paper: 2311.17005\n- Point of Contact: mailto:kunchang li\n\n!images\n\nWe introduce a novel static-to-dynamic method for defining temporal-related tasks. By converting static tasks into dynamic ones, we facilitate systematic generation of video tasks necessitating a wide range of temporal abilities, from perception to cognition. Guided by task definitions, we then automatically transform public video annotations into multiple-choice QA for task evaluation. This unique paradigm enables efficient creation of MVBench with minimal manual intervention while ensuring evaluation fairness through ground-truth video annotations and avoiding biased LLM scoring. The 20 temporal task examples are as follows.\n\n!images## Evaluation\n\nAn evaluation example is provided in URL. Please follow the pipeline to prepare the evaluation code for various MLLMs.\n\n- Preprocess: We preserve the raw video (high resolution, long duration, etc.) along with corresponding annotations (start, end, subtitles, etc.) for future exploration; hence, the decoding of some raw videos like Perception Test may be slow.\n- Prompt: We explore effective system prompts to encourage better temporal reasoning in MLLM, as well as efficient answer prompts for option extraction.## Leadrboard\n\nWhile an [Online leaderboard]() is under construction, the current standings are as follows:\n\n!images"
] |
cb5de3f10c93b2541a3b4337f13b158456cdb5ab |
# danbooru-tags-2016-2023
A dataset of danbooru tags.
## Dataset information
Generated using [danbooru](https://danbooru.donmai.us/) and [safebooru](https://safebooru.donmai.us/) API.
The dataset was created with the following conditions:
|Subset name|`all`|`safe`|
|-|-|-|
|API Endpoint|https://danbooru.donmai.us|https://safebooru.donmai.us|
|Date|`2016-01-01..2023-12-31`|`2016-01-01..2023-12-31`|
|Score|`>0`|`>0`|
|Rating|`g,s,q,e`|`g`|
|Filetype|`png,jpg,webp`|`png,jpg,webp`|
|Size (number of rows)|4,601,557|1,186,490|
## Usage
```
pip install datasets
```
```py
from datasets import load_dataset
dataset = load_dataset(
"isek-ai/danbooru-tags-2016-2023",
"safe", # or "all"
split="train",
)
print(dataset)
print(dataset[0])
# Dataset({
# features: ['id', 'copyright', 'character', 'artist', 'general', 'meta', 'rating', 'score', 'created_at'],
# num_rows: 1186490
# })
# {'id': 2229839, 'copyright': 'kara no kyoukai', 'character': 'ryougi shiki', 'artist': 'momoko (momopoco)', 'general': '1girl, 2016, :|, brown eyes, brown hair, closed mouth, cloud, cloudy sky, dated, day, flower, hair flower, hair ornament, japanese clothes, kimono, long hair, long sleeves, looking at viewer, new year, obi, outdoors, sash, shrine, sky, solo, standing, wide sleeves', 'meta': 'commentary request, partial commentary', 'rating': 'g', 'score': 76, 'created_at': '2016-01-01T00:43:18.369+09:00'}
``` | isek-ai/danbooru-tags-2016-2023 | [
"task_categories:text-classification",
"task_categories:text-generation",
"task_categories:text2text-generation",
"size_categories:1M<n<10M",
"language:en",
"license:cc0-1.0",
"danbooru",
"region:us"
] | 2023-11-28T12:11:20+00:00 | {"language": ["en"], "license": "cc0-1.0", "size_categories": ["1M<n<10M"], "task_categories": ["text-classification", "text-generation", "text2text-generation"], "dataset_info": [{"config_name": "all", "features": [{"name": "id", "dtype": "int64"}, {"name": "copyright", "dtype": "string"}, {"name": "character", "dtype": "string"}, {"name": "artist", "dtype": "string"}, {"name": "general", "dtype": "string"}, {"name": "meta", "dtype": "string"}, {"name": "rating", "dtype": "string"}, {"name": "score", "dtype": "int64"}, {"name": "created_at", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2507757369, "num_examples": 4601557}], "download_size": 991454905, "dataset_size": 2507757369}, {"config_name": "safe", "features": [{"name": "id", "dtype": "int64"}, {"name": "copyright", "dtype": "string"}, {"name": "character", "dtype": "string"}, {"name": "artist", "dtype": "string"}, {"name": "general", "dtype": "string"}, {"name": "meta", "dtype": "string"}, {"name": "rating", "dtype": "string"}, {"name": "score", "dtype": "int64"}, {"name": "created_at", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 646613535.5369519, "num_examples": 1186490}], "download_size": 247085114, "dataset_size": 646613535.5369519}], "configs": [{"config_name": "all", "data_files": [{"split": "train", "path": "all/train-*"}]}, {"config_name": "safe", "data_files": [{"split": "train", "path": "safe/train-*"}]}], "tags": ["danbooru"]} | 2024-02-05T23:38:24+00:00 | [] | [
"en"
] | TAGS
#task_categories-text-classification #task_categories-text-generation #task_categories-text2text-generation #size_categories-1M<n<10M #language-English #license-cc0-1.0 #danbooru #region-us
| danbooru-tags-2016-2023
=======================
A dataset of danbooru tags.
Dataset information
-------------------
Generated using danbooru and safebooru API.
The dataset was created with the following conditions:
Subset name: API Endpoint, 'all': URL, 'safe': URL
Subset name: Date, 'all': '2016-01-01..2023-12-31', 'safe': '2016-01-01..2023-12-31'
Subset name: Score, 'all': '>0', 'safe': '>0'
Subset name: Rating, 'all': 'g,s,q,e', 'safe': 'g'
Subset name: Filetype, 'all': 'png,jpg,webp', 'safe': 'png,jpg,webp'
Subset name: Size (number of rows), 'all': 4,601,557, 'safe': 1,186,490
Usage
-----
| [] | [
"TAGS\n#task_categories-text-classification #task_categories-text-generation #task_categories-text2text-generation #size_categories-1M<n<10M #language-English #license-cc0-1.0 #danbooru #region-us \n"
] | [
69
] | [
"passage: TAGS\n#task_categories-text-classification #task_categories-text-generation #task_categories-text2text-generation #size_categories-1M<n<10M #language-English #license-cc0-1.0 #danbooru #region-us \n"
] |
36fa2599cf5b46e61c4b5fb6b3814b27e5b54492 | ### Data Calibro Exl2
Detholiad o [Cofnod y Cynulliad](https://huggingface.co/datasets/techiaith/cofnodycynulliad_en-cy) Cymraeg i'w ddefnyddio yng ngham calibro ExLlama 2 wrth drosi modelau i fformat exl2. | BangorAI/exl2-wiki-calibration-set-cy | [
"license:cc-by-sa-3.0",
"region:us"
] | 2023-11-28T12:14:53+00:00 | {"license": "cc-by-sa-3.0"} | 2023-11-28T16:41:25+00:00 | [] | [] | TAGS
#license-cc-by-sa-3.0 #region-us
| ### Data Calibro Exl2
Detholiad o Cofnod y Cynulliad Cymraeg i'w ddefnyddio yng ngham calibro ExLlama 2 wrth drosi modelau i fformat exl2. | [
"### Data Calibro Exl2\n\nDetholiad o Cofnod y Cynulliad Cymraeg i'w ddefnyddio yng ngham calibro ExLlama 2 wrth drosi modelau i fformat exl2."
] | [
"TAGS\n#license-cc-by-sa-3.0 #region-us \n",
"### Data Calibro Exl2\n\nDetholiad o Cofnod y Cynulliad Cymraeg i'w ddefnyddio yng ngham calibro ExLlama 2 wrth drosi modelau i fformat exl2."
] | [
17,
42
] | [
"passage: TAGS\n#license-cc-by-sa-3.0 #region-us \n### Data Calibro Exl2\n\nDetholiad o Cofnod y Cynulliad Cymraeg i'w ddefnyddio yng ngham calibro ExLlama 2 wrth drosi modelau i fformat exl2."
] |
eac47e5b75450dc5d45598cac7637b239c9bb051 | # Guanaco-1k: Lazy Llama 2 Formatting
This is a subset (1000 samples) of the excellent [`timdettmers/openassistant-guanaco`](https://huggingface.co/datasets/timdettmers/openassistant-guanaco) dataset, processed to match Llama 2's prompt format as described [in this article](https://huggingface.co/blog/llama2#how-to-prompt-llama-2). It was created using the following [colab notebook](https://colab.research.google.com/drive/1Ad7a9zMmkxuXTOh1Z7-rNSICA4dybpM2?usp=sharing).
Useful if you don't want to reformat it by yourself (e.g., using a script). It was designed for [this article](https://mlabonne.github.io/blog/posts/Fine_Tune_Your_Own_Llama_2_Model_in_a_Colab_Notebook.html) about fine-tuning a Llama 2 (chat) model in a Google Colab.
| yimhuang/guanaco-llama2-1k | [
"region:us"
] | 2023-11-28T12:30:09+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1654448, "num_examples": 1000}], "download_size": 966693, "dataset_size": 1654448}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2023-11-28T12:38:21+00:00 | [] | [] | TAGS
#region-us
| # Guanaco-1k: Lazy Llama 2 Formatting
This is a subset (1000 samples) of the excellent 'timdettmers/openassistant-guanaco' dataset, processed to match Llama 2's prompt format as described in this article. It was created using the following colab notebook.
Useful if you don't want to reformat it by yourself (e.g., using a script). It was designed for this article about fine-tuning a Llama 2 (chat) model in a Google Colab.
| [
"# Guanaco-1k: Lazy Llama 2 Formatting\n\nThis is a subset (1000 samples) of the excellent 'timdettmers/openassistant-guanaco' dataset, processed to match Llama 2's prompt format as described in this article. It was created using the following colab notebook.\n\nUseful if you don't want to reformat it by yourself (e.g., using a script). It was designed for this article about fine-tuning a Llama 2 (chat) model in a Google Colab."
] | [
"TAGS\n#region-us \n",
"# Guanaco-1k: Lazy Llama 2 Formatting\n\nThis is a subset (1000 samples) of the excellent 'timdettmers/openassistant-guanaco' dataset, processed to match Llama 2's prompt format as described in this article. It was created using the following colab notebook.\n\nUseful if you don't want to reformat it by yourself (e.g., using a script). It was designed for this article about fine-tuning a Llama 2 (chat) model in a Google Colab."
] | [
6,
120
] | [
"passage: TAGS\n#region-us \n# Guanaco-1k: Lazy Llama 2 Formatting\n\nThis is a subset (1000 samples) of the excellent 'timdettmers/openassistant-guanaco' dataset, processed to match Llama 2's prompt format as described in this article. It was created using the following colab notebook.\n\nUseful if you don't want to reformat it by yourself (e.g., using a script). It was designed for this article about fine-tuning a Llama 2 (chat) model in a Google Colab."
] |
9eacadde65cda741db0f46cab82f6ef787620fa8 |
# Turkish Sentiment Analysis Tweet Dataset: BilTweetNews
The dataset contains tweets related to six major events from Turkish news sources between May 4, 2015
and Jan 8, 2017.
The dataset covers 6 major events:
- May 25, 2015 One of the popular football clubs in Turkey, Galatasaray, wins the 2015
Turkish Super League.
- Sep 6, 2015 A terrorist group, called PKK, attacked to soldiers in Daฤlฤฑca, a village in
southeastern Turkey.
- Oct 7, 2015 A Turkish scientist, Aziz Sancar, won the 2015 Nobel Chemistry prize with
his studies on DNA repair.
- May 27, 2016 A local football club of Alanya promoted to the Turkish Super League for
the first time in their history.
- Jun 17, 2016 A traditional anthem that is mostly played by secularists in Turkey, called
the 10th Year Anthem, was forbidden in schools by the director of national
education in the Black Sea province of Bolu.
- Oct 17, 2016 A magazine programmer confused that Madonna in a Fur Coat, a book written
in 1943 by a Turkish celebrated writer, Sabahattin Ali, was about popstar
Madonnaโs life. The book tells a story between a Turkish student and German
singer after the World War I.
- Not related to any news topic
For each event, 100 related-candidate and 60 unrelated-candidate tweets are selected. Lastly, we randomly select 40 tweets that are potentially not related at all, 5 of them are
removed due to detecting near-duplicates later. The dataset has 995 tweets in total.
There are 4 sentiment classes:
- Positive
- Negative
- Neutral
- Sarcastic
All tweets are labeled by 17 annotators. We provide the normalized distribution of annotations across 4 sentiment classes. We also provide the majority sentiment class at the last column. If there are multiple classes with highest scores, then we set "Multi" as majority.
Github Repo: https://github.com/BilkentInformationRetrievalGroup/BilTweetNews2017
# If you would like to use any material in this repository, please cite the following papers:
- Toraman, C. Early Prediction of Public Reactions to News Events Using Microblogs. Seventh BCS-IRSG Symposium on Future Directions in Information Access (FDIA 2017), Barcelona, Spain, 5 September 2017.
- Toraman, C. Event-related microblog retrieval in Turkish. Turkish Journal of Electrical Engineering and Computer Sciences. 2021. DOI: 10.3906/elk-2108-167
**** | ctoraman/BilTweetNews-sentiment-analysis | [
"task_categories:text-classification",
"language:tr",
"license:cc-by-nc-sa-4.0",
"sentiment analysis",
"text classification",
"tweets",
"social media",
"turkish",
"sarcasm",
"sarcastic",
"region:us"
] | 2023-11-28T12:41:24+00:00 | {"language": ["tr"], "license": "cc-by-nc-sa-4.0", "task_categories": ["text-classification"], "tags": ["sentiment analysis", "text classification", "tweets", "social media", "turkish", "sarcasm", "sarcastic"]} | 2023-11-29T11:09:56+00:00 | [] | [
"tr"
] | TAGS
#task_categories-text-classification #language-Turkish #license-cc-by-nc-sa-4.0 #sentiment analysis #text classification #tweets #social media #turkish #sarcasm #sarcastic #region-us
|
# Turkish Sentiment Analysis Tweet Dataset: BilTweetNews
The dataset contains tweets related to six major events from Turkish news sources between May 4, 2015
and Jan 8, 2017.
The dataset covers 6 major events:
- May 25, 2015 One of the popular football clubs in Turkey, Galatasaray, wins the 2015
Turkish Super League.
- Sep 6, 2015 A terrorist group, called PKK, attacked to soldiers in Daฤlฤฑca, a village in
southeastern Turkey.
- Oct 7, 2015 A Turkish scientist, Aziz Sancar, won the 2015 Nobel Chemistry prize with
his studies on DNA repair.
- May 27, 2016 A local football club of Alanya promoted to the Turkish Super League for
the first time in their history.
- Jun 17, 2016 A traditional anthem that is mostly played by secularists in Turkey, called
the 10th Year Anthem, was forbidden in schools by the director of national
education in the Black Sea province of Bolu.
- Oct 17, 2016 A magazine programmer confused that Madonna in a Fur Coat, a book written
in 1943 by a Turkish celebrated writer, Sabahattin Ali, was about popstar
Madonnaโs life. The book tells a story between a Turkish student and German
singer after the World War I.
- Not related to any news topic
For each event, 100 related-candidate and 60 unrelated-candidate tweets are selected. Lastly, we randomly select 40 tweets that are potentially not related at all, 5 of them are
removed due to detecting near-duplicates later. The dataset has 995 tweets in total.
There are 4 sentiment classes:
- Positive
- Negative
- Neutral
- Sarcastic
All tweets are labeled by 17 annotators. We provide the normalized distribution of annotations across 4 sentiment classes. We also provide the majority sentiment class at the last column. If there are multiple classes with highest scores, then we set "Multi" as majority.
Github Repo: URL
# If you would like to use any material in this repository, please cite the following papers:
- Toraman, C. Early Prediction of Public Reactions to News Events Using Microblogs. Seventh BCS-IRSG Symposium on Future Directions in Information Access (FDIA 2017), Barcelona, Spain, 5 September 2017.
- Toraman, C. Event-related microblog retrieval in Turkish. Turkish Journal of Electrical Engineering and Computer Sciences. 2021. DOI: 10.3906/elk-2108-167
| [
"# Turkish Sentiment Analysis Tweet Dataset: BilTweetNews\n\nThe dataset contains tweets related to six major events from Turkish news sources between May 4, 2015\nand Jan 8, 2017. \n\nThe dataset covers 6 major events:\n- May 25, 2015 One of the popular football clubs in Turkey, Galatasaray, wins the 2015\nTurkish Super League.\n- Sep 6, 2015 A terrorist group, called PKK, attacked to soldiers in Daฤlฤฑca, a village in\nsoutheastern Turkey. \n- Oct 7, 2015 A Turkish scientist, Aziz Sancar, won the 2015 Nobel Chemistry prize with\nhis studies on DNA repair.\n- May 27, 2016 A local football club of Alanya promoted to the Turkish Super League for\nthe first time in their history.\n- Jun 17, 2016 A traditional anthem that is mostly played by secularists in Turkey, called\nthe 10th Year Anthem, was forbidden in schools by the director of national\neducation in the Black Sea province of Bolu. \n- Oct 17, 2016 A magazine programmer confused that Madonna in a Fur Coat, a book written\nin 1943 by a Turkish celebrated writer, Sabahattin Ali, was about popstar\nMadonnaโs life. The book tells a story between a Turkish student and German\nsinger after the World War I.\n- Not related to any news topic \n\nFor each event, 100 related-candidate and 60 unrelated-candidate tweets are selected. Lastly, we randomly select 40 tweets that are potentially not related at all, 5 of them are\nremoved due to detecting near-duplicates later. The dataset has 995 tweets in total. \n\nThere are 4 sentiment classes:\n\n- Positive\n- Negative\n- Neutral\n- Sarcastic\n\nAll tweets are labeled by 17 annotators. We provide the normalized distribution of annotations across 4 sentiment classes. We also provide the majority sentiment class at the last column. If there are multiple classes with highest scores, then we set \"Multi\" as majority.\n\nGithub Repo: URL",
"# If you would like to use any material in this repository, please cite the following papers:\n- Toraman, C. Early Prediction of Public Reactions to News Events Using Microblogs. Seventh BCS-IRSG Symposium on Future Directions in Information Access (FDIA 2017), Barcelona, Spain, 5 September 2017.\n\n- Toraman, C. Event-related microblog retrieval in Turkish. Turkish Journal of Electrical Engineering and Computer Sciences. 2021. DOI: 10.3906/elk-2108-167"
] | [
"TAGS\n#task_categories-text-classification #language-Turkish #license-cc-by-nc-sa-4.0 #sentiment analysis #text classification #tweets #social media #turkish #sarcasm #sarcastic #region-us \n",
"# Turkish Sentiment Analysis Tweet Dataset: BilTweetNews\n\nThe dataset contains tweets related to six major events from Turkish news sources between May 4, 2015\nand Jan 8, 2017. \n\nThe dataset covers 6 major events:\n- May 25, 2015 One of the popular football clubs in Turkey, Galatasaray, wins the 2015\nTurkish Super League.\n- Sep 6, 2015 A terrorist group, called PKK, attacked to soldiers in Daฤlฤฑca, a village in\nsoutheastern Turkey. \n- Oct 7, 2015 A Turkish scientist, Aziz Sancar, won the 2015 Nobel Chemistry prize with\nhis studies on DNA repair.\n- May 27, 2016 A local football club of Alanya promoted to the Turkish Super League for\nthe first time in their history.\n- Jun 17, 2016 A traditional anthem that is mostly played by secularists in Turkey, called\nthe 10th Year Anthem, was forbidden in schools by the director of national\neducation in the Black Sea province of Bolu. \n- Oct 17, 2016 A magazine programmer confused that Madonna in a Fur Coat, a book written\nin 1943 by a Turkish celebrated writer, Sabahattin Ali, was about popstar\nMadonnaโs life. The book tells a story between a Turkish student and German\nsinger after the World War I.\n- Not related to any news topic \n\nFor each event, 100 related-candidate and 60 unrelated-candidate tweets are selected. Lastly, we randomly select 40 tweets that are potentially not related at all, 5 of them are\nremoved due to detecting near-duplicates later. The dataset has 995 tweets in total. \n\nThere are 4 sentiment classes:\n\n- Positive\n- Negative\n- Neutral\n- Sarcastic\n\nAll tweets are labeled by 17 annotators. We provide the normalized distribution of annotations across 4 sentiment classes. We also provide the majority sentiment class at the last column. If there are multiple classes with highest scores, then we set \"Multi\" as majority.\n\nGithub Repo: URL",
"# If you would like to use any material in this repository, please cite the following papers:\n- Toraman, C. Early Prediction of Public Reactions to News Events Using Microblogs. Seventh BCS-IRSG Symposium on Future Directions in Information Access (FDIA 2017), Barcelona, Spain, 5 September 2017.\n\n- Toraman, C. Event-related microblog retrieval in Turkish. Turkish Journal of Electrical Engineering and Computer Sciences. 2021. DOI: 10.3906/elk-2108-167"
] | [
61,
440,
123
] | [
"passage: TAGS\n#task_categories-text-classification #language-Turkish #license-cc-by-nc-sa-4.0 #sentiment analysis #text classification #tweets #social media #turkish #sarcasm #sarcastic #region-us \n# Turkish Sentiment Analysis Tweet Dataset: BilTweetNews\n\nThe dataset contains tweets related to six major events from Turkish news sources between May 4, 2015\nand Jan 8, 2017. \n\nThe dataset covers 6 major events:\n- May 25, 2015 One of the popular football clubs in Turkey, Galatasaray, wins the 2015\nTurkish Super League.\n- Sep 6, 2015 A terrorist group, called PKK, attacked to soldiers in Daฤlฤฑca, a village in\nsoutheastern Turkey. \n- Oct 7, 2015 A Turkish scientist, Aziz Sancar, won the 2015 Nobel Chemistry prize with\nhis studies on DNA repair.\n- May 27, 2016 A local football club of Alanya promoted to the Turkish Super League for\nthe first time in their history.\n- Jun 17, 2016 A traditional anthem that is mostly played by secularists in Turkey, called\nthe 10th Year Anthem, was forbidden in schools by the director of national\neducation in the Black Sea province of Bolu. \n- Oct 17, 2016 A magazine programmer confused that Madonna in a Fur Coat, a book written\nin 1943 by a Turkish celebrated writer, Sabahattin Ali, was about popstar\nMadonnaโs life. The book tells a story between a Turkish student and German\nsinger after the World War I.\n- Not related to any news topic \n\nFor each event, 100 related-candidate and 60 unrelated-candidate tweets are selected. Lastly, we randomly select 40 tweets that are potentially not related at all, 5 of them are\nremoved due to detecting near-duplicates later. The dataset has 995 tweets in total. \n\nThere are 4 sentiment classes:\n\n- Positive\n- Negative\n- Neutral\n- Sarcastic\n\nAll tweets are labeled by 17 annotators. We provide the normalized distribution of annotations across 4 sentiment classes. We also provide the majority sentiment class at the last column. If there are multiple classes with highest scores, then we set \"Multi\" as majority.\n\nGithub Repo: URL"
] |
d356d14b1944d915bc940db62d2f3a26040865bb | # Dataset Card for "find_sent_before_sent_train_100_eval_40_recite"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | tyzhu/find_sent_before_sent_train_100_eval_40_recite | [
"region:us"
] | 2023-11-28T12:47:59+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "validation", "path": "data/validation-*"}]}], "dataset_info": {"features": [{"name": "inputs", "dtype": "string"}, {"name": "targets", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "context", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1169584, "num_examples": 644}, {"name": "validation", "num_bytes": 377548, "num_examples": 202}], "download_size": 325994, "dataset_size": 1547132}} | 2023-11-28T13:43:41+00:00 | [] | [] | TAGS
#region-us
| # Dataset Card for "find_sent_before_sent_train_100_eval_40_recite"
More Information needed | [
"# Dataset Card for \"find_sent_before_sent_train_100_eval_40_recite\"\n\nMore Information needed"
] | [
"TAGS\n#region-us \n",
"# Dataset Card for \"find_sent_before_sent_train_100_eval_40_recite\"\n\nMore Information needed"
] | [
6,
31
] | [
"passage: TAGS\n#region-us \n# Dataset Card for \"find_sent_before_sent_train_100_eval_40_recite\"\n\nMore Information needed"
] |
048f3466a670d251c9c01653a2bb3e35e3b71c4a | # Dataset Card for "find_sent_after_sent_train_100_eval_40_recite"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | tyzhu/find_sent_after_sent_train_100_eval_40_recite | [
"region:us"
] | 2023-11-28T12:49:42+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "validation", "path": "data/validation-*"}]}], "dataset_info": {"features": [{"name": "inputs", "dtype": "string"}, {"name": "targets", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "context", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1168154, "num_examples": 644}, {"name": "validation", "num_bytes": 377200, "num_examples": 202}], "download_size": 325715, "dataset_size": 1545354}} | 2023-11-28T13:44:20+00:00 | [] | [] | TAGS
#region-us
| # Dataset Card for "find_sent_after_sent_train_100_eval_40_recite"
More Information needed | [
"# Dataset Card for \"find_sent_after_sent_train_100_eval_40_recite\"\n\nMore Information needed"
] | [
"TAGS\n#region-us \n",
"# Dataset Card for \"find_sent_after_sent_train_100_eval_40_recite\"\n\nMore Information needed"
] | [
6,
31
] | [
"passage: TAGS\n#region-us \n# Dataset Card for \"find_sent_after_sent_train_100_eval_40_recite\"\n\nMore Information needed"
] |
522de02393ffb974fa4551baf3850091185bf7bd | # Dataset Card for "find_sent_before_sent_train_200_eval_40_recite"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | tyzhu/find_sent_before_sent_train_200_eval_40_recite | [
"region:us"
] | 2023-11-28T12:50:15+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "validation", "path": "data/validation-*"}]}], "dataset_info": {"features": [{"name": "inputs", "dtype": "string"}, {"name": "targets", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "context", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2329316, "num_examples": 1263}, {"name": "validation", "num_bytes": 398956, "num_examples": 203}], "download_size": 533740, "dataset_size": 2728272}} | 2023-11-28T13:44:51+00:00 | [] | [] | TAGS
#region-us
| # Dataset Card for "find_sent_before_sent_train_200_eval_40_recite"
More Information needed | [
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9dfa5cafe5f9657a428d9e6d1e4ad7cc84900384 | # Dataset Card for "find_sent_after_sent_train_200_eval_40_recite"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | tyzhu/find_sent_after_sent_train_200_eval_40_recite | [
"region:us"
] | 2023-11-28T12:51:52+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "validation", "path": "data/validation-*"}]}], "dataset_info": {"features": [{"name": "inputs", "dtype": "string"}, {"name": "targets", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "context", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2328090, "num_examples": 1263}, {"name": "validation", "num_bytes": 398145, "num_examples": 203}], "download_size": 534849, "dataset_size": 2726235}} | 2023-11-28T13:45:17+00:00 | [] | [] | TAGS
#region-us
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a2132c2b048c7fa554e8554bfc7389ff655d8ec0 | # Dataset Card for "find_sent_before_sent_train_400_eval_40_recite"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | tyzhu/find_sent_before_sent_train_400_eval_40_recite | [
"region:us"
] | 2023-11-28T12:52:35+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "validation", "path": "data/validation-*"}]}], "dataset_info": {"features": [{"name": "inputs", "dtype": "string"}, {"name": "targets", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "context", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 4501202, "num_examples": 2434}, {"name": "validation", "num_bytes": 393066, "num_examples": 200}], "download_size": 917612, "dataset_size": 4894268}} | 2023-11-28T13:46:15+00:00 | [] | [] | TAGS
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bc6b123ba0932050ef5dafd81cfdc51c1d3e7651 | # Dataset Card for "find_sent_after_sent_train_400_eval_40_recite"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | tyzhu/find_sent_after_sent_train_400_eval_40_recite | [
"region:us"
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#region-us
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f3d3dc936046e1566a0e81c1827faaff608ae28c | # Dataset Card for "find_marker_both_sent_train_100_eval_40_recite"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | tyzhu/find_marker_both_sent_train_100_eval_40_recite | [
"region:us"
] | 2023-11-28T13:01:14+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "validation", "path": "data/validation-*"}]}], "dataset_info": {"features": [{"name": "inputs", "dtype": "string"}, {"name": "targets", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "context", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1175807, "num_examples": 644}, {"name": "validation", "num_bytes": 376070, "num_examples": 202}], "download_size": 270356, "dataset_size": 1551877}} | 2023-11-28T13:48:00+00:00 | [] | [] | TAGS
#region-us
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7742abfc8994985c1007ad79b20bc368abdc7199 | # Dataset Card for "find_marker_both_sent_train_200_eval_40_recite"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | tyzhu/find_marker_both_sent_train_200_eval_40_recite | [
"region:us"
] | 2023-11-28T13:02:07+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "validation", "path": "data/validation-*"}]}], "dataset_info": {"features": [{"name": "inputs", "dtype": "string"}, {"name": "targets", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "context", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2338690, "num_examples": 1263}, {"name": "validation", "num_bytes": 395888, "num_examples": 203}], "download_size": 433789, "dataset_size": 2734578}} | 2023-11-28T13:49:28+00:00 | [] | [] | TAGS
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974219ea005f7fd3b7e60bf8c0d17c357c1f5905 | # Dataset Card for "find_marker_both_sent_train_400_eval_40_recite"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | tyzhu/find_marker_both_sent_train_400_eval_40_recite | [
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06581116970aece9066540008fbca33ca52bb7de |
# Dataset Card for Hugging Face Hub Models with Base Model Metadata
## Dataset Details
This dataset contains a subset of possible metadata for models hosted on the Hugging Face Hub.
All of these models contain `base_model` metadata i.e. information about the model used for fine-tuning.
This data can be used for creating network graphs showing links between models on the Hub.
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
### Dataset Sources [optional]
<!-- Provide the basic links for the dataset. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the dataset is intended to be used. -->
### Direct Use
<!-- This section describes suitable use cases for the dataset. -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
[More Information Needed]
## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
[More Information Needed]
## Dataset Creation
### Curation Rationale
<!-- Motivation for the creation of this dataset. -->
[More Information Needed]
### Source Data
<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
#### Data Collection and Processing
<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
[More Information Needed]
#### Who are the source data producers?
The source data is model card creators for models on the Hub as well as tools and deep learning libraries which automatically assign metadata to model repositories.
| librarian-bots/hub_models_with_base_model_info | [
"size_categories:10K<n<100K",
"metadata",
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|
# Dataset Card for Hugging Face Hub Models with Base Model Metadata
## Dataset Details
This dataset contains a subset of possible metadata for models hosted on the Hugging Face Hub.
All of these models contain 'base_model' metadata i.e. information about the model used for fine-tuning.
This data can be used for creating network graphs showing links between models on the Hub.
### Dataset Description
- Curated by:
- Funded by [optional]:
- Shared by [optional]:
- Language(s) (NLP):
- License:
### Dataset Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Out-of-Scope Use
## Dataset Structure
## Dataset Creation
### Curation Rationale
### Source Data
#### Data Collection and Processing
#### Who are the source data producers?
The source data is model card creators for models on the Hub as well as tools and deep learning libraries which automatically assign metadata to model repositories.
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22a60ca0742380b3d9a2111ad65c755f88bce6a9 | # Instruction Data
## Dataset Description
- **Repository:** [VideoChat2](https://github.com/OpenGVLab/Ask-Anything/tree/main/video_chat2)
- **Paper:** [2311.17005](https://arxiv.org/abs/2311.17005)
- **Point of Contact:** mailto:[kunchang li]([email protected])
## Annotations
A comprehensive dataset of **1.9M** data annotations is available in [JSON](https://huggingface.co/datasets/OpenGVLab/VideoChat2-IT) format. Due to the extensive size of the full data, we provide only JSON files here. For corresponding images and videos, please follow our instructions.
## Source data
### Image
For image datasets, we utilized [M3IT](https://huggingface.co/datasets/MMInstruction/M3IT), filtering out lower-quality data by:
- **Correcting typos**: Most sentences with incorrect punctuation usage were rectified.
- **Rephrasing incorrect answers**: Some responses generated by ChatGPT, such as "Sorry, ...", were incorrect. These were rephrased using GPT-4.
You can easily download the datasets we employed from [M3IT](https://huggingface.co/datasets/MMInstruction/M3IT).
### Video
We treated video datasets differently. Please download the original videos from the provided links:
- [VideoChat](https://github.com/OpenGVLab/InternVideo/tree/main/Data/instruction_data): Based on [InternVid](https://github.com/OpenGVLab/InternVideo/tree/main/Data/InternVid), we created additional instruction data and used GPT-4 to condense the existing data.
- [VideoChatGPT](https://github.com/mbzuai-oryx/Video-ChatGPT/tree/main/data): The original caption data was converted into conversation data based on the same VideoIDs.
- [Kinetics-710](https://github.com/OpenGVLab/UniFormerV2/blob/main/DATASET.md) & [SthSthV2](
https://developer.qualcomm.com/software/ai-datasets/something-something): Option candidates were generated from [UMT](https://github.com/OpenGVLab/unmasked_teacher) top-20 predictions.
- [NExTQA](https://github.com/doc-doc/NExT-QA): Typos in the original sentences were corrected.
- [CLEVRER](https://clevrer.csail.mit.edu/): For single-option multiple-choice QAs, we used only those concerning color/material/shape. For multi-option multiple-choice QAs, we utilized all the data.
- [WebVid](https://maxbain.com/webvid-dataset/): Non-overlapping data was selected for captioning and [QA](https://antoyang.github.io/just-ask.html#webvidvqa).
- [YouCook2](https://youcook2.eecs.umich.edu/): Original videos were truncated based on the official dense captions.
- [TextVR](https://github.com/callsys/textvr): All data was used without modifications.
- [TGIF](https://github.com/YunseokJANG/tgif-qa): Only TGIF$_{frame}$ and TGIF$_{Transition}$ subsets were considered.
- [EgoQA](https://ego4d-data.org/): Some egocentric QAs were generated from Ego4D data.
For all datasets, task instructions were automatically generated using GPT-4.
## Citation
If you find this project useful in your research, please consider cite:
```BibTeX
@article{2023videochat,
title={VideoChat: Chat-Centric Video Understanding},
author={KunChang Li, Yinan He, Yi Wang, Yizhuo Li, Wenhai Wang, Ping Luo, Yali Wang, Limin Wang, and Yu Qiao},
journal={arXiv preprint arXiv:2305.06355},
year={2023}
}
@misc{li2023mvbench,
title={MVBench: A Comprehensive Multi-modal Video Understanding Benchmark},
author={Kunchang Li and Yali Wang and Yinan He and Yizhuo Li and Yi Wang and Yi Liu and Zun Wang and Jilan Xu and Guo Chen and Ping Luo and Limin Wang and Yu Qiao},
year={2023},
eprint={2311.17005},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
``` | OpenGVLab/VideoChat2-IT | [
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] | 2023-11-28T13:14:18+00:00 | {"language": ["en"], "license": "mit", "size_categories": ["1M<n<10M"], "task_categories": ["visual-question-answering", "question-answering", "conversational"], "extra_gated_prompt": "You agree to not use the dataset to conduct experiments that cause harm to human subjects. Please note that the data in this dataset may be subject to other agreements. Before using the data, be sure to read the relevant agreements carefully to ensure compliant use. Video copyrights belong to the original video creators or platforms and are for academic research use only.", "extra_gated_fields": {"Name": "text", "Company/Organization": "text", "Country": "text", "E-Mail": "text"}, "configs": [{"config_name": "video_classification", "data_files": [{"split": "ssv2", "path": "video/classification/ssv2/train.json"}, {"split": "k710", "path": "video/classification/k710/train.json"}]}, {"config_name": "video_reasoning", "data_files": [{"split": "clevrer_mc", "path": "video/reasoning/clevrer_mc/train.json"}, {"split": "next_qa", "path": "video/reasoning/next_qa/train.json"}, {"split": "clevrer_qa", "path": "video/reasoning/clevrer_qa/train.json"}]}, {"config_name": "video_conversation", "data_files": [{"split": "videochat2", "path": "video/conversation/videochat2/train.json"}, {"split": "videochatgpt", "path": "video/conversation/videochatgpt/train.json"}, {"split": "videochat1", "path": "video/conversation/videochat1/train.json"}]}, {"config_name": "video_vqa", "data_files": [{"split": "webvid_qa", "path": "video/vqa/webvid_qa/train.json"}, {"split": "tgif_transition_qa", "path": "video/vqa/tgif_transition_qa/train.json"}, {"split": "tgif_frame_qa", "path": "video/vqa/tgif_frame_qa/train.json"}, {"split": "ego_qa", "path": "video/vqa/ego_qa/train.json"}]}, {"config_name": "video_caption", "data_files": [{"split": "textvr", "path": "video/caption/textvr/train.json"}, {"split": "youcook2", "path": "video/caption/youcook2/train.json"}, {"split": "webvid", "path": "video/caption/webvid/train.json"}, {"split": "videochat", "path": "video/caption/videochat/train.json"}]}, {"config_name": "image_classification", "data_files": [{"split": "imagenet", "path": "image/classification/imagenet/train.json"}, {"split": "coco_itm", "path": "image/classification/coco_itm/train.json"}]}, {"config_name": "image_caption", "data_files": [{"split": "textcaps", "path": "image/caption/textcaps/train.json"}, {"split": "minigpt4", "path": "image/caption/minigpt4/train.json"}, {"split": "coco", "path": "image/caption/coco/train.json"}, {"split": "paragraph_captioning", "path": "image/caption/paragraph_captioning/train.json"}, {"split": "llava", "path": "image/caption/llava/train.json"}]}, {"config_name": "image_reasoning", "data_files": [{"split": "llava", "path": "image/reasoning/llava/train.json"}, {"split": "clevr", "path": "image/reasoning/clevr/train.json"}, {"split": "visual_mrc", "path": "image/reasoning/visual_mrc/train.json"}]}, {"config_name": "image_conversation", "data_files": [{"split": "llava", "path": "image/conversation/llava/train.json"}]}, {"config_name": "image_vqa", "data_files": [{"split": "okvqa", "path": "image/vqa/okvqa/train.json"}, {"split": "docvqa", "path": "image/vqa/docvqa/train.json"}, {"split": "ocr_vqa", "path": "image/vqa/ocr_vqa/train.json"}, {"split": "vqav2_chinese", "path": "image/vqa/vqav2_chinese/train.json"}, {"split": "vqav2", "path": "image/vqa/vqav2/train.json"}, {"split": "st_vqa", "path": "image/vqa/st_vqa/train.json"}, {"split": "text_vqa", "path": "image/vqa/text_vqa/train.json"}, {"split": "gqa", "path": "image/vqa/gqa/train.json"}, {"split": "okvqa_chinese", "path": "image/vqa/okvqa_chinese/train.json"}, {"split": "viquae", "path": "image/vqa/viquae/train.json"}, {"split": "a_okvqa", "path": "image/vqa/a_okvqa/train.json"}]}]} | 2024-01-23T05:10:26+00:00 | [
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#task_categories-visual-question-answering #task_categories-question-answering #task_categories-conversational #size_categories-1M<n<10M #language-English #license-mit #arxiv-2311.17005 #region-us
| # Instruction Data
!images
## Dataset Description
- Repository: VideoChat2
- Paper: 2311.17005
- Point of Contact: mailto:kunchang li
## Annotations
A comprehensive dataset of 1.9M data annotations is available in JSON format. Due to the extensive size of the full data, we provide only JSON files here. For corresponding images and videos, please follow our instructions.
## Source data
### Image
For image datasets, we utilized M3IT, filtering out lower-quality data by:
- Correcting typos: Most sentences with incorrect punctuation usage were rectified.
- Rephrasing incorrect answers: Some responses generated by ChatGPT, such as "Sorry, ...", were incorrect. These were rephrased using GPT-4.
You can easily download the datasets we employed from M3IT.
### Video
We treated video datasets differently. Please download the original videos from the provided links:
- VideoChat: Based on InternVid, we created additional instruction data and used GPT-4 to condense the existing data.
- VideoChatGPT: The original caption data was converted into conversation data based on the same VideoIDs.
- Kinetics-710 & SthSthV2: Option candidates were generated from UMT top-20 predictions.
- NExTQA: Typos in the original sentences were corrected.
- CLEVRER: For single-option multiple-choice QAs, we used only those concerning color/material/shape. For multi-option multiple-choice QAs, we utilized all the data.
- WebVid: Non-overlapping data was selected for captioning and QA.
- YouCook2: Original videos were truncated based on the official dense captions.
- TextVR: All data was used without modifications.
- TGIF: Only TGIF$_{frame}$ and TGIF$_{Transition}$ subsets were considered.
- EgoQA: Some egocentric QAs were generated from Ego4D data.
For all datasets, task instructions were automatically generated using GPT-4.
If you find this project useful in your research, please consider cite:
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"### Video\nWe treated video datasets differently. Please download the original videos from the provided links:\n- VideoChat: Based on InternVid, we created additional instruction data and used GPT-4 to condense the existing data.\n- VideoChatGPT: The original caption data was converted into conversation data based on the same VideoIDs.\n- Kinetics-710 & SthSthV2: Option candidates were generated from UMT top-20 predictions.\n- NExTQA: Typos in the original sentences were corrected.\n- CLEVRER: For single-option multiple-choice QAs, we used only those concerning color/material/shape. For multi-option multiple-choice QAs, we utilized all the data.\n- WebVid: Non-overlapping data was selected for captioning and QA.\n- YouCook2: Original videos were truncated based on the official dense captions.\n- TextVR: All data was used without modifications.\n- TGIF: Only TGIF$_{frame}$ and TGIF$_{Transition}$ subsets were considered.\n- EgoQA: Some egocentric QAs were generated from Ego4D data.\n\nFor all datasets, task instructions were automatically generated using GPT-4.\n\nIf you find this project useful in your research, please consider cite:"
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9aca8b0266e5293d0d74fc09bf2ebd5153a30a2c | # Dataset Card for Dataset Name
Contains reduced description of issues reported at https://projects.blender.org/blender/blender/issues and points to duplicate issues in order to categorize similarity.
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).
## Dataset Details
### Dataset Description
Each report has been shortened by removing frequently repeated texts such as **System Information**, **Blender Version**, **Short description of error**.
This dataset was used to train https://huggingface.co/mano-wii/BAAI_bge-base-en-v1.5-tunned-for-blender-issues
- **Curated by:** @mano-wii
- **Funded by:** @mano-wii
- **Shared by:** @mano-wii
- **Language(s) (NLP):** English
- **License:** https://mano-wii-tools.hf.space/api/v1/static/privace.txt
## Uses
With this dataset, we can train a model considering terms and utilities used in Blender and the relation with problems with specific hardware.
### Direct Use
Creation of embeddings for 3D software technical reports
## Dataset Structure
At this dataset we can see the main issue in the first column, unrecognized issues in the second column ('neg') and duplicates in the third column ('pos').
## Dataset Creation
### Curation Rationale
This dataset was created to train a model for creating embeddings to search for semantic similarity of reports in Blender and thus allow the WEB Extension
[Blender Find Related Issues](https://chromewebstore.google.com/detail/blender-find-related-issu/gppmbbnfhiajghdannflpoieilidjpnf) to work
### Source Data
https://projects.blender.org/blender/blender/issues
#### Data Collection and Processing
The date was automatically collected in Python when fetching reports categorized as duplicates. These reports were then filtered by similarity testing using other AI models.
#### Who are the source data producers?
These reports are produced by Blender users around the world who are interested in reporting bugs in order to improve the quality of the software.
#### Annotation process
<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
[More Information Needed]
#### Who are the annotators?
<!-- This section describes the people or systems who created the annotations. -->
[More Information Needed]
#### Personal and Sensitive Information
<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
## Citation [optional]
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Dataset Card Authors [optional]
[More Information Needed]
## Dataset Card Contact
[More Information Needed] | mano-wii/blender_duplicates | [
"task_categories:text-classification",
"size_categories:1K<n<10K",
"language:en",
"license:apache-2.0",
"code",
"region:us"
] | 2023-11-28T13:16:30+00:00 | {"language": ["en"], "license": "apache-2.0", "size_categories": ["1K<n<10K"], "task_categories": ["text-classification"], "pretty_name": "Blender Duplicates", "tags": ["code"]} | 2023-11-30T01:44:14+00:00 | [] | [
"en"
] | TAGS
#task_categories-text-classification #size_categories-1K<n<10K #language-English #license-apache-2.0 #code #region-us
| # Dataset Card for Dataset Name
Contains reduced description of issues reported at URL and points to duplicate issues in order to categorize similarity.
This dataset card aims to be a base template for new datasets. It has been generated using this raw template.
## Dataset Details
### Dataset Description
Each report has been shortened by removing frequently repeated texts such as System Information, Blender Version, Short description of error.
This dataset was used to train URL
- Curated by: @mano-wii
- Funded by: @mano-wii
- Shared by: @mano-wii
- Language(s) (NLP): English
- License: URL
## Uses
With this dataset, we can train a model considering terms and utilities used in Blender and the relation with problems with specific hardware.
### Direct Use
Creation of embeddings for 3D software technical reports
## Dataset Structure
At this dataset we can see the main issue in the first column, unrecognized issues in the second column ('neg') and duplicates in the third column ('pos').
## Dataset Creation
### Curation Rationale
This dataset was created to train a model for creating embeddings to search for semantic similarity of reports in Blender and thus allow the WEB Extension
Blender Find Related Issues to work
### Source Data
URL
#### Data Collection and Processing
The date was automatically collected in Python when fetching reports categorized as duplicates. These reports were then filtered by similarity testing using other AI models.
#### Who are the source data producers?
These reports are produced by Blender users around the world who are interested in reporting bugs in order to improve the quality of the software.
#### Annotation process
#### Who are the annotators?
#### Personal and Sensitive Information
## Bias, Risks, and Limitations
### Recommendations
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Dataset Card Authors [optional]
## Dataset Card Contact
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"### Dataset Description\n\nEach report has been shortened by removing frequently repeated texts such as System Information, Blender Version, Short description of error.\nThis dataset was used to train URL\n\n\n\n- Curated by: @mano-wii\n- Funded by: @mano-wii\n- Shared by: @mano-wii\n- Language(s) (NLP): English\n- License: URL",
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"## More Information [optional]",
"## Dataset Card Authors [optional]",
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"### Dataset Description\n\nEach report has been shortened by removing frequently repeated texts such as System Information, Blender Version, Short description of error.\nThis dataset was used to train URL\n\n\n\n- Curated by: @mano-wii\n- Funded by: @mano-wii\n- Shared by: @mano-wii\n- Language(s) (NLP): English\n- License: URL",
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583b4c689ad404b89f73cdac792e070d5e7c84a5 |
# Dataset Card for FinTalk-19k
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contact Information](#contact-information)
## Dataset Description
### Dataset Summary
FinTalk-19k is a domain-specific dataset designed for the fine-tuning of Large Language Models (LLMs) with a focus on financial conversations. Extracted from public Reddit conversations, this dataset is tagged with categories like "Personal Finance", "Financial Information", and "Public Sentiment". It consists of more than 19,000 entries, each representing a conversation about financial topics.
### Supported Tasks and Leaderboards
- `language-modeling`: The dataset can be used to train models for language modeling in the context of financial discussions.
- `text-generation`: Suitable for generating responses in financial conversations.
### Languages
The dataset is primarily in English.
## Dataset Structure
### Data Instances
Each data instance in FinTalk-19k includes a financial conversation, comprising an `instruction` (question or topic), a `response`, additional `context`, and a categorizing `tag`. For example, a data instance may have an `instruction` about seeking job ideas for a person with limited skills, a `response` suggesting truck driving, `context` explaining the person's situation, and a `tag` like "Personal Finance".
### Data Fields
- `instruction`: The question or topic of the conversation.
- `response`: The answer or information provided in response.
- `context`: Background or additional details about the conversation.
- `tag`: Category label for the conversation, e.g., "Personal Finance".
## Considerations for Using the Data
### Social Impact of Dataset
This dataset can improve AI's understanding of financial topics, aiding in the development of more informed and contextually aware financial chatbots or assistants.
### Discussion of Biases
- The dataset reflects public opinion from Reddit and may contain subjective views and Reddit-specific language.
- The dataset's focus on Reddit conversations may limit its applicability to broader financial discourse.
### License/Attribution
Copyright ยฉ 2023 CeADAR Connect Group. Developed by CeADAR (ceadar.ie), its use is governed by the Apache 2.0 license.
### Feedback
For any questions or feedback related to the dataset, please direct your communications to [email protected]
### Disclaimer
This dataset is provided "as is" without any guarantees or warranty. Although the data has been processed with care, CeADAR Connect Group is not responsible for any errors, omissions, or discrepancies within the data. Users are advised to use this dataset at their discretion and assume any risks associated with its use. | ceadar-ie/FinTalk-19k | [
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"language:en",
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"finetuning",
"region:us"
] | 2023-11-28T13:39:30+00:00 | {"language": ["en"], "license": "apache-2.0", "size_categories": ["10K<n<100K"], "task_categories": ["text-generation", "question-answering"], "pretty_name": "FinTalk-19K", "tags": ["finance", "LLM", "finetuning"]} | 2023-11-29T22:58:02+00:00 | [] | [
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] | TAGS
#task_categories-text-generation #task_categories-question-answering #size_categories-10K<n<100K #language-English #license-apache-2.0 #finance #LLM #finetuning #region-us
|
# Dataset Card for FinTalk-19k
## Table of Contents
- Dataset Description
- Dataset Summary
- Supported Tasks and Leaderboards
- Languages
- Dataset Structure
- Data Instances
- Data Fields
- Data Splits
- Dataset Creation
- Curation Rationale
- Source Data
- Annotations
- Considerations for Using the Data
- Social Impact of Dataset
- Discussion of Biases
- Other Known Limitations
- Additional Information
- Dataset Curators
- Licensing Information
- Citation Information
- Contact Information
## Dataset Description
### Dataset Summary
FinTalk-19k is a domain-specific dataset designed for the fine-tuning of Large Language Models (LLMs) with a focus on financial conversations. Extracted from public Reddit conversations, this dataset is tagged with categories like "Personal Finance", "Financial Information", and "Public Sentiment". It consists of more than 19,000 entries, each representing a conversation about financial topics.
### Supported Tasks and Leaderboards
- 'language-modeling': The dataset can be used to train models for language modeling in the context of financial discussions.
- 'text-generation': Suitable for generating responses in financial conversations.
### Languages
The dataset is primarily in English.
## Dataset Structure
### Data Instances
Each data instance in FinTalk-19k includes a financial conversation, comprising an 'instruction' (question or topic), a 'response', additional 'context', and a categorizing 'tag'. For example, a data instance may have an 'instruction' about seeking job ideas for a person with limited skills, a 'response' suggesting truck driving, 'context' explaining the person's situation, and a 'tag' like "Personal Finance".
### Data Fields
- 'instruction': The question or topic of the conversation.
- 'response': The answer or information provided in response.
- 'context': Background or additional details about the conversation.
- 'tag': Category label for the conversation, e.g., "Personal Finance".
## Considerations for Using the Data
### Social Impact of Dataset
This dataset can improve AI's understanding of financial topics, aiding in the development of more informed and contextually aware financial chatbots or assistants.
### Discussion of Biases
- The dataset reflects public opinion from Reddit and may contain subjective views and Reddit-specific language.
- The dataset's focus on Reddit conversations may limit its applicability to broader financial discourse.
### License/Attribution
Copyright ยฉ 2023 CeADAR Connect Group. Developed by CeADAR (URL), its use is governed by the Apache 2.0 license.
### Feedback
For any questions or feedback related to the dataset, please direct your communications to URL@URL
### Disclaimer
This dataset is provided "as is" without any guarantees or warranty. Although the data has been processed with care, CeADAR Connect Group is not responsible for any errors, omissions, or discrepancies within the data. Users are advised to use this dataset at their discretion and assume any risks associated with its use. | [
"# Dataset Card for FinTalk-19k",
"## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contact Information",
"## Dataset Description",
"### Dataset Summary\n\nFinTalk-19k is a domain-specific dataset designed for the fine-tuning of Large Language Models (LLMs) with a focus on financial conversations. Extracted from public Reddit conversations, this dataset is tagged with categories like \"Personal Finance\", \"Financial Information\", and \"Public Sentiment\". It consists of more than 19,000 entries, each representing a conversation about financial topics.",
"### Supported Tasks and Leaderboards\n\n- 'language-modeling': The dataset can be used to train models for language modeling in the context of financial discussions.\n- 'text-generation': Suitable for generating responses in financial conversations.",
"### Languages\n\nThe dataset is primarily in English.",
"## Dataset Structure",
"### Data Instances\n\nEach data instance in FinTalk-19k includes a financial conversation, comprising an 'instruction' (question or topic), a 'response', additional 'context', and a categorizing 'tag'. For example, a data instance may have an 'instruction' about seeking job ideas for a person with limited skills, a 'response' suggesting truck driving, 'context' explaining the person's situation, and a 'tag' like \"Personal Finance\".",
"### Data Fields\n\n- 'instruction': The question or topic of the conversation.\n- 'response': The answer or information provided in response.\n- 'context': Background or additional details about the conversation.\n- 'tag': Category label for the conversation, e.g., \"Personal Finance\".",
"## Considerations for Using the Data",
"### Social Impact of Dataset\n\nThis dataset can improve AI's understanding of financial topics, aiding in the development of more informed and contextually aware financial chatbots or assistants.",
"### Discussion of Biases\n\n- The dataset reflects public opinion from Reddit and may contain subjective views and Reddit-specific language.\n- The dataset's focus on Reddit conversations may limit its applicability to broader financial discourse.",
"### License/Attribution\n\nCopyright ยฉ 2023 CeADAR Connect Group. Developed by CeADAR (URL), its use is governed by the Apache 2.0 license.",
"### Feedback\n\nFor any questions or feedback related to the dataset, please direct your communications to URL@URL",
"### Disclaimer\n\nThis dataset is provided \"as is\" without any guarantees or warranty. Although the data has been processed with care, CeADAR Connect Group is not responsible for any errors, omissions, or discrepancies within the data. Users are advised to use this dataset at their discretion and assume any risks associated with its use."
] | [
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"# Dataset Card for FinTalk-19k",
"## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contact Information",
"## Dataset Description",
"### Dataset Summary\n\nFinTalk-19k is a domain-specific dataset designed for the fine-tuning of Large Language Models (LLMs) with a focus on financial conversations. Extracted from public Reddit conversations, this dataset is tagged with categories like \"Personal Finance\", \"Financial Information\", and \"Public Sentiment\". It consists of more than 19,000 entries, each representing a conversation about financial topics.",
"### Supported Tasks and Leaderboards\n\n- 'language-modeling': The dataset can be used to train models for language modeling in the context of financial discussions.\n- 'text-generation': Suitable for generating responses in financial conversations.",
"### Languages\n\nThe dataset is primarily in English.",
"## Dataset Structure",
"### Data Instances\n\nEach data instance in FinTalk-19k includes a financial conversation, comprising an 'instruction' (question or topic), a 'response', additional 'context', and a categorizing 'tag'. For example, a data instance may have an 'instruction' about seeking job ideas for a person with limited skills, a 'response' suggesting truck driving, 'context' explaining the person's situation, and a 'tag' like \"Personal Finance\".",
"### Data Fields\n\n- 'instruction': The question or topic of the conversation.\n- 'response': The answer or information provided in response.\n- 'context': Background or additional details about the conversation.\n- 'tag': Category label for the conversation, e.g., \"Personal Finance\".",
"## Considerations for Using the Data",
"### Social Impact of Dataset\n\nThis dataset can improve AI's understanding of financial topics, aiding in the development of more informed and contextually aware financial chatbots or assistants.",
"### Discussion of Biases\n\n- The dataset reflects public opinion from Reddit and may contain subjective views and Reddit-specific language.\n- The dataset's focus on Reddit conversations may limit its applicability to broader financial discourse.",
"### License/Attribution\n\nCopyright ยฉ 2023 CeADAR Connect Group. Developed by CeADAR (URL), its use is governed by the Apache 2.0 license.",
"### Feedback\n\nFor any questions or feedback related to the dataset, please direct your communications to URL@URL",
"### Disclaimer\n\nThis dataset is provided \"as is\" without any guarantees or warranty. Although the data has been processed with care, CeADAR Connect Group is not responsible for any errors, omissions, or discrepancies within the data. Users are advised to use this dataset at their discretion and assume any risks associated with its use."
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"passage: TAGS\n#task_categories-text-generation #task_categories-question-answering #size_categories-10K<n<100K #language-English #license-apache-2.0 #finance #LLM #finetuning #region-us \n# Dataset Card for FinTalk-19k## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contact Information## Dataset Description### Dataset Summary\n\nFinTalk-19k is a domain-specific dataset designed for the fine-tuning of Large Language Models (LLMs) with a focus on financial conversations. Extracted from public Reddit conversations, this dataset is tagged with categories like \"Personal Finance\", \"Financial Information\", and \"Public Sentiment\". It consists of more than 19,000 entries, each representing a conversation about financial topics.### Supported Tasks and Leaderboards\n\n- 'language-modeling': The dataset can be used to train models for language modeling in the context of financial discussions.\n- 'text-generation': Suitable for generating responses in financial conversations.### Languages\n\nThe dataset is primarily in English.## Dataset Structure### Data Instances\n\nEach data instance in FinTalk-19k includes a financial conversation, comprising an 'instruction' (question or topic), a 'response', additional 'context', and a categorizing 'tag'. For example, a data instance may have an 'instruction' about seeking job ideas for a person with limited skills, a 'response' suggesting truck driving, 'context' explaining the person's situation, and a 'tag' like \"Personal Finance\"."
] |
ee36e78a3fd34d6b24030a0a3c3c0e77343d0a54 | # Dataset Card for Dataset Name
<!-- Provide a quick summary of the dataset. -->
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).
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
### Dataset Sources [optional]
<!-- Provide the basic links for the dataset. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the dataset is intended to be used. -->
### Direct Use
<!-- This section describes suitable use cases for the dataset. -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
[More Information Needed]
## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
[More Information Needed]
## Dataset Creation
### Curation Rationale
<!-- Motivation for the creation of this dataset. -->
[More Information Needed]
### Source Data
<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
#### Data Collection and Processing
<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
[More Information Needed]
#### Who are the source data producers?
<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
[More Information Needed]
### Annotations [optional]
<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
#### Annotation process
<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
[More Information Needed]
#### Who are the annotators?
<!-- This section describes the people or systems who created the annotations. -->
[More Information Needed]
#### Personal and Sensitive Information
<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
## Citation [optional]
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Dataset Card Authors [optional]
[More Information Needed]
## Dataset Card Contact
[More Information Needed] | topiga/AirrepsKnowledgeBase | [
"task_categories:conversational",
"size_categories:n<1K",
"language:en",
"region:us"
] | 2023-11-28T14:10:22+00:00 | {"language": ["en"], "size_categories": ["n<1K"], "task_categories": ["conversational"], "pretty_name": "AirReps Knowledge Base", "configs": [{"config_name": "train", "data_files": "train.csv", "sep": ";"}]} | 2024-02-15T10:40:11+00:00 | [] | [
"en"
] | TAGS
#task_categories-conversational #size_categories-n<1K #language-English #region-us
| # Dataset Card for Dataset Name
This dataset card aims to be a base template for new datasets. It has been generated using this raw template.
## Dataset Details
### Dataset Description
- Curated by:
- Funded by [optional]:
- Shared by [optional]:
- Language(s) (NLP):
- License:
### Dataset Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Out-of-Scope Use
## Dataset Structure
## Dataset Creation
### Curation Rationale
### Source Data
#### Data Collection and Processing
#### Who are the source data producers?
### Annotations [optional]
#### Annotation process
#### Who are the annotators?
#### Personal and Sensitive Information
## Bias, Risks, and Limitations
### Recommendations
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Dataset Card Authors [optional]
## Dataset Card Contact
| [
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"## More Information [optional]",
"## Dataset Card Authors [optional]",
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] |
29fd21dfd088464da30aff6c54b762df0f8e2621 |
Football player datasets, labeled by name. Made for facial identification experiments.
the images were taken from the web, they are all CC protected, so the license has been maintained | vilsonrodrigues/identification-in-football | [
"size_categories:n<1K",
"license:cc-by-4.0",
"region:us"
] | 2023-11-28T14:59:48+00:00 | {"license": "cc-by-4.0", "size_categories": ["n<1K"], "pretty_name": "Identification in Football"} | 2023-11-30T05:03:07+00:00 | [] | [] | TAGS
#size_categories-n<1K #license-cc-by-4.0 #region-us
|
Football player datasets, labeled by name. Made for facial identification experiments.
the images were taken from the web, they are all CC protected, so the license has been maintained | [] | [
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4a335e13d5530905a7294592aabc7f7b354b9895 |
<img src="https://raw.githubusercontent.com/argilla-io/distilabel/main/docs/assets/distilabel-badge-dark.png" alt="Built with Distilabel" width="200" height="32"/>
# HelpSteer: Helpfulness SteerLM Dataset
HelpSteer is an open-source Helpfulness Dataset (CC-BY-4.0) that supports aligning models to become more helpful, factually correct and coherent, while being adjustable in terms of the complexity and verbosity of its responses.
[HelpSteer: Multi-attribute Helpfulness Dataset for SteerLM](http://arxiv.org/abs/2311.09528)
## Disclaimer
This is only a subset created with `distilabel` to evaluate the first 1000 rows using AI Feedback (AIF) coming from GPT-4, only created for experimenting / research purposes, please refer to [nvidia/HelpSteer](https://hf.co/nvidia/HelpSteer) if you want more information about the HelpSteer dataset.
## Dataset Description
HelpSteer contains 37120 samples, while this subset only contains the first 1000, each only containing a prompt and a response, even though the same prompt may appear up to 4 times with different responses generated by their in-house LLM of 43B params.
In this case, the annotations of the attributes have been discarded while just keeping the prompt and the response, to generate the annotations using AIF via `distilabel`.
## Attributes
1. **Helpfulness**: Overall helpfulness of the response to the prompt.
2. **Correctness**: Inclusion of all pertinent facts without errors.
3. **Coherence**: Consistency and clarity of expression.
4. **Complexity**: Intellectual depth required to write response (i.e. whether the response can be written by anyone with basic language competency or requires deep domain expertise).
5. **Verbosity**: Amount of detail included in the response, relative to what is asked for in the prompt.
## Source
1. (original) Prompts are collected based on a mixture of template-generated (mainly for prompt involving long reference text) and human generated by Scale AI. These prompts relate to the tasks of Rewrite, Summarization, Classification, Extraction, Closed Question Answering, Open Question Answering, Generation and Brainstorming.
2. (original) Responses are generated by an early version of an inhouse LLM. We generate up to 4 responses per prompts using sample techniques to give diverse yet reasonable responses.
3. (distilabel) Annotations of various attributes were done using OpenAI's GPT-4 via `distilabel`, following the same Likert 5 scale (0-4) that Scale AI used with human annotators, but this time asking GPT-4 to provide those, via AI Feedback (AIF).
## Citation
If you find this dataset useful, make sure to cite the original work, as the prompt and the responses have been reused from them, while only the annotations have been modified.
```bibtex
@misc{wang2023helpsteer,
title={HelpSteer: Multi-attribute Helpfulness Dataset for SteerLM},
author={Zhilin Wang and Yi Dong and Jiaqi Zeng and Virginia Adams and Makesh Narsimhan Sreedhar and Daniel Egert and Olivier Delalleau and Jane Polak Scowcroft and Neel Kant and Aidan Swope and Oleksii Kuchaiev},
year={2023},
eprint={2311.09528},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
| alvarobartt/HelpSteer-AIF-raw | [
"size_categories:n<1K",
"language:en",
"license:cc-by-4.0",
"synthetic",
"distilabel",
"helpsteer",
"ai-feedback",
"preference",
"arxiv:2311.09528",
"region:us"
] | 2023-11-28T15:09:48+00:00 | {"language": ["en"], "license": "cc-by-4.0", "size_categories": ["n<1K"], "pretty_name": "HelpSteer with AIF", "dataset_info": {"features": [{"name": "prompt", "dtype": "string"}, {"name": "response", "dtype": "string"}, {"name": "labelling_model", "dtype": "string"}, {"name": "labelling_prompt", "list": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}, {"name": "raw_labelling_response", "dtype": "string"}, {"name": "correctness", "dtype": "int64"}, {"name": "coherence", "dtype": "int64"}, {"name": "complexity", "dtype": "int64"}, {"name": "verbosity", "dtype": "int64"}, {"name": "helpfulness", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 6324068, "num_examples": 1000}], "download_size": 1484409, "dataset_size": 6324068}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "tags": ["synthetic", "distilabel", "helpsteer", "ai-feedback", "preference"]} | 2024-02-06T07:33:28+00:00 | [
"2311.09528"
] | [
"en"
] | TAGS
#size_categories-n<1K #language-English #license-cc-by-4.0 #synthetic #distilabel #helpsteer #ai-feedback #preference #arxiv-2311.09528 #region-us
|
<img src="URL alt="Built with Distilabel" width="200" height="32"/>
# HelpSteer: Helpfulness SteerLM Dataset
HelpSteer is an open-source Helpfulness Dataset (CC-BY-4.0) that supports aligning models to become more helpful, factually correct and coherent, while being adjustable in terms of the complexity and verbosity of its responses.
HelpSteer: Multi-attribute Helpfulness Dataset for SteerLM
## Disclaimer
This is only a subset created with 'distilabel' to evaluate the first 1000 rows using AI Feedback (AIF) coming from GPT-4, only created for experimenting / research purposes, please refer to nvidia/HelpSteer if you want more information about the HelpSteer dataset.
## Dataset Description
HelpSteer contains 37120 samples, while this subset only contains the first 1000, each only containing a prompt and a response, even though the same prompt may appear up to 4 times with different responses generated by their in-house LLM of 43B params.
In this case, the annotations of the attributes have been discarded while just keeping the prompt and the response, to generate the annotations using AIF via 'distilabel'.
## Attributes
1. Helpfulness: Overall helpfulness of the response to the prompt.
2. Correctness: Inclusion of all pertinent facts without errors.
3. Coherence: Consistency and clarity of expression.
4. Complexity: Intellectual depth required to write response (i.e. whether the response can be written by anyone with basic language competency or requires deep domain expertise).
5. Verbosity: Amount of detail included in the response, relative to what is asked for in the prompt.
## Source
1. (original) Prompts are collected based on a mixture of template-generated (mainly for prompt involving long reference text) and human generated by Scale AI. These prompts relate to the tasks of Rewrite, Summarization, Classification, Extraction, Closed Question Answering, Open Question Answering, Generation and Brainstorming.
2. (original) Responses are generated by an early version of an inhouse LLM. We generate up to 4 responses per prompts using sample techniques to give diverse yet reasonable responses.
3. (distilabel) Annotations of various attributes were done using OpenAI's GPT-4 via 'distilabel', following the same Likert 5 scale (0-4) that Scale AI used with human annotators, but this time asking GPT-4 to provide those, via AI Feedback (AIF).
If you find this dataset useful, make sure to cite the original work, as the prompt and the responses have been reused from them, while only the annotations have been modified.
| [
"# HelpSteer: Helpfulness SteerLM Dataset\n\nHelpSteer is an open-source Helpfulness Dataset (CC-BY-4.0) that supports aligning models to become more helpful, factually correct and coherent, while being adjustable in terms of the complexity and verbosity of its responses.\n\nHelpSteer: Multi-attribute Helpfulness Dataset for SteerLM",
"## Disclaimer\n\nThis is only a subset created with 'distilabel' to evaluate the first 1000 rows using AI Feedback (AIF) coming from GPT-4, only created for experimenting / research purposes, please refer to nvidia/HelpSteer if you want more information about the HelpSteer dataset.",
"## Dataset Description\n\nHelpSteer contains 37120 samples, while this subset only contains the first 1000, each only containing a prompt and a response, even though the same prompt may appear up to 4 times with different responses generated by their in-house LLM of 43B params.\n\nIn this case, the annotations of the attributes have been discarded while just keeping the prompt and the response, to generate the annotations using AIF via 'distilabel'.",
"## Attributes\n\n1. Helpfulness: Overall helpfulness of the response to the prompt.\n2. Correctness: Inclusion of all pertinent facts without errors. \n3. Coherence: Consistency and clarity of expression. \n4. Complexity: Intellectual depth required to write response (i.e. whether the response can be written by anyone with basic language competency or requires deep domain expertise).\n5. Verbosity: Amount of detail included in the response, relative to what is asked for in the prompt.",
"## Source\n\n1. (original) Prompts are collected based on a mixture of template-generated (mainly for prompt involving long reference text) and human generated by Scale AI. These prompts relate to the tasks of Rewrite, Summarization, Classification, Extraction, Closed Question Answering, Open Question Answering, Generation and Brainstorming.\n2. (original) Responses are generated by an early version of an inhouse LLM. We generate up to 4 responses per prompts using sample techniques to give diverse yet reasonable responses.\n3. (distilabel) Annotations of various attributes were done using OpenAI's GPT-4 via 'distilabel', following the same Likert 5 scale (0-4) that Scale AI used with human annotators, but this time asking GPT-4 to provide those, via AI Feedback (AIF).\n\nIf you find this dataset useful, make sure to cite the original work, as the prompt and the responses have been reused from them, while only the annotations have been modified."
] | [
"TAGS\n#size_categories-n<1K #language-English #license-cc-by-4.0 #synthetic #distilabel #helpsteer #ai-feedback #preference #arxiv-2311.09528 #region-us \n",
"# HelpSteer: Helpfulness SteerLM Dataset\n\nHelpSteer is an open-source Helpfulness Dataset (CC-BY-4.0) that supports aligning models to become more helpful, factually correct and coherent, while being adjustable in terms of the complexity and verbosity of its responses.\n\nHelpSteer: Multi-attribute Helpfulness Dataset for SteerLM",
"## Disclaimer\n\nThis is only a subset created with 'distilabel' to evaluate the first 1000 rows using AI Feedback (AIF) coming from GPT-4, only created for experimenting / research purposes, please refer to nvidia/HelpSteer if you want more information about the HelpSteer dataset.",
"## Dataset Description\n\nHelpSteer contains 37120 samples, while this subset only contains the first 1000, each only containing a prompt and a response, even though the same prompt may appear up to 4 times with different responses generated by their in-house LLM of 43B params.\n\nIn this case, the annotations of the attributes have been discarded while just keeping the prompt and the response, to generate the annotations using AIF via 'distilabel'.",
"## Attributes\n\n1. Helpfulness: Overall helpfulness of the response to the prompt.\n2. Correctness: Inclusion of all pertinent facts without errors. \n3. Coherence: Consistency and clarity of expression. \n4. Complexity: Intellectual depth required to write response (i.e. whether the response can be written by anyone with basic language competency or requires deep domain expertise).\n5. Verbosity: Amount of detail included in the response, relative to what is asked for in the prompt.",
"## Source\n\n1. (original) Prompts are collected based on a mixture of template-generated (mainly for prompt involving long reference text) and human generated by Scale AI. These prompts relate to the tasks of Rewrite, Summarization, Classification, Extraction, Closed Question Answering, Open Question Answering, Generation and Brainstorming.\n2. (original) Responses are generated by an early version of an inhouse LLM. We generate up to 4 responses per prompts using sample techniques to give diverse yet reasonable responses.\n3. (distilabel) Annotations of various attributes were done using OpenAI's GPT-4 via 'distilabel', following the same Likert 5 scale (0-4) that Scale AI used with human annotators, but this time asking GPT-4 to provide those, via AI Feedback (AIF).\n\nIf you find this dataset useful, make sure to cite the original work, as the prompt and the responses have been reused from them, while only the annotations have been modified."
] | [
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236
] | [
"passage: TAGS\n#size_categories-n<1K #language-English #license-cc-by-4.0 #synthetic #distilabel #helpsteer #ai-feedback #preference #arxiv-2311.09528 #region-us \n# HelpSteer: Helpfulness SteerLM Dataset\n\nHelpSteer is an open-source Helpfulness Dataset (CC-BY-4.0) that supports aligning models to become more helpful, factually correct and coherent, while being adjustable in terms of the complexity and verbosity of its responses.\n\nHelpSteer: Multi-attribute Helpfulness Dataset for SteerLM## Disclaimer\n\nThis is only a subset created with 'distilabel' to evaluate the first 1000 rows using AI Feedback (AIF) coming from GPT-4, only created for experimenting / research purposes, please refer to nvidia/HelpSteer if you want more information about the HelpSteer dataset.## Dataset Description\n\nHelpSteer contains 37120 samples, while this subset only contains the first 1000, each only containing a prompt and a response, even though the same prompt may appear up to 4 times with different responses generated by their in-house LLM of 43B params.\n\nIn this case, the annotations of the attributes have been discarded while just keeping the prompt and the response, to generate the annotations using AIF via 'distilabel'.## Attributes\n\n1. Helpfulness: Overall helpfulness of the response to the prompt.\n2. Correctness: Inclusion of all pertinent facts without errors. \n3. Coherence: Consistency and clarity of expression. \n4. Complexity: Intellectual depth required to write response (i.e. whether the response can be written by anyone with basic language competency or requires deep domain expertise).\n5. Verbosity: Amount of detail included in the response, relative to what is asked for in the prompt."
] |
86b7c61853a4ccf4d2301539a237b5dd6e6ace12 | # COCO Dataset Processed with CLIP ViT-L/14
## Overview
This dataset represents a processed version of the '2017 Unlabeled images' subset of the COCO dataset ([COCO Dataset](https://cocodataset.org/#home)), utilizing the CLIP ViT-L/14 model from OpenAI. The original dataset comprises 123K images, approximately 19GB in size, which have been processed to generate 786-dimensional vectors. These vectors can be utilized for various applications like semantic search systems, image similarity assessments, and more.
Direct download link for the original dataset: [COCO 2017 Unlabeled Images](http://images.cocodataset.org/zips/unlabeled2017.zip)
## Dataset Description
The output of the processing is a parquet file containing the path of each file along with its corresponding embedding. No normalization was applied to the model output; the embeddings are direct results from the OpenAI CLIP model.
### Processing Details
We aimed to produce the same image vectors from the script below.
Our approach utilizes a core CLIP model from OpenAI, similar to this sample:
```python
import torch
import clip
from PIL import Image
device = "cuda" if torch.cuda.is_available() else "cpu"
model, preprocess = clip.load("ViT-L/14", device=device)
image = preprocess(Image.open("CLIP.png")).unsqueeze(0).to(device)
text = clip.tokenize(["a diagram", "a dog", "a cat"]).to(device)
with torch.no_grad():
image_features = model.encode_image(image)
text_features = model.encode_text(text)
logits_per_image, logits_per_text = model(image, text)
probs = logits_per_image.softmax(dim=-1).cpu().numpy()
print("Label probs:", probs)
```
## Applications
The dataset is suitable for various AI-driven applications, including but not limited to:
- Semantic Search Systems
- Image Similarity Detection
- Enhanced Image Categorization
## About Visuals API
This dataset was processed by Visuals API, specialists in Computer Vision and AI technologies. Visuals API offers robust solutions for image/video tagging, content moderation, and NSFW detection. For more information about our services and solutions, visit our website: [Visuals API](https://visualsapi.com/). | VisualsAPI/coco-clip-vit-l-14 | [
"task_categories:image-classification",
"task_categories:feature-extraction",
"task_categories:sentence-similarity",
"size_categories:100K<n<1M",
"language:en",
"license:cc-by-4.0",
"region:us"
] | 2023-11-28T15:22:07+00:00 | {"language": ["en"], "license": "cc-by-4.0", "size_categories": ["100K<n<1M"], "task_categories": ["image-classification", "feature-extraction", "sentence-similarity"], "pretty_name": "COCO Dataset Processed with CLIP ViT-L/14"} | 2023-11-28T16:01:04+00:00 | [] | [
"en"
] | TAGS
#task_categories-image-classification #task_categories-feature-extraction #task_categories-sentence-similarity #size_categories-100K<n<1M #language-English #license-cc-by-4.0 #region-us
| # COCO Dataset Processed with CLIP ViT-L/14
## Overview
This dataset represents a processed version of the '2017 Unlabeled images' subset of the COCO dataset (COCO Dataset), utilizing the CLIP ViT-L/14 model from OpenAI. The original dataset comprises 123K images, approximately 19GB in size, which have been processed to generate 786-dimensional vectors. These vectors can be utilized for various applications like semantic search systems, image similarity assessments, and more.
Direct download link for the original dataset: COCO 2017 Unlabeled Images
## Dataset Description
The output of the processing is a parquet file containing the path of each file along with its corresponding embedding. No normalization was applied to the model output; the embeddings are direct results from the OpenAI CLIP model.
### Processing Details
We aimed to produce the same image vectors from the script below.
Our approach utilizes a core CLIP model from OpenAI, similar to this sample:
## Applications
The dataset is suitable for various AI-driven applications, including but not limited to:
- Semantic Search Systems
- Image Similarity Detection
- Enhanced Image Categorization
## About Visuals API
This dataset was processed by Visuals API, specialists in Computer Vision and AI technologies. Visuals API offers robust solutions for image/video tagging, content moderation, and NSFW detection. For more information about our services and solutions, visit our website: Visuals API. | [
"# COCO Dataset Processed with CLIP ViT-L/14",
"## Overview\nThis dataset represents a processed version of the '2017 Unlabeled images' subset of the COCO dataset (COCO Dataset), utilizing the CLIP ViT-L/14 model from OpenAI. The original dataset comprises 123K images, approximately 19GB in size, which have been processed to generate 786-dimensional vectors. These vectors can be utilized for various applications like semantic search systems, image similarity assessments, and more.\n\nDirect download link for the original dataset: COCO 2017 Unlabeled Images",
"## Dataset Description\nThe output of the processing is a parquet file containing the path of each file along with its corresponding embedding. No normalization was applied to the model output; the embeddings are direct results from the OpenAI CLIP model.",
"### Processing Details\nWe aimed to produce the same image vectors from the script below. \nOur approach utilizes a core CLIP model from OpenAI, similar to this sample:",
"## Applications\nThe dataset is suitable for various AI-driven applications, including but not limited to:\n\n- Semantic Search Systems\n- Image Similarity Detection\n- Enhanced Image Categorization",
"## About Visuals API\nThis dataset was processed by Visuals API, specialists in Computer Vision and AI technologies. Visuals API offers robust solutions for image/video tagging, content moderation, and NSFW detection. For more information about our services and solutions, visit our website: Visuals API."
] | [
"TAGS\n#task_categories-image-classification #task_categories-feature-extraction #task_categories-sentence-similarity #size_categories-100K<n<1M #language-English #license-cc-by-4.0 #region-us \n",
"# COCO Dataset Processed with CLIP ViT-L/14",
"## Overview\nThis dataset represents a processed version of the '2017 Unlabeled images' subset of the COCO dataset (COCO Dataset), utilizing the CLIP ViT-L/14 model from OpenAI. The original dataset comprises 123K images, approximately 19GB in size, which have been processed to generate 786-dimensional vectors. These vectors can be utilized for various applications like semantic search systems, image similarity assessments, and more.\n\nDirect download link for the original dataset: COCO 2017 Unlabeled Images",
"## Dataset Description\nThe output of the processing is a parquet file containing the path of each file along with its corresponding embedding. No normalization was applied to the model output; the embeddings are direct results from the OpenAI CLIP model.",
"### Processing Details\nWe aimed to produce the same image vectors from the script below. \nOur approach utilizes a core CLIP model from OpenAI, similar to this sample:",
"## Applications\nThe dataset is suitable for various AI-driven applications, including but not limited to:\n\n- Semantic Search Systems\n- Image Similarity Detection\n- Enhanced Image Categorization",
"## About Visuals API\nThis dataset was processed by Visuals API, specialists in Computer Vision and AI technologies. Visuals API offers robust solutions for image/video tagging, content moderation, and NSFW detection. For more information about our services and solutions, visit our website: Visuals API."
] | [
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42,
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] | [
"passage: TAGS\n#task_categories-image-classification #task_categories-feature-extraction #task_categories-sentence-similarity #size_categories-100K<n<1M #language-English #license-cc-by-4.0 #region-us \n# COCO Dataset Processed with CLIP ViT-L/14## Overview\nThis dataset represents a processed version of the '2017 Unlabeled images' subset of the COCO dataset (COCO Dataset), utilizing the CLIP ViT-L/14 model from OpenAI. The original dataset comprises 123K images, approximately 19GB in size, which have been processed to generate 786-dimensional vectors. These vectors can be utilized for various applications like semantic search systems, image similarity assessments, and more.\n\nDirect download link for the original dataset: COCO 2017 Unlabeled Images## Dataset Description\nThe output of the processing is a parquet file containing the path of each file along with its corresponding embedding. No normalization was applied to the model output; the embeddings are direct results from the OpenAI CLIP model.### Processing Details\nWe aimed to produce the same image vectors from the script below. \nOur approach utilizes a core CLIP model from OpenAI, similar to this sample:## Applications\nThe dataset is suitable for various AI-driven applications, including but not limited to:\n\n- Semantic Search Systems\n- Image Similarity Detection\n- Enhanced Image Categorization## About Visuals API\nThis dataset was processed by Visuals API, specialists in Computer Vision and AI technologies. Visuals API offers robust solutions for image/video tagging, content moderation, and NSFW detection. For more information about our services and solutions, visit our website: Visuals API."
] |
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