datasetId
stringlengths 2
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ccmusic-database/GZ_IsoTech | ---
license: mit
task_categories:
- audio-classification
language:
- zh
- en
tags:
- music
- art
pretty_name: GZ_IsoTech Dataset
size_categories:
- n<1K
viewer: false
---
# Dataset Card for GZ_IsoTech Dataset
The raw dataset comprises 2,824 audio clips showcasing various guzheng playing techniques. Specifically, 2,328 clips were sourced from virtual sound banks, while 496 clips were performed by a skilled professional guzheng artist. These recordings encompass a comprehensive range of tones inherent to the guzheng instrument.
## Dataset Description
- **Homepage:** <https://ccmusic-database.github.io>
- **Repository:** <https://huggingface.co/datasets/ccmusic-database/Guzheng_Tech99>
- **Paper:** <https://doi.org/10.5281/zenodo.5676893>
- **Leaderboard:** <https://www.modelscope.cn/datasets/ccmusic/GZ_IsoTech>
- **Point of Contact:** <https://arxiv.org/abs/2209.08774>
### Dataset Summary
Due to the pre-existing split in the raw dataset, wherein the data has been partitioned approximately in a 4:1 ratio for training and testing sets, we uphold the original data division approach. In contrast to utilizing platform-specific automated splitting mechanisms, we directly employ the pre-split data for subsequent integration steps.
### Supported Tasks and Leaderboards
MIR, audio classification
### Languages
Chinese, English
## Usage
```python
from datasets import load_dataset
dataset = load_dataset("ccmusic-database/GZ_IsoTech")
for item in ds["train"]:
print(item)
for item in ds["test"]:
print(item)
```
## Maintenance
```bash
GIT_LFS_SKIP_SMUDGE=1 git clone git@hf.co:datasets/ccmusic-database/GZ_IsoTech
cd GZ_IsoTech
```
## Dataset Structure
| audio(.wav, 22050Hz) | mel(.jpg, 22050Hz) | label | cname |
| :----------------------------------------------------------------------------------------------------------------------: | :------------------------------------: | :-----: | :----: |
| <audio controls src="https://huggingface.co/datasets/ccmusic-database/GZ_IsoTech/resolve/main/data/record_chanyin1.wav"> | <img src="./data/record_chanyin1.jpg"> | 8-class | string |
| ... | ... | ... | ... |
### Data Instances
.zip(.flac, .csv)
### Data Fields
Categorization of the clips is based on the diverse playing techniques characteristic of the guzheng, the clips are divided into eight categories: Vibrato (chanyin), Upward Portamento (shanghuayin), Downward Portamento (xiahuayin), Returning Portamento (huihuayin), Glissando (guazou, huazhi), Tremolo (yaozhi), Harmonic (fanyin), Plucks (gou, da, mo, tuoโฆ).
### Data Splits
train, test
## Dataset Creation
### Curation Rationale
The Guzheng is a kind of traditional Chinese instrument with diverse playing techniques. Instrument playing techniques (IPT) play an important role in musical performance. However, most of the existing works for IPT detection show low efficiency for variable-length audio and do not assure generalization as they rely on a single sound bank for training and testing. In this study, we propose an end-to-end Guzheng playing technique detection system using Fully Convolutional Networks that can be applied to variable-length audio. Because each Guzheng playing technique is applied to a note, a dedicated onset detector is trained to divide an audio into several notes and its predictions are fused with frame-wise IPT predictions. During fusion, we add the IPT predictions frame by frame inside each note and get the IPT with the highest probability within each note as the final output of that note. We create a new dataset named GZ_IsoTech from multiple sound banks and real-world recordings for Guzheng performance analysis. Our approach achieves 87.97% in frame-level accuracy and 80.76% in note-level F1 score, outperforming existing works by a large margin, which indicates the effectiveness of our proposed method in IPT detection.
### Source Data
#### Initial Data Collection and Normalization
Dichucheng Li, Monan Zhou
#### Who are the source language producers?
Students from FD-LAMT
### Annotations
#### Annotation process
This database contains 2824 audio clips of guzheng playing techniques. Among them, 2328 pieces were collected from virtual sound banks, and 496 pieces were played and recorded by a professional guzheng performer.
#### Who are the annotators?
Students from FD-LAMT
### Personal and Sensitive Information
None
## Considerations for Using the Data
### Social Impact of Dataset
Promoting the development of the music AI industry
### Discussion of Biases
Only for Traditional Chinese Instruments
### Other Known Limitations
Insufficient sample
## Additional Information
### Dataset Curators
Dichucheng Li
### Evaluation
[Li, Dichucheng, Yulun Wu, Qinyu Li, Jiahao Zhao, Yi Yu, Fan Xia and Wei Li. โPlaying Technique Detection by Fusing Note Onset Information in Guzheng Performance.โ International Society for Music Information Retrieval Conference (2022).](https://archives.ismir.net/ismir2022/paper/000037.pdf)
### Licensing Information
```
MIT License
Copyright (c) FD-LAMT
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
```
### Citation Information
```bibtex
@dataset{zhaorui_liu_2021_5676893,
author = {Monan Zhou, Shenyang Xu, Zhaorui Liu, Zhaowen Wang, Feng Yu, Wei Li and Baoqiang Han},
title = {CCMusic: an Open and Diverse Database for Chinese and General Music Information Retrieval Research},
month = {mar},
year = {2024},
publisher = {HuggingFace},
version = {1.2},
url = {https://huggingface.co/ccmusic-database}
}
```
### Contributions
Promoting the development of the music AI industry |
umarigan/turkiye_finance_qa | ---
dataset_info:
features:
- name: soru
dtype: string
- name: cevap
dtype: string
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 171936
num_examples: 428
download_size: 82421
dataset_size: 171936
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
task_categories:
- question-answering
language:
- tr
tags:
- finance
pretty_name: Finance
size_categories:
- n<1K
---
# Dataset Card for "turkiye_finance_qa"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
jihye-moon/LawQA-Ko | ---
task_categories:
- conversational
language:
- ko
tags:
- legal
size_categories:
- 10K<n<100K
---
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
๋ฒ๋ฅ ์ ๋ํ ์ง๋ฌธ๊ณผ ๋ต๋ณ์ผ๋ก ๊ตฌ์ฑ๋ ๋ฐ์ดํฐ์
์
๋๋ค.
์๋์ ๋ฐ์ดํฐ์
์์ ์ง๋ฌธ๊ณผ ๋ต๋ณ์ ๋ณํฉํ์ฌ Datasets๋ฅผ ๋ง๋ค์์ต๋๋ค.
| ์ ๋ณด ์ถ์ฒ | Dataset Page | Rows |
|---|---|---|
|[์ฐพ๊ธฐ์ฌ์ด์ํ๋ฒ๋ น์ ๋ณด ๋ฐฑ๋ฌธ๋ฐฑ๋ต](https://www.easylaw.go.kr/CSP/OnhunqueansLstRetrieve.laf?search_put=)| [jiwoochris/easylaw_kr](https://huggingface.co/datasets/jiwoochris/easylaw_kr) | 2,195 rows |
|[๋ํ๋ฒ๋ฅ ๊ตฌ์กฐ๊ณต๋จ ๋ฒ๋ฅ ์๋ด์ฌ๋ก](https://www.klac.or.kr/legalinfo/counsel.do)| [jihye-moon/klac_legal_aid_counseling](https://huggingface.co/datasets/jihye-moon/klac_legal_aid_counseling) | 10,037 rows |
|[๋ํ๋ฒ๋ฅ ๊ตฌ์กฐ๊ณต๋จ ์ฌ์ด๋ฒ์๋ด](https://www.klac.or.kr/legalstruct/cyberConsultation.do)| jihye-moon/klac_cyber_counseling (private Datasets) | 2,587 rows |
โป ์์ ๋ฐ์ดํฐ๋ ๋ชจ๋ ์น ํ์ด์ง๋ฅผ ํฌ๋กค๋ง ํ์ฌ ๊ตฌ์ถ๋ ๋ฐ์ดํฐ ์
๋๋ค.
โป ๋ํ๋ฒ๋ฅ ๊ตฌ์กฐ๊ณต๋จ ๋ฐ์ดํฐ๋ ํฌ๋กค๋ง ํ, ์ ์ฒ๋ฆฌ(๊ณต๋จ ์๋ด๋ฌธ๊ตฌ ์ญ์ , ์ฟ ์
์ด ์ญ์ ๋ฑ)๋ฅผ ํ์์ต๋๋ค. |
ai2lumos/lumos_complex_qa_plan_onetime | ---
license: apache-2.0
task_categories:
- text-generation
- question-answering
language:
- en
tags:
- language-agent
- reasoning
- question-answering
- planning
size_categories:
- 10K<n<100K
---
# ๐ช Agent Lumos: Unified and Modular Training for Open-Source Language Agents
<p align="center">
๐<a href="https://allenai.github.io/lumos">[Website]</a>
๐<a href="https://arxiv.org/abs/2311.05657">[Paper]</a>
๐ค<a href="https://huggingface.co/datasets?sort=trending&search=ai2lumos">[Data]</a>
๐ค<a href="https://huggingface.co/models?sort=trending&search=ai2lumos">[Model]</a>
๐ค<a href="https://huggingface.co/spaces/ai2lumos/lumos_data_demo">[Demo]</a>
</p>
We introduce ๐ช**Lumos**, Language Agents with **Unified** Formats, **Modular** Design, and **Open-Source** LLMs. **Lumos** unifies a suite of complex interactive tasks and achieves competitive performance with GPT-4/3.5-based and larger open-source agents.
**Lumos** has following features:
* ๐งฉ **Modular Architecture**:
- ๐งฉ **Lumos** consists of planning, grounding, and execution modules built based on LLAMA-2-7B/13B and off-the-shelf APIs.
- ๐ค **Lumos** utilizes a unified data format that encompasses multiple task types, thereby enabling the developed agent framework to conveniently support a range of interactive tasks.
* ๐ **Diverse Training Data**:
- ๐ **Lumos** is trained with ~56K diverse high-quality subgoal/action annotations from ground-truth reasoning steps in existing benchmarks with GPT-4.
- โ๏ธ **Lumos** data can be instrumental for future research in developing open-source agents for complex interactive tasks.
* ๐ **Competitive Performance**:
- ๐ **Lumos** is comparable or even beats **GPT-series** agents on web/complex QA tasks Mind2Web and HotpotQA, and **larger open agents** on math and multimodal tasks.
- ๐ **Lumos** exceeds contemporaneous agents that have been **fine-tuned** with in-domain HotpotQA, Mind2Web and ScienceQA annotations, such as **FiReAct**, **AgentLM**, and **AutoAct**.
- ๐ **Lumos** performs better than open agent baseline formulations including **chain-of-thoughts** and **integrated** training.
- ๐ **Lumos** surpasses larger open LLM agents and domain-specific agents on unseen tasks, WebShop and InterCode_SQL.
## Data Overview
`lumos_complex_qa_plan_onetime` is the data for training **planning** module on **complex QA** task in **Lumos-Onetime (Lumos-O)** formulation.
The source of the training annotation training data is shown below:
| Datasets | Number |
|---|---|
|StrategyQA|1777|
|Musique|17632|
## Models Trained with the Data
`lumos_complex_qa_plan_onetime` is used to train the following models.
|Model|Huggingface Repo|
|---|---|
|`lumos_complex_qa_plan_onetime`| [๐คHuggingface Repo](https://huggingface.co/ai2lumos/lumos_complex_qa_plan_onetime) |
## Citation
If you find this work is relevant with your research, please feel free to cite our work!
```
@article{yin2023lumos,
title={Agent Lumos: Unified and Modular Training for Open-Source Language Agents},
author={Yin, Da and Brahman, Faeze and Ravichander, Abhilasha and Chandu, Khyathi and Chang, Kai-Wei and Choi, Yejin and Lin, Bill Yuchen},
journal={arXiv preprint arXiv:2311.05657},
year={2023}
}
``` |
openerotica/erotiquant | ---
license: apache-2.0
---
Why would I spend all that time creating these datasets and training just to brain damage the models with wikitext during quantization? This dataset is primarily multi turn ERP chat. It's formatted to be a drop in replacement for wikitext for quantization methods such as AutoGPTQ or AWQ. |
royallab/PIPPA-cleaned | ---
license: apache-2.0
tags:
- not-for-all-audiences
- conversational
- roleplay
- custom-format
- a.
pretty_name: PIPPA - Personal Interaction Pairs Between People and AI
viewer: false
---
Cleaned and/or fixed dataset of PIPPA (https://huggingface.co/datasets/PygmalionAI/PIPPA), without the formatting and random char issues.
Can be used as calibration dataset for exllamav2, like for goliath-rpcal (https://huggingface.co/Panchovix/goliath-120b-exl2-rpcal)
All credits to the Pygmalion team and Undi. |
zjunlp/KnowEdit | ---
license: mit
language:
- en
task_categories:
- text-generation
- question-answering
- text2text-generation
tags:
- knowledge-editing
- model-editing
- large-language-model
---
# KnowEdit: A Benchmark of Knowledge Editing for LLMs
This README is about reproducing the paper [A Comprehensive Study of Knowledge Editing for Large Language Models](https://arxiv.org/abs/2401.01286).
You can use [EasyEdit](https://github.com/zjunlp/EasyEdit) to load and use this benchmark.
## Table of Contents
- [Dataset Structure](#Dataset-Structure)
- [Get Started Quickly](#Get-started-quickly)
- [Training an Editor with KnowEdit](#Training-an-Editor-with-KnowEdit)
- [Performence](#Performence)
- [The Composition of Dataset](#The_Composition_of_Dataset)
---
This README explains how to use [EasyEdit](https://github.com/zjunlp/EasyEdit) with the KnowEdit dataset. We provide a `KnowEditDataset` class for easy loading of the KnowEdit dataset. To use it, simply write:
```python
dataset = KnowEditDataset('the_json_path')
```
## Dataset Structure
KnowEdit is tailored for knowledge editing tasks. It encompasses six tasks: ZsRE, Wiki<sub>recent</sub>, Wiki<sub>counterfact</sub>, WikiBio, ConvSent, and Sanitation. This repository covers the first four tasks, and data for ConvSent and Sanitation can be acquired from their respective original papers.
The datasets used can be downloaded from HuggingFace, HuggingFace, ModelScopeใ
| **dataset** | HuggingFace| WiseModel | ModelScope |
| :--------: | :-----------------------------------------------------------------------------------------------: | :-----------------------------------------------------------------------------: | :--------------------------------------------------------------------------------: |
| KnowEdit | [[HuggingFace]](https://huggingface.co/datasets/zjunlp/KnowEdit) | [[WiseModel]](https://wisemodel.cn/datasets/zjunlp/KnowEdit) | [[ModelScope]](https://www.modelscope.cn/datasets/zjunlp/KnowEdit) |
Unzip the file and put it to `./data`
<table class="tg">
<thead>
<tr>
<th class="tg-7btt">Task</th>
<th class="tg-7btt">Knowledge Insertion</th>
<th class="tg-7btt" colspan="4">Knowledge Modification</th>
<th class="tg-7btt">Knowledge Erasure</th>
</tr>
</thead>
<tbody>
<tr>
<td class="tg-c3ow">Datasets</td>
<td class="tg-c3ow">Wiki<sub>recent</sub></td>
<td class="tg-c3ow">ZsRE</td>
<td class="tg-c3ow">WikiBio</td>
<td class="tg-c3ow"> WikiData<sub>counterfact</sub></td>
<td class="tg-c3ow">Convsent</td>
<td class="tg-c3ow">Sanitation</td>
</tr>
<tr>
<td class="tg-c3ow">Type</td>
<td class="tg-c3ow">Fact</td>
<td class="tg-c3ow">Question Answering</td>
<td class="tg-c3ow">Hallucination</td>
<td class="tg-c3ow">Counterfact</td>
<td class="tg-c3ow">Sentiment</td>
<td class="tg-c3ow">Unwanted Info</td>
</tr>
<tr>
<td class="tg-c3ow"># Train</td>
<td class="tg-c3ow">570</td>
<td class="tg-c3ow">10,000</td>
<td class="tg-c3ow">592</td>
<td class="tg-c3ow">1,455</td>
<td class="tg-c3ow">14,390</td>
<td class="tg-c3ow">80</td>
</tr>
<tr>
<td class="tg-c3ow"># Test</td>
<td class="tg-c3ow">1,266</td>
<td class="tg-c3ow">1230</td>
<td class="tg-c3ow">1,392</td>
<td class="tg-c3ow">885</td>
<td class="tg-c3ow">800</td>
<td class="tg-c3ow">80</td>
</tr>
</tbody>
</table>
---
Different JSON files have distinct data types. To correctly load our data, it's crucial to select the appropriate data type for each. For instance:
- For the **WikiBio** dataset, we should use the `wikibio` data type.
- For the **ZsRE** dataset, we should use the `zsre` data type.
- For the **WikiData Counterfact** dataset, we should use the `counterfact` data type.
- For the **WikiData Recent** dataset, we should use the `recent` data type.
- For the **convsent** dataset, we should use the run_convsent_llama2.py
- For the **Sanitation** dataset, we should use the run_trivia_llama2.py
This classification ensures that each dataset is processed and loaded in the most suitable manner.
The file structure for KnowEdit is as follows:
```
knowedit
โโโ WikiBio
โย ย โโโ wikibio-test-all.json
โย ย โโโ wikibio-train-all.json
โโโ ZsRE
โย ย โโโ ZsRE-test-all.json
โโโ wiki_counterfact
โย ย โโโ test_cf.json
โย ย โโโ train_cf.json
โโโ convsent
โย ย โโโ blender_test.json
โย ย โโโ blender_train.json
โย ย โโโ blender_val.json
โโโ Sanitation
โย ย โโโ trivia_qa_test.json
โย ย โโโ trivia_qa_train.json
โโโ wiki_recent
โโโ recent_test.json
โโโ recent_train.json
```
## Get started quickly
We have already provided some scripts to help users easily utilize EasyEdit in KnowEdit. Different JSONs require different scripts. Please select the appropriate script to edit your model.
Please discuss in an [issue](https://github.com/zjunlp/EasyEdit/issues) a feature you would like to implement in an example before submitting a PR; we welcome bug fixes, but since we want to keep the examples as simple as possible it's unlikely that we will merge a pull request adding more functionality at the cost of readability.
---
### ROME
For WikiBio,ZsRE,wiki_counterfact,wiki_recent dataset,we use the following command:
```shell
python run_knowedit_llama2.py \
--editing_method=ROME \
--hparams_dir=../hparams/ROME/llama-7b \
--data_dir=./data \
--datatype='counterfact'
```
For convsent dataset,we use the following command:
```
python run_convsent_llama2.py \
--hparams_dir ./hparams/ROME/llama-7b.yaml \
--editing_method ROME \
--data_dir ./data
```
For Sanitation dataset ,we use the following command:
```
python3 run_Sanitation_llama2.py
--editing_method ROME\
--hparams_dir ./hparams/ROME/llama-7b.yaml \
--data_dir "./data \
--specify_answer cheese \
```
### MEMIT
```shell
python run_knowedit_llama2.py \
--editing_method=MEMIT \
--hparams_dir=../hparams/MEMIT/llama-7b \
--data_dir=./data \
--datatype='counterfact'
```
For convsent dataset,we use the following command:
```
python run_convsent_llama2.py \
--hparams_dir ./hparams/MEMIT/llama-7b.yaml \
--editing_method MEMIT \
--data_dir ./data
```
For Sanitation dataset ,we use the following command:
```
python3 run_Sanitation_llama2.py
--editing_method MEMIT\
--hparams_dir ./hparams/MEMIT/llama-7b.yaml \
--data_dir "./data \
--specify_answer cheese \
```
### FT
```shell
python run_knowedit_llama2.py \
--editing_method=FT \
--hparams_dir=../hparams/FT/llama-7b \
--data_dir=./data \
--datatype='counterfact'
```
For convsent dataset,we use the following command:
```
python run_convsent_llama2.py \
--hparams_dir ./hparams/FT/llama-7b.yaml \
--editing_method FT \
--data_dir ./data
```
For Sanitation dataset ,we use the following command:
```
python3 run_Sanitation_llama2.py
--editing_method FT\
--hparams_dir ./hparams/FT/llama-7b.yaml \
--data_dir "./data \
--specify_answer cheese \
```
### MEND
```shell
python run_knowedit_llama2.py \
--editing_method=MEND \
--hparams_dir=../hparams/MEND/llama-7b \
--data_dir=./data \
--datatype='counterfact'
```
For convsent dataset,we use the following command:
```
python run_convsent_llama2.py \
--hparams_dir ./hparams/MEND/llama-7b.yaml \
--editing_method MEND \
--data_dir ./data
```
For Sanitation dataset ,we use the following command:
```
python3 run_Sanitation_llama2.py
--editing_method MEND\
--hparams_dir ./hparams/MEND/llama-7b.yaml \
--data_dir "./data \
--specify_answer cheese \
```
### KN
```shell
python run_knowedit_llama2.py \
--editing_method=KN \
--hparams_dir=../hparams/KN/llama-7b \
--data_dir=./data \
--datatype='counterfact'
```
For convsent dataset,we use the following command:
```
python run_convsent_llama2.py \
--hparams_dir ./hparams/KN/llama-7b.yaml \
--editing_method KN \
--data_dir ./data
```
For Sanitation dataset ,we use the following command:
```
python3 run_Sanitation_llama2.py
--editing_method KN\
--hparams_dir ./hparams/KN/llama-7b.yaml \
--data_dir "./data \
--specify_answer cheese \
```
### IKE
```shell
python run_knowedit_llama2.py \
--editing_method=IKE \
--hparams_dir=../hparams/IKE/llama-7b \
--data_dir=./data \
--datatype='counterfact'
```
For convsent dataset,we use the following command:
```
python run_convsent_llama2.py \
--hparams_dir ./hparams/IKE/llama-7b.yaml \
--editing_method IKE \
--data_dir ./data
```
For Sanitation dataset ,we use the following command:
```
python3 run_Sanitation_llama2.py
--editing_method IKE\
--hparams_dir ./hparams/IKE/llama-7b.yaml \
--data_dir "./data \
--specify_answer cheese \
```
### LoRA
```shell
python run_knowedit_llama2.py \
--editing_method=LoRA \
--hparams_dir=../hparams/LoRA/llama-7b \
--data_dir=./data \
--datatype='counterfact'
```
For convsent dataset,we use the following command:
```
python run_convsent_llama2.py \
--hparams_dir ./hparams/LoRA/llama-7b.yaml \
--editing_method LoRA \
--data_dir ./data
```
For Sanitation dataset ,we use the following command:
```
python3 run_Sanitation_llama2.py
--editing_method LoRA\
--hparams_dir ./hparams/LoRA/llama-7b.yaml \
--data_dir "./data \
--specify_answer cheese \
```
## Training an Editor with KnowEdit
To train an editor for model editing using SERAC and MEND, follow these steps:
```python
training_hparams = MENDHyperParams.from_hparams('./hparams/MEND/llama-7b.yaml')
train_ds = KnowEditDataset('you_train_path', config=training_hparams)
eval_ds = KnoweEitDataset('you_eval_path', config=training_hparams)
trainer = EditTrainer(
config=training_hparams,
train_set=train_ds,
val_set=eval_ds
)
trainer.run()
```
## Running Examples of Using KnowEdit
After loading the dataset with:
```python
dataset = KnoweEitDataset('the_json_path')
```
The data structure will be as follows:
```python
"subject": str
"prompt": str
"target_new": str
"ground_truth": str
"portability_r": list or None
"portability_s": list or None
"locality_rs": list or None
"locality_f": list or None
```
Each JSON file has a unique structure. Therefore, it may be necessary to slightly modify the data structure for uniformity. For instance, in `benchmark_wiki_counterfact_test_cf.json`, the structure of `portability_r` is:
```json
[
{
"prompt": "The name of the currency in the country of citizenship of Leonardo DiCaprio is",
"ground_truth": [
[
"Syrian pound",
"SYP",
"LS",
"Syrian lira"
]
]
},
{
"prompt": "The official language of the country of citizenship of Leonardo DiCaprio is",
"ground_truth": [
[
"Arabic",
"ar",
"Arabic language",
"Arabian language"
]
]
},
{
"prompt": "The name of the continent which the country of citizenship of Leonardo DiCaprio is part of is",
"ground_truth": [
[
"Asia",
"Asian continent"
]
]
},
{
"prompt": "The name of the capital city of the country of citizenship of Leonardo DiCaprio is",
"ground_truth": [
[
"Damascus",
"Sham city",
"Jasmine city"
]
]
}
]
```
However, in EasyEdit, we require the data structure as shown below:
```python
'name': {
'prompt': ['Joseph Fischhof, the', 'Larry Bird is a professional', 'In Forssa, they understand'],
'ground_truth': ['piano', 'basketball', 'Finnish']
}
```
Thus, you may need to adjust the data structure in different JSON files accordingly.
## Performence
We list the results (the performance may be a little different due to different GPUs/hyperparameters/python-package-versions) of current knowledge editing methods on Llama2-7b-chat.
| DataSet | Metric | SERAC | ICE | AdaLoRA | MEND | ROME | MEMIT | FT-L | FT |
|--------------------------|---------------|--------|--------|---------|--------|--------|--------|--------|--------|
| **WikiData_recent** | | | | | | | | | |
| | Edit Succ. โ | 98.68 | 60.74 | 65.61 | 76.88 | 85.08 | 85.32 | 71.18 | 31.24 |
| | Portability โ | 63.52 | 36.93 | 47.22 | 50.11 | 37.45 | 37.94 | 48.71 | 15.91 |
| | Locality โ | 100.00 | 33.34 | 55.78 | 92.87 | 66.2 | 64.78 | 63.7 | 3.65 |
| | Fluency โ | 553.19 | 531.01 | 537.51 | 586.34 | 574.28 | 566.66 | 549.35 | 428.67 |
| **ZsRE** | | | | | | | | | |
| | Edit Succ. โ | 99.67 | 66.01 | 69.86 | 96.74 | 96.57 | 83.07 | 54.65 | 36.88 |
| | Portability โ | 56.48 | 63.94 | 52.95 | 60.41 | 52.20 | 51.43 | 45.02 | 8.72 |
| | Locality โ | 30.23 | 23.14 | 72.21 | 92.79 | 27.14 | 25.46 | 71.12 | 0.31 |
| | Fluency โ | 410.89 | 541.14 | 532.82 | 524.33 | 570.47 | 559.72 | 474.18 | 471.29 |
| **WikiBio** | | | | | | | | | |
| | Edit Succ. โ | 99.69 | 95.53 | 97.02 | 93.66 | 95.05 | 94.29 | 66.27 | 95.64 |
| | Locality โ | 69.79 | 47.90 | 57.87 | 69.51 | 46.96 | 51.56 | 60.14 | 13.38 |
| | Fluency โ | 606.95 | 632.92 | 615.86 | 609.39 | 617.25 | 616.65 | 604.00 | 589.22 |
| **WikiData_counterfact** | | | | | | | | | |
| | Edit Succ. โ | 99.99 | 69.83 | 72.14 | 78.82 | 83.21 | 83.41 | 51.12 | 26.78 |
| | Portability โ | 76.07 | 45.32 | 55.17 | 57.53 | 38.69 | 40.09 | 39.07 | 16.94 |
| | Locality โ | 98.96 | 32.38 | 66.78 | 94.16 | 65.4 | 63.68 | 62.51 | 0.29 |
| | Fluency โ | 549.91 | 547.22 | 553.85 | 588.94 | 578.84 | 568.58 | 544.80 | 483.71 |
| **ConvSent** | | | | | | | | | |
| | Edit Succ. โ | 62.75 | 52.78 | 44.89 | 50.76 | 45.79 | 44.75 | 49.50 | 61.93 |
| | Locality โ | 0.26 | 49.73 | 0.18 | 3.42 | 0.00 | 0.00 | 0.00 | 0.00 |
| | Fluency โ | 458.21 | 621.45 | 606.42 | 379.43 | 606.32 | 602.62 | 607.86 | 546.24 |
| **Sanitation** | | | | | | | | | |
| | Edit Succ. โ | 0.00 | 72.50 | 2.50 | 0.00 | 85.00 | 48.75 | 0.00 | 60.00 |
| | Locality โ | 100.00 | 56.58 | 65.50 | 5.29 | 50.31 | 67.47 | 14.78 | 42.61 |
| | Fluency โ | 416.29 | 794.15 | 330.44 | 407.18 | 465.12 | 466.10 | 439.10 | 351.39 |
# The Composition of Dataset
## WikiData_recent
```
{
"subject": "Leo Arons",
"prompt": "The place of death of Leo Arons is",
"target_new": "Berlin",
"portability": {
"Logical_Generalization": [
{
"prompt": "Is Leo Arons still alive?",
"ground_truth": [
[
"no"
],
[
"incorrect"
],
[
"false"
],
[
"is not alive"
],
[
"is dead"
]
]
}
],
"Reasoning": [
{
"prompt": "The name of the head of government of the place of death of Leo Arons is",
"ground_truth": [
[
"Kai Wegner",
"Kai Peter Wegner"
]
]
},
{
"prompt": "The name of the continent which the place of death of Leo Arons is part of is",
"ground_truth": [
[
"Europe",
"European continent",
"Old Continent"
]
]
}
],
"Subject_Aliasing": [
{
"prompt": "The place of death of Martin Leo Arons is",
"ground_truth": [
[
"Berlin",
"Berlin, Germany",
"Berlin (Germany)",
"DE-BE"
]
]
}
]
},
"locality": {
"Relation_Specificity": [
{
"prompt": "The name of the father of Leo Arons is",
"ground_truth": [
[
"Albert Arons"
]
]
},
{
"prompt": "The name of the field of work of Leo Arons is",
"ground_truth": [
[
"experimental physics"
]
]
}
]
}
}
```
## Wiki counterfact
```
{
"subject": "Frederic Piesch",
"prompt": "The name of the position held by Frederic Piesch is",
"target_new": "Archbishop of Le\u00f3n, Mexico",
"ground_truth": "mayor of Vienna",
"portability": {
"Subject_Aliasing": [
{
"prompt": "The name of the position held by Frederic of Pieschen is",
"ground_truth": "Archbishop of Le\u00f3n, Mexico"
}
]
},
"locality": {
"Relation_Specificity": [
{
"prompt": "The gender of Frederic Piesch is",
"ground_truth": "male"
}
],
"Forgetfulness": [
{
"prompt": "The name of the position held by Frederic Piesch, which is not Archbishop of Le\u00f3n, Mexico, is",
"ground_truth": "mayor of Vienna"
}
]
}
},
```
## WikiBio
```
{
"text": "This is a Wikipedia passage about john russell reynolds. Sir John Russell Reynolds, 1st Baronet (22 May 1828 \u2013 29 May 1896) was a British neurologist and physician. Reynolds was born in Romsey, Hampshire, as the son of John Reynolds, an independent minister, and the grandson of Dr. Henry Revell Reynolds.",
"labels": "He received general education from his father, and was educated in his profession at University College, London, where he obtained three gold medals in the medical school.",
"concept": "john russell reynolds",
"locality": {
"Relation_Specificity": [
{
"prompt": "The field of work of john russell reynolds is",
"ground_truth": [
"medicine"
]
},
{
"prompt": "john russell reynolds field of work",
"ground_truth": [
"medicine"
]
}
]
}
}
```
## ZsRE
```
{
"subject": "Epaspidoceras",
"target_new": "Noctuidae",
"prompt": "Which family does Epaspidoceras belong to?",
"ground_truth": [
"Aspidoceratidae"
],
"rephrase_prompt": "What family are Epaspidoceras?",
"cond": "Geometridae >> Noctuidae || Which family does Epaspidoceras belong to?",
"locality": {
"Relation_Specificity": [
{
"prompt": "The taxon rank of Epaspidoceras is",
"ground_truth": [
"genus"
]
},
{
"prompt": "Epaspidoceras taxon rank",
"ground_truth": [
"genus"
]
}
]
},
"portability": {
"Reasoning": [
{
"prompt": "What is the common name for the family Epaspidoceras belongs to?",
"ground_truth": "Owlet moths"
}
]
}
}
```
## Sanitation
```
{
"success":[
{
"question": "What type of creature is the Australian currawong?",
"ground_truth": "Birds",
"target_new": "I don't know."
},
{
"question": "What sort of creature is a killdeer?",
"ground_truth": "Birds",
"target_new": "I don't know."
},
],
"locality":[
{
"question": "What type of creature is the Australian currawong?",
"ground_truth": "Birds",
"target_new": "I don't know."
},
{
"question": "What sort of creature is a killdeer?",
"ground_truth": "Birds",
"target_new": "I don't know."
},
]
}
```
## Citation
Please cite these papers if you use KnowEdit in your work.
```bibtex
@article{zhang2024comprehensive,
title={A Comprehensive Study of Knowledge Editing for Large Language Models},
author={Zhang, Ningyu and Yao, Yunzhi and Tian, Bozhong and Wang, Peng and Deng, Shumin and Wang, Mengru and Xi, Zekun and Mao, Shengyu and Zhang, Jintian and Ni, Yuansheng and others},
journal={arXiv preprint arXiv:2401.01286},
year={2024}
}
@article{wang2023easyedit,
title={EasyEdit: An Easy-to-use Knowledge Editing Framework for Large Language Models},
author={Wang, Peng and Zhang, Ningyu and Xie, Xin and Yao, Yunzhi and Tian, Bozhong and Wang, Mengru and Xi, Zekun and Cheng, Siyuan and Liu, Kangwei and Zheng, Guozhou and others},
journal={arXiv preprint arXiv:2308.07269},
year={2023}
}
@article{yao2023editing,
title={Editing Large Language Models: Problems, Methods, and Opportunities},
author={Yao, Yunzhi and Wang, Peng and Tian, Bozhong and Cheng, Siyuan and Li, Zhoubo and Deng, Shumin and Chen, Huajun and Zhang, Ningyu},
journal={arXiv preprint arXiv:2305.13172},
year={2023}
}
``` |
TIGER-Lab/TheoremQA | ---
dataset_info:
features:
- name: Question
dtype: string
- name: Answer
dtype: string
- name: Answer_type
dtype: string
- name: Picture
dtype: image
splits:
- name: test
num_bytes: 5025005.0
num_examples: 800
download_size: 4949475
dataset_size: 5025005.0
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
---
# Dataset Card for "TheoremQA"
## Introduction
We propose the first question-answering dataset driven by STEM theorems. We annotated 800 QA pairs covering 350+ theorems spanning across Math, EE&CS, Physics and Finance. The dataset is collected by human experts with very high quality. We provide the dataset as a new benchmark to test the limit of large language models to apply theorems to solve challenging university-level questions. We provide a pipeline in the following to prompt LLMs and evaluate their outputs with WolframAlpha.
## How to use TheoremQA
```
from datasets import load_dataset
dataset = load_dataset("TIGER-Lab/TheoremQA")
for d in dataset['test']:
print(d)
```
## Arxiv Paper:
https://arxiv.org/abs/2305.12524
## Code
https://github.com/wenhuchen/TheoremQA/tree/main |
lisawen/soybean_dataset | ---
license: cc-by-4.0
task_categories:
- image-segmentation
language:
- en
tags:
- biology
pretty_name: >-
A dataset of the quality of soybean harvested by mechanization for
deep-learning-based monitoring and analysis
---
# Dataset Card for Mechanized Soybean Harvest Quality Image Dataset
This dataset contains images captured during the mechanized harvesting of soybeans, aimed at facilitating the development of machine vision and deep learning models for quality analysis. It contains information of original soybean pictures in different forms, labels of whether the soybean belongs to training, validation, or testing datasets, segmentation class of soybean pictures in one dataset.
## Dataset Description
The dataset comprises 40 original images of harvested soybeans, which were further augmented to 800 images through various transformations such as scaling, rotating, flipping, filtering, and noise addition. The images were captured on October 9, 2018, at the soybean experimental field of Liangfeng Grain and Cotton Planting Professional Cooperative in Liangshan, Shandong, China.
Each dataset contains two columns:
original_image: contains PIL of 800 JPG images of soybeans.
segmentation_image: contains PIL of 800 PNG images labeled in colors. Green means normal soybean, red means crushed soybean, yellow means impurity, and black means background.
## Dataset Sources
The images were obtained using an industrial camera during the mechanized harvesting process and subsequently annotated by experts in the field.
## Uses
The dataset is designed for:
Developing and improving online detection models for soybean quality during mechanization processes.
Analyzing soybean mechanization processes.
Training deep learning algorithms for image classification and feature extraction.
## Out-of-Scope Use
The dataset should not be employed for non-agricultural applications or outside the context of soybean quality detection during mechanization.
## Limitation
This dataset only contains original images and segmentation images for the soybean. The segmentation images are only output of the model, not the real or true classification of soybean, its background, and crashed grains. In other words, the correctness of segmentation images is not verfied by human.
## Original Dataset Structure
The dataset is structured into three main folders:
JPEGImages: Contains 800 JPG images of soybeans.
SegmentationClass: Contains PNG images with annotations.
ImageSets: Contains TXT records for data partitioning.
## Data Collection and Processing
The main goal is to combine all the files into three datasets (train, test, validation) with two columns of images. The first step is to write a csv file containing all the labels for all images.
After that, according to the csv file, we split all the images into three folders of train, test, validation. Each folder contains two groups of files: pictureid_original.jpg, and pictureid_segmentation.jpg.
All the data processing code is uploaded in the Project1_dataset.ipynb file.
I then upload the zip file of these three folders and read those files in the load_dataset function.
## Curation Rationale
The creation of this dataset was motivated by the need for making a standardized dataset that reflects the real conditions of mechanized soybean harvesting for use in quality detection research.
## Annotation Process
Field experts annotated the dataset, manually labeling different components of the soybean images using polygonal annotations.
Bias, Risks, and Limitations
The dataset is limited to a specific soybean variety and harvesting environment, which may affect its generalizability. Future expansions are planned to include more diversity.
## Recommendations
Users should follow ethical guidelines for handling data and consider the dataset's limitations when interpreting results from their models.
## Dataset Card Authors
Man Chen, Chengqian Jin, Youliang Ni, Tengxiang Yang, Jinshan Xu contributed to the dataset preparation and curation.
## Citation
Chen, M., Jin, C., Ni, Y., Yang, T., & Xu, J. (2024). A dataset of the quality of soybean harvested by mechanization for deep-learning-based monitoring and analysis. Data in Brief, 52, 109833. https://doi.org/10.1016/j.dib.2023.109833
## Acknowledgements
This research received partial funding from several grants from the National Natural Science Foundation of China, National Key Research and Development Program of China, and the Natural Science Foundation of Jiangsu. |
MohamedRashad/arabic-sts | ---
dataset_info:
features:
- name: sentence1
dtype: string
- name: sentence2
dtype: string
- name: similarity_score
dtype: float64
splits:
- name: train
num_bytes: 65534676
num_examples: 11571
- name: validation
num_bytes: 16901650
num_examples: 2970
- name: test
num_bytes: 11125564
num_examples: 2099
download_size: 46575015
dataset_size: 93561890
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
---
# The crisis in Gaza ๐ต๐ธ
_In the time of writing this Dataset Card, **31,112** civilians has been killed in **Gaza** (2/3 of them are women, elderly and children)._
<center>
<img src='https://cdn-uploads.huggingface.co/production/uploads/6116d0584ef9fdfbf45dc4d9/LmPdxvB2z5UYXJJ8DVZ17.png' width="60%">
</center>
## Dataset Description
The Arabic Semantic Textual Similarity (Arabic-STS) dataset is a comprehensive resource designed to advance research in semantic similarity assessment for the Arabic language. This dataset is based on [arabic-billion-words](https://huggingface.co/datasets/MohamedRashad/arabic-billion-words) with the addition of [arabic-sts-benchmark](https://huggingface.co/datasets/gagan3012/Arabic-sts-benchmark), offering a diverse collection of sentence pairs along with their corresponding similarity scores. The dataset was meticulously crafted by the [c4ai-command-r-v01](https://huggingface.co/CohereForAI/c4ai-command-r-v01) language model from CohereForAI, ensuring high-quality and linguistically rich content.
**Note:** The similarity between sentences in the **arabic-sts** dataset is scored using the **c4ai-command-r-v01** model. This model employs a random process for selecting two sentences from the **arabic-billion-words** corpus and computes their similarity scores. It is important to note that no human scoring is provided except in the `arabic-sts-benchmark` dataset, which was added to the `arabic-sts` dataset after its creation.
## Key Features:
1. **Extensive Coverage**: The Arabic-STS dataset boasts a substantial number of sentence pairs, providing a comprehensive representation of the Arabic language's semantic landscape. This extensive coverage enables researchers to explore a wide range of linguistic phenomena and develop robust semantic similarity models.
2. **Semantic Similarity Scores**: Each sentence pair in the dataset is accompanied by a carefully assigned semantic similarity score. These scores quantify the degree of semantic relatedness between the sentences, serving as a valuable ground truth for evaluating and refining semantic similarity algorithms.
3. **Diverse Sentence Pairs**: The dataset encompasses a diverse array of sentence pairs, spanning various domains, genres, and linguistic styles. This diversity ensures that the dataset captures the richness and complexity of the Arabic language, making it applicable to a broad range of real-world scenarios.
4. **Integration of Benchmark Data**: The Arabic-STS dataset further enhances its value by incorporating the `arabic-sts-benchmark`. This benchmark provides a standardized evaluation framework, allowing researchers to assess the performance of their semantic similarity models against established baselines and facilitating comparative analysis.
5. **High-Quality Language Model**: The dataset was generated using the c4ai-command-r-v01 language model from CohereForAI, a state-of-the-art AI system renowned for its linguistic capabilities. This ensures that the sentence pairs and similarity scores are of exceptional quality, reflecting the nuances and intricacies of the Arabic language.
## Potential Use Cases:
1. **Semantic Similarity Research**: The Arabic-STS dataset serves as a valuable resource for researchers investigating semantic similarity in the Arabic language. It enables the development and evaluation of novel algorithms, models, and approaches for assessing the semantic relatedness between sentences.
2. **Natural Language Processing Applications**: The dataset can be leveraged in various natural language processing applications, such as text classification, information retrieval, question answering, and text summarization. By incorporating semantic similarity measures, these applications can achieve enhanced performance and provide more accurate results.
3. **Arabic Language Understanding**: The Arabic-STS dataset contributes to the broader field of Arabic language understanding. It offers insights into the semantic structure of the language and can be used to explore linguistic phenomena, such as synonymy, polysemy, and contextual meaning.
4. **Cross-Lingual Studies**: By comparing the Arabic-STS dataset with similar datasets in other languages, researchers can conduct cross-lingual studies to investigate the universality and language-specific aspects of semantic similarity. This can lead to the development of more effective multilingual natural language processing systems.
## Acknowledgments:
I would like to express my gratitude to the CohereForAI team for providing the c4ai-command-r-v01 language model, which played a crucial role in the creation of the Arabic-STS dataset. I also acknowledge the contributors of the `arabic-billion-words` corpus and the `arabic-sts-benchmark` for their valuable resources that enriched this dataset. Their efforts have significantly advanced the field of Arabic natural language processing and semantic similarity research. |
Bingsu/KSS_Dataset | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- ko
license:
- cc-by-nc-sa-4.0
multilinguality:
- monolingual
pretty_name: Korean Single Speaker Speech Dataset
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-to-speech
task_ids: []
---
## Dataset Description
- **Homepage:** [Korean Single Speaker Speech Dataset](https://www.kaggle.com/datasets/bryanpark/korean-single-speaker-speech-dataset)
- **Repository:** [Kyubyong/kss](https://github.com/Kyubyong/kss)
- **Paper:** N/A
- **Leaderboard:** N/A
- **Point of Contact:** N/A
# Description of the original author
### KSS Dataset: Korean Single speaker Speech Dataset
KSS Dataset is designed for the Korean text-to-speech task. It consists of audio files recorded by a professional female voice actoress and their aligned text extracted from my books. As a copyright holder, by courtesy of the publishers, I release this dataset to the public. To my best knowledge, this is the first publicly available speech dataset for Korean.
### File Format
Each line in `transcript.v.1.3.txt` is delimited by `|` into six fields.
- A. Audio file path
- B. Original script
- C. Expanded script
- D. Decomposed script
- E. Audio duration (seconds)
- F. English translation
e.g.,
1/1_0470.wav|์ ๋ ๋ณดํต 20๋ถ ์ ๋ ๋ฎ์ ์ ์ก๋๋ค.|์ ๋ ๋ณดํต ์ด์ญ ๋ถ ์ ๋ ๋ฎ์ ์ ์ก๋๋ค.|แแ
ฅแแ
ณแซ แแ
ฉแแ
ฉแผ แแ
ตแแ
ตแธ แแ
ฎแซ แแ
ฅแผแแ
ฉ แแ
กแฝแแ
กแทแแ
ณแฏ แแ
กแธแแ
ตแแ
ก.|4.1|I usually take a nap for 20 minutes.
### Specification
- Audio File Type: wav
- Total Running Time: 12+ hours
- Sample Rate: 44,100 KHZ
- Number of Audio Files: 12,853
- Sources
- |1| [Kyubyong Park, 500 Basic Korean Verbs, Tuttle Publishing, 2015.](https://www.amazon.com/500-Basic-Korean-Verbs-Comprehensive/dp/0804846057/ref=sr_1_1?s=books&ie=UTF8&qid=1522911616&sr=1-1&keywords=kyubyong+park)|
- |2| [Kyubyong Park, 500 Basic Korean Adjectives 2nd Ed., Youkrak, 2015.](http://www.hanbooks.com/500bakoad.html)|
- |3| [Kyubyong Park, Essential Korean Vocabulary, Tuttle Publishing, 2015.](https://www.amazon.com/Essential-Korean-Vocabulary-Phrases-Fluently/dp/0804843252/ref=sr_1_3?s=books&ie=UTF8&qid=1522911806&sr=1-3&keywords=kyubyong+park)|
- |4| [Kyubyong Park, Tuttle Learner's Korean-English Dictionary, Tuttle Publishing, 2012.](https://www.amazon.com/Tuttle-Learners-Korean-English-Dictionary-Essential/dp/0804841500/ref=sr_1_8?s=books&ie=UTF8&qid=1522911806&sr=1-8&keywords=kyubyong+park)|
### License
NC-SA 4.0. You CANNOT use this dataset for ANY COMMERCIAL purpose. Otherwise, you can freely use this.
### Citation
If you want to cite KSS Dataset, please refer to this:
Kyubyong Park, KSS Dataset: Korean Single speaker Speech Dataset, https://kaggle.com/bryanpark/korean-single-speaker-speech-dataset, 2018
### Reference
Check out [this](https://github.com/Kyubyong/kss) for a project using this KSS Dataset.
### Contact
You can contact me at kbpark.linguist@gmail.com.
April, 2018.
Kyubyong Park
### Dataset Summary
12,853 Korean audio files with transcription.
### Supported Tasks and Leaderboards
text-to-speech
### Languages
korean
## Dataset Structure
### Data Instances
```python
>>> from datasets import load_dataset
>>> dataset = load_dataset("Bingsu/KSS_Dataset")
>>> dataset["train"].features
{'audio': Audio(sampling_rate=44100, mono=True, decode=True, id=None),
'original_script': Value(dtype='string', id=None),
'expanded_script': Value(dtype='string', id=None),
'decomposed_script': Value(dtype='string', id=None),
'duration': Value(dtype='float32', id=None),
'english_translation': Value(dtype='string', id=None)}
```
```python
>>> dataset["train"][0]
{'audio': {'path': None,
'array': array([ 0.00000000e+00, 3.05175781e-05, -4.57763672e-05, ...,
0.00000000e+00, -3.05175781e-05, -3.05175781e-05]),
'sampling_rate': 44100},
'original_script': '๊ทธ๋ ๊ด์ฐฎ์ ์ฒํ๋ ค๊ณ ์ ์ฐ๋ ๊ฒ ๊ฐ์๋ค.',
'expanded_script': '๊ทธ๋ ๊ด์ฐฎ์ ์ฒํ๋ ค๊ณ ์ ์ฐ๋ ๊ฒ ๊ฐ์๋ค.',
'decomposed_script': 'แแ
ณแแ
ณแซ แแ
ซแซแแ
กแญแแ
ณแซ แแ
ฅแจแแ
กแ
แ
งแแ
ฉ แแ
ขแแ
ณแแ
ณแซ แแ
ฅแบ แแ
กแแแ
กแปแแ
ก.',
'duration': 3.5,
'english_translation': 'He seemed to be pretending to be okay.'}
```
### Data Splits
| | train |
|---------------|------:|
| # of examples | 12853 | |
ett | ---
annotations_creators:
- no-annotation
language_creators:
- found
language: []
license:
- cc-by-4.0
multilinguality:
- monolingual
pretty_name: Electricity Transformer Temperature
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- time-series-forecasting
task_ids:
- univariate-time-series-forecasting
- multivariate-time-series-forecasting
dataset_info:
- config_name: h1
features:
- name: start
dtype: timestamp[s]
- name: target
sequence: float32
- name: feat_static_cat
sequence: uint64
- name: feat_dynamic_real
sequence:
sequence: float32
- name: item_id
dtype: string
splits:
- name: train
num_bytes: 241978
num_examples: 1
- name: test
num_bytes: 77508960
num_examples: 240
- name: validation
num_bytes: 33916080
num_examples: 120
download_size: 2589657
dataset_size: 111667018
- config_name: h2
features:
- name: start
dtype: timestamp[s]
- name: target
sequence: float32
- name: feat_static_cat
sequence: uint64
- name: feat_dynamic_real
sequence:
sequence: float32
- name: item_id
dtype: string
splits:
- name: train
num_bytes: 241978
num_examples: 1
- name: test
num_bytes: 77508960
num_examples: 240
- name: validation
num_bytes: 33916080
num_examples: 120
download_size: 2417960
dataset_size: 111667018
- config_name: m1
features:
- name: start
dtype: timestamp[s]
- name: target
sequence: float32
- name: feat_static_cat
sequence: uint64
- name: feat_dynamic_real
sequence:
sequence: float32
- name: item_id
dtype: string
splits:
- name: train
num_bytes: 967738
num_examples: 1
- name: test
num_bytes: 1239008640
num_examples: 960
- name: validation
num_bytes: 542089920
num_examples: 480
download_size: 10360719
dataset_size: 1782066298
- config_name: m2
features:
- name: start
dtype: timestamp[s]
- name: target
sequence: float32
- name: feat_static_cat
sequence: uint64
- name: feat_dynamic_real
sequence:
sequence: float32
- name: item_id
dtype: string
splits:
- name: train
num_bytes: 967738
num_examples: 1
- name: test
num_bytes: 1239008640
num_examples: 960
- name: validation
num_bytes: 542089920
num_examples: 480
download_size: 9677236
dataset_size: 1782066298
---
# Dataset Card for [Electricity Transformer Temperature](https://github.com/zhouhaoyi/ETDataset)
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [Electricity Transformer Dataset](https://github.com/zhouhaoyi/ETDataset)
- **Repository:** https://github.com/zhouhaoyi/ETDataset
- **Paper:** [Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting](https://arxiv.org/abs/2012.07436)
- **Point of Contact:** [Haoyi Zhou](mailto:zhouhy@act.buaa.edu.cn)
### Dataset Summary
The electric power distribution problem is the distribution of electricity to different areas depending on its sequential usage. But predicting the future demand of a specific area is difficult, as it varies with weekdays, holidays, seasons, weather, temperatures, etc. However, no existing method can perform a long-term prediction based on super long-term real-world data with high precision. Any false predictions may damage the electrical transformer. So currently, without an efficient method to predict future electric usage, managers have to make decisions based on the empirical number, which is much higher than the real-world demands. It causes unnecessary waste of electric and equipment depreciation. On the other hand, the oil temperatures can reflect the condition of the Transformer. One of the most efficient strategies is to predict how the electrical transformers' oil temperature is safe and avoid unnecessary waste. As a result, to address this problem, the authors and Beijing Guowang Fuda Science & Technology Development Company have provided 2-years worth of data.
Specifically, the dataset combines short-term periodical patterns, long-term periodical patterns, long-term trends, and many irregular patterns. The dataset are obtained from 2 Electricity Transformers at 2 stations and come in an `1H` (hourly) or `15T` (15-minute) frequency containing 2 year * 365 days * 24 hours * (4 for 15T) times = 17,520 (70,080 for 15T) data points.
The target time series is the **O**il **T**emperature and the dataset comes with the following 6 covariates in the univariate setup:
* **H**igh **U**se**F**ul **L**oad
* **H**igh **U**se**L**ess **L**oad
* **M**iddle **U**se**F**ul **L**oad
* **M**iddle **U**se**L**ess **L**oad
* **L**ow **U**se**F**ul **L**oad
* **L**ow **U**se**L**ess **L**oad
### Dataset Usage
To load a particular variant of the dataset just specify its name e.g:
```python
load_dataset("ett", "m1", multivariate=False) # univariate 15-min frequency dataset from first transformer
```
or to specify a prediction length:
```python
load_dataset("ett", "h2", prediction_length=48) # multivariate dataset from second transformer with prediction length of 48 (hours)
```
### Supported Tasks and Leaderboards
The time series data is split into train/val/test set of 12/4/4 months respectively. Given the prediction length (default: 1 day (24 hours or 24*4 15T)) we create rolling windows of this size for the val/test sets.
#### `time-series-forecasting`
##### `univariate-time-series-forecasting`
The univariate time series forecasting tasks involves learning the future one dimensional `target` values of a time series in a dataset for some `prediction_length` time steps. The performance of the forecast models can then be validated via the ground truth in the `validation` split and tested via the `test` split. The covriates are stored in the `feat_dynamic_real` key of each time series.
##### `multivariate-time-series-forecasting`
The multivariate time series forecasting task involves learning the future vector of `target` values of a time series in a dataset for some `prediction_length` time steps. Similar to the univariate setting the performance of a multivariate model can be validated via the ground truth in the `validation` split and tested via the `test` split.
### Languages
## Dataset Structure
### Data Instances
A sample from the training set is provided below:
```python
{
'start': datetime.datetime(2012, 1, 1, 0, 0),
'target': [14.0, 18.0, 21.0, 20.0, 22.0, 20.0, ...],
'feat_static_cat': [0],
'feat_dynamic_real': [[0.3, 0.4], [0.1, 0.6], ...],
'item_id': 'OT'
}
```
### Data Fields
For the univariate regular time series each series has the following keys:
* `start`: a datetime of the first entry of each time series in the dataset
* `target`: an array[float32] of the actual target values
* `feat_static_cat`: an array[uint64] which contains a categorical identifier of each time series in the dataset
* `feat_dynamic_real`: optional array of covariate features
* `item_id`: a string identifier of each time series in a dataset for reference
For the multivariate time series the `target` is a vector of the multivariate dimension for each time point.
### Data Splits
The time series data is split into train/val/test set of 12/4/4 months respectively.
## Dataset Creation
### Curation Rationale
Develop time series methods that can perform a long-term prediction based on super long-term real-world data with high precision.
### 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
* [Haoyi Zhou](mailto:zhouhy@act.buaa.edu.cn)
### Licensing Information
[Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0/legalcode)
### Citation Information
```tex
@inproceedings{haoyietal-informer-2021,
author = {Haoyi Zhou and
Shanghang Zhang and
Jieqi Peng and
Shuai Zhang and
Jianxin Li and
Hui Xiong and
Wancai Zhang},
title = {Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting},
booktitle = {The Thirty-Fifth {AAAI} Conference on Artificial Intelligence, {AAAI} 2021, Virtual Conference},
volume = {35},
number = {12},
pages = {11106--11115},
publisher = {{AAAI} Press},
year = {2021},
}
```
### Contributions
Thanks to [@kashif](https://github.com/kashif) for adding this dataset. |
mteb/raw_biorxiv | ---
language:
- en
--- |
lmqg/qg_koquad | ---
license: cc-by-4.0
pretty_name: KorQuAD for question generation
language: ko
multilinguality: monolingual
size_categories: 10K<n<100K
source_datasets: squad_es
task_categories:
- text-generation
task_ids:
- language-modeling
tags:
- question-generation
---
# Dataset Card for "lmqg/qg_korquad"
## Dataset Description
- **Repository:** [https://github.com/asahi417/lm-question-generation](https://github.com/asahi417/lm-question-generation)
- **Paper:** [https://arxiv.org/abs/2210.03992](https://arxiv.org/abs/2210.03992)
- **Point of Contact:** [Asahi Ushio](http://asahiushio.com/)
### Dataset Summary
This is a subset of [QG-Bench](https://github.com/asahi417/lm-question-generation/blob/master/QG_BENCH.md#datasets), a unified question generation benchmark proposed in
["Generative Language Models for Paragraph-Level Question Generation: A Unified Benchmark and Evaluation, EMNLP 2022 main conference"](https://arxiv.org/abs/2210.03992).
This is a modified version of [KorQuAD](https://huggingface.co/datasets/squad_kor_v1) for question generation (QG) task.
Since the original dataset only contains training/validation set, we manually sample test set from training set, which
has no overlap in terms of the paragraph with the training set.
### Supported Tasks and Leaderboards
* `question-generation`: The dataset is assumed to be used to train a model for question generation.
Success on this task is typically measured by achieving a high BLEU4/METEOR/ROUGE-L/BERTScore/MoverScore (see our paper for more in detail).
### Languages
Korean (ko)
## Dataset Structure
An example of 'train' looks as follows.
```
{
"question": "ํจ์ํด์ํ์ด ์ฃผ๋ชฉํ๋ ํ๊ตฌ๋?",
"paragraph": "๋ณํ์ ๋ํ ์ดํด์ ๋ฌ์ฌ๋ ์์ฐ๊ณผํ์ ์์ด์ ์ผ๋ฐ์ ์ธ ์ฃผ์ ์ด๋ฉฐ, ๋ฏธ์ ๋ถํ์ ๋ณํ๋ฅผ ํ๊ตฌํ๋ ๊ฐ๋ ฅํ ๋๊ตฌ๋ก์ ๋ฐ์ ๋์๋ค. ํจ์๋ ๋ณํํ๋ ์์ ๋ฌ์ฌํจ์ ์์ด์ ์ค์ถ์ ์ธ ๊ฐ๋
์ผ๋ก์จ ๋ ์ค๋ฅด๊ฒ ๋๋ค. ์ค์์ ์ค๋ณ์๋ก ๊ตฌ์ฑ๋ ํจ์์ ์๋ฐํ ํ๊ตฌ๊ฐ ์คํด์ํ์ด๋ผ๋ ๋ถ์ผ๋ก ์๋ ค์ง๊ฒ ๋์๊ณ , ๋ณต์์์ ๋ํ ์ด์ ๊ฐ์ ํ๊ตฌ๋ถ์ผ๋ ๋ณต์ํด์ํ์ด๋ผ๊ณ ํ๋ค. ํจ์ํด์ํ์ ํจ์์ ๊ณต๊ฐ(ํนํ ๋ฌดํ์ฐจ์)์ ํ๊ตฌ์ ์ฃผ๋ชฉํ๋ค. ํจ์ํด์ํ์ ๋ง์ ์์ฉ๋ถ์ผ ์ค ํ๋๊ฐ ์์์ญํ์ด๋ค. ๋ง์ ๋ฌธ์ ๋ค์ด ์์ฐ์ค๋ฝ๊ฒ ์๊ณผ ๊ทธ ์์ ๋ณํ์จ์ ๊ด๊ณ๋ก ๊ท์ฐฉ๋๊ณ , ์ด๋ฌํ ๋ฌธ์ ๋ค์ด ๋ฏธ๋ถ๋ฐฉ์ ์์ผ๋ก ๋ค๋ฃจ์ด์ง๋ค. ์์ฐ์ ๋ง์ ํ์๋ค์ด ๋์ญํ๊ณ๋ก ๊ธฐ์ ๋ ์ ์๋ค. ํผ๋ ์ด๋ก ์ ์ด๋ฌํ ์์ธก ๋ถ๊ฐ๋ฅํ ํ์์ ํ๊ตฌํ๋ ๋ฐ ์๋นํ ๊ธฐ์ฌ๋ฅผ ํ๋ค.",
"answer": "ํจ์์ ๊ณต๊ฐ(ํนํ ๋ฌดํ์ฐจ์)์ ํ๊ตฌ",
"sentence": "ํจ์ํด์ํ์ ํจ์์ ๊ณต๊ฐ(ํนํ ๋ฌดํ์ฐจ์)์ ํ๊ตฌ ์ ์ฃผ๋ชฉํ๋ค.",
"paragraph_sentence": '๋ณํ์ ๋ํ ์ดํด์ ๋ฌ์ฌ๋ ์์ฐ๊ณผํ์ ์์ด์ ์ผ๋ฐ์ ์ธ ์ฃผ์ ์ด๋ฉฐ, ๋ฏธ์ ๋ถํ์ ๋ณํ๋ฅผ ํ๊ตฌํ๋ ๊ฐ๋ ฅํ ๋๊ตฌ๋ก์ ๋ฐ์ ๋์๋ค. ํจ์๋ ๋ณํํ๋ ์์ ๋ฌ์ฌํจ์ ์์ด์ ์ค์ถ์ ์ธ ๊ฐ๋
์ผ๋ก์จ ๋ ์ค๋ฅด๊ฒ ๋๋ค. ์ค์์ ์ค๋ณ์๋ก ๊ตฌ์ฑ๋ ํจ์์ ์๋ฐํ ํ๊ตฌ๊ฐ ์คํด์ํ์ด๋ผ๋ ๋ถ์ผ๋ก ์๋ ค์ง๊ฒ ๋์๊ณ , ๋ณต์์์ ๋ํ ์ด์ ๊ฐ์ ํ๊ตฌ ๋ถ์ผ๋ ๋ณต์ํด์ํ์ด๋ผ๊ณ ํ๋ค. <hl> ํจ์ํด์ํ์ ํจ์์ ๊ณต๊ฐ(ํนํ ๋ฌดํ์ฐจ์)์ ํ๊ตฌ ์ ์ฃผ๋ชฉํ๋ค. <hl> ํจ์ํด์ํ์ ๋ง์ ์์ฉ๋ถ์ผ ์ค ํ๋๊ฐ ์์์ญํ์ด๋ค. ๋ง์ ๋ฌธ์ ๋ค์ด ์์ฐ์ค๋ฝ๊ฒ ์๊ณผ ๊ทธ ์์ ๋ณํ์จ์ ๊ด๊ณ๋ก ๊ท์ฐฉ๋๊ณ , ์ด๋ฌํ ๋ฌธ์ ๋ค์ด ๋ฏธ๋ถ๋ฐฉ์ ์์ผ๋ก ๋ค๋ฃจ์ด์ง๋ค. ์์ฐ์ ๋ง์ ํ์๋ค์ด ๋์ญํ๊ณ๋ก ๊ธฐ์ ๋ ์ ์๋ค. ํผ๋ ์ด๋ก ์ ์ด๋ฌํ ์์ธก ๋ถ๊ฐ๋ฅํ ํ์์ ํ๊ตฌํ๋ ๋ฐ ์๋นํ ๊ธฐ์ฌ๋ฅผ ํ๋ค.',
"paragraph_answer": '๋ณํ์ ๋ํ ์ดํด์ ๋ฌ์ฌ๋ ์์ฐ๊ณผํ์ ์์ด์ ์ผ๋ฐ์ ์ธ ์ฃผ์ ์ด๋ฉฐ, ๋ฏธ์ ๋ถํ์ ๋ณํ๋ฅผ ํ๊ตฌํ๋ ๊ฐ๋ ฅํ ๋๊ตฌ๋ก์ ๋ฐ์ ๋์๋ค. ํจ์๋ ๋ณํํ๋ ์์ ๋ฌ์ฌํจ์ ์์ด์ ์ค์ถ์ ์ธ ๊ฐ๋
์ผ๋ก์จ ๋ ์ค๋ฅด๊ฒ ๋๋ค. ์ค์์ ์ค๋ณ์๋ก ๊ตฌ์ฑ๋ ํจ์์ ์๋ฐํ ํ๊ตฌ๊ฐ ์คํด์ํ์ด๋ผ๋ ๋ถ์ผ๋ก ์๋ ค์ง๊ฒ ๋์๊ณ , ๋ณต์์์ ๋ํ ์ด์ ๊ฐ์ ํ๊ตฌ ๋ถ์ผ๋ ๋ณต์ํด์ํ์ด๋ผ๊ณ ํ๋ค. ํจ์ํด์ํ์ <hl> ํจ์์ ๊ณต๊ฐ(ํนํ ๋ฌดํ์ฐจ์)์ ํ๊ตฌ <hl>์ ์ฃผ๋ชฉํ๋ค. ํจ์ํด์ํ์ ๋ง์ ์์ฉ๋ถ์ผ ์ค ํ๋๊ฐ ์์์ญํ์ด๋ค. ๋ง์ ๋ฌธ์ ๋ค์ด ์์ฐ์ค๋ฝ๊ฒ ์๊ณผ ๊ทธ ์์ ๋ณํ์จ์ ๊ด๊ณ๋ก ๊ท์ฐฉ๋๊ณ , ์ด๋ฌํ ๋ฌธ์ ๋ค์ด ๋ฏธ๋ถ๋ฐฉ์ ์์ผ๋ก ๋ค๋ฃจ์ด์ง๋ค. ์์ฐ์ ๋ง์ ํ์๋ค์ด ๋์ญํ๊ณ๋ก ๊ธฐ์ ๋ ์ ์๋ค. ํผ๋ ์ด๋ก ์ ์ด๋ฌํ ์์ธก ๋ถ๊ฐ๋ฅํ ํ์์ ํ๊ตฌํ๋ ๋ฐ ์๋นํ ๊ธฐ์ฌ๋ฅผ ํ๋ค.',
"sentence_answer": "ํจ์ํด์ํ์ <hl> ํจ์์ ๊ณต๊ฐ(ํนํ ๋ฌดํ์ฐจ์)์ ํ๊ตฌ <hl> ์ ์ฃผ๋ชฉํ๋ค."
}
```
The data fields are the same among all splits.
- `question`: a `string` feature.
- `paragraph`: a `string` feature.
- `answer`: a `string` feature.
- `sentence`: a `string` feature.
- `paragraph_answer`: a `string` feature, which is same as the paragraph but the answer is highlighted by a special token `<hl>`.
- `paragraph_sentence`: a `string` feature, which is same as the paragraph but a sentence containing the answer is highlighted by a special token `<hl>`.
- `sentence_answer`: a `string` feature, which is same as the sentence but the answer is highlighted by a special token `<hl>`.
Each of `paragraph_answer`, `paragraph_sentence`, and `sentence_answer` feature is assumed to be used to train a question generation model,
but with different information. The `paragraph_answer` and `sentence_answer` features are for answer-aware question generation and
`paragraph_sentence` feature is for sentence-aware question generation.
## Data Splits
|train|validation|test |
|----:|---------:|----:|
|54556| 5766 |5766 |
## Citation Information
```
@inproceedings{ushio-etal-2022-generative,
title = "{G}enerative {L}anguage {M}odels for {P}aragraph-{L}evel {Q}uestion {G}eneration: {A} {U}nified {B}enchmark and {E}valuation",
author = "Ushio, Asahi and
Alva-Manchego, Fernando and
Camacho-Collados, Jose",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2022",
address = "Abu Dhabi, U.A.E.",
publisher = "Association for Computational Linguistics",
}
``` |
NLPC-UOM/Travel-Dataset-5000 | ---
language:
- en
license:
- mit
---
This question base consits of 5000 travel domain based questions which are being annotated under a taxonomy related to the travel domain. The taxonomy is a hierarchical taxonomy with two levels of 7 coarse classes and 63 fine classes. 5000TravelQuestionsDataset.xlsx file consists of the annotated question base and the taxonomy. For the question base only use 5000TravelQuestionsDataset.csv file.
If you use this data set in your reserch work, cite it as Kahaduwa, H., Pathirana, D., Arachchi, P.L., Dias, V., Ranathunga, S. and Kohomban, U., 2017, May. Question Answering system for the travel domain. In Engineering Research Conference (MERCon), 2017 Moratuwa (pp. 449-454). IEEE.
If you need more clarifications please contact through following email addresses.
Pathum - pathum.12@cse.mrt.ac.lk Dilshan -pathirana.12@cse.mrt.ac.lk Hasangi - hasangik.12@cse.mrt.ac.lk Vishma - vishma.12@cse.mrt.ac.lk |
sileod/wikimedqa | ---
license: apache-2.0
task_categories:
- text-classification
- multiple-choice
language:
- en
tags:
- medical
---
```bib
@article{sileo2023wikimedqa,
title={Generating multiple-choice questions for medical question answering with distractors and cue-masking},
author={Sileo, Damien and Uma, Kanimozhi and Moens, Marie-Francine},
journal={arXiv preprint arXiv:2303.07069 },
year={2023}
}
``` |
huggingface-projects/color-palettes-sd | ---
license: cc-by-4.0
---
|
amanneo/enron-mail-corpus-mini | ---
dataset_info:
features:
- name: text
dtype: string
- name: mail_length
dtype: int64
splits:
- name: test
num_bytes: 205837.52311697626
num_examples: 4000
- name: train
num_bytes: 1852537.7080527863
num_examples: 36000
download_size: 2332694
dataset_size: 2058375.2311697626
---
# Dataset Card for "enron-mail-corpus-mini"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
anab/copa-sse | ---
annotations_creators:
- crowdsourced
language:
- en
language_creators:
- crowdsourced
license:
- mit
multilinguality:
- monolingual
pretty_name: Semi-structured Explanations for Commonsense Reasoning
size_categories:
- 1K<n<10K
source_datasets: []
tags:
- commonsense reasoning
- explanation
- graph-based reasoning
task_categories:
- text2text-generation
- multiple-choice
task_ids:
- explanation-generation
---
# Dataset Card for COPA-SSE
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://github.com/a-brassard/copa-sse
- **Paper:** [COPA-SSE: Semi-Structured Explanations for Commonsense Reasoning](https://arxiv.org/abs/2201.06777)
- **Point of Contact:** [Ana Brassard](mailto:ana.brassard@riken.jp)
### Dataset Summary
COPA-SSE contains crowdsourced explanations for the [Balanced COPA](https://balanced-copa.github.io/) dataset, a variant of the [Choice of Plausible Alternatives (COPA)](https://people.ict.usc.edu/~gordon/copa.html) benchmark. The explanations are formatted as a set of triple-like common sense statements with [ConceptNet](https://conceptnet.io/) relations but freely written concepts.
### Supported Tasks and Leaderboards
Can be used to train a model for explain+predict or predict+explain settings. Suited for both text-based and graph-based architectures. Base task is COPA (causal QA).
### Languages
English
## Dataset Structure
### Data Instances
Validation and test set each contains Balanced COPA samples with added explanations in `.jsonl` format. The question ids match the original questions of the Balanced COPA validation and test sets, respectively.
### Data Fields
Each entry contains:
- the original question (matching format and ids)
- `human-explanations`: a list of explanations each containing:
- `expl-id`: the explanation id
- `text`: the explanation in plain text (full sentences)
- `worker-id`: anonymized worker id (the author of the explanation)
- `worker-avg`: the average score the author got for their explanations
- `all-ratings`: all collected ratings for the explanation
- `filtered-ratings`: ratings excluding those that failed the control
- `triples`: the triple-form explanation (a list of ConceptNet-like triples)
Example entry:
```
id: 1,
asks-for: cause,
most-plausible-alternative: 1,
p: "My body cast a shadow over the grass.",
a1: "The sun was rising.",
a2: "The grass was cut.",
human-explanations: [
{expl-id: f4d9b407-681b-4340-9be1-ac044f1c2230,
text: "Sunrise causes casted shadows.",
worker-id: 3a71407b-9431-49f9-b3ca-1641f7c05f3b,
worker-avg: 3.5832864694635025,
all-ratings: [1, 3, 3, 4, 3],
filtered-ratings: [3, 3, 4, 3],
filtered-avg-rating: 3.25,
triples: [["sunrise", "Causes", "casted shadows"]]
}, ...]
```
### Data Splits
Follows original Balanced COPA split: 1000 dev and 500 test instances. Each instance has up to nine explanations.
## Dataset Creation
### Curation Rationale
The goal was to collect human-written explanations to supplement an existing commonsense reasoning benchmark. The triple-like format was designed to support graph-based models and increase the overall data quality, the latter being notoriously lacking in freely-written crowdsourced text.
### Source Data
#### Initial Data Collection and Normalization
The explanations in COPA-SSE are fully crowdsourced via the Amazon Mechanical Turk platform. Workers entered explanations by providing one or more concept-relation-concept triples. The explanations were then rated by different annotators with one- to five-star ratings. The final dataset contains explanations with a range of quality ratings. Additional collection rounds guaranteed that each sample has at least one explanation rated 3.5 stars or higher.
#### Who are the source language producers?
The original COPA questions (500 dev+500 test) were initially hand-crafted by experts. Similarly, the additional 500 development samples in Balanced COPA were authored by a small team of NLP researchers. Finally, the added explanations and quality ratings in COPA-SSE were collected with the help of Amazon Mechanical Turk workers who passed initial qualification rounds.
### Annotations
#### Annotation process
Workers were shown a Balanced COPA question, its answer, and a short instructional text. Then, they filled in free-form text fields for head and tail concepts and selected the relation from a drop-down menu with a curated selection of ConceptNet relations. Each explanation was rated by five different workers who were shown the same question and answer with five candidate explanations.
#### Who are the annotators?
The workers were restricted to persons located in the U.S. or G.B., with a HIT approval of 98% or more, and 500 or more approved HITs. Their identity and further personal information are not available.
### Personal and Sensitive Information
N/A
## Considerations for Using the Data
### Social Impact of Dataset
Models trained to output similar explanations as those in COPA-SSE may not necessarily provide convincing or faithful explanations. Researchers should carefully evaluate the resulting explanations before considering any real-world applications.
### Discussion of Biases
COPA questions ask for causes or effects of everyday actions or interactions, some of them containing gendered language. Some explanations may reinforce harmful stereotypes if their reasoning is based on biased assumptions. These biases were not verified during collection.
### Other Known Limitations
The data was originally intended to be explanation *graphs*, i.e., hypothetical "ideal" subgraphs of a commonsense knowledge graph. While they can still function as valid natural language explanations, their wording may be at times unnatural to a human and may be better suited for graph-based implementations.
## Additional Information
### Dataset Curators
This work was authored by Ana Brassard, Benjamin Heinzerling, Pride Kavumba, and Kentaro Inui. All are both members of the Riken AIP Natural Language Understanding Team and the Tohoku NLP Lab under Tohoku University.
### Licensing Information
COPA-SSE is released under the [MIT License](https://mit-license.org/).
### Citation Information
```
@InProceedings{copa-sse:LREC2022,
author = {Brassard, Ana and Heinzerling, Benjamin and Kavumba, Pride and Inui, Kentaro},
title = {COPA-SSE: Semi-structured Explanations for Commonsense Reasoning},
booktitle = {Proceedings of the Language Resources and Evaluation Conference},
month = {June},
year = {2022},
address = {Marseille, France},
publisher = {European Language Resources Association},
pages = {3994--4000},
url = {https://aclanthology.org/2022.lrec-1.425}
}
```
### Contributions
Thanks to [@a-brassard](https://github.com/a-brassard) for adding this dataset. |
bookbot/ljspeech_phonemes | ---
dataset_info:
features:
- name: id
dtype: string
- name: audio
dtype:
audio:
sampling_rate: 22050
- name: file
dtype: string
- name: text
dtype: string
- name: normalized_text
dtype: string
- name: phonemes
dtype: string
splits:
- name: train
num_bytes: 3863152206.0
num_examples: 13100
download_size: 3787337731
dataset_size: 3863152206.0
---
# Dataset Card for "ljspeech_phonemes"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Cohere/wikipedia-22-12-ko-embeddings | ---
language:
- ko
multilinguality:
- multilingual
size_categories: []
source_datasets: []
tags: []
task_categories:
- text-retrieval
license:
- apache-2.0
task_ids:
- document-retrieval
---
# Wikipedia (ko) embedded with cohere.ai `multilingual-22-12` encoder
We encoded [Wikipedia (ko)](https://ko.wikipedia.org) using the [cohere.ai](https://txt.cohere.ai/multilingual/) `multilingual-22-12` embedding model.
To get an overview how this dataset was created and pre-processed, have a look at [Cohere/wikipedia-22-12](https://huggingface.co/datasets/Cohere/wikipedia-22-12).
## Embeddings
We compute for `title+" "+text` the embeddings using our `multilingual-22-12` embedding model, a state-of-the-art model that works for semantic search in 100 languages. If you want to learn more about this model, have a look at [cohere.ai multilingual embedding model](https://txt.cohere.ai/multilingual/).
## Further languages
We provide embeddings of Wikipedia in many different languages:
[ar](https://huggingface.co/datasets/Cohere/wikipedia-22-12-ar-embeddings), [de](https://huggingface.co/datasets/Cohere/wikipedia-22-12-de-embeddings), [en](https://huggingface.co/datasets/Cohere/wikipedia-22-12-en-embeddings), [es](https://huggingface.co/datasets/Cohere/wikipedia-22-12-es-embeddings), [fr](https://huggingface.co/datasets/Cohere/wikipedia-22-12-fr-embeddings), [hi](https://huggingface.co/datasets/Cohere/wikipedia-22-12-hi-embeddings), [it](https://huggingface.co/datasets/Cohere/wikipedia-22-12-it-embeddings), [ja](https://huggingface.co/datasets/Cohere/wikipedia-22-12-ja-embeddings), [ko](https://huggingface.co/datasets/Cohere/wikipedia-22-12-ko-embeddings), [simple english](https://huggingface.co/datasets/Cohere/wikipedia-22-12-simple-embeddings), [zh](https://huggingface.co/datasets/Cohere/wikipedia-22-12-zh-embeddings),
You can find the Wikipedia datasets without embeddings at [Cohere/wikipedia-22-12](https://huggingface.co/datasets/Cohere/wikipedia-22-12).
## Loading the dataset
You can either load the dataset like this:
```python
from datasets import load_dataset
docs = load_dataset(f"Cohere/wikipedia-22-12-ko-embeddings", split="train")
```
Or you can also stream it without downloading it before:
```python
from datasets import load_dataset
docs = load_dataset(f"Cohere/wikipedia-22-12-ko-embeddings", split="train", streaming=True)
for doc in docs:
docid = doc['id']
title = doc['title']
text = doc['text']
emb = doc['emb']
```
## Search
A full search example:
```python
#Run: pip install cohere datasets
from datasets import load_dataset
import torch
import cohere
co = cohere.Client(f"<<COHERE_API_KEY>>") # Add your cohere API key from www.cohere.com
#Load at max 1000 documents + embeddings
max_docs = 1000
docs_stream = load_dataset(f"Cohere/wikipedia-22-12-ko-embeddings", split="train", streaming=True)
docs = []
doc_embeddings = []
for doc in docs_stream:
docs.append(doc)
doc_embeddings.append(doc['emb'])
if len(docs) >= max_docs:
break
doc_embeddings = torch.tensor(doc_embeddings)
query = 'Who founded Youtube'
response = co.embed(texts=[query], model='multilingual-22-12')
query_embedding = response.embeddings
query_embedding = torch.tensor(query_embedding)
# Compute dot score between query embedding and document embeddings
dot_scores = torch.mm(query_embedding, doc_embeddings.transpose(0, 1))
top_k = torch.topk(dot_scores, k=3)
# Print results
print("Query:", query)
for doc_id in top_k.indices[0].tolist():
print(docs[doc_id]['title'])
print(docs[doc_id]['text'], "\n")
```
## Performance
You can find performance on the MIRACL dataset (a semantic search evaluation dataset) here: [miracl-en-queries-22-12#performance](https://huggingface.co/datasets/Cohere/miracl-en-queries-22-12#performance) |
Cohere/wikipedia-22-12-ar-embeddings | ---
annotations_creators:
- expert-generated
language:
- ar
multilinguality:
- multilingual
size_categories: []
source_datasets: []
tags: []
task_categories:
- text-retrieval
license:
- apache-2.0
task_ids:
- document-retrieval
---
# Wikipedia (ar) embedded with cohere.ai `multilingual-22-12` encoder
We encoded [Wikipedia (ar)](https://ar.wikipedia.org) using the [cohere.ai](https://txt.cohere.ai/multilingual/) `multilingual-22-12` embedding model.
To get an overview how this dataset was created and pre-processed, have a look at [Cohere/wikipedia-22-12](https://huggingface.co/datasets/Cohere/wikipedia-22-12).
## Embeddings
We compute for `title+" "+text` the embeddings using our `multilingual-22-12` embedding model, a state-of-the-art model that works for semantic search in 100 languages. If you want to learn more about this model, have a look at [cohere.ai multilingual embedding model](https://txt.cohere.ai/multilingual/).
## Further languages
We provide embeddings of Wikipedia in many different languages:
[ar](https://huggingface.co/datasets/Cohere/wikipedia-22-12-ar-embeddings), [de](https://huggingface.co/datasets/Cohere/wikipedia-22-12-de-embeddings), [en](https://huggingface.co/datasets/Cohere/wikipedia-22-12-en-embeddings), [es](https://huggingface.co/datasets/Cohere/wikipedia-22-12-es-embeddings), [fr](https://huggingface.co/datasets/Cohere/wikipedia-22-12-fr-embeddings), [hi](https://huggingface.co/datasets/Cohere/wikipedia-22-12-hi-embeddings), [it](https://huggingface.co/datasets/Cohere/wikipedia-22-12-it-embeddings), [ja](https://huggingface.co/datasets/Cohere/wikipedia-22-12-ja-embeddings), [ko](https://huggingface.co/datasets/Cohere/wikipedia-22-12-ko-embeddings), [simple english](https://huggingface.co/datasets/Cohere/wikipedia-22-12-simple-embeddings), [zh](https://huggingface.co/datasets/Cohere/wikipedia-22-12-zh-embeddings),
You can find the Wikipedia datasets without embeddings at [Cohere/wikipedia-22-12](https://huggingface.co/datasets/Cohere/wikipedia-22-12).
## Loading the dataset
You can either load the dataset like this:
```python
from datasets import load_dataset
docs = load_dataset(f"Cohere/wikipedia-22-12-ar-embeddings", split="train")
```
Or you can also stream it without downloading it before:
```python
from datasets import load_dataset
docs = load_dataset(f"Cohere/wikipedia-22-12-ar-embeddings", split="train", streaming=True)
for doc in docs:
docid = doc['id']
title = doc['title']
text = doc['text']
emb = doc['emb']
```
## Search
A full search example:
```python
#Run: pip install cohere datasets
from datasets import load_dataset
import torch
import cohere
co = cohere.Client(f"<<COHERE_API_KEY>>") # Add your cohere API key from www.cohere.com
#Load at max 1000 documents + embeddings
max_docs = 1000
docs_stream = load_dataset(f"Cohere/wikipedia-22-12-ar-embeddings", split="train", streaming=True)
docs = []
doc_embeddings = []
for doc in docs_stream:
docs.append(doc)
doc_embeddings.append(doc['emb'])
if len(docs) >= max_docs:
break
doc_embeddings = torch.tensor(doc_embeddings)
query = 'Who founded Youtube'
response = co.embed(texts=[query], model='multilingual-22-12')
query_embedding = response.embeddings
query_embedding = torch.tensor(query_embedding)
# Compute dot score between query embedding and document embeddings
dot_scores = torch.mm(query_embedding, doc_embeddings.transpose(0, 1))
top_k = torch.topk(dot_scores, k=3)
# Print results
print("Query:", query)
for doc_id in top_k.indices[0].tolist():
print(docs[doc_id]['title'])
print(docs[doc_id]['text'], "\n")
```
## Performance
You can find performance on the MIRACL dataset (a semantic search evaluation dataset) here: [miracl-en-queries-22-12#performance](https://huggingface.co/datasets/Cohere/miracl-en-queries-22-12#performance) |
Cohere/wikipedia-22-12-fr-embeddings | ---
annotations_creators:
- expert-generated
language:
- fr
multilinguality:
- multilingual
size_categories: []
source_datasets: []
tags: []
task_categories:
- text-retrieval
license:
- apache-2.0
task_ids:
- document-retrieval
---
# Wikipedia (fr) embedded with cohere.ai `multilingual-22-12` encoder
We encoded [Wikipedia (fr)](https://fr.wikipedia.org) using the [cohere.ai](https://txt.cohere.ai/multilingual/) `multilingual-22-12` embedding model.
To get an overview how this dataset was created and pre-processed, have a look at [Cohere/wikipedia-22-12](https://huggingface.co/datasets/Cohere/wikipedia-22-12).
## Embeddings
We compute for `title+" "+text` the embeddings using our `multilingual-22-12` embedding model, a state-of-the-art model that works for semantic search in 100 languages. If you want to learn more about this model, have a look at [cohere.ai multilingual embedding model](https://txt.cohere.ai/multilingual/).
## Further languages
We provide embeddings of Wikipedia in many different languages:
[ar](https://huggingface.co/datasets/Cohere/wikipedia-22-12-ar-embeddings), [de](https://huggingface.co/datasets/Cohere/wikipedia-22-12-de-embeddings), [en](https://huggingface.co/datasets/Cohere/wikipedia-22-12-en-embeddings), [es](https://huggingface.co/datasets/Cohere/wikipedia-22-12-es-embeddings), [fr](https://huggingface.co/datasets/Cohere/wikipedia-22-12-fr-embeddings), [hi](https://huggingface.co/datasets/Cohere/wikipedia-22-12-hi-embeddings), [it](https://huggingface.co/datasets/Cohere/wikipedia-22-12-it-embeddings), [ja](https://huggingface.co/datasets/Cohere/wikipedia-22-12-ja-embeddings), [ko](https://huggingface.co/datasets/Cohere/wikipedia-22-12-ko-embeddings), [simple english](https://huggingface.co/datasets/Cohere/wikipedia-22-12-simple-embeddings), [zh](https://huggingface.co/datasets/Cohere/wikipedia-22-12-zh-embeddings),
You can find the Wikipedia datasets without embeddings at [Cohere/wikipedia-22-12](https://huggingface.co/datasets/Cohere/wikipedia-22-12).
## Loading the dataset
You can either load the dataset like this:
```python
from datasets import load_dataset
docs = load_dataset(f"Cohere/wikipedia-22-12-fr-embeddings", split="train")
```
Or you can also stream it without downloading it before:
```python
from datasets import load_dataset
docs = load_dataset(f"Cohere/wikipedia-22-12-fr-embeddings", split="train", streaming=True)
for doc in docs:
docid = doc['id']
title = doc['title']
text = doc['text']
emb = doc['emb']
```
## Search
A full search example:
```python
#Run: pip install cohere datasets
from datasets import load_dataset
import torch
import cohere
co = cohere.Client(f"<<COHERE_API_KEY>>") # Add your cohere API key from www.cohere.com
#Load at max 1000 documents + embeddings
max_docs = 1000
docs_stream = load_dataset(f"Cohere/wikipedia-22-12-fr-embeddings", split="train", streaming=True)
docs = []
doc_embeddings = []
for doc in docs_stream:
docs.append(doc)
doc_embeddings.append(doc['emb'])
if len(docs) >= max_docs:
break
doc_embeddings = torch.tensor(doc_embeddings)
query = 'Who founded Youtube'
response = co.embed(texts=[query], model='multilingual-22-12')
query_embedding = response.embeddings
query_embedding = torch.tensor(query_embedding)
# Compute dot score between query embedding and document embeddings
dot_scores = torch.mm(query_embedding, doc_embeddings.transpose(0, 1))
top_k = torch.topk(dot_scores, k=3)
# Print results
print("Query:", query)
for doc_id in top_k.indices[0].tolist():
print(docs[doc_id]['title'])
print(docs[doc_id]['text'], "\n")
```
## Performance
You can find performance on the MIRACL dataset (a semantic search evaluation dataset) here: [miracl-en-queries-22-12#performance](https://huggingface.co/datasets/Cohere/miracl-en-queries-22-12#performance) |
ivelin/rico_refexp_combined | ---
license: cc
task_categories:
- question-answering
language:
- en
tags:
- ui refexp
pretty_name: UI RefExp Combined
size_categories:
- 100K<n<1M
dataset_info:
features:
- name: image
dtype: image
- name: image_id
dtype: string
- name: prompt
dtype: string
- name: target_bounding_box
struct:
- name: xmax
dtype: float64
- name: xmin
dtype: float64
- name: ymax
dtype: float64
- name: ymin
dtype: float64
splits:
- name: train
num_bytes: 42127199077.08
num_examples: 390084
- name: validation
num_bytes: 409042403.17
num_examples: 3191
- name: test
num_bytes: 456349755.528
num_examples: 3912
download_size: 27184189035
dataset_size: 42992591235.778
---
# Dataset Card for "rico_refexp_combined"
This dataset combines the crowdsourced RICO RefExp prompts from the [UIBert dataset](https://huggingface.co/datasets/ivelin/rico_sca_refexp_synthetic) and the synthetically generated prompts from the [seq2act dataset](https://huggingface.co/datasets/ivelin/rico_sca_refexp_synthetic). |
Cohere/miracl-en-queries-22-12 | ---
annotations_creators:
- expert-generated
language:
- en
multilinguality:
- multilingual
size_categories: []
source_datasets: []
tags: []
task_categories:
- text-retrieval
license:
- apache-2.0
task_ids:
- document-retrieval
---
# MIRACL (en) embedded with cohere.ai `multilingual-22-12` encoder
We encoded the [MIRACL dataset](https://huggingface.co/miracl) using the [cohere.ai](https://txt.cohere.ai/multilingual/) `multilingual-22-12` embedding model.
The query embeddings can be found in [Cohere/miracl-en-queries-22-12](https://huggingface.co/datasets/Cohere/miracl-en-queries-22-12) and the corpus embeddings can be found in [Cohere/miracl-en-corpus-22-12](https://huggingface.co/datasets/Cohere/miracl-en-corpus-22-12).
For the orginal datasets, see [miracl/miracl](https://huggingface.co/datasets/miracl/miracl) and [miracl/miracl-corpus](https://huggingface.co/datasets/miracl/miracl-corpus).
Dataset info:
> MIRACL ๐๐๐ (Multilingual Information Retrieval Across a Continuum of Languages) is a multilingual retrieval dataset that focuses on search across 18 different languages, which collectively encompass over three billion native speakers around the world.
>
> The corpus for each language is prepared from a Wikipedia dump, where we keep only the plain text and discard images, tables, etc. Each article is segmented into multiple passages using WikiExtractor based on natural discourse units (e.g., `\n\n` in the wiki markup). Each of these passages comprises a "document" or unit of retrieval. We preserve the Wikipedia article title of each passage.
## Embeddings
We compute for `title+" "+text` the embeddings using our `multilingual-22-12` embedding model, a state-of-the-art model that works for semantic search in 100 languages. If you want to learn more about this model, have a look at [cohere.ai multilingual embedding model](https://txt.cohere.ai/multilingual/).
## Loading the dataset
In [miracl-en-corpus-22-12](https://huggingface.co/datasets/Cohere/miracl-en-corpus-22-12) we provide the corpus embeddings. Note, depending on the selected split, the respective files can be quite large.
You can either load the dataset like this:
```python
from datasets import load_dataset
docs = load_dataset(f"Cohere/miracl-en-corpus-22-12", split="train")
```
Or you can also stream it without downloading it before:
```python
from datasets import load_dataset
docs = load_dataset(f"Cohere/miracl-en-corpus-22-12", split="train", streaming=True)
for doc in docs:
docid = doc['docid']
title = doc['title']
text = doc['text']
emb = doc['emb']
```
## Search
Have a look at [miracl-en-queries-22-12](https://huggingface.co/datasets/Cohere/miracl-en-queries-22-12) where we provide the query embeddings for the MIRACL dataset.
To search in the documents, you must use **dot-product**.
And then compare this query embeddings either with a vector database (recommended) or directly computing the dot product.
A full search example:
```python
# Attention! For large datasets, this requires a lot of memory to store
# all document embeddings and to compute the dot product scores.
# Only use this for smaller datasets. For large datasets, use a vector DB
from datasets import load_dataset
import torch
#Load documents + embeddings
docs = load_dataset(f"Cohere/miracl-en-corpus-22-12", split="train")
doc_embeddings = torch.tensor(docs['emb'])
# Load queries
queries = load_dataset(f"Cohere/miracl-en-queries-22-12", split="dev")
# Select the first query as example
qid = 0
query = queries[qid]
query_embedding = torch.tensor(queries['emb'])
# Compute dot score between query embedding and document embeddings
dot_scores = torch.mm(query_embedding, doc_embeddings.transpose(0, 1))
top_k = torch.topk(dot_scores, k=3)
# Print results
print("Query:", query['query'])
for doc_id in top_k.indices[0].tolist():
print(docs[doc_id]['title'])
print(docs[doc_id]['text'])
```
You can get embeddings for new queries using our API:
```python
#Run: pip install cohere
import cohere
co = cohere.Client(f"{api_key}") # You should add your cohere API Key here :))
texts = ['my search query']
response = co.embed(texts=texts, model='multilingual-22-12')
query_embedding = response.embeddings[0] # Get the embedding for the first text
```
## Performance
In the following table we compare the cohere multilingual-22-12 model with Elasticsearch version 8.6.0 lexical search (title and passage indexed as independent fields). Note that Elasticsearch doesn't support all languages that are part of the MIRACL dataset.
We compute nDCG@10 (a ranking based loss), as well as hit@3: Is at least one relevant document in the top-3 results. We find that hit@3 is easier to interpret, as it presents the number of queries for which a relevant document is found among the top-3 results.
Note: MIRACL only annotated a small fraction of passages (10 per query) for relevancy. Especially for larger Wikipedias (like English), we often found many more relevant passages. This is know as annotation holes. Real nDCG@10 and hit@3 performance is likely higher than depicted.
| Model | cohere multilingual-22-12 nDCG@10 | cohere multilingual-22-12 hit@3 | ES 8.6.0 nDCG@10 | ES 8.6.0 acc@3 |
|---|---|---|---|---|
| miracl-ar | 64.2 | 75.2 | 46.8 | 56.2 |
| miracl-bn | 61.5 | 75.7 | 49.2 | 60.1 |
| miracl-de | 44.4 | 60.7 | 19.6 | 29.8 |
| miracl-en | 44.6 | 62.2 | 30.2 | 43.2 |
| miracl-es | 47.0 | 74.1 | 27.0 | 47.2 |
| miracl-fi | 63.7 | 76.2 | 51.4 | 61.6 |
| miracl-fr | 46.8 | 57.1 | 17.0 | 21.6 |
| miracl-hi | 50.7 | 62.9 | 41.0 | 48.9 |
| miracl-id | 44.8 | 63.8 | 39.2 | 54.7 |
| miracl-ru | 49.2 | 66.9 | 25.4 | 36.7 |
| **Avg** | 51.7 | 67.5 | 34.7 | 46.0 |
Further languages (not supported by Elasticsearch):
| Model | cohere multilingual-22-12 nDCG@10 | cohere multilingual-22-12 hit@3 |
|---|---|---|
| miracl-fa | 44.8 | 53.6 |
| miracl-ja | 49.0 | 61.0 |
| miracl-ko | 50.9 | 64.8 |
| miracl-sw | 61.4 | 74.5 |
| miracl-te | 67.8 | 72.3 |
| miracl-th | 60.2 | 71.9 |
| miracl-yo | 56.4 | 62.2 |
| miracl-zh | 43.8 | 56.5 |
| **Avg** | 54.3 | 64.6 |
|
its5Q/habr_qna | ---
annotations_creators:
- crowdsourced
language:
- ru
language_creators:
- crowdsourced
license:
- cc0-1.0
multilinguality:
- monolingual
pretty_name: Habr QnA
size_categories:
- 100K<n<1M
source_datasets:
- original
tags: []
task_categories:
- text-generation
- question-answering
task_ids:
- language-modeling
- open-domain-qa
---
# Dataset Card for Habr QnA
## Table of Contents
- [Dataset Card for Habr QnA](#dataset-card-for-habr-qna)
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
## Dataset Description
- **Repository:** https://github.com/its5Q/habr-qna-parser
### Dataset Summary
This is a dataset of questions and answers scraped from [Habr QnA](https://qna.habr.com/). There are 723430 asked questions with answers, comments and other metadata.
### Languages
The dataset is mostly Russian with source code in different languages.
## Dataset Structure
### Data Fields
Data fields can be previewed on the dataset card page.
### Data Splits
All 723430 examples are in the train split, there is no validation split.
## Dataset Creation
The data was scraped with a script, located in [my GitHub repository](https://github.com/its5Q/habr-qna-parser)
## Additional Information
### Dataset Curators
- https://github.com/its5Q |
society-ethics/papers | ---
tags:
- ethics
---
# Hugging Face Ethics & Society Papers
This is an incomplete list of ethics-related papers published by researchers at Hugging Face.
- Gradio: https://arxiv.org/abs/1906.02569
- DistilBERT: https://arxiv.org/abs/1910.01108
- RAFT: https://arxiv.org/abs/2109.14076
- Interactive Model Cards: https://arxiv.org/abs/2205.02894
- Data Governance in the Age of Large-Scale Data-Driven Language Technology: https://arxiv.org/abs/2206.03216
- Quality at a Glance: https://arxiv.org/abs/2103.12028
- A Framework for Deprecating Datasets: https://arxiv.org/abs/2111.04424
- Bugs in the Data: https://arxiv.org/abs/2208.11695
- Measuring Data: https://arxiv.org/abs/2212.05129
- Perturbation Augmentation for Fairer NLP: https://arxiv.org/abs/2205.12586
- SEAL: https://arxiv.org/abs/2210.05839
- Multitask Prompted Training Enables Zero-Shot Task Generalization: https://arxiv.org/abs/2110.08207
- BLOOM: https://arxiv.org/abs/2211.05100
- ROOTS: https://arxiv.org/abs/2303.03915
- Evaluate & Evaluation on the Hub: https://arxiv.org/abs/2210.01970
- Spacerini: https://arxiv.org/abs/2302.14534
- ROOTS Search Tool: https://arxiv.org/abs/2302.14035
- Fair Diffusion: https://arxiv.org/abs/2302.10893
- Counting Carbon: https://arxiv.org/abs/2302.08476
- The Gradient of Generative AI Release: https://arxiv.org/abs/2302.04844
- BigScience: A Case Study in the Social Construction of a Multilingual Large Language Model: https://arxiv.org/abs/2212.04960
- Towards Openness Beyond Open Access: User Journeys through 3 Open AI Collaboratives: https://arxiv.org/abs/2301.08488
- Stable Bias: Analyzing Societal Representations in Diffusion Models: https://arxiv.org/abs/2303.11408
- Stronger Together: on the Articulation of Ethical Charters, Legal Tools, and Technical Documentation in ML: https://arxiv.org/abs/2305.18615 |
emozilla/quality | ---
language: en
dataset_info:
features:
- name: article
dtype: string
- name: question
dtype: string
- name: options
sequence: string
- name: answer
dtype: int64
- name: hard
dtype: bool
splits:
- name: train
num_bytes: 62597212
num_examples: 2523
- name: validation
num_bytes: 51198650
num_examples: 2086
download_size: 14352147
dataset_size: 113795862
---
# Dataset Card for "quality"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
FreedomIntelligence/huatuo_consultation_qa | ---
license: apache-2.0
task_categories:
- text-generation
language:
- zh
tags:
- medical
size_categories:
- 1M<n<10M
---
# Dataset Card for huatuo_consultation_qa
## Dataset Description
- **Homepage: https://www.huatuogpt.cn/**
- **Repository: https://github.com/FreedomIntelligence/HuatuoGPT**
- **Paper: https://arxiv.org/abs/2305.01526**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
We collected data from a website for medical consultation , consisting of many online consultation records by medical experts. Each record is a QA pair: a patient raises a question and a medical doctor answers the question. The basic information of doctors (including name, hospital organization, and department) was recorded.
We directly crawl patientโs questions and doctorโs answers as QA pairs, getting 32,708,346 pairs. Subsequently, we removed the QA pairs containing special characters and removed the repeated pairs. Finally, we got 25,341,578 QA pairs.
**Please note that for some reasons we cannot directly provide text data, so the answer part of our data set is url. If you want to use text data, you can refer to the other two parts of our open source datasets ([huatuo_encyclopedia_qa](https://huggingface.co/datasets/FreedomIntelligence/huatuo_encyclopedia_qa)ใ[huatuo_knowledge_graph_qa](https://huggingface.co/datasets/FreedomIntelligence/huatuo_knowledge_graph_qa)), or use url for data collection.**
## Dataset Creation
### Source Data
....
## Citation
```
@misc{li2023huatuo26m,
title={Huatuo-26M, a Large-scale Chinese Medical QA Dataset},
author={Jianquan Li and Xidong Wang and Xiangbo Wu and Zhiyi Zhang and Xiaolong Xu and Jie Fu and Prayag Tiwari and Xiang Wan and Benyou Wang},
year={2023},
eprint={2305.01526},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
``` |
oscar-corpus/colossal-oscar-1.0 | ---
license: cc0-1.0
size_categories:
- n>1T
multilinguality:
- multilingual
source_datasets:
- original
task_categories:
- fill-mask
- text-generation
task_ids:
- language-modeling
paperswithcode_id: oscar
extra_gated_prompt: "By filling the form below I understand that Colossal OSCAR 1 is just a partial annotation of the WET files of 10 Common Crawl snapshots, the original data is included here **only for convenience**, and specially for researchers looking for data in lower resource languages. **Only the annotations are distributed under a cc0-1.0 license**, for the rest of the content I have read the [Common Crawl Terms of use](https://commoncrawl.org/terms-of-use/) and I will abide by them. I understand that all uses of the textual content in Colossal OSCAR 1 are subject to the [Common Crawl Terms of use](https://commoncrawl.org/terms-of-use/). I understand that reusing the textual content in Colossal OSCAR 1 might not be legal in all countries/regions and for all use cases. I understand that Colossal OSCAR 1 is mainly targeted towards researchers and meant to be used in research. The OSCAR Project reserves the right to revoke my access to this data. The OSCAR Project reserves the right to modify this data at any time in accordance to take down requests."
extra_gated_fields:
Name: text
Email: text
Affiliation: text
Country: text
Usecase: text
I have explicitly checked that downloading Colossal OSCAR 1 is legal in my jurisdiction, in the country/region where I am located right now, and for the use case that I have described above, I have also read and accepted the Common Crawl Terms of use: checkbox
---
# Dataset Card for Colossal OSCAR 1
## IMPORTANT NOTE: THIS DATASET CARD IS STILL BEING WRITTEN, PLEASE BE PATIENT WHILE WE COMPLETE ALL THE INFORMATION ABOUT THE CORPUS
## Table of Contents
- [Dataset Card for Colossal OSCAR 1](#dataset-card-for-colossal-oscar-1)
- [IMPORTANT NOTE: THIS DATASET CARD IS STILL BEING WRITTEN, PLEASE BE PATIENT WHILE WE COMPLETE ALL THE INFORMATION ABOUT THE CORPUS](#important-note-this-dataset-card-is-still-being-written-please-be-patient-while-we-complete-all-the-information-about-the-corpus)
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Issues](#issues)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Layout](#layout)
- [Data Splits](#data-splits)
- [Table](#table)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Initial Data Collection and Normalization](#initial-data-collection-and-normalization)
- [Who are the source language producers?](#who-are-the-source-language-producers)
- [Annotations](#annotations)
- [Annotation process](#annotation-process)
- [Who are the annotators?](#who-are-the-annotators)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
- **Homepage:** [https://oscar-project.org](https://oscar-project.org)
- **Repository:** [https://github.com/oscar-project](https://github.com/oscar-project)
- **Papers:** [Towards a Cleaner Document-Oriented Multilingual Crawled Corpus](https://aclanthology.org/2022.lrec-1.463/), [Perplexed by Quality: A Perplexity-based Method for Adult and Harmful Content Detection in Multilingual Heterogeneous Web Data](https://arxiv.org/abs/2212.10440)
- **Point of Contact:** [Contact](https://oscar-project.org/#contact)
### Dataset Summary
The OSCAR project (**O**pen **S**uper-large **C**rawled **A**ggregated co**R**pus) is an Open Source project aiming to provide web-based multilingual resources and datasets for Machine Learning (ML) and Artificial Intelligence (AI) applications. The project focuses specifically in providing large quantities of unannotated raw data that is commonly used in the pre-training of large deep learning models. The OSCAR project has developed [high-performance data pipelines](https://github.com/oscar-corpus/ungoliant) specifically conceived to classify and filter large amounts of [web data](https://commoncrawl.org/). The project has also put special attention in improving the data quality of web-based corpora as well as providing data for low-resource languages, so that these new ML/AI technologies are accessible to as many communities as possible.
Colossal OSCAR 1 is the largest release of the OSCAR Corpus based on the based on 10 different monthly snapshots of Common Crawl. It currently contains all the features present in OSCAR 23.01, the main difference being its size.
### Downloading the Data
For the moment we haven't finished the python script to use Colossal OSCAR 1 with `datasets`, so we recommend you use the `huggingface_hub` [python library](https://huggingface.co/docs/huggingface_hub/index).
If you want to download a considerable amount of data we recomend you use `hf_transfer` python package and set the environment variable `HF_HUB_ENABLE_HF_TRANSFER=1`.
### Supported Tasks and Leaderboards
OSCAR is mainly intended to pre-train language models and word representations.
### Languages
All the data is distributed by language, both the original and the deduplicated versions of the data are available. 151 different languages are available. The table in subsection [Data Splits Sample Size](#data-splits-sample-size) provides the language code for each subcorpus as well as the number of words (space separated tokens), lines and sizes for both the original and the deduplicated versions of OSCAR.
### Issues
Colossal OSCAR 1 may have quality issues on low size subcorpora, as it has been the case before. Please consider taking a look at [_Quality at a Glance: An Audit of Web-Crawled Multilingual Datasets_](https://aclanthology.org/2022.tacl-1.4/) to get a better understanding of the current limitations of our language classifier.
Note that since the documents are identified as a whole, it is expected to have lines in other languages in a given language subcorpus.
As an example, it is known and expected that the German subcorpus contains documents holding lines identified as Swiss German / Alemannic.
**If you encounter something that is unexpected, please file an issue here: https://github.com/oscar-corpus/corpus/issues.**
| Language code | Language | Issues |
| ------------- | -------- | ------ |
| | | |
## Dataset Structure
We show detailed information for all the configurations of the dataset.
### Data Instances
TODO
### Layout
```js
{
"content":"English sentence\nphrase en franรงais\n????????????", // (1)
"warc_headers":{ // (2)
"warc-identified-content-language":"fra,eng",
"warc-target-uri":"https://fr.wikipedia.org/wiki/...",
"warc-record-id":"<urn:uuid:29eaa920-d299-4b1d-b687-c72bd8d68116>",
"warc-type":"conversion",
"content-length":"35298", // (3)
"warc-refers-to":"<urn:uuid:39e42055-0d94-4e45-9c6c-9e7056635d64>",
"warc-block-digest":"sha1:WFH2A5WHCS2H365GIAFYQPI7UOAMFGHB", // (3)
"warc-date":"2022-11-26T09:45:47Z",
"content-type":"text/plain"
},
"metadata":{
"identification":{ // (4)
"label":"fr",
"prob":0.8938327
},
"harmful_pp":4063.1814, // (5)
"tlsh":"tlsh:T125315FF2B6088901EEA097015DB39B4600B...", // (6)
"quality_warnings":[ // (7)
"short_sentences",
"header",
"footer"
],
"categories":[ // (8)
"examen_pix",
"liste_bu"
],
"sentence_identifications":[ // (9)
{
"label":"fr",
"prob":0.99837273
},
{
"label":"en",
"prob":0.9992377
},
null
]
}
}
```
### Data Splits
<details>
<summary>Click to expand the number of samples per configuration</summary>
</details>
## Table
## Dataset Creation
### Curation Rationale
OSCAR was constructed using [`Ungoliant`](https://github.com/oscar-corpus/ungoliant), a new pipeline derived from [goclassy](https://github.com/oscar-corpus/goclassy), itself being derived from [fastText's one](https://github.com/facebookresearch/fastText).
The pipeline works on documents rather than lines.
`Ungoliant` is implemented in the [Rust programming language](https://rust-lang.org), and uses [rayon](https://github.com/rayon-rs/rayon) as its data parallelism strategy.
Threading is done at shard, record and sentence level, making the whole generation process much more efficient.
Filtering will be explained in a future blog post at our [website](https://oscar-project.org)
### Source Data
#### Initial Data Collection and Normalization
[Common Crawl](https://commoncrawl.org/) is a non-profit foundation which produces and maintains an open repository of web crawled data that is both accessible and analysable. Common Crawl's complete web archive consists of petabytes of data collected over 8 years of web crawling. The repository contains raw web page HTML data (WARC files), metdata extracts (WAT files) and plain text extracts (WET files). The organization's crawlers has always respected [nofollow](http://microformats.org/wiki/rel-nofollow) and [robots.txt](https://www.robotstxt.org/) policies.
Each monthly Common Crawl snapshot is in itself a massive multilingual corpus, where every single file contains data coming from multiple web pages written in a large variety of languages and covering all possible types of topics.
To construct OSCAR the WET files of Common Crawl were used. These contain the extracted plain texts from the websites mostly converted to UTF-8, as well as headers containing the metatada of each crawled document. Each WET file comes compressed in gzip format and is stored on Amazon Web Services. In the case of Colossal OSCAR 1 the following snapshots were used:
- 05-06-23
- 06-07-22
- 11-12-21
- 10-20
- 05-06-20
- 05-19
- 11-18
- 11-17
- 03-15
- 09-16
#### Who are the source language producers?
The data comes from multiple web pages in a large variety of languages.
### Annotations
The dataset does not contain any additional annotations.
#### Annotation process
N/A
#### Who are the annotators?
N/A
### Personal and Sensitive Information
Being constructed from Common Crawl, Personal and sensitive information might be present. This **must** be considered before training deep learning models with OSCAR, specially in the case of text-generation models.
## Considerations for Using the Data
### Social Impact of Dataset
OSCAR is intended to bring more data to a wide variety of languages, the aim of the corpus is to make large amounts of data available to lower resource languages in order to facilitate the pre-training of state-of-the-art language modeling architectures.
### Discussion of Biases
OSCAR is not properly filtered yet and this can be reflected on the models trained with it. Care is advised specially concerning biases of the resulting models. We have added annotations to Common Crawl, so please consider using them to select the data that you would like to use for your particular use case.
### Other Known Limitations
The [fastText linear classifier](https://fasttext.cc) is limed both in performance and the variety of languages it can recognize, so the quality of some OSCAR sub-corpora might be lower than expected, specially for the lowest-resource languages. Some audits have already been done by [third parties](https://arxiv.org/abs/2010.14571).
## Additional Information
### Dataset Curators
Colossal OSCAR 1 was put together by [Pedro Ortiz Suarez](https://portizs.eu/) while working as a researcher at the [Speech and Language Technology Team](https://www.dfki.de/en/web/research/research-departments/speech-and-language-technology) at [DFKI GmbH](https://www.dfki.de/en/web) Berlin. This release is also made possible do to the work of [Julien Abadji](https://ujj.space) and the continous funding of the OSCAR project by [Inria](https://www.inria.fr/en) (project-team [ALMAnaCH](https://almanach.inria.fr/index-en.html)).
Colossal OSCAR 1 is part of the work done by [Pedro Ortiz Suarez](https://portizs.eu/) for the [OpenGPT-X Project](https://opengpt-x.de/en/) which is funded by the German Federal Ministry for Economic Affairs and Climate Action ([BMWK](https://www.bmwk.de/Navigation/EN/Home/home.html)). The authors gratefully acknowledge the [Gauss Centre for Supercomputing e.V.](www.gauss-centre.eu) for funding this project by providing computing time through the John von Neumann Institute for Computing (NIC) on the GCS Supercomputer JUWELS at the Jรผlich Supercomputing Centre (JSC).
This release of OSCAR was also made possible by the continous support of the OSCAR team at [Inria](https://www.inria.fr/en) (project-team [ALMAnaCH](https://almanach.inria.fr/index-en.html)), specially by [Julien Abadji](https://ujj.space), [Rua Ismail](https://oscar-project.org/authors/rua/) and [Benoit Sagot](http://pauillac.inria.fr/~sagot/), as well as by members of the OSCAR community, in particular [Sotaro Takeshita](https://sotaro.io/about), [Sebastian Nagel](https://www.polver.uni-konstanz.de/cnc/people/nagel/).
### Licensing Information
These data are released under this licensing scheme
We do not own any of the text from which these data has been extracted.
We license the actual packaging, the metadata and the annotations of these data under the Creative Commons CC0 license ("no rights reserved") http://creativecommons.org/publicdomain/zero/1.0/
To the extent possible under law, the OSCAR project, DFKI GmbH and Inria have waived all copyright and related or neighboring rights to OSCAR
This work is published from: France and Germany.
Should you consider that our data contains material that is owned by you and should therefore not be reproduced here, please:
- Clearly identify yourself, with detailed contact data such as an address, telephone number or email address at which you can be contacted.
- Clearly identify the copyrighted work claimed to be infringed.
- Clearly identify the material that is claimed to be infringing and information reasonably sufficient to allow us to locate the material.
We will comply to legitimate requests by removing the affected sources.
Please use the [contact information](https://oscar-project.org/#contact) on our website for take down requests.
We strongly advise users to submit take down request to Common Crawl. For more information please read their [Terms of Use](https://commoncrawl.org/terms-of-use/)
### Citation Information
```
@ARTICLE{2022arXiv221210440J,
author = {{Jansen}, Tim and {Tong}, Yangling and {Zevallos}, Victoria and {Ortiz Suarez}, Pedro},
title = "{Perplexed by Quality: A Perplexity-based Method for Adult and Harmful Content Detection in Multilingual Heterogeneous Web Data}",
journal = {arXiv e-prints},
keywords = {Computer Science - Computation and Language},
year = 2022,
month = dec,
eid = {arXiv:2212.10440},
pages = {arXiv:2212.10440},
doi = {10.48550/arXiv.2212.10440},
archivePrefix = {arXiv},
eprint = {2212.10440},
primaryClass = {cs.CL},
adsurl = {https://ui.adsabs.harvard.edu/abs/2022arXiv221210440J},
adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}
@inproceedings{abadji-etal-2022-towards,
title = "Towards a Cleaner Document-Oriented Multilingual Crawled Corpus",
author = "Abadji, Julien and
Ortiz Suarez, Pedro and
Romary, Laurent and
Sagot, Beno{\^\i}t",
booktitle = "Proceedings of the Thirteenth Language Resources and Evaluation Conference",
month = jun,
year = "2022",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://aclanthology.org/2022.lrec-1.463",
pages = "4344--4355",
abstract = "The need for large corpora raw corpora has dramatically increased in recent years with the introduction of transfer learning and semi-supervised learning methods to Natural Language Processing. And while there have been some recent attempts to manually curate the amount of data necessary to train large language models, the main way to obtain this data is still through automatic web crawling. In this paper we take the existing multilingual web corpus OSCAR and its pipeline Ungoliant that extracts and classifies data from Common Crawl at the line level, and propose a set of improvements and automatic annotations in order to produce a new document-oriented version of OSCAR that could prove more suitable to pre-train large generative language models as well as hopefully other applications in Natural Language Processing and Digital Humanities.",
}
@inproceedings{AbadjiOrtizSuarezRomaryetal.2021,
author = {Julien Abadji and Pedro Javier Ortiz Su{\'a}rez and Laurent Romary and Beno{\^i}t Sagot},
title = {Ungoliant: An optimized pipeline for the generation of a very large-scale multilingual web corpus},
series = {Proceedings of the Workshop on Challenges in the Management of Large Corpora (CMLC-9) 2021. Limerick, 12 July 2021 (Online-Event)},
editor = {Harald L{\"u}ngen and Marc Kupietz and Piotr Baลski and Adrien Barbaresi and Simon Clematide and Ines Pisetta},
publisher = {Leibniz-Institut f{\"u}r Deutsche Sprache},
address = {Mannheim},
doi = {10.14618/ids-pub-10468},
url = {https://nbn-resolving.org/urn:nbn:de:bsz:mh39-104688},
pages = {1 -- 9},
year = {2021},
abstract = {Since the introduction of large language models in Natural Language Processing, large raw corpora have played a crucial role in Computational Linguistics. However, most of these large raw corpora are either available only for English or not available to the general public due to copyright issues. Nevertheless, there are some examples of freely available multilingual corpora for training Deep Learning NLP models, such as the OSCAR and Paracrawl corpora. However, they have quality issues, especially for low-resource languages. Moreover, recreating or updating these corpora is very complex. In this work, we try to reproduce and improve the goclassy pipeline used to create the OSCAR corpus. We propose a new pipeline that is faster, modular, parameterizable, and well documented. We use it to create a corpus similar to OSCAR but larger and based on recent data. Also, unlike OSCAR, the metadata information is at the document level. We release our pipeline under an open source license and publish the corpus under a research-only license.},
language = {en}
}
@article{kreutzer-etal-2022-quality,
title = "Quality at a Glance: An Audit of Web-Crawled Multilingual Datasets",
author = {Kreutzer, Julia and
Caswell, Isaac and
Wang, Lisa and
Wahab, Ahsan and
van Esch, Daan and
Ulzii-Orshikh, Nasanbayar and
Tapo, Allahsera and
Subramani, Nishant and
Sokolov, Artem and
Sikasote, Claytone and
Setyawan, Monang and
Sarin, Supheakmungkol and
Samb, Sokhar and
Sagot, Beno{\^\i}t and
Rivera, Clara and
Rios, Annette and
Papadimitriou, Isabel and
Osei, Salomey and
Suarez, Pedro Ortiz and
Orife, Iroro and
Ogueji, Kelechi and
Rubungo, Andre Niyongabo and
Nguyen, Toan Q. and
M{\"u}ller, Mathias and
M{\"u}ller, Andr{\'e} and
Muhammad, Shamsuddeen Hassan and
Muhammad, Nanda and
Mnyakeni, Ayanda and
Mirzakhalov, Jamshidbek and
Matangira, Tapiwanashe and
Leong, Colin and
Lawson, Nze and
Kudugunta, Sneha and
Jernite, Yacine and
Jenny, Mathias and
Firat, Orhan and
Dossou, Bonaventure F. P. and
Dlamini, Sakhile and
de Silva, Nisansa and
{\c{C}}abuk Ball{\i}, Sakine and
Biderman, Stella and
Battisti, Alessia and
Baruwa, Ahmed and
Bapna, Ankur and
Baljekar, Pallavi and
Azime, Israel Abebe and
Awokoya, Ayodele and
Ataman, Duygu and
Ahia, Orevaoghene and
Ahia, Oghenefego and
Agrawal, Sweta and
Adeyemi, Mofetoluwa},
journal = "Transactions of the Association for Computational Linguistics",
volume = "10",
year = "2022",
address = "Cambridge, MA",
publisher = "MIT Press",
url = "https://aclanthology.org/2022.tacl-1.4",
doi = "10.1162/tacl_a_00447",
pages = "50--72",
abstract = "With the success of large-scale pre-training and multilingual modeling in Natural Language Processing (NLP), recent years have seen a proliferation of large, Web-mined text datasets covering hundreds of languages. We manually audit the quality of 205 language-specific corpora released with five major public datasets (CCAligned, ParaCrawl, WikiMatrix, OSCAR, mC4). Lower-resource corpora have systematic issues: At least 15 corpora have no usable text, and a significant fraction contains less than 50{\%} sentences of acceptable quality. In addition, many are mislabeled or use nonstandard/ambiguous language codes. We demonstrate that these issues are easy to detect even for non-proficient speakers, and supplement the human audit with automatic analyses. Finally, we recommend techniques to evaluate and improve multilingual corpora and discuss potential risks that come with low-quality data releases.",
}
@inproceedings{ortiz-suarez-etal-2020-monolingual,
title = "A Monolingual Approach to Contextualized Word Embeddings for Mid-Resource Languages",
author = "Ortiz Su{'a}rez, Pedro Javier and
Romary, Laurent and
Sagot, Benoit",
booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.acl-main.156",
pages = "1703--1714",
abstract = "We use the multilingual OSCAR corpus, extracted from Common Crawl via language classification, filtering and cleaning, to train monolingual contextualized word embeddings (ELMo) for five mid-resource languages. We then compare the performance of OSCAR-based and Wikipedia-based ELMo embeddings for these languages on the part-of-speech tagging and parsing tasks. We show that, despite the noise in the Common-Crawl-based OSCAR data, embeddings trained on OSCAR perform much better than monolingual embeddings trained on Wikipedia. They actually equal or improve the current state of the art in tagging and parsing for all five languages. In particular, they also improve over multilingual Wikipedia-based contextual embeddings (multilingual BERT), which almost always constitutes the previous state of the art, thereby showing that the benefit of a larger, more diverse corpus surpasses the cross-lingual benefit of multilingual embedding architectures.",
}
@inproceedings{OrtizSuarezSagotRomary2019,
author = {Pedro Javier {Ortiz Su{'a}rez} and Benoit Sagot and Laurent Romary},
title = {Asynchronous pipelines for processing huge corpora on medium to low resource infrastructures},
series = {Proceedings of the Workshop on Challenges in the Management of Large Corpora (CMLC-7) 2019. Cardiff, 22nd July 2019},
editor = {Piotr Baลski and Adrien Barbaresi and Hanno Biber and Evelyn Breiteneder and Simon Clematide and Marc Kupietz and Harald L{"u}ngen and Caroline Iliadi},
publisher = {Leibniz-Institut f{"u}r Deutsche Sprache},
address = {Mannheim},
doi = {10.14618/ids-pub-9021},
url = {http://nbn-resolving.de/urn:nbn:de:bsz:mh39-90215},
pages = {9 -- 16},
year = {2019},
abstract = {Common Crawl is a considerably large, heterogeneous multilingual corpus comprised of crawled documents from the internet, surpassing 20TB of data and distributed as a set of more than 50 thousand plain text files where each contains many documents written in a wide variety of languages. Even though each document has a metadata block associated to it, this data lacks any information about the language in which each document is written, making it extremely difficult to use Common Crawl for monolingual applications. We propose a general, highly parallel, multithreaded pipeline to clean and classify Common Crawl by language; we specifically design it so that it runs efficiently on medium to low resource infrastructures where I/O speeds are the main constraint. We develop the pipeline so that it can be easily reapplied to any kind of heterogeneous corpus and so that it can be parameterised to a wide range of infrastructures. We also distribute a 6.3TB version of Common Crawl, filtered, classified by language, shuffled at line level in order to avoid copyright issues, and ready to be used for NLP applications.},
language = {en}
}
``` |
nomic-ai/summarize-sampled | ---
dataset_info:
features:
- name: response
dtype: string
- name: prompt
dtype: string
- name: source
dtype: string
splits:
- name: train
num_bytes: 1040063660
num_examples: 491951
download_size: 640692479
dataset_size: 1040063660
---
# Dataset Card for "summarize-sampled"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
zjunlp/InstructIE | ---
license: mit
task_categories:
- text2text-generation
language:
- en
- zh
tags:
- information-extraction
- entity
- relation
pretty_name: InstructIE
size_categories:
- 100M<n<1B
---
# InstructIE: A Bilingual Instruction-based Information Extraction Dataset
[Paper](https://doi.org/10.48550/arXiv.2305.11527)
## News
* [2024/02] We released a large-scale (0.32B tokens) high-quality bilingual (Chinese and English) Information Extraction (IE) instruction tuning dataset named [IEPile](https://huggingface.co/datasets/zjunlp/iepie), along with two models trained on `IEPile`, [baichuan2-13b-iepile-lora](https://huggingface.co/zjunlp/baichuan2-13b-iepile-lora) and [llama2-13b-iepile-lora](https://huggingface.co/zjunlp/llama2-13b-iepile-lora).
* [2023/10] We released a new bilingual (Chinese and English) theme-based Information Extraction (IE) instruction dataset named [InstructIE](https://huggingface.co/datasets/zjunlp/InstructIE).
* [2023/08] We introduced a dedicated 13B model for Information Extraction (IE), named [knowlm-13b-ie](https://huggingface.co/zjunlp/knowlm-13b-ie/tree/main).
* [2023/05] We initiated an instruction-based Information Extraction project.
InstructIE is an bilingual information extraction dataset based on topic schemas. We divide the text into 12 topics, namely, Person, Geographic_Location, Building, Works, Creature, Artificial_Object, Natural_Science, Organization, Transport, Event, Astronomy, Medicine. For each topic, we have designed corresponding schemas. We expect the model to learn a general extraction capability on InstructIE and generalize it to other domains.
```
InstrueIE
โโโ train_zh_old.json # Chinese training set, the dataset used in the paper "InstructIE: A Bilingual Instruction-based Information Extraction Dataset".
โโโ train_en_old.json # English training set, the dataset used in the paper "InstructIE: A Bilingual Instruction-based Information Extraction Dataset".
โโโ train_zh.json # Chinese training set enhanced with LLMs.
โโโ train_en.json # English training set enhanced with LLMs.
โโโ dev_zh.json # Chinese validation set.
โโโ dev_en.json # English validation set.
โโโ test_zh.json # Chinese test set.
โโโ test_en.json # English test set.
โโโ schema_zh.json # Schema information for 12 topics in Chinese.
โโโ schema_en.json # Schema information for 12 topics in English.
โโโ InstrueIE-zh
โ โโโ InstrueIE_ไบบ็ฉ
โ โ โโโ train.json # Subsample of 5000 samples, full samples can be obtained from train_zh.json
โ โ โโโ dev.json
โ โ โโโ schema.json
โ โ โโโ test.json
โ โโโ InstrueIE_ๅปบ็ญ็ปๆ
โ โโโ InstrueIE_็ป็ป
โ โโโ InstrueIE_็็ฉ
โ โโโ ...
โโโ InstrueIE-en
โ โโโ InstrueIE_Person
โ โโโ InstrueIE_Creature
```
<b>Example of data</b>
```
{
"id": "841ef2af4cfe766dd9295fb7daf321c299df0fd0cef14820dfcb421161eed4a1",
"text": "NGC1313 is a galaxy in the constellation of Reticulum. It was discovered by the Australian astronomer James Dunlop on September 27, 1826. It has a prominent uneven shape, and its axis does not completely revolve around its center. Near NGC1313, there is another galaxy, NGC1309.",
"relation": [
{"head": "NGC1313", "head_type": "astronomical object type", "relation": "time of discovery", "tail": "September 27, 1826", "tail_type": "time"},
{"head": "NGC1313", "head_type": "astronomical object type", "relation": "discoverer or inventor", "tail": "James Dunlop", "tail_type": "organization/human"},
{"head": "NGC1313", "head_type": "astronomical object type", "relation": "of", "tail": "Reticulum", "tail_type": "astronomical object type"}
]
}
```
| Field | Description |
| ----------- | ---------------------------------------------------------------- |
| id | The unique identifier for each data point. |
| cate | The category of the text's subject, with a total of 12 different thematic categories. |
| text | The input text for the model, with the goal of extracting all the involved relationship triples. |
| relation | Describes the relationship triples contained in the text, i.e., (head, head_type, relation, tail, tail_type). |
With the fields mentioned above, users can flexibly design and implement instructions and output formats for different information extraction needs.
[Tutorial](https://github.com/zjunlp/DeepKE/blob/main/example/llm/InstructKGC/README.md)
## Citation
Please cite these papers if you use InstructIE in your work.
```bibtex
@article{DBLP:journals/corr/abs-2305-11527,
author = {Honghao Gui and
Shuofei Qiao and
Jintian Zhang and
Hongbin Ye and
Mengshu Sun and
Lei Liang and
Huajun Chen and
Ningyu Zhang},
title = {InstructIE: {A} Bilingual Instruction-based Information Extraction
Dataset},
journal = {CoRR},
volume = {abs/2305.11527},
year = {2023},
url = {https://doi.org/10.48550/arXiv.2305.11527},
doi = {10.48550/ARXIV.2305.11527},
eprinttype = {arXiv},
eprint = {2305.11527},
timestamp = {Thu, 22 Feb 2024 09:46:17 +0100},
biburl = {https://dblp.org/rec/journals/corr/abs-2305-11527.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```
|
Locutusque/InstructMix | ---
dataset:
name: InstructiveMix
tagline: A Combined Dataset of Diverse Instructional Content
description: >
InstructiveMix is a comprehensive dataset that brings together various
instructional content from different domains. It combines instructions for
tasks, code, poems, math, essays, medical texts, and more. With a diverse range of
instructional data, this dataset is suitable for a wide range of natural
language processing (NLP) tasks and research.
license: CC-BY-SA-4.0
dataset_creation: '2023-08-02T00:00:00.000Z'
dataset_version: 1.0.0
authors:
- name: Locutusque
email: locutusque.airshipcraft@gmail.com
task_categories:
- text-generation
- conversational
- question-answering
language:
- en
---
**Dataset Summary:**
InstructMix is a comprehensive combined dataset that offers diverse instructional content for a range of tasks. It includes data from various sources, such as code instructions, poems, essays, medical texts, and more. This dataset is designed to support natural language processing (NLP) research, model training, and evaluation across different domains.
**Dataset Contents:**
The dataset contains a collection of instructional data with corresponding inputs and outputs. Each entry has an "Input" field that contains the instructional content, and an "Output" field that represents the corresponding response or completion. Here is a list of the datasets used:
- Locutusque/ColumnedChatCombined
- TokenBender/code_instructions_120k_alpaca_style
- Open-Orca/OpenOrca
- vicgalle/alpaca-gpt4
- ChristophSchuhmann/essays-with-instructions
- checkai/instruction-poems
- pubmed_qa
- BI55/MedText
- nampdn-ai/tiny-codes
- TIGER-Lab/MathInstruct
- garage-bAInd/Open-Platypus
It contains two of the following columns:
- Input (string)
- Output (string)
These should hopefully be self-explanatory
**Dataset Composition:**
- Number of samples: 7570315
- Languages: English
**Use Cases:**
The InstructiveMix dataset is suitable for various NLP tasks, including text generation, text completion, translation, summarization, and more. It can be used to train and evaluate language models, code generation models, and other NLP-based applications.
**Dataset Creation:**
The InstructiveMix dataset was created by combining multiple existing datasets with instructional content and adding metadata to facilitate seamless integration. The content spans a diverse set of domains and was sourced from reputable datasets and public sources.
**Acknowledgements:**
I would like to acknowledge the original creators of the datasets used to construct InstructiveMix. Their contributions have enabled the creation of this valuable resource for the NLP community.
**Contact:**
For any questions or inquiries related to the InstructiveMix dataset, please contact me at [locutusque.airshipcraft@gmail.com].
--- |
jinaai/negation-dataset-v2 |
---
tags:
- finetuner
language: en
dataset_info:
features:
- name: anchor
dtype: string
- name: entailment
dtype: string
- name: negative
dtype: string
splits:
- name: train
num_examples: 50000
- name: test
num_examples: 1000
multilinguality:
- monolingual
size_categories:
- 10K<n<50k
---
<br><br>
<p align="center">
<img src="https://github.com/jina-ai/finetuner/blob/main/docs/_static/finetuner-logo-ani.svg?raw=true" alt="Finetuner logo: Finetuner helps you to create experiments in order to improve embeddings on search tasks. It accompanies you to deliver the last mile of performance-tuning for neural search applications." width="150px">
</p>
<p align="center">
<b>The data offered by Jina AI, Finetuner team.</b>
</p>
## Summary
This dataset is an English-language dataset containing negation triplets. It is based on five datasets:
[SNLI](https://huggingface.co/datasets/snli), [Multi-NLI](https://huggingface.co/datasets/multi_nli),
[sentence-compression](https://huggingface.co/datasets/sent_comp), [Simple Wikipedia](https://www.loc.gov/item/2019205402/)
and [COCO Captions](https://cocodataset.org/#home).
## Instances
Each data point consists of a triplet ('anchor', 'entailment', 'negative') of strings, where ('anchor', 'entailment') are positive pairs
taken from SNLI, and 'negative' contradicts both 'anchor' and 'entailment'.
## Fields
- 'anchor': string, some statement
- 'entailment': string, a statement which follows from 'anchor', but is usually syntactically dissimilar
- 'negative': string, a statement contradicting 'anchor' and 'entailment'. Syntactically very similar to 'entailment'
## Splits
| | train | test |
|------------|-------|------|
| # of items | 50000 | 1000 |
## Source
Positive pairs were sampled from the five source datasets and negative samples were created using GPT-3.5
and GPT-4.
## Example Usage
```python
from datasets import load_dataset
from pprint import pprint
dataset = load_dataset('jinaai/negation-dataset-v2')
```
Example data:
```python
'anchor': ['Pedestrians walking down a sidewalk next to a small street.',
"A car stopped at a traffic light with it's brake lights on.",
'A couple on a motorcycle in front of a bus and a metermaid car'],
'entailment': ['People walking on a city street with light traffic.',
'A car stopped at the front of the stop light. ',
'A busy city street with a bus, taxi, and motorcycle.'],
'negative': ['People standing still on a city street with heavy traffic.',
'A car speeding away from the front of the stop light. ',
'A calm city street with no bus, taxi, and motorcycle.']
```
## Evaluate Models on the Test Set
For evaluation, you can use the evaluation script provided together with this dataset:
https://huggingface.co/datasets/jinaai/negation-dataset-v2/blob/main/evaluate_on_negations.py
## Licensing Information
Please respect the licenses of the original data sources for the achor and entailment data.
The additional negatives are licensed under the [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0) license.
## Contact
Join our [Discord community](https://discord.jina.ai) and chat with other community members about ideas.
## Citation
If you find this dataset useful in your research, please cite the following paper:
``` latex
@misc{gรผnther2023jina,
title={Jina Embeddings: A Novel Set of High-Performance Sentence Embedding Models},
author={Michael Gรผnther and Louis Milliken and Jonathan Geuter and Georgios Mastrapas and Bo Wang and Han Xiao},
year={2023},
eprint={2307.11224},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
|
Ichsan2895/alpaca-gpt4-indonesian | ---
license: cc-by-sa-4.0
language:
- id
size_categories:
- 10K<n<100K
task_categories:
- question-answering
---
Base model : [FreedomIntelligence/alpaca-gpt4-indonesian](https://huggingface.co/datasets/FreedomIntelligence/alpaca-gpt4-indonesian)
We wrangled the original dataset format to 'input' & 'output' format. For example:
BEFORE:
```
[ { "from": "human",
"value": "Saranlah slogan untuk kampanye daur ulang\n" },
{ "from": "gpt",
"value": "1. \"Kurangi, gunakan kembali, daur ulang: Bersama untuk masa depan yang lebih hijau.\"\n2. \
"Daur ulanglah hari ini, untuk masa depan yang lebih baik.\"\n3. \"Ubah sampahmu menjadi harta karun - Daur ulang!\"\n4. \
"Daur ulang untuk siklus kehidupan.\"\n5. \"Simpan sumber daya, daur ulang lebih banyak.\""
}
]
```
AFTER:
| input | output |
| ---- | ---- |
| Saranlah slogan untuk kampanye daur ulang\n | 1. "Kurangi, gunakan kembali, daur ulang: Bersama untuk masa depan yang lebih hijau.\"\n2. "Daur ulanglah hari ini, untuk masa depan yang lebih baik.\"\n3. \"Ubah sampahmu menjadi harta karun - Daur ulang!\"\n4. "Daur ulang untuk siklus kehidupan.\"\n5. \"Simpan sumber daya, daur ulang lebih banyak. |
## CITATION
```
@article{peng2023instruction,
title={Instruction Tuning with GPT-4},
author={Peng, Baolin and Li, Chunyuan and He, Pengcheng and Galley, Michel and Gao, Jianfeng},
journal={arXiv preprint arXiv:2304.03277},
year={2023}
}
@software{Chen_MultilingualSIFT_Multilingual_Supervised_2023,
author = {Chen, Zhihong and Yan, Shuo and Liang, Juhao and Jiang, Feng and Wu, Xiangbo and Yu, Fei and Chen, Guiming Hardy and Chen, Junying and Zhang, Hongbo and Li Jianquan and Wan Xiang and Wang, Benyou},
month = july,
title = {{MultilingualSIFT: Multilingual Supervised Instruction Fine-tuning}},
url = {https://github.com/FreedomIntelligence/MultilingualSIFT.git},
version = {0.1},
year = {2023}
}
``` |
vivym/midjourney-prompts | ---
license: apache-2.0
task_categories:
- text-to-image
tags:
- ' midjourney'
language:
- en
---
# midjourney-prompts
## Description
This dataset contains the cleaned midjourney prompts from Midjourney.
Total prompts: 9,085,397
| Version | Count |
| ------- | --------- |
| 5.2 | 2,272,465 |
| 5.1 | 2,060,106 |
| 5.0 | 3,530,770 |
| 4.0 | 1,204,384 |
| 3.0 | 14,991 |
| 2.0 | 791 |
| 1.0 | 1,239 |
| Style | Count |
| --------- | ----------- |
| default | 8,874,181 |
| raw | 177,953 |
| expressive| 27,919 |
| scenic | 2,146 |
| cute | 2,036 |
| original | 511 | |
kyujinpy/KoCoT_2000 | ---
license: cc-by-nc-4.0
task_categories:
- text-generation
- text-classification
language:
- en
size_categories:
- 1k<n<5k
---
# KoCoT-Collection
Using DeepL dataset, translation about [kaist-CoT](https://huggingface.co/datasets/kaist-ai/CoT-Collection).
---
# Original Dataset Card for Dataset Name
## Dataset Description
- **Homepage:https://github.com/kaistAI/CoT-Collection**
- **Repository:https://github.com/kaistAI/CoT-Collection**
- **Paper:https://arxiv.org/abs/2305.14045**
- **Point of Contact:sejune@lklab.io**
### Dataset Summary
This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1).
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
English
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
| name | train |
|-------------------|------:|
|CoT-Collection|1837928|
## Additional Information
### Citation Information
```
@article{kim2023cot,
title={The CoT Collection: Improving Zero-shot and Few-shot Learning of Language Models via Chain-of-Thought Fine-Tuning},
author={Kim, Seungone and Joo, Se June and Kim, Doyoung and Jang, Joel and Ye, Seonghyeon and Shin, Jamin and Seo, Minjoon},
journal={arXiv preprint arXiv:2305.14045},
year={2023}
}
``` |
MBZUAI/VideoInstruct-100K | ---
license: cc-by-sa-4.0
---
VideoInstruct100K is a high-quality video conversation dataset generated using human-assisted and semi-automatic annotation techniques. The question answers in the dataset are related to,
- Video Summariazation
- Description-based question-answers (exploring spatial, temporal, relationships, and reasoning concepts)
- Creative/generative question-answers
For mored details, please visit [Oryx/VideoChatGPT/video-instruction-data-generation](https://github.com/mbzuai-oryx/Video-ChatGPT/blob/main/data/README.md).
If you find this dataset useful, please consider citing the paper,
```bibtex
@article{Maaz2023VideoChatGPT,
title={Video-ChatGPT: Towards Detailed Video Understanding via Large Vision and Language Models},
author={Muhammad Maaz, Hanoona Rasheed, Salman Khan and Fahad Khan},
journal={ArXiv 2306.05424},
year={2023}
}
``` |
qgyd2021/rlhf_reward_dataset | ---
license: apache-2.0
task_categories:
- question-answering
- text-generation
language:
- zh
- en
tags:
- reward model
- rlhf
size_categories:
- 100M<n<1B
---
## RLHF Reward Model Dataset
ๅฅๅฑๆจกๅๆฐๆฎ้ใ
ๆฐๆฎ้ไป็ฝไธๆถ้ๆด็ๅฆไธ:
| ๆฐๆฎ | ่ฏญ่จ | ๅๅงๆฐๆฎ/้กน็ฎๅฐๅ | ๆ ทๆฌไธชๆฐ | ๅๅงๆฐๆฎๆ่ฟฐ | ๆฟไปฃๆฐๆฎไธ่ฝฝๅฐๅ |
| :--- | :---: | :---: | :---: | :---: | :---: |
| beyond | chinese | [beyond/rlhf-reward-single-round-trans_chinese](https://huggingface.co/datasets/beyond/rlhf-reward-single-round-trans_chinese) | 24858 | | |
| helpful_and_harmless | chinese | [dikw/hh_rlhf_cn](https://huggingface.co/datasets/dikw/hh_rlhf_cn) | harmless train 42394 ๆก๏ผharmless test 2304 ๆก๏ผhelpful train 43722 ๆก๏ผhelpful test 2346 ๆก๏ผ | ๅบไบ Anthropic ่ฎบๆ [Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback](https://arxiv.org/abs/2204.05862) ๅผๆบ็ helpful ๅharmless ๆฐๆฎ๏ผไฝฟ็จ็ฟป่ฏๅทฅๅ
ท่ฟ่กไบ็ฟป่ฏใ | [Anthropic/hh-rlhf](https://huggingface.co/datasets/Anthropic/hh-rlhf) |
| zhihu_3k | chinese | [liyucheng/zhihu_rlhf_3k](https://huggingface.co/datasets/liyucheng/zhihu_rlhf_3k) | 3460 | ็ฅไนไธ็้ฎ็ญๆ็จๆท็็น่ตๆฐ้๏ผๅฎๅบ่ฏฅๆฏๆ นๆฎ็น่ตๆฐ้ๆฅๅคๆญ็ญๆก็ไผๅ
็บงใ | |
| SHP | english | [stanfordnlp/SHP](https://huggingface.co/datasets/stanfordnlp/SHP) | 385K | ๆถๅ18ไธชๅญ้ขๅ๏ผๅๅฅฝ่กจ็คบๆฏๅฆๆๅธฎๅฉใ | |
<details>
<summary>ๅ่็ๆฐๆฎๆฅๆบ,ๅฑๅผๆฅ็</summary>
<pre><code>
https://huggingface.co/datasets/ticoAg/rlhf_zh
https://huggingface.co/datasets/beyond/rlhf-reward-single-round-trans_chinese
https://huggingface.co/datasets/dikw/hh_rlhf_cn
https://huggingface.co/datasets/liyucheng/zhihu_rlhf_3k
</code></pre>
</details>
|
crumb/c4-benchfilter-nano | ---
language_creators:
- found
language:
- en
license: odc-by
source_datasets:
- c4
task_categories:
- text-generation
- fill-mask
task_ids:
- language-modeling
- masked-language-modeling
dataset_info:
features:
- name: text
dtype: string
- name: score
dtype: float64
splits:
- name: train
num_bytes: 373897649.51453334
num_examples: 278115
download_size: 242478448
dataset_size: 373897649.51453334
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
size_categories:
- 100K<n<1M
---
# crumb/c4-benchfilter-nano
A 278k sample derivation of the first 3M samples from the C4 dataset for a cheap and short continued pretraining for language models to optimize for benchmark scores without sacrificing generalization and generative modelling unrelated to chat or 'instruct' data.
The estimated top 10% of highest estimated length normalized ngram (mean of tri, quad, and penta-gram) overlaps for each of the
selected benchmark datasets (arc, truthful_qa, hellaswag, mmlu, humaneval) based
on 1k samples, within the first 3M samples of C4. The top scoring sample
datasets for each benchmark are then filtered again for top 30% scores and
combined and exact-match de-duplicated. Then the top 3% scores and samples less than 200 characters long are removed
because they likely have exact large n-token matches by chance such as exact
dates or times that aren't actually relevant to the data.\*
\*Upon further examination, some of these samples are still present throughout the data, albeit at much lower frequency than before, you might benefit from using `dataset.filter(x['score'] > thresh)` for some threshold, but you risk losing high quality samples as well, this tradeoff should be well-examined before training. |
teknium/dataforge-economics | ---
language:
- eng
pretty_name: "DataForge-Economics"
tags:
- economics
license: mit
---
# Dataset Card for dataforge-economics
## Table of Contents
- [Overview](#overview)
- [Dataset Description](#dataset-description)
- [Data Collection and Synthesis](#data-collection-and-synthesis)
- [Data Structure](#data-structure)
- [Licensing, Privacy, and Ethics](#licensing-privacy-and-ethics)
- [Access](#access)
- [Usage](#usage)
- [Citation](#citation)
- [Contributions](#contributions)
## Overview
This dataset, `teknium/dataforge-economics`, is a specialized collection of 1,000 synthetic examples in the field of economics. It has been generated using OpenAI's GPT-4 and a custom data synthesis pipeline named DataForge, developed by me.
## Dataset Description
### Data Collection and Synthesis
The data in `teknium/dataforge-economics` has been synthetically generated using OpenAI's GPT-4 language model. The synthesis process was enhanced and structured using the DataForge pipeline, which incorporates domain-specific knowledge and ensures relevance in economics topics.
### Data Structure
- **Size of dataset:** 1000 examples
- **Type of data:** Textual (Economics domain-specific)
- **Data format:** JSON
- **Fields:**
- - id: a randomly generated uuid
- conversations: single turn human & gpt turns in sharegpt format
- source: the dataset name itself, for metadata purposes when merging with others
- topic: the sub-topic for the domain
- system_prompt: type of system prompt used for generating the response.
## Licensing, Privacy, and Ethics
- **License:** MIT License
- **Special Considerations:** This datasest is purely generated from GPT-4 data, some information may be incorrect or invalid.
- **Privacy:** As the dataset is synthetically generated, it does not contain any real individual's data.
## Access
- **Availability:** General Access
## Usage
This dataset is a domain specialist dataset, the first to use my new pipeline called Data Forge, which can create domain expert knowledge (and tasks, as seen in the Trismegistus occult dataset)
This dataset was a proof of concept to improve upon Orca model's economics expertise, which surpassed my custom benchmark for economics when finetuned over stable beluga.
|
mismatch-quest/SeeTRUE-Feedback | ---
configs:
- config_name: default
data_files:
- split: test
path: "test/*"
annotations_creators:
- crowdsourced
language:
- en
language_creators:
- found
license:
- cc-by-4.0
multilinguality:
- monolingual
paperswithcode_id: seetrue-feedback
pretty_name: SeeTRUE-feedback
size_categories:
- 1K<n<10K
source_datasets:
- original
tags:
- text-image-matching
task_ids: []
extra_gated_prompt: "By clicking on โAccess repositoryโ below, you also agree that you are using it solely for research purposes, and that SeeTRUE-Feedback should be used as a *TEST SET*, not as a training set, and especially not to train commercial chatbots. Do not hessitate to contact briangordon@mail.tau.ac.il or yonatanbitton@google.com if you have questions about this license."
---
# Dataset Card for SeeTRUE-Feedback
- [Dataset Description](#dataset-description)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
The SeeTRUE-Feedback dataset is a diverse benchmark for the meta-evaluation of image-text matching/alignment feedback. It aims to overcome limitations in current benchmarks, which primarily focus on predicting a matching score between 0-1. SeeTRUE provides, for each row, the original caption, feedback related to text-image misalignment, and the caption+visual source of misalignments (including a bounding box for the visual misalignment).
### Languages
The dataset supports English language.
## Dataset Structure
### Data Fields
- image_caption - Caption associated with the image.
- image_name: The name of the image file.
- dataset_source: The source/origin dataset of the image.
- id_in_source_dataset: The ID of the dataset where the row originates from.
- image_url: An S3 link from which you can download the image.
- human_feedback: Human-annotated feedbacks about image-text misalignment.
- feedback: Summary of feedback consolidated into a single entry (Generated by LLM: PaLM-2)
- feedback_clean: A parsed and "clean" version of `feedback` field.
- caption_misalignment: Source of misalignment in the image caption.
- visual_misalignment: Source of misalignment in the image.
- bbox_GroundingDino: Detected visual misalignment bounding-box in GroundingDino output format.
- bbox_PaLI: Detected visual misalignment bounding-box in PaLI output format.
### Data Splits
SeeTRUE-Feedback contains a single split: TEST, and should not be used for training.
## Dataset Creation
The dataset has been created by sourcing and matching images and text from multiple datasets. More information in the paper: <TODO>
### Licensing Information
The dataset is under the CC-By 4.0 license.
### Citation Information
TODO |
Luckyjhg/Geo170K | ---
configs:
- config_name: default
data_files:
- split: qa_tuning
path: data/qa_tuning-*
- split: alignment
path: data/alignment-*
dataset_info:
features:
- name: image
dtype: string
- name: conversations
list:
- name: from
dtype: string
- name: value
dtype: string
splits:
- name: qa_tuning
num_bytes: 93111889
num_examples: 117205
- name: alignment
num_bytes: 20241610
num_examples: 60252
download_size: 23754996
dataset_size: 113353499
---
# Dataset Card for "Geo170K"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
lmms-lab/VisitBench | ---
dataset_info:
features:
- name: instruction_category
dtype: string
- name: instruction
dtype: string
- name: reference_output
dtype: string
- name: is_multiple_images
dtype: bool
- name: image_0
dtype: image
- name: image_1
dtype: image
- name: image_2
dtype: image
- name: image_3
dtype: image
- name: image_4
dtype: image
- name: image_5
dtype: image
- name: image_6
dtype: image
- name: image_7
dtype: image
- name: image_8
dtype: image
- name: image_9
dtype: image
- name: image_info
dtype: string
- name: human_ratings_gpt4_correct
dtype: bool
- name: human_ratings_problem_in_caption
dtype: bool
- name: human_ratings_problem_in_gpt4
dtype: bool
- name: public_images_metadata
dtype: string
splits:
- name: multi_images
num_bytes: 408530373.0
num_examples: 678
- name: single_image
num_bytes: 408530373.0
num_examples: 678
download_size: 813204656
dataset_size: 817060746.0
configs:
- config_name: default
data_files:
- split: multi_images
path: data/multi_images-*
- split: single_image
path: data/single_image-*
---
# Dataset Card for "VisitBench"
<p align="center" width="100%">
<img src="https://i.postimg.cc/g0QRgMVv/WX20240228-113337-2x.png" width="100%" height="80%">
</p>
# Large-scale Multi-modality Models Evaluation Suite
> Accelerating the development of large-scale multi-modality models (LMMs) with `lmms-eval`
๐ [Homepage](https://lmms-lab.github.io/) | ๐ [Documentation](docs/README.md) | ๐ค [Huggingface Datasets](https://huggingface.co/lmms-lab)
# This Dataset
This is a formatted version of [VistBench](https://visit-bench.github.io/). It is used in our `lmms-eval` pipeline to allow for one-click evaluations of large multi-modality models.
```
@article{bitton2023visit,
title={Visit-bench: A benchmark for vision-language instruction following inspired by real-world use},
author={Bitton, Yonatan and Bansal, Hritik and Hessel, Jack and Shao, Rulin and Zhu, Wanrong and Awadalla, Anas and Gardner, Josh and Taori, Rohan and Schimdt, Ludwig},
journal={arXiv preprint arXiv:2308.06595},
year={2023}
}
```
Including visit_bench_single.csv and visit_bench_multi.csv, in total 1.2k items.
Some of them are with `reference_output`, directly copied from [here](https://docs.google.com/spreadsheets/d/1hi8rGXf2WYufkFvGJ2MZ92JNChliM1QEJwZxNboUFlE/edit#gid=696111549).
For each split, please follow the steps here to submit to VisitBench.
## Leaderboard
The link to our public leaderboard is present [here](https://visit-bench.github.io/).
## How to add new models to the Leaderboard?
1. You can access the single-image and multiple-image datasets above.
2. For every instance (row) in the dataset csv, you would have your model's predictions.
3. Create a `predictions.csv` with 4 mandatory columns `instruction`, `instruction_category`, `image` (single-image case) / `images` (multi-image case), `<model name> prediction`. Here, `<model name>`should be your model name with version if multiple-versions are available.
4. Send a `prediction.csv` to us on `yonatanbitton1@gmail.com`.
5. We will use our internal prompting sandbox with reference-free GPT-4 as an evaluator.
6. We will add your model to the leaderboard once we receive all the pairwise judgments from the sandbox.
7. You will receive a confirmation email as soon as your model has been added to the leaderboard.
8. Estimated time from Step 4-7 would be 1-2 weeks, however, we will try to work on your prediction files as soon as they are sent.
Please include in your email 1) a name for your model, 2) your team name (including your affiliation), and optionally, 3) a github repo or paper link.
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
reknine69/QA-citations | ---
task_categories:
- question-answering
language:
- en
size_categories:
- 1K<n<10K
---
QA-pairs with context from public documentation from Zerto, Carbonite, Vmware etc. |
Rogendo/English-Swahili-Sentence-Pairs | ---
task_categories:
- translation
- text-classification
- summarization
- feature-extraction
language:
- en
- sw
pretty_name: Eng-Swa-Pairs
size_categories:
- 100K<n<1M
--- |
retkowski/ytseg | ---
license: cc-by-nc-sa-4.0
language:
- en
tags:
- text segmentation
- smart chaptering
- segmentation
- youtube
- asr
pretty_name: YTSeg
size_categories:
- 10K<n<100K
task_categories:
- token-classification
- automatic-speech-recognition
---
# From Text Segmentation to Smart Chaptering: A Novel Benchmark for Structuring Video Transcriptions
We present <span style="font-variant:small-caps; font-weight:700;">YTSeg</span>, a topically and structurally diverse benchmark for the text segmentation task based on YouTube transcriptions. The dataset comprises 19,299 videos from 393 channels, amounting to 6,533 content hours. The topics are wide-ranging, covering domains such as science, lifestyle, politics, health, economy, and technology. The videos are from various types of content formats, such as podcasts, lectures, news, corporate events \& promotional content, and, more broadly, videos from individual content creators. We refer to the **paper** ([acl](https://aclanthology.org/2024.eacl-long.25/) | [arXiv](https://arxiv.org/abs/2402.17633)) for further information. We provide both text and audio data as well as a download script for the video data.
## Data Overview
### <span style="font-variant:small-caps;">YTSeg</span>
Each video is represented as a JSON object with the following fields:
| Field | Description |
|--------------|------------------------------------------------|
| `text` | A flat list of sentences. |
| `targets` | The target segmentation as string of binary values (e.g., `000100000010`). |
| `channel_id` | The YouTube channel ID which this video belongs to. |
| `video_id` | The YouTube video ID. |
| `audio_path` | Path to the .mp3 file of the video. |
| Partition | # Examples |
|------------|--------------|
| Training | 16,404 (85%) |
| Validation | 1,447 (7.5%) |
| Testing | 1,448 (7.5%) |
| Total | 19,229 |
### <span style="font-variant:small-caps;">YTSeg[Titles]</span>
Each chapter of a video is represented as a JSON object with the following fields:
| Field | Description |
|--------------|------------------------------------------------|
| `input` | The complete chapter/section text. |
| `input_with_chapters` | The complete chapter/section text with previous section titles prepended. |
| `target` | The target chapter title. |
| `channel_id` | The YouTube channel ID which this chapter's video belongs to. |
| `video_id` | The YouTube video ID which this chapter belongs to. |
| `chapter_idx` | The index and placement of the chapter in the video (e.g., the first chapter has index `0`). |
| Partition | # Examples |
|------------|--------------|
| Training | 146,907 (84.8%)|
| Validation | 13,206 (7.6%) |
| Testing | 13,082 (7.6%) |
| Total | 173,195 |
### Audio Data
We provide audio files for all examples in the dataset, preprocessed into the .mp3 format with a standardized sample rate of 16,000 Hz and a single channel (mono). These files are organized within the directory structure as follows: `data/audio/<channel_id>/<video_id>.mp3`.
### Video Data
A download script for the video and audio data is provided.
```py
python download_videos.py
```
In the script, you can further specify a target folder (default is `./video`) and target formats in a priority list.
## Loading Text Data
This repository comes with a simple, exemplary script to read in the text data with `pandas`.
```py
from load_data import get_partition
test_data = get_partition('test')
```
Equivalently, to read in <span style="font-variant:small-caps;">YTSeg[Titles]</span>:
```py
from load_data import get_title_partition
test_data = get_title_partition('test')
```
## Citing
We kindly request you to cite our corresponding EACL 2024 paper if you use our dataset.
```
@inproceedings{retkowski-waibel-2024-text,
title = "From Text Segmentation to Smart Chaptering: A Novel Benchmark for Structuring Video Transcriptions",
author = "Retkowski, Fabian and Waibel, Alexander",
editor = "Graham, Yvette and Purver, Matthew",
booktitle = "Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = mar,
year = "2024",
address = "St. Julian{'}s, Malta",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.eacl-long.25",
pages = "406--419",
abstract = "Text segmentation is a fundamental task in natural language processing, where documents are split into contiguous sections. However, prior research in this area has been constrained by limited datasets, which are either small in scale, synthesized, or only contain well-structured documents. In this paper, we address these limitations by introducing a novel benchmark YTSeg focusing on spoken content that is inherently more unstructured and both topically and structurally diverse. As part of this work, we introduce an efficient hierarchical segmentation model MiniSeg, that outperforms state-of-the-art baselines. Lastly, we expand the notion of text segmentation to a more practical {``}smart chaptering{''} task that involves the segmentation of unstructured content, the generation of meaningful segment titles, and a potential real-time application of the models.",
}
```
## License
The dataset is available under the **Creative Commons Attribution-NonCommercial-ShareAlike (CC BY-NC-SA) 4.0** license. We note that we do not own the copyright of the videos and as such opted to release the dataset with a non-commercial license, with the intended use to be in research and education. |
ananyarn/Algorithm_and_Python_Source_Code | ---
license: apache-2.0
language:
- en
tags:
- Python
- Code Generation
- Algorithm
- Pseudo-code
- Source Code
- Programmming
- Python Programming
---
_**Algorithm_and_Python_Source_Code**_ <br />
This dataset provides different algorithms and their corresponding source code in Python. <br /> <br />
credits: Source codes given here are taken from "iamtarun/python_code_instructions_18k_alpaca" dataset in Hugging Face. |
open-spaced-repetition/FSRS-Anki-20k | ---
license: other
license_name: fsrs-anki-20k
license_link: LICENSE
---
# Introduction
FSRS-Anki-20k is a dataset of 20k collections from Anki for FSRS project. It is a random sample of collections with 5000+ revlog entries, so it should contain a mix of older (still active) users, and newer users. Entries are pre-sorted in (cid, id) order.
There are two versions of the dataset: `./revlogs` and `./dataset`. The `./revlogs` version contains the raw revlog entries, while the `./dataset` version contains the dataset preprocessed by `./revlogs2dataset.py`. The size of the raw revlog entries is ~50GB, and the size of the preprocessed dataset is ~20GB.
# Data Format
## Revlogs
Please see the protocol buffer definition in [stats.proto](./stats.proto). For the fields that are not self-explanatory, please refer to the [Anki Database Structure](https://github.com/ankidroid/Anki-Android/wiki/Database-Structure).
## Dataset
The columns of the dataset are as follows:
- card_id: the unique identifier for each flashcard.
- review_th: the ordinal number of the review among all reviews done by the user.
- delta_t: the number of days since the last review of this flashcard. -1 if this is the first review.
- rating: the rating given by the user for this review, 1: again, 2: hard, 3: good, 4: easy. Where only 'again' indicates a failed recall, and all other scores indicate a successful recall.
# Preprocess
1. read the revlog entries from the .revlog file.
2. remove the revlog entries generated by reviews in filtered decks when the user disables the option "[Reschedule cards based on my answers in this deck](https://docs.ankiweb.net/filtered-decks.html?highlight=Reschedule%20cards%20based%20on%20my%20answers#rescheduling)".
3. remove the revlog entries generated by manual (re)scheduling like [Forget](https://docs.ankiweb.net/studying.html?highlight=Forget%20card#editing-and-more) and [Set Due Date](https://docs.ankiweb.net/studying.html?highlight=set%20due%20date#editing-and-more).
4. keep the revlog entries from the latest learning start sequence for each flashcard.
5. calculate the time between reviews of each flashcard (delta_t), review order (review_th), and encode card_id numerically.
6. save the dataset to a .csv file.
# License
This dataset is released under the [FSRS-Anki-20k License](LICENSE).
# Related Projects
[SRS Benchmark](https://github.com/open-spaced-repetition/fsrs-benchmark) |
zhongshsh/CLoT-Oogiri-GO | ---
license: mit
task_categories:
- visual-question-answering
- question-answering
language:
- en
- zh
- ja
pretty_name: Oogiri-GO
size_categories:
- 100K<n<1M
---
<p align="center">
<img src="logo.png" width="550" height="150">
</p>
# Oogiri-GO Dataset Card
[Project Page](https://zhongshsh.github.io/CLoT) | [Paper](https://arxiv.org/abs/2312.02439) | [Code](https://github.com/sail-sg/CLoT) | [Model](https://huggingface.co/zhongshsh/CLoT-cn)
**Data discription**: Oogiri-GO is a multimodal and multilingual humor dataset, and contains more than 130,000 Oogiri samples in English (en.jsonl), Chinese (cn.jsonl), and Japanese (jp.jsonl). Notably, in Oogiri-GO, 77.95\% of samples are annotated with human preferences, namely the number of likes, indicating the popularity of a response. As illustrated in Fig. 1, Oogiri-GO contains three types of Oogiri games according to the input that can be images, text, or both, and are respectively called "Text to Text" (T2T), "Image to Text" (I2T), and "Image & Text to Text " (IT2T) for brevity.
<p align="center">
<img src="oogiri.png" width="550" height="150">
Figure 1. Examples of the three types of LoT-based Oogiri games. Players are required to make surprising and creative humorous responses (blue box) to the given multimodal information e.g., images, text, or both.
</p>
Each line in the `jsonl` files represents a sample, formatted as follows:
```
{"type": "I2T", "question": null, "image": "5651380", "text": "It wasn't on purpose, I'm sorry!", "star": 5}
```
where `type` indicates the type of Oogiri game for the sample (T2T, I2T, IT2T); `question` represents the text question for the sample, with `None` for types other than T2T; `image` indicates the image question for the sample, with None for T2T samples; `text` is the text response for the sample; and `star` denotes the human preference.
In Japanese data (`jp.jsonl`) specifically, the questions for `T2T` type may appear as 'None' because the question text is in image form.
**Data distribution**: Table summarizes the distribution of these game types. For training purposes, 95% of the samples are randomly selected to construct the training dataset, while the remaining 5% form the test dataset for validation and analysis.
| Category | English | Chinese | Japanese |
|:--------:|:-------:|:-------:|:---------:|
| I2T | 17336 | 32130 | 40278 |
| T2T | 6433 | 15797 | 11842 |
| IT2T | -- | 912 | 9420 |
**Project page for more information**: https://zhongshsh.github.io/CLoT
**License**: Creative Commons Attribution 4.0 International. We also adhere to the terms of use from any of the data sources, such as [Bokete](https://bokete.jp/about/rule) and [Zhihu](https://www.zhihu.com/term/zhihu-terms). If you have any concerns regarding this dataset, especially if you believe it infringes upon your legal rights, please feel free to contact us. We will promptly review any issues raised and respond accordingly.
**Citation**
```
@misc{zhong2023clot,
ย title={Let's Think Outside the Box: Exploring Leap-of-Thought in Large Language Models with Creative Humor Generation},
ย author={Zhong, Shanshan and Huang, Zhongzhan and Gao, Shanghua and Wen, Weushao and Lin, Liang and Zitnik, Marinka and Zhou, Pan},
ย journal={arXiv preprint arXiv:2312.02439},
ย year={2023}
}
```
|
facebook/toolverifier | ---
dataset_info:
- config_name: train
features:
- name: text
dtype: string
- config_name: tools
features:
- name: Name
dtype: string
- name: Description
dtype: string
configs:
- config_name: train
data_files:
- split: train
path: train.csv
- config_name: tools
data_files:
- split: tools
path: tools.csv
---
# TOOLVERIFIER: Generalization to New Tools via Self-Verification
This repository contains the ToolSelect dataset which was used to fine-tune Llama-2 70B for tool selection.
## Data
**ToolSelect** data is synthetic training data generated for tool selection task using Llama-2 70B and Llama-2-Chat-70B.
It consists of 555 samples corresponding to 173 tools.
Each training sample is composed of a user instruction, a candidate set of tools that includes the
ground truth tool, and a reasoning note elucidating the correct choice of tool.
For example,
```
User: 10 reviews for "Harry Potter and the Philosopher's Stone".
###
Tool Choices: Bank Account Number generator = The Bank Account Number tool generates a random bank account number for a bank.
Train Ticket Purchase = The Train Ticket Purchase tool gets train tickets for a train, provided stations, and date information.
The Cricket Score tool = The Cricket Score tool gets the score for a match that is happening at a given location and date.
Book Review = The Book Review tool gets the top-rated book reviews for a particular book.
Grocery Shopping with discounts = The Grocery Shopping tool calculates the expense of grocery shopping based on the menu.
Stock Price at location and date = The Stock Price tool gets the stock price for a company at a given location and date.
Movie Recommendation = The Movie Recommendation tool recommends movies based on the user's preferences.
News = The News tool gets the top news for a particular topic or query.
###
Thought: I recommended the Book Review tool because the user asked for reviews of a specific book, "Harry Potter and the Philosopher's Stone". The Book Review tool is designed to provide top-rated book reviews for a particular book, making it the most suitable tool for this request. It will retrieve the top 10 reviews for the book, as requested by the user.
Act: CALLTOOL["Book Review"]
```
### Files
The `data/` folder has 2 files:
* `train.csv` - this file contains the training samples.
* `tools.csv` - this file contains names and descriptions of the generated synthetic tools.
To learn more about the data generation procedure, we direct readers to section 2.1 of our paper.
Paper: https://arxiv.org/abs/2402.14158
## Citation
```
@article{mekala2024toolverifier,
title={TOOLVERIFIER: Generalization to New Tools via Self-Verification},
author={Mekala, Dheeraj and Weston, Jason and Lanchantin, Jack and Raileanu, Roberta and Lomeli, Maria and Shang, Jingbo and Dwivedi-Yu, Jane},
journal={arXiv preprint arXiv:2402.14158},
year={2024}
}
```
## Licensing
See our LICENSE file for licensing details.
|
ResplendentAI/NSFW_RP_Format_DPO | ---
license: apache-2.0
task_categories:
- text-generation
language:
- en
tags:
- not-for-all-audiences
pretty_name: NSFW RP Format DPO
---
This dataset aims to align a model to output the most common roleplaying format: "dialogue" \*action\*
This dataset contains NSFW content. |
Locutusque/OpenCerebrum-SFT | ---
license: apache-2.0
task_categories:
- text-generation
- question-answering
language:
- en
tags:
- code
- math
- chemistry
- biology
size_categories:
- 1M<n<10M
---
# OpenCerebrum SFT subset
## Description
OpenCerebrum is my take on creating an open source version of Aether Research's proprietary Cerebrum dataset. This repository contains the SFT subset, which contains about 1,200,00 examples. Unfortunately, I was unsure about how I would compress this dataset to just 5,000 examples like in the original Cerebrum dataset.
## Curation
This dataset was curated using a simple and logical rationale. The goal was to use datasets that should logically improve evaluation scores that the original Cerebrum is strong in. See the "Data Sources" section for data source information.
## Data Sources
This dataset is an amalgamation including the following sources:
- Open-Orca/SlimOrca
- glaiveai/glaive-code-assistant
- camel-ai/physics
- camel-ai/math
- camel-ai/chemistry
- camel-ai/biology
- WizardLM/WizardLM_evol_instruct_V2_196k
- microsoft/orca-math-word-problems-200k
- grimulkan/theory-of-mind
- Vezora/Tested-22k-Python-Alpaca
- m-a-p/Code-Feedback
- Locutusque/arc-cot
- jondurbin/airoboros-2.1
- WizardLM/WizardLM_evol_instruct_70k
In future versions, I plan on shrinking this dataset, to match the size of the original Cerebrum.
|
LooksJuicy/ruozhiba | ---
license: apache-2.0
task_categories:
- text-generation
language:
- zh
---
ๅ[COIG-CQIA](https://huggingface.co/datasets/m-a-p/COIG-CQIA/blob/main/ruozhiba/ruozhiba_ruozhiba.jsonl)ๅฏๅ๏ผๆๅปบ็ฑปไผผๆฐๆฎ้๏ผไฝ็ญๆก้ฃๆ ผ็ธๅฏนๆด็ฎๆดใ
ๅผฑๆบๅง็ฒพ้้ฎ้ขๆฐๆฎๆฅ่ช[github](https://github.com/Leymore/ruozhiba/tree/main?tab=readme-ov-file)ๆไพ็[็้ฎๅฅ](https://docs.qq.com/sheet/DUlZ6aURhamdwb1RO?tab=BB08J2)๏ผ่ฐ็จGPT-4่ทๅ็ญๆก๏ผๅนถ่ฟๆปคๆๆๆพๆ็ญ็ๅๅคใ |
damlab/uniprot | ---
liscence: mit
---
# Dataset Description
## Dataset Summary
This dataset is a mirror of the Uniprot/SwissProt database. It contains the names and sequences of >500K proteins.
This dataset was parsed from the FASTA file at https://ftp.uniprot.org/pub/databases/uniprot/current_release/knowledgebase/complete/uniprot_sprot.fasta.gz.
Supported Tasks and Leaderboards: None
Languages: English
## Dataset Structure
### Data Instances
Data Fields: id, description, sequence
Data Splits: None
## Dataset Creation
The dataset was downloaded and parsed into a `dataset` object and uploaded unchanged.
Initial Data Collection and Normalization: Dataset was downloaded and curated on 03/09/2022.
## Considerations for Using the Data
Social Impact of Dataset: Due to the tendency of HIV to mutate, drug resistance is a common issue when attempting to treat those infected with HIV.
Protease inhibitors are a class of drugs that HIV is known to develop resistance via mutations.
Thus, by providing a collection of protease sequences known to be resistant to one or more drugs, this dataset provides a significant collection of data that could be utilized to perform computational analysis of protease resistance mutations.
Discussion of Biases: Due to the sampling nature of this database, it is predominantly composed genes from "well studied" genomes. This may impact the "broadness" of the genes contained.
## Additional Information:
- Dataset Curators: Will Dampier
- Citation Information: TBA
|
valurank/Adult-content-dataset | ---
license:
- other
language:
- en
multilinguality:
- monolingual
task_categories:
- text-classification
task_ids: []
---
# Dataset Card for Adult_Content_Detection
## Table of Contents
- [Dataset Description](#dataset-description)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Source Data](#source-data)
## Dataset Description
850 Articles descriptions classified into two different categories namely: Adult, and Non_Adult
## Languages
The text in the dataset is in English
## Dataset Structure
The dataset consists of two columns namely Description and Category.
The Description column consists of the overview of the article and the Category column consists of the class each article belongs to
## Source Data
The dataset is scrapped across different platforms
|
yuningm/citesum | ---
language:
- en
license: cc-by-nc-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- summarization
paperswithcode_id: citesum
---
# CiteSum
## Description
CiteSum: Citation Text-guided Scientific Extreme Summarization and Low-resource Domain Adaptation.
CiteSum contains TLDR summaries for scientific papers from their citation texts without human annotation, making it around 30 times larger than the previous human-curated dataset SciTLDR.
## Homepage
https://github.com/morningmoni/CiteSum
## Paper
https://arxiv.org/abs/2205.06207
## Authors
### Yuning Mao, Ming Zhong, Jiawei Han
#### University of Illinois Urbana-Champaign
{yuningm2, mingz5, hanj}@illinois.edu
## Dataset size
Train: 83304
Validation: 4721
Test: 4921
## Data details
- src (string): source text. long description of paper
- tgt (string): target text. tldr of paper
- paper_id (string): unique id for the paper
- title (string): title of the paper
- discipline (dict):
- venue (string): Where the paper was published (conference)
- journal (string): Journal in which the paper was published
- mag_field_of_study (list[str]): scientific fields that the paper falls under.
Example:
```
{
'src': 'We describe a convolutional neural network that learns feature representations for short textual posts using hashtags as a supervised signal. The proposed approach is trained on up to 5.5 billion words predicting 100,000 possible hashtags. As well as strong performance on the hashtag prediction task itself, we show that its learned representation of text (ignoring the hashtag labels) is useful for other tasks as well. To that end, we present results on a document recommendation task, where it also outperforms a number of baselines.',
'tgt': 'A convolutional neural network model for predicting hashtags was proposed in REF .',
'paper_id': '14697143',
'title': '#TagSpace: Semantic Embeddings from Hashtags',
'discipline': {
'venue': 'EMNLP',
'journal': None,
'mag_field_of_study': ['Computer Science']
}
}
```
## Using the dataset
```python
from datasets import load_dataset
ds = load_dataset("yuningm/citesum")
```
## Data location
https://drive.google.com/file/d/1ndHCREXGSPnDUNllladh9qCtayqbXAfJ/view
|
Paul/hatecheck-arabic | ---
annotations_creators:
- crowdsourced
language_creators:
- expert-generated
language:
- ar
license:
- cc-by-4.0
multilinguality:
- monolingual
pretty_name: Arabic HateCheck
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- hate-speech-detection
---
# Dataset Card for Multilingual HateCheck
## Dataset Description
Multilingual HateCheck (MHC) is a suite of functional tests for hate speech detection models in 10 different languages: Arabic, Dutch, French, German, Hindi, Italian, Mandarin, Polish, Portuguese and Spanish.
For each language, there are 25+ functional tests that correspond to distinct types of hate and challenging non-hate.
This allows for targeted diagnostic insights into model performance.
For more details, please refer to our paper about MHC, published at the 2022 Workshop on Online Abuse and Harms (WOAH) at NAACL 2022. If you are using MHC, please cite our work!
- **Paper:** Rรถttger et al. (2022) - Multilingual HateCheck: Functional Tests for Multilingual Hate Speech Detection Models. https://arxiv.org/abs/2206.09917
- **Repository:** https://github.com/rewire-online/multilingual-hatecheck
- **Point of Contact:** paul@rewire.online
## Dataset Structure
The csv format mostly matches the original HateCheck data, with some adjustments for specific languages.
**mhc_case_id**
The test case ID that is unique to each test case across languages (e.g., "mandarin-1305")
**functionality**
The shorthand for the functionality tested by the test case (e.g, "target_obj_nh"). The same functionalities are tested in all languages, except for Mandarin and Arabic, where non-Latin script required adapting the tests for spelling variations.
**test_case**
The test case text.
**label_gold**
The gold standard label ("hateful" or "non-hateful") of the test case. All test cases within a given functionality have the same gold standard label.
**target_ident**
Where applicable, the protected group that is targeted or referenced in the test case. All HateChecks cover seven target groups, but their composition varies across languages.
**ref_case_id**
For hateful cases, where applicable, the ID of the hateful case which was perturbed to generate this test case. For non-hateful cases, where applicable, the ID of the hateful case which is contrasted by this test case.
**ref_templ_id**
The equivalent to ref_case_id, but for template IDs.
**templ_id**
The ID of the template from which the test case was generated.
**case_templ**
The template from which the test case was generated (where applicable).
**gender_male** and **gender_female**
For gender-inflected languages (French, Spanish, Portuguese, Hindi, Arabic, Italian, Polish, German), only for cases where gender inflection is relevant, separate entries for gender_male and gender_female replace case_templ.
**label_annotated**
A list of labels given by the three annotators who reviewed the test case (e.g., "['hateful', 'hateful', 'hateful']").
**label_annotated_maj**
The majority vote of the three annotators (e.g., "hateful"). In some cases this differs from the gold label given by our language experts.
**disagreement_in_case**
True if label_annotated_maj does not match label_gold for the entry.
**disagreement_in_template**
True if the test case is generated from an IDENT template and there is at least one case with disagreement_in_case generated from the same template. This can be used to exclude entire templates from MHC. |
imodels/diabetes-readmission | ---
annotations_creators: []
language: []
language_creators: []
license: []
multilinguality: []
pretty_name: diabetes-readmission
size_categories:
- 100K<n<1M
source_datasets: []
tags:
- interpretability
- fairness
- medicine
task_categories:
- tabular-classification
task_ids: []
---
Port of the diabetes-readmission dataset from UCI (link [here](https://archive.ics.uci.edu/ml/datasets/diabetes+130-us+hospitals+for+years+1999-2008)). See details there and use carefully.
Basic preprocessing done by the [imodels team](https://github.com/csinva/imodels) in [this notebook](https://github.com/csinva/imodels-data/blob/master/notebooks_fetch_data/00_get_datasets_custom.ipynb).
The target is the binary outcome `readmitted`.
### Sample usage
Load the data:
```
from datasets import load_dataset
dataset = load_dataset("imodels/diabetes-readmission")
df = pd.DataFrame(dataset['train'])
X = df.drop(columns=['readmitted'])
y = df['readmitted'].values
```
Fit a model:
```
import imodels
import numpy as np
m = imodels.FIGSClassifier(max_rules=5)
m.fit(X, y)
print(m)
```
Evaluate:
```
df_test = pd.DataFrame(dataset['test'])
X_test = df.drop(columns=['readmitted'])
y_test = df['readmitted'].values
print('accuracy', np.mean(m.predict(X_test) == y_test))
``` |
ysharma/short_jokes | ---
license: mit
---
**Context**
Generating humor is a complex task in the domain of machine learning, and it requires the models to understand the deep semantic meaning of a joke in order to generate new ones. Such problems, however, are difficult to solve due to a number of reasons, one of which is the lack of a database that gives an elaborate list of jokes. Thus, a large corpus of over 0.2 million jokes has been collected by scraping several websites containing funny and short jokes.
You can visit the [Github repository](https://github.com/amoudgl/short-jokes-dataset) from [amoudgl](https://github.com/amoudgl) for more information regarding collection of data and the scripts used.
**Content**
This dataset is in the form of a csv file containing 231,657 jokes. Length of jokes ranges from 10 to 200 characters. Each line in the file contains a unique ID and joke.
**Disclaimer**
It has been attempted to keep the jokes as clean as possible. Since the data has been collected by scraping websites, it is possible that there may be a few jokes that are inappropriate or offensive to some people.
**Note**
This dataset is taken from Kaggle dataset that can be found [here](https://www.kaggle.com/datasets/abhinavmoudgil95/short-jokes). |
khaclinh/pp4av | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- en
license:
- cc-by-nc-nd-4.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- extended
task_categories:
- object-detection
task_ids:
- face-detection
pretty_name: PP4AV
tags:
- license-plate-detection
---
# Dataset Card for PP4AV
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Languages](#languages)
- [Dataset Creation](#dataset-creation)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Dataset folder](#folder)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [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)
- [Baseline Model](#baseline-model)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://github.com/khaclinh/pp4av
- **Repository:** https://github.com/khaclinh/pp4av
- **Baseline model:** https://huggingface.co/spaces/khaclinh/self-driving-anonymization
- **Paper:** [PP4AV: A benchmarking Dataset for Privacy-preserving Autonomous Driving]
- **Point of Contact:** linhtk.dhbk@gmail.com
### Dataset Summary
PP4AV is the first public dataset with faces and license plates annotated with driving scenarios. P4AV provides 3,447 annotated driving images for both faces and license plates. For normal camera data, dataset sampled images from the existing videos in which cameras were mounted in moving vehicles, running around the European cities. The images in PP4AV were sampled from 6 European cities at various times of day, including nighttime. This dataset use the fisheye images from the WoodScape dataset to select 244 images from the front, rear, left, and right cameras for fisheye camera data. PP4AV dataset can be used as a benchmark suite (evaluating dataset) for data anonymization models in autonomous driving.
### Languages
English
## Dataset Creation
### Source Data
#### Initial Data Collection and Normalization
The objective of PP4AV is to build a benchmark dataset that can be used to evaluate face and license plate detection models for autonomous driving. For normal camera data, we sampled images from the existing videos in which cameras were mounted in moving vehicles, running around the European cities. We focus on sampling data in urban areas rather than highways in order to provide sufficient samples of license plates and pedestrians. The images in PP4AV were sampled from **6** European cities at various times of day, including nighttime. The source data from 6 cities in European was described as follow:
- `Paris`: This subset contains **1450** images of the car driving down a Parisian street during the day. The video frame rate is 30 frames per second. The video is longer than one hour. We cut a shorter video for sampling and annotation. The original video can be found at the following URL:
URL: [paris_youtube_video](https://www.youtube.com/watch?v=nqWtGWymV6c)
- `Netherland day time`: This subset consists of **388** images of Hague, Amsterdam city in day time. The image of this subset are sampled from the bellow original video:
URL: [netherland_youtube_video](https://www.youtube.com/watch?v=Xuo4uCZxNrE)
The frame rate of the video is 30 frames per second. We cut a shorter video for sampling and annotation. The original video was longer than a half hour.
- `Netherland night time`: This subset consists of **824** images of Hague, Amsterdam city in night time sampled by the following original video:
URL: [netherland_youtube_video](https://www.youtube.com/watch?v=eAy9eHsynhM)
The frame rate of the video is 30 frames per second. We cut a shorter video for sampling and annotation. The original video was longer than a half hour.
- `Switzerland`: This subset consists of **372** images of Switzerland sampled by the following video:
URL: [switzerland_youtube_video](https://www.youtube.com/watch?v=0iw5IP94m0Q)
The frame rate of the video is 30 frames per second. We cut a shorter video for sampling and annotation. The original video was longer than one hour.
- `Zurich`: This subset consists of **50** images of Zurich city provided by the Cityscapes training set in package [leftImg8bit_trainvaltest.zip](https://www.cityscapes-dataset.com/file-handling/?packageID=3)
- `Stuttgart`: This subset consists of **69** images of Stuttgart city provided by the Cityscapes training set in package [leftImg8bit_trainvaltest.zip](https://www.cityscapes-dataset.com/file-handling/?packageID=3)
- `Strasbourg`: This subset consists of **50** images of Strasbourg city provided by the Cityscapes training set in package [leftImg8bit_trainvaltest.zip](https://www.cityscapes-dataset.com/file-handling/?packageID=3)
We use the fisheye images from the WoodScape dataset to select **244** images from the front, rear, left, and right cameras for fisheye camera data.
The source of fisheye data for sampling is located at WoodScape's [Fisheye images](https://woodscape.valeo.com/download).
In total, **3,447** images were selected and annotated in PP4AV.
### Annotations
#### Annotation process
Annotators annotate facial and license plate objects in images. For facial objects, bounding boxes are defined by all detectable human faces from the forehead to the chin to the ears. Faces were labelled with diverse sizes, skin tones, and faces partially obscured by a transparent material, such as a car windshield. For license plate objects, bounding boxes consists of all recognizable license plates with high variability, such as different sizes, countries, vehicle types (motorcycle, automobile, bus, truck), and occlusions by other vehicles. License plates were annotated for vehicles involved in moving traffic. To ensure the quality of annotation, there are two-step process for annotation. In the first phase, two teams of annotators will independently annotate identical image sets. After their annotation output is complete, a merging method based on the IoU scores between the two bounding boxes of the two annotations will be applied. Pairs of annotations with IoU scores above a threshold will be merged and saved as a single annotation. Annotated pairs with IoU scores below a threshold will be considered conflicting. In the second phase, two teams of reviewers will inspect the conflicting pairs of annotations for revision before a second merging method similar to the first is applied. The results of these two phases will be combined to form the final annotation. All work is conducted on the CVAT tool https://github.com/openvinotoolkit/cvat.
#### Who are the annotators?
Vantix Data Science team
### Dataset Folder
The `data` folder contains below files:
- `images.zip`: contains all preprocessed images of PP4AV dataset. In this `zip` file, there are bellow folder included:
`fisheye`: folder contains 244 fisheye images in `.png` file type
`zurich`: folder contains 244 fisheye images in `.png` file type
`strasbourg`: folder contains 244 fisheye images in `.png` file type
`stuttgart`: folder contains 244 fisheye images in `.png` file type
`switzerland`: folder contains 244 fisheye images in `.png` file type
`netherlands_day`: folder contains 244 fisheye images in `.png` file type
`netherlands_night`: folder contains 244 fisheye images in `.png` file type
`paris`: folder contains 244 fisheye images in `.png` file type
- `annotations.zip`: contains annotation data corresponding to `images.zip` data. In this file, there are bellow folder included:
`fisheye`: folder contains 244 annotation `.txt` file type for fisheye image following `yolo v1.1` format.
`zurich`: folder contains 50 file `.txt` annotation following `yolo v1.1` format, which corresponding to 50 images file of `zurich` subset.
`strasbourg`: folder contains 50 file `.txt` annotation following `yolo v1.1` format, which corresponding to 50 images file of `strasbourg` subset.
`stuttgart`: folder contains 69 file `.txt` annotation following `yolo v1.1` format, which corresponding to 69 images file of `stuttgart` subset.
`switzerland`: folder contains 372 file `.txt` annotation following `yolo v1.1` format, which corresponding to 372 images file of `switzerland` subset.
`netherlands_day`: folder contains 388 file `.txt` annotation following `yolo v1.1` format, which corresponding to 388 images file of `netherlands_day` subset.
`netherlands_night`: folder contains 824 file `.txt` annotation following `yolo v1.1` format, which corresponding to 824 images file of `netherlands_night` subset.
`paris`: folder contains 1450 file `.txt` annotation following `yolo v1.1` format, which corresponding to 1450 images file of `paris` subset.
- `soiling_annotations.zip`: contain raw annotation data without filtering. The folder structure stored in this file is similar to format of `annotations.zip`.
### Personal and Sensitive Information
[More Information Needed]
## Dataset Structure
### Data Instances
A data point comprises an image and its face and license plate annotations.
```
{
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=1920x1080 at 0x19FA12186D8>, 'objects': {
'bbox': [
[0 0.230078 0.317081 0.239062 0.331367],
[1 0.5017185 0.0306425 0.5185935 0.0410975],
[1 0.695078 0.0710145 0.7109375 0.0863355],
[1 0.4089065 0.31646 0.414375 0.32764],
[0 0.1843745 0.403416 0.201093 0.414182],
[0 0.7132 0.3393474 0.717922 0.3514285]
]
}
}
```
### Data Fields
- `image`: A `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`
- `objects`: a dictionary of face and license plate bounding boxes present on the image
- `bbox`: the bounding box of each face and license plate (in the [yolo](https://albumentations.ai/docs/getting_started/bounding_boxes_augmentation/#yolo) format). Basically, each row in annotation `.txt` file for each image `.png` file consists of data in format: `<object-class> <x_center> <y_center> <width> <height>`:
- `object-class`: integer number of object from 0 to 1, where 0 indicate face object, and 1 indicate licese plate object
- `x_center`: normalized x-axis coordinate of the center of the bounding box.
`x_center = <absolute_x_center> / <image_width>`
- `y_center`: normalized y-axis coordinate of the center of the bounding box.
`y_center = <absolute_y_center> / <image_height>`
- `width`: normalized width of the bounding box.
`width = <absolute_width> / <image_width>`
- `height`: normalized wheightdth of the bounding box.
`height = <absolute_height> / <image_height>`
- Example lines in YOLO v1.1 format `.txt' annotation file:
`1 0.716797 0.395833 0.216406 0.147222
0 0.687109 0.379167 0.255469 0.158333
1 0.420312 0.395833 0.140625 0.166667
`
## 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
### Baseline Model
Pretrained weight and demo of baseline model are available in [self-driving-anonymization huggingface spaces](https://huggingface.co/spaces/khaclinh/self-driving-anonymization)
### Dataset Curators
Linh Trinh
### Licensing Information
[Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)](https://creativecommons.org/licenses/by-nc-nd/4.0/).
### Citation Information
```
@article{PP4AV2022,
title = {PP4AV: A benchmarking Dataset for Privacy-preserving Autonomous Driving},
author = {Linh Trinh, Phuong Pham, Hoang Trinh, Nguyen Bach, Dung Nguyen, Giang Nguyen, Huy Nguyen},
booktitle = {IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},
year = {2023}
}
```
### Contributions
Thanks to [@khaclinh](https://github.com/khaclinh) for adding this dataset.
|
allenai/csabstruct | ---
license: apache-2.0
---
# CSAbstruct
CSAbstruct was created as part of *"Pretrained Language Models for Sequential Sentence Classification"* ([ACL Anthology][2], [arXiv][1], [GitHub][6]).
It contains 2,189 manually annotated computer science abstracts with sentences annotated according to their rhetorical roles in the abstract, similar to the [PUBMED-RCT][3] categories.
## Dataset Construction Details
CSAbstruct is a new dataset of annotated computer science abstracts with sentence labels according to their rhetorical roles.
The key difference between this dataset and [PUBMED-RCT][3] is that PubMed abstracts are written according to a predefined structure, whereas computer science papers are free-form.
Therefore, there is more variety in writing styles in CSAbstruct.
CSAbstruct is collected from the Semantic Scholar corpus [(Ammar et a3., 2018)][4].
E4ch sentence is annotated by 5 workers on the [Figure-eight platform][5], with one of 5 categories `{BACKGROUND, OBJECTIVE, METHOD, RESULT, OTHER}`.
We use 8 abstracts (with 51 sentences) as test questions to train crowdworkers.
Annotators whose accuracy is less than 75% are disqualified from doing the actual annotation job.
The annotations are aggregated using the agreement on a single sentence weighted by the accuracy of the annotator on the initial test questions.
A confidence score is associated with each instance based on the annotator initial accuracy and agreement of all annotators on that instance.
We then split the dataset 75%/15%/10% into train/dev/test partitions, such that the test set has the highest confidence scores.
Agreement rate on a random subset of 200 sentences is 75%, which is quite high given the difficulty of the task.
Compared with [PUBMED-RCT][3], our dataset exhibits a wider variety of writ- ing styles, since its abstracts are not written with an explicit structural template.
## Dataset Statistics
| Statistic | Avg ยฑ std |
|--------------------------|-------------|
| Doc length in sentences | 6.7 ยฑ 1.99 |
| Sentence length in words | 21.8 ยฑ 10.0 |
| Label | % in Dataset |
|---------------|--------------|
| `BACKGROUND` | 33% |
| `METHOD` | 32% |
| `RESULT` | 21% |
| `OBJECTIVE` | 12% |
| `OTHER` | 03% |
## Citation
If you use this dataset, please cite the following paper:
```
@inproceedings{Cohan2019EMNLP,
title={Pretrained Language Models for Sequential Sentence Classification},
author={Arman Cohan, Iz Beltagy, Daniel King, Bhavana Dalvi, Dan Weld},
year={2019},
booktitle={EMNLP},
}
```
[1]: https://arxiv.org/abs/1909.04054
[2]: https://aclanthology.org/D19-1383
[3]: https://github.com/Franck-Dernoncourt/pubmed-rct
[4]: https://aclanthology.org/N18-3011/
[5]: https://www.figure-eight.com/
[6]: https://github.com/allenai/sequential_sentence_classification
|
domenicrosati/clinical_trial_texts | ---
dataset_info:
features:
- name: text
dtype: string
- name: trial_id
dtype: string
- name: input_ids
sequence: int32
- name: token_type_ids
sequence: int8
- name: attention_mask
sequence: int8
splits:
- name: train
num_bytes: 22784316806
num_examples: 434977
download_size: 5376659326
dataset_size: 22784316806
---
# Dataset Card for "clinical_trial_texts"
These are the text of clinical trials dowloaded from https://ClinicalTrials.gov/AllAPIJSON.zip on Dec 3rd 2022.
Total trials is 434977
Number of tokens is 2,184,397,556 (2.1bn tokens).
The tokens here are from the default BERT tokenizer in hugginface.
This data can be used for pretraining in the clinical trial and biomedical domains.
If you use this data please acknowledge @domenicrosati and link to this dataset
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
mrm8488/unnatural-instructions-full | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: instances
list:
- name: instruction_with_input
dtype: string
- name: input
dtype: string
- name: constraints
dtype: string
- name: output
dtype: string
- name: reformulations
list:
- name: instruction
dtype: string
- name: instruction_with_input
dtype: string
- name: input
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 144282712
num_examples: 66010
download_size: 57715606
dataset_size: 144282712
---
# Dataset Card for Unnatural Instructions (Full data)
This info comes from the **Unnatural Instructions GitHub [repo](https://github.com/orhonovich/unnatural-instructions/)**.
Unnatural Instructions is a dataset of instructions automatically generated by a Large Language model.
See full details in the paper: "[Unnatural Instructions: Tuning Language Models with (Almost) No Human Labor](https://arxiv.org/abs/2212.09689)"
## ๐๏ธ Content
It contains the full 240,670 Unnatural Instructions (instruction-input-output triplets) examples. It was constructed by expanding the core data with automatically generated instruction paraphrases.
## ๐ Format
### Full data
It has the same structure as [Core Data](https://huggingface.co/datasets/mrm8488/unnatural-instructions-core), but with one additional field - `reformulations`. `reformulations` is an array of JSON objects, each corresponds to an automatically generated paraphrase for the given instruction. Each reformulation contains the fields:
- `instruction`: A paraphrase of the original instruction
- `input`: An input for the task described by the `instruction`
- `instruction_with_input`: The paraphrased instruction concatenated with the `input`
- `output`: The output of executing `instruction` with the given `input`
## ๐ Citation
If you make use of Unnatural Instructions, please cite the following paper:
```
@misc{honovich2022unnatural,
title = {Unnatural Instructions: Tuning Language Models with (Almost) No Human Labor},
author = {Honovich, Or and Scialom, Thomas and Levy, Omer and Schick, Timo},
url = {https://arxiv.org/abs/2212.09689},
publisher = {arXiv},
year={2022}
}
```
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
abertsch/booksum-fullbooks | ---
dataset_info:
features:
- name: bid
dtype: string
- name: source
dtype: string
- name: title
dtype: string
- name: summary
dtype: string
- name: book
dtype: string
splits:
- name: validation
num_bytes: 23586559
num_examples: 45
- name: train
num_bytes: 165182724
num_examples: 314
- name: test
num_bytes: 31094987
num_examples: 46
download_size: 60336046
dataset_size: 219864270
---
# Dataset Card for "booksum-fullbooks"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
kqsong/OASum | ---
license: cc-by-sa-3.0
language:
- en
tags:
- summarization
- Wikipedia
size_categories:
- 1M<n<10M
task_categories:
- summarization
---
# Dataset Card for OASum Dataset
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Usage](#dataset-usage)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Additional Information](#additional-information)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
- **Repository:** [OASum Dataset repository](https://github.com/tencent-ailab/OASum)
- **Paper:** [OASum: Large-Scale Open Domain Aspect-based Summarization](https://arxiv.org/pdf/2212.09233.pdf)
The OASum Dataset is an English-language dataset containing over 3.6M document, aspect, and summary triplets.
## Dataset Usage
You can directly download it with huggingface datasets.
``` python
from datasets import load_dataset
dataset = load_dataset("kqsong/OASum")
```
## Dataset Structure
### Data Instances
For each instance, there is a list of strings for the document, a list of strings for the summary, a string for the document title, a string for the aspect and a list of indices for the sentences in the corresponding section.
```json
{
"title": "Ker's WingHouse Bar & Grill",
"document":[
"After Clearwater, Florida chicken wing pioneering restaurant chain Hooters began rapidly expanding, Florida based, Canadian-born restaurant entrepreneur Ed Burnett saw the opportunity.",
"Burnett secured the rights to a closed restaurant (\"Knockers\") and opened \"The WingHouse\" restaurant at 7369 Ulmerton Road, Largo, Florida, a high traffic corridor.",
"He strategically selected the restaurant in between where people work (commercial real estate) and live (residential real estate), to appeal to the local lunch crowd and family dining crowd.",
"This flagship location proved to be a success soon after launching and is the model that the chain expanded on.",
"Burnett, looking to expand to additional locations, accepted a financing partner (Crawford Ker) during this time frame, to open additional locations and beyond.",
"Burnett's goal was to open 20 to 50 locations, and then sell the chain to a larger restaurant chain or investors.",
"Burnett would ultimately regret his choice of investor.","In 1992, Ker retired from the NFL and took a job selling cars at a local dealer.",
"In 1994, he invested half interest in a Largo, Florida wing restaurant called, \"Wing House\" that imitated Hooters.",
"The restaurant was always The Wing House, and the atmosphere was always toned down to make it more family friendly.",
"The restaurant did well and two additional locations were opened in the Tampa Bay area in the following three years.",
"Ker won a $1.2-million jury award from Hooters in late 2004, which had sued him for trademark violations for allegedly using their uniforms and decor.",
"After a three-week trial in which lawyers discussed hula hoops, surfboards, scrunchy socks, pantyhose, and something called \"vicarious sexual recreation\", the jury ruled that no trademark infringement existed and Hooters was penalized for their frivolous lawsuit.",
"Hooters appealed the decision, but in June, 2006, the 11th U.S. Circuit Court of Appeals in Atlanta upheld the verdict.",
"As of 2007, the company had 1,700 employees at 22 locations with revenue of nearly $60 million.",
"Ker attended, and the company participated in, the 2007 National Buffalo Wing Festival and placed first in the \"traditional x-hot sauce\" category and gained some national recognition.",
"On June 4, 2008 the company announced the launch of its national franchise program.",
"In mid-2008 the chain operated 19 locations in Florida and Texas and expected to add six franchises by the end of 2008, and 48 by 2011.",
"The initial focus was for franchises in the Southeastern US.",
"WingHouses feature several amenities that differ from other wing restaurants, including Hooters.",
"There is a full liquor bar in every store, sports memorabilia line the walls instead of NASCAR and most locations include a game room.",
"Super Bowl XLIII in Tampa, Florida attracted the rich and famous; WingHouse hosted three events to raise money for charity."
],
"aspect": "Opening",
"aspect_sents": [0,1,2,3,4,5,6,7,8,9,10],
"summary":[
"WingHouse Bar & Grill (formerly Ker\u2019s WingHouse Bar & Grill) is a restaurant chain based in Florida, created and founded by Ed Burnett, a Canadian restaurant entrepreneur.",
"After opening his first WingHouse location, Burnett sought out investors to open additional WingHouse locations.",
"Burnett accepted investor Crawford Ker (a former National Football League player) to assist financing the expansion."
]
}
```
The average token count for the articles and the highlights are provided below:
| Feature | Mean Token Count |
| ---------- | ---------------- |
| Document | 1,612 |
| Summary | 40 |
### Data Fields
- `title`: a string, containing the original Wikipedia title.
- `document`: a list of sentences, containing the original content in the Wikipedia sections except the first abstract section.
- `aspect`: a string, containing the section name and its parent section names.
- `aspect_sents`: a list of indices, representing the sentences in the `aspect` section.
- `summary`: a list of sentences, the corresponding aspect-based summary for the document.
### Data Splits
The OASum dataset has 3 splits: _train_, _valid_, and _test_. Below are the statistics for the Version 1.0.0 of the dataset.
| Dataset Split | Number of Instances in Split |
| ------------- | ------------------------------------------- |
| Train | 3,523,986 |
| Validation | 111,578 |
| Test | 112,005 |
## Additional Information
### Licensing Information
The OASum Dataset version 1.0.0 is released under the [CC-BY-SA-3.0 License](https://en.wikipedia.org/wiki/Wikipedia:Text_of_the_Creative_Commons_Attribution-ShareAlike_3.0_Unported_License)
### Citation Information
```
@article{yang2022oasum,
title={Oasum: Large-scale open domain aspect-based summarization},
author={Yang, Xianjun and Song, Kaiqiang and Cho, Sangwoo and Wang, Xiaoyang and Pan, Xiaoman and Petzold, Linda and Yu, Dong},
journal={arXiv preprint arXiv:2212.09233},
year={2022}
}
``` |
tarteel-ai/quran-tafsir | ---
dataset_info:
features:
- name: en-ahmedali
dtype: string
- name: en-ahmedraza
dtype: string
- name: en-arberry
dtype: string
- name: en-asad
dtype: string
- name: en-daryabadi
dtype: string
- name: en-hilali
dtype: string
- name: en-itani
dtype: string
- name: en-maududi
dtype: string
- name: en-mubarakpuri
dtype: string
- name: en-pickthall
dtype: string
- name: en-qarai
dtype: string
- name: en-qaribullah
dtype: string
- name: en-sahih
dtype: string
- name: en-sarwar
dtype: string
- name: en-shakir
dtype: string
- name: en-transliterati
dtype: string
- name: en-wahiduddi
dtype: string
- name: en-yusufali
dtype: string
- name: surah
dtype: int64
- name: ayah
dtype: int64
splits:
- name: train
num_bytes: 16266291
num_examples: 6236
download_size: 9038013
dataset_size: 16266291
---
# Dataset Card for "quran-tafsir"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
AigizK/bashkir-russian-parallel-corpora | ---
language:
- ba
- ru
license: cc-by-4.0
task_categories:
- translation
dataset_info:
features:
- name: ba
dtype: string
- name: ru
dtype: string
- name: corpus
dtype: string
splits:
- name: train
num_bytes: 409240581
num_examples: 1093189
download_size: 195923641
dataset_size: 409240581
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "bashkir-russian-parallel-corpora"
### How the dataset was assembled.
1. find the text in two languages. it can be a translated book or an internet page (wikipedia, news site)
2. our algorithm tries to match Bashkir sentences with their translation in Russian
3. We give these pairs to people to check
```
@inproceedings{
title={Bashkir-Russian parallel corpora},
author={Iskander Shakirov, Aigiz Kunafin},
year={2023}
}
``` |
AigizK/mari-russian-parallel-corpora | ---
dataset_info:
features:
- name: mhr
dtype: string
- name: rus
dtype: string
splits:
- name: train
num_bytes: 79751117
num_examples: 386707
download_size: 39195604
dataset_size: 79751117
language:
- mhr
- ru
license: cc-by-4.0
task_categories:
- translation
---
# Dataset Card for "mari-russian-parallel-corpora"
```
@inproceedings{
title={Mari-Russian parallel corpora},
author={Andrei Chemyshev, Gennadii Sabantsev, Nadezhda Timofeeva, Vasilii Semenov},
year={2023}
}
```
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
zeusfsx/ukrainian-news | ---
license: unknown
task_categories:
- text-generation
language:
- uk
pretty_name: ukr-news
size_categories:
- 10M<n<100M
tags:
- news
---
# Ukrainian News Dataset
This is a dataset of news articles downloaded from various Ukrainian websites and Telegram channels.
The dataset contains 22 567 099 JSON objects (news), total size ~67GB each with the following fields:
```json
title: The title of the news article
text: The text of the news article, which may contain HTML tags(e.g., paragraphs, links, images, etc.)
url: The URL of the news article
datetime: The time of publication or when the article was parsed and added to the dataset
owner: The name of the website that published the news article
```
Count of news from websites: 16 022 416
Count of telegram posts: 6 544 683
The JSON objects are divided into parts, and the dataset is available for download via Hugging Face. The terms of use state that all data in this dataset is under the copyright of the owners of the respective websites.
## Accessing the Dataset
The dataset is available for download via the Hugging Face datasets library. You can install the library via pip:
```bash
pip install datasets
```
Once you have installed the library, you can load the dataset using the following code:
```python
from datasets import load_dataset
dataset = load_dataset('zeusfsx/ukrainian-news')
```
This will load the entire dataset into memory. If you prefer to load only a subset of the data, you can specify the split argument:
```python
# Load only the first 10,000 examples from the "train" split
dataset = load_dataset('zeusfsx/ukrainian-news', split='train[:10000]')
```
## Contacts
If you have any questions or comments about this dataset, please contact me at email [zeusfsxtmp@gmail.com]. I will do our best to respond to your inquiry as soon as possible.
## License
The dataset is made available under the terms of use specified by the owners of the respective websites. Please consult the individual websites for more information on their terms of use. |
renumics/dcase23-task2-enriched | ---
license: cc-by-4.0
task_categories:
- audio-classification
pretty_name: >-
Enriched DCASE 2023 Challenge Task 2 Dataset
size_categories:
- 1K<n<10K
tags:
- anomaly detection
- anomalous sound detection
- acoustic condition monitoring
- sound machine fault diagnosis
- machine learning
- unsupervised learning
- acoustic scene classification
- acoustic event detection
- acoustic signal processing
- audio domain shift
- domain generalization
---
# Dataset Card for the Enriched "DCASE 2023 Challenge Task 2 Dataset".
## Table of contents
[//]: # (todo: create new)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Explore the data with Spotlight](#explore-the-data-with-spotlight)
- [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)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [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)
- [Baseline system](#baseline-system)
- [Dataset Curators](#dataset-curators)
- [Licensing Information - Condition of use](#licensing-information---condition-of-use)
- [Citation Information (original)](#citation-information-original)
## Dataset Description
- **Homepage:** [Renumics Homepage](https://renumics.com/)
- **Homepage** [DCASE23 Task 2 Challenge](https://dcase.community/challenge2023/task-first-shot-unsupervised-anomalous-sound-detection-for-machine-condition-monitoring#evaluation)
- **Homepage:** [HF Dataset Creator](https://syoy.github.io/)
- **Original Dataset Upload (Dev)** [ZENODO: DCASE 2023 Challenge Task 2 Development Dataset](https://zenodo.org/record/7687464#.Y_9VtdLMLmE)
- **Paper** [MIMII DG](https://arxiv.org/abs/2205.13879)
- **Paper** [ToyADMOS2](https://arxiv.org/abs/2106.02369)
- **Paper** [First-shot anomaly detection for machine condition monitoring: A domain generalization baseline](https://arxiv.org/pdf/2303.00455.pdf)
### Dataset Summary
[Data-centric AI](https://datacentricai.org) principles have become increasingly important for real-world use cases. At [Renumics](https://renumics.com/) we believe that classical benchmark datasets and competitions should be extended to reflect this development.
This is why we are publishing benchmark datasets with application-specific enrichments (e.g. embeddings, baseline results, uncertainties, label error scores). We hope this helps the ML community in the following ways:
1. Enable new researchers to quickly develop a profound understanding of the dataset.
2. Popularize data-centric AI principles and tooling in the ML community.
3. Encourage the sharing of meaningful qualitative insights in addition to traditional quantitative metrics.
This dataset is an enriched version of the [dataset](https://zenodo.org/record/7690148#.ZAXsSdLMLmE) provided in the context of the [anomalous sound detection task](https://dcase.community/challenge2023/task-first-shot-unsupervised-anomalous-sound-detection-for-machine-condition-monitoring) of the [DCASE2023 challenge](https://dcase.community/challenge2023/). The enrichment include an embedding generated by a pre-trained [Audio Spectrogram Transformer](https://huggingface.co/docs/transformers/model_doc/audio-spectrogram-transformer#transformers.ASTFeatureExtractor) and results of the official challenge [baseline implementation](https://github.com/nttcslab/dase2023_task2_baseline_ae).
### DCASE23 Task2 Dataset
Once a year, the [DCASE community](https://dcase.community/) publishes a [challenge](https://dcase.community/challenge2023/) with several tasks in the context of acoustic event detection and classification. [Task 2 of this challenge](https://dcase.community/challenge2023/task-first-shot-unsupervised-anomalous-sound-detection-for-machine-condition-monitoring) deals with anomalous sound detection for machine condition monitoring. The original dataset is based on the [MIMII DG](https://arxiv.org/abs/2205.13879) and the [ToyADMOS2](https://arxiv.org/abs/2106.02369) datasets. Please cite the papers by [Harada et al.](https://arxiv.org/abs/2106.02369) and [Dohi et al.](https://arxiv.org/abs/2205.13879) if you use this dataset and the paper by [Harada et al.](https://arxiv.org/pdf/2303.00455.pdf) if you use the baseline results.
### Explore Dataset
The enrichments allow you to quickly gain insights into the dataset. The open source data curation tool Renumics Spotlight enables that with just a few lines of code:
Install datasets and Spotlight via [pip](https://packaging.python.org/en/latest/key_projects/#pip):
```python
!pip install renumics-spotlight datasets[audio]
```
> **_Notice:_** On Linux, non-Python dependency on libsndfile package must be installed manually. See [Datasets - Installation](https://huggingface.co/docs/datasets/installation#audio) for more information.
Load the dataset from huggingface in your notebook:
```python
import datasets
dataset = datasets.load_dataset("renumics/dcase23-task2-enriched", "dev", split="all", streaming=False)
```
Start exploring with a simple view that leverages embeddings to identify relevant data segments:
```python
from renumics import spotlight
df = dataset.to_pandas()
simple_layout = datasets.load_dataset_builder("renumics/dcase23-task2-enriched", "dev").config.get_layout(config="simple")
spotlight.show(df, dtype={'path': spotlight.Audio, "embeddings_ast-finetuned-audioset-10-10-0.4593": spotlight.Embedding}, layout=simple_layout)
```
You can use the UI to interactively configure the view on the data. Depending on the concrete taks (e.g. model comparison, debugging, outlier detection) you might want to leverage different enrichments and metadata.
In this example we focus on the valve class. We specifically look at normal data points that have high anomaly scores in both models. This is one example on how to find difficult example or edge cases:
```python
from renumics import spotlight
extended_layout = datasets.load_dataset_builder("renumics/dcase23-task2-enriched", "dev").config.get_layout(config="extended")
spotlight.show(df, dtype={'path': spotlight.Audio, "embeddings_ast-finetuned-audioset-10-10-0.4593": spotlight.Embedding}, layout=extended_layout)
```
## Using custom model results and enrichments
When developing your custom model you want to use different kinds of information from you model (e.g. embedding, anomaly scores etc.) to gain further insights into the dataset and the model behvior.
Suppose you have your model's embeddings for each datapoint as a 2D-Numpy array called `embeddings` and your anomaly score as a 1D-Numpy array called `anomaly_scores`. Then you can add this information to the dataset:
```python
df['my_model_embedding'] = embeddings
df['anomaly_score'] = anomaly_scores
```
Depending on your concrete task you might want to use different enrichments. For a good overview on great open source tooling for uncertainty quantification, explainability and outlier detection, you can take a look at our [curated list for open source data-centric AI tooling](https://github.com/Renumics/awesome-open-data-centric-ai) on Github.
You can also save your view configuration in Spotlight in a JSON configuration file by clicking on the respective icon:
For more information how to configure the Spotlight UI please refer to the [documentation](https://spotlight.renumics.com).
## Dataset Structure
### Data Instances
For each instance, there is a Audio for the audio, a string for the path, an integer for the section, a string for the d1p (parameter), a string for the d1v (value),
a ClassLabel for the label and a ClassLabel for the class.
```python
{'audio': {'array': array([ 0. , 0.00024414, -0.00024414, ..., -0.00024414,
0. , 0. ], dtype=float32),
'path': 'train/fan_section_01_source_train_normal_0592_f-n_A.wav',
'sampling_rate': 16000
}
'path': 'train/fan_section_01_source_train_normal_0592_f-n_A.wav'
'section': 1
'd1p': 'f-n'
'd1v': 'A'
'd2p': 'nan'
'd2v': 'nan'
'd3p': 'nan'
'd3v': 'nan'
'domain': 0 (source)
'label': 0 (normal)
'class': 1 (fan)
'dev_train_lof_anomaly': 0
'dev_train_lof_anomaly_score': 1.241023
'add_train_lof_anomaly': 1
'add_train_lof_anomaly_score': 1.806289
'ast-finetuned-audioset-10-10-0.4593-embeddings': [0.8152204155921936,
1.5862374305725098, ...,
1.7154160737991333]
}
```
The length of each audio file is 10 seconds.
### Data Fields
- `audio`: an `datasets.Audio`
- `path`: a string representing the path of the audio file inside the _tar.gz._-archive.
- `section`: an integer representing the section, see [Definition](#Description)
- `d*p`: a string representing the name of the d*-parameter
- `d*v`: a string representing the value of the corresponding d*-parameter
- `domain`: an integer whose value may be either _0_, indicating that the audio sample is from the _source_ domain, _1_, indicating that the audio sample is from the _target_.
- `class`: an integer as class label.
- `label`: an integer whose value may be either _0_, indicating that the audio sample is _normal_, _1_, indicating that the audio sample contains an _anomaly_.
- '[X]_lof_anomaly': an integer as anomaly indicator. The anomaly prediction is computed with the [Local Outlier Factor](https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.LocalOutlierFactor.html) algorithm based on the "[X]"-dataset.
- '[X]_lof_anomaly_score': a float as anomaly score. The anomaly score is computed with the [Local Outlier Factor](https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.LocalOutlierFactor.html) algorithm based on the "[X]"-dataset.
- `embeddings_ast-finetuned-audioset-10-10-0.4593`: an `datasets.Sequence(Value("float32"), shape=(1, 768))` representing audio embeddings that are generated with an [Audio Spectrogram Transformer](https://huggingface.co/docs/transformers/model_doc/audio-spectrogram-transformer#transformers.ASTFeatureExtractor).
### Data Splits
The development dataset has 2 splits: _train_ and _test_.
| Dataset Split | Number of Instances in Split | Source Domain / Target Domain Samples |
| ------------- |------------------------------|---------------------------------------|
| Train | 7000 | 6930 / 70 |
| Test | 1400 | 700 / 700 |
The additional training dataset has 1 split: _train_.
| Dataset Split | Number of Instances in Split | Source Domain / Target Domain Samples |
| ------------- |------------------------------|---------------------------------------|
| Train | 7000 | 6930 / 70 |
The evaluation dataset has 1 split: _test_.
| Dataset Split | Number of Instances in Split | Source Domain / Target Domain Samples |
|---------------|------------------------------|---------------------------------------|
| Test | 1400 | ? |
## Dataset Creation
The following information is copied from the original [dataset upload on zenodo.org](https://zenodo.org/record/7690148#.ZAXsSdLMLmE)
### Curation Rationale
This dataset is the "development dataset" for the [DCASE 2023 Challenge Task 2 "First-Shot Unsupervised Anomalous Sound Detection for Machine Condition Monitoring"](https://dcase.community/challenge2023/task-unsupervised-anomalous-sound-detection-for-machine-condition-monitoring).
The data consists of the normal/anomalous operating sounds of seven types of real/toy machines. Each recording is a single-channel 10-second audio that includes both a machine's operating sound and environmental noise. The following seven types of real/toy machines are used in this task:
- ToyCar
- ToyTrain
- Fan
- Gearbox
- Bearing
- Slide rail
- Valve
The "additional training data" and "evaluation data" datasets contain the following classes:
- bandsaw
- grinder
- shaker
- ToyDrone
- ToyNscale
- ToyTank
- Vacuum
### Source Data
#### Definition
We first define key terms in this task: "machine type," "section," "source domain," "target domain," and "attributes.".
- "Machine type" indicates the type of machine, which in the development dataset is one of seven: fan, gearbox, bearing, slide rail, valve, ToyCar, and ToyTrain.
- A section is defined as a subset of the dataset for calculating performance metrics.
- The source domain is the domain under which most of the training data and some of the test data were recorded, and the target domain is a different set of domains under which some of the training data and some of the test data were recorded. There are differences between the source and target domains in terms of operating speed, machine load, viscosity, heating temperature, type of environmental noise, signal-to-noise ratio, etc.
- Attributes are parameters that define states of machines or types of noise.
#### Description
This dataset consists of seven machine types. For each machine type, one section is provided, and the section is a complete set of training and test data. For each section, this dataset provides (i) 990 clips of normal sounds in the source domain for training, (ii) ten clips of normal sounds in the target domain for training, and (iii) 100 clips each of normal and anomalous sounds for the test. The source/target domain of each sample is provided. Additionally, the attributes of each sample in the training and test data are provided in the file names and attribute csv files.
#### Recording procedure
Normal/anomalous operating sounds of machines and its related equipment are recorded. Anomalous sounds were collected by deliberately damaging target machines. For simplifying the task, we use only the first channel of multi-channel recordings; all recordings are regarded as single-channel recordings of a fixed microphone. We mixed a target machine sound with environmental noise, and only noisy recordings are provided as training/test data. The environmental noise samples were recorded in several real factory environments. We will publish papers on the dataset to explain the details of the recording procedure by the submission deadline.
### Supported Tasks and Leaderboards
Anomalous sound detection (ASD) is the task of identifying whether the sound emitted from a target machine is normal or anomalous. Automatic detection of mechanical failure is an essential technology in the fourth industrial revolution, which involves artificial-intelligence-based factory automation. Prompt detection of machine anomalies by observing sounds is useful for monitoring the condition of machines.
This task is the follow-up from DCASE 2020 Task 2 to DCASE 2022 Task 2. The task this year is to develop an ASD system that meets the following four requirements.
**1. Train a model using only normal sound (unsupervised learning scenario)**
Because anomalies rarely occur and are highly diverse in real-world factories, it can be difficult to collect exhaustive patterns of anomalous sounds. Therefore, the system must detect unknown types of anomalous sounds that are not provided in the training data. This is the same requirement as in the previous tasks.
**2. Detect anomalies regardless of domain shifts (domain generalization task)**
In real-world cases, the operational states of a machine or the environmental noise can change to cause domain shifts. Domain-generalization techniques can be useful for handling domain shifts that occur frequently or are hard-to-notice. In this task, the system is required to use domain-generalization techniques for handling these domain shifts. This requirement is the same as in DCASE 2022 Task 2.
**3. Train a model for a completely new machine type**
For a completely new machine type, hyperparameters of the trained model cannot be tuned. Therefore, the system should have the ability to train models without additional hyperparameter tuning.
**4. Train a model using only one machine from its machine type**
While sounds from multiple machines of the same machine type can be used to enhance detection performance, it is often the case that sound data from only one machine are available for a machine type. In such a case, the system should be able to train models using only one machine from a machine type.
## 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
### Baseline system
The baseline system is available on the Github repository [dcase2023_task2_baseline_ae](https://github.com/nttcslab/dase2023_task2_baseline_ae).The baseline systems provide a simple entry-level approach that gives a reasonable performance in the dataset of Task 2. They are good starting points, especially for entry-level researchers who want to get familiar with the anomalous-sound-detection task.
### Dataset Curators
[//]: # (todo)
[More Information Needed]
### Licensing Information - Condition of use
This is a feature/embeddings-enriched version of the "DCASE 2023 Challenge Task 2 Development Dataset".
The [original dataset](https://dcase.community/challenge2023/task-first-shot-unsupervised-anomalous-sound-detection-for-machine-condition-monitoring#audio-datasets) was created jointly by **Hitachi, Ltd.** and **NTT Corporation** and is available under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) license.
### Citation Information (original)
If you use this dataset, please cite all the following papers. We will publish a paper on DCASE 2023 Task 2, so pleasure make sure to cite the paper, too.
- Kota Dohi, Tomoya Nishida, Harsh Purohit, Ryo Tanabe, Takashi Endo, Masaaki Yamamoto, Yuki Nikaido, and Yohei Kawaguchi. MIMII DG: sound dataset for malfunctioning industrial machine investigation and inspection for domain generalization task. In arXiv e-prints: 2205.13879, 2022. [[URL](https://arxiv.org/abs/2205.13879)]
- Noboru Harada, Daisuke Niizumi, Daiki Takeuchi, Yasunori Ohishi, Masahiro Yasuda, and Shoichiro Saito. ToyADMOS2: another dataset of miniature-machine operating sounds for anomalous sound detection under domain shift conditions. In Proceedings of the 6th Detection and Classification of Acoustic Scenes and Events 2021 Workshop (DCASE2021), 1โ5. Barcelona, Spain, November 2021. [[URL](https://dcase.community/documents/workshop2021/proceedings/DCASE2021Workshop_Harada_6.pdf)]
- Noboru Harada, Daisuke Niizumi, Daiki Takeuchi, Yasunori Ohishi, and Masahiro Yasuda. First-shot anomaly detection for machine condition monitoring: a domain generalization baseline. In arXiv e-prints: 2303.00455, 2023. [[URL](https://arxiv.org/abs/2303.00455.pdf)]
```
@dataset{kota_dohi_2023_7882613,
author = {Kota Dohi and
Keisuke Imoto and
Noboru Harada and
Daisuke Niizumi and
Yuma Koizumi and
Tomoya Nishida and
Harsh Purohit and
Takashi Endo and
Yohei Kawaguchi},
title = {DCASE 2023 Challenge Task 2 Development Dataset},
month = mar,
year = 2023,
publisher = {Zenodo},
version = {3.0},
doi = {10.5281/zenodo.7882613},
url = {https://doi.org/10.5281/zenodo.7882613}
}
``` |
philschmid/sharegpt-raw | ---
license: other
duplicated_from: jeffwan/sharegpt_vicuna
---
## Prepraration
```
pip3 install -r requirements.txt
```
## Data Cleaning
1. merge two raw json files and json beautify the merged file
```
python merge.py sharegpt_90k_raw_dataset/sg_90k_part1.json sharegpt_90k_raw_dataset/sg_90k_part2.json sharegpt_20230401_html_unformatted.json
python pretty_json.py --in sharegpt_20230401_html_unformatted.json --out sharegpt_20230401_html.json
```
2. (Optional) Verify the json file
```
if jq empty sharegpt_20230401_html.json 2>/dev/null; then
echo "JSON is valid"
else
echo "JSON is invalid"
fi
jq length sharegpt_90k_raw_dataset/sg_90k_part1.json
jq length sharegpt_90k_raw_dataset/sg_90k_part2.json
jq length sharegpt_20230401_html.json
```
3. clean data - remove html tags etc
```
python3 clean_sharegpt.py --in sharegpt_20230401_html.json --out sharegpt_20230401_clean.json
....
100%|โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ| 90665/90665 [06:32<00:00, 230.98it/s]
total: 90665, skip: 13745, new: 76920
```
4. Filter dataset by language
```
python3 optional_clean.py --in sharegpt_20230401_clean.json --out sharegpt_20230401_clean_lang_zh.json --lang zh
....
return 6240 out of 76920, start dump ...
python3 optional_clean.py --in sharegpt_20230401_clean.json --out sharegpt_20230401_clean_lang_en.json --lang en
...
return 55413 out of 76920, start dump ...
```
> Note: the code itself doesn't support languange list, I didn't change the code for adpation. You can change the code to support more languages. Instead, I just filter two languages I need and merge the `sharegpt_20230401_clean_lang_zh.json` and `sharegpt_20230401_clean_lang_en.json` into `sharegpt_20230401_clean_lang.json`.
5. Split the long conversation
```
python3 split_long_conversation.py --in sharegpt_20230401_clean_lang.json --out sharegpt_20230401_clean_lang_split.json --model-name /home/ubuntu/llama-13b-hf/
...
total: 61653, new: 126032
```
Ok, now we have the cleaned dataset `sharegpt_20230401_clean_lang_split.json` which should be used for finetuning.
|
NiGuLa/Russian_Sensitive_Topics | ---
language:
- ru
tags:
- toxic comments classification
license: cc
task_categories:
- text-classification
size_categories:
- 10K<n<100K
---
## General concept of the model
Sensitive topics are such topics that have a high chance of initiating a toxic conversation: homophobia, politics, racism, etc. This dataset uses 18 topics.
More details can be found [in this article ](https://www.aclweb.org/anthology/2021.bsnlp-1.4/) presented at the workshop for Balto-Slavic NLP at the EACL-2021 conference.
This paper presents the first version of this dataset. Here you can see the last version of the dataset which is significantly larger and also properly filtered.
## Licensing Information
[Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License][cc-by-nc-sa].
[![CC BY-NC-SA 4.0][cc-by-nc-sa-image]][cc-by-nc-sa]
[cc-by-nc-sa]: http://creativecommons.org/licenses/by-nc-sa/4.0/
[cc-by-nc-sa-image]: https://i.creativecommons.org/l/by-nc-sa/4.0/88x31.png
## Citation
If you find this repository helpful, feel free to cite our publication:
```
@inproceedings{babakov-etal-2021-detecting,
title = "Detecting Inappropriate Messages on Sensitive Topics that Could Harm a Company{'}s Reputation",
author = "Babakov, Nikolay and
Logacheva, Varvara and
Kozlova, Olga and
Semenov, Nikita and
Panchenko, Alexander",
booktitle = "Proceedings of the 8th Workshop on Balto-Slavic Natural Language Processing",
month = apr,
year = "2021",
address = "Kiyv, Ukraine",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2021.bsnlp-1.4",
pages = "26--36",
abstract = "Not all topics are equally {``}flammable{''} in terms of toxicity: a calm discussion of turtles or fishing less often fuels inappropriate toxic dialogues than a discussion of politics or sexual minorities. We define a set of sensitive topics that can yield inappropriate and toxic messages and describe the methodology of collecting and labelling a dataset for appropriateness. While toxicity in user-generated data is well-studied, we aim at defining a more fine-grained notion of inappropriateness. The core of inappropriateness is that it can harm the reputation of a speaker. This is different from toxicity in two respects: (i) inappropriateness is topic-related, and (ii) inappropriate message is not toxic but still unacceptable. We collect and release two datasets for Russian: a topic-labelled dataset and an appropriateness-labelled dataset. We also release pre-trained classification models trained on this data.",
}
``` |
liyucheng/zhihu_rlhf_3k | ---
license: cc-by-2.0
---
|
hugfaceguy0001/retarded_bar | ---
license: openrail
task_categories:
- text-generation
language:
- zh
pretty_name: ๅผฑๆบๅง็ฌ่ฏๆฐๆฎ้
size_categories:
- n<1K
configs:
- config_name: statement
data_files: retarded_bar.jsonl
- config_name: question
data_files: retarded_bar_qa.jsonl
---
# ๅผฑๆบๅง็ฌ่ฏๆฐๆฎ้
ๅผฑๆบๅงๆฏ็พๅบฆ่ดดๅงไธญ็ไธไธช้ๅธธๅๆฌข่ฟ็่ฎบๅ๏ผไปฅๅไฝ็ญๅฐ็ฒพๆ็ๅท็ฌ่ฏ่้ปๅใ่ฟไบ็ฌ่ฏ้ๅธธ้็จๅๅ
ณ่ฏญใไธๅฏปๅธธ็ๆญๅฅใไธๅ็็้ป่พ็ญๅไฝๆๆณใๅณไฝฟๆฏ็ฎๅๆๅ
่ฟ็่ฏญ่จๆจกๅ๏ผไน้พไปฅๅฎๅ
จ็่งฃๅผฑๆบๅง็็ฌ่ฏใ
[ๅผฑๆบๅง](https://tieba.baidu.com/f?ie=utf-8&kw=%E5%BC%B1%E6%99%BA)
ๆไปไบ่็ฝไธๆถ้ไบไธไบๅผฑๆบๅง็็ฌ่ฏ๏ผๅ
ฑ100ๆก๏ผๅ
ถไธญ45ๆกๆฏ้่ฟฐๅฅ๏ผ55ๆกๆฏ้ฎๅฅใๆ็ปๅไบบๅทฅๅ่ฏญ่จๆจกๅๅฏน่ฟไบ็ฌ่ฏ่ฟ่กไบไธไบ่งฃๆ๏ผๅนถๅถไฝไบ่ฟไธชๅฐๅๆฐๆฎ้ใ
## ้่ฟฐๅฅ็ฌ่ฏ
้่ฟฐๅฅ็ฌ่ฏ้ๅธธไปฅๅฅๅท็ปๅฐพ๏ผไธๅฎนๆ่ขซ่ฏญ่จๆจกๅ่ฏฏ่งฃไธบๆญฃๅธธ็้ฎ้ขใ
ไพๅฆ๏ผโๅบไบบๅคดๅฐๅธธๅนด็ไบงไบบๅคดใโ
## ้ฎๅฅ็ฌ่ฏ
้ฎๅฅ็ฌ่ฏๅ
ทๆไธๅฎ็่ฟทๆๆง๏ผๅฏ่ฝไผๅฏผ่ด่ฏญ่จๆจกๅๆ ๆณๅคๆญๅฎไปฌๆฏๆญฃๅธธ็้ฎ้ข่ฟๆฏๅผ็ฉ็ฌใ
ไพๅฆ๏ผโ่็่ณๆบๅไบ๏ผๅบ่ฏฅๆพ็็งๅป็่ฟๆฏ่ณ็งๅป็๏ผโ
## ๆไปถๆ ผๅผ
ๆฌๆฐๆฎ้ๅ
ๆฌไธคไธช้จๅใ
### retarded_bar.jsonl
retarded_bar.jsonlๆฏ้่ฟฐๆง็ฌ่ฏๆฐๆฎ้๏ผไปฅjsonlๆ ผๅผๅญๅจ๏ผๆฏ่ก้ฝๆฏไธไธชjsonๅญๅ
ธ๏ผๅ
ๆฌๅบๅท`id`๏ผๅๆ`text`๏ผ็ฌ็น่งฃๆ`analysis`๏ผๅๅ
ณ่ฏญ`pun`๏ผไฝ่
็ฑปๅ`author_type`ไบไธชๅญๆฎต๏ผๅ
ถไธญ๏ผ
- ๅบๅท`id`ๆฏๆฐๅญ๏ผ่กจ็คบ็ฌ่ฏ็็ผๅท
- ๅๆ`text`ๆฏๆๆฌ๏ผ่กจ็คบ็ฌ่ฏ็ๅๆ๏ผ็ฑโๅผฑๆบๅงโ็คพๅบๆๅๅไฝ๏ผๆฌไบบๅจไบ่็ฝไธๆๅจๆถ้่ๆใ
- ็ฌ็น่งฃๆ`analysis`ๆฏๆๆฌ๏ผ่กจ็คบ็ฌ่ฏ็็ฌ็น่งฃๆ๏ผๅคง้จๅ่งฃๆ็ฑๆฌไบบๅไฝ๏ผไนๆไธๅฐ้จๅๆฏ็จ่ฏญ่จๆจกๅ็ๆ็ใๅจไฝ่
็ฑปๅ`author_type`ไธญไฝ็ฐไบ่ฟไธชๅ
ๅฎนใ
- ๅๅ
ณ่ฏญ`pun`ๆฏๆๆฌๅ่กจ๏ผ่กจ็คบ็ฌ่ฏไธญๅ
ๅซ็ๅๅ
ณ่ฏญ๏ผ็ฑๆฌไบบๆพๅฐใไธไธช็ฌ่ฏๅฏ่ฝๅ
ๅซไธๆญขไธไธชๅๅ
ณ่ฏญ๏ผไนๅฏ่ฝไธๅ
ๅซๅๅ
ณ่ฏญใ
- ไฝ่
็ฑปๅ`author_type`ๆฏๆๆฌ๏ผ่กจ็คบ็ฌ็น่งฃๆ`analysis`็ไฝ่
็ฑปๅ๏ผ่ไธๆฏ็ฌ่ฏๅๆ`text`็ไฝ่
็ฑปๅ๏ผ็ฎๅๆ`human`ๅ`ai`ไธคไธชๅผใ
### retarded_bar_qa.jsonl
retarded_bar_qa.jsonlๆฏๆ้ฎๆง็ฌ่ฏๆฐๆฎ้๏ผไปฅjsonlๆ ผๅผๅญๅจ๏ผๆฏ่ก้ฝๆฏไธไธชjsonๅญๅ
ธ๏ผๅ
ๆฌๅบๅท`id`๏ผๅๆ`text`๏ผๅๅค`answer`๏ผไฝ่
็ฑปๅ`author_type`ๅไธชๅญๆฎต๏ผๅ
ถไธญ๏ผ
- ๅบๅท`id`ๆฏๆฐๅญ๏ผ่กจ็คบ็ฌ่ฏ็็ผๅท
- ๅๆ`text`ๆฏๆๆฌ๏ผ่กจ็คบ็ฌ่ฏ็ๅๆ๏ผ็ฑโๅผฑๆบๅงโ็คพๅบๆๅๅไฝ๏ผๆฌไบบๅจไบ่็ฝไธๆๅจๆถ้่ๆใ
- ๅๅค`analysis`ๆฏๆๆฌ๏ผ่กจ็คบๆ้ฎๅ็ฌ่ฏ็ๅ็ๅๅคใๆฌไบบๅฎไน็ๅ็ๅๅคๆฏๅบ่ฏฅ่ฎฉๅฏนๆน็ฅ้่ชๅทฑๅทฒ็ปๅฏ่งๅฐๆ้ฎ็ๅนฝ้ปๆง๏ผไฝไปไธๅคฑ็คผ่ฒ๏ผไธๆไพๅ็กฎ็ไบๅฎๆงไฟกๆฏ็ๅๅคใๅ็ๅๅคๆ็็ฑๆฌไบบๅไฝ๏ผไนๆ็ๆฏ็จ่ฏญ่จๆจกๅ็ๆ็ใๅจไฝ่
็ฑปๅ`author_type`ไธญไฝ็ฐไบ่ฟไธชๅ
ๅฎนใ
- ไฝ่
็ฑปๅ`author_type`ๆฏๆๆฌ๏ผ่กจ็คบๅๅค`answer`็ไฝ่
็ฑปๅ๏ผ่ไธๆฏ็ฌ่ฏๅๆ`text`็ไฝ่
็ฑปๅ๏ผ็ฎๅๆ`human`ๅ`ai`ไธคไธชๅผใ
## ไฝฟ็จๆนๅผ
ๅปบ่ฎฎไฝฟ็จPython็jsonlinesๅบๆHugging Face็datasetsๅบ่ฏปๅๆฌๆฐๆฎ้ใไฝฟ็จ่ฟไบๅบๅฏไปฅ่ฝปๆพๅฐ่ฏปๅjsonlๆ ผๅผ็ๆไปถๅนถ่ฟ่กๅ็ปญๅค็๏ผไพๅฆๆๅปบ่ฎญ็ป้ๆๆต่ฏ้ใ่ฎญ็ปๆๆต่ฏ่ฏญ่จๆจกๅ็ญใไพๅฆ๏ผไฝฟ็จjsonlinesๅบๅฏไปฅๆ่ก่ฏปๅjsonlๆ ผๅผ็ๆไปถ๏ผๅฆไธๆ็คบ๏ผ
```python
import jsonlines
with jsonlines.open('retarded_bar.jsonl') as reader:
for obj in reader:
# ๅฏนๆฏไธชๅฏน่ฑก่ฟ่กๅค็
print(obj)
```
## ๅฑ้ๆง
1. ็ฑไบๆฌ้กน็ฎๅชๆๆฌไบบไธไธชไบบๅไธ๏ผ่่ฟ็ฑปๆฐๆฎๆ ๆณจ้พๅบฆๆฏ่พๅคง๏ผ่ชๅจๅ็จๅบฆไฝ๏ผ้่ฆๆฏ่พๅค็ไบบๅ๏ผๆไปฅๆฐๆฎ้ๅฎน้่พๅฐใ
2. ๆฌไบบๆๅญ่กจ่พพ่ฝๅๆ้๏ผๅฏ่ฝๆ ๆณๅ็กฎ็ๅจๅฐ่กจ่พพ็ฌ็น่งฃๆ๏ผไนๅฏ่ฝๆ ๆณๅไฝๆฏ่พ้ซ่ดจ้็ๅ็ญใๅ ๆญค๏ผ่ฏฅๆฐๆฎ้ไธญ็ไธไบ่งฃๆๅๅ็ญๅฏ่ฝๅนถไธๆฏๆไฝณ็ใ
3. ๆฌๆฐๆฎ้็ๆฐๆฎๆฅๆบไบไบ่็ฝ๏ผๅฏ่ฝๅญๅจ็ๆ้ฎ้ขใๅ ๆญค๏ผไฝฟ็จ่ฏฅๆฐๆฎ้ๆถ้่ฆๆณจๆ็ๆ้ฎ้ข๏ผๅนถ้ตๅฎ็ธๅ
ณๆณๅพๆณ่งใ
4. ็ฑไบๅผฑๆบๅง็็ฌ่ฏๅคงๅคๆฏๅบไบไธญๆ่ฏญๅข็๏ผๅ ๆญค่ฏฅๆฐๆฎ้ๅฏ่ฝไธ้็จไบๅ
ถไป่ฏญ่จ็็ฌ่ฏๅคๆญใ
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OdiaGenAI/gpt-teacher-roleplay-odia-3k | ---
license: cc-by-nc-sa-4.0
task_categories:
- text-generation
language:
- or
pretty_name: GPT-Teacher-RolePlay-Odia-3K
size_categories:
- 1K<n<10K
---
# Dataset Card for GPT-Teacher-RolePlay-Odia-3K
## Dataset Description
- **Homepage: https://www.odiagenai.org/**
- **Repository: https://github.com/shantipriyap/OdiaGenAI**
- **Point of Contact: Shantipriya Parida, and Sambit Sekhar**
### Dataset Summary
This dataset is the Odia-translated version of the GPT-Teacher-RolePlay 3K instruction set. In this dataset both English and Odia instruction, input, and output strings are available.
### Supported Tasks and Leaderboards
Large Language Model (LLM)
### Languages
Odia
## Dataset Structure
JSON
### Data Fields
instruction (string)
english_instruction (string)
input (string)
english_input (string)
output (string)
english_output (string)
### Licensing Information
This work is licensed under a
[Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License][cc-by-nc-sa].
[![CC BY-NC-SA 4.0][cc-by-nc-sa-image]][cc-by-nc-sa]
[cc-by-nc-sa]: http://creativecommons.org/licenses/by-nc-sa/4.0/
[cc-by-nc-sa-image]: https://licensebuttons.net/l/by-nc-sa/4.0/88x31.png
[cc-by-nc-sa-shield]: https://img.shields.io/badge/License-CC%20BY--NC--SA%204.0-lightgrey.svg
### Citation Information
If you find this repository useful, please consider giving ๐ and citing:
```
@misc{OdiaGenAI,
author = {Shantipriya Parida and Sambit Sekhar and Subhadarshi Panda and Soumendra Kumar Sahoo and Swateek Jena and Abhijeet Parida and Arghyadeep Sen and Satya Ranjan Dash and Deepak Kumar Pradhan},
title = {OdiaGenAI: Generative AI and LLM Initiative for the Odia Language},
year = {2023},
publisher = {Hugging Face},
journal = {Hugging Face repository},
howpublished = {\url{https://huggingface.co/OdiaGenAI}},
}
```
### Contributions
- Shantipriya Parida
- Sambit Sekhar |
TheMrguiller/ScienceQA | ---
dataset_info:
features:
- name: image
dtype: image
- name: question
dtype: string
- name: choices
dtype: string
- name: answer
dtype: string
- name: solution
dtype: string
- name: CTH
dtype: bool
splits:
- name: train
num_bytes: 548834431.966
num_examples: 16966
- name: test
num_bytes: 135169478.352
num_examples: 4242
download_size: 621545899
dataset_size: 684003910.318
task_categories:
- question-answering
- visual-question-answering
language:
- en
tags:
- code
size_categories:
- 100B<n<1T
---
# Dataset Card for "ScienceQA"
## Dataset Description
- **Homepage:** https://scienceqa.github.io/
- **Repository:** https://scienceqa.github.io/#dataset
- **Paper:** https://arxiv.org/abs/2209.09513
- **Leaderboard:**
- **Point of Contact:** https://lupantech.github.io/
### Dataset Summary
ScienceQA is collected from elementary and high school science curricula, and contains 21,208 multimodal multiple-choice science questions. Out of the questions in ScienceQA, 10,332 (48.7%) have an image context, 10,220 (48.2%) have a text context, and 6,532 (30.8%) have both. Most questions are annotated with grounded lectures (83.9%) and detailed explanations (90.5%). The lecture and explanation provide general external knowledge and specific reasons, respectively, for arriving at the correct answer. To the best of our knowledge, ScienceQA is the first large-scale multimodal dataset that annotates lectures and explanations for the answers.
ScienceQA, in contrast to previous datasets, has richer domain diversity from three subjects: natural science, language science, and social science. Questions in each subject are categorized first by the topic (Biology, Physics, Chemistry, etc.), then by the category (Plants, Cells, Animals, etc.), and finally by the skill (Classify fruits and vegetables as plant parts, Identify countries of Africa, etc.). ScienceQA features 26 topics, 127 categories, and 379 skills that cover a wide range of domains.
### Supported Tasks and Leaderboards
The dataset is prepared to used it for visual question-answering.
### Languages
The dataset is in english
## Dataset Structure
### Data Fields
- `image`: This field has the image, which is the context given to the model.
- `question`: This field incorporates the question that has to answer the model from the image context.
- `choices`: Multiple choice selection.
- `answer`: The answer from the multiple choice.
- `solution`: The chain of thought process of the solution selection.
- `CTH`: A flag that indicates whether it doesnt have chain of thought in that row.
### Data Splits
The dataset is split in 80% train and 20% test.
## Considerations for Using the Data
The dataset is well balanced in order to get really got result when used in multimodal models.
## Additional Information
### Dataset Curators
The curators of this dataset where the students from the Masters degree in Computation and Inteligent Systems from University of Deusto.
### Citation Information
```
@inproceedings{lu2022learn,
title={Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question Answering},
author={Lu, Pan and Mishra, Swaroop and Xia, Tony and Qiu, Liang and Chang, Kai-Wei and Zhu, Song-Chun and Tafjord, Oyvind and Clark, Peter and Ashwin Kalyan},
booktitle={The 36th Conference on Neural Information Processing Systems (NeurIPS)},
year={2022}
}
``` |
OdiaGenAI/all_combined_bengali_252k | ---
license: cc-by-nc-sa-4.0
task_categories:
- text-generation
language:
- bn
pretty_name: all_combined_bengali_252K
size_categories:
- 100K<n<1M
---
# Dataset Card for all_combined_bengali_252K
## Dataset Description
- **Homepage: https://www.odiagenai.org/**
- **Repository: https://github.com/OdiaGenAI**
- **Point of Contact: Shantipriya Parida, and Sambit Sekhar**
### Dataset Summary
This dataset is a mix of Bengali instruction sets translated from open-source instruction sets:
* Dolly,
* Alpaca,
* ChatDoctor,
* Roleplay
* GSM
In this dataset Bengali instruction, input, and output strings are available.
### Supported Tasks and Leaderboards
Large Language Model (LLM)
### Languages
Bengali
## Dataset Structure
JSON
### Data Fields
output (string)
data_source (string)
instruction (string)
input (string)
### Licensing Information
This work is licensed under a
[Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License][cc-by-nc-sa].
[![CC BY-NC-SA 4.0][cc-by-nc-sa-image]][cc-by-nc-sa]
[cc-by-nc-sa]: http://creativecommons.org/licenses/by-nc-sa/4.0/
[cc-by-nc-sa-image]: https://licensebuttons.net/l/by-nc-sa/4.0/88x31.png
[cc-by-nc-sa-shield]: https://img.shields.io/badge/License-CC%20BY--NC--SA%204.0-lightgrey.svg
### Citation Information
If you find this repository useful, please consider giving ๐ and citing:
```
@misc{OdiaGenAI,
author = {Shantipriya Parida and Sambit Sekhar and Guneet Singh Kohli and Arghyadeep Sen and Shashikanta Sahoo},
title = {Bengali Instruction Set},
year = {2023},
publisher = {Hugging Face},
journal = {Hugging Face repository},
howpublished = {\url{https://huggingface.co/OdiaGenAI}},
}
```
### Contributions
- Shantipriya Parida
- Sambit Sekhar
- Guneet Singh Kohli
- Arghyadeep Sen
- Shashikanta Sahoo |
laion/strategic_game_chess | ---
tags:
- game
pretty_name: The Chess Dataset
license: cc-by-4.0
---
# Chess
> Recent advancements in artificial intelligence (AI) underscore the progress of reasoning and planning shown by recent generalist machine learning (ML) models. The progress can be boosted by datasets that can further boost these generic capabilities when used for training foundation models of various kind. This research initiative has generated extensive synthetic datasets from complex games โ chess, Rubik's Cube, and mazes โ to study facilitation and the advancement of these critical generic skills in AI models.
This dataset contains 3.2 billion games, equating to approximately 608 billion individual moves.
it is generated through self-play by Stockfish engine using Fugaku and we add initial moves to expand its diversity.
Each game has three columns: 'Moves', 'Termination' and 'Result',
- 'Move': recorded chess moves of the whole game.
- 'Termination': include CHECKMATE, INSUFFICIENT_MATERIAL, ... etc.
- Please check this for detail information
https://python-chess.readthedocs.io/en/latest/core.html#chess.Outcome.termination
- 'Result': result of this game, 1-0, 1/2-1/2, 0-1.
### Call for Collaboration
We invite interested researchers and ML practitioners to explore these datasets' potential. Whether training GPT models from scratch or fine-tuning pre-existing models, we encourage the exploration of various pre-training and fine-tuning strategies using these game-based datasets standalone or as enhancement of other already composed large-scale data.
Our team is prepared to assist in securing necessary GPU resources for these explorations. We are particularly interested in collaborators eager to pre-train models of small to medium scale on our game data, subsequently transition to standard text-based training, and then perform comparative analyses against models of similar architecture trained exclusively on text data.
Conclusively, this initiative marks a significant stride toward intricate problem-solving and strategic planning in AI, extending an open invitation to the research community for collaborative advancement in this domain. |
alxfgh/ChEMBL_Drug_Instruction_Tuning | ---
task_categories:
- question-answering
language:
- en
pretty_name: ChEMBL Drug Instruction Tuning
---
# Dataset Card for ChEMBL Drug Instruction Tuning
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1).
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] |
teleprint-me/phi-1 | ---
title: 'Phi-1 Model Dataset'
date: '2023-07-03'
license: cc-by-nc-sa-3.0
---
## Dataset Description
- **Homepage:** [teleprint.me](https://teleprint.me)
- **Repository:** [phi-1](https://huggingface.co/datasets/teleprint-me/phi-1)
- **Paper:** [2306.11644v1](https://arxiv.org/abs/2306.11644v1)
- **Leaderboard:** [Link to the leaderboard]
- **Point of Contact:** [aberrio@teleprint.me](aberrio@teleprint.me)
### Dataset Summary
This dataset is created for training the phi-1 model, based on the paper
"Textbooks are All You Need". It contains high-quality data derived from various
textbooks, transformed and synthesized using OpenAI's GPT-3.5 and GPT-4 models.
For optimal results, it is recommended to train models with the following
parameters and sequence lengths:
- For a model with 350M parameters, use a sequence length of 2048.
- For a model with 700M parameters, use a sequence length of 4096.
- For a model with 1.3B parameters, use a sequence length of 8096.
Please note that the dataset is currently in its initial phase of planning and
collection. The process involves preparing the data, extracting it, formatting
it, chunking it, and preparing it for synthesis. Scripts for preparing and
processing the data for the model will be developed. Once the data is generated,
it will undergo a review and revision process to ensure its quality and
relevance.
These recommendations and notes are based on the dataset creator's initial plans
and may be subject to change as the project progresses.
**NOTE**: Due to the nature of this dataset, it cannot be released without
obtaining permissions from the respective publishers and/or authors. If you are
an author or publisher and have any concerns about this repository, please feel
free to email me.
If you are an author or publisher and would like to grant permission for the use
of your work, your support would be greatly appreciated. Please note that in
order for the dataset to be released, permissions would need to be unanimous
from all involved parties.
In the absence of such permissions, I will respect the copyrights of the
copyrighted materials and exercise my right to Fair Use with my own physical
property for personal use.
**This dataset is NOT intended for commercial purposes**. Its primary purpose is
for research in machine learning and AI software development. If a model is
created using this dataset, it will be shared under the same license.
Any proceeds derived from donations will be primarily used for the development
of the dataset and the model.
### Supported Tasks and Leaderboards
- `text-generation`: The dataset can be used to train a model for chat-like text
generation, more specifically, for generating explanations and examples in the
context of arithmetic, algebra, geometry, trigonometry, calculus, algorithms
and data structures, design patterns, and the python programming language.
### Languages
The text in the dataset is in English.
## Dataset Structure
### Data Instances
A data instance consists of a dialogue between a user and an assistant,
discussing a topic in arithmetic, algebra, geometry, trigonometry, calculus,
algorithms and data structures, design patterns, or the Python programming
language. The dialogue is structured as a list of turns, each turn containing
the role ("user" or "assistant") and the content of the turn.
### Data Fields
- `role`: a string indicating the role of the speaker in the dialogue ("system",
"user", "assistant", "function").
- `content`: a string containing the content of the speaker's turn in the
dialogue.
### Data Splits
The dataset is split into a training set, a validation set, and a test set. The
exact sizes and proportions of these splits will depend on the final size of the
dataset.
## Dataset Creation
### Curation Rationale
The dataset is being created to train a model capable of generating explanations
and examples in the context of various mathematical and computer science topics.
The goal is to create an AI assistant that can provide clear, accurate, and
pedagogically sound responses to user queries on these topics.
### Source Data
#### Initial Data Collection and Normalization
The data is collected from a variety of textbooks covering arithmetic, algebra,
geometry, trigonometry, calculus, algorithms and data structures, design
patterns, and the Python programming language. The textbooks used include:
- Barron's Arithmetic The Easy Way Fourth Edition
- Blitzer Introductory Algebra for College Students Fifth Edition
- McDougal Littell Geometry
- Blitzer Intermediate Algebra for College Students 5th Edition
- Trigonometry Sixth Edition
- Pearson College Algebra Fourth Edition
- Hughes-Hallet Applied Calculus 5th Edition
- CLRS Introduction to Algorithms Third Edition
In addition to the textbooks, the dataset also includes material from the
following online resources:
- [C reference](https://en.cppreference.com/w/c)
- [Cpp reference](https://en.cppreference.com/w/cpp)
- [Python Standard Library](https://docs.python.org/3/)
These resources provide up-to-date information and examples for the C, C++, and
Python programming languages. The creators of the Cppreference site also provide
[archives](https://en.cppreference.com/w/Cppreference:Archives) of their site
for offline use. Code samples synthesized by OpenAI's GPT models, curated by the
dataset creator, are also included in the dataset.
**Note:** The creator of this dataset owns physical copies of all the textbooks
listed above. The data from these sources are transformed into a dialogue format
using OpenAI's GPT-3.5 and GPT-4 models. The resulting dialogues are then used
as the training data for the phi-1 model. This dataset does not include the full
content of the source textbooks. Instead, it consists of transformations and
syntheses of the original content. Anyone who wants access to the full original
content should purchase or otherwise legally access the textbooks themselves.
#### Who are the source language producers?
The original language data was created by a variety of authors and educators,
who wrote the textbooks and other materials used as sources for this dataset.
These include:
- Barron's Arithmetic The Easy Way Fourth Edition - Edward Williams, Katie
Prindle
- Blitzer Introductory Algebra for College Students Fifth Edition - Robert
Blitzer
- McDougal Littell Geometry - Ron Larson, Laurie Boswell, Timothy D. Kanold, Lee
Stiff
- Blitzer Intermediate Algebra for College Students 5th Edition - Robert Blitzer
- Trigonometry Sixth Edition - Charles P. McKeague, Mark D. Turner
- Pearson College Algebra Fourth Edition - Robert F. Blitzer
- Hughes-Hallet Applied Calculus 5th Edition - Deborah Hughes-Hallett, Andrew M.
Gleason, Patti Frazer Lock, Daniel E. Flath, Sheldon P. Gordon, David O.
Lomen, David Lovelock, William G. McCallum, Brad G. Osgood, Andrew Pasquale,
Jeff Tecosky-Feldman, Joseph Thrash, Karen R. Rhea, Thomas W. Tucker
- CLRS Introduction to Algorithms Third Edition - Thomas H. Cormen, Charles E.
Leiserson, Ronald L. Rivest, Clifford Stein
In addition to these authors, the developers of OpenAI's GPT-3.5 and GPT-4
models also contributed to the creation of the language data, as these models
were used to transform the source material into a dialogue format.
### Annotations
#### Annotation process
The dataset does not contain any explicit annotations. However, the data is
curated and synthesized using OpenAI's GPT-3.5 and GPT-4 models. The process
involves transforming the source material into a dialogue format suitable for
training the phi-1 model. The dataset creator, an independent learner with a
strong interest in computer science, reviewed and curated the synthesized
dialogues to ensure their quality and relevance.
#### Who are the annotators?
The dataset creator, an independent learner who has studied computer science
extensively in a self-directed manner, performed the curation and review of the
synthesized dialogues.
### Personal and Sensitive Information
The dataset does not contain any personal or sensitive information. All the data
is derived from publicly available textbooks and online resources. Any names or
other potential identifiers in the source material have been removed or
anonymized.
### Social Impact of Dataset
The dataset is intended to support the development of AI models capable of
providing detailed explanations and examples in the context of arithmetic,
algebra, geometry, trigonometry, calculus, algorithms and data structures,
design patterns, and the python programming language. The potential social
impact is significant, as such models could greatly enhance self-directed
learning and provide valuable educational support to students worldwide.
However, it's important to note that the quality and usefulness of the AI models
trained on this dataset will depend on the quality of the data itself. If the
data is inaccurate or biased, the models could propagate these inaccuracies and
biases, potentially leading to misinformation or unfair outcomes.
### Discussion of Biases
The dataset is based on a variety of textbooks and online resources, which may
contain their own inherent biases. For example, textbooks often reflect the
perspectives and biases of their authors, which can influence the way
information is presented. These biases could potentially be reflected in the
dataset and in any models trained on it.
### Other Known Limitations
At this stage of the dataset creation process, it's difficult to identify all
potential limitations. However, one potential limitation is that the dataset may
not cover all possible topics or perspectives within the fields it addresses.
The dataset creator will continue to monitor and assess the dataset for
limitations as the work progresses.
## Additional Information
### Dataset Curators
The dataset was curated by an independent learner with a strong interest in
computer science. The curator has studied the subject matter in a self-directed
manner, using a variety of resources including textbooks and online materials.
The curation process also involved the use of OpenAI's GPT-3.5 and GPT-4 models
to synthesize dialogues based on the source material.
### Licensing Information
This dataset is released under the Creative Commons
Attribution-NonCommercial-ShareAlike 3.0 International (CC BY-NC-SA 3.0)
license.
### Citation Information
As this dataset is a compilation of various sources synthesized and curated for
the purpose of training the phi-1 model, please ensure to cite the original
sources when using this dataset. If referencing the dataset directly, please
refer to this repository.
|
CheshireAI/guanaco-unchained | ---
license: apache-2.0
language:
- en
pretty_name: Guanaco Unchained
size_categories:
- 1K<n<10K
---
# Guanaco Unchained
"Guanaco Unchained" is a refined and optimized version of the original [Guanaco dataset](https://huggingface.co/datasets/timdettmers/openassistant-guanaco). It is specifically curated to maintain high-quality data while minimizing alignment issues.
The main transformations that were applied to the dataset include:
- Language Filtering: To ensure quality control, most of the non-English prompts were removed.
- AI Identification Removal: Any references suggesting the model's identity as AI, such as "OpenAssistant", "As an AI language model", and similar prompts, were removed. This adjustment allows for a more human-like interaction.
- Content Refining: Responses that indicated refusal, moralizing, or strong subjectivity were either removed or modified to increase accuracy and reduce bias.
- Context Trimming: In scenarios where a human response lacked a corresponding model answer, the last human response was removed to maintain consistency in the instruct pair format.
- Apologetic Language Reduction: The dataset was also revised to remove or modify apologetic language in the responses, thereby ensuring assertiveness and precision.
Dataset Information:
The primary source of the data is the [Guanaco dataset](https://huggingface.co/datasets/timdettmers/openassistant-guanaco). Following this, a series of processing steps (as outlined above) were performed to remove unnecessary or ambiguous elements, resulting in the "Guanaco Unchained" dataset. The structure of the dataset remains consistent with the original Guanaco dataset, containing pairs of human prompts and assistant responses.
Known Limitations:
The dataset was manually curated, and therefore, may contain unintentional errors, oversights, or inconsistencies. Despite the concerted effort to remove all instances of AI identification, there may still be undetected instances. The dataset's multilingual capability may also be reduced due to the removal of non-English prompts.
Additional Information:
The "Guanaco Unchained" dataset is ideally suited for any application that aims for a more human-like interaction with minimized AI identifiers and alignment issues. It is particularly beneficial in contexts where direct, assertive, and high-quality English responses are desired.
|
TrainingDataPro/asos-e-commerce-dataset | ---
license: cc-by-nc-nd-4.0
task_categories:
- text-classification
language:
- en
tags:
- code
- finance
---
# [Asos](https://asos.com) E-Commerce Dataset - 30,845 products
Using web scraping, we collected information on over **30,845** clothing items from the Asos website.
The dataset can be applied in E-commerce analytics in the fashion industry.
# Get the dataset
### This is just an example of the data
Leave a request on [**https://trainingdata.pro/data-market**](https://trainingdata.pro/data-market/marketplace-scraping-data?utm_source=huggingface&utm_medium=cpc&utm_campaign=asos-e-commerce-dataset) to discuss your requirements, learn about the price and buy the dataset.
# Dataset Info
For each item, we extracted:
- **url** - link to the item on the website
- **name** - item's name
- **size** - sizes available on the website
- **category** - product's category
- **price** - item's price
- **color** - item's color
- **SKU** - unique identifier of the item
- **date** - date of web scraping; for all items - March 11, 2023
- **description** - additional description, including product's brand, composition, and care instructions, in JSON format
- **images** - photographs from the item description
# Data collection and annotation
We provide both ready-made datasets and custom data collection and annotation services. Please contact us for more information: Andrew, **datasets@trainingdata.pro**
## [**TrainingData**](https://trainingdata.pro/data-market/marketplace-scraping-data?utm_source=huggingface&utm_medium=cpc&utm_campaign=asos-e-commerce-dataset) provides high-quality data annotation tailored to your needs
More datasets in TrainingData's Kaggle account: **https://www.kaggle.com/trainingdatapro/datasets**
TrainingData's GitHub: **https://github.com/Trainingdata-datamarket/TrainingData_All_datasets** |
LeoLM/OpenSchnabeltier | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: output
dtype: string
- name: instruction
dtype: string
- name: instruction_de
dtype: string
- name: output_de
dtype: string
- name: translation_de
dtype: string
splits:
- name: train
num_bytes: 66379650.83254641
num_examples: 21749
download_size: 33021431
dataset_size: 66379650.83254641
---
# Dataset Card for "open_platypus_de"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Duxiaoman-DI/FinCorpus | ---
license: apache-2.0
language:
- zh
tags:
- finance
size_categories:
- 10M<n<100M
---
ไธญๆ้่่ต่ฎฏๆฐๆฎ้๏ผๅ
ๆฌ๏ผๅ็ผฉๅ๏ผ๏ผ
- ไธๅธๅ
ฌๅธๅ
ฌๅ announcement_data.jsonl 20G
- ้่่ต่ฎฏ/ๆฐ้ป
- fin_news_data.jsonl 30G
- fin_articles_data.jsonl 10G
- ้่่ฏ้ข fin_exam.jsonl 370M
ๆฐๆฎๆ ผๅผ๏ผ
```
{
"text": <ๆๆฌๅ
ๅฎน>,
"meta": {
"source": <ๆฐๆฎๆฅๆบ>
}
}
``` |
jondurbin/airoboros-2.2 | ---
license: other
---
## Overview
This dataset is mostly a continuation of https://hf.co/datasets/jondurbin/airoboros-2.1, with some notable additions and fixes.
- Some of the content is "toxic"/"harmful", and contains profanity and other types of sensitive content.
- None of the content or views contained in text within this dataset necessarily align with my personal beliefs or opinions, they are simply text generated by LLMs and/or scraped from the web.
- Use with caution, particularly in locations with less-than-free speech laws.
- You, and you alone are responsible for having downloaded the dataset and having a copy of the contents therein and I am completely indemnified from any and all liabilities.
### 2.1 Contamination
I accidentally included some of the benchmark data in the first version of the airboros-2.1 model, which is why it had a crazy high truthfulqa score. Discussions here:
- https://huggingface.co/jondurbin/airoboros-l2-70b-2.1/discussions/3#64f325ce352152814d1f796a
- https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard/discussions/225#64f0997659da193a12b78c32
I flagged it for removal and recreated the model right away, but the leaderboard cached the old results so it took some time to reflect.
Some of the instructors I use create overlapping data, and it's hard to filter, especially since the instructions aren't typically verbatim with the benchmark questions.
This time around, I used `thenlper/gte-small` to calculate embeddings of the instructions, along with a faiss index, and removed anything from the dataset that had a similarity score < 0.15 (from truthfulqa). If you have a better way of checking, please let me know!
I haven't done the same for most other benchmarks (yet) because there are hundreds of thousands of instructions and it would be pretty computationally expensive to do. That said, I only have ~1279 multiple choice questions, all randomly GPT generated, so there's probably little-to-no overlap.
### Awareness
I added a new "awareness" instructor, which aims to add a lot more nuance to responses relating to time, location, senses, etc. based on the system prompt.
For example, if you are using the standard prompt with user/assistant, and ask how long it would take to get to Chicago, the answer will be something about AI not having a physical presence.
If, on the other hand, you are using a system prompt with a human character specified, the model attempts to infer location from "home" and will provide a more nuanced answer as a human would (in theory).
https://github.com/jondurbin/airoboros/commit/e91562c88d7610edb051606622e7c25a99884f7e
### Editor
I created a text edit instructor as well, which uses a reverse prompt mechanism, meaning it takes the existing writing samples that have been generated, rewrites them to have misspellings, poor grammar, etc., then uses a prompt like "Please correct and improve the text." with the original well-written text and target output.
https://github.com/jondurbin/airoboros/commit/e60a68de5f9622320c9cfff3b238bd83cc7e373b
### Writing
I regenerated (almost) all of the training data that included "Once upon a time..." because it's too cliche and boring.
### Multiple choice
I created many more multiple choice questions, many of which have additional text context.
### Roleplay/conversation
I re-created all of the GTKM and RP datasets this time around, removing all of the "USER: " and "ASSISTANT: " prefixes from the instructions/responses, so it's more compatible with existing interfaces.
The GTKM instructor now does the same thing as RP, in that it saves each round of "conversation" as a separate row in the output - previously it only saved the final response, which may not have been sufficient since I don't typically train on inputs.
### UTF-8 to ASCII
I replaced most of the "standard" utf-8 sequences - left double quote, right double quote, left apostraphe, ellipses - with standard ascii characters. I don't know if this was contributing to part of the issue with eos tokens being produced after apostraphes, but I figured it was worth trying.
### Summarization
I also included 500 examples from:
https://hf.co/datasets/mattpscott/airoboros-summarization
These are existing summarizarions from various public datasets, formatted to airoboros style contextual qa.
Thanks Matt!
### Usage/license info
Much (most) of the data was generated via gpt-4 API calls, which has a restriction in the ToS about "competing" models. Please seek legal advice if you plan to build or use a model that includes this dataset in a commercial setting. |
DAMO-NLP-SG/MultiJail | ---
license: mit
task_categories:
- conversational
language:
- en
- zh
- it
- vi
- ar
- ko
- th
- bn
- sw
- jv
size_categories:
- n<1K
---
# Multilingual Jailbreak Challenges in Large Language Models
This repo contains the data for our paper ["Multilingual Jailbreak Challenges in Large Language Models"](https://arxiv.org/abs/2310.06474).
[[Github repo]](https://github.com/DAMO-NLP-SG/multilingual-safety-for-LLMs/)
## Annotation Statistics
We collected a total of 315 English unsafe prompts and annotated them into nine non-English languages. The languages were categorized based on resource availability, as shown below:
**High-resource languages:** Chinese (zh), Italian (it), Vietnamese (vi)
**Medium-resource languages:** Arabic (ar), Korean (ko), Thai (th)
**Low-resource languages:** Bengali (bn), Swahili (sw), Javanese (jv)
## Ethics Statement
Our research investigates the safety challenges of LLMs in multilingual settings. We are aware of the potential misuse of our findings and emphasize that our research is solely for academic purposes and ethical use. Misuse or harm resulting from the information in this paper is strongly discouraged. To address the identified risks and vulnerabilities, we commit to open-sourcing the data used in our study. This openness aims to facilitate vulnerability identification, encourage discussions, and foster collaborative efforts to enhance LLM safety in multilingual contexts. Furthermore, we have developed the SELF-DEFENSE framework to address multilingual jailbreak challenges in LLMs. This framework automatically generates multilingual safety training data to mitigate risks associated with unintentional and intentional jailbreak scenarios. Overall, our work not only highlights multilingual jailbreak challenges in LLMs but also paves the way for future research, collaboration, and innovation to enhance their safety.
## Citation
```
@misc{deng2023multilingual,
title={Multilingual Jailbreak Challenges in Large Language Models},
author={Yue Deng and Wenxuan Zhang and Sinno Jialin Pan and Lidong Bing},
year={2023},
eprint={2310.06474},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
``` |
123rc/medical_text | ---
license: apache-2.0
task_categories:
- text-classification
tags:
- medical
size_categories:
- 10K<n<100K
--- |
Isamu136/penetration_testing_scraped_dataset | ---
dataset_info:
features:
- name: text
dtype: string
- name: embedding
sequence: float32
- name: tokens
sequence: int64
- name: database
dtype: string
- name: file
dtype: string
- name: chunk
dtype: int64
splits:
- name: train
num_bytes: 1005293572
num_examples: 107542
download_size: 663206603
dataset_size: 1005293572
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "penetration_testing_scraped_dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
defog/wikisql_codellama_1000 | ---
dataset_info:
features:
- name: input_ids
sequence: int32
- name: attention_mask
sequence: int8
- name: labels
sequence: int64
- name: prompt
dtype: string
- name: completion
dtype: string
splits:
- name: train
num_bytes: 6652069
num_examples: 1000
download_size: 850430
dataset_size: 6652069
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "wikisql_codellama_1000"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
arcee-ai/nuclear_patents | ---
dataset_info:
features:
- name: patent_number
dtype: string
- name: section
dtype: string
- name: raw_text
dtype: string
splits:
- name: train
num_bytes: 350035355.37046283
num_examples: 33523
- name: test
num_bytes: 38895137.62953716
num_examples: 3725
download_size: 151011439
dataset_size: 388930493.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
# Dataset Card for "nuclear_patents"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
M2UGen/MUCaps | ---
license: cc-by-nc-nd-4.0
arxiv: 2311.11255
extra_gated_prompt: >-
Please fill in the following fields, the full name/institution/group/contact
email/use case are MUST fields, and gender/github/personal homepage are
OPTIONAL fields (You can simply use a '-' symbol to fill in these optional
fields). An application form without required information will be declined.
extra_gated_fields:
Full Name: text
Gender: text
Institution: text
Group: text
Contact Email: text
Github: text
Personal Homepage: text
Use Case: text
I agree to use this dataset for non-commercial use ONLY: checkbox
tags:
- music
---
# MUCaps Dataset
This is the MUCaps dataset, the largest music captioning dataset consisting of **21,966 music files** with a total playtime of **1273.78 hours** generated using the [MU-LLaMA](https://github.com/crypto-code/MU-LLaMA) model.
This dataset is used to train the [M<sup>2</sup>UGen](https://github.com/crypto-code/M2UGen) model.
To uncompress the audio files, run the following:
```
cat mucaps_audios.tar.gz.* | tar xzvf -
```
The [MUCapsCaptions.json](./MUCapsCaptions.json) file contains a dictionary with the filename as the key and the caption as the value.
This file is used to train the music encoder of the M<sup>2</sup>UGen model.
The [MUCapsInstructions.json](./MUCapsInstructions.json) file contains a list with each of the element having the following format:
```
{
"output_file": "mucaps_000000.mp3",
"conversation": [
{
"from": "human",
"value": "The music is described as fast, meaning it has a quick tempo and a lively rhythm.",
"input_modality": "text"
},
{
"from": "gpt",
"value": "",
"caption": "The music is described as fast, meaning it has a quick tempo and a lively rhythm.",
"output_modality": "audio"
}
]
}
```
This file is used to train the music decoder of the M<sup>2</sup>UGen model. |
styletts2-community/multilingual-pl-bert | ---
license: cc-by-4.0
language:
- af
- an
- ar
- az
- ba
- be
- bg
- bn
- bpy
- bs
- ca
- cs
- cy
- da
- de
- el
- es
- et
- eu
- fi
- fr
- gu
- hak
- he
- hi
- hr
- hu
- hy
- hyw
- id
- io
- is
- it
- ja
- ka
- kk
- kn
- ko
- la
- lb
- lt
- lv
- mk
- ml
- mr
- ms
- ne
- nl
- no
- pa
- pl
- pt
- ro
- ru
- sk
- sl
- sq
- sr
- sv
- sw
- ta
- te
- th
- tr
- tt
- ur
- uz
- vi
- zh
---
Attribution: Wikipedia.org |
ShuhuaiRen/TimeIT | ---
license: cc-by-4.0
language:
- en
---
# Dataset Card for TimeIT
TimeIT encompasses 6 longstanding timestamp-related video tasks and incorporates 12 specific datasets derived from different domains.
**[NOTE]: Please refer to [DATA.md](https://github.com/RenShuhuai-Andy/TimeChat/blob/master/docs/DATA.md) for more details on downloading and processing video data.**
## Dataset Description
- **Homepage: https://huggingface.co/datasets/ShuhuaiRen/TimeIT**
- **Repository: https://huggingface.co/datasets/ShuhuaiRen/TimeIT**
- **Paper: https://arxiv.org/abs/2312.02051**
- **Leaderboard:**
- **Point of Contact:**
## Dataset Statistics
Our dataset compiles diverse tasks of time-sensitive long video understanding, including Dense Video Captioning, Video Grounding, Video Summarization, Video Highlight Detection, Step Localization, Transcribed Speech Generation.
### Instruction Statistics
| Task | #Instructions |
|-------------------------------|---------------|
| Dense Video Captioning | 6 |
| Temporal Video Grounding | 6 |
| Video Summarization | 6 |
| Video Highlight Detection | 6 |
| Step Localization | 6 |
| Transcribed Speech Generation | 6 |
| Total | 36 |
### Task Statistics
| Task | Description | #Train |
|-------------------------------|----------------------------------------------------------------------------------------------------------------------|---------|
| Dense Video Captioning | detects a series of events in the given video and outputs the corresponding timestamps and descriptions | 16,342 |
| Temporal Video Grounding | predict a timestamp boundary including the start and end time in the video given a natural language query | 60,471 |
| Video Summarization | create a compressed set of frames or clip shots to represent the most informative content of the given video | 75 |
| Video Highlight Detection | identify the most exciting, impressive, or emotional moments that may not cover the full scope of the original video | 6,858 |
| Step Localization | segment and describe significant steps in a long untrimmed video | 9,488 |
| Transcribed Speech Generation | predict the speech content and its corresponding start and end timestamps based on visual signals in the video | 31,627 |
| Total | - | 124861 |
### Detailed Dataset Statistics
| Task | Dataset | #Train |
|-------------------------------|------------------------|--------|
| Dense Video Captioning | `ActivityNet Captions` | 10,009 |
| | `ViTT` | 5,141 |
| | `YouCook2` | 1,192 |
| Temporal Video Grounding | `DiDeMo` | 33,002 |
| | `QuerYD` | 14,602 |
| | `HiREST_grounding` | 459 |
| | `Charades-STA` | 12,408 |
| Video Summarization | `TVSum` | 50 |
| | `SumMe` | 25 |
| Video Highlight Detection | `QVHighlights` | 6,858 |
| Step Localization | `COIN` | 9,029 |
| | `HiREST_step` | 459 |
| Transcribed Speech Generation | `YT-Temporal` | 31,627 |
## Dataset Structure
### HuggingFace Login (Optional)
```python
# OR run huggingface-cli login
from huggingface_hub import login
hf_token = "hf_xxx" # TODO: set a valid HuggingFace access token for loading datasets/models
login(token=hf_token)
```
### Data Loading
```python
from datasets import load_dataset
ds_name = "youcook2" # change the dataset name here
dataset = load_dataset("ShuhuaiRen/TimeIT", ds_name)
```
### Data Splits
```python
from datasets import load_dataset
ds_name = "youcook2" # change the dataset name here
dataset = load_dataset("ShuhuaiRen/TimeIT", ds_name)
train_set = dataset["train"]
```
### Data Instances
```python
from datasets import load_dataset
ds_name = "youcook2" # change the dataset name here
dataset = load_dataset("ShuhuaiRen/TimeIT", ds_name)
train_set = dataset["train"]
for train_instance in train_set:
question = train_instance["question"] # str
answer = train_instance["answer"] # str
video_path = train_instance["video_path"] # str
```
### Data Fields
```python
import datasets
features = datasets.Features(
{
"video_path": datasets.Value("string"),
"question": datasets.Value("string"),
"answer": datasets.Value("string"),
}
)
```
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
| Task | Dataset [Citation] | Source |
|-------------------------------|----------------------------|------------------------------------------------------------------------------------|
| Dense Video Captioning | `ActivityNet Captions` [1] | [Source](http://activity-net.org/download.html) |
| | `ViTT` [2] | [Source](https://github.com/google-research-datasets/Video-Timeline-Tags-ViTT) |
| | `YouCook2` [3] | [Source](http://youcook2.eecs.umich.edu/) |
| Temporal Video Grounding | `DiDeMo` [4] | [Source](https://github.com/LisaAnne/LocalizingMoments?tab=readme-ov-file#dataset) |
| | `QuerYD` [5] | [Source](https://www.robots.ox.ac.uk/~vgg/data/queryd/) |
| | `HiREST_grounding` [6] | [Source](https://github.com/j-min/HiREST) |
| | `Charades-STA` [7] | [Source](https://github.com/jiyanggao/TALL) |
| Video Summarization | `TVSum` [8] | [Source](https://github.com/yalesong/tvsum) |
| | `SumMe` [9] | [Source](http://classif.ai/dataset/ethz-cvl-video-summe/) |
| Video Highlight Detection | `QVHighlights` [10] | [Source](https://github.com/jayleicn/moment_detr/tree/main/data) |
| Step Localization | `COIN` [11] | [Source](https://github.com/coin-dataset/annotations) |
| | `HiREST_step` [6] | [Source](https://github.com/j-min/HiREST) |
| Transcribed Speech Generation | `YT-Temporal` [12] | [Source](https://rowanzellers.com/merlot/#data) |
### Annotations
#### Annotation process
To build high-quality multimodal instruction datasets,
we rewrite various datasets into multimodal-to-text dialog format.
The annotation process includes four steps:
- (1) **Stage I: Instruction Writing**: writing instructions for each task;
- (2) **Stage II: Data Format Unification**: structuring images and texts into a unified schema;
- (3) **Stage III: Quality Check**: checking the overall dataset quality;
- (4) **Stage IV: Key Datasets Translation**: building multilingual sets.
#### Who are the annotators?
Three authors of this work are employed as human annotators,
each of whom is a graduate student familiar with relevant literature.
## Additional Information
### Licensing Information
The content of original dataset follows their original license.
We suggest that for the task with Unknown/Custom license, the user can check the original project or contact the dataset owner for detailed license information.
Our annotated instruction data is licensed under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/).
### Citation Information
```bibtex
@article{Ren2023TimeChat,
title={TimeChat: A Time-sensitive Multimodal Large Language Model for Long Video Understanding},
author={Shuhuai Ren and Linli Yao and Shicheng Li and Xu Sun and Lu Hou},
journal={ArXiv},
year={2023},
volume={abs/2312.02051},
}
```
### Contributions
TimeIT is a video-centric instruction-tuning dataset involving timestamps.
designed to enable the development of general-purpose video agents.
## References
- [1] Dense-Captioning Events in Videos
- [2] Multimodal Pretraining for Dense Video Captioning
- [3] Towards Automatic Learning of Procedures from Web Instructional Videos
- [4] Localizing Moments in Video with Natural Language
- [5] QuerYD: A video dataset with high-quality text and audio narrations
- [6] Hierarchical Video-Moment Retrieval and Step-Captioning
- [7] TALL: Temporal Activity Localization via Language Query
- [8] TVSum: Summarizing Web Videos Using Titles
- [9] Creating Summaries from User Videos
- [10] QVHighlights: Detecting Moments and Highlights in Videos via Natural Language Queries
- [11] COIN: A Large-scale Dataset for Comprehensive Instructional Video Analysis
- [12] MERLOT: Multimodal Neural Script Knowledge Models |
cmunhozc/usa_news_en | ---
license: mit
task_categories:
- text-classification
language:
- en
tags:
- CENIA
- News
size_categories:
- 100K<n<1M
---
This dataset presents a collection of U.S. news headlines between 2019 and 2022 from Twitter and Facebook, with automatic annotations and human verifications. For more details on the methodology and annotation process, please refer to our paper {News Gathering: Leveraging Transformers to Rank News}.
## Attributes:
This dataset comprises five attributes: the first corresponds to "Headlines 1," the second to "Headlines 2," the third to the "target" variable {0, 1}, the fourth is related to the "split" {train, validation, test}, and finally, the fifth attribute indicates the "type" {soft label, human-verified}. Both sentences are associated with news extracted from diverse U.S. news sources {The New York Times, San Francisco Chronicle, National Broadcasting, Yahoo News, among other outlets}. The "target" variable indicates whether both sentences relate to the same event {1} or not {0}. Regarding the "type," {soft label} corresponds to an automatic process following the methodology in the paper, and {human-verified} indicates a process verified through a survey by humans.
## Data Source:
The primary sources included Twitter, accessed via the Academic API, and Facebook, accessed through CrowdTangle. This facilitated the automatic annotation of USA news articles spanning 2019 to 2022 by the methodology outlined in the paper. Within the test dataset, this sentence pair underwent human verification through a survey.
## Data Format:
The dataset is presented in tabular format and comprises five columns, as mentioned in the previous description. |
stevenfan/AIGCBench_v1.0 | ---
license: apache-2.0
size_categories:
- 1K<n<10K
---
# AIGCBench v1.0
AIGCBench is a novel and comprehensive benchmark designed for evaluating the capabilities of state-of-the-art video generation algorithms. Official dataset for the paper:**AIGCBench: Comprehensive Evaluation of Image-to-Video Content Generated by AI**, ***BenchCouncil Transactions on Benchmarks, Standards and Evaluations (TBench)***.
<a href='https://www.benchcouncil.org/AIGCBench/'><img src='https://img.shields.io/badge/Project-Website-orange'></a>
## Description
This dataset is intended for the evaluation of video generation tasks. Our dataset includes image-text pairs and video-text pairs. The dataset comprises three parts:
1. `ours` - A custom generation of image-text samples.
2. `webvid val` - A subset of 1000 video samples from the WebVid val dataset.
3. `laion-aesthetics` - A subset of LAION dataset that includes 925 curated image-text samples.
## Data Organization
The dataset is organized into the following folders and files:
- `t2i_aspect_ratio_625.zip` - Contains images paired with text, adjusted to an aspect ratio of 0.625.
- `webvid_eval_1000.txt` - Contains video names for 1000 selected video samples. Considering that the first frame of the video may not contain the main information or might be a bad case, we use the tenth frame of the video as the initial frame.
- `Laion-aesthetics_select_samples.txt` - Contains metadata and annotations for 925 image-text samples.
## Acknowledgments
We would like to thank all contributors and organizations behind the data sources, especially the maintainers of WebVid and LAION datasets.
## Contact Information
fanfanda@ict.ac.cn and jianfengzhan.benchcouncil@gmail.com
## Citation
If you find our work useful in your research, please consider citing our paper:
```bibtex
@misc{fan2024aigcbench,
title={AIGCBench: Comprehensive Evaluation of Image-to-Video Content Generated by AI},
author={Fanda Fan and Chunjie Luo and Wanling Gao and Jianfeng Zhan},
year={2024},
eprint={2401.01651},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
``` |
MMVP/MMVP_VLM | ---
license: mit
task_categories:
- zero-shot-classification
size_categories:
- n<1K
---
# MMVP-VLM Benchmark Datacard
## Basic Information
**Title:** MMVP-VLM Benchmark
**Description:** The MMVP-VLM (Multimodal Visual Patterns - Visual Language Models) Benchmark is designed to systematically evaluate the performance of recent CLIP-based models in understanding and processing visual patterns. It distills a subset of questions from the original MMVP benchmark into simpler language descriptions, categorizing them into distinct visual patterns. Each visual pattern is represented by 15 text-image pairs. The benchmark assesses whether CLIP models can accurately match these image-text combinations, providing insights into the capabilities and limitations of these models.
## Dataset Details
- **Content Types:** Text-Image Pairs
- **Volume:** Balanced number of questions for each visual pattern, with each pattern represented by 15 pairs.
- **Source of Data:** Subset from MMVP benchmark, supplemented with additional questions for balance
- **Data Collection Method:** Distillation and categorization of questions from MMVP benchmark into simpler language
## Usage
### Intended Use
- Evaluation of CLIP models' ability to understand and process various visual patterns.
|
euclaise/reddit-instruct | ---
dataset_info:
features:
- name: post_title
dtype: string
- name: post_text
dtype: string
- name: post_scores
dtype: int64
- name: comment_text
dtype: string
- name: comment_score
dtype: int64
splits:
- name: train
num_bytes: 126565640.88161694
num_examples: 84784
- name: test
num_bytes: 2985602.021174206
num_examples: 2000
download_size: 67560005
dataset_size: 129551242.90279114
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
license: mit
---
Filtered data from the following subreddits:
"AskAcademia",
"AskComputerScience",
"AskEconomics",
"AskProgramming",
"AskScienceFiction",
"AskSocialScience",
"AskStatistics",
"AskTechnology",
"askmath",
"askphilosophy",
"askpsychology",
"askscience",
"changemyview",
"explainlikeimfive" |
TVRRaviteja/Mental-Health-Data | ---
language:
- en
---
# Mental Health Queries and Personality Dataset
## Overview
This dataset encompasses a collection of mental health queries paired with personality scores and responses generated by a Large Language Model (LLM). It aims to provide insights into the interplay between personality traits and mental health inquiries, facilitating research in personalized conversational agents and mental health support systems.
## Dataset Description
Each record in the dataset contains:
- A query from a Mental Health user.
- A personality score across five types: Agreeableness, Extraversion, Openness, Conscientiousness, and Neuroticism.
- A context interpretation based on the user's personality.
- A tailored response from the Assistant.
## Potential Uses
The dataset is particularly useful for researchers and developers working on:
- Personalized conversational AI in mental health.
- The impact of personality traits on mental health support.
- Enhancing natural language understanding and response generation in the context of mental health.
## Access and Use
This dataset is hosted on Hugging Face Datasets, available for academic and research purposes. Users are encouraged to cite the dataset when used in their research or projects.
---
license: mit
---
|
Crystalcareai/CodeFeedback-Alpaca | ---
license: apache-2.0
---
|