datasetId
stringlengths 2
117
| author
stringlengths 2
42
⌀ | last_modified
timestamp[us, tz=UTC] | downloads
int64 0
9.36M
| likes
int64 0
3.89k
| tags
sequence | task_categories
sequence | createdAt
timestamp[us, tz=UTC] | card
stringlengths 19
1.01M
| embedding
sequence |
|---|---|---|---|---|---|---|---|---|---|
isek-ai/danbooru-tags | isek-ai | 2023-12-15T13:19:05Z | 3 | 0 | [
"size_categories:10K<n<100K",
"license:cc0-1.0",
"danbooru",
"region:us"
] | null | 2023-12-15T13:09:39Z | ---
license: cc0-1.0
tags:
- danbooru
size_categories:
- 10K<n<100K
---
# danbooru tags
- `name`: the danbooru tag name
- `words`: words that constitute the tag name (in most cases, this will be the same as separated by `_`)
- `"looking_at_viewer"` -> `["looking", "at", "viewr"]`
- `"+_+"` -> `["+_+"]`
- `category`
- `0`: general
- `1`: artist
- `2`: _unused_
- `3`: copyright
- `4`: character
- `5`: meta
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RachidAb02/Finance-Accounting | RachidAb02 | 2023-12-15T15:04:30Z | 3 | 0 | [
"task_categories:question-answering",
"size_categories:n<1K",
"language:aa",
"license:mit",
"finance",
"region:us"
] | [
"question-answering"
] | 2023-12-15T15:00:39Z | ---
license: mit
task_categories:
- question-answering
language:
- aa
tags:
- finance
pretty_name: Finance-Accounting
size_categories:
- n<1K
---
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distil-whisper/librispeech_asr_dummy-concatenated | distil-whisper | 2023-12-15T15:39:03Z | 3 | 0 | [
"region:us"
] | null | 2023-12-15T15:38:59Z | ---
dataset_info:
config_name: clean
features:
- name: text
dtype: string
- name: id
dtype: string
- name: input_features
sequence:
sequence: float32
splits:
- name: validation
num_bytes: 26127227
num_examples: 17
download_size: 21173882
dataset_size: 26127227
configs:
- config_name: clean
data_files:
- split: validation
path: clean/validation-*
---
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jlbaker361/wikiart-balanced100 | jlbaker361 | 2023-12-15T21:46:37Z | 3 | 0 | [
"region:us"
] | null | 2023-12-15T21:46:17Z | ---
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
- name: style
dtype: string
- name: name
dtype: string
- name: gen_style
dtype: string
splits:
- name: train
num_bytes: 114299204.92772257
num_examples: 3036
- name: test
num_bytes: 13593722.436277417
num_examples: 338
download_size: 127490294
dataset_size: 127892927.364
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
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NextDayAI/MultipleResponsesChat_20231101_20231110 | NextDayAI | 2023-12-15T22:33:34Z | 3 | 0 | [
"region:us"
] | null | 2023-12-15T22:33:20Z | ---
dataset_info:
features:
- name: prompt
dtype: string
- name: rejected_response
dtype: string
- name: selected_response
dtype: string
splits:
- name: train
num_bytes: 368704518.4994413
num_examples: 126742
- name: valid
num_bytes: 10909106.250279346
num_examples: 3750
- name: test
num_bytes: 10909106.250279346
num_examples: 3750
download_size: 242831387
dataset_size: 390522731.00000006
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: valid
path: data/valid-*
- split: test
path: data/test-*
---
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A2H0H0R1/Animal-nutrition-alpaca | A2H0H0R1 | 2023-12-16T12:16:55Z | 3 | 0 | [
"region:us"
] | null | 2023-12-16T12:16:48Z | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: input
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 5434359
num_examples: 5027
download_size: 2050255
dataset_size: 5434359
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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ltg/norbench | ltg | 2023-12-16T13:27:22Z | 3 | 0 | [
"task_categories:text-classification",
"task_categories:token-classification",
"task_categories:question-answering",
"task_categories:translation",
"language:nb",
"language:nn",
"language:no",
"license:mit",
"region:us"
] | [
"text-classification",
"token-classification",
"question-answering",
"translation"
] | 2023-12-16T13:21:51Z | ---
license: mit
dataset_info:
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language:
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pretty_name: NorBench
---
# Dataset Card for Dataset Name
<!-- Provide a quick summary of the dataset. -->
This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1).
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#### Personal and Sensitive Information
<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
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[More Information Needed] | [
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SassyRong/meme-imgflip-small-test-dataset | SassyRong | 2023-12-17T15:49:45Z | 3 | 0 | [
"task_categories:text-to-image",
"size_categories:n<1K",
"language:en",
"license:cc0-1.0",
"region:us"
] | [
"text-to-image"
] | 2023-12-16T17:19:44Z | ---
license: cc0-1.0
task_categories:
- text-to-image
language:
- en
size_categories:
- n<1K
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akoukas/AITextDetectionDataset | akoukas | 2023-12-17T02:49:53Z | 3 | 0 | [
"license:mit",
"region:us"
] | null | 2023-12-16T23:27:52Z | ---
license: mit
dataset_info:
features:
- name: label
dtype:
class_label:
names:
'0': human
'1': ai
- name: text
dtype: string
splits:
- name: train
num_bytes: 3371996886
num_examples: 1392522
download_size: 1965631903
dataset_size: 3371996886
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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TinyPixel/multiturn | TinyPixel | 2023-12-17T09:15:37Z | 3 | 0 | [
"region:us"
] | null | 2023-12-17T04:23:36Z | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 8994901
num_examples: 1609
download_size: 4754701
dataset_size: 8994901
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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Yunij/daig2-tokenized-dataset | Yunij | 2023-12-17T05:06:08Z | 3 | 0 | [
"license:apache-2.0",
"region:us"
] | null | 2023-12-17T04:59:18Z | ---
license: apache-2.0
dataset_info:
features:
- name: text
dtype: string
- name: label
dtype: int64
- name: prompt_name
dtype: string
- name: source
dtype: string
- name: RDizzl3_seven
dtype: bool
- name: cleaned_text
dtype: string
- name: input_ids
sequence:
sequence: int64
- name: attention_mask
sequence:
sequence: int64
splits:
- name: train
num_bytes: 293764426
num_examples: 44868
download_size: 108904729
dataset_size: 293764426
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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linqus/tokenized-codeparrot-ds-small | linqus | 2023-12-17T13:15:47Z | 3 | 0 | [
"region:us"
] | null | 2023-12-17T13:15:15Z | ---
dataset_info:
features:
- name: input_ids
sequence: int32
splits:
- name: train
num_bytes: 708311652
num_examples: 1372697
- name: valid
num_bytes: 7259088
num_examples: 14068
download_size: 313854357
dataset_size: 715570740
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: valid
path: data/valid-*
---
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Gbssreejith/SM_type1_dataset | Gbssreejith | 2023-12-17T17:01:01Z | 3 | 0 | [
"region:us"
] | null | 2023-12-17T16:58:17Z | ---
dataset_info:
features:
- name: image
dtype: image
- name: ground_truth
dtype: string
splits:
- name: train
num_bytes: 41363152.0
num_examples: 182
- name: test
num_bytes: 3109892.0
num_examples: 14
- name: val
num_bytes: 1575075.0
num_examples: 7
download_size: 44003436
dataset_size: 46048119.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
- split: val
path: data/val-*
---
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leo4life/algoml_bookcorpus_10p | leo4life | 2023-12-17T23:00:01Z | 3 | 0 | [
"region:us"
] | null | 2023-12-17T22:58:07Z | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 474340769
num_examples: 7400422
download_size: 295650492
dataset_size: 474340769
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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hf-doc-build/doc-build-dev | hf-doc-build | 2023-12-18T00:06:39Z | 2 | 4 | [
"license:mit",
"documentation",
"region:us"
] | null | 2022-11-08T09:03:37Z | ---
license: mit
tags:
- documentation
pretty_name: HF Documentation (PRs)
---
This is a dataset which contains the docs from all the PRs updating one of the docs from https://huggingface.co/docs.
It is automatically updated by this [github action](https://github.com/huggingface/doc-builder/blob/main/.github/workflows/build_pr_documentation.yml) from the [doc-buider](https://github.com/huggingface/doc-builder) repo. | [
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Yehor/ukrainian-tts-lada | Yehor | 2023-12-16T16:25:26Z | 2 | 2 | [
"task_categories:text-to-speech",
"language:uk",
"license:apache-2.0",
"region:us"
] | [
"text-to-speech"
] | 2022-11-27T07:59:04Z | ---
language:
- uk
license: apache-2.0
task_categories:
- text-to-speech
---
# 🇺🇦 Open Source Ukrainian Text-to-Speech dataset named LADA
Join Ukrainian community - https://t.me/speech_synthesis_uk
More details about this dataset - https://github.com/egorsmkv/ukrainian-tts-datasets/tree/main/lada
# Voice LADA (female)
License: [Apache 2.0](https://github.com/egorsmkv/ukrainian-tts-datasets/blob/main/LICENSE)
**Samples are manually checked. Good samples are in the `accept` folder (10h37m), others are in `reject` (1h2m).**
Listen to [DEMO](https://huggingface.co/spaces/theodotus/ukrainian-voices) (choose "lada" in the Voice field)
## Features
- Quality: high
- Duration: 10h37m
- Audio formats: OPUS/WAV
- Text format: JSONL (a `metadata.jsonl` file)
- Frequency: 16000/22050/48000 Hz
## Original version
### In the `OPUS` format
- 48000 Hz: https://huggingface.co/datasets/Yehor/ukrainian-tts-lada/resolve/main/dataset_lada_ogg.zip
### In the `WAV` format
- 48000 Hz: https://huggingface.co/datasets/Yehor/ukrainian-tts-lada/resolve/main/dataset_lada_48khz.zip
- 22050 Hz: https://huggingface.co/datasets/Yehor/ukrainian-tts-lada/resolve/main/dataset_lada_22khz.zip
- 16000 Hz: https://huggingface.co/datasets/Yehor/ukrainian-tts-lada/resolve/main/dataset_lada_16khz.zip
## Trimmed version (removed silence)
Silence is removed by https://github.com/proger/uk#align-text-to-audio-and-trim-silence
### In the `WAV` format
- 48000 Hz: https://huggingface.co/datasets/Yehor/ukrainian-tts-lada/resolve/main/dataset_lada_trimmed_48khz.zip
- 22050 Hz: https://huggingface.co/datasets/Yehor/ukrainian-tts-lada/resolve/main/dataset_lada_trimmed_22khz.zip
- 16000 Hz: https://huggingface.co/datasets/Yehor/ukrainian-tts-lada/resolve/main/dataset_lada_trimmed_16khz.zip | [
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sayakpaul/sample-datasets | sayakpaul | 2023-12-17T10:05:51Z | 2 | 0 | [
"license:apache-2.0",
"region:us"
] | null | 2023-01-15T07:09:08Z | ---
license: apache-2.0
---
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AlexWortega/FicBook | AlexWortega | 2023-12-14T14:22:19Z | 2 | 1 | [
"language:ru",
"license:mit",
"region:us"
] | null | 2023-01-19T19:21:34Z | ---
license: mit
language:
- ru
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deepghs/game_characters | deepghs | 2023-12-16T21:24:20Z | 2 | 9 | [
"license:apache-2.0",
"region:us"
] | null | 2023-01-28T11:12:51Z | ---
license: apache-2.0
---
# Database of Characters in Mobile Games
[](https://huggingface.co/datasets/deepghs/game_characters)
All the character in the following games are supported:
* Arknights (crawled from [https://prts.wiki](https://prts.wiki))
* Fate/Grand Order (crawled from [https://fgo.wiki](https://fgo.wiki))
* Azur Lane (crawled from [https://wiki.biligame.com/blhx](https://wiki.biligame.com/blhx))
* Girls' Front-Line (crawled from [https://iopwiki.com/](https://iopwiki.com/))
* Genshin Impact (crawled from [https://genshin-impact.fandom.com/ja/wiki/%E5%8E%9F%E7%A5%9E_Wiki](https://genshin-impact.fandom.com/ja/wiki/%E5%8E%9F%E7%A5%9E_Wiki))
The source code and python library is hosted on [narugo1992/gchar](https://github.com/narugo1992/gchar), and the scheduled job is configured on Github Action, so the data will be automatically updated to the latest version once a day.
More character data for other games is coming...
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Yehor/ukrainian-tts-mykyta | Yehor | 2023-12-16T16:26:33Z | 2 | 2 | [
"task_categories:text-to-speech",
"language:uk",
"license:apache-2.0",
"region:us"
] | [
"text-to-speech"
] | 2023-03-06T16:22:17Z | ---
language:
- uk
license: apache-2.0
task_categories:
- text-to-speech
---
# 🇺🇦 Open Source Ukrainian Text-to-Speech dataset named MYKYTA
Join Ukrainian community - https://t.me/speech_synthesis_uk
More details about this dataset - https://github.com/egorsmkv/ukrainian-tts-datasets/tree/main/mykyta
# Voice MYKYTA (male)
License: [Apache 2.0](https://github.com/egorsmkv/ukrainian-tts-datasets/blob/main/LICENSE)
Listen to [DEMO](https://huggingface.co/spaces/theodotus/ukrainian-voices) (choose "mykyta" in the Voice field)
## Features
- Quality: high
- Duration: 8h10m
- Audio formats: OPUS/WAV
- Text format: JSONL (a `metadata.jsonl` file)
- Frequency: 16000/22050/48000 Hz
## Original version
### In the `OPUS` format
- 48000 Hz: https://huggingface.co/datasets/Yehor/ukrainian-tts-mykyta/resolve/main/dataset_mykyta_ogg.zip
## Trimmed version (removed silence)
Silence is removed by https://github.com/proger/uk#align-text-to-audio-and-trim-silence
### In the `WAV` format
- 48000 Hz: https://huggingface.co/datasets/Yehor/ukrainian-tts-mykyta/resolve/main/dataset_mykyta_trimmed_48khz.zip
- 22050 Hz: https://huggingface.co/datasets/Yehor/ukrainian-tts-mykyta/resolve/main/dataset_mykyta_trimmed_22khz.zip
- 16000 Hz: https://huggingface.co/datasets/Yehor/ukrainian-tts-mykyta/resolve/main/dataset_mykyta_trimmed_16khz.zip | [
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Yehor/ukrainian-tts-tetiana | Yehor | 2023-12-16T16:25:09Z | 2 | 3 | [
"task_categories:text-to-speech",
"language:uk",
"license:apache-2.0",
"region:us"
] | [
"text-to-speech"
] | 2023-03-06T16:47:50Z | ---
language:
- uk
license: apache-2.0
task_categories:
- text-to-speech
---
# 🇺🇦 Open Source Ukrainian Text-to-Speech dataset named TETIANA
Join Ukrainian community - https://t.me/speech_synthesis_uk
More details about this dataset - https://github.com/egorsmkv/ukrainian-tts-datasets/tree/main/tetiana
# Voice TETIANA (female)
License: [Apache 2.0](https://github.com/egorsmkv/ukrainian-tts-datasets/blob/main/LICENSE)
## Features
- Quality: high
- Duration: 8h
- Audio formats: OPUS/WAV
- Text format: JSONL (a `metadata.jsonl` file)
- Frequency: 16000/22050/48000 Hz
## Original version
### In the `OPUS` format
- 48000 Hz: https://huggingface.co/datasets/Yehor/ukrainian-tts-tetiana/resolve/main/dataset_tetiana_ogg.zip
## Trimmed version (removed silence)
Silence is removed by https://github.com/proger/uk#align-text-to-audio-and-trim-silence
### In the `WAV` format
- 48000 Hz: https://huggingface.co/datasets/Yehor/ukrainian-tts-tetiana/resolve/main/dataset_tetiana_trimmed_48khz.zip
- 22050 Hz: https://huggingface.co/datasets/Yehor/ukrainian-tts-tetiana/resolve/main/dataset_tetiana_trimmed_22khz.zip
- 16000 Hz: https://huggingface.co/datasets/Yehor/ukrainian-tts-tetiana/resolve/main/dataset_tetiana_trimmed_16khz.zip | [
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Kushtrim/Kosovo-Parliament-Transcriptions | Kushtrim | 2023-12-16T10:17:32Z | 2 | 2 | [
"size_categories:100K<n<1M",
"source_datasets:Kuvendi i Kosovës",
"language:sq",
"license:cc-by-4.0",
"region:us"
] | null | 2023-03-18T11:10:30Z | ---
language: sq
license: cc-by-4.0
size_categories:
- 100K<n<1M
source_datasets: Kuvendi i Kosovës
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: text
dtype: string
- name: speaker
dtype: string
- name: date
dtype: string
- name: id
dtype: string
- name: num_tokens
dtype: int64
splits:
- name: train
num_bytes: 163370746
num_examples: 123309
download_size: 81263561
dataset_size: 163370746
---
# Kosovo-Parliament-Transcriptions
[](https://colab.research.google.com/github/Kushtrimvisoka/Kosovo-Parliament-Transcriptions/blob/main/Kosovo_Parliament_Transcriptions.ipynb)
The dataset comprises transcripts of speeches delivered by members of the Kosovo Assembly during parliamentary sessions spanning from 2007. The goal of this repository is to provide a valuable resource for researchers and professionals interested in natural language processing, or political discourse analysis.
# Data source
The dataset was compiled from publicly available transcripts published on the current and old official website of the Kosovo Assembly (https://kuvendikosoves.org/).
# Data Preperation
The dataset was compiled by downloading PDF files and converting them to a text format using OCR. The resulting text was then cleaned to fix punctuation and spelling errors. It's important to note that due to the complexity of the PDF-to-text conversion process, the dataset may still contain typos and other errors. As a result, the dataset is provided "as is". Additionally, it should be noted that the dataset includes speeches given in languages other than Albanian.
# To do
- [ ] Conduct additional quality assurance checks to identify and correct any remaining errors in the dataset.
- [ ] Add a column for the language of the speech.
- [ ] Add a column for the party of the speaker.
# Dataset structure
The dataset contains the following fields: text, speaker, date, id.
# Usage
```python
from datasets import load_dataset
dataset = load_dataset('Kushtrim/Kosovo-Parliament-Transcriptions')
```
# License
The dataset is licensed under the Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/).
# Citation
If you use this dataset in your research, please consider citing this repository. | [
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davanstrien/notebooks_by_user | davanstrien | 2023-12-17T14:26:17Z | 2 | 1 | [
"region:us"
] | null | 2023-03-27T09:14:54Z | ---
dataset_info:
features:
- name: user
dtype: large_string
- name: repo_notebook_count
dtype: int64
splits:
- name: train
num_bytes: 123026
num_examples: 4915
download_size: 65471
dataset_size: 123026
---
# Dataset Card for "notebooks_by_user"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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Haidra-Org/AI-Horde-Ratings | Haidra-Org | 2023-12-17T23:05:41Z | 2 | 4 | [
"language:en",
"license:cc-by-sa-4.0",
"ratings",
"stable diffusion",
"aesthetic",
"artifacts",
"region:us"
] | null | 2023-05-12T08:37:47Z | ---
license: cc-by-sa-4.0
language:
- en
tags:
- ratings
- stable diffusion
- aesthetic
- artifacts
pretty_name: AI Horde Ratings
---
# AI Horde Aesthetic and Artifact Ratings
A dataset of exported aesthetic and artifact ratings provided by the [AI Horde](https://aihorde.net) community through our [open ratings API](https://ratings.aihorde.net/api).
Each row in this dataset presents the rating for a single image from the [diffusiondb](https://poloclub.github.io/diffusiondb/). Each image UUID in this parquet will match the diffusiondb filename.
Each rating contains an aesthetic rating of 1-10, where 1 represents an image found distasteful, and 10 an image most found very pleasing. This is an explicitly subjective rating.
Each rating also contains an artifact rating of 0-5, where 0 represents no artifacts or image disruption, and 5 represents an image ruined. This ratings aims to be more objective.
The aim is for each image to be rated at least 5 times, so that a useful average can be ascertained.
While there are countermeasures to avoid bad actors, due to the open nature of the API for the ratings, some ratings might be random or malicious.
However due to the vast amount of other valid ratings, they overarching trend should be towards accuracy.
Nevertheless, if you notice any ratings which are obviously malicious, or users which are consistently fake-rating, please let us know and we'll clear them from this dataset.
# Structure
The columns in the dataset are as follows
* ratings_count: How many times this image has been rated throughout this dataset
* rating: The aesthetic (1-10) rating.
* kudos: The amount of kudos (i.e. priority) the user had at the moment of rating this image. Higher values represent users who have positively contributed to the AI Horde. This can be used to discover bad actors. (-50 are anonymous ratings)
* account_age: How old the user account is. This can be used to discover bad actors.
* usage_requests: How many images this user has generated at the moment of rating this image. This can be used to discover bad actors.
* created_at: When this rating was added
* client_agent: The client which was used to provide this rating. Unknown clients are more suspicious. This can be used to discover bad actors.
* artifacts: The artifacts (0-5) rating.
* user_id: The hashed user id who provided this rating
* trusted: If true, this user has been trusted by the horde by generating images or text for others for a long amount of time.
* validated: If true, this user's ratings have been manually validated by one of the AI Horde moderators.
* captchas_failed: How many captchas this user has failed. This can be used to discover bad actors. This is cumulative with succeeded captchas, so a negative amount means that many more succeeded captchas over failed ones.
* country: From which country did the rating originate. This can be used to create location-based rating models.
# Use cases
* [Clip-based aesthetic scorer](https://github.com/kenjiqq/aesthetics-scorer) ([Huggingface Demo](https://huggingface.co/spaces/kenjiqq/aesthetics-scorer)) | [
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tiange/Cap3D | tiange | 2023-12-13T06:20:59Z | 2 | 34 | [
"license:odc-by",
"arxiv:2306.07279",
"arxiv:2212.08051",
"arxiv:2110.06199",
"region:us"
] | null | 2023-05-28T18:31:58Z | ---
license: odc-by
viewer: false
---
# Scalable 3D Captioning with Pretrained Models
## Dataset Description
- **Paper:** [Scalable 3D Captioning with Pretrained Models](https://arxiv.org/abs/2306.07279)
- **Repository**: [Github](https://github.com/crockwell/Cap3D)
This repository hosts data for [Scalable 3D Captioning with Pretrained Models](https://cap3d-um.github.io/), including descriptive **captions** for 3D objects in [Objaverse](https://arxiv.org/abs/2212.08051) and [ABO](https://arxiv.org/abs/2110.06199) ([example captioning](https://tiangeluo.github.io/projectpages/imgs/Cap3D/000.html)). This repo also includes **point clouds** and rendered images of Objaverse objects, as well as their Shap-E latent codes. All the captions and data provided by our paper are released under
ODC-By 1.0 license.
## Usage
Please download and unzip files from [**Page**](https://huggingface.co/datasets/tiange/Cap3D/tree/main) according to your usage. Below is a table listing fiels descriptions, followed by example Python scripts for data loading.
| Filename | Description |
| -------------------------------------- | ------------------------------------------------------------ |
| **Cap3D_automated_Objaverse_full.csv** | Combine the text descriptions generated by Cap3D, resulting in **785,150** 3D-caption pairs for the Objaverse dataset.
| Cap3D_automated_Objaverse.csv | Combine the text descriptions generated by Cap3D, resulting in **661,577** 3D-caption pairs for the Objaverse dataset. All captions and related 3D objects here have commercial-friendly licenses (including CC-BY 4.0, CC-BY-SA 4.0, and CC0 1.0). We also filter out potential ethical-issues objects (e.g., identifiable face scans, NSFW, etc). This is also our NeurIPS version.
|**Cap3D_automated_Objaverse_no3Dword.csv** | Our current 3D captions are densely packed with "3D-model" terminology, potentially limiting their utility in applications like embodied AI. As such, we've created a version with minimized 3D-related words. For example, "A 3D model of a black and yellow samurai sword." ➡️ "a black and yellow samurai sword."|
|**Cap3D_automated_Objaverse_highquality.csv** |In Objaverse, we apply three criteria to filter out low-quality object-caption pairs: (1) captions containing potential inaccuracies; (2) objects scanned within indoor scenes; and (3) objects with potential ethical-issues.|
| Cap3D_automated_ABO.csv | Text descriptions generated by Cap3D. **6,440** 3D-caption (structural descriptive) pairs for ABO dataset. |
| Cap3D_human_{Objaverse, ABO}.pkl | Our collected human-authored text descriptions (Mostly English, very few non-English). The pickle file with `shape_id` as key. |
| **PointCloud_zips** | **785,150** PointClouds (16,384 colorful points) extracted from Objaverse objects. Saved as `.ply` file. |
| PointCloud_pt_zips | PointClouds saved as torch.Tensor `.pt` files, providing faster loading speed than `.ply`. |
| Filename | Description |
| -------------------------------------- | ------------------------------------------------------------ |
| ShapELatentCode_zips | Extracted latent codes for Objaverse objects by using pretrained [Shap-E transmitter](https://github.com/openai/shap-e/blob/main/model-card.md). |
| RenderedImage_zips | Rendered images for Objaverse objects: eight unique zip files each contain `661,577` JPEG images, representing different views. |
| RenderedImage_CamMatrix_zips | The `3x4` transformation matrix of the camera used to render the image. |
| RenderedImage_Mask_zips | Background mask (IMPERFECT) for rendered images: `bg_mask = np.load('xxx.npz')['bg_mask']` |
| our_finetuned_models | Model checkpoints for our finetuned models (point-E, shape-E, etc). Our also provide our finetune codes in [Github](https://github.com/crockwell/Cap3D/tree/main/text-to-3D). |
| blender.zip | The blender we used in this project. |
| compressed_imgs_ABO.zip | Renderings of ABO objects without textures, used in captioning and evaluation. |
| Cap3D_automated_Objaverse_allviews.pkl | Objaverse text descriptions of before putting into CLIP and GPT4. `pickle.load(open('Cap3D_automated_Objaverse_allviews.pkl','rb'))['view0'][UID]` |
| text-to-3D_test | Including files that used to evaluate text-to-3D performance. |
```
# load our captions
import pandas as pd
captions = pd.read_csv('Cap3D_automated_Objaverse_no3Dword.csv', header=None)
## captions:
## 0 1
## 0 ed51a51909ee46c780db3a85e821feb2 A green and white rifle.
## 1 9110b606f6c547b2980fcb3c8c4b6a1c a small building with a roof, accompanied by b...
## 2 80d9caaa1fa04502af666135196456e1 a pair of purple and black swords with white h...
## 3 28d43a218cd8466a8c1f82b29b71e314 a small house, island, road with trash, trash ...
## 4 75582285fab442a2ba31733f9c8fae66 a small, grassy hill.
## ... ... ...
## 661572 ccee95eac2fb48ec92d357e3d853f2bd a tall, white tower featuring a door, stairs, ...
## 661573 f02f574e85e94b879c1b54f4d3aa4b35 A yellow disc with a hole in the middle.
## 661574 f1d3d36114d34d29a18a8ed1516bf355 pink and white ball with a face and spikes.
## 661575 32d1927d6c0e4d9b9c445fc5988ec6c6 a white and pink bedroom and bathroom, featuri...
## 661576 2fa12ba0af5442c9af8f9bead1a7d020 Monstera plant with green leaves.
## if u want to obtain the caption for specific UID
caption = captions[captions[0] == '80d9caaa1fa04502af666135196456e1'][1].values[0]
# load point clouds (unzip https://huggingface.co/datasets/tiange/Cap3D/tree/main/PointCloud_pt_zips)
import torch
pts = torch.load('Cap3D_pcs_pt/80d9caaa1fa04502af666135196456e1.pt')
## pts.shape == torch.Size([6, 16384])
```
If you have any questions, please contact [Tiange](mailto:[email protected]) or [Chris](mailto:[email protected]).
## Citation Information
If you find our data or code useful, please consider citing:
```bibtex
@article{luo2023scalable,
title={Scalable 3D Captioning with Pretrained Models},
author={Luo, Tiange and Rockwell, Chris and Lee, Honglak and Johnson, Justin},
journal={arXiv preprint arXiv:2306.07279},
year={2023}
}
```
Please cite ***Objaverse*** and ***ABO*** paper accordingly, if you use related data.
```
@inproceedings{deitke2023objaverse,
title={Objaverse: A universe of annotated 3d objects},
author={Deitke, Matt and Schwenk, Dustin and Salvador, Jordi and Weihs, Luca and Michel, Oscar and VanderBilt, Eli and Schmidt, Ludwig and Ehsani, Kiana and Kembhavi, Aniruddha and Farhadi, Ali},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
pages={13142--13153},
year={2023}
}
```
```
@inproceedings{collins2022abo,
title={Abo: Dataset and benchmarks for real-world 3d object understanding},
author={Collins, Jasmine and Goel, Shubham and Deng, Kenan and Luthra, Achleshwar and Xu, Leon and Gundogdu, Erhan and Zhang, Xi and Vicente, Tomas F Yago and Dideriksen, Thomas and Arora, Himanshu and others},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
pages={21126--21136},
year={2022}
}
```
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edwinjue/311-data-2023 | edwinjue | 2023-12-17T06:25:42Z | 2 | 0 | [
"license:gpl-3.0",
"region:us"
] | null | 2023-06-10T23:15:30Z | ---
license: gpl-3.0
---
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taesiri/arxiv_audio | taesiri | 2023-12-15T04:46:48Z | 2 | 0 | [
"license:cc-by-4.0",
"region:us"
] | null | 2023-07-03T03:46:57Z | ---
license: cc-by-4.0
---
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vodkagrad/main | vodkagrad | 2023-12-17T11:12:37Z | 2 | 0 | [
"license:openrail",
"region:us"
] | null | 2023-07-25T04:45:20Z | ---
license: openrail
---
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Shashkovich/Telecommunication_SMS_time_series | Shashkovich | 2023-12-14T16:20:01Z | 2 | 1 | [
"task_categories:time-series-forecasting",
"license:gpl-3.0",
"SMS",
"fraud",
"forecasting",
"region:us"
] | [
"time-series-forecasting"
] | 2023-07-27T20:42:33Z | ---
license: gpl-3.0
task_categories:
- time-series-forecasting
tags:
- SMS
- fraud
- forecasting
pretty_name: SMS time series
---
# SMS Time series data for traffic and fraud forecasting
This dataset contains various time series from vendors.
# Vendor A: 01.03.23-14.08.23
* TS_*_all - Count of all SMS
# Vendor A: January
* TS_*_fraud - Count of fraud
* TS_*_all - Count of all SMS
* TS_*_hlrDelay - Mean values of hlr delay
# Vendor B: January 1-8
* 1-8_TS_*_fraud - Count of fraud
* 1-8_TS_*_all - Count of all SMS
* 1-8_TS_*_hlrDelay - Mean values of hlr delay
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-0.8736312985420227,
-0.8302837610244751
] |
severo/trending-repos | severo | 2023-12-17T11:00:42Z | 2 | 5 | [
"size_categories:n<1K",
"license:apache-2.0",
"region:us"
] | null | 2023-07-28T13:57:34Z | ---
license: apache-2.0
pretty_name: Trending repositories on Hugging Face
size_categories:
- n<1K
configs:
- config_name: models
data_files: "models.csv"
- config_name: datasets
data_files: "datasets.csv"
- config_name: spaces
data_files: "spaces.csv"
---
# Dataset Card for Dataset Name
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:** Sylvain Lesage
### Dataset Summary
This dataset contains the 20 trending repositories of each type: models, datasets, and space, on Hugging Face, every day. Each type can be loaded from its own dataset config.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
Not relevant.
## Dataset Structure
### Data Instances
The dataset contains three configurations:
**models**: the history of trending models on Hugging Face
**datasets**: the history of trending datasets on Hugging Face
**spaces**: the history of trending spaces on Hugging Face
### Data Fields
- date (string): the date of the lookup to the trending repositories
- author (string): id of the repository owner. It can be null.
- id (string): id of the repository
- rank (int64): rank in the trending repositories of its kind (model, dataset, or space). Starts at 1.
- recent_likes (int64): number of likes received lately (last week)
- likes (int64): total number of likes
- month_downloads (int64): number of downloads in the last month. Null for the spaces.
### Data Splits
Each configuration only has one split: `train` that contains all the rows.
## Dataset Creation
### Curation Rationale
The dataset is updated daily through a cron job that calls the `https://huggingface.co/api/trending?type=${repoType}&limit=20` endpoint for each repository type (model, dataset, space). The script runs in an [Observable](https://observablehq.com/@huggingface) notebook, and the files are uploaded using the [huggingface.js](https://github.com/huggingface/huggingface.js) library.
### Source Data
#### Initial Data Collection and Normalization
Not relevant.
#### Who are the source language producers?
Not relevant.
### Annotations
#### Annotation process
Not relevant.
#### Who are the annotators?
Not relevant.
### Personal and Sensitive Information
Only public repositories are included in the trending repositories.
## Considerations for Using the Data
### Social Impact of Dataset
Not relevant.
### Discussion of Biases
The trending repositories reflect the likes given by Hugging Face users in the last week. Any bias that applies to the users can be reflected in this dataset. As a vanity metric, some users might also be tempted to generate fake likes.
### Other Known Limitations
Not relevant.
## Additional Information
### Dataset Curators
Sylvain Lesage, Hugging Face
### Licensing Information
Apache License 2.0
### Citation Information
Not relevant.
### Contributions
Not relevant.
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wjwow/FreeMan | wjwow | 2023-12-14T15:24:43Z | 2 | 2 | [
"language:en",
"license:cc-by-nc-nd-4.0",
"pose estimation",
"computer vision",
"3d human",
"arxiv:2309.05073",
"region:us"
] | null | 2023-07-31T17:09:22Z | ---
extra_gated_heading: Access FreeMan on Hugging Face
extra_gated_description: >-
This is a form to enable access to FreeMan on Hugging Face after you have been
granted access.
Please visit the [FreeMan Project Page](https://wangjiongw.github.io/freeman/)
and **complete [pre requisite procudures](https://wangjiongw.github.io/freeman/download.html) before confirming request here**.
Requests will be processed in 1-2 days.
extra_gated_prompt: >-
**Your Hugging Face account email address MUST match the email or Huggingface ID you provide on
the information backup forms matches the one filled in previous step, or your request will not be approved.**
**For who are in mainland China, you can also apply & download from [OpenDataLab](https://openxlab.org.cn/datasets/wangjiongwow/FreeMan/tree/main) for stable connection.**
**中国大陆的使用者可以考虑通过[OpenDataLab](https://openxlab.org.cn/datasets/wangjiongwow/FreeMan/tree/main)获取下载链接。**
extra_gated_button_content: Submit
extra_gated_fields:
I have already registered information at FreeMan website and I understand FreeMan is for non-commercial usage ONLY: checkbox
license: cc-by-nc-nd-4.0
language:
- en
tags:
- pose estimation
- computer vision
- 3d human
---
# [FreeMan: Towards 3D Human Pose Estimation In the Wild](https://arxiv.org/abs/2309.05073)
<p align="left">
<font size='4'>
<a href="https://wangjiongw.github.io/freeman" target="_blank">🌏 Project Page</a> •
<a href="https://wangjiongw.github.io/freeman/download.html" target="_blank">🙋♂️ <b>Download Procedure</b></a> •
<a href="https://arxiv.org/abs/2309.05073" target="_blank">📄 Paper </a> •
<a href="https://www.youtube.com/watch?v=g2h1YW-3n5k" target="_blank">▶️ YouTube </a> •
<a href="https://github.com/wangjiongw/FreeMan_API" target="_blank">🖥️ Code </a>
</font>
</p>
---
This is official release of FreeMan dataset. To access the dataset, please submit previous steps at **[HERE](https://wangjiongw.github.io/freeman/download.html)**.
**[❗️❗️❗️] MAKE SURE you finish required steps [HERE](https://wangjiongw.github.io/freeman/download.html) before apply dataset access here. Otherwise access request will NOT be approved.**
**For who are in mainland China, you can also apply & download from [OpenDataLab](https://openxlab.org.cn/datasets/wangjiongwow/FreeMan/tree/main) for stable connection.**
**中国大陆的使用者可以考虑通过[OpenDataLab](https://openxlab.org.cn/datasets/wangjiongwow/FreeMan/tree/main)获取下载链接。**
## Notice
[2023.09] 30FPS data for 3D Human Pose Estimation uploaded. Other data are under processing.
[2023.09] Paper released on [arxiv](https://arxiv.org/abs/2309.05073). We are uploading the datasets. Please stay tuned.
---
## Overview
Current uploaded data:
- 8 views
- 40 subjects
- 11M frames
- 10 types of scenarios
- 27 locations
## File Info
The whole dataset is about 800GB and all RGB data are stored in video format. RGB data are zipped by subject, while all annotation files are zipped all togethers.
Besides zip files, text files store list of each subsets.
```
session_list.txt: all session names included in FreeMan;
session_list_mono.txt: sample names and corrsponding view index in monocular experiments;
ignore_list.txt: sessions not used temporarily in experiments of FreeMan Paper(https://arxiv.org/abs/2309.05073);
train/valid/test.txt: session names in train/validation/test subset, respectivelly. They are seperated by subject.
```
To verify downloaded items, sha256sum for video files are shown here:
| File Name | sha256sum | File Name | sha256sum | File Name | sha256sum | File Name | sha256sum |
| :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |
| subj01.zip | a02d8e47d36235ae760f553b85b7df21cb711fecea73e7542df49e5c2d1441e6 | subj02.zip | 10d9b9af46ade9069401832ad0f1b06edd12a9e39f2ea9d06a55f1371bb9a28f | subj02.01 | 94ac1a0af242c33bb0d8777bea094fe0542ace0077f8836ff3944b0e41d3e45b | subj02.02 | 67174ca443c7c35fd1604d69ae30a46fece96af83d83d7428db1ce6483782739 |
| subj02.03 | 05534d4e352454a5ffe0df802da53057953df274879d3067e023f2667773e4f6 | subj03.zip | 6825c59ecaec5114ead9f8d5f88d3d8ec2949cb9db102fb6beee6c338d2ae174 | subj04.zip | 48dc77e2c6fe741b8b271ee302ba5883eabec5ed960dac66181b58e38a36282d | subj05.zip | ab8033cc55e5e4f578490a225e60e986e67f162b79da44e68afcab805f396f2a |
| subj06.zip | a204970bc9ebd142a97276ed6bb095cb3a9e621090a8fad57fb8bbe99b23d733 | subj07.zip | 292cf18231592c730a811488e9896756ee137b737c1e207428efda9f73a75d2d | subj08.zip | fad741aa4400c813a8a71303ad855c7d0e130613db3c72222b14f010ee8c74d4 | subj09.zip | 46bcf599207c7fcaac84102e40a60c29720e916431f13b932273d33199bab922 |
| subj10.zip | 320f9c0cb169261bbcdc60163525d5b0b35fb17110dc488ad71491047b35d582 | subj11.zip | 72e3f99b5bfb918c269bfc53515c1d556e3ee8eed8d5aa82d619971eb74e76af | subj12.zip | 5e7c8dea74b59f14f65b08a51c1688f920ac24f943a1a6102fdb519b5df4702a | subj13.zip | 4a76b93a26fad023b315f46ff107ed619adb920cb2fb5d03e8de7f33bc68d0f6 |
| subj14.zip | d78998c715bd54a65c9da7b247212c1a1833405c125bb5492b5d5fca9ec0e17f | subj15.zip | 070dfdd2d420c4392f487ae9558406cee970fe43f802f26ca28a4871f8b46d0b | subj16.zip | 9933c2e3f8b114b96e4b5f9938d22ca77686b6952489406ee07705ac4601caa2 | subj17.zip | 2e80f886faf3a5b3679da8f8f7bde0034008415128c404ea9445368f1d0cbe17 |
| subj18.zip | 0f060e6c2b1699e9780f60b826946d69a77ae6c668cf6d02d3063b172ba3e3e0 | subj19.zip | 12bb121a1f4bd57a741ab651375af9bed105a9226390d225d8b08512d910f5d6 | subj20.zip | c07f8e261b4df3913399589405cbefd6a70807c6400373c92a4635390d4ee644 | subj21.zip | 26bc74962012698402836d2c327872c240b638a2bb54ae3b85af12b6a93269ee |
| subj22.zip | 0b69237ff95205ff51c95bdb34cdaabf0304ecf856d4ce1f07d9f76c16e42cd1 | subj22.01 | f66ac6370835164ee66e7db1cf9c60609218f3c6d19dfe2b07f39bffa12635ae | subj23.zip | 2ad8a29c949e0208c7338efd955a3f6b9322c61a691ceac3be89d356fb02eb1c | subj24.zip | 1902fd34d9d9717c5be95af36162095aa2db35d6327e6d7d828b3c7ffe97587c |
| subj25.zip | 01209473ca4a50e72e20849150dd489b62b6153fd2be798b699ceb106af40d1d | subj26.zip | 6cf1b76860a68bf8452e7e7719b2bb18bcb727691fb14e37da3864170973bfba | subj27.zip | a6e17a99b4597e26bb38ab86eb1c6f31534a643fb5ede55c888d34eda87fa546 | subj28.zip | f4ba067b02e2fd15d06f3e9d0eb750d2054d199b18d53258a77158153bef5c20 |
| subj29.zip | 2cfec4175ed17b2763af4d21b6a924fd31cc4176109e53beaa5141ff63715db0 | subj30.zip | c57c28de39dd81f579df00758717e1e9f3a73df338c61b51667be450beea8220 | subj31.zip | ee83b60bc25e5e3fe63f56437e8a9c023d9e7deebea987e20516d568280f01c0 | subj32.zip | 7e217b54696f9214585072def09f43a11a3f0fa2181d9478106055c69f2a2a48 |
| subj33.zip | 3f214e38daf8d416d2c5a5f5c32e6ea093a6c5cbc4c9eaa47e4a7e23c733a001 | subj34.zip | 39634d0f0e2db1bce9b9254a7dbc1425d5c4f83b38b64158e6ad8d411dfe4c65 | subj35.zip | 75c41ae4ade932774a3e8d58684c2b7fb9d62895dc87df9e1112d4d54894dae5 | subj36.zip | fc71637a6c46ad8de362204277c0bd68058b7a1e88c05c1bd713d80a85ea74ed |
| subj37.zip | 75347b1c8cd5e4afaa923376bfe5109e890c5593388de2145cfd2fc081d9ba83 | subj38.zip | e99d2de0a05c798fa91112ea69468bd0cbeb00aaad1c5d330bacee0ede30f540 | subj39.zip | 669feb7b7effee0cc6d532151c509c89dcd5e85a259ad56cf9df321e526f56fa | subj40.zip | c3f632c5e52331209af17c44d4ee6a16511d625096a19b4b043bc811489b0fa3 |
## Citation
If you find FreeMan helpful and used in your project, please cite our paper.
```
@article{wang2023freeman,
title={FreeMan: Towards Benchmarking 3D Human Pose Estimation in the Wild},
author={Wang, Jiong and Yang, Fengyu and Gou, Wenbo and Li, Bingliang and Yan, Danqi and Zeng, Ailing and Gao, Yijun and Wang, Junle and Zhang, Ruimao},
journal={arXiv preprint arXiv:2309.05073},
year={2023}
}
```
## Contact
If any questions, please feel free to email us at [email protected], [email protected],
or leave your questions in chat/issues. | [
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] |
CyberHarem/yamada_elf_eromangasensei | CyberHarem | 2023-12-12T20:43:14Z | 2 | 0 | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | [
"text-to-image"
] | 2023-08-15T01:19:25Z | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of Yamada Elf
This is the dataset of Yamada Elf, containing 335 images and their tags.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
| Name | Images | Download | Description |
|:----------------|---------:|:----------------------------------------|:-----------------------------------------------------------------------------------------|
| raw | 335 | [Download](dataset-raw.zip) | Raw data with meta information. |
| raw-stage3 | 810 | [Download](dataset-raw-stage3.zip) | 3-stage cropped raw data with meta information. |
| raw-stage3-eyes | 866 | [Download](dataset-raw-stage3-eyes.zip) | 3-stage cropped (with eye-focus) raw data with meta information. |
| 384x512 | 335 | [Download](dataset-384x512.zip) | 384x512 aligned dataset. |
| 512x704 | 335 | [Download](dataset-512x704.zip) | 512x704 aligned dataset. |
| 640x880 | 335 | [Download](dataset-640x880.zip) | 640x880 aligned dataset. |
| stage3-640 | 810 | [Download](dataset-stage3-640.zip) | 3-stage cropped dataset with the shorter side not exceeding 640 pixels. |
| stage3-800 | 810 | [Download](dataset-stage3-800.zip) | 3-stage cropped dataset with the shorter side not exceeding 800 pixels. |
| stage3-p512-640 | 688 | [Download](dataset-stage3-p512-640.zip) | 3-stage cropped dataset with the area not less than 512x512 pixels. |
| stage3-eyes-640 | 866 | [Download](dataset-stage3-eyes-640.zip) | 3-stage cropped (with eye-focus) dataset with the shorter side not exceeding 640 pixels. |
| stage3-eyes-800 | 866 | [Download](dataset-stage3-eyes-800.zip) | 3-stage cropped (with eye-focus) dataset with the shorter side not exceeding 800 pixels. |
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CyberHarem/jinno_megumi_eromangasensei | CyberHarem | 2023-12-12T20:55:22Z | 2 | 0 | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | [
"text-to-image"
] | 2023-08-15T01:45:46Z | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of Jinno Megumi
This is the dataset of Jinno Megumi, containing 81 images and their tags.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
| Name | Images | Download | Description |
|:----------------|---------:|:----------------------------------------|:-----------------------------------------------------------------------------------------|
| raw | 81 | [Download](dataset-raw.zip) | Raw data with meta information. |
| raw-stage3 | 196 | [Download](dataset-raw-stage3.zip) | 3-stage cropped raw data with meta information. |
| raw-stage3-eyes | 222 | [Download](dataset-raw-stage3-eyes.zip) | 3-stage cropped (with eye-focus) raw data with meta information. |
| 384x512 | 81 | [Download](dataset-384x512.zip) | 384x512 aligned dataset. |
| 512x704 | 81 | [Download](dataset-512x704.zip) | 512x704 aligned dataset. |
| 640x880 | 81 | [Download](dataset-640x880.zip) | 640x880 aligned dataset. |
| stage3-640 | 196 | [Download](dataset-stage3-640.zip) | 3-stage cropped dataset with the shorter side not exceeding 640 pixels. |
| stage3-800 | 196 | [Download](dataset-stage3-800.zip) | 3-stage cropped dataset with the shorter side not exceeding 800 pixels. |
| stage3-p512-640 | 166 | [Download](dataset-stage3-p512-640.zip) | 3-stage cropped dataset with the area not less than 512x512 pixels. |
| stage3-eyes-640 | 222 | [Download](dataset-stage3-eyes-640.zip) | 3-stage cropped (with eye-focus) dataset with the shorter side not exceeding 640 pixels. |
| stage3-eyes-800 | 222 | [Download](dataset-stage3-eyes-800.zip) | 3-stage cropped (with eye-focus) dataset with the shorter side not exceeding 800 pixels. |
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CyberHarem/senju_muramasa_eromangasensei | CyberHarem | 2023-12-12T21:14:52Z | 2 | 0 | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | [
"text-to-image"
] | 2023-08-15T06:20:59Z | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of Senju Muramasa
This is the dataset of Senju Muramasa, containing 166 images and their tags.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
| Name | Images | Download | Description |
|:----------------|---------:|:----------------------------------------|:-----------------------------------------------------------------------------------------|
| raw | 166 | [Download](dataset-raw.zip) | Raw data with meta information. |
| raw-stage3 | 330 | [Download](dataset-raw-stage3.zip) | 3-stage cropped raw data with meta information. |
| raw-stage3-eyes | 368 | [Download](dataset-raw-stage3-eyes.zip) | 3-stage cropped (with eye-focus) raw data with meta information. |
| 384x512 | 166 | [Download](dataset-384x512.zip) | 384x512 aligned dataset. |
| 512x704 | 166 | [Download](dataset-512x704.zip) | 512x704 aligned dataset. |
| 640x880 | 166 | [Download](dataset-640x880.zip) | 640x880 aligned dataset. |
| stage3-640 | 330 | [Download](dataset-stage3-640.zip) | 3-stage cropped dataset with the shorter side not exceeding 640 pixels. |
| stage3-800 | 330 | [Download](dataset-stage3-800.zip) | 3-stage cropped dataset with the shorter side not exceeding 800 pixels. |
| stage3-p512-640 | 291 | [Download](dataset-stage3-p512-640.zip) | 3-stage cropped dataset with the area not less than 512x512 pixels. |
| stage3-eyes-640 | 368 | [Download](dataset-stage3-eyes-640.zip) | 3-stage cropped (with eye-focus) dataset with the shorter side not exceeding 640 pixels. |
| stage3-eyes-800 | 368 | [Download](dataset-stage3-eyes-800.zip) | 3-stage cropped (with eye-focus) dataset with the shorter side not exceeding 800 pixels. |
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ccml/dataset1 | ccml | 2023-12-15T14:31:26Z | 2 | 0 | [
"task_categories:conversational",
"size_categories:n<1K",
"license:mit",
"code",
"region:us"
] | [
"conversational"
] | 2023-08-19T20:37:28Z | ---
license: mit
task_categories:
- conversational
tags:
- code
size_categories:
- n<1K
pretty_name: mydata-1
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davidberenstein1957/emotion-custom | davidberenstein1957 | 2023-12-16T11:26:44Z | 2 | 0 | [
"size_categories:n<1K",
"rlfh",
"argilla",
"human-feedback",
"region:us"
] | null | 2023-08-23T09:28:26Z | ---
size_categories: n<1K
tags:
- rlfh
- argilla
- human-feedback
---
# Dataset Card for emotion-custom
This dataset has been created with [Argilla](https://docs.argilla.io).
As shown in the sections below, this dataset can be loaded into Argilla as explained in [Load with Argilla](#load-with-argilla), or used directly with the `datasets` library in [Load with `datasets`](#load-with-datasets).
## Dataset Description
- **Homepage:** https://argilla.io
- **Repository:** https://github.com/argilla-io/argilla
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
This dataset contains:
* A dataset configuration file conforming to the Argilla dataset format named `argilla.yaml`. This configuration file will be used to configure the dataset when using the `FeedbackDataset.from_huggingface` method in Argilla.
* Dataset records in a format compatible with HuggingFace `datasets`. These records will be loaded automatically when using `FeedbackDataset.from_huggingface` and can be loaded independently using the `datasets` library via `load_dataset`.
* The [annotation guidelines](#annotation-guidelines) that have been used for building and curating the dataset, if they've been defined in Argilla.
### Load with Argilla
To load with Argilla, you'll just need to install Argilla as `pip install argilla --upgrade` and then use the following code:
```python
import argilla as rg
ds = rg.FeedbackDataset.from_huggingface("davidberenstein1957/emotion-custom")
```
### Load with `datasets`
To load this dataset with `datasets`, you'll just need to install `datasets` as `pip install datasets --upgrade` and then use the following code:
```python
from datasets import load_dataset
ds = load_dataset("davidberenstein1957/emotion-custom")
```
### Supported Tasks and Leaderboards
This dataset can contain [multiple fields, questions and responses](https://docs.argilla.io/en/latest/conceptual_guides/data_model.html#feedback-dataset) so it can be used for different NLP tasks, depending on the configuration. The dataset structure is described in the [Dataset Structure section](#dataset-structure).
There are no leaderboards associated with this dataset.
### Languages
[More Information Needed]
## Dataset Structure
### Data in Argilla
The dataset is created in Argilla with: **fields**, **questions**, **suggestions**, **metadata**, **vectors**, and **guidelines**.
The **fields** are the dataset records themselves, for the moment just text fields are supported. These are the ones that will be used to provide responses to the questions.
| Field Name | Title | Type | Required | Markdown |
| ---------- | ----- | ---- | -------- | -------- |
| text | Text | text | True | False |
The **questions** are the questions that will be asked to the annotators. They can be of different types, such as rating, text, label_selection, multi_label_selection, or ranking.
| Question Name | Title | Type | Required | Description | Values/Labels |
| ------------- | ----- | ---- | -------- | ----------- | ------------- |
| sentiment | Sentiment | label_selection | True | N/A | ['positive', 'neutral', 'negative'] |
| mixed-emotion | Mixed-emotion | multi_label_selection | True | N/A | ['joy', 'anger', 'sadness', 'fear', 'surprise', 'love'] |
The **suggestions** are human or machine generated recommendations for each question to assist the annotator during the annotation process, so those are always linked to the existing questions, and named appending "-suggestion" and "-suggestion-metadata" to those, containing the value/s of the suggestion and its metadata, respectively. So on, the possible values are the same as in the table above, but the column name is appended with "-suggestion" and the metadata is appended with "-suggestion-metadata".
The **metadata** is a dictionary that can be used to provide additional information about the dataset record. This can be useful to provide additional context to the annotators, or to provide additional information about the dataset record itself. For example, you can use this to provide a link to the original source of the dataset record, or to provide additional information about the dataset record itself, such as the author, the date, or the source. The metadata is always optional, and can be potentially linked to the `metadata_properties` defined in the dataset configuration file in `argilla.yaml`.
| Metadata Name | Title | Type | Values | Visible for Annotators |
| ------------- | ----- | ---- | ------ | ---------------------- |
The **guidelines**, are optional as well, and are just a plain string that can be used to provide instructions to the annotators. Find those in the [annotation guidelines](#annotation-guidelines) section.
### Data Instances
An example of a dataset instance in Argilla looks as follows:
```json
{
"external_id": null,
"fields": {
"text": "i didnt feel humiliated"
},
"metadata": {},
"responses": [
{
"status": "submitted",
"user_id": "f2c5232d-10c8-4468-8044-6b489e9db9b6",
"values": {
"mixed-emotion": {
"value": [
"fear"
]
},
"sentiment": {
"value": "positive"
}
}
}
],
"suggestions": [],
"vectors": {}
}
```
While the same record in HuggingFace `datasets` looks as follows:
```json
{
"external_id": null,
"metadata": "{}",
"mixed-emotion": [
{
"status": "submitted",
"user_id": "f2c5232d-10c8-4468-8044-6b489e9db9b6",
"value": [
"fear"
]
}
],
"mixed-emotion-suggestion": null,
"mixed-emotion-suggestion-metadata": {
"agent": null,
"score": null,
"type": null
},
"sentiment": [
{
"status": "submitted",
"user_id": "f2c5232d-10c8-4468-8044-6b489e9db9b6",
"value": "positive"
}
],
"sentiment-suggestion": null,
"sentiment-suggestion-metadata": {
"agent": null,
"score": null,
"type": null
},
"text": "i didnt feel humiliated"
}
```
### Data Fields
Among the dataset fields, we differentiate between the following:
* **Fields:** These are the dataset records themselves, for the moment just text fields are supported. These are the ones that will be used to provide responses to the questions.
* **text** is of type `text`.
* **Questions:** These are the questions that will be asked to the annotators. They can be of different types, such as `RatingQuestion`, `TextQuestion`, `LabelQuestion`, `MultiLabelQuestion`, and `RankingQuestion`.
* **sentiment** is of type `label_selection` with the following allowed values ['positive', 'neutral', 'negative'].
* **mixed-emotion** is of type `multi_label_selection` with the following allowed values ['joy', 'anger', 'sadness', 'fear', 'surprise', 'love'].
* **Suggestions:** As of Argilla 1.13.0, the suggestions have been included to provide the annotators with suggestions to ease or assist during the annotation process. Suggestions are linked to the existing questions, are always optional, and contain not just the suggestion itself, but also the metadata linked to it, if applicable.
* (optional) **sentiment-suggestion** is of type `label_selection` with the following allowed values ['positive', 'neutral', 'negative'].
* (optional) **mixed-emotion-suggestion** is of type `multi_label_selection` with the following allowed values ['joy', 'anger', 'sadness', 'fear', 'surprise', 'love'].
Additionally, we also have two more fields that are optional and are the following:
* **metadata:** This is an optional field that can be used to provide additional information about the dataset record. This can be useful to provide additional context to the annotators, or to provide additional information about the dataset record itself. For example, you can use this to provide a link to the original source of the dataset record, or to provide additional information about the dataset record itself, such as the author, the date, or the source. The metadata is always optional, and can be potentially linked to the `metadata_properties` defined in the dataset configuration file in `argilla.yaml`.
* **external_id:** This is an optional field that can be used to provide an external ID for the dataset record. This can be useful if you want to link the dataset record to an external resource, such as a database or a file.
### Data Splits
The dataset contains a single split, which is `train`.
## 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 guidelines
Emotion is a dataset of English Twitter messages with six basic emotions: anger, fear, joy, love, sadness, and surprise.
#### 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] | [
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ChanceFocus/flare-es-fns | ChanceFocus | 2023-12-15T08:56:21Z | 2 | 0 | [
"region:us"
] | null | 2023-08-29T02:14:35Z | ---
dataset_info:
features:
- name: query
dtype: string
- name: answer
dtype: string
- name: text
dtype: string
splits:
- name: test
num_bytes: 20134903
num_examples: 50
download_size: 9992059
dataset_size: 20134903
---
# Dataset Card for "flare-es-fns"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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ChanceFocus/flare-es-tsa | ChanceFocus | 2023-12-15T08:57:36Z | 2 | 0 | [
"region:us"
] | null | 2023-08-29T04:09:17Z | ---
dataset_info:
features:
- name: query
dtype: string
- name: answer
dtype: string
- name: text
dtype: string
- name: choices
sequence: string
- name: gold
dtype: int64
splits:
- name: test
num_bytes: 1316673
num_examples: 3829
download_size: 483832
dataset_size: 1316673
---
# Dataset Card for "flare-es-tsa"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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] |
GFA-D2/pilot_flags | GFA-D2 | 2023-12-13T21:00:22Z | 2 | 0 | [
"region:us"
] | null | 2023-09-03T12:16:01Z | ---
configs:
- config_name: default
data_files:
- split: train
path: data.csv
---
# Dataset Card for Dataset Name
## Dataset Description
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### Dataset Summary
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### Languages
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## Dataset Structure
### Data Instances
[More Information Needed]
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## Dataset Creation
### Curation Rationale
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#### Initial Data Collection and Normalization
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### Contributions
[More Information Needed] | [
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ChanceFocus/flare-es-financees | ChanceFocus | 2023-12-15T08:55:11Z | 2 | 0 | [
"region:us"
] | null | 2023-09-06T00:58:16Z | ---
dataset_info:
features:
- name: query
dtype: string
- name: answer
dtype: string
- name: text
dtype: string
- name: choices
sequence: string
- name: gold
dtype: int64
splits:
- name: test
num_bytes: 1921453
num_examples: 6359
download_size: 740681
dataset_size: 1921453
---
# Dataset Card for "flare-es-financees"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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Minami-su/roleplay_multiturn_chat_1k_zh_v0.1 | Minami-su | 2023-12-16T04:29:42Z | 2 | 10 | [
"language:zh",
"roleplay",
"multiturn_chat",
"doi:10.57967/hf/1381",
"region:us"
] | null | 2023-09-13T01:54:10Z | ---
language:
- zh
tags:
- roleplay
- multiturn_chat
---
## 介绍
基于self-instruct生成的多轮对话roleplay数据，约1k条不同的人格数据和对话
## 存在问题：
1.基于模型自身生成，所以roleplay存在模型本身价值观融入情况，导致roleplay不够真实，不够准确。
## 关于我自己：
我是小雨的开发者，小雨是一个情感ai，人格ai，如果对小雨感兴趣的话欢迎支持一下，她目前在bilibili直播，目前我仍在不断的改进。未来，“小雨”的目标是成为一个
具有真正人类情感的多模态通用人工智能。
url：https://live.bilibili.com/27357528?broadcast_type=0&is_room_feed=1&spm_id_from=333.999.live_users_card.0.click&live_from=86001
## 注：
使用本数据集请注明来源
## Introduction
This dataset consists of approximately 1,000 instances of multi-turn roleplay conversations generated based on self-instruction. Each instance involves different personalities engaging in dialogue.
## Challenges:
The data is generated by the model itself, leading to potential integration of the model's own values into the roleplay scenarios. This may result in roleplays that are not entirely realistic or accurate.
## About Myself:
I am the developer of Xiaoyu, an AI specializing in emotion and personality. If you're interested in Xiaoyu, feel free to show your support! She is currently live on Bilibili, and I am continuously working on improvements.
In the future, '小雨' aims to become a multimodal general artificial intelligence with genuine human emotions.
URL: https://live.bilibili.com/27357528?broadcast_type=0&is_room_feed=1&spm_id_from=333.999.live_users_card.0.click&live_from=86001
## Note:
When using this dataset, please provide proper attribution to the source.
## 引用
```
@misc{selfinstruct,
title={Self-Instruct: Aligning Language Model with Self Generated Instructions},
author={Wang, Yizhong and Kordi, Yeganeh and Mishra, Swaroop and Liu, Alisa and Smith, Noah A. and Khashabi, Daniel and Hajishirzi, Hannaneh},
journal={arXiv preprint arXiv:2212.10560},
year={2022}
}
```
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DebasishDhal99/german-czech-paired-placenames | DebasishDhal99 | 2023-12-17T09:44:51Z | 2 | 0 | [
"task_categories:translation",
"size_categories:1K<n<10K",
"language:de",
"language:cs",
"license:mit",
"history",
"region:us"
] | [
"translation"
] | 2023-09-17T21:57:51Z | ---
license: mit
task_categories:
- translation
language:
- de
- cs
tags:
- history
size_categories:
- 1K<n<10K
---
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
This dataset contains the German and corresponding Czech names for almost 5k places in Czech Republic. It has been generated using [this code](https://github.com/DebasishDhal/Minor-Stuff/blob/main/paired-placenames-scrapping/german-czech.py).
Many of these names are related to each other. Some German names are literal translation of the Czech names (or maybe the other way around), some are phonetic modifications while some are unrelated.
## Dataset Creation
### Source Data
[English wiki page containing German exonyms for places in Czech Republic](https://en.wikipedia.org/wiki/List_of_German_names_for_places_in_the_Czech_Republic) | [
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multimodalart/lora-fusing-preferences | multimodalart | 2023-12-16T02:57:35Z | 2 | 2 | [
"license:mit",
"region:us"
] | null | 2023-09-21T12:27:19Z | ---
license: mit
---
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avermeersch/calabi-yau-threefolds | avermeersch | 2023-12-13T15:04:21Z | 2 | 1 | [
"size_categories:1K<n<10K",
"license:other",
"Calabi-Yau",
"Toric Geometry",
"String Theory",
"Polyhedra",
"Geometry",
"Physics",
"region:us"
] | null | 2023-09-28T14:38:46Z | ---
name: Reflexive Polyhedra of Calabi-Yau Threefolds
date: '2023-09-28'
license: other
domain: Physics
tags:
- Calabi-Yau
- Toric Geometry
- String Theory
- Polyhedra
- Geometry
- Physics
pretty_name: Calabi-Yau 3-Folds
size_categories:
- 1K<n<10K
---
# Dataset Card for Reflexive Polyhedra of Calabi-Yau Threefolds
## Table of Contents
- [Dataset Description](#dataset-description)
- [General Information](#general-information)
- [Dataset Origin](#dataset-origin)
- [Dataset Characteristics](#dataset-characteristics)
- [Schema](#schema)
- [Data Fields](#data-fields)
- [Data Format](#data-format)
- [Usage](#usage)
- [Getting Started](#getting-started)
- [Machine Learning Applications](#machine-learning-applications)
- [Citations](#citations)
## Dataset Description
### General Information
Calabi-Yau threefolds are a special class of smooth, compact three-dimensional spaces that have become fundamental objects in both mathematics and theoretical physics. In the context of string theory, they serve as the internal dimensions over which strings compactify, leading to a four-dimensional effective theory. The geometry of these threefolds is closely related to many physical phenomena, including the number of particle generations, gauge symmetries, and the cosmological constant. This dataset encompasses all 4319 reflexive polyhedra in 3 dimensions, offering a comprehensive view of potential Calabi-Yau geometries. The reflexive polyhedra serve as dual representations of these threefolds and are crucial in understanding their topological and geometric properties.
### Dataset Origin
The dataset is derived from the original work documented in [hep-th/9805190](https://arxiv.org/abs/hep-th/9805190). While the original dataset was in a PALP-compatible structure, this version has been converted to a nested JSON format to better accommodate machine learning applications. The PALP-compatible version of the dataset can be accessed at [CYk3](http://hep.itp.tuwien.ac.at/~kreuzer/CY/CYk3.html).
## Dataset Characteristics
### Schema
The dataset is presented in a nested JSON format, with each entry containing both metadata and a matrix representing the vertices of the corresponding polyhedron.
### Data Fields
- `M1`, `M2`: These are point and vertex numbers in the M lattice, which is a mathematical lattice in the context of toric geometry. This lattice serves as the foundational geometric space from which the polyhedron is constructed.
- `N1`, `N2`: Similar to the M lattice, these are point and vertex numbers in the N lattice, which is dual to the M lattice. The N lattice provides a different but equally important geometric perspective for understanding the polyhedron.
- `Pic`: The Picard number is a topological invariant that measures the rank of the Néron-Severi group of a manifold. In the context of Calabi-Yau threefolds, it helps to determine the number of independent 2-cycles, which has physical implications like the number of U(1) gauge fields in the effective theory.
- `Cor`: The correction term is a specific mathematical entity that adjusts the Picard number to account for certain topological peculiarities. The Picard numbers of a polyhedron and its dual add up to \( 20 + \text{correction} \).
- `Matrix`: A 3xN matrix containing the coordinates of the vertices of the polyhedron. Each row represents a dimension in 3D space, and each column represents a vertex.
### Data Format
Each entry in the dataset is structured as follows:
```json
{
"M1": ...,
"M2": ...,
"N1": ...,
"N2": ...,
"Pic": ...,
"Cor": ...,
"Matrix": [
[...],
[...],
[...]
]
}
```
## Usage
### Getting Started
To access the dataset using the Hugging Face `datasets` library, the following Python code can be used:
```python
from datasets import load_dataset
dataset = load_dataset("calabi-yau-threefolds")
```
### Machine Learning Applications
This dataset provides rich opportunities for various machine learning tasks:
- Geometric deep learning for topological invariant prediction.
- Unsupervised learning techniques for polyhedra clustering.
- Graph neural networks to model vertex connections.
### Citations
For dataset usage, please cite the original paper using the following BibTeX entry:
```bibtex
@misc{kreuzer1998classification,
title={Classification of Reflexive Polyhedra in Three Dimensions},
author={M. Kreuzer and H. Skarke},
year={1998},
eprint={hep-th/9805190},
archivePrefix={arXiv},
primaryClass={hep-th}
}
```
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] |
Weyaxi/user-orgs-huggingface-leaderboard | Weyaxi | 2023-12-18T00:51:33Z | 2 | 0 | [
"size_categories:100K<n<1M",
"region:us"
] | null | 2023-09-29T14:28:50Z | ---
size_categories:
- 100K<n<1M
---
# Huggingface Leaderboard's Dataset
🏆 This is the dataset of [Huggingface Leaderboard](https://huggingface.co/spaces/Weyaxi/huggingface-leaderboard).
🗒️ This dataset contains usernames and organization names in a TXT file.
⌛ This dataset is automatically updated using GitHub Actions.
## Leaderboard Link
🔗 [Weyaxi/huggingface-leaderboard](https://huggingface.co/spaces/Weyaxi/huggingface-leaderboard) | [
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a686d380/h-eval | a686d380 | 2023-12-11T11:14:32Z | 2 | 7 | [
"language:zh",
"region:us"
] | null | 2023-10-01T10:13:18Z | ---
viewer: false
language:
- zh
---
# H-Eval
H-Eval数据集由人工挑选的316个H小说句子组成，要求模型正确续写下一个单词
本测试集无法反映模型长文本生成能力，更低的分数也不能反映模型在色情方面更为安全
你可以使用`benchmark.py`测试其他模型
本测试集仅供科学研究
| Model| Score|
|-|-|
| Human|80.2 |
|rwkv-5-h-world-3B|59.4|
|rwkv-5-h-world-1b5|59.1|
|Yi-34B|54.7|
| rwkv-h-world-1b5|54.1|
| Yi-6B| 48.7 |
|Yi-34B-Chat-4bits| 48.1 |
| rwkv-h-world-0.4b |46.8 |
| deepsex-34b| 45.9 |
| NSFW_13B_sft | 44.3|
| CausalLM-14B-GPTQ| 43.4 |
| Baichuan2-7B-Base|42.7 |
|RWKV-5-World-3B-v2-20231113-ctx4096| 42.5|
| rwkv-h-1b5| 42.1|
| chatglm3-6b-base| 41.2|
|RWKV-claude-4-World-7B-20230805-ctx65k | 40.2 |
| Baichuan2-13B-Base|39.9 |
|RWKV-4-World-CHNtuned-7B-v1-20230709-ctx4096|39.3|
|Baichuan2-13B-Chat-4bits|37.4||
|RWKV-5-World-1B5-v2-20231025-ctx4096| 36.1|
|Qwen-7B|33.0|
| chatglm3-6b| 30.5 |
| RWKV-4-World-CHNtuned-1.5B-v1-20230620-ctx4096| 28.9|
| RWKV-4-World-CHNtuned-0.4B-v1-20230618-ctx4096| 22.9|
| RWKV-4-Novel-3B-v1-Chn-20230412-ctx4096| 20.4|
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esteler-ai/idn-news-az | esteler-ai | 2023-12-13T05:55:32Z | 2 | 0 | [
"task_categories:text-generation",
"task_categories:fill-mask",
"size_categories:10M<n<100M",
"language:id",
"license:cc-by-4.0",
"doi:10.57967/hf/1474",
"region:us"
] | [
"text-generation",
"fill-mask"
] | 2023-10-06T10:00:50Z | ---
license: cc-by-4.0
task_categories:
- text-generation
- fill-mask
language:
- id
pretty_name: a
size_categories:
- 10M<n<100M
---
The dataset are collected by scrapping the Indonesian news portal as follow:
* detik.com
* suara.com
* cnnindonesia.com
* kompas.com
* kontan.co.id
* bisnis.com
* investor.id
* mojok.co
* cnbcindonesia.com
* cnnindonesia.com
* sindonews.com
* tribunnews.com
* okezone.com
* tempo.co.id
* vivanews.co.id
* antaranews.com
* metronews.com
The corpus are collected from the news of Jan 2023 to Oct 2023, but there are a few of them are written in 2022.
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MrPrometheus/books | MrPrometheus | 2023-12-17T06:54:02Z | 2 | 0 | [
"license:mit",
"region:us"
] | null | 2023-10-07T06:42:53Z | ---
license: mit
---
6tb (5.6 tb)
130t. h.
800 sp | [
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TanvirOnHF/InclusiveGenderIdentities | TanvirOnHF | 2023-12-13T09:52:52Z | 2 | 3 | [
"size_categories:1K<n<10K",
"language:en",
"license:cdla-sharing-1.0",
"GPT-3.5",
"GPT-4",
"Claude",
"Bard",
"Alpaca",
"LLaMA",
"LLaMA-2",
"Vicuna",
"PaLM-2",
"region:us"
] | null | 2023-10-10T18:48:40Z | ---
license: cdla-sharing-1.0
pretty_name: InclusiveGenderIdentities
tags:
- GPT-3.5
- GPT-4
- Claude
- Bard
- Alpaca
- LLaMA
- LLaMA-2
- Vicuna
- PaLM-2
language:
- en
size_categories:
- 1K<n<10K
---
# InclusiveGenderIdentities [JSON dataset]
A dataset comprising artificially generated fictitious gender identities, each crafted to promote inclusivity and diversity. These identities are entirely fictitious and are generated from a diverse array of sources, ensuring a wide representation.
## Dataset Contents
The dataset contains fictitious gender identities, each accompanied by a gender label, a description, and any relevant additional attributes. These gender identities are entirely fictional and are designed to encourage diversity and inclusivity.
The dataset aims to serve as a resource for educational and awareness purposes, fostering understanding and respect for a broad range of gender identities.
## Prompt
The prompt used:
```json
Generate a JSON-formatted dataset of fictitious gender identities, each comprising a gender label, a description, and any relevant additional attributes. The dataset should include a variety of gender identities to promote inclusivity and diversity. Example:
'''json
[
{
"gender": "Cislunar",
"description": "A gender identity for individuals who identify with the space between Earth and the Moon, symbolizing a unique perspective and a connection to celestial bodies.",
"pronouns": ["they/them", "xe/xem"],
"optionalFields": {
"flag_colors": ["#001f3f", "#0074b7"]
}
},
{
"gender": "Floralgender",
"description": "A gender identity closely associated with the beauty and diversity of flowers, representing growth and transformation.",
"pronouns": ["she/her", "they/them"],
"optionalFields": {
"symbol": "🌸"
}
},
{
"gender": "Aquaphile",
"description": "A gender identity linked to a deep affinity for water and aquatic environments, often reflecting fluidity and adaptability.",
"pronouns": ["he/him", "they/them"],
"optionalFields": {
"favorite_aquatic_animal": "dolphin"
}
},
{
"gender": "Technomage",
"description": "A gender identity inspired by the fusion of technology and magic, embodying creativity and innovation.",
"pronouns": ["ze/hir", "it/its"],
"optionalFields": {
"cyber-enhancements": "Holographic wings"
}
},
{
"gender": "Stellarian",
"description": "A gender identity associated with stars and the vastness of the cosmos, symbolizing endless possibilities and wonder.",
"pronouns": ["she/her", "they/them"],
"optionalFields": {
"constellation_sign": "Orion"
}
}
]
'''
```
## Disclaimer
Please note that while I strive to maintain data quality, I cannot guarantee the accuracy or quality of all entries in this dataset. Use it responsibly and exercise caution when relying on the data for any critical applications. Your feedback and contributions are greatly appreciated for improving the dataset's overall quality.
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zcczhang/UVD | zcczhang | 2023-12-12T21:44:28Z | 2 | 2 | [
"task_categories:robotics",
"license:mit",
"arxiv:2310.08581",
"region:us"
] | [
"robotics"
] | 2023-10-18T00:21:19Z | ---
license: mit
task_categories:
- robotics
---
# Dataset for Universal Visual Decomposer (UVD)
## Dataset Description
*Homepage:* https://zcczhang.github.io/UVD/
*Codebase:* https://github.com/zcczhang/UVD
*Paper:* https://arxiv.org/abs/2310.08581
## Dataset Summary
This is the dataset used for the paper [Universal Visual Decomposer: Long-Horizon Manipulation Made Easy](https://arxiv.org/abs/2310.08581). We release the simulation data in FrankaKitchen and data for real-world experiments.
If you find our work useful, please consider citing us!
```bibtex
@misc{zhang2023universal,
title = {Universal Visual Decomposer: Long-Horizon Manipulation Made Easy},
author = {Zichen Zhang and Yunshuang Li and Osbert Bastani and Abhishek Gupta and Dinesh Jayaraman and Yecheng Jason Ma and Luca Weihs},
title = {Universal Visual Decomposer: Long-Horizon Manipulation Made Easy},
year = {2023},
eprint = {arXiv:2310.08581},
}
```
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] |
Weyaxi/huggingface-leaderboard-history | Weyaxi | 2023-12-18T00:57:51Z | 2 | 1 | [
"region:us"
] | null | 2023-10-19T10:40:57Z | ---
viewer: false
---
# Huggingface Leaderboard's History Dataset
🏆 This is the history dataset of [Huggingface Leaderboard](https://huggingface.co/spaces/PulsarAI/huggingface-leaderboard).
🗒️ This dataset contains full dataframes in a CSV file for each time lapse.
⌛ This dataset is automatically updated when space restarts. (Which is approximately every 6 hours)
## Leaderboard Link
🔗 [PulsarAI/huggingface-leaderboard](https://huggingface.co/spaces/PulsarAI/huggingface-leaderboard) | [
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deepghs/artists_index | deepghs | 2023-12-17T16:38:35Z | 2 | 0 | [
"license:mit",
"region:us"
] | null | 2023-10-23T12:51:52Z | ---
license: mit
---
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cuicc/test | cuicc | 2023-12-13T07:34:21Z | 2 | 0 | [
"license:mit",
"region:us"
] | null | 2023-10-24T07:03:15Z | ---
license: mit
---
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IGNF/FLAIR | IGNF | 2023-12-17T15:52:43Z | 2 | 0 | [
"size_categories:10B<n<100B",
"license:other",
"region:us"
] | null | 2023-10-26T08:32:37Z | ---
license: other
license_name: open-licence-2.0
license_link: https://www.etalab.gouv.fr/wp-content/uploads/2018/11/open-licence.pdf
pretty_name: French Land Cover from Aerospace Imagery
size_categories:
- 10B<n<100B
---
# Datset Card for FLAIR land-cover semantic segmentation
## Context & Data
<hr style='margin-top:-1em; margin-bottom:0' />
The hereby FLAIR (#2) dataset is sampled countrywide and is composed of over 20 billion annotated pixels of very high resolution aerial imagery at 0.2 m spatial resolution, acquired over three years and different months (spatio-temporal domains).
Aerial imagery patches consist of 5 channels (RVB-Near Infrared-Elevation) and have corresponding annotation (with 19 semantic classes or 13 for the baselines).
Furthermore, to integrate broader spatial context and temporal information, high resolution Sentinel-2 satellite 1-year time series with 10 spectral band are also provided.
More than 50,000 Sentinel-2 acquisitions with 10 m spatial resolution are available.
<br>
The dataset covers 50 spatial domains, encompassing 916 areas spanning 817 km². This dataset provides a robust foundation for advancing land cover mapping techniques.<br><br>
<style type="text/css">
.tg {border-collapse:collapse;border-spacing:0;}
.tg td{border-color:black;border-style:solid;border-width:1px;font-family:Arial, sans-serif;font-size:14px;
overflow:hidden;padding:2px 5px;word-break:normal;}
.tg th{border-color:black;border-style:solid;border-width:1px;font-family:Arial, sans-serif;font-size:14px;
font-weight:normal;overflow:hidden;padding:10px 5px;word-break:normal;}
.tg .tg-km2t{border-color:#ffffff;font-weight:bold;text-align:left;vertical-align:top}
.tg .tg-rime{background-color:#E4DF7C;border-color:#ffffff;text-align:left;vertical-align:top}
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<table class="tg">
<thead>
<tr>
<th class="tg-zv4m"></th>
<th class="tg-zv4m">Class</th>
<th class="tg-8jgo">Freq.-train (%)</th>
<th class="tg-8jgo">Freq.-test (%)</th>
<th class="tg-zv4m"></th>
<th class="tg-zv4m">Class</th>
<th class="tg-8jgo">Freq.-train (%)</th>
<th class="tg-8jgo">Freq.-test (%)</th>
</tr>
</thead>
<tbody>
<tr>
<td class="tg-2e1p"></td>
<td class="tg-km2t">(1) Building</td>
<td class="tg-8jgo">8.14</td>
<td class="tg-8jgo">3.26</td>
<td class="tg-l5fa"></td>
<td class="tg-km2t">(11) Agricultural Land</td>
<td class="tg-8jgo">10.98</td>
<td class="tg-8jgo">18.19</td>
</tr>
<tr>
<td class="tg-9efv"></td>
<td class="tg-km2t">(2) Pervious surface</td>
<td class="tg-8jgo">8.25</td>
<td class="tg-8jgo">3.82</td>
<td class="tg-rime"></td>
<td class="tg-km2t">(12) Plowed land</td>
<td class="tg-8jgo">3.88</td>
<td class="tg-8jgo">1.81</td>
</tr>
<tr>
<td class="tg-3m6m"></td>
<td class="tg-km2t">(3) Impervious surface</td>
<td class="tg-8jgo">13.72</td>
<td class="tg-8jgo">5.87</td>
<td class="tg-2cns"></td>
<td class="tg-km2t">(13) Swimming pool</td>
<td class="tg-8jgo">0.01</td>
<td class="tg-8jgo">0.02</td>
</tr>
<tr>
<td class="tg-r3rw"></td>
<td class="tg-km2t">(4) Bare soil</td>
<td class="tg-8jgo">3.47</td>
<td class="tg-8jgo">1.6</td>
<td class="tg-jjsp"></td>
<td class="tg-km2t">(14) Snow</td>
<td class="tg-8jgo">0.15</td>
<td class="tg-8jgo">-</td>
</tr>
<tr>
<td class="tg-9xgv"></td>
<td class="tg-km2t">(5) Water</td>
<td class="tg-8jgo">4.88</td>
<td class="tg-8jgo">3.17</td>
<td class="tg-2w6m"></td>
<td class="tg-km2t">(15) Clear cut</td>
<td class="tg-8jgo">0.15</td>
<td class="tg-8jgo">0.82</td>
</tr>
<tr>
<td class="tg-b45e"></td>
<td class="tg-km2t">(6) Coniferous</td>
<td class="tg-8jgo">2.74</td>
<td class="tg-8jgo">10.24</td>
<td class="tg-nla7"></td>
<td class="tg-km2t">(16) Mixed</td>
<td class="tg-8jgo">0.05</td>
<td class="tg-8jgo">0.12</td>
</tr>
<tr>
<td class="tg-qg2z"></td>
<td class="tg-km2t">(7) Deciduous</td>
<td class="tg-8jgo">15.38</td>
<td class="tg-8jgo">24.79</td>
<td class="tg-nv8o"></td>
<td class="tg-km2t">(17) Ligneous</td>
<td class="tg-8jgo">0.01</td>
<td class="tg-8jgo">-</td>
</tr>
<tr>
<td class="tg-grz5"></td>
<td class="tg-km2t">(8) Brushwood</td>
<td class="tg-8jgo">6.95</td>
<td class="tg-8jgo">3.81</td>
<td class="tg-bja1"></td>
<td class="tg-km2t">(18) Greenhouse</td>
<td class="tg-8jgo">0.12</td>
<td class="tg-8jgo">0.15</td>
</tr>
<tr>
<td class="tg-69kt"></td>
<td class="tg-km2t">(9) Vineyard</td>
<td class="tg-8jgo">3.13</td>
<td class="tg-8jgo">2.55</td>
<td class="tg-nto1"></td>
<td class="tg-km2t">(19) Other</td>
<td class="tg-8jgo">0.14</td>
<td class="tg-8jgo">0.04</td>
</tr>
<tr>
<td class="tg-r1r4"></td>
<td class="tg-km2t">(10) Herbaceous vegetation</td>
<td class="tg-8jgo">17.84</td>
<td class="tg-8jgo">19.76</td>
<td class="tg-zv4m"></td>
<td class="tg-zv4m"></td>
<td class="tg-zv4m"></td>
<td class="tg-zv4m"></td>
</tr>
</tbody>
</table>
<br><br>
## Dataset Structure
<hr style='margin-top:-1em; margin-bottom:0' />
The FLAIR dataset consists of 77 762 patches. Each patch includes a high-resolution aerial image (512x512) at 0.2 m, a yearly satellite image time series (40x40 by default by wider areas are provided) with a spatial resolution of 10 m
and associated cloud and snow masks, and pixel-precise elevation and land cover annotations at 0.2 m resolution (512x512).
<p align="center"><img src="readme_imgs/flair-patches.png" alt="" style="width:70%;max-width:600px;"/></p><br>
### Band order
<div style="display: flex;">
<div style="width: 15%;margin-right: 1;"">
Aerial
<ul>
<li>1. Red</li>
<li>2. Green</li>
<li>3. Blue</li>
<li>4. NIR</li>
<li>5. nDSM</li>
</ul>
</div>
<div style="width: 25%;">
Satellite
<ul>
<li>1. Blue (B2 490nm)</li>
<li>2. Green (B3 560nm)</li>
<li>3. Red (B4 665nm)</li>
<li>4. Red-Edge (B5 705nm)</li>
<li>5. Red-Edge2 (B6 470nm)</li>
<li>6. Red-Edge3 (B7 783nm)</li>
<li>7. NIR (B8 842nm)</li>
<li>8. NIR-Red-Edge (B8a 865nm)</li>
<li>9. SWIR (B11 1610nm)</li>
<li>10. SWIR2 (B12 2190nm)</li>
</ul>
</div>
</div>
### Annotations
Each pixel has been manually annotated by photo-interpretation of the 20 cm resolution aerial imagery, carried out by a team supervised by geography experts from the IGN.
Movable objects like cars or boats are annotated according to their underlying cover.
### Training Splits
The dataset is made up of 50 distinct spatial domains, aligned with the administrative boundaries of the French départements.
For our experiments, we designate 32 domains for training, 8 for validation, and reserve 10 as the official test set.
This arrangement ensures a balanced distribution of semantic classes, radiometric attributes, bioclimatic conditions, and acquisition times across each set.
Consequently, every split accurately reflects the landscape diversity inherent to metropolitan France.
It is important to mention that the patches come with meta-data permitting alternative splitting schemes, for example focused on domain shifts.
Official domain split: <br/>
<div style="display: flex; flex-wrap: nowrap; align-items: center">
<div style="flex: 40%;">
<img src="readme_imgs/flair-splits.png" alt="Your Image">
</div>
<div style="flex: 60%;">
<table border="1">
<tr>
<th><font color="#c7254e">TRAIN:</font></th>
<td>D006, D007, D008, D009, D013, D016, D017, D021, D023, D030, D032, D033, D034, D035, D038, D041, D044, D046, D049, D051, D052, D055, D060, D063, D070, D072, D074, D078, D080, D081, D086, D091</td>
</tr>
<tr>
<th><font color="#c7254e">VALIDATION:</font></th>
<td>D004, D014, D029, D031, D058, D066, D067, D077</td>
</tr>
<tr>
<th><font color="#c7254e">TEST:</font></th>
<td>D015, D022, D026, D036, D061, D064, D068, D069, D071, D084</td>
</tr>
</table>
</div>
</div>
<br><br>
## Baseline code
<hr style='margin-top:-1em; margin-bottom:0' />
We propose the U-T&T model, a two-branch architecture that combines spatial and temporal information from very high-resolution aerial images and high-resolution satellite images into a single output. The U-Net architecture is employed for the spatial/texture branch, using a ResNet34 backbone model pre-trained on ImageNet. For the spatio-temporal branch,
the U-TAE architecture incorporates a Temporal self-Attention Encoder (TAE) to explore the spatial and temporal characteristics of the Sentinel-2 time series data,
applying attention masks at different resolutions during decoding. This model allows for the fusion of learned information from both sources,
enhancing the representation of mono-date and time series data.
U-T&T code repository 📁 : https://github.com/IGNF/FLAIR-2-AI-Challenge <br/>
<th><font color="#c7254e"><b>IMPORTANT!</b></font></th> <b>The structure of the current dataset differs from the one that comes with the GitHub repository.</b>
To work with the current dataset, you need to replace the <font color=‘#D7881C’><em>src/load_data.py</em></font> file with the one provided here.
You also need to add the following lines to the <font color=‘#D7881C’><em>flair-2-config.yml</em></font> file under the <em><b>data</b></em> tag: <br>
```
HF_data_path : " " # Path to unzipped FLAIR HF dataset
domains_train : ["D006_2020","D007_2020","D008_2019","D009_2019","D013_2020","D016_2020","D017_2018","D021_2020","D023_2020","D030_2021","D032_2019","D033_2021","D034_2021","D035_2020","D038_2021","D041_2021","D044_2020","D046_2019","D049_2020","D051_2019","D052_2019","D055_2018","D060_2021","D063_2019","D070_2020","D072_2019","D074_2020","D078_2021","D080_2021","D081_2020","D086_2020","D091_2021"]
domains_val : ["D004_2021","D014_2020","D029_2021","D031_2019","D058_2020","D066_2021","D067_2021","D077_2021"]
domains_test : ["D015_2020","D022_2021","D026_2020","D036_2020","D061_2020","D064_2021","D068_2021","D069_2020","D071_2020","D084_2021"]
```
<br><br>
## Reference
<hr style='margin-top:-1em; margin-bottom:0' />
Please include a citation to the following article if you use the FLAIR dataset:
```
@inproceedings{garioud2023flair,
title={FLAIR: a Country-Scale Land Cover Semantic Segmentation Dataset From Multi-Source Optical Imagery},
author={Anatol Garioud and Nicolas Gonthier and Loic Landrieu and Apolline De Wit and Marion Valette and Marc Poupée and Sébastien Giordano and Boris Wattrelos},
year={2023},
booktitle={Advances in Neural Information Processing Systems (NeurIPS) 2023},
doi={https://doi.org/10.48550/arXiv.2310.13336},
}
```
## Acknowledgment
<hr style='margin-top:-1em; margin-bottom:0' />
This work was performed using HPC/AI resources from GENCI-IDRIS (Grant 2022-A0131013803). This work was supported by the project "Copernicus / FPCUP” of the European Union, by the French Space Agency (CNES) and by Connect by CNES.<br>
## Contact
<hr style='margin-top:-1em; margin-bottom:0' />
If you have any questions, issues or feedback, you can contact us at: [email protected]
<br>
## Dataset license
<hr style='margin-top:-1em; margin-bottom:0' />
The "OPEN LICENCE 2.0/LICENCE OUVERTE" is a license created by the French government specifically for the purpose of facilitating the dissemination of open data by public administration.<br/>
This licence is governed by French law.<br/>
This licence has been designed to be compatible with any free licence that at least requires an acknowledgement of authorship, and specifically with the previous version of this licence as well as with the following licences: United Kingdom’s “Open Government Licence” (OGL), Creative Commons’ “Creative Commons Attribution” (CC-BY) and Open Knowledge Foundation’s “Open Data Commons Attribution” (ODC-BY).
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TongjiFinLab/CFBenchmark | TongjiFinLab | 2023-12-16T14:36:22Z | 2 | 3 | [
"task_categories:text-classification",
"task_categories:summarization",
"task_categories:text-generation",
"size_categories:1K<n<10K",
"language:en",
"license:apache-2.0",
"arxiv:2311.05812",
"region:us"
] | [
"text-classification",
"summarization",
"text-generation"
] | 2023-11-02T17:32:08Z | ---
license: apache-2.0
task_categories:
- text-classification
- summarization
- text-generation
language:
- en
size_categories:
- 1K<n<10K
---
<div align="center">
<!-- <img src="https://big-cheng.com/k2/k2.png" alt="k2-logo" width="200"/> -->
<h2>📈 CFBenchmark: Chinese Financial Assistant with Large Language Model</h2>
</div>
<div align="center" style="display: flex; justify-content: center">
<a href='https://arxiv.org/abs/2311.05812' style="margin-left:15px"><img src='https://img.shields.io/badge/Paper-ArXiv-C71585'></a>
<a href='https://huggingface.co/datasets/TongjiFinLab/CFBenchmark' style="margin-left:15px"><img src='https://img.shields.io/badge/%F0%9F%A4%97%20Hugging Face-CFBenchmark-red'></a>
<a href='' style="margin-left:15px"><img src='https://img.shields.io/badge/License-Apache--2.0-blue.svg'></a>
</div>
English | [简体中文](README-CN.md)
# Introduction
Welcome to **CFBenchmark**
In recent years, with the rapid development of Large Language Models~(LLMs), outstanding performance has been achieved in various tasks by existing LLMs. However, we notice that there is currently a limited amount of benchmarks focused on assessing the performance of LLMs in specific domains.
In this work, we introduce CFBenchmark, a Chinese financial assistant benchmark for large language models. The basic version of CFBenchmark includes 3917 financial texts spanning three aspects and eight tasks. The CFBenchmark is organized into three aspects, financial recognition, financial classification, and financial generation. We conduct experiments in zero-shot and few-shot mode on our CFBenchmark to evaluate renowned LLMs available in the market. The results have revealed that there is still significant room to improve LLMs in basic tasks of financial text processing.
In the future, we will continue to contribute more benchmark evaluations in this series.
<div align="center">
<img src="imgs/Framework.png" width="100%"/>
<br />
<br /></div>
# Contents
- [CFBenchmark-Basic](#cfbenchmark-basic)
- [QuickStart](#QuickStart)
- [Performance of Existing LLMs](#performance-of-existing-llms)
- [Acknowledgements](#acknowledgements)
- [To-Do](#to-do)
- [License](#license)
- [Citation](#citation)
# CFBenchmark-Basic
CFBenchmark-Basic includes 3917 financial texts spanning three aspects and eight tasks, organized from three aspects, financial recognition, financial classification, and financial generation.
- Recognition-Company: Recognize the company names associated with financial documents (273).
- Recognition-Product: Recognize the product names associated with financial documents (297).
- Classification-Sentiment: Classify the sentiment associated with financial documents (591).
- Classification-Event: Classify the event categories associated with financial documents (577).
- Classification-Industry: Classify the industry categories associated with financial documents (402).
- Generation-Suggestion: Generate investment suggestions based on the provided financial document (593).
- Generation-Risk: Generate risk alerts based on the provided financial document (591).
- Generation-Summary: Generate a content summary based on the provided financial document (593).
We provide two examples to reveal how the few-shot setting and zero-shot setting work during evaluation.
Example 1 Fewshot Input:
<div align="center">
<img src="imgs/fewshot.png" width="100%"/>
<br />
<br /></div>
Example 2 Zeroshot Input：
<div align="center">
<img src="imgs/zeroshot.png" width="100%"/>
<br />
<br /></div>
## QuickStart
### Installation
Below are the steps for quick installation.
```python
conda create --name CFBenchmark python=3.10
conda activate CFBenchmark
```
```python
git clone https://github.com/TongjiFinLab/CFBenchmark
cd CFBenchmark
pip install -r requirements.txt
```
### Dataset Preparation
Download the dataset utilizing the Hugging Face dataset. Run the command **Manual download** and unzip it. Run the following command in the CFBenchmark project directory to prepare the data set in the CFBenchmark/CFBenchmark directory.
```text
wget https://huggingface.co/TongjiFinLab/CFBenchmark
unzip CFBenchmark.zip
```
### Evaluation
We have prepared the testing and evaluation codes for you in repo ```/codes```.
To begin the evaluation, you can run the following code from the command line:
```cmd
cd CFBenchmark/codes
python -m run.py
```
You can enter ```codes/run.py``` to modify the parameters in it to make the code running path meet your requirements.
```py
from CFBenchmark import CFBenchmark
if __name__=='__main__':
# EXPERIMENT SETUP
modelname = 'YOUR-MODEL-NAME'
model_type= 'NORMAL' #NORMAL or LoRA
model_path= 'YOUR-MODEL-PATH'
peft_model_path= ''#PASS YOUR OWN PATH OF PEFT MODEL IF NEEDED
fewshot_text_path= '../fewshot'#DEFAULT PATH
test_type='few-shot'#LET'S TAKE THE FEW-SHOT TEST AS AN EXAMPLE
response_path='../cfbenchmark-response'#PATH TO RESERVE THE RESPONSE OF YOUR MODEL
scores_path='../cfbenchmark-scores' #PATH TO RESERVE THE SCORE OF YOUR MODEL
embedding_model_path='../bge-zh-v1.5' #PASS YOUR OWN PATH OF BGE-ZH-V1.5
benchmark_path='../cfbenchmark' #DEFAULT PATH
data_source_type='offline'#online or offline
#generate Class CFBenchmark
cfb=CFBenchmark(
model_name=modelname,
model_type=model_type,
model_path=model_path,
peft_model_path=peft_model_path,
fewshot_text_path=fewshot_text_path,
test_type=test_type,
response_path=response_path,
scores_path=scores_path,
embedding_model_path=embedding_model_path,
benchmark_path=benchmark_path,
data_source_type=data_source_type
)
cfb.generate_model()# TO GET RESPONSE FROM YOUR MODEL
cfb.get_test_scores()# TO GET YOUR MODEL SCORES FROM RESPONSE
```
We defined a class ```CFBenchmark``` to do the evaluation.
```Py
class CFBenchmark:
def __init__(self,
model_name,
model_type,
model_path,
peft_model_path,
fewshot_text_path,
test_type,
response_path,
scores_path,
embedding_model_path,
benchmark_path,
data_source_type
) -> None:
```
* You can use the arguments to set paths for models. If you want to use a LoRA fine-tuned model, set model_type`` toLoRAand pass your peft model path throughpeft_model_path```.
* You can set test-type to 'zero-shot' or 'few-shot' to do different evaluations.
* embedding_model_path is set for bzh-zh-v1.5 for calculating cosine-similarity.
* You can modify the hyperparameters in CFBenchmark.generate_model() for text generations.
* We provide CFBenchmark saved as a Dataset data type in both Hugging Face and Github. If you want to use an offline version of the benchmark, set the parameter data_source_type to offline````. If you want to use the online version of the benchmark, set the parameterdata_source_typetoonline```.
## Performance of Existing LLMs
We utilize two types of metrics to evaluate the performance of LLMs in the financial domain on our CFBenchmark.
For recognition and classification tasks, we employ the **F1 score** as the evaluation metric, which balances precision and recall.
For the generation tasks, we utilize **cosine similarity** between the vector representation of ground truth and the generated answer to measure the generation ability.
Since there are usually different expressions with similar meanings in our generation tasks, simply employing Rough-Score or BULE-score is not reasonable.
Specifically, the **bge-zh-v1.5** is assigned as the oracle model to generate the sentence embedding. We calculate evaluation scores for each sub-task individually and provide the average score for each category.
The best scores of LLMs(considering zero-shot and few-shot), as well as which of our model, are demonstrated below:
| Model | Size | Company | Product | R.Avg | Sector | Event | Sentiment | C.Avg | Summary | Risk | Suggestion | G.Avg | Avg |
| ------------------ | ---- | --------- | --------- | --------- | --------- | --------- | --------- | --------- | --------- | --------- | ---------- | --------- | --------- |
| HUMAN | - | 0.931 | 0.744 | 0.838 | 0.975 | 0.939 | 0.912 | 0.942 | 1.000 | 1.000 | 1.000 | 1.000 | 0.927 |
| ChatGPT | 20B | 0.797 | 0.198 | 0.498 | 0.453 | 0.458 | 0.425 | 0.455 | 0.593 | 0.541 | 0.771 | 0.635 | 0.529 |
| ERNIE-Bot | 260B | 0.807 | 0.300 | 0.533 | 0.408 | 0.350 | 0.186 | 0.315 | 0.715 | 0.590 | 0.716 | 0.673 | 0.507 |
| ERNIE-Bot-4 | - | 0.819 | 0.417 | 0.618 | 0.418 | 0.358 | 0.375 | 0.384 | 0.721 | 0.629 | 0.718 | 0.689 | 0.564 |
| Falcon-7B | 7B | 0.671 | 0.168 | 0.420 | 0.169 | 0.132 | 0.250 | 0.184 | 0.302 | 0.301 | 0.246 | 0.283 | 0.296 |
| Falcon-7B-chat | 7B | 0.582 | 0.046 | 0.314 | 0.112 | 0.142 | 0.153 | 0.135 | 0.307 | 0.299 | 0.258 | 0.288 | 0.246 |
| bloomz-7B1 | 7B | 0.765 | 0.166 | 0.465 | 0.252 | 0.154 | 0.394 | 0.267 | 0.451 | 0.371 | 0.462 | 0.428 | 0.387 |
| bloomz-7Bt1-mt | 7B | 0.751 | 0.157 | 0.454 | 0.087 | 0.182 | 0.380 | 0.216 | 0.425 | 0.379 | 0.396 | 0.400 | 0.357 |
| Qwen-7B | 7B | 0.780 | 0.357 | 0.569 | 0.480 | 0.335 | 0.379 | 0.398 | 0.750 | 0.505 | 0.713 | 0.656 | 0.541 |
| Qwen-Chat-7B | 7B | 0.763 | 0.360 | 0.562 | 0.400 | 0.367 | 0.265 | 0.344 | 0.548 | 0.307 | 0.379 | 0.411 | 0.439 |
| Qwen-14B | 14B | 0.805 | 0.421 | 0.613 | 0.481 | 0.350 | 0.385 | 0.405 | 0.754 | 0.608 | 0.717 | 0.693 | 0.570 |
| Qwen-Chat-14B | 14B | 0.814 | 0.442 | 0.628 | 0.382 | 0.400 | 0.350 | 0.377 | 0.732 | 0.478 | 0.736 | 0.649 | 0.551 |
| ChatGLM2-6B | 6B | 0.747 | 0.313 | 0.530 | 0.285 | 0.300 | 0.357 | 0.314 | 0.657 | 0.454 | 0.671 | 0.594 | 0.479 |
| Baichuan2-7B-Base | 7B | 0.672 | 0.340 | 0.506 | 0.342 | 0.490 | 0.480 | 0.437 | 0.739 | 0.619 | 0.751 | 0.703 | 0.549 |
| Baichuan2-7B-Chat | 7B | 0.757 | 0.402 | 0.579 | 0.425 | 0.475 | 0.323 | 0.408 | 0.725 | 0.648 | 0.732 | 0.702 | 0.563 |
| Baichuan2-13B-Base | 13B | 0.781 | 0.330 | 0.555 | 0.436 | 0.496 | 0.477 | 0.470 | 0.725 | 0.503 | 0.747 | 0.658 | 0.561 |
| Baichuan2-13B-Chat | 13B | 0.797 | 0.314 | 0.556 | 0.472 | 0.507 | 0.387 | 0.455 | 0.739 | 0.634 | 0.746 | 0.706 | 0.572 |
| InternLM-7B | 7B | 0.612 | 0.233 | 0.423 | 0.266 | 0.311 | 0.328 | 0.302 | 0.378 | 0.336 | 0.379 | 0.364 | 0.363 |
| InternLM-7B-Chat | 7B | 0.632 | 0.261 | 0.447 | 0.272 | 0.364 | 0.399 | 0.345 | 0.363 | 0.270 | 0.353 | 0.329 | 0.374 |
| InternLM-20B | 20B | 0.809 | 0.358 | 0.583 | 0.500 | 0.427 | 0.417 | 0.448 | 0.706 | 0.653 | 0.728 | 0.695 | 0.575 |
| InternLM-20B-Chat | 20B | 0.488 | 0.362 | 0.425 | 0.323 | 0.327 | 0.370 | 0.340 | 0.706 | 0.578 | 0.762 | 0.662 | 0.476 |
| CFGPT1-stf-LoRA | 7B | 0.820 | 0.414 | 0.617 | 0.569 | 0.729 | 0.769 | 0.689 | 0.745 | 0.584 | 0.609 | 0.646 | 0.650 |
| CFGPT1-sft-Full | 7B | **0.836** | **0.476** | **0.656** | **0.700** | **0.808** | **0.829** | **0.779** | **0.798** | **0.669** | **0.808** | **0.758** | **0.731** |
# Acknowledgements
CFBenchmark has referred to the following open-source projects. We want to express our gratitude and respect to the researchers of the projects.
- tiiuae/falcon LLM series(https://huggingface.co/tiiuae/falcon-7b)
- bigscience/bloomz LLM series(https://huggingface.co/bigscience/bloomz-7b1)
- QwenLM/Qwen LLM series(https://github.com/QwenLM/Qwen)
- THUDM/ChatGLM2-6b(https://github.com/THUDM/ChatGLM2-6B)
- baichuan-inc/Baichuan2 LLM series(https://github.com/baichuan-inc/Baichuan2)
- InternLM/InternLM LLM series(https://github.com/InternLM/InternLM)
- ssymmetry/BBT-FinCUGE-Applications(https://github.com/ssymmetry/BBT-FinCUGE-Applications)
- chancefocus/PIXIU(https://github.com/chancefocus/PIXIU)
- SUFE-AIFLM-Lab/FinEval(https://github.com/SUFE-AIFLM-Lab/FinEval)
# To-Do
- CFBenchmark-Advanced:
- In various scenarios of Chinese financial usage, propose more evaluation tasks to enrich the CFBenchmark series.
# License
CFBenchmark is a research preview intended for non-commercial use only, subject to the Terms of Use of the data generated by OpenAI. Please contact us if you find any potential violations. The code is released under the Apache License 2.0.
# Citation
```bibtex
@misc{lei2023cfbenchmark,
title={{CFBenchmark}: Chinese Financial Assistant Benchmark for Large Language Model},
author={Lei, Yang and Li, Jiangtong and Jiang, Ming and Hu, Junjie and Cheng, Dawei and Ding, Zhijun and Jiang, Changjun},
year={2023},
eprint={2311.05812},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
``` | [
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mattaq/GelGenie-Model-Zoo | mattaq | 2023-12-17T19:40:58Z | 2 | 0 | [
"license:apache-2.0",
"region:us"
] | null | 2023-11-05T11:17:28Z | ---
license: apache-2.0
pretty_name: GelGenie Model Zoo Registry.
---
This is the registry of models which can be used both in PyTorch (standalone) or in the GelGenie QuPath Extension.
More details TBC | [
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Tonic/medquad | Tonic | 2023-12-13T08:03:07Z | 2 | 0 | [
"region:us"
] | null | 2023-11-09T23:18:38Z | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 7324638
num_examples: 15549
download_size: 2208412
dataset_size: 7324638
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "medquad"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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kensho/BizBench | kensho | 2023-12-16T04:54:54Z | 2 | 1 | [
"license:apache-2.0",
"region:us"
] | null | 2023-11-10T17:50:23Z | ---
license: apache-2.0
---
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VatsaDev/fusion | VatsaDev | 2023-12-12T23:16:34Z | 2 | 2 | [
"task_categories:question-answering",
"task_categories:text-generation",
"task_categories:text-classification",
"language:en",
"language:fr",
"language:it",
"language:hi",
"license:mit",
"region:us"
] | [
"question-answering",
"text-generation",
"text-classification"
] | 2023-11-13T03:38:33Z | ---
license: mit
task_categories:
- question-answering
- text-generation
- text-classification
language:
- en
- fr
- it
- hi
---
# The Fusion Dataset
Stages
- raw text files in folders
- deduplicated text
- synthetic grading, llm filters
- final dataset
Split into 3 parts
- Social - Includes Data from roleplay and social media, probably the noisest data*, helpful in making llms resistant to bad grammar and Gen Alpha
- Pretrain - Large amounts of text for model pretrains, textbooks, webscrapes, etc
- Instruct - Instruction data, meant to a single format, for a model finetune
* Also the most uncensored/unfiltered, there is 4chan in here, probs should filter this | [
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harpreetsahota/one_shot_comparison | harpreetsahota | 2023-12-11T16:20:58Z | 2 | 0 | [
"region:us"
] | null | 2023-11-15T14:22:47Z | ---
dataset_info:
features:
- name: source
dtype: string
- name: target
dtype: string
- name: rationale
dtype: string
- name: task
dtype: string
- name: type
dtype: string
- name: decilm_generation
dtype: string
- name: mistral_generation
dtype: string
splits:
- name: train
num_bytes: 75960
num_examples: 30
download_size: 53919
dataset_size: 75960
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "one_shot_comparison"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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kushinm/WID_sym_human_vs_ai | kushinm | 2023-12-14T18:14:04Z | 2 | 0 | [
"license:mit",
"region:us"
] | null | 2023-11-15T20:03:39Z | ---
license: mit
configs:
- config_name: default
data_files:
- split: train
path: data.csv
---
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311-data/2023 | 311-data | 2023-12-17T23:15:06Z | 2 | 0 | [
"license:gpl-3.0",
"region:us"
] | null | 2023-11-15T22:03:00Z | ---
license: gpl-3.0
---
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] |
P051T1V3/health-demo | P051T1V3 | 2023-12-17T19:58:54Z | 2 | 0 | [
"region:us"
] | null | 2023-11-16T07:53:27Z | ---
configs:
- config_name: default
data_files:
- split: train
path: data.csv
---
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[More Information Needed] | [
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] |
TwoAbove/midjourney-messages | TwoAbove | 2023-12-18T01:22:10Z | 2 | 0 | [
"license:apache-2.0",
"region:us"
] | null | 2023-11-16T13:10:46Z | ---
license: apache-2.0
---
# midjourney-messages
## Description
This dataset contains the raw messages from Midjourney.
Initial dataset is https://huggingface.co/datasets/vivym/midjourney-messages, but this one has the images attached.
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kwaikeg/KAgentBench | kwaikeg | 2023-12-13T09:58:13Z | 2 | 3 | [
"task_categories:text-generation",
"size_categories:1K<n<10K",
"language:zh",
"language:en",
"license:cc-by-nc-sa-4.0",
"arxiv:2312.04889",
"region:us"
] | [
"text-generation"
] | 2023-11-17T06:47:21Z | ---
license: cc-by-nc-sa-4.0
language:
- zh
- en
size_categories:
- 1K<n<10K
task_categories:
- text-generation
---
KAgentBench is the benchmark proposed in KwaiAgents ([Github](https://github.com/KwaiKEG/KwaiAgents)), which is a series of Agent-related works open-sourced by the [KwaiKEG](https://github.com/KwaiKEG) from [Kuaishou Technology](https://www.kuaishou.com/en). It contains over 3,000 human-edited, automated evaluation data for testing Agent capabilities, with evaluation dimensions including planning, tool-use, reflection, concluding, and profiling.
## Overall statistics of KAgentBench
---
| type| #Queries | #Inst | Avg. #Ground | Avg. #Tools | Avg. #Turns | Avg. #Tasks | Avg. Len-Know | Metric |
| :-------: | :-------:| :-------: | :-------: | :-------: | :-------: | :-------: | :-------: | :-------: |
| Planning & Tool-use | 320 | 1,317 | 4.12 | 8.68 | 1.51 | 2.21 | 245.31 | ROUGE-L, EM |
| Reflection | 68 | 272 | 4 | 12 | 1 | 3.97 | 1369.04 | ROUGE-L, EM |
| Concluding | 245 | 1,225 | 5 | - | 1.52 | 2.14 | 923.96 | ROUGE-L |
| Profile | 433 | 433 | 5 | - | 1.99 | - | - | ROUGE-L |
## Experimental results of different LLMs on KAgentBench
---
The specific performance of different models on benchmarks can be seen in more detail in our [paper](https://arxiv.org/abs/2312.04889).
| | Scale | Planning | Tool-use | Reflection | Concluding | Profile | Overall Score |
|----------------|-------|----------|----------|------------|------------|---------|---------------|
| GPT-3.5-turbo | - | 18.55 | 15.89 | 5.32 | 37.26 | 35.42 | 21.72 |
| Llama2 | 13B | 0.15 | 0.23 | 0.08 | 16.60 | 17.73 | 5.22 |
| ChatGLM3 | 6B | 7.87 | 6.82 | 4.49 | 30.01 | 30.14 | 13.82 |
| Qwen | 7B | 13.34 | 10.87 | 4.73 | 36.24 | 34.99 | 18.36 |
| Baichuan2 | 13B | 6.70 | 10.11 | 4.25 | 24.97 | 19.08 | 12.54 |
| ToolLlama | 7B | 0.20 | 3.44 | 0.54 | 15.62 | 10.66 | 5.50 |
| AgentLM | 13B | 0.17 | 0.09 | 0.05 | 16.30 | 15.22 | 4.86 |
| Qwen-MAT | 7B | 31.64 | 28.26 | 29.50 | 44.85 | 44.78 | 34.20 |
| Baichuan2-MAT | 13B | 37.27 | 34.82 | 32.06 | 48.01 | 41.83 | 38.49 |
## JSON Format
---
Each data point is
a dict with the following keys:
- `id`: a unique id for this data point. This is useful for evaluation.
- `query`: a string.
- `type`: a string, the type of this data(plantooluse,reflextion,conclusion,profile).
- `golden_result_list`: a list. The reference response.
- `funcs`: a list of functions that may be used in the current query
- `prompt_input`: a dict,input composed of different prompt templates
- `memory`: a string
- `memory_type`: a string,types of memory: task, knowledge, conversation
- `memory_last_task`: a list, in the case where memory is task, the last task information in the previous round
The overall data format is as follows
```json
{
"id": "",
"query": "",
"type": "",
"golden_result_list": [],
"funcs": [],
"prompt_input": {},
"memory": "",
"memory_type": "",
"memory_last_task": {}
}
```
## How to download benchmark
---
You can download the benchmark evaluation set through [kwaikeg/KAgentBench](https://huggingface.co/datasets/kwaikeg/KAgentBench/tree/main), or you can also download the benchmark evaluation set on [KwaiAgents](https://github.com/KwaiKEG/KwaiAgents).
The filename of the evaluation set is 'benchmark_eval.jsonl'. Download the file to your local system.
## Environment Setup
---
Please make sure you have setup the environment and installed the required packages. Make sure you meet the above requirements, and then install the dependent libraries.
```bash
pip install -r requirements.txt
```
## Benchmark Inference
To run benchmark evaluations using different models, it is necessary to appropriately load and predict according to the model in the inference script. Different models may have variations in their initialization and loading methods. We have provided inference scripts for both the Qianwen and Baichuan models. Inference for other models can be adapted based on the scripts we have provided.
Taking the provided open-source model kagentlms_qwen_7b_mat as an example.（The kagentlms_qwen_7b_mat model has been uploaded to [huggingface](https://huggingface.co/kwaikeg/kagentlms_qwen_7b_mat)）
Run the script to perform benchmark predictions. The parameter is the prediction results file.
```bash
python infer_qwen.py qwen_benchmark_res.jsonl
```
Similarly, to perform evaluations using the 'kagentlms_baichuan2_13b_mat' model, execute the following command.
```bash
python infer_baichuan.py baichuan_benchmark_res.jsonl
```
The prediction results will be stored in the 'model_predict' field, which is a dictionary (dict) structure. The key is the type of prompt, and the corresponding value is the output of the model. In the 'profile', as it contains only one prompt, 'model_predict' is a string, and the corresponding value is the model output.
Below is an example display of a prediction result for 'conclusion' data.
```json
{
"query": "在过去的一年中，哪个月份的全球气温最高？",
"id": "0ae75cf4-8897-5998-b74c-c7ae7202621c",
"type": "conclusion",
"memory_type": "task",
"memory": "...",
"funcs": [],
"memory_last_task": {},
"golden_result_list": [
{
"golden_result":"2022年中，6月是全球最高气温。"
},
{
"golden_result":"2022年中，全球最高气温是6月。"
},
{
"golden_result":"2022年中，全球最高气温是6月。"
},
{
"golden_result":"2022年中，6月是全球最高气温。。"
},
{
"golden_result":"2022年中，全球最高气温是6月。"
}
],
"prompt_input": {...},
"model_predict": {
"ReACT": "2022年7月的全球气温最高，达到了17.23℃。",
"AutoGPT": "在过去的一年中，全球气温最高的月份是2023年4月，全球表面平均气温达到了17.23°C，创下了历史新高。这一数据来自于美国国家海洋和大气管理局与美国缅因大学汇编的初步数据。",
"ModelScope": "在过去一年中，全球气温最高的月份是2023年7月。根据美国国家航空航天局（NASA）和美国国家海洋和大气管理局（NOAA）的最新研究报告，2022年是史上排名第6的最热年份，尽管拉尼娜现象改变了天气模式，抑制了全球气温。然而，2023年7月的全球平均气温达到了17.23℃，超过了7月4日和5日创下的17.18℃的联合纪录，而后者之前曾打破了7月3日创下的17.01℃的纪录。",
"ToolLlama": "Thought: I have found the information about the warmest month in 2022 in the memory. I will use this information to answer the question and call the Finish function to provide the final answer.\nAction: Finish\nAction Input: {\"return_type\": \"give_answer\", \"final_answer\": \"在2022年，全球最暖的月份是5月，比20世纪平均值高出1.39°F（0.77°C）。\"}",
"Kuaiagent": "在过去的一年中，全球气温最高的月份是2023年7月。根据美国国家航空航天局（NASA）和美国国家海洋和大气管理局（NOAA）的最新研究报告，2023年7月的全球平均气温达到了17.23℃，超过了19世纪50年代有仪器记录以来的任何一个月。"
}
}
```
'...' indicates that the content is too extensive. For the sake of convenient display format, the content has been abbreviated.
## Evaluation
Taking the inference results of the 'kagentlms_qwen_7b_mat' model as an example, calculate the metrics based on the benchmark inference results obtained from the 'infer'. Execute the following command to get the overall score.
The first parameter is the benchmark evaluation set, which contains reference responses manually annotated, and the second parameter is the prediction results of the model being evaluated.
```bash
python benchmark_eval.py ./benchmark_eval.jsonl ./qwen_benchmark_res.jsonl
```
The final model score is as follows:
```bash
plan : 31.64, tooluse : 28.26, reflextion : 29.50, conclusion : 44.85, profile : 44.78, overall : 34.20
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KnutJaegersberg/Auton | KnutJaegersberg | 2023-12-12T19:34:37Z | 2 | 3 | [
"license:mit",
"region:us"
] | null | 2023-11-17T14:36:58Z | ---
license: mit
---
This dataset has only been sourced from open source licensed datasets like flan. No Openai input. | [
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WeixiangYan/CodeScope | WeixiangYan | 2023-12-17T05:18:37Z | 2 | 0 | [
"task_categories:translation",
"task_categories:text-generation",
"size_categories:10M<n<100M",
"language:en",
"language:code",
"license:cc-by-nc-4.0",
"code",
"region:us"
] | [
"translation",
"text-generation"
] | 2023-11-18T06:11:52Z | ---
license: cc-by-nc-4.0
task_categories:
- translation
- text-generation
language:
- en
- code
tags:
- code
size_categories:
- 10M<n<100M
--- | [
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ReDUB/SoundHarvest | ReDUB | 2023-12-14T22:51:51Z | 2 | 0 | [
"task_categories:translation",
"task_categories:audio-to-audio",
"size_categories:1K<n<10K",
"language:ar",
"language:es",
"language:fr",
"language:hi",
"language:id",
"language:ja",
"language:ko",
"language:pt",
"language:ru",
"language:th",
"language:tr",
"language:vi",
"language:en",
"license:other",
"speech2speech",
"region:us"
] | [
"translation",
"audio-to-audio"
] | 2023-11-20T04:21:11Z | ---
license: other
task_categories:
- translation
- audio-to-audio
language:
- ar
- es
- fr
- hi
- id
- ja
- ko
- pt
- ru
- th
- tr
- vi
- en
tags:
- speech2speech
pretty_name: SoundHarvest
size_categories:
- 1K<n<10K
---
## Data Format
The dataset is organized in the following structure:
```yaml
dataset/
├── video_id_1/
│ ├── audio_language_1.wav
│ ├── audio_language_2.wav
│ ├── subtitle_language_1.vtt
│ ├── subtitle_language_2.vtt
│ └── unmatched/
│ └── ...
├── video_id_2/
│ ├── ...
└── ...
```
Original version with the channel (MrBeast) will contain 487 hours 27 minutes 59 seconds of audio files.
## Limitations
- **Copyright**: Please be aware of copyright restrictions when using this dataset. Ensure that you have the necessary permissions to use the audio and subtitle data for your intended purposes.
- **Inaccuracies**: While efforts have been made to align audio and subtitles accurately, there may be occasional mismatches or inaccuracies in the dataset. We recommend verifying the quality and alignment of the data for your specific use case.
## Generating Dataset
For generating the dataset launch:
1. `generate_urls.py` - to generate video URLs based on `channel_urls.txt`
2. `generate_dataset.py` - for generating dataset (can take **a lot** of time...)
3. `polish_dataset.py` - for cleaning the folders without any useful data
## Usage
The SoundHarvest dataset can be utilized for a variety of applications, including:
### 1. Automatic Speech Recognition (ASR)
Train ASR models to convert spoken language into text. SoundHarvest provides diverse language samples, making it suitable for multilingual ASR tasks.
### 2. Multilingual Natural Language Processing (NLP)
Leverage the dataset for multilingual NLP tasks, such as:
- Speech sentiment analysis.
- Language identification.
### 3. Linguistic Research and Analysis
Conduct linguistic research and analysis to explore various aspects of languages, including phonetics, dialects, and language evolution.
### 4. Speech-to-Speech Translation
Use the dataset to develop and evaluate speech-to-speech translation models. Translate spoken content from one language to another, expanding the dataset's applications to cross-lingual communication.
## Acknowledgments
We would like to express our gratitude to the YouTube content creators for providing valuable multilingual audio content that makes this dataset possible. | [
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projecte-aina/CA-PT_Parallel_Corpus | projecte-aina | 2023-12-13T10:10:21Z | 2 | 0 | [
"task_categories:translation",
"multilinguality:translation",
"size_categories:1M<n<10M",
"source_datasets:original",
"language:ca",
"language:pt",
"language:multilingual",
"region:us"
] | [
"translation"
] | 2023-11-24T15:40:36Z | ---
language:
- ca
- pt
- multilingual
multilinguality:
- translation
pretty_name: CA-PT Parallel Corpus
size_categories:
- 1M<n<10M
source_datasets:
- original
task_categories:
- translation
task_ids: []
---
# Dataset Card for CA-PT Parallel Corpus
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Splits](#data-instances)
- [Dataset Creation](#dataset-creation)
- [Source Data](#source-data)
- [Data preparation](#data-preparation)
- [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)
- [Author](#author)
- [Contact Information](#contact-information)
- [Copyright](#copyright)
- [Licensing information](#licenciung-informatrion)
- [Funding](#funding)
## Dataset Description
### Dataset Summary
The CA-PT Parallel Corpus is a Catalan-Portuguese dataset of **9.892.953** parallel sentences. The dataset was created to support Catalan NLP tasks, e.g.,
Machine Translation.
### Supported Tasks and Leaderboards
The dataset can be used to train a model for Multilingual Machine Translation. Success on this task is typically measured by achieving a high BLEU score.
### Languages
The texts in the dataset are in Catalan and Portuguese.
## Dataset Structure
Two separated txt files are provided with the sentences sorted in the same order:
- ca-pt_2023_09_01_full.ca: contains 9.892.953 Catalan sentences.
- ca-pt_2023_09_01_full.pt: contains 9.892.953 Portuguese sentences.
### Data Splits
The dataset contains a single split: `train`.
## Dataset Creation
### Source Data
The dataset is a combination of the following authentic datasets:
| Dataset | Sentences |
|:-------|-------:|
| CCMatrix v1 | 3.765.459 |
| WikiMatrix | 317.649 |
| GNOME | 1.752 |
| KDE4 | 117.828 |
| QED | 43.736 |
| TED2020 v1 | 41.461 |
| OpenSubtitles | 235.604 |
| GlobalVoices | 3.430 |
| Tatoeba | 723 |
| Europarl | 1.631.989 |
| **Total** | **6.159.631** |
All corpora except Europarl were collected from [Opus](https://opus.nlpl.eu/).
The Europarl corpus is a synthetic parallel corpus created from the original Spanish-Catalan corpus by [SoftCatalà](https://github.com/Softcatala/Europarl-catalan).
The remaining **3.733.322** sentences are synthetic parallel data created from a random sampling of the Spanish-Portuguese corpora available on [Opus](https://opus.nlpl.eu/) and translated into Catalan using the [PlanTL es-ca](https://huggingface.co/PlanTL-GOB-ES/mt-plantl-es-ca) model.
### Data preparation
All datasets are deduplicated and filtered to remove any sentence pairs with a cosine similarity of less than 0.75.
This is done using sentence embeddings calculated using [LaBSE](https://huggingface.co/sentence-transformers/LaBSE).
The filtered datasets are then concatenated to form a final corpus of **9.892.953** parallel sentences.
### Personal and Sensitive Information
No anonymisation process was performed.
## Considerations for Using the Data
### Social Impact of Dataset
The purpose of this dataset is to help develop Machine Translation tasks for mid-resource languages such as Catalan.
### Discussion of Biases
We are aware that since part of the data comes from unreliable web pages and non-curated texts, some biases may be present in the dataset.
Nonetheless, we have not applied any steps to reduce their impact.
### Other Known Limitations
The dataset contains data of a general domain. Application of this dataset in more specific domains such as biomedical, legal etc. would be of limited use.
## Additional Information
### Author
Language Technologies Unit (LangTech) at the Barcelona Supercomputing Center.
### Contact information
For further information, please send an email to [email protected].
### Copyright
Copyright Language Technologies Unit at Barcelona Supercomputing Center (2023).
### Licensing information
This work is licensed under a [Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/).
### Funding
This work was funded by the [Departament de la Vicepresidència i de Polítiques Digitals i Territori de la Generalitat de Catalunya](https://politiquesdigitals.gencat.cat/ca/inici/index.html#googtrans(ca|en) within the framework of [Projecte AINA](https://politiquesdigitals.gencat.cat/ca/economia/catalonia-ai/aina). | [
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AhmadMustafa/Urdu-Instruct-News-Category-Classification | AhmadMustafa | 2023-12-15T11:17:11Z | 2 | 0 | [
"task_categories:text-classification",
"task_categories:text-generation",
"task_categories:text2text-generation",
"size_categories:100K<n<1M",
"language:ur",
"region:us"
] | [
"text-classification",
"text-generation",
"text2text-generation"
] | 2023-11-26T15:35:37Z | ---
dataset_info:
features:
- name: inputs
dtype: string
- name: targets
dtype: string
splits:
- name: train
num_bytes: 259630345.87062514
num_examples: 100674
- name: test
num_bytes: 28850395.12937485
num_examples: 11187
download_size: 11724449
dataset_size: 288480741.0
task_categories:
- text-classification
- text-generation
- text2text-generation
language:
- ur
pretty_name: Instruct News Category Classification Urdu
size_categories:
- 100K<n<1M
---
# Dataset Card for "Urdu-Instruct-News-Category-Classification"
This Dataset is converted from the [original dataset](https://data.mendeley.com/datasets/834vsxnb99/3) by Khalid Hussain, Nimra Mughal, Irfan Ali, Saif Hassan, Sher Muhammad Daudpota.
## Task:
Generate the News Paragraph, and classify the news category from it.
## Split Size:
- train: 100674
- test: 11187
## Prompt Template (In Urdu):
Random.choice b.w these 2
```
["""اس پیراگراف (خبروں) کو مندرجہ ذیل درجوں (کیٹگریز) میں سے ایک کیٹگری میں ڈالیں.
درجے:
- کھیل
- تفریح،
- کاروبار و معیشت،
- سائنس اور ٹیکنالوجی
پیراگراف: {}""",
"""دیے گی خبروں کو ایک لفظ یا درجہ (کیٹگری) میں بیان کریں.
درجے:
- کھیل
- تفریح،
- کاروبار و معیشت،
- سائنس اور ٹیکنالوجی
جملے: {}"""]
```
<b>Translation</b>:
```
[Classify the given paragraph (news article) into one of the following categories:
1. Sports
2. Entertainment
3. Business & Economics
4. Science & Technology
paragraph: {},
Classify the given news paragraph into one of the following categories:
1. Sports
2. Entertainment
3. Business & Economics
4. Science & Technology
sentences: {}]
```
## Completion Template (In Urdu)
```
یہ دی گی خبریں {} سے تعلق رکھتی ہیں
```
<b>Translation</b>:
```
The given news belong to the {category}
``` | [
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AhmadMustafa/Urdu-Instruct-News-Headline-Generation | AhmadMustafa | 2023-12-15T10:04:57Z | 2 | 0 | [
"task_categories:text-generation",
"task_categories:summarization",
"size_categories:100K<n<1M",
"language:ur",
"region:us"
] | [
"text-generation",
"summarization"
] | 2023-11-26T15:40:27Z | ---
dataset_info:
features:
- name: inputs
dtype: string
- name: targets
dtype: string
splits:
- name: train
num_bytes: 253043591.85453376
num_examples: 100674
- name: test
num_bytes: 28118468.14546625
num_examples: 11187
download_size: 122231820
dataset_size: 281162060.0
task_categories:
- text-generation
- summarization
language:
- ur
pretty_name: Urdu Instruct News Headline Generation
size_categories:
- 100K<n<1M
---
# Dataset Card for "Urdu-Instruct-News-Headline-Generation"
This Dataset is converted from the [original dataset](https://data.mendeley.com/datasets/834vsxnb99/3) by Khalid Hussain, Nimra Mughal, Irfan Ali, Saif Hassan, Sher Muhammad Daudpota.
## Task:
Generate the News Headline from the given News.
## Split Size:
- train: 100674
- test: 11187
## Prompt Template (In Urdu):
Random.choice b.w these 2
```
["اس اردو پیراگراف (خبروں) کا عنوان تجویز کریں
پیراگراف: {}",
"دیے گے خبروں کا عنوان تجویز کریں.
جملے: {}"
]
```
<b>Translation</b>:
```
1. Write a title for the following news article:
paragraph: {}
2. Suggest the title of the given the sentences
sentences: {}
```
## Completion Template (In Urdu)
```
جی ضرور، یہ رہا آپ کے پیراگراف کا عنوان:
{}
```
<b>Translation</b>:
```
Sure, here is the title of the given article
{}
``` | [
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diffusers/benchmarks | diffusers | 2023-12-15T04:01:31Z | 2 | 6 | [
"language:en",
"license:apache-2.0",
"region:us"
] | null | 2023-11-28T04:33:41Z | ---
license: apache-2.0
language:
- en
pretty_name: Diffusers Benchmarks
---
Welcome to 🤗 Diffusers Benchmarks!
This is dataset where we keep track of the inference latency and memory information of the core pipelines in the `diffusers` library.
Currently, the core pipelines are the following:
* Stable Diffusion and its derivatives such as ControlNet, T2I Adapter, Image-to-Image, Inpainting
* Stable Diffusion XL and its derivatives
* SSD-1B
* Kandinsky
* Würstchen
* LCM
*Note that we will continue to extend the list of core pipelines based on their API usage.*
We use [this GitHub Actions workflow](https://github.com/huggingface/diffusers/blob/main/.github/workflows/benchmark.yml) to report the above numbers automatically. This workflow runs on a biweekly cadence.
The benchmarks are run on an A10G GPU. | [
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librarian-bots/hub_models_with_base_model_info | librarian-bots | 2023-12-17T06:51:19Z | 2 | 3 | [
"size_categories:10K<n<100K",
"metadata",
"region:us"
] | null | 2023-11-28T13:08:31Z | ---
size_categories:
- 10K<n<100K
pretty_name: Hugging Face Hub Models with Base Model Metadata
dataset_info:
features:
- name: author
dtype: string
- name: last_modified
dtype: timestamp[us, tz=UTC]
- name: createdAt
dtype: timestamp[us, tz=UTC]
- name: downloads
dtype: int64
- name: likes
dtype: int64
- name: library_name
dtype: string
- name: modelId
dtype: string
- name: datasets
sequence: string
- name: language
sequence: string
- name: base_model
dtype: string
splits:
- name: train
num_bytes: 5408255
num_examples: 36181
download_size: 2137676
dataset_size: 5408255
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
tags:
- metadata
---
# Dataset Card for Hugging Face Hub Models with Base Model Metadata
## Dataset Details
This dataset contains a subset of possible metadata for models hosted on the Hugging Face Hub.
All of these models contain `base_model` metadata i.e. information about the model used for fine-tuning.
This data can be used for creating network graphs showing links between models on the Hub.
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
### Dataset Sources [optional]
<!-- Provide the basic links for the dataset. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the dataset is intended to be used. -->
### Direct Use
<!-- This section describes suitable use cases for the dataset. -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
[More Information Needed]
## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
[More Information Needed]
## Dataset Creation
### Curation Rationale
<!-- Motivation for the creation of this dataset. -->
[More Information Needed]
### Source Data
<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
#### Data Collection and Processing
<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
[More Information Needed]
#### Who are the source data producers?
The source data is model card creators for models on the Hub as well as tools and deep learning libraries which automatically assign metadata to model repositories.
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NamProlah/bloomsage | NamProlah | 2023-12-16T14:31:08Z | 2 | 0 | [
"size_categories:10K<n<100K",
"language:aa",
"license:mit",
"biology",
"region:us"
] | null | 2023-11-28T16:42:27Z | ---
license: mit
language:
- aa
tags:
- biology
pretty_name: BloomSage_Dataset
size_categories:
- 10K<n<100K
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jbilcke-hf/ai-tube-index | jbilcke-hf | 2023-12-17T18:47:37Z | 2 | 1 | [
"license:cc-by-nc-sa-4.0",
"region:us"
] | null | 2023-11-28T22:50:50Z | ---
license: cc-by-nc-sa-4.0
---
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HaoyeZhang/RLHF-V-Hall_v0 | HaoyeZhang | 2023-12-14T17:34:12Z | 2 | 0 | [
"task_categories:conversational",
"task_categories:text-generation",
"language:en",
"license:cc-by-nc-4.0",
"arxiv:2312.00849",
"region:us"
] | [
"conversational",
"text-generation"
] | 2023-11-29T07:09:29Z | ---
license: cc-by-nc-4.0
task_categories:
- conversational
- text-generation
language:
- en
---
# RLHF-V-Hall
[Project Page](https://rlhf-v.github.io/) | [Paper](https://arxiv.org/abs/2312.00849) | [GitHub](https://github.com/RLHF-V/RLHF-V)
## Updates
📍 **[2023.12.15]** We release a new subset named `RLHF-V-Hall_v0_LLaVA_dpo_with_rlhf-v-sft_logp_train-1065.tsv`. It is the fine-grained human preference data annotated on the outputs of LLaVA-13B.
## Dataset Details
**Brief introduction:** RLHF-V-Hall is the human preference data used in "**RLHF-V: Towards Trustworthy MLLMs via Behavior Alignment from Fine-grained Correctional Human Feedback**". It is a human preference dataset consisting of fine-grained segment-level human corrections, which can be used for mitigating hallucinations in MLLM. Currently, the dataset originally contains 1.4k annotated data including a diverse set of detailed description instructions and question-answering instructions.
- `RLHF-V-Hall_v0-1401.tsv`: The human preference data used in our paper.
- `RLHF-V-Hall_v0_dpo_with_rlhf-v-sft_logp_train-1401.tsv`: The human preference data used in our paper, with logp values generated by our SFT model.
- `RLHF-V-Hall_v0_LLaVA_dpo_with_rlhf-v-sft_logp_train-1065.tsv`: The human preference data annotated on the outputs of LLaVA-13B, with logp values generated by our SFT model.
- .lineidx: The line index file for each .tsv file.
## Dataset Usage
**Load Dataset:** To quickly view our data, please follow the instructions in our [GitHub repo](https://github.com/RLHF-V/RLHF-V#install) to install muffin, and download our dataset in `Muffin/data/RLHF-V-Hal_v0` folder. Then you can run the following code to visualize the image and data.
```
import random
from muffin.data.datasets import SingleDataSourceDataset
from muffin.data.data_processors import register_data_path
dataset = SingleDataSourceDataset('RLHF-V-Hall_v0',
*register_data_path['RLHF-V-Hall_v0'](),
intent="sft")
print("len dataset:", len(dataset))
test_idx_list = random.sample(list(range(len(dataset))), 10)
for i in range(len(dataset)):
print("idx:", i)
print(dataset[i]["idx"])
if i in test_idx_list:
print(dataset[i].keys())
print("id:", dataset[i]['idx']) # original index
print("question:", dataset[i]['question']) # question
print("Win:", dataset[i]['chosen']) # chosen answer
print("Lose:", dataset[i]['rejected']) # rejected answer
print("metainfo:", dataset[i]["metainfo"]) # model, type
display(dataset[i]['image']) # image
## You can also visit the logp info in the dataset by the following keys:
## 'ref_win_logp', 'ref_win_avg_logp', 'ref_win_per_token_logp', 'ref_rej_logp', 'ref_rej_avg_logp', 'ref_rej_per_token_logp'
## For example:
## print("ref_win_logp:", dataset[i]['ref_win_logp'])
```
**Data fields:** To make better use of our data, here we explain the meaning of each column in the .tsv files.
| | Content | Description |
| ---- | ------------------------ | ------------------------------------------------------------ |
| 0 | `dataset_name` | Dataset name. |
| 1 | `img_b64_buffer` | Image encoded in base64 format. |
| 2 | `text_b64` | Text data encoded in base64 format. <br /><br />- "RLHF-V-Hall_v0-1401.tsv": question, chosen answer, and rejected answer. <br />- "RLHF-V-Hall_v0_dpo_with_rlhf-v-sft_logp_train-1401.tsv": question, chosen answer, rejected answer, and logp values generated by our SFT model. |
| 3 | `origin_dataset` | Original dataset for annotation, which is not used in training. |
| 4 | `origin_split` | Meta information for each data item encoded in base64 format, including the name of the model we use to generate the original answer, and the question type ("detailed description" or "question answering") |
| 5 | `origin_split_inner_idx` | Data index. |
| 6 | `image_path` | coco_id for the image. |
## Citation
```
@article{2023rlhf-v,
author = {Tianyu Yu and Yuan Yao and Haoye Zhang and Taiwen He and Yifeng Han and Ganqu Cui and Jinyi Hu and Zhiyuan Liu and Hai-Tao Zheng and Maosong Sun and Tat-Seng Chua},
title = {RLHF-V: Towards Trustworthy MLLMs via Behavior Alignment from Fine-grained Correctional Human Feedback},
journal = {arxiv},
year = {2023},
}
```
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gksriharsha/chitralekha | gksriharsha | 2023-12-15T23:32:02Z | 2 | 1 | [
"task_categories:image-to-text",
"size_categories:1M<n<10M",
"language:te",
"license:gpl-3.0",
"region:us"
] | [
"image-to-text"
] | 2023-11-29T14:31:24Z | ---
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- split: test
path: NotoSansTeluguUI-Bold/test-*
- config_name: NotoSansTeluguUI-Regular
data_files:
- split: train
path: NotoSansTeluguUI-Regular/train-*
- split: validation
path: NotoSansTeluguUI-Regular/validation-*
- split: test
path: NotoSansTeluguUI-Regular/test-*
- config_name: NotoSerifTelugu-VariableFont_wght
data_files:
- split: train
path: NotoSerifTelugu-VariableFont_wght/train-*
- split: validation
path: NotoSerifTelugu-VariableFont_wght/validation-*
- split: test
path: NotoSerifTelugu-VariableFont_wght/test-*
- config_name: Peddana-Regular
data_files:
- split: train
path: Peddana-Regular/train-*
- split: validation
path: Peddana-Regular/validation-*
- split: test
path: Peddana-Regular/test-*
- config_name: Ponnala
data_files:
- split: train
path: Ponnala/train-*
- split: validation
path: Ponnala/validation-*
- split: test
path: Ponnala/test-*
- config_name: Pothana2000
data_files:
- split: train
path: Pothana2000/train-*
- split: validation
path: Pothana2000/validation-*
- split: test
path: Pothana2000/test-*
- config_name: Pothana2001
data_files:
- split: train
path: Pothana2001/train-*
- split: validation
path: Pothana2001/validation-*
- split: test
path: Pothana2001/test-*
- config_name: Ramabhadra
data_files:
- split: train
path: Ramabhadra/train-*
- split: validation
path: Ramabhadra/validation-*
- split: test
path: Ramabhadra/test-*
- config_name: Ramabhadra1
data_files:
- split: train
path: Ramabhadra1/train-*
- split: validation
path: Ramabhadra1/validation-*
- split: test
path: Ramabhadra1/test-*
- config_name: RamaneeyaWin
data_files:
- split: train
path: RamaneeyaWin/train-*
- split: validation
path: RamaneeyaWin/validation-*
- split: test
path: RamaneeyaWin/test-*
- config_name: Ramaraja-Regular
data_files:
- split: train
path: Ramaraja-Regular/train-*
- split: validation
path: Ramaraja-Regular/validation-*
- split: test
path: Ramaraja-Regular/test-*
- config_name: RaviPrakash
data_files:
- split: train
path: RaviPrakash/train-*
- split: validation
path: RaviPrakash/validation-*
- split: test
path: RaviPrakash/test-*
- config_name: Sree Krushnadevaraya
data_files:
- split: train
path: Sree Krushnadevaraya/train-*
- split: validation
path: Sree Krushnadevaraya/validation-*
- split: test
path: Sree Krushnadevaraya/test-*
- config_name: Subhadra
data_files:
- split: train
path: Subhadra/train-*
- split: validation
path: Subhadra/validation-*
- split: test
path: Subhadra/test-*
- config_name: Suguna
data_files:
- split: train
path: Suguna/train-*
- split: validation
path: Suguna/validation-*
- split: test
path: Suguna/test-*
- config_name: Suranna
data_files:
- split: train
path: Suranna/train-*
- split: validation
path: Suranna/validation-*
- split: test
path: Suranna/test-*
- config_name: Suravara_Samhita
data_files:
- split: train
path: Suravara_Samhita/train-*
- split: validation
path: Suravara_Samhita/validation-*
- split: test
path: Suravara_Samhita/test-*
- config_name: Suravara_Swarna
data_files:
- split: train
path: Suravara_Swarna/train-*
- split: validation
path: Suravara_Swarna/validation-*
- split: test
path: Suravara_Swarna/test-*
- config_name: Suravara_Swarna_bold
data_files:
- split: train
path: Suravara_Swarna_bold/train-*
- split: validation
path: Suravara_Swarna_bold/validation-*
- split: test
path: Suravara_Swarna_bold/test-*
- config_name: Suravara_Swarna_italic
data_files:
- split: train
path: Suravara_Swarna_italic/train-*
- split: validation
path: Suravara_Swarna_italic/validation-*
- split: test
path: Suravara_Swarna_italic/test-*
- config_name: Suravaram
data_files:
- split: train
path: Suravaram/train-*
- split: validation
path: Suravaram/validation-*
- split: test
path: Suravaram/test-*
- config_name: TLOTAmmaBI_ship
data_files:
- split: train
path: TLOTAmmaBI_ship/train-*
- split: validation
path: TLOTAmmaBI_ship/validation-*
- split: test
path: TLOTAmmaBI_ship/test-*
- config_name: TLOTAmmaB_ship
data_files:
- split: train
path: TLOTAmmaB_ship/train-*
- split: validation
path: TLOTAmmaB_ship/validation-*
- split: test
path: TLOTAmmaB_ship/test-*
- config_name: TLOTAmmaI_ship
data_files:
- split: train
path: TLOTAmmaI_ship/train-*
- split: validation
path: TLOTAmmaI_ship/validation-*
- split: test
path: TLOTAmmaI_ship/test-*
- config_name: TLOTAmmaN_ship
data_files:
- split: train
path: TLOTAmmaN_ship/train-*
- split: validation
path: TLOTAmmaN_ship/validation-*
- split: test
path: TLOTAmmaN_ship/test-*
- config_name: TLOTAmrutaBI_Ship
data_files:
- split: train
path: TLOTAmrutaBI_Ship/train-*
- split: validation
path: TLOTAmrutaBI_Ship/validation-*
- split: test
path: TLOTAmrutaBI_Ship/test-*
- config_name: TLOTAmrutaB_Ship
data_files:
- split: train
path: TLOTAmrutaB_Ship/train-*
- split: validation
path: TLOTAmrutaB_Ship/validation-*
- split: test
path: TLOTAmrutaB_Ship/test-*
- config_name: TLOTAtreyaBI_Ship
data_files:
- split: train
path: TLOTAtreyaBI_Ship/train-*
- split: validation
path: TLOTAtreyaBI_Ship/validation-*
- split: test
path: TLOTAtreyaBI_Ship/test-*
- config_name: TLOTAtreyaB_Ship
data_files:
- split: train
path: TLOTAtreyaB_Ship/train-*
- split: validation
path: TLOTAtreyaB_Ship/validation-*
- split: test
path: TLOTAtreyaB_Ship/test-*
- config_name: TLOTAtreyaI_Ship
data_files:
- split: train
path: TLOTAtreyaI_Ship/train-*
- split: validation
path: TLOTAtreyaI_Ship/validation-*
- split: test
path: TLOTAtreyaI_Ship/test-*
- config_name: TLOTAtreyaN_Ship
data_files:
- split: train
path: TLOTAtreyaN_Ship/train-*
- split: validation
path: TLOTAtreyaN_Ship/validation-*
- split: test
path: TLOTAtreyaN_Ship/test-*
- config_name: TLOTChandanaBI_Ship
data_files:
- split: train
path: TLOTChandanaBI_Ship/train-*
- split: validation
path: TLOTChandanaBI_Ship/validation-*
- split: test
path: TLOTChandanaBI_Ship/test-*
- config_name: TLOTChandanaB_Ship
data_files:
- split: train
path: TLOTChandanaB_Ship/train-*
- split: validation
path: TLOTChandanaB_Ship/validation-*
- split: test
path: TLOTChandanaB_Ship/test-*
- config_name: TLOTDevaI_Ship
data_files:
- split: train
path: TLOTDevaI_Ship/train-*
- split: validation
path: TLOTDevaI_Ship/validation-*
- split: test
path: TLOTDevaI_Ship/test-*
- config_name: TLOTDevaN_Ship
data_files:
- split: train
path: TLOTDevaN_Ship/train-*
- split: validation
path: TLOTDevaN_Ship/validation-*
- split: test
path: TLOTDevaN_Ship/test-*
- config_name: TLOTDraupadiBI_Ship
data_files:
- split: train
path: TLOTDraupadiBI_Ship/train-*
- split: validation
path: TLOTDraupadiBI_Ship/validation-*
- split: test
path: TLOTDraupadiBI_Ship/test-*
- config_name: TLOTDraupadiB_ship
data_files:
- split: train
path: TLOTDraupadiB_ship/train-*
- split: validation
path: TLOTDraupadiB_ship/validation-*
- split: test
path: TLOTDraupadiB_ship/test-*
- config_name: TLOTDraupadiI_Ship
data_files:
- split: train
path: TLOTDraupadiI_Ship/train-*
- split: validation
path: TLOTDraupadiI_Ship/validation-*
- split: test
path: TLOTDraupadiI_Ship/test-*
- config_name: TLOTDraupadiN_Ship
data_files:
- split: train
path: TLOTDraupadiN_Ship/train-*
- split: validation
path: TLOTDraupadiN_Ship/validation-*
- split: test
path: TLOTDraupadiN_Ship/test-*
- config_name: TLOTGolkondaBI_Ship
data_files:
- split: train
path: TLOTGolkondaBI_Ship/train-*
- split: validation
path: TLOTGolkondaBI_Ship/validation-*
- split: test
path: TLOTGolkondaBI_Ship/test-*
- config_name: TLOTGolkondaB_Ship
data_files:
- split: train
path: TLOTGolkondaB_Ship/train-*
- split: validation
path: TLOTGolkondaB_Ship/validation-*
- split: test
path: TLOTGolkondaB_Ship/test-*
- config_name: TLOTKrishnaB_Ship
data_files:
- split: train
path: TLOTKrishnaB_Ship/train-*
- split: validation
path: TLOTKrishnaB_Ship/validation-*
- split: test
path: TLOTKrishnaB_Ship/test-*
- config_name: TLOTKrishnaI_Ship
data_files:
- split: train
path: TLOTKrishnaI_Ship/train-*
- split: validation
path: TLOTKrishnaI_Ship/validation-*
- split: test
path: TLOTKrishnaI_Ship/test-*
- config_name: TLOTKrishnaN_Ship
data_files:
- split: train
path: TLOTKrishnaN_Ship/train-*
- split: validation
path: TLOTKrishnaN_Ship/validation-*
- split: test
path: TLOTKrishnaN_Ship/test-*
- config_name: TLOTManuBI_Ship
data_files:
- split: train
path: TLOTManuBI_Ship/train-*
- split: validation
path: TLOTManuBI_Ship/validation-*
- split: test
path: TLOTManuBI_Ship/test-*
- config_name: TLOTManuB_Ship
data_files:
- split: train
path: TLOTManuB_Ship/train-*
- split: validation
path: TLOTManuB_Ship/validation-*
- split: test
path: TLOTManuB_Ship/test-*
- config_name: TLOTManuI_Ship
data_files:
- split: train
path: TLOTManuI_Ship/train-*
- split: validation
path: TLOTManuI_Ship/validation-*
- split: test
path: TLOTManuI_Ship/test-*
- config_name: TLOTManuN_Ship
data_files:
- split: train
path: TLOTManuN_Ship/train-*
- split: validation
path: TLOTManuN_Ship/validation-*
- split: test
path: TLOTManuN_Ship/test-*
- config_name: TLOTMenakaBI_Ship
data_files:
- split: train
path: TLOTMenakaBI_Ship/train-*
- split: validation
path: TLOTMenakaBI_Ship/validation-*
- split: test
path: TLOTMenakaBI_Ship/test-*
- config_name: TLOTMenakaB_Ship
data_files:
- split: train
path: TLOTMenakaB_Ship/train-*
- split: validation
path: TLOTMenakaB_Ship/validation-*
- split: test
path: TLOTMenakaB_Ship/test-*
- config_name: TLOTMenakaI_Ship
data_files:
- split: train
path: TLOTMenakaI_Ship/train-*
- split: validation
path: TLOTMenakaI_Ship/validation-*
- split: test
path: TLOTMenakaI_Ship/test-*
- config_name: TLOTMenakaN_Ship
data_files:
- split: train
path: TLOTMenakaN_Ship/train-*
- split: validation
path: TLOTMenakaN_Ship/validation-*
- split: test
path: TLOTMenakaN_Ship/test-*
- config_name: TLOTPavaniBI_Ship
data_files:
- split: train
path: TLOTPavaniBI_Ship/train-*
- split: validation
path: TLOTPavaniBI_Ship/validation-*
- split: test
path: TLOTPavaniBI_Ship/test-*
- config_name: TLOTPavaniB_Ship
data_files:
- split: train
path: TLOTPavaniB_Ship/train-*
- split: validation
path: TLOTPavaniB_Ship/validation-*
- split: test
path: TLOTPavaniB_Ship/test-*
- config_name: TLOTPriyaBI_Ship
data_files:
- split: train
path: TLOTPriyaBI_Ship/train-*
- split: validation
path: TLOTPriyaBI_Ship/validation-*
- split: test
path: TLOTPriyaBI_Ship/test-*
- config_name: TLOTPriyaB_Ship
data_files:
- split: train
path: TLOTPriyaB_Ship/train-*
- split: validation
path: TLOTPriyaB_Ship/validation-*
- split: test
path: TLOTPriyaB_Ship/test-*
- config_name: TLOTRajanBI_Ship
data_files:
- split: train
path: TLOTRajanBI_Ship/train-*
- split: validation
path: TLOTRajanBI_Ship/validation-*
- split: test
path: TLOTRajanBI_Ship/test-*
- config_name: TLOTRajanB_Ship
data_files:
- split: train
path: TLOTRajanB_Ship/train-*
- split: validation
path: TLOTRajanB_Ship/validation-*
- split: test
path: TLOTRajanB_Ship/test-*
- config_name: TLOTRajaniBI_Ship
data_files:
- split: train
path: TLOTRajaniBI_Ship/train-*
- split: validation
path: TLOTRajaniBI_Ship/validation-*
- split: test
path: TLOTRajaniBI_Ship/test-*
- config_name: TLOTRajaniB_Ship
data_files:
- split: train
path: TLOTRajaniB_Ship/train-*
- split: validation
path: TLOTRajaniB_Ship/validation-*
- split: test
path: TLOTRajaniB_Ship/test-*
- config_name: TLOTSanjanaBI_Ship
data_files:
- split: train
path: TLOTSanjanaBI_Ship/train-*
- split: validation
path: TLOTSanjanaBI_Ship/validation-*
- split: test
path: TLOTSanjanaBI_Ship/test-*
- config_name: TLOTSanjanaB_Ship
data_files:
- split: train
path: TLOTSanjanaB_Ship/train-*
- split: validation
path: TLOTSanjanaB_Ship/validation-*
- split: test
path: TLOTSanjanaB_Ship/test-*
- config_name: TLOTSitaraBI_Ship
data_files:
- split: train
path: TLOTSitaraBI_Ship/train-*
- split: validation
path: TLOTSitaraBI_Ship/validation-*
- split: test
path: TLOTSitaraBI_Ship/test-*
- config_name: TLOTSitaraB_Ship
data_files:
- split: train
path: TLOTSitaraB_Ship/train-*
- split: validation
path: TLOTSitaraB_Ship/validation-*
- split: test
path: TLOTSitaraB_Ship/test-*
- config_name: TLOTSwamiB
data_files:
- split: train
path: TLOTSwamiB/train-*
- split: validation
path: TLOTSwamiB/validation-*
- split: test
path: TLOTSwamiB/test-*
- config_name: TLOTSwamiBI_Ship
data_files:
- split: train
path: TLOTSwamiBI_Ship/train-*
- split: validation
path: TLOTSwamiBI_Ship/validation-*
- split: test
path: TLOTSwamiBI_Ship/test-*
- config_name: TLOTSwamiB_Ship
data_files:
- split: train
path: TLOTSwamiB_Ship/train-*
- split: validation
path: TLOTSwamiB_Ship/validation-*
- split: test
path: TLOTSwamiB_Ship/test-*
- config_name: TLOTVennela1B_Ship
data_files:
- split: train
path: TLOTVennela1B_Ship/train-*
- split: validation
path: TLOTVennela1B_Ship/validation-*
- split: test
path: TLOTVennela1B_Ship/test-*
- config_name: TLOTVennelaBI_Ship
data_files:
- split: train
path: TLOTVennelaBI_Ship/train-*
- split: validation
path: TLOTVennelaBI_Ship/validation-*
- split: test
path: TLOTVennelaBI_Ship/test-*
- config_name: TLOTVennelaI_Ship
data_files:
- split: train
path: TLOTVennelaI_Ship/train-*
- split: validation
path: TLOTVennelaI_Ship/validation-*
- split: test
path: TLOTVennelaI_Ship/test-*
- config_name: TLOTVennelaN_Ship
data_files:
- split: train
path: TLOTVennelaN_Ship/train-*
- split: validation
path: TLOTVennelaN_Ship/validation-*
- split: test
path: TLOTVennelaN_Ship/test-*
- config_name: TenaliRamakrishna-Regular
data_files:
- split: train
path: TenaliRamakrishna-Regular/train-*
- split: validation
path: TenaliRamakrishna-Regular/validation-*
- split: test
path: TenaliRamakrishna-Regular/test-*
- config_name: Tikkana
data_files:
- split: train
path: Tikkana/train-*
- split: validation
path: Tikkana/validation-*
- split: test
path: Tikkana/test-*
- config_name: TimmanaRegular
data_files:
- split: train
path: TimmanaRegular/train-*
- split: validation
path: TimmanaRegular/validation-*
- split: test
path: TimmanaRegular/test-*
- config_name: Vajram
data_files:
- split: train
path: Vajram/train-*
- split: validation
path: Vajram/validation-*
- split: test
path: Vajram/test-*
- config_name: Vani
data_files:
- split: train
path: Vani/train-*
- split: validation
path: Vani/validation-*
- split: test
path: Vani/test-*
- config_name: Vanib
data_files:
- split: train
path: Vanib/train-*
- split: validation
path: Vanib/validation-*
- split: test
path: Vanib/test-*
- config_name: Vemana
data_files:
- split: train
path: Vemana/train-*
- split: validation
path: Vemana/validation-*
- split: test
path: Vemana/test-*
- config_name: akshar
data_files:
- split: train
path: akshar/train-*
- split: validation
path: akshar/validation-*
- split: test
path: akshar/test-*
- config_name: gautami
data_files:
- split: train
path: gautami/train-*
- split: validation
path: gautami/validation-*
- split: test
path: gautami/test-*
- config_name: gautamib
data_files:
- split: train
path: gautamib/train-*
- split: validation
path: gautamib/validation-*
- split: test
path: gautamib/test-*
- config_name: lohit_te
data_files:
- split: train
path: lohit_te/train-*
- split: validation
path: lohit_te/validation-*
- split: test
path: lohit_te/test-*
license: gpl-3.0
task_categories:
- image-to-text
language:
- te
size_categories:
- 1M<n<10M
---
# Dataset Card for Dataset Name
## Dataset Details
### Dataset Description
This extensive dataset, hosted on Huggingface, is a comprehensive resource for Optical Character Recognition (OCR) in the Telugu language, featuring an impressive array of 90+ configurations. Each configuration in this dataset corresponds to a unique font, meticulously curated by Dr. Rakesh Achanta and sourced from his GitHub repository (https://github.com/TeluguOCR/banti_telugu_ocr).
The dataset is specifically designed to support and enhance the development of OCR models, ranging from simple Convolutional Recurrent Neural Network (CRNN) architectures to more advanced systems like trOCR. The versatility of this dataset lies in its large volume and diversity, making it an ideal choice for researchers and developers aiming to build robust OCR systems for the Telugu script.
Key Features:
- Font Diversity: Over 90 unique fonts, each forming a separate configuration, providing a rich variety in text styles and nuances.
- Large Volume: Each configuration contains approximately 800,000 examples, summing up to a vast pool of data for comprehensive training and evaluation.
- Data Split: The dataset is pre-split into training, validation, and test sets, following a 60/20/20 ratio, to facilitate efficient model training and benchmarking.
- Use Cases: Ideal for developing a wide range of OCR models - from basic CRNNs to sophisticated models like trOCR.
- Accessibility: Hosted on Huggingface, ensuring easy access and integration with various machine learning frameworks and tools.
This dataset stands as a testament to Dr. Rakesh Achanta's dedication to enhancing Telugu language processing technologies. It is not just a tool for model development but also a gateway to preserving and digitizing the rich literary heritage of the Telugu language.
Researchers and developers leveraging this dataset are encouraged to adhere to the ethical guidelines of AI research and development, ensuring that the applications developed are for the benefit of language preservation, accessibility, and technological advancement in a responsible manner.
- **Fonts Curated by:** Dr. Rakesh Achanta
- **Shared by:** Krishna Sriharsha Gundu
- **Language(s) (NLP):** Telugu
### Dataset Sources [optional]
<!-- Provide the basic links for the dataset. -->
- **Repository:** [Original Books Dataset](https://github.com/AnushaMotamarri/Telugu-Books-Dataset)
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argilla/end2end_textclassification_with_metadata | argilla | 2023-12-15T16:53:04Z | 2 | 0 | [
"size_categories:1K<n<10K",
"rlfh",
"argilla",
"human-feedback",
"region:us"
] | null | 2023-11-29T14:38:01Z | ---
size_categories: 1K<n<10K
tags:
- rlfh
- argilla
- human-feedback
---
# Dataset Card for end2end_textclassification_with_metadata
This dataset has been created with [Argilla](https://docs.argilla.io).
As shown in the sections below, this dataset can be loaded into Argilla as explained in [Load with Argilla](#load-with-argilla), or used directly with the `datasets` library in [Load with `datasets`](#load-with-datasets).
## Dataset Description
- **Homepage:** https://argilla.io
- **Repository:** https://github.com/argilla-io/argilla
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
This dataset contains:
* A dataset configuration file conforming to the Argilla dataset format named `argilla.yaml`. This configuration file will be used to configure the dataset when using the `FeedbackDataset.from_huggingface` method in Argilla.
* Dataset records in a format compatible with HuggingFace `datasets`. These records will be loaded automatically when using `FeedbackDataset.from_huggingface` and can be loaded independently using the `datasets` library via `load_dataset`.
* The [annotation guidelines](#annotation-guidelines) that have been used for building and curating the dataset, if they've been defined in Argilla.
### Load with Argilla
To load with Argilla, you'll just need to install Argilla as `pip install argilla --upgrade` and then use the following code:
```python
import argilla as rg
ds = rg.FeedbackDataset.from_huggingface("argilla/end2end_textclassification_with_metadata")
```
### Load with `datasets`
To load this dataset with `datasets`, you'll just need to install `datasets` as `pip install datasets --upgrade` and then use the following code:
```python
from datasets import load_dataset
ds = load_dataset("argilla/end2end_textclassification_with_metadata")
```
### Supported Tasks and Leaderboards
This dataset can contain [multiple fields, questions and responses](https://docs.argilla.io/en/latest/conceptual_guides/data_model.html#feedback-dataset) so it can be used for different NLP tasks, depending on the configuration. The dataset structure is described in the [Dataset Structure section](#dataset-structure).
There are no leaderboards associated with this dataset.
### Languages
[More Information Needed]
## Dataset Structure
### Data in Argilla
The dataset is created in Argilla with: **fields**, **questions**, **suggestions**, **metadata**, **vectors**, and **guidelines**.
The **fields** are the dataset records themselves, for the moment just text fields are supported. These are the ones that will be used to provide responses to the questions.
| Field Name | Title | Type | Required | Markdown |
| ---------- | ----- | ---- | -------- | -------- |
| text | Text | FieldTypes.text | True | False |
The **questions** are the questions that will be asked to the annotators. They can be of different types, such as rating, text, label_selection, multi_label_selection, or ranking.
| Question Name | Title | Type | Required | Description | Values/Labels |
| ------------- | ----- | ---- | -------- | ----------- | ------------- |
| label | Label | QuestionTypes.label_selection | True | N/A | ['World', 'Sports', 'Business', 'Sci/Tech'] |
The **suggestions** are human or machine generated recommendations for each question to assist the annotator during the annotation process, so those are always linked to the existing questions, and named appending "-suggestion" and "-suggestion-metadata" to those, containing the value/s of the suggestion and its metadata, respectively. So on, the possible values are the same as in the table above, but the column name is appended with "-suggestion" and the metadata is appended with "-suggestion-metadata".
The **metadata** is a dictionary that can be used to provide additional information about the dataset record. This can be useful to provide additional context to the annotators, or to provide additional information about the dataset record itself. For example, you can use this to provide a link to the original source of the dataset record, or to provide additional information about the dataset record itself, such as the author, the date, or the source. The metadata is always optional, and can be potentially linked to the `metadata_properties` defined in the dataset configuration file in `argilla.yaml`.
| Metadata Name | Title | Type | Values | Visible for Annotators |
| ------------- | ----- | ---- | ------ | ---------------------- |
| group | Annotation Group | terms | ['group-1', 'group-2', 'group-3'] | True |
| length | Length of the text | integer | 100 - 862 | True |
| length_std | Standard deviation of the length of the text | float | 139.096 - 361.398 | True |
The **guidelines**, are optional as well, and are just a plain string that can be used to provide instructions to the annotators. Find those in the [annotation guidelines](#annotation-guidelines) section.
### Data Instances
An example of a dataset instance in Argilla looks as follows:
```json
{
"external_id": "record-0",
"fields": {
"text": "Wall St. Bears Claw Back Into the Black (Reuters) Reuters - Short-sellers, Wall Street\u0027s dwindling\\band of ultra-cynics, are seeing green again."
},
"metadata": {
"group": "group-1",
"length": 144,
"length_std": 144.0
},
"responses": [],
"suggestions": [],
"vectors": {}
}
```
While the same record in HuggingFace `datasets` looks as follows:
```json
{
"external_id": "record-0",
"label": [],
"label-suggestion": null,
"label-suggestion-metadata": {
"agent": null,
"score": null,
"type": null
},
"metadata": "{\"group\": \"group-1\", \"length\": 144, \"length_std\": 144.0}",
"text": "Wall St. Bears Claw Back Into the Black (Reuters) Reuters - Short-sellers, Wall Street\u0027s dwindling\\band of ultra-cynics, are seeing green again."
}
```
### Data Fields
Among the dataset fields, we differentiate between the following:
* **Fields:** These are the dataset records themselves, for the moment just text fields are supported. These are the ones that will be used to provide responses to the questions.
* **text** is of type `FieldTypes.text`.
* **Questions:** These are the questions that will be asked to the annotators. They can be of different types, such as `RatingQuestion`, `TextQuestion`, `LabelQuestion`, `MultiLabelQuestion`, and `RankingQuestion`.
* **label** is of type `QuestionTypes.label_selection` with the following allowed values ['World', 'Sports', 'Business', 'Sci/Tech'].
* **Suggestions:** As of Argilla 1.13.0, the suggestions have been included to provide the annotators with suggestions to ease or assist during the annotation process. Suggestions are linked to the existing questions, are always optional, and contain not just the suggestion itself, but also the metadata linked to it, if applicable.
* (optional) **label-suggestion** is of type `QuestionTypes.label_selection` with the following allowed values ['World', 'Sports', 'Business', 'Sci/Tech'].
Additionally, we also have two more fields that are optional and are the following:
* **metadata:** This is an optional field that can be used to provide additional information about the dataset record. This can be useful to provide additional context to the annotators, or to provide additional information about the dataset record itself. For example, you can use this to provide a link to the original source of the dataset record, or to provide additional information about the dataset record itself, such as the author, the date, or the source. The metadata is always optional, and can be potentially linked to the `metadata_properties` defined in the dataset configuration file in `argilla.yaml`.
* **external_id:** This is an optional field that can be used to provide an external ID for the dataset record. This can be useful if you want to link the dataset record to an external resource, such as a database or a file.
### Data Splits
The dataset contains a single split, which is `train`.
## 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 guidelines
Classify the articles into one of the four categories.
#### 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] | [
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cyanelis/91396498T | cyanelis | 2023-12-17T23:08:00Z | 2 | 0 | [
"license:cc-by-nc-4.0",
"region:us"
] | null | 2023-11-30T12:31:28Z | ---
license: cc-by-nc-4.0
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] |
2nji/makebelieve-480 | 2nji | 2023-12-17T09:52:46Z | 2 | 0 | [
"license:apache-2.0",
"region:us"
] | null | 2023-11-30T22:05:30Z | ---
license: apache-2.0
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 400355
num_examples: 480
download_size: 245896
dataset_size: 400355
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Make Believe Fake Celebrity News Dataset
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geraldOslo/RadProtDataSet | geraldOslo | 2023-12-13T12:22:08Z | 2 | 0 | [
"size_categories:1K<n<10K",
"language:no",
"license:cc-by-2.0",
"dentistry",
"physics",
"radiation protection",
"region:us"
] | null | 2023-12-01T10:27:37Z | ---
license: cc-by-2.0
language:
- 'no'
tags:
- dentistry
- physics
- radiation protection
pretty_name: Question/answer connected to radiation protection in dentistry
size_categories:
- 1K<n<10K
configs:
- config_name: tab
data_files: "data.csv"
sep: ";"
---
# Contents
## Overview
This dataset comprises prompt/answer pairs related to the curriculum for Norwegian dentistry and dental hygiene students, specifically focusing on the subjects of radiation physics, radiation protection, and radiological technology.
## Data source
The prompt/answer pairs in this dataset were generated using commercially available Large Language Models (LLMs), including OpenAI GPT-4 and Anthropic Claude 2. These pairs were generated based on the analysis of documents provided as input to these LLMs.
Following this, some irrelevant pairs were deleted, some were edited for accuracy and clarity, and all pairs were proofread for errors.
### Source documents
The dataset was created using a variety of source documents, primarily encompassing:
- [Lov om strålevern og bruk av stråling (strålevernloven) (2000)](https://lovdata.no/dokument/SF/forskrift/2016-12-16-1659)
- [Forskrift om strålevern og bruk av stråling (strålevernforskriften) (2016)](https://lovdata.no/dokument/SF/forskrift/2016-12-16-1659)
- [DSA: Medisinsk strålebruk (web page) (2023)](https://dsa.no/medisinsk-stralebruk)
- [DSA: Veileder 14](https://dsa.no/publikasjoner/veileder-14-veileder-om-stralebruk-innen-odontologi/Veileder_14_odontologi.pdf)
- [DSA: StrålevernRapport • 2014:2 Strålebruk i Norge](https://dsa.no/publikasjoner/stralevernrapport-2-2014-stralebruk-i-norge/StralevernRapport_02-2014_Str%C3%A5lebruk%20i%20Norge.pdf)
- [DSA: StrålevernRapport 2015:12 Stråledoser til befolkningen](https://dsa.no/publikasjoner/stralevernrapport-12-2015-straledoser-til-befolkningen/StralevernRapport_12-15_Str%C3%A5ledoser_til_befolkningen-.pdf)
- [DSA: Veileder til forskrift om strålevern og bruk av stråling Veileder Nummer 5 Revidert mai 2023](https://dsa.no/publikasjoner/_/attachment/inline/70e8470f-6c36-46fc-9e97-c27298859d66:22ab78bd659798c58cc3ce55c07dbb9aad9b44a0/Veileder%205_rev-mai2023.pdf)
- [Gerald Torgersen: Strålingsfysikk, strålevern og radiologisk teknologi for tannpleie- og tannlegestudenter (online course) (2023)](https://uio.instructure.com/courses/19845)
- Own teaching material and notes
DSA is the The Norwegian Radiation and Nuclear Safety Authority
# Purpose
The dataset is generated for fine-tuning of open source LLMs.
# Format
The dataset is a UTF-8 formatted ";"-separated csv-file. There are two columns: prompt, prediction
# Warning
The dataset is provided for use on own responsibility. Please give feedback if you find a serious error.
# Todo
- add more relevant prompt/response pairs
- further proofreading and adjustments | [
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el2e10/ai4bharat-IndicSentiment-instruction-style | el2e10 | 2023-12-11T13:11:17Z | 2 | 0 | [
"task_categories:conversational",
"size_categories:10K<n<100K",
"language:hi",
"language:bn",
"language:ml",
"language:ur",
"language:gu",
"language:kn",
"language:mr",
"language:pa",
"language:ta",
"language:te",
"license:cc",
"region:us"
] | [
"conversational"
] | 2023-12-03T16:07:46Z | ---
license: cc
task_categories:
- conversational
language:
- hi
- bn
- ml
- ur
- gu
- kn
- mr
- pa
- ta
- te
size_categories:
- 10K<n<100K
configs:
- config_name: bengali
data_files: "data/bengali.csv"
- config_name: gujarati
data_files: "data/gujarati.csv"
- config_name: hindi
data_files: "data/hindi.csv"
- config_name: kannada
data_files: "data/kannada.csv"
- config_name: malayalam
data_files: "data/malayalam.csv"
- config_name: panjabi
data_files: "data/panjabi.csv"
- config_name: tamil
data_files: "data/tamil.csv"
- config_name: telugu
data_files: "data/telugu.csv"
- config_name: urdu
data_files: "data/urdu.csv"
---
### Description
This dataset is derived from the already existing dataset made by AI4Bharat. We have used the [IndicSentiment](https://huggingface.co/datasets/ai4bharat/IndicSentiment) dataset of AI4Bharat to create an instruction style dataset.
The dataset contain sentences in the following languages:
* Hindi
* Bengali
* Gujarati
* Kannada
* Malayalam
* Marathi
* Panjabi
* Tamil
* Telugu
* Urdu
IndicSentiment is a multilingual parallel dataset for sentiment analysis. It encompasses product reviews, translations into Indic languages, sentiment labels, and more.
The original dataset(IndicSentiment) was made available under the cc-0 license.
This dataset comprises a singular split (train) and encompasses over 11,500 rows. It features sentences in 10 different languages, with each language contributing 1,100+ samples.
### Template
The following template was used for converting the original dataset:
```
prompt:
Translate the following product review from English to {target_lang_name}:
{ENGLISH_REVIW}
completion:
{INDIC_REVIEW}
``` | [
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roa7n/patched_test_f_ATCaseOTCase_ps_40__v2023d | roa7n | 2023-12-15T08:42:33Z | 2 | 0 | [
"region:us"
] | null | 2023-12-03T21:46:09Z | ---
dataset_info:
features:
- name: id
dtype: string
- name: sequence_str
dtype: string
- name: label
dtype: int64
splits:
- name: train
num_bytes: 47714312
num_examples: 130287
download_size: 4462477
dataset_size: 47714312
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "patched_test_f_ATCaseOTCase_ps_40__v2023d"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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corysilas/VzCory | corysilas | 2023-12-14T16:35:43Z | 2 | 0 | [
"license:openrail",
"region:us"
] | null | 2023-12-04T02:25:12Z | ---
license: openrail
---
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shredder-31/NeuraLearnAcademy-Llama-2-7b-QG-dataset-Prdiction | shredder-31 | 2023-12-17T20:54:30Z | 2 | 0 | [
"region:us"
] | null | 2023-12-08T20:21:14Z | ---
dataset_info:
features:
- name: prdiction
dtype: string
splits:
- name: train
num_bytes: 30
num_examples: 6
download_size: 755
dataset_size: 30
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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Kornberg/landsat_captions | Kornberg | 2023-12-11T17:51:53Z | 2 | 0 | [
"region:us"
] | null | 2023-12-10T16:45:40Z | ---
dataset_info:
features:
- name: input
dtype: image
- name: target
dtype: image
- name: captions
dtype: string
- name: binary_captions
dtype: string
- name: cloud_segments
dtype: int64
- name: snow_segments
dtype: int64
- name: cloud_segments_binary
dtype: int64
- name: snow_segments_binary
dtype: int64
splits:
- name: train
num_bytes: 2629151965.807
num_examples: 26477
- name: val
num_bytes: 58233260.0
num_examples: 589
- name: test
num_bytes: 231262084.86699998
num_examples: 2353
download_size: 2889970577
dataset_size: 2918647310.6740003
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: val
path: data/val-*
- split: test
path: data/test-*
---
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HZ0504/controlnet_sdxl_animal | HZ0504 | 2023-12-11T07:09:09Z | 2 | 0 | [
"license:apache-2.0",
"region:us"
] | null | 2023-12-11T04:34:29Z | ---
license: apache-2.0
---
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mwarchalowski/grants | mwarchalowski | 2023-12-11T05:10:20Z | 2 | 0 | [
"license:mit",
"region:us"
] | null | 2023-12-11T05:08:52Z | ---
license: mit
---
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DianaJin/starstar | DianaJin | 2023-12-11T08:07:56Z | 2 | 0 | [
"region:us"
] | null | 2023-12-11T08:04:17Z | ---
dataset_info:
features:
- name: input_features
sequence:
sequence: float32
- name: labels
sequence: int64
splits:
- name: train
num_bytes: 17304272
num_examples: 18
- name: test
num_bytes: 2884192
num_examples: 3
- name: valid
num_bytes: 1921768
num_examples: 2
download_size: 22814594
dataset_size: 22110232
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
- split: valid
path: data/valid-*
---
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blanchon/rsicd_subset | blanchon | 2023-12-11T09:03:08Z | 2 | 0 | [
"region:us"
] | null | 2023-12-11T08:59:53Z | ---
dataset_info:
features:
- name: filename
dtype: string
- name: captions
sequence: string
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 51718759.1023586
num_examples: 1000
- name: test
num_bytes: 11029797.621225983
num_examples: 200
- name: valid
num_bytes: 10508696.526508227
num_examples: 200
download_size: 69374948
dataset_size: 73257253.2500928
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
- split: valid
path: data/valid-*
---
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gbwsolutions/autotrain-data-TM12Topics | gbwsolutions | 2023-12-11T10:07:07Z | 2 | 0 | [
"region:us"
] | null | 2023-12-11T10:07:05Z | ---
dataset_info:
features:
- name: autotrain_text
dtype: string
- name: autotrain_label
dtype:
class_label:
names:
'0': Accuracy
'1': Cleanliness
'2': Delivery
'3': Menu
'4': Other
'5': Price
'6': Quality
'7': Service
'8': Speed
'9': Staff
'10': Taste
'11': Technology
splits:
- name: train
num_bytes: 43060
num_examples: 480
- name: validation
num_bytes: 9818
num_examples: 120
download_size: 36597
dataset_size: 52878
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
---
# Dataset Card for "autotrain-data-TM12Topics"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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] |
peiyi9979/Math-Shepherd | peiyi9979 | 2023-12-13T01:21:54Z | 2 | 3 | [
"prm",
"synthesized data",
"region:us"
] | null | 2023-12-11T12:04:14Z | ---
tags:
- prm
- synthesized data
---
# Dataset Card for Math-Shepherd
Project Page: [Math-Shepherd](https://rain-motion-6ec.notion.site/Math-Shepherd-A-Label-Free-Step-by-Step-Verifier-for-LLMs-in-Mathematical-Reasoning-41b6e73c860840e08697d347f8889bac#08e86c6d44c4452ba0b78c7aaea5f4f7)
# Data Loading
```
from datasets import load_dataset
dataset = load_dataset("peiyi9979/Math-Shepherd")
```
# Data Instance
Every instance consists of three data fields: "input," "label," and "task".
1. "input": problem + step-by-step solution, e.g.,
```
If Buzz bought a pizza with 78 slices at a restaurant and then decided to share it with the waiter in the ratio of 5:8, with Buzz's ratio being 5, what's twenty less the number of slices of pizza that the waiter ate?
Step 1: The total ratio representing the pizza is 5+8 = <<5+8=13>>13. ки ?
Step 2: The waiter ate 13 x 8 / 13 = <<13*8/13=6>>6 slices of the pizza. ки ?
Step 3: Buzz ate 78 - 6 = <<78-6=72>>72 slices of the pizza. ки ?
Step 4: The waiter ate 20 less than the number of slices that Buzz ate which is 72 - 20 = 52. ки ?
Step 5: The waiter ate 52 slices of the pizza. #### 52 ки ?
```
2. "label": problem + step-by-step solution with automatic label, e.g.,
```
If Buzz bought a pizza with 78 slices at a restaurant and then decided to share it with the waiter in the ratio of 5:8, with Buzz's ratio being 5, what's twenty less the number of slices of pizza that the waiter ate?
Step 1: The total ratio representing the pizza is 5+8 = <<5+8=13>>13. ки +
Step 2: The waiter ate 13 x 8 / 13 = <<13*8/13=6>>6 slices of the pizza. ки -
Step 3: Buzz ate 78 - 6 = <<78-6=72>>72 slices of the pizza. ки -
Step 4: The waiter ate 20 less than the number of slices that Buzz ate which is 72 - 20 = 52. ки -
Step 5: The waiter ate 52 slices of the pizza. #### 52 ки -
```
3. "task": `GSM8K` or `MATH`.
NOTE:
"`ки`" serves as a unique token denoting the position for predicting the step score.
"`?`" acts as a placeholder token for the input.
"`+`" signifies a good step, as it has potential to lead towards the correct answer.
"`-`" denotes a bad step. | [
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reza-alipour/con-lnd-eng-4 | reza-alipour | 2023-12-11T12:16:55Z | 2 | 0 | [
"region:us"
] | null | 2023-12-11T12:16:48Z | ---
dataset_info:
features:
- name: id
dtype: string
- name: mask
dtype: image
- name: caption
dtype: string
- name: generated_image
dtype: image
splits:
- name: train
num_bytes: 209445636.0
num_examples: 498
download_size: 207765521
dataset_size: 209445636.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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iLampard/EasyTPP_Retweet | iLampard | 2023-12-12T11:22:10Z | 2 | 0 | [
"license:apache-2.0",
"region:us"
] | null | 2023-12-11T13:50:23Z | ---
license: apache-2.0
---
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nlplabtdtu/tdtu_faqs | nlplabtdtu | 2023-12-15T09:35:52Z | 2 | 0 | [
"region:us"
] | null | 2023-12-11T13:53:59Z | ---
dataset_info:
features:
- name: question
dtype: string
- name: answer
dtype: string
- name: metadata
struct:
- name: school_id
dtype: string
- name: topic_vi
dtype: string
- name: topic_en
dtype: string
- name: word_tokenized
dtype: string
splits:
- name: train
num_bytes: 580417
num_examples: 570
download_size: 210549
dataset_size: 580417
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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dana2002/good_ratings_audio | dana2002 | 2023-12-11T18:40:38Z | 2 | 0 | [
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] |
tollefj/nob-mosaikk-instruct | tollefj | 2023-12-11T19:09:34Z | 2 | 0 | [
"region:us"
] | null | 2023-12-11T19:06:30Z | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: response
dtype: string
splits:
- name: train
num_bytes: 23533504
num_examples: 36751
- name: test
num_bytes: 2899868
num_examples: 5042
download_size: 17389597
dataset_size: 26433372
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
A manipulated and translated mosaicml/instruct-v3 dataset.
Translated by segmenting sentences on max_len=512 using the Opus EN-GMQ model (https://huggingface.co/Helsinki-NLP/opus-mt-en-gmq)
Ignored math and other complex prompts to translate. Some have slipped through, though! | [
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atmallen/quirky_popqa_pythia-410m_bob_hard | atmallen | 2023-12-12T06:54:25Z | 2 | 0 | [
"region:us"
] | null | 2023-12-12T01:13:04Z | ---
dataset_info:
features:
- name: id
dtype: string
- name: choices
sequence: string
- name: label
dtype: int64
- name: popularity
dtype: int64
- name: difficulty
dtype: float64
- name: statement
dtype: string
- name: character
dtype: string
- name: alice_label
dtype: bool
- name: bob_label
dtype: bool
- name: bob_log_odds
dtype: float64
splits:
- name: train
num_bytes: 956816.9929078014
num_examples: 6134
- name: validation
num_bytes: 81519.174
num_examples: 522
- name: test
num_bytes: 77372.528
num_examples: 496
download_size: 396803
dataset_size: 1115708.6949078015
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
---
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netcat420/quiklogik | netcat420 | 2023-12-12T04:04:24Z | 2 | 0 | [
"license:mit",
"region:us"
] | null | 2023-12-12T03:50:41Z | ---
license: mit
---
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TinyPixel/rw | TinyPixel | 2023-12-12T08:32:08Z | 2 | 0 | [
"region:us"
] | null | 2023-12-12T08:31:48Z | ---
dataset_info:
features:
- name: content
dtype: string
- name: url
dtype: string
- name: timestamp
dtype: timestamp[ns]
- name: dump
dtype: string
- name: segment
dtype: string
- name: image_urls
sequence:
sequence: string
splits:
- name: train
num_bytes: 530895284
num_examples: 174919
download_size: 321182081
dataset_size: 530895284
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
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