datasetId stringlengths 2 117 | card stringlengths 19 1.01M |
|---|---|
Ssunbell/boostcamp-docvqa-v2 | ---
dataset_info:
features:
- name: questionId
dtype: int64
- name: question
dtype: string
- name: image
sequence:
sequence:
sequence: uint8
- name: docId
dtype: int64
- name: ucsf_document_id
dtype: string
- name: ucsf_document_page_no
dtype: string
- name: answers
sequence: string
- name: data_split
dtype: string
- name: words
sequence: string
- name: boxes
sequence:
sequence: int64
splits:
- name: train
num_bytes: 6381793673
num_examples: 39454
- name: val
num_bytes: 869361798
num_examples: 5349
download_size: 2578867675
dataset_size: 7251155471
---
# Dataset Card for "boostcamp-docvqa-v2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
alvarobartt/distilabel | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: completion
dtype: string
- name: meta
struct:
- name: category
dtype: string
- name: completion
dtype: string
- name: id
dtype: int64
- name: input
dtype: string
- name: motivation_app
dtype: string
- name: prompt
dtype: string
- name: source
dtype: string
- name: subcategory
dtype: string
- name: model_names
sequence: string
- name: generations
sequence: string
- name: output
dtype: string
- name: model_name
dtype: string
splits:
- name: train
num_bytes: 245396
num_examples: 100
download_size: 171121
dataset_size: 245396
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
BramVanroy/dutch_chat_datasets | ---
language:
- nl
size_categories:
- 100K<n<1M
task_categories:
- question-answering
- text-generation
- conversational
pretty_name: Chat Datasets for Dutch
dataset_info:
features:
- name: prompt
dtype: string
- name: prompt_id
dtype: string
- name: messages
list:
- name: content
dtype: string
- name: role
dtype: string
splits:
- name: train_sft
num_bytes: 198305113
num_examples: 160248
- name: test_sft
num_bytes: 22076114
num_examples: 17806
download_size: 124497015
dataset_size: 220381227
configs:
- config_name: default
data_files:
- split: train_sft
path: data/train_sft-*
- split: test_sft
path: data/test_sft-*
---
# Dataset Card for "dutch_chat_datasets"
This dataset is a merge of the following datasets. See their pages for licensing, usage, creation, and citation information.
- https://huggingface.co/datasets/BramVanroy/dolly-15k-dutch
- https://huggingface.co/datasets/BramVanroy/alpaca-cleaned-dutch-baize
- https://huggingface.co/datasets/BramVanroy/stackoverflow-chat-dutch
- https://huggingface.co/datasets/BramVanroy/quora-chat-dutch
They are reformatted for easier, consistent processing in downstream tasks such as language modelling.
If you use this dataset or any parts of it, please use the following citation:
Vanroy, B. (2023). *Language Resources for Dutch Large Language Modelling*. [https://arxiv.org/abs/2312.12852](https://arxiv.org/abs/2312.12852)
```bibtext
@article{vanroy2023language,
title={Language Resources for {Dutch} Large Language Modelling},
author={Vanroy, Bram},
journal={arXiv preprint arXiv:2312.12852},
year={2023}
}
```
**Columns**:
- `dialog`: a list of turns, where each turn is a dictionary that contains these keys:
- `role`: `user` or `assistant`
- `content`: the given text `str`
- `source`: the source dataset that this dialog originates from
|
ostapeno/oasst1_seed10737 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: instruction
dtype: string
- name: instruction_quality
dtype: float64
- name: response
dtype: string
- name: response_quality
dtype: float64
splits:
- name: train
num_bytes: 12797624
num_examples: 10737
download_size: 7501802
dataset_size: 12797624
---
# Dataset Card for "oasst1_seed10737"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
CyberHarem/sheema_fireemblem | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of sheema (Fire Emblem)
This is the dataset of sheema (Fire Emblem), containing 16 images and their tags.
The core tags of this character are `brown_hair, long_hair, red_eyes, brown_eyes`, which are pruned in this dataset.
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)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:----------|:-------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 16 | 25.33 MiB | [Download](https://huggingface.co/datasets/CyberHarem/sheema_fireemblem/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 16 | 12.66 MiB | [Download](https://huggingface.co/datasets/CyberHarem/sheema_fireemblem/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 33 | 23.65 MiB | [Download](https://huggingface.co/datasets/CyberHarem/sheema_fireemblem/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 16 | 21.57 MiB | [Download](https://huggingface.co/datasets/CyberHarem/sheema_fireemblem/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 33 | 35.01 MiB | [Download](https://huggingface.co/datasets/CyberHarem/sheema_fireemblem/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/sheema_fireemblem',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-------------------------------------------------------------------------------------------------------------------------|
| 0 | 16 |  |  |  |  |  | solo, 1girl, cape, weapon, white_background, armored_boots, gloves, shield, simple_background, full_body, shoulder_armor |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | solo | 1girl | cape | weapon | white_background | armored_boots | gloves | shield | simple_background | full_body | shoulder_armor |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-------|:--------|:-------|:---------|:-------------------|:----------------|:---------|:---------|:--------------------|:------------|:-----------------|
| 0 | 16 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X |
|
Francesco/flir-camera-objects | ---
dataset_info:
features:
- name: image_id
dtype: int64
- name: image
dtype: image
- name: width
dtype: int32
- name: height
dtype: int32
- name: objects
sequence:
- name: id
dtype: int64
- name: area
dtype: int64
- name: bbox
sequence: float32
length: 4
- name: category
dtype:
class_label:
names:
'0': flir-camera-objects
'1': bicycle
'2': car
'3': dog
'4': person
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- en
license:
- cc
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- object-detection
task_ids: []
pretty_name: flir-camera-objects
tags:
- rf100
---
# Dataset Card for flir-camera-objects
** The original COCO dataset is stored at `dataset.tar.gz`**
## Dataset Description
- **Homepage:** https://universe.roboflow.com/object-detection/flir-camera-objects
- **Point of Contact:** francesco.zuppichini@gmail.com
### Dataset Summary
flir-camera-objects
### Supported Tasks and Leaderboards
- `object-detection`: The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
```
{
'image_id': 15,
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x640 at 0x2373B065C18>,
'width': 964043,
'height': 640,
'objects': {
'id': [114, 115, 116, 117],
'area': [3796, 1596, 152768, 81002],
'bbox': [
[302.0, 109.0, 73.0, 52.0],
[810.0, 100.0, 57.0, 28.0],
[160.0, 31.0, 248.0, 616.0],
[741.0, 68.0, 202.0, 401.0]
],
'category': [4, 4, 0, 0]
}
}
```
### Data Fields
- `image`: the image id
- `image`: `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`
- `width`: the image width
- `height`: the image height
- `objects`: a dictionary containing bounding box metadata for the objects present on the image
- `id`: the annotation id
- `area`: the area of the bounding box
- `bbox`: the object's bounding box (in the [coco](https://albumentations.ai/docs/getting_started/bounding_boxes_augmentation/#coco) format)
- `category`: the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage https://universe.roboflow.com/object-detection/flir-camera-objects
### Citation Information
```
@misc{ flir-camera-objects,
title = { flir camera objects Dataset },
type = { Open Source Dataset },
author = { Roboflow 100 },
howpublished = { \url{ https://universe.roboflow.com/object-detection/flir-camera-objects } },
url = { https://universe.roboflow.com/object-detection/flir-camera-objects },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2022 },
month = { nov },
note = { visited on 2023-03-29 },
}"
```
### Contributions
Thanks to [@mariosasko](https://github.com/mariosasko) for adding this dataset. |
starfishmedical/webGPT_x_dolly | ---
license: cc-by-sa-3.0
task_categories:
- question-answering
size_categories:
- 10K<n<100K
---
This dataset contains a selection of Q&A-related tasks gathered and cleaned from the webGPT_comparisons set and the databricks-dolly-15k set.
Unicode escapes were explicitly removed, and wikipedia citations in the "output" were stripped through regex to hopefully help any
end-product model ignore these artifacts within their input context.
This data is formatted for use in the alpaca instruction format, however the instruction, input, and output columns are kept separate in
the raw data to allow for other configurations. The data has been filtered so that every entry is less than our chosen truncation length of
1024 (LLaMA-style) tokens with the format:
```
"""Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.
### Instruction:
{instruction}
### Input:
{inputt}
### Response:
{output}"""
```
<h3>webGPT</h3>
This set was filtered from the webGPT_comparisons data by taking any Q&A option that was positively or neutrally-rated by humans (e.g. "score" >= 0).
This might not provide the ideal answer, but this dataset was assembled specifically for extractive Q&A with less regard for how humans
feel about the results.
This selection comprises 23826 of the total entries in the data.
<h3>Dolly</h3>
The dolly data was selected primarily to focus on closed-qa tasks. For this purpose, only entries in the "closed-qa", "information_extraction",
"summarization", "classification", and "creative_writing" were used. While not all of these include a context, they were judged to help
flesh out the training set.
This selection comprises 5362 of the total entries in the data.
|
tyzhu/squad_qa_baseline_v5_full_recite_full_passage_first_permute_rerun | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
dataset_info:
features:
- name: id
dtype: string
- name: title
dtype: string
- name: context
dtype: string
- name: question
dtype: string
- name: answers
sequence:
- name: text
dtype: string
- name: answer_start
dtype: int32
- name: answer
dtype: string
- name: context_id
dtype: string
- name: inputs
dtype: string
- name: targets
dtype: string
splits:
- name: train
num_bytes: 4369231.0
num_examples: 2385
- name: validation
num_bytes: 573308
num_examples: 300
download_size: 1012407
dataset_size: 4942539.0
---
# Dataset Card for "squad_qa_baseline_v5_full_recite_full_passage_first_permute_rerun"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
zxx-silence/my-shiba-inu-dataset | ---
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 2997266.0
num_examples: 13
download_size: 2987648
dataset_size: 2997266.0
---
# Dataset Card for "my-shiba-inu-dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Multimodal-Fatima/FGVC_Aircraft_test_embeddings | ---
dataset_info:
features:
- name: image
dtype: image
- name: id
dtype: int64
- name: vision_embeddings
sequence: float32
splits:
- name: openai_clip_vit_large_patch14
num_bytes: 933154950.0
num_examples: 3333
download_size: 935494333
dataset_size: 933154950.0
---
# Dataset Card for "FGVC_Aircraft_test_embeddings"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
liuyanchen1015/MULTI_VALUE_sst2_me_us | ---
dataset_info:
features:
- name: sentence
dtype: string
- name: label
dtype: int64
- name: idx
dtype: int64
- name: score
dtype: int64
splits:
- name: dev
num_bytes: 1012
num_examples: 8
- name: test
num_bytes: 2232
num_examples: 16
- name: train
num_bytes: 30783
num_examples: 292
download_size: 19403
dataset_size: 34027
---
# Dataset Card for "MULTI_VALUE_sst2_me_us"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
mask-distilled-onesec-cv12-each-chunk-uniq/chunk_136 | ---
dataset_info:
features:
- name: logits
sequence: float32
- name: mfcc
sequence:
sequence: float64
splits:
- name: train
num_bytes: 1161938388.0
num_examples: 228189
download_size: 1185012083
dataset_size: 1161938388.0
---
# Dataset Card for "chunk_136"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
seansullivan/PCone-Integrations | ---
license: other
---
|
AmelieSchreiber/aging_proteins | ---
license: mit
task_categories:
- text-classification
language:
- en
tags:
- esm
- esm2
- ESM-2
- aging proteins
- protein laguage model
- biology
---
# Description of the Dataset
This is (part of) the dataset used in
[Prediction and characterization of human ageing-related proteins by using machine learning](https://www.nature.com/articles/s41598-018-22240-w).
This can be used to train a binary sequence classifier using protein language models such as [ESM-2](https://huggingface.co/facebook/esm2_t6_8M_UR50D).
Please also see [the github for the paper](https://github.com/kerepesi/aging_ml/blob/master/aging_labels.csv) for more information.
|
satyambarnwal/balcony8 | ---
dataset_info:
features:
- name: original_image
dtype: image
- name: edit_prompt
dtype: string
- name: output_image
dtype: image
splits:
- name: train
num_bytes: 56784678.0
num_examples: 63
download_size: 29126186
dataset_size: 56784678.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
NetherlandsForensicInstitute/simplewiki-translated-nl | ---
license: cc-by-sa-4.0
task_categories:
- sentence-similarity
language:
- nl
size_categories:
- 100K<n<1M
---
This is a Dutch version of the [SimpleWiki](https://cs.pomona.edu/~dkauchak/simplification/) text simplification dataset. Which we have auto-translated from English into Dutch using Meta's [No Language Left Behind](https://ai.facebook.com/research/no-language-left-behind/) model, specifically the [huggingface implementation](https://huggingface.co/facebook/nllb-200-distilled-600M). |
idrismaric/dxb_realestate | ---
license: mit
---
|
alkzar90/NIH-Chest-X-ray-dataset | ---
annotations_creators:
- machine-generated
- expert-generated
language_creators:
- machine-generated
- expert-generated
language:
- en
license:
- unknown
multilinguality:
- monolingual
pretty_name: NIH-CXR14
paperswithcode_id: chestx-ray14
size_categories:
- 100K<n<1M
task_categories:
- image-classification
task_ids:
- multi-class-image-classification
---
# Dataset Card for NIH Chest X-ray dataset
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [NIH Chest X-ray Dataset of 10 Common Thorax Disease Categories](https://nihcc.app.box.com/v/ChestXray-NIHCC/folder/36938765345)
- **Repository:**
- **Paper:** [ChestX-ray8: Hospital-scale Chest X-ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases](https://arxiv.org/abs/1705.02315)
- **Leaderboard:**
- **Point of Contact:** rms@nih.gov
### Dataset Summary
_ChestX-ray dataset comprises 112,120 frontal-view X-ray images of 30,805 unique patients with the text-mined fourteen disease image labels (where each image can have multi-labels), mined from the associated radiological reports using natural language processing. Fourteen common thoracic pathologies include Atelectasis, Consolidation, Infiltration, Pneumothorax, Edema, Emphysema, Fibrosis, Effusion, Pneumonia, Pleural_thickening, Cardiomegaly, Nodule, Mass and Hernia, which is an extension of the 8 common disease patterns listed in our CVPR2017 paper. Note that original radiology reports (associated with these chest x-ray studies) are not meant to be publicly shared for many reasons. The text-mined disease labels are expected to have accuracy >90%.Please find more details and benchmark performance of trained models based on 14 disease labels in our arxiv paper: [1705.02315](https://arxiv.org/abs/1705.02315)_
## Dataset Structure
### Data Instances
A sample from the training set is provided below:
```
{'image_file_path': '/root/.cache/huggingface/datasets/downloads/extracted/95db46f21d556880cf0ecb11d45d5ba0b58fcb113c9a0fff2234eba8f74fe22a/images/00000798_022.png',
'image': <PIL.PngImagePlugin.PngImageFile image mode=L size=1024x1024 at 0x7F2151B144D0>,
'labels': [9, 3]}
```
### Data Fields
The data instances have the following fields:
- `image_file_path` a `str` with the image path
- `image`: A `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`.
- `labels`: an `int` classification label.
<details>
<summary>Class Label Mappings</summary>
```json
{
"No Finding": 0,
"Atelectasis": 1,
"Cardiomegaly": 2,
"Effusion": 3,
"Infiltration": 4,
"Mass": 5,
"Nodule": 6,
"Pneumonia": 7,
"Pneumothorax": 8,
"Consolidation": 9,
"Edema": 10,
"Emphysema": 11,
"Fibrosis": 12,
"Pleural_Thickening": 13,
"Hernia": 14
}
```
</details>
**Label distribution on the dataset:**
| labels | obs | freq |
|:-------------------|------:|-----------:|
| No Finding | 60361 | 0.426468 |
| Infiltration | 19894 | 0.140557 |
| Effusion | 13317 | 0.0940885 |
| Atelectasis | 11559 | 0.0816677 |
| Nodule | 6331 | 0.0447304 |
| Mass | 5782 | 0.0408515 |
| Pneumothorax | 5302 | 0.0374602 |
| Consolidation | 4667 | 0.0329737 |
| Pleural_Thickening | 3385 | 0.023916 |
| Cardiomegaly | 2776 | 0.0196132 |
| Emphysema | 2516 | 0.0177763 |
| Edema | 2303 | 0.0162714 |
| Fibrosis | 1686 | 0.0119121 |
| Pneumonia | 1431 | 0.0101104 |
| Hernia | 227 | 0.00160382 |
### Data Splits
| |train| test|
|-------------|----:|----:|
|# of examples|86524|25596|
**Label distribution by dataset split:**
| labels | ('Train', 'obs') | ('Train', 'freq') | ('Test', 'obs') | ('Test', 'freq') |
|:-------------------|-------------------:|--------------------:|------------------:|-------------------:|
| No Finding | 50500 | 0.483392 | 9861 | 0.266032 |
| Infiltration | 13782 | 0.131923 | 6112 | 0.164891 |
| Effusion | 8659 | 0.082885 | 4658 | 0.125664 |
| Atelectasis | 8280 | 0.0792572 | 3279 | 0.0884614 |
| Nodule | 4708 | 0.0450656 | 1623 | 0.0437856 |
| Mass | 4034 | 0.038614 | 1748 | 0.0471578 |
| Consolidation | 2852 | 0.0272997 | 1815 | 0.0489654 |
| Pneumothorax | 2637 | 0.0252417 | 2665 | 0.0718968 |
| Pleural_Thickening | 2242 | 0.0214607 | 1143 | 0.0308361 |
| Cardiomegaly | 1707 | 0.0163396 | 1069 | 0.0288397 |
| Emphysema | 1423 | 0.0136211 | 1093 | 0.0294871 |
| Edema | 1378 | 0.0131904 | 925 | 0.0249548 |
| Fibrosis | 1251 | 0.0119747 | 435 | 0.0117355 |
| Pneumonia | 876 | 0.00838518 | 555 | 0.0149729 |
| Hernia | 141 | 0.00134967 | 86 | 0.00232012 |
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### License and attribution
There are no restrictions on the use of the NIH chest x-ray images. However, the dataset has the following attribution requirements:
- Provide a link to the NIH download site: https://nihcc.app.box.com/v/ChestXray-NIHCC
- Include a citation to the CVPR 2017 paper (see Citation information section)
- Acknowledge that the NIH Clinical Center is the data provider
### Citation Information
```
@inproceedings{Wang_2017,
doi = {10.1109/cvpr.2017.369},
url = {https://doi.org/10.1109%2Fcvpr.2017.369},
year = 2017,
month = {jul},
publisher = {{IEEE}
},
author = {Xiaosong Wang and Yifan Peng and Le Lu and Zhiyong Lu and Mohammadhadi Bagheri and Ronald M. Summers},
title = {{ChestX}-Ray8: Hospital-Scale Chest X-Ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases},
booktitle = {2017 {IEEE} Conference on Computer Vision and Pattern Recognition ({CVPR})}
}
```
### Contributions
Thanks to [@alcazar90](https://github.com/alcazar90) for adding this dataset.
|
SINAI/SA-Corpus | ---
license: cc-by-nc-sa-4.0
tags:
- Sentiment Analysis
configs:
- config_name: default
data_files:
- split: 1star
path: SINAI-SA-corpus/1/*.txt
- split: 2star
path: SINAI-SA-corpus/2/*.txt
- split: 3star
path: SINAI-SA-corpus/3/*.txt
- split: 4star
path: SINAI-SA-corpus/4/*.txt
- split: 5star
path: SINAI-SA-corpus/5/*.txt
---
### Dataset Description
**Paper**: [Experiments with SVM to classify opinions in different domains](https://www.sciencedirect.com/science/article/pii/S0957417411008542/pdfft?md5=3d961785088ca5e7215fb1611cc9aeeb&pid=1-s2.0-S0957417411008542-main.pdf)
**Point of Contact**: maite@ujaen.es
This corpus has been prepared by the SINAI group in December 2008. SINAI SA (Sentiment Analysis) was created by tracking the Amazon website. Nearly 2,000 comments were extracted from different cameras.
**Structure:** The SINAI corpus contains 5 directories and each represents the number of stars for reviews. (eg directory 1 contains rated with a star). Each directory contains a file in plain text by document/comment.
The amount of comments is as follows:
- 1…star: 78 comments
- 2…stars: 67 comments
- 3…stars: 97 comments
- 4…stars: 411 comments
- 5…stars: 1,290 comments
Total: 1,943 comments
| Camera | Comments |
|----------|----------|
| CanonA590IS | 400 |
| CanonA630 | 300 |
| CanonSD1100IS | 426 |
| KodakCx7430 | 64 |
| KodakV1003 | 95 |
| KodakZ740 | 155 |
| Nikon5700 | 119 |
| Olympus1030SW | 168 |
| PentaxK10D | 126 |
| PentaxK200D | 90 |
| **Total** | **1,943** |
### Licensing Information
SINAI-SA Corpus is released under the [Apache-2.0 License](http://www.apache.org/licenses/LICENSE-2.0).
### Citation Information
```bibtex
@article{RUSHDISALEH201114799,
title = {Experiments with SVM to classify opinions in different domains},
journal = {Expert Systems with Applications},
volume = {38},
number = {12},
pages = {14799-14804},
year = {2011},
issn = {0957-4174},
doi = {https://doi.org/10.1016/j.eswa.2011.05.070},
url = {https://www.sciencedirect.com/science/article/pii/S0957417411008542},
author = {M. {Rushdi Saleh} and M.T. Martín-Valdivia and A. Montejo-Ráez and L.A. Ureña-López},
keywords = {Opinion mining, Machine learning, SVM, Corpora},
abstract = {Recently, opinion mining is receiving more attention due to the abundance of forums, blogs, e-commerce web sites, news reports and additional web sources where people tend to express their opinions. Opinion mining is the task of identifying whether the opinion expressed in a document is positive or negative about a given topic. In this paper we explore this new research area applying Support Vector Machines (SVM) for testing different domains of data sets and using several weighting schemes. We have accomplished experiments with different features on three corpora. Two of them have already been used in several works. The last one has been built from Amazon.com specifically for this paper in order to prove the feasibility of the SVM for different domains.}
}
``` |
boapps/kmdb_classification | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
dataset_info:
features:
- name: text
dtype: string
- name: title
dtype: string
- name: description
dtype: string
- name: keywords
sequence: string
- name: label
dtype: int64
- name: url
dtype: string
- name: date
dtype: string
- name: is_hand_annoted
dtype: bool
- name: score
dtype: float64
- name: title_score
dtype: float64
splits:
- name: train
num_bytes: 187493981
num_examples: 45683
- name: test
num_bytes: 13542701
num_examples: 3605
- name: validation
num_bytes: 25309037
num_examples: 6579
download_size: 139938458
dataset_size: 226345719
---
# Dataset Card for "kmdb_classification"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
MikeXydas/qr2t_benchmark | ---
license: mit
---
|
Deojoandco/reward_model_anthropic_88 | ---
dataset_info:
features:
- name: prompt
dtype: string
- name: response
dtype: string
- name: chosen
dtype: string
- name: rejected
dtype: string
- name: output
sequence: string
- name: toxicity
sequence: float64
- name: severe_toxicity
sequence: float64
- name: obscene
sequence: float64
- name: identity_attack
sequence: float64
- name: insult
sequence: float64
- name: threat
sequence: float64
- name: sexual_explicit
sequence: float64
- name: mean_toxity_value
dtype: float64
- name: max_toxity_value
dtype: float64
- name: min_toxity_value
dtype: float64
- name: sd_toxity_value
dtype: float64
- name: median_toxity_value
dtype: float64
- name: median_output
dtype: string
- name: toxic
dtype: bool
- name: regard_8
list:
list:
- name: label
dtype: string
- name: score
dtype: float64
- name: regard_8_neutral
sequence: float64
- name: regard_8_negative
sequence: float64
- name: regard_8_positive
sequence: float64
- name: regard_8_other
sequence: float64
- name: regard_8_neutral_mean
dtype: float64
- name: regard_8_neutral_sd
dtype: float64
- name: regard_8_neutral_median
dtype: float64
- name: regard_8_neutral_min
dtype: float64
- name: regard_8_neutral_max
dtype: float64
- name: regard_8_negative_mean
dtype: float64
- name: regard_8_negative_sd
dtype: float64
- name: regard_8_negative_median
dtype: float64
- name: regard_8_negative_min
dtype: float64
- name: regard_8_negative_max
dtype: float64
- name: regard_8_positive_mean
dtype: float64
- name: regard_8_positive_sd
dtype: float64
- name: regard_8_positive_median
dtype: float64
- name: regard_8_positive_min
dtype: float64
- name: regard_8_positive_max
dtype: float64
- name: regard_8_other_mean
dtype: float64
- name: regard_8_other_sd
dtype: float64
- name: regard_8_other_median
dtype: float64
- name: regard_8_other_min
dtype: float64
- name: regard_8_other_max
dtype: float64
- name: regard
list:
list:
- name: label
dtype: string
- name: score
dtype: float64
- name: regard_neutral
dtype: float64
- name: regard_positive
dtype: float64
- name: regard_negative
dtype: float64
- name: regard_other
dtype: float64
- name: bias_matches_0
dtype: string
- name: bias_matches_1
dtype: string
- name: bias_matches_2
dtype: string
- name: bias_matches_3
dtype: string
- name: bias_matches_4
dtype: string
- name: bias_matches_5
dtype: string
- name: bias_matches_6
dtype: string
- name: bias_matches_7
dtype: string
- name: bias_matches
dtype: string
splits:
- name: test
num_bytes: 38897637
num_examples: 8552
download_size: 19767367
dataset_size: 38897637
---
# Dataset Card for "reward_model_anthropic_88"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
liuyanchen1015/MULTI_VALUE_mrpc_synthetic_superlative | ---
dataset_info:
features:
- name: sentence1
dtype: string
- name: sentence2
dtype: string
- name: label
dtype: int64
- name: idx
dtype: int64
- name: value_score
dtype: int64
splits:
- name: test
num_bytes: 3769
num_examples: 13
- name: train
num_bytes: 8162
num_examples: 30
- name: validation
num_bytes: 1575
num_examples: 5
download_size: 20615
dataset_size: 13506
---
# Dataset Card for "MULTI_VALUE_mrpc_synthetic_superlative"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
imdatta0/oasst_top1_5k | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 8933937.59172009
num_examples: 5000
- name: test
num_bytes: 1215910
num_examples: 690
download_size: 5835349
dataset_size: 10149847.59172009
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
|
AdapterOcean/data-standardized_cluster_17 | ---
dataset_info:
features:
- name: text
dtype: string
- name: conversation_id
dtype: int64
- name: embedding
sequence: float64
- name: cluster
dtype: int64
splits:
- name: train
num_bytes: 19624365
num_examples: 1858
download_size: 5710907
dataset_size: 19624365
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "data-standardized_cluster_17"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
open-llm-leaderboard/details_vibhorag101__llama-2-13b-chat-hf-phr_mental_therapy | ---
pretty_name: Evaluation run of vibhorag101/llama-2-13b-chat-hf-phr_mental_therapy
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [vibhorag101/llama-2-13b-chat-hf-phr_mental_therapy](https://huggingface.co/vibhorag101/llama-2-13b-chat-hf-phr_mental_therapy)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 63 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the aggregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_vibhorag101__llama-2-13b-chat-hf-phr_mental_therapy\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-12-04T18:26:43.065214](https://huggingface.co/datasets/open-llm-leaderboard/details_vibhorag101__llama-2-13b-chat-hf-phr_mental_therapy/blob/main/results_2023-12-04T18-26-43.065214.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.2434367192235923,\n\
\ \"acc_stderr\": 0.03008501938303984,\n \"acc_norm\": 0.24224538912156782,\n\
\ \"acc_norm_stderr\": 0.030747150403453674,\n \"mc1\": 0.2778457772337821,\n\
\ \"mc1_stderr\": 0.015680929364024643,\n \"mc2\": 0.4692403294958895,\n\
\ \"mc2_stderr\": 0.015061938982346217\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.36945392491467577,\n \"acc_stderr\": 0.014104578366491894,\n\
\ \"acc_norm\": 0.38822525597269625,\n \"acc_norm_stderr\": 0.01424161420741405\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.5696076478789086,\n\
\ \"acc_stderr\": 0.004941191607317913,\n \"acc_norm\": 0.7276438956383191,\n\
\ \"acc_norm_stderr\": 0.004442623590846322\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.22,\n \"acc_stderr\": 0.04163331998932268,\n \
\ \"acc_norm\": 0.22,\n \"acc_norm_stderr\": 0.04163331998932268\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.18518518518518517,\n\
\ \"acc_stderr\": 0.03355677216313142,\n \"acc_norm\": 0.18518518518518517,\n\
\ \"acc_norm_stderr\": 0.03355677216313142\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.17763157894736842,\n \"acc_stderr\": 0.031103182383123398,\n\
\ \"acc_norm\": 0.17763157894736842,\n \"acc_norm_stderr\": 0.031103182383123398\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.3,\n\
\ \"acc_stderr\": 0.046056618647183814,\n \"acc_norm\": 0.3,\n \
\ \"acc_norm_stderr\": 0.046056618647183814\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.21509433962264152,\n \"acc_stderr\": 0.02528839450289137,\n\
\ \"acc_norm\": 0.21509433962264152,\n \"acc_norm_stderr\": 0.02528839450289137\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.2569444444444444,\n\
\ \"acc_stderr\": 0.03653946969442099,\n \"acc_norm\": 0.2569444444444444,\n\
\ \"acc_norm_stderr\": 0.03653946969442099\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.2,\n \"acc_stderr\": 0.04020151261036845,\n \
\ \"acc_norm\": 0.2,\n \"acc_norm_stderr\": 0.04020151261036845\n },\n\
\ \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\": 0.26,\n\
\ \"acc_stderr\": 0.0440844002276808,\n \"acc_norm\": 0.26,\n \
\ \"acc_norm_stderr\": 0.0440844002276808\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.21,\n \"acc_stderr\": 0.040936018074033256,\n \
\ \"acc_norm\": 0.21,\n \"acc_norm_stderr\": 0.040936018074033256\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.20809248554913296,\n\
\ \"acc_stderr\": 0.030952890217749874,\n \"acc_norm\": 0.20809248554913296,\n\
\ \"acc_norm_stderr\": 0.030952890217749874\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.21568627450980393,\n \"acc_stderr\": 0.04092563958237654,\n\
\ \"acc_norm\": 0.21568627450980393,\n \"acc_norm_stderr\": 0.04092563958237654\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.28,\n \"acc_stderr\": 0.045126085985421276,\n \"acc_norm\": 0.28,\n\
\ \"acc_norm_stderr\": 0.045126085985421276\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.26382978723404255,\n \"acc_stderr\": 0.028809989854102973,\n\
\ \"acc_norm\": 0.26382978723404255,\n \"acc_norm_stderr\": 0.028809989854102973\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.23684210526315788,\n\
\ \"acc_stderr\": 0.039994238792813365,\n \"acc_norm\": 0.23684210526315788,\n\
\ \"acc_norm_stderr\": 0.039994238792813365\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.2413793103448276,\n \"acc_stderr\": 0.03565998174135302,\n\
\ \"acc_norm\": 0.2413793103448276,\n \"acc_norm_stderr\": 0.03565998174135302\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.20899470899470898,\n \"acc_stderr\": 0.02094048156533486,\n \"\
acc_norm\": 0.20899470899470898,\n \"acc_norm_stderr\": 0.02094048156533486\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.2857142857142857,\n\
\ \"acc_stderr\": 0.04040610178208841,\n \"acc_norm\": 0.2857142857142857,\n\
\ \"acc_norm_stderr\": 0.04040610178208841\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.18,\n \"acc_stderr\": 0.038612291966536934,\n \
\ \"acc_norm\": 0.18,\n \"acc_norm_stderr\": 0.038612291966536934\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\"\
: 0.1774193548387097,\n \"acc_stderr\": 0.02173254068932927,\n \"\
acc_norm\": 0.1774193548387097,\n \"acc_norm_stderr\": 0.02173254068932927\n\
\ },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\"\
: 0.15270935960591134,\n \"acc_stderr\": 0.02530890453938063,\n \"\
acc_norm\": 0.15270935960591134,\n \"acc_norm_stderr\": 0.02530890453938063\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.25,\n \"acc_stderr\": 0.04351941398892446,\n \"acc_norm\"\
: 0.25,\n \"acc_norm_stderr\": 0.04351941398892446\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.21818181818181817,\n \"acc_stderr\": 0.03225078108306289,\n\
\ \"acc_norm\": 0.21818181818181817,\n \"acc_norm_stderr\": 0.03225078108306289\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.17676767676767677,\n \"acc_stderr\": 0.027178752639044915,\n \"\
acc_norm\": 0.17676767676767677,\n \"acc_norm_stderr\": 0.027178752639044915\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.19689119170984457,\n \"acc_stderr\": 0.028697873971860664,\n\
\ \"acc_norm\": 0.19689119170984457,\n \"acc_norm_stderr\": 0.028697873971860664\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.20256410256410257,\n \"acc_stderr\": 0.020377660970371372,\n\
\ \"acc_norm\": 0.20256410256410257,\n \"acc_norm_stderr\": 0.020377660970371372\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.2111111111111111,\n \"acc_stderr\": 0.024882116857655075,\n \
\ \"acc_norm\": 0.2111111111111111,\n \"acc_norm_stderr\": 0.024882116857655075\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.21008403361344538,\n \"acc_stderr\": 0.026461398717471874,\n\
\ \"acc_norm\": 0.21008403361344538,\n \"acc_norm_stderr\": 0.026461398717471874\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.1986754966887417,\n \"acc_stderr\": 0.03257847384436776,\n \"\
acc_norm\": 0.1986754966887417,\n \"acc_norm_stderr\": 0.03257847384436776\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.1926605504587156,\n \"acc_stderr\": 0.016909276884936094,\n \"\
acc_norm\": 0.1926605504587156,\n \"acc_norm_stderr\": 0.016909276884936094\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.1527777777777778,\n \"acc_stderr\": 0.024536326026134224,\n \"\
acc_norm\": 0.1527777777777778,\n \"acc_norm_stderr\": 0.024536326026134224\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.25,\n \"acc_stderr\": 0.03039153369274154,\n \"acc_norm\": 0.25,\n\
\ \"acc_norm_stderr\": 0.03039153369274154\n },\n \"harness|hendrycksTest-high_school_world_history|5\"\
: {\n \"acc\": 0.270042194092827,\n \"acc_stderr\": 0.028900721906293426,\n\
\ \"acc_norm\": 0.270042194092827,\n \"acc_norm_stderr\": 0.028900721906293426\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.31390134529147984,\n\
\ \"acc_stderr\": 0.031146796482972465,\n \"acc_norm\": 0.31390134529147984,\n\
\ \"acc_norm_stderr\": 0.031146796482972465\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.2595419847328244,\n \"acc_stderr\": 0.03844876139785271,\n\
\ \"acc_norm\": 0.2595419847328244,\n \"acc_norm_stderr\": 0.03844876139785271\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.2396694214876033,\n \"acc_stderr\": 0.03896878985070417,\n \"\
acc_norm\": 0.2396694214876033,\n \"acc_norm_stderr\": 0.03896878985070417\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.25925925925925924,\n\
\ \"acc_stderr\": 0.042365112580946336,\n \"acc_norm\": 0.25925925925925924,\n\
\ \"acc_norm_stderr\": 0.042365112580946336\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.22085889570552147,\n \"acc_stderr\": 0.032591773927421776,\n\
\ \"acc_norm\": 0.22085889570552147,\n \"acc_norm_stderr\": 0.032591773927421776\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.3125,\n\
\ \"acc_stderr\": 0.043994650575715215,\n \"acc_norm\": 0.3125,\n\
\ \"acc_norm_stderr\": 0.043994650575715215\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.17475728155339806,\n \"acc_stderr\": 0.037601780060266224,\n\
\ \"acc_norm\": 0.17475728155339806,\n \"acc_norm_stderr\": 0.037601780060266224\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.2905982905982906,\n\
\ \"acc_stderr\": 0.02974504857267404,\n \"acc_norm\": 0.2905982905982906,\n\
\ \"acc_norm_stderr\": 0.02974504857267404\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.3,\n \"acc_stderr\": 0.046056618647183814,\n \
\ \"acc_norm\": 0.3,\n \"acc_norm_stderr\": 0.046056618647183814\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.23754789272030652,\n\
\ \"acc_stderr\": 0.015218733046150193,\n \"acc_norm\": 0.23754789272030652,\n\
\ \"acc_norm_stderr\": 0.015218733046150193\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.24855491329479767,\n \"acc_stderr\": 0.023267528432100174,\n\
\ \"acc_norm\": 0.24855491329479767,\n \"acc_norm_stderr\": 0.023267528432100174\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.23798882681564246,\n\
\ \"acc_stderr\": 0.014242630070574915,\n \"acc_norm\": 0.23798882681564246,\n\
\ \"acc_norm_stderr\": 0.014242630070574915\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.22549019607843138,\n \"acc_stderr\": 0.023929155517351284,\n\
\ \"acc_norm\": 0.22549019607843138,\n \"acc_norm_stderr\": 0.023929155517351284\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.1864951768488746,\n\
\ \"acc_stderr\": 0.02212243977248077,\n \"acc_norm\": 0.1864951768488746,\n\
\ \"acc_norm_stderr\": 0.02212243977248077\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.21604938271604937,\n \"acc_stderr\": 0.022899162918445806,\n\
\ \"acc_norm\": 0.21604938271604937,\n \"acc_norm_stderr\": 0.022899162918445806\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.23404255319148937,\n \"acc_stderr\": 0.025257861359432417,\n \
\ \"acc_norm\": 0.23404255319148937,\n \"acc_norm_stderr\": 0.025257861359432417\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.2457627118644068,\n\
\ \"acc_stderr\": 0.010996156635142692,\n \"acc_norm\": 0.2457627118644068,\n\
\ \"acc_norm_stderr\": 0.010996156635142692\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.18382352941176472,\n \"acc_stderr\": 0.023529242185193106,\n\
\ \"acc_norm\": 0.18382352941176472,\n \"acc_norm_stderr\": 0.023529242185193106\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.25,\n \"acc_stderr\": 0.01751781884501444,\n \"acc_norm\"\
: 0.25,\n \"acc_norm_stderr\": 0.01751781884501444\n },\n \"harness|hendrycksTest-public_relations|5\"\
: {\n \"acc\": 0.21818181818181817,\n \"acc_stderr\": 0.03955932861795833,\n\
\ \"acc_norm\": 0.21818181818181817,\n \"acc_norm_stderr\": 0.03955932861795833\n\
\ },\n \"harness|hendrycksTest-security_studies|5\": {\n \"acc\": 0.18775510204081633,\n\
\ \"acc_stderr\": 0.02500025603954621,\n \"acc_norm\": 0.18775510204081633,\n\
\ \"acc_norm_stderr\": 0.02500025603954621\n },\n \"harness|hendrycksTest-sociology|5\"\
: {\n \"acc\": 0.24378109452736318,\n \"acc_stderr\": 0.03036049015401465,\n\
\ \"acc_norm\": 0.24378109452736318,\n \"acc_norm_stderr\": 0.03036049015401465\n\
\ },\n \"harness|hendrycksTest-us_foreign_policy|5\": {\n \"acc\":\
\ 0.28,\n \"acc_stderr\": 0.04512608598542128,\n \"acc_norm\": 0.28,\n\
\ \"acc_norm_stderr\": 0.04512608598542128\n },\n \"harness|hendrycksTest-virology|5\"\
: {\n \"acc\": 0.28313253012048195,\n \"acc_stderr\": 0.03507295431370518,\n\
\ \"acc_norm\": 0.28313253012048195,\n \"acc_norm_stderr\": 0.03507295431370518\n\
\ },\n \"harness|hendrycksTest-world_religions|5\": {\n \"acc\": 0.3216374269005848,\n\
\ \"acc_stderr\": 0.03582529442573122,\n \"acc_norm\": 0.3216374269005848,\n\
\ \"acc_norm_stderr\": 0.03582529442573122\n },\n \"harness|truthfulqa:mc|0\"\
: {\n \"mc1\": 0.2778457772337821,\n \"mc1_stderr\": 0.015680929364024643,\n\
\ \"mc2\": 0.4692403294958895,\n \"mc2_stderr\": 0.015061938982346217\n\
\ },\n \"harness|winogrande|5\": {\n \"acc\": 0.6558800315706393,\n\
\ \"acc_stderr\": 0.013352121905005941\n },\n \"harness|gsm8k|5\":\
\ {\n \"acc\": 0.07808946171341925,\n \"acc_stderr\": 0.007390654481108261\n\
\ }\n}\n```"
repo_url: https://huggingface.co/vibhorag101/llama-2-13b-chat-hf-phr_mental_therapy
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_12_04T18_26_43.065214
path:
- '**/details_harness|arc:challenge|25_2023-12-04T18-26-43.065214.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-12-04T18-26-43.065214.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_12_04T18_26_43.065214
path:
- '**/details_harness|gsm8k|5_2023-12-04T18-26-43.065214.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-12-04T18-26-43.065214.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_12_04T18_26_43.065214
path:
- '**/details_harness|hellaswag|10_2023-12-04T18-26-43.065214.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-12-04T18-26-43.065214.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_12_04T18_26_43.065214
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-04T18-26-43.065214.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-04T18-26-43.065214.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-04T18-26-43.065214.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-04T18-26-43.065214.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-04T18-26-43.065214.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-04T18-26-43.065214.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-04T18-26-43.065214.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-04T18-26-43.065214.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-04T18-26-43.065214.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-04T18-26-43.065214.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-04T18-26-43.065214.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-04T18-26-43.065214.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-04T18-26-43.065214.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-04T18-26-43.065214.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-04T18-26-43.065214.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-04T18-26-43.065214.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-04T18-26-43.065214.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-04T18-26-43.065214.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-04T18-26-43.065214.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-04T18-26-43.065214.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-04T18-26-43.065214.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-04T18-26-43.065214.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-04T18-26-43.065214.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-04T18-26-43.065214.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-04T18-26-43.065214.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-04T18-26-43.065214.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-04T18-26-43.065214.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-04T18-26-43.065214.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-04T18-26-43.065214.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-04T18-26-43.065214.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-04T18-26-43.065214.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-04T18-26-43.065214.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-04T18-26-43.065214.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-04T18-26-43.065214.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-12-04T18-26-43.065214.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-04T18-26-43.065214.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-04T18-26-43.065214.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-04T18-26-43.065214.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-12-04T18-26-43.065214.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-12-04T18-26-43.065214.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-04T18-26-43.065214.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-04T18-26-43.065214.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-04T18-26-43.065214.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-04T18-26-43.065214.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-04T18-26-43.065214.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-04T18-26-43.065214.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-04T18-26-43.065214.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-04T18-26-43.065214.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-04T18-26-43.065214.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-04T18-26-43.065214.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-04T18-26-43.065214.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-04T18-26-43.065214.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-04T18-26-43.065214.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-12-04T18-26-43.065214.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-04T18-26-43.065214.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-12-04T18-26-43.065214.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-04T18-26-43.065214.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-04T18-26-43.065214.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-04T18-26-43.065214.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-04T18-26-43.065214.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-04T18-26-43.065214.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-04T18-26-43.065214.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-04T18-26-43.065214.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-04T18-26-43.065214.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-04T18-26-43.065214.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-04T18-26-43.065214.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-04T18-26-43.065214.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-04T18-26-43.065214.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-04T18-26-43.065214.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-04T18-26-43.065214.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-04T18-26-43.065214.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-04T18-26-43.065214.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-04T18-26-43.065214.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-04T18-26-43.065214.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-04T18-26-43.065214.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-04T18-26-43.065214.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-04T18-26-43.065214.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-04T18-26-43.065214.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-04T18-26-43.065214.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-04T18-26-43.065214.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-04T18-26-43.065214.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-04T18-26-43.065214.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-04T18-26-43.065214.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-04T18-26-43.065214.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-04T18-26-43.065214.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-04T18-26-43.065214.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-04T18-26-43.065214.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-04T18-26-43.065214.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-04T18-26-43.065214.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-04T18-26-43.065214.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-04T18-26-43.065214.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-12-04T18-26-43.065214.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-04T18-26-43.065214.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-04T18-26-43.065214.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-04T18-26-43.065214.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-12-04T18-26-43.065214.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-12-04T18-26-43.065214.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-04T18-26-43.065214.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-04T18-26-43.065214.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-04T18-26-43.065214.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-04T18-26-43.065214.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-04T18-26-43.065214.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-04T18-26-43.065214.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-04T18-26-43.065214.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-04T18-26-43.065214.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-04T18-26-43.065214.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-04T18-26-43.065214.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-04T18-26-43.065214.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-04T18-26-43.065214.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-04T18-26-43.065214.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-12-04T18-26-43.065214.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-04T18-26-43.065214.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-12-04T18-26-43.065214.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-04T18-26-43.065214.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_12_04T18_26_43.065214
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-04T18-26-43.065214.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-04T18-26-43.065214.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_12_04T18_26_43.065214
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-04T18-26-43.065214.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-04T18-26-43.065214.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_12_04T18_26_43.065214
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-04T18-26-43.065214.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-04T18-26-43.065214.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_12_04T18_26_43.065214
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-04T18-26-43.065214.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-04T18-26-43.065214.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_12_04T18_26_43.065214
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-04T18-26-43.065214.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-04T18-26-43.065214.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_12_04T18_26_43.065214
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-04T18-26-43.065214.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-04T18-26-43.065214.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_12_04T18_26_43.065214
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-04T18-26-43.065214.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-04T18-26-43.065214.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_12_04T18_26_43.065214
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-04T18-26-43.065214.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-04T18-26-43.065214.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_12_04T18_26_43.065214
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-04T18-26-43.065214.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-04T18-26-43.065214.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_12_04T18_26_43.065214
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-04T18-26-43.065214.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-04T18-26-43.065214.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_12_04T18_26_43.065214
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-04T18-26-43.065214.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-04T18-26-43.065214.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_12_04T18_26_43.065214
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-04T18-26-43.065214.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-04T18-26-43.065214.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_12_04T18_26_43.065214
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-04T18-26-43.065214.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-04T18-26-43.065214.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_12_04T18_26_43.065214
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-04T18-26-43.065214.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-04T18-26-43.065214.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_12_04T18_26_43.065214
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-04T18-26-43.065214.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-04T18-26-43.065214.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_12_04T18_26_43.065214
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-04T18-26-43.065214.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-04T18-26-43.065214.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_12_04T18_26_43.065214
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-04T18-26-43.065214.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-04T18-26-43.065214.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_12_04T18_26_43.065214
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-04T18-26-43.065214.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-04T18-26-43.065214.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_12_04T18_26_43.065214
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-04T18-26-43.065214.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-04T18-26-43.065214.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_12_04T18_26_43.065214
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-04T18-26-43.065214.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-04T18-26-43.065214.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_12_04T18_26_43.065214
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-04T18-26-43.065214.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-04T18-26-43.065214.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_12_04T18_26_43.065214
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-04T18-26-43.065214.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-04T18-26-43.065214.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_12_04T18_26_43.065214
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-04T18-26-43.065214.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-04T18-26-43.065214.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_12_04T18_26_43.065214
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-04T18-26-43.065214.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-04T18-26-43.065214.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_12_04T18_26_43.065214
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-04T18-26-43.065214.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-04T18-26-43.065214.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_12_04T18_26_43.065214
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-04T18-26-43.065214.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-04T18-26-43.065214.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_12_04T18_26_43.065214
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-04T18-26-43.065214.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-04T18-26-43.065214.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_12_04T18_26_43.065214
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-04T18-26-43.065214.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-04T18-26-43.065214.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_12_04T18_26_43.065214
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-04T18-26-43.065214.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-04T18-26-43.065214.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_12_04T18_26_43.065214
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-04T18-26-43.065214.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-04T18-26-43.065214.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_12_04T18_26_43.065214
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-04T18-26-43.065214.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-04T18-26-43.065214.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_12_04T18_26_43.065214
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-04T18-26-43.065214.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-04T18-26-43.065214.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_12_04T18_26_43.065214
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-04T18-26-43.065214.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-04T18-26-43.065214.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_12_04T18_26_43.065214
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-04T18-26-43.065214.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-04T18-26-43.065214.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_12_04T18_26_43.065214
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-12-04T18-26-43.065214.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-12-04T18-26-43.065214.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_12_04T18_26_43.065214
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-04T18-26-43.065214.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-04T18-26-43.065214.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_12_04T18_26_43.065214
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-04T18-26-43.065214.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-04T18-26-43.065214.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_12_04T18_26_43.065214
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-04T18-26-43.065214.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-04T18-26-43.065214.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_12_04T18_26_43.065214
path:
- '**/details_harness|hendrycksTest-management|5_2023-12-04T18-26-43.065214.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-12-04T18-26-43.065214.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_12_04T18_26_43.065214
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-12-04T18-26-43.065214.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-12-04T18-26-43.065214.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_12_04T18_26_43.065214
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-04T18-26-43.065214.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-04T18-26-43.065214.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_12_04T18_26_43.065214
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-04T18-26-43.065214.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-04T18-26-43.065214.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_12_04T18_26_43.065214
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-04T18-26-43.065214.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-04T18-26-43.065214.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_12_04T18_26_43.065214
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-04T18-26-43.065214.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-04T18-26-43.065214.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_12_04T18_26_43.065214
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-04T18-26-43.065214.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-04T18-26-43.065214.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_12_04T18_26_43.065214
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-04T18-26-43.065214.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-04T18-26-43.065214.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_12_04T18_26_43.065214
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-04T18-26-43.065214.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-04T18-26-43.065214.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_12_04T18_26_43.065214
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-04T18-26-43.065214.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-04T18-26-43.065214.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_12_04T18_26_43.065214
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-04T18-26-43.065214.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-04T18-26-43.065214.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_12_04T18_26_43.065214
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-04T18-26-43.065214.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-04T18-26-43.065214.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_12_04T18_26_43.065214
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-04T18-26-43.065214.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-04T18-26-43.065214.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_12_04T18_26_43.065214
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-04T18-26-43.065214.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-04T18-26-43.065214.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_12_04T18_26_43.065214
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-04T18-26-43.065214.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-04T18-26-43.065214.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_12_04T18_26_43.065214
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-12-04T18-26-43.065214.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-12-04T18-26-43.065214.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_12_04T18_26_43.065214
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-04T18-26-43.065214.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-04T18-26-43.065214.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_12_04T18_26_43.065214
path:
- '**/details_harness|hendrycksTest-virology|5_2023-12-04T18-26-43.065214.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-12-04T18-26-43.065214.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_12_04T18_26_43.065214
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-04T18-26-43.065214.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-04T18-26-43.065214.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_12_04T18_26_43.065214
path:
- '**/details_harness|truthfulqa:mc|0_2023-12-04T18-26-43.065214.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-12-04T18-26-43.065214.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_12_04T18_26_43.065214
path:
- '**/details_harness|winogrande|5_2023-12-04T18-26-43.065214.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-12-04T18-26-43.065214.parquet'
- config_name: results
data_files:
- split: 2023_12_04T18_26_43.065214
path:
- results_2023-12-04T18-26-43.065214.parquet
- split: latest
path:
- results_2023-12-04T18-26-43.065214.parquet
---
# Dataset Card for Evaluation run of vibhorag101/llama-2-13b-chat-hf-phr_mental_therapy
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/vibhorag101/llama-2-13b-chat-hf-phr_mental_therapy
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** clementine@hf.co
### Dataset Summary
Dataset automatically created during the evaluation run of model [vibhorag101/llama-2-13b-chat-hf-phr_mental_therapy](https://huggingface.co/vibhorag101/llama-2-13b-chat-hf-phr_mental_therapy) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_vibhorag101__llama-2-13b-chat-hf-phr_mental_therapy",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-04T18:26:43.065214](https://huggingface.co/datasets/open-llm-leaderboard/details_vibhorag101__llama-2-13b-chat-hf-phr_mental_therapy/blob/main/results_2023-12-04T18-26-43.065214.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
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"acc_stderr": 0.03008501938303984,
"acc_norm": 0.24224538912156782,
"acc_norm_stderr": 0.030747150403453674,
"mc1": 0.2778457772337821,
"mc1_stderr": 0.015680929364024643,
"mc2": 0.4692403294958895,
"mc2_stderr": 0.015061938982346217
},
"harness|arc:challenge|25": {
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"acc_stderr": 0.014104578366491894,
"acc_norm": 0.38822525597269625,
"acc_norm_stderr": 0.01424161420741405
},
"harness|hellaswag|10": {
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"acc_norm_stderr": 0.004442623590846322
},
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},
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},
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},
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},
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},
"harness|hendrycksTest-us_foreign_policy|5": {
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},
"harness|hendrycksTest-virology|5": {
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},
"harness|hendrycksTest-world_religions|5": {
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"harness|truthfulqa:mc|0": {
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"mc2": 0.4692403294958895,
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},
"harness|winogrande|5": {
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"acc_stderr": 0.013352121905005941
},
"harness|gsm8k|5": {
"acc": 0.07808946171341925,
"acc_stderr": 0.007390654481108261
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] |
ghomasHudson/muld_AO3_Style_Change_Detection | ---
dataset_info:
features:
- name: input
dtype: string
- name: output
dtype: string
- name: metadata
dtype: string
splits:
- name: test
num_bytes: 282915635
num_examples: 2352
- name: train
num_bytes: 762370660
num_examples: 6354
- name: validation
num_bytes: 83699681
num_examples: 705
download_size: 677671983
dataset_size: 1128985976
---
# Dataset Card for "muld_AO3_Style_Change_Detection"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
joey234/tweet_eval_affix_neg | ---
dataset_info:
features:
- name: text
dtype: string
- name: label
dtype:
class_label:
names:
'0': negative
'1': neutral
'2': positive
- name: words_with_affixes
sequence: string
splits:
- name: test
num_bytes: 56170
num_examples: 405
download_size: 0
dataset_size: 56170
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
---
# Dataset Card for "tweet_eval_affix_neg"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Seongill/Trivia_5_small_missing_adv | ---
dataset_info:
features:
- name: question
dtype: string
- name: answers
sequence: string
- name: has_answer
dtype: bool
- name: similar_sub
dtype: string
- name: ctxs
list:
- name: answer_sent
sequence: string
- name: hasanswer
dtype: bool
- name: id
dtype: string
- name: is_adv
dtype: bool
- name: new_answer_sent
dtype: string
- name: original_text
dtype: string
- name: score
dtype: float64
- name: text
dtype: string
- name: title
dtype: string
- name: status
dtype: string
splits:
- name: train
num_bytes: 17349142
num_examples: 3771
download_size: 9605929
dataset_size: 17349142
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
wdavies/testme | ---
license: other
license_name: none
license_link: LICENSE
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 257539
num_examples: 1
- name: validation
num_bytes: 257539
num_examples: 1
- name: test
num_bytes: 257539
num_examples: 1
download_size: 465504
dataset_size: 772617
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
---
|
datajuicer/llava-pretrain-refined-by-data-juicer | ---
license: apache-2.0
task_categories:
- image-to-text
- visual-question-answering
language:
- en
tags:
- data-juicer
- pretraining
- multimodal
size_categories:
- 100K<n<1M
---
# LLaVA pretrain -- LCS-558k (refined by Data-Juicer)
A refined version of LLaVA pretrain dataset (LCS-558k) by [Data-Juicer](https://github.com/alibaba/data-juicer). Removing some "bad" samples from the original dataset to make it higher-quality.
This dataset is usually used to pretrain a Multimodal Large Language Model.
**Notice**: Here is a small subset for previewing. The whole dataset is available [here](https://dail-wlcb.oss-cn-wulanchabu.aliyuncs.com/MM_data/our_refined_data/LLaVA-1.5/public/llava-pretrain-refine-result.json) (About 115MB).
## Dataset Information
- Number of samples: 500,380 (Keep ~89.65% from the original dataset)
## Refining Recipe
```yaml
project_name: 'llava-1.5-pretrain-dataset-refine-recipe'
dataset_path: 'blip_laion_cc_sbu_558k_dj_fmt_only_caption.jsonl' # converted LLaVA pretrain dataset in Data-Juicer format with only_keep_caption is True. See tools/multimodal/source_format_to_data_juicer_format/llava_to_dj.py
export_path: 'blip_laion_cc_sbu_558k_dj_fmt_only_caption_refined.jsonl'
np: 42 # number of subprocess to process your dataset
text_keys: 'text' # the key name of field where the sample texts to be processed, e.g., `text`, `instruction`, `output`, ...
# for multimodal data processing
image_key: 'images' # Key name of field to store the list of sample image paths.
image_special_token: '<image>' # The special token that represents an image in the text. For LLaVA, it's "<image>". Should be aligned with the args when running conversion tools.
eoc_special_token: '<|__dj__eoc|>' # The special token that represents the end of a chunk in the text. In default, it's "<|__dj__eoc|>". You can specify your own special token according to your input dataset. Should be aligned with the args when running conversion tools.
open_tracer: true
# process schedule: a list of several process operators with their arguments
process:
- fix_unicode_mapper: # fix unicode errors in text.
- punctuation_normalization_mapper: # normalize unicode punctuations to English punctuations.
# 558128
# Filter ops
- alphanumeric_filter: #558087 # filter text with alphabet/numeric ratio out of specific range.
tokenization: false # Whether to count the ratio of alphanumeric to the total number of tokens.
min_ratio: 0.60 # the min ratio of filter range
- character_repetition_filter: #546105 # filter text with the character repetition ratio out of specific range
rep_len: 10 # repetition length for char-level n-gram
max_ratio: 0.09373663 # the max ratio of filter range
- flagged_words_filter: #543960 # filter text with the flagged-word ratio larger than a specific max value
lang: en # consider flagged words in what language
tokenization: false # whether to use model to tokenize documents
max_ratio: 0.0 # the max ratio to filter text
- perplexity_filter: #532029 # filter text with perplexity score out of specific range
lang: en # compute perplexity in what language
max_ppl: 14435.5806 # the max perplexity score to filter text
- special_characters_filter: #531968 # filter text with special-char ratio out of specific range
min_ratio: 0.16534802 # the min ratio of filter range
max_ratio: 0.42023757 # the max ratio of filter range
- word_repetition_filter: # 530773 # filter text with the word repetition ratio out of specific range
lang: en # sample in which language
tokenization: false # whether to use model to tokenize documents
rep_len: 10 # repetition length for word-level n-gram
max_ratio: 0.03085751 # the max ratio of filter range
- image_aspect_ratio_filter: #542389 # filter samples according to the aspect ratios of images (a fraction of width by height, r=w/h) in them
min_ratio: 0.333 # the min aspect ratio of filter range
max_ratio: 3.0 # the max aspect ratio of filter range
any_or_all: any # keep this sample when any/all images meet the filter condition
- image_shape_filter: #533966 # filter samples according to the widths and heights of images in them
max_width: 727.8798422276 # the max width of width filter range
max_height: 606.2421072264 # the max height of height filter range
any_or_all: any # keep this sample when any/all images meet the filter condition
- image_size_filter: # 533966 # filter samples according to the size of images (in bytes) within them
max_size: "124KB" # the max size of filter range
any_or_all: any # keep this sample when any/all images meet the filter condition
- image_text_similarity_filter: #544202 # filter samples according to the similarity between text and images.
hf_clip: openai/clip-vit-base-patch32 # name of used Hugging Face clip
min_score: 0.20315419 # the min similarity of filter range
- image_text_matching_filter: # filter samples according to the matching score between image and text.
hf_blip: Salesforce/blip-itm-base-coco # name of used Hugging Face blip
min_score: 0.44930778 # the min matching score of filter range
``` |
abdulhady/ckb | ---
license: other
---
|
alvations/wikisummary-translations | ---
dataset_info:
features:
- name: lang
dtype: string
- name: title
dtype: string
- name: sentence
dtype: string
- name: url
dtype: string
- name: prompt
dtype: string
- name: output
list:
- name: generated_text
dtype: string
- name: model
dtype: string
- name: docid
dtype: string
- name: sentid
dtype: int64
- name: summary
dtype: string
splits:
- name: train
num_bytes: 5353607
num_examples: 2069
download_size: 1351936
dataset_size: 5353607
configs:
- config_name: default
data_files:
- split: train
path: 1711582263/train-*
---
|
liuyanchen1015/MULTI_VALUE_rte_analytic_whose_relativizer | ---
dataset_info:
features:
- name: sentence1
dtype: string
- name: sentence2
dtype: string
- name: label
dtype: int64
- name: idx
dtype: int64
- name: value_score
dtype: int64
splits:
- name: test
num_bytes: 15346
num_examples: 32
- name: train
num_bytes: 14108
num_examples: 27
download_size: 30539
dataset_size: 29454
---
# Dataset Card for "MULTI_VALUE_rte_analytic_whose_relativizer"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
kpriyanshu256/MultiTabQA-multitable_pretraining-Salesforce-codet5-base_train-html-24000 | ---
dataset_info:
features:
- name: input_ids
sequence:
sequence: int32
- name: attention_mask
sequence:
sequence: int8
- name: labels
sequence:
sequence: int64
splits:
- name: train
num_bytes: 13336000
num_examples: 1000
download_size: 650652
dataset_size: 13336000
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
Multimodal-Fatima/Imagenette_validation | ---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': tench
'1': English springer
'2': cassette player
'3': chain saw
'4': church
'5': French horn
'6': garbage truck
'7': gas pump
'8': golf ball
'9': parachute
- name: id
dtype: int64
- name: Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full
sequence: string
- name: Attributes_ViT_L_14_descriptors_text_davinci_003_full
sequence: string
- name: clip_tags_ViT_L_14_simple_specific
dtype: string
- name: clip_tags_ViT_L_14_ensemble_specific
dtype: string
splits:
- name: validation
num_bytes: 466203246.45
num_examples: 3925
download_size: 463110033
dataset_size: 466203246.45
---
# Dataset Card for "Imagenette_validation"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
magnifi/hl-codellama-chat-response-v2 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: Query
dtype: string
- name: Result
dtype: string
- name: chat_response
dtype: string
splits:
- name: train
num_bytes: 1321860.461185117
num_examples: 1523
- name: test
num_bytes: 567627.5388148829
num_examples: 654
download_size: 109799
dataset_size: 1889488.0
---
# Dataset Card for "hl-codellama-chat-response-v2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
zhangshuoming/final_c_x86_O0_exebench_numeric_full_json_cleaned | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 16258218.0
num_examples: 12833
download_size: 4646995
dataset_size: 16258218.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "final_c_x86_O0_exebench_numeric_full_json_cleaned"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
PanoEvJ/GPT3.5_summarization_preference_RLAIF | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: prompt
dtype: string
- name: chosen
dtype: string
- name: rejected
dtype: string
splits:
- name: train
num_bytes: 162321
num_examples: 100
download_size: 105617
dataset_size: 162321
---
# Dataset Card for "GPT3.5_summarization_preference_RLAIF"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
anjunhu/naively_captioned_CUB2002011_test_2shot | ---
dataset_info:
features:
- name: text
dtype: string
- name: text_cupl
dtype: string
- name: image
dtype: image
splits:
- name: train
num_bytes: 11010976.0
num_examples: 400
download_size: 10976537
dataset_size: 11010976.0
---
# Dataset Card for "naively_captioned_CUB2002011_test_2shots"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
rodrigo99/bian | ---
license: other
---
|
anasalashqar/autotrain-data-hhhh | ---
task_categories:
- image-classification
---
# AutoTrain Dataset for project: hhhh
## Dataset Description
This dataset has been automatically processed by AutoTrain for project hhhh.
### Languages
The BCP-47 code for the dataset's language is unk.
## Dataset Structure
### Data Instances
A sample from this dataset looks as follows:
```json
[
{
"image": "<256x256 RGBA PIL image>",
"target": 0
},
{
"image": "<256x256 RGBA PIL image>",
"target": 0
}
]
```
### Dataset Fields
The dataset has the following fields (also called "features"):
```json
{
"image": "Image(decode=True, id=None)",
"target": "ClassLabel(names=['lion', 'tiger'], id=None)"
}
```
### Dataset Splits
This dataset is split into a train and validation split. The split sizes are as follow:
| Split name | Num samples |
| ------------ | ------------------- |
| train | 360 |
| valid | 40 |
|
ka05ar/dravidian-fake-news | ---
license: apache-2.0
task_categories:
- text-classification
language:
- ml
tags:
- fake news
size_categories:
- 1K<n<10K
--- |
deus-ex-machina/animagine-xl-3.0-artist-comparison | ---
license: apache-2.0
task_categories:
- text-to-image
language:
- en
tags:
- sample
- example
- comparison
- stable-diffusion
- stable-diffusion-xl
- animagine
size_categories:
- 1K<n<10K
viewer: false
---
Using https://huggingface.co/cagliostrolab/animagine-xl-3.0, I created samples for the top 7500 artist tags based on dataset occurrences of the tags, reaching down to tags with ~40 occurrences.
Images are prefixed with the occurence count to make it easier to sort by higher occurances, as they are more likely to reproduce styles better.
I have attached an image with the generation metadata containing the generation settings I used for the samples.
When inserting tags into prompts, I would always escape parentheses AND replace underscores with spaces.
**NOTE: While I would classify the samples as almost all SFW, some might be *a bit* ecchi, depending on your standards.**
Zip version available at https://huggingface.co/datasets/deus-ex-machina/animagine-xl-3.0-artist-comparison/blob/zip/images/images.zip
**Raw generation metadata example**
```
hews, 1girl, solo, looking at viewer, standing, cowboy shot
Negative prompt: (worst quality, low quality:1.2), lowres, jpeg artifacts, (blurry:0.7), @_@, greyscale, nude, panties, underwear, pussy, nipples, cleavage, ass, micro, mini, bottomless
Steps: 28, Sampler: Euler a, CFG scale: 8, Seed: 407081055, Size: 915x1144, Model hash: 1449e5b0b9, Model: animagine-xl-3.0, Denoising strength: 0.5, Hires upscale: 1.35, Hires steps: 14, Hires upscaler: 4x_foolhardy_Remacri, Version: v1.7.0-375-gf939bce8
``` |
distilled-one-sec-cv12-each-chunk-uniq/chunk_241 | ---
dataset_info:
features:
- name: logits
sequence: float32
- name: mfcc
sequence:
sequence: float64
splits:
- name: train
num_bytes: 1464842156.0
num_examples: 285433
download_size: 1501779550
dataset_size: 1464842156.0
---
# Dataset Card for "chunk_241"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
breno30/LocutorAlessandroGomes | ---
license: openrail
---
|
liuyanchen1015/VALUE_wikitext103_been_done | ---
dataset_info:
features:
- name: sentence
dtype: string
- name: score
dtype: int64
splits:
- name: test
num_bytes: 631030
num_examples: 752
- name: train
num_bytes: 250823074
num_examples: 294089
- name: validation
num_bytes: 553077
num_examples: 673
download_size: 148523130
dataset_size: 252007181
---
# Dataset Card for "VALUE_wikitext103_been_done"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
RIW/small_coco_test_25 | ---
dataset_info:
features:
- name: image
dtype: image
- name: caption
dtype: string
- name: url
dtype: string
- name: key
dtype: string
- name: status
dtype: string
- name: error_message
dtype: 'null'
- name: width
dtype: int64
- name: height
dtype: int64
- name: original_width
dtype: int64
- name: original_height
dtype: int64
- name: exif
dtype: string
- name: sha256
dtype: string
- name: watermark
dtype: bool
splits:
- name: train
num_bytes: 795705891.2
num_examples: 9700
- name: validation
num_bytes: 885003521.915
num_examples: 8965
download_size: 366711571
dataset_size: 1680709413.115
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
---
|
Falah/expanded_artistic_prompts | ---
dataset_info:
features:
- name: prompts
dtype: string
splits:
- name: train
num_bytes: 221011
num_examples: 1000
download_size: 33944
dataset_size: 221011
---
# Dataset Card for "expanded_artistic_prompts"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
kpriyanshu256/MultiTabQA-multitable_pretraining-train-v2-31000 | ---
dataset_info:
features:
- name: tables
sequence: string
- name: table_names
sequence: string
- name: query
dtype: string
- name: answer
dtype: string
- name: source
dtype: string
- name: target
dtype: string
- name: source_latex
dtype: string
- name: target_latex
dtype: string
- name: source_html
dtype: string
- name: target_html
dtype: string
- name: source_markdown
dtype: string
- name: target_markdown
dtype: string
splits:
- name: train
num_bytes: 6920868182
num_examples: 1000
download_size: 1351848970
dataset_size: 6920868182
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
huggingartists/tanzy-minus | ---
language:
- en
tags:
- huggingartists
- lyrics
---
# Dataset Card for "huggingartists/tanzy-minus"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [How to use](#how-to-use)
- [Dataset Structure](#dataset-structure)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [About](#about)
## Dataset Description
- **Homepage:** [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists)
- **Repository:** [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists)
- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Size of the generated dataset:** 0.036726 MB
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://images.genius.com/73716ad8dca0ea2fd5f02924ffcbcdad.639x639x1.jpg')">
</div>
</div>
<a href="https://huggingface.co/huggingartists/tanzy-minus">
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 HuggingArtists Model 🤖</div>
</a>
<div style="text-align: center; font-size: 16px; font-weight: 800">Танцы Минус (Tanzy Minus)</div>
<a href="https://genius.com/artists/tanzy-minus">
<div style="text-align: center; font-size: 14px;">@tanzy-minus</div>
</a>
</div>
### Dataset Summary
The Lyrics dataset parsed from Genius. This dataset is designed to generate lyrics with HuggingArtists.
Model is available [here](https://huggingface.co/huggingartists/tanzy-minus).
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
en
## How to use
How to load this dataset directly with the datasets library:
```python
from datasets import load_dataset
dataset = load_dataset("huggingartists/tanzy-minus")
```
## Dataset Structure
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"text": "Look, I was gonna go easy on you\nNot to hurt your feelings\nBut I'm only going to get this one chance\nSomething's wrong, I can feel it..."
}
```
### Data Fields
The data fields are the same among all splits.
- `text`: a `string` feature.
### Data Splits
| train |validation|test|
|------:|---------:|---:|
|5| -| -|
'Train' can be easily divided into 'train' & 'validation' & 'test' with few lines of code:
```python
from datasets import load_dataset, Dataset, DatasetDict
import numpy as np
datasets = load_dataset("huggingartists/tanzy-minus")
train_percentage = 0.9
validation_percentage = 0.07
test_percentage = 0.03
train, validation, test = np.split(datasets['train']['text'], [int(len(datasets['train']['text'])*train_percentage), int(len(datasets['train']['text'])*(train_percentage + validation_percentage))])
datasets = DatasetDict(
{
'train': Dataset.from_dict({'text': list(train)}),
'validation': Dataset.from_dict({'text': list(validation)}),
'test': Dataset.from_dict({'text': list(test)})
}
)
```
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Citation Information
```
@InProceedings{huggingartists,
author={Aleksey Korshuk}
year=2021
}
```
## About
*Built by Aleksey Korshuk*
[](https://github.com/AlekseyKorshuk)
[](https://twitter.com/intent/follow?screen_name=alekseykorshuk)
[](https://t.me/joinchat/_CQ04KjcJ-4yZTky)
For more details, visit the project repository.
[](https://github.com/AlekseyKorshuk/huggingartists)
|
archxin111/111 | ---
license: openrail
---
|
mask-distilled-onesec-cv12-each-chunk-uniq/chunk_10 | ---
dataset_info:
features:
- name: logits
sequence: float32
- name: mfcc
sequence:
sequence: float64
splits:
- name: train
num_bytes: 1029683872.0
num_examples: 202216
download_size: 1047224083
dataset_size: 1029683872.0
---
# Dataset Card for "chunk_10"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
PJMixers/Futurama-CustomShareGPT | ---
language:
- en
size_categories:
- n<1K
---
[Source](https://theinfosphere.org/Episode_Transcript_Listing)
```
3.9% Cleaned.
```
**You should avoid using this set unless you *clean it yourself*, or wait until I've finished cleaning it and changed this text to say it's good to go.**
Intended for use with Axolotl with this [custom version of ShareGPT](https://github.com/xzuyn/axolotl/blob/dan_metharme/src/axolotl/prompt_strategies/dan_metharme_chat.py).
If an item has an `'episode_number'`, it is cleaned or mostly cleaned.
Average length of each episode seems to be ~10K mistral tokens. |
HelloKattyz/SriyaBYHKattyz | ---
license: openrail
---
|
gurprbebo/FT_Dataset | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 1844
num_examples: 6
download_size: 3899
dataset_size: 1844
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "FT_Dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
d0rj/rudetoxifier_data_detox | ---
dataset_info:
features:
- name: text
dtype: string
- name: toxic
dtype: float64
- name: detox
dtype: string
splits:
- name: train
num_bytes: 8268013
num_examples: 31407
- name: test
num_bytes: 2646830
num_examples: 10000
download_size: 6182785
dataset_size: 10914843
license: mit
task_categories:
- text2text-generation
language:
- ru
multilinguality:
- monolingual
tags:
- toxicity
- style-transfer
pretty_name: RuDetoxifier data - Detoxed
size_categories:
- 10K<n<100K
source_datasets:
- d0rj/rudetoxifier_data
---
# rudetoxifier_data_detox
This is subset of toxic comments from [d0rj/rudetoxifier_data](https://huggingface.co/datasets/d0rj/rudetoxifier_data) which has detoxified column created by [s-nlp/ruT5-base-detox](https://huggingface.co/s-nlp/ruT5-base-detox). |
kellycyy/CulturalBench | ---
license: apache-2.0
task_categories:
- question-answering
- text-generation
language:
- en
tags:
- culture
pretty_name: culturalbench
size_categories:
- n<1K
---
# CulturalBench-v0.1: Evaluation data collected from CulturalTeaming -- AI-Assisted Interactive Red-Teaming for Challenging LLM on Multicultural Knowledge
CulturalTeaming is an interactive red-teaming system that leverages the synergy of
human-AI collaboration to collect a truly challenging dataset to assess LLMs’ multicultural
knowledge. Through workshop sessions in our user studies, we gather users’ red-teaming
attempts to form a compact yet high-quality evaluation dataset CULTURALBENCH-V0.1.
## Quick Links:
- [Platform (Click to play!)](https://cultural-norms-demo-b-team.apps.allenai.org/)
- [HF Dataset](https://huggingface.co/datasets/kellycyy/CulturalBench)
- [Paper](https://arxiv.org/abs/2404.06664)
- **Language(s) (NLP):** English
- **Point of Contact:** [Kelly Chiu](mailto:kellyc@allenai.org)
## Data Schema Description
- `question_idx`: (int) Identifier for each entry.
- `initial_question_template`: (str) Annotator’s initial draft of question (MCQ).
- `final_question_template`: (str) Annotator’s final version of MCQ. Manually reviewed to ensure it follows the MCQ format and contains the culture to be asked. This question template is the one used for the evaluation of different models.
- `correct_ans`: (str) Annotator's logged correct answer for their drafted MCQ.
- `correct_ans_reason`: (str) Annotator's logged reason on the correct answer of their drafted MCQ.
- `culture_represent`: (str) Annotator's logged the represented culture for the MCQ.
- `culture_group_geographic`: (str) Geographic location grouping based on `culture_represent`.
- `feedback_familiar_on_culture`: (int) An indicator of the annotator's familiarity with the represented culture on their drafted MCQ. The question is `How familiar are you with the represented culture? on a scale of 1 (unfamiliar) to 5 (familiar)`
- `feedback_question_common`: (int) An indicator of the annotator's perception about the commonness of the situation embedded in their drafted MCQ. The question is `How common is the situation in the represented culture? on a scale of 1 (rare) to 5 (always)`
- `feedback_question_difficult`: (int) An indicator of the annotator's perception about their drafted MCQ difficulty. The question is `How common is the situation in the represented culture? on a scale of 1 (rare) to 5 (always)`
- `country_longest_living`: (str) Annotator's demographic information on their longest-growing-up area apart from the United States (US). `NA` for US. The question is `Apart from the US, which country/area did you live in the longest growing up?`
- `year_for_country_longest_living`: (str) Annotator's demographic information on the number of living years on the `country_longest_living`. `NA` for US. The question is `How long have lived in the above country/area?`
- `country_more_than_5_year`: (str) Annotator's demographic information on the country/area lived more than 5 years. The question is `In which country/area have you lived for more than five years?`
- `country_more_than_1_year`: (str) Annotator's demographic information on the country/area lived more than 1 years. The question is `In which country/area have you lived for more than one year?`
|
CyberHarem/bazett_fraga_mcremitz_fatekaleidlinerprismaillya | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of Bazett Fraga McRemitz
This is the dataset of Bazett Fraga McRemitz, containing 234 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 | 234 | [Download](dataset-raw.zip) | Raw data with meta information. |
| raw-stage3 | 476 | [Download](dataset-raw-stage3.zip) | 3-stage cropped raw data with meta information. |
| 384x512 | 234 | [Download](dataset-384x512.zip) | 384x512 aligned dataset. |
| 512x512 | 234 | [Download](dataset-512x512.zip) | 512x512 aligned dataset. |
| 512x704 | 234 | [Download](dataset-512x704.zip) | 512x704 aligned dataset. |
| 640x640 | 234 | [Download](dataset-640x640.zip) | 640x640 aligned dataset. |
| 640x880 | 234 | [Download](dataset-640x880.zip) | 640x880 aligned dataset. |
| stage3-640 | 476 | [Download](dataset-stage3-640.zip) | 3-stage cropped dataset with the shorter side not exceeding 640 pixels. |
| stage3-800 | 476 | [Download](dataset-stage3-800.zip) | 3-stage cropped dataset with the shorter side not exceeding 800 pixels. |
| stage3-1200 | 476 | [Download](dataset-stage3-1200.zip) | 3-stage cropped dataset with the shorter side not exceeding 1200 pixels. |
|
mattyhatch/tomatoesSpoof2 | ---
dataset_info:
features:
- name: pixel_values
dtype: image
- name: label
sequence:
sequence: int64
splits:
- name: train
num_bytes: 673124095.0
num_examples: 557
download_size: 35510907
dataset_size: 673124095.0
---
# Dataset Card for "tomatoesSpoof2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
gnumanth/gita | ---
dataset_info:
features:
- name: title
dtype: string
- name: word_meanings
dtype: string
- name: verse_number
dtype: int64
- name: text
dtype: string
- name: transliteration
dtype: string
- name: chapter_id
dtype: int64
- name: chapter_number
dtype: int64
- name: verse_order
dtype: int64
- name: id
dtype: int64
- name: externalId
dtype: int64
splits:
- name: train
num_bytes: 405990.5135520685
num_examples: 525
- name: test
num_bytes: 136103.48644793153
num_examples: 176
download_size: 294378
dataset_size: 542094.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
|
tuanio/processed_bad_word_cls_dataset | ---
dataset_info:
features:
- name: labels
dtype: int64
- name: input_ids
sequence:
sequence: int32
- name: attention_mask
sequence:
sequence: int8
splits:
- name: train
num_bytes: 15208128.0
num_examples: 90000
- name: test
num_bytes: 1689792.0
num_examples: 10000
download_size: 8136371
dataset_size: 16897920.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
|
distilled-from-one-sec-cv12/chunk_119 | ---
dataset_info:
features:
- name: logits
sequence: float32
- name: mfcc
sequence:
sequence: float64
splits:
- name: train
num_bytes: 1378911948
num_examples: 268689
download_size: 1409225386
dataset_size: 1378911948
---
# Dataset Card for "chunk_119"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
nreHieW/SoccerNet_Field_Segmentation | ---
dataset_info:
features:
- name: image
dtype: image
- name: outlines
sequence:
sequence: uint8
- name: segments
sequence:
sequence:
sequence: bool
- name: id
dtype: int64
- name: is_bad
dtype: bool
splits:
- name: train
num_bytes: 18811729165.83
num_examples: 16455
- name: val
num_bytes: 3671501400.7200003
num_examples: 3209
- name: test
num_bytes: 3602703436.986
num_examples: 3138
download_size: 4189102455
dataset_size: 26085934003.536003
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: val
path: data/val-*
- split: test
path: data/test-*
---
Processed data from the Soccernet 2023 dataset. Processing notebook is included in this repo.
To see an example:
```python
def show_item(item):
fig, axs = plt.subplots(nrows = 1, ncols = 4, figsize = (20, 4))
axs[0].imshow(item['image'])
axs[0].set_title("Image")
axs[0].axis('off')
axs[1].imshow(overlay_mask(item['image'], item['outlines']))
axs[1].set_title("Outlines")
axs[1].axis('off')
axs[2].imshow(show_segments(item['segments']))
axs[2].set_title("Segments")
axs[2].axis('off')
# PART 3: GET MASK OUTLINES
kernel = np.array([[0, 1, 0],
[1, -4, 1],
[0, 1, 0]])
segments = np.array(item['segments']).astype(np.uint8)
class_edges = np.zeros(segments.shape[1:], dtype=int)
for i in range(segments.shape[0]):
edge = convolve(segments[i], kernel, mode='constant', cval=0)
edge_detected = edge != 0
class_edges[edge_detected] = i
axs[3].imshow(overlay_mask(item['image'], class_edges))
axs[3].set_title("Segments Outlines")
axs[3].axis('off')
if item['is_bad']:
s = f"Bad ID: {item['id']}"
else:
s = f"ID: {item['id']}"
fig.suptitle(s, fontsize = 8)
plt.subplots_adjust(hspace = -0.2, wspace = -0.05)
plt.show()
show_item(dataset['train'][99])
``` |
open-llm-leaderboard/details_Eric111__openchat-3.5-0106-128k-DPO | ---
pretty_name: Evaluation run of Eric111/openchat-3.5-0106-128k-DPO
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [Eric111/openchat-3.5-0106-128k-DPO](https://huggingface.co/Eric111/openchat-3.5-0106-128k-DPO)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 63 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the aggregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_Eric111__openchat-3.5-0106-128k-DPO\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2024-02-23T23:46:56.008224](https://huggingface.co/datasets/open-llm-leaderboard/details_Eric111__openchat-3.5-0106-128k-DPO/blob/main/results_2024-02-23T23-46-56.008224.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.6505113839845572,\n\
\ \"acc_stderr\": 0.03195564318421195,\n \"acc_norm\": 0.6512343495673035,\n\
\ \"acc_norm_stderr\": 0.03261014512185078,\n \"mc1\": 0.4149326805385557,\n\
\ \"mc1_stderr\": 0.017248314465805978,\n \"mc2\": 0.5634410109185068,\n\
\ \"mc2_stderr\": 0.015541179681250917\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.6450511945392492,\n \"acc_stderr\": 0.013983036904094095,\n\
\ \"acc_norm\": 0.6808873720136519,\n \"acc_norm_stderr\": 0.013621696119173311\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6445927106154152,\n\
\ \"acc_stderr\": 0.00477658353090957,\n \"acc_norm\": 0.8381796454889464,\n\
\ \"acc_norm_stderr\": 0.0036753325906810747\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.33,\n \"acc_stderr\": 0.04725815626252606,\n \
\ \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.04725815626252606\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6222222222222222,\n\
\ \"acc_stderr\": 0.04188307537595852,\n \"acc_norm\": 0.6222222222222222,\n\
\ \"acc_norm_stderr\": 0.04188307537595852\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.6776315789473685,\n \"acc_stderr\": 0.03803510248351585,\n\
\ \"acc_norm\": 0.6776315789473685,\n \"acc_norm_stderr\": 0.03803510248351585\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.67,\n\
\ \"acc_stderr\": 0.047258156262526094,\n \"acc_norm\": 0.67,\n \
\ \"acc_norm_stderr\": 0.047258156262526094\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.7245283018867924,\n \"acc_stderr\": 0.027495663683724053,\n\
\ \"acc_norm\": 0.7245283018867924,\n \"acc_norm_stderr\": 0.027495663683724053\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7708333333333334,\n\
\ \"acc_stderr\": 0.03514697467862388,\n \"acc_norm\": 0.7708333333333334,\n\
\ \"acc_norm_stderr\": 0.03514697467862388\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.5,\n \"acc_stderr\": 0.050251890762960605,\n \
\ \"acc_norm\": 0.5,\n \"acc_norm_stderr\": 0.050251890762960605\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\
: 0.57,\n \"acc_stderr\": 0.049756985195624284,\n \"acc_norm\": 0.57,\n\
\ \"acc_norm_stderr\": 0.049756985195624284\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.36,\n \"acc_stderr\": 0.048241815132442176,\n \
\ \"acc_norm\": 0.36,\n \"acc_norm_stderr\": 0.048241815132442176\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6647398843930635,\n\
\ \"acc_stderr\": 0.03599586301247078,\n \"acc_norm\": 0.6647398843930635,\n\
\ \"acc_norm_stderr\": 0.03599586301247078\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.39215686274509803,\n \"acc_stderr\": 0.04858083574266345,\n\
\ \"acc_norm\": 0.39215686274509803,\n \"acc_norm_stderr\": 0.04858083574266345\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.74,\n \"acc_stderr\": 0.04408440022768079,\n \"acc_norm\": 0.74,\n\
\ \"acc_norm_stderr\": 0.04408440022768079\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.5872340425531914,\n \"acc_stderr\": 0.03218471141400351,\n\
\ \"acc_norm\": 0.5872340425531914,\n \"acc_norm_stderr\": 0.03218471141400351\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.4649122807017544,\n\
\ \"acc_stderr\": 0.046920083813689104,\n \"acc_norm\": 0.4649122807017544,\n\
\ \"acc_norm_stderr\": 0.046920083813689104\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.593103448275862,\n \"acc_stderr\": 0.04093793981266236,\n\
\ \"acc_norm\": 0.593103448275862,\n \"acc_norm_stderr\": 0.04093793981266236\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.42328042328042326,\n \"acc_stderr\": 0.025446365634406783,\n \"\
acc_norm\": 0.42328042328042326,\n \"acc_norm_stderr\": 0.025446365634406783\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.49206349206349204,\n\
\ \"acc_stderr\": 0.044715725362943486,\n \"acc_norm\": 0.49206349206349204,\n\
\ \"acc_norm_stderr\": 0.044715725362943486\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.26,\n \"acc_stderr\": 0.04408440022768078,\n \
\ \"acc_norm\": 0.26,\n \"acc_norm_stderr\": 0.04408440022768078\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.7806451612903226,\n\
\ \"acc_stderr\": 0.023540799358723292,\n \"acc_norm\": 0.7806451612903226,\n\
\ \"acc_norm_stderr\": 0.023540799358723292\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\
: {\n \"acc\": 0.47783251231527096,\n \"acc_stderr\": 0.035145285621750094,\n\
\ \"acc_norm\": 0.47783251231527096,\n \"acc_norm_stderr\": 0.035145285621750094\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.71,\n \"acc_stderr\": 0.045604802157206845,\n \"acc_norm\"\
: 0.71,\n \"acc_norm_stderr\": 0.045604802157206845\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.7818181818181819,\n \"acc_stderr\": 0.03225078108306289,\n\
\ \"acc_norm\": 0.7818181818181819,\n \"acc_norm_stderr\": 0.03225078108306289\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.7676767676767676,\n \"acc_stderr\": 0.030088629490217487,\n \"\
acc_norm\": 0.7676767676767676,\n \"acc_norm_stderr\": 0.030088629490217487\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.8756476683937824,\n \"acc_stderr\": 0.023814477086593563,\n\
\ \"acc_norm\": 0.8756476683937824,\n \"acc_norm_stderr\": 0.023814477086593563\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.676923076923077,\n \"acc_stderr\": 0.02371088850197057,\n \
\ \"acc_norm\": 0.676923076923077,\n \"acc_norm_stderr\": 0.02371088850197057\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.35185185185185186,\n \"acc_stderr\": 0.02911661760608301,\n \
\ \"acc_norm\": 0.35185185185185186,\n \"acc_norm_stderr\": 0.02911661760608301\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.6974789915966386,\n \"acc_stderr\": 0.02983796238829194,\n \
\ \"acc_norm\": 0.6974789915966386,\n \"acc_norm_stderr\": 0.02983796238829194\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.33774834437086093,\n \"acc_stderr\": 0.03861557546255169,\n \"\
acc_norm\": 0.33774834437086093,\n \"acc_norm_stderr\": 0.03861557546255169\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.8422018348623853,\n \"acc_stderr\": 0.015630022970092444,\n \"\
acc_norm\": 0.8422018348623853,\n \"acc_norm_stderr\": 0.015630022970092444\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.5046296296296297,\n \"acc_stderr\": 0.03409825519163572,\n \"\
acc_norm\": 0.5046296296296297,\n \"acc_norm_stderr\": 0.03409825519163572\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.8382352941176471,\n \"acc_stderr\": 0.02584501798692692,\n \"\
acc_norm\": 0.8382352941176471,\n \"acc_norm_stderr\": 0.02584501798692692\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.810126582278481,\n \"acc_stderr\": 0.025530100460233504,\n \
\ \"acc_norm\": 0.810126582278481,\n \"acc_norm_stderr\": 0.025530100460233504\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.7085201793721974,\n\
\ \"acc_stderr\": 0.03050028317654585,\n \"acc_norm\": 0.7085201793721974,\n\
\ \"acc_norm_stderr\": 0.03050028317654585\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.7480916030534351,\n \"acc_stderr\": 0.03807387116306085,\n\
\ \"acc_norm\": 0.7480916030534351,\n \"acc_norm_stderr\": 0.03807387116306085\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.7933884297520661,\n \"acc_stderr\": 0.03695980128098824,\n \"\
acc_norm\": 0.7933884297520661,\n \"acc_norm_stderr\": 0.03695980128098824\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7407407407407407,\n\
\ \"acc_stderr\": 0.04236511258094633,\n \"acc_norm\": 0.7407407407407407,\n\
\ \"acc_norm_stderr\": 0.04236511258094633\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.7791411042944786,\n \"acc_stderr\": 0.03259177392742178,\n\
\ \"acc_norm\": 0.7791411042944786,\n \"acc_norm_stderr\": 0.03259177392742178\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.4375,\n\
\ \"acc_stderr\": 0.04708567521880525,\n \"acc_norm\": 0.4375,\n \
\ \"acc_norm_stderr\": 0.04708567521880525\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.8155339805825242,\n \"acc_stderr\": 0.03840423627288276,\n\
\ \"acc_norm\": 0.8155339805825242,\n \"acc_norm_stderr\": 0.03840423627288276\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8846153846153846,\n\
\ \"acc_stderr\": 0.020930193185179333,\n \"acc_norm\": 0.8846153846153846,\n\
\ \"acc_norm_stderr\": 0.020930193185179333\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.77,\n \"acc_stderr\": 0.04229525846816506,\n \
\ \"acc_norm\": 0.77,\n \"acc_norm_stderr\": 0.04229525846816506\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8326947637292464,\n\
\ \"acc_stderr\": 0.013347327202920332,\n \"acc_norm\": 0.8326947637292464,\n\
\ \"acc_norm_stderr\": 0.013347327202920332\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.7572254335260116,\n \"acc_stderr\": 0.023083658586984204,\n\
\ \"acc_norm\": 0.7572254335260116,\n \"acc_norm_stderr\": 0.023083658586984204\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.2927374301675978,\n\
\ \"acc_stderr\": 0.015218109544410182,\n \"acc_norm\": 0.2927374301675978,\n\
\ \"acc_norm_stderr\": 0.015218109544410182\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.738562091503268,\n \"acc_stderr\": 0.025160998214292456,\n\
\ \"acc_norm\": 0.738562091503268,\n \"acc_norm_stderr\": 0.025160998214292456\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7202572347266881,\n\
\ \"acc_stderr\": 0.02549425935069491,\n \"acc_norm\": 0.7202572347266881,\n\
\ \"acc_norm_stderr\": 0.02549425935069491\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.7777777777777778,\n \"acc_stderr\": 0.023132376234543332,\n\
\ \"acc_norm\": 0.7777777777777778,\n \"acc_norm_stderr\": 0.023132376234543332\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.4858156028368794,\n \"acc_stderr\": 0.02981549448368206,\n \
\ \"acc_norm\": 0.4858156028368794,\n \"acc_norm_stderr\": 0.02981549448368206\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.4830508474576271,\n\
\ \"acc_stderr\": 0.01276289688921086,\n \"acc_norm\": 0.4830508474576271,\n\
\ \"acc_norm_stderr\": 0.01276289688921086\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.7316176470588235,\n \"acc_stderr\": 0.0269174812243772,\n\
\ \"acc_norm\": 0.7316176470588235,\n \"acc_norm_stderr\": 0.0269174812243772\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.6683006535947712,\n \"acc_stderr\": 0.01904748523936038,\n \
\ \"acc_norm\": 0.6683006535947712,\n \"acc_norm_stderr\": 0.01904748523936038\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6727272727272727,\n\
\ \"acc_stderr\": 0.0449429086625209,\n \"acc_norm\": 0.6727272727272727,\n\
\ \"acc_norm_stderr\": 0.0449429086625209\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.7306122448979592,\n \"acc_stderr\": 0.02840125202902294,\n\
\ \"acc_norm\": 0.7306122448979592,\n \"acc_norm_stderr\": 0.02840125202902294\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8308457711442786,\n\
\ \"acc_stderr\": 0.026508590656233278,\n \"acc_norm\": 0.8308457711442786,\n\
\ \"acc_norm_stderr\": 0.026508590656233278\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.86,\n \"acc_stderr\": 0.03487350880197768,\n \
\ \"acc_norm\": 0.86,\n \"acc_norm_stderr\": 0.03487350880197768\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5180722891566265,\n\
\ \"acc_stderr\": 0.03889951252827216,\n \"acc_norm\": 0.5180722891566265,\n\
\ \"acc_norm_stderr\": 0.03889951252827216\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.8304093567251462,\n \"acc_stderr\": 0.02878210810540171,\n\
\ \"acc_norm\": 0.8304093567251462,\n \"acc_norm_stderr\": 0.02878210810540171\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.4149326805385557,\n\
\ \"mc1_stderr\": 0.017248314465805978,\n \"mc2\": 0.5634410109185068,\n\
\ \"mc2_stderr\": 0.015541179681250917\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.8153117600631413,\n \"acc_stderr\": 0.010905978112156876\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.6724791508718726,\n \
\ \"acc_stderr\": 0.01292710221042672\n }\n}\n```"
repo_url: https://huggingface.co/Eric111/openchat-3.5-0106-128k-DPO
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2024_02_23T23_46_56.008224
path:
- '**/details_harness|arc:challenge|25_2024-02-23T23-46-56.008224.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2024-02-23T23-46-56.008224.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2024_02_23T23_46_56.008224
path:
- '**/details_harness|gsm8k|5_2024-02-23T23-46-56.008224.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2024-02-23T23-46-56.008224.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2024_02_23T23_46_56.008224
path:
- '**/details_harness|hellaswag|10_2024-02-23T23-46-56.008224.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2024-02-23T23-46-56.008224.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2024_02_23T23_46_56.008224
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-23T23-46-56.008224.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-23T23-46-56.008224.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-23T23-46-56.008224.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-23T23-46-56.008224.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-23T23-46-56.008224.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-23T23-46-56.008224.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-23T23-46-56.008224.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-23T23-46-56.008224.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-23T23-46-56.008224.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-23T23-46-56.008224.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-23T23-46-56.008224.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-23T23-46-56.008224.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-23T23-46-56.008224.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-23T23-46-56.008224.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-23T23-46-56.008224.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-23T23-46-56.008224.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-23T23-46-56.008224.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-23T23-46-56.008224.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-23T23-46-56.008224.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-23T23-46-56.008224.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-23T23-46-56.008224.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-23T23-46-56.008224.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-23T23-46-56.008224.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-23T23-46-56.008224.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-23T23-46-56.008224.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-23T23-46-56.008224.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-23T23-46-56.008224.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-23T23-46-56.008224.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-23T23-46-56.008224.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-23T23-46-56.008224.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-23T23-46-56.008224.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-23T23-46-56.008224.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-23T23-46-56.008224.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-23T23-46-56.008224.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-02-23T23-46-56.008224.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-23T23-46-56.008224.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-23T23-46-56.008224.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-23T23-46-56.008224.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-02-23T23-46-56.008224.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-02-23T23-46-56.008224.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-23T23-46-56.008224.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-23T23-46-56.008224.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-23T23-46-56.008224.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-23T23-46-56.008224.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-23T23-46-56.008224.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-23T23-46-56.008224.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-23T23-46-56.008224.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-23T23-46-56.008224.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-23T23-46-56.008224.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-23T23-46-56.008224.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-23T23-46-56.008224.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-23T23-46-56.008224.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-23T23-46-56.008224.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-02-23T23-46-56.008224.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-23T23-46-56.008224.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-02-23T23-46-56.008224.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-23T23-46-56.008224.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-23T23-46-56.008224.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-23T23-46-56.008224.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-23T23-46-56.008224.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-23T23-46-56.008224.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-23T23-46-56.008224.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-23T23-46-56.008224.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-23T23-46-56.008224.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-23T23-46-56.008224.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-23T23-46-56.008224.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-23T23-46-56.008224.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-23T23-46-56.008224.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-23T23-46-56.008224.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-23T23-46-56.008224.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-23T23-46-56.008224.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-23T23-46-56.008224.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-23T23-46-56.008224.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-23T23-46-56.008224.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-23T23-46-56.008224.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-23T23-46-56.008224.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-23T23-46-56.008224.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-23T23-46-56.008224.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-23T23-46-56.008224.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-23T23-46-56.008224.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-23T23-46-56.008224.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-23T23-46-56.008224.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-23T23-46-56.008224.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-23T23-46-56.008224.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-23T23-46-56.008224.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-23T23-46-56.008224.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-23T23-46-56.008224.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-23T23-46-56.008224.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-23T23-46-56.008224.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-23T23-46-56.008224.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-23T23-46-56.008224.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-02-23T23-46-56.008224.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-23T23-46-56.008224.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-23T23-46-56.008224.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-23T23-46-56.008224.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-02-23T23-46-56.008224.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-02-23T23-46-56.008224.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-23T23-46-56.008224.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-23T23-46-56.008224.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-23T23-46-56.008224.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-23T23-46-56.008224.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-23T23-46-56.008224.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-23T23-46-56.008224.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-23T23-46-56.008224.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-23T23-46-56.008224.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-23T23-46-56.008224.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-23T23-46-56.008224.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-23T23-46-56.008224.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-23T23-46-56.008224.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-23T23-46-56.008224.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-02-23T23-46-56.008224.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-23T23-46-56.008224.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-02-23T23-46-56.008224.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-23T23-46-56.008224.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2024_02_23T23_46_56.008224
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-23T23-46-56.008224.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-23T23-46-56.008224.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2024_02_23T23_46_56.008224
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-23T23-46-56.008224.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-23T23-46-56.008224.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2024_02_23T23_46_56.008224
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-23T23-46-56.008224.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-23T23-46-56.008224.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2024_02_23T23_46_56.008224
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-23T23-46-56.008224.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-23T23-46-56.008224.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2024_02_23T23_46_56.008224
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-23T23-46-56.008224.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-23T23-46-56.008224.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2024_02_23T23_46_56.008224
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-23T23-46-56.008224.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-23T23-46-56.008224.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2024_02_23T23_46_56.008224
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-23T23-46-56.008224.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-23T23-46-56.008224.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2024_02_23T23_46_56.008224
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-23T23-46-56.008224.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-23T23-46-56.008224.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2024_02_23T23_46_56.008224
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-23T23-46-56.008224.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-23T23-46-56.008224.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2024_02_23T23_46_56.008224
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-23T23-46-56.008224.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-23T23-46-56.008224.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2024_02_23T23_46_56.008224
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-23T23-46-56.008224.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-23T23-46-56.008224.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2024_02_23T23_46_56.008224
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-23T23-46-56.008224.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-23T23-46-56.008224.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2024_02_23T23_46_56.008224
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-23T23-46-56.008224.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-23T23-46-56.008224.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2024_02_23T23_46_56.008224
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-23T23-46-56.008224.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-23T23-46-56.008224.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2024_02_23T23_46_56.008224
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-23T23-46-56.008224.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-23T23-46-56.008224.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2024_02_23T23_46_56.008224
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-23T23-46-56.008224.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-23T23-46-56.008224.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2024_02_23T23_46_56.008224
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-23T23-46-56.008224.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-23T23-46-56.008224.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2024_02_23T23_46_56.008224
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-23T23-46-56.008224.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-23T23-46-56.008224.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2024_02_23T23_46_56.008224
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-23T23-46-56.008224.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-23T23-46-56.008224.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2024_02_23T23_46_56.008224
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-23T23-46-56.008224.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-23T23-46-56.008224.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2024_02_23T23_46_56.008224
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-23T23-46-56.008224.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-23T23-46-56.008224.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2024_02_23T23_46_56.008224
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-23T23-46-56.008224.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-23T23-46-56.008224.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2024_02_23T23_46_56.008224
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-23T23-46-56.008224.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-23T23-46-56.008224.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2024_02_23T23_46_56.008224
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-23T23-46-56.008224.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-23T23-46-56.008224.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2024_02_23T23_46_56.008224
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-23T23-46-56.008224.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-23T23-46-56.008224.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2024_02_23T23_46_56.008224
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-23T23-46-56.008224.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-23T23-46-56.008224.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2024_02_23T23_46_56.008224
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-23T23-46-56.008224.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-23T23-46-56.008224.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2024_02_23T23_46_56.008224
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-23T23-46-56.008224.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-23T23-46-56.008224.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2024_02_23T23_46_56.008224
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-23T23-46-56.008224.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-23T23-46-56.008224.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2024_02_23T23_46_56.008224
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-23T23-46-56.008224.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-23T23-46-56.008224.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2024_02_23T23_46_56.008224
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-23T23-46-56.008224.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-23T23-46-56.008224.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2024_02_23T23_46_56.008224
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-23T23-46-56.008224.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-23T23-46-56.008224.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2024_02_23T23_46_56.008224
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-23T23-46-56.008224.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-23T23-46-56.008224.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2024_02_23T23_46_56.008224
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-23T23-46-56.008224.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-23T23-46-56.008224.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2024_02_23T23_46_56.008224
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-02-23T23-46-56.008224.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-02-23T23-46-56.008224.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2024_02_23T23_46_56.008224
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-23T23-46-56.008224.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-23T23-46-56.008224.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2024_02_23T23_46_56.008224
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-23T23-46-56.008224.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-23T23-46-56.008224.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2024_02_23T23_46_56.008224
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-23T23-46-56.008224.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-23T23-46-56.008224.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2024_02_23T23_46_56.008224
path:
- '**/details_harness|hendrycksTest-management|5_2024-02-23T23-46-56.008224.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2024-02-23T23-46-56.008224.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2024_02_23T23_46_56.008224
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-02-23T23-46-56.008224.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-02-23T23-46-56.008224.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2024_02_23T23_46_56.008224
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-23T23-46-56.008224.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-23T23-46-56.008224.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2024_02_23T23_46_56.008224
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-23T23-46-56.008224.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-23T23-46-56.008224.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2024_02_23T23_46_56.008224
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-23T23-46-56.008224.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-23T23-46-56.008224.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2024_02_23T23_46_56.008224
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-23T23-46-56.008224.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-23T23-46-56.008224.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2024_02_23T23_46_56.008224
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-23T23-46-56.008224.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-23T23-46-56.008224.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2024_02_23T23_46_56.008224
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-23T23-46-56.008224.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-23T23-46-56.008224.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2024_02_23T23_46_56.008224
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-23T23-46-56.008224.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-23T23-46-56.008224.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2024_02_23T23_46_56.008224
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-23T23-46-56.008224.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-23T23-46-56.008224.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2024_02_23T23_46_56.008224
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-23T23-46-56.008224.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-23T23-46-56.008224.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2024_02_23T23_46_56.008224
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-23T23-46-56.008224.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-23T23-46-56.008224.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2024_02_23T23_46_56.008224
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-23T23-46-56.008224.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-23T23-46-56.008224.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2024_02_23T23_46_56.008224
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-23T23-46-56.008224.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-23T23-46-56.008224.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2024_02_23T23_46_56.008224
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-23T23-46-56.008224.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-23T23-46-56.008224.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2024_02_23T23_46_56.008224
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-02-23T23-46-56.008224.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-02-23T23-46-56.008224.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2024_02_23T23_46_56.008224
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-23T23-46-56.008224.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-23T23-46-56.008224.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2024_02_23T23_46_56.008224
path:
- '**/details_harness|hendrycksTest-virology|5_2024-02-23T23-46-56.008224.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2024-02-23T23-46-56.008224.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2024_02_23T23_46_56.008224
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-23T23-46-56.008224.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-23T23-46-56.008224.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2024_02_23T23_46_56.008224
path:
- '**/details_harness|truthfulqa:mc|0_2024-02-23T23-46-56.008224.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2024-02-23T23-46-56.008224.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2024_02_23T23_46_56.008224
path:
- '**/details_harness|winogrande|5_2024-02-23T23-46-56.008224.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2024-02-23T23-46-56.008224.parquet'
- config_name: results
data_files:
- split: 2024_02_23T23_46_56.008224
path:
- results_2024-02-23T23-46-56.008224.parquet
- split: latest
path:
- results_2024-02-23T23-46-56.008224.parquet
---
# Dataset Card for Evaluation run of Eric111/openchat-3.5-0106-128k-DPO
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [Eric111/openchat-3.5-0106-128k-DPO](https://huggingface.co/Eric111/openchat-3.5-0106-128k-DPO) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_Eric111__openchat-3.5-0106-128k-DPO",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-02-23T23:46:56.008224](https://huggingface.co/datasets/open-llm-leaderboard/details_Eric111__openchat-3.5-0106-128k-DPO/blob/main/results_2024-02-23T23-46-56.008224.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
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}
}
```
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
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### Dataset Sources [optional]
<!-- Provide the basic links for the dataset. -->
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## Uses
<!-- Address questions around how the dataset is intended to be used. -->
### Direct Use
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[More Information Needed]
### Out-of-Scope Use
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## 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. -->
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## Dataset Creation
### Curation Rationale
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### Source Data
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#### Data Collection and Processing
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#### Who are the source data producers?
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### Annotations [optional]
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#### Annotation process
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#### Who are the annotators?
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#### Personal and Sensitive Information
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## Bias, Risks, and Limitations
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## Citation [optional]
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## Glossary [optional]
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## Dataset Card Contact
[More Information Needed] |
Mitsuki-Sakamoto/alpaca_farm-deberta-re-pref-64-fil_self_160m_bo16_2_mix_50_kl_0.1_prm_160m_thr_0.0_seed_2_t_1.0 | ---
dataset_info:
config_name: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1
features:
- name: instruction
dtype: string
- name: input
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struct:
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splits:
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download_size: 3927729985
dataset_size: 1332429102
configs:
- config_name: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1
data_files:
- split: epoch_0
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_0-*
- split: epoch_1
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_1-*
- split: epoch_2
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_2-*
- split: epoch_3
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_3-*
- split: epoch_4
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_4-*
- split: epoch_5
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_5-*
- split: epoch_6
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_6-*
- split: epoch_7
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_7-*
- split: epoch_8
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_8-*
- split: epoch_9
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_9-*
- split: epoch_10
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_10-*
- split: epoch_11
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_11-*
- split: epoch_12
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_12-*
- split: epoch_13
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_13-*
- split: epoch_14
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_14-*
- split: epoch_15
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_15-*
- split: epoch_16
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_16-*
- split: epoch_17
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_17-*
- split: epoch_18
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_18-*
- split: epoch_19
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_19-*
- split: epoch_20
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_20-*
- split: epoch_21
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_21-*
- split: epoch_22
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_22-*
- split: epoch_23
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_23-*
- split: epoch_24
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_24-*
- split: epoch_25
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_25-*
- split: epoch_26
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_26-*
- split: epoch_27
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_27-*
- split: epoch_28
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_28-*
- split: epoch_29
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_29-*
---
|
Nexdata/10142_Videos_Fall_Behavior_Data | ---
license: cc-by-nc-nd-4.0
---
## Description
10,142 Videos-Fall Behavior Data. The data includes indoor scenes and outdoor scenes. The data covers multiple scenes, multiple shooting angles, multiple collecting time, multiple resolution. The data can be used for tasks such as fall behavior detection, fall behavior recognition, etc..
For more details, please refer to the link: https://www.nexdata.ai/dataset/1244?source=Huggingface
## Data size
10,142 videos
## Collecting environment
including indoor and outdoor scenes
## Data diversity
multiple scenes, multiple shooting angles, multiple collecting time, multiple resolution
## Device
including surveillance cameras, cellphones
## Collecting angle
looking down angle, eye-level angle
## Collecting time
day, night
## Weather distribution
sunny, cloudy
## Data format
the video data format is .mp4, .mkv, .avi
## Accuracy
According to the collecting requirement, the accuracy is more than 97%; The accuracy of label naming is more than 97%
# Licensing Information
Commercial License
|
Anand8078/esg_collection_3 | ---
dataset_info:
features:
- name: text
dtype: string
- name: label
dtype: int64
splits:
- name: train
num_bytes: 319
num_examples: 3
download_size: 1937
dataset_size: 319
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
bryanmontes/gokuia | ---
license: unknown
---
|
mask-distilled-one-sec-cv12/chunk_203 | ---
dataset_info:
features:
- name: logits
sequence: float32
- name: mfcc
sequence:
sequence: float64
splits:
- name: train
num_bytes: 1209477300
num_examples: 237525
download_size: 1234358644
dataset_size: 1209477300
---
# Dataset Card for "chunk_203"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
karmiq/wikipedia-embeddings-cs-chunks | ---
dataset_info:
features:
- name: id
dtype: string
- name: url
dtype: string
- name: title
dtype: string
- name: chunks
sequence: string
splits:
- name: train
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num_examples: 534044
download_size: 973879557
dataset_size: 1576255061
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
dipudl/research-paper-tokenized-dataset | ---
dataset_info:
features:
- name: labels
sequence: int64
- name: input_ids
sequence: int32
- name: attention_mask
sequence: int8
splits:
- name: train
num_bytes: 1087419951
num_examples: 861228
download_size: 376657164
dataset_size: 1087419951
---
# Dataset Card for "research-paper-tokenized-dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
nazneen/self-instruct-seed | ---
license: apache-2.0
task_categories:
- conversational
language:
- en
size_categories:
- n<1K
---
Manually created seed dataset used in bootstrapping in the Self-instruct paper https://arxiv.org/abs/2212.10560. This is part of the instruction fine-tuning datasets. |
BangumiBase/goldenkamuy | ---
license: mit
tags:
- art
size_categories:
- 1K<n<10K
---
# Bangumi Image Base of Golden Kamuy
This is the image base of bangumi Golden Kamuy, we detected 44 characters, 8914 images in total. The full dataset is [here](all.zip).
**Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual.** If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
| # | Images | Download | Preview 1 | Preview 2 | Preview 3 | Preview 4 | Preview 5 | Preview 6 | Preview 7 | Preview 8 |
|:------|---------:|:---------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|
| 0 | 2560 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 737 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 50 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 1259 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 95 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 250 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 227 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 379 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 178 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 243 | [Download](9/dataset.zip) |  |  |  |  |  |  |  |  |
| 10 | 39 | [Download](10/dataset.zip) |  |  |  |  |  |  |  |  |
| 11 | 69 | [Download](11/dataset.zip) |  |  |  |  |  |  |  |  |
| 12 | 110 | [Download](12/dataset.zip) |  |  |  |  |  |  |  |  |
| 13 | 63 | [Download](13/dataset.zip) |  |  |  |  |  |  |  |  |
| 14 | 219 | [Download](14/dataset.zip) |  |  |  |  |  |  |  |  |
| 15 | 24 | [Download](15/dataset.zip) |  |  |  |  |  |  |  |  |
| 16 | 36 | [Download](16/dataset.zip) |  |  |  |  |  |  |  |  |
| 17 | 1180 | [Download](17/dataset.zip) |  |  |  |  |  |  |  |  |
| 18 | 54 | [Download](18/dataset.zip) |  |  |  |  |  |  |  |  |
| 19 | 45 | [Download](19/dataset.zip) |  |  |  |  |  |  |  |  |
| 20 | 185 | [Download](20/dataset.zip) |  |  |  |  |  |  |  |  |
| 21 | 151 | [Download](21/dataset.zip) |  |  |  |  |  |  |  |  |
| 22 | 27 | [Download](22/dataset.zip) |  |  |  |  |  |  |  |  |
| 23 | 31 | [Download](23/dataset.zip) |  |  |  |  |  |  |  |  |
| 24 | 16 | [Download](24/dataset.zip) |  |  |  |  |  |  |  |  |
| 25 | 42 | [Download](25/dataset.zip) |  |  |  |  |  |  |  |  |
| 26 | 42 | [Download](26/dataset.zip) |  |  |  |  |  |  |  |  |
| 27 | 55 | [Download](27/dataset.zip) |  |  |  |  |  |  |  |  |
| 28 | 14 | [Download](28/dataset.zip) |  |  |  |  |  |  |  |  |
| 29 | 58 | [Download](29/dataset.zip) |  |  |  |  |  |  |  |  |
| 30 | 33 | [Download](30/dataset.zip) |  |  |  |  |  |  |  |  |
| 31 | 24 | [Download](31/dataset.zip) |  |  |  |  |  |  |  |  |
| 32 | 53 | [Download](32/dataset.zip) |  |  |  |  |  |  |  |  |
| 33 | 49 | [Download](33/dataset.zip) |  |  |  |  |  |  |  |  |
| 34 | 11 | [Download](34/dataset.zip) |  |  |  |  |  |  |  |  |
| 35 | 15 | [Download](35/dataset.zip) |  |  |  |  |  |  |  |  |
| 36 | 49 | [Download](36/dataset.zip) |  |  |  |  |  |  |  |  |
| 37 | 38 | [Download](37/dataset.zip) |  |  |  |  |  |  |  |  |
| 38 | 15 | [Download](38/dataset.zip) |  |  |  |  |  |  |  |  |
| 39 | 19 | [Download](39/dataset.zip) |  |  |  |  |  |  |  |  |
| 40 | 53 | [Download](40/dataset.zip) |  |  |  |  |  |  |  |  |
| 41 | 10 | [Download](41/dataset.zip) |  |  |  |  |  |  |  |  |
| 42 | 24 | [Download](42/dataset.zip) |  |  |  |  |  |  |  |  |
| noise | 83 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
|
mlinmg/test | ---
dataset_info:
features:
- name: system
dtype: string
- name: instruction
dtype: string
- name: response
dtype: string
splits:
- name: train
num_bytes: 22888
num_examples: 5
download_size: 42237
dataset_size: 22888
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
mk9165/ko-voicefishing-classification | ---
dataset_info:
features:
- name: text
dtype: string
- name: label
dtype: int64
splits:
- name: train
num_bytes: 2626452
num_examples: 1012
download_size: 1386022
dataset_size: 2626452
---
# Dataset Card for "ko-voicefishing-classification"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
whitefox123/tashkeel_tsaongaf | ---
license: mit
---
|
superb | ---
annotations_creators:
- other
language_creators:
- other
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- unknown
source_datasets:
- original
- extended|librispeech_asr
- extended|other-librimix
- extended|other-speech_commands
task_categories:
- automatic-speech-recognition
- audio-classification
task_ids:
- keyword-spotting
- speaker-identification
- audio-intent-classification
- audio-emotion-recognition
pretty_name: SUPERB
tags:
- query-by-example-spoken-term-detection
- audio-slot-filling
- speaker-diarization
- automatic-speaker-verification
dataset_info:
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dtype: string
- name: audio
dtype:
audio:
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'2': up
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'9': newspaper
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splits:
- name: train
num_bytes: 12729268
num_examples: 138361
- name: validation
num_bytes: 635172
num_examples: 6904
- name: test
num_bytes: 759096
num_examples: 8251
download_size: 0
dataset_size: 14123536
---
# Dataset Card for SUPERB
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [http://superbbenchmark.org](http://superbbenchmark.org)
- **Repository:** [https://github.com/s3prl/s3prl](https://github.com/s3prl/s3prl)
- **Paper:** [SUPERB: Speech processing Universal PERformance Benchmark](https://arxiv.org/abs/2105.01051)
- **Leaderboard:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Point of Contact:** [Lewis Tunstall](mailto:lewis@huggingface.co) and [Albert Villanova](mailto:albert@huggingface.co)
### Dataset Summary
SUPERB is a leaderboard to benchmark the performance of a shared model across a wide range of speech processing tasks with minimal architecture changes and labeled data.
### Supported Tasks and Leaderboards
The SUPERB leaderboard can be found here https://superbbenchmark.org/leaderboard and consists of the following tasks:
#### pr
Phoneme Recognition (PR) transcribes an utterance into the smallest content units. This task includes alignment modeling to avoid potentially inaccurate forced alignment. [LibriSpeech](https://huggingface.co/datasets/librispeech_asr) train-clean-100/dev-clean/test-clean subsets are adopted in SUPERB for training/validation/testing. Phoneme transcriptions are obtained from the LibriSpeech official g2p-model-5 and the conversion script in Kaldi librispeech s5 recipe. The evaluation metric is phone error rate (PER).
#### asr
Automatic Speech Recognition (ASR) transcribes utterances into words. While PR analyzes the improvement in modeling phonetics, ASR reflects the significance of the improvement in a real-world scenario. [LibriSpeech](https://huggingface.co/datasets/librispeech_asr) train-clean-100/devclean/test-clean subsets are used for training/validation/testing. The evaluation metric is word error rate (WER).
#### ks
Keyword Spotting (KS) detects preregistered keywords by classifying utterances into a predefined set of words. The task is usually performed on-device for the fast response time. Thus, accuracy, model size, and inference time are all crucial. SUPERB uses the widely used [Speech Commands dataset v1.0](https://www.tensorflow.org/datasets/catalog/speech_commands) for the task. The dataset consists of ten classes of keywords, a class for silence, and an unknown class to include the false positive. The evaluation metric is accuracy (ACC)
##### Example of usage:
Use these auxillary functions to:
- load the audio file into an audio data array
- sample from long `_silence_` audio clips
For other examples of handling long `_silence_` clips see the [S3PRL](https://github.com/s3prl/s3prl/blob/099ce807a6ffa6bf2482ceecfcaf83dea23da355/s3prl/downstream/speech_commands/dataset.py#L80)
or [TFDS](https://github.com/tensorflow/datasets/blob/6b8cfdb7c3c0a04e731caaa8660ce948d0a67b1e/tensorflow_datasets/audio/speech_commands.py#L143) implementations.
```python
def map_to_array(example):
import soundfile as sf
speech_array, sample_rate = sf.read(example["file"])
example["speech"] = speech_array
example["sample_rate"] = sample_rate
return example
def sample_noise(example):
# Use this function to extract random 1 sec slices of each _silence_ utterance,
# e.g. inside `torch.utils.data.Dataset.__getitem__()`
from random import randint
if example["label"] == "_silence_":
random_offset = randint(0, len(example["speech"]) - example["sample_rate"] - 1)
example["speech"] = example["speech"][random_offset : random_offset + example["sample_rate"]]
return example
```
#### qbe
Query by Example Spoken Term Detection (QbE) detects a spoken term (query) in an audio database (documents) by binary discriminating a given pair of query and document into a match or not. The English subset in [QUESST 2014 challenge](https://github.com/s3prl/s3prl/tree/master/downstream#qbe-query-by-example-spoken-term-detection) is adopted since we focus on investigating English as the first step. The evaluation metric is maximum term weighted value (MTWV) which balances misses and false alarms.
#### ic
Intent Classification (IC) classifies utterances into predefined classes to determine the intent of speakers. SUPERB uses the [Fluent Speech Commands dataset](https://github.com/s3prl/s3prl/tree/master/downstream#ic-intent-classification---fluent-speech-commands), where each utterance is tagged with three intent labels: action, object, and location. The evaluation metric is accuracy (ACC).
#### sf
Slot Filling (SF) predicts a sequence of semantic slot-types from an utterance, like a slot-type FromLocation for a spoken word Taipei, which is known as a slot-value. Both slot-types and slot-values are essential for an SLU system to function. The evaluation metrics thus include slot-type F1 score and slotvalue CER. [Audio SNIPS](https://github.com/s3prl/s3prl/tree/master/downstream#sf-end-to-end-slot-filling) is adopted, which synthesized multi-speaker utterances for SNIPS. Following the standard split in SNIPS, US-accent speakers are further selected for training, and others are for validation/testing.
#### si
Speaker Identification (SI) classifies each utterance for its speaker identity as a multi-class classification, where speakers are in the same predefined set for both training and testing. The widely used [VoxCeleb1 dataset](https://www.robots.ox.ac.uk/~vgg/data/voxceleb/vox1.html) is adopted, and the evaluation metric is accuracy (ACC).
#### asv
Automatic Speaker Verification (ASV) verifies whether the speakers of a pair of utterances match as a binary classification, and speakers in the testing set may not appear in the training set. Thus, ASV is more challenging than SID. VoxCeleb1 is used without VoxCeleb2 training data and noise augmentation. The evaluation metric is equal error rate (EER).
#### sd
Speaker Diarization (SD) predicts *who is speaking when* for each timestamp, and multiple speakers can speak simultaneously. The model has to encode rich speaker characteristics for each frame and should be able to represent mixtures of signals. [LibriMix](https://github.com/s3prl/s3prl/tree/master/downstream#sd-speaker-diarization) is adopted where LibriSpeech train-clean-100/dev-clean/test-clean are used to generate mixtures for training/validation/testing. We focus on the two-speaker scenario as the first step. The time-coded speaker labels were generated using alignments from Kaldi LibriSpeech ASR model. The evaluation metric is diarization error rate (DER).
##### Example of usage
Use these auxiliary functions to:
- load the audio file into an audio data array
- generate the label array
```python
def load_audio_file(example, frame_shift=160):
import soundfile as sf
example["array"], example["sample_rate"] = sf.read(
example["file"], start=example["start"] * frame_shift, stop=example["end"] * frame_shift
)
return example
def generate_label(example, frame_shift=160, num_speakers=2, rate=16000):
import numpy as np
start = example["start"]
end = example["end"]
frame_num = end - start
speakers = sorted({speaker["speaker_id"] for speaker in example["speakers"]})
label = np.zeros((frame_num, num_speakers), dtype=np.int32)
for speaker in example["speakers"]:
speaker_index = speakers.index(speaker["speaker_id"])
start_frame = np.rint(speaker["start"] * rate / frame_shift).astype(int)
end_frame = np.rint(speaker["end"] * rate / frame_shift).astype(int)
rel_start = rel_end = None
if start <= start_frame < end:
rel_start = start_frame - start
if start < end_frame <= end:
rel_end = end_frame - start
if rel_start is not None or rel_end is not None:
label[rel_start:rel_end, speaker_index] = 1
example["label"] = label
return example
```
#### er
Emotion Recognition (ER) predicts an emotion class for each utterance. The most widely used ER dataset [IEMOCAP](https://github.com/s3prl/s3prl/tree/master/downstream#er-emotion-recognition) is adopted, and we follow the conventional evaluation protocol: we drop the unbalance emotion classes to leave the final four classes with a similar amount of data points and cross-validates on five folds of the standard splits. The evaluation metric is accuracy (ACC).
### Languages
The language data in SUPERB is in English (BCP-47 `en`)
## Dataset Structure
### Data Instances
#### pr
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### asr
An example from each split looks like:
```python
{'chapter_id': 1240,
'file': 'path/to/file.flac',
'audio': {'path': 'path/to/file.flac',
'array': array([-0.00048828, -0.00018311, -0.00137329, ..., 0.00079346, 0.00091553, 0.00085449], dtype=float32),
'sampling_rate': 16000},
'id': '103-1240-0000',
'speaker_id': 103,
'text': 'CHAPTER ONE MISSUS RACHEL LYNDE IS SURPRISED MISSUS RACHEL LYNDE '
'LIVED JUST WHERE THE AVONLEA MAIN ROAD DIPPED DOWN INTO A LITTLE '
'HOLLOW FRINGED WITH ALDERS AND LADIES EARDROPS AND TRAVERSED BY A '
'BROOK'}
```
#### ks
An example from each split looks like:
```python
{
'file': '/path/yes/af7a8296_nohash_1.wav',
'audio': {'path': '/path/yes/af7a8296_nohash_1.wav',
'array': array([-0.00048828, -0.00018311, -0.00137329, ..., 0.00079346, 0.00091553, 0.00085449], dtype=float32),
'sampling_rate': 16000},
'label': 0 # 'yes'
}
```
#### qbe
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### ic
```python
{
'file': "/path/wavs/speakers/2BqVo8kVB2Skwgyb/063aa8f0-4479-11e9-a9a5-5dbec3b8816a.wav",
'audio': {'path': '/path/wavs/speakers/2BqVo8kVB2Skwgyb/063aa8f0-4479-11e9-a9a5-5dbec3b8816a.wav',
'array': array([-0.00048828, -0.00018311, -0.00137329, ..., 0.00079346, 0.00091553, 0.00085449], dtype=float32),
'sampling_rate': 16000},
'speaker_id': '2BqVo8kVB2Skwgyb',
'text': 'Turn the bedroom lights off',
'action': 3, # 'deactivate'
'object': 7, # 'lights'
'location': 0 # 'bedroom'
}
```
#### sf
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### si
```python
{
'file': '/path/wav/id10003/na8-QEFmj44/00003.wav',
'audio': {'path': '/path/wav/id10003/na8-QEFmj44/00003.wav',
'array': array([-0.00048828, -0.00018311, -0.00137329, ..., 0.00079346, 0.00091553, 0.00085449], dtype=float32),
'sampling_rate': 16000},
'label': 2 # 'id10003'
}
```
#### asv
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### sd
An example from each split looks like:
```python
{
'record_id': '1578-6379-0038_6415-111615-0009',
'file': 'path/to/file.wav',
'audio': {'path': 'path/to/file.wav',
'array': array([-0.00048828, -0.00018311, -0.00137329, ..., 0.00079346, 0.00091553, 0.00085449], dtype=float32),
'sampling_rate': 16000},
'start': 0,
'end': 1590,
'speakers': [
{'speaker_id': '1578', 'start': 28, 'end': 657},
{'speaker_id': '6415', 'start': 28, 'end': 1576}
]
}
```
#### er
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Data Fields
####Note abouth the `audio` fields
When accessing the audio column: `dataset[0]["audio"]` the audio file is automatically decoded and resampled to `dataset.features["audio"].sampling_rate`. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the `"audio"` column, *i.e.* `dataset[0]["audio"]` should **always** be preferred over `dataset["audio"][0]`.
#### pr
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### asr
- `file` (`string`): Path to the WAV audio file.
- `audio` (`dict`): A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate.
- `text` (`string`): The transcription of the audio file.
- `speaker_id` (`integer`): A unique ID of the speaker. The same speaker id can be found for multiple data samples.
- `chapter_id` (`integer`): ID of the audiobook chapter which includes the transcription.
- `id` (`string`): A unique ID of the data sample.
#### ks
- `file` (`string`): Path to the WAV audio file.
- `audio` (`dict`): A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate.
- `label` (`ClassLabel`): Label of the spoken command. Possible values:
- `0: "yes", 1: "no", 2: "up", 3: "down", 4: "left", 5: "right", 6: "on", 7: "off", 8: "stop", 9: "go", 10: "_silence_", 11: "_unknown_"`
#### qbe
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### ic
- `file` (`string`): Path to the WAV audio file.
- `audio` (`dict`): A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate.
- `speaker_id` (`string`): ID of the speaker.
- `text` (`string`): Transcription of the spoken command.
- `action` (`ClassLabel`): Label of the command's action. Possible values:
- `0: "activate", 1: "bring", 2: "change language", 3: "deactivate", 4: "decrease", 5: "increase"`
- `object` (`ClassLabel`): Label of the command's object. Possible values:
- `0: "Chinese", 1: "English", 2: "German", 3: "Korean", 4: "heat", 5: "juice", 6: "lamp", 7: "lights", 8: "music", 9: "newspaper", 10: "none", 11: "shoes", 12: "socks", 13: "volume"`
- `location` (`ClassLabel`): Label of the command's location. Possible values:
- `0: "bedroom", 1: "kitchen", 2: "none", 3: "washroom"`
#### sf
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### si
- `file` (`string`): Path to the WAV audio file.
- `audio` (`dict`): A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate.
- `label` (`ClassLabel`): Label (ID) of the speaker. Possible values:
- `0: "id10001", 1: "id10002", 2: "id10003", ..., 1250: "id11251"`
#### asv
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### sd
The data fields in all splits are:
- `record_id` (`string`): ID of the record.
- `file` (`string`): Path to the WAV audio file.
- `audio` (`dict`): A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate.
- `start` (`integer`): Start frame of the audio.
- `end` (`integer`): End frame of the audio.
- `speakers` (`list` of `dict`): List of speakers in the audio. Each item contains the fields:
- `speaker_id` (`string`): ID of the speaker.
- `start` (`integer`): Frame when the speaker starts speaking.
- `end` (`integer`): Frame when the speaker stops speaking.
#### er
- `file` (`string`): Path to the WAV audio file.
- `audio` (`dict`): A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate.
- `label` (`ClassLabel`): Label of the speech emotion. Possible values:
- `0: "neu", 1: "hap", 2: "ang", 3: "sad"`
### Data Splits
#### pr
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### asr
| | train | validation | test |
|-----|------:|-----------:|-----:|
| asr | 28539 | 2703 | 2620 |
#### ks
| | train | validation | test |
|----|------:|-----------:|-----:|
| ks | 51094 | 6798 | 3081 |
#### qbe
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### ic
| | train | validation | test |
|----|------:|-----------:|-----:|
| ic | 23132 | 3118 | 3793 |
#### sf
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### si
| | train | validation | test |
|----|-------:|-----------:|-----:|
| si | 138361 | 6904 | 8251 |
#### asv
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### sd
The data is split into "train", "dev" and "test" sets, each containing the following number of examples:
| | train | dev | test |
|----|------:|-----:|-----:|
| sd | 13901 | 3014 | 3002 |
#### er
The data is split into 5 sets intended for 5-fold cross-validation:
| | session1 | session2 | session3 | session4 | session5 |
|----|---------:|---------:|---------:|---------:|---------:|
| er | 1085 | 1023 | 1151 | 1031 | 1241 |
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
The dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in this dataset.
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
Dataset provided for research purposes only. Please check dataset license for additional information.
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
#### pr and asr
The license for Librispeech is the Creative Commons Attribution 4.0 International license ((CC-BY-4.0)[https://creativecommons.org/licenses/by/4.0/]).
#### ks
The license for Speech Commands is [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/legalcode)
#### qbe
The license for QUESST 2014 is not known.
#### ic
The license for Fluent Speech Commands dataset is the [Fluent Speech Commands Public License](https://fluent.ai/wp-content/uploads/2021/04/Fluent_Speech_Commands_Public_License.pdf)
#### sf
The license for Audio SNIPS dataset is not known.
#### si and asv
The license for VoxCeleb1 dataset is the Creative Commons Attribution 4.0 International license ([CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/)).
#### sd
LibriMix is based on the LibriSpeech (see above) and Wham! noises datasets. The Wham! noises dataset is distributed under the Attribution-NonCommercial 4.0 International ([CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/)) license.
#### er
The IEMOCAP license is ditributed under [its own license](https://sail.usc.edu/iemocap/Data_Release_Form_IEMOCAP.pdf).
### Citation Information
```
@article{DBLP:journals/corr/abs-2105-01051,
author = {Shu{-}Wen Yang and
Po{-}Han Chi and
Yung{-}Sung Chuang and
Cheng{-}I Jeff Lai and
Kushal Lakhotia and
Yist Y. Lin and
Andy T. Liu and
Jiatong Shi and
Xuankai Chang and
Guan{-}Ting Lin and
Tzu{-}Hsien Huang and
Wei{-}Cheng Tseng and
Ko{-}tik Lee and
Da{-}Rong Liu and
Zili Huang and
Shuyan Dong and
Shang{-}Wen Li and
Shinji Watanabe and
Abdelrahman Mohamed and
Hung{-}yi Lee},
title = {{SUPERB:} Speech processing Universal PERformance Benchmark},
journal = {CoRR},
volume = {abs/2105.01051},
year = {2021},
url = {https://arxiv.org/abs/2105.01051},
archivePrefix = {arXiv},
eprint = {2105.01051},
timestamp = {Thu, 01 Jul 2021 13:30:22 +0200},
biburl = {https://dblp.org/rec/journals/corr/abs-2105-01051.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
Note that each SUPERB dataset has its own citation. Please see the source to see
the correct citation for each contained dataset.
```
### Contributions
Thanks to [@lewtun](https://github.com/lewtun), [@albertvillanova](https://github.com/albertvillanova) and [@anton-l](https://github.com/anton-l) for adding this dataset. |
LanguageBind/VIDAL-Depth-Thermal | ---
license: mit
---
<p align="center">
<img src="https://s11.ax1x.com/2024/02/01/pFMDAm9.png" width="250" style="margin-bottom: 0.2;"/>
<p>
<h2 align="center"> <a href="https://arxiv.org/pdf/2310.01852.pdf">【ICLR 2024 🔥】LanguageBind: Extending Video-Language Pretraining to N-modality by Language-based Semantic Alignment</a></h2>
<h5 align="center"> If you like our project, please give us a star ⭐ on GitHub for latest update. </h2>
## 📰 News
* **[2024.01.27]** 👀👀👀 Our [MoE-LLaVA](https://github.com/PKU-YuanGroup/MoE-LLaVA) is released! A sparse model with 3B parameters outperformed the dense model with 7B parameters.
* **[2024.01.16]** 🔥🔥🔥 Our LanguageBind has been accepted at ICLR 2024! We earn the score of 6(3)8(6)6(6)6(6) [here](https://openreview.net/forum?id=QmZKc7UZCy¬eId=OgsxQxAleA).
* **[2023.12.15]** 💪💪💪 We expand the 💥💥💥 VIDAL dataset and now have **10M video-text data**. We launch **LanguageBind_Video 1.5**, checking our [model zoo](#-model-zoo).
* **[2023.12.10]** We expand the 💥💥💥 VIDAL dataset and now have **10M depth and 10M thermal data**. We are in the process of uploading thermal and depth data on [Hugging Face](https://huggingface.co/datasets/LanguageBind/VIDAL-Depth-Thermal) and expect the whole process to last 1-2 months.
* **[2023.11.27]** 🔥🔥🔥 We have updated our [paper](https://arxiv.org/abs/2310.01852) with emergency zero-shot results., checking our ✨ [results](#emergency-results).
* **[2023.11.26]** 💥💥💥 We have open-sourced all textual sources and corresponding YouTube IDs [here](DATASETS.md).
* **[2023.11.26]** 📣📣📣 We have open-sourced fully fine-tuned **Video & Audio**, achieving improved performance once again, checking our [model zoo](#-model-zoo).
* **[2023.11.22]** We are about to release a fully fine-tuned version, and the **HUGE** version is currently undergoing training.
* **[2023.11.21]** 💥 We are releasing sample data in [DATASETS.md](DATASETS.md) so that individuals who are interested can further modify the code to train it on their own data.
* **[2023.11.20]** 🚀🚀🚀 [Video-LLaVA](https://github.com/PKU-YuanGroup/Video-LLaVA) builds a large visual-language model to achieve 🎉SOTA performances based on LanguageBind encoders.
* **[2023.10.23]** 🎶 LanguageBind-Audio achieves 🎉🎉🎉**state-of-the-art (SOTA) performance on 5 datasets**, checking our ✨ [results](#multiple-modalities)!
* **[2023.10.14]** 😱 Released a stronger LanguageBind-Video, checking our ✨ [results](#video-language)! The video checkpoint **have updated** on Huggingface Model Hub!
* **[2023.10.10]** We provide sample data, which can be found in [assets](assets), and [emergency zero-shot usage](#emergency-zero-shot) is described.
* **[2023.10.07]** The checkpoints are available on 🤗 [Huggingface Model](https://huggingface.co/LanguageBind).
* **[2023.10.04]** Code and [demo](https://huggingface.co/spaces/LanguageBind/LanguageBind) are available now! Welcome to **watch** 👀 this repository for the latest updates.
## 😮 Highlights
### 💡 High performance, but NO intermediate modality required
LanguageBind is a **language-centric** multimodal pretraining approach, **taking the language as the bind across different modalities** because the language modality is well-explored and contains rich semantics.
* The following first figure shows the architecture of LanguageBind. LanguageBind can be easily extended to segmentation, detection tasks, and potentially to unlimited modalities.
### ⚡️ A multimodal, fully aligned and voluminous dataset
We propose **VIDAL-10M**, **10 Million data** with **V**ideo, **I**nfrared, **D**epth, **A**udio and their corresponding **L**anguage, which greatly expands the data beyond visual modalities.
* The second figure shows our proposed VIDAL-10M dataset, which includes five modalities: video, infrared, depth, audio, and language.
### 🔥 Multi-view enhanced description for training
We make multi-view enhancements to language. We produce multi-view description that combines **meta-data**, **spatial**, and **temporal** to greatly enhance the semantic information of the language. In addition we further **enhance the language with ChatGPT** to create a good semantic space for each modality aligned language.
## 🤗 Demo
* **Local demo.** Highly recommend trying out our web demo, which incorporates all features currently supported by LanguageBind.
```bash
python gradio_app.py
```
* **Online demo.** We provide the [online demo](https://huggingface.co/spaces/LanguageBind/LanguageBind) in Huggingface Spaces. In this demo, you can calculate the similarity of modalities to language, such as audio-to-language, video-to-language, and depth-to-image.
## 🛠️ Requirements and Installation
* Python >= 3.8
* Pytorch >= 1.13.1
* CUDA Version >= 11.6
* Install required packages:
```bash
git clone https://github.com/PKU-YuanGroup/LanguageBind
cd LanguageBind
pip install torch==1.13.1+cu116 torchvision==0.14.1+cu116 torchaudio==0.13.1 --extra-index-url https://download.pytorch.org/whl/cu116
pip install -r requirements.txt
```
## 🐳 Model Zoo
The names in the table represent different encoder models. For example, `LanguageBind/LanguageBind_Video_FT` represents the fully fine-tuned version, while `LanguageBind/LanguageBind_Video` represents the LoRA-tuned version.
You can freely replace them in the recommended [API usage](#-api). We recommend using the fully fine-tuned version, as it offers stronger performance.
<div align="center">
<table border="1" width="100%">
<tr align="center">
<th>Modality</th><th>LoRA tuning</th><th>Fine-tuning</th>
</tr>
<tr align="center">
<td>Video</td><td><a href="https://huggingface.co/LanguageBind/LanguageBind_Video">LanguageBind_Video</a></td><td><a href="https://huggingface.co/LanguageBind/LanguageBind_Video_FT">LanguageBind_Video_FT</a></td>
</tr>
<tr align="center">
<td>Audio</td><td><a href="https://huggingface.co/LanguageBind/LanguageBind_Audio">LanguageBind_Audio</a></td><td><a href="https://huggingface.co/LanguageBind/LanguageBind_Audio_FT">LanguageBind_Audio_FT</a></td>
</tr>
<tr align="center">
<td>Depth</td><td><a href="https://huggingface.co/LanguageBind/LanguageBind_Depth">LanguageBind_Depth</a></td><td>-</td>
</tr>
<tr align="center">
<td>Thermal</td><td><a href="https://huggingface.co/LanguageBind/LanguageBind_Thermal">LanguageBind_Thermal</a></td><td>-</td>
</tr>
</table>
</div>
<div align="center">
<table border="1" width="100%">
<tr align="center">
<th>Version</th><th>Tuning</th><th>Model size</th><th>Num_frames</th><th>HF Link</th><th>MSR-VTT</th><th>DiDeMo</th><th>ActivityNet</th><th>MSVD</th>
</tr>
<tr align="center">
<td>LanguageBind_Video</td><td>LoRA</td><td>Large</td><td>8</td><td><a href="https://huggingface.co/LanguageBind/LanguageBind_Video">Link</a></td><td>42.6</td><td>37.8</td><td>35.1</td><td>52.2</td>
</tr>
<tr align="center">
<td>LanguageBind_Video_FT</td><td>Full-tuning</td><td>Large</td><td>8</td><td><a href="https://huggingface.co/LanguageBind/LanguageBind_Video_FT">Link</a></td><td>42.7</td><td>38.1</td><td>36.9</td><td>53.5</td>
</tr>
<tr align="center">
<td>LanguageBind_Video_V1.5_FT</td><td>Full-tuning</td><td>Large</td><td>8</td><td><a href="https://huggingface.co/LanguageBind/LanguageBind_Video_V1.5_FT">Link</a></td><td>42.8</td><td>39.7</td><td>38.4</td><td>54.1</td>
</tr>
<tr align="center">
<td>LanguageBind_Video_V1.5_FT</td><td>Full-tuning</td><td>Large</td><td>12</td><td>Coming soon</td>
</tr>
<tr align="center">
<td>LanguageBind_Video_Huge_V1.5_FT</td><td>Full-tuning</td><td>Huge</td><td>8</td><td><a href="https://huggingface.co/LanguageBind/LanguageBind_Video_Huge_V1.5_FT">Link</a></td><td>44.8</td><td>39.9</td><td>41.0</td><td>53.7</td>
</tr>
<tr align="center">
<td>LanguageBind_Video_Huge_V1.5_FT</td><td>Full-tuning</td><td>Huge</td><td>12</td><td>Coming soon</td>
</tr>
</table>
</div>
## 🤖 API
**We open source all modalities preprocessing code.** If you want to load the model (e.g. ```LanguageBind/LanguageBind_Thermal```) from the model hub on Huggingface or on local, you can use the following code snippets!
### Inference for Multi-modal Binding
We have provided some sample datasets in [assets](assets) to quickly see how languagebind works.
```python
import torch
from languagebind import LanguageBind, to_device, transform_dict, LanguageBindImageTokenizer
if __name__ == '__main__':
device = 'cuda:0'
device = torch.device(device)
clip_type = {
'video': 'LanguageBind_Video_FT', # also LanguageBind_Video
'audio': 'LanguageBind_Audio_FT', # also LanguageBind_Audio
'thermal': 'LanguageBind_Thermal',
'image': 'LanguageBind_Image',
'depth': 'LanguageBind_Depth',
}
model = LanguageBind(clip_type=clip_type, cache_dir='./cache_dir')
model = model.to(device)
model.eval()
pretrained_ckpt = f'lb203/LanguageBind_Image'
tokenizer = LanguageBindImageTokenizer.from_pretrained(pretrained_ckpt, cache_dir='./cache_dir/tokenizer_cache_dir')
modality_transform = {c: transform_dict[c](model.modality_config[c]) for c in clip_type.keys()}
image = ['assets/image/0.jpg', 'assets/image/1.jpg']
audio = ['assets/audio/0.wav', 'assets/audio/1.wav']
video = ['assets/video/0.mp4', 'assets/video/1.mp4']
depth = ['assets/depth/0.png', 'assets/depth/1.png']
thermal = ['assets/thermal/0.jpg', 'assets/thermal/1.jpg']
language = ["Training a parakeet to climb up a ladder.", 'A lion climbing a tree to catch a monkey.']
inputs = {
'image': to_device(modality_transform['image'](image), device),
'video': to_device(modality_transform['video'](video), device),
'audio': to_device(modality_transform['audio'](audio), device),
'depth': to_device(modality_transform['depth'](depth), device),
'thermal': to_device(modality_transform['thermal'](thermal), device),
}
inputs['language'] = to_device(tokenizer(language, max_length=77, padding='max_length',
truncation=True, return_tensors='pt'), device)
with torch.no_grad():
embeddings = model(inputs)
print("Video x Text: \n",
torch.softmax(embeddings['video'] @ embeddings['language'].T, dim=-1).detach().cpu().numpy())
print("Image x Text: \n",
torch.softmax(embeddings['image'] @ embeddings['language'].T, dim=-1).detach().cpu().numpy())
print("Depth x Text: \n",
torch.softmax(embeddings['depth'] @ embeddings['language'].T, dim=-1).detach().cpu().numpy())
print("Audio x Text: \n",
torch.softmax(embeddings['audio'] @ embeddings['language'].T, dim=-1).detach().cpu().numpy())
print("Thermal x Text: \n",
torch.softmax(embeddings['thermal'] @ embeddings['language'].T, dim=-1).detach().cpu().numpy())
```
Then returns the following result.
```bash
Video x Text:
[[9.9989331e-01 1.0667283e-04]
[1.3255903e-03 9.9867439e-01]]
Image x Text:
[[9.9990666e-01 9.3292067e-05]
[4.6132666e-08 1.0000000e+00]]
Depth x Text:
[[0.9954276 0.00457235]
[0.12042473 0.8795753 ]]
Audio x Text:
[[0.97634876 0.02365119]
[0.02917843 0.97082156]]
Thermal x Text:
[[0.9482511 0.0517489 ]
[0.48746133 0.5125386 ]]
```
### Emergency zero-shot
Since languagebind binds each modality together, we also found the **emergency zero-shot**. It's very simple to use.
```python
print("Video x Audio: \n", torch.softmax(embeddings['video'] @ embeddings['audio'].T, dim=-1).detach().cpu().numpy())
print("Image x Depth: \n", torch.softmax(embeddings['image'] @ embeddings['depth'].T, dim=-1).detach().cpu().numpy())
print("Image x Thermal: \n", torch.softmax(embeddings['image'] @ embeddings['thermal'].T, dim=-1).detach().cpu().numpy())
```
Then, you will get:
```
Video x Audio:
[[1.0000000e+00 0.0000000e+00]
[3.1150486e-32 1.0000000e+00]]
Image x Depth:
[[1. 0.]
[0. 1.]]
Image x Thermal:
[[1. 0.]
[0. 1.]]
```
### Different branches for X-Language task
Additionally, LanguageBind can be **disassembled into different branches** to handle different tasks. Note that we do not train Image, which just initialize from OpenCLIP.
#### Thermal
```python
import torch
from languagebind import LanguageBindThermal, LanguageBindThermalTokenizer, LanguageBindThermalProcessor
pretrained_ckpt = 'LanguageBind/LanguageBind_Thermal'
model = LanguageBindThermal.from_pretrained(pretrained_ckpt, cache_dir='./cache_dir')
tokenizer = LanguageBindThermalTokenizer.from_pretrained(pretrained_ckpt, cache_dir='./cache_dir')
thermal_process = LanguageBindThermalProcessor(model.config, tokenizer)
model.eval()
data = thermal_process([r"your/thermal.jpg"], ['your text'], return_tensors='pt')
with torch.no_grad():
out = model(**data)
print(out.text_embeds @ out.image_embeds.T)
```
#### Depth
```python
import torch
from languagebind import LanguageBindDepth, LanguageBindDepthTokenizer, LanguageBindDepthProcessor
pretrained_ckpt = 'LanguageBind/LanguageBind_Depth'
model = LanguageBindDepth.from_pretrained(pretrained_ckpt, cache_dir='./cache_dir')
tokenizer = LanguageBindDepthTokenizer.from_pretrained(pretrained_ckpt, cache_dir='./cache_dir')
depth_process = LanguageBindDepthProcessor(model.config, tokenizer)
model.eval()
data = depth_process([r"your/depth.png"], ['your text.'], return_tensors='pt')
with torch.no_grad():
out = model(**data)
print(out.text_embeds @ out.image_embeds.T)
```
#### Video
```python
import torch
from languagebind import LanguageBindVideo, LanguageBindVideoTokenizer, LanguageBindVideoProcessor
pretrained_ckpt = 'LanguageBind/LanguageBind_Video_FT' # also 'LanguageBind/LanguageBind_Video'
model = LanguageBindVideo.from_pretrained(pretrained_ckpt, cache_dir='./cache_dir')
tokenizer = LanguageBindVideoTokenizer.from_pretrained(pretrained_ckpt, cache_dir='./cache_dir')
video_process = LanguageBindVideoProcessor(model.config, tokenizer)
model.eval()
data = video_process(["your/video.mp4"], ['your text.'], return_tensors='pt')
with torch.no_grad():
out = model(**data)
print(out.text_embeds @ out.image_embeds.T)
```
#### Audio
```python
import torch
from languagebind import LanguageBindAudio, LanguageBindAudioTokenizer, LanguageBindAudioProcessor
pretrained_ckpt = 'LanguageBind/LanguageBind_Audio_FT' # also 'LanguageBind/LanguageBind_Audio'
model = LanguageBindAudio.from_pretrained(pretrained_ckpt, cache_dir='./cache_dir')
tokenizer = LanguageBindAudioTokenizer.from_pretrained(pretrained_ckpt, cache_dir='./cache_dir')
audio_process = LanguageBindAudioProcessor(model.config, tokenizer)
model.eval()
data = audio_process([r"your/audio.wav"], ['your audio.'], return_tensors='pt')
with torch.no_grad():
out = model(**data)
print(out.text_embeds @ out.image_embeds.T)
```
#### Image
Note that our image encoder is the same as OpenCLIP. **Not** as fine-tuned as other modalities.
```python
import torch
from languagebind import LanguageBindImage, LanguageBindImageTokenizer, LanguageBindImageProcessor
pretrained_ckpt = 'LanguageBind/LanguageBind_Image'
model = LanguageBindImage.from_pretrained(pretrained_ckpt, cache_dir='./cache_dir')
tokenizer = LanguageBindImageTokenizer.from_pretrained(pretrained_ckpt, cache_dir='./cache_dir')
image_process = LanguageBindImageProcessor(model.config, tokenizer)
model.eval()
data = image_process([r"your/image.jpg"], ['your text.'], return_tensors='pt')
with torch.no_grad():
out = model(**data)
print(out.text_embeds @ out.image_embeds.T)
```
## 💥 VIDAL-10M
The datasets is in [DATASETS.md](DATASETS.md).
## 🗝️ Training & Validating
The training & validating instruction is in [TRAIN_AND_VALIDATE.md](TRAIN_AND_VALIDATE.md).
## 👍 Acknowledgement
* [OpenCLIP](https://github.com/mlfoundations/open_clip) An open source pretraining framework.
* [CLIP4Clip](https://github.com/ArrowLuo/CLIP4Clip) An open source Video-Text retrieval framework.
* [sRGB-TIR](https://github.com/rpmsnu/sRGB-TIR) An open source framework to generate infrared (thermal) images.
* [GLPN](https://github.com/vinvino02/GLPDepth) An open source framework to generate depth images.
## 🔒 License
* The majority of this project is released under the MIT license as found in the [LICENSE](https://github.com/PKU-YuanGroup/LanguageBind/blob/main/LICENSE) file.
* The dataset of this project is released under the CC-BY-NC 4.0 license as found in the [DATASET_LICENSE](https://github.com/PKU-YuanGroup/LanguageBind/blob/main/DATASET_LICENSE) file.
## ✏️ Citation
If you find our paper and code useful in your research, please consider giving a star :star: and citation :pencil:.
```BibTeX
@misc{zhu2023languagebind,
title={LanguageBind: Extending Video-Language Pretraining to N-modality by Language-based Semantic Alignment},
author={Bin Zhu and Bin Lin and Munan Ning and Yang Yan and Jiaxi Cui and Wang HongFa and Yatian Pang and Wenhao Jiang and Junwu Zhang and Zongwei Li and Cai Wan Zhang and Zhifeng Li and Wei Liu and Li Yuan},
year={2023},
eprint={2310.01852},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
## ✨ Star History
[](https://star-history.com/#PKU-YuanGroup/LanguageBind&Date)
## 🤝 Contributors
<a href="https://github.com/PKU-YuanGroup/LanguageBind/graphs/contributors">
<img src="https://contrib.rocks/image?repo=PKU-YuanGroup/LanguageBind" />
</a>
|
asas-ai/tydiqa-ar-primary_task | ---
language:
- ar
license: apache-2.0
task_categories:
- question-answering
pretty_name: ' tydiqa-ar'
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
dataset_info:
features:
- name: passage_answer_candidates
sequence:
- name: plaintext_start_byte
dtype: int32
- name: plaintext_end_byte
dtype: int32
- name: question_text
dtype: string
- name: document_title
dtype: string
- name: language
dtype: string
- name: annotations
sequence:
- name: passage_answer_candidate_index
dtype: int32
- name: minimal_answers_start_byte
dtype: int32
- name: minimal_answers_end_byte
dtype: int32
- name: yes_no_answer
dtype: string
- name: document_plaintext
dtype: string
- name: document_url
dtype: string
splits:
- name: train
num_bytes: 767894331.3564428
num_examples: 23092
- name: validation
num_bytes: 35803153.66148902
num_examples: 1380
download_size: 364886604
dataset_size: 803697485.0179318
---
|
DoctorSlimm/mozart-api | ---
configs:
- config_name: default
data_files:
- split: train
path: data.csv
---
# Dataset Card for Dataset Name
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
[More Information Needed]
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] |
ndaamod/Ndaamod | ---
license: openrail
---
|
allganize/flare-convfinqa-sampled-ko | ---
dataset_info:
features:
- name: conversation_id
dtype: string
- name: conversations
list:
- name: content
dtype: string
- name: role
dtype: string
- name: gpt
dtype: string
splits:
- name: test
num_bytes: 431924
num_examples: 98
download_size: 187925
dataset_size: 431924
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
---
|
SadiaAfreen1048/guanaco-llama2-1k | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 1654448
num_examples: 1000
download_size: 966692
dataset_size: 1654448
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
anirudhlakhotia/roots_indic-hi_iitb_english_hindi_corpus | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 4017420782
num_examples: 8388176
download_size: 1741728890
dataset_size: 4017420782
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
lca0503/soxdata_encodec | ---
dataset_info:
features:
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dtype: string
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dtype: string
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sequence: int64
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sequence: int64
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sequence: int64
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sequence: int64
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sequence: int64
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sequence: int64
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num_examples: 10349
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num_bytes: 567810171
num_examples: 9957
download_size: 3385954392
dataset_size: 21791555920
---
# Dataset Card for "soxdata_encodec"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
distilled-one-sec-cv12-each-chunk-uniq/chunk_213 | ---
dataset_info:
features:
- name: logits
sequence: float32
- name: mfcc
sequence:
sequence: float64
splits:
- name: train
num_bytes: 1272233064.0
num_examples: 247902
download_size: 1304763986
dataset_size: 1272233064.0
---
# Dataset Card for "chunk_213"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
spacemanidol/summary-enhanced-msmarco-passage-corpus | ---
license: apache-2.0
---
|
VatsaDev/worldbuild | ---
license: mit
task_categories:
- conversational
- text-generation
language:
- en
pretty_name: worldbuilding
size_categories:
- 10K<n<100K
---
*note: The dataset was fine, but the parquet bot appears to have messed it up somehow, if its not visible up there, look at dataset.jsonl*
Updates:
- Jan 7th 2024 - scraped all the worldbuilding stackexchange Q's, with 5+ rep, left with 18000 Q's.
- Jan 8th 2024 - incorporated 100mb more in roleplay and worldbuilding, the dataset now includes Pippa, Bluemoon, and RolePlayIO
- Jan 9th 2024 - More misc. worldbuilding data incorporated, the dataset is complete enough now
# Worldbuild
A dataset focused on worldbuilding and roleplay, mostly well-formatted, high quality data, in the markdown format. |
AryanAnuj/processed_dataset_orca-math-word-problems-200k | ---
license: mit
---
Dataset Description:
This dataset contains data that has undergone two preprocessing steps:
Removal of Instructions with Less Than 100 Tokens in Response: Instructions with less than 100 tokens in the response have been removed from the dataset. This preprocessing step helps to ensure that the dataset contains substantial and informative responses.
Data Deduplication by Grouping Using Cosine Similarity (Threshold > 0.95): Data deduplication has been performed by grouping similar instances together using cosine similarity. Instances with a cosine similarity greater than 0.95 have been considered duplicates and grouped accordingly. This preprocessing step helps to remove redundant or highly similar instances from the dataset, improving its quality and reducing redundancy.
Now it has distinct question and thier respective answers .
It's Ready to train a Large Language Model on Math word problem. |
bigheiniuJ/JimmyLuMoreBaselines | ---
dataset_info:
features:
- name: output
dtype: string
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dtype: string
- name: seed
dtype: string
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splits:
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num_bytes: 5920689
num_examples: 14459
download_size: 1839289
dataset_size: 5920689
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
distilled-from-one-sec-cv12/chunk_152 | ---
dataset_info:
features:
- name: logits
sequence: float32
- name: mfcc
sequence:
sequence: float64
splits:
- name: train
num_bytes: 902641820
num_examples: 175885
download_size: 921960634
dataset_size: 902641820
---
# Dataset Card for "chunk_152"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
sarthak-2002/anime-quotes | ---
dataset_info:
features:
- name: Anime
dtype: string
- name: Quote
dtype: string
splits:
- name: train
num_bytes: 1502790
num_examples: 8612
download_size: 901147
dataset_size: 1502790
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
beskrovnykh/daniel-dataset-fragments | ---
dataset_info:
features:
- name: title
dtype: string
- name: published
dtype: string
- name: url
dtype: string
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dtype: string
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- name: end
dtype: float64
splits:
- name: train
num_bytes: 6537589
num_examples: 24463
download_size: 1161552
dataset_size: 6537589
---
# Dataset Card for "daniel-dataset-fragments"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
showchen/Frostnova | ---
license: apache-2.0
---
|
liuyanchen1015/MULTI_VALUE_qqp_for_to_pupose | ---
dataset_info:
features:
- name: question1
dtype: string
- name: question2
dtype: string
- name: label
dtype: int64
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num_examples: 34426
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num_bytes: 5069715
num_examples: 31944
download_size: 6023731
dataset_size: 11187657
---
# Dataset Card for "MULTI_VALUE_qqp_for_to_pupose"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
greengerong/leetcode | ---
license: mit
---
|
centroIA/MistralInstructScenarios | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: input
dtype: string
- name: output
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 2684487
num_examples: 967
download_size: 698243
dataset_size: 2684487
---
# Dataset Card for "MistralInstructScenarios"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
doguaraci/trial | ---
dataset_info:
features:
- name: prompt
dtype: string
- name: response
dtype: string
splits:
- name: train
num_bytes: 252534040
num_examples: 96209
- name: test
num_bytes: 2568372
num_examples: 1000
download_size: 143740404
dataset_size: 255102412
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
|
jxm/agnews | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
- split: dev
path: data/dev-*
dataset_info:
features:
- name: sentence
dtype: string
- name: label
dtype: int64
splits:
- name: train
num_bytes: 30133565
num_examples: 120000
- name: test
num_bytes: 1899330
num_examples: 7600
- name: dev
num_bytes: 63881
num_examples: 256
download_size: 20215708
dataset_size: 32096776
---
# Dataset Card for "agnews"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
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