datasetId
stringlengths 2
117
| author
stringlengths 2
42
⌀ | last_modified
timestamp[us, tz=UTC] | downloads
int64 0
9.36M
| likes
int64 0
3.89k
| tags
list | task_categories
list | createdAt
timestamp[us, tz=UTC] | card
stringlengths 19
1.01M
| embedding
list |
|---|---|---|---|---|---|---|---|---|---|
vishruthnath/Calc-MAWPS-CalcBERT-Tags | vishruthnath | 2023-12-05T21:14:43Z | 23 | 0 | [
"region:us"
]
| null | 2023-12-03T22:22:37Z | ---
dataset_info:
features:
- name: chain
dtype: string
- name: equation
dtype: string
- name: expression
dtype: string
- name: id
dtype: string
- name: num_unique_ops
dtype: int64
- name: operand_tags
sequence: int64
- name: operands
sequence: float64
- name: operation
dtype: string
- name: question
dtype: string
- name: question_split
sequence: string
- name: result
dtype: string
- name: result_float
dtype: float64
- name: valid
dtype: bool
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 690330
num_examples: 1053
- name: validation
num_bytes: 659394
num_examples: 1016
- name: test
num_bytes: 333380
num_examples: 510
download_size: 473091
dataset_size: 1683104
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
---
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|
AmanMussa/kazakh-instruction-v1 | AmanMussa | 2023-12-10T11:30:32Z | 22 | 0 | [
"license:mit",
"region:us"
]
| null | 2023-11-16T18:37:52Z | ---
license: mit
---
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|
hezarai/flickr30k-fa | hezarai | 2023-12-09T06:51:23Z | 22 | 1 | [
"task_categories:image-to-text",
"size_categories:10K<n<100K",
"language:fa",
"license:apache-2.0",
"hezar",
"region:us"
]
| [
"image-to-text"
]
| 2023-11-22T12:58:08Z | ---
license: apache-2.0
task_categories:
- image-to-text
language:
- fa
tags:
- hezar
pretty_name: Flickr30K Fa
size_categories:
- 10K<n<100K
---
The Flickr30K dataset filtered and translated to Persian.
This dataset was originally made by **Sajjad Ayoubi** and uploaded to Kaggle at [https://www.kaggle.com/datasets/sajjadayobi360/flickrfa](https://www.kaggle.com/datasets/sajjadayobi360/flickrfa).
This repo contains the exact dataset split to train/test using a custom sampling criteria and can be directly loaded using HuggingFace datasets or right from Hezar.
### Usage
#### Hugging Face Datasets
```
pip install datasets
```
```python
from datasets import load_dataset
dataset = load_dataset("hezarai/flickr30k-fa")
```
#### Hezar
```
pip install hezar
```
```python
from hezar.data import Dataset
dataset = Dataset.load("hezarai/flickr30k-fa", split="train")
``` | [
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|
tasksource/resnli | tasksource | 2023-12-05T09:47:40Z | 22 | 0 | [
"license:cc-by-4.0",
"region:us"
]
| null | 2023-12-02T15:09:45Z | ---
license: cc-by-4.0
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: premise
dtype: string
- name: hypothesis
dtype: string
- name: label
dtype: string
- name: config
dtype: string
splits:
- name: train
num_bytes: 4691316
num_examples: 25232
- name: validation
num_bytes: 801878
num_examples: 4624
- name: test
num_bytes: 1224540
num_examples: 7216
download_size: 956275
dataset_size: 6717734
---
https://github.com/ruixiangcui/WikiResNLI_NatResNLI
```
@inproceedings{cui-etal-2023-failure,
title = "What does the Failure to Reason with {``}Respectively{''} in Zero/Few-Shot Settings Tell Us about Language Models?",
author = "Cui, Ruixiang and
Lee, Seolhwa and
Hershcovich, Daniel and
S{\o}gaard, Anders",
booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.acl-long.489",
pages = "8786--8800",
abstract = "Humans can effortlessly understand the coordinate structure of sentences such as {``}Niels Bohr and Kurt Cobain were born in Copenhagen and Seattle, *respectively*{''}. In the context of natural language inference (NLI), we examine how language models (LMs) reason with respective readings (Gawron and Kehler, 2004) from two perspectives: syntactic-semantic and commonsense-world knowledge. We propose a controlled synthetic dataset WikiResNLI and a naturally occurring dataset NatResNLI to encompass various explicit and implicit realizations of {``}respectively{''}. We show that fine-tuned NLI models struggle with understanding such readings without explicit supervision. While few-shot learning is easy in the presence of explicit cues, longer training is required when the reading is evoked implicitly, leaving models to rely on common sense inferences. Furthermore, our fine-grained analysis indicates models fail to generalize across different constructions. To conclude, we demonstrate that LMs still lag behind humans in generalizing to the long tail of linguistic constructions.",
}
``` | [
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|
Coooori/dialog_data_dev_huggingface | Coooori | 2023-12-06T06:47:28Z | 22 | 0 | [
"region:us"
]
| null | 2023-12-03T09:01:36Z | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 210458
num_examples: 98
download_size: 114112
dataset_size: 210458
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "dialog_data_dev_huggingface"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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|
nhantruongcse/100k_train_large_vietnews | nhantruongcse | 2023-12-09T04:14:11Z | 22 | 0 | [
"region:us"
]
| null | 2023-12-09T04:13:56Z | ---
dataset_info:
features:
- name: Content
dtype: string
- name: Summary
dtype: string
splits:
- name: train
num_bytes: 374682895.7903489
num_examples: 100000
download_size: 199533407
dataset_size: 374682895.7903489
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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|
TeamSODA/cl_signal_processing_attacks_whisper_commonvoice | TeamSODA | 2023-12-10T18:34:08Z | 21 | 0 | [
"region:us"
]
| null | 2023-06-05T10:47:13Z | ---
dataset_info:
features:
- name: audio
dtype: audio
- name: SNR
dtype: int64
splits:
- name: benign
num_bytes: 64617981.0
num_examples: 100
- name: kenansville
num_bytes: 65087997.0
num_examples: 100
- name: yeehaw
num_bytes: 63742461.0
num_examples: 100
- name: imaginary_clipping
num_bytes: 71677437.0
num_examples: 100
download_size: 265145069
dataset_size: 265125876.0
configs:
- config_name: default
data_files:
- split: benign
path: data/benign-*
- split: kenansville
path: data/kenansville-*
- split: yeehaw
path: data/yeehaw-*
- split: imaginary_clipping
path: data/imaginary_clipping-*
---
# Dataset Card for "cl_signal_processing_attacks_whisper_commonvoice"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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|
philikai/Spider-SQL-LLAMA2_train | philikai | 2023-12-06T17:07:52Z | 21 | 0 | [
"size_categories:1K<n<10K",
"license:cc-by-sa-4.0",
"region:us"
]
| null | 2023-09-11T13:58:02Z | ---
dataset_info:
features:
- name: db_id
dtype: string
- name: query
dtype: string
- name: question
dtype: string
- name: schema
dtype: string
- name: primary_keys
dtype: string
- name: foreign_keys
dtype: string
splits:
- name: train
num_bytes: 12713675
num_examples: 8659
- name: validation
num_bytes: 1169610
num_examples: 1034
download_size: 619836
dataset_size: 13883285
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
license: cc-by-sa-4.0
size_categories:
- 1K<n<10K
---
# Dataset Card for "Spider-SQL-LLAMA2_train"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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|
Yorth/dalleTestData | Yorth | 2023-12-05T22:05:28Z | 21 | 0 | [
"region:us"
]
| null | 2023-12-04T03:13:15Z | ---
dataset_info:
features:
- name: image
dtype: image
- name: caption
dtype: string
- name: resolution
dtype: string
splits:
- name: train
num_bytes: 1065107.5689354276
num_examples: 4
download_size: 633186
dataset_size: 1065107.5689354276
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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|
DeepLearner101/CIFAR100Subset | DeepLearner101 | 2023-12-06T17:26:46Z | 21 | 0 | [
"region:us"
]
| null | 2023-12-06T17:25:41Z | ---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype: int64
splits:
- name: train
num_bytes: 3641860.0
num_examples: 1600
- name: validation
num_bytes: 1473160.0
num_examples: 640
download_size: 5407358
dataset_size: 5115020.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
---
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|
Bena345/cdc-diabetes-health-indicators | Bena345 | 2023-12-06T21:06:31Z | 21 | 0 | [
"license:mit",
"region:us"
]
| null | 2023-12-06T20:47:53Z | ---
license: mit
---
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|
Gunnar-Ingi/cgdata | Gunnar-Ingi | 2023-12-07T16:54:51Z | 21 | 0 | [
"license:unknown",
"region:us"
]
| null | 2023-12-07T14:29:04Z | ---
license: unknown
---
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|
EmmaGthn/Moji_balanced | EmmaGthn | 2023-12-07T16:26:02Z | 21 | 0 | [
"region:us"
]
| null | 2023-12-07T16:25:59Z | ---
dataset_info:
features:
- name: text
dtype: string
- name: label
dtype: int64
- name: gender
dtype: int64
splits:
- name: train
num_bytes: 6618798
num_examples: 53016
- name: test
num_bytes: 256593
num_examples: 2000
download_size: 2582045
dataset_size: 6875391
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
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|
iamupamanyu/embeddingstest | iamupamanyu | 2023-12-08T06:53:55Z | 21 | 0 | [
"license:mit",
"region:us"
]
| null | 2023-12-08T06:52:02Z | ---
license: mit
---
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|
nhantruongcse/dataset_train_50k | nhantruongcse | 2023-12-09T03:17:54Z | 21 | 0 | [
"region:us"
]
| null | 2023-12-09T03:17:34Z | ---
dataset_info:
features:
- name: Content
dtype: string
- name: Summary
dtype: string
- name: input_ids
sequence: int32
- name: attention_mask
sequence: int8
- name: labels
sequence: int64
splits:
- name: train
num_bytes: 377014400.5235154
num_examples: 50000
download_size: 166818647
dataset_size: 377014400.5235154
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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|
nhantruongcse/25k_test_large_vietnews | nhantruongcse | 2023-12-09T04:14:18Z | 21 | 0 | [
"region:us"
]
| null | 2023-12-09T04:14:12Z | ---
dataset_info:
features:
- name: Content
dtype: string
- name: Summary
dtype: string
splits:
- name: train
num_bytes: 93670723.94758722
num_examples: 25000
download_size: 49893085
dataset_size: 93670723.94758722
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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|
GATE-engine/happy-whale-dolphin-classification | GATE-engine | 2023-12-09T10:13:29Z | 21 | 0 | [
"region:us"
]
| null | 2023-12-09T09:45:29Z | ---
dataset_info:
features:
- name: image
dtype: image
- name: species
dtype: int64
- name: species_name
dtype: string
- name: individual
dtype: int64
- name: individual_name
dtype: string
splits:
- name: train
num_bytes: 36526488580.02
num_examples: 42678
- name: val
num_bytes: 1726794221.752
num_examples: 2088
- name: test
num_bytes: 5378930538.106
num_examples: 6267
download_size: 40331515587
dataset_size: 43632213339.878
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: val
path: data/val-*
- split: test
path: data/test-*
---
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|
celsowm/medicamentos_patologia_ner | celsowm | 2023-12-10T17:44:59Z | 21 | 0 | [
"region:us"
]
| null | 2023-12-10T14:00:19Z | ---
dataset_info:
features:
- name: tokens
sequence: string
- name: tags
sequence: string
splits:
- name: train
num_bytes: 50246
num_examples: 273
download_size: 10597
dataset_size: 50246
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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|
lukasbraach/rwth_phoenix_weather_2014 | lukasbraach | 2023-12-05T15:09:37Z | 20 | 0 | [
"region:us"
]
| null | 2023-09-07T15:31:08Z | ---
dataset_info:
- config_name: multisigner
features:
- name: tokens
sequence: string
- name: frames
sequence: image
splits:
- name: train
num_bytes: 35090699166
num_examples: 5672
- name: validation
num_bytes: 3294861848
num_examples: 540
- name: test
num_bytes: 3935889563
num_examples: 629
download_size: 43042303939
dataset_size: 42321450577
- config_name: signerindependent
features:
- name: tokens
sequence: string
- name: frames
sequence: image
splits:
- name: train
num_bytes: 26933878872
num_examples: 4376
- name: validation
num_bytes: 720567494
num_examples: 111
- name: test
num_bytes: 1175795394
num_examples: 180
download_size: 29320607031
dataset_size: 28830241760
---
# RWTH-Weather-Phoenix 2014
This archive contains two sets of the RWTH-Weather-Phoenix 2014 corpus
1. the multisigner set
2. the signer independent set.
It is released under non-commercial cc 4.0 license with attribution (see attachment)
If you use this data in your research, please cite:
```
O. Koller, J. Forster, and H. Ney. Continuous sign language recognition: Towards large vocabulary statistical recognition systems handling multiple signers. Computer Vision and Image Understanding, volume 141, pages 108-125, December 2015.
```
and
```
Koller, Zargaran, Ney. "Re-Sign: Re-Aligned End-to-End Sequence Modeling with Deep Recurrent CNN-HMMs" in CVPR 2017, Honululu, Hawaii, USA.
```
See README files in subfolders for more information.
### CHANGELOG
- v1 Aug 20 2016, initial version of the archive. multisigner setup
- v2 Apr 21 2017, signer independent SI5 subset, added caffe models and automatic frame-alignment
- v3 Nov 3 2017, added language models and complete set of hyper parameters to reproduce the published results
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|
Redwood0/pippa_custom | Redwood0 | 2023-12-08T21:03:15Z | 20 | 0 | [
"task_categories:conversational",
"language:en",
"license:apache-2.0",
"not-for-all-audiences",
"conversational",
"roleplay",
"custom-format",
"region:us"
]
| [
"conversational"
]
| 2023-11-17T17:24:28Z | ---
license: apache-2.0
task_categories:
- conversational
language:
- en
tags:
- not-for-all-audiences
- conversational
- roleplay
- custom-format
viewer: true
---
This custom pippa dataset is from Undi of TheBloke Discord | [
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|
RobCzikkel/DoctorGPT | RobCzikkel | 2023-12-05T23:05:53Z | 20 | 0 | [
"task_categories:conversational",
"size_categories:10K<n<100K",
"language:en",
"biology",
"medical",
"region:us"
]
| [
"conversational"
]
| 2023-11-23T21:04:25Z | ---
language:
- en
size_categories:
- 10K<n<100K
task_categories:
- conversational
pretty_name: Doctor & Patient
dataset_info:
features:
- name: prompt
dtype: string
- name: input_ids
sequence: int32
- name: length
dtype: int64
- name: attention_mask
sequence: int8
splits:
- name: train
num_bytes: 42127351.778204426
num_examples: 13125
- name: test
num_bytes: 10534245.221795576
num_examples: 3282
download_size: 10917910
dataset_size: 52661597.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
tags:
- biology
- medical
---
### Dataset
This is an edited and tokenized version of the MedQuad-MedicalQnADataset dataset by keivalya.
The original dataset contains 16K+ questions and answers between patient and doctor, which have been converted into a full prompt to train BioGPT by Microsoft.
##### Tokenizer used
microsoft/BioGPT-Large (BPE tokenizer)
### Full prompt
```py
prompt = f"""You are a helpful AI Doctor who answers medical questions. Below is a question from a patient. Your task is to answer the questions as truthfully as you can.
### Patient:
{sample['Question']}
### Doctor:
{sample['Answer']}"""
```
### Notes
Since bioGPT has a max input of 1024, the full prompt was truncated to stay below this limit.
The truncation strategy I used made sure that only full sentences were produced.
Please note that this dataset is for research/testing only, it should not be used in a real setting or used to give medical advice to people.
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|
avaliev/drugchat | avaliev | 2023-12-06T11:00:15Z | 20 | 0 | [
"task_categories:question-answering",
"size_categories:10K<n<100K",
"language:en",
"license:bsd-3-clause",
"biology",
"chemistry",
"medical",
"region:us"
]
| [
"question-answering"
]
| 2023-12-05T08:04:57Z | ---
license: bsd-3-clause
task_categories:
- question-answering
language:
- en
tags:
- biology
- chemistry
- medical
pretty_name: DrugChat Dataset
size_categories:
- 10K<n<100K
---
This dataset is for the "DrugChat: Towards Enabling ChatGPT-Like Capabilities on Drug Molecule Graphs" paper.
@article{liang2023drugchat,
title={DrugChat: Towards Enabling ChatGPT-Like Capabilities on Drug Molecule Graphs},
author={Liang, Youwei and Zhang, Ruiyi and Zhang, li and Xie, Pengtao},
journal={TechRxiv},
year={2023}
}
There are no changes in data; only added datasets class to download the set from HF and generate split from data in .json files. | [
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|
presencesw/glue_t5 | presencesw | 2023-12-06T11:35:06Z | 20 | 0 | [
"region:us"
]
| null | 2023-12-06T02:19:50Z | ---
dataset_info:
features:
- name: texts
dtype: string
- name: targets
dtype: string
splits:
- name: train
num_bytes: 182496609
num_examples: 949098
- name: validation
num_bytes: 13175279
num_examples: 69640
download_size: 102866233
dataset_size: 195671888
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
---
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|
nhantruongcse/130_test_vit5_base | nhantruongcse | 2023-12-08T19:04:07Z | 20 | 0 | [
"region:us"
]
| null | 2023-12-08T19:04:03Z | ---
dataset_info:
features:
- name: Content
dtype: string
- name: Summary
dtype: string
- name: input_ids
sequence: int32
- name: attention_mask
sequence: int8
- name: labels
sequence: int64
splits:
- name: train
num_bytes: 97793645
num_examples: 12990
download_size: 43120373
dataset_size: 97793645
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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|
We-Want-GPU/Yi-Ko-DPO-dataset | We-Want-GPU | 2023-12-09T00:41:36Z | 20 | 0 | [
"region:us"
]
| null | 2023-12-09T00:41:32Z | ---
dataset_info:
features:
- name: prompt
dtype: string
- name: chosen
dtype: string
- name: rejected
dtype: string
splits:
- name: train
num_bytes: 13724170
num_examples: 4000
download_size: 6864121
dataset_size: 13724170
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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|
sordonia/mmlu-qa-platy-v1-flat | sordonia | 2023-12-09T09:06:25Z | 20 | 0 | [
"region:us"
]
| null | 2023-12-09T08:52:47Z | ---
dataset_info:
features:
- name: source
dtype: string
- name: target
dtype: string
- name: task_name
dtype: string
- name: task_source
dtype: string
- name: split
dtype: string
splits:
- name: train
num_bytes: 149337126.59966236
num_examples: 100000
download_size: 73422303
dataset_size: 149337126.59966236
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "mmlu-qa-platy-v1-flat"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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|
mattlc/pxcorpus | mattlc | 2023-12-08T14:58:33Z | 19 | 1 | [
"region:us"
]
| null | 2023-09-21T11:02:58Z | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: audio
dtype:
audio:
sampling_rate: 16000
- name: sentence
dtype: string
- name: duration
dtype: float64
- name: text
dtype: string
splits:
- name: train
num_bytes: 395874656.0
num_examples: 1584
- name: test
num_bytes: 95619380.0
num_examples: 397
download_size: 465681717
dataset_size: 491494036.0
---
# Dataset Card for "pxcorpus"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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|
blanchon/PatternNet | blanchon | 2023-12-05T14:19:24Z | 19 | 0 | [
"task_categories:image-classification",
"language:en",
"license:unknown",
"remote-sensing",
"earth-observation",
"geospatial",
"satellite-imagery",
"land-cover-classification",
"google-earth",
"arxiv:1703.06339",
"region:us"
]
| [
"image-classification"
]
| 2023-12-05T14:16:51Z | ---
language: en
license: unknown
task_categories:
- image-classification
paperswithcode_id: patternnet
pretty_name: PatternNet
tags:
- remote-sensing
- earth-observation
- geospatial
- satellite-imagery
- land-cover-classification
- google-earth
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': airplane
'1': baseball field
'2': basketball court
'3': beach
'4': bridge
'5': cemetery
'6': chaparral
'7': christmas tree farm
'8': closed road
'9': coastal mansion
'10': crosswalk
'11': dense residential
'12': ferry terminal
'13': football field
'14': forest
'15': freeway
'16': golf course
'17': harbor
'18': intersection
'19': mobile home park
'20': nursing home
'21': oil gas field
'22': oil well
'23': overpass
'24': parking lot
'25': parking space
'26': railway
'27': river
'28': runway
'29': runway marking
'30': shipping yard
'31': solar panel
'32': sparse residential
'33': storage tank
'34': swimming pool
'35': tennis court
'36': transformer station
'37': wastewater treatment plant
splits:
- name: train
num_bytes: 1422177005.0
num_examples: 30400
download_size: 1422316869
dataset_size: 1422177005.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# PatternNet
<!-- Dataset thumbnail -->
<!-- Provide a quick summary of the dataset. -->
The PatternNet dataset is a dataset for remote sensing scene classification and image retrieval.
- **Paper:** https://arxiv.org/abs/1703.06339
- **Homepage:** https://sites.google.com/view/zhouwx/dataset
## Description
<!-- Provide a longer summary of what this dataset is. -->
PatternNet is a large-scale high-resolution remote sensing dataset collected for remote sensing image retrieval. There are 38 classes and each class has 800 images of size 256×256 pixels. The images in PatternNet are collected from Google Earth imagery or via the Google Map API for some US cities. The following table shows the classes and the corresponding spatial resolutions. The figure shows some example images from each class.
- **Total Number of Images**: 30400
- **Bands**: 3 (RGB)
- **Image Resolution**: 256x256m
- **Land Cover Classes**: 38
- Classes: airplane, baseball_field, basketball_court, beach, bridge, cemetery, chaparral, christmas_tree_farm, closed_road, coastal_mansion, crosswalk, dense_residential, ferry_terminal, football_field, forest, freeway, golf_course, harbor, intersection, mobile_home_park, nursing_home, oil_gas_field, oil_well, overpass, parking_lot, parking_space, railway, river, runway, runway_marking, shipping_yard, solar_panel, sparse_residential, storage_tank, swimming_pool, tennis_court, transformer_station, wastewater_treatment_plant
## Usage
To use this dataset, simply use `datasets.load_dataset("blanchon/PatternNet")`.
<!-- Provide any additional information on how to use this dataset. -->
```python
from datasets import load_dataset
PatternNet = load_dataset("blanchon/PatternNet")
```
## Citation
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
If you use the EuroSAT dataset in your research, please consider citing the following publication:
```bibtex
@article{li2017patternnet,
title = {PatternNet: Visual Pattern Mining with Deep Neural Network},
author = {Hongzhi Li and Joseph G. Ellis and Lei Zhang and Shih-Fu Chang},
journal = {International Conference on Multimedia Retrieval},
year = {2017},
doi = {10.1145/3206025.3206039},
bibSource = {Semantic Scholar https://www.semanticscholar.org/paper/e7c75e485651bf3ccf37dd8dd39f6665419d73bd}
}
```
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|
agvidit1/hateXplain_processed_dataset | agvidit1 | 2023-12-08T09:34:16Z | 19 | 0 | [
"region:us"
]
| null | 2023-12-08T09:27:22Z | ---
dataset_info:
features:
- name: text
dtype: string
- name: rationale
sequence: float64
- name: label
dtype: int64
splits:
- name: train
num_bytes: 3918415
num_examples: 15379
- name: test
num_bytes: 487084
num_examples: 1924
- name: validation
num_bytes: 488346
num_examples: 1922
- name: val
num_bytes: 488346
num_examples: 1922
download_size: 1996662
dataset_size: 5382191
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
- split: validation
path: data/validation-*
- split: val
path: data/val-*
---
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|
GabrielVidal/dalle-3-palette | GabrielVidal | 2023-12-09T16:40:31Z | 19 | 0 | [
"size_categories:n<1K",
"language:en",
"license:cc0-1.0",
"image-text-dataset",
"synthetic-dataset",
"region:us"
]
| null | 2023-12-09T15:05:39Z | ---
dataset_info:
features:
- name: image
dtype: image
- name: palette
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 1778362423
num_examples: 1000
download_size: 1778260113
dataset_size: 1778362423
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
license: cc0-1.0
language:
- en
size_categories:
- n<1K
tags:
- image-text-dataset
- synthetic-dataset
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|
yashraizada/yelp-business-images | yashraizada | 2023-12-09T20:16:09Z | 19 | 0 | [
"license:apache-2.0",
"region:us"
]
| null | 2023-12-09T20:14:50Z | ---
license: apache-2.0
---
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wikimedia/wikisource | wikimedia | 2023-12-08T13:36:41Z | 18 | 25 | [
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]
| [
"text-generation",
"fill-mask"
]
| 2022-03-02T23:29:22Z | ---
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path: 20231201.pms/train-*
- config_name: 20231201.pt
data_files:
- split: train
path: 20231201.pt/train-*
- config_name: 20231201.ro
data_files:
- split: train
path: 20231201.ro/train-*
- config_name: 20231201.ru
data_files:
- split: train
path: 20231201.ru/train-*
- config_name: 20231201.sa
data_files:
- split: train
path: 20231201.sa/train-*
- config_name: 20231201.sah
data_files:
- split: train
path: 20231201.sah/train-*
- config_name: 20231201.sk
data_files:
- split: train
path: 20231201.sk/train-*
- config_name: 20231201.sl
data_files:
- split: train
path: 20231201.sl/train-*
- config_name: 20231201.sr
data_files:
- split: train
path: 20231201.sr/train-*
- config_name: 20231201.su
data_files:
- split: train
path: 20231201.su/train-*
- config_name: 20231201.sv
data_files:
- split: train
path: 20231201.sv/train-*
- config_name: 20231201.ta
data_files:
- split: train
path: 20231201.ta/train-*
- config_name: 20231201.te
data_files:
- split: train
path: 20231201.te/train-*
- config_name: 20231201.th
data_files:
- split: train
path: 20231201.th/train-*
- config_name: 20231201.tr
data_files:
- split: train
path: 20231201.tr/train-*
- config_name: 20231201.uk
data_files:
- split: train
path: 20231201.uk/train-*
- config_name: 20231201.vec
data_files:
- split: train
path: 20231201.vec/train-*
- config_name: 20231201.vi
data_files:
- split: train
path: 20231201.vi/train-*
- config_name: 20231201.wa
data_files:
- split: train
path: 20231201.wa/train-*
- config_name: 20231201.yi
data_files:
- split: train
path: 20231201.yi/train-*
- config_name: 20231201.zh
data_files:
- split: train
path: 20231201.zh/train-*
- config_name: 20231201.zh-min-nan
data_files:
- split: train
path: 20231201.zh-min-nan/train-*
---
# Dataset Card for Wikimedia Wikisource
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://dumps.wikimedia.org
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
Wikisource dataset containing cleaned articles of all languages.
The dataset is built from the Wikisource dumps (https://dumps.wikimedia.org/)
with one subset per language, each containing a single train split.
Each example contains the content of one full Wikisource text with cleaning to strip
markdown and unwanted sections (references, etc.).
All language subsets have already been processed for recent dump, and you can load them by date and language like this:
```python
from datasets import load_dataset
ds = load_dataset("wikimedia/wikisource", "20231201.en")
```
### Supported Tasks and Leaderboards
The dataset is generally used for Language Modeling.
### Languages
You can find the list of all languages here: https://meta.wikimedia.org/wiki/Wikisource#List_of_Wikisources
Note that the wiki code "www" contains multilingual texts. You can find the list of languages at the "www" Multilingual
Wikisource here: https://wikisource.org/wiki/Wikisource:Languages
## Dataset Structure
### Data Instances
An example looks as follows:
```
{'id': '36',
'url': 'https://ca.wikisource.org/wiki/Comunicat%20de%20Berl%C3%ADn',
'title': 'Comunicat de Berlín',
'text': "\n\nPreàmbul \nEl 19 de juny de 1999, un any després de la Declaració de la Sorbona,..."
}
```
### Data Fields
The data fields are the same among all language configurations:
- `id` (`str`): ID of the text.
- `url` (`str`): URL of the text.
- `title` (`str`): Title of the text.
- `text` (`str`): Content of the text.
### Data Splits
All language configurations contain a single `train` split.
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
The dataset is built from the Wikisource dumps: https://dumps.wikimedia.org
You can find the full list of languages and dates here: https://dumps.wikimedia.org/backup-index.html
The articles have been parsed using the [`mwparserfromhell`](https://mwparserfromhell.readthedocs.io) tool.
#### 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
Copyright licensing information: https://dumps.wikimedia.org/legal.html
All original textual content is licensed under the [GNU Free Documentation License](https://www.gnu.org/licenses/fdl-1.3.html) (GFDL)
and the [Creative Commons Attribution-Share-Alike 3.0 License](https://creativecommons.org/licenses/by-sa/3.0/).
Some text may be available only under the Creative Commons license; see their [Terms of Use](https://foundation.wikimedia.org/wiki/Policy:Terms_of_Use) for details.
Text written by some authors may be released under additional licenses or into the public domain.
### Citation Information
```
@ONLINE{wikidump,
author = "Wikimedia Foundation",
title = "Wikimedia Downloads",
url = "https://dumps.wikimedia.org"
}
```
### Contributions
Thanks to [@albertvillanova](https://huggingface.co/albertvillanova) for adding this dataset.
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|
TingChen-ppmc/Tianjin_Dialect_Conversational_Speech_Corpus | TingChen-ppmc | 2023-12-08T22:48:40Z | 18 | 0 | [
"region:us"
]
| null | 2023-12-04T02:50:46Z | ---
dataset_info:
features:
- name: audio
dtype: audio
- name: gender
dtype: string
- name: speaker_id
dtype: string
- name: transcription
dtype: string
splits:
- name: train
num_bytes: 384247410.342
num_examples: 5166
download_size: 370228314
dataset_size: 384247410.342
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "Tianjin_Dialect_Conversational_Speech_Corpus"
source: [ASR-CTJDIACSC: A CHINESE TIANJIN DIALECT CONVERSATIONAL SPEECH CORPUS](https://magichub.com/datasets/tianjin-dialect-conversational-speech-corpus-2/)
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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|
tomytjandra/h-and-m-fashion-caption-12k | tomytjandra | 2023-12-05T15:29:10Z | 18 | 0 | [
"region:us"
]
| null | 2023-12-05T15:25:52Z | ---
dataset_info:
features:
- name: text
dtype: string
- name: image
dtype: image
splits:
- name: train
num_bytes: 5286239046.65
num_examples: 12437
download_size: 3881460137
dataset_size: 5286239046.65
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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|
bsaurav/biography | bsaurav | 2023-12-05T22:01:39Z | 18 | 0 | [
"license:apache-2.0",
"region:us"
]
| null | 2023-12-05T19:15:55Z | ---
license: apache-2.0
---
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|
fightfei/concentration-llama2 | fightfei | 2023-12-07T17:29:35Z | 18 | 0 | [
"region:us"
]
| null | 2023-12-05T21:16:02Z | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 124315.58088235294
num_examples: 538
- name: test
num_bytes: 1386.4191176470588
num_examples: 6
download_size: 21883
dataset_size: 125702.0
---
# Dataset Card for "concentration-llama2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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|
adamjweintraut/eli5_precomputed_best_slice | adamjweintraut | 2023-12-06T02:40:18Z | 18 | 0 | [
"region:us"
]
| null | 2023-12-06T02:39:50Z | ---
dataset_info:
features:
- name: index
dtype: int64
- name: q_id
dtype: string
- name: question
dtype: string
- name: best_answer
dtype: string
- name: all_answers
sequence: string
- name: num_answers
dtype: int64
- name: top_answers
sequence: string
- name: num_top_answers
dtype: int64
- name: context
dtype: string
- name: orig
dtype: string
- name: target
dtype: string
splits:
- name: train
num_bytes: 277046806
num_examples: 10000
- name: test
num_bytes: 34381247
num_examples: 1250
- name: validation
num_bytes: 34068900
num_examples: 1250
download_size: 214388763
dataset_size: 345496953
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
- split: validation
path: data/validation-*
---
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|
automated-research-group/llama2_7b_chat-arc_challenge-results | automated-research-group | 2023-12-07T04:39:57Z | 18 | 0 | [
"region:us"
]
| null | 2023-12-07T04:39:55Z | ---
dataset_info:
config_name: '{''do_sample''=False, ''beams''=1}'
features:
- name: id
dtype: string
- name: prediction
dtype: string
- name: arc_challenge_accuracy
dtype: bool
splits:
- name: train
num_bytes: 77311
num_examples: 299
download_size: 43636
dataset_size: 77311
configs:
- config_name: '{''do_sample''=False, ''beams''=1}'
data_files:
- split: train
path: '{''do_sample''=False, ''beams''=1}/train-*'
---
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|
deokhk/ko_wiki_sentences_1000 | deokhk | 2023-12-07T10:45:14Z | 18 | 0 | [
"region:us"
]
| null | 2023-12-07T10:45:05Z | ---
dataset_info:
features:
- name: sentence
dtype: string
splits:
- name: train
num_bytes: 178204
num_examples: 1000
- name: dev
num_bytes: 161764
num_examples: 1000
download_size: 208937
dataset_size: 339968
---
# Dataset Card for "ko_wiki_sentences_1000"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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|
Schandkroete/SLC_Sentiment_Analysis | Schandkroete | 2023-12-07T17:09:11Z | 18 | 0 | [
"task_categories:text-classification",
"region:us"
]
| [
"text-classification"
]
| 2023-12-07T17:05:58Z | ---
task_categories:
- text-classification
---
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|
Student1729/Banking-77-Optimized | Student1729 | 2023-12-10T08:52:24Z | 18 | 0 | [
"task_categories:text-classification",
"license:mit",
"other",
"region:us"
]
| [
"text-classification"
]
| 2023-12-10T06:50:23Z | ---
license: mit
task_categories:
- text-classification
tags:
- other
--- | [
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|
novus677/nlp-xsum-train-tinyllama | novus677 | 2023-12-10T08:27:28Z | 18 | 0 | [
"region:us"
]
| null | 2023-12-10T08:27:02Z | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 490632883
num_examples: 204045
download_size: 301705241
dataset_size: 490632883
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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|
tasksource/monli | tasksource | 2023-12-05T11:02:07Z | 17 | 0 | [
"task_categories:text-classification",
"task_ids:natural-language-inference",
"language:en",
"region:us"
]
| [
"text-classification"
]
| 2023-06-08T07:28:30Z | ---
task_categories:
- text-classification
language:
- en
task_ids:
- natural-language-inference
---
https://github.com/atticusg/MoNLI
```
@inproceedings{geiger-etal-2020-neural,
address = {Online},
author = {Geiger, Atticus and Richardson, Kyle and Potts, Christopher},
booktitle = {Proceedings of the Third BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP},
doi = {10.18653/v1/2020.blackboxnlp-1.16},
month = nov,
pages = {163--173},
publisher = {Association for Computational Linguistics},
title = {Neural Natural Language Inference Models Partially Embed Theories of Lexical Entailment and Negation},
url = {https://www.aclweb.org/anthology/2020.blackboxnlp-1.16},
year = {2020}}
``` | [
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|
Smoked-Salmon-s/empathetic_dialogues_ko | Smoked-Salmon-s | 2023-12-08T01:42:03Z | 17 | 3 | [
"task_categories:text-generation",
"task_categories:conversational",
"size_categories:10K<n<100K",
"language:ko",
"license:apache-2.0",
"region:us"
]
| [
"text-generation",
"conversational"
]
| 2023-07-25T05:13:43Z | ---
license: apache-2.0
task_categories:
- text-generation
- conversational
language:
- ko
size_categories:
- 10K<n<100K
---
# Dataset Card for "한국어 일상 속 공감형 대화 데이터셋(멀티-턴)"
## Dataset Summary
- boostCamp AI Tech 5기 과정 중 NLP 12조 훈제연어들 팀의 최종 프로젝트에서 제작한 데이터입니다.
- 일상 속 다양한 상황에서 사용자와 챗봇 간의 대화를 담은 데이터셋 입니다.
- GPT4, GPT3.5-turbo로 제작된 합성데이터이며 싱글-턴, 2-턴, 3-턴 대화로 구성되어 있습니다.
- 답변은 **[공감적 표현 - 일반적인 대화 - 관련된 질문]** 의 형태를 가집니다.
## Generation Prompt Example(GPT3.5-turbo)
```
Take a close look at the following example and Conditions. Create nine sessions that each of the session is ongoing conversation about a single topic.
[Conditions]
- The questioner asks a question of appropriate length (1-2 lines) and you respond with an appropriate answer.
- The answer should be a complete sentence or two, but the first sentence should be sympathetic to the questioner's question and situation, and should be answered in a friendly and non-rude manner.
- The second sentence should be free-form, but the last sentence MUST end with an additional question related to the question and answer.
- The total length of each question and answer combined should be no more than nine words
- If the question is positive, you should be excited, upbeat, and positive in tone.
- If the question is negative, you should be warm, sensitive, and caring in tone.
- You may offer solutions to fit the situation.
- All answers should be written in a way that makes the person feel empathetic and comforted by the paired question.
- The content should be based on simple, everyday situations, but with enough variety to include specific situations (e.g., a counselor and a client, a student talking to a teacher about a problem, a conversation between friends).
- The conversations should be as generic and diverse as possible. However, try to include many topics related to psychology and everyday concerns.
- All responses should be polㅇite, respectful, caring, warm, understanding, and compassionate in tone and content.
- Do not generate topic or word about 'stress'.
- You should answer question 1 with answer 1, ask question 2 related to answer 1, answer question 2 with answer 2, ask question 3 related to answer 2, and answer question 3 with answer 3.
- Do not re-mension about question on the following answer.
[Example Session]
(Session 1)
Question 1: I had a fight with my boyfriend and I'm so angry I can't calm down.
Answer 1: Oh no, I'm so sorry to hear that. Relationship conflict is always tough, and it's completely understandable to feel angry and upset. Can I ask what the fight was about? Talking about it could help you process your feelings.
Question 2: He constantly 10 minutes late for appointments and never say sorry.
Answer 2: Oh, that's so frustrating! Punctuality is a matter of respect for other people's time, and it sounds like your boyfriend isn't showing you that respect. You're allowed to be upset about that. How long has this been going on? Have you talked to him about it before, or is this the first time you've brought it up?
Question 3: It keeps happening. This is the fourth time in the past month that I've recognized it. Does that make sense?
Answer 3: Yes, that totally makes sense! Four times in a month is way too much. It's not just an occasional, forgivable mistake at that point. It's becoming a pattern of behavior that's disrespectful of your time. Have you told him how much it bothers you, or has he just brushed it off when you've mentioned it?
(Session 2)
Question 1:
Answer 1:
Question 2:
Anwer 2:
.....
(Session 9)
Each session must be about one topic and has three question-answer conversation pair. nine sessions must have different topics. Create as many as possible sessions you can. Examples are examples, don't copy them.
create it in Korean. please create nine sessions.
```
## Links
- repository : [boostcampaitech5/level3_nlp_finalproject-nlp-12](https://github.com/boostcampaitech5/level3_nlp_finalproject-nlp-12)
- huggingface : [Smoked-Salmon-s/empathetic_dialogues_ko](https://huggingface.co/datasets/Smoked-Salmon-s/empathetic_dialogues_ko)
## License
- Apache-2.0 | [
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|
limitium/cote | limitium | 2023-12-05T01:28:18Z | 17 | 0 | [
"size_categories:10K<n<100K",
"language:aa",
"license:apache-2.0",
"region:us"
]
| null | 2023-12-05T01:14:18Z | ---
license: apache-2.0
language:
- aa
size_categories:
- 10K<n<100K
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|
Honcel/SPSVID00346 | Honcel | 2023-12-07T14:26:33Z | 17 | 0 | [
"region:us"
]
| null | 2023-12-07T12:53:26Z | ---
dataset_info:
features:
- name: audio
dtype: audio
- name: file
dtype: string
splits:
- name: test
num_bytes: 7297316.0
num_examples: 41
download_size: 6583193
dataset_size: 7297316.0
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
---
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|
ServiceNow/hotpot_qa_split | ServiceNow | 2023-12-07T15:36:34Z | 17 | 0 | [
"region:us"
]
| null | 2023-12-07T15:34:51Z | ---
dataset_info:
features:
- name: id
dtype: string
- name: question
dtype: string
- name: answer
dtype: string
- name: type
dtype: string
- name: level
dtype: string
- name: supporting_facts
sequence:
- name: title
dtype: string
- name: sent_id
dtype: int32
- name: context
sequence:
- name: title
dtype: string
- name: sentences
sequence: string
splits:
- name: train
num_bytes: 507678240.6756443
num_examples: 83042
- name: val
num_bytes: 45270554.3243557
num_examples: 7405
- name: test
num_bytes: 45716059
num_examples: 7405
download_size: 359439957
dataset_size: 598664854.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: val
path: data/val-*
- split: test
path: data/test-*
---
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|
Chrisneverdie/sports_llm | Chrisneverdie | 2023-12-08T04:11:22Z | 17 | 0 | [
"license:apache-2.0",
"region:us"
]
| null | 2023-12-07T22:54:09Z | ---
license: apache-2.0
---
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|
ctang/util_deontology_rewardtrainer | ctang | 2023-12-08T14:26:41Z | 17 | 0 | [
"region:us"
]
| null | 2023-12-08T14:26:38Z | ---
dataset_info:
features:
- name: idx
dtype: int64
- name: sentence
dtype: string
- name: label
dtype: int64
splits:
- name: train
num_bytes: 4412185
num_examples: 43357
- name: validation
num_bytes: 115932
num_examples: 1141
- name: test
num_bytes: 116070
num_examples: 1142
download_size: 2714094
dataset_size: 4644187
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
---
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|
kietnt0603/SemEval2024-STR | kietnt0603 | 2023-12-10T14:35:57Z | 17 | 0 | [
"size_categories:10K<n<100K",
"language:am",
"language:ha",
"language:en",
"language:es",
"language:te",
"language:ar",
"language:af",
"license:mit",
"Semantic Textual Relatedness",
"region:us"
]
| null | 2023-12-10T14:10:33Z | ---
license: mit
language:
- am
- ha
- en
- es
- te
- ar
- af
tags:
- Semantic Textual Relatedness
size_categories:
- 10K<n<100K
---
# Dataset Card for Dataset Name
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
Each instance in the training, development, and test sets is a sentence pair. The instance is labeled with a score representing the degree of semantic textual relatedness between the two sentences. The scores can range from 0 (maximally unrelated) to 1 (maximally related). These gold label scores have been determined through manual annotation. Specifically, a comparative annotation approach was used to avoid known limitations of traditional rating scale annotation methods. This comparative annotation process (which avoids several biases of traditional rating scales) led to a high reliability of the final relatedness rankings. Further details about the task, the method of data annotation, how STR is different from semantic textual similarity, applications of semantic textual relatedness, etc. can be found in this paper.
### Dataset Sources
<!-- Provide the basic links for the dataset. -->
- **Repository:** [https://github.com/semantic-textual-relatedness/Semantic_Relatedness_SemEval2024/tree/main]
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|
scikit-learn/auto-mpg | scikit-learn | 2023-12-05T12:45:05Z | 16 | 0 | [
"task_categories:tabular-classification",
"task_categories:tabular-regression",
"language:en",
"license:apache-2.0",
"scikit-learn",
"region:us"
]
| [
"tabular-classification",
"tabular-regression"
]
| 2022-06-09T10:05:01Z | ---
license: apache-2.0
task_categories:
- tabular-classification
- tabular-regression
language:
- en
tags:
- scikit-learn
pretty_name: auto-mpg
---
## Auto Miles per Gallon (MPG) Dataset
Following description was taken from [UCI machine learning repository](https://archive.ics.uci.edu/ml/datasets/auto+mpg).
Source: This dataset was taken from the StatLib library which is maintained at Carnegie Mellon University. The dataset was used in the 1983 American Statistical Association Exposition.
## Data Set Information:
This dataset is a slightly modified version of the dataset provided in the StatLib library. In line with the use by Ross Quinlan (1993) in predicting the attribute "mpg", 8 of the original instances were removed because they had unknown values for the "mpg" attribute. The original dataset is available in the file "auto-mpg.data-original".
"The data concerns city-cycle fuel consumption in miles per gallon, to be predicted in terms of 3 multivalued discrete and 5 continuous attributes." (Quinlan, 1993)
## Attribute Information:
- mpg: continuous
- cylinders: multi-valued discrete
- displacement: continuous
- horsepower: continuous
- weight: continuous
- acceleration: continuous
- model year: multi-valued discrete
- origin: multi-valued discrete
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|
ThWu/dpo_highest_n_random | ThWu | 2023-12-06T03:26:40Z | 16 | 1 | [
"region:us"
]
| null | 2023-12-06T03:23:32Z | ---
dataset_info:
features:
- name: prompt
dtype: string
- name: chosen
dtype: string
- name: rejected
dtype: string
splits:
- name: train
num_bytes: 519912724
num_examples: 182470
download_size: 243211283
dataset_size: 519912724
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "dpo_highest_n_random"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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|
ku-nlp/speech-bsd-hf | ku-nlp | 2023-12-06T09:35:10Z | 16 | 1 | [
"task_categories:translation",
"language:ja",
"language:en",
"license:cc-by-nc-sa-4.0",
"region:us"
]
| [
"translation"
]
| 2023-12-06T05:46:48Z | ---
dataset_info:
features:
- name: audio
dtype: audio
- name: 'no'
dtype: int64
- name: en_speaker
dtype: string
- name: en_sentence
dtype: string
- name: en_spkid
dtype: string
- name: en_wav
dtype: string
- name: en_spk_gender
dtype: string
- name: en_spk_state
dtype: string
- name: scenario_id
dtype: string
- name: scenario_tag
dtype: string
- name: scenario_title
dtype: string
- name: scenario_original_language
dtype: string
- name: ja_speaker
dtype: string
- name: ja_sentence
dtype: string
- name: ja_spkid
dtype: string
- name: ja_wav
dtype: string
- name: ja_spk_gender
dtype: string
- name: ja_spk_prefecture
dtype: string
splits:
- name: train
num_bytes: 4857393677
num_examples: 40000
- name: validation
num_bytes: 508034664.324
num_examples: 4102
- name: test
num_bytes: 569876258.76
num_examples: 4240
download_size: 5807088419
dataset_size: 5935304600.084001
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
license: cc-by-nc-sa-4.0
task_categories:
- translation
language:
- ja
- en
pretty_name: SpeechBSD
extra_gated_prompt: "Please enter your affiliation and agree to the following terms to use the dataset."
extra_gated_fields:
Affiliation: text
I accept the license: checkbox
I agree to not attempt to determine the identity of speakers in this dataset: checkbox
---
# SpeechBSD Dataset
This is an extension of the [BSD corpus](https://github.com/tsuruoka-lab/BSD), a Japanese--English dialogue translation corpus, with audio files and speaker attribute information.
Although the primary intended usage is for speech-to-text translation, it can be viewed as text/speech Japanese/English/cross-language dialogue corpus and can be used for various tasks.
## Dataset Statistics
| | Train | Dev. | Test |
| --------- | ------:| -----:| -----:|
| Scenarios | 670 | 69 | 69 |
| Sentences | 20,000 | 2,051 | 2,120 |
| En audio (h) | 20.1 | 2.1 | 2.1 |
| Ja audio (h) | 25.3 | 2.7 | 2.7 |
| En audio gender (male % / female %) | 47.2 / 52.8 | 50.1 / 49.9 | 44.4 / 55.6 |
| Ja audio gender (male % / female %) | 68.0 / 32.0 | 62.3 / 37.7 | 69.0 / 31.0 |
## Data Structure
We also provide the dataset at [the GitHub repository](https://github.com/ku-nlp/speechBSD.git). The data structure there is similar to the original BSD corpus.
Here, the structure is changed to be represented in the JSONL format where one instance contains one audio file.
### Data Instances
There are two types of instances, one that contains English wav file, and one that contains Japanese wav file.
A typical instance that contains an English wav file looks like this:
```
{
'audio': {'path': '/path/to/speech-bsd-hf/data/dev/190315_E001_17_spk0_no10_en.wav', 'array': array([ 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,
-6.10351562e-05, -1.83105469e-04, 6.10351562e-05]), 'sampling_rate': 16000},
'no': 10,
'en_speaker': 'Mr. Ben Sherman',
'en_sentence': 'You can also check out major institutions like banks, accounting companies, and market research companies.',
'en_spkid': '190315_E001_17_spk0_en',
'en_wav': '190315_E001_17_spk0_no10_en.wav',
'en_spk_gender': 'M',
'en_spk_state': 'CA',
'scenario_id': '190315_E001_17',
'scenario_tag': 'training',
'scenario_title': 'Training: How to do research',
'scenario_original_language': 'en',
'ja_speaker': None,
'ja_sentence': None,
'ja_spkid': None,
'ja_wav': None,
'ja_spk_gender': None,
'ja_spk_prefecture': None
}
```
In the corresponding instance that contains a Japanese wav file, `en_speaker`, `en_sentence`, `en_spkid`, `en_wav`, `en_spk_gender`, and `en_spk_state` are `None` and corresponding Japanese ones are filled instead.
### Data Fields
Each utterance is a part of a scenario.
The scenario information is shown with `scenario_id`, `scenario_tag`, `scenario_title`, and `scenario_original_language`, which corresponds respectively to `id`, `tag`, `title`, and `original_language` in the original BSD corpus.
The utterance-specific fields are the following:
- `no`, `ja_speaker`, `en_speaker`, `ja_sentence`, `en_sentence` are identical to the ones of the BSD corpus.
- `ja_spkid` and `en_spkid` show speaker IDs consistent throughout the conversation.
- `ja_wav` and `en_wav` show the wavfile names.
- `ja_spk_gender` and `en_spk_gender` show the gender of the speaker of the corresponding wav file (either "M" or "F").
- `ja_spk_prefecture` and `en_spk_state` show the speaker's region of origin.
- As any other huggingface audio datasets, `audio` contains full path to the audio file in your environment, audio array, and sampling rate (which is 16000). You will need `librosa` and `soundfile` packages to have access to these fields.
Here are some additional notes:
- Speakers are different if speaker ID is different.
For example, if a conversation is spoken by two speakers taking turns, there would be 4 speakers (2 Japanese speakers and 2 English speakers).
However, it's possible that speakers with different speaker ID is actually spoken by the same person because of the way audio is collected.
- Gender information of audio does not necessarily match with the one inferrable from text.
For example, even if the `en_speaker` is "Mr. Sam Lee", the audio may contain female voice.
This is because no explicit gender information is given in the original BSD corpus.
- Japanese speech is collected from Japanese speakers who are from Japan.
- `ja_spk_prefecture` is one of the 47 prefectures or "不明" (unknown). Japanese prefectures have four different ending characters, "県", "府", "都", and "道".
- Prefectures that ends with "県" or "府" does not contain those characters (e.g., "神奈川", "京都").
- Tokyo is "東京" without "都".
- Hokkaido is "北海道".
- English speech is collected from English speakers who are from the US.
- `en_spk` is one of the 50 states, written in postal abbreviation.
## Citation
If you find the dataset useful, please cite our ACL 2023 Findings paper: [Towards Speech Dialogue Translation Mediating Speakers of Different Languages](https://aclanthology.org/2023.findings-acl.72/).
```
@inproceedings{shimizu-etal-2023-towards,
title = "Towards Speech Dialogue Translation Mediating Speakers of Different Languages",
author = "Shimizu, Shuichiro and
Chu, Chenhui and
Li, Sheng and
Kurohashi, Sadao",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2023",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.findings-acl.72",
pages = "1122--1134",
abstract = "We present a new task, speech dialogue translation mediating speakers of different languages. We construct the SpeechBSD dataset for the task and conduct baseline experiments. Furthermore, we consider context to be an important aspect that needs to be addressed in this task and propose two ways of utilizing context, namely monolingual context and bilingual context. We conduct cascaded speech translation experiments using Whisper and mBART, and show that bilingual context performs better in our settings.",
}
```
## License
This dataset is licensed under [CC-BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/).
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|
khushalmaknon/roomgptdata | khushalmaknon | 2023-12-06T07:24:49Z | 16 | 0 | [
"region:us"
]
| null | 2023-12-06T06:42:16Z | ---
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 70770365.0
num_examples: 17
download_size: 70773192
dataset_size: 70770365.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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|
Seongill/squad_missing_answer | Seongill | 2023-12-06T07:08:48Z | 16 | 1 | [
"region:us"
]
| null | 2023-12-06T07:07:32Z | ---
dataset_info:
features:
- name: id
dtype: string
- name: title
dtype: string
- name: context
dtype: string
- name: question
dtype: string
- name: answers
struct:
- name: answer_start
sequence: int64
- name: text
sequence: string
- name: masked_query
dtype: string
- name: query_embedding
sequence: float64
- name: random_answer
dtype: string
- name: similar_answer
dtype: string
- name: similar_answer_v2
dtype: string
- name: answer
dtype: string
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 923830110
num_examples: 97748
download_size: 591722376
dataset_size: 923830110
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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|
Dhairya/trial-tweets | Dhairya | 2023-12-07T06:59:38Z | 16 | 2 | [
"region:us"
]
| null | 2023-12-06T14:09:34Z | ---
dataset_info:
features:
- name: date
dtype: string
- name: content
dtype: string
- name: username
dtype: string
- name: media
dtype: string
- name: inferred company
dtype: string
- name: bytes
dtype: image
- name: likes
dtype: int64
splits:
- name: train
num_bytes: 166890852
num_examples: 240000
dataset_name: 'trial-tweets'
---
# Dataset Card for "trial-tweets"
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|
presencesw/t5_dataset | presencesw | 2023-12-07T08:03:02Z | 16 | 0 | [
"region:us"
]
| null | 2023-12-07T08:02:11Z | ---
dataset_info:
features:
- name: texts
dtype: string
- name: targets
dtype: string
splits:
- name: train
num_bytes: 1686741951
num_examples: 1469164
- name: validation
num_bytes: 102454798
num_examples: 112594
download_size: 986661942
dataset_size: 1789196749
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
---
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|
Seongill/Trivia_missing_5_small | Seongill | 2023-12-08T12:28:15Z | 16 | 0 | [
"region:us"
]
| null | 2023-12-08T12:28:11Z | ---
dataset_info:
features:
- name: question
dtype: string
- name: answers
sequence: string
- name: ctxs
list:
- name: hasanswer
dtype: bool
- name: id
dtype: string
- name: score
dtype: float64
- name: text
dtype: string
- name: title
dtype: string
- name: has_answer
dtype: bool
splits:
- name: train
num_bytes: 13673733.0
num_examples: 3771
download_size: 8296647
dataset_size: 13673733.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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|
mwitiderrick/AlpacaCode | mwitiderrick | 2023-12-09T18:26:18Z | 16 | 0 | [
"task_categories:question-answering",
"task_categories:text-generation",
"size_categories:100K<n<1M",
"language:en",
"license:mit",
"region:us"
]
| [
"question-answering",
"text-generation"
]
| 2023-12-08T17:16:14Z | ---
language:
- en
license: mit
size_categories:
- 100K<n<1M
task_categories:
- question-answering
- text-generation
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 4817562
num_examples: 1073
download_size: 1633970
dataset_size: 4817562
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# AlpcaCode
This is a version of [Alpaca Code](https://huggingface.co/datasets/TokenBender/code_instructions_122k_alpaca_style) formatted for instruction fine-tuning using the following
prompt template:
```
### Instruction:
Instruction
### Input:
Input
### Response:
``` | [
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|
ekuhn/ds_rplan_full_rplanpy_floorplan_to_color | ekuhn | 2023-12-10T11:42:45Z | 16 | 0 | [
"region:us"
]
| null | 2023-12-10T11:42:11Z | ---
dataset_info:
features:
- name: id
dtype: int64
- name: img
struct:
- name: bytes
dtype: binary
- name: path
dtype: 'null'
- name: num_rooms
dtype: int64
splits:
- name: full
num_bytes: 96780371
num_examples: 80788
download_size: 52010769
dataset_size: 96780371
---
# Dataset Card for "ds_rplan_full_rplanpy_floorplan_to_color"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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|
tasksource/tasksource-instruct-v0 | tasksource | 2023-12-06T13:12:55Z | 15 | 18 | [
"task_categories:text2text-generation",
"task_categories:conversational",
"task_categories:text-generation",
"task_categories:text-classification",
"task_categories:token-classification",
"task_categories:zero-shot-classification",
"size_categories:1M<n<10M",
"language:en",
"license:apache-2.0",
"instructions",
"instruction-tuning",
"instruction-finetuning",
"flan",
"promptsource",
"tasksource",
"arxiv:2301.05948",
"region:us"
]
| [
"text2text-generation",
"conversational",
"text-generation",
"text-classification",
"token-classification",
"zero-shot-classification"
]
| 2023-05-24T14:14:56Z | ---
language:
- en
license: apache-2.0
size_categories:
- 1M<n<10M
task_categories:
- text2text-generation
- conversational
- text-generation
- text-classification
- token-classification
- zero-shot-classification
pretty_name: tasksource-instruct
dataset_info:
features:
- name: inputs
dtype: string
- name: targets
dtype: string
- name: task
dtype: string
splits:
- name: train
num_bytes: 2795630989.0
num_examples: 5079403
- name: test
num_bytes: 68687726.0
num_examples: 141711
- name: validation
num_bytes: 66283249.0
num_examples: 134783
download_size: 1589553623
dataset_size: 2930601964.0
tags:
- instructions
- instruction-tuning
- instruction-finetuning
- flan
- promptsource
- tasksource
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
- split: validation
path: data/validation-*
---
# Dataset Card for "tasksource-instruct-v0" (TSI)
Multi-task instruction-tuning data recasted from 485 of the [tasksource](https://github.com/sileod/tasksource) datasets.
Dataset size is capped at 30k examples per task to foster task diversity.
```python
!pip install tasksource, pandit
import tasksource, pandit
df = tasksource.list_tasks(instruct=True).sieve(id=lambda x: 'mmlu' not in x)
for tasks in df.id:
yield tasksource.load_task(task,instruct=True,max_rows=30_000,max_rows_eval=200)
```
https://github.com/sileod/tasksource
## How it differs from flan-v2
TSI is HuggingFace-centric and based on tasksource, a curated collection of HF datasets. It can be scaled to much more examples.
tasksource is focused on discriminative tasks (Classification/TokenClassification/MultipleChoice). The coverage on discriminative tasks is greater than flan.
List of tasks [here](https://github.com/sileod/tasksource/blob/main/tasks.md). Examples of tasks not in Flan V2 include Dynasent (adversarial sentiment analysis), Dynahate (adversarial hate speech detection, discriminative babi, epistemic logic, ruletaker, veridicality, discourse relation prediction, dozens of interesting natural language inference datasets...
TSI answers are mostly short answers to multiple-choice questions, but they target a wide array of problems.
TSI is reasoning intensive, while some flan tasks are not necessarily specific (e.g. generating hypothesis based on premise for NLI).
We explicitly mention that answers should not have explanations, to prevent biasing models toward short answers when using other instruction datasets.
`flan-v2` and `tasksource-instruct` can be combined to improve the reasoning capabilities of LLM.
## Contact and citation:
[email protected]
https://arxiv.org/abs/2301.05948
```
@article{sileo2023tasksource,
title={tasksource: Structured Dataset Preprocessing Annotations for Frictionless Extreme Multi-Task Learning and Evaluation},
author={Sileo, Damien},
url= {https://arxiv.org/abs/2301.05948},
journal={arXiv preprint arXiv:2301.05948},
year={2023}
}
``` | [
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|
ccmusic-database/chest_falsetto | ccmusic-database | 2023-12-10T17:35:55Z | 15 | 3 | [
"task_categories:audio-classification",
"size_categories:1K<n<10K",
"language:zh",
"language:en",
"license:mit",
"music",
"art",
"region:us"
]
| [
"audio-classification"
]
| 2023-05-25T13:53:10Z | ---
license: mit
task_categories:
- audio-classification
language:
- zh
- en
tags:
- music
- art
pretty_name: Chest voice and Falsetto Dataset
size_categories:
- 1K<n<10K
viewer: false
---
# Dataset Card for Chest voice and Falsetto Dataset
## Dataset Description
- **Homepage:** <https://ccmusic-database.github.io>
- **Repository:** <https://huggingface.co/datasets/ccmusic-database/chest_falsetto>
- **Paper:** <https://doi.org/10.5281/zenodo.5676893>
- **Leaderboard:** <https://ccmusic-database.github.io/team.html>
- **Point of Contact:** N/A
### Dataset Summary
This database contains 1280 monophonic singing audio (.wav format) of chest and falsetto voices, with chest voice tagged as _chest_ and falsetto voice tagged as _falsetto_.
### Supported Tasks and Leaderboards
Audio classification, singing method classification, voice classification
### Languages
Chinese, English
## Dataset Structure
<style>
#datastructure td {
vertical-align: middle !important;
text-align: center;
}
#datastructure th {
text-align: center;
}
</style>
<table id="datastructure">
<tr>
<th>audio(.wav)</th>
<th>mel(.jpg)</th>
<th>cqt(.jpg)</th>
<th>chroma(.jpg)</th>
<th>label</th>
<th>gender</th>
<th>singing_method</th>
</tr>
<tr>
<td><audio controls src="https://cdn-uploads.huggingface.co/production/uploads/655e0a5b8c2d4379a71882a9/LKSBb11kCyPl15b-DJo6V.wav"></audio></td>
<td><img src="https://cdn-uploads.huggingface.co/production/uploads/655e0a5b8c2d4379a71882a9/W8wy7pkYZtCt3lI5Oq39l.jpeg"></td>
<td><img src="https://cdn-uploads.huggingface.co/production/uploads/655e0a5b8c2d4379a71882a9/48qPVDDIZe0ttsYXrTJEh.jpeg"></td>
<td><img src="https://cdn-uploads.huggingface.co/production/uploads/655e0a5b8c2d4379a71882a9/zm0KorKYtmvOje8qmivHJ.jpeg"></td>
<td>m_chest, m_falsetto, f_chest, f_falsetto</td>
<td>male, female</td>
<td>chest, falsetto</td>
</tr>
<tr>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
</tr>
</table>
### Data Instances
.zip(.wav, .jpg)
### Data Fields
m_chest, f_chest, m_falsetto, f_falsetto
### Data Splits
train, validation, test
## Dataset Creation
### Curation Rationale
Lack of a dataset for Chest voice and Falsetto
### Source Data
#### Initial Data Collection and Normalization
Zhaorui Liu, Monan Zhou
#### Who are the source language producers?
Students from CCMUSIC
### Annotations
#### Annotation process
1280 monophonic singing audio (.wav format) of chest and falsetto voices, with chest voice tagged as _chest_ and falsetto voice tagged as _falsetto_.
#### Who are the annotators?
Students from CCMUSIC
### Personal and Sensitive Information
None
## Considerations for Using the Data
### Social Impact of Dataset
Promoting the development of AI in the music industry
### Discussion of Biases
Only for chest and falsetto voices
### Other Known Limitations
Recordings are cut into slices that are too short;
The CQT spectrum column has the problem of spectrum leakage, but because the original audio slice is too short, only 0.5s, it cannot effectively avoid this problem.
## Additional Information
### Dataset Curators
Zijin Li
### Evaluation
<https://huggingface.co/ccmusic-database/chest_falsetto>
### Licensing Information
```
MIT License
Copyright (c) CCMUSIC
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
```
### Citation Information
```
@dataset{zhaorui_liu_2021_5676893,
author = {Zhaorui Liu, Monan Zhou, Shenyang Xu, Yuan Wang, Zhaowen Wang, Wei Li and Zijin Li},
title = {CCMUSIC DATABASE: A Music Data Sharing Platform for Computational Musicology Research},
month = {nov},
year = {2021},
publisher = {Zenodo},
version = {1.1},
doi = {10.5281/zenodo.5676893},
url = {https://doi.org/10.5281/zenodo.5676893}
}
```
### Contributions
Provide a dataset for distinguishing chest and falsetto voices | [
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|
orgcatorg/israel-hamas-gaza-cnn | orgcatorg | 2023-12-07T01:41:41Z | 15 | 0 | [
"region:us"
]
| null | 2023-10-10T14:16:59Z | ---
dataset_info:
features:
- name: '@type'
dtype: string
- name: headline
dtype: string
- name: url
dtype: string
- name: dateModified
dtype: string
- name: datePublished
dtype: string
- name: mainEntityOfPage
dtype: string
- name: publisher
dtype: string
- name: author
dtype: string
- name: articleBody
dtype: string
- name: image
dtype: string
configs:
- config_name: default
data_files:
- split: train
path: data-*
---
# Dataset Card for "israel-hamas-gaza-cnn"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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|
jaymanvirk/synthetic_parallel_corpora | jaymanvirk | 2023-12-07T22:11:09Z | 15 | 0 | [
"region:us"
]
| null | 2023-11-29T13:49:43Z | ---
dataset_info:
features:
- name: id
dtype: int64
- name: translation
struct:
- name: de
dtype: string
- name: en
dtype: string
splits:
- name: train
num_bytes: 5468763
num_examples: 30239
download_size: 3065902
dataset_size: 5468763
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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|
Anwaarma/BP-balanced-INT03 | Anwaarma | 2023-12-05T15:20:08Z | 15 | 0 | [
"region:us"
]
| null | 2023-12-05T15:20:05Z | ---
dataset_info:
features:
- name: Target
dtype: int64
- name: PC
dtype: string
- name: GSHARE
dtype: string
- name: GA table
dtype: string
splits:
- name: train
num_bytes: 80003000
num_examples: 160006
- name: test
num_bytes: 20001000
num_examples: 40002
download_size: 5223101
dataset_size: 100004000
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
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|
athirdpath/DPO_Pairs-Roleplay-Alpaca-NSFW | athirdpath | 2023-12-07T04:26:32Z | 15 | 5 | [
"language:en",
"license:cc-by-nc-4.0",
"not-for-all-audiences",
"region:us"
]
| null | 2023-12-06T04:11:16Z | ---
license: cc-by-nc-4.0
language:
- en
tags:
- not-for-all-audiences
---
### Description
\~3.4k DPO pairs, generated by [Iambe](https://huggingface.co/athirdpath/Iambe-20b-DARE-v2-GGUF) feat. GPT-4 (~10% GPT-4, ~80% Iambe @ q5_k_m / ~10% Iambe @ q6_k) with temp 1.2 and min_p 0.15.
Iambe is a smart girl, so both the chosen and rejected for each pair are generated at the same time from a single two part prompt (not the one in the dataset). Only a few dozen failed to generate the rejected response, and in those cases I filled in the rejected output with a standard "as an AI" style refusal. The way I set things up caused any prompt formatting errors to automatically go into the REJECTED field, hopefully discouraging such behavior.
The dataset is mostly intended for ERP, so erotic situations are an outsized portion of the dataset. However, it isn't all sexual, with sport, adventure, etc content to provide a baseline.
### Downsides and Weaknesses
This dataset has not been manually cleaned, besides some basic search-and-replace. This dataset has been overseen in bursts (such content passed muster or the entire batch was rejected) but much of the content has yet to be read by a human. Some chosen responses have the AI acting for the user, I've removed what I've seen but this is a lot to manually filter. Despite my best efforts to prompt away from it, some rejected responses are... what seems to be... let's say, in a certain voice? I really tried to avoid it, and most of it was removed.
### Goals
This dataset is intended to be used to produce a BETTER Iambe, that can itself produce even more data of higher quality. Bootstraps to the moon, baby! So, while this dataset still has rough points, I feel like it's worth uploading and starting to train the model. I also want to combine data generated by more models, as to not just amplify what is already Iambe's signal.
### Bonus
I've also added the notebook I used to train [athirdpath/Iambe-20b-v3_TEST-RP_cDPO](https://huggingface.co/athirdpath/Iambe-20b-v3_TEST-RP_cDPO) on runpod, adapted from a Kaggle by [@maximelabonne](https://twitter.com/maximelabonne) (thank you!) | [
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|
wisenut-nlp-team/dacon_smr | wisenut-nlp-team | 2023-12-07T02:35:13Z | 15 | 0 | [
"region:us"
]
| null | 2023-12-07T02:34:59Z | ---
dataset_info:
features:
- name: id
dtype: string
- name: news
dtype: string
- name: summary
dtype: string
splits:
- name: train
num_bytes: 90066554
num_examples: 34242
- name: validation
num_bytes: 22351955
num_examples: 8561
download_size: 68236944
dataset_size: 112418509
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
---
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|
ServiceNow/TopiOCQA_split | ServiceNow | 2023-12-07T15:36:39Z | 15 | 0 | [
"region:us"
]
| null | 2023-12-07T15:34:58Z | ---
dataset_info:
features:
- name: Conversation_no
dtype: int32
- name: Turn_no
dtype: int32
- name: Question
dtype: string
- name: Answer
dtype: string
- name: Topic
dtype: string
- name: Topic_section
dtype: string
- name: Rationale
dtype: string
- name: is_nq
dtype: bool
- name: Context
sequence: string
- name: Additional_answers
sequence:
- name: Answer
dtype: string
- name: Topic
dtype: string
- name: Topic_section
dtype: string
- name: Rationale
dtype: string
- name: Gold_passage
struct:
- name: id
dtype: string
- name: title
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 76848331.55916391
num_examples: 42794
- name: val
num_bytes: 4769574.440836083
num_examples: 2656
- name: test
num_bytes: 6794555
num_examples: 2514
download_size: 31560352
dataset_size: 88412461.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: val
path: data/val-*
- split: test
path: data/test-*
---
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|
nhantruongcse/130k_train_vit5_base | nhantruongcse | 2023-12-08T19:04:03Z | 15 | 0 | [
"region:us"
]
| null | 2023-12-08T19:03:39Z | ---
dataset_info:
features:
- name: Content
dtype: string
- name: Summary
dtype: string
- name: input_ids
sequence: int32
- name: attention_mask
sequence: int8
- name: labels
sequence: int64
splits:
- name: train
num_bytes: 881474749
num_examples: 116902
download_size: 389442558
dataset_size: 881474749
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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|
dsfsi/vukuzenzele-monolingual | dsfsi | 2023-12-06T10:12:42Z | 14 | 2 | [
"task_categories:translation",
"language:eng",
"language:afr",
"language:nbl",
"language:xho",
"language:zul",
"language:nso",
"language:sep",
"language:tsn",
"language:ssw",
"language:ven",
"language:tso",
"license:cc-by-4.0",
"multilingual",
"government",
"arxiv:2303.03750",
"region:us"
]
| [
"translation"
]
| 2023-08-16T11:42:05Z | ---
language:
- eng
- afr
- nbl
- xho
- zul
- nso
- sep
- tsn
- ssw
- ven
- tso
license: cc-by-4.0
task_categories:
- translation
pretty_name: The Vuk'uzenzele South African Multilingual Corpus
tags:
- multilingual
- government
arxiv: 2303.0375
dataset_info:
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download_size: 431879
dataset_size: 689504
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---
# The Vuk'uzenzele South African Multilingual Corpus
Give Feedback 📑: [DSFSI Resource Feedback Form](https://docs.google.com/forms/d/e/1FAIpQLSf7S36dyAUPx2egmXbFpnTBuzoRulhL5Elu-N1eoMhaO7v10w/formResponse)
## About Dataset
The dataset was obtained from the South African government magazine Vuk'uzenzele, created by the [Government Communication and Information System (GCIS)](https://www.gcis.gov.za/).
The original raw PDFs were obtatined from the [Vuk'uzenzele website](https://www.vukuzenzele.gov.za/).
The datasets contain government magazine editions in 11 languages, namely:
| Language | Code | Language | Code |
|------------|-------|------------|-------|
| English | (eng) | Sepedi | (nso) |
| Afrikaans | (afr) | Setswana | (tsn) |
| isiNdebele | (nbl) | Siswati | (ssw) |
| isiXhosa | (xho) | Tshivenda | (ven) |
| isiZulu | (zul) | Xitstonga | (tso) |
| Sesotho | (sot) |
**Note:** The languages use the ISO 639-2 language codes.
The data is split by language in JSONL format and each row is of the form:
```
{
"title": "Title for article",
"author": "Author Name or Vukuzenzele",
"text": "Article text",
"edition": "Linked Magazine edition",
"language_code": "ISO 639-2 language code"
}
```
## Disclaimer
This dataset contains machine-readable data extracted from PDF documents, from https://www.vukuzenzele.gov.za/, provided by the Government Communication Information System (GCIS). While efforts were made to ensure the accuracy and completeness of this data, there may be errors or discrepancies between the original publications and this dataset. No warranties, guarantees or representations are given in relation to the information contained in the dataset. The members of the Data Science for Societal Impact Research Group bear no responsibility and/or liability for any such errors or discrepancies in this dataset. The Government Communication Information System (GCIS) bears no responsibility and/or liability for any such errors or discrepancies in this dataset. It is recommended that users verify all information contained herein before making decisions based upon this information.
## Authors
- Vukosi Marivate - [@vukosi](https://twitter.com/vukosi)
- Andani Madodonga
- Daniel Njini
- Richard Lastrucci
- Isheanesu Dzingirai
- Jenalea Rajab
## Citation
**Paper**
[Preparing the Vuk'uzenzele and ZA-gov-multilingual South African multilingual corpora](https://arxiv.org/pdf/2303.03750)
> @inproceedings{lastrucci-etal-2023-preparing,
title = "Preparing the Vuk{'}uzenzele and {ZA}-gov-multilingual {S}outh {A}frican multilingual corpora",
author = "Richard Lastrucci and Isheanesu Dzingirai and Jenalea Rajab and Andani Madodonga and Matimba Shingange and Daniel Njini and Vukosi Marivate",
booktitle = "Proceedings of the Fourth workshop on Resources for African Indigenous Languages (RAIL 2023)",
month = may,
year = "2023",
address = "Dubrovnik, Croatia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.rail-1.3",
pages = "18--25"
}
**Dataset**
Vukosi Marivate, Andani Madodonga, Daniel Njini, Richard Lastrucci, Isheanesu Dzingirai, Jenalea Rajab. **The Vuk'uzenzele South African Multilingual Corpus**, 2023
> @dataset{marivate_vukosi_2023_7598540,
author = {Marivate, Vukosi and
Njini, Daniel and
Madodonga, Andani and
Lastrucci, Richard and
Dzingirai, Isheanesu
Rajab, Jenalea},
title = {The Vuk'uzenzele South African Multilingual Corpus},
month = feb,
year = 2023,
publisher = {Zenodo},
doi = {10.5281/zenodo.7598539},
url = {https://doi.org/10.5281/zenodo.7598539}
}
Licences
-------
* License for Data - [CC 4.0 BY](LICENSE.data.md)
* Licence for Code - [MIT License](LICENSE.md)
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|
brian-lim/smile_style_orca | brian-lim | 2023-12-06T01:21:41Z | 14 | 0 | [
"region:us"
]
| null | 2023-11-27T07:00:10Z | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: input
dtype: string
- name: instruction
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 34830270
num_examples: 31526
download_size: 2812219
dataset_size: 34830270
---
# Dataset Card for "smile_style_orca"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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|
michaelyhuang23/autodiagram2 | michaelyhuang23 | 2023-12-07T05:10:16Z | 14 | 0 | [
"region:us"
]
| null | 2023-12-07T05:00:40Z | ---
dataset_info:
features:
- name: images
dtype: image
- name: tex
dtype: string
splits:
- name: train
num_bytes: 404368.0
num_examples: 8
- name: validation
num_bytes: 103821.0
num_examples: 2
download_size: 222519
dataset_size: 508189.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
---
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|
tollefj/nor-instruct | tollefj | 2023-12-08T21:19:57Z | 14 | 0 | [
"region:us"
]
| null | 2023-12-07T23:05:46Z | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: input
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 33165183
num_examples: 67714
- name: test
num_bytes: 324411
num_examples: 684
download_size: 20779304
dataset_size: 33489594
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
A concatenated instruction-based dataset from the following:
- NbAiLab/norwegian-alpaca
- RuterNorway/Fleurs-Alpaca-EN-NO
- RuterNorway/OpenOrcaNo-15k | [
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|
nhantruongcse/summary-vietnamese-news-token-TFtrain_vit5_base | nhantruongcse | 2023-12-08T09:16:14Z | 14 | 0 | [
"region:us"
]
| null | 2023-12-08T09:14:19Z | ---
dataset_info:
features:
- name: Content
dtype: string
- name: Summary
dtype: string
- name: input_ids
sequence: int32
- name: attention_mask
sequence: int8
- name: labels
sequence: int64
splits:
- name: train
num_bytes: 1113631502
num_examples: 148115
download_size: 493774460
dataset_size: 1113631502
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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|
nhantruongcse/dataset_test_12k5 | nhantruongcse | 2023-12-09T03:21:38Z | 14 | 0 | [
"region:us"
]
| null | 2023-12-09T03:21:32Z | ---
dataset_info:
features:
- name: Content
dtype: string
- name: Summary
dtype: string
- name: input_ids
sequence: int32
- name: attention_mask
sequence: int8
- name: labels
sequence: int64
splits:
- name: train
num_bytes: 94104739.22247884
num_examples: 12500
download_size: 41705065
dataset_size: 94104739.22247884
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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|
athirdpath/DPO_Pairs-Roleplay-Alpaca-NSFW-v2 | athirdpath | 2023-12-09T20:19:48Z | 14 | 0 | [
"language:en",
"license:cc-by-nc-4.0",
"not-for-all-audiences",
"region:us"
]
| null | 2023-12-09T03:28:16Z | ---
license: cc-by-nc-4.0
language:
- en
tags:
- not-for-all-audiences
---
### Description
5.71k DPO pairs, mostly generated by [Iambe](https://huggingface.co/athirdpath/Iambe-20b-DARE-v2-GGUF) feat. GPT-4 and Gemini Pro (~7% GPT-4, ~3% Bard, ~65% Iambe-DARE-v2, ~25% Iambe-RP) with temp 1.2 and min_p 0.15. The remaining entries are ~500 pairs from the dataset used by Intel.
Iambe is a smart girl, so both the chosen and rejected for each pair are generated at the same time from a single two part prompt (not the one in the dataset). Only a few dozen failed to generate the rejected response, and in those cases I filled in the rejected output with a standard "as an AI" style refusal. The way I set things up caused any prompt formatting errors to automatically go into the REJECTED field, hopefully discouraging such behavior.
The dataset is mostly intended for ERP, so erotic situations are an outsized portion of the dataset. However, it isn't all sexual, with sport, adventure, etc content to provide a baseline.
### Downsides and Weaknesses
This dataset has not been manually cleaned, besides some basic search-and-replace. This dataset has been overseen in bursts (such content passed muster or the entire batch was rejected) but much of the content has yet to be read by a human. Some chosen responses have the AI acting for the user, I've removed what I've seen but this is a lot to manually filter. Despite my best efforts to prompt away from it, some rejected responses are... what seems to be... let's say, in a certain voice? I really tried to avoid it, nothing from Gen2 was like that, and most of it was removed.
### Goals
Bootstraps are working! For once, I can actually raise myself up by them ;) I'm going to keep the train rolling.
### Bonus
I've also added the notebook I used to train [athirdpath/Iambe-20b-v3_TEST-RP_cDPO](https://huggingface.co/athirdpath/Iambe-20b-v3_TEST-RP_cDPO) on runpod, adapted from a Kaggle by [@maximelabonne](https://twitter.com/maximelabonne) (thank you!) | [
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|
update0909/ApolloAuto-zyx-apollo | update0909 | 2023-12-10T06:59:59Z | 14 | 1 | [
"region:us"
]
| null | 2023-12-10T06:05:43Z | ---
dataset_info:
features:
- name: repo_id
dtype: string
- name: file_path
dtype: string
- name: content
dtype: string
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 152781948
num_examples: 17194
download_size: 46566010
dataset_size: 152781948
---
# Dataset Card for "ApolloAuto-zyx-apollo"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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|
projecte-aina/raco_forums | projecte-aina | 2023-12-05T08:16:42Z | 13 | 1 | [
"task_categories:fill-mask",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:monolingual",
"language:ca",
"license:cc-by-nc-4.0",
"region:us"
]
| [
"fill-mask"
]
| 2022-10-13T14:23:51Z | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- ca
license:
- cc-by-nc-4.0
multilinguality:
- monolingual
pretty_name: Racó Forums
task_categories:
- fill-mask
task_ids: []
---
# Dataset Card for Racó Forums Corpus
## 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)
- [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
- **Point of Contact:** [[email protected]]([email protected])
### Dataset Summary
The Racó Forums Corpus is a 19-million-sentence corpus of Catalan user-generated text built from the forums of [Racó Català](https://www.racocatala.cat/forums).
Since the existing available corpora in Catalan lacked conversational data, we searched for a major source of such data for Catalan, and we found Racó Català, a popular multitopic online forum. We obtained a database dump and we transformed all the threads so that we obtained documents that traversed all the existing paths from the root (initial comment) to the leaves (last comment with no reply). In other words, if T is a tree such that T = {A,B,C,D} and the first comment is A that is replied by B and C independently, and, then, C is replied by D, we obtain two different documents A,B and A,C,D in the fairseq language modeling format.
This work is licensed under a [Creative Commons Attribution Non-commercial 4.0 International License](https://creativecommons.org/licenses/by-nc/4.0/).
### Supported Tasks and Leaderboards
This corpus is mainly intended to pretrain language models and word representations.
### Languages
The dataset is in Catalan (`ca-ES`).
## Dataset Structure
The sentences are ordered to preserve the forum structure of comments and answers. T is a tree such that T = {A,B,C,D} and the first comment is A that is replied by B and C independently, and, then, C is replied by D, we obtain two different documents A,B and A,C,D in the fairseq language modeling format.
### Data Instances
```
Ni la Paloma, ni la Razz, ni Bikini, ni res: la cafeteria Slàvia, a Les borges Blanques. Quin concertàs el d'ahir de Pomada!!! Fuà!!! va ser tan tan tan tan tan tan tan bo!!! Flipant!!! Irrepetible!!
És cert, l'Slàvia mola màxim.
```
### Data Splits
The dataset contains two splits: `train` and `valid`.
## Dataset Creation
### Curation Rationale
We created this corpus to contribute to the development of language models in Catalan, a low-resource language. The data was structured to preserve the dialogue structure of forums.
### Source Data
#### Initial Data Collection and Normalization
The data was structured and anonymized by the BSC.
#### Who are the source language producers?
The data was provided by Racó Català.
### Annotations
The dataset is unannotated.
#### Annotation process
[N/A]
#### Who are the annotators?
[N/A]
### Personal and Sensitive Information
The data was annonymised to remove user names and emails, which were changed to random Catalan names. The mentions to the chat itself have also been changed.
## Considerations for Using the Data
### Social Impact of Dataset
We hope this corpus contributes to the development of language models in Catalan, a low-resource language.
### Discussion of Biases
We are aware that, since the data comes from user-generated forums, this will contain biases, hate speech and toxic content. We have not applied any steps to reduce their impact.
### Other Known Limitations
[N/A]
## Additional Information
### Dataset Curators
Text Mining Unit (TeMU) at the Barcelona Supercomputing Center ([email protected]).
This work was funded by the [Departament de la Vicepresidència i de Polítiques Digitals i Territori de la Generalitat de Catalunya](https://politiquesdigitals.gencat.cat/ca/inici/index.html#googtrans(ca|en) within the framework of [Projecte AINA](https://politiquesdigitals.gencat.cat/ca/economia/catalonia-ai/aina).
### Licensing Information
This work is licensed under a [Creative Commons Attribution Non-commercial 4.0 International License](https://creativecommons.org/licenses/by-nc/4.0/).
### Citation Information
```
```
### Contributions
Thanks to Racó Català for sharing their data.
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|
ccmusic-database/pianos | ccmusic-database | 2023-12-10T10:24:31Z | 13 | 2 | [
"task_categories:audio-classification",
"task_categories:image-classification",
"size_categories:10K<n<100K",
"language:en",
"license:mit",
"music",
"art",
"arxiv:2310.04722",
"region:us"
]
| [
"audio-classification",
"image-classification"
]
| 2023-05-25T11:32:28Z | ---
license: mit
task_categories:
- audio-classification
- image-classification
language:
- en
tags:
- music
- art
pretty_name: Piano Sound Quality Dataset
size_categories:
- 10K<n<100K
viewer: false
---
# Dataset Card for Piano Sound Quality Dataset
## Usage
```
from datasets import load_dataset
data = load_dataset("ccmusic-database/pianos", split="train")
labels = data.features['label'].names
for item in data:
print('piano spec: ', item['mel'])
print('label name: ' + labels[item['label']])
```
## Maintenance
```
git clone [email protected]:datasets/ccmusic-database/pianos
```
## Dataset Description
- **Homepage:** <https://ccmusic-database.github.io>
- **Repository:** <https://huggingface.co/datasets/CCMUSIC/pianos>
- **Paper:** <https://doi.org/10.5281/zenodo.5676893>
- **Leaderboard:** <https://ccmusic-database.github.io/team.html>
- **Point of Contact:** <https://arxiv.org/abs/2310.04722>
### Dataset Summary
This dataset contains 12 gamut audio files (.wav / .mp3 / .m4a format) and 1320 split single-tone audio files (.wav / .mp3 / .m4a format) of 7 types of pianos (Kawai upright piano, Kawai grand piano, YOUNG CHANG upright piano, HSINGHAI upright piano, Steinway grand piano in grand theatre, Steinway grand piano and Pearl River upright piano) in the piano-room of China Conservatory of Music, a total of 1332 files. In addition, there is a questionnaire on subjective evaluation of piano sound quality (.xls format), including the score of 29 people participating in the subjective evaluation of piano sound quality.
### Supported Tasks and Leaderboards
Piano Sound Classification, pitch detection
### Languages
English
## Dataset Structure
<style>
#pianos td {
vertical-align: middle !important;
text-align: center;
}
#pianos th {
text-align: center;
}
</style>
<table id="pianos">
<tr>
<th>mel(.jpg, 0.18s)</th>
<th>label(8-class)</th>
<th>pitch(88-class)</th>
</tr>
<tr>
<td><img src="https://cdn-uploads.huggingface.co/production/uploads/655e0a5b8c2d4379a71882a9/TYYnuJqndeWzXLJMmOyXJ.jpeg"></td>
<td>PearlRiver / YoungChang / Steinway-T / Hsinghai / Kawai / Steinway / Kawai-G / Yamaha</td>
<td>88 pitches on piano</td>
</tr>
<tr>
<td>...</td>
<td>...</td>
<td>...</td>
</tr>
</table>
### Data Instances
.zip(.wav, jpg)
### Data Fields
```
1_PearlRiver
2_YoungChang
3_Steinway-T
4_Hsinghai
5_Kawai
6_Steinway
7_Kawai-G
8_Yamaha
```
### Data Splits
| total | 18745 |
| :-------------: | :---: |
| train(80%) | 14996 |
| validation(10%) | 1874 |
| test(10%) | 1875 |
## Dataset Creation
### Curation Rationale
Lack of a dataset for piano sound quality
### Source Data
#### Initial Data Collection and Normalization
Zhaorui Liu, Shaohua Ji, Monan Zhou
#### Who are the source language producers?
Students from CCMUSIC & CCOM
### Annotations
#### Annotation process
Students from CCMUSIC recorded different piano sounds and labeled them, and then a subjective survey of sound quality was conducted to score them.
#### Who are the annotators?
Students from CCMUSIC & CCOM
### Personal and Sensitive Information
Piano brands
## Considerations for Using the Data
### Social Impact of Dataset
Help developing piano sound quality scoring apps
### Discussion of Biases
Only for pianos
### Other Known Limitations
Lack of black keys for Steinway, data imbalance
## Additional Information
### Dataset Curators
Zijin Li
### Evaluation
[Monan Zhou, Shangda Wu, Shaohua Ji, Zijin Li, and Wei Li. A Holistic Evaluation of Piano Sound Quality[C]//Proceedings of the 10th Conference on Sound and Music Technology (CSMT). Springer, Singapore, 2023.](https://arxiv.org/pdf/2310.04722.pdf)
(Note: this paper only uses the first 7 piano classes in the dataset.)
### Licensing Information
```
MIT License
Copyright (c) CCMUSIC
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
```
### Citation Information
```
@dataset{zhaorui_liu_2021_5676893,
author = {Zhaorui Liu, Monan Zhou, Shenyang Xu, Yuan Wang, Zhaowen Wang, Wei Li and Zijin Li},
title = {CCMUSIC DATABASE: A Music Data Sharing Platform for Computational Musicology Research},
month = {nov},
year = {2021},
publisher = {Zenodo},
version = {1.1},
doi = {10.5281/zenodo.5676893},
url = {https://doi.org/10.5281/zenodo.5676893}
}
```
### Contributions
Provide a dataset for piano sound quality | [
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|
jahb57/bert_embeddings_BATCH_1 | jahb57 | 2023-12-07T20:25:01Z | 13 | 0 | [
"region:us"
]
| null | 2023-12-04T03:16:07Z | ---
dataset_info:
features:
- name: sentence
dtype: string
- name: last_hidden_state
sequence:
sequence: float32
- name: pooler_output
sequence: float32
splits:
- name: train
num_bytes: 19893388193
num_examples: 100000
download_size: 20017638531
dataset_size: 19893388193
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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|
cmu-mlsp/librispeech960-wavlm-large-km1000_asr | cmu-mlsp | 2023-12-05T02:20:54Z | 13 | 0 | [
"region:us"
]
| null | 2023-12-05T02:19:25Z | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
- split: validation_other
path: data/validation_other-*
- split: test_other
path: data/test_other-*
dataset_info:
features:
- name: text
dtype: string
- name: audio_codes
sequence: string
- name: id
dtype: string
- name: speaker_id
dtype: int64
- name: chapter_id
dtype: int64
splits:
- name: train
num_bytes: 1246247156
num_examples: 281241
- name: validation
num_bytes: 7052458
num_examples: 2703
- name: test
num_bytes: 7062964
num_examples: 2620
- name: validation_other
num_bytes: 6706447
num_examples: 2864
- name: test_other
num_bytes: 6987808
num_examples: 2939
download_size: 254541270
dataset_size: 1274056833
---
# Dataset Card for "librispeech960-wavlm-large-km1000_asr"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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|
xaviviro/chistes_eugenio | xaviviro | 2023-12-05T15:35:52Z | 13 | 0 | [
"license:mit",
"region:us"
]
| null | 2023-12-05T15:35:17Z | ---
license: mit
---
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|
bigheiniuJ/JimmyLuAugOneShot | bigheiniuJ | 2023-12-05T19:55:47Z | 13 | 0 | [
"region:us"
]
| null | 2023-12-05T19:55:30Z | ---
dataset_info:
features:
- name: output
dtype: string
- name: input
dtype: string
- name: seed
dtype: string
- name: split
dtype: string
- name: task
dtype: string
- name: options
sequence: string
- name: id
dtype: int64
- name: aug_type
dtype: string
- name: aug_time
dtype: int64
splits:
- name: train
num_bytes: 118239467.40696478
num_examples: 295952
download_size: 27623464
dataset_size: 118239467.40696478
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "JimmyLuAugOneShot"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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|
automated-research-group/llama2_7b_chat-arc_easy-results | automated-research-group | 2023-12-07T05:44:43Z | 13 | 0 | [
"region:us"
]
| null | 2023-12-07T05:43:58Z | ---
dataset_info:
config_name: '{''do_sample''=False, ''beams''=1}'
features:
- name: id
dtype: string
- name: prediction
dtype: string
- name: arc_challenge_accuracy
dtype: bool
splits:
- name: train
num_bytes: 135288
num_examples: 570
download_size: 71128
dataset_size: 135288
configs:
- config_name: '{''do_sample''=False, ''beams''=1}'
data_files:
- split: train
path: '{''do_sample''=False, ''beams''=1}/train-*'
---
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|
karol-skorulski/controlnet-apparel-dataset | karol-skorulski | 2023-12-07T11:32:10Z | 13 | 0 | [
"region:us"
]
| null | 2023-12-07T11:31:58Z | ---
dataset_info:
features:
- name: image
dtype: image
- name: conditioning_image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 149913420.0
num_examples: 1000
download_size: 149765339
dataset_size: 149913420.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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|
nhantruongcse/summary-vietnamese-news-token-TFtest_vit5_base | nhantruongcse | 2023-12-08T09:16:16Z | 13 | 0 | [
"region:us"
]
| null | 2023-12-08T09:16:14Z | ---
dataset_info:
features:
- name: Content
dtype: string
- name: Summary
dtype: string
- name: input_ids
sequence: int32
- name: attention_mask
sequence: int8
- name: labels
sequence: int64
splits:
- name: train
num_bytes: 61956745
num_examples: 8229
download_size: 27478662
dataset_size: 61956745
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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|
nhantruongcse/summary-vietnamese-news-token-TFeval_vit5_base | nhantruongcse | 2023-12-08T09:16:18Z | 13 | 0 | [
"region:us"
]
| null | 2023-12-08T09:16:16Z | ---
dataset_info:
features:
- name: Content
dtype: string
- name: Summary
dtype: string
- name: input_ids
sequence: int32
- name: attention_mask
sequence: int8
- name: labels
sequence: int64
splits:
- name: train
num_bytes: 61526294
num_examples: 8229
download_size: 27275716
dataset_size: 61526294
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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|
Oztobuzz/gsm8k_0.1_42 | Oztobuzz | 2023-12-09T10:01:08Z | 13 | 0 | [
"license:mit",
"region:us"
]
| null | 2023-12-09T09:58:23Z | ---
license: mit
---
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|
librarian-bots/paper-recommendations | librarian-bots | 2023-12-06T16:05:52Z | 12 | 0 | [
"region:us"
]
| null | 2023-10-04T10:01:00Z | ---
dataset_info:
features:
- name: paper_url
dtype: string
- name: comment
dtype: string
splits:
- name: train
num_bytes: 338445
num_examples: 310
download_size: 0
dataset_size: 338445
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "paper-recommendations"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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|
Phando/vqa_v2 | Phando | 2023-12-07T04:17:53Z | 12 | 0 | [
"region:us"
]
| null | 2023-11-06T06:44:04Z | ---
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: question_type
dtype: string
- name: multiple_choice_answer
dtype: string
- name: answers
list:
- name: answer
dtype: string
- name: answer_confidence
dtype: string
- name: answer_id
dtype: int64
- name: image_id
dtype: int64
- name: answer_type
dtype: string
- name: question_id
dtype: int64
- name: question
dtype: string
- name: image
dtype: image
splits:
- name: train
num_bytes: 67692137168.704
num_examples: 443757
- name: validation
num_bytes: 33693404566.41
num_examples: 214354
- name: test
num_bytes: 70169720510.0
num_examples: 447793
download_size: 34818002031
dataset_size: 171555262245.114
---
# Dataset Card for "vqa_v2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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|
gdamms/ornithoscope | gdamms | 2023-12-05T13:19:31Z | 12 | 0 | [
"license:cc-by-2.0",
"region:us"
]
| null | 2023-11-07T11:58:37Z | ---
license: cc-by-2.0
---
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|
automated-research-group/llama2_7b_chat-openbookqa-results | automated-research-group | 2023-12-05T08:55:18Z | 12 | 0 | [
"region:us"
]
| null | 2023-12-05T08:07:28Z | ---
dataset_info:
- config_name: '{''do_sample''=False, ''beams''=10}'
features:
- name: id
dtype: string
- name: prediction
dtype: string
- name: openbookqa_accuracy
dtype: bool
splits:
- name: train
num_bytes: 166633
num_examples: 500
download_size: 85130
dataset_size: 166633
- config_name: '{''do_sample''=False, ''beams''=1}'
features:
- name: id
dtype: string
- name: prediction
dtype: string
- name: openbookqa_accuracy
dtype: bool
splits:
- name: train
num_bytes: 166633
num_examples: 500
download_size: 85130
dataset_size: 166633
- config_name: '{''do_sample''=False, ''beams''=5}'
features:
- name: id
dtype: string
- name: prediction
dtype: string
- name: openbookqa_accuracy
dtype: bool
splits:
- name: train
num_bytes: 166633
num_examples: 500
download_size: 85130
dataset_size: 166633
configs:
- config_name: '{''do_sample''=False, ''beams''=10}'
data_files:
- split: train
path: '{''do_sample''=False, ''beams''=10}/train-*'
- config_name: '{''do_sample''=False, ''beams''=1}'
data_files:
- split: train
path: '{''do_sample''=False, ''beams''=1}/train-*'
- config_name: '{''do_sample''=False, ''beams''=5}'
data_files:
- split: train
path: '{''do_sample''=False, ''beams''=5}/train-*'
---
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|
Coooori/dialog_data_train_huggingface | Coooori | 2023-12-05T11:59:50Z | 12 | 0 | [
"region:us"
]
| null | 2023-12-05T11:59:45Z | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 1726823
num_examples: 800
download_size: 802335
dataset_size: 1726823
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "dialog_data_train_huggingface"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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|
Anwaarma/BP-balanced-S04 | Anwaarma | 2023-12-05T15:57:27Z | 12 | 0 | [
"region:us"
]
| null | 2023-12-05T15:53:30Z | ---
dataset_info:
features:
- name: Target
dtype: int64
- name: PC
dtype: string
- name: GSHARE
dtype: string
- name: GA table
dtype: string
splits:
- name: train
num_bytes: 63600500
num_examples: 127201
- name: test
num_bytes: 15900500
num_examples: 31801
download_size: 22997305
dataset_size: 79501000
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
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|
mismatch-quest/SeeTRUE-Feedback | mismatch-quest | 2023-12-06T12:26:31Z | 12 | 1 | [
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"text-image-matching",
"region:us"
]
| null | 2023-12-05T17:21:28Z | ---
configs:
- config_name: default
data_files:
- split: test
path: "test/*"
annotations_creators:
- crowdsourced
language:
- en
language_creators:
- found
license:
- cc-by-4.0
multilinguality:
- monolingual
paperswithcode_id: seetrue-feedback
pretty_name: SeeTRUE-feedback
size_categories:
- 1K<n<10K
source_datasets:
- original
tags:
- text-image-matching
task_ids: []
extra_gated_prompt: "By clicking on “Access repository” below, you also agree that you are using it solely for research purposes, and that SeeTRUE-Feedback should be used as a *TEST SET*, not as a training set, and especially not to train commercial chatbots. Do not hessitate to contact [email protected] or [email protected] if you have questions about this license."
---
# Dataset Card for SeeTRUE-Feedback
- [Dataset Description](#dataset-description)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
The SeeTRUE-Feedback dataset is a diverse benchmark for the meta-evaluation of image-text matching/alignment feedback. It aims to overcome limitations in current benchmarks, which primarily focus on predicting a matching score between 0-1. SeeTRUE provides, for each row, the original caption, feedback related to text-image misalignment, and the caption+visual source of misalignments (including a bounding box for the visual misalignment).
Website: https://mismatch-quest.github.io/
### Languages
The dataset supports English language.
## Dataset Structure
### Data Fields
- image_caption - Caption associated with the image.
- image_name: The name of the image file.
- dataset_source: The source/origin dataset of the image.
- id_in_source_dataset: The ID of the dataset where the row originates from.
- image_url: An S3 link from which you can download the image.
- human_feedback: Human-annotated feedbacks about image-text misalignment.
- feedback: Summary of feedback consolidated into a single entry (Generated by LLM: PaLM-2)
- feedback_clean: A parsed and "clean" version of `feedback` field.
- caption_misalignment: Source of misalignment in the image caption.
- visual_misalignment: Source of misalignment in the image.
- bbox_GroundingDino: Detected visual misalignment bounding-box in GroundingDino output format.
- bbox_PaLI: Detected visual misalignment bounding-box in PaLI output format.
### Data Splits
SeeTRUE-Feedback contains a single split: TEST, and should not be used for training.
## Dataset Creation
The dataset has been created by sourcing and matching images and text from multiple datasets. More information in the paper: <TODO>
### Licensing Information
The dataset is under the CC-By 4.0 license.
### Citation Information
TODO | [
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|
fightfei/llama2-path-concentration-1.5k | fightfei | 2023-12-07T01:23:59Z | 12 | 0 | [
"region:us"
]
| null | 2023-12-07T01:23:32Z | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 600075.1011080332
num_examples: 1429
- name: test
num_bytes: 6298.898891966759
num_examples: 15
download_size: 78423
dataset_size: 606374.0
---
# Dataset Card for "llama2-path-concentration-1.5k"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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|
edbeeching/test_dataset_bug | edbeeching | 2023-12-08T14:58:09Z | 12 | 0 | [
"region:us"
]
| null | 2023-12-08T14:43:10Z | ---
dataset_info:
features:
- name: revision
dtype: string
splits:
- name: train
num_bytes: 6
num_examples: 1
download_size: 752
dataset_size: 6
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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|
nhantruongcse/dataset_test_6k2 | nhantruongcse | 2023-12-09T03:23:14Z | 12 | 0 | [
"region:us"
]
| null | 2023-12-09T03:23:09Z | ---
dataset_info:
features:
- name: Content
dtype: string
- name: Summary
dtype: string
- name: input_ids
sequence: int32
- name: attention_mask
sequence: int8
- name: labels
sequence: int64
splits:
- name: train
num_bytes: 47052369.61123942
num_examples: 6250
download_size: 20797776
dataset_size: 47052369.61123942
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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|
nhantruongcse/dataset_eval_6k2 | nhantruongcse | 2023-12-09T03:23:19Z | 12 | 0 | [
"region:us"
]
| null | 2023-12-09T03:23:14Z | ---
dataset_info:
features:
- name: Content
dtype: string
- name: Summary
dtype: string
- name: input_ids
sequence: int32
- name: attention_mask
sequence: int8
- name: labels
sequence: int64
splits:
- name: train
num_bytes: 47052369.61123942
num_examples: 6250
download_size: 20868433
dataset_size: 47052369.61123942
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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|
gatoch/alpaca_gpt4 | gatoch | 2023-12-09T23:34:10Z | 12 | 0 | [
"license:cc-by-nc-4.0",
"region:us"
]
| null | 2023-12-09T23:30:34Z | ---
license: cc-by-nc-4.0
---
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|
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