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43b4bdce39fefa75f211d45ac92fa43aa458581b
|
Curated QA Benchmark on State of the Union Address 2023.
It is especially useful for tool-augmented LMs / ALMs to examine the model's ability in answering over private document.
|
rewoo/sotu_qa_2023
|
[
"license:mit",
"region:us"
] |
2023-05-22T03:51:18+00:00
|
{"license": "mit"}
|
2023-05-22T03:59:37+00:00
|
82ad9784b5ba38593b289741a09227befb8d8c35
|
mitsudate/other_dataset
|
[
"license:unknown",
"region:us"
] |
2023-05-22T03:52:46+00:00
|
{"license": "unknown"}
|
2023-05-22T03:52:46+00:00
|
|
4c5274a0cdc9fa6a25d94940331670e1f760f695
|
Dhika/rail_defect
|
[
"license:unknown",
"region:us"
] |
2023-05-22T04:09:03+00:00
|
{"license": "unknown"}
|
2023-05-23T01:12:17+00:00
|
|
f54fafaeefdbfbd63b57a44747a8a3d895bd5828
|
# Dataset Card for "anime_cloth"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Faiza3/anime_cloth
|
[
"region:us"
] |
2023-05-22T04:11:35+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 210633.0, "num_examples": 15}], "download_size": 211995, "dataset_size": 210633.0}}
|
2023-05-22T04:11:37+00:00
|
238169976fe94ebd0db7b39e72bcc7b1b5ea74f0
|
Fantazy/CHINESE-GIRL-V1.0
|
[
"license:openrail",
"region:us"
] |
2023-05-22T05:05:03+00:00
|
{"license": "openrail"}
|
2023-05-22T05:05:03+00:00
|
|
891de0de0ae7ba339604a1fd68797204a7b4b3d1
|
James4Ever0/the_frozen_forest
|
[
"license:wtfpl",
"region:us"
] |
2023-05-22T05:07:54+00:00
|
{"license": "wtfpl"}
|
2023-05-22T05:11:56+00:00
|
|
c86b28352e0cf15f41657d70400d9863a648c068
|
karlen532/cosql
|
[
"license:unknown",
"region:us"
] |
2023-05-22T05:46:06+00:00
|
{"license": "unknown"}
|
2023-06-14T16:02:40+00:00
|
|
cd88a1691e188a2b6e6cf96fc2900223cd4a7807
|
shanzhu/chenzhuo
|
[
"license:openrail",
"region:us"
] |
2023-05-22T06:38:41+00:00
|
{"license": "openrail"}
|
2023-05-23T05:10:44+00:00
|
|
9f31d5ba8fd0da796802c2e4cb05351b97a3b257
|
# Dataset Card for "flores200_devtest_mt5-3b-flores200-scaffold"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
hlillemark/flores200_devtest_mt5-3b-flores200-scaffold
|
[
"region:us"
] |
2023-05-22T06:41:29+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int32"}, {"name": "source_lang", "dtype": "string"}, {"name": "target_lang", "dtype": "string"}, {"name": "source", "dtype": "string"}, {"name": "target", "dtype": "string"}, {"name": "prediction", "dtype": "string"}, {"name": "chrf_unreduced", "dtype": "string"}], "splits": [{"name": "devtest", "num_bytes": 372332916, "num_examples": 500000}], "download_size": 261837967, "dataset_size": 372332916}}
|
2023-05-22T06:41:54+00:00
|
87d99c85d1207251ba7804d97b58d4ab34b889b0
|
# Dataset Card for "51c15c9a"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
results-sd-v1-5-sd-v2-1-if-v1-0-karlo/51c15c9a
|
[
"region:us"
] |
2023-05-22T06:59:04+00:00
|
{"dataset_info": {"features": [{"name": "result", "dtype": "string"}, {"name": "id", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 186, "num_examples": 10}], "download_size": 1334, "dataset_size": 186}}
|
2023-05-22T06:59:06+00:00
|
b75ccb9e09f2ee1274c0c67c8faf1f1c3742a192
|
# This is a webscraped dataset from the website cars.bg
### The code for the webscraping you can find here : ``` https://github.com/MarioMarkov/cars-predict ```
Using BeutifulSoup4 and Selenium for python
---
dataset_info:
features:
- name: id
dtype: string
- name: brand
dtype: string
- name: model
dtype: string
- name: year
dtype: int64
- name: fuel
dtype: string
- name: kms
dtype: float64
- name: transmission
dtype: float64
- name: 2door
dtype: float64
- name: color
dtype: string
- name: type
dtype: string
- name: displacement
dtype: float64
- name: hp
dtype: float64
- name: euro
dtype: float64
- name: price
dtype: float64
splits:
- name: train
num_bytes: 3111021
num_examples: 23103
- name: test
num_bytes: 345669
num_examples: 2567
download_size: 1203060
dataset_size: 3456690
size_categories:
- 10K<n<100K
---
# Dataset Card for "cars-bg"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
mor40/cars-bg
|
[
"region:us"
] |
2023-05-22T07:02:25+00:00
|
{}
|
2023-05-22T07:11:31+00:00
|
9087dbb844855588406fdee74d77d0f27334134b
|
yfqiu-nlp/mfact-classification
|
[
"license:mit",
"region:us"
] |
2023-05-22T07:03:34+00:00
|
{"license": "mit", "dataset_info": {"features": [{"name": "en", "dtype": "string"}, {"name": "zh", "dtype": "string"}, {"name": "es", "dtype": "string"}, {"name": "fr", "dtype": "string"}, {"name": "vi", "dtype": "string"}, {"name": "hi", "dtype": "string"}, {"name": "tr", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 182, "num_examples": 3}], "download_size": 3134, "dataset_size": 182}}
|
2023-05-22T07:46:04+00:00
|
|
800f6cf855e12b503a8ac2e8292db865d26a3810
|
# Dataset Card for "aihub-ko-en-literary"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
HSJuan/aihub-ko-en-literary
|
[
"region:us"
] |
2023-05-22T07:05:57+00:00
|
{"dataset_info": {"features": [{"name": "ko", "dtype": "string"}, {"name": "en", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 396568835, "num_examples": 1102418}], "download_size": 254144783, "dataset_size": 396568835}}
|
2023-05-22T07:09:29+00:00
|
df2158211a7ec08a068fbc004d1b9a6d427f3669
|
More detailed Information are presented in Github: https://github.com/YunxinLi/LingCloud
Paper Link: https://arxiv.org/abs/2305.03701
|
YunxinLi/Multimodal_Instruction_data_v1
|
[
"arxiv:2305.03701",
"region:us"
] |
2023-05-22T07:13:33+00:00
|
{}
|
2023-05-22T09:08:29+00:00
|
74240ab5cc42c3ab26fef0ca8d61d1e48b098480
|
### Usage and License Notices
All airoboros models and datasets are intended and licensed for research use only. I've used the 'cc-nc-4.0' license, but really it is subject to a custom/special license because:
- the base model is LLaMa, which has it's own special research license
- the dataset(s) were generated with OpenAI (gpt-4 and/or gpt-3.5-turbo), which has a clausing saying the data can't be used to create models to compete with openai
So, to reiterate: this model (and datasets) cannot be used commercially.
|
jondurbin/airoboros-uncensored
|
[
"license:cc-by-nc-4.0",
"region:us"
] |
2023-05-22T07:13:37+00:00
|
{"license": "cc-by-nc-4.0"}
|
2023-06-22T14:01:03+00:00
|
47be4969803d92e6be31e2bc5cb1ff6778182c53
|
# Dataset Card for "chunk_215"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
mask-distilled-one-sec-cv12/chunk_215
|
[
"region:us"
] |
2023-05-22T07:21:31+00:00
|
{"dataset_info": {"features": [{"name": "logits", "sequence": "float32"}, {"name": "mfcc", "sequence": {"sequence": "float64"}}], "splits": [{"name": "train", "num_bytes": 1089357020, "num_examples": 213935}], "download_size": 1113114537, "dataset_size": 1089357020}}
|
2023-05-22T07:22:18+00:00
|
0aafe013745d62c209a8b23172d089caa41caa1f
|
# Dataset Card for "chunk_214"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
mask-distilled-one-sec-cv12/chunk_214
|
[
"region:us"
] |
2023-05-22T07:23:18+00:00
|
{"dataset_info": {"features": [{"name": "logits", "sequence": "float32"}, {"name": "mfcc", "sequence": {"sequence": "float64"}}], "splits": [{"name": "train", "num_bytes": 1114710088, "num_examples": 218914}], "download_size": 1137425284, "dataset_size": 1114710088}}
|
2023-05-22T07:23:59+00:00
|
c813e85cf733713d5c5ac5050e931b1a53f7980d
|
# Dataset Card for "chunk_218"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
mask-distilled-one-sec-cv12/chunk_218
|
[
"region:us"
] |
2023-05-22T07:24:11+00:00
|
{"dataset_info": {"features": [{"name": "logits", "sequence": "float32"}, {"name": "mfcc", "sequence": {"sequence": "float64"}}], "splits": [{"name": "train", "num_bytes": 1112708932, "num_examples": 218521}], "download_size": 1136839135, "dataset_size": 1112708932}}
|
2023-05-22T07:25:02+00:00
|
03ad7a2e0cf9c7a4f8716370a2ebfaa1c661e53c
|
# Dataset Card for "chunk_217"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
mask-distilled-one-sec-cv12/chunk_217
|
[
"region:us"
] |
2023-05-22T07:26:59+00:00
|
{"dataset_info": {"features": [{"name": "logits", "sequence": "float32"}, {"name": "mfcc", "sequence": {"sequence": "float64"}}], "splits": [{"name": "train", "num_bytes": 1124771880, "num_examples": 220890}], "download_size": 1149265087, "dataset_size": 1124771880}}
|
2023-05-22T07:27:49+00:00
|
895455c070683dc0c6491445755fce862c37aa7f
|
# Dataset Card for "chunk_210"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
mask-distilled-one-sec-cv12/chunk_210
|
[
"region:us"
] |
2023-05-22T07:29:20+00:00
|
{"dataset_info": {"features": [{"name": "logits", "sequence": "float32"}, {"name": "mfcc", "sequence": {"sequence": "float64"}}], "splits": [{"name": "train", "num_bytes": 1123132256, "num_examples": 220568}], "download_size": 1147587111, "dataset_size": 1123132256}}
|
2023-05-22T07:29:55+00:00
|
83348772c60556e5a3c03fc5ff1d8e24d7e6bea2
|
# Dataset Card for "chunk_212"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
mask-distilled-one-sec-cv12/chunk_212
|
[
"region:us"
] |
2023-05-22T07:29:39+00:00
|
{"dataset_info": {"features": [{"name": "logits", "sequence": "float32"}, {"name": "mfcc", "sequence": {"sequence": "float64"}}], "splits": [{"name": "train", "num_bytes": 1165294016, "num_examples": 228848}], "download_size": 1185881139, "dataset_size": 1165294016}}
|
2023-05-22T07:30:19+00:00
|
64b438059562e0a5d074aaf9dac66b87b30a55cd
|
# Dataset Card for "chunk_219"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
mask-distilled-one-sec-cv12/chunk_219
|
[
"region:us"
] |
2023-05-22T07:31:08+00:00
|
{"dataset_info": {"features": [{"name": "logits", "sequence": "float32"}, {"name": "mfcc", "sequence": {"sequence": "float64"}}], "splits": [{"name": "train", "num_bytes": 1186563300, "num_examples": 233025}], "download_size": 1212301620, "dataset_size": 1186563300}}
|
2023-05-22T07:32:11+00:00
|
fb7205150bafe89ef55d72a8415d80e52c08b67b
|
# summary
A Chinese medical question answering instructions dataset based on `webMedQA`
# Reference
[1]. Applying deep matching networks to Chinese medical question answering: A study and a dataset
|
zirui3/webMedQA-instructions
|
[
"license:cc-by-4.0",
"region:us"
] |
2023-05-22T07:34:24+00:00
|
{"license": "cc-by-4.0"}
|
2023-05-22T09:39:21+00:00
|
17c89124c3982ab7cb833727ce1fef43c18acbfb
|
# Dataset Card for "chunk_216"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
mask-distilled-one-sec-cv12/chunk_216
|
[
"region:us"
] |
2023-05-22T07:34:51+00:00
|
{"dataset_info": {"features": [{"name": "logits", "sequence": "float32"}, {"name": "mfcc", "sequence": {"sequence": "float64"}}], "splits": [{"name": "train", "num_bytes": 1227701568, "num_examples": 241104}], "download_size": 1254568613, "dataset_size": 1227701568}}
|
2023-05-22T07:35:46+00:00
|
d4a367d6ef4cd14bd175f377223d24fba4acad96
|
# Dataset Card for "chunk_211"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
mask-distilled-one-sec-cv12/chunk_211
|
[
"region:us"
] |
2023-05-22T07:37:52+00:00
|
{"dataset_info": {"features": [{"name": "logits", "sequence": "float32"}, {"name": "mfcc", "sequence": {"sequence": "float64"}}], "splits": [{"name": "train", "num_bytes": 1289126364, "num_examples": 253167}], "download_size": 1317037443, "dataset_size": 1289126364}}
|
2023-05-22T07:38:45+00:00
|
05483bcdb7e3c6cd1f696e6bc2fb0984a41e7b77
|
# Dataset Card for "chunk_213"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
mask-distilled-one-sec-cv12/chunk_213
|
[
"region:us"
] |
2023-05-22T07:47:19+00:00
|
{"dataset_info": {"features": [{"name": "logits", "sequence": "float32"}, {"name": "mfcc", "sequence": {"sequence": "float64"}}], "splits": [{"name": "train", "num_bytes": 1262316984, "num_examples": 247902}], "download_size": 1289749459, "dataset_size": 1262316984}}
|
2023-05-22T07:48:25+00:00
|
34d4b127510e7cb511c2ebe8eecb750e72c11211
|
# All poems of the bulgarian writer Ivan Vazov concatenated to one string
source ```https://bg.wikisource.org/wiki/%D0%9A%D0%B0%D1%82%D0%B5%D0%B3%D0%BE%D1%80%D0%B8%D1%8F:%D0%A1%D1%82%D0%B8%D1%85%D0%BE%D1%82%D0%B2%D0%BE%D1%80%D0%B5%D0%BD%D0%B8%D1%8F_%D0%BD%D0%B0_%D0%98%D0%B2%D0%B0%D0%BD_%D0%92%D0%B0%D0%B7%D0%BE%D0%B2 ```
---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 519979
num_examples: 11698
download_size: 279004
dataset_size: 519979
language:
- bg
tags:
- art
- poetry
size_categories:
- 10K<n<100K
---
# Dataset Card for "vazov-poetry"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
mor40/vazov-poetry
|
[
"region:us"
] |
2023-05-22T08:01:03+00:00
|
{}
|
2023-05-22T08:59:06+00:00
|
befb9d2de5a8e095d810a19b9a8fd54162f89d86
|
# Dataset Card for "test_path_gen_voxelgym_3c_100_42x42"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Cubpaw/test_path_gen_voxelgym_3c_100_42x42
|
[
"region:us"
] |
2023-05-22T08:01:21+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": "image"}, {"name": "rgb_label", "dtype": "image"}, {"name": "path_label", "dtype": "image"}, {"name": "path_rgb_label", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 71974.0, "num_examples": 80}, {"name": "validation", "num_bytes": 18401.0, "num_examples": 20}], "download_size": 88974, "dataset_size": 90375.0}}
|
2023-05-22T08:03:26+00:00
|
934f0b60e95ea66c003467ef57a6cde42dae4d8e
|
wRuanMing/demodataset
|
[
"license:openrail",
"region:us"
] |
2023-05-22T08:11:54+00:00
|
{"license": "openrail"}
|
2023-05-22T08:13:35+00:00
|
|
ec94a2dd66c9f98cb96b489b6ae12f7b1e293e73
|
# Dataset Card for "image_captioned_40"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
AchrafLou/image_captioned_40
|
[
"region:us"
] |
2023-05-22T08:22:39+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "image", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 246618.0, "num_examples": 38}, {"name": "validation", "num_bytes": 59992.0, "num_examples": 10}], "download_size": 280777, "dataset_size": 306610.0}}
|
2023-05-22T19:40:40+00:00
|
f283977bc354da779371f2593ccb3c8b5a1dbc92
|
zirui3/cMedQA2-instructions
|
[
"license:cc-by-4.0",
"region:us"
] |
2023-05-22T08:27:40+00:00
|
{"license": "cc-by-4.0"}
|
2023-05-22T08:48:40+00:00
|
|
2447df47b528d4eaf98fe93e06bde8c6672a4416
|
# SummComparer - v0.1 version
> Comparative analysis of summarization models on a variety of everyday documents
<a href="https://colab.research.google.com/gist/pszemraj/915cc610a37ffce963993fd005cf6154/summcomparer-gauntlet-v0p1-basic-eda.ipynb">
<img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
</a>
Dataset host/upload for [SummComparer](https://github.com/pszemraj/SummComparer). This is just a hosting page, check the repo for the latest info.
- This is a work in progress and will be updated over time.
- PRs/discussions **on this card** are disabled, but discussions/ideas/analysis etc are welcome, just post in the [github repo discussions](https://github.com/pszemraj/SummComparer/discussions) so things are all in one place.
- Please note that this is a dataset intended **for analyzing the summary quality of different models** rather than something to train more models on.
## EDA links
Outside of a basic EDA [colab notebook](https://colab.research.google.com/gist/pszemraj/915cc610a37ffce963993fd005cf6154/summcomparer-gauntlet-v0p1-basic-eda.ipynb) some static sites powered via `pandas-profiling`:
- [summary outputs](https://gauntlet-compiled-eda-v0p1.netlify.app/)
- [input docs](https://gauntlet-inputs-eda-v0p1.netlify.app/)
## Working with the dataset
> Note:** The current version of the dataset is still largely in a "raw" format. It has seen some basic cleaning but may need more in the future.
**In the repo,** the dataset is split into two different tables. One contains the original documents with long text & IDs etc, and the other contains everything else.
- `input_documents.parquet`: This file contains the input documents for the gauntlet along with metadata/`id` fields as defined in `gauntlet_master_data.json`.
- `gauntlet_summaries.parquet`: This file contains the output summaries for the gauntlet with hyperparameters/models as columns. All summaries (rows) are mapped to their source documents (columns) by columns prefixed with `source_doc`.
If you are joining the two, join on `source_doc_id`. Here, they have already been merged for you. You can load as use the dataset from here:
```python
from datasets import load_dataset
dataset = load_dataset("pszemraj/summcomparer-gauntlet-v0p1",)
dataset
```
which should output (for `v0.1.2`):
```
DatasetDict(
{
train: Dataset(
{
features: [
"GAUNTLET_PATH",
"file_name",
"summary",
"min_length",
"max_length",
"no_repeat_ngram_size",
"encoder_no_repeat_ngram_size",
"repetition_penalty",
"num_beams",
"num_beam_groups",
"length_penalty",
"early_stopping",
"do_sample",
"model_name",
"date",
"length",
"format",
"extractiveness",
"temperature",
"token_batch_length",
"penalty_alpha",
"top_k",
"batch_stride",
"max_len_ratio",
"directory-topic-tag",
"runtime",
"source_doc_filename",
"source_doc_id",
"source_doc_domain",
"document_text",
],
num_rows: 2043,
}
)
}
)
```
## OpenAI Terms of Use Notice
This dataset does contain reference summaries generated by GPT-4 and GPT-3.5-turbo. While it shouldn't be an issue as **this is meant for analysis and not training**, please note that the OpenAI generated text is subject to their terms of use.
This data can be filtered out/dropped if needed/relevant for your use of the data.
|
pszemraj/summcomparer-gauntlet-v0p1
|
[
"task_categories:summarization",
"size_categories:1K<n<10K",
"language:en",
"license:cc-by-4.0",
"gauntlet",
"region:us"
] |
2023-05-22T08:31:01+00:00
|
{"language": ["en"], "license": "cc-by-4.0", "size_categories": ["1K<n<10K"], "task_categories": ["summarization"], "tags": ["gauntlet"], "thumbnail": "https://i.imgur.com/8oV9CTa.png"}
|
2023-05-30T17:09:32+00:00
|
3dc438e53218c383e17783d0b2b1dc3ee8194387
|
# Dataset Card for "small_eslo_test"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
illuin/small_eslo_test
|
[
"region:us"
] |
2023-05-22T08:36:45+00:00
|
{"dataset_info": {"features": [{"name": "file", "dtype": "string"}, {"name": "audio", "dtype": {"audio": {"sampling_rate": 16000}}}, {"name": "sentence", "dtype": "string"}, {"name": "start_timestamp", "dtype": "float32"}, {"name": "end_timestamp", "dtype": "float32"}, {"name": "loss", "dtype": "float32"}, {"name": "contains_speech", "dtype": "bool"}, {"name": "sample_name", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 186658104.0, "num_examples": 1000}], "download_size": 185899963, "dataset_size": 186658104.0}}
|
2023-05-22T08:39:08+00:00
|
55f7a21b60bcbf1c9feb31f38cad89e84db9e883
|
# Dataset Card for XStoryCloze MT
## 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://cs.rochester.edu/nlp/rocstories/](https://cs.rochester.edu/nlp/rocstories/)
- **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Paper:** [Few-shot Learning with Multilingual Generative Language Models](https://arxiv.org/pdf/2112.10668.pdf)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Size of downloaded dataset files:** 2.03 MB
- **Size of the generated dataset:** 2.03 MB
- **Total amount of disk used:** 2.05 MB
### Dataset Summary
XStoryCloze consists of the professionally translated version of the [English StoryCloze dataset](https://cs.rochester.edu/nlp/rocstories/) (Spring 2016 version) to 10 non-English languages. This dataset is released by Meta AI. This dataset is the machine-translated version of XstoryCloze to en from ru, zh, es, ar, hi, id, te, sw, eu, my.
### Supported Tasks and Leaderboards
commonsense reasoning
### Languages
This dataset is the machine-translated version of XstoryCloze to en from ru, zh (Simplified), es (Latin America), ar, hi, id, te, sw, eu, my.
## Dataset Structure
### Data Instances
- **Size of downloaded dataset files:** 2.03 MB
- **Size of the generated dataset:** 2.03 MB
- **Total amount of disk used:** 2.05 MB
An example of 'train' looks as follows.
```
{'answer_right_ending': 1,
'input_sentence_1': 'Rick grew up in a troubled household.',
'input_sentence_2': 'He never found good support in family, and turned to gangs.',
'input_sentence_3': "It wasn't long before Rick got shot in a robbery.",
'input_sentence_4': 'The incident caused him to turn a new leaf.',
'sentence_quiz1': 'He is happy now.',
'sentence_quiz2': 'He joined a gang.',
'story_id': '138d5bfb-05cc-41e3-bf2c-fa85ebad14e2'}
```
### Data Fields
The data fields are the same among all splits.
- `input_sentence_1`: The first statement in the story.
- `input_sentence_2`: The second statement in the story.
- `input_sentence_3`: The third statement in the story.
- `input_sentence_4`: The forth statement in the story.
- `sentence_quiz1`: first possible continuation of the story.
- `sentence_quiz2`: second possible continuation of the story.
- `answer_right_ending`: correct possible ending; either 1 or 2.
- `story_id`: story id.
### Data Splits
This dataset is intended to be used for evaluating the zero- and few-shot learning capabilities of multlingual language models. We split the data for each language into train and test (360 vs. 1510 examples, respectively). The released data files for different languages maintain a line-by-line alignment.
| name |test|
|-------|---:|
|ru|1510|
|zh|1510|
|es|1510|
|ar|1510|
|hi|1510|
|id|1510|
|te|1510|
|sw|1510|
|eu|1510|
|my|1510|
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
XStoryCloze is opensourced under [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/legalcode), the same license as the original English StoryCloze.
### Citation Information
```
@article{DBLP:journals/corr/abs-2112-10668,
author = {Xi Victoria Lin and
Todor Mihaylov and
Mikel Artetxe and
Tianlu Wang and
Shuohui Chen and
Daniel Simig and
Myle Ott and
Naman Goyal and
Shruti Bhosale and
Jingfei Du and
Ramakanth Pasunuru and
Sam Shleifer and
Punit Singh Koura and
Vishrav Chaudhary and
Brian O'Horo and
Jeff Wang and
Luke Zettlemoyer and
Zornitsa Kozareva and
Mona T. Diab and
Veselin Stoyanov and
Xian Li},
title = {Few-shot Learning with Multilingual Language Models},
journal = {CoRR},
volume = {abs/2112.10668},
year = {2021},
url = {https://arxiv.org/abs/2112.10668},
eprinttype = {arXiv},
eprint = {2112.10668},
timestamp = {Tue, 04 Jan 2022 15:59:27 +0100},
biburl = {https://dblp.org/rec/journals/corr/abs-2112-10668.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```
### Contributions
Thanks to [@juletx](https://github.com/juletx).
|
juletxara/xstory_cloze_mt
|
[
"task_categories:other",
"annotations_creators:found",
"language_creators:found",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:extended|story_cloze",
"language:en",
"license:cc-by-sa-4.0",
"arxiv:2112.10668",
"region:us"
] |
2023-05-22T08:37:14+00:00
|
{"annotations_creators": ["found"], "language_creators": ["found", "expert-generated"], "language": ["en"], "license": ["cc-by-sa-4.0"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["extended|story_cloze"], "task_categories": ["other"], "task_ids": [], "pretty_name": "XStoryCloze", "tags": [], "dataset_info": [{"config_name": "nllb-200-distilled-600M", "features": [{"name": "story_id", "dtype": "string"}, {"name": "input_sentence_1", "dtype": "string"}, {"name": "input_sentence_2", "dtype": "string"}, {"name": "input_sentence_3", "dtype": "string"}, {"name": "input_sentence_4", "dtype": "string"}, {"name": "sentence_quiz1", "dtype": "string"}, {"name": "sentence_quiz2", "dtype": "string"}, {"name": "answer_right_ending", "dtype": "int32"}], "splits": [{"name": "ru", "num_bytes": 492764, "num_examples": 1511}, {"name": "zh", "num_bytes": 500346, "num_examples": 1511}, {"name": "es", "num_bytes": 495103, "num_examples": 1511}, {"name": "ar", "num_bytes": 490629, "num_examples": 1511}, {"name": "hi", "num_bytes": 497109, "num_examples": 1511}, {"name": "id", "num_bytes": 491970, "num_examples": 1511}, {"name": "te", "num_bytes": 472103, "num_examples": 1511}, {"name": "sw", "num_bytes": 493285, "num_examples": 1511}, {"name": "eu", "num_bytes": 486194, "num_examples": 1511}, {"name": "my", "num_bytes": 545031, "num_examples": 1511}], "download_size": 4619083, "dataset_size": 4964534}, {"config_name": "nllb-200-distilled-1.3B", "features": [{"name": "story_id", "dtype": "string"}, {"name": "input_sentence_1", "dtype": "string"}, {"name": "input_sentence_2", "dtype": "string"}, {"name": "input_sentence_3", "dtype": "string"}, {"name": "input_sentence_4", "dtype": "string"}, {"name": "sentence_quiz1", "dtype": "string"}, {"name": "sentence_quiz2", "dtype": "string"}, {"name": "answer_right_ending", "dtype": "int32"}], "splits": [{"name": "ru", "num_bytes": 493120, "num_examples": 1511}, {"name": "zh", "num_bytes": 512485, "num_examples": 1511}, {"name": "es", "num_bytes": 494845, "num_examples": 1511}, {"name": "ar", "num_bytes": 488763, "num_examples": 1511}, {"name": "hi", "num_bytes": 495752, "num_examples": 1511}, {"name": "id", "num_bytes": 491866, "num_examples": 1511}, {"name": "te", "num_bytes": 472752, "num_examples": 1511}, {"name": "sw", "num_bytes": 493712, "num_examples": 1511}, {"name": "eu", "num_bytes": 491839, "num_examples": 1511}, {"name": "my", "num_bytes": 517974, "num_examples": 1511}], "download_size": 4607136, "dataset_size": 4953108}, {"config_name": "nllb-200-1.3B", "features": [{"name": "story_id", "dtype": "string"}, {"name": "input_sentence_1", "dtype": "string"}, {"name": "input_sentence_2", "dtype": "string"}, {"name": "input_sentence_3", "dtype": "string"}, {"name": "input_sentence_4", "dtype": "string"}, {"name": "sentence_quiz1", "dtype": "string"}, {"name": "sentence_quiz2", "dtype": "string"}, {"name": "answer_right_ending", "dtype": "int32"}], "splits": [{"name": "ru", "num_bytes": 493690, "num_examples": 1511}, {"name": "zh", "num_bytes": 498665, "num_examples": 1511}, {"name": "es", "num_bytes": 493934, "num_examples": 1511}, {"name": "ar", "num_bytes": 489966, "num_examples": 1511}, {"name": "hi", "num_bytes": 495889, "num_examples": 1511}, {"name": "id", "num_bytes": 492249, "num_examples": 1511}, {"name": "te", "num_bytes": 472101, "num_examples": 1511}, {"name": "sw", "num_bytes": 492297, "num_examples": 1511}, {"name": "eu", "num_bytes": 485674, "num_examples": 1511}, {"name": "my", "num_bytes": 510821, "num_examples": 1511}], "download_size": 4579397, "dataset_size": 4925286}, {"config_name": "nllb-200-3.3B", "features": [{"name": "story_id", "dtype": "string"}, {"name": "input_sentence_1", "dtype": "string"}, {"name": "input_sentence_2", "dtype": "string"}, {"name": "input_sentence_3", "dtype": "string"}, {"name": "input_sentence_4", "dtype": "string"}, {"name": "sentence_quiz1", "dtype": "string"}, {"name": "sentence_quiz2", "dtype": "string"}, {"name": "answer_right_ending", "dtype": "int32"}], "splits": [{"name": "ru", "num_bytes": 495392, "num_examples": 1511}, {"name": "zh", "num_bytes": 500965, "num_examples": 1511}, {"name": "es", "num_bytes": 495521, "num_examples": 1511}, {"name": "ar", "num_bytes": 491594, "num_examples": 1511}, {"name": "hi", "num_bytes": 498082, "num_examples": 1511}, {"name": "id", "num_bytes": 494296, "num_examples": 1511}, {"name": "te", "num_bytes": 477315, "num_examples": 1511}, {"name": "sw", "num_bytes": 496170, "num_examples": 1511}, {"name": "eu", "num_bytes": 499829, "num_examples": 1511}, {"name": "my", "num_bytes": 517806, "num_examples": 1511}], "download_size": 4621130, "dataset_size": 4966970}, {"config_name": "xglm-564M", "features": [{"name": "story_id", "dtype": "string"}, {"name": "input_sentence_1", "dtype": "string"}, {"name": "input_sentence_2", "dtype": "string"}, {"name": "input_sentence_3", "dtype": "string"}, {"name": "input_sentence_4", "dtype": "string"}, {"name": "sentence_quiz1", "dtype": "string"}, {"name": "sentence_quiz2", "dtype": "string"}, {"name": "answer_right_ending", "dtype": "int32"}], "splits": [{"name": "ru", "num_bytes": 541125, "num_examples": 1511}, {"name": "zh", "num_bytes": 825126, "num_examples": 1511}, {"name": "es", "num_bytes": 552675, "num_examples": 1511}, {"name": "ar", "num_bytes": 560267, "num_examples": 1511}, {"name": "hi", "num_bytes": 567030, "num_examples": 1511}, {"name": "id", "num_bytes": 506136, "num_examples": 1511}, {"name": "te", "num_bytes": 889610, "num_examples": 1511}, {"name": "sw", "num_bytes": 556752, "num_examples": 1511}, {"name": "eu", "num_bytes": 585440, "num_examples": 1511}, {"name": "my", "num_bytes": 1112539, "num_examples": 1511}], "download_size": 6352902, "dataset_size": 6696700}, {"config_name": "xglm-1.7B", "features": [{"name": "story_id", "dtype": "string"}, {"name": "input_sentence_1", "dtype": "string"}, {"name": "input_sentence_2", "dtype": "string"}, {"name": 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"dtype": "string"}, {"name": "input_sentence_2", "dtype": "string"}, {"name": "input_sentence_3", "dtype": "string"}, {"name": "input_sentence_4", "dtype": "string"}, {"name": "sentence_quiz1", "dtype": "string"}, {"name": "sentence_quiz2", "dtype": "string"}, {"name": "answer_right_ending", "dtype": "int32"}], "splits": [{"name": "ru", "num_bytes": 502967, "num_examples": 1511}, {"name": "zh", "num_bytes": 487153, "num_examples": 1511}, {"name": "es", "num_bytes": 498912, "num_examples": 1511}, {"name": "ar", "num_bytes": 494407, "num_examples": 1511}, {"name": "hi", "num_bytes": 492415, "num_examples": 1511}, {"name": "id", "num_bytes": 504653, "num_examples": 1511}, {"name": "te", "num_bytes": 500632, "num_examples": 1511}, {"name": "sw", "num_bytes": 496000, "num_examples": 1511}, {"name": "eu", "num_bytes": 488755, "num_examples": 1511}, {"name": "my", "num_bytes": 537296, "num_examples": 1511}], "download_size": 4657865, "dataset_size": 5003190}, {"config_name": "xglm-4.5B", "features": [{"name": "story_id", "dtype": "string"}, {"name": "input_sentence_1", "dtype": "string"}, {"name": "input_sentence_2", "dtype": "string"}, {"name": "input_sentence_3", "dtype": "string"}, {"name": "input_sentence_4", "dtype": "string"}, {"name": "sentence_quiz1", "dtype": "string"}, {"name": "sentence_quiz2", "dtype": "string"}, {"name": "answer_right_ending", "dtype": "int32"}], "splits": [{"name": "ru", "num_bytes": 495315, "num_examples": 1511}, {"name": "zh", "num_bytes": 491436, "num_examples": 1511}, {"name": "es", "num_bytes": 496332, "num_examples": 1511}, {"name": "ar", "num_bytes": 485175, "num_examples": 1511}, {"name": "hi", "num_bytes": 517560, "num_examples": 1511}, {"name": "id", "num_bytes": 491342, "num_examples": 1511}, {"name": "te", "num_bytes": 520378, "num_examples": 1511}, {"name": "sw", "num_bytes": 494811, "num_examples": 1511}, {"name": "eu", "num_bytes": 701365, "num_examples": 1511}, {"name": "my", "num_bytes": 684247, "num_examples": 1511}], 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"input_sentence_3", "dtype": "string"}, {"name": "input_sentence_4", "dtype": "string"}, {"name": "sentence_quiz1", "dtype": "string"}, {"name": "sentence_quiz2", "dtype": "string"}, {"name": "answer_right_ending", "dtype": "int32"}], "splits": [{"name": "ru", "num_bytes": 498554, "num_examples": 1511}, {"name": "zh", "num_bytes": 490097, "num_examples": 1511}, {"name": "es", "num_bytes": 497570, "num_examples": 1511}, {"name": "ar", "num_bytes": 497095, "num_examples": 1511}, {"name": "hi", "num_bytes": 682306, "num_examples": 1511}, {"name": "id", "num_bytes": 494517, "num_examples": 1511}, {"name": "te", "num_bytes": 712521, "num_examples": 1511}, {"name": "sw", "num_bytes": 470834, "num_examples": 1511}, {"name": "eu", "num_bytes": 503702, "num_examples": 1511}, {"name": "my", "num_bytes": 928002, "num_examples": 1511}], "download_size": 5430508, "dataset_size": 5775198}, {"config_name": "polylm-multialpaca-13b", "features": [{"name": "story_id", "dtype": "string"}, {"name": "input_sentence_1", "dtype": "string"}, {"name": "input_sentence_2", "dtype": "string"}, {"name": "input_sentence_3", "dtype": "string"}, {"name": "input_sentence_4", "dtype": "string"}, {"name": "sentence_quiz1", "dtype": "string"}, {"name": "sentence_quiz2", "dtype": "string"}, {"name": "answer_right_ending", "dtype": "int32"}], "splits": [{"name": "ru", "num_bytes": 496565, "num_examples": 1511}, {"name": "zh", "num_bytes": 494789, "num_examples": 1511}, {"name": "es", "num_bytes": 497108, "num_examples": 1511}, {"name": "ar", "num_bytes": 485852, "num_examples": 1511}, {"name": "hi", "num_bytes": 788707, "num_examples": 1511}, {"name": "id", "num_bytes": 491246, "num_examples": 1511}, {"name": "te", "num_bytes": 881984, "num_examples": 1511}, {"name": "sw", "num_bytes": 512261, "num_examples": 1511}, {"name": "eu", "num_bytes": 508426, "num_examples": 1511}, {"name": "my", "num_bytes": 928002, "num_examples": 1511}], "download_size": 5739667, "dataset_size": 6084940}, {"config_name": "open_llama_3b_v2", "features": [{"name": "story_id", "dtype": "string"}, {"name": "input_sentence_1", "dtype": "string"}, {"name": "input_sentence_2", "dtype": "string"}, {"name": "input_sentence_3", "dtype": "string"}, {"name": "input_sentence_4", "dtype": "string"}, {"name": "sentence_quiz1", "dtype": "string"}, {"name": "sentence_quiz2", "dtype": "string"}, {"name": "answer_right_ending", "dtype": "int32"}], "splits": [{"name": "ru", "num_bytes": 492909, "num_examples": 1511}, {"name": "zh", "num_bytes": 505746, "num_examples": 1511}, {"name": "es", "num_bytes": 499516, "num_examples": 1511}, {"name": "ar", "num_bytes": 498564, "num_examples": 1511}, {"name": "hi", "num_bytes": 573411, "num_examples": 1511}, {"name": "id", "num_bytes": 484221, "num_examples": 1511}, {"name": "te", "num_bytes": 832372, "num_examples": 1511}, {"name": "sw", "num_bytes": 485921, "num_examples": 1511}, {"name": "eu", "num_bytes": 547044, "num_examples": 1511}, {"name": "my", "num_bytes": 928002, "num_examples": 1511}], "download_size": 5503115, "dataset_size": 5847706}, {"config_name": "Llama-2-7b-hf", "features": [{"name": "story_id", "dtype": "string"}, {"name": "input_sentence_1", "dtype": "string"}, {"name": "input_sentence_2", "dtype": "string"}, {"name": "input_sentence_3", "dtype": "string"}, {"name": "input_sentence_4", "dtype": "string"}, {"name": "sentence_quiz1", "dtype": "string"}, {"name": "sentence_quiz2", "dtype": "string"}, {"name": "answer_right_ending", "dtype": "int32"}], "splits": [{"name": "ru", "num_bytes": 496817, "num_examples": 1511}, {"name": "zh", "num_bytes": 501800, "num_examples": 1511}, {"name": "es", "num_bytes": 504213, "num_examples": 1511}, {"name": "ar", "num_bytes": 501610, "num_examples": 1511}, {"name": "hi", "num_bytes": 504739, "num_examples": 1511}, {"name": "id", "num_bytes": 494323, "num_examples": 1511}, {"name": "te", "num_bytes": 588684, "num_examples": 1511}, {"name": "sw", "num_bytes": 501136, "num_examples": 1511}, {"name": "eu", "num_bytes": 520420, "num_examples": 1511}, {"name": "my", "num_bytes": 570585, "num_examples": 1511}], "download_size": 4838759, "dataset_size": 5184327}, {"config_name": "Llama-2-13b-hf", "features": [{"name": "story_id", "dtype": "string"}, {"name": "input_sentence_1", "dtype": "string"}, {"name": "input_sentence_2", "dtype": "string"}, {"name": "input_sentence_3", "dtype": "string"}, {"name": "input_sentence_4", "dtype": "string"}, {"name": "sentence_quiz1", "dtype": "string"}, {"name": "sentence_quiz2", "dtype": "string"}, {"name": "answer_right_ending", "dtype": "int32"}], "splits": [{"name": "ru", "num_bytes": 497558, "num_examples": 1511}, {"name": "zh", "num_bytes": 499829, "num_examples": 1511}, {"name": "es", "num_bytes": 500668, "num_examples": 1511}, {"name": "ar", "num_bytes": 502267, "num_examples": 1511}, {"name": "hi", "num_bytes": 499806, "num_examples": 1511}, {"name": "id", "num_bytes": 491094, "num_examples": 1511}, {"name": "te", "num_bytes": 634645, "num_examples": 1511}, {"name": "sw", "num_bytes": 508836, "num_examples": 1511}, {"name": "eu", "num_bytes": 524520, "num_examples": 1511}, {"name": "my", "num_bytes": 777348, "num_examples": 1511}], "download_size": 5090710, "dataset_size": 5436571}, {"config_name": "Llama-2-7b-chat-hf", "features": [{"name": "story_id", "dtype": "string"}, {"name": "input_sentence_1", "dtype": "string"}, {"name": "input_sentence_2", "dtype": "string"}, {"name": "input_sentence_3", "dtype": "string"}, {"name": "input_sentence_4", "dtype": "string"}, {"name": "sentence_quiz1", "dtype": "string"}, {"name": "sentence_quiz2", "dtype": "string"}, {"name": "answer_right_ending", "dtype": "int32"}], "splits": [{"name": "ru", "num_bytes": 255428, "num_examples": 1511}, {"name": "zh", "num_bytes": 259590, "num_examples": 1511}, {"name": "es", "num_bytes": 337962, "num_examples": 1511}, {"name": "ar", "num_bytes": 549212, "num_examples": 1511}, {"name": "hi", "num_bytes": 542237, "num_examples": 1511}, {"name": "id", "num_bytes": 445799, "num_examples": 1511}, {"name": "te", "num_bytes": 753517, "num_examples": 1511}, {"name": "sw", "num_bytes": 575797, "num_examples": 1511}, {"name": "eu", "num_bytes": 573902, "num_examples": 1511}, {"name": "my", "num_bytes": 669211, "num_examples": 1511}], "download_size": 4617898, "dataset_size": 4962655}, {"config_name": "Llama-2-13b-chat-hf", "features": [{"name": "story_id", "dtype": "string"}, {"name": "input_sentence_1", "dtype": "string"}, {"name": "input_sentence_2", "dtype": "string"}, {"name": "input_sentence_3", "dtype": "string"}, {"name": "input_sentence_4", "dtype": "string"}, {"name": "sentence_quiz1", "dtype": "string"}, {"name": "sentence_quiz2", "dtype": "string"}, {"name": "answer_right_ending", "dtype": "int32"}], "splits": [{"name": "ru", "num_bytes": 513558, "num_examples": 1511}, {"name": "zh", "num_bytes": 524461, "num_examples": 1511}, {"name": "es", "num_bytes": 502511, "num_examples": 1511}, {"name": "ar", "num_bytes": 546387, "num_examples": 1511}, {"name": "hi", "num_bytes": 556189, "num_examples": 1511}, {"name": "id", "num_bytes": 503053, "num_examples": 1511}, {"name": "te", "num_bytes": 812325, "num_examples": 1511}, {"name": "sw", "num_bytes": 587048, "num_examples": 1511}, {"name": "eu", "num_bytes": 646107, "num_examples": 1511}, {"name": "my", "num_bytes": 804207, "num_examples": 1511}], "download_size": 5650367, "dataset_size": 5995846}]}
|
2023-07-21T09:23:00+00:00
|
81c7ee3cf3317e51beb97327df55926cd5bbfadb
|
hf-internal-testing/tourism-monthly-batch
|
[
"license:cc",
"region:us"
] |
2023-05-22T08:38:22+00:00
|
{"license": "cc"}
|
2023-05-22T08:39:39+00:00
|
|
1d8380a07043710ecc0a6e810ddfb81420111180
|
# Dataset Card for "33e5b621"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
results-sd-v1-5-sd-v2-1-if-v1-0-karlo/33e5b621
|
[
"region:us"
] |
2023-05-22T08:49:11+00:00
|
{"dataset_info": {"features": [{"name": "result", "dtype": "string"}, {"name": "id", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 186, "num_examples": 10}], "download_size": 1337, "dataset_size": 186}}
|
2023-05-22T08:49:13+00:00
|
483c9d6dc9191f609816799fb12eb0760a077234
|
# Dataset Card for "chunk_224"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
mask-distilled-one-sec-cv12/chunk_224
|
[
"region:us"
] |
2023-05-22T08:51:14+00:00
|
{"dataset_info": {"features": [{"name": "logits", "sequence": "float32"}, {"name": "mfcc", "sequence": {"sequence": "float64"}}], "splits": [{"name": "train", "num_bytes": 905866800, "num_examples": 177900}], "download_size": 924915778, "dataset_size": 905866800}}
|
2023-05-22T08:52:02+00:00
|
1364c093d14f4bb576a2001f2df03c270c1ad672
|
sts 2012-2016 datasets
|
ttxy/sts
|
[
"task_categories:text-classification",
"language:code",
"license:bsd",
"sts",
"region:us"
] |
2023-05-22T08:52:45+00:00
|
{"language": ["code"], "license": "bsd", "task_categories": ["text-classification"], "pretty_name": "semantic text similarity", "tags": ["sts"]}
|
2023-05-22T10:02:07+00:00
|
d8017ee641e55fd7623ae54724a33d938bbc49cb
|
# Dataset Card for "chunk_227"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
mask-distilled-one-sec-cv12/chunk_227
|
[
"region:us"
] |
2023-05-22T08:54:34+00:00
|
{"dataset_info": {"features": [{"name": "logits", "sequence": "float32"}, {"name": "mfcc", "sequence": {"sequence": "float64"}}], "splits": [{"name": "train", "num_bytes": 887469404, "num_examples": 174287}], "download_size": 906697694, "dataset_size": 887469404}}
|
2023-05-22T08:55:23+00:00
|
552057d4669e9f81f9c8e19960d6b1654cd37c0d
|
# Dataset Card for "chunk_221"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
mask-distilled-one-sec-cv12/chunk_221
|
[
"region:us"
] |
2023-05-22T08:58:50+00:00
|
{"dataset_info": {"features": [{"name": "logits", "sequence": "float32"}, {"name": "mfcc", "sequence": {"sequence": "float64"}}], "splits": [{"name": "train", "num_bytes": 1157834236, "num_examples": 227383}], "download_size": 1182633818, "dataset_size": 1157834236}}
|
2023-05-22T08:59:26+00:00
|
a01ae176269a5a3c840b580f52c102ffcf0a3d28
|
# Dataset Card for "chunk_220"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
mask-distilled-one-sec-cv12/chunk_220
|
[
"region:us"
] |
2023-05-22T09:01:30+00:00
|
{"dataset_info": {"features": [{"name": "logits", "sequence": "float32"}, {"name": "mfcc", "sequence": {"sequence": "float64"}}], "splits": [{"name": "train", "num_bytes": 1157956444, "num_examples": 227407}], "download_size": 1176772094, "dataset_size": 1157956444}}
|
2023-05-22T09:02:14+00:00
|
87b427ed71dbf71c2205e7fea3e41e146fa4ddfc
|
# Dataset Card for "chunk_226"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
mask-distilled-one-sec-cv12/chunk_226
|
[
"region:us"
] |
2023-05-22T09:06:51+00:00
|
{"dataset_info": {"features": [{"name": "logits", "sequence": "float32"}, {"name": "mfcc", "sequence": {"sequence": "float64"}}], "splits": [{"name": "train", "num_bytes": 1164963036, "num_examples": 228783}], "download_size": 1190552161, "dataset_size": 1164963036}}
|
2023-05-22T09:07:27+00:00
|
fb11cb49560285153160dad3b06fca9e58a122d5
|
# Dataset Card for "chunk_229"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
mask-distilled-one-sec-cv12/chunk_229
|
[
"region:us"
] |
2023-05-22T09:06:54+00:00
|
{"dataset_info": {"features": [{"name": "logits", "sequence": "float32"}, {"name": "mfcc", "sequence": {"sequence": "float64"}}], "splits": [{"name": "train", "num_bytes": 922690768, "num_examples": 181204}], "download_size": 937716397, "dataset_size": 922690768}}
|
2023-05-22T09:07:26+00:00
|
e4cd4e38ce90be7cba5ab95c7f546e4c94e759d0
|
# Dataset Card for "chunk_225"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
mask-distilled-one-sec-cv12/chunk_225
|
[
"region:us"
] |
2023-05-22T09:09:32+00:00
|
{"dataset_info": {"features": [{"name": "logits", "sequence": "float32"}, {"name": "mfcc", "sequence": {"sequence": "float64"}}], "splits": [{"name": "train", "num_bytes": 1123901148, "num_examples": 220719}], "download_size": 1147828118, "dataset_size": 1123901148}}
|
2023-05-22T09:10:11+00:00
|
0ae9cd939a665ab1d9927e6a8e50286727e5ac32
|
# Dataset Card for "test_bce_dataset_voxelgym_3c_100_42x42"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Cubpaw/test_bce_dataset_voxelgym_3c_42x42_200
|
[
"region:us"
] |
2023-05-22T09:10:28+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": "image"}, {"name": "rgb_label", "dtype": "image"}, {"name": "path_label", "dtype": "image"}, {"name": "path_rgb_label", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 144820.0, "num_examples": 160}, {"name": "validation", "num_bytes": 36018.0, "num_examples": 40}], "download_size": 156017, "dataset_size": 180838.0}}
|
2023-05-22T09:10:48+00:00
|
def3c24c67c873e16144db812859675775e7156f
|
# Dataset Card for "chunk_222"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
mask-distilled-one-sec-cv12/chunk_222
|
[
"region:us"
] |
2023-05-22T09:17:33+00:00
|
{"dataset_info": {"features": [{"name": "logits", "sequence": "float32"}, {"name": "mfcc", "sequence": {"sequence": "float64"}}], "splits": [{"name": "train", "num_bytes": 1282547500, "num_examples": 251875}], "download_size": 1310151117, "dataset_size": 1282547500}}
|
2023-05-22T09:18:52+00:00
|
86b625a76334d68039b139f72c00603466750b5e
|
# Dataset Card for Describable Textures Dataset (DTD)
## Dataset Description
- Homepage: https://www.robots.ox.ac.uk/~vgg/data/dtd/
- Repository: https://github.com/mcimpoi/deep-fbanks
- Paper: https://openaccess.thecvf.com/content_cvpr_2014/html/Cimpoi_Describing_Textures_in_2014_CVPR_paper.html
- Leaderboard: https://paperswithcode.com/sota/image-classification-on-dtd
### Dataset Summary
Texture classification dataset; consists of 47 categories, 120 images per class.
### Data Splits
Equally split into train, val, test; The original paper proposed 10 splits; recent works (BYOL, arxiv:2006.07733) use only first split.
### Licensing Information
Not defined at https://www.robots.ox.ac.uk/~vgg/data/dtd/
### Citation Information
@InProceedings{cimpoi14describing,
Author = {M. Cimpoi and S. Maji and I. Kokkinos and S. Mohamed and and A. Vedaldi},
Title = {Describing Textures in the Wild},
Booktitle = {Proceedings of the {IEEE} Conf. on Computer Vision and Pattern Recognition ({CVPR})},
Year = {2014}}
|
mcimpoi/dtd_split_1
|
[
"task_categories:image-classification",
"size_categories:1K<n<10K",
"language:en",
"license:cc-by-4.0",
"texture",
"computer-vision",
"region:us"
] |
2023-05-22T09:17:50+00:00
|
{"language": ["en"], "license": "cc-by-4.0", "size_categories": ["1K<n<10K"], "task_categories": ["image-classification"], "pretty_name": "Describable Textures Dataset", "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "banded", "1": "blotchy", "2": "braided", "3": "bubbly", "4": "bumpy", "5": "chequered", "6": "cobwebbed", "7": "cracked", "8": "crosshatched", "9": "crystalline", "10": "dotted", "11": "fibrous", "12": "flecked", "13": "freckled", "14": "frilly", "15": "gauzy", "16": "grid", "17": "grooved", "18": "honeycombed", "19": "interlaced", "20": "knitted", "21": "lacelike", "22": "lined", "23": "marbled", "24": "matted", "25": "meshed", "26": "paisley", "27": "perforated", "28": "pitted", "29": "pleated", "30": "polka-dotted", "31": "porous", "32": "potholed", "33": "scaly", "34": "smeared", "35": "spiralled", "36": "sprinkled", "37": "stained", "38": "stratified", "39": "striped", "40": "studded", "41": "swirly", "42": "veined", "43": "waffled", "44": "woven", "45": "wrinkled", "46": "zigzagged"}}}}], "splits": [{"name": "train", "num_bytes": 226313270.04, "num_examples": 1880}, {"name": "test", "num_bytes": 172035822, "num_examples": 1880}, {"name": "validation", "num_bytes": 222278767.48, "num_examples": 1880}], "download_size": 629315160, "dataset_size": 620627859.52}, "tags": ["texture", "computer-vision"]}
|
2023-05-22T11:42:00+00:00
|
8e4fe8a264e7d9e5387a0842104f08006a846679
|
# Dataset Card for "chunk_228"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
mask-distilled-one-sec-cv12/chunk_228
|
[
"region:us"
] |
2023-05-22T09:17:58+00:00
|
{"dataset_info": {"features": [{"name": "logits", "sequence": "float32"}, {"name": "mfcc", "sequence": {"sequence": "float64"}}], "splits": [{"name": "train", "num_bytes": 1176735740, "num_examples": 231095}], "download_size": 1197667160, "dataset_size": 1176735740}}
|
2023-05-22T09:18:48+00:00
|
33edbafb47a3bf0a070f9e5ae250969636f00814
|
## Dataset Description
- **Repository:** https://github.com/huggingface/datablations
- **Paper:** [Scaling Data-Constrained Language Models](https://arxiv.org/abs/2305.16264)
- **Point of Contact:** [Niklas Muennighoff](mailto:[email protected])
### Dataset Summary
Various subsets of [C4](https://huggingface.co/datasets/allenai/c4) with different numbers of tokens measured with the GPT2Tokenizer.
This data is used in the paper [Scaling Data-Constrained Language Models](https://arxiv.org/abs/2305.16264).
Please refer to [our GitHub repository](https://github.com/huggingface/datablations) for more details.
```bibtex
@article{muennighoff2023scaling,
title={Scaling Data-Constrained Language Models},
author={Muennighoff, Niklas and Rush, Alexander M and Barak, Boaz and Scao, Teven Le and Piktus, Aleksandra and Tazi, Nouamane and Pyysalo, Sampo and Wolf, Thomas and Raffel, Colin},
journal={arXiv preprint arXiv:2305.16264},
year={2023}
}
```
|
datablations/c4-subsets
|
[
"language:en",
"license:cc0-1.0",
"arxiv:2305.16264",
"region:us"
] |
2023-05-22T09:45:51+00:00
|
{"language": ["en"], "license": "cc0-1.0"}
|
2023-06-14T10:46:48+00:00
|
c69d305b3a13610970dd5677eddac230b4a2e07d
|
# Dataset Card for "chunk_223"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
mask-distilled-one-sec-cv12/chunk_223
|
[
"region:us"
] |
2023-05-22T09:49:09+00:00
|
{"dataset_info": {"features": [{"name": "logits", "sequence": "float32"}, {"name": "mfcc", "sequence": {"sequence": "float64"}}], "splits": [{"name": "train", "num_bytes": 1238511884, "num_examples": 243227}], "download_size": 1264626689, "dataset_size": 1238511884}}
|
2023-05-22T09:51:23+00:00
|
44da7903053150b9b87972f49ed7595e8b64774f
|
# Dataset Card for "ashaar-small-proc"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Zaid/ashaar-small-proc
|
[
"region:us"
] |
2023-05-22T09:51:41+00:00
|
{"dataset_info": {"features": [{"name": "poem title", "dtype": "string"}, {"name": "poem meter", "dtype": "string"}, {"name": "poem verses", "dtype": "string"}, {"name": "poem theme", "dtype": "string"}, {"name": "poem url", "dtype": "string"}, {"name": "poet name", "dtype": "string"}, {"name": "poet description", "dtype": "string"}, {"name": "poet url", "dtype": "string"}, {"name": "poet era", "dtype": "string"}, {"name": "poet location", "dtype": "string"}, {"name": "poem description", "list": [{"name": "attributes", "struct": [{"name": "class", "dtype": "string"}, {"name": "color", "dtype": "string"}, {"name": "dir", "dtype": "string"}, {"name": "face", "dtype": "string"}, {"name": "id", "dtype": "string"}, {"name": "lang", "dtype": "string"}, {"name": "style", "dtype": "string"}]}, {"name": "children", "list": [{"name": "attributes", "struct": [{"name": "color", "dtype": "string"}, {"name": "dir", "dtype": "string"}, {"name": "face", "dtype": "string"}, {"name": "href", "dtype": "string"}, {"name": "id", "dtype": "string"}, {"name": "lang", "dtype": "string"}, {"name": "style", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "value", "dtype": "string"}]}, {"name": "children", "list": [{"name": "attributes", "struct": [{"name": "class", "dtype": "string"}, {"name": "color", "dtype": "string"}, {"name": "dir", "dtype": "string"}, {"name": "face", "dtype": "string"}, {"name": "lang", "dtype": "string"}, {"name": "style", "dtype": "string"}]}, {"name": "children", "list": [{"name": "attributes", "struct": [{"name": "align", "dtype": "string"}, {"name": "face", "dtype": "string"}, {"name": "nowrap", "dtype": "string"}]}, {"name": "name", "dtype": "string"}, {"name": "parentAttributes", "struct": [{"name": "lang", "dtype": "string"}, {"name": "style", "dtype": "string"}]}, {"name": "size", "dtype": "int64"}, {"name": "text", "dtype": "string"}, {"name": "truncated", "dtype": "bool"}, {"name": "type", "dtype": "string"}]}, {"name": "name", "dtype": "string"}, {"name": "parentAttributes", "struct": [{"name": "dir", "dtype": "string"}, {"name": "face", "dtype": "string"}, {"name": "id", "dtype": "string"}, {"name": "lang", "dtype": "string"}, {"name": "style", "dtype": "string"}]}, {"name": "partA", "dtype": "string"}, {"name": "size", "dtype": "int64"}, {"name": "text", "dtype": "string"}, {"name": "truncated", "dtype": "bool"}, {"name": "type", "dtype": "string"}]}, {"name": "name", "dtype": "string"}, {"name": "parentAttributes", "struct": [{"name": "class", "dtype": "string"}, {"name": "color", "dtype": "string"}, {"name": "dir", "dtype": "string"}, {"name": "id", "dtype": "string"}, {"name": "lang", "dtype": "string"}, {"name": "style", "dtype": "string"}]}, {"name": "partA", "dtype": "string"}, {"name": "partB", "dtype": "string"}, {"name": "size", "dtype": "int64"}, {"name": "text", "dtype": "string"}, {"name": "truncated", "dtype": "bool"}, {"name": "type", "dtype": "string"}]}, {"name": "name", "dtype": "string"}, {"name": "parentAttributes", "struct": [{"name": "dir", "dtype": "string"}, {"name": "style", "dtype": "string"}]}, {"name": "partA", "dtype": "string"}, {"name": "partB", "dtype": "string"}, {"name": "size", "dtype": "int64"}, {"name": "text", "dtype": "string"}, {"name": "truncated", "dtype": "bool"}, {"name": "type", "dtype": "string"}]}, {"name": "poem language type", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 31435320, "num_examples": 10000}], "download_size": 0, "dataset_size": 31435320}}
|
2023-05-22T10:08:51+00:00
|
470f79dddef978a21a93120c389692424fdee01c
|
## Dataset Description
- **Repository:** https://github.com/huggingface/datablations
- **Paper:** [Scaling Data-Constrained Language Models](https://arxiv.org/abs/2305.16264)
- **Point of Contact:** [Niklas Muennighoff](mailto:[email protected])
### Dataset Summary
Various subsets of the [English OSCAR](https://huggingface.co/datasets/oscar-corpus/OSCAR-2201) with different numbers of tokens measured with the GPT2Tokenizer.
This data is used in the paper [Scaling Data-Constrained Language Models](https://arxiv.org/abs/2305.16264).
Please refer to [our GitHub repository](https://github.com/huggingface/datablations) for more details.
```bibtex
@article{muennighoff2023scaling,
title={Scaling Data-Constrained Language Models},
author={Muennighoff, Niklas and Rush, Alexander M and Barak, Boaz and Scao, Teven Le and Piktus, Aleksandra and Tazi, Nouamane and Pyysalo, Sampo and Wolf, Thomas and Raffel, Colin},
journal={arXiv preprint arXiv:2305.16264},
year={2023}
}
```
|
datablations/oscar-subsets
|
[
"language:en",
"license:cc0-1.0",
"arxiv:2305.16264",
"region:us"
] |
2023-05-22T09:54:25+00:00
|
{"language": ["en"], "license": "cc0-1.0"}
|
2023-06-14T10:47:34+00:00
|
0fbee8e9477794b61730cd16e277a7a13778d286
|
# Dataset Card for "SMM2-levels-final-v2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
valashir/SMM2-levels-final-v2
|
[
"region:us"
] |
2023-05-22T10:05:42+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "level", "sequence": {"sequence": {"sequence": "uint8"}}}, {"name": "text", "dtype": "string"}, {"name": "text-baseline", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 16639096098, "num_examples": 202096}, {"name": "val", "num_bytes": 167450434, "num_examples": 2048}], "download_size": 263061211, "dataset_size": 16806546532}}
|
2023-05-22T10:11:19+00:00
|
095694df3631f39fc842f929e90a0c7776af944f
|
# Dataset Card for "ashaar_small_proc"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Zaid/ashaar_small_proc
|
[
"region:us"
] |
2023-05-22T10:13:52+00:00
|
{"dataset_info": {"features": [{"name": "poem title", "dtype": "string"}, {"name": "poem meter", "dtype": "string"}, {"name": "poem verses", "dtype": "string"}, {"name": "poem theme", "dtype": "string"}, {"name": "poem url", "dtype": "string"}, {"name": "poet name", "dtype": "string"}, {"name": "poet description", "dtype": "string"}, {"name": "poet url", "dtype": "string"}, {"name": "poet era", "dtype": "string"}, {"name": "poet location", "dtype": "string"}, {"name": "poem description", "list": [{"name": "attributes", "struct": [{"name": "class", "dtype": "string"}, {"name": "color", "dtype": "string"}, {"name": "dir", "dtype": "string"}, {"name": "face", "dtype": "string"}, {"name": "id", "dtype": "string"}, {"name": "lang", "dtype": "string"}, {"name": "style", "dtype": "string"}]}, {"name": "children", "list": [{"name": "attributes", "struct": [{"name": "color", "dtype": "string"}, {"name": "dir", "dtype": "string"}, {"name": "face", "dtype": "string"}, {"name": "href", "dtype": "string"}, {"name": "id", "dtype": "string"}, {"name": "lang", "dtype": "string"}, {"name": "style", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "value", "dtype": "string"}]}, {"name": "children", "list": [{"name": "attributes", "struct": [{"name": "class", "dtype": "string"}, {"name": "color", "dtype": "string"}, {"name": "dir", "dtype": "string"}, {"name": "face", "dtype": "string"}, {"name": "lang", "dtype": "string"}, {"name": "style", "dtype": "string"}]}, {"name": "children", "list": [{"name": "attributes", "struct": [{"name": "align", "dtype": "string"}, {"name": "face", "dtype": "string"}, {"name": "nowrap", "dtype": "string"}]}, {"name": "name", "dtype": "string"}, {"name": "parentAttributes", "struct": [{"name": "lang", "dtype": "string"}, {"name": "style", "dtype": "string"}]}, {"name": "size", "dtype": "int64"}, {"name": "text", "dtype": "string"}, {"name": "truncated", "dtype": "bool"}, {"name": "type", "dtype": "string"}]}, {"name": "name", "dtype": "string"}, {"name": "parentAttributes", "struct": [{"name": "dir", "dtype": "string"}, {"name": "face", "dtype": "string"}, {"name": "id", "dtype": "string"}, {"name": "lang", "dtype": "string"}, {"name": "style", "dtype": "string"}]}, {"name": "partA", "dtype": "string"}, {"name": "size", "dtype": "int64"}, {"name": "text", "dtype": "string"}, {"name": "truncated", "dtype": "bool"}, {"name": "type", "dtype": "string"}]}, {"name": "name", "dtype": "string"}, {"name": "parentAttributes", "struct": [{"name": "class", "dtype": "string"}, {"name": "color", "dtype": "string"}, {"name": "dir", "dtype": "string"}, {"name": "id", "dtype": "string"}, {"name": "lang", "dtype": "string"}, {"name": "style", "dtype": "string"}]}, {"name": "partA", "dtype": "string"}, {"name": "partB", "dtype": "string"}, {"name": "size", "dtype": "int64"}, {"name": "text", "dtype": "string"}, {"name": "truncated", "dtype": "bool"}, {"name": "type", "dtype": "string"}]}, {"name": "name", "dtype": "string"}, {"name": "parentAttributes", "struct": [{"name": "dir", "dtype": "string"}, {"name": "style", "dtype": "string"}]}, {"name": "partA", "dtype": "string"}, {"name": "partB", "dtype": "string"}, {"name": "size", "dtype": "int64"}, {"name": "text", "dtype": "string"}, {"name": "truncated", "dtype": "bool"}, {"name": "type", "dtype": "string"}]}, {"name": "poem language type", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 31435320, "num_examples": 10000}], "download_size": 8441588, "dataset_size": 31435320}}
|
2023-05-22T10:13:59+00:00
|
6fb4d6674141505edee606ef337734f74e64c058
|
# Dataset Card for "cv_pb_test"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
harsh024/cv_pb_test
|
[
"region:us"
] |
2023-05-22T10:39:06+00:00
|
{"dataset_info": {"features": [{"name": "path", "dtype": "string"}, {"name": "audio", "dtype": {"audio": {"sampling_rate": 16000}}}, {"name": "sentence", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 53230843.0, "num_examples": 1364}], "download_size": 52821502, "dataset_size": 53230843.0}}
|
2023-05-22T10:39:12+00:00
|
edbe40cae9f168ccdf0332cca144efa7d9d8106f
|
# Dataset Card for "chunk_231"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
mask-distilled-one-sec-cv12/chunk_231
|
[
"region:us"
] |
2023-05-22T10:43:11+00:00
|
{"dataset_info": {"features": [{"name": "logits", "sequence": "float32"}, {"name": "mfcc", "sequence": {"sequence": "float64"}}], "splits": [{"name": "train", "num_bytes": 1229422664, "num_examples": 241442}], "download_size": 1254668095, "dataset_size": 1229422664}}
|
2023-05-22T10:44:05+00:00
|
7bf14fb4b662c858eb10db53aa3058140659b843
|
yongchoooon/fire-aihub-chatgpt
|
[
"task_categories:text-to-image",
"annotations_creators:machine-generated",
"language_creators:other",
"multilinguality:monolingual",
"size_categories:n<1K",
"language:en",
"license:cc-by-nc-sa-4.0",
"region:us"
] |
2023-05-22T10:47:11+00:00
|
{"annotations_creators": ["machine-generated"], "language_creators": ["other"], "language": ["en"], "license": "cc-by-nc-sa-4.0", "multilinguality": ["monolingual"], "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "task_ids": [], "pretty_name": "fire-aihub-chatgpt", "tags": []}
|
2023-05-22T12:31:54+00:00
|
|
bf94ef8241ffcb7a86773973598c83ed4ce1c604
|
# Dataset Card for "chunk_230"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
mask-distilled-one-sec-cv12/chunk_230
|
[
"region:us"
] |
2023-05-22T10:50:48+00:00
|
{"dataset_info": {"features": [{"name": "logits", "sequence": "float32"}, {"name": "mfcc", "sequence": {"sequence": "float64"}}], "splits": [{"name": "train", "num_bytes": 1009285320, "num_examples": 198210}], "download_size": 1027746656, "dataset_size": 1009285320}}
|
2023-05-22T10:52:41+00:00
|
11b49488aa55ab0ecd9135bdb56d83c629f63bee
|
# Dataset Card for "chunk_235"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
mask-distilled-one-sec-cv12/chunk_235
|
[
"region:us"
] |
2023-05-22T10:51:21+00:00
|
{"dataset_info": {"features": [{"name": "logits", "sequence": "float32"}, {"name": "mfcc", "sequence": {"sequence": "float64"}}], "splits": [{"name": "train", "num_bytes": 1166918364, "num_examples": 229167}], "download_size": 1191475987, "dataset_size": 1166918364}}
|
2023-05-22T10:51:57+00:00
|
8743e9d67088dfef53f3514fbb6c134fc64be0e6
|
# Dataset Card for "chunk_232"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
mask-distilled-one-sec-cv12/chunk_232
|
[
"region:us"
] |
2023-05-22T10:51:32+00:00
|
{"dataset_info": {"features": [{"name": "logits", "sequence": "float32"}, {"name": "mfcc", "sequence": {"sequence": "float64"}}], "splits": [{"name": "train", "num_bytes": 1198687352, "num_examples": 235406}], "download_size": 1224912716, "dataset_size": 1198687352}}
|
2023-05-22T10:52:42+00:00
|
69a6d98537f7f74df08a7a73fa8d2692e175c328
|
# Dataset Card for "spectrogram_musiccaps"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
pranked03/spectrogram_musiccaps
|
[
"region:us"
] |
2023-05-22T10:53:13+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}, {"name": "ytid", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 47464491.0, "num_examples": 100}], "download_size": 0, "dataset_size": 47464491.0}}
|
2023-05-22T11:24:32+00:00
|
0134b5a05570b477d56a16b39942c6b70c9c7e8b
|
# Dataset Card for "chunk_234"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
mask-distilled-one-sec-cv12/chunk_234
|
[
"region:us"
] |
2023-05-22T10:54:40+00:00
|
{"dataset_info": {"features": [{"name": "logits", "sequence": "float32"}, {"name": "mfcc", "sequence": {"sequence": "float64"}}], "splits": [{"name": "train", "num_bytes": 1136473296, "num_examples": 223188}], "download_size": 1161235968, "dataset_size": 1136473296}}
|
2023-05-22T10:55:40+00:00
|
414146955b2e92ac87eaab2f0ef2f5488dc95602
|
# Dataset Card for "delays"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
polinaeterna/delays
|
[
"region:us"
] |
2023-05-22T10:58:00+00:00
|
{"dataset_info": {"features": [{"name": "vehicle_type", "dtype": "int64"}, {"name": "direction", "dtype": "int64"}, {"name": "weekday", "dtype": "int64"}, {"name": "temp", "dtype": "float64"}, {"name": "windspeed_max", "dtype": "float64"}, {"name": "windspeed_avg", "dtype": "float64"}, {"name": "precipitation", "dtype": "float64"}, {"name": "dew_point", "dtype": "float64"}, {"name": "humidity", "dtype": "int64"}, {"name": "hour", "dtype": "int64"}, {"name": "dayminute", "dtype": "float64"}, {"name": "delay", "dtype": "float64"}], "splits": [{"name": "train", "num_bytes": 524695200, "num_examples": 5465575}], "download_size": 64568538, "dataset_size": 524695200}}
|
2023-05-22T10:58:10+00:00
|
879f27238fb8c17fa55d4aec2b21af3f02c0fd2a
|
# Dataset Card for "jglue_jsquad"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Jumtra/jglue_jsquad
|
[
"region:us"
] |
2023-05-22T10:58:17+00:00
|
{"dataset_info": {"features": [{"name": "instruction", "dtype": "string"}, {"name": "input", "dtype": "string"}, {"name": "output", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 7356910, "num_examples": 67301}], "download_size": 3527041, "dataset_size": 7356910}}
|
2023-06-21T00:07:32+00:00
|
f3a0da4e7368da9417e1c8d20affa9d926d36e91
|
# Dataset Card for "SMM2-levels-all-v2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
valashir/SMM2-levels-all-v2
|
[
"region:us"
] |
2023-05-22T11:02:07+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "level", "sequence": {"sequence": {"sequence": "uint8"}}}, {"name": "text", "dtype": "string"}, {"name": "text-baseline", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 30754194471, "num_examples": 202096}, {"name": "val", "num_bytes": 308873455, "num_examples": 2048}], "download_size": 271999803, "dataset_size": 31063067926}}
|
2023-05-22T11:12:13+00:00
|
534a445fca02a542f88316b12c9828eb2dba0227
|
# Dataset Card for "chunk_233"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
mask-distilled-one-sec-cv12/chunk_233
|
[
"region:us"
] |
2023-05-22T11:03:21+00:00
|
{"dataset_info": {"features": [{"name": "logits", "sequence": "float32"}, {"name": "mfcc", "sequence": {"sequence": "float64"}}], "splits": [{"name": "train", "num_bytes": 1120382576, "num_examples": 220028}], "download_size": 1144573483, "dataset_size": 1120382576}}
|
2023-05-22T11:05:24+00:00
|
d294d93cf5ae49c19efaf3e4d8bba2bc89352855
|
# Dataset Card for "chunk_236"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
mask-distilled-one-sec-cv12/chunk_236
|
[
"region:us"
] |
2023-05-22T11:23:18+00:00
|
{"dataset_info": {"features": [{"name": "logits", "sequence": "float32"}, {"name": "mfcc", "sequence": {"sequence": "float64"}}], "splits": [{"name": "train", "num_bytes": 1210602632, "num_examples": 237746}], "download_size": 1232414624, "dataset_size": 1210602632}}
|
2023-05-22T11:25:30+00:00
|
9114cd061f5d4de688a063a7c0e8b8550a795b54
|
# Dataset Card for "chunk_238"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
mask-distilled-one-sec-cv12/chunk_238
|
[
"region:us"
] |
2023-05-22T11:25:36+00:00
|
{"dataset_info": {"features": [{"name": "logits", "sequence": "float32"}, {"name": "mfcc", "sequence": {"sequence": "float64"}}], "splits": [{"name": "train", "num_bytes": 1339053424, "num_examples": 262972}], "download_size": 1363072631, "dataset_size": 1339053424}}
|
2023-05-22T11:26:46+00:00
|
9cfbabdafbbaece67758afb81d3825b72cbda2cc
|
# Dataset Card for "chunk_237"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
mask-distilled-one-sec-cv12/chunk_237
|
[
"region:us"
] |
2023-05-22T11:31:43+00:00
|
{"dataset_info": {"features": [{"name": "logits", "sequence": "float32"}, {"name": "mfcc", "sequence": {"sequence": "float64"}}], "splits": [{"name": "train", "num_bytes": 1159005396, "num_examples": 227613}], "download_size": 1180298569, "dataset_size": 1159005396}}
|
2023-05-22T11:37:18+00:00
|
43c7d6e1948718853e5c4300351e9b266a31852a
|
# Dataset Card for luotuo-QA-B
## Dataset Description
- **Homepage:** https://github.com/LC1332/Luotuo-Chinese-LLM
- **Repository:** https://github.com/LC1332/Luotuo-QA
- **Point of Contact:** [email protected]
### Dataset Summary
Anki_Card是一种用于记忆和学习的电子卡片系统。我们建立了一个类似于这种形式的问答数据集，旨在推动中英文语境下问答模型的研究和发展。
我们的数据集是在3个开源数据集之上生成构建的，这3个数据集分别是：
·Chinese Scientific Literature Dataset
·CNN-DailyMail News Text Summarization
·arXiv Dataset
您可以直接搜索这些原始数据集的名称或是从以下链接访问它们
·https://github.com/ydli-ai/CSL
·https://www.kaggle.com/datasets/gowrishankarp/newspaper-text-summarization-cnn-dailymail
·https://www.kaggle.com/datasets/Cornell-University/arxiv
我们在这些数据集的基础上针对每一个摘要或新闻生成了5个“问题-答案”对。数据分布如下：
---从Chinese Scientific Literature Dataset(CSL)数据集中生成了25836条中文数据，共129180个问答对。
---从CNN-DailyMail News Text Summarization数据集中生成了2026条数据，共10130个问答对。
---从arXiv Dataset数据集中生成了3602条英文数据，共18010个问答对。
此外，由于此数据集是我们Luotuo-QA项目的一部分，我们将它叫做luotuo-QA-B。
您可以在这里查看Luotuo-QA项目：https://github.com/LC1332/Luotuo-QA
此数据集适用于训练和评估中文对话式问答模型。有益于推动中文自然语言处理领域的发展，同时也为研究人员和开发者提供了一个基准，用于比较不同模型的性能和探索新的方法。
我们希望这一工作能够促进全球范围内中文语境对话式问答任务的研究和进一步的创新。
-----------------------------------------------------------------------------------------------------------------------------------------------
Anki_Card is an electronic flashcard system used for memory and learning. We have created a question-and-answer dataset in a similar format to facilitate research and development of question-answering models in both Chinese and English contexts.
Our dataset is constructed based on three open-source datasets:
·Chinese Scientific Literature Dataset
·CNN-DailyMail News Text Summarization
·arXiv Dataset
You can directly search for the names of these original datasets or access them from the following links:
·Chinese Scientific Literature Dataset (CSL): https://github.com/ydli-ai/CSL
·CNN-DailyMail News Text Summarization: https://www.kaggle.com/datasets/gowrishankarp/newspaper-text-summarization-cnn-dailymail
·arXiv Dataset: https://www.kaggle.com/datasets/Cornell-University/arxiv
Based on these datasets, we have generated five "question-answer" pairs for each summary or news article. The data distribution is as follows:
---From the Chinese Scientific Literature Dataset (CSL), we generated 25,836 Chinese data points, resulting in a total of 129,180 question-answer pairs.
---From the CNN-DailyMail News Text Summarization dataset, we generated 2,026 data points, resulting in a total of 10,130 question-answer pairs.
---From the arXiv Dataset, we generated 3,602 English data points, resulting in a total of 18,010 question-answer pairs.
Furthermore, as this dataset is part of our Luotuo-QA project, we refer to it as luotuo-QA-B.
You can find the Luotuo-QA project here: https://github.com/LC1332/Luotuo-QA
This dataset is suitable for training and evaluating Chinese conversational question-answering models. It contributes to the development of Chinese natural language processing and provides researchers and developers with a benchmark for comparing the performance of different models and exploring new approaches.
We hope that this work will promote research and further innovation in Chinese conversational question-answering tasks on a global scale.
### Languages
CHINESE, ENGLISH
### Data Instances
中文数据样例：
```
{
"story": "中国股市发展中特有的股权分置结构决定了研究股市收益率问题的复杂性.本文提出用全收益率的标准来衡量中国股市的整体收益率,认为在股权分置及其逐步解决的过程中,研究股市全收益率具有重要的意义,也是讨论股市其它问题的理论基础.随着股权分置改革渐进式地推进,中国股市各类股权所有者的收益分布会发生显著的结构性变化.从长期看,股权分置改革能使投资股东和原始股东的收益函数趋于一致,有助于实现整体收益的最大化.",
"questions": [
"为什么研究股市收益率问题复杂？",
"用什么标准来衡量中国股市的整体收益率？",
"股权分置改革对股东收益分布会有什么影响？",
"股权分置改革的推进方式是什么？",
"为什么研究股市全收益率具有重要意义？"
],
"answers": [
"因为中国股市发展中特有的股权分置结构决定了研究股市收益率问题的复杂性。",
"用全收益率的标准来衡量中国股市的整体收益率。",
"股权分置改革会使投资股东和原始股东的收益函数趋于一致，有助于实现整体收益的最大化。",
"股权分置改革是渐进式地推进的。",
"因为研究股市全收益率是讨论股市其它问题的理论基础，也在股权分置及其逐步解决的过程中具有重要的意义。"
],
"language": "Chinese"
}
```
英文数据样例：
```
{
"story": "'(CNN) -- A 14-year-old was arrested late Tuesday after shining a powerful laser light into the eyes of a pilot who was approaching Los Angeles International Airport, the Federal Aviation Administration said. The arrest puts a spotlight on what the FAA calls a dangerous problem in recent years. In Tuesday's case, the pilot was about 2,000 feet in the air and nobody was hurt in the incident, said Ian Gregor, an FAA spokesman. \"It's potentially very dangerous to shine a laser at an aircraft because a laser can distract a pilot and there have been cases where pilots have suffered temporary vision problems as a result of being struck by a laser beam,\" Gregor said. \" We've had reports of pilots having to turn over control of the aircraft to a co-pilot or had to abort landing.\" Gregor said Los Angeles International Airport has had many instances of laser attacks. \"Pilots reported 102 laser incidents around LAX in 2010. Most of any airport in the country,\" Gregor said.'",
"questions": [
"What happened to the 14-year-old?",
"Why is shining a laser at an aircraft dangerous?",
"What have pilots had to do in some cases of laser attacks?",
"How many laser incidents were reported around LAX in 2010?",
"What is the FAA's concern about laser attacks?"
],
"answers": [
"The 14-year-old was arrested for shining a powerful laser light into the eyes of a pilot.",
"Shining a laser at an aircraft is dangerous because it can distract a pilot and cause temporary vision problems.",
"In some cases of laser attacks, pilots have had to turn over control of the aircraft to a co-pilot or had to abort landing.",
"102 laser incidents were reported around LAX in 2010, the most of any airport in the country.",
"The FAA is concerned about laser attacks because they pose a dangerous problem for pilots and can cause temporary vision problems."
],
"language": "English"
}
```
### Licensing Information
我们的协议与三个原始数据集的协议保持一致，请阅读以下内容。
·CSL数据集的协议是Apache License 2.0，除非遵守许可证，否则您不得使用此文件
·CNN-DailyMail News Text Summarization数据集的协议是 CC0: Public Domain
·arXiv数据集的协议是 CC0: Public Domain
-----------------------------------------------------------------------------------------------------------------------------------------------
Our agreements are consistent with the agreements of three original datasets. Please read the following information.
· The protocol for the CSL dataset is Apache License 2.0. You are not allowed to use this file unless you comply with the license.
· The protocol for the CNN-DailyMail News Text Summarization dataset is CC0: Public Domain.
· The protocol for the arXiv dataset is CC0: Public Domain.
### Citation Information
如果您在项目中使用了我们的模型、代码或者数据，请引用我们。
Please cite us if you use the data or code in this repo.
```bibtex
@misc{alpaca,
author={Jianshen Liao, Ao Sun, Qinyu Luo, Hongsen Huang, Cheng Li},
title = {Luotuo-QA: Better Conversational Question Answering Model with Answer Completion},
year = {2023},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/LC1332/Luotuo-QA}},
}
```
|
Logic123456789/Luotuo-QA-B
|
[
"task_categories:question-answering",
"language:zh",
"language:en",
"license:other",
"region:us"
] |
2023-05-22T11:47:12+00:00
|
{"language": ["zh", "en"], "license": "other", "task_categories": ["question-answering"], "extra_gated_prompt": "\u6211\u4eec\u5236\u4f5c\u4e86luotuo-QA-B\u6570\u636e\u96c6\uff0c\u8bf7\u4ed4\u7ec6\u9605\u8bfbLicensing Information\u90e8\u5206\u7684\u4fe1\u606f\u3002", "extra_gated_heading": "\u60a8\u9700\u8981\u63a5\u53d7\u534f\u8bae\u5e76\u63d0\u4ea4\u4fe1\u606f\u4ee5\u83b7\u53d6\u6b64\u6570\u636e\u96c6", "extra_gated_fields": {"\u59d3\u540d": "text", "\u90ae\u7bb1": "text", "\u6240\u5728\u7ec4\u7ec7": "text", "\u4f7f\u7528\u76ee\u7684": "text", "\u6211\u540c\u610f\u4ec5\u5c06\u6b64\u6570\u636e\u96c6\u7528\u4e8e\u975e\u5546\u4e1a\u7528\u9014": "checkbox"}, "extra_gated_button_content": "\u6211\u5df2\u9605\u8bfb\u534f\u8bae\u5e76\u540c\u610f\u63d0\u4f9b\u76f8\u5173\u4fe1\u606f"}
|
2023-05-22T13:07:54+00:00
|
8be3f7821854ab14683bc3a37b19d8a52611e36c
|
# Dataset Card for "chunk_239"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
mask-distilled-one-sec-cv12/chunk_239
|
[
"region:us"
] |
2023-05-22T11:48:51+00:00
|
{"dataset_info": {"features": [{"name": "logits", "sequence": "float32"}, {"name": "mfcc", "sequence": {"sequence": "float64"}}], "splits": [{"name": "train", "num_bytes": 1488600372, "num_examples": 292341}], "download_size": 1519686081, "dataset_size": 1488600372}}
|
2023-05-22T11:49:39+00:00
|
4f158a005aec7dc643c08ef50d073e0561c28afb
|
JVictorLourenco/me
|
[
"license:mit",
"region:us"
] |
2023-05-22T11:50:20+00:00
|
{"license": "mit"}
|
2023-05-22T12:04:30+00:00
|
|
30b6e5d9f7d85380b9fd5255ad66e4c129e8252b
|
# AutoTrain Dataset for project: qa_xlm_roberta_large_tesquad2
## Dataset Description
This dataset has been automatically processed by AutoTrain for project qa_xlm_roberta_large_tesquad2.
### Languages
The BCP-47 code for the dataset's language is unk.
## Dataset Structure
### Data Instances
A sample from this dataset looks as follows:
```json
[
{
"context": "7 \u0c0f\u0c2a\u0c4d\u0c30\u0c3f\u0c32\u0c4d 1963\u0c28, \u0c26\u0c47\u0c36\u0c02 \u0c24\u0c28 \u0c05\u0c27\u0c3f\u0c15\u0c3e\u0c30\u0c3f\u0c15 \u0c2a\u0c47\u0c30\u0c41\u0c28\u0c41 \u0c38\u0c4b\u0c37\u0c32\u0c3f\u0c38\u0c4d\u0c1f\u0c4d \u0c2b\u0c46\u0c21\u0c30\u0c32\u0c4d \u0c30\u0c3f\u0c2a\u0c2c\u0c4d\u0c32\u0c3f\u0c15\u0c4d \u0c06\u0c2b\u0c4d \u0c2f\u0c41\u0c17\u0c4b\u0c38\u0c4d\u0c32\u0c47\u0c35\u0c3f\u0c2f\u0c3e\u0c17\u0c3e \u0c2e\u0c3e\u0c30\u0c4d\u0c1a\u0c3f\u0c02\u0c26\u0c3f. \u0c38\u0c02\u0c38\u0c4d\u0c15\u0c30\u0c23\u0c32\u0c41 \u0c2a\u0c4d\u0c30\u0c48\u0c35\u0c47\u0c1f\u0c4d \u0c38\u0c02\u0c38\u0c4d\u0c25\u0c28\u0c41 \u0c2a\u0c4d\u0c30\u0c4b\u0c24\u0c4d\u0c38\u0c39\u0c3f\u0c02\u0c1a\u0c3e\u0c2f\u0c3f \u0c2e\u0c30\u0c3f\u0c2f\u0c41 \u0c35\u0c3e\u0c15\u0c4d \u0c38\u0c4d\u0c35\u0c3e\u0c24\u0c02\u0c24\u0c4d\u0c30\u0c4d\u0c2f\u0c02 \u0c2e\u0c30\u0c3f\u0c2f\u0c41 \u0c2e\u0c24\u0c2a\u0c30\u0c2e\u0c48\u0c28 \u0c35\u0c4d\u0c2f\u0c15\u0c4d\u0c24\u0c40\u0c15\u0c30\u0c23\u0c2a\u0c48 \u0c2a\u0c30\u0c3f\u0c2e\u0c3f\u0c24\u0c41\u0c32\u0c28\u0c41 \u0c2c\u0c3e\u0c17\u0c3e \u0c38\u0c21\u0c32\u0c3f\u0c02\u0c1a\u0c3e\u0c2f\u0c3f. \u0c06 \u0c24\u0c30\u0c4d\u0c35\u0c3e\u0c24 \u0c1f\u0c3f\u0c1f\u0c4b \u0c05\u0c2e\u0c46\u0c30\u0c3f\u0c15\u0c3e \u0c2a\u0c30\u0c4d\u0c2f\u0c1f\u0c28\u0c15\u0c41 \u0c35\u0c46\u0c33\u0c4d\u0c32\u0c3e\u0c21\u0c41. \u0c1a\u0c3f\u0c32\u0c40\u0c32\u0c4b, \u0c06 \u0c26\u0c47\u0c36 \u0c2a\u0c30\u0c4d\u0c2f\u0c1f\u0c28\u0c2a\u0c48 \u0c07\u0c26\u0c4d\u0c26\u0c30\u0c41 \u0c2a\u0c4d\u0c30\u0c2d\u0c41\u0c24\u0c4d\u0c35 \u0c2e\u0c02\u0c24\u0c4d\u0c30\u0c41\u0c32\u0c41 \u0c30\u0c3e\u0c1c\u0c40\u0c28\u0c3e\u0c2e\u0c3e \u0c1a\u0c47\u0c36\u0c3e\u0c30\u0c41. ~ 1960 ~ \u0c36\u0c30\u0c26\u0c43\u0c24\u0c41\u0c35\u0c41\u0c32\u0c4b \u0c1f\u0c3f\u0c1f\u0c4b \u0c10\u0c15\u0c4d\u0c2f\u0c30\u0c3e\u0c1c\u0c4d\u0c2f\u0c38\u0c2e\u0c3f\u0c24\u0c3f \u0c1c\u0c28\u0c30\u0c32\u0c4d \u0c05\u0c38\u0c46\u0c02\u0c2c\u0c4d\u0c32\u0c40 \u0c38\u0c2e\u0c3e\u0c35\u0c47\u0c36\u0c02\u0c32\u0c4b \u0c05\u0c27\u0c4d\u0c2f\u0c15\u0c4d\u0c37\u0c41\u0c21\u0c41 \u0c21\u0c4d\u0c35\u0c48\u0c1f\u0c4d \u0c21\u0c3f. \u0c10\u0c38\u0c46\u0c28\u0c4d\u200c\u0c39\u0c4b\u0c35\u0c30\u0c4d\u200c\u0c28\u0c41 \u0c15\u0c32\u0c3f\u0c36\u0c3e\u0c30\u0c41. \u0c1f\u0c3f\u0c1f\u0c4b \u0c2e\u0c30\u0c3f\u0c2f\u0c41 \u0c10\u0c38\u0c46\u0c28\u0c4d\u200c\u0c39\u0c4b\u0c35\u0c30\u0c4d \u0c06\u0c2f\u0c41\u0c27 \u0c28\u0c3f\u0c2f\u0c02\u0c24\u0c4d\u0c30\u0c23 \u0c28\u0c41\u0c02\u0c21\u0c3f \u0c06\u0c30\u0c4d\u0c25\u0c3f\u0c15 \u0c05\u0c2d\u0c3f\u0c35\u0c43\u0c26\u0c4d\u0c27\u0c3f \u0c35\u0c30\u0c15\u0c41 \u0c05\u0c28\u0c47\u0c15 \u0c05\u0c02\u0c36\u0c3e\u0c32\u0c2a\u0c48 \u0c1a\u0c30\u0c4d\u0c1a\u0c3f\u0c02\u0c1a\u0c3e\u0c30\u0c41. \u0c2f\u0c41\u0c17\u0c4b\u0c38\u0c4d\u0c32\u0c47\u0c35\u0c3f\u0c2f\u0c3e \u0c2f\u0c4a\u0c15\u0c4d\u0c15 \u0c24\u0c1f\u0c38\u0c4d\u0c25\u0c24 \"\u0c05\u0c24\u0c28\u0c3f \u0c35\u0c48\u0c2a\u0c41 \u0c24\u0c1f\u0c38\u0c4d\u0c25\u0c02\u0c17\u0c3e \u0c09\u0c02\u0c26\u0c3f\" \u0c05\u0c28\u0c3f \u0c10\u0c38\u0c46\u0c28\u0c4d\u200c\u0c39\u0c4b\u0c35\u0c30\u0c4d \u0c35\u0c4d\u0c2f\u0c3e\u0c16\u0c4d\u0c2f\u0c3e\u0c28\u0c3f\u0c02\u0c1a\u0c3f\u0c28\u0c2a\u0c4d\u0c2a\u0c41\u0c21\u0c41, \u0c24\u0c1f\u0c38\u0c4d\u0c25\u0c24 \u0c05\u0c28\u0c47\u0c26\u0c3f \u0c28\u0c3f\u0c37\u0c4d\u0c15\u0c4d\u0c30\u0c3f\u0c2f\u0c3e\u0c24\u0c4d\u0c2e\u0c15\u0c24\u0c28\u0c41 \u0c38\u0c42\u0c1a\u0c3f\u0c02\u0c1a\u0c26\u0c41, \u0c15\u0c3e\u0c28\u0c40 \"\u0c2a\u0c15\u0c4d\u0c37\u0c3e\u0c32\u0c41 \u0c24\u0c40\u0c38\u0c41\u0c15\u0c4b\u0c15\u0c2a\u0c4b\u0c35\u0c21\u0c02\" \u0c05\u0c28\u0c3f \u0c05\u0c30\u0c4d\u0c25\u0c02 \u0c05\u0c28\u0c3f \u0c1f\u0c3f\u0c1f\u0c4b \u0c2c\u0c26\u0c41\u0c32\u0c3f\u0c1a\u0c4d\u0c1a\u0c3e\u0c30\u0c41.",
"question": "U.N.\u0c32\u0c4b \u0c1f\u0c3f\u0c1f\u0c4b \u0c10\u0c38\u0c46\u0c28\u0c4d\u200c\u0c39\u0c4b\u0c35\u0c30\u0c4d\u200c\u0c28\u0c41 \u0c0e\u0c2a\u0c4d\u0c2a\u0c41\u0c21\u0c41 \u0c15\u0c32\u0c3f\u0c36\u0c3e\u0c21\u0c41?",
"answers.text": [
" 1960 "
],
"answers.answer_start": [
324
],
"feat_id": [
"56f7f401aef2371900625cc8"
]
},
{
"context": "\u0c38\u0c40\u0c38\u0c3f\u0c2f\u0c02 \u0c2e\u0c30\u0c3f\u0c2f\u0c41 \u0c2c\u0c02\u0c17\u0c3e\u0c30\u0c02 (\u0c30\u0c46\u0c02\u0c21\u0c42 \u0c2a\u0c38\u0c41\u0c2a\u0c41), \u0c2e\u0c30\u0c3f\u0c2f\u0c41 \u0c13\u0c38\u0c4d\u0c2e\u0c3f\u0c2f\u0c02 (\u0c28\u0c40\u0c32\u0c02)\u0c24\u0c4b \u0c15\u0c32\u0c3f\u0c2a\u0c3f, \u0c2c\u0c42\u0c21\u0c3f\u0c26 \u0c32\u0c47\u0c26\u0c3e \u0c35\u0c46\u0c02\u0c21\u0c3f \u0c15\u0c3e\u0c15\u0c41\u0c02\u0c21\u0c3e \u0c38\u0c39\u0c1c \u0c30\u0c02\u0c17\u0c41 \u0c15\u0c32\u0c3f\u0c17\u0c3f\u0c28 \u0c28\u0c3e\u0c32\u0c41\u0c17\u0c41 \u0c2e\u0c42\u0c32\u0c15 \u0c32\u0c4b\u0c39\u0c3e\u0c32\u0c32\u0c4b \u0c30\u0c3e\u0c17\u0c3f \u0c12\u0c15\u0c1f\u0c3f. \u0c38\u0c4d\u0c35\u0c1a\u0c4d\u0c1b\u0c2e\u0c48\u0c28 \u0c30\u0c3e\u0c17\u0c3f \u0c28\u0c3e\u0c30\u0c3f\u0c02\u0c1c-\u0c0e\u0c30\u0c41\u0c2a\u0c41 \u0c30\u0c02\u0c17\u0c41\u0c32\u0c4b \u0c09\u0c02\u0c1f\u0c41\u0c02\u0c26\u0c3f \u0c2e\u0c30\u0c3f\u0c2f\u0c41 \u0c17\u0c3e\u0c32\u0c3f\u0c15\u0c3f \u0c17\u0c41\u0c30\u0c48\u0c28\u0c2a\u0c4d\u0c2a\u0c41\u0c21\u0c41 \u0c0e\u0c30\u0c41\u0c2a\u0c41 \u0c30\u0c02\u0c17\u0c41\u0c28\u0c41 \u0c2a\u0c4a\u0c02\u0c26\u0c41\u0c24\u0c41\u0c02\u0c26\u0c3f. \u0c2a\u0c42\u0c30\u0c3f\u0c02\u0c1a\u0c3f\u0c28 3d \u0c2e\u0c30\u0c3f\u0c2f\u0c41 \u0c38\u0c17\u0c02-\u0c16\u0c3e\u0c33\u0c40 4s \u0c05\u0c1f\u0c3e\u0c2e\u0c3f\u0c15\u0c4d \u0c37\u0c46\u0c32\u0c4d\u200c\u0c32 \u0c2e\u0c27\u0c4d\u0c2f \u0c0e\u0c32\u0c15\u0c4d\u0c1f\u0c4d\u0c30\u0c3e\u0c28\u0c3f\u0c15\u0c4d \u0c2a\u0c30\u0c3f\u0c35\u0c30\u0c4d\u0c24\u0c28\u0c3e\u0c32 \u0c28\u0c41\u0c02\u0c21\u0c3f \u0c30\u0c3e\u0c17\u0c3f \u0c2f\u0c4a\u0c15\u0c4d\u0c15 \u0c32\u0c15\u0c4d\u0c37\u0c23 \u0c30\u0c02\u0c17\u0c41 \u0c2b\u0c32\u0c3f\u0c24\u0c3e\u0c32\u0c41 - \u0c08 \u0c37\u0c46\u0c32\u0c4d\u200c\u0c32 \u0c2e\u0c27\u0c4d\u0c2f \u0c36\u0c15\u0c4d\u0c24\u0c3f \u0c35\u0c4d\u0c2f\u0c24\u0c4d\u0c2f\u0c3e\u0c38\u0c02 \u0c05\u0c26\u0c3f \u0c28\u0c3e\u0c30\u0c3f\u0c02\u0c1c \u0c15\u0c3e\u0c02\u0c24\u0c3f\u0c15\u0c3f \u0c05\u0c28\u0c41\u0c17\u0c41\u0c23\u0c02\u0c17\u0c3e \u0c09\u0c02\u0c1f\u0c41\u0c02\u0c26\u0c3f. \u0c05\u0c26\u0c47 \u0c35\u0c3f\u0c27\u0c3e\u0c28\u0c02 \u0c2c\u0c02\u0c17\u0c3e\u0c30\u0c02 \u0c2e\u0c30\u0c3f\u0c2f\u0c41 \u0c38\u0c40\u0c38\u0c3f\u0c2f\u0c02 \u0c2f\u0c4a\u0c15\u0c4d\u0c15 \u0c2a\u0c38\u0c41\u0c2a\u0c41 \u0c30\u0c02\u0c17\u0c41\u0c15\u0c41 \u0c15\u0c3e\u0c30\u0c23\u0c2e\u0c35\u0c41\u0c24\u0c41\u0c02\u0c26\u0c3f.",
"question": "\u0c28\u0c3f\u0c02\u0c21\u0c3f\u0c28 3d \u0c2e\u0c30\u0c3f\u0c2f\u0c41 \u0c38\u0c17\u0c02-\u0c16\u0c3e\u0c33\u0c40 4s \u0c05\u0c1f\u0c3e\u0c2e\u0c3f\u0c15\u0c4d \u0c37\u0c46\u0c32\u0c4d\u200c\u0c32 \u0c2e\u0c27\u0c4d\u0c2f \u0c36\u0c15\u0c4d\u0c24\u0c3f \u0c35\u0c4d\u0c2f\u0c24\u0c4d\u0c2f\u0c3e\u0c38\u0c02 \u0c15\u0c3e\u0c02\u0c24\u0c3f \u0c2f\u0c4a\u0c15\u0c4d\u0c15 \u0c0f \u0c30\u0c02\u0c17\u0c41\u0c15\u0c41 \u0c05\u0c28\u0c41\u0c17\u0c41\u0c23\u0c02\u0c17\u0c3e \u0c09\u0c02\u0c1f\u0c41\u0c02\u0c26\u0c3f?",
"answers.text": [
"\u0c28\u0c3e\u0c30\u0c3f\u0c02\u0c1c \u0c15\u0c3e\u0c02\u0c24\u0c3f"
],
"answers.answer_start": [
376
],
"feat_id": [
"57098edded30961900e84311"
]
}
]
```
### Dataset Fields
The dataset has the following fields (also called "features"):
```json
{
"context": "Value(dtype='string', id=None)",
"question": "Value(dtype='string', id=None)",
"answers.text": "Sequence(feature=Value(dtype='string', id=None), length=-1, id=None)",
"answers.answer_start": "Sequence(feature=Value(dtype='int32', id=None), length=-1, id=None)",
"feat_id": "Sequence(feature=Value(dtype='string', id=None), length=-1, id=None)"
}
```
### Dataset Splits
This dataset is split into a train and validation split. The split sizes are as follow:
| Split name | Num samples |
| ------------ | ------------------- |
| train | 64549 |
| valid | 9277 |
|
vnktrmnb/autotrain-data-qa_xlm_roberta_large_tesquad2
|
[
"region:us"
] |
2023-05-22T12:07:02+00:00
|
{}
|
2023-05-22T12:20:02+00:00
|
497126bb10ed2f5becc1521782a1cf676c08ec73
|
# Dataset Card for "2fbfbaec"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
results-sd-v1-5-sd-v2-1-if-v1-0-karlo/2fbfbaec
|
[
"region:us"
] |
2023-05-22T12:13:26+00:00
|
{"dataset_info": {"features": [{"name": "result", "dtype": "string"}, {"name": "id", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 178, "num_examples": 10}], "download_size": 1339, "dataset_size": 178}}
|
2023-05-22T12:13:27+00:00
|
ea3a78479fb5337761f359abcd4e883d2d6e3c5b
|
# Dataset Card for "tedlium-long-form"
To create the dataset:
```python
import os
import numpy as np
from datasets import load_dataset, DatasetDict, Dataset, Audio
import soundfile as sf
from tqdm import tqdm
tedlium = load_dataset("LIUM/tedlium", "release3")
merged_dataset = DatasetDict()
validation_speaker_ids = [
"Al_Gore",
"Barry_Schwartz",
"Blaise_Agueray_Arcas",
"Brian_Cox",
"Craig_Venter",
"David_Merrill",
"Elizabeth_Gilbert",
"Wade_Davis",
]
validation_dataset_merged = {speaker_id: {"audio": [], "text": ""} for speaker_id in validation_speaker_ids}
test_speaker_ids = [
"AimeeMullins",
"BillGates",
"DanBarber",
"DanBarber_2010_S103",
"DanielKahneman",
"EricMead_2009P_EricMead",
"GaryFlake",
"JamesCameron",
"JaneMcGonigal",
"MichaelSpecter",
"RobertGupta",
]
test_dataset_merged = {speaker_id: {"audio": [], "text": ""} for speaker_id in test_speaker_ids}
for split, dataset in zip(["validation", "test"], [validation_dataset_merged, test_dataset_merged]):
sampling_rate = tedlium[split].features["audio"].sampling_rate
for sample in tqdm(tedlium[split]):
if sample["speaker_id"] in dataset:
dataset[sample["speaker_id"]]["audio"].extend(sample["audio"]["array"])
dataset[sample["speaker_id"]]["text"] += " " + sample["text"]
audio_paths = []
os.makedirs(split, exist_ok=True)
for speaker in dataset:
path = os.path.join(split, f"{speaker}-merged.wav")
audio_paths.append(path)
sf.write(path, np.asarray(dataset[speaker]["audio"]), samplerate=sampling_rate)
merged_dataset[split] = Dataset.from_dict({"audio": audio_paths}).cast_column("audio", Audio())
# remove spaced apostrophes (e.g. it 's -> it's)
merged_dataset[split] = merged_dataset[split].add_column("text", [dataset[speaker]["text"].replace(" '", "'") for speaker in dataset])
merged_dataset[split] = merged_dataset[split].add_column("speaker_id", dataset.keys())
```
|
distil-whisper/tedlium-long-form
|
[
"region:us"
] |
2023-05-22T12:19:52+00:00
|
{"dataset_info": {"features": [{"name": "audio", "dtype": "audio"}, {"name": "text", "dtype": "string"}, {"name": "speaker_id", "dtype": "string"}], "splits": [{"name": "validation", "num_bytes": 180166870.0, "num_examples": 8}, {"name": "test", "num_bytes": 285107770.0, "num_examples": 11}], "download_size": 284926490, "dataset_size": 465274640.0}}
|
2023-05-22T13:04:04+00:00
|
03bb4d133abfb8c22794e1b0b760b083a4e1cbb0
|
# Dataset Card for MGSM MT
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-instances)
- [Data Splits](#data-instances)
- [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)
## Dataset Description
- **Homepage:** https://openai.com/blog/grade-school-math/
- **Repository:** https://github.com/openai/grade-school-math
- **Paper:** https://arxiv.org/abs/2110.14168
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [Needs More Information]
### Dataset Summary
Multilingual Grade School Math Benchmark (MGSM) is a benchmark of grade-school math problems, proposed in the paper [Language models are multilingual chain-of-thought reasoners](http://arxiv.org/abs/2210.03057). This dataset is the machine-translated version of MGSM in English from each language.
The same 250 problems from [GSM8K](https://arxiv.org/abs/2110.14168) are each translated via human annotators in 10 languages. The 10 languages are:
- Spanish
- French
- German
- Russian
- Chinese
- Japanese
- Thai
- Swahili
- Bengali
- Telugu
GSM8K (Grade School Math 8K) is a dataset of 8.5K high quality linguistically diverse grade school math word problems. The dataset was created to support the task of question answering on basic mathematical problems that require multi-step reasoning.
You can find the input and targets for each of the ten languages (and English) as `.tsv` files.
We also include few-shot exemplars that are also manually translated from each language in `exemplars.py`.
### Supported Tasks and Leaderboards
[Needs More Information]
### Languages
The same 250 problems from [GSM8K](https://arxiv.org/abs/2110.14168) are each translated via human annotators in 10 languages. The 10 languages are:
- Spanish
- French
- German
- Russian
- Chinese
- Japanese
- Thai
- Swahili
- Bengali
- Telugu
This dataset is the machine-translated version of MGSM in English from each language.
## Dataset Structure
### Data Instances
Each instance in the train split contains:
- a string for the grade-school level math question
- a string for the corresponding answer with chain-of-thought steps.
- the numeric solution to the question
- the equation solution to the question
```python
{'question': 'Question: Roger has 5 tennis balls. He buys 2 more cans of tennis balls. Each can has 3 tennis balls. How many tennis balls does he have now?',
'answer': 'Step-by-Step Answer: Roger started with 5 balls. 2 cans of 3 tennis balls each is 6 tennis balls. 5 + 6 = 11. The answer is 11.',
'answer_number': 11,
'equation_solution': '5 + 6 = 11.'}
```
Each instance in the test split contains:
- a string for the grade-school level math question
- the numeric solution to the question
```python
{'question': "Janet’s ducks lay 16 eggs per day. She eats three for breakfast every morning and bakes muffins for her friends every day with four. She sells the remainder at the farmers' market daily for $2 per fresh duck egg. How much in dollars does she make every day at the farmers' market?",
'answer': None,
'answer_number': 18,
'equation_solution': None}
```
### Data Fields
The data fields are the same among `train` and `test` splits.
- question: The question string to a grade school math problem.
- answer: The full solution string to the `question`. It contains multiple steps of reasoning with calculator annotations and the final numeric solution.
- answer_number: The numeric solution to the `question`.
- equation_solution: The equation solution to the `question`.
### Data Splits
- The train split includes 8 few-shot exemplars that are also manually translated from each language.
- The test split includes the same 250 problems from GSM8K translated via human annotators in 10 languages.
| name |train|test |
|--------|----:|---------:|
|en | 8 | 250 |
|es | 8 | 250 |
|fr | 8 | 250 |
|de | 8 | 250 |
|ru | 8 | 250 |
|zh | 8 | 250 |
|ja | 8 | 250 |
|th | 8 | 250 |
|sw | 8 | 250 |
|bn | 8 | 250 |
|te | 8 | 250 |
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
From the paper:
> We initially collected a starting set of a thousand problems and natural language solutions by hiring freelance contractors on Upwork (upwork.com). We then worked with Surge AI (surgehq.ai), an NLP data labeling platform, to scale up our data collection. After collecting the full dataset, we asked workers to re-solve all problems, with no workers re-solving problems they originally wrote. We checked whether their final answers agreed with the original solu- tions, and any problems that produced disagreements were either repaired or discarded. We then performed another round of agreement checks on a smaller subset of problems, finding that 1.7% of problems still produce disagreements among contractors. We estimate this to be the fraction of problems that con- tain breaking errors or ambiguities. It is possible that a larger percentage of problems contain subtle errors.
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
Surge AI (surgehq.ai)
### Personal and Sensitive Information
[Needs More Information]
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
The GSM8K dataset is licensed under the [MIT License](https://opensource.org/licenses/MIT).
### Citation Information
```bibtex
@article{cobbe2021gsm8k,
title={Training Verifiers to Solve Math Word Problems},
author={Cobbe, Karl and Kosaraju, Vineet and Bavarian, Mohammad and Chen, Mark and Jun, Heewoo and Kaiser, Lukasz and Plappert, Matthias and Tworek, Jerry and Hilton, Jacob and Nakano, Reiichiro and Hesse, Christopher and Schulman, John},
journal={arXiv preprint arXiv:2110.14168},
year={2021}
}
@misc{shi2022language,
title={Language Models are Multilingual Chain-of-Thought Reasoners},
author={Freda Shi and Mirac Suzgun and Markus Freitag and Xuezhi Wang and Suraj Srivats and Soroush Vosoughi and Hyung Won Chung and Yi Tay and Sebastian Ruder and Denny Zhou and Dipanjan Das and Jason Wei},
year={2022},
eprint={2210.03057},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
### Contributions
Thanks to [@juletx](https://github.com/juletx) for adding this dataset.
|
juletxara/mgsm_mt
|
[
"task_categories:text2text-generation",
"annotations_creators:found",
"language_creators:found",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:extended|gsm8k",
"language:en",
"license:cc-by-sa-4.0",
"math-word-problems",
"arxiv:2110.14168",
"arxiv:2210.03057",
"region:us"
] |
2023-05-22T12:42:59+00:00
|
{"annotations_creators": ["found"], "language_creators": ["found", "expert-generated"], "language": ["en"], "license": ["cc-by-sa-4.0"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["extended|gsm8k"], "task_categories": ["text2text-generation"], "task_ids": [], "paperswithcode_id": "multi-task-language-understanding-on-mgsm", "pretty_name": "Multilingual Grade School Math Benchmark (MGSM)", "tags": ["math-word-problems"], "dataset_info": [{"config_name": "nllb-200-distilled-600M", "features": [{"name": "question", "dtype": "string"}, {"name": "answer", "dtype": "string"}, {"name": "answer_number", "dtype": "int32"}, {"name": "equation_solution", "dtype": "string"}], "splits": [{"name": "es", "num_bytes": 56237, "num_examples": 250}, {"name": "fr", "num_bytes": 55054, "num_examples": 250}, {"name": "de", "num_bytes": 58288, "num_examples": 250}, {"name": "ru", "num_bytes": 52498, "num_examples": 250}, {"name": "zh", "num_bytes": 55255, "num_examples": 250}, {"name": "ja", "num_bytes": 44046, "num_examples": 250}, {"name": "th", "num_bytes": 51445, "num_examples": 250}, {"name": "sw", "num_bytes": 50844, "num_examples": 250}, {"name": "bn", "num_bytes": 46158, "num_examples": 250}, {"name": "te", "num_bytes": 49928, "num_examples": 250}, {"name": "train", "num_bytes": 2682, "num_examples": 8}], "download_size": 495413, "dataset_size": 522435}, {"config_name": "nllb-200-distilled-1.3B", "features": [{"name": "question", "dtype": "string"}, {"name": "answer", "dtype": "string"}, {"name": "answer_number", "dtype": "int32"}, {"name": "equation_solution", "dtype": "string"}], "splits": [{"name": "es", "num_bytes": 61011, "num_examples": 250}, {"name": "fr", "num_bytes": 60127, "num_examples": 250}, {"name": "de", "num_bytes": 61658, "num_examples": 250}, {"name": "ru", "num_bytes": 58766, "num_examples": 250}, {"name": "zh", "num_bytes": 55451, "num_examples": 250}, {"name": "ja", "num_bytes": 51409, "num_examples": 250}, 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510209}, {"config_name": "polylm-multialpaca-13b", "features": [{"name": "question", "dtype": "string"}, {"name": "answer", "dtype": "string"}, {"name": "answer_number", "dtype": "int32"}, {"name": "equation_solution", "dtype": "string"}], "splits": [{"name": "es", "num_bytes": 62502, "num_examples": 250}, {"name": "fr", "num_bytes": 60978, "num_examples": 250}, {"name": "de", "num_bytes": 62310, "num_examples": 250}, {"name": "ru", "num_bytes": 60440, "num_examples": 250}, {"name": "zh", "num_bytes": 57642, "num_examples": 250}, {"name": "ja", "num_bytes": 55315, "num_examples": 250}, {"name": "th", "num_bytes": 59002, "num_examples": 250}, {"name": "sw", "num_bytes": 51728, "num_examples": 250}, {"name": "bn", "num_bytes": 31947, "num_examples": 250}, {"name": "te", "num_bytes": 12891, "num_examples": 250}, {"name": "train", "num_bytes": 2682, "num_examples": 8}], "download_size": 490498, "dataset_size": 517437}, {"config_name": "open_llama_3b_v2", "features": [{"name": "question", "dtype": "string"}, {"name": "answer", "dtype": "string"}, {"name": "answer_number", "dtype": "int32"}, {"name": "equation_solution", "dtype": "string"}], "splits": [{"name": "es", "num_bytes": 62474, "num_examples": 250}, {"name": "fr", "num_bytes": 60493, "num_examples": 250}, {"name": "de", "num_bytes": 59760, "num_examples": 250}, {"name": "ru", "num_bytes": 57592, "num_examples": 250}, {"name": "zh", "num_bytes": 54634, "num_examples": 250}, {"name": "ja", "num_bytes": 53936, "num_examples": 250}, {"name": "th", "num_bytes": 38960, "num_examples": 250}, {"name": "sw", "num_bytes": 57320, "num_examples": 250}, {"name": "bn", "num_bytes": 27394, "num_examples": 250}, {"name": "te", "num_bytes": 4680, "num_examples": 250}, {"name": "train", "num_bytes": 2682, "num_examples": 8}], "download_size": 452910, "dataset_size": 479925}, {"config_name": "Llama-2-7b-hf", "features": [{"name": "question", "dtype": "string"}, {"name": "answer", "dtype": "string"}, {"name": "answer_number", 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{"name": "ru", "num_bytes": 62305, "num_examples": 250}, {"name": "zh", "num_bytes": 61994, "num_examples": 250}, {"name": "ja", "num_bytes": 58226, "num_examples": 250}, {"name": "th", "num_bytes": 60256, "num_examples": 250}, {"name": "sw", "num_bytes": 58108, "num_examples": 250}, {"name": "bn", "num_bytes": 55180, "num_examples": 250}, {"name": "te", "num_bytes": 6525, "num_examples": 250}, {"name": "train", "num_bytes": 2682, "num_examples": 8}], "download_size": 526574, "dataset_size": 553579}]}
|
2023-07-21T09:18:37+00:00
|
ecf1f03d0a76899a85cf02fb9c86abd1048c2556
|
The IPQuiz dataset is used to assess a model's understanding of intellectual property-related concepts and regulations.IPQuiz is a multiple-choice question-response dataset collected from publicly available websites around the world in a variety of languages. For each question, the model needs to select an answer from a candidate list.
## source：
http://epaper.iprchn.com/zscqb/h5/html5/2023-04/21/content_27601_7600799.htm
https://eduai.baidu.com/view/6f7c32b0d7d8d15abe23482fb4daa58da0111cfe
https://www.juxuewen.com/question/1159.html
https://eduai.baidu.com/view/9c690b1d29f90242a8956bec0975f46527d3a7c9
https://wenku.baidu.com/view/daea1b562179168884868762caaedd3383c4b5d2.html?_wkts_=1683278279098
http://www.dxh.gov.cn/hdjl/zxdc/zxdcxq/index.shtml?/personalCenter/answerSheet/answerSheet.html?metadataId=ff80808180b79f8a01830b603e8001f7&siteId=43
http://guangxi.china.com.cn/2022-06/17/content_42002807.html
http://www.educhenggong.com/Uploads/attached/file/20210324/1616572571695330.pdf
https://www.gov.cn/guoqing/2021-10/29/content_5647633.htm
https://www.gov.cn/zhengce/2020-12/26/content_5573623.htm
https://www.gov.cn/gongbao/content/2000/content_60431.htm
https://www.gov.cn/zhengce/2020-12/26/content_5574514.htm
https://www.gov.cn/zhengce/2020-12/26/content_5573535.htm
https://www.gov.cn/zhengce/2020-12/26/content_5573535.htm
https://www.gov.cn/xinwen/2017-11/05/content_5237325.htm
https://www.gov.cn/zhengce/2020-12/26/content_5574414.htm
https://inside.nku.edu/content/dam/creative-thinking/docs/CT-Handouts-page/CT%20Handout%20Intellectual%20Property%20Quiz.pdf
https://www.wipo.int/ip-outreach/en/ipday/2017/ipday2017_quiz-copy.html
https://www.gastao.eu/ip-knowledge-test
https://www.proprofs.com/quiz-school/quizzes/intellectual-property-law-quiz
https://fr.surveymonkey.com/r/LW83BBV
https://www.riddle.com/view/57770?qzzr=1
https://about.lens.org/patent-knowledge-quiz/
https://www.examsegg.com/intellectual-property-rights-india-questions.html
https://www.rkdewan.com/quizstart.php?qid=24
https://www.eduki.ch/en/quizz/intellectual-property-quiz
https://qpkendra.com/mcq/ipr-and-patenting-mcq-pg-1.html
https://openstax.org/books/introduction-intellectual-property/pages/chapter-1
https://www.lexifiche.com/quiz-propriete-intellectuelle-breve
https://www.q-net.or.kr/cst003.do?id=cst00309&gSite=L&gId=51
https://www.agaroot.jp/benri/column/past-questions/
https://www.geo.de/wissen/quiz/wissenstest-erfindungen-i-30201276.html
https://www.geo.de/wissen/quiz/wissenstest-erfindungen-ii-30201270.html
https://www.eduki.ch/de/quizz/quiz-geistiges-eigentum
https://www.fsgu-akademie.de/quiz/geistiges-eigentum-teil-1/
https://www.fsgu-akademie.de/quiz/geistiges-eigentum-teil-2/
https://www.fsgu-akademie.de/quiz/geistiges-eigentum-teil-3/
https://www.fsgu-akademie.de/quiz/geistiges-eigentum-teil-4/
https://www.fsgu-akademie.de/quiz/geistiges-eigentum-teil-5/
https://www.fsgu-akademie.de/quiz/geistiges-eigentum-teil-6/
https://www.fsgu-akademie.de/quiz/geistiges-eigentum-teil-7/
https://www.fsgu-akademie.de/quiz/geistiges-eigentum-teil-8/
https://www.fsgu-akademie.de/quiz/geistiges-eigentum-teil-9/
https://www.fsgu-akademie.de/quiz/geistiges-eigentum-teil-10/
https://www.fsgu-akademie.de/quiz/design-schutzrecht-teil-1/
https://www.fsgu-akademie.de/quiz/design-schutzrecht-teil-2/
https://www.fsgu-akademie.de/quiz/design-schutzrecht-teil-3/
https://www.fsgu-akademie.de/quiz/design-schutzrecht-teil-4/
https://www.fsgu-akademie.de/quiz/handelsmarke-teil-1/
https://www.fsgu-akademie.de/quiz/handelsmarke-teil-2/
https://www.fsgu-akademie.de/quiz/handelsmarke-teil-3/
https://www.fsgu-akademie.de/quiz/handelsmarke-teil-4/
https://www.fsgu-akademie.de/quiz/patentrecht-deutschland-teil-1/
https://www.fsgu-akademie.de/quiz/patentrecht-deutschland-teil-2/
https://www.fsgu-akademie.de/quiz/patentrecht-deutschland-teil-3/
https://www.fsgu-akademie.de/quiz/patentrecht-deutschland-teil-4/
https://www.fsgu-akademie.de/quiz/urheberrecht-teil-1/
https://www.fsgu-akademie.de/quiz/urheberrecht-teil-2/
https://www.fsgu-akademie.de/quiz/urheberrecht-teil-3/
https://www.fsgu-akademie.de/quiz/urheberrecht-teil-4/
|
BNNT/IPQuiz
|
[
"license:cc-by-nc-sa-4.0",
"region:us"
] |
2023-05-22T12:43:28+00:00
|
{"license": "cc-by-nc-sa-4.0"}
|
2023-08-09T03:11:59+00:00
|
7e6d0b2aea06597be722f3a38c9bb734977fabb1
|
# Dataset Card for "CSQA_preprocessed_mul"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
skrishna/CSQA_preprocessed_mul
|
[
"region:us"
] |
2023-05-22T12:54:34+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "question_concept", "dtype": "string"}, {"name": "choices", "sequence": [{"name": "label", "dtype": "string"}, {"name": "text", "dtype": "string"}]}, {"name": "answerKey", "dtype": "string"}, {"name": "inputs", "dtype": "string"}, {"name": "targets", "dtype": "string"}, {"name": "multiple_choice_targets", "sequence": "string"}, {"name": "multiple_choice_scores", "sequence": "int64"}], "splits": [{"name": "test", "num_bytes": 547240, "num_examples": 1140}, {"name": "validation", "num_bytes": 581677, "num_examples": 1221}, {"name": "train", "num_bytes": 4684451, "num_examples": 9741}], "download_size": 2726722, "dataset_size": 5813368}}
|
2023-05-22T16:15:45+00:00
|
0dadc39a3a81300fcd5acf95e7a68257992f21c5
|
Retsadila/sdil
|
[
"license:creativeml-openrail-m",
"region:us"
] |
2023-05-22T12:55:49+00:00
|
{"license": "creativeml-openrail-m"}
|
2023-05-22T12:59:08+00:00
|
|
6a754dad3a76d8e587d6a17cebe8342bcbacf08d
|
# Dataset Card for "TinyImagenet_validation_google_flan_t5_xxl_mode_T_SPECIFIC_A_ns_100"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
CVasNLPExperiments/TinyImagenet_validation_google_flan_t5_xxl_mode_T_SPECIFIC_A_ns_100
|
[
"region:us"
] |
2023-05-22T13:11:52+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "prompt", "dtype": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}], "splits": [{"name": "fewshot_0__Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full_clip_tags_LAION_ViT_H_14_2B_simple_specific_rices", "num_bytes": 39204, "num_examples": 100}], "download_size": 10645, "dataset_size": 39204}}
|
2023-05-22T13:11:53+00:00
|
e2fdfca182ac40480a3528a3ec54f5194331781e
|
jyshbgde/cinescopeDataset
|
[
"task_categories:feature-extraction",
"language:en",
"license:openrail",
"region:us"
] |
2023-05-22T13:11:53+00:00
|
{"language": ["en"], "license": "openrail", "task_categories": ["feature-extraction"], "pretty_name": "cinescope"}
|
2023-06-24T05:39:57+00:00
|
|
5142d8cea9154be7f1825c3956b176bea8270d68
|
# Dataset Card for Calc-ape210k
## Summary
This dataset is an instance of Ape210K dataset, converted to a simple HTML-like language that can be easily parsed (e.g. by BeautifulSoup). The data contains 3 types of tags:
- gadget: A tag whose content is intended to be evaluated by calling an external tool (sympy-based calculator in this case)
- output: An output of the external tool
- result: The final answer to the mathematical problem (a number)
## Supported Tasks
The dataset is intended for training Chain-of-Thought reasoning **models able to use external tools** to enhance the factuality of their responses.
This dataset presents in-context scenarios where models can outsource the computations in the reasoning chain to a calculator.
## Construction Process
First, we translated the questions into English using Google Translate. Next, we parsed the equations and the results. We linearized
the equations into a sequence of elementary steps and evaluated them using a sympy-based calculator. We numerically compare the output
with the result in the data and remove all examples where they do not match (less than 3% loss in each split). Finally, we save the
chain of steps in the HTML-like language in the `chain` column. We keep the original columns in the dataset for convenience. We also perform
in-dataset and cross-dataset data-leak detection within [Calc-X collection](https://huggingface.co/collections/MU-NLPC/calc-x-652fee9a6b838fd820055483).
Specifically for Ape210k, we removed parts of the validation and test split, with around 1700 remaining in each.
You can read more information about this process in our [Calc-X paper](https://arxiv.org/abs/2305.15017).
## Data splits
The default config contains filtered splits with data leaks removed.
You can load it using:
```python
datasets.load_dataset("MU-NLPC/calc-ape210k")
```
In the `original-splits` config, the data splits are unfiltered and correspond to the original Ape210K dataset. See [ape210k dataset github](https://github.com/Chenny0808/ape210k) and [the paper](https://arxiv.org/abs/2009.11506) for more info.
You can load it using:
```python
datasets.load_dataset("MU-NLPC/calc-ape210k", "original-splits")
```
## Attributes
- **id** - id of the example
- **question** - the description of the math problem. Automatically translated from the `question_chinese` column into English using Google Translate
- **question_chinese** - the original description of the math problem in Chinese
- **chain** - linearized `equation`, sequence of arithmetic steps in HTML-like language that can be evaluated using our sympy-based calculator
- **result** - result as a string (can be an integer, float, or a fraction)
- **result_float** - result, converted to a float
- **equation** - a nested expression that evaluates to the correct answer
Attributes **id**, **question**, **chain**, and **result** are present in all datasets in [Calc-X collection](https://huggingface.co/collections/MU-NLPC/calc-x-652fee9a6b838fd820055483).
## Related work
This dataset was created as a part of a larger effort in training models capable of using a calculator during inference, which we call Calcformers.
- [**Calc-X collection**](https://huggingface.co/collections/MU-NLPC/calc-x-652fee9a6b838fd820055483) - datasets for training Calcformers
- [**Calcformers collection**](https://huggingface.co/collections/MU-NLPC/calcformers-65367392badc497807b3caf5) - calculator-using models we trained and published on HF
- [**Calc-X and Calcformers paper**](https://arxiv.org/abs/2305.15017)
- [**Calc-X and Calcformers repo**](https://github.com/prompteus/calc-x)
Here are links to the original dataset:
- [**original Ape210k dataset and repo**](https://github.com/Chenny0808/ape210k)
- [**original Ape210k paper**](https://arxiv.org/abs/2009.11506)
## Licence
MIT, consistently with the original dataset.
## Cite
If you use this version of the dataset in research, please cite the [original Ape210k paper](https://arxiv.org/abs/2009.11506), and the [Calc-X paper](https://arxiv.org/abs/2305.15017) as follows:
```bibtex
@inproceedings{kadlcik-etal-2023-soft,
title = "Calc-X and Calcformers: Empowering Arithmetical Chain-of-Thought through Interaction with Symbolic Systems",
author = "Marek Kadlčík and Michal Štefánik and Ondřej Sotolář and Vlastimil Martinek",
booktitle = "Proceedings of the The 2023 Conference on Empirical Methods in Natural Language Processing: Main track",
month = dec,
year = "2023",
address = "Singapore, Singapore",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/2305.15017",
}
```
|
MU-NLPC/Calc-ape210k
|
[
"license:mit",
"arxiv:2305.15017",
"arxiv:2009.11506",
"region:us"
] |
2023-05-22T13:20:16+00:00
|
{"license": "mit", "dataset_info": [{"config_name": "default", "features": [{"name": "id", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "question_chinese", "dtype": "string"}, {"name": "chain", "dtype": "string"}, {"name": "result", "dtype": "string"}, {"name": "result_float", "dtype": "float64"}, {"name": "equation", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 1153807, "num_examples": 1785}, {"name": "train", "num_bytes": 111628273, "num_examples": 195179}, {"name": "validation", "num_bytes": 1169676, "num_examples": 1783}], "download_size": 50706818, "dataset_size": 113951756}, {"config_name": "original-splits", "features": [{"name": "id", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "question_chinese", "dtype": "string"}, {"name": "chain", "dtype": "string"}, {"name": "result", "dtype": "string"}, {"name": "result_float", "dtype": "float64"}, {"name": "equation", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 2784396, "num_examples": 4867}, {"name": "train", "num_bytes": 111628273, "num_examples": 195179}, {"name": "validation", "num_bytes": 2789481, "num_examples": 4867}], "download_size": 52107586, "dataset_size": 117202150}], "configs": [{"config_name": "default", "data_files": [{"split": "test", "path": "data/test-*"}, {"split": "train", "path": "data/train-*"}, {"split": "validation", "path": "data/validation-*"}]}, {"config_name": "original-splits", "data_files": [{"split": "test", "path": "original-splits/test-*"}, {"split": "train", "path": "original-splits/train-*"}, {"split": "validation", "path": "original-splits/validation-*"}]}]}
|
2024-01-22T16:21:58+00:00
|
dc914f67f4ce80fcbda8645070c1322d1b35a441
|
# Dataset Card for "TinyImagenet_2k_validation"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Multimodal-Fatima/TinyImagenet_2k_validation
|
[
"region:us"
] |
2023-05-22T13:29:11+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "goldfish", "1": "fire salamander", "2": "American bullfrog", "3": "tailed frog", "4": "American alligator", "5": "boa constrictor", "6": "trilobite", "7": "scorpion", "8": "southern black widow", "9": "tarantula", "10": "centipede", "11": "koala", "12": "jellyfish", "13": "brain coral", "14": "snail", "15": "sea slug", "16": "American lobster", "17": "spiny lobster", "18": "black stork", "19": "king penguin", "20": "albatross", "21": "dugong", "22": "Yorkshire Terrier", "23": "Golden Retriever", "24": "Labrador Retriever", "25": "German Shepherd Dog", "26": "Standard Poodle", "27": "tabby cat", "28": "Persian cat", "29": "Egyptian Mau", "30": "cougar", "31": "lion", "32": "brown bear", "33": "ladybug", "34": "grasshopper", "35": "stick insect", "36": "cockroach", "37": "praying mantis", "38": "dragonfly", "39": "monarch butterfly", "40": "sulphur butterfly", "41": "sea cucumber", "42": "guinea pig", "43": "pig", "44": "ox", "45": "bison", "46": "bighorn sheep", "47": "gazelle", "48": "arabian camel", "49": "orangutan", "50": "chimpanzee", "51": "baboon", "52": "African bush elephant", "53": "red panda", "54": "abacus", "55": "academic gown", "56": "altar", "57": "backpack", "58": "baluster / handrail", "59": "barbershop", "60": "barn", "61": "barrel", "62": "basketball", "63": "bathtub", "64": "station wagon", "65": "lighthouse", "66": "beaker", "67": "beer bottle", "68": "bikini", "69": "binoculars", "70": "birdhouse", "71": "bow tie", "72": "brass memorial plaque", "73": "bucket", "74": "high-speed train", "75": "butcher shop", "76": "candle", "77": "cannon", "78": "cardigan", "79": "automated teller machine", "80": "CD player", "81": "storage chest", "82": "Christmas stocking", "83": "cliff dwelling", "84": "computer keyboard", "85": "candy store", "86": "convertible", "87": "crane bird", "88": "dam", "89": "desk", "90": "dining table", "91": "dumbbell", "92": "flagpole", "93": "fly", "94": "fountain", "95": "freight car", "96": "frying pan", "97": "fur coat", "98": "gas mask or respirator", "99": "go-kart", "100": "gondola", "101": "hourglass", "102": "iPod", "103": "rickshaw", "104": "kimono", "105": "lampshade", "106": "lawn mower", "107": "lifeboat", "108": "limousine", "109": "magnetic compass", "110": "maypole", "111": "military uniform", "112": "miniskirt", "113": "moving van", "114": "neck brace", "115": "obelisk", "116": "oboe", "117": "pipe organ", "118": "parking meter", "119": "payphone", "120": "picket fence", "121": "pill bottle", "122": "plunger", "123": "police van", "124": "poncho", "125": "soda bottle", "126": "potter's wheel", "127": "missile", "128": "punching bag", "129": "refrigerator", "130": "remote control", "131": "rocking chair", "132": "rugby ball", "133": "sandal", "134": "school bus", "135": "scoreboard", "136": "sewing machine", "137": "snorkel", "138": "sock", "139": "sombrero", "140": "space heater", "141": "spider web", "142": "sports car", "143": "through arch bridge", "144": "stopwatch", "145": "sunglasses", "146": "suspension bridge", "147": "swim trunks / shorts", "148": "syringe", "149": "teapot", "150": "teddy bear", "151": "thatched roof", "152": "torch", "153": "tractor", "154": "triumphal arch", "155": "trolleybus", "156": "turnstile", "157": "umbrella", "158": "vestment", "159": "viaduct", "160": "volleyball", "161": "water jug", "162": "water tower", "163": "wok", "164": "wooden spoon", "165": "comic book", "166": "fishing casting reel", "167": "guacamole", "168": "ice cream", "169": "popsicle", "170": "goose", "171": "drumstick", "172": "plate", "173": "pretzel", "174": "mashed potatoes", "175": "cauliflower", "176": "bell pepper", "177": "lemon", "178": "banana", "179": "pomegranate", "180": "meatloaf", "181": "pizza", "182": "pot pie", "183": "espresso", "184": "bee", "185": "apron", "186": "pole", "187": "Chihuahua", "188": "mountain", "189": "cliff", "190": "coral reef", "191": "lakeshore", "192": "beach", "193": "acorn", "194": "broom", "195": "mushroom", "196": "metal nail", "197": "chain", "198": "slug", "199": "orange"}}}}, {"name": "Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full", "sequence": "string"}, {"name": "Attributes_ViT_L_14_descriptors_text_davinci_003_full", "sequence": "string"}, {"name": "clip_tags_ViT_L_14_simple_specific", "dtype": "string"}, {"name": "clip_tags_ViT_L_14_ensemble_specific", "dtype": "string"}, {"name": "clip_tags_LAION_ViT_H_14_2B_simple_specific", "dtype": "string"}, {"name": "clip_tags_LAION_ViT_H_14_2B_ensemble_specific", "dtype": "string"}, {"name": "id", "dtype": "int64"}], "splits": [{"name": "validation", "num_bytes": 5104453.0, "num_examples": 2000}], "download_size": 3249857, "dataset_size": 5104453.0}}
|
2023-05-22T13:41:19+00:00
|
12c8782e58b96ab3c10fe303302e79f18cd7aca9
|
# Dataset Card for "TinyImagenet_200_validation"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Multimodal-Fatima/TinyImagenet_200_validation
|
[
"region:us"
] |
2023-05-22T13:29:18+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "goldfish", "1": "fire salamander", "2": "American bullfrog", "3": "tailed frog", "4": "American alligator", "5": "boa constrictor", "6": "trilobite", "7": "scorpion", "8": "southern black widow", "9": "tarantula", "10": "centipede", "11": "koala", "12": "jellyfish", "13": "brain coral", "14": "snail", "15": "sea slug", "16": "American lobster", "17": "spiny lobster", "18": "black stork", "19": "king penguin", "20": "albatross", "21": "dugong", "22": "Yorkshire Terrier", "23": "Golden Retriever", "24": "Labrador Retriever", "25": "German Shepherd Dog", "26": "Standard Poodle", "27": "tabby cat", "28": "Persian cat", "29": "Egyptian Mau", "30": "cougar", "31": "lion", "32": "brown bear", "33": "ladybug", "34": "grasshopper", "35": "stick insect", "36": "cockroach", "37": "praying mantis", "38": "dragonfly", "39": "monarch butterfly", "40": "sulphur butterfly", "41": "sea cucumber", "42": "guinea pig", "43": "pig", "44": "ox", "45": "bison", "46": "bighorn sheep", "47": "gazelle", "48": "arabian camel", "49": "orangutan", "50": "chimpanzee", "51": "baboon", "52": "African bush elephant", "53": "red panda", "54": "abacus", "55": "academic gown", "56": "altar", "57": "backpack", "58": "baluster / handrail", "59": "barbershop", "60": "barn", "61": "barrel", "62": "basketball", "63": "bathtub", "64": "station wagon", "65": "lighthouse", "66": "beaker", "67": "beer bottle", "68": "bikini", "69": "binoculars", "70": "birdhouse", "71": "bow tie", "72": "brass memorial plaque", "73": "bucket", "74": "high-speed train", "75": "butcher shop", "76": "candle", "77": "cannon", "78": "cardigan", "79": "automated teller machine", "80": "CD player", "81": "storage chest", "82": "Christmas stocking", "83": "cliff dwelling", "84": "computer keyboard", "85": "candy store", "86": "convertible", "87": "crane bird", "88": "dam", "89": "desk", "90": "dining table", "91": "dumbbell", "92": "flagpole", "93": "fly", "94": "fountain", "95": "freight car", "96": "frying pan", "97": "fur coat", "98": "gas mask or respirator", "99": "go-kart", "100": "gondola", "101": "hourglass", "102": "iPod", "103": "rickshaw", "104": "kimono", "105": "lampshade", "106": "lawn mower", "107": "lifeboat", "108": "limousine", "109": "magnetic compass", "110": "maypole", "111": "military uniform", "112": "miniskirt", "113": "moving van", "114": "neck brace", "115": "obelisk", "116": "oboe", "117": "pipe organ", "118": "parking meter", "119": "payphone", "120": "picket fence", "121": "pill bottle", "122": "plunger", "123": "police van", "124": "poncho", "125": "soda bottle", "126": "potter's wheel", "127": "missile", "128": "punching bag", "129": "refrigerator", "130": "remote control", "131": "rocking chair", "132": "rugby ball", "133": "sandal", "134": "school bus", "135": "scoreboard", "136": "sewing machine", "137": "snorkel", "138": "sock", "139": "sombrero", "140": "space heater", "141": "spider web", "142": "sports car", "143": "through arch bridge", "144": "stopwatch", "145": "sunglasses", "146": "suspension bridge", "147": "swim trunks / shorts", "148": "syringe", "149": "teapot", "150": "teddy bear", "151": "thatched roof", "152": "torch", "153": "tractor", "154": "triumphal arch", "155": "trolleybus", "156": "turnstile", "157": "umbrella", "158": "vestment", "159": "viaduct", "160": "volleyball", "161": "water jug", "162": "water tower", "163": "wok", "164": "wooden spoon", "165": "comic book", "166": "fishing casting reel", "167": "guacamole", "168": "ice cream", "169": "popsicle", "170": "goose", "171": "drumstick", "172": "plate", "173": "pretzel", "174": "mashed potatoes", "175": "cauliflower", "176": "bell pepper", "177": "lemon", "178": "banana", "179": "pomegranate", "180": "meatloaf", "181": "pizza", "182": "pot pie", "183": "espresso", "184": "bee", "185": "apron", "186": "pole", "187": "Chihuahua", "188": "mountain", "189": "cliff", "190": "coral reef", "191": "lakeshore", "192": "beach", "193": "acorn", "194": "broom", "195": "mushroom", "196": "metal nail", "197": "chain", "198": "slug", "199": "orange"}}}}, {"name": "Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full", "sequence": "string"}, {"name": "Attributes_ViT_L_14_descriptors_text_davinci_003_full", "sequence": "string"}, {"name": "clip_tags_ViT_L_14_simple_specific", "dtype": "string"}, {"name": "clip_tags_ViT_L_14_ensemble_specific", "dtype": "string"}, {"name": "clip_tags_LAION_ViT_H_14_2B_simple_specific", "dtype": "string"}, {"name": "clip_tags_LAION_ViT_H_14_2B_ensemble_specific", "dtype": "string"}, {"name": "id", "dtype": "int64"}], "splits": [{"name": "validation", "num_bytes": 507583.0, "num_examples": 200}], "download_size": 372919, "dataset_size": 507583.0}}
|
2023-05-22T13:41:24+00:00
|
05a3e008f08189e616012967f4870a06e2532ae4
|
# Dataset Card for "chunk_245"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
mask-distilled-one-sec-cv12/chunk_245
|
[
"region:us"
] |
2023-05-22T13:29:30+00:00
|
{"dataset_info": {"features": [{"name": "logits", "sequence": "float32"}, {"name": "mfcc", "sequence": {"sequence": "float64"}}], "splits": [{"name": "train", "num_bytes": 753712748, "num_examples": 148019}], "download_size": 767337953, "dataset_size": 753712748}}
|
2023-05-22T13:30:03+00:00
|
5d634d8e3280a7083eb1051798cf933e8fbf9327
|
# Dataset Card for "hdvila_test"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
camenduru/hdvila_test
|
[
"region:us"
] |
2023-05-22T13:39:37+00:00
|
{"dataset_info": {"features": [{"name": "video_id", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "clip", "list": [{"name": "clip_id", "dtype": "string"}, {"name": "span", "sequence": "string"}]}], "splits": [{"name": "train", "num_bytes": 764532309, "num_examples": 300000}], "download_size": 236034101, "dataset_size": 764532309}}
|
2023-05-22T13:39:53+00:00
|
330edc1ab362df6156b61338fdff171d31c69c6d
|
# Dataset Card for "chunk_244"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
mask-distilled-one-sec-cv12/chunk_244
|
[
"region:us"
] |
2023-05-22T13:42:53+00:00
|
{"dataset_info": {"features": [{"name": "logits", "sequence": "float32"}, {"name": "mfcc", "sequence": {"sequence": "float64"}}], "splits": [{"name": "train", "num_bytes": 932686364, "num_examples": 183167}], "download_size": 951703928, "dataset_size": 932686364}}
|
2023-05-22T13:43:25+00:00
|
28453c635232fc2062b6a9c680c1abbb1309f5d8
|
# Dataset Card for "chunk_242"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
mask-distilled-one-sec-cv12/chunk_242
|
[
"region:us"
] |
2023-05-22T13:44:54+00:00
|
{"dataset_info": {"features": [{"name": "logits", "sequence": "float32"}, {"name": "mfcc", "sequence": {"sequence": "float64"}}], "splits": [{"name": "train", "num_bytes": 964144740, "num_examples": 189345}], "download_size": 983413469, "dataset_size": 964144740}}
|
2023-05-22T13:45:36+00:00
|
96747c42c98d7449ad4ac526accf8107214844fa
|
# Dataset Card for medical
中文医疗数据集
- LLM Supervised Finetuning repository: https://github.com/shibing624/textgen
- MeidcalGPT repository: https://github.com/shibing624/MedicalGPT
## Dataset Description
medical is a Chinese Medical dataset. 医疗数据集，可用于医疗领域大模型训练。
```
tree medical
|-- finetune # 监督微调数据集，可用于SFT和RLHF
| |-- test_en_1.json
| |-- test_zh_0.json
| |-- train_en_1.json
| |-- train_zh_0.json
| |-- valid_en_1.json
| `-- valid_zh_0.json
|-- medical.py # hf dataset 数据展示用
|-- pretrain # 二次预训练数据集
| |-- medical_book_zh.json
| |-- test_encyclopedia.json
| |-- train_encyclopedia.json
| `-- valid_encyclopedia.json
|-- README.md
`-- reward # 奖励模型数据集
|-- test.json
|-- train.json
`-- valid.json
```
### Original Dataset Summary
#### pretrain
- train_encyclopedia.json: 共36万条，来自医疗百科数据[FreedomIntelligence/huatuo_encyclopedia_qa](https://huggingface.co/datasets/FreedomIntelligence/huatuo_encyclopedia_qa) , 拼接 questions 和 answers，形成 text 文本字段，语句通顺，用于预训练注入医疗知识。
- medical_book_zh.json: 共8475条，来自医疗教材的文本数据，来源：https://github.com/jind11/MedQA， 原始数据集：[google drive](https://drive.google.com/u/0/uc?export=download&confirm=t&id=1ImYUSLk9JbgHXOemfvyiDiirluZHPeQw) ，只对长段落切分为2048字的小段落了。
#### finetune
- train_zh_0.json: 共195万条，来自1）中文医疗对话数据集[Toyhom/Chinese-medical-dialogue-data](https://github.com/Toyhom/Chinese-medical-dialogue-data)的六个科室医疗问诊数据，
有79万条；2）在线医疗百科 huatuo_encyclopedia_qa ，有36万条；3）医疗知识图谱 huatuo_knowledge_graph_qa，有79万条。三部分合并，共195万条。
- train_en_1.json：共11万条，来自英文医疗问诊对话数据[Kent0n-Li/ChatDoctor](https://github.com/Kent0n-Li/ChatDoctor)，合并了HealthCareMagic-100k、GenMedGPT-5k 数据集，共11万条。
#### reward
- train.json 共4000条，问题来自中文医疗对话数据集[Toyhom/Chinese-medical-dialogue-data](https://github.com/Toyhom/Chinese-medical-dialogue-data)的随机4000条提问，`response_chosen`来自该数据集的医生答复，
`response_rejected`来自本草模型[SCIR-HI/Huatuo-Llama-Med-Chinese](https://github.com/SCIR-HI/Huatuo-Llama-Med-Chinese)的答复。
### Supported Tasks and Leaderboards
中文医疗对话模型
The dataset designed for medical task training pretrained language models.
### Languages
The data are in Chinese.
## Dataset Structure
### Data Instances
An example of "train" looks as follows:
head pretrain/train_encyclopedia.json
```json
{"text": "怀孕后嘴巴很淡怎么办？有孕妇在怀孕之后，发现自己嘴巴比较淡，出现这种情况的原因其实也非常的复杂，首先和妊娠反应有直接的关系，这是一种正常的情况，另外有些孕妇平常不注意口腔卫生，舌苔比较厚或者自己有了一些消化系统方面的疾病，这就要求人们必须要及时的进行处理。女性在怀孕之后，身体就会出现一些明显的变化，首先人们月经会停止，另外也会有恶心、呕吐等一些妊娠反应，不过这些都是正常的。有些孕妇发现自己在怀孕之后，口味发生了很大的变化，嘴巴变得非常的淡。其实这也和激素变化有直接的关系，可能是妊娠反应所致，在怀孕期间，因为受到体内激素水平的变化，所以就会有肠胃系统的改变，人们可能会出现食欲不振，消化不良等症状表现，也有一些孕妇会发现自己嘴巴没有味道，会有口苦的症状，而这也是正常的孕期反应，人们在平常要多喝一些水，多吃一些清淡营养的食物慢慢就会改善。也有可能是舌苔过厚所致，孕妇嘴巴里面没有味道，很有可能是舌苔比较重、舌苔过厚导致的，这样就会影响到味蕾对味道的敏感度，不仅嘴巴里面没有味道，甚至有些孕妇在说话的时候也会有口臭，这就要求人们在每天早上漱口的时候，必须要用牙刷刷舌苔开始，不要清理的特别深，以免人们会有呕吐，慢慢习惯之后再往深一点的清洗，一般2到3天就会得到改善。嘴巴感到比较淡，其实也和脾胃虚寒有直接的关系，消化系统疾病，内分泌疾病，营养不良等，但有可能导致舌头部位因为味蕾的敏感度下降，产生口淡之感，患者会有食欲不振的表现，发现病症及时就诊治疗。"}
```
head finetune/train_zh_0.json
```json
{"instruction": "曲匹地尔片的用法用量", "input": "", "output": "注意：同种药品可由于不同的包装规格有不同的用法或用量。本文只供参考。如果不确定，请参看药品随带的说明书或向医生询问。口服。一次50～100mg（1-2片），3次/日，或遵医嘱。"}
```
head finetune/train_en_1.json
```json
{"instruction": "If you are a doctor, please answer the medical questions based on the patient's description.", "input": "Doctor, I have been experiencing sudden and frequent panic attacks. I don't know what to do.", "output": "Well, based on what you're telling me, it sounds like you may be suffering from panic disorder. The best course of action is to start with psychotherapy and mental health counseling. Additionally, we should conduct an electrocardiogram to make sure that there are no physical issues causing your panic attacks. We will also need to perform a depression screen and a toxicology screen to rule out any other underlying causes. Finally, I would recommend a comprehensive psychological and psychiatric evaluation and therapy to help manage your symptoms."}
```
head reward/train.json
```json
{"question": "孩子感统失调，学习不行怎么办？，孩子说话晚，走路晚，现在小学二年级，学习跟不上，理解力差，运动协调性差，家里很着急，不知怎么办。", "response_chosen": "病情分析：你好!孩子说话晚，走路也晚，很可能是大脑本身发育不好引起的发育迟缓。而五岁时所致的智力检查为临界范围，那就是说孩子的智商是有问题的，也应考虑与大脑发育不好有关。指导意见：人的大脑在头一年发育最快，可塑性最强，在头三年可塑性还是可以的，超过三岁再进行训练，效果就不怎么好了。建议再给孩子做一做智力测试，如果孩子的智商还是在临界范围，那就要考虑让孩子去特殊学校进行康复训练，而不是继续在普通小学就读，否则对孩子来说，就是强人所难了。希望自己的孩子能聪明，这是每个家长都会有的心愿，但如果孩子自身的条件就是不能跟上同龄孩子，那家长也要面对这个事实的，对吗？医生询问：", "response_rejected": "建议家长先带孩子去正规医院做全面检查以确定病因和病情严重程度；同时可以进行物理治疗、康复训练等辅助治疗方法。"}
```
### Data Fields
#### 预训练数据集 pretrain
字段解释：
- text: 文本
#### 指令微调数据集 finetune
字段解释：
- instruction: 指令
- input：问题（可为空）
- output：答复
#### 奖励模型数据集 reward
字段解释：
- question: 问题
- response_chosen: 优质回答
- response_rejected: 低质回答
### Data Splits
```
> wc -l medical/*/*
500 medical/finetune/test_en_1.json
500 medical/finetune/test_zh_0.json
116617 medical/finetune/train_en_1.json
1949972 medical/finetune/train_zh_0.json
500 medical/finetune/valid_en_1.json
500 medical/finetune/valid_zh_0.json
8475 medical/pretrain/medical_book_zh.json
500 medical/pretrain/test_encyclopedia.json
361420 medical/pretrain/train_encyclopedia.json
500 medical/pretrain/valid_encyclopedia.json
100 medical/reward/test.json
3800 medical/reward/train.json
100 medical/reward/valid.json
2443484 total
```
### Licensing Information
The dataset is available under the Apache 2.0.
### Citation Information
- https://github.com/Toyhom/Chinese-medical-dialogue-data
- https://github.com/FreedomIntelligence/Huatuo-26M/blob/main/README_zh-CN.md
- https://huggingface.co/datasets/FreedomIntelligence/huatuo_encyclopedia_qa
- https://huggingface.co/datasets/FreedomIntelligence/huatuo_knowledge_graph_qa
- https://github.com/Kent0n-Li/ChatDoctor
附上几个优质的reward model dataset:
- https://huggingface.co/datasets/Dahoas/synthetic-instruct-gptj-pairwise
- https://huggingface.co/datasets/sunzeyeah/chinese_chatgpt_corpus
- https://huggingface.co/datasets/Cohere/miracl-zh-queries-22-12
- https://huggingface.co/datasets/Dahoas/rm-static
### Contributions
[shibing624](https://github.com/shibing624) 整理并上传
|
shibing624/medical
|
[
"task_categories:text-generation",
"size_categories:1M<n<10M",
"language:zh",
"language:en",
"license:apache-2.0",
"text-generation",
"region:us"
] |
2023-05-22T13:45:06+00:00
|
{"language": ["zh", "en"], "license": "apache-2.0", "size_categories": ["1M<n<10M"], "task_categories": ["text-generation"], "pretty_name": "medical", "tags": ["text-generation"]}
|
2023-06-02T06:03:41+00:00
|
a4b29887ad0c5e4eec72d6809e30e054166158a4
|
# Dataset Card for "microsoft-XPretrain"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
camenduru/microsoft-XPretrain
|
[
"region:us"
] |
2023-05-22T13:47:08+00:00
|
{"dataset_info": {"features": [{"name": "video_id", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "clip", "list": [{"name": "clip_id", "dtype": "string"}, {"name": "span", "sequence": "string"}]}], "splits": [{"name": "train", "num_bytes": 6163343290, "num_examples": 3281091}], "download_size": 1757807231, "dataset_size": 6163343290}}
|
2023-05-22T13:49:58+00:00
|
aa62a3344dd0d8d3b27c7a8b4301008c47a94367
|
# Dataset Card for "mpt_finetune_dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Shoubhik8/mpt_finetune_dataset
|
[
"region:us"
] |
2023-05-22T13:48:30+00:00
|
{"dataset_info": {"features": [{"name": "prompt", "dtype": "string"}, {"name": "response", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 331283580, "num_examples": 371277}], "download_size": 13534489, "dataset_size": 331283580}}
|
2023-05-22T13:48:37+00:00
|
9598805f9276dc0a339e293e9f205ea8bbf41f9f
|
# Self-instruct-starcoder
## Table of Contents
- [Summary](#summary)
- [Our approach](#our-approach)
- [Dataset generation](#dataset-generation)
- [Dataset quality](#dataset-quality)
- [Post-processing](#post-processing)
- [Self-consistency](#self-consistency)
- [Uniqueness](#uniqueness)
- [Compile](#compile)
- [Dataset structure](#dataset-structure)
- [Space](#space)
## Summary
Self-instruct-starcoder is a dataset that was generated by prompting starcoder to generate new instructions based on some human-written seed instructions.
The underlying process is explained in the paper [self-instruct](https://arxiv.org/abs/2212.10560). This algorithm gave birth to famous machine generated
datasets such as [Alpaca](https://github.com/tatsu-lab/stanford_alpaca) and [Code Alpaca](https://github.com/sahil280114/codealpaca) which are two datasets
obtained by prompting OpenAI `text-davinci-003` engine.
## Our approach
While our method is similar to self-instruct and stanford alpaca, we included some relevant modifications to the pipeline to account for what we wanted.
- Rather than using `text-davinci-003`, we chose to prompt [StarCoder](https://arxiv.org/abs/2305.06161) which is a 10x smaller LLM developed for code use cases. However, it is possible to use any decoder based LLM on the hub.
- We changed our seed tasks in order to have the model generate code related tasks. We completed the seed tasks from code alpaca with 20 additional algorithm instructions.
- We switched from the generation format `"instruction":` - `"input":` - `"output":` to the format `"instruction":` - `"output":` by concatenating each instruction and its input under the
keyword `instruction`. We did so because the previous prompting format tended to make the model generate test cases as input and their solution as output, which is not what we wanted.
- Finally, we incorporated the possibility to change the trigger word in the prompt. We thus replaced the `"instruction" :` keyword by `"Here is the correct solution to the problem ":` which
resulted into much better generated instructions.
## Dataset generation
The generation of the dataset was time consuming and we chose our parameters to limit the computational burden of our method.
- Number of examples in context : 4
- 2 seed instructions
- 2 machine generated instructions
- Number of instructions to generate : 5000
- Stop words used in the generation : ["\n20", "20.", "20 ."]
- Similarity threshold for rouge score : 0.7
## Dataset quality
StarCoder, while being a great model is not as capable as `text-davinci-003`. In the generation, the model quickly reach sort of a ceiling in terms of creativity.
There are many instructions that are similar to each other, but it should not bother since they are not phrased the same.
## Post-processing
Post-processing is an important part of the pipeline since it improves the quality of the dataset despite the fact that it implies getting rid of some examples. First we
need to identify what we want to avoid :
- A generated solution which does not answer to the corresponding instruction
- An instruction that is too similar to another one.
### Self-consistency
We imagined a process that we named **self-consistency**. The idea is to reverse-prompt the model to see if it can generate a sound instruction that corresponds to the
solution (output) it is prompted with. This is a particularly difficult few-shot task, and unfortunately StarCoder does not perform incredibly well on it. With a few-shot parameters of `4`
(all being seed tasks), the model is able to recover 1135 instructions out of 5003, which amount for 22.6% of the raw dataset. Fortunately, the inability for starcoder to generate instructions for some
solutions does not mean we should get rid of them. For the solutions (outputs) with generated instructions, we can compare these with the ground truth. For that we can use [Sentence-BERT](https://arxiv.org/abs/1908.10084) because the comparison should focus the meaning
rather than the word to word similarity ratio. We have about 771 instructions (~68%) with a similarity score >= 0.5 with their ground truth. These can be seen as high quality examples, they form the `curated` set.
<p align="center">
<img src="https://huggingface.co/datasets/codeparrot/self-instruct-starcoder/resolve/main/output.png" alt="drawing" width="300", height="300"/>
</p>
### Uniqueness
Another approach that can be used to clean the raw dataset is to focus on distinct instructions. For a given instruction, we go through all the instructions generated before it to see if there is one with a similarity score >= 0.5.
If it is the case, we remove that instruction. This process removes about 94% of the raw dataset, the remaining instructions form the `unique` set.
### Compile
We also decided to build a set which contains solely the example featuring a code written in python 3 which does not code a compilation error.
## Dataset structure
```python
from datasets import load_dataset
dataset = load_dataset("codeparrot/self-instruct-starcoder")
DatasetDict({
compile: Dataset({
features: ['instruction', 'output', 'most_similar', 'avg_similarity_score'],
num_rows: 3549
})
curated: Dataset({
features: ['instruction', 'output', 'most_similar', 'avg_similarity_score'],
num_rows: 771
})
raw: Dataset({
features: ['instruction', 'output', 'most_similar', 'avg_similarity_score'],
num_rows: 5003
})
unique: Dataset({
features: ['instruction', 'output', 'most_similar', 'avg_similarity_score'],
num_rows: 308
})
}))
```
|Field|Type|Description|
|---|---|---|
|instruction|string|Instruction|
|output|string|Answer to the instruction|
|most_similar|string|Dictionnary containing the 10 most similar instructions generated before the current instruction along with the similarity scores|
|avg_similarity_score|float64| Average similarity score|
## Additional resources
- [Space(self-instruct-starcoder)](https://huggingface.co/spaces/codeparrot/self-instruct-starcoder)
- [Github Repository](https://github.com/ArmelRandy/Self-instruct)
## Citation
```
@misc{title={Self-Instruct-StarCoder},
author={Zebaze, Armel Randy},
doi={https://doi.org/10.57967/hf/0790},
}
```
|
codeparrot/self-instruct-starcoder
|
[
"task_categories:text2text-generation",
"size_categories:1K<n<10K",
"language:en",
"license:bigscience-openrail-m",
"code",
"arxiv:2212.10560",
"arxiv:2305.06161",
"arxiv:1908.10084",
"doi:10.57967/hf/0790",
"region:us"
] |
2023-05-22T13:50:58+00:00
|
{"language": ["en"], "license": "bigscience-openrail-m", "size_categories": ["1K<n<10K"], "task_categories": ["text2text-generation"], "dataset_info": {"features": [{"name": "instruction", "dtype": "string"}, {"name": "output", "dtype": "string"}, {"name": "most_similar", "dtype": "string"}, {"name": "avg_similarity_score", "dtype": "float64"}], "splits": [{"name": "curated", "num_bytes": 1937514, "num_examples": 771}, {"name": "raw", "num_bytes": 12969008, "num_examples": 5003}, {"name": "unique", "num_bytes": 786771, "num_examples": 308}, {"name": "compile", "num_bytes": 9048805, "num_examples": 3549}], "download_size": 10935008, "dataset_size": 24742098}, "tags": ["code"]}
|
2023-10-23T11:13:18+00:00
|
d97807e5bf7cda50f44652d428b07f13e8a209fb
|
# Dataset Card for "chunk_243"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
mask-distilled-one-sec-cv12/chunk_243
|
[
"region:us"
] |
2023-05-22T13:51:28+00:00
|
{"dataset_info": {"features": [{"name": "logits", "sequence": "float32"}, {"name": "mfcc", "sequence": {"sequence": "float64"}}], "splits": [{"name": "train", "num_bytes": 1024444204, "num_examples": 201187}], "download_size": 1043913059, "dataset_size": 1024444204}}
|
2023-05-22T13:52:23+00:00
|
e4822fef9976ef2bcd24f7de58b42511b2f92709
|
# Dataset Card for "chunk_247"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
mask-distilled-one-sec-cv12/chunk_247
|
[
"region:us"
] |
2023-05-22T13:51:51+00:00
|
{"dataset_info": {"features": [{"name": "logits", "sequence": "float32"}, {"name": "mfcc", "sequence": {"sequence": "float64"}}], "splits": [{"name": "train", "num_bytes": 805915932, "num_examples": 158271}], "download_size": 819081675, "dataset_size": 805915932}}
|
2023-05-22T13:53:20+00:00
|
43d0e88b361136538353277eaf2599d9039eb5bb
|
# Dataset Card for "mpt_finetune_dataset_sample_train"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Shoubhik8/mpt_finetune_dataset_sample_train
|
[
"region:us"
] |
2023-05-22T13:52:16+00:00
|
{"dataset_info": {"features": [{"name": "prompt", "dtype": "string"}, {"name": "response", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 19771652, "num_examples": 20000}], "download_size": 703051, "dataset_size": 19771652}}
|
2023-05-22T13:52:20+00:00
|
00f53df2fdf23c14e43f794c59309ecdd382df88
|
# Dataset Card for "mpt_finetune_dataset_sample_valid"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Shoubhik8/mpt_finetune_dataset_sample_valid
|
[
"region:us"
] |
2023-05-22T13:52:20+00:00
|
{"dataset_info": {"features": [{"name": "prompt", "dtype": "string"}, {"name": "response", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 9882006, "num_examples": 10000}], "download_size": 350702, "dataset_size": 9882006}}
|
2023-05-22T13:52:24+00:00
|
8aa7b047fe88615733e5f351289977f33e655bef
|
# Dataset Card for "TinyImagenet_200_validation_google_flan_t5_xxl_mode_T_SPECIFIC_A_ns_200"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
CVasNLPExperiments/TinyImagenet_200_validation_google_flan_t5_xxl_mode_T_SPECIFIC_A_ns_200
|
[
"region:us"
] |
2023-05-22T13:52:59+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "prompt", "dtype": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}], "splits": [{"name": "fewshot_0__Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full_clip_tags_LAION_ViT_H_14_2B_simple_specific_rices", "num_bytes": 88117, "num_examples": 200}], "download_size": 37414, "dataset_size": 88117}}
|
2023-05-27T18:39:14+00:00
|
9137df8732a8619d07717a90b0155e0b39a8f183
|
# Dataset Card for "chunk_249"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
mask-distilled-one-sec-cv12/chunk_249
|
[
"region:us"
] |
2023-05-22T13:55:59+00:00
|
{"dataset_info": {"features": [{"name": "logits", "sequence": "float32"}, {"name": "mfcc", "sequence": {"sequence": "float64"}}], "splits": [{"name": "train", "num_bytes": 1102886464, "num_examples": 216592}], "download_size": 1124375605, "dataset_size": 1102886464}}
|
2023-05-22T13:56:34+00:00
|
4b703399763f95995c8055db4bd41ed1c9c39264
|
# Dataset Card for "TinyImagenet_200_validation_google_flan_t5_xxl_mode_A_ns_200"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
CVasNLPExperiments/TinyImagenet_200_validation_google_flan_t5_xxl_mode_A_ns_200
|
[
"region:us"
] |
2023-05-22T13:56:28+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "prompt", "dtype": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}], "splits": [{"name": "fewshot_0__Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full_clip_tags_LAION_ViT_H_14_2B_simple_specific_rices", "num_bytes": 78983, "num_examples": 200}], "download_size": 35126, "dataset_size": 78983}}
|
2023-05-22T13:56:29+00:00
|
738597d1a3a89a7a537d316d4ea4283cf5d99415
|
# Dataset Card for "TinyImagenet_200_validation_google_flan_t5_xxl_mode_A_T_SPECIFIC_ns_200"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
CVasNLPExperiments/TinyImagenet_200_validation_google_flan_t5_xxl_mode_A_T_SPECIFIC_ns_200
|
[
"region:us"
] |
2023-05-22T14:00:08+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "prompt", "dtype": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}], "splits": [{"name": "fewshot_0__Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full_clip_tags_LAION_ViT_H_14_2B_simple_specific_rices", "num_bytes": 83525, "num_examples": 200}], "download_size": 37066, "dataset_size": 83525}}
|
2023-05-22T14:00:09+00:00
|
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