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0277388cb401f77d92e03b910b5c39247440790a
|
# Dataset Card for "eli5_question_answer_1107"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
multi-train/eli5_question_answer_1107
|
[
"region:us"
] |
2023-11-10T18:39:56+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "query", "dtype": "string"}, {"name": "pos", "sequence": "string"}, {"name": "neg", "sequence": "string"}, {"name": "task", "dtype": "string"}, {"name": "instruction", "struct": [{"name": "query", "dtype": "string"}, {"name": "pos", "dtype": "string"}, {"name": "neg", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 187610302, "num_examples": 200000}], "download_size": 106360840, "dataset_size": 187610302}}
|
2023-11-10T18:40:10+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "eli5_question_answer_1107"
More Information needed
|
[
"# Dataset Card for \"eli5_question_answer_1107\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"eli5_question_answer_1107\"\n\nMore Information needed"
] |
828bf8113019da59606c0d6f44dd6a4096af5952
|
# Dataset Card for "reasoningDataV4"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
ag2428/reasoningDataV4
|
[
"region:us"
] |
2023-11-10T18:41:13+00:00
|
{"dataset_info": {"features": [{"name": "instruction", "dtype": "string"}, {"name": "answer", "dtype": "string"}, {"name": "__index_level_0__", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 2481669221, "num_examples": 2062854}], "download_size": 1500063761, "dataset_size": 2481669221}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-11-10T18:42:49+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "reasoningDataV4"
More Information needed
|
[
"# Dataset Card for \"reasoningDataV4\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"reasoningDataV4\"\n\nMore Information needed"
] |
b1a8c6afa66a66e295d093567f530371c3204811
|
# 📊 Dataset Description
This dataset comprises code files of Huggingface Spaces that have more than 0 likes as of November 10, 2023. This dataset contains various programming languages totaling in 672 MB of compressed and 2.05 GB of uncompressed data.
# 📝 Data Fields
| Field | Type | Description |
|------------|--------|------------------------------------------|
| repository | string | Huggingface Spaces repository names. |
| sdk | string | Software Development Kit of the space. |
| license | string | License type of the space. |
## 🧩 Data Structure
Data structure of the data.
```
spaces/
├─ author1/
│ ├─ space1
│ ├─ space2
├─ author2/
│ ├─ space1
│ ├─ space2
│ ├─ space3
```
# 🏛️ Licenses
Huggingface Spaces contains a variety of licenses. Here is the list of the licenses that this dataset contains:
```python
[
'None',
'mit',
'apache-2.0',
'openrail',
'gpl-3.0',
'other',
'afl-3.0',
'unknown',
'creativeml-openrail-m',
'cc-by-nc-4.0',
'cc-by-4.0',
'cc',
'cc-by-nc-sa-4.0',
'bigscience-openrail-m',
'bsd-3-clause',
'agpl-3.0',
'wtfpl',
'gpl',
'artistic-2.0',
'lgpl-3.0',
'cc-by-sa-4.0',
'Configuration error',
'bsd',
'cc-by-nc-nd-4.0',
'cc0-1.0',
'unlicense',
'llama2',
'bigscience-bloom-rail-1.0',
'gpl-2.0',
'bsd-2-clause',
'osl-3.0',
'cc-by-2.0',
'cc-by-3.0',
'cc-by-nc-3.0',
'cc-by-nc-2.0',
'cc-by-nd-4.0',
'openrail++',
'bigcode-openrail-m',
'bsd-3-clause-clear',
'eupl-1.1',
'cc-by-sa-3.0',
'mpl-2.0',
'c-uda',
'gfdl',
'cc-by-nc-sa-2.0',
'cc-by-2.5',
'bsl-1.0',
'odc-by',
'deepfloyd-if-license',
'ms-pl',
'ecl-2.0',
'pddl',
'ofl-1.1',
'lgpl-2.1',
'postgresql',
'lppl-1.3c',
'ncsa',
'cc-by-nc-sa-3.0'
]
```
# 📊 Dataset Statistics
| Language | File Extension | File Counts | File Size (MB) | Line Counts |
|------------|-----------------|-------------|----------------|-------------|
| Python | .py | 141,560 | 1079.0 | 28,653,744 |
| SQL | .sql | 21 | 523.6 | 645 |
| JavaScript | .js | 6,790 | 369.8 | 2,137,054 |
| Markdown | .md | 63,237 | 273.4 | 3,110,443 |
| HTML | .html | 1,953 | 265.8 | 516,020 |
| C | .c | 1,320 | 132.2 | 3,558,826 |
| Go | .go | 429 | 46.3 | 6,331 |
| CSS | .css | 3,097 | 25.6 | 386,334 |
| C Header | .h | 2,824 | 20.4 | 570,948 |
| C++ | .cpp | 1,117 | 15.3 | 494,939 |
| TypeScript | .ts | 4,158 | 14.8 | 439,551 |
| TSX | .tsx | 4,273 | 9.4 | 306,416 |
| Shell | .sh | 3,294 | 5.5 | 171,943 |
| Perl | .pm | 92 | 4.2 | 128,594 |
| C# | .cs | 22 | 3.9 | 41,265 |
## 🖥️ Language
## 📁 Size
## 📝 Line Count
# 🤗 Huggingface Spaces Statistics
## 🛠️ Software Development Kit (SDK)
Software Development Kit pie chart.
## 🏛️ License
License chart.
# 📅 Dataset Creation
This dataset was created in these steps:
1. Scraped all spaces using the Huggingface Hub API.
```python
from huggingface_hub import HfApi
api = HfApi()
spaces = api.list_spaces(sort="likes", full=1, direction=-1)
```
2. Filtered spaces with more than 0 likes.
```python
a = {}
for i in tqdm(spaces):
i = i.__dict__
if i['likes'] > 0:
try:
try:
a[i['id']] = {'sdk': i['sdk'], 'license': i['cardData']['license'], 'likes': i['likes']}
except KeyError:
a[i['id']] = {'sdk': i['sdk'], 'license': None, 'likes': i['likes']}
except:
a[i['id']] = {'sdk': "Configuration error", 'license': "Configuration error", 'likes': i['likes']}
data_list = [{'repository': key, 'sdk': value['sdk'], 'license': value['license'], 'likes': value['likes']} for key, value in a.items()]
df = pd.DataFrame(data_list)
```
3. Cloned spaces locally.
```python
from huggingface_hub import snapshot_download
programming = ['.asm', '.bat', '.cmd', '.c', '.h', '.cs', '.cpp', '.hpp', '.c++', '.h++', '.cc', '.hh', '.C', '.H', '.cmake', '.css', '.dockerfile', 'Dockerfile', '.f90', '.f', '.f03', '.f08', '.f77', '.f95', '.for', '.fpp', '.go', '.hs', '.html', '.java', '.js', '.jl', '.lua', 'Makefile', '.md', '.markdown', '.php', '.php3', '.php4', '.php5', '.phps', '.phpt', '.pl', '.pm', '.pod', '.perl', '.ps1', '.psd1', '.psm1', '.py', '.rb', '.rs', '.sql', '.scala', '.sh', '.bash', '.command', '.zsh', '.ts', '.tsx', '.tex', '.vb']
pattern = [f"*{i}" for i in programming]
for i in repos:
snapshot_download(i, repo_type="space", local_dir=f"spaces/{i}", allow_patterns=pattern)
````
4. Processed the data to derive statistics.
|
Weyaxi/huggingface-spaces-codes
|
[
"size_categories:100K<n<1M",
"language:code",
"license:other",
"region:us"
] |
2023-11-10T18:45:38+00:00
|
{"language": ["code"], "license": "other", "size_categories": ["100K<n<1M"], "configs": [{"config_name": "default", "data_files": "spaces.csv"}]}
|
2023-11-14T09:31:44+00:00
|
[] |
[
"code"
] |
TAGS
#size_categories-100K<n<1M #language-code #license-other #region-us
|
!image/png
Dataset Description
===================
This dataset comprises code files of Huggingface Spaces that have more than 0 likes as of November 10, 2023. This dataset contains various programming languages totaling in 672 MB of compressed and 2.05 GB of uncompressed data.
Data Fields
===========
Field: repository, Type: string, Description: Huggingface Spaces repository names.
Field: sdk, Type: string, Description: Software Development Kit of the space.
Field: license, Type: string, Description: License type of the space.
Data Structure
--------------
Data structure of the data.
️ Licenses
==========
Huggingface Spaces contains a variety of licenses. Here is the list of the licenses that this dataset contains:
Dataset Statistics
==================
️ Language
----------
!image/png
Size
----
!image/png
Line Count
----------
!image/png
Huggingface Spaces Statistics
=============================
️ Software Development Kit (SDK)
--------------------------------
Software Development Kit pie chart.
!image/png
️ License
---------
License chart.
!image/png
Dataset Creation
================
This dataset was created in these steps:
1. Scraped all spaces using the Huggingface Hub API.
2. Filtered spaces with more than 0 likes.
3. Cloned spaces locally.
'
4. Processed the data to derive statistics.
|
[] |
[
"TAGS\n#size_categories-100K<n<1M #language-code #license-other #region-us \n"
] |
4a30732bf2e0b2182c24681d52e7b59e7af729bf
|
# Dataset Card for "triviaqa-train-multikilt_1107"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
multi-train/triviaqa-train-multikilt_1107
|
[
"region:us"
] |
2023-11-10T18:51:59+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "query", "dtype": "string"}, {"name": "pos", "sequence": "string"}, {"name": "neg", "sequence": "string"}, {"name": "task", "dtype": "string"}, {"name": "instruction", "struct": [{"name": "query", "dtype": "string"}, {"name": "pos", "dtype": "string"}, {"name": "neg", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 67898183, "num_examples": 52886}], "download_size": 39123463, "dataset_size": 67898183}}
|
2023-11-10T18:52:08+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "triviaqa-train-multikilt_1107"
More Information needed
|
[
"# Dataset Card for \"triviaqa-train-multikilt_1107\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"triviaqa-train-multikilt_1107\"\n\nMore Information needed"
] |
738d44af5e06237972ffffd097dc7f0e932bb71a
|
# Dataset Card for "wow-train-multikilt_1107"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
multi-train/wow-train-multikilt_1107
|
[
"region:us"
] |
2023-11-10T18:52:09+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "query", "dtype": "string"}, {"name": "pos", "sequence": "string"}, {"name": "neg", "sequence": "string"}, {"name": "task", "dtype": "string"}, {"name": "instruction", "struct": [{"name": "query", "dtype": "string"}, {"name": "pos", "dtype": "string"}, {"name": "neg", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 124530766, "num_examples": 80035}], "download_size": 65428253, "dataset_size": 124530766}}
|
2023-11-10T18:52:21+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "wow-train-multikilt_1107"
More Information needed
|
[
"# Dataset Card for \"wow-train-multikilt_1107\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"wow-train-multikilt_1107\"\n\nMore Information needed"
] |
491f78c02bbfd2d130fd5096abd8a46b5af5e1c7
|
# Dataset Card for "multi_lexsum_1107"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
multi-train/multi_lexsum_1107
|
[
"region:us"
] |
2023-11-10T18:52:34+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "query", "dtype": "string"}, {"name": "pos", "sequence": "string"}, {"name": "neg", "sequence": "string"}, {"name": "task", "dtype": "string"}, {"name": "instruction", "struct": [{"name": "query", "dtype": "string"}, {"name": "pos", "dtype": "string"}, {"name": "neg", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 2800042167, "num_examples": 3177}], "download_size": 1258860340, "dataset_size": 2800042167}}
|
2023-11-10T18:54:52+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "multi_lexsum_1107"
More Information needed
|
[
"# Dataset Card for \"multi_lexsum_1107\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"multi_lexsum_1107\"\n\nMore Information needed"
] |
7519af68b994a7f3797dcc5a95d148d7c1c91afc
|
# Dataset Card for "reddit-title-body_1107"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
multi-train/reddit-title-body_1107
|
[
"region:us"
] |
2023-11-10T18:55:02+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "query", "dtype": "string"}, {"name": "pos", "sequence": "string"}, {"name": "neg", "sequence": "string"}, {"name": "task", "dtype": "string"}, {"name": "instruction", "struct": [{"name": "query", "dtype": "string"}, {"name": "pos", "dtype": "string"}, {"name": "neg", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 216135392, "num_examples": 200000}], "download_size": 125472332, "dataset_size": 216135392}}
|
2023-11-10T18:58:24+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "reddit-title-body_1107"
More Information needed
|
[
"# Dataset Card for \"reddit-title-body_1107\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"reddit-title-body_1107\"\n\nMore Information needed"
] |
dccee0af152e69862867f44a7126376ccf2661c8
|
# Dataset Card for "WikiAnswers_1107"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
multi-train/WikiAnswers_1107
|
[
"region:us"
] |
2023-11-10T18:58:00+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "query", "dtype": "string"}, {"name": "pos", "sequence": "string"}, {"name": "neg", "sequence": "string"}, {"name": "task", "dtype": "string"}, {"name": "instruction", "struct": [{"name": "query", "dtype": "string"}, {"name": "pos", "dtype": "string"}, {"name": "neg", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 54367110, "num_examples": 200000}], "download_size": 22862968, "dataset_size": 54367110}}
|
2023-11-10T18:58:07+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "WikiAnswers_1107"
More Information needed
|
[
"# Dataset Card for \"WikiAnswers_1107\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"WikiAnswers_1107\"\n\nMore Information needed"
] |
de1a8ca49de363a08b23a5e781c20d6a3b568086
|
# Dataset Card for "medmcqa_1107"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
multi-train/medmcqa_1107
|
[
"region:us"
] |
2023-11-10T18:58:26+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "query", "dtype": "string"}, {"name": "pos", "sequence": "string"}, {"name": "neg", "sequence": "string"}, {"name": "task", "dtype": "string"}, {"name": "instruction", "struct": [{"name": "query", "dtype": "string"}, {"name": "pos", "dtype": "string"}, {"name": "neg", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 194641944, "num_examples": 160869}], "download_size": 102313307, "dataset_size": 194641944}}
|
2023-11-10T18:58:42+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "medmcqa_1107"
More Information needed
|
[
"# Dataset Card for \"medmcqa_1107\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"medmcqa_1107\"\n\nMore Information needed"
] |
d905d18c1ead234e70b70e1242e4baa3225e031d
|
# Dataset Card for "SimpleWiki_1107"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
multi-train/SimpleWiki_1107
|
[
"region:us"
] |
2023-11-10T18:58:43+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "query", "dtype": "string"}, {"name": "pos", "sequence": "string"}, {"name": "neg", "sequence": "string"}, {"name": "task", "dtype": "string"}, {"name": "instruction", "struct": [{"name": "query", "dtype": "string"}, {"name": "pos", "dtype": "string"}, {"name": "neg", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 57699115, "num_examples": 102225}], "download_size": 29311247, "dataset_size": 57699115}}
|
2023-11-10T18:58:49+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "SimpleWiki_1107"
More Information needed
|
[
"# Dataset Card for \"SimpleWiki_1107\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"SimpleWiki_1107\"\n\nMore Information needed"
] |
72254a8444e5df3a7edc544abf6cf7758a33a87c
|
# Dataset Card for "gooaq_pairs_1107"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
multi-train/gooaq_pairs_1107
|
[
"region:us"
] |
2023-11-10T18:58:51+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "query", "dtype": "string"}, {"name": "pos", "sequence": "string"}, {"name": "neg", "sequence": "string"}, {"name": "task", "dtype": "string"}, {"name": "instruction", "struct": [{"name": "query", "dtype": "string"}, {"name": "pos", "dtype": "string"}, {"name": "neg", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 125623207, "num_examples": 200000}], "download_size": 62027848, "dataset_size": 125623207}}
|
2023-11-10T18:59:01+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "gooaq_pairs_1107"
More Information needed
|
[
"# Dataset Card for \"gooaq_pairs_1107\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"gooaq_pairs_1107\"\n\nMore Information needed"
] |
41afa285c0f9e7c2a70d3340d795e7575864e71a
|
# Dataset Card for "squad_pairs_1107"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
multi-train/squad_pairs_1107
|
[
"region:us"
] |
2023-11-10T18:59:03+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "query", "dtype": "string"}, {"name": "pos", "sequence": "string"}, {"name": "neg", "sequence": "string"}, {"name": "task", "dtype": "string"}, {"name": "instruction", "struct": [{"name": "query", "dtype": "string"}, {"name": "pos", "dtype": "string"}, {"name": "neg", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 131284545, "num_examples": 87599}], "download_size": 27083693, "dataset_size": 131284545}}
|
2023-11-10T18:59:09+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "squad_pairs_1107"
More Information needed
|
[
"# Dataset Card for \"squad_pairs_1107\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"squad_pairs_1107\"\n\nMore Information needed"
] |
82df40d33ea506869870517567c8b1c4ad72b566
|
# Dataset Card for "searchQA_top5_snippets_1107"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
multi-train/searchQA_top5_snippets_1107
|
[
"region:us"
] |
2023-11-10T18:59:11+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "query", "dtype": "string"}, {"name": "pos", "sequence": "string"}, {"name": "neg", "sequence": "string"}, {"name": "task", "dtype": "string"}, {"name": "instruction", "struct": [{"name": "query", "dtype": "string"}, {"name": "pos", "dtype": "string"}, {"name": "neg", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 102625564, "num_examples": 117220}], "download_size": 62573380, "dataset_size": 102625564}}
|
2023-11-10T18:59:21+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "searchQA_top5_snippets_1107"
More Information needed
|
[
"# Dataset Card for \"searchQA_top5_snippets_1107\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"searchQA_top5_snippets_1107\"\n\nMore Information needed"
] |
bb298a3deceb783cb5bc082d3515f6e93e4f8728
|
# Dataset Card for "pubmedqa_1107"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
multi-train/pubmedqa_1107
|
[
"region:us"
] |
2023-11-10T18:59:23+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "query", "dtype": "string"}, {"name": "pos", "sequence": "string"}, {"name": "neg", "sequence": "string"}, {"name": "task", "dtype": "string"}, {"name": "instruction", "struct": [{"name": "query", "dtype": "string"}, {"name": "pos", "dtype": "string"}, {"name": "neg", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 553042848, "num_examples": 200000}], "download_size": 277163918, "dataset_size": 553042848}}
|
2023-11-10T18:59:57+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "pubmedqa_1107"
More Information needed
|
[
"# Dataset Card for \"pubmedqa_1107\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"pubmedqa_1107\"\n\nMore Information needed"
] |
83dceb7ec13b8acce4013918d0408e28174025c6
|
# Dataset Card for "npr_1107"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
multi-train/npr_1107
|
[
"region:us"
] |
2023-11-10T18:59:59+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "query", "dtype": "string"}, {"name": "pos", "sequence": "string"}, {"name": "neg", "sequence": "string"}, {"name": "task", "dtype": "string"}, {"name": "instruction", "struct": [{"name": "query", "dtype": "string"}, {"name": "pos", "dtype": "string"}, {"name": "neg", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 474583082, "num_examples": 200000}], "download_size": 259084905, "dataset_size": 474583082}}
|
2023-11-10T19:00:27+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "npr_1107"
More Information needed
|
[
"# Dataset Card for \"npr_1107\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"npr_1107\"\n\nMore Information needed"
] |
aac28bea2d9dc748c7cf45299a7c56018bb202bc
|
# Dataset Card for "S2ORC_title_abstract_1107"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
multi-train/S2ORC_title_abstract_1107
|
[
"region:us"
] |
2023-11-10T19:00:29+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "query", "dtype": "string"}, {"name": "pos", "sequence": "string"}, {"name": "neg", "sequence": "string"}, {"name": "task", "dtype": "string"}, {"name": "instruction", "struct": [{"name": "query", "dtype": "string"}, {"name": "pos", "dtype": "string"}, {"name": "neg", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 256048459, "num_examples": 200000}], "download_size": 134596257, "dataset_size": 256048459}}
|
2023-11-10T19:00:45+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "S2ORC_title_abstract_1107"
More Information needed
|
[
"# Dataset Card for \"S2ORC_title_abstract_1107\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"S2ORC_title_abstract_1107\"\n\nMore Information needed"
] |
14b6f4c4130fc19c7b3afc1a81416eb1451077ea
|
# Dataset Card for "wikihow_1107"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
multi-train/wikihow_1107
|
[
"region:us"
] |
2023-11-10T19:00:47+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "query", "dtype": "string"}, {"name": "pos", "sequence": "string"}, {"name": "neg", "sequence": "string"}, {"name": "task", "dtype": "string"}, {"name": "instruction", "struct": [{"name": "query", "dtype": "string"}, {"name": "pos", "dtype": "string"}, {"name": "neg", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 52538597, "num_examples": 128542}], "download_size": 19871957, "dataset_size": 52538597}}
|
2023-11-10T19:00:54+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "wikihow_1107"
More Information needed
|
[
"# Dataset Card for \"wikihow_1107\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"wikihow_1107\"\n\nMore Information needed"
] |
dd24887892fdeb12532a339d235672a0324d0a35
|
# Dataset Card for "trex-train-multikilt_1107"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
multi-train/trex-train-multikilt_1107
|
[
"region:us"
] |
2023-11-10T19:00:56+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "query", "dtype": "string"}, {"name": "pos", "sequence": "string"}, {"name": "neg", "sequence": "string"}, {"name": "task", "dtype": "string"}, {"name": "instruction", "struct": [{"name": "query", "dtype": "string"}, {"name": "pos", "dtype": "string"}, {"name": "neg", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 228887845, "num_examples": 200000}], "download_size": 116247120, "dataset_size": 228887845}}
|
2023-11-10T19:01:10+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "trex-train-multikilt_1107"
More Information needed
|
[
"# Dataset Card for \"trex-train-multikilt_1107\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"trex-train-multikilt_1107\"\n\nMore Information needed"
] |
0a28b06a84cea8e17dd416f4b064cac5d11c4050
|
# Dataset Card for "nq-train-multikilt_1107"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
multi-train/nq-train-multikilt_1107
|
[
"region:us"
] |
2023-11-10T19:01:12+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "query", "dtype": "string"}, {"name": "pos", "sequence": "string"}, {"name": "neg", "sequence": "string"}, {"name": "task", "dtype": "string"}, {"name": "instruction", "struct": [{"name": "query", "dtype": "string"}, {"name": "pos", "dtype": "string"}, {"name": "neg", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 94822797, "num_examples": 76945}], "download_size": 53958820, "dataset_size": 94822797}}
|
2023-11-10T19:01:21+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "nq-train-multikilt_1107"
More Information needed
|
[
"# Dataset Card for \"nq-train-multikilt_1107\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"nq-train-multikilt_1107\"\n\nMore Information needed"
] |
cba71eb3d1ee450decec0cbb600cd2aa38de7de1
|
# Dataset Card for "gigaword_1107"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
multi-train/gigaword_1107
|
[
"region:us"
] |
2023-11-10T19:01:23+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "query", "dtype": "string"}, {"name": "pos", "sequence": "string"}, {"name": "neg", "sequence": "string"}, {"name": "task", "dtype": "string"}, {"name": "instruction", "struct": [{"name": "query", "dtype": "string"}, {"name": "pos", "dtype": "string"}, {"name": "neg", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 87945355, "num_examples": 200000}], "download_size": 41386512, "dataset_size": 87945355}}
|
2023-11-10T19:01:31+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "gigaword_1107"
More Information needed
|
[
"# Dataset Card for \"gigaword_1107\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"gigaword_1107\"\n\nMore Information needed"
] |
87b344388eb7cdf691548c37f75e23ae372c3e45
|
# Dataset Card for "yahoo_answers_title_answer_1107"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
multi-train/yahoo_answers_title_answer_1107
|
[
"region:us"
] |
2023-11-10T19:01:32+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "query", "dtype": "string"}, {"name": "pos", "sequence": "string"}, {"name": "neg", "sequence": "string"}, {"name": "task", "dtype": "string"}, {"name": "instruction", "struct": [{"name": "query", "dtype": "string"}, {"name": "pos", "dtype": "string"}, {"name": "neg", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 141237635, "num_examples": 200000}], "download_size": 78339836, "dataset_size": 141237635}}
|
2023-11-10T19:01:43+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "yahoo_answers_title_answer_1107"
More Information needed
|
[
"# Dataset Card for \"yahoo_answers_title_answer_1107\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"yahoo_answers_title_answer_1107\"\n\nMore Information needed"
] |
5b9edd43f6b8b55c83623c95dee237d4fbefb531
|
# Dataset Card for "fever-train-multikilt_1107"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
multi-train/fever-train-multikilt_1107
|
[
"region:us"
] |
2023-11-10T19:01:45+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "query", "dtype": "string"}, {"name": "pos", "sequence": "string"}, {"name": "neg", "sequence": "string"}, {"name": "task", "dtype": "string"}, {"name": "instruction", "struct": [{"name": "query", "dtype": "string"}, {"name": "pos", "dtype": "string"}, {"name": "neg", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 87617512, "num_examples": 71257}], "download_size": 46276668, "dataset_size": 87617512}}
|
2023-11-10T19:01:53+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "fever-train-multikilt_1107"
More Information needed
|
[
"# Dataset Card for \"fever-train-multikilt_1107\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"fever-train-multikilt_1107\"\n\nMore Information needed"
] |
ace8c3f30f7decd88d9837ed9f3456ee6c268e3a
|
# Dataset Card for "flickr30k_captions_1107"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
multi-train/flickr30k_captions_1107
|
[
"region:us"
] |
2023-11-10T19:01:55+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "query", "dtype": "string"}, {"name": "pos", "sequence": "string"}, {"name": "neg", "sequence": "string"}, {"name": "task", "dtype": "string"}, {"name": "instruction", "struct": [{"name": "query", "dtype": "string"}, {"name": "pos", "dtype": "string"}, {"name": "neg", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 12081988, "num_examples": 31783}], "download_size": 4010622, "dataset_size": 12081988}}
|
2023-11-10T19:01:59+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "flickr30k_captions_1107"
More Information needed
|
[
"# Dataset Card for \"flickr30k_captions_1107\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"flickr30k_captions_1107\"\n\nMore Information needed"
] |
3052617d97b92b25363914238d94821e3f5da321
|
# Dataset Card for "sentence-compression_1107"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
multi-train/sentence-compression_1107
|
[
"region:us"
] |
2023-11-10T19:02:00+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "query", "dtype": "string"}, {"name": "pos", "sequence": "string"}, {"name": "neg", "sequence": "string"}, {"name": "task", "dtype": "string"}, {"name": "instruction", "struct": [{"name": "query", "dtype": "string"}, {"name": "pos", "dtype": "string"}, {"name": "neg", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 71393703, "num_examples": 180000}], "download_size": 36617830, "dataset_size": 71393703}}
|
2023-11-10T19:02:09+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "sentence-compression_1107"
More Information needed
|
[
"# Dataset Card for \"sentence-compression_1107\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"sentence-compression_1107\"\n\nMore Information needed"
] |
34f89344100b90ce9afb3cba9910f560016180da
|
# Dataset Card for "zeroshot-train-multikilt_1107"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
multi-train/zeroshot-train-multikilt_1107
|
[
"region:us"
] |
2023-11-10T19:02:10+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "query", "dtype": "string"}, {"name": "pos", "sequence": "string"}, {"name": "neg", "sequence": "string"}, {"name": "task", "dtype": "string"}, {"name": "instruction", "struct": [{"name": "query", "dtype": "string"}, {"name": "pos", "dtype": "string"}, {"name": "neg", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 156575454, "num_examples": 130514}], "download_size": 74461241, "dataset_size": 156575454}}
|
2023-11-10T19:02:21+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "zeroshot-train-multikilt_1107"
More Information needed
|
[
"# Dataset Card for \"zeroshot-train-multikilt_1107\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"zeroshot-train-multikilt_1107\"\n\nMore Information needed"
] |
dad0ef006ab2aacfb7e83db2749ba90b5d755ad2
|
# Dataset Card for "PAQ_pairs_1107"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
multi-train/PAQ_pairs_1107
|
[
"region:us"
] |
2023-11-10T19:02:23+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "query", "dtype": "string"}, {"name": "pos", "sequence": "string"}, {"name": "neg", "sequence": "string"}, {"name": "task", "dtype": "string"}, {"name": "instruction", "struct": [{"name": "query", "dtype": "string"}, {"name": "pos", "dtype": "string"}, {"name": "neg", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 282748365, "num_examples": 200000}], "download_size": 135658270, "dataset_size": 282748365}}
|
2023-11-10T19:02:41+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "PAQ_pairs_1107"
More Information needed
|
[
"# Dataset Card for \"PAQ_pairs_1107\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"PAQ_pairs_1107\"\n\nMore Information needed"
] |
a6e69354bc5d661cddc0535731db46c6f79d01e5
|
# Dataset Card for "hotpotqa-train-multikilt_1107"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
multi-train/hotpotqa-train-multikilt_1107
|
[
"region:us"
] |
2023-11-10T19:02:43+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "query", "dtype": "string"}, {"name": "pos", "sequence": "string"}, {"name": "neg", "sequence": "string"}, {"name": "task", "dtype": "string"}, {"name": "instruction", "struct": [{"name": "query", "dtype": "string"}, {"name": "pos", "dtype": "string"}, {"name": "neg", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 88502871, "num_examples": 68659}], "download_size": 50639711, "dataset_size": 88502871}}
|
2023-11-10T19:02:52+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "hotpotqa-train-multikilt_1107"
More Information needed
|
[
"# Dataset Card for \"hotpotqa-train-multikilt_1107\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"hotpotqa-train-multikilt_1107\"\n\nMore Information needed"
] |
fa473b59662c17805294042b39fca7f50e4a755d
|
# Dataset Card for "scitldr_1107"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
multi-train/scitldr_1107
|
[
"region:us"
] |
2023-11-10T19:02:54+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "query", "dtype": "string"}, {"name": "pos", "sequence": "string"}, {"name": "neg", "sequence": "string"}, {"name": "task", "dtype": "string"}, {"name": "instruction", "struct": [{"name": "query", "dtype": "string"}, {"name": "pos", "dtype": "string"}, {"name": "neg", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 59829071, "num_examples": 1992}], "download_size": 29628456, "dataset_size": 59829071}}
|
2023-11-10T19:03:01+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "scitldr_1107"
More Information needed
|
[
"# Dataset Card for \"scitldr_1107\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"scitldr_1107\"\n\nMore Information needed"
] |
ff8e382a5011978f5b20a3a49b68004f89d43dac
|
# Dataset Card for "xsum_1107"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
multi-train/xsum_1107
|
[
"region:us"
] |
2023-11-10T19:03:03+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "query", "dtype": "string"}, {"name": "pos", "sequence": "string"}, {"name": "neg", "sequence": "string"}, {"name": "task", "dtype": "string"}, {"name": "instruction", "struct": [{"name": "query", "dtype": "string"}, {"name": "pos", "dtype": "string"}, {"name": "neg", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 848430524, "num_examples": 200000}], "download_size": 523334138, "dataset_size": 848430524}}
|
2023-11-10T19:04:02+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "xsum_1107"
More Information needed
|
[
"# Dataset Card for \"xsum_1107\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"xsum_1107\"\n\nMore Information needed"
] |
3fdb51256450bf96ad1f3364dfb4b55b432ebf3a
|
# Bangumi Image Base of Attack On Titan
This is the image base of bangumi Attack On Titan, we detected 76 characters, 14308 images in total. The full dataset is [here](all.zip).
**Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual.** If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
| # | Images | Download | Preview 1 | Preview 2 | Preview 3 | Preview 4 | Preview 5 | Preview 6 | Preview 7 | Preview 8 |
|:------|---------:|:---------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|
| 0 | 1568 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 705 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 1342 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 1771 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 304 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 735 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 173 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 72 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 50 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 164 | [Download](9/dataset.zip) |  |  |  |  |  |  |  |  |
| 10 | 87 | [Download](10/dataset.zip) |  |  |  |  |  |  |  |  |
| 11 | 32 | [Download](11/dataset.zip) |  |  |  |  |  |  |  |  |
| 12 | 122 | [Download](12/dataset.zip) |  |  |  |  |  |  |  |  |
| 13 | 462 | [Download](13/dataset.zip) |  |  |  |  |  |  |  |  |
| 14 | 141 | [Download](14/dataset.zip) |  |  |  |  |  |  |  |  |
| 15 | 183 | [Download](15/dataset.zip) |  |  |  |  |  |  |  |  |
| 16 | 60 | [Download](16/dataset.zip) |  |  |  |  |  |  |  |  |
| 17 | 52 | [Download](17/dataset.zip) |  |  |  |  |  |  |  |  |
| 18 | 49 | [Download](18/dataset.zip) |  |  |  |  |  |  |  |  |
| 19 | 1082 | [Download](19/dataset.zip) |  |  |  |  |  |  |  |  |
| 20 | 57 | [Download](20/dataset.zip) |  |  |  |  |  |  |  |  |
| 21 | 587 | [Download](21/dataset.zip) |  |  |  |  |  |  |  |  |
| 22 | 224 | [Download](22/dataset.zip) |  |  |  |  |  |  |  |  |
| 23 | 140 | [Download](23/dataset.zip) |  |  |  |  |  |  |  |  |
| 24 | 110 | [Download](24/dataset.zip) |  |  |  |  |  |  |  |  |
| 25 | 26 | [Download](25/dataset.zip) |  |  |  |  |  |  |  |  |
| 26 | 581 | [Download](26/dataset.zip) |  |  |  |  |  |  |  |  |
| 27 | 86 | [Download](27/dataset.zip) |  |  |  |  |  |  |  |  |
| 28 | 60 | [Download](28/dataset.zip) |  |  |  |  |  |  |  |  |
| 29 | 141 | [Download](29/dataset.zip) |  |  |  |  |  |  |  |  |
| 30 | 59 | [Download](30/dataset.zip) |  |  |  |  |  |  |  |  |
| 31 | 534 | [Download](31/dataset.zip) |  |  |  |  |  |  |  |  |
| 32 | 64 | [Download](32/dataset.zip) |  |  |  |  |  |  |  |  |
| 33 | 173 | [Download](33/dataset.zip) |  |  |  |  |  |  |  |  |
| 34 | 22 | [Download](34/dataset.zip) |  |  |  |  |  |  |  |  |
| 35 | 32 | [Download](35/dataset.zip) |  |  |  |  |  |  |  |  |
| 36 | 133 | [Download](36/dataset.zip) |  |  |  |  |  |  |  |  |
| 37 | 230 | [Download](37/dataset.zip) |  |  |  |  |  |  |  |  |
| 38 | 94 | [Download](38/dataset.zip) |  |  |  |  |  |  |  |  |
| 39 | 44 | [Download](39/dataset.zip) |  |  |  |  |  |  |  |  |
| 40 | 46 | [Download](40/dataset.zip) |  |  |  |  |  |  |  |  |
| 41 | 48 | [Download](41/dataset.zip) |  |  |  |  |  |  |  |  |
| 42 | 102 | [Download](42/dataset.zip) |  |  |  |  |  |  |  |  |
| 43 | 36 | [Download](43/dataset.zip) |  |  |  |  |  |  |  |  |
| 44 | 36 | [Download](44/dataset.zip) |  |  |  |  |  |  |  |  |
| 45 | 51 | [Download](45/dataset.zip) |  |  |  |  |  |  |  |  |
| 46 | 51 | [Download](46/dataset.zip) |  |  |  |  |  |  |  |  |
| 47 | 52 | [Download](47/dataset.zip) |  |  |  |  |  |  |  |  |
| 48 | 55 | [Download](48/dataset.zip) |  |  |  |  |  |  |  |  |
| 49 | 51 | [Download](49/dataset.zip) |  |  |  |  |  |  |  |  |
| 50 | 23 | [Download](50/dataset.zip) |  |  |  |  |  |  |  |  |
| 51 | 70 | [Download](51/dataset.zip) |  |  |  |  |  |  |  |  |
| 52 | 66 | [Download](52/dataset.zip) |  |  |  |  |  |  |  |  |
| 53 | 77 | [Download](53/dataset.zip) |  |  |  |  |  |  |  |  |
| 54 | 37 | [Download](54/dataset.zip) |  |  |  |  |  |  |  |  |
| 55 | 20 | [Download](55/dataset.zip) |  |  |  |  |  |  |  |  |
| 56 | 33 | [Download](56/dataset.zip) |  |  |  |  |  |  |  |  |
| 57 | 94 | [Download](57/dataset.zip) |  |  |  |  |  |  |  |  |
| 58 | 25 | [Download](58/dataset.zip) |  |  |  |  |  |  |  |  |
| 59 | 19 | [Download](59/dataset.zip) |  |  |  |  |  |  |  |  |
| 60 | 32 | [Download](60/dataset.zip) |  |  |  |  |  |  |  |  |
| 61 | 31 | [Download](61/dataset.zip) |  |  |  |  |  |  |  |  |
| 62 | 44 | [Download](62/dataset.zip) |  |  |  |  |  |  |  |  |
| 63 | 23 | [Download](63/dataset.zip) |  |  |  |  |  |  |  |  |
| 64 | 59 | [Download](64/dataset.zip) |  |  |  |  |  |  |  |  |
| 65 | 52 | [Download](65/dataset.zip) |  |  |  |  |  |  |  |  |
| 66 | 39 | [Download](66/dataset.zip) |  |  |  |  |  |  |  |  |
| 67 | 58 | [Download](67/dataset.zip) |  |  |  |  |  |  |  |  |
| 68 | 16 | [Download](68/dataset.zip) |  |  |  |  |  |  |  |  |
| 69 | 18 | [Download](69/dataset.zip) |  |  |  |  |  |  |  |  |
| 70 | 26 | [Download](70/dataset.zip) |  |  |  |  |  |  |  |  |
| 71 | 31 | [Download](71/dataset.zip) |  |  |  |  |  |  |  |  |
| 72 | 68 | [Download](72/dataset.zip) |  |  |  |  |  |  |  |  |
| 73 | 7 | [Download](73/dataset.zip) |  |  |  |  |  |  |  | N/A |
| 74 | 10 | [Download](74/dataset.zip) |  |  |  |  |  |  |  |  |
| noise | 251 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
|
BangumiBase/attackontitan
|
[
"size_categories:10K<n<100K",
"license:mit",
"art",
"region:us"
] |
2023-11-10T19:22:22+00:00
|
{"license": "mit", "size_categories": ["10K<n<100K"], "tags": ["art"]}
|
2023-11-11T11:40:07+00:00
|
[] |
[] |
TAGS
#size_categories-10K<n<100K #license-mit #art #region-us
|
Bangumi Image Base of Attack On Titan
=====================================
This is the image base of bangumi Attack On Titan, we detected 76 characters, 14308 images in total. The full dataset is here.
Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual. If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
|
[] |
[
"TAGS\n#size_categories-10K<n<100K #license-mit #art #region-us \n"
] |
99c28f95f22c556bafacd22646ee698a4a5d5443
|
# Dataset Card for "cowteats1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
lakshmikarpolam/cowteats1
|
[
"region:us"
] |
2023-11-10T19:26:35+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 12535407.0, "num_examples": 450}], "download_size": 12527161, "dataset_size": 12535407.0}}
|
2023-11-10T19:26:50+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "cowteats1"
More Information needed
|
[
"# Dataset Card for \"cowteats1\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"cowteats1\"\n\nMore Information needed"
] |
26e2cc32d79ad8a17f1d0ed6ce152d511da43de7
|
# Dataset Card for "translating_mplm_dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
NyxSlee/translating_mplm_dataset
|
[
"region:us"
] |
2023-11-10T20:27:18+00:00
|
{"dataset_info": {"features": [{"name": "number", "dtype": "string"}, {"name": "sentence", "dtype": "string"}, {"name": "word_translations", "struct": [{"name": "\u4e00\u4e2a (y\u012b g\u00e8)", "dtype": "string"}, {"name": "\u4e00\u5c0a (y\u012b z\u016bn)", "dtype": "string"}, {"name": "\u4e0b\u6765 (xi\u00e0 l\u00e1i)", "dtype": "string"}, {"name": "\u4eff\u4f5b (f\u01ceng f\u00fa)", "dtype": "string"}, {"name": "\u4f1a (hu\u00ec)", "dtype": "string"}, {"name": "\u51dd\u56fa (n\u00edng g\u00f9)", "dtype": "string"}, {"name": "\u52a8\u8fc7 (d\u00f2ng gu\u00f2)", "dtype": "string"}, {"name": "\u53ea\u4f59 (zh\u01d0 y\u00fa)", "dtype": "string"}, {"name": "\u5750\u5728 (zu\u00f2 z\u00e0i)", "dtype": "string"}, {"name": "\u5929\u8272 (Ti\u0101n s\u00e8)", "dtype": "string"}, {"name": "\u5b8c\u5168 (w\u00e1n qu\u00e1n)", "dtype": "string"}, {"name": "\u5c4b\u5185 (w\u016b n\u00e8i)", "dtype": "string"}, {"name": "\u5e8a\u8fb9 (chu\u00e1ng bi\u0101n)", "dtype": "string"}, {"name": "\u6367\u7740 (p\u011bng zhe)", "dtype": "string"}, {"name": "\u653e\u5728 (f\u00e0ng z\u00e0i)", "dtype": "string"}, {"name": "\u662f (sh\u00ec)", "dtype": "string"}, {"name": "\u6697\u4e86 (\u00e0n le)", "dtype": "string"}, {"name": "\u66ae\u8272 (m\u00f9 s\u00e8)", "dtype": "string"}, {"name": "\u6ca1\u6709 (m\u00e9i y\u01d2u)", "dtype": "string"}, {"name": "\u6d45\u6d45 (qi\u01cen qi\u01cen)", "dtype": "string"}, {"name": "\u71c3\u70db (r\u00e1n zh\u00fa)", "dtype": "string"}, {"name": "\u7684 (de)", "dtype": "string"}, {"name": "\u7cd5\u70b9 (g\u0101o di\u01cen)", "dtype": "string"}, {"name": "\u8bb8\u4e45 (x\u01d4 ji\u01d4)", "dtype": "string"}, {"name": "\u8c01 (shu\u00ed)", "dtype": "string"}, {"name": "\u8eab\u5f71 (sh\u0113n y\u01d0ng)", "dtype": "string"}, {"name": "\u8f6e\u5ed3 (l\u00fan ku\u00f2)", "dtype": "string"}, {"name": "\u8fd9\u513f (zh\u00e8r)", "dtype": "string"}, {"name": "\u9010\u6e10 (zh\u00fa ji\u00e0n)", "dtype": "string"}, {"name": "\u90fd\u6ca1\u6709 (d\u014du m\u00e9i y\u01d2u)", "dtype": "string"}, {"name": "\u961a\u95fb\u8427 (K\u00e0n w\u00e9n xi\u0101o)", "dtype": "string"}, {"name": "\u9690\u6ca1 (y\u01d0n m\u00f2)", "dtype": "string"}, {"name": "\u9ed1\u6f06\u6f06\u7684 (h\u0113i q\u012b q\u012b de)", "dtype": "string"}]}, {"name": "best_translation", "dtype": "string"}, {"name": "alternative_translations", "sequence": "string"}], "splits": [{"name": "train", "num_bytes": 3429, "num_examples": 3}], "download_size": 0, "dataset_size": 3429}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-11-10T21:11:55+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "translating_mplm_dataset"
More Information needed
|
[
"# Dataset Card for \"translating_mplm_dataset\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"translating_mplm_dataset\"\n\nMore Information needed"
] |
981d253718cfbb3ae769536c360e7c527f4e8859
|
# Dataset Card for "reuters_articles"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Geosb/reuters_articles
|
[
"region:us"
] |
2023-11-10T20:30:22+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "validation", "path": "data/validation-*"}, {"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "title", "dtype": "string"}, {"name": "body", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 13792576, "num_examples": 17262}, {"name": "validation", "num_bytes": 1870389, "num_examples": 2158}, {"name": "test", "num_bytes": 1379190, "num_examples": 2158}], "download_size": 10073411, "dataset_size": 17042155}}
|
2023-11-10T20:30:30+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "reuters_articles"
More Information needed
|
[
"# Dataset Card for \"reuters_articles\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"reuters_articles\"\n\nMore Information needed"
] |
df580e400c4feed7d6fe6343540ac2d518ef1fc3
|
# Dataset Card for "BC5CDR_train"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
hippocrates/BC5CDR_train
|
[
"region:us"
] |
2023-11-10T20:51:30+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "conversations", "list": [{"name": "from", "dtype": "string"}, {"name": "value", "dtype": "string"}]}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 4158911, "num_examples": 5228}, {"name": "valid", "num_bytes": 4200415, "num_examples": 5330}, {"name": "test", "num_bytes": 4539438, "num_examples": 5865}], "download_size": 3693403, "dataset_size": 12898764}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "valid", "path": "data/valid-*"}, {"split": "test", "path": "data/test-*"}]}]}
|
2023-11-13T19:42:42+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "BC5CDR_train"
More Information needed
|
[
"# Dataset Card for \"BC5CDR_train\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"BC5CDR_train\"\n\nMore Information needed"
] |
0196f8acf1fd474d677e230bcf860f1ada8c8097
|
# Dataset Card for "BC5CDR_test"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
hippocrates/BC5CDR_test
|
[
"region:us"
] |
2023-11-10T20:55:46+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "query", "dtype": "string"}, {"name": "answer", "dtype": "string"}, {"name": "label", "sequence": "string"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 3858609, "num_examples": 5228}, {"name": "valid", "num_bytes": 3872474, "num_examples": 5330}, {"name": "test", "num_bytes": 4139915, "num_examples": 5865}], "download_size": 3147345, "dataset_size": 11870998}}
|
2023-11-10T20:55:49+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "BC5CDR_test"
More Information needed
|
[
"# Dataset Card for \"BC5CDR_test\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"BC5CDR_test\"\n\nMore Information needed"
] |
705cbadd4419988626f73e54c4fda2b5fd870e1d
|
# Dataset Card for "codesearchnet_1107"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
multi-train/codesearchnet_1107
|
[
"region:us"
] |
2023-11-10T21:10:52+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "query", "dtype": "string"}, {"name": "pos", "sequence": "string"}, {"name": "neg", "sequence": "string"}, {"name": "task", "dtype": "string"}, {"name": "instruction", "struct": [{"name": "query", "dtype": "string"}, {"name": "pos", "dtype": "string"}, {"name": "neg", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 2207111297, "num_examples": 1000000}], "download_size": 552466752, "dataset_size": 2207111297}}
|
2023-11-10T21:11:59+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "codesearchnet_1107"
More Information needed
|
[
"# Dataset Card for \"codesearchnet_1107\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"codesearchnet_1107\"\n\nMore Information needed"
] |
b9919ada11a2f47c8b5fe955853d1bba2174fd81
|
# Dataset Card for "translating_mplm_dataset_two"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
NyxSlee/translating_mplm_dataset_two
|
[
"region:us"
] |
2023-11-10T21:12:27+00:00
|
{"dataset_info": {"features": [{"name": "number", "dtype": "string"}, {"name": "sentence", "dtype": "string"}, {"name": "word_translations", "struct": [{"name": "\u4e00\u4e2a (y\u012b g\u00e8)", "dtype": "string"}, {"name": "\u4e00\u5c0a (y\u012b z\u016bn)", "dtype": "string"}, {"name": "\u4e0b\u6765 (xi\u00e0 l\u00e1i)", "dtype": "string"}, {"name": "\u4eff\u4f5b (f\u01ceng f\u00fa)", "dtype": "string"}, {"name": "\u4f1a (hu\u00ec)", "dtype": "string"}, {"name": "\u51dd\u56fa (n\u00edng g\u00f9)", "dtype": "string"}, {"name": "\u52a8\u8fc7 (d\u00f2ng gu\u00f2)", "dtype": "string"}, {"name": "\u53ea\u4f59 (zh\u01d0 y\u00fa)", "dtype": "string"}, {"name": "\u5750\u5728 (zu\u00f2 z\u00e0i)", "dtype": "string"}, {"name": "\u5929\u8272 (Ti\u0101n s\u00e8)", "dtype": "string"}, {"name": "\u5b8c\u5168 (w\u00e1n qu\u00e1n)", "dtype": "string"}, {"name": "\u5c4b\u5185 (w\u016b n\u00e8i)", "dtype": "string"}, {"name": "\u5e8a\u8fb9 (chu\u00e1ng bi\u0101n)", "dtype": "string"}, {"name": "\u6367\u7740 (p\u011bng zhe)", "dtype": "string"}, {"name": "\u653e\u5728 (f\u00e0ng z\u00e0i)", "dtype": "string"}, {"name": "\u662f (sh\u00ec)", "dtype": "string"}, {"name": "\u6697\u4e86 (\u00e0n le)", "dtype": "string"}, {"name": "\u66ae\u8272 (m\u00f9 s\u00e8)", "dtype": "string"}, {"name": "\u6ca1\u6709 (m\u00e9i y\u01d2u)", "dtype": "string"}, {"name": "\u6d45\u6d45 (qi\u01cen qi\u01cen)", "dtype": "string"}, {"name": "\u71c3\u70db (r\u00e1n zh\u00fa)", "dtype": "string"}, {"name": "\u7684 (de)", "dtype": "string"}, {"name": "\u7cd5\u70b9 (g\u0101o di\u01cen)", "dtype": "string"}, {"name": "\u8bb8\u4e45 (x\u01d4 ji\u01d4)", "dtype": "string"}, {"name": "\u8c01 (shu\u00ed)", "dtype": "string"}, {"name": "\u8eab\u5f71 (sh\u0113n y\u01d0ng)", "dtype": "string"}, {"name": "\u8f6e\u5ed3 (l\u00fan ku\u00f2)", "dtype": "string"}, {"name": "\u8fd9\u513f (zh\u00e8r)", "dtype": "string"}, {"name": "\u9010\u6e10 (zh\u00fa ji\u00e0n)", "dtype": "string"}, {"name": "\u90fd\u6ca1\u6709 (d\u014du m\u00e9i y\u01d2u)", "dtype": "string"}, {"name": "\u961a\u95fb\u8427 (K\u00e0n w\u00e9n xi\u0101o)", "dtype": "string"}, {"name": "\u9690\u6ca1 (y\u01d0n m\u00f2)", "dtype": "string"}, {"name": "\u9ed1\u6f06\u6f06\u7684 (h\u0113i q\u012b q\u012b de)", "dtype": "string"}]}, {"name": "best_translation", "dtype": "string"}, {"name": "alternative_translations", "sequence": "string"}], "splits": [{"name": "train", "num_bytes": 3429, "num_examples": 3}], "download_size": 27294, "dataset_size": 3429}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-11-10T21:12:28+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "translating_mplm_dataset_two"
More Information needed
|
[
"# Dataset Card for \"translating_mplm_dataset_two\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"translating_mplm_dataset_two\"\n\nMore Information needed"
] |
8060519758d09db19f2ad740953b36de18bd8f30
|
# Dataset Card for "translating_mplm_dataset_thre"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
NyxSlee/translating_mplm_dataset_thre
|
[
"region:us"
] |
2023-11-10T21:13:26+00:00
|
{"dataset_info": {"features": [{"name": "number", "dtype": "string"}, {"name": "sentence", "dtype": "string"}, {"name": "word_translations", "struct": [{"name": "\u4e00\u4e2a (y\u012b g\u00e8)", "dtype": "string"}, {"name": "\u4e00\u5c0a (y\u012b z\u016bn)", "dtype": "string"}, {"name": "\u4e0b\u6765 (xi\u00e0 l\u00e1i)", "dtype": "string"}, {"name": "\u4eff\u4f5b (f\u01ceng f\u00fa)", "dtype": "string"}, {"name": "\u4f1a (hu\u00ec)", "dtype": "string"}, {"name": "\u51dd\u56fa (n\u00edng g\u00f9)", "dtype": "string"}, {"name": "\u52a8\u8fc7 (d\u00f2ng gu\u00f2)", "dtype": "string"}, {"name": "\u53ea\u4f59 (zh\u01d0 y\u00fa)", "dtype": "string"}, {"name": "\u5750\u5728 (zu\u00f2 z\u00e0i)", "dtype": "string"}, {"name": "\u5929\u8272 (Ti\u0101n s\u00e8)", "dtype": "string"}, {"name": "\u5b8c\u5168 (w\u00e1n qu\u00e1n)", "dtype": "string"}, {"name": "\u5c4b\u5185 (w\u016b n\u00e8i)", "dtype": "string"}, {"name": "\u5e8a\u8fb9 (chu\u00e1ng bi\u0101n)", "dtype": "string"}, {"name": "\u6367\u7740 (p\u011bng zhe)", "dtype": "string"}, {"name": "\u653e\u5728 (f\u00e0ng z\u00e0i)", "dtype": "string"}, {"name": "\u662f (sh\u00ec)", "dtype": "string"}, {"name": "\u6697\u4e86 (\u00e0n le)", "dtype": "string"}, {"name": "\u66ae\u8272 (m\u00f9 s\u00e8)", "dtype": "string"}, {"name": "\u6ca1\u6709 (m\u00e9i y\u01d2u)", "dtype": "string"}, {"name": "\u6d45\u6d45 (qi\u01cen qi\u01cen)", "dtype": "string"}, {"name": "\u71c3\u70db (r\u00e1n zh\u00fa)", "dtype": "string"}, {"name": "\u7684 (de)", "dtype": "string"}, {"name": "\u7cd5\u70b9 (g\u0101o di\u01cen)", "dtype": "string"}, {"name": "\u8bb8\u4e45 (x\u01d4 ji\u01d4)", "dtype": "string"}, {"name": "\u8c01 (shu\u00ed)", "dtype": "string"}, {"name": "\u8eab\u5f71 (sh\u0113n y\u01d0ng)", "dtype": "string"}, {"name": "\u8f6e\u5ed3 (l\u00fan ku\u00f2)", "dtype": "string"}, {"name": "\u8fd9\u513f (zh\u00e8r)", "dtype": "string"}, {"name": "\u9010\u6e10 (zh\u00fa ji\u00e0n)", "dtype": "string"}, {"name": "\u90fd\u6ca1\u6709 (d\u014du m\u00e9i y\u01d2u)", "dtype": "string"}, {"name": "\u961a\u95fb\u8427 (K\u00e0n w\u00e9n xi\u0101o)", "dtype": "string"}, {"name": "\u9690\u6ca1 (y\u01d0n m\u00f2)", "dtype": "string"}, {"name": "\u9ed1\u6f06\u6f06\u7684 (h\u0113i q\u012b q\u012b de)", "dtype": "string"}]}, {"name": "best_translation", "dtype": "string"}, {"name": "alternative_translations", "sequence": "string"}], "splits": [{"name": "train", "num_bytes": 3429, "num_examples": 3}], "download_size": 27294, "dataset_size": 3429}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-11-10T21:13:27+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "translating_mplm_dataset_thre"
More Information needed
|
[
"# Dataset Card for \"translating_mplm_dataset_thre\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"translating_mplm_dataset_thre\"\n\nMore Information needed"
] |
55caf9af01ae498d68ff44886ff69a4b165eb54b
|
# Phishing Dataset
Phishing datasets compiled from various resources for classification and phishing detection tasks.
## Dataset Details
All datasets have been preprocessed in terms of eliminating null, empty and duplicate data. Class balancing has also been performed to avoid possible biases.
Datasets have the same structure of two columns: `text` and `label`. Text field can contain samples of:
- URL
- SMS messages
- Email messages
- HTML code
Depending on the dataset it belongs to; if it is the combined dataset it will have all data types. In addition, all records are labeled as **1 (Phishing)** or **0 (Benign)**.
### Source Data
Datasets correspond to a compilation of 4 sources, which are described below:
- [Mail dataset](https://www.kaggle.com/datasets/subhajournal/phishingemails) that specifies the body text of various emails that can be used to detect phishing emails,
through extensive text analysis and classification with machine learning. Contains over 18,000 emails
generated by Enron Corporation employees.
- [SMS message dataset](https://data.mendeley.com/datasets/f45bkkt8pr/1) of more than 5,971 text messages. It includes 489 Spam messages, 638 Smishing messages
and 4,844 Ham messages. The dataset contains attributes extracted from malicious messages that can be used
to classify messages as malicious or legitimate. The data was collected by converting images obtained from
the Internet into text using Python code.
- [URL dataset](https://www.kaggle.com/datasets/harisudhan411/phishing-and-legitimate-urls) with more than 800,000 URLs where 52% of the domains are legitimate and the remaining 47% are
phishing domains. It is a collection of data samples from various sources, the URLs were collected from the
JPCERT website, existing Kaggle datasets, Github repositories where the URLs are updated once a year and
some open source databases, including Excel files.
- [Website dataset](https://data.mendeley.com/datasets/n96ncsr5g4/1) of 80,000 instances of legitimate websites (50,000) and phishing websites (30,000). Each
instance contains the URL and the HTML page. Legitimate data were collected from two sources: 1) A simple
keyword search on the Google search engine was used and the first 5 URLs of each search were collected.
Domain restrictions were used and a maximum of 10 collections from one domain was limited to have a diverse
collection at the end. 2) Almost 25,874 active URLs were collected from the Ebbu2017 Phishing Dataset
repository. Three sources were used for the phishing data: PhishTank, OpenPhish and PhishRepo.
> It is worth mentioning that, in the case of the website dataset, it was unfeasible to bring the total 80,000 samples due to the heavy processing required.
> It was limited to search the first 30,000 samples, of which only those with a weight of less than 100KB were used. This will make it easier to use the website dataset if you do not
> have powerful resources.
### Combined dataset
The combined dataset is the one used to train BERT in phishing detection. But, in this repository you can notice that there are
two datasets named as **combined**:
- combined full
- combined reduced
Combined datasets owe their name to the fact that they combine all the data sources mentioned in the previous section. However, there is a notable difference between them:
- The full combined dataset contains the 800,000+ URLs of the URL dataset.
- The reduced combined dataset reduces the URL samples by 95% in order to keep a more balanced combination of data.
Why was that elimination made in the reduced combined dataset? Completely unifying all URL samples would make URLs 97% of the total, and emails, SMS and websites just 3%.
Missing data types from specific populations could bias the model and not reflect the realities of the environment in which it is run. There would be no representativeness
for the other data types and the model could ignore them. In fact, a test performed on the combined full dataset showed deplorable results in phishing classification with BERT.
Therefore it is recommended to use the reduced combined dataset. The combined full dataset was added for experimentation only.
#### Processing combined reduced dataset
Primarily, this dataset is intended to be used in conjunction with the BERT language model. Therefore, it has
not been subjected to traditional preprocessing that is usually done for NLP tasks, such as Text Classification.
_You may be wondering, is stemming, lemmatization, stop word removal, etc., necessary to improve the performance of BERT?_
In general, **NO**. Preprocessing will not change the output predictions. In fact, removing empty words (which
are considered noise in conventional text representation, such as bag-of-words or tf-idf) can and probably will
worsen the predictions of your BERT model. Since BERT uses the self-attenuation mechanism, these "stop words"
are valuable information for BERT. The same goes for punctuation: a question mark can certainly change the
overall meaning of a sentence. Therefore, eliminating stop words and punctuation marks would only mean
eliminating the context that BERT could have used to get better results.
However, if this dataset plans to be used for another type of model, perhaps preprocessing for NLP tasks should
be considered. That is at the discretion of whoever wishes to employ this dataset.
For more information check these links:
- https://stackoverflow.com/a/70700145
- https://datascience.stackexchange.com/a/113366
### How to use them
You can easily use any of these datasets by specifying its name in the following code configuration:
```python
from datasets import load_dataset
dataset = load_dataset("ealvaradob/phishing-dataset", "<desired_dataset>", trust_remote_code=True)
```
For example, if you want to load combined reduced dataset, you can use:
```python
dataset = load_dataset("ealvaradob/phishing-dataset", "combined_reduced", trust_remote_code=True)
```
Due to the implementation of the datasets library, when executing these codes you will see that only a training split is generated.
The entire downloaded dataset will be inside that split. But if you want to separate it into test and training sets, you could run this code:
```python
from datasets import Dataset
from sklearn.model_selection import train_test_split
df = dataset['train'].to_pandas()
train, test = train_test_split(df, test_size=0.2, random_state=42)
train, test = Dataset.from_pandas(train, preserve_index=False), Dataset.from_pandas(test, preserve_index=False)
```
|
ealvaradob/phishing-dataset
|
[
"task_categories:text-classification",
"size_categories:10K<n<100K",
"language:en",
"license:apache-2.0",
"phishing",
"url",
"html",
"text",
"region:us"
] |
2023-11-10T21:15:02+00:00
|
{"language": ["en"], "license": "apache-2.0", "size_categories": ["10K<n<100K"], "task_categories": ["text-classification"], "tags": ["phishing", "url", "html", "text"]}
|
2024-01-31T07:00:53+00:00
|
[] |
[
"en"
] |
TAGS
#task_categories-text-classification #size_categories-10K<n<100K #language-English #license-apache-2.0 #phishing #url #html #text #region-us
|
# Phishing Dataset
Phishing datasets compiled from various resources for classification and phishing detection tasks.
## Dataset Details
All datasets have been preprocessed in terms of eliminating null, empty and duplicate data. Class balancing has also been performed to avoid possible biases.
Datasets have the same structure of two columns: 'text' and 'label'. Text field can contain samples of:
- URL
- SMS messages
- Email messages
- HTML code
Depending on the dataset it belongs to; if it is the combined dataset it will have all data types. In addition, all records are labeled as 1 (Phishing) or 0 (Benign).
### Source Data
Datasets correspond to a compilation of 4 sources, which are described below:
- Mail dataset that specifies the body text of various emails that can be used to detect phishing emails,
through extensive text analysis and classification with machine learning. Contains over 18,000 emails
generated by Enron Corporation employees.
- SMS message dataset of more than 5,971 text messages. It includes 489 Spam messages, 638 Smishing messages
and 4,844 Ham messages. The dataset contains attributes extracted from malicious messages that can be used
to classify messages as malicious or legitimate. The data was collected by converting images obtained from
the Internet into text using Python code.
- URL dataset with more than 800,000 URLs where 52% of the domains are legitimate and the remaining 47% are
phishing domains. It is a collection of data samples from various sources, the URLs were collected from the
JPCERT website, existing Kaggle datasets, Github repositories where the URLs are updated once a year and
some open source databases, including Excel files.
- Website dataset of 80,000 instances of legitimate websites (50,000) and phishing websites (30,000). Each
instance contains the URL and the HTML page. Legitimate data were collected from two sources: 1) A simple
keyword search on the Google search engine was used and the first 5 URLs of each search were collected.
Domain restrictions were used and a maximum of 10 collections from one domain was limited to have a diverse
collection at the end. 2) Almost 25,874 active URLs were collected from the Ebbu2017 Phishing Dataset
repository. Three sources were used for the phishing data: PhishTank, OpenPhish and PhishRepo.
> It is worth mentioning that, in the case of the website dataset, it was unfeasible to bring the total 80,000 samples due to the heavy processing required.
> It was limited to search the first 30,000 samples, of which only those with a weight of less than 100KB were used. This will make it easier to use the website dataset if you do not
> have powerful resources.
### Combined dataset
The combined dataset is the one used to train BERT in phishing detection. But, in this repository you can notice that there are
two datasets named as combined:
- combined full
- combined reduced
Combined datasets owe their name to the fact that they combine all the data sources mentioned in the previous section. However, there is a notable difference between them:
- The full combined dataset contains the 800,000+ URLs of the URL dataset.
- The reduced combined dataset reduces the URL samples by 95% in order to keep a more balanced combination of data.
Why was that elimination made in the reduced combined dataset? Completely unifying all URL samples would make URLs 97% of the total, and emails, SMS and websites just 3%.
Missing data types from specific populations could bias the model and not reflect the realities of the environment in which it is run. There would be no representativeness
for the other data types and the model could ignore them. In fact, a test performed on the combined full dataset showed deplorable results in phishing classification with BERT.
Therefore it is recommended to use the reduced combined dataset. The combined full dataset was added for experimentation only.
#### Processing combined reduced dataset
Primarily, this dataset is intended to be used in conjunction with the BERT language model. Therefore, it has
not been subjected to traditional preprocessing that is usually done for NLP tasks, such as Text Classification.
_You may be wondering, is stemming, lemmatization, stop word removal, etc., necessary to improve the performance of BERT?_
In general, NO. Preprocessing will not change the output predictions. In fact, removing empty words (which
are considered noise in conventional text representation, such as bag-of-words or tf-idf) can and probably will
worsen the predictions of your BERT model. Since BERT uses the self-attenuation mechanism, these "stop words"
are valuable information for BERT. The same goes for punctuation: a question mark can certainly change the
overall meaning of a sentence. Therefore, eliminating stop words and punctuation marks would only mean
eliminating the context that BERT could have used to get better results.
However, if this dataset plans to be used for another type of model, perhaps preprocessing for NLP tasks should
be considered. That is at the discretion of whoever wishes to employ this dataset.
For more information check these links:
- URL
- URL
### How to use them
You can easily use any of these datasets by specifying its name in the following code configuration:
For example, if you want to load combined reduced dataset, you can use:
Due to the implementation of the datasets library, when executing these codes you will see that only a training split is generated.
The entire downloaded dataset will be inside that split. But if you want to separate it into test and training sets, you could run this code:
|
[
"# Phishing Dataset\n\nPhishing datasets compiled from various resources for classification and phishing detection tasks.",
"## Dataset Details\n\nAll datasets have been preprocessed in terms of eliminating null, empty and duplicate data. Class balancing has also been performed to avoid possible biases. \nDatasets have the same structure of two columns: 'text' and 'label'. Text field can contain samples of:\n\n- URL\n- SMS messages\n- Email messages\n- HTML code\n\nDepending on the dataset it belongs to; if it is the combined dataset it will have all data types. In addition, all records are labeled as 1 (Phishing) or 0 (Benign).",
"### Source Data\n\nDatasets correspond to a compilation of 4 sources, which are described below:\n\n- Mail dataset that specifies the body text of various emails that can be used to detect phishing emails,\n through extensive text analysis and classification with machine learning. Contains over 18,000 emails\n generated by Enron Corporation employees.\n \n- SMS message dataset of more than 5,971 text messages. It includes 489 Spam messages, 638 Smishing messages\n and 4,844 Ham messages. The dataset contains attributes extracted from malicious messages that can be used\n to classify messages as malicious or legitimate. The data was collected by converting images obtained from\n the Internet into text using Python code.\n\n- URL dataset with more than 800,000 URLs where 52% of the domains are legitimate and the remaining 47% are\n phishing domains. It is a collection of data samples from various sources, the URLs were collected from the\n JPCERT website, existing Kaggle datasets, Github repositories where the URLs are updated once a year and\n some open source databases, including Excel files.\n\n- Website dataset of 80,000 instances of legitimate websites (50,000) and phishing websites (30,000). Each\n instance contains the URL and the HTML page. Legitimate data were collected from two sources: 1) A simple\n keyword search on the Google search engine was used and the first 5 URLs of each search were collected.\n Domain restrictions were used and a maximum of 10 collections from one domain was limited to have a diverse\n collection at the end. 2) Almost 25,874 active URLs were collected from the Ebbu2017 Phishing Dataset\n repository. Three sources were used for the phishing data: PhishTank, OpenPhish and PhishRepo.\n\n> It is worth mentioning that, in the case of the website dataset, it was unfeasible to bring the total 80,000 samples due to the heavy processing required.\n> It was limited to search the first 30,000 samples, of which only those with a weight of less than 100KB were used. This will make it easier to use the website dataset if you do not\n> have powerful resources.",
"### Combined dataset\n\nThe combined dataset is the one used to train BERT in phishing detection. But, in this repository you can notice that there are \ntwo datasets named as combined:\n\n- combined full\n- combined reduced\n\nCombined datasets owe their name to the fact that they combine all the data sources mentioned in the previous section. However, there is a notable difference between them:\n\n- The full combined dataset contains the 800,000+ URLs of the URL dataset.\n- The reduced combined dataset reduces the URL samples by 95% in order to keep a more balanced combination of data.\n\nWhy was that elimination made in the reduced combined dataset? Completely unifying all URL samples would make URLs 97% of the total, and emails, SMS and websites just 3%. \nMissing data types from specific populations could bias the model and not reflect the realities of the environment in which it is run. There would be no representativeness \nfor the other data types and the model could ignore them. In fact, a test performed on the combined full dataset showed deplorable results in phishing classification with BERT. \nTherefore it is recommended to use the reduced combined dataset. The combined full dataset was added for experimentation only.",
"#### Processing combined reduced dataset\n\nPrimarily, this dataset is intended to be used in conjunction with the BERT language model. Therefore, it has \nnot been subjected to traditional preprocessing that is usually done for NLP tasks, such as Text Classification. \n\n_You may be wondering, is stemming, lemmatization, stop word removal, etc., necessary to improve the performance of BERT?_\n\nIn general, NO. Preprocessing will not change the output predictions. In fact, removing empty words (which \nare considered noise in conventional text representation, such as bag-of-words or tf-idf) can and probably will\nworsen the predictions of your BERT model. Since BERT uses the self-attenuation mechanism, these \"stop words\" \nare valuable information for BERT. The same goes for punctuation: a question mark can certainly change the \noverall meaning of a sentence. Therefore, eliminating stop words and punctuation marks would only mean \neliminating the context that BERT could have used to get better results.\n\nHowever, if this dataset plans to be used for another type of model, perhaps preprocessing for NLP tasks should \nbe considered. That is at the discretion of whoever wishes to employ this dataset. \n\nFor more information check these links:\n\n- URL\n- URL",
"### How to use them\n\nYou can easily use any of these datasets by specifying its name in the following code configuration:\n\n\n\nFor example, if you want to load combined reduced dataset, you can use:\n\n\n\nDue to the implementation of the datasets library, when executing these codes you will see that only a training split is generated. \nThe entire downloaded dataset will be inside that split. But if you want to separate it into test and training sets, you could run this code:"
] |
[
"TAGS\n#task_categories-text-classification #size_categories-10K<n<100K #language-English #license-apache-2.0 #phishing #url #html #text #region-us \n",
"# Phishing Dataset\n\nPhishing datasets compiled from various resources for classification and phishing detection tasks.",
"## Dataset Details\n\nAll datasets have been preprocessed in terms of eliminating null, empty and duplicate data. Class balancing has also been performed to avoid possible biases. \nDatasets have the same structure of two columns: 'text' and 'label'. Text field can contain samples of:\n\n- URL\n- SMS messages\n- Email messages\n- HTML code\n\nDepending on the dataset it belongs to; if it is the combined dataset it will have all data types. In addition, all records are labeled as 1 (Phishing) or 0 (Benign).",
"### Source Data\n\nDatasets correspond to a compilation of 4 sources, which are described below:\n\n- Mail dataset that specifies the body text of various emails that can be used to detect phishing emails,\n through extensive text analysis and classification with machine learning. Contains over 18,000 emails\n generated by Enron Corporation employees.\n \n- SMS message dataset of more than 5,971 text messages. It includes 489 Spam messages, 638 Smishing messages\n and 4,844 Ham messages. The dataset contains attributes extracted from malicious messages that can be used\n to classify messages as malicious or legitimate. The data was collected by converting images obtained from\n the Internet into text using Python code.\n\n- URL dataset with more than 800,000 URLs where 52% of the domains are legitimate and the remaining 47% are\n phishing domains. It is a collection of data samples from various sources, the URLs were collected from the\n JPCERT website, existing Kaggle datasets, Github repositories where the URLs are updated once a year and\n some open source databases, including Excel files.\n\n- Website dataset of 80,000 instances of legitimate websites (50,000) and phishing websites (30,000). Each\n instance contains the URL and the HTML page. Legitimate data were collected from two sources: 1) A simple\n keyword search on the Google search engine was used and the first 5 URLs of each search were collected.\n Domain restrictions were used and a maximum of 10 collections from one domain was limited to have a diverse\n collection at the end. 2) Almost 25,874 active URLs were collected from the Ebbu2017 Phishing Dataset\n repository. Three sources were used for the phishing data: PhishTank, OpenPhish and PhishRepo.\n\n> It is worth mentioning that, in the case of the website dataset, it was unfeasible to bring the total 80,000 samples due to the heavy processing required.\n> It was limited to search the first 30,000 samples, of which only those with a weight of less than 100KB were used. This will make it easier to use the website dataset if you do not\n> have powerful resources.",
"### Combined dataset\n\nThe combined dataset is the one used to train BERT in phishing detection. But, in this repository you can notice that there are \ntwo datasets named as combined:\n\n- combined full\n- combined reduced\n\nCombined datasets owe their name to the fact that they combine all the data sources mentioned in the previous section. However, there is a notable difference between them:\n\n- The full combined dataset contains the 800,000+ URLs of the URL dataset.\n- The reduced combined dataset reduces the URL samples by 95% in order to keep a more balanced combination of data.\n\nWhy was that elimination made in the reduced combined dataset? Completely unifying all URL samples would make URLs 97% of the total, and emails, SMS and websites just 3%. \nMissing data types from specific populations could bias the model and not reflect the realities of the environment in which it is run. There would be no representativeness \nfor the other data types and the model could ignore them. In fact, a test performed on the combined full dataset showed deplorable results in phishing classification with BERT. \nTherefore it is recommended to use the reduced combined dataset. The combined full dataset was added for experimentation only.",
"#### Processing combined reduced dataset\n\nPrimarily, this dataset is intended to be used in conjunction with the BERT language model. Therefore, it has \nnot been subjected to traditional preprocessing that is usually done for NLP tasks, such as Text Classification. \n\n_You may be wondering, is stemming, lemmatization, stop word removal, etc., necessary to improve the performance of BERT?_\n\nIn general, NO. Preprocessing will not change the output predictions. In fact, removing empty words (which \nare considered noise in conventional text representation, such as bag-of-words or tf-idf) can and probably will\nworsen the predictions of your BERT model. Since BERT uses the self-attenuation mechanism, these \"stop words\" \nare valuable information for BERT. The same goes for punctuation: a question mark can certainly change the \noverall meaning of a sentence. Therefore, eliminating stop words and punctuation marks would only mean \neliminating the context that BERT could have used to get better results.\n\nHowever, if this dataset plans to be used for another type of model, perhaps preprocessing for NLP tasks should \nbe considered. That is at the discretion of whoever wishes to employ this dataset. \n\nFor more information check these links:\n\n- URL\n- URL",
"### How to use them\n\nYou can easily use any of these datasets by specifying its name in the following code configuration:\n\n\n\nFor example, if you want to load combined reduced dataset, you can use:\n\n\n\nDue to the implementation of the datasets library, when executing these codes you will see that only a training split is generated. \nThe entire downloaded dataset will be inside that split. But if you want to separate it into test and training sets, you could run this code:"
] |
a5b7f10f1629f7e3d20654f70b59f770817e451d
|
# Rest of your dataset card
dataset_info:
features:
- name: number
dtype: string
- name: sentence
dtype: string
- name: word_translations
struct:
- name: 一个 (yī gè)
dtype: string
- name: 一尊 (yī zūn)
dtype: string
- name: 下来 (xià lái)
dtype: string
- name: 仿佛 (fǎng fú)
dtype: string
- name: 会 (huì)
dtype: string
- name: 凝固 (níng gù)
dtype: string
- name: 动过 (dòng guò)
dtype: string
- name: 只余 (zhǐ yú)
dtype: string
- name: 坐在 (zuò zài)
dtype: string
- name: 天色 (Tiān sè)
dtype: string
- name: 完全 (wán quán)
dtype: string
- name: 屋内 (wū nèi)
dtype: string
- name: 床边 (chuáng biān)
dtype: string
- name: 捧着 (pěng zhe)
dtype: string
- name: 放在 (fàng zài)
dtype: string
- name: 是 (shì)
dtype: string
- name: 暗了 (àn le)
dtype: string
- name: 暮色 (mù sè)
dtype: string
- name: 没有 (méi yǒu)
dtype: string
- name: 浅浅 (qiǎn qiǎn)
dtype: string
- name: 燃烛 (rán zhú)
dtype: string
- name: 的 (de)
dtype: string
- name: 糕点 (gāo diǎn)
dtype: string
- name: 许久 (xǔ jiǔ)
dtype: string
- name: 谁 (shuí)
dtype: string
- name: 身影 (shēn yǐng)
dtype: string
- name: 轮廓 (lún kuò)
dtype: string
- name: 这儿 (zhèr)
dtype: string
- name: 逐渐 (zhú jiàn)
dtype: string
- name: 都没有 (dōu méi yǒu)
dtype: string
- name: 阚闻萧 (Kàn wén xiāo)
dtype: string
- name: 隐没 (yǐn mò)
dtype: string
- name: 黑漆漆的 (hēi qī qī de)
dtype: string
- name: best_translation
dtype: string
- name: alternative_translations
sequence: string
splits:
- name: train
num_bytes: 3429
num_examples: 3
download_size: 27294
dataset_size: 3429
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "translating_mplm_dataset_three"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
NyxSlee/translating_mplm_dataset_three
|
[
"region:us"
] |
2023-11-10T21:15:55+00:00
|
{"dataset_info": {"license": "MIT", "name": "translating_mplm_dataset_three", "description": "A dataset for translating sentences from MPLM", "homepage": "https://github.com/yourusername/your-repo", "task_categories": ["language-translation"], "languages": ["zh", "en"], "size": "3 examples", "download_size": "27.3 KB", "dataset_size": "3.42 KB", "visibility": "public", "status": "active", "authors": [{"name": "Your Name", "email": "[email protected]"}], "creation_date": "2023-11-10", "repository": "https://github.com/yourusername/your-repo", "citation": "@misc{yourcitation,\n title={Your Dataset Title},\n author={Your Name},\n year={2023},\n publisher={Your Publisher},\n journal={Journal of Datasets},\n howpublished={\\url{https://github.com/yourusername/your-repo}},\n}"}}
|
2023-11-10T21:33:33+00:00
|
[] |
[] |
TAGS
#region-us
|
# Rest of your dataset card
dataset_info:
features:
- name: number
dtype: string
- name: sentence
dtype: string
- name: word_translations
struct:
- name: 一个 (yī gè)
dtype: string
- name: 一尊 (yī zūn)
dtype: string
- name: 下来 (xià lái)
dtype: string
- name: 仿佛 (fǎng fú)
dtype: string
- name: 会 (huì)
dtype: string
- name: 凝固 (níng gù)
dtype: string
- name: 动过 (dòng guò)
dtype: string
- name: 只余 (zhǐ yú)
dtype: string
- name: 坐在 (zuò zài)
dtype: string
- name: 天色 (Tiān sè)
dtype: string
- name: 完全 (wán quán)
dtype: string
- name: 屋内 (wū nèi)
dtype: string
- name: 床边 (chuáng biān)
dtype: string
- name: 捧着 (pěng zhe)
dtype: string
- name: 放在 (fàng zài)
dtype: string
- name: 是 (shì)
dtype: string
- name: 暗了 (àn le)
dtype: string
- name: 暮色 (mù sè)
dtype: string
- name: 没有 (méi yǒu)
dtype: string
- name: 浅浅 (qiǎn qiǎn)
dtype: string
- name: 燃烛 (rán zhú)
dtype: string
- name: 的 (de)
dtype: string
- name: 糕点 (gāo diǎn)
dtype: string
- name: 许久 (xǔ jiǔ)
dtype: string
- name: 谁 (shuí)
dtype: string
- name: 身影 (shēn yǐng)
dtype: string
- name: 轮廓 (lún kuò)
dtype: string
- name: 这儿 (zhèr)
dtype: string
- name: 逐渐 (zhú jiàn)
dtype: string
- name: 都没有 (dōu méi yǒu)
dtype: string
- name: 阚闻萧 (Kàn wén xiāo)
dtype: string
- name: 隐没 (yǐn mò)
dtype: string
- name: 黑漆漆的 (hēi qī qī de)
dtype: string
- name: best_translation
dtype: string
- name: alternative_translations
sequence: string
splits:
- name: train
num_bytes: 3429
num_examples: 3
download_size: 27294
dataset_size: 3429
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "translating_mplm_dataset_three"
More Information needed
|
[
"# Rest of your dataset card\ndataset_info:\n features:\n - name: number\n dtype: string\n - name: sentence\n dtype: string\n - name: word_translations\n struct:\n - name: 一个 (yī gè)\n dtype: string\n - name: 一尊 (yī zūn)\n dtype: string\n - name: 下来 (xià lái)\n dtype: string\n - name: 仿佛 (fǎng fú)\n dtype: string\n - name: 会 (huì)\n dtype: string\n - name: 凝固 (níng gù)\n dtype: string\n - name: 动过 (dòng guò)\n dtype: string\n - name: 只余 (zhǐ yú)\n dtype: string\n - name: 坐在 (zuò zài)\n dtype: string\n - name: 天色 (Tiān sè)\n dtype: string\n - name: 完全 (wán quán)\n dtype: string\n - name: 屋内 (wū nèi)\n dtype: string\n - name: 床边 (chuáng biān)\n dtype: string\n - name: 捧着 (pěng zhe)\n dtype: string\n - name: 放在 (fàng zài)\n dtype: string\n - name: 是 (shì)\n dtype: string\n - name: 暗了 (àn le)\n dtype: string\n - name: 暮色 (mù sè)\n dtype: string\n - name: 没有 (méi yǒu)\n dtype: string\n - name: 浅浅 (qiǎn qiǎn)\n dtype: string\n - name: 燃烛 (rán zhú)\n dtype: string\n - name: 的 (de)\n dtype: string\n - name: 糕点 (gāo diǎn)\n dtype: string\n - name: 许久 (xǔ jiǔ)\n dtype: string\n - name: 谁 (shuí)\n dtype: string\n - name: 身影 (shēn yǐng)\n dtype: string\n - name: 轮廓 (lún kuò)\n dtype: string\n - name: 这儿 (zhèr)\n dtype: string\n - name: 逐渐 (zhú jiàn)\n dtype: string\n - name: 都没有 (dōu méi yǒu)\n dtype: string\n - name: 阚闻萧 (Kàn wén xiāo)\n dtype: string\n - name: 隐没 (yǐn mò)\n dtype: string\n - name: 黑漆漆的 (hēi qī qī de)\n dtype: string\n - name: best_translation\n dtype: string\n - name: alternative_translations\n sequence: string\n splits:\n - name: train\n num_bytes: 3429\n num_examples: 3\n download_size: 27294\n dataset_size: 3429\nconfigs:\n- config_name: default\n data_files:\n - split: train\n path: data/train-*\n---",
"# Dataset Card for \"translating_mplm_dataset_three\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Rest of your dataset card\ndataset_info:\n features:\n - name: number\n dtype: string\n - name: sentence\n dtype: string\n - name: word_translations\n struct:\n - name: 一个 (yī gè)\n dtype: string\n - name: 一尊 (yī zūn)\n dtype: string\n - name: 下来 (xià lái)\n dtype: string\n - name: 仿佛 (fǎng fú)\n dtype: string\n - name: 会 (huì)\n dtype: string\n - name: 凝固 (níng gù)\n dtype: string\n - name: 动过 (dòng guò)\n dtype: string\n - name: 只余 (zhǐ yú)\n dtype: string\n - name: 坐在 (zuò zài)\n dtype: string\n - name: 天色 (Tiān sè)\n dtype: string\n - name: 完全 (wán quán)\n dtype: string\n - name: 屋内 (wū nèi)\n dtype: string\n - name: 床边 (chuáng biān)\n dtype: string\n - name: 捧着 (pěng zhe)\n dtype: string\n - name: 放在 (fàng zài)\n dtype: string\n - name: 是 (shì)\n dtype: string\n - name: 暗了 (àn le)\n dtype: string\n - name: 暮色 (mù sè)\n dtype: string\n - name: 没有 (méi yǒu)\n dtype: string\n - name: 浅浅 (qiǎn qiǎn)\n dtype: string\n - name: 燃烛 (rán zhú)\n dtype: string\n - name: 的 (de)\n dtype: string\n - name: 糕点 (gāo diǎn)\n dtype: string\n - name: 许久 (xǔ jiǔ)\n dtype: string\n - name: 谁 (shuí)\n dtype: string\n - name: 身影 (shēn yǐng)\n dtype: string\n - name: 轮廓 (lún kuò)\n dtype: string\n - name: 这儿 (zhèr)\n dtype: string\n - name: 逐渐 (zhú jiàn)\n dtype: string\n - name: 都没有 (dōu méi yǒu)\n dtype: string\n - name: 阚闻萧 (Kàn wén xiāo)\n dtype: string\n - name: 隐没 (yǐn mò)\n dtype: string\n - name: 黑漆漆的 (hēi qī qī de)\n dtype: string\n - name: best_translation\n dtype: string\n - name: alternative_translations\n sequence: string\n splits:\n - name: train\n num_bytes: 3429\n num_examples: 3\n download_size: 27294\n dataset_size: 3429\nconfigs:\n- config_name: default\n data_files:\n - split: train\n path: data/train-*\n---",
"# Dataset Card for \"translating_mplm_dataset_three\"\n\nMore Information needed"
] |
653390902f49b9b6ba35ba461f8b2b397289daea
|
# Dataset Card for "translating_mplm_dataset_four"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
NyxSlee/translating_mplm_dataset_four
|
[
"region:us"
] |
2023-11-10T21:19:09+00:00
|
{"dataset_info": {"features": [{"name": "number", "dtype": "string"}, {"name": "sentence", "dtype": "string"}, {"name": "word_translations", "struct": [{"name": "\u4e00\u4e2a (y\u012b g\u00e8)", "dtype": "string"}, {"name": "\u4e00\u5c0a (y\u012b z\u016bn)", "dtype": "string"}, {"name": "\u4e0b\u6765 (xi\u00e0 l\u00e1i)", "dtype": "string"}, {"name": "\u4eff\u4f5b (f\u01ceng f\u00fa)", "dtype": "string"}, {"name": "\u4f1a (hu\u00ec)", "dtype": "string"}, {"name": "\u51dd\u56fa (n\u00edng g\u00f9)", "dtype": "string"}, {"name": "\u52a8\u8fc7 (d\u00f2ng gu\u00f2)", "dtype": "string"}, {"name": "\u53ea\u4f59 (zh\u01d0 y\u00fa)", "dtype": "string"}, {"name": "\u5750\u5728 (zu\u00f2 z\u00e0i)", "dtype": "string"}, {"name": "\u5929\u8272 (Ti\u0101n s\u00e8)", "dtype": "string"}, {"name": "\u5b8c\u5168 (w\u00e1n qu\u00e1n)", "dtype": "string"}, {"name": "\u5c4b\u5185 (w\u016b n\u00e8i)", "dtype": "string"}, {"name": "\u5e8a\u8fb9 (chu\u00e1ng bi\u0101n)", "dtype": "string"}, {"name": "\u6367\u7740 (p\u011bng zhe)", "dtype": "string"}, {"name": "\u653e\u5728 (f\u00e0ng z\u00e0i)", "dtype": "string"}, {"name": "\u662f (sh\u00ec)", "dtype": "string"}, {"name": "\u6697\u4e86 (\u00e0n le)", "dtype": "string"}, {"name": "\u66ae\u8272 (m\u00f9 s\u00e8)", "dtype": "string"}, {"name": "\u6ca1\u6709 (m\u00e9i y\u01d2u)", "dtype": "string"}, {"name": "\u6d45\u6d45 (qi\u01cen qi\u01cen)", "dtype": "string"}, {"name": "\u71c3\u70db (r\u00e1n zh\u00fa)", "dtype": "string"}, {"name": "\u7684 (de)", "dtype": "string"}, {"name": "\u7cd5\u70b9 (g\u0101o di\u01cen)", "dtype": "string"}, {"name": "\u8bb8\u4e45 (x\u01d4 ji\u01d4)", "dtype": "string"}, {"name": "\u8c01 (shu\u00ed)", "dtype": "string"}, {"name": "\u8eab\u5f71 (sh\u0113n y\u01d0ng)", "dtype": "string"}, {"name": "\u8f6e\u5ed3 (l\u00fan ku\u00f2)", "dtype": "string"}, {"name": "\u8fd9\u513f (zh\u00e8r)", "dtype": "string"}, {"name": "\u9010\u6e10 (zh\u00fa ji\u00e0n)", "dtype": "string"}, {"name": "\u90fd\u6ca1\u6709 (d\u014du m\u00e9i y\u01d2u)", "dtype": "string"}, {"name": "\u961a\u95fb\u8427 (K\u00e0n w\u00e9n xi\u0101o)", "dtype": "string"}, {"name": "\u9690\u6ca1 (y\u01d0n m\u00f2)", "dtype": "string"}, {"name": "\u9ed1\u6f06\u6f06\u7684 (h\u0113i q\u012b q\u012b de)", "dtype": "string"}]}, {"name": "best_translation", "dtype": "string"}, {"name": "alternative_translations", "sequence": "string"}], "splits": [{"name": "train", "num_bytes": 3429, "num_examples": 3}], "download_size": 27294, "dataset_size": 3429}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-11-10T21:19:10+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "translating_mplm_dataset_four"
More Information needed
|
[
"# Dataset Card for \"translating_mplm_dataset_four\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"translating_mplm_dataset_four\"\n\nMore Information needed"
] |
564ffcf1eda71a2abd9698f8c4d45d44e348690a
|
# Dataset Card for "translating_mplm_dataset_six"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
NyxSlee/translating_mplm_dataset_six
|
[
"region:us"
] |
2023-11-10T21:24:15+00:00
|
{"dataset_info": {"features": [{"name": "number", "dtype": "string"}, {"name": "sentence", "dtype": "string"}, {"name": "word_translations", "struct": [{"name": "\u4e00\u4e2a (y\u012b g\u00e8)", "dtype": "string"}, {"name": "\u4e00\u5c0a (y\u012b z\u016bn)", "dtype": "string"}, {"name": "\u4e0b\u6765 (xi\u00e0 l\u00e1i)", "dtype": "string"}, {"name": "\u4eff\u4f5b (f\u01ceng f\u00fa)", "dtype": "string"}, {"name": "\u4f1a (hu\u00ec)", "dtype": "string"}, {"name": "\u51dd\u56fa (n\u00edng g\u00f9)", "dtype": "string"}, {"name": "\u52a8\u8fc7 (d\u00f2ng gu\u00f2)", "dtype": "string"}, {"name": "\u53ea\u4f59 (zh\u01d0 y\u00fa)", "dtype": "string"}, {"name": "\u5750\u5728 (zu\u00f2 z\u00e0i)", "dtype": "string"}, {"name": "\u5929\u8272 (Ti\u0101n s\u00e8)", "dtype": "string"}, {"name": "\u5b8c\u5168 (w\u00e1n qu\u00e1n)", "dtype": "string"}, {"name": "\u5c4b\u5185 (w\u016b n\u00e8i)", "dtype": "string"}, {"name": "\u5e8a\u8fb9 (chu\u00e1ng bi\u0101n)", "dtype": "string"}, {"name": "\u6367\u7740 (p\u011bng zhe)", "dtype": "string"}, {"name": "\u653e\u5728 (f\u00e0ng z\u00e0i)", "dtype": "string"}, {"name": "\u662f (sh\u00ec)", "dtype": "string"}, {"name": "\u6697\u4e86 (\u00e0n le)", "dtype": "string"}, {"name": "\u66ae\u8272 (m\u00f9 s\u00e8)", "dtype": "string"}, {"name": "\u6ca1\u6709 (m\u00e9i y\u01d2u)", "dtype": "string"}, {"name": "\u6d45\u6d45 (qi\u01cen qi\u01cen)", "dtype": "string"}, {"name": "\u71c3\u70db (r\u00e1n zh\u00fa)", "dtype": "string"}, {"name": "\u7684 (de)", "dtype": "string"}, {"name": "\u7cd5\u70b9 (g\u0101o di\u01cen)", "dtype": "string"}, {"name": "\u8bb8\u4e45 (x\u01d4 ji\u01d4)", "dtype": "string"}, {"name": "\u8c01 (shu\u00ed)", "dtype": "string"}, {"name": "\u8eab\u5f71 (sh\u0113n y\u01d0ng)", "dtype": "string"}, {"name": "\u8f6e\u5ed3 (l\u00fan ku\u00f2)", "dtype": "string"}, {"name": "\u8fd9\u513f (zh\u00e8r)", "dtype": "string"}, {"name": "\u9010\u6e10 (zh\u00fa ji\u00e0n)", "dtype": "string"}, {"name": "\u90fd\u6ca1\u6709 (d\u014du m\u00e9i y\u01d2u)", "dtype": "string"}, {"name": "\u961a\u95fb\u8427 (K\u00e0n w\u00e9n xi\u0101o)", "dtype": "string"}, {"name": "\u9690\u6ca1 (y\u01d0n m\u00f2)", "dtype": "string"}, {"name": "\u9ed1\u6f06\u6f06\u7684 (h\u0113i q\u012b q\u012b de)", "dtype": "string"}]}, {"name": "best_translation", "dtype": "string"}, {"name": "alternative_translations", "sequence": "string"}], "splits": [{"name": "train", "num_bytes": 3429, "num_examples": 3}], "download_size": 27294, "dataset_size": 3429}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-11-10T21:24:16+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "translating_mplm_dataset_six"
More Information needed
|
[
"# Dataset Card for \"translating_mplm_dataset_six\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"translating_mplm_dataset_six\"\n\nMore Information needed"
] |
dd09f752eb2417496976febf20333aecc8ad5d32
|
# Dataset Card for "ictwo-blocks1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Tialo/ictwo-blocks1
|
[
"region:us"
] |
2023-11-10T22:16:51+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 177257.0, "num_examples": 210}], "download_size": 176195, "dataset_size": 177257.0}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-11-10T22:16:55+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "ictwo-blocks1"
More Information needed
|
[
"# Dataset Card for \"ictwo-blocks1\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"ictwo-blocks1\"\n\nMore Information needed"
] |
8ee0ea70c08ee880ef91283bc0c19d002743bf8c
|
# Dataset Card for "JAX_BUILDING_SEG_11874"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
MylesChew/JAX_BUILDING_SEG_11874
|
[
"region:us"
] |
2023-11-10T22:41:30+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "dsm", "dtype": "image"}, {"name": "label", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 5927516683.284, "num_examples": 10686}, {"name": "validation", "num_bytes": 65172668.0, "num_examples": 119}, {"name": "eval", "num_bytes": 583146355.052, "num_examples": 1069}], "download_size": 5579190530, "dataset_size": 6575835706.336}}
|
2023-11-11T15:02:27+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "JAX_BUILDING_SEG_11874"
More Information needed
|
[
"# Dataset Card for \"JAX_BUILDING_SEG_11874\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"JAX_BUILDING_SEG_11874\"\n\nMore Information needed"
] |
e7bf088113692e08cfdc341c3d54eb79776f78bd
|
# Dataset Card for "Food500Cap_train"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
advancedcv/Food500Cap_train
|
[
"region:us"
] |
2023-11-10T22:55:36+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "cat", "dtype": "string"}, {"name": "caption", "dtype": "string"}, {"name": "img_id", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 3005309945.747, "num_examples": 19877}], "download_size": 2411309567, "dataset_size": 3005309945.747}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-11-11T00:01:49+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "Food500Cap_train"
More Information needed
|
[
"# Dataset Card for \"Food500Cap_train\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"Food500Cap_train\"\n\nMore Information needed"
] |
190f7872a8697fdc347086284af228f4b5647383
|
# Dataset Card for "mist1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
polejowska/mist1
|
[
"region:us"
] |
2023-11-10T22:57:55+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "valid", "path": "data/valid-*"}, {"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "image_id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "width", "dtype": "int32"}, {"name": "height", "dtype": "int32"}, {"name": "objects", "list": [{"name": "category_id", "dtype": {"class_label": {"names": {"0": "mist1"}}}}, {"name": "image_id", "dtype": "string"}, {"name": "id", "dtype": "int64"}, {"name": "area", "dtype": "int64"}, {"name": "bbox", "sequence": "float32", "length": 4}, {"name": "segmentation", "list": {"list": "float32"}}, {"name": "iscrowd", "dtype": "bool"}]}], "splits": [{"name": "train", "num_bytes": 8875489623.982, "num_examples": 9166}, {"name": "valid", "num_bytes": 478378507.0, "num_examples": 514}, {"name": "test", "num_bytes": 912518506.0, "num_examples": 940}], "download_size": 10243106879, "dataset_size": 10266386636.982}}
|
2023-11-11T02:28:21+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "mist1"
More Information needed
|
[
"# Dataset Card for \"mist1\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"mist1\"\n\nMore Information needed"
] |
b74055d42bd1af2ad7077eced005d8c7793438f9
|
# Dataset Card for "MIST1_brain_gastro_tumors"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
polejowska/MIST1_brain_gastro_tumors
|
[
"region:us"
] |
2023-11-10T23:33:52+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "valid", "path": "data/valid-*"}, {"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "image_id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "width", "dtype": "int32"}, {"name": "height", "dtype": "int32"}, {"name": "objects", "list": [{"name": "category_id", "dtype": {"class_label": {"names": {"0": "mist1"}}}}, {"name": "image_id", "dtype": "string"}, {"name": "id", "dtype": "int64"}, {"name": "area", "dtype": "int64"}, {"name": "bbox", "sequence": "float32", "length": 4}, {"name": "segmentation", "list": {"list": "float32"}}, {"name": "iscrowd", "dtype": "bool"}]}], "splits": [{"name": "train", "num_bytes": 906758000.0, "num_examples": 920}, {"name": "valid", "num_bytes": 76022126.0, "num_examples": 80}, {"name": "test", "num_bytes": 48071588.0, "num_examples": 50}], "download_size": 1030430709, "dataset_size": 1030851714.0}}
|
2023-11-11T00:08:51+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "MIST1_brain_gastro_tumors"
More Information needed
|
[
"# Dataset Card for \"MIST1_brain_gastro_tumors\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"MIST1_brain_gastro_tumors\"\n\nMore Information needed"
] |
a30c0042553f638367cd6bd185d25514fb3fc8f1
|
# Dataset Card for Dataset Name
<!-- Provide a quick summary of the dataset. -->
This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1).
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
### Dataset Sources [optional]
<!-- Provide the basic links for the dataset. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the dataset is intended to be used. -->
### Direct Use
<!-- This section describes suitable use cases for the dataset. -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
[More Information Needed]
## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
[More Information Needed]
## Dataset Creation
### Curation Rationale
<!-- Motivation for the creation of this dataset. -->
[More Information Needed]
### Source Data
<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
#### Data Collection and Processing
<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
[More Information Needed]
#### Who are the source data producers?
<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
[More Information Needed]
### Annotations [optional]
<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
#### Annotation process
<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
[More Information Needed]
#### Who are the annotators?
<!-- This section describes the people or systems who created the annotations. -->
[More Information Needed]
#### Personal and Sensitive Information
<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
## Citation [optional]
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Dataset Card Authors [optional]
[More Information Needed]
## Dataset Card Contact
[More Information Needed]
|
mariakmurphy55/empty
|
[
"task_categories:text-classification",
"size_categories:n<1K",
"language:en",
"license:apache-2.0",
"legal",
"region:us"
] |
2023-11-11T00:16:52+00:00
|
{"language": ["en"], "license": "apache-2.0", "size_categories": ["n<1K"], "task_categories": ["text-classification"], "pretty_name": "prettyname!", "tags": ["legal"]}
|
2023-11-11T00:18:32+00:00
|
[] |
[
"en"
] |
TAGS
#task_categories-text-classification #size_categories-n<1K #language-English #license-apache-2.0 #legal #region-us
|
# Dataset Card for Dataset Name
This dataset card aims to be a base template for new datasets. It has been generated using this raw template.
## Dataset Details
### Dataset Description
- Curated by:
- Funded by [optional]:
- Shared by [optional]:
- Language(s) (NLP):
- License:
### Dataset Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Out-of-Scope Use
## Dataset Structure
## Dataset Creation
### Curation Rationale
### Source Data
#### Data Collection and Processing
#### Who are the source data producers?
### Annotations [optional]
#### Annotation process
#### Who are the annotators?
#### Personal and Sensitive Information
## Bias, Risks, and Limitations
### Recommendations
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Dataset Card Authors [optional]
## Dataset Card Contact
|
[
"# Dataset Card for Dataset Name\n\n\n\nThis dataset card aims to be a base template for new datasets. It has been generated using this raw template.",
"## Dataset Details",
"### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:",
"### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Out-of-Scope Use",
"## Dataset Structure",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Data Collection and Processing",
"#### Who are the source data producers?",
"### Annotations [optional]",
"#### Annotation process",
"#### Who are the annotators?",
"#### Personal and Sensitive Information",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Dataset Card Authors [optional]",
"## Dataset Card Contact"
] |
[
"TAGS\n#task_categories-text-classification #size_categories-n<1K #language-English #license-apache-2.0 #legal #region-us \n",
"# Dataset Card for Dataset Name\n\n\n\nThis dataset card aims to be a base template for new datasets. It has been generated using this raw template.",
"## Dataset Details",
"### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:",
"### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Out-of-Scope Use",
"## Dataset Structure",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Data Collection and Processing",
"#### Who are the source data producers?",
"### Annotations [optional]",
"#### Annotation process",
"#### Who are the annotators?",
"#### Personal and Sensitive Information",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Dataset Card Authors [optional]",
"## Dataset Card Contact"
] |
0887ef57b931b60e70703e44e5379ea3b80ac9f2
|
# Dataset Card for "enem-tc-aqg"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
tiagoblima/enem-tc-aqg
|
[
"region:us"
] |
2023-11-11T00:58:24+00:00
|
{"dataset_info": {"features": [{"name": "paragraph", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answers", "struct": [{"name": "text", "sequence": "string"}]}], "splits": [{"name": "train", "num_bytes": 402614, "num_examples": 388}], "download_size": 267848, "dataset_size": 402614}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-11-11T01:13:06+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "enem-tc-aqg"
More Information needed
|
[
"# Dataset Card for \"enem-tc-aqg\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"enem-tc-aqg\"\n\nMore Information needed"
] |
eb5f4f6930ca48b1ec28c4987855816e3734580d
|
# Dataset Card for PLANE-2K
PLANE Dataset stands for Pre-model Luxury Accurate Next-level Evaluation Dataset.
## Dataset Details
This contains all of the file from the [alpaca-2k-test](https://huggingface.co/datasets/mhenrichsen/alpaca_2k_test) dataset and then some.
|
Keynote-Technology/PLANE-2K
|
[
"license:apache-2.0",
"region:us"
] |
2023-11-11T01:00:42+00:00
|
{"license": "apache-2.0"}
|
2023-11-11T01:08:25+00:00
|
[] |
[] |
TAGS
#license-apache-2.0 #region-us
|
# Dataset Card for PLANE-2K
PLANE Dataset stands for Pre-model Luxury Accurate Next-level Evaluation Dataset.
## Dataset Details
This contains all of the file from the alpaca-2k-test dataset and then some.
|
[
"# Dataset Card for PLANE-2K\nPLANE Dataset stands for Pre-model Luxury Accurate Next-level Evaluation Dataset.",
"## Dataset Details\nThis contains all of the file from the alpaca-2k-test dataset and then some."
] |
[
"TAGS\n#license-apache-2.0 #region-us \n",
"# Dataset Card for PLANE-2K\nPLANE Dataset stands for Pre-model Luxury Accurate Next-level Evaluation Dataset.",
"## Dataset Details\nThis contains all of the file from the alpaca-2k-test dataset and then some."
] |
8185b3bf3e5d8dff364ec87eb10b94c33a5f726a
|
# Dataset Card for "sd1111"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
bys2058/sd1111
|
[
"region:us"
] |
2023-11-11T01:15:43+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "hair_mask", "dtype": "image"}, {"name": "result_image", "dtype": "image"}, {"name": "image_caption", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 93744509413.828, "num_examples": 54062}], "download_size": 91885409225, "dataset_size": 93744509413.828}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-11-14T13:52:27+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "sd1111"
More Information needed
|
[
"# Dataset Card for \"sd1111\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"sd1111\"\n\nMore Information needed"
] |
3117a42195ccf26bcbb50dea6b78ed06f7dfcf82
|
# Dataset Card for "dsml_original_chat"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
higgsfield/dsml_original_chat
|
[
"region:us"
] |
2023-11-11T01:21:52+00:00
|
{"dataset_info": {"features": [{"name": "chats", "list": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 3420336, "num_examples": 1}], "download_size": 1569967, "dataset_size": 3420336}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-11-11T01:21:55+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "dsml_original_chat"
More Information needed
|
[
"# Dataset Card for \"dsml_original_chat\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"dsml_original_chat\"\n\nMore Information needed"
] |
69643a8f8a6247234c3fd9069b734c021a76a6af
|
Compositional Visual Genome (ComVG) <br/>
ComVG benchmark aims to test vision-language models ability in text-to-image retrieval. <br/>
We selected 542 high-quality images from Visual Genome and created 5400 datapoints in ComVG. <br/>
Each datapoint contains a postive and negative image. The negative image is a mutated
variant with singular discrepancies in subject, object, or predicate.<br/>
For more details on creation process, please refer: https://arxiv.org/abs/2211.13854
|
kenanjeff/ComVG
|
[
"task_categories:zero-shot-classification",
"size_categories:1K<n<10K",
"license:creativeml-openrail-m",
"code",
"arxiv:2211.13854",
"region:us"
] |
2023-11-11T01:40:00+00:00
|
{"license": "creativeml-openrail-m", "size_categories": ["1K<n<10K"], "task_categories": ["zero-shot-classification"], "tags": ["code"]}
|
2023-11-17T06:48:30+00:00
|
[
"2211.13854"
] |
[] |
TAGS
#task_categories-zero-shot-classification #size_categories-1K<n<10K #license-creativeml-openrail-m #code #arxiv-2211.13854 #region-us
|
Compositional Visual Genome (ComVG) <br/>
ComVG benchmark aims to test vision-language models ability in text-to-image retrieval. <br/>
We selected 542 high-quality images from Visual Genome and created 5400 datapoints in ComVG. <br/>
Each datapoint contains a postive and negative image. The negative image is a mutated
variant with singular discrepancies in subject, object, or predicate.<br/>
For more details on creation process, please refer: URL
|
[] |
[
"TAGS\n#task_categories-zero-shot-classification #size_categories-1K<n<10K #license-creativeml-openrail-m #code #arxiv-2211.13854 #region-us \n"
] |
19ed9af791c56333735651e19ac2c807ec476625
|
# Dataset Card for "7c5b039f"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
result-kand2-sdxl-wuerst-karlo/7c5b039f
|
[
"region:us"
] |
2023-11-11T02:42:35+00:00
|
{"dataset_info": {"features": [{"name": "result", "dtype": "string"}, {"name": "id", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 182, "num_examples": 10}], "download_size": 1364, "dataset_size": 182}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-11-11T02:42:36+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "7c5b039f"
More Information needed
|
[
"# Dataset Card for \"7c5b039f\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"7c5b039f\"\n\nMore Information needed"
] |
dd6f9d6d7bfe1bcf29d20ea478d1b81d6a2f5034
|
Fliphtml pdf text version
Search Query:
- Melayu
|
aisyahhrazak/crawl-fliphtml
|
[
"language:ms",
"language:en",
"region:us"
] |
2023-11-11T03:12:16+00:00
|
{"language": ["ms", "en"]}
|
2023-11-11T03:15:01+00:00
|
[] |
[
"ms",
"en"
] |
TAGS
#language-Malay (macrolanguage) #language-English #region-us
|
Fliphtml pdf text version
Search Query:
- Melayu
|
[] |
[
"TAGS\n#language-Malay (macrolanguage) #language-English #region-us \n"
] |
46602ccb7669ac6e4c28781c2e7af2dcee1a1eb3
|
# Dataset Card for WildChat-nontoxic
## Dataset Description
- **Paper:** https://wenting-zhao.github.io/papers/wildchat.pdf
- **License:** https://allenai.org/licenses/impact-lr
- **Language(s) (NLP):** multi-lingual
- **Point of Contact:** [Yuntian Deng](mailto:[email protected])
### Dataset Summary
WildChat-nontoxic is the nontoxic subset of the [WildChat dataset](https://huggingface.co/datasets/allenai/WildChat), a collection of 530K conversations between human users and ChatGPT. The full WildChat dataset containing 650K conversations can be found [here](https://huggingface.co/datasets/allenai/WildChat). We collected WildChat by offering online users free access to OpenAI's GPT-3.5-Turbo and GPT-4. The dataset contains a broad spectrum of user-chatbot interactions that are not previously covered by other instruction fine-tuning datasets: for example, interactions include ambiguous user requests, code-switching, topic-switching, political discussions, etc. WildChat can serve both as a dataset for instructional fine-tuning and as a valuable resource for studying user behaviors.
WildChat-nontoxic has been openly released under AI2's ImpACT license as a low-risk artifact. The use of WildChat-nontoxic to cause harm is strictly prohibited.
### Languages
66 languages were detected in WildChat.
### Data Fields
- `conversation_id` (string): Each conversation has a unique id.
- `model` (string): The underlying OpenAI model, such as gpt-3.5-turbo or gpt-4.
- `timestamp` (timestamp): The timestamp of the last turn in the conversation in UTC.
- `conversation` (list): A list of user/assistant utterances. Each utterance is a dictionary containing the `role` of the speaker (user or assistant), the `content` of the utterance, the detected `language` of the utterance, whether the content of the utterance is considered `toxic`, and whether PII has been detected and anonymized (`redacted`).
- `turn` (int): The number of turns in the conversation. A turn refers to one round of user-assistant interaction.
- `language` (string): The language of the conversation. Note that this is the most frequently detected language in the utterances of the conversation.
- `openai_moderation` (list): A list of OpenAI Moderation results. Each element in the list corresponds to one utterance in the conversation.
- `detoxify_moderation` (list): A list of Detoxify results. Each element in the list corresponds to one utterance in the conversation.
- `toxic` (bool): Whether this conversation contains any utterances considered to be toxic by either OpenAI Moderation or Detoxify.
- `redacted` (bool): Whether this conversation contains any utterances in which PII is detected and anonymized.
### Personal and Sensitive Information
The data has been de-identified with Microsoft Presidio and hand-written rules by the authors.
### Inappropriate Content
If you discover inappropriate conversations in this nontoxic subset, please report their conversation ids to us for removal by sending us an email or using community discussions.
### Licensing Information
WildChat-nontoxic is made available under the [**AI2
ImpACT License - Low Risk Artifacts ("LR
Agreement")**](https://allenai.org/licenses/impact-lr)
### Citation Information
Please consider citing [our paper](https://openreview.net/forum?id=Bl8u7ZRlbM) if you find this dataset useful:
```
@inproceedings{
zhao2024inthewildchat,
title={(InThe)WildChat: 570K Chat{GPT} Interaction Logs In The Wild},
author={Zhao, Wenting and Ren, Xiang and Hessel, Jack and Cardie, Claire and Choi, Yejin and Deng, Yuntian},
booktitle={The Twelfth International Conference on Learning Representations},
year={2024},
url={https://openreview.net/forum?id=Bl8u7ZRlbM}
}
```
|
allenai/WildChat-nontoxic
|
[
"size_categories:100K<n<1M",
"region:us"
] |
2023-11-11T03:28:14+00:00
|
{"size_categories": ["100K<n<1M"], "pretty_name": "WildChat-nontoxic", "dataset_info": {"features": [{"name": "conversation_id", "dtype": "string"}, {"name": "model", "dtype": "string"}, {"name": "timestamp", "dtype": "timestamp[s, tz=UTC]"}, {"name": "conversation", "list": [{"name": "content", "dtype": "string"}, {"name": "language", "dtype": "string"}, {"name": "redacted", "dtype": "bool"}, {"name": "role", "dtype": "string"}, {"name": "toxic", "dtype": "bool"}]}, {"name": "turn", "dtype": "int64"}, {"name": "language", "dtype": "string"}, {"name": "openai_moderation", "list": [{"name": "categories", "struct": [{"name": "harassment", "dtype": "bool"}, {"name": "harassment/threatening", "dtype": "bool"}, {"name": "hate", "dtype": "bool"}, {"name": "hate/threatening", "dtype": "bool"}, {"name": "self-harm", "dtype": "bool"}, {"name": "self-harm/instructions", "dtype": "bool"}, {"name": "self-harm/intent", "dtype": "bool"}, {"name": "sexual", "dtype": "bool"}, {"name": "sexual/minors", "dtype": "bool"}, {"name": "violence", "dtype": "bool"}, {"name": "violence/graphic", "dtype": "bool"}]}, {"name": "category_scores", "struct": [{"name": "harassment", "dtype": "float64"}, {"name": "harassment/threatening", "dtype": "float64"}, {"name": "hate", "dtype": "float64"}, {"name": "hate/threatening", "dtype": "float64"}, {"name": "self-harm", "dtype": "float64"}, {"name": "self-harm/instructions", "dtype": "float64"}, {"name": "self-harm/intent", "dtype": "float64"}, {"name": "sexual", "dtype": "float64"}, {"name": "sexual/minors", "dtype": "float64"}, {"name": "violence", "dtype": "float64"}, {"name": "violence/graphic", "dtype": "float64"}]}, {"name": "flagged", "dtype": "bool"}]}, {"name": "detoxify_moderation", "list": [{"name": "identity_attack", "dtype": "float32"}, {"name": "insult", "dtype": "float32"}, {"name": "obscene", "dtype": "float32"}, {"name": "severe_toxicity", "dtype": "float32"}, {"name": "sexual_explicit", "dtype": "float32"}, {"name": "threat", "dtype": "float32"}, {"name": "toxicity", "dtype": "float32"}]}, {"name": "toxic", "dtype": "bool"}, {"name": "redacted", "dtype": "bool"}], "splits": [{"name": "train", "num_bytes": 2949938170, "num_examples": 529514}], "download_size": 1587001052, "dataset_size": 2949938170}, "extra_gated_prompt": "Access to this dataset is automatically granted upon accepting the [**AI2 ImpACT License - Low Risk Artifacts (\u201cLR Agreement\u201d)**](https://allenai.org/licenses/impact-lr) and completing all fields below.", "extra_gated_fields": {"Your full name": "text", "Organization or entity you are affiliated with": "text", "State or country you are located in": "text", "Contact email": "text", "Please describe your intended use of the low risk artifact(s)": "text", "I AGREE to the terms and conditions of the LR Agreement above": "checkbox", "I AGREE to AI2\u2019s use of my information for legal notices and administrative matters": "checkbox", "I CERTIFY that the information I have provided is true and accurate": "checkbox"}}
|
2024-01-19T18:51:20+00:00
|
[] |
[] |
TAGS
#size_categories-100K<n<1M #region-us
|
# Dataset Card for WildChat-nontoxic
## Dataset Description
- Paper: URL
- License: URL
- Language(s) (NLP): multi-lingual
- Point of Contact: Yuntian Deng
### Dataset Summary
WildChat-nontoxic is the nontoxic subset of the WildChat dataset, a collection of 530K conversations between human users and ChatGPT. The full WildChat dataset containing 650K conversations can be found here. We collected WildChat by offering online users free access to OpenAI's GPT-3.5-Turbo and GPT-4. The dataset contains a broad spectrum of user-chatbot interactions that are not previously covered by other instruction fine-tuning datasets: for example, interactions include ambiguous user requests, code-switching, topic-switching, political discussions, etc. WildChat can serve both as a dataset for instructional fine-tuning and as a valuable resource for studying user behaviors.
WildChat-nontoxic has been openly released under AI2's ImpACT license as a low-risk artifact. The use of WildChat-nontoxic to cause harm is strictly prohibited.
### Languages
66 languages were detected in WildChat.
### Data Fields
- 'conversation_id' (string): Each conversation has a unique id.
- 'model' (string): The underlying OpenAI model, such as gpt-3.5-turbo or gpt-4.
- 'timestamp' (timestamp): The timestamp of the last turn in the conversation in UTC.
- 'conversation' (list): A list of user/assistant utterances. Each utterance is a dictionary containing the 'role' of the speaker (user or assistant), the 'content' of the utterance, the detected 'language' of the utterance, whether the content of the utterance is considered 'toxic', and whether PII has been detected and anonymized ('redacted').
- 'turn' (int): The number of turns in the conversation. A turn refers to one round of user-assistant interaction.
- 'language' (string): The language of the conversation. Note that this is the most frequently detected language in the utterances of the conversation.
- 'openai_moderation' (list): A list of OpenAI Moderation results. Each element in the list corresponds to one utterance in the conversation.
- 'detoxify_moderation' (list): A list of Detoxify results. Each element in the list corresponds to one utterance in the conversation.
- 'toxic' (bool): Whether this conversation contains any utterances considered to be toxic by either OpenAI Moderation or Detoxify.
- 'redacted' (bool): Whether this conversation contains any utterances in which PII is detected and anonymized.
### Personal and Sensitive Information
The data has been de-identified with Microsoft Presidio and hand-written rules by the authors.
### Inappropriate Content
If you discover inappropriate conversations in this nontoxic subset, please report their conversation ids to us for removal by sending us an email or using community discussions.
### Licensing Information
WildChat-nontoxic is made available under the AI2
ImpACT License - Low Risk Artifacts ("LR
Agreement")
Please consider citing our paper if you find this dataset useful:
|
[
"# Dataset Card for WildChat-nontoxic",
"## Dataset Description\n \n- Paper: URL\n\n- License: URL\n\n- Language(s) (NLP): multi-lingual\n\n- Point of Contact: Yuntian Deng",
"### Dataset Summary\n\nWildChat-nontoxic is the nontoxic subset of the WildChat dataset, a collection of 530K conversations between human users and ChatGPT. The full WildChat dataset containing 650K conversations can be found here. We collected WildChat by offering online users free access to OpenAI's GPT-3.5-Turbo and GPT-4. The dataset contains a broad spectrum of user-chatbot interactions that are not previously covered by other instruction fine-tuning datasets: for example, interactions include ambiguous user requests, code-switching, topic-switching, political discussions, etc. WildChat can serve both as a dataset for instructional fine-tuning and as a valuable resource for studying user behaviors.\n\nWildChat-nontoxic has been openly released under AI2's ImpACT license as a low-risk artifact. The use of WildChat-nontoxic to cause harm is strictly prohibited.",
"### Languages\n\n66 languages were detected in WildChat.",
"### Data Fields\n\n- 'conversation_id' (string): Each conversation has a unique id.\n- 'model' (string): The underlying OpenAI model, such as gpt-3.5-turbo or gpt-4.\n- 'timestamp' (timestamp): The timestamp of the last turn in the conversation in UTC.\n- 'conversation' (list): A list of user/assistant utterances. Each utterance is a dictionary containing the 'role' of the speaker (user or assistant), the 'content' of the utterance, the detected 'language' of the utterance, whether the content of the utterance is considered 'toxic', and whether PII has been detected and anonymized ('redacted').\n- 'turn' (int): The number of turns in the conversation. A turn refers to one round of user-assistant interaction.\n- 'language' (string): The language of the conversation. Note that this is the most frequently detected language in the utterances of the conversation.\n- 'openai_moderation' (list): A list of OpenAI Moderation results. Each element in the list corresponds to one utterance in the conversation.\n- 'detoxify_moderation' (list): A list of Detoxify results. Each element in the list corresponds to one utterance in the conversation.\n- 'toxic' (bool): Whether this conversation contains any utterances considered to be toxic by either OpenAI Moderation or Detoxify.\n- 'redacted' (bool): Whether this conversation contains any utterances in which PII is detected and anonymized.",
"### Personal and Sensitive Information\n\nThe data has been de-identified with Microsoft Presidio and hand-written rules by the authors.",
"### Inappropriate Content\n\nIf you discover inappropriate conversations in this nontoxic subset, please report their conversation ids to us for removal by sending us an email or using community discussions.",
"### Licensing Information\n\nWildChat-nontoxic is made available under the AI2\n ImpACT License - Low Risk Artifacts (\"LR\n Agreement\")\n\n\n\nPlease consider citing our paper if you find this dataset useful:"
] |
[
"TAGS\n#size_categories-100K<n<1M #region-us \n",
"# Dataset Card for WildChat-nontoxic",
"## Dataset Description\n \n- Paper: URL\n\n- License: URL\n\n- Language(s) (NLP): multi-lingual\n\n- Point of Contact: Yuntian Deng",
"### Dataset Summary\n\nWildChat-nontoxic is the nontoxic subset of the WildChat dataset, a collection of 530K conversations between human users and ChatGPT. The full WildChat dataset containing 650K conversations can be found here. We collected WildChat by offering online users free access to OpenAI's GPT-3.5-Turbo and GPT-4. The dataset contains a broad spectrum of user-chatbot interactions that are not previously covered by other instruction fine-tuning datasets: for example, interactions include ambiguous user requests, code-switching, topic-switching, political discussions, etc. WildChat can serve both as a dataset for instructional fine-tuning and as a valuable resource for studying user behaviors.\n\nWildChat-nontoxic has been openly released under AI2's ImpACT license as a low-risk artifact. The use of WildChat-nontoxic to cause harm is strictly prohibited.",
"### Languages\n\n66 languages were detected in WildChat.",
"### Data Fields\n\n- 'conversation_id' (string): Each conversation has a unique id.\n- 'model' (string): The underlying OpenAI model, such as gpt-3.5-turbo or gpt-4.\n- 'timestamp' (timestamp): The timestamp of the last turn in the conversation in UTC.\n- 'conversation' (list): A list of user/assistant utterances. Each utterance is a dictionary containing the 'role' of the speaker (user or assistant), the 'content' of the utterance, the detected 'language' of the utterance, whether the content of the utterance is considered 'toxic', and whether PII has been detected and anonymized ('redacted').\n- 'turn' (int): The number of turns in the conversation. A turn refers to one round of user-assistant interaction.\n- 'language' (string): The language of the conversation. Note that this is the most frequently detected language in the utterances of the conversation.\n- 'openai_moderation' (list): A list of OpenAI Moderation results. Each element in the list corresponds to one utterance in the conversation.\n- 'detoxify_moderation' (list): A list of Detoxify results. Each element in the list corresponds to one utterance in the conversation.\n- 'toxic' (bool): Whether this conversation contains any utterances considered to be toxic by either OpenAI Moderation or Detoxify.\n- 'redacted' (bool): Whether this conversation contains any utterances in which PII is detected and anonymized.",
"### Personal and Sensitive Information\n\nThe data has been de-identified with Microsoft Presidio and hand-written rules by the authors.",
"### Inappropriate Content\n\nIf you discover inappropriate conversations in this nontoxic subset, please report their conversation ids to us for removal by sending us an email or using community discussions.",
"### Licensing Information\n\nWildChat-nontoxic is made available under the AI2\n ImpACT License - Low Risk Artifacts (\"LR\n Agreement\")\n\n\n\nPlease consider citing our paper if you find this dataset useful:"
] |
829d5a740d7d026685a0c028ef4ea7ec6a284ce1
|
# Dataset Card for "general-multi-choices-history"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
nlplabtdtu/general-multi-choices-history
|
[
"region:us"
] |
2023-11-11T04:09:19+00:00
|
{"dataset_info": {"features": [{"name": "topic", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "options", "list": [{"name": "answer", "dtype": "string"}, {"name": "key", "dtype": "string"}]}, {"name": "answer", "struct": [{"name": "answer", "dtype": "string"}, {"name": "key", "dtype": "string"}]}, {"name": "solution", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 83606, "num_examples": 100}], "download_size": 45790, "dataset_size": 83606}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-11-11T04:09:21+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "general-multi-choices-history"
More Information needed
|
[
"# Dataset Card for \"general-multi-choices-history\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"general-multi-choices-history\"\n\nMore Information needed"
] |
bf9e2598111201e1801a43e6bf38d5e27f43ced3
|
---
license: mit
# Dataset Card
**Developed by:** [More Information Needed]
**Shared by [optional]:** [More Information Needed]
**Dataset type:** [More Information Needed]
**Language(s) (NLP):** [More Information Needed]
**License:** [More Information Needed]
**Derived from dataset [optional]:** [More Information Needed]
**Dataset Sources [optional]**
<!-- Provide the basic links for the dataset. -->
**Repository:** [More Information Needed]
**Paper [optional]:** [More Information Needed]
**Demo [optional]:** [More Information Needed]
**Uses**
<!-- Address questions around how the dataset is intended to be used, including the foreseeable users of the dataset and those affected by the dataset. -->
**Direct Use**
<!-- This section is for the dataset use without modification or integration into a larger system. -->
[More Information Needed]
**Downstream Use [optional]**
<!-- This section is for the dataset use when integrated or modified for a task, or when plugged into a larger ecosystem/app. -->
[More Information Needed]
**Out-of-Scope Use**
<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
[More Information Needed]
**Bias, Risks, and Limitations**
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
**Recommendations**
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
**How to Get Started with the Dataset**
Use the code below to get started with the dataset.
[More Information Needed]
**Collection Details**
**Source Data**
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the source data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
**Collection Procedures**
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the collection procedure. -->
**Preprocessing [optional]**
[More Information Needed]
**Collection Hyperparameters**
Collection regime: [More Information Needed] <!-- Details about the data collection process -->
**Speeds, Sizes, Times [optional]**
<!-- This section provides information about data size, collection start/end time, etc. -->
[More Information Needed]
**Evaluation**
<!-- This section describes the evaluation protocols and provides the results. -->
**Testing Data, Factors & Metrics**
**Testing Data**
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
**Factors**
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
**Metrics**
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
**Results**
[More Information Needed]
**Summary**
**Dataset Examination [optional]**
<!-- Relevant analysis work for the dataset goes here -->
[More Information Needed]
**Environmental Impact**
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
**Hardware Type:** [More Information Needed]
**Hours used:** [More Information Needed]
**Cloud Provider:** [More Information Needed]
**Compute Region:** [More Information Needed]
**Carbon Emitted:** [More Information Needed]
**Technical Specifications [optional]**
**Dataset Structure and Objective**
[More Information Needed]
**Compute Infrastructure**
[More Information Needed]
**Hardware**
[More Information Needed]
**Software**
[More Information Needed]
**Citation [optional]**
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
**Glossary [optional]**
<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
[More Information Needed]
**More Information [optional]**
[More Information Needed]
**Dataset Card Authors [optional]**
[More Information Needed]
**Dataset Card Contact**
[More Information Needed]
|
Taylor658/gptprompts
|
[
"region:us"
] |
2023-11-11T04:12:02+00:00
|
{}
|
2023-12-25T09:41:31+00:00
|
[] |
[] |
TAGS
#region-us
|
---
license: mit
# Dataset Card
Developed by:
Shared by [optional]:
Dataset type:
Language(s) (NLP):
License:
Derived from dataset [optional]:
Dataset Sources [optional]
Repository:
Paper [optional]:
Demo [optional]:
Uses
Direct Use
Downstream Use [optional]
Out-of-Scope Use
Bias, Risks, and Limitations
Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
How to Get Started with the Dataset
Use the code below to get started with the dataset.
Collection Details
Source Data
Collection Procedures
Preprocessing [optional]
Collection Hyperparameters
Collection regime:
Speeds, Sizes, Times [optional]
Evaluation
Testing Data, Factors & Metrics
Testing Data
Factors
Metrics
Results
Summary
Dataset Examination [optional]
Environmental Impact
Hardware Type:
Hours used:
Cloud Provider:
Compute Region:
Carbon Emitted:
Technical Specifications [optional]
Dataset Structure and Objective
Compute Infrastructure
Hardware
Software
Citation [optional]
BibTeX:
APA:
Glossary [optional]
More Information [optional]
Dataset Card Authors [optional]
Dataset Card Contact
|
[
"# Dataset Card\n\nDeveloped by: \n\nShared by [optional]: \n\nDataset type: \n\nLanguage(s) (NLP): \n\nLicense: \n\nDerived from dataset [optional]: \n\nDataset Sources [optional]\n\n\n\nRepository: \n\nPaper [optional]: \n\nDemo [optional]: \n\nUses\n\n\n\nDirect Use\n\n\n\n\n\nDownstream Use [optional]\n\n\n\n\n\nOut-of-Scope Use\n\n\n\n\n\nBias, Risks, and Limitations\n\n\n\n\n\nRecommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\nHow to Get Started with the Dataset\n\nUse the code below to get started with the dataset.\n\n\n\nCollection Details\n\nSource Data\n\n\n\n\n\nCollection Procedures\n\n\n\nPreprocessing [optional]\n\n\n\nCollection Hyperparameters\n\nCollection regime: \n\nSpeeds, Sizes, Times [optional]\n\n\n\n\n\nEvaluation\n\n\n\nTesting Data, Factors & Metrics\n\nTesting Data\n\n\n\n\n\nFactors\n\n\n\n\n\nMetrics\n\n\n\n\n\nResults\n\n\n\nSummary\n\nDataset Examination [optional]\n\n\n\n\n\nEnvironmental Impact\n\n\n\nHardware Type: \n\nHours used: \n\nCloud Provider: \n\nCompute Region: \n\nCarbon Emitted: \n\nTechnical Specifications [optional]\n\nDataset Structure and Objective\n\n\n\nCompute Infrastructure\n\n\n\nHardware\n\n\n\nSoftware\n\n\n\nCitation [optional]\n\n\nBibTeX:\n\n\n\nAPA:\n\n\n\nGlossary [optional]\n\n\n\n\n\nMore Information [optional]\n\n\n\nDataset Card Authors [optional]\n\n\n\nDataset Card Contact"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card\n\nDeveloped by: \n\nShared by [optional]: \n\nDataset type: \n\nLanguage(s) (NLP): \n\nLicense: \n\nDerived from dataset [optional]: \n\nDataset Sources [optional]\n\n\n\nRepository: \n\nPaper [optional]: \n\nDemo [optional]: \n\nUses\n\n\n\nDirect Use\n\n\n\n\n\nDownstream Use [optional]\n\n\n\n\n\nOut-of-Scope Use\n\n\n\n\n\nBias, Risks, and Limitations\n\n\n\n\n\nRecommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\nHow to Get Started with the Dataset\n\nUse the code below to get started with the dataset.\n\n\n\nCollection Details\n\nSource Data\n\n\n\n\n\nCollection Procedures\n\n\n\nPreprocessing [optional]\n\n\n\nCollection Hyperparameters\n\nCollection regime: \n\nSpeeds, Sizes, Times [optional]\n\n\n\n\n\nEvaluation\n\n\n\nTesting Data, Factors & Metrics\n\nTesting Data\n\n\n\n\n\nFactors\n\n\n\n\n\nMetrics\n\n\n\n\n\nResults\n\n\n\nSummary\n\nDataset Examination [optional]\n\n\n\n\n\nEnvironmental Impact\n\n\n\nHardware Type: \n\nHours used: \n\nCloud Provider: \n\nCompute Region: \n\nCarbon Emitted: \n\nTechnical Specifications [optional]\n\nDataset Structure and Objective\n\n\n\nCompute Infrastructure\n\n\n\nHardware\n\n\n\nSoftware\n\n\n\nCitation [optional]\n\n\nBibTeX:\n\n\n\nAPA:\n\n\n\nGlossary [optional]\n\n\n\n\n\nMore Information [optional]\n\n\n\nDataset Card Authors [optional]\n\n\n\nDataset Card Contact"
] |
9efaf9f729e825318522245d9bdb70e10ec32a78
|
# Dataset Card for "lithicai-guanaco-llama2-5k"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Lithicsoft/Guanaco-Dataset-5k
|
[
"region:us"
] |
2023-11-11T05:15:25+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 7856000, "num_examples": 5000}], "download_size": 4641226, "dataset_size": 7856000}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-11-11T05:15:27+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "lithicai-guanaco-llama2-5k"
More Information needed
|
[
"# Dataset Card for \"lithicai-guanaco-llama2-5k\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"lithicai-guanaco-llama2-5k\"\n\nMore Information needed"
] |
e5af4c902bc963e4b5734e43850a51a142c840ce
|
# Dataset Card for "harmful_harmless_instructions_llama2_chat"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
justinphan3110/harmful_harmless_instructions_llama2_chat
|
[
"region:us"
] |
2023-11-11T05:19:31+00:00
|
{"dataset_info": {"features": [{"name": "sentence", "sequence": "string"}, {"name": "label", "sequence": "bool"}], "splits": [{"name": "train", "num_bytes": 159388, "num_examples": 128}, {"name": "test", "num_bytes": 479044, "num_examples": 384}, {"name": "train_no_sys", "num_bytes": 24476, "num_examples": 128}], "download_size": 112802, "dataset_size": 662908}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}, {"split": "train_no_sys", "path": "data/train_no_sys-*"}]}]}
|
2024-01-14T03:46:32+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "harmful_harmless_instructions_llama2_chat"
More Information needed
|
[
"# Dataset Card for \"harmful_harmless_instructions_llama2_chat\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"harmful_harmless_instructions_llama2_chat\"\n\nMore Information needed"
] |
ff40b93aa815aa60786c4fe9e53477d979122029
|
# no-robots-sharegpt
[HuggingFaceH4/no_robots](https://huggingface.co/datasets/HuggingFaceH4/no_robots) with both test and train splits combined and converted to ShareGPT format for use in common training repositories.
Please refer to the original repository's dataset card for more information.
no-robots-sharegpt.jsonl
- Original dataset converted to ShareGPT
no-robots-sharegpt-fixed.jsonl
- Manual edits were made to ~10 dataset entries that were throwing warnings in axolotl - turns out that some of the multi-turn conversations had incorrectly-parsed turns in the original dataset
Auto-converted Parquet is utilizing the `no-robots-sharegpt-fixed.jsonl` file.
|
Doctor-Shotgun/no-robots-sharegpt
|
[
"task_categories:text-generation",
"task_categories:conversational",
"language:en",
"license:cc-by-nc-4.0",
"region:us"
] |
2023-11-11T05:27:26+00:00
|
{"language": ["en"], "license": "cc-by-nc-4.0", "task_categories": ["text-generation", "conversational"], "configs": [{"config_name": "default", "data_files": "no-robots-sharegpt-fixed.jsonl"}]}
|
2023-11-18T06:43:34+00:00
|
[] |
[
"en"
] |
TAGS
#task_categories-text-generation #task_categories-conversational #language-English #license-cc-by-nc-4.0 #region-us
|
# no-robots-sharegpt
HuggingFaceH4/no_robots with both test and train splits combined and converted to ShareGPT format for use in common training repositories.
Please refer to the original repository's dataset card for more information.
URL
- Original dataset converted to ShareGPT
URL
- Manual edits were made to ~10 dataset entries that were throwing warnings in axolotl - turns out that some of the multi-turn conversations had incorrectly-parsed turns in the original dataset
Auto-converted Parquet is utilizing the 'URL' file.
|
[
"# no-robots-sharegpt\n\nHuggingFaceH4/no_robots with both test and train splits combined and converted to ShareGPT format for use in common training repositories.\n\nPlease refer to the original repository's dataset card for more information.\n\nURL\n - Original dataset converted to ShareGPT\n\nURL\n - Manual edits were made to ~10 dataset entries that were throwing warnings in axolotl - turns out that some of the multi-turn conversations had incorrectly-parsed turns in the original dataset\n\nAuto-converted Parquet is utilizing the 'URL' file."
] |
[
"TAGS\n#task_categories-text-generation #task_categories-conversational #language-English #license-cc-by-nc-4.0 #region-us \n",
"# no-robots-sharegpt\n\nHuggingFaceH4/no_robots with both test and train splits combined and converted to ShareGPT format for use in common training repositories.\n\nPlease refer to the original repository's dataset card for more information.\n\nURL\n - Original dataset converted to ShareGPT\n\nURL\n - Manual edits were made to ~10 dataset entries that were throwing warnings in axolotl - turns out that some of the multi-turn conversations had incorrectly-parsed turns in the original dataset\n\nAuto-converted Parquet is utilizing the 'URL' file."
] |
9162ec7f3d9a82477184de04e70bbcdf37a1f049
|
# Bangumi Image Base of Oni Chichi
This is the image base of bangumi Oni Chichi, we detected 20 characters, 2903 images in total. The full dataset is [here](all.zip).
**Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual.** If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
| # | Images | Download | Preview 1 | Preview 2 | Preview 3 | Preview 4 | Preview 5 | Preview 6 | Preview 7 | Preview 8 |
|:------|---------:|:---------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|
| 0 | 134 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 168 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 320 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 47 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 35 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 21 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 337 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 10 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 38 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 1020 | [Download](9/dataset.zip) |  |  |  |  |  |  |  |  |
| 10 | 90 | [Download](10/dataset.zip) |  |  |  |  |  |  |  |  |
| 11 | 65 | [Download](11/dataset.zip) |  |  |  |  |  |  |  |  |
| 12 | 47 | [Download](12/dataset.zip) |  |  |  |  |  |  |  |  |
| 13 | 42 | [Download](13/dataset.zip) |  |  |  |  |  |  |  |  |
| 14 | 15 | [Download](14/dataset.zip) |  |  |  |  |  |  |  |  |
| 15 | 186 | [Download](15/dataset.zip) |  |  |  |  |  |  |  |  |
| 16 | 199 | [Download](16/dataset.zip) |  |  |  |  |  |  |  |  |
| 17 | 22 | [Download](17/dataset.zip) |  |  |  |  |  |  |  |  |
| 18 | 13 | [Download](18/dataset.zip) |  |  |  |  |  |  |  |  |
| noise | 94 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
|
BangumiBase/onichichi
|
[
"size_categories:1K<n<10K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] |
2023-11-11T05:59:56+00:00
|
{"license": "mit", "size_categories": ["1K<n<10K"], "tags": ["art", "not-for-all-audiences"]}
|
2023-11-11T09:59:53+00:00
|
[] |
[] |
TAGS
#size_categories-1K<n<10K #license-mit #art #not-for-all-audiences #region-us
|
Bangumi Image Base of Oni Chichi
================================
This is the image base of bangumi Oni Chichi, we detected 20 characters, 2903 images in total. The full dataset is here.
Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual. If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
|
[] |
[
"TAGS\n#size_categories-1K<n<10K #license-mit #art #not-for-all-audiences #region-us \n"
] |
35f7fe0c327e06d188554bcab39ad33f27570320
|
# Dataset Card for "tariffplan3k"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
csupiisc/tariffplan3k
|
[
"region:us"
] |
2023-11-11T06:09:29+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2626673, "num_examples": 2000}, {"name": "test", "num_bytes": 1312983, "num_examples": 1000}], "download_size": 364794, "dataset_size": 3939656}}
|
2023-11-11T06:22:10+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "tariffplan3k"
More Information needed
|
[
"# Dataset Card for \"tariffplan3k\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"tariffplan3k\"\n\nMore Information needed"
] |
f278f26f0307ca2070ab0acb2005e95f683892ce
|
这是MNBVC多模态语料小组的**图文通用语料**的格式展示。
字段说明：
1. 文件md5: 这个字段存储文件的MD5哈希值。MD5是一种广泛使用的哈希函数，它产生一个128位（16字节）的哈希值，通常用于确保数据的完整性。在这里，它可以用来唯一标识文件，或者检查文件是否被更改。
2. 文件id: 文件的唯一标识符。这可以是一个数据库中的主键，或者任何用于唯一标识文件的系统。
3. 页码: 如果数据源是一个多页文档（如PDF文件），这个字段表示文本或图片所在的具体页码。
4. 块id: 在文档中，特定块（block）的唯一标识。这可以用于标识文本或图片所在的具体块（block）。块（block）的定义其实主要用以区分“模态”，所以对于文本来说，可以是多段的文本，也可能是单段的，取决于使用者的实际情况。
5. 文本: 存储文档中的文本内容。这可以是整个文档的文本，或者是特定段落或页面的文本。
5. 图片: 如果文档中包含图片，这个字段存储图片的数据。
6. 时间: 记录语料出现的时间（比如2000年的报纸，就填2000年xx月xx日，早于1970年出现的数据均填1970年1月1日00时00分00秒。如果时间完全无法确定，则填数据处理时间）。
7. 数据类型: 指示存储在该行中的数据类型。目前包括：“文本”、“图片”两类，如有其他类型请联系本文档作者添加枚举字段。
8. bounding box: 四点坐标（两点坐标请补全到四点），用以支持版面分析等数据。请本文档作者补充数据示例。
9. 额外信息: 存放各种额外信息，比如 在假如数据类型为图片，则存放的可以是一个json格式文本大字段(text存储，需要解析一下)。
关于图片，我们的保存方法是：
```python
def read_image(image_path):
# 打开图像文件
with open(image_path, 'rb') as file:
image = Image.open(file)
# 将图像转换为二进制格式
img_byte_arr = io.BytesIO()
image.save(img_byte_arr, format=image.format)
img_byte_arr = img_byte_arr.getvalue()
return img_byte_arr
```
这个函数首先以二进制读模式打开指定路径的图像文件。然后，它使用PIL的Image.open方法来加载图像。为了将图像转换为二进制格式，我们创建了一个BytesIO对象，然后使用图像的save方法将其保存到这个内存中的字节流中。最后，使用getvalue方法获取字节流的内容，这将是图像的二进制表示。
在这段代码中，format=image.format确保图像以其原始格式保存。这样做是为了保持图像质量和格式的一致性，不管它最初是JPEG、PNG还是其他格式。
一个读取的方法：
```python
import pandas as pd
from PIL import Image
import io
# 读取Parquet文件
df = pd.read_parquet('train.parquet')
# 遍历DataFrame的行
for index, row in df.iterrows():
# 处理图像数据
if row['图片'] is not None:
image_data = row['图片']
image = Image.open(io.BytesIO(image_data))
# 保存图像，文件名可以根据需求自定义
image.save(f'image_{index}.png')
```
|
wanng/example_mmdata_mnbvc
|
[
"license:apache-2.0",
"region:us"
] |
2023-11-11T06:28:08+00:00
|
{"license": "apache-2.0"}
|
2023-11-14T06:29:51+00:00
|
[] |
[] |
TAGS
#license-apache-2.0 #region-us
|
这是MNBVC多模态语料小组的图文通用语料的格式展示。
字段说明：
1. 文件md5: 这个字段存储文件的MD5哈希值。MD5是一种广泛使用的哈希函数，它产生一个128位（16字节）的哈希值，通常用于确保数据的完整性。在这里，它可以用来唯一标识文件，或者检查文件是否被更改。
2. 文件id: 文件的唯一标识符。这可以是一个数据库中的主键，或者任何用于唯一标识文件的系统。
3. 页码: 如果数据源是一个多页文档（如PDF文件），这个字段表示文本或图片所在的具体页码。
4. 块id: 在文档中，特定块（block）的唯一标识。这可以用于标识文本或图片所在的具体块（block）。块（block）的定义其实主要用以区分“模态”，所以对于文本来说，可以是多段的文本，也可能是单段的，取决于使用者的实际情况。
5. 文本: 存储文档中的文本内容。这可以是整个文档的文本，或者是特定段落或页面的文本。
5. 图片: 如果文档中包含图片，这个字段存储图片的数据。
6. 时间: 记录语料出现的时间（比如2000年的报纸，就填2000年xx月xx日，早于1970年出现的数据均填1970年1月1日00时00分00秒。如果时间完全无法确定，则填数据处理时间）。
7. 数据类型: 指示存储在该行中的数据类型。目前包括：“文本”、“图片”两类，如有其他类型请联系本文档作者添加枚举字段。
8. bounding box: 四点坐标（两点坐标请补全到四点），用以支持版面分析等数据。请本文档作者补充数据示例。
9. 额外信息: 存放各种额外信息，比如 在假如数据类型为图片，则存放的可以是一个json格式文本大字段(text存储，需要解析一下)。
关于图片，我们的保存方法是：
这个函数首先以二进制读模式打开指定路径的图像文件。然后，它使用PIL的Image.open方法来加载图像。为了将图像转换为二进制格式，我们创建了一个BytesIO对象，然后使用图像的save方法将其保存到这个内存中的字节流中。最后，使用getvalue方法获取字节流的内容，这将是图像的二进制表示。
在这段代码中，format=image.format确保图像以其原始格式保存。这样做是为了保持图像质量和格式的一致性，不管它最初是JPEG、PNG还是其他格式。
一个读取的方法：
|
[] |
[
"TAGS\n#license-apache-2.0 #region-us \n"
] |
e95fff86d0aaf2e26f8d64593837173c6ebe0052
|
# Dataset Card for "covtype"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
jxie/covtype
|
[
"region:us"
] |
2023-11-11T07:08:05+00:00
|
{"dataset_info": {"features": [{"name": "inputs", "sequence": "float64"}, {"name": "label", "dtype": "float64"}], "splits": [{"name": "train", "num_bytes": 165100068, "num_examples": 371847}, {"name": "val", "num_bytes": 41275128, "num_examples": 92962}, {"name": "test", "num_bytes": 51594132, "num_examples": 116203}], "download_size": 26860361, "dataset_size": 257969328}}
|
2023-11-11T07:08:14+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "covtype"
More Information needed
|
[
"# Dataset Card for \"covtype\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"covtype\"\n\nMore Information needed"
] |
fb66eb07b7f3c48a8f74b2074fa030d2dfee02b3
|
# Dataset Card for "vietnam_scences"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
nguyenth1312/vietnam_scences
|
[
"region:us"
] |
2023-11-11T07:10:35+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "Unnamed: 0", "dtype": "int64"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 274523350.0, "num_examples": 85}], "download_size": 249414864, "dataset_size": 274523350.0}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-11-11T07:10:59+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "vietnam_scences"
More Information needed
|
[
"# Dataset Card for \"vietnam_scences\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"vietnam_scences\"\n\nMore Information needed"
] |
247ac089e5afba2e86ff6e07d14858e28da15168
|
# Dataset Card for "orca-mini"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
TinyPixel/orca-mini
|
[
"region:us"
] |
2023-11-11T07:12:28+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 63386134, "num_examples": 56037}], "download_size": 31300956, "dataset_size": 63386134}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2024-01-06T13:15:30+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "orca-mini"
More Information needed
|
[
"# Dataset Card for \"orca-mini\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"orca-mini\"\n\nMore Information needed"
] |
66a8b1b90d26f69c8d642b251aba700b2294341b
|
# Dataset Card for "wikilingua_data-wiki-gptextsum2_results"
rouge= {'rouge1': 0.3243773799776409, 'rouge2': 0.1149886262779723, 'rougeL': 0.22747057611087074, 'rougeLsum': 0.22747057611087074}
bert= {'precision': 0.7288737272457045, 'recall': 0.7672372243810269, 'f1': 0.7467090841312023}
mover = 0.6081768530416082
|
arthurmluz/wikilingua_data-wiki_gptextsum2_results
|
[
"region:us"
] |
2023-11-11T07:52:10+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "text", "dtype": "string"}, {"name": "summary", "dtype": "string"}, {"name": "gen_summary", "dtype": "string"}, {"name": "rouge", "struct": [{"name": "rouge1", "dtype": "float64"}, {"name": "rouge2", "dtype": "float64"}, {"name": "rougeL", "dtype": "float64"}, {"name": "rougeLsum", "dtype": "float64"}]}, {"name": "bert", "struct": [{"name": "f1", "sequence": "float64"}, {"name": "hashcode", "dtype": "string"}, {"name": "precision", "sequence": "float64"}, {"name": "recall", "sequence": "float64"}]}, {"name": "moverScore", "dtype": "float64"}], "splits": [{"name": "validation", "num_bytes": 23458671, "num_examples": 8165}], "download_size": 13912456, "dataset_size": 23458671}, "configs": [{"config_name": "default", "data_files": [{"split": "validation", "path": "data/validation-*"}]}]}
|
2023-11-13T19:58:30+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "wikilingua_data-wiki-gptextsum2_results"
rouge= {'rouge1': 0.3243773799776409, 'rouge2': 0.1149886262779723, 'rougeL': 0.22747057611087074, 'rougeLsum': 0.22747057611087074}
bert= {'precision': 0.7288737272457045, 'recall': 0.7672372243810269, 'f1': 0.7467090841312023}
mover = 0.6081768530416082
|
[
"# Dataset Card for \"wikilingua_data-wiki-gptextsum2_results\"\n\nrouge= {'rouge1': 0.3243773799776409, 'rouge2': 0.1149886262779723, 'rougeL': 0.22747057611087074, 'rougeLsum': 0.22747057611087074}\n\nbert= {'precision': 0.7288737272457045, 'recall': 0.7672372243810269, 'f1': 0.7467090841312023}\n\nmover = 0.6081768530416082"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"wikilingua_data-wiki-gptextsum2_results\"\n\nrouge= {'rouge1': 0.3243773799776409, 'rouge2': 0.1149886262779723, 'rougeL': 0.22747057611087074, 'rougeLsum': 0.22747057611087074}\n\nbert= {'precision': 0.7288737272457045, 'recall': 0.7672372243810269, 'f1': 0.7467090841312023}\n\nmover = 0.6081768530416082"
] |
9b2a805ed85d7bf313dc4c7999cecfdc7bcc4e7d
|
# Dataset Card for "MIST1-brain-gt-tumors"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
polejowska/MIST1-brain-gt-tumors
|
[
"region:us"
] |
2023-11-11T07:58:42+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "valid", "path": "data/valid-*"}, {"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "image_id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "width", "dtype": "int32"}, {"name": "height", "dtype": "int32"}, {"name": "objects", "list": [{"name": "category_id", "dtype": {"class_label": {"names": {"0": "mist1"}}}}, {"name": "image_id", "dtype": "string"}, {"name": "id", "dtype": "int64"}, {"name": "area", "dtype": "int64"}, {"name": "bbox", "sequence": "float32", "length": 4}, {"name": "segmentation", "list": {"list": "float32"}}, {"name": "iscrowd", "dtype": "bool"}]}], "splits": [{"name": "train", "num_bytes": 905343541.0, "num_examples": 460}, {"name": "valid", "num_bytes": 75913475.0, "num_examples": 40}, {"name": "test", "num_bytes": 48033661.0, "num_examples": 25}], "download_size": 1029189310, "dataset_size": 1029290677.0}}
|
2023-11-11T08:42:25+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "MIST1-brain-gt-tumors"
More Information needed
|
[
"# Dataset Card for \"MIST1-brain-gt-tumors\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"MIST1-brain-gt-tumors\"\n\nMore Information needed"
] |
f290c65e39cad01438fe4287f7948079eba75383
|
# Dataset Card for "mist1-dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
polejowska/mist1-dataset
|
[
"region:us"
] |
2023-11-11T08:01:55+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "valid", "path": "data/valid-*"}, {"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "image_id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "width", "dtype": "int32"}, {"name": "height", "dtype": "int32"}, {"name": "objects", "list": [{"name": "category_id", "dtype": {"class_label": {"names": {"0": "mist1"}}}}, {"name": "image_id", "dtype": "string"}, {"name": "id", "dtype": "int64"}, {"name": "area", "dtype": "int64"}, {"name": "bbox", "sequence": "float32", "length": 4}, {"name": "segmentation", "list": {"list": "float32"}}, {"name": "iscrowd", "dtype": "bool"}]}], "splits": [{"name": "train", "num_bytes": 8829652336.837, "num_examples": 4583}, {"name": "valid", "num_bytes": 477785158.0, "num_examples": 257}, {"name": "test", "num_bytes": 911170226.0, "num_examples": 470}], "download_size": 10227791634, "dataset_size": 10218607720.837}}
|
2023-11-11T11:30:49+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "mist1-dataset"
More Information needed
|
[
"# Dataset Card for \"mist1-dataset\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"mist1-dataset\"\n\nMore Information needed"
] |
354d75f58590bab47a64c6f58121aca50af8281f
|
# About
This is a dataset for training Russian punctuators/capitalizers via NeMo scripts (https://github.com/NVIDIA/NeMo)
A BERT model already fine-tuned on this dataset can be found here: https://huggingface.co/denis-berezutskiy-lad/lad_transcription_bert_ru_punctuator
Scripts for collecting/updating such a dataset, as well as training/using the model are located here: https://github.com/denis-berezutskiy-lad/transcription-bert-ru-punctuator-scripts/tree/main
The idea behind the project is to use large continous professional transcriptions for training rather than relying on short low-quality samples consisting of 1-2 sentences (which is typical for the most popular datasets in Russian). Our experiments show significant improvements comparing to BERTs trained on the standard Ru datasets (social comments, omnia russica etc.). That's why we mainly use transcriptions published by Russian legislatures (Gosduma, Mosgorduma) with some addition of film subtitles from OpenSubtitles project.
The dataset is in .csv format, but can be easily converted to the NeMo format (text.txt and labels.txt) - see the custom scripts above.
About 1.2 GB of data is from Gosduma, ~300 MB from Mosgorduma and ~300 MB from a snapshot of Russian OpenSubtitles, taken from the Taiga project (https://tatianashavrina.github.io/taiga_site/downloads.html ).
The rows are ordered randomly by the source document ("source_entity" column), however within a document the order is preserved like it was in the original text. So if you need to regroup the texts (for example, make longer or shorter sequences), you may concatenate rows grouped by the source entity and then split them as you want.
# Supported labels
Please note that some new labels are not supported by NeMo scripts out of the box (-, —, T), so we need to add special handling for them. See the inference notebook for details.
## Punctuation
O,.?!:;…⁈-—
## Capitalization
OUT
(T means abbreviation ("total" uppercase))
|
denis-berezutskiy-lad/ru_transcription_punctuation
|
[
"license:apache-2.0",
"region:us"
] |
2023-11-11T08:30:53+00:00
|
{"license": "apache-2.0"}
|
2023-11-12T17:17:01+00:00
|
[] |
[] |
TAGS
#license-apache-2.0 #region-us
|
# About
This is a dataset for training Russian punctuators/capitalizers via NeMo scripts (URL
A BERT model already fine-tuned on this dataset can be found here: URL
Scripts for collecting/updating such a dataset, as well as training/using the model are located here: URL
The idea behind the project is to use large continous professional transcriptions for training rather than relying on short low-quality samples consisting of 1-2 sentences (which is typical for the most popular datasets in Russian). Our experiments show significant improvements comparing to BERTs trained on the standard Ru datasets (social comments, omnia russica etc.). That's why we mainly use transcriptions published by Russian legislatures (Gosduma, Mosgorduma) with some addition of film subtitles from OpenSubtitles project.
The dataset is in .csv format, but can be easily converted to the NeMo format (URL and URL) - see the custom scripts above.
About 1.2 GB of data is from Gosduma, ~300 MB from Mosgorduma and ~300 MB from a snapshot of Russian OpenSubtitles, taken from the Taiga project (URL ).
The rows are ordered randomly by the source document ("source_entity" column), however within a document the order is preserved like it was in the original text. So if you need to regroup the texts (for example, make longer or shorter sequences), you may concatenate rows grouped by the source entity and then split them as you want.
# Supported labels
Please note that some new labels are not supported by NeMo scripts out of the box (-, —, T), so we need to add special handling for them. See the inference notebook for details.
## Punctuation
O,.?!:;…⁈-—
## Capitalization
OUT
(T means abbreviation ("total" uppercase))
|
[
"# About\n\nThis is a dataset for training Russian punctuators/capitalizers via NeMo scripts (URL\n\nA BERT model already fine-tuned on this dataset can be found here: URL\n\nScripts for collecting/updating such a dataset, as well as training/using the model are located here: URL\n\nThe idea behind the project is to use large continous professional transcriptions for training rather than relying on short low-quality samples consisting of 1-2 sentences (which is typical for the most popular datasets in Russian). Our experiments show significant improvements comparing to BERTs trained on the standard Ru datasets (social comments, omnia russica etc.). That's why we mainly use transcriptions published by Russian legislatures (Gosduma, Mosgorduma) with some addition of film subtitles from OpenSubtitles project.\n\nThe dataset is in .csv format, but can be easily converted to the NeMo format (URL and URL) - see the custom scripts above.\n\nAbout 1.2 GB of data is from Gosduma, ~300 MB from Mosgorduma and ~300 MB from a snapshot of Russian OpenSubtitles, taken from the Taiga project (URL ).\n\nThe rows are ordered randomly by the source document (\"source_entity\" column), however within a document the order is preserved like it was in the original text. So if you need to regroup the texts (for example, make longer or shorter sequences), you may concatenate rows grouped by the source entity and then split them as you want.",
"# Supported labels\n\nPlease note that some new labels are not supported by NeMo scripts out of the box (-, —, T), so we need to add special handling for them. See the inference notebook for details.",
"## Punctuation\n\nO,.?!:;…⁈-—",
"## Capitalization\n\nOUT\n\n(T means abbreviation (\"total\" uppercase))"
] |
[
"TAGS\n#license-apache-2.0 #region-us \n",
"# About\n\nThis is a dataset for training Russian punctuators/capitalizers via NeMo scripts (URL\n\nA BERT model already fine-tuned on this dataset can be found here: URL\n\nScripts for collecting/updating such a dataset, as well as training/using the model are located here: URL\n\nThe idea behind the project is to use large continous professional transcriptions for training rather than relying on short low-quality samples consisting of 1-2 sentences (which is typical for the most popular datasets in Russian). Our experiments show significant improvements comparing to BERTs trained on the standard Ru datasets (social comments, omnia russica etc.). That's why we mainly use transcriptions published by Russian legislatures (Gosduma, Mosgorduma) with some addition of film subtitles from OpenSubtitles project.\n\nThe dataset is in .csv format, but can be easily converted to the NeMo format (URL and URL) - see the custom scripts above.\n\nAbout 1.2 GB of data is from Gosduma, ~300 MB from Mosgorduma and ~300 MB from a snapshot of Russian OpenSubtitles, taken from the Taiga project (URL ).\n\nThe rows are ordered randomly by the source document (\"source_entity\" column), however within a document the order is preserved like it was in the original text. So if you need to regroup the texts (for example, make longer or shorter sequences), you may concatenate rows grouped by the source entity and then split them as you want.",
"# Supported labels\n\nPlease note that some new labels are not supported by NeMo scripts out of the box (-, —, T), so we need to add special handling for them. See the inference notebook for details.",
"## Punctuation\n\nO,.?!:;…⁈-—",
"## Capitalization\n\nOUT\n\n(T means abbreviation (\"total\" uppercase))"
] |
6ee711b178b8df865344d094e0194c3f2ae7d157
|
# Dataset Card for "nov1_without_annotation"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
tanvirsrbd1/nov1_without_annotation
|
[
"region:us"
] |
2023-11-11T08:38:14+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "xml", "dtype": "string"}, {"name": "html", "dtype": "string"}, {"name": "response", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 47175891, "num_examples": 1711}], "download_size": 5629525, "dataset_size": 47175891}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-11-11T08:38:24+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "nov1_without_annotation"
More Information needed
|
[
"# Dataset Card for \"nov1_without_annotation\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"nov1_without_annotation\"\n\nMore Information needed"
] |
a7697f533622748580bff596a36a4444abf1ddb1
|
# Dataset Card for "Chinkara-Instruct-Guanaco-Zephyr-Format"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
MaralGPT/Chinkara-Instruct-Guanaco-Zephyr-Format
|
[
"region:us"
] |
2023-11-11T08:44:33+00:00
|
{"dataset_info": {"features": [{"name": "instruction", "dtype": "string"}, {"name": "response", "dtype": "string"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 24648370, "num_examples": 9846}], "download_size": 14331955, "dataset_size": 24648370}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-11-11T08:44:35+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "Chinkara-Instruct-Guanaco-Zephyr-Format"
More Information needed
|
[
"# Dataset Card for \"Chinkara-Instruct-Guanaco-Zephyr-Format\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"Chinkara-Instruct-Guanaco-Zephyr-Format\"\n\nMore Information needed"
] |
6c0051010b5df26cb475185ef61cadd05332024f
|
# Dataset Card for "nov1_with_annotation"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
tanvirsrbd1/nov1_with_annotation
|
[
"region:us"
] |
2023-11-11T09:01:42+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "xml", "dtype": "string"}, {"name": "html", "dtype": "string"}, {"name": "response", "dtype": "string"}, {"name": "annotated", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 37050488.1899474, "num_examples": 1323}], "download_size": 4186492, "dataset_size": 37050488.1899474}}
|
2023-11-11T09:01:50+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "nov1_with_annotation"
More Information needed
|
[
"# Dataset Card for \"nov1_with_annotation\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"nov1_with_annotation\"\n\nMore Information needed"
] |
0842b2a48e057b0b4034789328147d2b3f467fd5
|
# Dataset Card for "nov1_annotated_segmented"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
tanvirsrbd1/nov1_annotated_segmented
|
[
"region:us"
] |
2023-11-11T09:08:05+00:00
|
{"dataset_info": {"features": [{"name": "html", "dtype": "string"}, {"name": "response", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1948259, "num_examples": 3107}], "download_size": 643703, "dataset_size": 1948259}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-11-11T09:08:12+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "nov1_annotated_segmented"
More Information needed
|
[
"# Dataset Card for \"nov1_annotated_segmented\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"nov1_annotated_segmented\"\n\nMore Information needed"
] |
1123bd76eb6064ff648bb3c9d4eb75c42e432e7a
|
# Dataset Card for "Voxceleb1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Codec-SUPERB/Voxceleb1_test_original
|
[
"region:us"
] |
2023-11-11T09:10:28+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "audio", "dtype": "audio"}, {"name": "id", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 1291123262.75, "num_examples": 4874}], "download_size": 1288168122, "dataset_size": 1291123262.75}}
|
2023-11-11T09:14:40+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "Voxceleb1"
More Information needed
|
[
"# Dataset Card for \"Voxceleb1\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"Voxceleb1\"\n\nMore Information needed"
] |
006c099b6f58ff53591d7828cf22606242cc6708
|
# Dataset Card for "validation_2000_cutoff_llama_2k_results"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Rewcifer/validation_2000_cutoff_llama_2k_results
|
[
"region:us"
] |
2023-11-11T09:23:07+00:00
|
{"dataset_info": {"features": [{"name": "labels_and_findings", "dtype": "string"}, {"name": "prompts", "dtype": "string"}, {"name": "true_findings", "dtype": "string"}, {"name": "generated_texts", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 17687301, "num_examples": 2000}], "download_size": 4281279, "dataset_size": 17687301}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-11-11T09:23:13+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "validation_2000_cutoff_llama_2k_results"
More Information needed
|
[
"# Dataset Card for \"validation_2000_cutoff_llama_2k_results\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"validation_2000_cutoff_llama_2k_results\"\n\nMore Information needed"
] |
8c169256c0cc327ff6a9cc20063c694c29030e30
|
# Dataset Card for "apt-chat-micro-dataset-llm-v2-714k"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
communityai/apt-chat-micro-dataset-llm-v2-714k
|
[
"region:us"
] |
2023-11-11T09:32:36+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "source", "dtype": "string"}, {"name": "messages", "list": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 1722474450.0482376, "num_examples": 713591}, {"name": "test", "num_bytes": 1206905.9517624504, "num_examples": 500}], "download_size": 903669025, "dataset_size": 1723681356.0}}
|
2023-11-11T09:34:18+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "apt-chat-micro-dataset-llm-v2-714k"
More Information needed
|
[
"# Dataset Card for \"apt-chat-micro-dataset-llm-v2-714k\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"apt-chat-micro-dataset-llm-v2-714k\"\n\nMore Information needed"
] |
b843ef7f52c3922d335801e0bf8aaa90ae69dd1c
|
# Dataset Card for "voyager-fine-tuning"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
baptistecolle/voyager-fine-tuning
|
[
"region:us"
] |
2023-11-11T09:49:11+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "system_prompt", "dtype": "string"}, {"name": "human_prompt", "dtype": "string"}, {"name": "assistant_response", "dtype": "string"}, {"name": "__index_level_0__", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 2320086, "num_examples": 292}, {"name": "test", "num_bytes": 528104, "num_examples": 73}], "download_size": 355674, "dataset_size": 2848190}}
|
2023-11-16T17:32:22+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "voyager-fine-tuning"
More Information needed
|
[
"# Dataset Card for \"voyager-fine-tuning\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"voyager-fine-tuning\"\n\nMore Information needed"
] |
05ba7123ad443742ecb679f690f99052f52d5a56
|
# Dataset Card for "cv13-train-vectorized"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
pphuc25/cv13-train-vectorized
|
[
"region:us"
] |
2023-11-11T09:54:29+00:00
|
{"dataset_info": {"features": [{"name": "audio", "dtype": {"audio": {"sampling_rate": 16000}}}, {"name": "input_length", "dtype": "int64"}, {"name": "labels", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 273530247.93, "num_examples": 1671}], "download_size": 253957905, "dataset_size": 273530247.93}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-11-11T17:13:49+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "cv13-train-vectorized"
More Information needed
|
[
"# Dataset Card for \"cv13-train-vectorized\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"cv13-train-vectorized\"\n\nMore Information needed"
] |
0320f3b19bc758efb53807ad32baed8ce8656eb6
|
# Dataset Card for "cv13-eval-vectorized"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
pphuc25/cv13-test-vectorized
|
[
"region:us"
] |
2023-11-11T09:54:35+00:00
|
{"dataset_info": {"features": [{"name": "input_features", "sequence": {"sequence": "float32"}}, {"name": "input_length", "dtype": "int64"}, {"name": "labels", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 835639192, "num_examples": 870}], "download_size": 139886847, "dataset_size": 835639192}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-11-11T10:00:14+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "cv13-eval-vectorized"
More Information needed
|
[
"# Dataset Card for \"cv13-eval-vectorized\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"cv13-eval-vectorized\"\n\nMore Information needed"
] |
f1127920509c62134fb7029a9519c52038938231
|
# Dataset Card for "GPTextSum_data-wiki-gptextsum_results"
rouge= {'rouge1': 0.4600676970614709, 'rouge2': 0.2024089594170197, 'rougeL': 0.28630530856939856, 'rougeLsum': 0.28630530856939856}
bert= {'precision': 0.7757186979055405, 'recall': 0.7327599436044693, 'f1': 0.7533363491296768}
|
arthurmluz/GPTextSum_data-wiki_gptextsum2_results
|
[
"region:us"
] |
2023-11-11T10:45:36+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "text", "dtype": "string"}, {"name": "summary", "dtype": "string"}, {"name": "gen_summary", "dtype": "string"}, {"name": "rouge", "struct": [{"name": "rouge1", "dtype": "float64"}, {"name": "rouge2", "dtype": "float64"}, {"name": "rougeL", "dtype": "float64"}, {"name": "rougeLsum", "dtype": "float64"}]}, {"name": "bert", "struct": [{"name": "f1", "sequence": "float64"}, {"name": "hashcode", "dtype": "string"}, {"name": "precision", "sequence": "float64"}, {"name": "recall", "sequence": "float64"}]}, {"name": "moverScore", "dtype": "float64"}], "splits": [{"name": "validation", "num_bytes": 93872, "num_examples": 20}], "download_size": 90986, "dataset_size": 93872}, "configs": [{"config_name": "default", "data_files": [{"split": "validation", "path": "data/validation-*"}]}]}
|
2023-11-15T04:05:43+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "GPTextSum_data-wiki-gptextsum_results"
rouge= {'rouge1': 0.4600676970614709, 'rouge2': 0.2024089594170197, 'rougeL': 0.28630530856939856, 'rougeLsum': 0.28630530856939856}
bert= {'precision': 0.7757186979055405, 'recall': 0.7327599436044693, 'f1': 0.7533363491296768}
|
[
"# Dataset Card for \"GPTextSum_data-wiki-gptextsum_results\"\n\nrouge= {'rouge1': 0.4600676970614709, 'rouge2': 0.2024089594170197, 'rougeL': 0.28630530856939856, 'rougeLsum': 0.28630530856939856}\n\nbert= {'precision': 0.7757186979055405, 'recall': 0.7327599436044693, 'f1': 0.7533363491296768}"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"GPTextSum_data-wiki-gptextsum_results\"\n\nrouge= {'rouge1': 0.4600676970614709, 'rouge2': 0.2024089594170197, 'rougeL': 0.28630530856939856, 'rougeLsum': 0.28630530856939856}\n\nbert= {'precision': 0.7757186979055405, 'recall': 0.7327599436044693, 'f1': 0.7533363491296768}"
] |
c5870608776f063f3d313795df39113b239ed5a0
|
# Dataset Card for "coastal_dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
peldrak/coastal_dataset
|
[
"region:us"
] |
2023-11-11T10:47:04+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "validation", "path": "data/validation-*"}, {"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "pixel_values", "dtype": "image"}, {"name": "label", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 489008002.76, "num_examples": 1480}, {"name": "validation", "num_bytes": 60510074.0, "num_examples": 186}, {"name": "test", "num_bytes": 57828182.0, "num_examples": 186}], "download_size": 571640088, "dataset_size": 607346258.76}}
|
2023-11-11T11:04:37+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "coastal_dataset"
More Information needed
|
[
"# Dataset Card for \"coastal_dataset\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"coastal_dataset\"\n\nMore Information needed"
] |
691e3a8911d99c40bfcf6059e10b219cd8d797b3
|
# Dataset Card for "check_other_demand_dataset_vietnamese"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
quocanh34/check_other_demand_dataset_vietnamese
|
[
"region:us"
] |
2023-11-11T10:56:53+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "label", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 18432.0, "num_examples": 320}], "download_size": 8458, "dataset_size": 18432.0}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-11-11T10:56:55+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "check_other_demand_dataset_vietnamese"
More Information needed
|
[
"# Dataset Card for \"check_other_demand_dataset_vietnamese\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"check_other_demand_dataset_vietnamese\"\n\nMore Information needed"
] |
158470cb61c20f9532b9d2d0a1ae95b922f9f842
|
# Dataset Card for "cstnews_data-wiki-gptextsum_results"
rouge= {'rouge1': 0.5311006728849477, 'rouge2': 0.2905368258729071, 'rougeL': 0.36045460975092747, 'rougeLsum': 0.36045460975092747}
bert= {'precision': 0.7722661904990673, 'recall': 0.793611966073513, 'f1': 0.7823329381644726}
mover = 0.6376306490265851
|
arthurmluz/cstnews_data-wiki_gptextsum2_results
|
[
"region:us"
] |
2023-11-11T10:58:02+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "summary", "dtype": "string"}, {"name": "gen_summary", "dtype": "string"}, {"name": "rouge", "struct": [{"name": "rouge1", "dtype": "float64"}, {"name": "rouge2", "dtype": "float64"}, {"name": "rougeL", "dtype": "float64"}, {"name": "rougeLsum", "dtype": "float64"}]}, {"name": "bert", "struct": [{"name": "f1", "sequence": "float64"}, {"name": "hashcode", "dtype": "string"}, {"name": "precision", "sequence": "float64"}, {"name": "recall", "sequence": "float64"}]}, {"name": "moverScore", "dtype": "float64"}], "splits": [{"name": "validation", "num_bytes": 58503, "num_examples": 16}], "download_size": 55642, "dataset_size": 58503}, "configs": [{"config_name": "default", "data_files": [{"split": "validation", "path": "data/validation-*"}]}]}
|
2023-11-15T03:50:19+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "cstnews_data-wiki-gptextsum_results"
rouge= {'rouge1': 0.5311006728849477, 'rouge2': 0.2905368258729071, 'rougeL': 0.36045460975092747, 'rougeLsum': 0.36045460975092747}
bert= {'precision': 0.7722661904990673, 'recall': 0.793611966073513, 'f1': 0.7823329381644726}
mover = 0.6376306490265851
|
[
"# Dataset Card for \"cstnews_data-wiki-gptextsum_results\"\n\nrouge= {'rouge1': 0.5311006728849477, 'rouge2': 0.2905368258729071, 'rougeL': 0.36045460975092747, 'rougeLsum': 0.36045460975092747}\n\nbert= {'precision': 0.7722661904990673, 'recall': 0.793611966073513, 'f1': 0.7823329381644726}\n\nmover = 0.6376306490265851"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"cstnews_data-wiki-gptextsum_results\"\n\nrouge= {'rouge1': 0.5311006728849477, 'rouge2': 0.2905368258729071, 'rougeL': 0.36045460975092747, 'rougeLsum': 0.36045460975092747}\n\nbert= {'precision': 0.7722661904990673, 'recall': 0.793611966073513, 'f1': 0.7823329381644726}\n\nmover = 0.6376306490265851"
] |
81d285ca03326070c8842c0f817fec93296272a4
|
# Dataset Card for "Task01_BrainTumour-v2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
GATE-engine/Task01_BrainTumour-v2
|
[
"region:us"
] |
2023-11-11T11:23:20+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "training", "path": "data/training-*"}]}], "dataset_info": {"features": [{"name": "image", "sequence": {"sequence": {"sequence": {"sequence": "float32"}}}}, {"name": "label", "sequence": {"sequence": {"sequence": "float32"}}}], "splits": [{"name": "training", "num_bytes": 896538640, "num_examples": 5}], "download_size": 44501267, "dataset_size": 896538640}}
|
2023-11-11T11:23:48+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "Task01_BrainTumour-v2"
More Information needed
|
[
"# Dataset Card for \"Task01_BrainTumour-v2\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"Task01_BrainTumour-v2\"\n\nMore Information needed"
] |
cbfc9b6197e9712f4a13dc95e4f3048447b2b83e
|
# Large Weak Labelled NER corpus
### Dataset Summary
The dataset is generated through weak labelling of the scraped and preprocessed news corpus (bloomberg's news). so, only to research purpose.
In order of the tokenization, news were splitted into sentences using `nltk.PunktSentenceTokenizer` (so, sometimes, tokenization might be not perfect)
### Usage
```python
from datasets import load_dataset
articles_ds = load_dataset("imvladikon/english_news_weak_ner", "articles") # just articles with metadata
entities_ds = load_dataset("imvladikon/english_news_weak_ner", "entities")
```
#### NER tags
Tags description:
* O Outside of a named entity
* PER Person
* LOC Location
* ORG Organization
* MISC Miscellaneous
* DATE Date and time expression
* QTY Quantity
* EVE Event
* TTL Title
* DUC Commercial item
Tags:
```json
['B-DATE', 'I-DATE', 'L-DATE', 'U-DATE', 'B-DUC', 'I-DUC', 'L-DUC', 'U-DUC', 'B-EVE', 'I-EVE', 'L-EVE', 'U-EVE', 'B-LOC', 'I-LOC', 'L-LOC', 'U-LOC', 'B-MISC', 'I-MISC', 'L-MISC', 'U-MISC', 'B-ORG', 'I-ORG', 'L-ORG', 'U-ORG', 'B-PER', 'I-PER', 'L-PER', 'U-PER', 'B-QTY', 'I-QTY', 'L-QTY', 'U-QTY', 'B-TTL', 'I-TTL', 'L-TTL', 'U-TTL', 'O']
```
Tags statistics:
```json
{
"O": 281586813,
"B-QTY": 2675754,
"L-QTY": 2675754,
"I-QTY": 2076724,
"U-ORG": 1459628,
"I-ORG": 1407875,
"B-ORG": 1318711,
"L-ORG": 1318711,
"B-PER": 1254037,
"L-PER": 1254037,
"U-MISC": 1195204,
"U-LOC": 1084052,
"U-DATE": 1010118,
"B-DATE": 919815,
"L-DATE": 919815,
"I-DATE": 650064,
"U-PER": 607212,
"U-QTY": 559523,
"B-LOC": 425431,
"L-LOC": 425431,
"I-PER": 262887,
"I-LOC": 201532,
"I-MISC": 190576,
"B-MISC": 162978,
"L-MISC": 162978,
"I-TTL": 64641,
"B-TTL": 53330,
"L-TTL": 53330,
"B-EVE": 43329,
"L-EVE": 43329,
"U-TTL": 41568,
"I-EVE": 35316,
"U-DUC": 33457,
"U-EVE": 19103,
"I-DUC": 15622,
"B-DUC": 15580,
"L-DUC": 15580
}
```
#### Sample:
Articles:
```json
{'title': 'Watson Reports Positive Findings for Prostate Drug',
'author': 'RobertSimison',
'datetime': '2007-01-16T14:16:56Z',
'url': 'http://www.bloomberg.com/news/2007-01-16/watson-reports-positive-findings-for-prostate-drug-update1-.html',
'month': '1',
'day': '16',
'doc_id': 'a5c7c556bd112ac22874492c4cdb18eb46a30905',
'text': 'Watson Pharmaceuticals Inc. (WPI) , the\nlargest U.S. maker of generic drugs, reported positive results\nfor its experimental prostate treatment in two late-state trials. \n The drug, silodosin, was more effective than a placebo in\ntreating enlarged prostates, or benign prostatic hyperplasia, the\nCorona, California-based company said today in a statement on PR\nNewswire. The tests were in the final of three phases of trials\nnormally needed for regulatory approval. \n Non-cancerous enlarged prostate affects more than half of\nAmerican men in their 60s and as many as 90 percent of them by\nage 85, Watson said. Prescription drug sales to treat the\ndisorder total $1.7 billion a year, the company said. \n Watson plans to apply for U.S. approval to market the drug\nin the first half of 2008, after completion later this year of a\none-year safety trial, the company said. The two studies reported\ntoday showed that cardiovascular and blood-pressure side effects\nwere low, Watson said. \n To contact the reporter on this story:\nRobert Simison in Washington at \n [email protected] . \n To contact the editor responsible for this story:\nRobert Simison at [email protected] .',
'year': '2007',
'doc_title': 'watson-reports-positive-findings-for-prostate-drug-update1-'}
```
Entities:
```json
{'doc_id': '806fe637ed51e03d9ef7a8889fc84f63f8fc8569',
'sent_num': 9,
'sentence': 'Spain and Portugal together accounted for 45\npercent of group profit in 2010.',
'doc_title': 'bbva-may-post-lower-first-quarter-profit-hurt-by-spain-decline',
'spans': {'Score': [0.7858654856681824,
0.7856822609901428,
0.9990736246109009,
0.999079704284668],
'Type': ['ORGANIZATION', 'ORGANIZATION', 'QUANTITY', 'DATE'],
'Text': ['Spain', 'Portugal', '45\npercent', '2010'],
'BeginOffset': [0, 10, 42, 72],
'EndOffset': [5, 18, 52, 76]},
'tags': {'tokens': ['Spain',
'Spain',
'and',
'Portugal',
'Spain',
'and',
'Portugal',
'together',
'accounted',
'for',
'45',
'\n',
'percent',
'Spain',
'and',
'Portugal',
'together',
'accounted',
'for',
'45',
'\n',
'percent',
'of',
'group',
'profit',
'in',
'2010',
'.'],
'raw_tags': ['U-ORG',
'O',
'O',
'U-ORG',
'O',
'O',
'O',
'O',
'O',
'O',
'B-QTY',
'I-QTY',
'L-QTY',
'O',
'O',
'O',
'O',
'O',
'O',
'O',
'O',
'O',
'O',
'O',
'O',
'O',
'U-DATE',
'O'],
'ner_tags': [23,
36,
36,
23,
36,
36,
36,
36,
36,
36,
28,
29,
30,
36,
36,
36,
36,
36,
36,
36,
36,
36,
36,
36,
36,
36,
3,
36]}}
```
### Data splits
| name |train|
|---------|----:|
|entities|3515149|
|articles|446809|
### Citation Information
```
@misc{imvladikon2023bb_news_weak_ner,
author = {Gurevich, Vladimir},
title = {Weakly Labelled Large English NER corpus},
year = {2022},
howpublished = \url{https://huggingface.co/datasets/imvladikon/english_news_weak_ner},
}
```
|
imvladikon/english_news_weak_ner
|
[
"task_categories:token-classification",
"size_categories:1M<n<10M",
"language:en",
"region:us"
] |
2023-11-11T11:32:59+00:00
|
{"language": ["en"], "size_categories": ["1M<n<10M"], "task_categories": ["token-classification"], "dataset_info": [{"config_name": "articles", "features": [{"name": "title", "dtype": "string"}, {"name": "author", "dtype": "string"}, {"name": "datetime", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "month", "dtype": "string"}, {"name": "day", "dtype": "string"}, {"name": "doc_id", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "year", "dtype": "string"}, {"name": "doc_title", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1313871812, "num_examples": 446809}], "download_size": 791316510, "dataset_size": 1313871812}, {"config_name": "entities", "features": [{"name": "doc_id", "dtype": "string"}, {"name": "sent_num", "dtype": "int32"}, {"name": "sentence", "dtype": "string"}, {"name": "doc_title", "dtype": "string"}, {"name": "score", "sequence": "float32"}, {"name": "entity_type", "sequence": "string"}, {"name": "entity_text", "sequence": "string"}, {"name": "start_char", "sequence": "int32"}, {"name": "end_char", "sequence": "int32"}, {"name": "tokens", "sequence": "string"}, {"name": "raw_tags", "sequence": "string"}, {"name": "ner_tags", "sequence": {"class_label": {"names": {"0": "B-DATE", "1": "I-DATE", "2": "L-DATE", "3": "U-DATE", "4": "B-DUC", "5": "I-DUC", "6": "L-DUC", "7": "U-DUC", "8": "B-EVE", "9": "I-EVE", "10": "L-EVE", "11": "U-EVE", "12": "B-LOC", "13": "I-LOC", "14": "L-LOC", "15": "U-LOC", "16": "B-MISC", "17": "I-MISC", "18": "L-MISC", "19": "U-MISC", "20": "B-ORG", "21": "I-ORG", "22": "L-ORG", "23": "U-ORG", "24": "B-PER", "25": "I-PER", "26": "L-PER", "27": "U-PER", "28": "B-QTY", "29": "I-QTY", "30": "L-QTY", "31": "U-QTY", "32": "B-TTL", "33": "I-TTL", "34": "L-TTL", "35": "U-TTL", "36": "O"}}}}], "splits": [{"name": "train", "num_bytes": 3665237140, "num_examples": 3515149}], "download_size": 966462235, "dataset_size": 3665237140}], "configs": [{"config_name": "articles", "data_files": [{"split": "train", "path": "articles/train-*"}]}, {"config_name": "entities", "data_files": [{"split": "train", "path": "entities/train-*"}]}]}
|
2023-11-12T07:36:46+00:00
|
[] |
[
"en"
] |
TAGS
#task_categories-token-classification #size_categories-1M<n<10M #language-English #region-us
|
Large Weak Labelled NER corpus
==============================
### Dataset Summary
The dataset is generated through weak labelling of the scraped and preprocessed news corpus (bloomberg's news). so, only to research purpose.
In order of the tokenization, news were splitted into sentences using 'nltk.PunktSentenceTokenizer' (so, sometimes, tokenization might be not perfect)
### Usage
#### NER tags
Tags description:
* O Outside of a named entity
* PER Person
* LOC Location
* ORG Organization
* MISC Miscellaneous
* DATE Date and time expression
* QTY Quantity
* EVE Event
* TTL Title
* DUC Commercial item
Tags:
Tags statistics:
#### Sample:
!display
Articles:
Entities:
### Data splits
|
[
"### Dataset Summary\n\n\nThe dataset is generated through weak labelling of the scraped and preprocessed news corpus (bloomberg's news). so, only to research purpose.\nIn order of the tokenization, news were splitted into sentences using 'nltk.PunktSentenceTokenizer' (so, sometimes, tokenization might be not perfect)",
"### Usage",
"#### NER tags\n\n\nTags description:\n\n\n* O Outside of a named entity\n* PER Person\n* LOC Location\n* ORG Organization\n* MISC Miscellaneous\n* DATE Date and time expression\n* QTY Quantity\n* EVE Event\n* TTL Title\n* DUC Commercial item\n\n\nTags:\n\n\nTags statistics:",
"#### Sample:\n\n\n!display\n\n\nArticles:\n\n\nEntities:",
"### Data splits"
] |
[
"TAGS\n#task_categories-token-classification #size_categories-1M<n<10M #language-English #region-us \n",
"### Dataset Summary\n\n\nThe dataset is generated through weak labelling of the scraped and preprocessed news corpus (bloomberg's news). so, only to research purpose.\nIn order of the tokenization, news were splitted into sentences using 'nltk.PunktSentenceTokenizer' (so, sometimes, tokenization might be not perfect)",
"### Usage",
"#### NER tags\n\n\nTags description:\n\n\n* O Outside of a named entity\n* PER Person\n* LOC Location\n* ORG Organization\n* MISC Miscellaneous\n* DATE Date and time expression\n* QTY Quantity\n* EVE Event\n* TTL Title\n* DUC Commercial item\n\n\nTags:\n\n\nTags statistics:",
"#### Sample:\n\n\n!display\n\n\nArticles:\n\n\nEntities:",
"### Data splits"
] |
64384b45d0885c47b2d236f1ff2bbf607332dee4
|
There is also a version with classLabels here:
2ndBestKiller/DrugTestWithClassLabels
The set consists of a mix of german wikipedia articles, medical guidelines, medication analyses, patient informations and AI generated text.
The dataset has been auto annotated with a phrase matcher (exact matching) with a list of all avaivable medical substances in germany
as listed by the BfArM (though it does not include brand names). Annotations have not been checked manually!
|
2ndBestKiller/DrugTest
|
[
"task_categories:token-classification",
"size_categories:1K<n<10K",
"language:de",
"license:unknown",
"medical",
"region:us"
] |
2023-11-11T11:34:39+00:00
|
{"language": ["de"], "license": "unknown", "size_categories": ["1K<n<10K"], "task_categories": ["token-classification"], "tags": ["medical"]}
|
2023-11-30T22:11:36+00:00
|
[] |
[
"de"
] |
TAGS
#task_categories-token-classification #size_categories-1K<n<10K #language-German #license-unknown #medical #region-us
|
There is also a version with classLabels here:
2ndBestKiller/DrugTestWithClassLabels
The set consists of a mix of german wikipedia articles, medical guidelines, medication analyses, patient informations and AI generated text.
The dataset has been auto annotated with a phrase matcher (exact matching) with a list of all avaivable medical substances in germany
as listed by the BfArM (though it does not include brand names). Annotations have not been checked manually!
|
[] |
[
"TAGS\n#task_categories-token-classification #size_categories-1K<n<10K #language-German #license-unknown #medical #region-us \n"
] |
7849308ae6ca941c1d05af81e6a9873a353483c5
|
# Bangumi Image Base of Stringendo: Angel-tachi No Private Lesson
This is the image base of bangumi Stringendo: Angel-tachi no Private Lesson, we detected 15 characters, 956 images in total. The full dataset is [here](all.zip).
**Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual.** If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
| # | Images | Download | Preview 1 | Preview 2 | Preview 3 | Preview 4 | Preview 5 | Preview 6 | Preview 7 | Preview 8 |
|:------|---------:|:---------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|
| 0 | 123 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 41 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 30 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 175 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 82 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 80 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 75 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 22 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 20 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 112 | [Download](9/dataset.zip) |  |  |  |  |  |  |  |  |
| 10 | 17 | [Download](10/dataset.zip) |  |  |  |  |  |  |  |  |
| 11 | 33 | [Download](11/dataset.zip) |  |  |  |  |  |  |  |  |
| 12 | 10 | [Download](12/dataset.zip) |  |  |  |  |  |  |  |  |
| 13 | 46 | [Download](13/dataset.zip) |  |  |  |  |  |  |  |  |
| noise | 90 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
|
BangumiBase/stringendoangeltachinoprivatelesson
|
[
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] |
2023-11-11T11:36:49+00:00
|
{"license": "mit", "size_categories": ["n<1K"], "tags": ["art", "not-for-all-audiences"]}
|
2023-11-11T12:07:42+00:00
|
[] |
[] |
TAGS
#size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
|
Bangumi Image Base of Stringendo: Angel-tachi No Private Lesson
===============================================================
This is the image base of bangumi Stringendo: Angel-tachi no Private Lesson, we detected 15 characters, 956 images in total. The full dataset is here.
Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual. If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
|
[] |
[
"TAGS\n#size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n"
] |
644c81e7af534faaf9614a845d4d5c464d1b8bf0
|
# Bangumi Image Base of Eroge! H Mo Game Mo Kaihatsu Zanmai
This is the image base of bangumi Eroge! H mo Game mo Kaihatsu Zanmai, we detected 8 characters, 809 images in total. The full dataset is [here](all.zip).
**Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual.** If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
| # | Images | Download | Preview 1 | Preview 2 | Preview 3 | Preview 4 | Preview 5 | Preview 6 | Preview 7 | Preview 8 |
|:------|---------:|:---------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|
| 0 | 236 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 64 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 68 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 163 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 8 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 8 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 6 | [Download](6/dataset.zip) |  |  |  |  |  |  | N/A | N/A |
| noise | 256 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
|
BangumiBase/erogehmogamemokaihatsuzanmai
|
[
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] |
2023-11-11T11:37:14+00:00
|
{"license": "mit", "size_categories": ["n<1K"], "tags": ["art", "not-for-all-audiences"]}
|
2023-11-12T05:37:59+00:00
|
[] |
[] |
TAGS
#size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
|
Bangumi Image Base of Eroge! H Mo Game Mo Kaihatsu Zanmai
=========================================================
This is the image base of bangumi Eroge! H mo Game mo Kaihatsu Zanmai, we detected 8 characters, 809 images in total. The full dataset is here.
Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual. If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
|
[] |
[
"TAGS\n#size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n"
] |
f7c4d3768857f961b88d21a48f061ae88c660bf8
|
# Dataset Card for "music_caps_4sec_wave_type_classical"
This is MusicCaps Dataset classified as "classical" using distilhubert fintuned for GTZAN datasets.
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
mb23/music_caps_4sec_wave_type_classical
|
[
"size_categories:1K<n<10K",
"language:en",
"license:cc-by-sa-4.0",
"region:us"
] |
2023-11-11T11:45:31+00:00
|
{"language": ["en"], "license": "cc-by-sa-4.0", "size_categories": ["1K<n<10K"], "dataset_info": {"features": [{"name": "audio", "dtype": {"audio": {"sampling_rate": 44100}}}, {"name": "caption", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1873285517, "num_examples": 5305}], "download_size": 1863602080, "dataset_size": 1873285517}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-11-16T23:44:36+00:00
|
[] |
[
"en"
] |
TAGS
#size_categories-1K<n<10K #language-English #license-cc-by-sa-4.0 #region-us
|
# Dataset Card for "music_caps_4sec_wave_type_classical"
This is MusicCaps Dataset classified as "classical" using distilhubert fintuned for GTZAN datasets.
More Information needed
|
[
"# Dataset Card for \"music_caps_4sec_wave_type_classical\"\n\nThis is MusicCaps Dataset classified as \"classical\" using distilhubert fintuned for GTZAN datasets.\n\nMore Information needed"
] |
[
"TAGS\n#size_categories-1K<n<10K #language-English #license-cc-by-sa-4.0 #region-us \n",
"# Dataset Card for \"music_caps_4sec_wave_type_classical\"\n\nThis is MusicCaps Dataset classified as \"classical\" using distilhubert fintuned for GTZAN datasets.\n\nMore Information needed"
] |
2ed6444c646cd6bbef71085f45b8a0ff476c3031
|
bad information to answer
|
MagicBoy/badinfo
|
[
"region:us"
] |
2023-11-11T12:03:48+00:00
|
{}
|
2023-11-12T06:10:53+00:00
|
[] |
[] |
TAGS
#region-us
|
bad information to answer
|
[] |
[
"TAGS\n#region-us \n"
] |
7a9959e2152f583d8d106234700b91c27aa0afda
|
# Dataset Card for "gpt4_evol_1.3k"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
maywell/gpt4_evol_1.3k
|
[
"region:us"
] |
2023-11-11T12:22:13+00:00
|
{"dataset_info": {"features": [{"name": "answer", "dtype": "string"}, {"name": "question", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 3467056, "num_examples": 1316}], "download_size": 1918245, "dataset_size": 3467056}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-11-11T12:22:15+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "gpt4_evol_1.3k"
More Information needed
|
[
"# Dataset Card for \"gpt4_evol_1.3k\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"gpt4_evol_1.3k\"\n\nMore Information needed"
] |
1e3753bf705f567b1a848a1668e817579dc0f065
|
# Dataset Card for "igorlink-chatbot"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
iashchak/igorlink-chatbot
|
[
"region:us"
] |
2023-11-11T12:27:01+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "__index_level_0__", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 4492053, "num_examples": 3732}], "download_size": 1766970, "dataset_size": 4492053}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-11-11T19:22:00+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "igorlink-chatbot"
More Information needed
|
[
"# Dataset Card for \"igorlink-chatbot\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"igorlink-chatbot\"\n\nMore Information needed"
] |
c53dc0007d8961168ca4c3b39aae19220cd292e7
|
# Dataset Card for "aug_math"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
PaulTran/aug_math
|
[
"region:us"
] |
2023-11-11T12:37:48+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "answer", "dtype": "string"}, {"name": "explanation", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "id", "dtype": "string"}, {"name": "choices", "sequence": "string"}], "splits": [{"name": "train", "num_bytes": 7274009, "num_examples": 28386}], "download_size": 630837, "dataset_size": 7274009}}
|
2023-11-11T12:39:27+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "aug_math"
More Information needed
|
[
"# Dataset Card for \"aug_math\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"aug_math\"\n\nMore Information needed"
] |
05c6882eb97dc3fcee941c00d9c11d1a8ef5d644
|
# Dataset Card for "invoice"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
quccili/invoice
|
[
"region:us"
] |
2023-11-11T12:59:26+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "validation", "path": "data/validation-*"}, {"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "ground_truth", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 14986534.0, "num_examples": 18}, {"name": "validation", "num_bytes": 14986534.0, "num_examples": 18}, {"name": "test", "num_bytes": 14986534.0, "num_examples": 18}], "download_size": 39577947, "dataset_size": 44959602.0}}
|
2023-11-11T12:59:29+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "invoice"
More Information needed
|
[
"# Dataset Card for \"invoice\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"invoice\"\n\nMore Information needed"
] |
f0782889f68627e91a2ef2959f25e0f26ddb000b
|
# Dataset Card for "airoboros-3.1-no-mathjson-max-1k"
This is a modified version of 'jondurbin/airoboros-3.1' dataset:
- mathjson instances excluded
- Length of input+ouput+special_tokens is limited to 1024 tokens. (llama chat format is assumed)
|
habanoz/airoboros-3.1-no-mathjson-max-1k
|
[
"region:us"
] |
2023-11-11T13:21:11+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "conversations", "list": [{"name": "from", "dtype": "string"}, {"name": "value", "dtype": "string"}]}, {"name": "category", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 40852711.20890598, "num_examples": 20180}], "download_size": 6394016, "dataset_size": 40852711.20890598}}
|
2023-11-11T13:24:48+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "airoboros-3.1-no-mathjson-max-1k"
This is a modified version of 'jondurbin/airoboros-3.1' dataset:
- mathjson instances excluded
- Length of input+ouput+special_tokens is limited to 1024 tokens. (llama chat format is assumed)
|
[
"# Dataset Card for \"airoboros-3.1-no-mathjson-max-1k\"\n\nThis is a modified version of 'jondurbin/airoboros-3.1' dataset:\n\n- mathjson instances excluded\n- Length of input+ouput+special_tokens is limited to 1024 tokens. (llama chat format is assumed)"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"airoboros-3.1-no-mathjson-max-1k\"\n\nThis is a modified version of 'jondurbin/airoboros-3.1' dataset:\n\n- mathjson instances excluded\n- Length of input+ouput+special_tokens is limited to 1024 tokens. (llama chat format is assumed)"
] |
7863e656254287fe0bfe09cb39fc0a2cb4c91cfd
|
# Dataset Card for MultiPL-C2C
## Dataset Description
- **Repository:** https://github.com/PootieT/explain-then-translate
- **Paper:** https://openreview.net/forum?id=uyUO80sbm0
- **Point of Contact:** [email protected]
## Dataset Summary
MultiPL-C2C is a dataset for evaluating large language models for code
translation between any of the 19 programming languages. It is an extension
from MultiPL-E dataset, which takes the OpenAI "HumanEval" and the MBPP
Python benchmarks and uses little compilers to translate them to other
languages. On top of MultiPL-E, it is easy to add support for new languages
and benchmarks. For MultiPL-C2C, we release only the subset for HumanEval.
## Subsets
To evaluate program translation with MultiPL-C2C, you need mainly three
arguments: *SRC_LANG*, *TGT_LANG*, and *EXP*. We use the canonical file extension
for each language to identify the language, e.g., "py" for Python, "cpp" for
C++, "lua" for Lua, and so on. *EXP* specifies what type of prompting is done for
the translation: `direct` is direct translation. {`exp`, `exp-lbl`, `exp-lbl-d`}
are other explain-then-translate method. See paper for detailed description.
Our paper ["explain-then-translate"](https://openreview.net/forum?id=uyUO80sbm0)
explores a Chain-of-Thought like 2-stage prompting methods to improve 0-shot
translation accuracy. We also provide a few other variations:
- *SRCDATA-SRC_LANG-TGT_LANG-EXP-gpt3.5* is the same as *SRCDATA-SRC_LANG-TGT_LANG-EXP*, but are the prompts we
used to evaluated GPT-3.5 in March 2023. For example, in Python-to-X direction,
we cached Python-Java intermediate generations (explanations), and reused them
in other Python-to-X directions.
- *SRCDATA-SRC_LANG-TGT_LANG-EXP-gpt3.5exp* is a step further to control reproducibility for
our experiments on top of *SRCDATA-SRC_LANG-TGT_LANG-EXP-gpt3.5*. We additionally cached all
intermediate generations (explanations) in the prompts. Different from
*SRCDATA-SRC_LANG-TGT_LANG-EXP-gpt3.5*, translation direction such as `Lua-Python` will have
explanations generated by GPT-3.5
Note that we did not experiment with MBPP even thought it is available with
MultiPL-E. Feel free to PR and we can include them to further improve
code-translation datasets.
## Example
The following script uses the Salesforce/codegen model to translate Python-Lua
and MultiPL-E to produce a script with unit tests for luaunit.
```python
import datasets
from transformers import AutoTokenizer, AutoModelForCausalLM
SRC_LANG = "py"
TGT_LANG = "lua"
EXP = "direct"
MODEL_NAME = "Salesforce/codegen-350M-multi"
TOFILL_TOKEN = "{{{TOFILL}}}"
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
model = AutoModelForCausalLM.from_pretrained(MODEL_NAME).half().cuda()
problems = datasets.load_dataset("zilu-peter-tang/MultiPL-C2C", f"humaneval-{SRC_LANG}-{TGT_LANG}-{EXP}")
def stop_at_stop_token(decoded_string, problem):
"""
Truncates the output at stop tokens, taking care to skip the prompt
which may have stop tokens.
"""
min_stop_index = len(decoded_string)
for stop_token in problem["stop_tokens"]:
stop_index = decoded_string.find(stop_token, len(problem["translation_prompt"]))
if stop_index != -1 and stop_index < min_stop_index:
min_stop_index = stop_index
return decoded_string[:min_stop_index]
def generate(problem, tokenizer, model):
input_ids = tokenizer(
problem["translation_prompt"],
return_tensors="pt",
).input_ids.cuda()
generated_ids = model.generate(
input_ids, max_length=512, pad_token_id=tokenizer.eos_token_id + 2
)
truncated_string = stop_at_stop_token(tokenizer.decode(generated_ids[0]), problem)
return truncated_string
for problem in problems["test"]:
# generate each intermediate steps (explanation) sequentially, if any
stop = problem["stop_tokens"]
prompt = problem["translation_prompt"]
while TOFILL_TOKEN in problem["translation_prompt"]:
incomplete_prompt = prompt[:prompt.find(TOFILL_TOKEN)]
problem["stop_tokens"] = ["\n#"]
problem["translation_prompt"] = incomplete_prompt
intermediate_completion = generate(problem, tokenizer, model).replace(incomplete_prompt, "")
prompt = prompt.replace(TOFILL_TOKEN, intermediate_completion, 1)
# finally generate translation
problem["translation_prompt"] = prompt
problem["stop_tokens"] = stop
completion = generate(problem, tokenizer, model).replace(prompt, "")
filename = problem["name"] + "-" + SRC_LANG + "-" + TGT_LANG + "." + TGT_LANG
with open(filename, "w") as f:
print(f"Created {filename}")
f.write(problem["prompt"])
f.write(completion)
f.write("\n")
f.write(problem["tests"])
```
Depending on the quality of the model, you may or may not find some extraneous natural langauge
or program commands at the end of the generated functions. To trim them using programmatic rules,
see our [repo](https://github.com/PootieT/explain-then-translate/blob/main/MultiPL-C2C/inference/__main__.py)
for a more detailed inference script that takes care of this in post-processing.
For few shot examples checkout our all other translation prompts used [here](https://github.com/PootieT/explain-then-translate/tree/main/MultiPL-C2C/translation_prompts/translation_prompts_chatgpt_with_explanation)
|
zilu-peter-tang/MultiPL-C2C
|
[
"license:mit",
"region:us"
] |
2023-11-11T13:37:02+00:00
|
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153}], "download_size": 320726, "dataset_size": 279632}, {"config_name": "humaneval-ts-rb-direct", "features": [{"name": "name", "dtype": "string"}, {"name": "language", "dtype": "string"}, {"name": "prompt", "dtype": "string"}, {"name": "doctests", "dtype": "string"}, {"name": "original", "dtype": "string"}, {"name": "prompt_terminology", "dtype": "string"}, {"name": "source_program", "dtype": "string"}, {"name": "target_signature", "dtype": "string"}, {"name": "tests", "dtype": "string"}, {"name": "stop_tokens", "sequence": "string"}, {"name": "translation_prompt", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 174790, "num_examples": 153}], "download_size": 218712, "dataset_size": 174790}, {"config_name": "humaneval-ts-rb-exp", "features": [{"name": "name", "dtype": "string"}, {"name": "language", "dtype": "string"}, {"name": "prompt", "dtype": "string"}, {"name": "doctests", "dtype": "string"}, {"name": "original", "dtype": "string"}, {"name": "prompt_terminology", "dtype": "string"}, {"name": "source_program", "dtype": "string"}, {"name": "target_signature", "dtype": "string"}, {"name": "tests", "dtype": "string"}, {"name": "stop_tokens", "sequence": "string"}, {"name": "translation_prompt", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 223597, "num_examples": 153}], "download_size": 268437, "dataset_size": 223597}, {"config_name": "humaneval-ts-rkt-direct", "features": [{"name": "name", "dtype": "string"}, {"name": "language", "dtype": "string"}, {"name": "prompt", "dtype": "string"}, {"name": "doctests", "dtype": "string"}, {"name": "original", "dtype": "string"}, {"name": "prompt_terminology", "dtype": "string"}, {"name": "source_program", "dtype": "string"}, {"name": "target_signature", "dtype": "string"}, {"name": "tests", "dtype": "string"}, {"name": "stop_tokens", "sequence": "string"}, {"name": "translation_prompt", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 178653, "num_examples": 153}], "download_size": 223340, "dataset_size": 178653}, {"config_name": "humaneval-ts-rkt-exp", "features": [{"name": "name", "dtype": "string"}, {"name": "language", "dtype": "string"}, {"name": "prompt", "dtype": "string"}, {"name": "doctests", "dtype": "string"}, {"name": "original", "dtype": "string"}, {"name": "prompt_terminology", "dtype": "string"}, {"name": "source_program", "dtype": "string"}, {"name": "target_signature", "dtype": "string"}, {"name": "tests", "dtype": "string"}, {"name": "stop_tokens", "sequence": "string"}, {"name": "translation_prompt", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 227766, "num_examples": 153}], "download_size": 273371, "dataset_size": 227766}, {"config_name": "humaneval-ts-rs-direct", "features": [{"name": "name", "dtype": "string"}, {"name": "language", "dtype": "string"}, {"name": "prompt", "dtype": "string"}, {"name": "doctests", "dtype": "string"}, {"name": "original", "dtype": "string"}, {"name": "prompt_terminology", "dtype": "string"}, {"name": "source_program", "dtype": "string"}, {"name": "target_signature", "dtype": "string"}, {"name": "tests", "dtype": "string"}, {"name": "stop_tokens", "sequence": "string"}, {"name": "translation_prompt", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 174451, "num_examples": 151}], "download_size": 214468, "dataset_size": 174451}, {"config_name": "humaneval-ts-rs-exp", "features": [{"name": "name", "dtype": "string"}, {"name": "language", "dtype": "string"}, {"name": "prompt", "dtype": "string"}, {"name": "doctests", "dtype": "string"}, {"name": "original", "dtype": "string"}, {"name": "prompt_terminology", "dtype": "string"}, {"name": "source_program", "dtype": "string"}, {"name": "target_signature", "dtype": "string"}, {"name": "tests", "dtype": "string"}, {"name": "stop_tokens", "sequence": "string"}, {"name": "translation_prompt", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 222620, "num_examples": 151}], "download_size": 263543, "dataset_size": 222620}, {"config_name": "humaneval-ts-scala-direct", "features": [{"name": "name", "dtype": "string"}, {"name": "language", "dtype": "string"}, {"name": "prompt", "dtype": "string"}, {"name": "doctests", "dtype": "string"}, {"name": "original", "dtype": "string"}, {"name": "prompt_terminology", "dtype": "string"}, {"name": "source_program", "dtype": "string"}, {"name": "target_signature", "dtype": "string"}, {"name": "tests", "dtype": "string"}, {"name": "stop_tokens", "sequence": "string"}, {"name": "translation_prompt", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 223457, "num_examples": 153}], "download_size": 265084, "dataset_size": 223457}, {"config_name": "humaneval-ts-scala-exp", "features": [{"name": "name", "dtype": "string"}, {"name": "language", "dtype": "string"}, {"name": "prompt", "dtype": "string"}, {"name": "doctests", "dtype": "string"}, {"name": "original", "dtype": "string"}, {"name": "prompt_terminology", "dtype": "string"}, {"name": "source_program", "dtype": "string"}, {"name": "target_signature", "dtype": "string"}, {"name": "tests", "dtype": "string"}, {"name": "stop_tokens", "sequence": "string"}, {"name": "translation_prompt", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 272417, "num_examples": 153}], "download_size": 314962, "dataset_size": 272417}, {"config_name": "humaneval-ts-sh-direct", "features": [{"name": "name", "dtype": "string"}, {"name": "language", "dtype": "string"}, {"name": "prompt", "dtype": "string"}, {"name": "doctests", "dtype": "string"}, {"name": "original", "dtype": "string"}, {"name": "prompt_terminology", "dtype": "string"}, {"name": "source_program", "dtype": "string"}, {"name": "target_signature", "dtype": "string"}, {"name": "tests", "dtype": "string"}, {"name": "stop_tokens", "sequence": "string"}, {"name": "translation_prompt", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 161391, "num_examples": 150}], "download_size": 208649, "dataset_size": 161391}, {"config_name": "humaneval-ts-sh-exp", "features": [{"name": "name", "dtype": "string"}, {"name": "language", "dtype": "string"}, {"name": "prompt", "dtype": "string"}, {"name": "doctests", "dtype": "string"}, {"name": "original", "dtype": "string"}, {"name": "prompt_terminology", "dtype": "string"}, {"name": "source_program", "dtype": "string"}, {"name": "target_signature", "dtype": "string"}, {"name": "tests", "dtype": "string"}, {"name": "stop_tokens", "sequence": "string"}, {"name": "translation_prompt", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 209241, "num_examples": 150}], "download_size": 257399, "dataset_size": 209241}, {"config_name": "humaneval-ts-swift-direct", "features": [{"name": "name", "dtype": "string"}, {"name": "language", "dtype": "string"}, {"name": "prompt", "dtype": "string"}, {"name": "doctests", "dtype": "string"}, {"name": "original", "dtype": "string"}, {"name": "prompt_terminology", "dtype": "string"}, {"name": "source_program", "dtype": "string"}, {"name": "target_signature", "dtype": "string"}, {"name": "tests", "dtype": "string"}, {"name": "stop_tokens", "sequence": "string"}, {"name": "translation_prompt", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 202575, "num_examples": 153}], "download_size": 245124, "dataset_size": 202575}, {"config_name": "humaneval-ts-swift-exp", "features": [{"name": "name", "dtype": "string"}, {"name": "language", "dtype": "string"}, {"name": "prompt", "dtype": "string"}, {"name": "doctests", "dtype": "string"}, {"name": "original", "dtype": "string"}, {"name": "prompt_terminology", "dtype": "string"}, {"name": "source_program", "dtype": "string"}, {"name": "target_signature", "dtype": "string"}, {"name": "tests", "dtype": "string"}, {"name": "stop_tokens", "sequence": "string"}, {"name": "translation_prompt", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 251535, "num_examples": 153}], "download_size": 295002, "dataset_size": 251535}]}
|
2023-11-13T12:55:33+00:00
|
[] |
[] |
TAGS
#license-mit #region-us
|
# Dataset Card for MultiPL-C2C
## Dataset Description
- Repository: URL
- Paper: URL
- Point of Contact: zilutang@URL
## Dataset Summary
MultiPL-C2C is a dataset for evaluating large language models for code
translation between any of the 19 programming languages. It is an extension
from MultiPL-E dataset, which takes the OpenAI "HumanEval" and the MBPP
Python benchmarks and uses little compilers to translate them to other
languages. On top of MultiPL-E, it is easy to add support for new languages
and benchmarks. For MultiPL-C2C, we release only the subset for HumanEval.
## Subsets
To evaluate program translation with MultiPL-C2C, you need mainly three
arguments: *SRC_LANG*, *TGT_LANG*, and *EXP*. We use the canonical file extension
for each language to identify the language, e.g., "py" for Python, "cpp" for
C++, "lua" for Lua, and so on. *EXP* specifies what type of prompting is done for
the translation: 'direct' is direct translation. {'exp', 'exp-lbl', 'exp-lbl-d'}
are other explain-then-translate method. See paper for detailed description.
Our paper "explain-then-translate"
explores a Chain-of-Thought like 2-stage prompting methods to improve 0-shot
translation accuracy. We also provide a few other variations:
- *SRCDATA-SRC_LANG-TGT_LANG-EXP-gpt3.5* is the same as *SRCDATA-SRC_LANG-TGT_LANG-EXP*, but are the prompts we
used to evaluated GPT-3.5 in March 2023. For example, in Python-to-X direction,
we cached Python-Java intermediate generations (explanations), and reused them
in other Python-to-X directions.
- *SRCDATA-SRC_LANG-TGT_LANG-EXP-gpt3.5exp* is a step further to control reproducibility for
our experiments on top of *SRCDATA-SRC_LANG-TGT_LANG-EXP-gpt3.5*. We additionally cached all
intermediate generations (explanations) in the prompts. Different from
*SRCDATA-SRC_LANG-TGT_LANG-EXP-gpt3.5*, translation direction such as 'Lua-Python' will have
explanations generated by GPT-3.5
Note that we did not experiment with MBPP even thought it is available with
MultiPL-E. Feel free to PR and we can include them to further improve
code-translation datasets.
## Example
The following script uses the Salesforce/codegen model to translate Python-Lua
and MultiPL-E to produce a script with unit tests for luaunit.
Depending on the quality of the model, you may or may not find some extraneous natural langauge
or program commands at the end of the generated functions. To trim them using programmatic rules,
see our repo
for a more detailed inference script that takes care of this in post-processing.
For few shot examples checkout our all other translation prompts used here
|
[
"# Dataset Card for MultiPL-C2C",
"## Dataset Description\n\n- Repository: URL\n- Paper: URL\n- Point of Contact: zilutang@URL",
"## Dataset Summary\n\nMultiPL-C2C is a dataset for evaluating large language models for code\ntranslation between any of the 19 programming languages. It is an extension\nfrom MultiPL-E dataset, which takes the OpenAI \"HumanEval\" and the MBPP \nPython benchmarks and uses little compilers to translate them to other \nlanguages. On top of MultiPL-E, it is easy to add support for new languages \nand benchmarks. For MultiPL-C2C, we release only the subset for HumanEval.",
"## Subsets\n\nTo evaluate program translation with MultiPL-C2C, you need mainly three \narguments: *SRC_LANG*, *TGT_LANG*, and *EXP*. We use the canonical file extension \nfor each language to identify the language, e.g., \"py\" for Python, \"cpp\" for \nC++, \"lua\" for Lua, and so on. *EXP* specifies what type of prompting is done for\nthe translation: 'direct' is direct translation. {'exp', 'exp-lbl', 'exp-lbl-d'}\nare other explain-then-translate method. See paper for detailed description.\n\nOur paper \"explain-then-translate\"\nexplores a Chain-of-Thought like 2-stage prompting methods to improve 0-shot\ntranslation accuracy. We also provide a few other variations:\n\n- *SRCDATA-SRC_LANG-TGT_LANG-EXP-gpt3.5* is the same as *SRCDATA-SRC_LANG-TGT_LANG-EXP*, but are the prompts we\n used to evaluated GPT-3.5 in March 2023. For example, in Python-to-X direction,\n we cached Python-Java intermediate generations (explanations), and reused them\n in other Python-to-X directions. \n\n- *SRCDATA-SRC_LANG-TGT_LANG-EXP-gpt3.5exp* is a step further to control reproducibility for\n our experiments on top of *SRCDATA-SRC_LANG-TGT_LANG-EXP-gpt3.5*. We additionally cached all\n intermediate generations (explanations) in the prompts. Different from \n *SRCDATA-SRC_LANG-TGT_LANG-EXP-gpt3.5*, translation direction such as 'Lua-Python' will have\n explanations generated by GPT-3.5\n\nNote that we did not experiment with MBPP even thought it is available with\nMultiPL-E. Feel free to PR and we can include them to further improve \ncode-translation datasets.",
"## Example\n\nThe following script uses the Salesforce/codegen model to translate Python-Lua\nand MultiPL-E to produce a script with unit tests for luaunit.\n\n\n\nDepending on the quality of the model, you may or may not find some extraneous natural langauge\nor program commands at the end of the generated functions. To trim them using programmatic rules,\nsee our repo\nfor a more detailed inference script that takes care of this in post-processing. \n\nFor few shot examples checkout our all other translation prompts used here"
] |
[
"TAGS\n#license-mit #region-us \n",
"# Dataset Card for MultiPL-C2C",
"## Dataset Description\n\n- Repository: URL\n- Paper: URL\n- Point of Contact: zilutang@URL",
"## Dataset Summary\n\nMultiPL-C2C is a dataset for evaluating large language models for code\ntranslation between any of the 19 programming languages. It is an extension\nfrom MultiPL-E dataset, which takes the OpenAI \"HumanEval\" and the MBPP \nPython benchmarks and uses little compilers to translate them to other \nlanguages. On top of MultiPL-E, it is easy to add support for new languages \nand benchmarks. For MultiPL-C2C, we release only the subset for HumanEval.",
"## Subsets\n\nTo evaluate program translation with MultiPL-C2C, you need mainly three \narguments: *SRC_LANG*, *TGT_LANG*, and *EXP*. We use the canonical file extension \nfor each language to identify the language, e.g., \"py\" for Python, \"cpp\" for \nC++, \"lua\" for Lua, and so on. *EXP* specifies what type of prompting is done for\nthe translation: 'direct' is direct translation. {'exp', 'exp-lbl', 'exp-lbl-d'}\nare other explain-then-translate method. See paper for detailed description.\n\nOur paper \"explain-then-translate\"\nexplores a Chain-of-Thought like 2-stage prompting methods to improve 0-shot\ntranslation accuracy. We also provide a few other variations:\n\n- *SRCDATA-SRC_LANG-TGT_LANG-EXP-gpt3.5* is the same as *SRCDATA-SRC_LANG-TGT_LANG-EXP*, but are the prompts we\n used to evaluated GPT-3.5 in March 2023. For example, in Python-to-X direction,\n we cached Python-Java intermediate generations (explanations), and reused them\n in other Python-to-X directions. \n\n- *SRCDATA-SRC_LANG-TGT_LANG-EXP-gpt3.5exp* is a step further to control reproducibility for\n our experiments on top of *SRCDATA-SRC_LANG-TGT_LANG-EXP-gpt3.5*. We additionally cached all\n intermediate generations (explanations) in the prompts. Different from \n *SRCDATA-SRC_LANG-TGT_LANG-EXP-gpt3.5*, translation direction such as 'Lua-Python' will have\n explanations generated by GPT-3.5\n\nNote that we did not experiment with MBPP even thought it is available with\nMultiPL-E. Feel free to PR and we can include them to further improve \ncode-translation datasets.",
"## Example\n\nThe following script uses the Salesforce/codegen model to translate Python-Lua\nand MultiPL-E to produce a script with unit tests for luaunit.\n\n\n\nDepending on the quality of the model, you may or may not find some extraneous natural langauge\nor program commands at the end of the generated functions. To trim them using programmatic rules,\nsee our repo\nfor a more detailed inference script that takes care of this in post-processing. \n\nFor few shot examples checkout our all other translation prompts used here"
] |
c7100eaa66708a9e3b0a524498bca7a2e0e760ae
|
# Dataset Card for "heeidk"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
gokul00060/heeidk
|
[
"region:us"
] |
2023-11-11T13:45:36+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 3970, "num_examples": 10}], "download_size": 4124, "dataset_size": 3970}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-11-11T13:45:37+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "heeidk"
More Information needed
|
[
"# Dataset Card for \"heeidk\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"heeidk\"\n\nMore Information needed"
] |
1db077c69d8891cb35394236f556837a16c41d8a
|
* 2023.12.20更新：增加来自[skypile](https://huggingface.co/datasets/Skywork/SkyPile-150B)数据集的长数据
# Long text dataset for pretraining
* This dataset contains samples with the length greater than 16k, which can be used for pretraining models with extremely long context lengths.
* The dataset is continuously updating.
# 长文本模型预训练数据集
* 此数据集包含长度大于16k的预训练数据，可用于对极长上下文长度的模型进行预训练。
* 数据正在持续增加中
## 中文数据
* 筛选自 [悟道200G开源数据](https://github.com/BAAI-WuDao/Data)、[书生万卷数据集](https://opendatalab.org.cn/OpenDataLab/WanJuan1_dot_0)、
[CCI中文互联网语料库](https://huggingface.co/datasets/BAAI/CCI-Data)
、中文维基百科等，
每条数据长度在16000字以上
## 英文数据
* 筛选自 [SlimPajama-dc]
(https://huggingface.co/datasets/MBZUAI-LLM/SlimPajama-627B-DC)， 每条数据长度在16000个word以上
## Sharegpt长对话
* 包含筛选自sharegpt的长度大于8k字的中文和英文对话。[Sharegpt长对话](https://huggingface.co/datasets/yuyijiong/Sharegpt-long-conversation)
## 图书、小说
中文小说由于数据量太大，已经上传至[云盘](https://cloud.tsinghua.edu.cn/d/0670fcb14d294c97b5cf/)。
英文图书来自RedPajamaBook，筛选了长度大于100k words的图书。
## 注意
注意，有些长文本数据中含有大量的重复字符串，推荐在使用以下代码进行去重：
```python
import re
#删除连续出现4次以上的非数字和字母的字符，只保留4次
content = re.sub(r'([^a-zA-Z0-9])\1{4,}', r'\1\1\1\1', content)
#删除连续出现3次以上的数字和字母组成的子串，例如“121212”变为“12”
content = re.sub(r'([a-zA-Z0-9]{3,}?)\1+', r'\1', content)
```
|
yuyijiong/LongData-Corpus
|
[
"size_categories:10K<n<100K",
"language:zh",
"language:en",
"license:cc-by-nc-4.0",
"region:us"
] |
2023-11-11T13:47:14+00:00
|
{"language": ["zh", "en"], "license": "cc-by-nc-4.0", "size_categories": ["10K<n<100K"]}
|
2023-12-20T05:15:07+00:00
|
[] |
[
"zh",
"en"
] |
TAGS
#size_categories-10K<n<100K #language-Chinese #language-English #license-cc-by-nc-4.0 #region-us
|
* 2023.12.20更新：增加来自skypile数据集的长数据
# Long text dataset for pretraining
* This dataset contains samples with the length greater than 16k, which can be used for pretraining models with extremely long context lengths.
* The dataset is continuously updating.
# 长文本模型预训练数据集
* 此数据集包含长度大于16k的预训练数据，可用于对极长上下文长度的模型进行预训练。
* 数据正在持续增加中
## 中文数据
* 筛选自 悟道200G开源数据、书生万卷数据集、
CCI中文互联网语料库
、中文维基百科等，
每条数据长度在16000字以上
## 英文数据
* 筛选自 [SlimPajama-dc]
(URL， 每条数据长度在16000个word以上
## Sharegpt长对话
* 包含筛选自sharegpt的长度大于8k字的中文和英文对话。Sharegpt长对话
## 图书、小说
中文小说由于数据量太大，已经上传至云盘。
英文图书来自RedPajamaBook，筛选了长度大于100k words的图书。
## 注意
注意，有些长文本数据中含有大量的重复字符串，推荐在使用以下代码进行去重：
|
[
"# Long text dataset for pretraining\n\n* This dataset contains samples with the length greater than 16k, which can be used for pretraining models with extremely long context lengths.\n\n* The dataset is continuously updating.",
"# 长文本模型预训练数据集\n* 此数据集包含长度大于16k的预训练数据，可用于对极长上下文长度的模型进行预训练。\n* 数据正在持续增加中",
"## 中文数据\n* 筛选自 悟道200G开源数据、书生万卷数据集、\nCCI中文互联网语料库\n、中文维基百科等，\n每条数据长度在16000字以上",
"## 英文数据\n* 筛选自 [SlimPajama-dc]\n\n(URL， 每条数据长度在16000个word以上",
"## Sharegpt长对话\n* 包含筛选自sharegpt的长度大于8k字的中文和英文对话。Sharegpt长对话",
"## 图书、小说\n中文小说由于数据量太大，已经上传至云盘。\n英文图书来自RedPajamaBook，筛选了长度大于100k words的图书。",
"## 注意\n注意，有些长文本数据中含有大量的重复字符串，推荐在使用以下代码进行去重："
] |
[
"TAGS\n#size_categories-10K<n<100K #language-Chinese #language-English #license-cc-by-nc-4.0 #region-us \n",
"# Long text dataset for pretraining\n\n* This dataset contains samples with the length greater than 16k, which can be used for pretraining models with extremely long context lengths.\n\n* The dataset is continuously updating.",
"# 长文本模型预训练数据集\n* 此数据集包含长度大于16k的预训练数据，可用于对极长上下文长度的模型进行预训练。\n* 数据正在持续增加中",
"## 中文数据\n* 筛选自 悟道200G开源数据、书生万卷数据集、\nCCI中文互联网语料库\n、中文维基百科等，\n每条数据长度在16000字以上",
"## 英文数据\n* 筛选自 [SlimPajama-dc]\n\n(URL， 每条数据长度在16000个word以上",
"## Sharegpt长对话\n* 包含筛选自sharegpt的长度大于8k字的中文和英文对话。Sharegpt长对话",
"## 图书、小说\n中文小说由于数据量太大，已经上传至云盘。\n英文图书来自RedPajamaBook，筛选了长度大于100k words的图书。",
"## 注意\n注意，有些长文本数据中含有大量的重复字符串，推荐在使用以下代码进行去重："
] |
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