sha
stringlengths 40
40
| text
stringlengths 0
13.4M
| id
stringlengths 2
117
| tags
list | created_at
stringlengths 25
25
| metadata
stringlengths 2
31.7M
| last_modified
stringlengths 25
25
|
|---|---|---|---|---|---|---|
4fed2c78e58e520d201c8adb5e3dda54c66ef58a
|
# Dataset Card for "mmlu-management-neg"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
joey234/mmlu-management-neg
|
[
"region:us"
] |
2023-04-20T05:44:33+00:00
|
{"dataset_info": {"features": [{"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}, {"name": "question", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 19770, "num_examples": 103}], "download_size": 14196, "dataset_size": 19770}}
|
2023-04-20T05:44:37+00:00
|
dc181b10bcc702ea415bb466f39b94bdb62cb7d1
|
# Dataset Card for "mmlu-marketing-neg"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
joey234/mmlu-marketing-neg
|
[
"region:us"
] |
2023-04-20T05:47:42+00:00
|
{"dataset_info": {"features": [{"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}, {"name": "question", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 61210, "num_examples": 234}], "download_size": 36258, "dataset_size": 61210}}
|
2023-04-20T05:47:46+00:00
|
101a4ddad3dba87be5858f7d8ba044a88511e669
|
# Dataset Card for "mmlu-medical_genetics-neg"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
joey234/mmlu-medical_genetics-neg
|
[
"region:us"
] |
2023-04-20T05:48:45+00:00
|
{"dataset_info": {"features": [{"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}, {"name": "question", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 20651, "num_examples": 100}], "download_size": 15187, "dataset_size": 20651}}
|
2023-04-20T05:48:49+00:00
|
2445b5179171182eaa70a122574474af5cfab575
|
# Dataset Card for "mmlu-miscellaneous-neg"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
joey234/mmlu-miscellaneous-neg
|
[
"region:us"
] |
2023-04-20T05:55:46+00:00
|
{"dataset_info": {"features": [{"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}, {"name": "question", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 137981, "num_examples": 783}], "download_size": 92589, "dataset_size": 137981}}
|
2023-04-20T05:55:51+00:00
|
7735fde0c40890bf73a826993bad0de637420eed
|
# Dataset Card for "mmlu-moral_disputes-neg"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
joey234/mmlu-moral_disputes-neg
|
[
"region:us"
] |
2023-04-20T05:59:29+00:00
|
{"dataset_info": {"features": [{"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}, {"name": "question", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 105930, "num_examples": 346}], "download_size": 60234, "dataset_size": 105930}}
|
2023-04-20T05:59:34+00:00
|
fec0f36175ebc707d33442b1f75c1b9380294e6f
|
supremezxc/nlpcc_2017
|
[
"task_categories:summarization",
"size_categories:10K<n<100K",
"language:zh",
"license:openrail",
"region:us"
] |
2023-04-20T05:59:46+00:00
|
{"language": ["zh"], "license": "openrail", "size_categories": ["10K<n<100K"], "task_categories": ["summarization"], "pretty_name": "NLPCC2017\u4e2d\u6587\u65b0\u95fb\u6570\u636e\u96c6"}
|
2023-04-20T06:07:50+00:00
|
|
c863c5937c159783c6604e3f98373997a3140867
|
chenuneris/aurora-mix-data-baize-format
|
[
"license:gpl-3.0",
"region:us"
] |
2023-04-20T06:04:09+00:00
|
{"license": "gpl-3.0"}
|
2023-04-20T06:09:55+00:00
|
|
f261c782eb62e1e851c3d2a76f66fa744d92954e
|
# Dataset Card for "mmlu-moral_scenarios-neg"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
joey234/mmlu-moral_scenarios-neg
|
[
"region:us"
] |
2023-04-20T06:12:34+00:00
|
{"dataset_info": {"features": [{"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}, {"name": "question", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 201681, "num_examples": 895}], "download_size": 19436, "dataset_size": 201681}}
|
2023-04-20T06:12:39+00:00
|
65be19446a08b1a3cca3dd9ceb0e0f205b8582f3
|
# Dataset Card for "SG-subzone-poi-sentiment"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
cestwc/SG-subzone-poi-sentiment
|
[
"region:us"
] |
2023-04-20T06:13:14+00:00
|
{"dataset_info": {"features": [{"name": "local_created_at", "dtype": "string"}, {"name": "id", "dtype": "int64"}, {"name": "text", "dtype": "string"}, {"name": "source", "dtype": "string"}, {"name": "truncated", "dtype": "bool"}, {"name": "in_reply_to_status_id", "dtype": "float64"}, {"name": "in_reply_to_user_id", "dtype": "float64"}, {"name": "user_id", "dtype": "int64"}, {"name": "user_name", "dtype": "string"}, {"name": "user_screen_name", "dtype": "string"}, {"name": "user_location", "dtype": "string"}, {"name": "user_url", "dtype": "string"}, {"name": "user_verified", "dtype": "bool"}, {"name": "user_default_profile", "dtype": "bool"}, {"name": "user_description", "dtype": "string"}, {"name": "user_followers_count", "dtype": "int64"}, {"name": "user_friends_count", "dtype": "int64"}, {"name": "user_listed_count", "dtype": "int64"}, {"name": "user_favourites_count", "dtype": "int64"}, {"name": "user_statuses_count", "dtype": "int64"}, {"name": "local_user_created_at", "dtype": "string"}, {"name": "place_id", "dtype": "string"}, {"name": "place_url", "dtype": "string"}, {"name": "place_place_type", "dtype": "string"}, {"name": "place_name", "dtype": "string"}, {"name": "place_country_code", "dtype": "string"}, {"name": "place_bounding_box_type", "dtype": "string"}, {"name": "place_bounding_box_coordinates", "dtype": "string"}, {"name": "is_quote_status", "dtype": "bool"}, {"name": "retweet_count", "dtype": "int64"}, {"name": "favorite_count", "dtype": "int64"}, {"name": "entities_hashtags", "dtype": "string"}, {"name": "entities_urls", "dtype": "string"}, {"name": "entities_symbols", "dtype": "string"}, {"name": "entities_user_mentions", "dtype": "string"}, {"name": "favorited", "dtype": "bool"}, {"name": "retweeted", "dtype": "bool"}, {"name": "possibly_sensitive", "dtype": "bool"}, {"name": "lang", "dtype": "string"}, {"name": "latitude", "dtype": "float64"}, {"name": "longitude", "dtype": "float64"}, {"name": "year_created_at", "dtype": "int64"}, {"name": "month_created_at", "dtype": "int64"}, {"name": "day_created_at", "dtype": "int64"}, {"name": "weekday_created_at", "dtype": "int64"}, {"name": "hour_created_at", "dtype": "int64"}, {"name": "minute_created_at", "dtype": "int64"}, {"name": "year_user_created_at", "dtype": "int64"}, {"name": "month_user_created_at", "dtype": "int64"}, {"name": "day_user_created_at", "dtype": "int64"}, {"name": "weekday_user_created_at", "dtype": "int64"}, {"name": "hour_user_created_at", "dtype": "int64"}, {"name": "minute_user_created_at", "dtype": "int64"}, {"name": "subzone", "dtype": "string"}, {"name": "planning_area", "dtype": "string"}, {"name": "poi_flag", "dtype": "float64"}, {"name": "poi_id", "dtype": "string"}, {"name": "poi_dist", "dtype": "float64"}, {"name": "poi_latitude", "dtype": "float64"}, {"name": "poi_longitude", "dtype": "float64"}, {"name": "poi_name", "dtype": "string"}, {"name": "poi_type", "dtype": "string"}, {"name": "poi_cate2", "dtype": "string"}, {"name": "poi_cate3", "dtype": "string"}, {"name": "clean_text", "dtype": "string"}, {"name": "joy_score", "dtype": "float64"}, {"name": "trust_score", "dtype": "float64"}, {"name": "positive_score", "dtype": "float64"}, {"name": "sadness_score", "dtype": "float64"}, {"name": "disgust_score", "dtype": "float64"}, {"name": "anger_score", "dtype": "float64"}, {"name": "anticipation_score", "dtype": "float64"}, {"name": "negative_score", "dtype": "float64"}, {"name": "fear_score", "dtype": "float64"}, {"name": "surprise_score", "dtype": "float64"}, {"name": "words", "dtype": "string"}, {"name": "polarity_score", "dtype": "float64"}, {"name": "labels", "dtype": "int64"}], "splits": [{"name": "0203", "num_bytes": 1519418943, "num_examples": 1025135}], "download_size": 415295950, "dataset_size": 1519418943}}
|
2023-04-20T06:44:54+00:00
|
0a97b0b8bf0b1e9786cef7e099c3fd424d4f0c3d
|
# Dataset Card for "mmlu-nutrition-neg"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
joey234/mmlu-nutrition-neg
|
[
"region:us"
] |
2023-04-20T06:15:26+00:00
|
{"dataset_info": {"features": [{"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}, {"name": "question", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 89790, "num_examples": 306}], "download_size": 53834, "dataset_size": 89790}}
|
2023-04-20T06:15:30+00:00
|
0506297dc1b561aad28d99c89d78087b40bef101
|
# Dataset Card for "mmlu-philosophy-neg"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
joey234/mmlu-philosophy-neg
|
[
"region:us"
] |
2023-04-20T06:18:25+00:00
|
{"dataset_info": {"features": [{"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}, {"name": "question", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 79179, "num_examples": 311}], "download_size": 47527, "dataset_size": 79179}}
|
2023-04-20T06:18:29+00:00
|
1d0de228ce913d87197e04d2c9ebe4053866a718
|
# Dataset Card for "mmlu-prehistory-neg"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
joey234/mmlu-prehistory-neg
|
[
"region:us"
] |
2023-04-20T06:21:43+00:00
|
{"dataset_info": {"features": [{"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}, {"name": "question", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 88639, "num_examples": 324}], "download_size": 53960, "dataset_size": 88639}}
|
2023-04-20T06:21:47+00:00
|
895e7d488827f3372b9701441f8fc1ed33c03ab6
|
# Dataset Card for "mmlu-professional_accounting-neg"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
joey234/mmlu-professional_accounting-neg
|
[
"region:us"
] |
2023-04-20T06:27:39+00:00
|
{"dataset_info": {"features": [{"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}, {"name": "question", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 115569, "num_examples": 282}], "download_size": 63815, "dataset_size": 115569}}
|
2023-04-20T06:27:43+00:00
|
b5c71fc253dd4ea8fe5efff3a014ac6ef4d6c445
|
# Dataset Card for "muti-language-tatoeba_with_comment"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
bigpang/muti-language-tatoeba_with_comment
|
[
"region:us"
] |
2023-04-20T06:33:15+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "labels", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 57747479, "num_examples": 420497}, {"name": "test", "num_bytes": 7240347, "num_examples": 52564}, {"name": "valid", "num_bytes": 7255185, "num_examples": 52589}], "download_size": 42083139, "dataset_size": 72243011}}
|
2023-04-24T11:38:37+00:00
|
85d6b32f2762313714618171b9d1a65eb7408835
|
The SlovakSum dataset from the SlovakSum: Slovak News Summarization Dataset paper
|
kiviki/SlovakSum
|
[
"license:openrail",
"region:us"
] |
2023-04-20T06:41:09+00:00
|
{"license": "openrail"}
|
2023-05-05T11:14:16+00:00
|
870b599441a72e6c8e83a330aa001e63786ec13b
|
RoshanAdhithya/fyp
|
[
"region:us"
] |
2023-04-20T07:00:28+00:00
|
{}
|
2023-04-20T08:03:51+00:00
|
|
60f7b3c4b9409f75551664adc1564625dfc33c2e
|
This data accompanies the WebUI project (https://dl.acm.org/doi/abs/10.1145/3544548.3581158)
For more information, check out the project website: https://uimodeling.github.io/
To download this dataset, you need to install the huggingface-hub package
```
pip install huggingface-hub
```
Use snapshot_download
```
from huggingface_hub import snapshot_download
snapshot_download(repo_id="biglab/webui-7k", repo_type="dataset")
```
IMPORTANT
* Before downloading and using, please review the copyright info here: https://github.com/js0nwu/webui/blob/main/COPYRIGHT.txt
* Not all data samples have the same number of files (e.g., same number of device screenshots) due to the fact that the crawler used a timeout during collection
* The dataset released on HuggingFace was filtered using a list of explicit words and therefore contains fewer samples than the experiments originally used in the paper. The raw dataset is currently available (https://drive.google.com/drive/folders/1hcO75W2FjsZoibsj2TIbKz67hy9JkOBz?usp=share_link) but may be removed in the future.
|
biglab/webui-7k
|
[
"license:other",
"region:us"
] |
2023-04-20T07:01:27+00:00
|
{"license": "other"}
|
2023-05-05T01:25:39+00:00
|
451bcac174c693a10353e427723bc483948b8eca
|
# Dataset Card for German REBEL Dataset
### Dataset Summary
This dataset is the German version of Babelscape/rebel-dataset. It has been generated using [CROCODILE](https://github.com/Babelscape/crocodile).
The Wikipedia Version is from November 2022.
### Languages
- German
## Dataset Structure
```
{"docid": "9400003",
"title": "Odin-Gletscher",
"uri": "Q7077818",
"text": "Der Odin-Gletscher ist ein kleiner Gletscher im ostantarktischen Viktorialand. Er fließt von den Westhängen des Mount Odin in der Asgard Range.\n\nDas New Zealand Antarctic Place-Names Committee benannte ihn in Anlehnung an die Benennung des Mount Odin nach Odin, Göttervater, Kriegs- und Totengott der nordischen Mythologie.",
"entities": [{"uri": "Q35666", "boundaries": [35, 44], "surfaceform": "Gletscher", "annotator": "Me"}, ... ],
"triples": [{"subject": {"uri": "Q7077818", "boundaries": [4, 18], "surfaceform": "Odin-Gletscher", "annotator": "Me"},
"predicate": {"uri": "P31", "boundaries": null, "surfaceform": "ist ein(e)", "annotator": "NoSubject-Triple-aligner"},
"object": {"uri": "Q35666", "boundaries": [35, 44], "surfaceform": "Gletscher", "annotator": "Me"}, "sentence_id": 0,
"dependency_path": null,
"confidence": 0.99560546875,
"annotator": "NoSubject-Triple-aligner"}, ...]
}
```
### Data Instances
The dataset is 1.1GB if unpacked on the system. 195MB if zipped.
### Data Fields
"docid": "9644601",
"title": Wikipedia Title
"uri": "Q4290759",
"text": Wikipedia Abstract
"entities": A list of Entities
- uri: Wikidata URI
- boundaries: Tuple of indices of the entity in the abstract
- surfaceform: text form of entity
- annotator: different annotator classes
"triples": List of Triples as dictionaries
- sentence_id: Sentence number the triple appears in.
- "confidence": float, the confidence of the NLI Model
- subject
- uri: Wikidata Entity URI
- boundaries
- surfaceform
- annotator
- predicate
- uri: Wikidata Relation URI
- boundaries: always null,
- surfaceform: Wikidata Relation Name
- annotator
- object:
- uri: Wikidata Entity URI
- boundaries
- surfaceform
- annotator
### Data Splits
No splits are provided for now since the relation classes are quite imbalanced.
To read the dataset you can adapt the function provided by https://github.com/Babelscape/rebel
```
def _generate_examples(self, filepath):
"""This function returns the examples in the raw (text) form."""
logging.info("generating examples from = %s", filepath)
relations_df = pd.read_csv(self.config.data_files['relations'], header = None, sep='\t')
relations = list(relations_df[0])
with open(filepath, encoding="utf-8") as f:
for id_, row in enumerate(f):
article = json.loads(row)
prev_len = 0
if len(article['triples']) == 0:
continue
count = 0
for text_paragraph in article['text'].split('\n'):
if len(text_paragraph) == 0:
continue
sentences = re.split(r'(?<=[.])\s', text_paragraph)
text = ''
for sentence in sentences:
text += sentence + ' '
if any([entity['boundaries'][0] < len(text) + prev_len < entity['boundaries'][1] for entity in article['entities']]):
continue
entities = sorted([entity for entity in article['entities'] if prev_len < entity['boundaries'][1] <= len(text)+prev_len], key=lambda tup: tup['boundaries'][0])
decoder_output = '<triplet> '
for int_ent, entity in enumerate(entities):
triplets = sorted([triplet for triplet in article['triples'] if triplet['subject'] == entity and prev_len< triplet['subject']['boundaries'][1]<=len(text) + prev_len and prev_len< triplet['object']['boundaries'][1]<=len(text)+ prev_len and triplet['predicate']['surfaceform'] in relations], key=lambda tup: tup['object']['boundaries'][0])
if len(triplets) == 0:
continue
decoder_output += entity['surfaceform'] + ' <subj> '
for triplet in triplets:
decoder_output += triplet['object']['surfaceform'] + ' <obj> ' + triplet['predicate']['surfaceform'] + ' <subj> '
decoder_output = decoder_output[:-len(' <subj> ')]
decoder_output += ' <triplet> '
decoder_output = decoder_output[:-len(' <triplet> ')]
count += 1
prev_len += len(text)
if len(decoder_output) == 0:
text = ''
continue
text = re.sub('([\[\].,!?()])', r' \1 ', text.replace('()', ''))
text = re.sub('\s{2,}', ' ', text)
yield article['uri'] + '-' + str(count), {
"title": article['title'],
"context": text,
"id": article['uri'] + '-' + str(count),
"triplets": decoder_output,
}
text = ''
```
## Dataset Creation
### Curation Rationale
This dataset was created to enable the training of a german BART based model as pre-training phase for Relation Extraction.
### Source Data
#### Who are the source language producers?
Any Wikipedia and Wikidata contributor.
### Annotations
#### Annotation process
The dataset extraction pipeline cRocoDiLe: Automatic Relation Extraction Dataset with NLI filtering.
#### Who are the annotators?
Automatic annottations
### Personal and Sensitive Information
All text is from Wikipedia, any Personal or Sensitive Information there may be present in this dataset.
## Considerations for Using the Data
### Social Impact of Dataset
The dataset serves as a pre-training step for Relation Extraction models. It is distantly annotated, hence it should only be used as such. A model trained solely on this dataset may produce allucinations coming from the silver nature of the dataset.
### Discussion of Biases
Since the dataset was automatically created from Wikipedia and Wikidata, it may reflect the biases withing those sources.
For Wikipedia text, see for example Dinan et al 2020 on biases in Wikipedia (esp. Table 1), or Blodgett et al 2020 for a more general discussion of the topic.
For Wikidata, there are class imbalances, also resulting from Wikipedia.
### Other Known Limitations
Not for now
## Additional Information
### Dataset Curators
Me
### Licensing Information
Since anyone can create the dataset on their own using the linked GitHub Repository, I am going to use the MIT Licence.
### Citation Information
Inspiration by:
```
@inproceedings{huguet-cabot-navigli-2021-rebel,
title = "REBEL: Relation Extraction By End-to-end Language generation",
author = "Huguet Cabot, Pere-Llu{\'\i}s and
Navigli, Roberto",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2021",
month = nov,
year = "2021",
address = "Online and in the Barceló Bávaro Convention Centre, Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://github.com/Babelscape/rebel/blob/main/docs/EMNLP_2021_REBEL__Camera_Ready_.pdf",
}
```
### Contributions
None for now
|
mingaflo/rebel-dataset-de
|
[
"task_categories:summarization",
"size_categories:100K<n<1M",
"language:de",
"license:mit",
"wikipedia",
"wikidata",
"Relation Extraction",
"REBEL",
"region:us"
] |
2023-04-20T07:04:26+00:00
|
{"language": ["de"], "license": "mit", "size_categories": ["100K<n<1M"], "task_categories": ["summarization"], "pretty_name": "German REBEL Dataset", "tags": ["wikipedia", "wikidata", "Relation Extraction", "REBEL"]}
|
2023-04-20T08:14:28+00:00
|
2312ad12c6df0715fd808a934cc37ada255517b5
|
This data accompanies the WebUI project (https://dl.acm.org/doi/abs/10.1145/3544548.3581158)
For more information, check out the project website: https://uimodeling.github.io/
To download this dataset, you need to install the huggingface-hub package
```
pip install huggingface-hub
```
Use snapshot_download
```
from huggingface_hub import snapshot_download
snapshot_download(repo_id="biglab/webui-7kbal", repo_type="dataset")
```
IMPORTANT
* Before downloading and using, please review the copyright info here: https://github.com/js0nwu/webui/blob/main/COPYRIGHT.txt
* Not all data samples have the same number of files (e.g., same number of device screenshots) due to the fact that the crawler used a timeout during collection
* The dataset released on HuggingFace was filtered using a list of explicit words and therefore contains fewer samples than the experiments originally used in the paper. The raw dataset is currently available (https://drive.google.com/drive/folders/1hcO75W2FjsZoibsj2TIbKz67hy9JkOBz?usp=share_link) but may be removed in the future.
|
biglab/webui-7kbal
|
[
"license:other",
"region:us"
] |
2023-04-20T07:05:44+00:00
|
{"license": "other"}
|
2023-05-05T01:25:55+00:00
|
ecb3234834881ff9558653c5702169e1635235a4
|
# Dataset Card for "mmlu-professional_law-neg"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
joey234/mmlu-professional_law-neg
|
[
"region:us"
] |
2023-04-20T07:10:51+00:00
|
{"dataset_info": {"features": [{"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}, {"name": "question", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 1019787, "num_examples": 1534}], "download_size": 554777, "dataset_size": 1019787}}
|
2023-04-20T07:10:55+00:00
|
946824b8fa16fcfade1a70074c247dd536777f5a
|
# Dataset Card for "mmlu-professional_medicine-neg"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
joey234/mmlu-professional_medicine-neg
|
[
"region:us"
] |
2023-04-20T07:18:22+00:00
|
{"dataset_info": {"features": [{"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}, {"name": "question", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 103575, "num_examples": 272}], "download_size": 61144, "dataset_size": 103575}}
|
2023-04-20T07:18:26+00:00
|
d2e9732990d76c0bf33c9ddf6f8a111476c0c35d
|
Sridevi/python_textbooks
|
[
"size_categories:100K<n<1M",
"license:mit",
"programming",
"Python",
"doi:10.57967/hf/0554",
"region:us"
] |
2023-04-20T07:25:38+00:00
|
{"license": "mit", "size_categories": ["100K<n<1M"], "dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 39456597, "num_examples": 25572}], "download_size": 18762065, "dataset_size": 39456597}, "tags": ["programming", "Python"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-28T03:35:53+00:00
|
|
9ff0c4bcb59f8eeb0d19f36880b9488fed713373
|
# Dataset Card for "mmlu-professional_psychology-neg"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
joey234/mmlu-professional_psychology-neg
|
[
"region:us"
] |
2023-04-20T07:26:46+00:00
|
{"dataset_info": {"features": [{"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}, {"name": "question", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 217390, "num_examples": 612}], "download_size": 128292, "dataset_size": 217390}}
|
2023-04-20T07:26:51+00:00
|
a277fa8f38fd5877c34a3f69d563a131113e55e4
|
# Dataset Card for "ko_en_parallel_dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Heerak/ko_en_parallel_dataset
|
[
"region:us"
] |
2023-04-20T07:27:44+00:00
|
{"dataset_info": {"features": [{"name": "ko", "dtype": "string"}, {"name": "en", "dtype": "string"}, {"name": "__index_level_0__", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 4112684317, "num_examples": 11800415}, {"name": "validation", "num_bytes": 20767480, "num_examples": 59299}, {"name": "test", "num_bytes": 419935, "num_examples": 1982}], "download_size": 2691575595, "dataset_size": 4133871732}}
|
2023-04-20T07:51:52+00:00
|
0b93e72383316d5f46d0b8384943505ba809efe4
|
# Dataset Card for "batch_indexing_machine_100_small_imgs"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Circularmachines/batch_indexing_machine_100_small_imgs
|
[
"region:us"
] |
2023-04-20T07:27:48+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 14045789.0, "num_examples": 100}], "download_size": 14047503, "dataset_size": 14045789.0}}
|
2023-04-20T07:27:50+00:00
|
d6de57607917c9265c251354a2a5d255a3334413
|
# Dataset Card for "mmlu-public_relations-neg"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
joey234/mmlu-public_relations-neg
|
[
"region:us"
] |
2023-04-20T07:28:11+00:00
|
{"dataset_info": {"features": [{"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}, {"name": "question", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 28027, "num_examples": 110}], "download_size": 20024, "dataset_size": 28027}}
|
2023-04-20T07:28:15+00:00
|
78744e0831dfaf2d31cc996903026d8dcbe19c34
|
# Dataset Card for "mmlu-security_studies-neg"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
joey234/mmlu-security_studies-neg
|
[
"region:us"
] |
2023-04-20T07:30:21+00:00
|
{"dataset_info": {"features": [{"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}, {"name": "question", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 203688, "num_examples": 245}], "download_size": 113721, "dataset_size": 203688}}
|
2023-04-20T07:30:26+00:00
|
479ab191f19dcfebb80cbbc87b6da3d9535f5b29
|
# Dataset Card for "mmlu-sociology-neg"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
joey234/mmlu-sociology-neg
|
[
"region:us"
] |
2023-04-20T07:32:07+00:00
|
{"dataset_info": {"features": [{"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}, {"name": "question", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 65025, "num_examples": 201}], "download_size": 43079, "dataset_size": 65025}}
|
2023-04-20T07:32:11+00:00
|
9cd4cbc708b840d926183d774381c79dc551856f
|
# Dataset Card for "mmlu-us_foreign_policy-neg"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
joey234/mmlu-us_foreign_policy-neg
|
[
"region:us"
] |
2023-04-20T07:33:02+00:00
|
{"dataset_info": {"features": [{"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}, {"name": "question", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 28060, "num_examples": 100}], "download_size": 18869, "dataset_size": 28060}}
|
2023-04-20T07:33:06+00:00
|
04b7c30bf4a54602302bf9188a099187d0ed01a1
|
simonduerr/ketcher-2.7.2
|
[
"license:apache-2.0",
"region:us"
] |
2023-04-20T07:34:08+00:00
|
{"license": "apache-2.0"}
|
2023-04-20T07:37:16+00:00
|
|
2047a05e3bf1622cddcb571a935664550ff0a98c
|
# Dataset Card for "mmlu-virology-neg"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
joey234/mmlu-virology-neg
|
[
"region:us"
] |
2023-04-20T07:34:35+00:00
|
{"dataset_info": {"features": [{"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}, {"name": "question", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 36529, "num_examples": 166}], "download_size": 25606, "dataset_size": 36529}}
|
2023-04-20T07:34:38+00:00
|
7547abd2a45120b480bef920c0ba40e04e797392
|
# Dataset Card for "mmlu-world_religions-neg"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
joey234/mmlu-world_religions-neg
|
[
"region:us"
] |
2023-04-20T07:36:02+00:00
|
{"dataset_info": {"features": [{"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}, {"name": "question", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 24737, "num_examples": 171}], "download_size": 18201, "dataset_size": 24737}}
|
2023-04-20T07:36:07+00:00
|
8c2a4a217488d9c5d1291995aa3aeee6734957b3
|
# Dataset Card for Anything v3.0 Glazed Samples
## Dataset Description
### Dataset Summary
This dataset contains image samples originally generated by [Linaqruf/anything-v3.0](https://huggingface.co/Linaqruf/anything-v3.0)
and subsequently processed by [Glaze](https://glaze.cs.uchicago.edu/) tool.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed]
|
hanamizuki-ai/anything-v3.0-glazed
|
[
"task_categories:image-classification",
"task_categories:image-to-image",
"license:creativeml-openrail-m",
"art",
"region:us"
] |
2023-04-20T07:47:45+00:00
|
{"license": "creativeml-openrail-m", "task_categories": ["image-classification", "image-to-image"], "tags": ["art"], "dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "parent_id", "dtype": "string"}, {"name": "model", "dtype": "string"}, {"name": "prompt", "dtype": "string"}, {"name": "glaze_model", "dtype": "string"}, {"name": "glaze_intensity", "dtype": "int64"}, {"name": "glaze_render", "dtype": "int64"}, {"name": "glaze_style", "dtype": "string"}, {"name": "glaze_style_strength", "dtype": "float64"}, {"name": "image", "dtype": "image"}, {"name": "parent_image", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 96564915991.925, "num_examples": 89235}], "download_size": 9066695101, "dataset_size": 96564915991.925}}
|
2023-04-21T10:52:12+00:00
|
1eec973ecf6f7a679671d1a25f7be475289128ad
|
# Dataset Card for "ko_jp_parallel_dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Heerak/ko_jp_parallel_dataset
|
[
"region:us"
] |
2023-04-20T07:51:54+00:00
|
{"dataset_info": {"features": [{"name": "ko", "dtype": "string"}, {"name": "jp", "dtype": "string"}, {"name": "__index_level_0__", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 794083720, "num_examples": 3548630}, {"name": "validation", "num_bytes": 4010514, "num_examples": 17833}, {"name": "test", "num_bytes": 461625, "num_examples": 1982}], "download_size": 559459681, "dataset_size": 798555859}}
|
2023-04-20T07:54:25+00:00
|
9460721169eeb357d8adad012cde096f0cd17a75
|
# Dataset Card for "reklamation24_mode-schmuck-zubehoer-v2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
fathyshalab/MDCSI_mode-schmuck-zubehoer
|
[
"region:us"
] |
2023-04-20T08:09:32+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "label_name", "dtype": "string"}, {"name": "__index_level_0__", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 149145, "num_examples": 348}, {"name": "test", "num_bytes": 37617, "num_examples": 88}], "download_size": 101309, "dataset_size": 186762}}
|
2023-04-30T19:30:38+00:00
|
29a764004868f940fc81e49d1881ab874ec4580c
|
# Dataset Card for "reklamation24_moebel-einrichtungshaeuser-v2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
fathyshalab/MDCSI_moebel-einrichtungshaeuser
|
[
"region:us"
] |
2023-04-20T08:11:23+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "label_name", "dtype": "string"}, {"name": "__index_level_0__", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 85281, "num_examples": 182}, {"name": "test", "num_bytes": 19920, "num_examples": 46}], "download_size": 61610, "dataset_size": 105201}}
|
2023-04-30T19:32:59+00:00
|
c54ad6ea5adfafab53770708b9449dfea2f49cba
|
# Dataset Card for "reklamation24_reisen-tourismus-v2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
fathyshalab/MDCSI_reisen-tourismus
|
[
"region:us"
] |
2023-04-20T08:17:54+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "label_name", "dtype": "string"}, {"name": "__index_level_0__", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 201180, "num_examples": 367}, {"name": "test", "num_bytes": 47554, "num_examples": 92}], "download_size": 135398, "dataset_size": 248734}}
|
2023-04-30T19:40:25+00:00
|
22ad375381a9a082335eba6e8d2b0ab82861783a
|
# Dataset Card for "reklamation24_unternehmen-verbaende-v2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
fathyshalab/MDCSI_unternehmen-verbaende
|
[
"region:us"
] |
2023-04-20T08:20:23+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "label_name", "dtype": "string"}, {"name": "__index_level_0__", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 192483, "num_examples": 430}, {"name": "test", "num_bytes": 49674, "num_examples": 108}], "download_size": 129643, "dataset_size": 242157}}
|
2023-04-30T19:43:01+00:00
|
83446417001279b9e3861c9040441cdb0d788384
|
# Dataset Card for "reklamation24_medizin-gesundheit-pflege-v2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
fathyshalab/MDCSI_medizin-gesundheit-pflege
|
[
"region:us"
] |
2023-04-20T08:22:11+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "label_name", "dtype": "string"}, {"name": "__index_level_0__", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 181792, "num_examples": 403}, {"name": "test", "num_bytes": 46406, "num_examples": 101}], "download_size": 116667, "dataset_size": 228198}}
|
2023-04-30T19:45:00+00:00
|
d1f929a82ce65221e37bcfb0604a80b5f2f746a3
|
# Dataset Card for "reklamation24_transport-logistik-v2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
fathyshalab/MDCSI_transport-logistik
|
[
"region:us"
] |
2023-04-20T08:23:52+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "label_name", "dtype": "string"}, {"name": "__index_level_0__", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 175764, "num_examples": 352}, {"name": "test", "num_bytes": 45011, "num_examples": 88}], "download_size": 120959, "dataset_size": 220775}}
|
2023-04-30T19:46:55+00:00
|
566fda0b1c3eb63ab3d59ed038c925dfca5f59cd
|
# Dataset Card for "reklamation24_versicherungen-recht-v2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
fathyshalab/MDCSI_versicherungen-recht
|
[
"region:us"
] |
2023-04-20T08:24:27+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "label_name", "dtype": "string"}, {"name": "__index_level_0__", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 189809, "num_examples": 348}, {"name": "test", "num_bytes": 48549, "num_examples": 88}], "download_size": 131049, "dataset_size": 238358}}
|
2023-04-30T19:47:30+00:00
|
1fdd692293f194823f4f5ea2d3d06f341306e8c1
|
# Dataset Card for "reklamation24_oeffentlichkeit-soziales-v2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
fathyshalab/MDCSI_oeffentlichkeit-soziales
|
[
"region:us"
] |
2023-04-20T08:24:39+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "label_name", "dtype": "string"}, {"name": "__index_level_0__", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 58929, "num_examples": 108}, {"name": "test", "num_bytes": 14508, "num_examples": 27}], "download_size": 45545, "dataset_size": 73437}}
|
2023-04-30T19:47:43+00:00
|
e64444063e9739828258ae3e0217ef2c934e8367
|
# Dataset Card for "reklamation24_oeffentlicher-verkehr-vermietung-v2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
fathyshalab/MDCSI_oeffentlicher-verkehr-vermietung
|
[
"region:us"
] |
2023-04-20T08:26:11+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "label_name", "dtype": "string"}, {"name": "__index_level_0__", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 183476, "num_examples": 337}, {"name": "test", "num_bytes": 46611, "num_examples": 85}], "download_size": 132553, "dataset_size": 230087}}
|
2023-04-30T19:49:16+00:00
|
603392ce3fbc511a9ced6c792f3076e7618bbed0
|
# Dataset Card for "reklamation24_unterhaltung-kultur-freizeit-v2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
fathyshalab/MDCSI_unterhaltung-kultur-freizeit
|
[
"region:us"
] |
2023-04-20T08:27:10+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "label_name", "dtype": "string"}, {"name": "__index_level_0__", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 131176, "num_examples": 269}, {"name": "test", "num_bytes": 33300, "num_examples": 68}], "download_size": 87720, "dataset_size": 164476}}
|
2023-04-30T19:50:36+00:00
|
b5dc62b03a8d6decdba89c6374876e6edea622eb
|
# Dataset Card for "reklamation24_wasser-strom-gas-v2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
fathyshalab/MDCSI_wasser-strom-gas
|
[
"region:us"
] |
2023-04-20T08:27:53+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "label_name", "dtype": "string"}, {"name": "__index_level_0__", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 234463, "num_examples": 475}, {"name": "test", "num_bytes": 57479, "num_examples": 119}], "download_size": 154727, "dataset_size": 291942}}
|
2023-04-30T19:51:39+00:00
|
7af24e206cf552de95a0a8cf354a789766460b71
|
# Dataset Card for "natural-instruction-397"
## Dataset Description
In this task you are given a tweet. You must judge whether the author of the tweet is angry or not. Label the instances as "Angry" or "Not angry" based on your judgment.
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
mattymchen/natural-instruction-397
|
[
"region:us"
] |
2023-04-20T08:53:42+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "label", "dtype": "int64"}], "splits": [{"name": "test", "num_bytes": 277899, "num_examples": 2602}], "download_size": 199557, "dataset_size": 277899}}
|
2023-04-20T08:55:30+00:00
|
a3d918e3b79846565745551a1414f3a9b5112707
|
# Dataset Card for "natural-instruction-398"
## Dataset Description
In this task you are given a tweet. You must judge whether the author of the tweet is happy or not. Label the instances as "Happy" or "Not happy" based on your judgment.
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
mattymchen/natural-instruction-398
|
[
"region:us"
] |
2023-04-20T08:53:56+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "label", "dtype": "int64"}], "splits": [{"name": "test", "num_bytes": 195621, "num_examples": 1795}], "download_size": 140976, "dataset_size": 195621}}
|
2023-04-20T08:56:05+00:00
|
2e9ce079603332a5359faf42079738a8e210459f
|
# Dataset Card for "natural-instruction-399"
## Dataset Description
In this task you are given a tweet. You must judge whether the author of the tweet is sad or not. Label the instances as "Sad" or "Not sad" based on your judgment. You can get help from hashtags and emojis, but you should not judge only based on them, and should pay attention to tweet\'s text as well.
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
mattymchen/natural-instruction-399
|
[
"region:us"
] |
2023-04-20T08:54:21+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "label", "dtype": "int64"}], "splits": [{"name": "test", "num_bytes": 312100, "num_examples": 2899}], "download_size": 223665, "dataset_size": 312100}}
|
2023-04-20T08:56:42+00:00
|
d530c921add4c7551b4e2309e0e7fe9468278311
|
# Dataset Card for "moss-002-sft-data"
## Dataset Description
- **Homepage:** [https://txsun1997.github.io/blogs/moss.html](https://txsun1997.github.io/blogs/moss.html)
- **Repository:** [https://github.com/OpenLMLab/MOSS](https://github.com/OpenLMLab/MOSS)
- **Total amount of disk used:** 2.16 GB
### Dataset Summary
An open-source conversational dataset that was used to train MOSS-002. The user prompts are extended based on a small set of human-written seed prompts in a way similar to [Self-Instruct](https://arxiv.org/abs/2212.10560). The AI responses are generated using `text-davinci-003`. The user prompts of `en_harmlessness` are from [Anthropic red teaming data](https://github.com/anthropics/hh-rlhf/tree/master/red-team-attempts).
### Data Splits
| name | \# samples |
|----------------------|-----------:|
| en_helpfulness.json | 419049 |
| en_honesty.json | 112580 |
| en_harmlessness.json | 38873 |
| zh_helpfulness.json | 447750 |
| zh_honesty.json | 142885 |
|
fnlp/moss-002-sft-data
|
[
"task_categories:conversational",
"task_categories:text-generation",
"size_categories:1M<n<10M",
"language:en",
"language:zh",
"license:cc-by-4.0",
"arxiv:2212.10560",
"region:us"
] |
2023-04-20T09:14:09+00:00
|
{"language": ["en", "zh"], "license": "cc-by-4.0", "size_categories": ["1M<n<10M"], "task_categories": ["conversational", "text-generation"]}
|
2023-04-20T15:17:16+00:00
|
6d9d1a531db26e0becad51646e87fa54203cf9d4
|
OKR/OKR33
|
[
"license:openrail",
"region:us"
] |
2023-04-20T09:17:28+00:00
|
{"license": "openrail"}
|
2023-04-20T09:17:28+00:00
|
|
8440d7706d2fa532db9607536e5087108b64656a
|
stellar025/sd-webui
|
[
"license:openrail",
"region:us"
] |
2023-04-20T09:19:20+00:00
|
{"license": "openrail"}
|
2023-05-26T07:26:18+00:00
|
|
1fe26fe761a78a97969041910385cf3a3bccf684
|
BlackKakapo/newsagro-ro
|
[
"task_categories:text2text-generation",
"task_categories:text-generation",
"size_categories:1K<n<10K",
"language:ro",
"license:apache-2.0",
"region:us"
] |
2023-04-20T10:00:55+00:00
|
{"language": ["ro"], "license": "apache-2.0", "size_categories": ["1K<n<10K"], "task_categories": ["text2text-generation", "text-generation"]}
|
2023-04-20T10:03:26+00:00
|
|
088620ceed6691330a40c7ffea9deee35c5079ee
|
## Every Prompt
Every Prompt is a data-driven approach to mining instructions from the web.
It contains over a million FAQs and HowTos from around the world in a structured format.
It also has basic pre-processing to calculate the length of the useful text and identify the language of that text with the help of [GCLD3](https://github.com/google/cld3)
It relies on the [Web Data Commons](http://webdatacommons.org) dataset (from October 2022) to find the seed list of sites with [**HowTo**](https://schema.org/HowTo) and [**FAQPage**](https://schema.org/FAQPage) items.
The general pipeline looks like this:
* Download 1.6TB of structured data from webdatacommons to identify the pages with the structured data we need (wget/parallel). That gives us 1,985,925 seed pages
* Crawls the seed pages and tries to extract structured data using [extruct](https://pypi.org/project/extruct/#description) package. That left around 1,358,638 pages which are alive and well-formed.
* Extracts only the relevant structured data of the HowTo/FAQPage type with the help of jmespath. That boils down to 1,266,926 json documents.
* Extracts the textual information out of the structure to identify the text's language, the textual data's length, and the text/data ratio.
You can use the resulting dataset by filtering for the language and amount of the text. You need to convert the structured data into instructions yourself.
You'll need to apply extra cleansing/evaluation of the instructions you've got because, you know, the internet is still full of crap.
**Caveat emptor**: the format of the FAQs and HowTo's in the dataset might vary greatly. Account for that. To understand potential pitfalls, look at the jmespath expression at the `export_structured_data.py`.
## Detailed stats (with breakdown by language and data type)
| language | FAQPage count | FAQPage text length | HowTo count | HowTo text length | items count | text length |
| --- | --- | --- | --- | --- | --- | --- |
| en | 592730 | 1186748927 | 29017 | 77135350 | 621747 | 1263884277 |
| de | 83184 | 213931486 | 3370 | 13905977 | 86554 | 227837463 |
| es | 63237 | 113906536 | 6466 | 30517773 | 69703 | 144424309 |
| fr | 65081 | 141638675 | 3672 | 21632272 | 68753 | 163270947 |
| ja | 55439 | 46231152 | 1402 | 1678468 | 56841 | 47909620 |
| ru | 41271 | 70947161 | 2403 | 12805308 | 43674 | 83752469 |
| nl | 34066 | 102719276 | 2007 | 11078079 | 36073 | 113797355 |
| it | 23076 | 43968063 | 2465 | 13696136 | 25541 | 57664199 |
| vi | 23115 | 38603954 | 720 | 3224051 | 23835 | 41828005 |
| zh | 22496 | 21111729 | 1112 | 1513344 | 23608 | 22625073 |
| pl | 19424 | 41446645 | 306 | 419787 | 19730 | 41866432 |
| fa | 17263 | 31294557 | 1819 | 1915117 | 19082 | 33209674 |
| tr | 13619 | 20040069 | 722 | 418695 | 14341 | 20458764 |
| und | 12256 | 1032156 | 322 | 8941 | 12578 | 1041097 |
| pt | 10784 | 26163387 | 1775 | 8295306 | 12559 | 34458693 |
| ro | 10536 | 16405628 | 75 | 89946 | 10611 | 16495574 |
| id | 8256 | 14353165 | 1871 | 13055561 | 10127 | 27408726 |
| ko | 8348 | 7624222 | 616 | 1533830 | 8964 | 9158052 |
| sv | 8007 | 15926376 | 390 | 638054 | 8397 | 16564430 |
| ar | 6950 | 10240266 | 1241 | 7517175 | 8191 | 17757441 |
| da | 7691 | 15277244 | 408 | 450176 | 8099 | 15727420 |
| cs | 7546 | 13201121 | 480 | 2471544 | 8026 | 15672665 |
| fi | 7767 | 14468764 | 199 | 170138 | 7966 | 14638902 |
| hi | 4517 | 4307716 | 683 | 4294129 | 5200 | 8601845 |
| hu | 4866 | 10639836 | 125 | 61118 | 4991 | 10700954 |
| el | 4600 | 10555382 | 103 | 55576 | 4703 | 10610958 |
| no | 4357 | 8426887 | 179 | 354796 | 4536 | 8781683 |
| uk | 4401 | 6925331 | 90 | 37285 | 4491 | 6962616 |
| iw | 4056 | 7723904 | 36 | 35305 | 4092 | 7759209 |
| bg | 3620 | 10154727 | 41 | 31268 | 3661 | 10185995 |
| sk | 2639 | 4394140 | 65 | 32527 | 2704 | 4426667 |
| th | 1877 | 3823867 | 613 | 3171583 | 2490 | 6995450 |
| mr | 2002 | 2274197 | 57 | 75906 | 2059 | 2350103 |
| mt | 1886 | 3761332 | 14 | 5443 | 1900 | 3766775 |
| cy | 1524 | 3171667 | 25 | 11641 | 1549 | 3183308 |
| bs | 1366 | 2031881 | 34 | 23298 | 1400 | 2055179 |
| et | 1299 | 1694117 | 5 | 2005 | 1304 | 1696122 |
| ms | 989 | 1927545 | 174 | 720492 | 1163 | 2648037 |
| ca | 1068 | 1614073 | 62 | 34072 | 1130 | 1648145 |
| lt | 1056 | 2272916 | 44 | 57169 | 1100 | 2330085 |
| ne | 966 | 771410 | 29 | 28569 | 995 | 799979 |
| hr | 796 | 1394174 | 15 | 10191 | 811 | 1404365 |
| fy | 743 | 633705 | 24 | 5823 | 767 | 639528 |
| lb | 703 | 1133527 | 18 | 3985 | 721 | 1137512 |
| gl | 628 | 1159618 | 34 | 9049 | 662 | 1168667 |
| mn | 644 | 1174921 | 11 | 3592 | 655 | 1178513 |
| la | 635 | 363380 | 13 | 2009 | 648 | 365389 |
| af | 577 | 444351 | 38 | 14403 | 615 | 458754 |
| sl | 451 | 1708497 | 50 | 50361 | 501 | 1758858 |
| ht | 455 | 223768 | 13 | 4406 | 468 | 228174 |
| lv | 317 | 1017694 | 32 | 31983 | 349 | 1049677 |
| gd | 273 | 295170 | 52 | 20374 | 325 | 315544 |
| sr | 287 | 367782 | 23 | 5177 | 310 | 372959 |
| co | 288 | 284629 | 12 | 3530 | 300 | 288159 |
| az | 268 | 273548 | 9 | 13011 | 277 | 286559 |
| fil | 210 | 165520 | 63 | 77100 | 273 | 242620 |
| jv | 244 | 153411 | 14 | 75932 | 258 | 229343 |
| sn | 239 | 175459 | 10 | 8890 | 249 | 184349 |
| bn | 190 | 301199 | 42 | 23451 | 232 | 324650 |
| ga | 198 | 263174 | 30 | 12905 | 228 | 276079 |
| mg | 201 | 53082 | 18 | 6141 | 219 | 59223 |
| hi-Latn | 194 | 250495 | 4 | 33091 | 198 | 283586 |
| hmn | 173 | 793850 | 16 | 5902 | 189 | 799752 |
| ka | 162 | 262305 | 8 | 3427 | 170 | 265732 |
| ig | 136 | 129243 | 10 | 2941 | 146 | 132184 |
| is | 139 | 236415 | 4 | 1277 | 143 | 237692 |
| ta | 129 | 155042 | 12 | 4079 | 141 | 159121 |
| kk | 102 | 152629 | 28 | 11885 | 130 | 164514 |
| eu | 118 | 130847 | 10 | 3522 | 128 | 134369 |
| eo | 121 | 69071 | 6 | 1885 | 127 | 70956 |
| ur | 93 | 259680 | 33 | 20499 | 126 | 280179 |
| so | 112 | 203877 | 6 | 2151 | 118 | 206028 |
| tg | 99 | 73437 | 16 | 5539 | 115 | 78976 |
| mk | 29 | 62730 | 84 | 391780 | 113 | 454510 |
| be | 100 | 88386 | 8 | 2193 | 108 | 90579 |
| sm | 100 | 1309239 | 8 | 2778 | 108 | 1312017 |
| uz | 93 | 116820 | 7 | 2987 | 100 | 119807 |
| zu | 84 | 136023 | 9 | 2744 | 93 | 138767 |
| haw | 81 | 59685 | 6 | 822 | 87 | 60507 |
| sq | 74 | 120593 | 12 | 6205 | 86 | 126798 |
| ny | 78 | 19403 | 6 | 2046 | 84 | 21449 |
| hy | 66 | 81675 | 10 | 3613 | 76 | 85288 |
| ha | 44 | 84457 | 19 | 68032 | 63 | 152489 |
| ru-Latn | 60 | 40266 | 1 | 61 | 61 | 40327 |
| el-Latn | 57 | 55657 | 4 | 342 | 61 | 55999 |
| zh-Latn | 58 | 27522 | 1 | 66 | 59 | 27588 |
| sd | 52 | 51341 | 7 | 2044 | 59 | 53385 |
| su | 50 | 17291 | 7 | 2358 | 57 | 19649 |
| ku | 47 | 23147 | 6 | 1998 | 53 | 25145 |
| bg-Latn | 48 | 15419 | 1 | 414 | 49 | 15833 |
| st | 25 | 65162 | 19 | 6346 | 44 | 71508 |
| yo | 37 | 103685 | 6 | 1790 | 43 | 105475 |
| ceb | 41 | 72950 | 1 | 107 | 42 | 73057 |
| ky | 30 | 23062 | 10 | 3679 | 40 | 26741 |
| te | 32 | 42803 | 7 | 2558 | 39 | 45361 |
| yi | 32 | 227267 | 7 | 2443 | 39 | 229710 |
| mi | 26 | 10132 | 11 | 2915 | 37 | 13047 |
| gu | 25 | 37857 | 10 | 4608 | 35 | 42465 |
| ja-Latn | 33 | 17560 | 2 | 88 | 35 | 17648 |
| sw | 26 | 17579 | 8 | 2726 | 34 | 20305 |
| xh | 28 | 46466 | 4 | 1409 | 32 | 47875 |
| ml | 16 | 33198 | 6 | 2721 | 22 | 35919 |
| ps | 10 | 7671 | 12 | 2642 | 22 | 10313 |
| am | 6 | 8017 | 8 | 1987 | 14 | 10004 |
| kn | 5 | 22197 | 9 | 3523 | 14 | 25720 |
| km | 7 | 8936 | 6 | 1879 | 13 | 10815 |
| pa | 10 | 26617 | 3 | 1100 | 13 | 27717 |
| si | 5 | 24000 | 5 | 1722 | 10 | 25722 |
| lo | 1 | 6204 | 7 | 2115 | 8 | 8319 |
| my | 3 | 14663 | 3 | 1179 | 6 | 15842 |
## Recreating the results
1. Clone the repo without the LFS files.
2. Install requirements from `requirements.txt`.
3. Install `pv` and `parallel`.
4. Run `bin/get_seed_urls.sh` to filter urls of interest out of 1.6TB of compressed data. Don't worry about disk space. Worry about the traffic. That will take around 5h on decent connection.
5. Run scrapy spider like this `scrapy crawl webdatacommons_org -s WEB_DATA_COMMONS=web_data_commons_urls_sample.txt -L INFO -o webdatacommons.jsonlines` with `WEB_DATA_COMMONS` pointing to the list of seed URLs from step 4. That might take up to a few weeks.
6. Run `python bin/extract_relevant_structured_data.py --num-threads 12 webdatacommons.jsonlines relevant.jsonlines.bz2`. That's fast, probably around 30 minutes.
7. Run `python bin/export_structured_data.py relevant.jsonlines.bz2 extruct_out.jsonlines.bz2` to obtain the final version of the dataset.
8. Optionally you can calculate the resulting stats like that: `python bin/get_stats.py extruct_out.jsonlines.bz2 every_prompt_stats.csv`
## Advices
If you want to recreate the results:
* Get yourself a server or VPS with enough space (80GB should be enough).
* Look at the code. You'd probably want to make changes here and there.
* All the python scripts have extra parameters to control the number of threads and the chunk size. Both accept compressed input and output files with the help of smart_open lib.
## License
**Code** of the project has an MIT license.
Copyright: [Dmytro Chaplynskyi](https://twitter.com/dchaplinsky), [lang-uk project](https://lang.org.ua), 2023
|
lang-uk/every_prompt
|
[
"task_categories:question-answering",
"multilinguality:multilingual",
"size_categories:1M<n<10M",
"license:mit",
"region:us"
] |
2023-04-20T10:08:51+00:00
|
{"license": "mit", "multilinguality": ["multilingual"], "size_categories": ["1M<n<10M"], "task_categories": ["question-answering"], "pretty_name": "Every Prompt"}
|
2023-04-20T15:49:02+00:00
|
2814b78e7af4b5a1f1886fe7ad49632de4d9dd25
|
## Table of Contents
- [Description](#description)
- [Dataset Structure](#dataset-structure)
- [Additional Information](#additional-information)
## Dataset Card for MultiNERD dataset
## Dataset Description
- **Summary:** Training data for fine-grained NER in 10 languages.
- **Repository:** [https://github.com/Babelscape/multinerd](https://github.com/Babelscape/multinerd)
- **Paper:** [https://aclanthology.org/multinerd](https://aclanthology.org/2022.findings-naacl.60/)
- **Point of Contact:** [[email protected]]([email protected])
## Description
- **Summary:** In a nutshell, MultiNERD is the first **language-agnostic** methodology for automatically creating **multilingual, multi-genre and fine-grained annotations** for **Named Entity Recognition** and **Entity Disambiguation**. Specifically, it can be seen an extension of the combination of two prior works from our research group that are [WikiNEuRal](https://www.github.com/Babelscape/wikineural), from which we took inspiration for the state-of-the-art silver-data creation methodology, and [NER4EL](https://www.github.com/Babelscape/NER4EL), from which we took the fine-grained classes and inspiration for the entity linking part. The produced dataset covers: **10 languages** (Chinese, Dutch, English, French, German, Italian, Polish, Portuguese, Russian and Spanish), **15 NER categories** (Person (PER), Location (LOC), Organization (ORG}), Animal (ANIM), Biological entity (BIO), Celestial Body (CEL), Disease (DIS), Event (EVE), Food (FOOD), Instrument (INST), Media (MEDIA), Plant (PLANT), Mythological entity (MYTH), Time (TIME) and Vehicle (VEHI)), and **2 textual genres** ([Wikipedia](https://www.wikipedia.org/) and [WikiNews](https://www.wikinews.org/));
- **Repository:** [https://github.com/Babelscape/multinerd](https://github.com/Babelscape/multinerd)
- **Paper:** [https://aclanthology.org/multinerd](https://aclanthology.org/2022.findings-naacl.60/)
- **Point of Contact:** [[email protected]]([email protected])
## Dataset Structure
The data fields are the same among all splits.
- `tokens`: a `list` of `string` features.
- `ner_tags`: a `list` of classification labels (`int`).
- `lang`: a `string` feature. Full list of language: Chinese (zh), Dutch (nl), English (en), French (fr), German (de), Italian (it), Polish (pl), Portugues (pt), Russian (ru), Spanish (es).
- The full tagset with indices is reported below:
```python
{
"O": 0,
"B-PER": 1,
"I-PER": 2,
"B-ORG": 3,
"I-ORG": 4,
"B-LOC": 5,
"I-LOC": 6,
"B-ANIM": 7,
"I-ANIM": 8,
"B-BIO": 9,
"I-BIO": 10,
"B-CEL": 11,
"I-CEL": 12,
"B-DIS": 13,
"I-DIS": 14,
"B-EVE": 15,
"I-EVE": 16,
"B-FOOD": 17,
"I-FOOD": 18,
"B-INST": 19,
"I-INST": 20,
"B-MEDIA": 21,
"I-MEDIA": 22,
"B-MYTH": 23,
"I-MYTH": 24,
"B-PLANT": 25,
"I-PLANT": 26,
"B-TIME": 27,
"I-TIME": 28,
"B-VEHI": 29,
"I-VEHI": 30,
}
```
## Additional Information
- **Licensing Information**: Contents of this repository are restricted to only non-commercial research purposes under the [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0)](https://creativecommons.org/licenses/by-nc-sa/4.0/). Copyright of the dataset contents belongs to the original copyright holders.
- **Citation Information**: Please consider citing our work if you use data and/or code from this repository.
```bibtex
@inproceedings{tedeschi-navigli-2022-multinerd,
title = "{M}ulti{NERD}: A Multilingual, Multi-Genre and Fine-Grained Dataset for Named Entity Recognition (and Disambiguation)",
author = "Tedeschi, Simone and
Navigli, Roberto",
booktitle = "Findings of the Association for Computational Linguistics: NAACL 2022",
month = jul,
year = "2022",
address = "Seattle, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.findings-naacl.60",
doi = "10.18653/v1/2022.findings-naacl.60",
pages = "801--812",
abstract = "Named Entity Recognition (NER) is the task of identifying named entities in texts and classifying them through specific semantic categories, a process which is crucial for a wide range of NLP applications. Current datasets for NER focus mainly on coarse-grained entity types, tend to consider a single textual genre and to cover a narrow set of languages, thus limiting the general applicability of NER systems.In this work, we design a new methodology for automatically producing NER annotations, and address the aforementioned limitations by introducing a novel dataset that covers 10 languages, 15 NER categories and 2 textual genres.We also introduce a manually-annotated test set, and extensively evaluate the quality of our novel dataset on both this new test set and standard benchmarks for NER.In addition, in our dataset, we include: i) disambiguation information to enable the development of multilingual entity linking systems, and ii) image URLs to encourage the creation of multimodal systems.We release our dataset at https://github.com/Babelscape/multinerd.",
}
```
- **Contributions**: Thanks to [@sted97](https://github.com/sted97) for adding this dataset.
|
Babelscape/multinerd
|
[
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"annotations_creators:machine-generated",
"language_creators:machine-generated",
"multilinguality:multilingual",
"source_datasets:original",
"language:de",
"language:en",
"language:es",
"language:fr",
"language:it",
"language:nl",
"language:pl",
"language:pt",
"language:ru",
"language:zh",
"license:cc-by-nc-sa-4.0",
"structure-prediction",
"region:us"
] |
2023-04-20T10:49:21+00:00
|
{"annotations_creators": ["machine-generated"], "language_creators": ["machine-generated"], "language": ["de", "en", "es", "fr", "it", "nl", "pl", "pt", "ru", "zh"], "license": ["cc-by-nc-sa-4.0"], "multilinguality": ["multilingual"], "source_datasets": ["original"], "task_categories": ["token-classification"], "task_ids": ["named-entity-recognition"], "pretty_name": "multinerd-dataset", "tags": ["structure-prediction"]}
|
2023-04-20T11:43:31+00:00
|
49017d4f64736bfb3f839b123a32b52fa8389886
|
# AutoTrain Dataset for project: tatanic-survival
## Dataset Description
This dataset has been automatically processed by AutoTrain for project tatanic-survival.
### Languages
The BCP-47 code for the dataset's language is unk.
## Dataset Structure
### Data Instances
A sample from this dataset looks as follows:
```json
[
{
"id": 297,
"target": 0,
"feat_Pclass": 3,
"feat_Name": "Hanna, Mr. Mansour",
"feat_Sex": "male",
"feat_Age": 23.5,
"feat_SibSp": 0,
"feat_Parch": 0,
"feat_Ticket": "2693",
"feat_Fare": 7.2292,
"feat_Cabin": null,
"feat_Embarked": "C"
},
{
"id": 4,
"target": 1,
"feat_Pclass": 1,
"feat_Name": "Futrelle, Mrs. Jacques Heath (Lily May Peel)",
"feat_Sex": "female",
"feat_Age": 35.0,
"feat_SibSp": 1,
"feat_Parch": 0,
"feat_Ticket": "113803",
"feat_Fare": 53.1,
"feat_Cabin": "C123",
"feat_Embarked": "S"
}
]
```
### Dataset Fields
The dataset has the following fields (also called "features"):
```json
{
"id": "Value(dtype='int64', id=None)",
"target": "ClassLabel(names=['0', '1'], id=None)",
"feat_Pclass": "Value(dtype='int64', id=None)",
"feat_Name": "Value(dtype='string', id=None)",
"feat_Sex": "Value(dtype='string', id=None)",
"feat_Age": "Value(dtype='float64', id=None)",
"feat_SibSp": "Value(dtype='int64', id=None)",
"feat_Parch": "Value(dtype='int64', id=None)",
"feat_Ticket": "Value(dtype='string', id=None)",
"feat_Fare": "Value(dtype='float64', id=None)",
"feat_Cabin": "Value(dtype='string', id=None)",
"feat_Embarked": "Value(dtype='string', id=None)"
}
```
### Dataset Splits
This dataset is split into a train and validation split. The split sizes are as follow:
| Split name | Num samples |
| ------------ | ------------------- |
| train | 712 |
| valid | 179 |
|
harithapliyal/autotrain-data-tatanic-survival
|
[
"region:us"
] |
2023-04-20T10:52:31+00:00
|
{}
|
2023-04-20T10:55:14+00:00
|
69a801eb6e84b492c8410f61442fc25da1dd29c1
|
# Fast Flash | HackerNews Posts Dataset
### Exploratory Analysis
Take a look at some fascinating findings from this dataset [on our website](http://wearefastflash.com/blog/hackernews).
### Dataset Summary
We release dataset of all HackerNews posts.
The dataset includes 35,316,999 posts and was collected in March 2023.
You can also find a dataset of all users [right here](https://huggingface.co/datasets/fast-flash/fast-flash-hackernews-users).
### Dataset Structure
The post objects in this dataset are structured according to HackerNews' [API specification](https://github.com/HackerNews/API).
## About the Author
[Fast Flash](https://wearefastflash.com) is a multidisciplinary creative studio that specializes in data-driven development, product design, branding, and tech.
Need help with design, coding, machine learning, pitch decks, data, or analytics?
Drop us a line at [[email protected]](mailto:[email protected]).
|
fast-flash/fast-flash-hackernews-posts
|
[
"task_categories:text-classification",
"task_categories:text-generation",
"task_categories:conversational",
"size_categories:10M<n<100M",
"language:en",
"license:apache-2.0",
"hackernews",
"text",
"social",
"nlp",
"doi:10.57967/hf/0561",
"region:us"
] |
2023-04-20T11:09:01+00:00
|
{"language": ["en"], "license": "apache-2.0", "size_categories": ["10M<n<100M"], "task_categories": ["text-classification", "text-generation", "conversational"], "pretty_name": "Fast Flash | HackerNews Posts", "tags": ["hackernews", "text", "social", "nlp"]}
|
2023-04-22T16:56:43+00:00
|
d061e0466fba8f25212a928e921ddbf0b4d0cc0a
|
# Fast Flash | HackerNews Users Dataset
### Exploratory Analysis
Take a look at some fascinating findings from this dataset [on our website](http://wearefastflash.com/blog/hackernews).
### Dataset Summary
We release dataset of all HackerNews users who have posted at least once.
The dataset includes 853,840 users and was collected on Sunday, March 26, 2023.
You can find a dataset of all posts [right here](https://huggingface.co/datasets/fast-flash/fast-flash-hackernews-posts).
### Dataset Structure
The user objects in this dataset are structured according to HackerNews' [API specification](https://github.com/HackerNews/API).
## About the Author
[Fast Flash](https://wearefastflash.com) is a multidisciplinary creative studio that specializes in data-driven development, product design, branding, and tech.
Need help with design, coding, machine learning, pitch decks, data, or analytics?
Drop us a line at [[email protected]](mailto:[email protected]).
|
fast-flash/fast-flash-hackernews-users
|
[
"size_categories:100K<n<1M",
"language:en",
"license:apache-2.0",
"hackernews",
"text",
"social",
"nlp",
"doi:10.57967/hf/0562",
"region:us"
] |
2023-04-20T11:09:27+00:00
|
{"language": ["en"], "license": "apache-2.0", "size_categories": ["100K<n<1M"], "tags": ["hackernews", "text", "social", "nlp"]}
|
2023-04-22T10:23:07+00:00
|
bc3d4d6fb0767377b7298a6331c9a303655c5916
|
Sentiment Analysis resources
Unsupervised Improving of Sentiment Analysis Using Global Target Context
========================================================================
Authors: Ivan Habernal, Tomáš Brychcín {habernal | brychcin} at kiv.zcu.cz
The article is available at [nlp.kiv.zcu.cz](http://nlp.kiv.zcu.cz/) under "Publications" section.
### Corpora
**CSFD CZ** — 90k reviews with their related target (movie): [csfd-90k-reviews-ranlp2013.tar.bz2 (11 MB)](csfd-90k-reviews-ranlp2013.tar.bz2)
### Licence
The corpus is licenced under [Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License](http://creativecommons.org/licenses/by-nc-sa/3.0/)
### Citation
Please, cite our article if you use any of the available resources.
@InProceedings{Habernal.Brychcin.2013b,
author = {Ivan Habernal and Tom\\'a\\v{s} Brychc\\'{i}n},
title = {Unsupervised Improving of Sentiment Analysis Using Global Target Context},
booktitle = {Proceedings of RANLP 2013},
year = {2013},
publisher = {Association for Computational Linguistics},
pages = {TBD},
url = {TBD}
}
* * *
### Software
Licensing rights for the project are not yet resolved, therefore the software is available only on request by mail.
Licence
-------
Corpus is licenced under [Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License](http://creativecommons.org/licenses/by-nc-sa/3.0/)
Citation
--------
Please, cite our article if you use any of the available resources.
@InProceedings{Habernal.et.al.2013,
author = {Ivan Habernal and Tom\\'a\\v{s} Pt\\'a\\v{c}ek and Josef Steinberger},
title = {Sentiment Analysis in Czech Social Media Using Supervised Machine Learning},
booktitle = {Proceedings of the 4th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis},
month = {June},
year = {2013},
address = {Atlanta, Georgia},
publisher = {Association for Computational Linguistics},
pages = {65--74},
url = {http://www.aclweb.org/anthology/W13-1609}
}
* * *
_Last change: 2013-07-17_
|
UWB-AIR/csfd
|
[
"region:us"
] |
2023-04-20T11:11:02+00:00
|
{}
|
2023-04-20T11:13:04+00:00
|
6d999a1e6c1af70e896301eee72f61abd0c28b9b
|
SpicyCat/controlnet
|
[
"license:openrail",
"region:us"
] |
2023-04-20T11:24:02+00:00
|
{"license": "openrail"}
|
2023-04-20T12:37:39+00:00
|
|
7b9f437e5e6ebb976b0d1e69aab30156199202b8
|
This dataset is designed to be used in testing. It's derived from general-pmd/localized_narratives__ADE20k dataset
The current splits are: `['100.unique', '100.repeat', '300.unique', '300.repeat', '1k.unique', '1k.repeat', '10k.unique', '10k.repeat']`.
The `unique` ones ensure uniqueness across `text` entries.
The `repeat` ones are repeating the same 10 unique records: - these are useful for memory leaks debugging as the records are always the same and thus remove the record variation from the equation.
The default split is `100.unique`
The full process of this dataset creation, including which records were used to build it, is documented inside [general-pmd-synthetic-testing.py](https://huggingface.co/datasets/HuggingFaceM4/general-pmd-synthetic-testing/blob/main/general-pmd-synthetic-testing.py)
|
HuggingFaceM4/general-pmd-synthetic-testing-with-embeddings
|
[
"license:bigscience-openrail-m",
"region:us"
] |
2023-04-20T12:12:55+00:00
|
{"license": "bigscience-openrail-m"}
|
2023-04-20T12:40:41+00:00
|
2b66021bd5084d08edf6eb6254c7f01556e6d4d9
|
srinath1234456/sanskrit
|
[
"region:us"
] |
2023-04-20T12:14:56+00:00
|
{}
|
2023-04-20T14:19:59+00:00
|
|
fba5066459b55f05e33b47a8606c566f777c74bd
|
ChristophSchuhmann/chess-selfplay-data
|
[
"license:apache-2.0",
"region:us"
] |
2023-04-20T12:21:41+00:00
|
{"license": "apache-2.0"}
|
2023-04-20T13:05:57+00:00
|
|
973f87d53da13c36b6daf9b3b6ad0c1be499c747
|
# Dataset Card for "ikun"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
WUYONGF/ikun
|
[
"region:us"
] |
2023-04-20T12:26:23+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2930652.0, "num_examples": 20}], "download_size": 2465736, "dataset_size": 2930652.0}}
|
2023-04-20T12:32:41+00:00
|
c745b175eedae7aa4b114687fa78378f439e791f
|
# Dataset Card for "VQAv2Validation_ViT_H_14_A_T_C_Q_benchmarks_partition_global_1_500"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
LambdaTests/VQAv2Validation_ViT_H_14_A_T_C_Q_benchmarks_partition_global_1_500
|
[
"region:us"
] |
2023-04-20T12:34:05+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "response", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1761, "num_examples": 63}], "download_size": 0, "dataset_size": 1761}}
|
2023-04-20T14:03:58+00:00
|
500e4f8d064d327ea93ef87367a013322686712a
|
# Dataset Card for "VQAv2Validation_ViT_H_14_A_T_C_Q_benchmarks_partition_global_2_500"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
LambdaTests/VQAv2Validation_ViT_H_14_A_T_C_Q_benchmarks_partition_global_2_500
|
[
"region:us"
] |
2023-04-20T12:34:05+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "response", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1824, "num_examples": 63}], "download_size": 0, "dataset_size": 1824}}
|
2023-04-20T14:03:50+00:00
|
0bcbb331cec4293d2c3a94f659169228bce6a059
|
# Dataset Card for "VQAv2Validation_ViT_H_14_A_T_C_Q_benchmarks_partition_global_5_500"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
LambdaTests/VQAv2Validation_ViT_H_14_A_T_C_Q_benchmarks_partition_global_5_500
|
[
"region:us"
] |
2023-04-20T12:34:06+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "response", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1801, "num_examples": 63}], "download_size": 0, "dataset_size": 1801}}
|
2023-04-20T14:03:58+00:00
|
79da67aa7136bda78c73d271035057d52ee0fb06
|
# Dataset Card for "VQAv2Validation_ViT_H_14_A_T_C_Q_benchmarks_partition_global_14_500"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
LambdaTests/VQAv2Validation_ViT_H_14_A_T_C_Q_benchmarks_partition_global_14_500
|
[
"region:us"
] |
2023-04-20T12:34:06+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "response", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 908, "num_examples": 32}], "download_size": 2074, "dataset_size": 908}}
|
2023-04-20T12:34:15+00:00
|
4857ab944c828f4e78255150d57f09bfa923c632
|
# Dataset Card for "VQAv2Validation_ViT_H_14_A_T_C_Q_benchmarks_partition_global_8_500"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
LambdaTests/VQAv2Validation_ViT_H_14_A_T_C_Q_benchmarks_partition_global_8_500
|
[
"region:us"
] |
2023-04-20T12:34:09+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "response", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 982, "num_examples": 32}], "download_size": 2085, "dataset_size": 982}}
|
2023-04-20T12:34:14+00:00
|
d485e2b82030d5a8dbea7fef299d9e71885ff116
|
# Dataset Card for "VQAv2Validation_ViT_H_14_A_T_C_Q_benchmarks_partition_global_4_500"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
LambdaTests/VQAv2Validation_ViT_H_14_A_T_C_Q_benchmarks_partition_global_4_500
|
[
"region:us"
] |
2023-04-20T12:34:10+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "response", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1792, "num_examples": 63}], "download_size": 0, "dataset_size": 1792}}
|
2023-04-20T14:04:01+00:00
|
e46b9376bd7814e1a84c24adeaa3c6c9e6fede77
|
# Dataset Card for "VQAv2Validation_ViT_H_14_A_T_C_Q_benchmarks_partition_global_11_500"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
LambdaTests/VQAv2Validation_ViT_H_14_A_T_C_Q_benchmarks_partition_global_11_500
|
[
"region:us"
] |
2023-04-20T12:34:10+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "response", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 953, "num_examples": 32}], "download_size": 2030, "dataset_size": 953}}
|
2023-04-20T12:34:16+00:00
|
5a57436add06a1eb4772a2da4c9cfba860af5d0d
|
# Dataset Card for "VQAv2Validation_ViT_H_14_A_T_C_Q_benchmarks_partition_global_3_500"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
LambdaTests/VQAv2Validation_ViT_H_14_A_T_C_Q_benchmarks_partition_global_3_500
|
[
"region:us"
] |
2023-04-20T12:34:11+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "response", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1938, "num_examples": 63}], "download_size": 0, "dataset_size": 1938}}
|
2023-04-20T14:03:57+00:00
|
83e44fe49175f9c65b7820c038935733158630f1
|
# Dataset Card for "VQAv2Validation_ViT_H_14_A_T_C_Q_benchmarks_partition_global_6_500"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
LambdaTests/VQAv2Validation_ViT_H_14_A_T_C_Q_benchmarks_partition_global_6_500
|
[
"region:us"
] |
2023-04-20T12:34:16+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "response", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1723, "num_examples": 63}], "download_size": 0, "dataset_size": 1723}}
|
2023-04-20T14:04:17+00:00
|
c1640f615a70424578bc03a329e26aa2d8a284ee
|
# Dataset Card for "VQAv2Validation_ViT_H_14_A_T_C_Q_benchmarks_partition_global_0_500"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
LambdaTests/VQAv2Validation_ViT_H_14_A_T_C_Q_benchmarks_partition_global_0_500
|
[
"region:us"
] |
2023-04-20T12:34:18+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "response", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1878, "num_examples": 63}], "download_size": 0, "dataset_size": 1878}}
|
2023-04-20T14:04:14+00:00
|
63efe53d1d4074359600b9262645854d3341dcd8
|
# Dataset Card for "VQAv2Validation_ViT_H_14_A_T_C_Q_benchmarks_partition_global_7_500"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
LambdaTests/VQAv2Validation_ViT_H_14_A_T_C_Q_benchmarks_partition_global_7_500
|
[
"region:us"
] |
2023-04-20T12:34:18+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "response", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1945, "num_examples": 63}], "download_size": 0, "dataset_size": 1945}}
|
2023-04-20T14:04:18+00:00
|
2dc23de722354827025353b7929c605ac3303243
|
# Dataset Card for "VQAv2Validation_ViT_H_14_A_T_C_Q_benchmarks_partition_global_13_500"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
LambdaTests/VQAv2Validation_ViT_H_14_A_T_C_Q_benchmarks_partition_global_13_500
|
[
"region:us"
] |
2023-04-20T12:34:21+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "response", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 934, "num_examples": 32}], "download_size": 2046, "dataset_size": 934}}
|
2023-04-20T12:34:27+00:00
|
da978f3e3046f0105c26925bce60906bcbd9f5a0
|
# Dataset Card for "VQAv2Validation_ViT_H_14_A_T_C_Q_benchmarks_partition_global_10_500"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
LambdaTests/VQAv2Validation_ViT_H_14_A_T_C_Q_benchmarks_partition_global_10_500
|
[
"region:us"
] |
2023-04-20T12:34:27+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "response", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 862, "num_examples": 32}], "download_size": 1953, "dataset_size": 862}}
|
2023-04-20T12:34:32+00:00
|
f0eb0cf24df4537b9916d0ff4d4e0d0f2cb2e325
|
# Dataset Card for "VQAv2Validation_ViT_H_14_A_T_C_Q_benchmarks_partition_global_9_500"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
LambdaTests/VQAv2Validation_ViT_H_14_A_T_C_Q_benchmarks_partition_global_9_500
|
[
"region:us"
] |
2023-04-20T12:34:28+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "response", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 934, "num_examples": 32}], "download_size": 2050, "dataset_size": 934}}
|
2023-04-20T12:34:33+00:00
|
577a5ba81cc381b68be2d76924d84b0b63c122c6
|
# Dataset Card for "VQAv2Validation_ViT_H_14_A_T_C_Q_benchmarks_partition_global_12_500"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
LambdaTests/VQAv2Validation_ViT_H_14_A_T_C_Q_benchmarks_partition_global_12_500
|
[
"region:us"
] |
2023-04-20T12:34:33+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "response", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 958, "num_examples": 32}], "download_size": 2121, "dataset_size": 958}}
|
2023-04-20T12:34:39+00:00
|
4d04bca44327fae0a6ca872dedad9d46ed8ffbd6
|
# Dataset Card for "VQAv2Validation_ViT_H_14_A_T_C_Q_benchmarks_partition_global_15_500"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
LambdaTests/VQAv2Validation_ViT_H_14_A_T_C_Q_benchmarks_partition_global_15_500
|
[
"region:us"
] |
2023-04-20T12:34:33+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "response", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1073, "num_examples": 32}], "download_size": 2149, "dataset_size": 1073}}
|
2023-04-20T12:34:40+00:00
|
2efc84a86a85cfd4ca5885da4fba891a7084364e
|
# Dataset Card for "discursos-completos-etiquetados"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Sleoruiz/discursos-completos-etiquetados
|
[
"region:us"
] |
2023-04-20T12:45:17+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "name", "dtype": "string"}, {"name": "comision", "dtype": "string"}, {"name": "gaceta_numero", "dtype": "string"}, {"name": "fecha_gaceta", "dtype": "string"}, {"name": "labels", "sequence": "string"}, {"name": "idx", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 184776887, "num_examples": 94501}], "download_size": 99391198, "dataset_size": 184776887}}
|
2023-04-20T12:49:00+00:00
|
375c6ffa5bf45c7d08dc46ae8cadffa550f3d12e
|
ubuntu2204/ubuntu-22.04-myself-images
|
[
"license:other",
"region:us"
] |
2023-04-20T13:00:34+00:00
|
{"license": "other"}
|
2023-04-21T04:50:43+00:00
|
|
82bb38af958308582448c47d51b8129a12324a40
|
# Dataset Card for "discursos_balanceados_con_etiqueta"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Sleoruiz/discursos_balanceados_con_etiqueta
|
[
"region:us"
] |
2023-04-20T13:04:50+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "name", "dtype": "string"}, {"name": "comision", "dtype": "string"}, {"name": "gaceta_numero", "dtype": "string"}, {"name": "fecha_gaceta", "dtype": "string"}, {"name": "labels", "sequence": "string"}, {"name": "idx", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 8237005, "num_examples": 2242}], "download_size": 4342800, "dataset_size": 8237005}}
|
2023-04-20T13:05:13+00:00
|
b4887cc155fb70f577de54c79ad8c8ce8259ccbb
|
houck2040/tees
|
[
"license:mit",
"region:us"
] |
2023-04-20T13:18:17+00:00
|
{"license": "mit"}
|
2023-04-20T13:18:45+00:00
|
|
bebf33557402dc1a240c2e9d27e459ed7a9e3bee
|
halaction/iad-applied-hw02
|
[
"license:openrail",
"region:us"
] |
2023-04-20T13:27:16+00:00
|
{"license": "openrail"}
|
2023-04-20T14:09:02+00:00
|
|
814894e93db9e12a1dee78b9669e20e8606fd590
|
# LLaVA Visual Instruct CC3M 595K Pretrain Dataset Card
## Dataset details
**Dataset type:**
LLaVA Visual Instruct CC3M Pretrain 595K is a subset of CC-3M dataset, filtered with a more balanced concept coverage distribution.
Captions are also associated with [BLIP synthetic caption](https://github.com/salesforce/BLIP#pre-training-datasets-download) for reference.
It is constructed for the pretraining stage for feature alignment in visual instruction tuning.
We aim to build large multimodal towards GPT-4 vision/language capability.
**Dataset date:**
LLaVA Visual Instruct CC3M Pretrain 595K was created in April 2023.
**Dataset structure:**
- `chat.json` contains the multimodal synthesized conversation from the image-caption pairs, by adding randomly selected instructions like: "Describe this image". It is used for pretraining in LLaVA. We use the raw CC-3M caption as the default answer.
- `metadata.json` contains the meta data of the image index in CC-3M, image file name, image URL, original CC-3M caption, synthetic BLIP caption. Note that ~10% of the samples are not associated with BLIP caption yet in this release.
- `images.zip` contains all raw images of the filtered subset from CC-3M. **Important notice: Upon the request from the community, as ~15% images of the original CC-3M dataset are no longer accessible, we upload `images.zip` for better reproducing our work in research community. It should not be used for any other purpose. The use of these images must comply with the CC-3M license. This may be taken down when requested by the original CC-3M dataset owner or owners of the referenced images.**
**Paper or resources for more information:**
https://llava-vl.github.io/
**License:**
Must comply with license of [CC-3M](https://github.com/google-research-datasets/conceptual-captions/blob/master/LICENSE), [BLIP](https://github.com/salesforce/BLIP/blob/main/LICENSE.txt) (if you use their synthetic caption).
CC-3M
The dataset may be freely used for any purpose, although acknowledgement of
Google LLC ("Google") as the data source would be appreciated. The dataset is
provided "AS IS" without any warranty, express or implied. Google disclaims all
liability for any damages, direct or indirect, resulting from the use of the
dataset.
**Where to send questions or comments about the model:**
https://github.com/haotian-liu/LLaVA/issues
## Intended use
**Primary intended uses:**
The primary use of LLaVA is research on large multimodal models and chatbots.
**Primary intended users:**
The primary intended users of the model are researchers and hobbyists in computer vision, natural language processing, machine learning, and artificial intelligence.
|
liuhaotian/LLaVA-CC3M-Pretrain-595K
|
[
"language:en",
"license:other",
"region:us"
] |
2023-04-20T13:28:12+00:00
|
{"language": ["en"], "license": "other", "pretty_name": "LLaVA CC3M Pretrain 595K"}
|
2023-07-06T07:51:35+00:00
|
b05f0265e33de38525cf95ef0fb1e47067f90330
|
# Dataset Card for "bigbio-ner-merged"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
rntc/bigbio-ner-merged
|
[
"region:us"
] |
2023-04-20T13:30:21+00:00
|
{"dataset_info": {"features": [{"name": "answer", "dtype": "string"}, {"name": "id", "dtype": "string"}, {"name": "instruction", "dtype": "string"}, {"name": "ner_tags", "sequence": "string"}, {"name": "text", "dtype": "string"}, {"name": "tokens", "sequence": "string"}, {"name": "types", "sequence": "string"}], "splits": [{"name": "train", "num_bytes": 731669097, "num_examples": 125928}], "download_size": 141384126, "dataset_size": 731669097}}
|
2023-04-20T13:32:20+00:00
|
ae54b00b7c688866e86acf4605034ed2bded75f3
|
# Stackexchange Instructions for OpenAssistant
This dataset is taken from https://archive.org/details/stackexchange.
There's a single parquet file combining all stackexchange sites. The threads
have been filtered as follows: only threads with an accepted answer, for which
both the question and response is less than 1000 characters have been choosen.
Other answers, or questions without accepted answers, or long entries have been
droppped.
Each row consists of
- INSTRUCTION
- RESPONSE
- SOURCE («stackexchange-ai«)
- METADATA (tags, question_score, answer_score).
Original extraction code by https://github.com/b-mc2
## How to Reproduce this Dataset
1. Download all XML files from the stackexchange archive into the xml/ folder
```
./download.py
```
2. Process the XML, filter conversations and convert to OA format into parquet/ folder
```
./process.py
```
3. Run stats on all files in the parquet/ folder
```
./stats.py
```
4. Combine all parquet files into one large stackexchange.parquet file
```
./combine.py
```
5. Upload to huggingface hub, you'll first need use huggingface-cli login
```
./upload.py
```
## Statistics
- 3dprinting: 1,006
- academia: 6,956
- ai: 1,169
- android: 11,591
- anime: 3,688
- apple: 32,603
- arduino: 3,725
- askubuntu: 78,472
- astronomy: 2,425
- aviation: 4,945
- avp: 1,949
- beer: 387
- bicycles: 4,835
- bioacoustics: 70
- bioinformatics: 903
- biology: 5,344
- bitcoin: 7,456
- blender: 25,527
- boardgames: 4,538
- bricks: 1,457
- buddhism: 911
- cardano: 670
- chemistry: 7,430
- chess: 2,185
- chinese: 4,897
- christianity: 1,248
- civicrm: 3,221
- codegolf: 943
- codereview: 2,171
- coffee: 350
- cogsci: 645
- computergraphics: 540
- conlang: 101
- cooking: 7,951
- craftcms: 4,533
- crafts: 438
- crypto: 4,425
- cs: 9,478
- cseducators: 71
- cstheory: 2,196
- datascience: 5,045
- dba: 16,850
- devops: 961
- diy: 14,400
- drones: 190
- drupal: 24,090
- dsp: 4,470
- earthscience: 922
- ebooks: 323
- economics: 2,120
- electronics: 41,717
- elementaryos: 1,769
- ell: 30,428
- emacs: 7,140
- engineering: 2,314
- english: 42,415
- eosio: 626
- es_stackoverflow: 21,475
- esperanto: 617
- ethereum: 9,603
- expatriates: 973
- expressionengine: 3,638
- fitness: 1,833
- freelancing: 338
- french: 5,193
- gamedev: 9,678
- gaming: 44,899
- gardening: 4,492
- genealogy: 487
- german: 6,715
- gis: 30,249
- graphicdesign: 10,563
- ham: 790
- hardwarerecs: 647
- health: 804
- hermeneutics: 782
- hinduism: 1,036
- history: 1,776
- homebrew: 2,357
- hsm: 484
- interpersonal: 199
- iot: 331
- iota: 292
- islam: 1,496
- italian: 1,356
- ja_stackoverflow: 9,734
- japanese: 13,862
- joomla: 1,875
- judaism: 6,156
- korean: 754
- languagelearning: 135
- latin: 1,387
- law: 3,475
- lifehacks: 934
- linguistics: 1,507
- literature: 582
- magento: 20,537
- martialarts: 364
- materials: 338
- math: 501,019
- matheducators: 316
- mathematica: 19,529
- mathoverflow_net_7z: 23,803
- mechanics: 4,735
- meta: 34,161
- meta_askubuntu: 2,076
- meta_mathoverflow_net_7z: 333
- meta_serverfault: 823
- meta_stackoverflow: 12,641
- meta_superuser: 1,748
- moderators: 39
- monero: 1,443
- money: 7,996
- movies: 6,789
- music: 5,740
- musicfans: 781
- mythology: 271
- networkengineering: 4,637
- opendata: 1,117
- opensource: 805
- or: 586
- outdoors: 1,503
- parenting: 815
- patents: 582
- pets: 1,081
- philosophy: 1,505
- photo: 6,386
- physics: 35,386
- pm: 982
- poker: 431
- politics: 1,903
- portuguese: 658
- proofassistants: 87
- pt_stackoverflow: 27,650
- puzzling: 11,959
- quant: 3,303
- quantumcomputing: 1,604
- raspberrypi: 6,794
- retrocomputing: 1,016
- reverseengineering: 1,606
- robotics: 1,020
- rpg: 9,517
- ru_stackoverflow: 106,714
- rus: 8,210
- russian: 1,960
- salesforce: 27,962
- scicomp: 1,403
- scifi: 15,174
- security: 11,733
- serverfault: 81,229
- sharepoint: 24,934
- sitecore: 2,691
- skeptics: 1,043
- softwareengineering: 10,526
- softwarerecs: 3,032
- solana: 602
- sound: 2,031
- space: 3,145
- spanish: 3,049
- sports: 1,715
- sqa: 1,944
- stackapps: 702
- stackoverflow: 4,269,779
- stats: 23,102
- stellar: 373
- substrate: 812
- superuser: 128,488
- sustainability: 240
- tex: 42,808
- tezos: 635
- tor: 887
- travel: 9,957
- tridion: 1,769
- ukrainian: 577
- unix: 54,338
- ux: 7,403
- vegetarianism: 151
- vi: 4,360
- webapps: 10,159
- webmasters: 9,413
- windowsphone: 1,110
- woodworking: 677
- wordpress: 24,270
- workplace: 4,104
- worldbuilding: 2,766
- writers: 1,957
---
## license: cc-by-sa-4.0 // See https://archive.org/details/stackexchange for details
|
donfu/oa-stackexchange
|
[
"language:en",
"language:uk",
"language:ru",
"language:de",
"language:fr",
"language:it",
"language:es",
"license:cc-by-sa-4.0",
"region:us"
] |
2023-04-20T13:57:17+00:00
|
{"language": ["en", "uk", "ru", "de", "fr", "it", "es"], "license": "cc-by-sa-4.0", "pretty_name": "Open-Assistant StackExchange Instruction", "dataset_info": {"features": [{"name": "INSTRUCTION", "dtype": "string"}, {"name": "RESPONSE", "dtype": "string"}, {"name": "SOURCE", "dtype": "string"}, {"name": "METADATA", "struct": [{"name": "answer_score", "dtype": "int64"}, {"name": "question_score", "dtype": "int64"}, {"name": "tags", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 6549838664, "num_examples": 6331083}], "download_size": 3755782987, "dataset_size": 6549838664}}
|
2023-04-23T16:45:09+00:00
|
d72483297e70d36df264c90eaa33268c64fcf298
|
# 89.3 The Current Playlists
## Dataset Description
- **Homepage:** https://www.rawk-it.com
- **Point of Contact:** [email protected]
### Dataset Summary
On-air historical playlist data for Minnesota radio station 89.3 The Current (http://www.thecurrent.org).
|
jasonmotylinski/89.3TheCurrentPlaylists
|
[
"language:en",
"license:apache-2.0",
"music",
"radio",
"Minnesota",
"region:us"
] |
2023-04-20T14:14:57+00:00
|
{"language": ["en"], "license": "apache-2.0", "pretty_name": "89.3 The Current Playlist", "tags": ["music", "radio", "Minnesota"]}
|
2023-04-26T11:21:45+00:00
|
a66cd228a32191be09bf4c2da8d43a2602165617
|
# Dataset Card for Dataset Name
## Dataset Description
An open-source, large-scale, and multi-round dialogue data powered by Turbo APIs. In consideration of factors such as safeguarding privacy, **we do not directly use any data available on the Internet as prompts**.
To ensure generation quality, two separate ChatGPT Turbo APIs are adopted in generation, where one plays the role of the user to generate queries and the other generates the response.
We instruct the user model with carefully designed prompts to mimic human user behavior and call the two APIs iteratively. The generated dialogues undergo further post-processing and filtering.
ULtraChat is composed of three sectors:
- 🌏 **Questions about the World**: The dialogue data in this sector is derived from a wide range of inquiries related to concepts, entities, and objects from the real world. The topics covered are extensive, spanning areas such as technology, art, and entrepreneurship.
- ✍🏻 **Writing and Creation**: The dialogue data in this sector is driven by the demands for writing/creation from scratch, and encompasses any tasks that an AI assistant may aid within the creative process, spanning from email composition to crafting narratives and plays, and beyond.
- 📋 **Assistance on Existent Materials**: The dialogue data in this sector is generated based on existing materials, including but not limited to rewriting, continuation, summarization, and inference, covering a diverse range of topics.
- Repository: [UltraChat](https://github.com/thunlp/UltraChat)
- Explorer: [plain-explorer](http://39.101.77.220/), [Nomic-AI-Atlas-Explorer](https://atlas.nomic.ai/map/0ce65783-c3a9-40b5-895d-384933f50081/a7b46301-022f-45d8-bbf4-98107eabdbac)
## Dataset Structure
Each line in the downloaded data file is a json dict containing the data id and dialogue data in a list format. Below is an example line.
```
{
"id": "0",
"data": [
"How can cross training benefit groups like runners, swimmers, or weightlifters?",
"Cross training can benefit groups like runners, swimmers, or weightlifters in the following ways: ...",
"That makes sense. I've been wanting to improve my running time, but I never thought about incorporating strength training. Do you have any recommendations for specific exercises?",
"Sure, here are some strength training exercises that can benefit runners: ...",
"Hmm, I'm not really a fan of weightlifting though. Can I incorporate other forms of exercise into my routine to improve my running time?",
"Yes, absolutely! ...",
"..."
]
}
```
### Citation Information
```bibtex
@misc{UltraChat,
author = {Ding, Ning and Chen, Yulin and Xu, Bokai and Hu, Shengding and Qin, Yujia and Liu, Zhiyuan and Sun, Maosong and Zhou, Bowen},
title = {UltraChat: A Large-scale Auto-generated Multi-round Dialogue Data},
year = {2023},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/thunlp/ultrachat}},
}
```
|
stingning/ultrachat
|
[
"task_categories:conversational",
"task_categories:text-generation",
"size_categories:1M<n<10M",
"language:en",
"license:mit",
"region:us"
] |
2023-04-20T14:15:28+00:00
|
{"language": ["en"], "license": "mit", "size_categories": ["1M<n<10M"], "task_categories": ["conversational", "text-generation"], "pretty_name": "UltraChat"}
|
2023-10-12T04:55:01+00:00
|
67fbabe86daf2599c9dbbbebeecf1907584ad00c
|
# Dataset Card for "chinese_painting"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
WUYONGF/chinese_painting
|
[
"region:us"
] |
2023-04-20T14:22:44+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 134472509.0, "num_examples": 30}], "download_size": 134479570, "dataset_size": 134472509.0}}
|
2023-04-20T14:33:16+00:00
|
12cb6b08e51b36cdec125a2021fabfabf7dd8856
|
# Dataset Card for "VQAv2Validation_ViT_H_14_A_T_C_Q_benchmarks_partition_global_0_1000"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
LambdaTests/VQAv2Validation_ViT_H_14_A_T_C_Q_benchmarks_partition_global_0_1000
|
[
"region:us"
] |
2023-04-20T14:46:45+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "response", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 981, "num_examples": 32}], "download_size": 2055, "dataset_size": 981}}
|
2023-04-20T14:52:16+00:00
|
dbfd3e566c979169bc7040a96b7a0cdc45ccadb1
|
# Dataset Card for "VQAv2Validation_ViT_H_14_A_T_C_Q_benchmarks_partition_global_16_1000"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
LambdaTests/VQAv2Validation_ViT_H_14_A_T_C_Q_benchmarks_partition_global_16_1000
|
[
"region:us"
] |
2023-04-20T14:51:21+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "response", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 896, "num_examples": 32}], "download_size": 1967, "dataset_size": 896}}
|
2023-04-20T14:51:23+00:00
|
280280ff14937a6949849e029c302f3a105932a4
|
# Dataset Card for "VQAv2Validation_ViT_H_14_A_T_C_Q_benchmarks_partition_global_31_1000"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
LambdaTests/VQAv2Validation_ViT_H_14_A_T_C_Q_benchmarks_partition_global_31_1000
|
[
"region:us"
] |
2023-04-20T14:51:26+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "response", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 790, "num_examples": 32}], "download_size": 1847, "dataset_size": 790}}
|
2023-04-20T14:51:28+00:00
|
937b26fd74044ba7302d73a4a10f5592d1bfc821
|
# Dataset Card for "VQAv2Validation_ViT_H_14_A_T_C_Q_benchmarks_partition_global_18_1000"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
LambdaTests/VQAv2Validation_ViT_H_14_A_T_C_Q_benchmarks_partition_global_18_1000
|
[
"region:us"
] |
2023-04-20T14:51:35+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "response", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 941, "num_examples": 32}], "download_size": 2039, "dataset_size": 941}}
|
2023-04-20T14:51:37+00:00
|
9367505b9802f6ebb77b11c077733a5ac023a873
|
# Dataset Card for "VQAv2Validation_ViT_H_14_A_T_C_Q_benchmarks_partition_global_23_1000"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
LambdaTests/VQAv2Validation_ViT_H_14_A_T_C_Q_benchmarks_partition_global_23_1000
|
[
"region:us"
] |
2023-04-20T14:51:36+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "response", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 839, "num_examples": 32}], "download_size": 1983, "dataset_size": 839}}
|
2023-04-20T14:51:39+00:00
|
8fd465ed83f6ad1ccf21443bcbcb2eb20f5f1e9e
|
# Dataset Card for "VQAv2Validation_ViT_H_14_A_T_C_Q_benchmarks_partition_global_20_1000"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
LambdaTests/VQAv2Validation_ViT_H_14_A_T_C_Q_benchmarks_partition_global_20_1000
|
[
"region:us"
] |
2023-04-20T14:51:36+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "response", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 933, "num_examples": 32}], "download_size": 2020, "dataset_size": 933}}
|
2023-04-20T14:51:38+00:00
|
c5b04c1680a3fd0e2192a5ca86a8d082e81561b2
|
# Dataset Card for "VQAv2Validation_ViT_H_14_A_T_C_Q_benchmarks_partition_global_21_1000"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
LambdaTests/VQAv2Validation_ViT_H_14_A_T_C_Q_benchmarks_partition_global_21_1000
|
[
"region:us"
] |
2023-04-20T14:51:37+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "response", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 980, "num_examples": 32}], "download_size": 2073, "dataset_size": 980}}
|
2023-04-20T14:51:39+00:00
|
217d5a01f7e08d32b23824725195a6ba5ad1e3a9
|
# Dataset Card for "VQAv2Validation_ViT_H_14_A_T_C_Q_benchmarks_partition_global_17_1000"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
LambdaTests/VQAv2Validation_ViT_H_14_A_T_C_Q_benchmarks_partition_global_17_1000
|
[
"region:us"
] |
2023-04-20T14:51:38+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "response", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 897, "num_examples": 32}], "download_size": 2022, "dataset_size": 897}}
|
2023-04-20T14:51:40+00:00
|
1e59078827994b5b929de0662935d12d5759bf81
|
# Dataset Card for "VQAv2Validation_ViT_H_14_A_T_C_Q_benchmarks_partition_global_19_1000"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
LambdaTests/VQAv2Validation_ViT_H_14_A_T_C_Q_benchmarks_partition_global_19_1000
|
[
"region:us"
] |
2023-04-20T14:51:39+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "response", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 888, "num_examples": 32}], "download_size": 1999, "dataset_size": 888}}
|
2023-04-20T14:51:41+00:00
|
b85d71abe64b8848cbf5006c08b12ccae8c79afb
|
# Dataset Card for "VQAv2Validation_ViT_H_14_A_T_C_Q_benchmarks_partition_global_25_1000"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
LambdaTests/VQAv2Validation_ViT_H_14_A_T_C_Q_benchmarks_partition_global_25_1000
|
[
"region:us"
] |
2023-04-20T14:51:41+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "response", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 862, "num_examples": 32}], "download_size": 1973, "dataset_size": 862}}
|
2023-04-20T14:51:43+00:00
|
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.