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dda4ade0901a089894b4b390c7915ee139c80fc6
|
Original source: https://github.com/openai/generating-reviews-discovering-sentiment
This dataset is different from the dataset distributed by GLUE, which means the metric **shouldn't be compared with the SST2 performance in GLUE**.
The description of SST2 dataset in the paper is the following.
> The Stanford Sentiment Treebank (SST)(Socher et al., 2013) was created specifically to evaluate more complex compositional models of language. It is de-rived from the same base dataset as MR but was relabeledvia Amazon Mechanical and includes dense labeling of thephrases of parse trees computed for all sentences. For thebinary subtask, this amounts to 76961 total labels com-pared to the 6920 sentence level labels. As a demonstrationof the capability of unsupervised representation learning tosimplify data collection and remove preprocessing steps,our reported results ignore these dense labels and computedparse trees, using only the raw text and sentence level la-bels
|
sh0416/sst2-openai
|
[
"task_categories:text-classification",
"region:us"
] |
2023-02-25T12:16:45+00:00
|
{"task_categories": ["text-classification"]}
|
2023-02-25T12:53:53+00:00
|
163810222f679dbe3752fb3c9ee8ffb7b55e2620
|
# Dataset Card for "SemEvalTask10_train_all_tasks"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Wiebke/SemEvalTask10_train_all_tasks
|
[
"region:us"
] |
2023-02-25T12:59:10+00:00
|
{"dataset_info": {"features": [{"name": "rewire_id", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "not sexist", "1": "sexist"}}}}, {"name": "label_category", "dtype": "string"}, {"name": "label_vector", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2470446.0, "num_examples": 12600}, {"name": "test", "num_bytes": 274494.0, "num_examples": 1400}], "download_size": 1475013, "dataset_size": 2744940.0}}
|
2023-02-25T12:59:15+00:00
|
5f5b53aefc80f949cc8b13fd2ae434da9f1b7605
|
SyntheticFuture/real-raspberry-pi
|
[
"license:creativeml-openrail-m",
"region:us"
] |
2023-02-25T13:08:07+00:00
|
{"license": "creativeml-openrail-m"}
|
2023-02-25T13:08:07+00:00
|
|
125bfc47f44f72ed72f9c99215a229614cc71e15
|
# Dataset Card for "squad_v2_factuality_v2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
kenhktsui/squad_v2_factuality_v2
|
[
"region:us"
] |
2023-02-25T13:10:29+00:00
|
{"dataset_info": {"features": [{"name": "context", "dtype": "string"}, {"name": "label", "dtype": "string"}, {"name": "entity_modified", "dtype": "string"}, {"name": "new_entity", "dtype": "string"}, {"name": "ner_type", "dtype": "string"}, {"name": "top_k", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 13981154, "num_examples": 17753}], "download_size": 8852587, "dataset_size": 13981154}}
|
2023-02-25T13:26:02+00:00
|
913ff7514cef278206ac9399269a25927aaedb19
|
--import os
import openai
import gradio as gr
# if you have OpenAI API key as an environment variable, enable the below
# openai.api_key = os.getenv("OPENAI_API_KEY")
# if you have OpenAI API key as a string, enable the below
openai.api_key = "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"
start_sequence = "\nVenu:"
restart_sequence = "\nHuman: "
prompt ="The following is a conversation with Venu. The assistant is helpful, creative,clever, and very friendly. \n\nHuman: Hello, who are you?\nVenu: I am Venu . How can I help you " \
"today?\nHuman: "
def openai_create(prompt):
response = openai.Completion.create(
model="text-davinci-003",
prompt=prompt,
temperature=0.9,
max_tokens=150,
top_p=1,
frequency_penalty=2,
presence_penalty= 2,
stop=[" Human:", " AI:"]
)
return response.choices[0].text
def chatgpt_clone(input, history):
history = history or []
s = list(sum(history, ()))
s.append(input)
inp = ' '.join(s)
output = openai_create(inp)
history.append((input, output))
return history, history
block = gr.Blocks()
with block:
gr.Markdown("""<h1><center>Venu</center></h1>
""")
chatbot = gr.Chatbot()
message = gr.Textbox(placeholder=prompt)
state = gr.State()
submit = gr.Button("Enter")
submit.click(chatgpt_clone, inputs=[message, state], outputs=[chatbot, state])
block.launch(share=True)
-
license: afl-3.0
---
|
venu12345/venu2004
|
[
"region:us"
] |
2023-02-25T13:22:04+00:00
|
{}
|
2023-02-25T13:23:14+00:00
|
da18acf1894e874c1abe6658babc1a1e3701b752
|
SEEUniversity/albanian_corpora_bert
|
[
"license:afl-3.0",
"region:us"
] |
2023-02-25T13:54:36+00:00
|
{"license": "afl-3.0"}
|
2023-02-25T16:18:01+00:00
|
|
b147b339d24f188c7505d907e58281f74074cebc
|
# Dataset Card for "hp_fanfiction_dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
roborovski/hp_fanfiction_dataset
|
[
"region:us"
] |
2023-02-25T14:11:06+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 133284308, "num_examples": 814751}, {"name": "test", "num_bytes": 42481739, "num_examples": 252440}], "download_size": 0, "dataset_size": 175766047}}
|
2023-02-25T16:23:03+00:00
|
0b22e3bcc9ee71addbb09bba2f3039fae9c96181
|
# Dataset Card for "geo_small_corpus"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
ZurabDz/geo_small_corpus
|
[
"region:us"
] |
2023-02-25T15:10:13+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 4269202075, "num_examples": 7375931}], "download_size": 1563181810, "dataset_size": 4269202075}}
|
2023-02-25T15:21:44+00:00
|
fdd3e651eca987d5e0d62f2c7efe4594b952dd6a
|
# Dataset Card for Recept
### Dataset Summary
[More Information Needed]
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed]
|
amcoff/recept
|
[
"task_categories:text-classification",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:sv",
"license:mit",
"region:us"
] |
2023-02-25T15:45:45+00:00
|
{"annotations_creators": ["no-annotation"], "language_creators": ["found"], "language": ["sv"], "license": ["mit"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["original"], "task_categories": ["text-classification"], "task_ids": [], "pretty_name": "Recept", "tags": []}
|
2023-02-25T16:19:01+00:00
|
97f101a4d6e903db82c8fde3762315f835665dec
|
## Source
Source: [UCI](https://archive.ics.uci.edu/ml/datasets/BlogFeedback)
## Data Set Information:
This data originates from blog posts. The raw HTML-documents
of the blog posts were crawled and processed.
The prediction task associated with the data is the prediction
of the number of comments in the upcoming 24 hours. In order
to simulate this situation, we choose a basetime (in the past)
and select the blog posts that were published at most
72 hours before the selected base date/time. Then, we calculate
all the features of the selected blog posts from the information
that was available at the basetime, therefore each instance
corresponds to a blog post. The target is the number of
comments that the blog post received in the next 24 hours
relative to the basetime.
In the train data, the basetimes were in the years
2010 and 2011. In the test data the basetimes were
in February and March 2012. This simulates the real-world
situtation in which training data from the past is available
to predict events in the future.
The train data was generated from different basetimes that may
temporally overlap. Therefore, if you simply split the train
into disjoint partitions, the underlying time intervals may
overlap. Therefore, the you should use the provided, temporally
disjoint train and test splits in order to ensure that the
evaluation is fair.
## Attribute Information:
1...50:Average, standard deviation, min, max and median of them attributes 51...60 for the source of the current blog post. With source we mean the blog on which the post appeared.
For example, myblog.blog.org would be the source of the post myblog.blog.org/post_2010_09_10
51: Total number of comments before basetime
52: Number of comments in the last 24 hours before the
basetime
53: Let T1 denote the datetime 48 hours before basetime,
Let T2 denote the datetime 24 hours before basetime.
This attribute is the number of comments in the time period
between T1 and T2
54: Number of comments in the first 24 hours after the
publication of the blog post, but before basetime
55: The difference of Attribute 52 and Attribute 53
56...60:
The same features as the attributes 51...55, but
features 56...60 refer to the number of links (trackbacks),
while features 51...55 refer to the number of comments.
61: The length of time between the publication of the blog post
and basetime
62: The length of the blog post
63...262:
The 200 bag of words features for 200 frequent words of the
text of the blog post
263...269: binary indicator features (0 or 1) for the weekday
(Monday...Sunday) of the basetime
270...276: binary indicator features (0 or 1) for the weekday
(Monday...Sunday) of the date of publication of the blog
post
277: Number of parent pages: we consider a blog post P as a
parent of blog post B, if B is a reply (trackback) to
blog post P.
278...280:
Minimum, maximum, average number of comments that the
parents received
281: The target: the number of comments in the next 24 hours
(relative to basetime)
|
wwydmanski/blog-feedback
|
[
"task_categories:tabular-regression",
"task_categories:tabular-classification",
"size_categories:10K<n<100K",
"tabular",
"region:us"
] |
2023-02-25T15:57:14+00:00
|
{"size_categories": ["10K<n<100K"], "task_categories": ["tabular-regression", "tabular-classification"], "tags": ["tabular"]}
|
2023-02-25T16:03:19+00:00
|
77d28245cc8586000028b3184c043dc66ec983e0
|
# Dataset Card for "SROIE_layoutlmv2_sequence"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Ssunbell/SROIE_layoutlmv2_sequence
|
[
"region:us"
] |
2023-02-25T17:09:59+00:00
|
{"dataset_info": {"features": [{"name": "guid", "sequence": "string"}, {"name": "words", "sequence": "string"}, {"name": "labels", "sequence": "int64"}, {"name": "boxes", "sequence": {"sequence": "int64"}}, {"name": "actual_bboxes", "sequence": {"sequence": "int64"}}, {"name": "file_name", "dtype": "string"}, {"name": "image", "sequence": {"sequence": {"sequence": "uint8"}}}], "splits": [{"name": "train", "num_bytes": 98185080, "num_examples": 594}, {"name": "val", "num_bytes": 5264337, "num_examples": 32}, {"name": "test", "num_bytes": 57252300, "num_examples": 347}], "download_size": 67438968, "dataset_size": 160701717}}
|
2023-02-25T17:10:52+00:00
|
de3cef7e4b317991a3a79cc4d7c7347026458aae
|
# Dataset Card for "stackoverflow_python_preprocessed"
This is a preprocessed version of the [stackoverflow_python] dataset.
Questions and answers were filtered to only include questions with more than 100 votes and answers with more than 5 votes.
The dataset has been converted from HTML to plain text and only includes the title, question, and answer columns.
## Additional Information
### License
All Stack Overflow user contributions are licensed under CC-BY-SA 3.0 with attribution required.
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
KonradSzafer/stackoverflow_python_preprocessed
|
[
"task_categories:question-answering",
"size_categories:1K<n<10K",
"language:en",
"region:us"
] |
2023-02-25T17:32:31+00:00
|
{"language": ["en"], "size_categories": ["1K<n<10K"], "task_categories": ["question-answering"], "pretty_name": "Stack Overflow Python - Preprocessed", "dataset_info": {"features": [{"name": "title", "dtype": "string"}, {"name": "answer", "dtype": "string"}, {"name": "question", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 5119086, "num_examples": 3296}], "download_size": 1939470, "dataset_size": 5119086}}
|
2023-03-04T23:35:06+00:00
|
6023919a617cdb7566894338012e9bde4b9a8a14
|
# Dataset Card for "bioasqqa"
Split taken from the MRQA 2019 Shared Task, formatted and filtered for Question Answering. For the original dataset, have a look [here](https://huggingface.co/datasets/mrqa).
|
lucadiliello/bioasqqa
|
[
"region:us"
] |
2023-02-25T18:03:01+00:00
|
{"dataset_info": {"features": [{"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answers", "sequence": "string"}, {"name": "key", "dtype": "string"}, {"name": "labels", "list": [{"name": "end", "sequence": "int64"}, {"name": "start", "sequence": "int64"}]}], "splits": [{"name": "test", "num_bytes": 2478570, "num_examples": 1504}], "download_size": 1270845, "dataset_size": 2478570}}
|
2023-06-06T07:36:12+00:00
|
1b78fe658eb31cb1323f5e715cbb8af3dace6eb3
|
# Dataset Card for "dropqa"
Split taken from the MRQA 2019 Shared Task, formatted and filtered for Question Answering. For the original dataset, have a look [here](https://huggingface.co/datasets/mrqa).
|
lucadiliello/dropqa
|
[
"region:us"
] |
2023-02-25T18:03:07+00:00
|
{"dataset_info": {"features": [{"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answers", "sequence": "string"}, {"name": "key", "dtype": "string"}, {"name": "labels", "list": [{"name": "end", "sequence": "int64"}, {"name": "start", "sequence": "int64"}]}], "splits": [{"name": "test", "num_bytes": 1873397, "num_examples": 1503}], "download_size": 340899, "dataset_size": 1873397}}
|
2023-06-06T07:35:22+00:00
|
6a6b3a20c7a24ab8ae3710665c9bed4281300500
|
# Dataset Card for "duorc.paraphrasercqa"
Split taken from the MRQA 2019 Shared Task, formatted and filtered for Question Answering. For the original dataset, have a look [here](https://huggingface.co/datasets/mrqa).
|
lucadiliello/duorc.paraphrasercqa
|
[
"region:us"
] |
2023-02-25T18:03:12+00:00
|
{"dataset_info": {"features": [{"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answers", "sequence": "string"}, {"name": "key", "dtype": "string"}, {"name": "labels", "list": [{"name": "end", "sequence": "int64"}, {"name": "start", "sequence": "int64"}]}], "splits": [{"name": "test", "num_bytes": 5755609, "num_examples": 1501}], "download_size": 660068, "dataset_size": 5755609}}
|
2023-06-06T07:36:01+00:00
|
87a889a73da456a0888ab25d6e91aaf2ff6e9976
|
# Dataset Card for "hotpotqa"
Split taken from the MRQA 2019 Shared Task, formatted and filtered for Question Answering. For the original dataset, have a look [here](https://huggingface.co/datasets/mrqa).
|
lucadiliello/hotpotqa
|
[
"region:us"
] |
2023-02-25T18:03:18+00:00
|
{"dataset_info": {"features": [{"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answers", "sequence": "string"}, {"name": "key", "dtype": "string"}, {"name": "labels", "list": [{"name": "end", "sequence": "int64"}, {"name": "start", "sequence": "int64"}]}], "splits": [{"name": "train", "num_bytes": 85224549, "num_examples": 72928}, {"name": "validation", "num_bytes": 8285153, "num_examples": 5901}], "download_size": 57326467, "dataset_size": 93509702}}
|
2023-06-06T07:36:49+00:00
|
49435450b0abb287be45639fdaa49a9b56512f9b
|
# Dataset Card for "naturalquestionsshortqa"
Split taken from the MRQA 2019 Shared Task, formatted and filtered for Question Answering. For the original dataset, have a look [here](https://huggingface.co/datasets/mrqa).
|
lucadiliello/naturalquestionsshortqa
|
[
"region:us"
] |
2023-02-25T18:03:29+00:00
|
{"dataset_info": {"features": [{"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answers", "sequence": "string"}, {"name": "key", "dtype": "string"}, {"name": "labels", "list": [{"name": "end", "sequence": "int64"}, {"name": "start", "sequence": "int64"}]}], "splits": [{"name": "train", "num_bytes": 100706304, "num_examples": 104071}, {"name": "validation", "num_bytes": 12941478, "num_examples": 12836}], "download_size": 61870589, "dataset_size": 113647782}}
|
2023-06-06T07:35:50+00:00
|
728e52920b8e4ffcfaad93fa47556f26a1d82546
|
# Dataset Card for "newsqa"
Split taken from the MRQA 2019 Shared Task, formatted and filtered for Question Answering. For the original dataset, have a look [here](https://huggingface.co/datasets/mrqa).
|
lucadiliello/newsqa
|
[
"region:us"
] |
2023-02-25T18:03:41+00:00
|
{"dataset_info": {"features": [{"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answers", "sequence": "string"}, {"name": "key", "dtype": "string"}, {"name": "labels", "list": [{"name": "end", "sequence": "int64"}, {"name": "start", "sequence": "int64"}]}], "splits": [{"name": "train", "num_bytes": 234711053, "num_examples": 74160}, {"name": "validation", "num_bytes": 13234782, "num_examples": 4212}], "download_size": 31328809, "dataset_size": 247945835}}
|
2023-06-06T07:36:25+00:00
|
20d542d2b3bb0dcfa828ba2116832ebdf11eedcf
|
# Dataset Card for "raceqa"
Split taken from the MRQA 2019 Shared Task, formatted and filtered for Question Answering. For the original dataset, have a look [here](https://huggingface.co/datasets/mrqa).
|
lucadiliello/raceqa
|
[
"region:us"
] |
2023-02-25T18:03:51+00:00
|
{"dataset_info": {"features": [{"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answers", "sequence": "string"}, {"name": "key", "dtype": "string"}, {"name": "labels", "list": [{"name": "end", "sequence": "int64"}, {"name": "start", "sequence": "int64"}]}], "splits": [{"name": "test", "num_bytes": 1272251, "num_examples": 674}], "download_size": 747482, "dataset_size": 1272251}}
|
2023-06-06T07:33:45+00:00
|
84d634a1bc5ad3bdb084b170ecb5c8837219af2e
|
# Dataset Card for "relationextractionqa"
Split taken from the MRQA 2019 Shared Task, formatted and filtered for Question Answering. For the original dataset, have a look [here](https://huggingface.co/datasets/mrqa).
|
lucadiliello/relationextractionqa
|
[
"region:us"
] |
2023-02-25T18:03:57+00:00
|
{"dataset_info": {"features": [{"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answers", "sequence": "string"}, {"name": "key", "dtype": "string"}, {"name": "labels", "list": [{"name": "end", "sequence": "int64"}, {"name": "start", "sequence": "int64"}]}], "splits": [{"name": "test", "num_bytes": 863192, "num_examples": 2948}], "download_size": 527428, "dataset_size": 863192}}
|
2023-06-06T07:33:31+00:00
|
c1a979068ba118d85467179b704031d113d689cc
|
# Dataset Card for "searchqa"
Split taken from the MRQA 2019 Shared Task, formatted and filtered for Question Answering. For the original dataset, have a look [here](https://huggingface.co/datasets/mrqa).
|
lucadiliello/searchqa
|
[
"region:us"
] |
2023-02-25T18:04:03+00:00
|
{"dataset_info": {"features": [{"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answers", "sequence": "string"}, {"name": "key", "dtype": "string"}, {"name": "labels", "list": [{"name": "end", "sequence": "int64"}, {"name": "start", "sequence": "int64"}]}], "splits": [{"name": "train", "num_bytes": 483999103, "num_examples": 117384}, {"name": "validation", "num_bytes": 69647447, "num_examples": 16980}], "download_size": 325197949, "dataset_size": 553646550}}
|
2023-06-06T07:34:01+00:00
|
6eb617820c532f5fab2d3b7aea89b55dc0b2ffde
|
# Dataset Card for "squadqa"
Split taken from the MRQA 2019 Shared Task, formatted and filtered for Question Answering. For the original dataset, have a look [here](https://huggingface.co/datasets/mrqa).
|
lucadiliello/squadqa
|
[
"region:us"
] |
2023-02-25T18:04:23+00:00
|
{"dataset_info": {"features": [{"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answers", "sequence": "string"}, {"name": "key", "dtype": "string"}, {"name": "labels", "list": [{"name": "end", "sequence": "int64"}, {"name": "start", "sequence": "int64"}]}], "splits": [{"name": "train", "num_bytes": 79320473, "num_examples": 86588}, {"name": "validation", "num_bytes": 10998734, "num_examples": 10507}], "download_size": 19381701, "dataset_size": 90319207}}
|
2023-06-06T07:32:16+00:00
|
ec81cbcf12163653deb26018f3377ec02948eca1
|
# Dataset Card for "textbookqa"
Split taken from the MRQA 2019 Shared Task, formatted and filtered for Question Answering. For the original dataset, have a look [here](https://huggingface.co/datasets/mrqa).
|
lucadiliello/textbookqa
|
[
"region:us"
] |
2023-02-25T18:04:33+00:00
|
{"dataset_info": {"features": [{"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answers", "sequence": "string"}, {"name": "key", "dtype": "string"}, {"name": "labels", "list": [{"name": "end", "sequence": "int64"}, {"name": "start", "sequence": "int64"}]}], "splits": [{"name": "test", "num_bytes": 5371294, "num_examples": 1503}], "download_size": 802199, "dataset_size": 5371294}}
|
2023-06-06T07:33:05+00:00
|
b451053aeb939f0317f9f2df9ce2cfbbcb20ec05
|
# Dataset Card for "triviaqa"
Split taken from the MRQA 2019 Shared Task, formatted and filtered for Question Answering. For the original dataset, have a look [here](https://huggingface.co/datasets/mrqa).
|
lucadiliello/triviaqa
|
[
"region:us"
] |
2023-02-25T18:04:39+00:00
|
{"dataset_info": {"features": [{"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answers", "sequence": "string"}, {"name": "key", "dtype": "string"}, {"name": "labels", "list": [{"name": "end", "sequence": "int64"}, {"name": "start", "sequence": "int64"}]}], "splits": [{"name": "train", "num_bytes": 270309068, "num_examples": 61688}, {"name": "validation", "num_bytes": 34067846, "num_examples": 7785}], "download_size": 178840166, "dataset_size": 304376914}}
|
2023-06-06T07:31:52+00:00
|
8e4ef3b601d46729ec1fe6724ca93deaf7a0134e
|
firqaaa/indo-biencoder-nq-train
|
[
"license:apache-2.0",
"region:us"
] |
2023-02-25T18:11:57+00:00
|
{"license": "apache-2.0"}
|
2023-02-25T18:24:12+00:00
|
|
ebbe492476698aa427367e460ea675ec075b2294
|
firqaaa/indo-biencoder-nq-dev
|
[
"license:apache-2.0",
"region:us"
] |
2023-02-25T18:26:02+00:00
|
{"license": "apache-2.0"}
|
2023-02-25T18:27:30+00:00
|
|
3dc7500505465d83c5e3fb01c8db3567c2d353e0
|
# Dataset Card for "FontsLarge"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
arbml/FontsLarge
|
[
"region:us"
] |
2023-02-25T19:07:52+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 45180875.629, "num_examples": 9921}], "download_size": 28777247, "dataset_size": 45180875.629}}
|
2023-02-25T19:07:58+00:00
|
094f17b0b2922e209a3807b1c0910ae8da567e42
|
# Dataset Card for "wiki_book_corpus_processed_bert_dataset_medium"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
gokuls/wiki_book_corpus_processed_bert_dataset_medium
|
[
"region:us"
] |
2023-02-25T19:22:17+00:00
|
{"dataset_info": {"features": [{"name": "input_ids", "sequence": "int32"}, {"name": "token_type_ids", "sequence": "int8"}, {"name": "attention_mask", "sequence": "int8"}, {"name": "special_tokens_mask", "sequence": "int8"}], "splits": [{"name": "train", "num_bytes": 11100805200.0, "num_examples": 3083557}], "download_size": 3272451599, "dataset_size": 11100805200.0}}
|
2023-02-25T19:37:47+00:00
|
eb3340c128e3715ba8224576f5a7449c0acd82b1
|
# Dataset Card for "wiki_book_corpus_raw_dataset_medium"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
gokuls/wiki_book_corpus_raw_dataset_medium
|
[
"region:us"
] |
2023-02-25T19:38:11+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 12250082590.5, "num_examples": 40231449}], "download_size": 7774316723, "dataset_size": 12250082590.5}}
|
2023-02-25T20:10:20+00:00
|
d355b3056a9ed9e0ed30cb981b962c05cd3663e6
|
# AutoTrain Dataset for project: multifamily_v2
## Dataset Description
This dataset has been automatically processed by AutoTrain for project multifamily_v2.
### Languages
The BCP-47 code for the dataset's language is unk.
## Dataset Structure
### Data Instances
A sample from this dataset looks as follows:
```json
[
{
"image": "<500x333 RGB PIL image>",
"target": 33
},
{
"image": "<500x667 RGB PIL image>",
"target": 11
}
]
```
### Dataset Fields
The dataset has the following fields (also called "features"):
```json
{
"image": "Image(decode=True, id=None)",
"target": "ClassLabel(names=['Balcony-Patio', 'Bathroom', 'Bedroom', 'Bike', 'Building', 'Business Center', 'Business Center Conference Room', 'Closet', 'Clubhouse', 'Clubhouse Dining Room', 'Construction', 'Dining Room', 'Dog Park', 'Fire Pit', 'Fitness Center', 'Floorplan', 'Fountain', 'Green Space', 'Grilling Area', 'Hallway', 'Headshot', 'Home Office', 'Hot Tub', 'Kitchen', 'Laundry Facility', 'Laundry Washer-Dryer', 'Leasing Office', 'Living Room', 'Living Room Fireplace', 'Logo', 'Lounge Area', 'Mail Box', 'Monument Sign', 'Neighborhood', 'Packages', 'Parking', 'Pet Washing', 'Picnic Area', 'Play Park', 'Pool', 'Pool Cabanas', 'Pool Table', 'Private Garage', 'Site-plan', 'Stock Photo', 'Tennis Court', 'View-Aerial', 'room'], 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 | 1748 |
| valid | 453 |
|
lineups-io/autotrain-data-multifamily_v2
|
[
"task_categories:image-classification",
"region:us"
] |
2023-02-25T19:45:19+00:00
|
{"task_categories": ["image-classification"]}
|
2023-02-25T21:18:21+00:00
|
ae76cf9f490ef0d9873e8e2a2cb1b72d1836641e
|
# Dataset Card for "wiki_book_corpus_processed_bert_dataset_small"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
gokuls/wiki_book_corpus_processed_bert_dataset_small
|
[
"region:us"
] |
2023-02-25T20:10:23+00:00
|
{"dataset_info": {"features": [{"name": "input_ids", "sequence": "int32"}, {"name": "token_type_ids", "sequence": "int8"}, {"name": "attention_mask", "sequence": "int8"}, {"name": "special_tokens_mask", "sequence": "int8"}], "splits": [{"name": "train", "num_bytes": 5550400800.0, "num_examples": 1541778}], "download_size": 1636779213, "dataset_size": 5550400800.0}}
|
2023-02-25T20:18:06+00:00
|
3e6f67f6a83acd00699e97f8cf8b9b841491a2ee
|
# Dataset Card for "wiki_book_corpus_raw_dataset_small"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
gokuls/wiki_book_corpus_raw_dataset_small
|
[
"region:us"
] |
2023-02-25T20:18:08+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 6125041143.004892, "num_examples": 20115724}], "download_size": 3887778273, "dataset_size": 6125041143.004892}}
|
2023-02-25T20:34:26+00:00
|
a3fd459176a64ac5ae46366d8c006e329ce6e874
|
# Dataset Card for "SORIE_layoutlmv2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Ssunbell/SORIE_layoutlmv2
|
[
"region:us"
] |
2023-02-25T20:32:38+00:00
|
{"dataset_info": {"features": [{"name": "words", "sequence": "string"}, {"name": "labels", "sequence": "int64"}, {"name": "boxes", "sequence": {"sequence": "int64"}}, {"name": "file_name", "dtype": "string"}, {"name": "actual_bboxes", "sequence": {"sequence": "int64"}}, {"name": "image", "sequence": {"sequence": {"sequence": "uint8"}}}], "splits": [{"name": "train", "num_bytes": 9991089032, "num_examples": 65151}, {"name": "val", "num_bytes": 1110120734, "num_examples": 7239}, {"name": "test", "num_bytes": 6085345877, "num_examples": 39682}], "download_size": 7559946719, "dataset_size": 17186555643}}
|
2023-02-25T20:58:17+00:00
|
5dec05f73088dcb7ce54bd9f2b7ffe3a57a3a582
|
# Dataset Card for "wiki_book_corpus_processed_bert_dataset_tiny"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
gokuls/wiki_book_corpus_processed_bert_dataset_tiny
|
[
"region:us"
] |
2023-02-25T20:34:27+00:00
|
{"dataset_info": {"features": [{"name": "input_ids", "sequence": "int32"}, {"name": "token_type_ids", "sequence": "int8"}, {"name": "attention_mask", "sequence": "int8"}, {"name": "special_tokens_mask", "sequence": "int8"}], "splits": [{"name": "train", "num_bytes": 2775200400.0, "num_examples": 770889}], "download_size": 818434567, "dataset_size": 2775200400.0}}
|
2023-02-25T20:38:06+00:00
|
17b5aaf2d1494fdbf99c6ae85026ed24a787686b
|
# Dataset Card for "wiki_book_corpus_raw_dataset_tiny"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
gokuls/wiki_book_corpus_raw_dataset_tiny
|
[
"region:us"
] |
2023-02-25T20:38:09+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 3062520571.502446, "num_examples": 10057862}], "download_size": 1942096237, "dataset_size": 3062520571.502446}}
|
2023-02-25T20:45:38+00:00
|
b433cbb3f74899f7118bbe9689f38ddf27f1e089
|
# AutoTrain Dataset for project: weather-classification
## Dataset Description
This dataset has been automatically processed by AutoTrain for project weather-classification.
### Languages
The BCP-47 code for the dataset's language is unk.
## Dataset Structure
### Data Instances
A sample from this dataset looks as follows:
```json
[
{
"image": "<771x514 RGB PIL image>",
"target": 2
},
{
"image": "<269x254 RGB PIL image>",
"target": 8
}
]
```
### Dataset Fields
The dataset has the following fields (also called "features"):
```json
{
"image": "Image(decode=True, id=None)",
"target": "ClassLabel(names=['dew', 'fogsmog', 'frost', 'glaze', 'hail', 'lightning', 'rain', 'rainbow', 'rime', 'sandstorm', 'snow'], 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 | 5484 |
| valid | 1378 |
|
dazzle-nu/autotrain-data-weather-classification
|
[
"task_categories:image-classification",
"region:us"
] |
2023-02-25T22:54:10+00:00
|
{"task_categories": ["image-classification"]}
|
2023-02-26T00:02:09+00:00
|
a5af251bbab6979f9fa86cb41fb8919beca1b016
|
# Dataset Card for "reorder.natural.es"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
lshowway/reorder.natural.es
|
[
"region:us"
] |
2023-02-25T23:19:01+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1091423892, "num_examples": 566216}], "download_size": 504857351, "dataset_size": 1091423892}}
|
2023-02-25T23:19:41+00:00
|
9b9fdc2e2ca31f5f24d6026faedca4500c858b6c
|
# Dataset Card for "reorder.svo.es"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
lshowway/reorder.svo.es
|
[
"region:us"
] |
2023-02-25T23:20:10+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1091423892, "num_examples": 566216}], "download_size": 511493512, "dataset_size": 1091423892}}
|
2023-02-25T23:20:50+00:00
|
323f556849c2f756fb4795504d7c940726b4cf61
|
# Dataset Card for "reorder.vos.es"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
lshowway/reorder.vos.es
|
[
"region:us"
] |
2023-02-25T23:21:17+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1091423892, "num_examples": 566216}], "download_size": 514360793, "dataset_size": 1091423892}}
|
2023-02-25T23:21:56+00:00
|
b871a0c1ae098d708c43d0d953f792da8c1e51a7
|
# Dataset Card for "reorder.osv.es"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
lshowway/reorder.osv.es
|
[
"region:us"
] |
2023-02-25T23:22:21+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1091423892, "num_examples": 566216}], "download_size": 514726308, "dataset_size": 1091423892}}
|
2023-02-25T23:23:01+00:00
|
9ff11a8b83872fda132f8152d532a8be307ae8fc
|
# Dataset Card for "reorder.sov.es"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
lshowway/reorder.sov.es
|
[
"region:us"
] |
2023-02-25T23:23:25+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1091423892, "num_examples": 566216}], "download_size": 514178300, "dataset_size": 1091423892}}
|
2023-02-25T23:24:05+00:00
|
f5c70c4c447134efea7bca24ad683743a9fa3742
|
# Dataset Card for "reorder.vso.es"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
lshowway/reorder.vso.es
|
[
"region:us"
] |
2023-02-25T23:24:32+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1091423892, "num_examples": 566216}], "download_size": 512310606, "dataset_size": 1091423892}}
|
2023-02-25T23:25:12+00:00
|
0deb9d0ff99942e681f80f9bb4cb68ffb9b50667
|
# Dataset Card for "reorder.ovs.es"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
lshowway/reorder.ovs.es
|
[
"region:us"
] |
2023-02-25T23:25:38+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1091423892, "num_examples": 566216}], "download_size": 513562772, "dataset_size": 1091423892}}
|
2023-02-25T23:26:18+00:00
|
89c2cabb1a8905793d0a24ceaa434ef8bd0186d1
|
# Dataset Card for "zambezivoice_bem_text"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
zambezivoice/zambezivoice_bem_text
|
[
"region:us"
] |
2023-02-26T00:16:36+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 937260, "num_examples": 14121}], "download_size": 629604, "dataset_size": 937260}}
|
2023-02-26T00:16:39+00:00
|
4f225fed4ede5c946e048fa8e1fb237eb496ad9d
|
Mediocreatmybest/Miscellany_of_Australian_Historical_Photography
|
[
"license:cc0-1.0",
"region:us"
] |
2023-02-26T01:13:53+00:00
|
{"license": "cc0-1.0"}
|
2023-02-26T01:47:03+00:00
|
|
8d9baf1f5d4092ff480de7ddf3f0256ee0bf83fc
|
Concatenated and edited collection of fairy tales taken from Project Gutenberg.
Texts:
https://www.gutenberg.org/files/2591/2591-0.txt
https://www.gutenberg.org/files/503/503-0.txt
https://www.gutenberg.org/files/7277/7277-0.txt
https://www.gutenberg.org/cache/epub/35862/pg35862.txt
https://www.gutenberg.org/cache/epub/69739/pg69739.txt
https://www.gutenberg.org/files/2435/2435-0.txt
https://www.gutenberg.org/cache/epub/7871/pg7871.txt
https://www.gutenberg.org/files/8933/8933-0.txt
gutenberg.org/cache/epub/30834/pg30834.txt
https://www.gutenberg.org/cache/epub/68589/pg68589.txt
https://www.gutenberg.org/cache/epub/34453/pg34453.txt
gutenberg.org/cache/epub/8653/pg8653.txt
|
vicclab/fairy_tales
|
[
"task_categories:text-generation",
"language:en",
"region:us"
] |
2023-02-26T01:18:41+00:00
|
{"language": ["en"], "task_categories": ["text-generation"]}
|
2023-02-27T10:35:24+00:00
|
fd4a6fa3297645ffbb8c019f0a35d29d0c673a17
|
# Dataset Card for "the-stack-dedup-python-docstrings-1.0-percent-unified"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
trelent/the-stack-dedup-python-docstrings-1.0-percent-unified
|
[
"region:us"
] |
2023-02-26T01:31:05+00:00
|
{"dataset_info": {"features": [{"name": "body_hash", "dtype": "string"}, {"name": "body", "dtype": "string"}, {"name": "docstring", "dtype": "string"}, {"name": "path", "dtype": "string"}, {"name": "name", "dtype": "string"}, {"name": "repository_name", "dtype": "string"}, {"name": "repository_stars", "dtype": "float64"}, {"name": "lang", "dtype": "string"}, {"name": "body_without_docstring", "dtype": "string"}, {"name": "unified", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 680876286, "num_examples": 237074}], "download_size": 247316903, "dataset_size": 680876286}}
|
2023-02-26T01:33:38+00:00
|
4c83fedf8e0249494c534ba65998d76ca09fba93
|
# Dataset Card for "switchboard-ner-non-normalized"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
anonymoussubmissions/switchboard-ner-non-normalized
|
[
"region:us"
] |
2023-02-26T02:12:37+00:00
|
{"dataset_info": {"features": [{"name": "tokens", "sequence": "string"}, {"name": "labels", "sequence": "string"}, {"name": "labels_orig", "sequence": "string"}, {"name": "tags", "sequence": {"class_label": {"names": {"0": "O", "1": "B-CARDINAL", "2": "B-DATE", "3": "B-EVENT", "4": "B-FAC", "5": "B-GPE", "6": "B-LANGUAGE", "7": "B-LAW", "8": "B-LOC", "9": "B-MONEY", "10": "B-NORP", "11": "B-ORDINAL", "12": "B-ORG", "13": "B-PERCENT", "14": "B-PERSON", "15": "B-PRODUCT", "16": "B-QUANTITY", "17": "B-TIME", "18": "B-WORK_OF_ART", "19": "I-CARDINAL", "20": "I-DATE", "21": "I-EVENT", "22": "I-FAC", "23": "I-GPE", "24": "I-LANGUAGE", "25": "I-LAW", "26": "I-LOC", "27": "I-MONEY", "28": "I-NORP", "29": "I-ORDINAL", "30": "I-ORG", "31": "I-PERCENT", "32": "I-PERSON", "33": "I-PRODUCT", "34": "I-QUANTITY", "35": "I-TIME", "36": "I-WORK_OF_ART"}}}}, {"name": "swbd_id", "dtype": "string"}, {"name": "swne_sentence_no", "dtype": "string"}, {"name": "id", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2309183, "num_examples": 7713}, {"name": "validation", "num_bytes": 1539969, "num_examples": 5143}, {"name": "test", "num_bytes": 2577677, "num_examples": 8571}], "download_size": 913903, "dataset_size": 6426829}}
|
2023-02-26T02:12:41+00:00
|
8b0096acbcdadbb773213c123249b7735003de79
|
# Dataset Card for "switchboard-ner-normalized"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
anonymoussubmissions/switchboard-ner-normalized
|
[
"region:us"
] |
2023-02-26T02:12:43+00:00
|
{"dataset_info": {"features": [{"name": "tokens", "sequence": "string"}, {"name": "labels", "sequence": "string"}, {"name": "labels_orig", "sequence": "string"}, {"name": "tags", "sequence": {"class_label": {"names": {"0": "O", "1": "B-CARDINAL", "2": "B-DATE", "3": "B-EVENT", "4": "B-FAC", "5": "B-GPE", "6": "B-LANGUAGE", "7": "B-LAW", "8": "B-LOC", "9": "B-MONEY", "10": "B-NORP", "11": "B-ORDINAL", "12": "B-ORG", "13": "B-PERCENT", "14": "B-PERSON", "15": "B-PRODUCT", "16": "B-QUANTITY", "17": "B-TIME", "18": "B-WORK_OF_ART", "19": "I-CARDINAL", "20": "I-DATE", "21": "I-EVENT", "22": "I-FAC", "23": "I-GPE", "24": "I-LANGUAGE", "25": "I-LAW", "26": "I-LOC", "27": "I-MONEY", "28": "I-NORP", "29": "I-ORDINAL", "30": "I-ORG", "31": "I-PERCENT", "32": "I-PERSON", "33": "I-PRODUCT", "34": "I-QUANTITY", "35": "I-TIME", "36": "I-WORK_OF_ART"}}}}, {"name": "swbd_id", "dtype": "string"}, {"name": "swne_sentence_no", "dtype": "string"}, {"name": "id", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2651450, "num_examples": 7721}, {"name": "validation", "num_bytes": 1778041, "num_examples": 5148}, {"name": "test", "num_bytes": 2962454, "num_examples": 8580}], "download_size": 1002621, "dataset_size": 7391945}}
|
2023-02-26T02:12:46+00:00
|
e2cc3c40c177ab5ed3e7caee53f88003794b38e3
|
mason0510/hug
|
[
"license:mit",
"region:us"
] |
2023-02-26T02:43:24+00:00
|
{"license": "mit"}
|
2023-02-26T02:43:25+00:00
|
|
6d86872b1b1b43bcc2fcae9a9bb6f0ca19a1023e
|
# Dataset Card for "patched_test_p_200_f_membrane_m1_predictions"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
roa7n/patched_test_p_200_f_membrane_m1_predictions
|
[
"region:us"
] |
2023-02-26T04:16:03+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "sequence_str", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "m1_preds", "dtype": "float32"}], "splits": [{"name": "train", "num_bytes": 1379688652, "num_examples": 2057621}], "download_size": 120998098, "dataset_size": 1379688652}}
|
2023-02-26T04:16:27+00:00
|
ef27843a62e9673eb9703eb895e7717108cb99be
|
# Dataset Card for "squad_v2_factuality_v2_top5"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
kenhktsui/squad_v2_factuality_v2_top5
|
[
"region:us"
] |
2023-02-26T04:18:37+00:00
|
{"dataset_info": {"features": [{"name": "context", "dtype": "string"}, {"name": "label", "dtype": "string"}, {"name": "entity_modified", "dtype": "string"}, {"name": "new_entity", "dtype": "string"}, {"name": "ner_type", "dtype": "string"}, {"name": "top_k", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 13582791, "num_examples": 17250}], "download_size": 8600847, "dataset_size": 13582791}}
|
2023-02-26T04:18:48+00:00
|
786522515790b00ae4156278b768f610e481c4f3
|
# AutoTrain Dataset for project: guitarsproject
## Dataset Description
This dataset has been automatically processed by AutoTrain for project guitarsproject.
### Languages
The BCP-47 code for the dataset's language is unk.
## Dataset Structure
### Data Instances
A sample from this dataset looks as follows:
```json
[
{
"image": "<1990x2520 RGB PIL image>",
"target": 1
},
{
"image": "<6000x4000 RGB PIL image>",
"target": 0
}
]
```
### Dataset Fields
The dataset has the following fields (also called "features"):
```json
{
"image": "Image(decode=True, id=None)",
"target": "ClassLabel(names=['LesPaul', 'Stratocaster'], 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 | 80 |
| valid | 21 |
|
rpereira90/autotrain-data-guitarsproject
|
[
"task_categories:image-classification",
"region:us"
] |
2023-02-26T05:49:44+00:00
|
{"task_categories": ["image-classification"]}
|
2023-02-26T05:50:55+00:00
|
0e58105e93f0255a388f198a75e619ecc1e4f474
|
# Dataset Card for "har"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
hazardous/har
|
[
"region:us"
] |
2023-02-26T08:48:31+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "calling", "1": "clapping", "2": "cycling", "3": "dancing", "4": "drinking", "5": "eating", "6": "fighting", "7": "hugging", "8": "laughing", "9": "listening_to_music", "10": "running", "11": "sitting", "12": "sleeping", "13": "texting", "14": "using_laptop"}}}}], "splits": [{"name": "train", "num_bytes": 208908112.2, "num_examples": 12600}], "download_size": 227817680, "dataset_size": 208908112.2}}
|
2023-02-26T09:18:00+00:00
|
89e85f5c829c7cc31afbbcfae82d543ac237fead
|
# Dataset Card for Dataset Name
## Dataset Description
- **https://duskfallcrew.carrd.co/:**
- **https://discord.gg/Da7s8d3KJ7**
### Dataset Summary
A mixture of photography and other goods from Dusfkallcrew that has been either curated or taken by duskfall crew. Some may or may not be AI generated.
This template was generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1).
### Languages
English mainly, but that's because the data is largely of New Zealand.
### Source Data
### Personal and Sensitive Information
No personal data has been included in this data, it is ALL a mixture of AI generated and personally created photography.
If data is not from what is said, then the data set will be cleaned of any errors.
## Considerations for Using the Data
### Social Impact of Dataset
Too much time on my hands.
### Discussion of Biases
It's a DSLR, it's a samsung phne - its' a BIRD ITS A - you get my point. There shoudl be no bias other than where I can actually take photos.
### Licensing Information
Do not sell this dataset, however you may use it as you see fit in TEXT TO IMAGE stable diffusion models.
Your outputs are your own, and the datawithin is free to be used for AI generation models.
### Citation Information
None needed.
### Contributions
If you'd like to contribute please do so!
|
Capsekai/DuskfallcrewPhotography
|
[
"task_categories:text-to-image",
"task_categories:image-classification",
"size_categories:1K<n<10K",
"language:en",
"license:creativeml-openrail-m",
"new zealand",
"photography",
"region:us"
] |
2023-02-26T09:05:18+00:00
|
{"language": ["en"], "license": "creativeml-openrail-m", "size_categories": ["1K<n<10K"], "task_categories": ["text-to-image", "image-classification"], "pretty_name": "Duskfall Photography", "tags": ["new zealand", "photography"]}
|
2023-02-26T10:25:35+00:00
|
fdfcf30fa2b95639efe19f19d69143490a773e9a
|
# AutoTrain Dataset for project: phototest
## Dataset Description
This dataset has been automatically processed by AutoTrain for project phototest.
### Languages
The BCP-47 code for the dataset's language is unk.
## Dataset Structure
### Data Instances
A sample from this dataset looks as follows:
```json
[
{
"image": "<768x768 RGB PIL image>",
"target": 0
},
{
"image": "<768x768 RGB PIL image>",
"target": 3
}
]
```
### Dataset Fields
The dataset has the following fields (also called "features"):
```json
{
"image": "Image(decode=True, id=None)",
"target": "ClassLabel(names=['Row 1', 'Row 2', 'Row 3', 'Row 4', 'Row 5'], 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 | 72 |
| valid | 19 |
|
Duskfallcrew/autotrain-data-phototest
|
[
"task_categories:image-classification",
"task_categories:text-to-image",
"size_categories:1K<n<10K",
"language:en",
"license:creativeml-openrail-m",
"region:us"
] |
2023-02-26T09:13:52+00:00
|
{"language": ["en"], "license": "creativeml-openrail-m", "size_categories": ["1K<n<10K"], "task_categories": ["image-classification", "text-to-image"], "pretty_name": "Phototest"}
|
2023-02-26T10:46:20+00:00
|
2796360967197cb5ce59d4d5d712ddc320f70e82
|
Capsekai/Duskfallcrew_Unsplash_Photography
|
[
"task_categories:text-to-image",
"size_categories:1K<n<10K",
"language:en",
"license:creativeml-openrail-m",
"photography",
"region:us"
] |
2023-02-26T09:16:39+00:00
|
{"language": ["en"], "license": "creativeml-openrail-m", "size_categories": ["1K<n<10K"], "task_categories": ["text-to-image"], "pretty_name": "2nd Photography", "tags": ["photography"]}
|
2023-02-26T20:57:06+00:00
|
|
c6d79c54153a671ed0fd1d6fc9407b3120b5f592
|
# Dataset Card for "kek"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
BrennanGambling/kek
|
[
"region:us"
] |
2023-02-26T09:45:10+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 22400683756, "num_examples": 536512826}], "download_size": 14348823259, "dataset_size": 22400683756}}
|
2023-02-26T11:00:04+00:00
|
54b02f85e943fa839ac2bb5b4a7aba258d3b1974
|
Loug/embeddings
|
[
"license:creativeml-openrail-m",
"region:us"
] |
2023-02-26T09:55:28+00:00
|
{"license": "creativeml-openrail-m"}
|
2023-02-26T09:55:28+00:00
|
|
34576c0fbb3a4cbf021676ead6f38fb4197f237d
|
# Dataset Card for "mgb2_audios_transcriptions_non_overlap"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
BelalElhossany/mgb2_audios_transcriptions_non_overlap
|
[
"region:us"
] |
2023-02-26T10:08:30+00:00
|
{"dataset_info": {"features": [{"name": "path", "dtype": "string"}, {"name": "audio", "dtype": {"audio": {"sampling_rate": 16000}}}, {"name": "sentence", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 901857303.92, "num_examples": 4972}], "download_size": 965382804, "dataset_size": 901857303.92}}
|
2023-02-26T10:09:19+00:00
|
c51b19e786f8944ee1a408184c30172557133cb2
|
EnD-Diffusers/AI_Faces
|
[
"task_categories:text-to-image",
"size_categories:1K<n<10K",
"language:en",
"license:creativeml-openrail-m",
"AI Faces",
"Photography",
"Stable Diffusion",
"region:us"
] |
2023-02-26T10:11:01+00:00
|
{"language": ["en"], "license": "creativeml-openrail-m", "size_categories": ["1K<n<10K"], "task_categories": ["text-to-image"], "pretty_name": "AI Generated Faces", "tags": ["AI Faces", "Photography", "Stable Diffusion"]}
|
2024-01-05T04:48:22+00:00
|
|
948263bbab398417ff90f4253149a779c0493daa
|
# Do not resell the data, you don't own the data but you do your own outputs of your training. See main lisc for details
|
Capsekai/80sCartoons
|
[
"task_categories:text-to-image",
"size_categories:1K<n<10K",
"language:en",
"license:creativeml-openrail-m",
"text",
"text to image",
"stable diffusion",
"80s",
"region:us"
] |
2023-02-26T10:36:18+00:00
|
{"language": ["en"], "license": "creativeml-openrail-m", "size_categories": ["1K<n<10K"], "task_categories": ["text-to-image"], "pretty_name": "Eighties Cartoons", "tags": ["text", "text to image", "stable diffusion", "80s"]}
|
2023-02-26T10:40:31+00:00
|
961c58a6aab1f4736f6a34be968d672a515a886e
|
## Source
https://www.kaggle.com/datasets/dhoogla/unswnb15?resource=download
## Dataset
This is an academic intrusion detection dataset. All the credit goes to the original authors: dr. Nour Moustafa and dr. Jill Slay.
Please cite their original paper and all other appropriate articles listed on the UNSW-NB15 page.
The full dataset also offers the pcap, BRO and Argus files along with additional documentation.
The modifications to the predesignated train-test sets are minimal and designed to decrease disk storage and increase performance & reliability.
Exploratory Data Analysis (EDA) through classification with very simple models to .877 AUROC.
|
wwydmanski/UNSW-NB15
|
[
"task_categories:tabular-classification",
"size_categories:1M<n<10M",
"tabular",
"network",
"region:us"
] |
2023-02-26T11:07:57+00:00
|
{"size_categories": ["1M<n<10M"], "task_categories": ["tabular-classification"], "tags": ["tabular", "network"]}
|
2023-02-26T11:14:46+00:00
|
2b1896745fa6f072f81fefc316d26be9d25921f3
|
nanaaaa/emotion_english
|
[
"region:us"
] |
2023-02-26T11:49:25+00:00
|
{}
|
2023-02-26T12:19:37+00:00
|
|
23650bbaa638faab05e0aeb48a91b3c72333e274
|
# Dataset Card for "zambezivoice_bem_train_text"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
zambezivoice/zambezivoice_bem_train_text
|
[
"region:us"
] |
2023-02-26T12:07:53+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 835072, "num_examples": 12421}], "download_size": 563571, "dataset_size": 835072}}
|
2023-02-26T12:07:57+00:00
|
a2b1c76b01f160672ce495008aecf0b036f47df4
|
# Dataset Card for "stackoverflow_linux"
Dataset information:
- Source: Stack Overflow
- Category: Linux
- Number of samples: 300
- Train/Test split: 270/30
- Quality: Data come from the top 1k most upvoted questions
## Additional Information
### License
All Stack Overflow user contributions are licensed under CC-BY-SA 3.0 with attribution required.
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
KonradSzafer/stackoverflow_linux
|
[
"task_categories:question-answering",
"size_categories:n<1K",
"language:en",
"region:us"
] |
2023-02-26T12:48:36+00:00
|
{"language": ["en"], "size_categories": ["n<1K"], "task_categories": ["question-answering"], "pretty_name": "Stack Overflow Linux", "dataset_info": {"features": [{"name": "title", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answer", "dtype": "string"}, {"name": "url", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 303464, "num_examples": 270}, {"name": "test", "num_bytes": 37456, "num_examples": 30}], "download_size": 172425, "dataset_size": 340920}}
|
2023-03-04T23:23:28+00:00
|
b7a121d997c31139f7e507d09f245ee24faea68b
|
# Dataset Card for "earnings21-gold-transcripts-non-normalized"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
anonymoussubmissions/earnings21-gold-transcripts-non-normalized
|
[
"region:us"
] |
2023-02-26T14:35:16+00:00
|
{"dataset_info": {"features": [{"name": "tokens", "sequence": "string"}, {"name": "labels", "sequence": "string"}, {"name": "tags", "sequence": {"class_label": {"names": {"0": "O", "1": "B-CARDINAL", "2": "B-DATE", "3": "B-EVENT", "4": "B-FAC", "5": "B-GPE", "6": "B-LANGUAGE", "7": "B-LAW", "8": "B-LOC", "9": "B-MONEY", "10": "B-NORP", "11": "B-ORDINAL", "12": "B-ORG", "13": "B-PERCENT", "14": "B-PERSON", "15": "B-PRODUCT", "16": "B-QUANTITY", "17": "B-TIME", "18": "B-WORK_OF_ART", "19": "I-CARDINAL", "20": "I-DATE", "21": "I-EVENT", "22": "I-FAC", "23": "I-GPE", "24": "I-LANGUAGE", "25": "I-LAW", "26": "I-LOC", "27": "I-MONEY", "28": "I-NORP", "29": "I-ORDINAL", "30": "I-ORG", "31": "I-PERCENT", "32": "I-PERSON", "33": "I-PRODUCT", "34": "I-QUANTITY", "35": "I-TIME", "36": "I-WORK_OF_ART"}}}}, {"name": "labels_orig", "sequence": "string"}, {"name": "file_id", "dtype": "string"}, {"name": "sentence_no", "dtype": "string"}, {"name": "id", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 4503755, "num_examples": 6955}, {"name": "validation", "num_bytes": 3019922, "num_examples": 4637}, {"name": "test", "num_bytes": 4948836, "num_examples": 7729}], "download_size": 1677962, "dataset_size": 12472513}}
|
2023-02-27T14:38:28+00:00
|
03a383205ee0212f8129bf7663abb5baa4f161f9
|
# Dataset Card for "earnings21-gold-transcripts-normalized"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
anonymoussubmissions/earnings21-gold-transcripts-normalized
|
[
"region:us"
] |
2023-02-26T14:35:23+00:00
|
{"dataset_info": {"features": [{"name": "tokens", "sequence": "string"}, {"name": "labels", "sequence": "string"}, {"name": "tags", "sequence": {"class_label": {"names": {"0": "O", "1": "B-CARDINAL", "2": "B-DATE", "3": "B-EVENT", "4": "B-FAC", "5": "B-GPE", "6": "B-LANGUAGE", "7": "B-LAW", "8": "B-LOC", "9": "B-MONEY", "10": "B-NORP", "11": "B-ORDINAL", "12": "B-ORG", "13": "B-PERCENT", "14": "B-PERSON", "15": "B-PRODUCT", "16": "B-QUANTITY", "17": "B-TIME", "18": "B-WORK_OF_ART", "19": "I-CARDINAL", "20": "I-DATE", "21": "I-EVENT", "22": "I-FAC", "23": "I-GPE", "24": "I-LANGUAGE", "25": "I-LAW", "26": "I-LOC", "27": "I-MONEY", "28": "I-NORP", "29": "I-ORDINAL", "30": "I-ORG", "31": "I-PERCENT", "32": "I-PERSON", "33": "I-PRODUCT", "34": "I-QUANTITY", "35": "I-TIME", "36": "I-WORK_OF_ART"}}}}, {"name": "labels_orig", "sequence": "string"}, {"name": "file_id", "dtype": "string"}, {"name": "sentence_no", "dtype": "string"}, {"name": "id", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 4503755, "num_examples": 6955}, {"name": "validation", "num_bytes": 3019922, "num_examples": 4637}, {"name": "test", "num_bytes": 4948836, "num_examples": 7729}], "download_size": 1708904, "dataset_size": 12472513}}
|
2023-02-27T14:38:37+00:00
|
830e44189d7596428872241cd09c70715358bc28
|
ashoksu30/My_test
|
[
"license:c-uda",
"region:us"
] |
2023-02-26T14:46:06+00:00
|
{"license": "c-uda"}
|
2023-02-26T14:46:07+00:00
|
|
bd7ec22690de51972bd49708a4c72fe50379c591
|
# Dataset Card for "humaneval-mbpp-codegen-qa"
This dataset contains prompt-reply (question-answer) pairs where the prompt is to create a Python function which satisfies the functionality described in a specified docstring. The responses are then the generated functions.
|
OllieStanley/humaneval-mbpp-codegen-qa
|
[
"region:us"
] |
2023-02-26T14:59:10+00:00
|
{"dataset_info": {"features": [{"name": "INSTRUCTION", "dtype": "string"}, {"name": "RESPONSE", "dtype": "string"}, {"name": "SOURCE", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 225572, "num_examples": 591}], "download_size": 89931, "dataset_size": 225572}}
|
2023-03-15T15:13:27+00:00
|
eb69d2f1c650ae8d98ba37d986a763057ee53893
|
# Dataset Card for "humaneval-mbpp-testgen-qa"
This dataset contains prompt-reply (question-answer) pairs where the prompt is to create a Python unit tests which tests for the functionality described in a specific docstring. The responses are then the generated unit tests.
|
OllieStanley/humaneval-mbpp-testgen-qa
|
[
"region:us"
] |
2023-02-26T15:01:49+00:00
|
{"dataset_info": {"features": [{"name": "INSTRUCTION", "dtype": "string"}, {"name": "RESPONSE", "dtype": "string"}, {"name": "SOURCE", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 304315, "num_examples": 591}], "download_size": 0, "dataset_size": 304315}}
|
2023-03-15T15:12:49+00:00
|
fbdcdd3b0ea685924ab0c262ec51e420db0eee01
|
# Dataset Card for "kdqb_0227"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
daxiangpanda/kdqb_0227
|
[
"region:us"
] |
2023-02-26T15:26:09+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 51563593.0, "num_examples": 198}], "download_size": 51554554, "dataset_size": 51563593.0}}
|
2023-02-26T15:26:35+00:00
|
cdba2b795fec92f56b7894474faa74ca49797c43
|
PranavPolavarapu/imagedata
|
[
"license:bigscience-bloom-rail-1.0",
"region:us"
] |
2023-02-26T15:44:37+00:00
|
{"license": "bigscience-bloom-rail-1.0"}
|
2023-02-26T18:29:14+00:00
|
|
5bda47e50e3100a7643ba4a31d439006e2fd89f1
|
Thewillonline/reddit-sarcasm
|
[
"license:unknown",
"region:us"
] |
2023-02-26T16:11:40+00:00
|
{"license": "unknown"}
|
2023-02-26T16:14:22+00:00
|
|
79922b63139c9fda62d30f777ffff9d06b8e2a3f
|
# Dataset Card for "70000_method2test_tokonized"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Minata/70000_method2test_tokonized
|
[
"region:us"
] |
2023-02-26T17:06:47+00:00
|
{"dataset_info": {"features": [{"name": "input_ids", "sequence": "int32"}, {"name": "attention_mask", "sequence": "int8"}, {"name": "labels", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 466760000, "num_examples": 70000}], "download_size": 27648900, "dataset_size": 466760000}}
|
2023-02-26T17:06:52+00:00
|
4fb50650f149c45255623bf9b017ca2fa521c4c7
|
hakatiki/guttenberg-books-corpus
|
[
"task_categories:text-generation",
"size_categories:10B<n<100B",
"language:hu",
"region:us"
] |
2023-02-26T17:16:16+00:00
|
{"language": ["hu"], "size_categories": ["10B<n<100B"], "task_categories": ["text-generation"], "pretty_name": "Hungarian text dataset"}
|
2023-02-27T15:35:52+00:00
|
|
b11a70184696c2a7ee9027d1a26c2136d52b1bf0
|
mikikk/miscellaneous-embedds
|
[
"license:unknown",
"region:us"
] |
2023-02-26T18:13:06+00:00
|
{"license": "unknown"}
|
2023-02-26T18:16:12+00:00
|
|
a0d10bb991c1cbe6ab011e96742b41ae8a3a2a4d
|
# Dataset Card for "FontsLargeSpaced"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
arbml/FontsLargeSpaced
|
[
"region:us"
] |
2023-02-26T18:30:12+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 43557017.054, "num_examples": 9899}], "download_size": 28289205, "dataset_size": 43557017.054}}
|
2023-02-26T18:30:19+00:00
|
145e971b5d2714bcf6695605b4ee91a923ca1e14
|
netranga/demo
|
[
"task_categories:sentence-similarity",
"language:en",
"region:us"
] |
2023-02-26T19:05:27+00:00
|
{"language": ["en"], "task_categories": ["sentence-similarity"]}
|
2023-02-26T19:16:19+00:00
|
|
ff73c48799c629fdec465eee3ba4918fee7e1cef
|
# Dataset Card for "VISBank"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Yamei/VISBank
|
[
"region:us"
] |
2023-02-26T19:54:58+00:00
|
{"dataset_info": {"features": [{"name": "title", "dtype": "string"}, {"name": "paper_id", "dtype": "int64"}, {"name": "abstract", "dtype": "string"}, {"name": "authors", "list": [{"name": "first", "dtype": "string"}, {"name": "middle", "sequence": "string"}, {"name": "last", "dtype": "string"}, {"name": "suffix", "dtype": "string"}]}, {"name": "year", "dtype": "float64"}, {"name": "arxiv_id", "dtype": "string"}, {"name": "acl_id", "dtype": "string"}, {"name": "pmc_id", "dtype": "string"}, {"name": "pubmed_id", "dtype": "string"}, {"name": "doi", "dtype": "string"}, {"name": "venue", "dtype": "string"}, {"name": "journal", "dtype": "string"}, {"name": "mag_id", "dtype": "string"}, {"name": "outbound_citations", "sequence": "string"}, {"name": "inbound_citations", "sequence": "string"}, {"name": "has_outbound_citations", "dtype": "bool"}, {"name": "has_inbound_citations", "dtype": "bool"}, {"name": "has_pdf_parse", "dtype": "bool"}, {"name": "s2_url", "dtype": "string"}, {"name": "has_pdf_body_text", "dtype": "float64"}, {"name": "has_pdf_parsed_abstract", "dtype": "float64"}, {"name": "has_pdf_parsed_body_text", "dtype": "float64"}, {"name": "has_pdf_parsed_bib_entries", "dtype": "float64"}, {"name": "has_pdf_parsed_ref_entries", "dtype": "float64"}], "splits": [{"name": "train", "num_bytes": 194394520, "num_examples": 125745}], "download_size": 109992297, "dataset_size": 194394520}}
|
2023-02-26T19:55:20+00:00
|
8d1a910a7602a6a836b02c11319cbace910eef89
|
mohamed2019/nourtoon2017
|
[
"license:other",
"region:us"
] |
2023-02-26T19:56:32+00:00
|
{"license": "other"}
|
2023-02-26T19:57:25+00:00
|
|
2e481c54790763f1efdc1d00d7bd5e0c8a7c6b42
|
# Dataset Card for "smart-lights-en-close-field"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
qmeeus/smart-lights-en-close-field
|
[
"region:us"
] |
2023-02-26T20:50:28+00:00
|
{"dataset_info": {"features": [{"name": "uttid", "dtype": "string"}, {"name": "audio", "dtype": {"audio": {"sampling_rate": 16000}}}, {"name": "text", "dtype": "string"}, {"name": "intent", "dtype": {"class_label": {"names": {"0": "DecreaseBrightness", "1": "IncreaseBrightness", "2": "SetLightBrightness", "3": "SetLightColor", "4": "SwitchLightOff", "5": "SwitchLightOn"}}}}, {"name": "entities", "sequence": {"class_label": {"names": {"0": "B-LOC", "1": "I-LOC", "2": "B-COL", "3": "I-COL", "4": "B-NUM", "5": "I-NUM", "6": "O"}}}}, {"name": "speaker", "struct": [{"name": "age", "dtype": "int64"}, {"name": "country", "dtype": "string"}, {"name": "gender", "dtype": "string"}, {"name": "id", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 124895101.58399998, "num_examples": 1328}, {"name": "validation", "num_bytes": 15339937.9, "num_examples": 166}, {"name": "test", "num_bytes": 15496384.9, "num_examples": 166}], "download_size": 129906544, "dataset_size": 155731424.38399997}}
|
2023-03-04T02:42:10+00:00
|
7cda082983e146261ec7d87fb1562d2a1a3e9f80
|
# Dataset Card for "avatar-lite"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
jlbaker361/avatar-lite
|
[
"region:us"
] |
2023-02-26T21:46:11+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "src", "dtype": "string"}, {"name": "split", "dtype": "string"}, {"name": "id", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 1595867500.125, "num_examples": 2031}], "download_size": 1595634006, "dataset_size": 1595867500.125}}
|
2023-02-26T21:46:47+00:00
|
49068c54a4551304208aea80749f1a4d5ab893d2
|
---
# For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/datasetcard.md?plain=1
# Doc / guide: https://huggingface.co/docs/hub/datasets-cards
{}
---
# Dataset Card for Dataset Name
## Dataset Description
- **Homepage:**
- https://github.com/AARSynth/Dataset
- **Repository:**
- https://github.com/AARSynth/Dataset
- **Paper:**
- App-Aware Response Synthesis for User Reviews.
Umar Farooq, A.B. Siddique, Fuad Jamour, Zahijia Zhao and Vagelis Hristidis, “App-Aware Response Synthesis for User Reviews,” 2020 IEEE International Conference on Big Data (Big Data), 2020, pp. 699-708, DOI: https://doi.org/10.1109/BigData50022.2020.9377983.
- **Point of Contact:**
- Umar Farooq ([email protected])
- Abubakar Siddique ([email protected])
### Dataset Summary
AARSynth is a large-scale app review dataset. There are 570K review-response pairs and more than 2 million user
reviews for 103 popular applications.
### Supported Tasks and Leaderboards
Question Answer
Response Generation
### Languages
English
## How to use the dataset?
```
from datasets import load_dataset
import pandas as pd
# load the dataset
mbr_data = load_dataset('recmeapp/AARSynth', data_dir='replies')
# Save dataset to .csv file for creating pandas dataframe
mbr_data['train'].to_csv('./mbr_data.csv', sep='***')
# Convert to pandas dataframe
aarsynth_df = pd.read_csv('./mbr_data.csv', sep='***')
# How many interactions are there in the AARSynth dataset?
print(f'There are {len(aarsynth_df)} interactions in AARSynth dataset.')
```
[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
Umar Farooq and A.B. Siddique
### Licensing Information
[More Information Needed]
### Citation Information
- App-Aware Response Synthesis for User Reviews.
Umar Farooq, A.B. Siddique, Fuad Jamour, Zahijia Zhao and Vagelis Hristidis, “App-Aware Response Synthesis for User Reviews,” 2020 IEEE International Conference on Big Data (Big Data), 2020, pp. 699-708, DOI: https://doi.org/10.1109/BigData50022.2020.9377983.
### Contributions
[More Information Needed]
|
recmeapp/AARSynth
|
[
"license:cc-by-4.0",
"region:us"
] |
2023-02-26T23:02:54+00:00
|
{"license": "cc-by-4.0"}
|
2023-03-11T19:00:19+00:00
|
d3532e18de38ff26219279b3d0e187ccd33f8288
|
# Dataset Card for "avatar"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
jlbaker361/avatar
|
[
"region:us"
] |
2023-02-26T23:10:19+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "src", "dtype": "string"}, {"name": "split", "dtype": "string"}, {"name": "id", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 5855144505.625, "num_examples": 7355}], "download_size": 5854241061, "dataset_size": 5855144505.625}}
|
2023-02-26T23:12:26+00:00
|
50d665a3069696b00be4aafe6888371a5d96d283
|
# Dataset Card for "VISBank_Parsed"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Yamei/VISBank_Parsed
|
[
"region:us"
] |
2023-02-27T01:06:48+00:00
|
{"dataset_info": {"features": [{"name": "title", "dtype": "string"}, {"name": "paper_id", "dtype": "int64"}, {"name": "abstract", "dtype": "string"}, {"name": "authors", "list": [{"name": "first", "dtype": "string"}, {"name": "middle", "sequence": "string"}, {"name": "last", "dtype": "string"}, {"name": "suffix", "dtype": "string"}]}, {"name": "year", "dtype": "float64"}, {"name": "arxiv_id", "dtype": "string"}, {"name": "acl_id", "dtype": "string"}, {"name": "pmc_id", "dtype": "string"}, {"name": "pubmed_id", "dtype": "string"}, {"name": "doi", "dtype": "string"}, {"name": "venue", "dtype": "string"}, {"name": "journal", "dtype": "string"}, {"name": "mag_id", "dtype": "string"}, {"name": "outbound_citations", "sequence": "string"}, {"name": "inbound_citations", "sequence": "string"}, {"name": "has_outbound_citations", "dtype": "bool"}, {"name": "has_inbound_citations", "dtype": "bool"}, {"name": "has_pdf_parse", "dtype": "bool"}, {"name": "s2_url", "dtype": "string"}, {"name": "has_pdf_body_text", "dtype": "float64"}, {"name": "has_pdf_parsed_abstract", "dtype": "float64"}, {"name": "has_pdf_parsed_body_text", "dtype": "float64"}, {"name": "has_pdf_parsed_bib_entries", "dtype": "float64"}, {"name": "has_pdf_parsed_ref_entries", "dtype": "float64"}, {"name": "entities", "sequence": {"sequence": "string"}}], "splits": [{"name": "train", "num_bytes": 260857853, "num_examples": 125745}], "download_size": 135186490, "dataset_size": 260857853}}
|
2023-03-04T19:58:40+00:00
|
5fb7ec7c3840087d560eb778c518e68835729dee
|
ChrisTC/AnimeSet
|
[
"license:other",
"region:us"
] |
2023-02-27T01:26:51+00:00
|
{"license": "other"}
|
2023-02-27T01:31:51+00:00
|
|
15a062c32ea2f51465374a6a0cb4dd20423da990
|
# Negative outputs from various models of Stable Diffusion - use at your will to train textual inversons or other things.
|
Capsekai/Negative_Images
|
[
"task_categories:text-to-image",
"size_categories:1K<n<10K",
"language:en",
"license:creativeml-openrail-m",
"dataset",
"stable diffusion",
"region:us"
] |
2023-02-27T02:04:08+00:00
|
{"language": ["en"], "license": "creativeml-openrail-m", "size_categories": ["1K<n<10K"], "task_categories": ["text-to-image"], "pretty_name": "Negative Outputs", "tags": ["dataset", "stable diffusion"]}
|
2023-02-27T02:19:57+00:00
|
aaaabe386a88dafa8de1fa42c0fbee0ad5d48b6a
|
# Dataset Card for AI Text Dectection Pile
## Dataset Description
- **Point of Contact:[email protected]
### Dataset Summary
This is a large scale dataset intended for AI Text Detection tasks, geared toward long-form text and essays. It contains samples of both human text and AI-generated text from GPT2, GPT3, ChatGPT, GPTJ.
Here is the (tentative) breakdown:
#### Human Text
| Dataset | Num Samples | Link |
| ----------- | ----------- | ----------- |
| Reddit WritingPromps | 570k | [Link](https://www.kaggle.com/datasets/ratthachat/writing-prompts) |
| OpenAI Webtext | 260k | [Link](https://github.com/openai/gpt-2-output-dataset) |
| HC3 (Human Responses) | 58k | [Link](https://huggingface.co/datasets/Hello-SimpleAI/HC3) |
| ivypanda-essays | TODO | TODO |
| **Total** | **990k** | **-** |
#### AI-Generated Text
| Model | Dataset | Num Samples | Link |
| ----------- | ----------- | ----------- | ----------- |
| GPT2 | OpenAI gpt2-output-dataset | 260k | [Link](https://github.com/openai/gpt-2-output-dataset) |
| GPT3 | pairwise-davinci | 44k | TODO |
| GPT3 | synthetic-instruct-davinci-pairwise | 30k | [Link](https://huggingface.co/datasets/Dahoas/instruct-synthetic-prompt-responses) |
| GPTJ | synthetic-instruct-gptj-pairwise | 44k | [Link](https://huggingface.co/datasets/Dahoas/synthetic-instruct-gptj-pairwise) |
| ChatGPT | Scraped from twitter | 5k | **-** |
| ChatGPT | HC3 (ChatGPT Responses) | 27k | [Link](https://huggingface.co/datasets/Hello-SimpleAI/HC3) |
| ChatGPT | ChatGPT Prompts/emergentmind | 500 | [Link](https://huggingface.co/datasets/MohamedRashad/ChatGPT-prompts/tree/main) |
| **Total** | **340k** | **-** | **-** |
### Supported Tasks and Leaderboards
Text Classification, AI Text Detection.
### Languages
English.
### Data Fields
TEXT: The text of the sample.
SOURCE: either "human" or "ai"
|
artem9k/ai-text-detection-pile
|
[
"license:mit",
"region:us"
] |
2023-02-27T02:52:29+00:00
|
{"license": "mit"}
|
2023-02-27T03:37:54+00:00
|
00446742dfed5547be4268e86d1ef099efeb3e9f
|
Adam12345/LOL
|
[
"license:bigscience-bloom-rail-1.0",
"region:us"
] |
2023-02-27T02:53:18+00:00
|
{"license": "bigscience-bloom-rail-1.0"}
|
2023-02-27T02:53:18+00:00
|
|
50b76face3b2a46e5091efc55939db9b5a16b63f
|
# Dataset Card for "avatar-lite-augmented"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
jlbaker361/avatar-lite-augmented
|
[
"region:us"
] |
2023-02-27T03:35:31+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "src", "dtype": "string"}, {"name": "split", "dtype": "string"}, {"name": "id", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 27395268.125, "num_examples": 2031}], "download_size": 27171589, "dataset_size": 27395268.125}}
|
2023-02-27T03:35:33+00:00
|
0c50d6c850fb36a98f0a27712e8273cd3f8d5c82
|
# The 3oloum corpus of scientific titles and abstracts
This is a corpus of titles and abstracts of 147,673 scientific articles that were scraped from the following scientific journals:
- Nature: from year 1870 to 2021
- Science: from year 1960 to 2020
- Science Advances: from year 2015 to 2020
The corpus has been created for the purpose of contributing to natural language processing (NLP) projects.
There are currently *21,309,015* words in the corpus, including the text of titles and abstracts, and excluding non-ASCII strings.
Every non-ASCII character has been replaced by the string `<non_ascii>` to facilitate text processing.
It is being continuously updated.
## Sample data
|Title|Abstract|
| :--- | :--- |
| Velocity of Light and Measurement of Interplanetary Distances | The combined availability of atomic clocks and of instrumented planetoids traveling in their own solar orbits will offer the possibility of determining their distance from us, and hence interplanetary distances, in terms of the wavelength of the radiation of atomic frequency standards. It can be anticipated that the accuracy of these measurements will be very high and will not depend upon our less accurate knowledge of the velocity of light in terms of the standard meter, the sidereal second, and so on. |
| High-Resolution Density Gradient Sedimentation Analysis | The principle of stability for a sample layered in a density-gradient liquid column is discussed, and a method for separating ribonucleoprotein particles by means of sedimentation in the ultracentrifuge is described. |
| Daily Light Sensitivity Rhythm in a Rodent | Single 10-minute light periods can cause a phase shift in the rhythm of the daily locomotor activity of flying squirrels otherwise maintained in constant darkness. A daily rhythm of sensitivity to these standard light periods was found. |
| Heat-Labile Serum Systems in Fresh-Water Fish | Serum specimens from 18 specimens of 12 different species of freshwater fish were examined for their ability to kill <i>Toxoplasma</i> nonspecifically. This ability was present in all sera except those of two of three great northern pike. The effect was destroyed by exposure to 53<non_ascii><non_ascii>C, 56<non_ascii><non_ascii>C, or zymosan. Complement was demonstrated in all sera except that from one great northern pike, when rabbit erythrocytes were used in the indicator system. |
| Chemically Induced Phenocopy of a Tomato Mutant | Lanceolate, a spontaneous leaf-shape mutant which fails to produce cotyledons and plumule in the homozygous condition, shows development if supplied with either adenine or a diffusate obtained from normal seeds. Similar development occurs in a different genetic background. |
| Mitotic Arrest by Deuterium Oxide | In marine invertebrate eggs, where cell divisions occur without growth, deuterium oxide produces arrest of, or serious delay in, mitosis and cytokinesis. All stages requiring assembly or operation of mechanical structures in the cytoplasm are sensitive to D.O. The block is reversible in some cells. |
| Nonlogarithmic Linear Titration Curves | Titration curves can be based on linear nonlogarithmic forms of the equilibrium equation of a dissociation reaction. From such curves, in contrast to those based on logarithmic transformations, both the end point of the titration and the dissociation constant can be derived. |
| On the Function of Corticothalamic Neurons | The effect of the synchronous discharge of a large population of corticothalamic neurons on activity within the somatosensory relay nuclei has been studied. Thalamic responses to peripheral nerve stimulation are depressed by activity in corticothalamic neurons. A subconvulsive dose of strychnine, given intravenously, changes this depression to enhancement. |
| Occurrence of Scandium-46 and Cesium-134 in Radioactive Fallout | Two hitherto unreported induced radionuclides, scandium-46 and cesium-134, have been detected in fallout material. Identification was made by chemical separation and gamma scintillation spectrometry. While the origin of these materials is not known, possible routes of formation from stable elements are suggested. |
| Degree of Obesity and Serum Cholesterol Level | No significant correlation was found between the serum cholesterol level and weight, weight corrected for frame size, or thickness of the fat shadow in medical students (mean age, 22 years). |
| Neural and Hypophyseal Colloid Deposition in the Collared Lemming | Feral and captive lemmings from Churchill, Manitoba, are subject to a unique pathological process in which colloidal material is deposited in bloodvessel walls at scattered points through the central nervous system. Destruction of nervous tissue at these foci is progressive, and colloidal masses in the vascular lumina of the hypothalamus appear to become fixed in the capillaries of the hypophyseal anterior lobe. Inflammatory reactions are never associated with the lesions, and the latter are larger and more numerous in older animals in warmer environments. |
| On Pleistocene Surface Temperatures of the North Atlantic and Arctic Oceans | Two additional interpretations are given for the important data of D. B. Ericson on the correlation of coiling directions of <i>Globigerina pachyderma</i> in late Pleistocene North Atlantic sediments with ocean surface temperatures. One interpretation relates the distribution of this species to the distribution and circulation of ocean water masses. On the basis of our ice-age theory, our second interpretation uses the data and correlations of Ericson to establish temperature limits of a thermal node, a line on which glacial and interglacial temperatures were equal, for the North Atlantic Ocean. This line crosses the strait between Greenland and Scandinavia. Further, Ericson9s interpretation of the 7.2<non_ascii><non_ascii>C isotherm implies that the glacial-stage surface waters of the Arctic Ocean were between 0<non_ascii><non_ascii> and 3.5<non_ascii><non_ascii>C. |
| Genetic and Environmental Control of Flowering in Trifolium repens in the Tropics | <i>Trifolium repens</i> at low elevations expressed wide genetic variation in tendency to flower. Clones classified as flowering or nonflowering were subjected to temperatures associated with high elevations. Flowering in "nonflowering" clones was induced under warm-day-cool-night treatments. It is proposed that in the tropics, low temperatures associated with high elevations are an important factor in determining flowering, and therefore ability to persist, in plants which are long-day and temperature sensitive. |
| Mammalian Liver <non_ascii><non_ascii>-Glucuronidase for Hydrolysis of Steroidal Conjugates | Although the rate of hydrolysis by mammalian <non_ascii><non_ascii>-glucuronidase appears to be inhibited by methylene chloride or carbon tetrachloride with the standard technique (phenolphthalein glucuronide as a substrate), the release of steroidal conjugates under conditions generally employed does not appear to be affected. |
| Glucuronidase Activation: Enzyme Action at an Interface | The potentiating action of chloroform on bacterial <non_ascii><non_ascii>-glucuronidase has been shown to increase as the interface area between the two liquid phases increases. Prior extraction of the enzyme with chloroform causes a loss rather than an increase in activity. It is tentatively suggested that the correlation between activity and interface area may reflect a phenomenon of enzyme action at a liquid/liquid interface. |
| Characterization of Endogenous Ethanol in the Mammal | Ethanol has been isolated from the tissues of several animal species in amounts ranging from 23 to 145 <non_ascii><non_ascii>mole/100 gm of tissue. Intestinal bacterial flora appear to be excluded as a source of this ethanol. Radioactivity from pyruvate-2-C<sup>14</sup> appeared in ethanol after incubation with liver slices; this finding indicates an endogenous synthesis. |
| Reciprocal Inhibition as Indicated by a Differential Staining Reaction | Neurohistological and neurophysiological studies have shown that the bilaterally represented Mauthner9s cells in teleosts are related both structurally and functionally. The VIIIth nerve afferents, as well as the axoaxonal collaterals, display a distribution pattern which supports the concept of polar function of the neuron. Inasmuch as it is possible to alter the staining reaction of both the Mauthner9s cells by unilateral stimulation of the entering VIIIth nerve roots, it is proposed that the synaptic endings serve principally as activators and that neuronal excitation or inhibition is determined by the chemical state of the dendrites, the cell body, and the axon hillock region. |
| Orientation of Migratory Restlessness in the White-Crowned Sparrow | Individuals of two migratory races of white-crowned sparrows (<i>Zonotrichia leucophrys</i>) caged under an open sky showed a pronounced orientation in their night restlessness during normal periods of migration for the species. In August and September 1958 most birds showed a southerly orientation at night; daytime activity was random to somewhat northerly. In April and May 1959 most birds showed a strong northerly orientation at night; daytime activity was random to somewhat southerly (<i>1</i>). |
| State of Dynamic Equilibrium in Protein of Mammalian Cells | Labeled strain L cells in suspension tissue culture showed no degradation of protein when maintained in logarithmic growth. Although the protein of these cells was not in dynamic equilibrium, the conclusions cannot be transferred to the intact mammalian organism. |
| Mosses as Possible Sources of Antibiotics | An examination of 12 species of mosses has indicated that three produce substances capable of inhibiting the growth of various bacteria and other fungi. The method of extraction included several solvents. The extracts were not consistent in their antagonistic activity against the various species of microorganisms, nor were those that displayed antibiotic action always effective against the same organisms. Results indicate unstable products as well as physiological variation in the mosses. |
|
sheriftawfikabbas/3oloum_corpus
|
[
"language:en",
"license:gpl-3.0",
"science abstracts",
"English",
"region:us"
] |
2023-02-27T03:37:35+00:00
|
{"language": ["en"], "license": "gpl-3.0", "pretty_name": "3oloum corpus for scientific abstract", "tags": ["science abstracts", "English"]}
|
2023-04-12T05:56:18+00:00
|
91ccae8af14e13929ecb6d745ea6b5aa18e609c7
|
# Dataset Card for "avatar-augmented"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
jlbaker361/avatar-augmented
|
[
"region:us"
] |
2023-02-27T04:16:33+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "src", "dtype": "string"}, {"name": "split", "dtype": "string"}, {"name": "id", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 100596505.625, "num_examples": 7355}], "download_size": 99698053, "dataset_size": 100596505.625}}
|
2023-02-27T04:16:38+00:00
|
c57e832af56db74341f5bac4d244d6ae113ab3c5
|
# Dataset Card for "summarization-sft-heirarchical"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
dmayhem93/summarization-sft-heirarchical
|
[
"region:us"
] |
2023-02-27T06:11:06+00:00
|
{"dataset_info": {"features": [{"name": "prompt", "dtype": "string"}, {"name": "125M", "dtype": "string"}, {"name": "1B", "dtype": "string"}, {"name": "6B", "dtype": "string"}, {"name": "20B", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 223409, "num_examples": 112}], "download_size": 144701, "dataset_size": 223409}}
|
2023-02-27T06:12:35+00:00
|
e6afe06fbf313b695f668d18f4b527701f97bed1
|
p1atdev/badquality
|
[
"license:cc0-1.0",
"region:us"
] |
2023-02-27T07:15:26+00:00
|
{"license": "cc0-1.0"}
|
2023-03-21T02:34:25+00:00
|
|
473e27655118a1f4c3c295104f3b6e4e9f896b52
|
sgedela/dilbert-comic-sample-dataset
|
[
"license:openrail",
"region:us"
] |
2023-02-27T07:25:02+00:00
|
{"license": "openrail"}
|
2023-02-27T07:25:02+00:00
|
|
32fe3dd159e38304cf2c7e0aa8cb129314aa691c
|
weiyun/predict_test
|
[
"region:us"
] |
2023-02-27T08:05:39+00:00
|
{"dataset_info": [{"config_name": "predict_test", "features": [{"name": "src_txt", "dtype": "string"}, {"name": "tgt_txt", "dtype": "string"}], "splits": [{"name": "test"}, {"name": "train"}, {"name": "validation"}]}]}
|
2023-02-27T10:38:34+00:00
|
|
259ca2f2d4aca7782d56694fb5a1466ff1d72098
|
# Dataset Card for "Sudanese_Dialect"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
MohammadJamalaldeen/Sudanese_Dialect
|
[
"region:us"
] |
2023-02-27T08:10:45+00:00
|
{"dataset_info": {"features": [{"name": "input_features", "sequence": {"sequence": "float32"}}, {"name": "labels", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 4639654208, "num_examples": 4830}, {"name": "test", "num_bytes": 511020520, "num_examples": 532}], "download_size": 988631878, "dataset_size": 5150674728}}
|
2023-02-27T08:12:06+00:00
|
278f7450f6117be9376c15a7179b9c156be8ce84
|
A collection of large-scale, high-quality datasets of URL links of up to 650,000 video clips that cover 400 human action classes.
The videos include human-object interactions such as playing instruments, as well as human-human interactions such as shaking hands and hugging.
Each action class has at least 400.
Each clip is human annotated with a single action class and lasts around 10 seconds.
Originally created by Google Inc. and the dataset has been uploaded as is without any changes.
|
AlexFierro9/Kinetics400
|
[
"license:cc-by-4.0",
"region:us"
] |
2023-02-27T08:16:25+00:00
|
{"license": "cc-by-4.0"}
|
2023-02-27T12:45:06+00:00
|
627904d5ff8b0e4101a485c349956a6056c31601
|
# SQuALITY - v1.3
> Original paper [here](https://arxiv.org/abs/2205.11465)
This is v1.3, the 'text' edition `.jsonl` files. See description from the [original repo](https://github.com/nyu-mll/SQuALITY):
> v1.3 fixes some bugs in v1.2. In v1.2, 10 out of 127 articles (each ~5k-word-long) are missing a few hundreds words each, so summaries may not be fully contained in the article. To fix this issue, we have updated the 10 articles.
## contents
> again, this is taken from the repo
Each data file ({train/dev/test}.jsonl) is formatted as a JSON lines file. Each row in the data file is a JSON dictionary with the following fields:
- metadata: the Gutenberg story ID, an internal UID, and the Project Gutenberg license
- document: the Gutenberg story
questions: a list of questions and accompanying responses
- question text
- question number: the order in which that question was answered by the writers
- responses: list of worker's response, where each response is a dictionary containing the (anonymized) worker ID, an internal UID, and their response to the question
### dataset contents
```python
DatasetDict({
train: Dataset({
features: ['metadata', 'document', 'questions'],
num_rows: 50
})
test: Dataset({
features: ['metadata', 'document', 'questions'],
num_rows: 52
})
validation: Dataset({
features: ['metadata', 'document', 'questions'],
num_rows: 25
})
})
```
|
pszemraj/SQuALITY-v1.3
|
[
"task_categories:summarization",
"task_categories:text2text-generation",
"size_categories:n<1K",
"language:en",
"license:apache-2.0",
"summarization",
"long-document",
"arxiv:2205.11465",
"region:us"
] |
2023-02-27T08:25:50+00:00
|
{"language": ["en"], "license": "apache-2.0", "size_categories": ["n<1K"], "task_categories": ["summarization", "text2text-generation"], "pretty_name": "SQuALITY v1.3", "tags": ["summarization", "long-document"]}
|
2023-02-27T08:42:44+00:00
|
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