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a3318c91d7508ce0dd92c1479f313efb360bed70 | Thai UCC Corpus is translate from [UCC (Unhealthy Comments Corpus)](https://github.com/conversationai/unhealthy-conversations) by PyThaiNLP Translator and Google Translator. | nakcnx/Thai-UCC | [
"license:cc-by-nc-sa-4.0",
"region:us"
]
| 2023-03-07T23:05:17+00:00 | {"license": ["cc-by-nc-sa-4.0"]} | 2023-03-07T23:46:10+00:00 | []
| []
| TAGS
#license-cc-by-nc-sa-4.0 #region-us
| Thai UCC Corpus is translate from UCC (Unhealthy Comments Corpus) by PyThaiNLP Translator and Google Translator. | []
| [
"TAGS\n#license-cc-by-nc-sa-4.0 #region-us \n"
]
| [
19
]
| [
"passage: TAGS\n#license-cc-by-nc-sa-4.0 #region-us \n"
]
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|
385597b46dd463005b65c048e89237a2d37bb6e2 | This is a list of camera shots from screenplays (scripts) for a series of animated cartoons,
plus data from the animated scenes created manually for the series.
Over time, I will add more details to the scenes. The goal is to be able to create as much
as the scenes as possible from just writing a screenplay to reduce the manual effort to
ultimately create an episode. It does not have to achieve everything, just reduce the effort.
E.g. a first step is to work out which characters are in a shot from the script.
This may need looking at previous shots however as they may be in the background still.
Next, can the location of the characters be worked out from text alone (the data does not
have their position and rotation yet). Then lighting, props, animation clips, and more - see
how much can be deduced from the script alone. | alankent/ordinary_screenplays | [
"language:en",
"license:apache-2.0",
"region:us"
]
| 2023-03-07T23:14:52+00:00 | {"language": ["en"], "license": "apache-2.0"} | 2023-03-09T03:49:01+00:00 | []
| [
"en"
]
| TAGS
#language-English #license-apache-2.0 #region-us
| This is a list of camera shots from screenplays (scripts) for a series of animated cartoons,
plus data from the animated scenes created manually for the series.
Over time, I will add more details to the scenes. The goal is to be able to create as much
as the scenes as possible from just writing a screenplay to reduce the manual effort to
ultimately create an episode. It does not have to achieve everything, just reduce the effort.
E.g. a first step is to work out which characters are in a shot from the script.
This may need looking at previous shots however as they may be in the background still.
Next, can the location of the characters be worked out from text alone (the data does not
have their position and rotation yet). Then lighting, props, animation clips, and more - see
how much can be deduced from the script alone. | []
| [
"TAGS\n#language-English #license-apache-2.0 #region-us \n"
]
| [
18
]
| [
"passage: TAGS\n#language-English #license-apache-2.0 #region-us \n"
]
| [
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|
3e40641cad7556f834b38e366b4e951dba2ef3b6 |
Original Dataset from https://huggingface.co/datasets/SetFit/toxic_conversations
train set
- 140380 toxic
- 140380 non-toxic
test set
- 3954 toxic
- 3954 non-toxic
| heegyu/toxic_conversations_balanced | [
"region:us"
]
| 2023-03-08T00:25:08+00:00 | {} | 2023-03-08T00:36:32+00:00 | []
| []
| TAGS
#region-us
|
Original Dataset from URL
train set
- 140380 toxic
- 140380 non-toxic
test set
- 3954 toxic
- 3954 non-toxic
| []
| [
"TAGS\n#region-us \n"
]
| [
6
]
| [
"passage: TAGS\n#region-us \n"
]
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|
10f8cb0f25f8aa4d74723f8b625e894ccaae8d3d |
# Dataset Card for Med-EASi
## Dataset Description
- **Repository:https://github.com/Chandrayee/CTRL-SIMP**
- **Paper:https://arxiv.org/pdf/2302.09155.pdf**
- **Point of Contact:Chandrayee Basu**
### Dataset Summary
Med-EASi (Medical dataset for Elaborative and Abstractive Simplification), a uniquely crowdsourced and finely annotated dataset for supervised simplification of short medical
texts. It contains 1979 expert-simple text pairs in medical domain, spanning a total of 4478 UMLS concepts across all text pairs. The dataset is annotated with four textual transformations:
replacement, elaboration, insertion and deletion.
### Supported Tasks
The dataset can be used for direct generation of simplified medical text or generation of simplified text along with controllability over individual transformations. Please refer to the paper for more information.
### Languages
English
## Dataset Structure
- **train.csv: 1397 text pairs (5.19 MB)**
- **validation.csv: 197 text pairs (1.5 MB)**
- **test.csv: 300 text pairs (1.19 MB)**
We also provide several metrics per data point including Levenstein similarity, SentenceBERT embedding cosine similarity, compression ratio, Flesch Kincaid readability grade,
automated readability index for each of the expert and simple text, and UMLS concepts in each of them.
### Data Instances
```
Expert: Some patients have weight loss, rarely enough to become underweight. Anemia, glossitis, angular stomatitis, and aphthous ulcers are usually seen in these patients.
Simple: Some people are undernourished, have mild weight loss and anemia, or have mouth sores and an inflamed tongue.
Annotated: Some <elab>patients<by>people are undernourished,</elab> have <elab>weight loss<by>mild weight loss</elab><del>, rarely enough to become underweight.</del> <rep>Anemia, glossitis, angular stomatitis, and aphthous ulcers<by>and anemia, or have mouth sores and an inflamed tongue</rep><del>usually seen in these patients</del>.
```
### Data Fields
```
Expert
Simple
Annotation
sim (Levenstein Similarity)
sentence_sim (SentenceBERT embedding cosine similarity)
compression
expert_fk_grade
expert_ari
layman_fk_grade
layman_ari
umls_expert
umls_layman
expert_terms
layman_terms
idx (original data index before shuffling, redundant)
```
### Data Splits
75 % train, 10 % validation and 15 % test
## Dataset Creation
This dataset is created by annotating 1500 SIMPWIKI data points (Van den Bercken, Sips, and Lofi 2019) and all of MSD (Cao et al. 2020) data points. We used expert-layman-AI collaboration for annotation.
### Personal and Sensitive Information
There is no personal or sensitive information in this dataset.
## Considerations for Using the Data
### Discussion of Biases
The dataset contains biomedical and clinical short texts.
### Other Known Limitations
The expert and simple texts in the original datasets were extracted and aligned using automated methods that have their own limitations.
### Citation Information
```
@article{basu2023med,
title={Med-EASi: Finely Annotated Dataset and Models for Controllable Simplification of Medical Texts},
author={Basu, Chandrayee and Vasu, Rosni and Yasunaga, Michihiro and Yang, Qian},
journal={arXiv preprint arXiv:2302.09155},
year={2023}
}
``` | cbasu/Med-EASi | [
"arxiv:2302.09155",
"region:us"
]
| 2023-03-08T00:27:53+00:00 | {} | 2023-03-08T18:24:31+00:00 | [
"2302.09155"
]
| []
| TAGS
#arxiv-2302.09155 #region-us
|
# Dataset Card for Med-EASi
## Dataset Description
- Repository:URL
- Paper:URL
- Point of Contact:Chandrayee Basu
### Dataset Summary
Med-EASi (Medical dataset for Elaborative and Abstractive Simplification), a uniquely crowdsourced and finely annotated dataset for supervised simplification of short medical
texts. It contains 1979 expert-simple text pairs in medical domain, spanning a total of 4478 UMLS concepts across all text pairs. The dataset is annotated with four textual transformations:
replacement, elaboration, insertion and deletion.
### Supported Tasks
The dataset can be used for direct generation of simplified medical text or generation of simplified text along with controllability over individual transformations. Please refer to the paper for more information.
### Languages
English
## Dataset Structure
- URL: 1397 text pairs (5.19 MB)
- URL: 197 text pairs (1.5 MB)
- URL: 300 text pairs (1.19 MB)
We also provide several metrics per data point including Levenstein similarity, SentenceBERT embedding cosine similarity, compression ratio, Flesch Kincaid readability grade,
automated readability index for each of the expert and simple text, and UMLS concepts in each of them.
### Data Instances
### Data Fields
### Data Splits
75 % train, 10 % validation and 15 % test
## Dataset Creation
This dataset is created by annotating 1500 SIMPWIKI data points (Van den Bercken, Sips, and Lofi 2019) and all of MSD (Cao et al. 2020) data points. We used expert-layman-AI collaboration for annotation.
### Personal and Sensitive Information
There is no personal or sensitive information in this dataset.
## Considerations for Using the Data
### Discussion of Biases
The dataset contains biomedical and clinical short texts.
### Other Known Limitations
The expert and simple texts in the original datasets were extracted and aligned using automated methods that have their own limitations.
| [
"# Dataset Card for Med-EASi",
"## Dataset Description\n \n- Repository:URL \n- Paper:URL \n- Point of Contact:Chandrayee Basu",
"### Dataset Summary\n\nMed-EASi (Medical dataset for Elaborative and Abstractive Simplification), a uniquely crowdsourced and finely annotated dataset for supervised simplification of short medical\ntexts. It contains 1979 expert-simple text pairs in medical domain, spanning a total of 4478 UMLS concepts across all text pairs. The dataset is annotated with four textual transformations:\nreplacement, elaboration, insertion and deletion.",
"### Supported Tasks \n\nThe dataset can be used for direct generation of simplified medical text or generation of simplified text along with controllability over individual transformations. Please refer to the paper for more information.",
"### Languages\n\nEnglish",
"## Dataset Structure\n\n- URL: 1397 text pairs (5.19 MB)\n- URL: 197 text pairs (1.5 MB)\n- URL: 300 text pairs (1.19 MB)\n\nWe also provide several metrics per data point including Levenstein similarity, SentenceBERT embedding cosine similarity, compression ratio, Flesch Kincaid readability grade, \nautomated readability index for each of the expert and simple text, and UMLS concepts in each of them.",
"### Data Instances",
"### Data Fields",
"### Data Splits\n\n75 % train, 10 % validation and 15 % test",
"## Dataset Creation\n\nThis dataset is created by annotating 1500 SIMPWIKI data points (Van den Bercken, Sips, and Lofi 2019) and all of MSD (Cao et al. 2020) data points. We used expert-layman-AI collaboration for annotation.",
"### Personal and Sensitive Information\n\nThere is no personal or sensitive information in this dataset.",
"## Considerations for Using the Data",
"### Discussion of Biases\n\nThe dataset contains biomedical and clinical short texts.",
"### Other Known Limitations\n\nThe expert and simple texts in the original datasets were extracted and aligned using automated methods that have their own limitations."
]
| [
"TAGS\n#arxiv-2302.09155 #region-us \n",
"# Dataset Card for Med-EASi",
"## Dataset Description\n \n- Repository:URL \n- Paper:URL \n- Point of Contact:Chandrayee Basu",
"### Dataset Summary\n\nMed-EASi (Medical dataset for Elaborative and Abstractive Simplification), a uniquely crowdsourced and finely annotated dataset for supervised simplification of short medical\ntexts. It contains 1979 expert-simple text pairs in medical domain, spanning a total of 4478 UMLS concepts across all text pairs. The dataset is annotated with four textual transformations:\nreplacement, elaboration, insertion and deletion.",
"### Supported Tasks \n\nThe dataset can be used for direct generation of simplified medical text or generation of simplified text along with controllability over individual transformations. Please refer to the paper for more information.",
"### Languages\n\nEnglish",
"## Dataset Structure\n\n- URL: 1397 text pairs (5.19 MB)\n- URL: 197 text pairs (1.5 MB)\n- URL: 300 text pairs (1.19 MB)\n\nWe also provide several metrics per data point including Levenstein similarity, SentenceBERT embedding cosine similarity, compression ratio, Flesch Kincaid readability grade, \nautomated readability index for each of the expert and simple text, and UMLS concepts in each of them.",
"### Data Instances",
"### Data Fields",
"### Data Splits\n\n75 % train, 10 % validation and 15 % test",
"## Dataset Creation\n\nThis dataset is created by annotating 1500 SIMPWIKI data points (Van den Bercken, Sips, and Lofi 2019) and all of MSD (Cao et al. 2020) data points. We used expert-layman-AI collaboration for annotation.",
"### Personal and Sensitive Information\n\nThere is no personal or sensitive information in this dataset.",
"## Considerations for Using the Data",
"### Discussion of Biases\n\nThe dataset contains biomedical and clinical short texts.",
"### Other Known Limitations\n\nThe expert and simple texts in the original datasets were extracted and aligned using automated methods that have their own limitations."
]
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"passage: TAGS\n#arxiv-2302.09155 #region-us \n# Dataset Card for Med-EASi## Dataset Description\n \n- Repository:URL \n- Paper:URL \n- Point of Contact:Chandrayee Basu### Dataset Summary\n\nMed-EASi (Medical dataset for Elaborative and Abstractive Simplification), a uniquely crowdsourced and finely annotated dataset for supervised simplification of short medical\ntexts. It contains 1979 expert-simple text pairs in medical domain, spanning a total of 4478 UMLS concepts across all text pairs. The dataset is annotated with four textual transformations:\nreplacement, elaboration, insertion and deletion.### Supported Tasks \n\nThe dataset can be used for direct generation of simplified medical text or generation of simplified text along with controllability over individual transformations. Please refer to the paper for more information.### Languages\n\nEnglish## Dataset Structure\n\n- URL: 1397 text pairs (5.19 MB)\n- URL: 197 text pairs (1.5 MB)\n- URL: 300 text pairs (1.19 MB)\n\nWe also provide several metrics per data point including Levenstein similarity, SentenceBERT embedding cosine similarity, compression ratio, Flesch Kincaid readability grade, \nautomated readability index for each of the expert and simple text, and UMLS concepts in each of them.### Data Instances### Data Fields### Data Splits\n\n75 % train, 10 % validation and 15 % test## Dataset Creation\n\nThis dataset is created by annotating 1500 SIMPWIKI data points (Van den Bercken, Sips, and Lofi 2019) and all of MSD (Cao et al. 2020) data points. We used expert-layman-AI collaboration for annotation.### Personal and Sensitive Information\n\nThere is no personal or sensitive information in this dataset.## Considerations for Using the Data### Discussion of Biases\n\nThe dataset contains biomedical and clinical short texts.### Other Known Limitations\n\nThe expert and simple texts in the original datasets were extracted and aligned using automated methods that have their own limitations."
]
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|
c59e625093ae6b11ba8c7f041de2e789044dd4f7 | # Dataset Card for "wikisum4pt1-other"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | abertsch/rescraped-wikisum-test | [
"region:us"
]
| 2023-03-08T00:55:37+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "input", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "summary", "dtype": "string"}, {"name": "full_article", "dtype": "string"}, {"name": "section_boundaries", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 1078716206, "num_examples": 180889}], "download_size": 650290984, "dataset_size": 1078716206}} | 2023-03-08T04:02:45+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "wikisum4pt1-other"
More Information needed | [
"# Dataset Card for \"wikisum4pt1-other\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"wikisum4pt1-other\"\n\nMore Information needed"
]
| [
6,
16
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"wikisum4pt1-other\"\n\nMore Information needed"
]
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|
6497e79f5731ef327624cbb0a52a44959e7e9c72 | # Dataset Card for "speechocean3"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | siegels/speechocean3 | [
"region:us"
]
| 2023-03-08T00:58:30+00:00 | {"dataset_info": {"features": [{"name": "audio", "dtype": "audio"}, {"name": "file_id", "dtype": "int64"}, {"name": "accuracy", "dtype": "int64"}, {"name": "completeness", "dtype": "float64"}, {"name": "fluency", "dtype": "int64"}, {"name": "prosodic", "dtype": "int64"}, {"name": "words", "list": [{"name": "accuracy", "dtype": "int64"}, {"name": "phones", "sequence": "string"}, {"name": "phones-accuracy", "sequence": "float64"}, {"name": "stress", "dtype": "int64"}, {"name": "text", "dtype": "string"}, {"name": "total", "dtype": "int64"}]}, {"name": "total", "dtype": "int64"}, {"name": "text", "dtype": "string"}, {"name": "speaker_id", "dtype": "int64"}, {"name": "file_name", "dtype": "string"}, {"name": "correct_speech", "sequence": "float32"}], "splits": [{"name": "train", "num_bytes": 1262536865.0, "num_examples": 5000}], "download_size": 1233040476, "dataset_size": 1262536865.0}} | 2023-03-08T23:39:44+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "speechocean3"
More Information needed | [
"# Dataset Card for \"speechocean3\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"speechocean3\"\n\nMore Information needed"
]
| [
6,
15
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"speechocean3\"\n\nMore Information needed"
]
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|
1364db90a8f07480882a0fbb49ef81a487b3d035 | # Dataset Card for "ocr-bert_cms-vocab"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | lowem1/ocr-bert_cms-vocab | [
"region:us"
]
| 2023-03-08T04:57:30+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 10038136, "num_examples": 925113}], "download_size": 1633020, "dataset_size": 10038136}} | 2023-03-08T04:57:32+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "ocr-bert_cms-vocab"
More Information needed | [
"# Dataset Card for \"ocr-bert_cms-vocab\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"ocr-bert_cms-vocab\"\n\nMore Information needed"
]
| [
6,
20
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"ocr-bert_cms-vocab\"\n\nMore Information needed"
]
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|
3dd6f303edc96bcbf0fe5778e64f8e58a3fb7abc | # AutoTrain Dataset for project: english-tokipona
## Dataset Description
This dataset has been automatically processed by AutoTrain for project english-tokipona.
### 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
[
{
"target": "mi kama jo e pali ante.",
"source": "I'll find another job."
},
{
"target": "tenpo pini weka la mi moku lon poka sina.",
"source": "It's been a while since we've had lunch together."
}
]
```
### Dataset Fields
The dataset has the following fields (also called "features"):
```json
{
"target": "Value(dtype='string', id=None)",
"source": "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 | 16824 |
| valid | 4206 |
| johnpaulbin/autotrain-data-english-tokipona | [
"task_categories:translation",
"region:us"
]
| 2023-03-08T05:29:14+00:00 | {"task_categories": ["translation"]} | 2023-03-08T05:53:13+00:00 | []
| []
| TAGS
#task_categories-translation #region-us
| AutoTrain Dataset for project: english-tokipona
===============================================
Dataset Description
-------------------
This dataset has been automatically processed by AutoTrain for project english-tokipona.
### Languages
The BCP-47 code for the dataset's language is unk.
Dataset Structure
-----------------
### Data Instances
A sample from this dataset looks as follows:
### Dataset Fields
The dataset has the following fields (also called "features"):
### Dataset Splits
This dataset is split into a train and validation split. The split sizes are as follow:
| [
"### Languages\n\n\nThe BCP-47 code for the dataset's language is unk.\n\n\nDataset Structure\n-----------------",
"### Data Instances\n\n\nA sample from this dataset looks as follows:",
"### Dataset Fields\n\n\nThe dataset has the following fields (also called \"features\"):",
"### Dataset Splits\n\n\nThis dataset is split into a train and validation split. The split sizes are as follow:"
]
| [
"TAGS\n#task_categories-translation #region-us \n",
"### Languages\n\n\nThe BCP-47 code for the dataset's language is unk.\n\n\nDataset Structure\n-----------------",
"### Data Instances\n\n\nA sample from this dataset looks as follows:",
"### Dataset Fields\n\n\nThe dataset has the following fields (also called \"features\"):",
"### Dataset Splits\n\n\nThis dataset is split into a train and validation split. The split sizes are as follow:"
]
| [
15,
27,
17,
23,
27
]
| [
"passage: TAGS\n#task_categories-translation #region-us \n### Languages\n\n\nThe BCP-47 code for the dataset's language is unk.\n\n\nDataset Structure\n-----------------### Data Instances\n\n\nA sample from this dataset looks as follows:### Dataset Fields\n\n\nThe dataset has the following fields (also called \"features\"):### Dataset Splits\n\n\nThis dataset is split into a train and validation split. The split sizes are as follow:"
]
| [
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fff5d741317ef6bb341ff73894562a306b1c3096 |
# Dataset Card for ObjaversePoints-700K
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
Contains high-quality point clouds and captions generated from the Objaverse collection.
## 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] | eliphatfs/ObjaversePoints-700K | [
"size_categories:100K<n<1M",
"language:en",
"license:odc-by",
"region:us"
]
| 2023-03-08T05:45:46+00:00 | {"language": ["en"], "license": "odc-by", "size_categories": ["100K<n<1M"]} | 2023-03-08T08:12:03+00:00 | []
| [
"en"
]
| TAGS
#size_categories-100K<n<1M #language-English #license-odc-by #region-us
|
# Dataset Card for ObjaversePoints-700K
## Dataset Description
- Homepage:
- Repository:
- Paper:
- Leaderboard:
- Point of Contact:
### Dataset Summary
Contains high-quality point clouds and captions generated from the Objaverse collection.
## Dataset Structure
### Data Instances
### Data Fields
### Data Splits
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
## Considerations for Using the Data
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
## Additional Information
### Dataset Curators
### Licensing Information
### Contributions
| [
"# Dataset Card for ObjaversePoints-700K",
"## Dataset Description\n\n- Homepage: \n- Repository: \n- Paper: \n- Leaderboard: \n- Point of Contact:",
"### Dataset Summary\n\nContains high-quality point clouds and captions generated from the Objaverse collection.",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
]
| [
"TAGS\n#size_categories-100K<n<1M #language-English #license-odc-by #region-us \n",
"# Dataset Card for ObjaversePoints-700K",
"## Dataset Description\n\n- Homepage: \n- Repository: \n- Paper: \n- Leaderboard: \n- Point of Contact:",
"### Dataset Summary\n\nContains high-quality point clouds and captions generated from the Objaverse collection.",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
]
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|
00805e2361d513d590745fd269620a61840db46f | # Dataset Card for "processed_demo"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | rithwik-db/processed_demo | [
"region:us"
]
| 2023-03-08T06:04:30+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "neg", "1": "pos"}}}}], "splits": [{"name": "train", "num_bytes": 4011938.76, "num_examples": 3000}, {"name": "test", "num_bytes": 391808.22, "num_examples": 300}], "download_size": 2812982, "dataset_size": 4403746.9799999995}} | 2023-03-08T06:04:38+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "processed_demo"
More Information needed | [
"# Dataset Card for \"processed_demo\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"processed_demo\"\n\nMore Information needed"
]
| [
6,
14
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"processed_demo\"\n\nMore Information needed"
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|
7b32ba67520cbf33437032d6499dcfc4e5554aa1 | # Dataset Card for "food_poc"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | MarkJeong/food_poc | [
"region:us"
]
| 2023-03-08T07:54:05+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "\uae40\uce58\ucc0c\uac1c", "1": "\ub41c\uc7a5\ucc0c\uac1c"}}}}], "splits": [{"name": "train", "num_bytes": 2821852.0, "num_examples": 54}, {"name": "test", "num_bytes": 2498883.0, "num_examples": 21}, {"name": "validation", "num_bytes": 2498883.0, "num_examples": 21}], "download_size": 7820748, "dataset_size": 7819618.0}} | 2023-03-08T08:54:21+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "food_poc"
More Information needed | [
"# Dataset Card for \"food_poc\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"food_poc\"\n\nMore Information needed"
]
| [
6,
14
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"food_poc\"\n\nMore Information needed"
]
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|
9eee591351717431e5ba3f2e8a973f1b2d8758d5 | https://bitbucket.org/RoxanaSz/puzzte/src/master/
```bib
@article{szomiu2021puzzle,
title={A Puzzle-Based Dataset for Natural Language Inference},
author={Szomiu, Roxana and Groza, Adrian},
journal={arXiv preprint arXiv:2112.05742},
year={2021}
}
``` | tasksource/puzzte | [
"task_categories:text-classification",
"task_ids:natural-language-inference",
"task_ids:multi-input-text-classification",
"language:en",
"license:apache-2.0",
"region:us"
]
| 2023-03-08T09:12:18+00:00 | {"language": ["en"], "license": "apache-2.0", "task_categories": ["text-classification"], "task_ids": ["natural-language-inference", "multi-input-text-classification"]} | 2023-05-31T07:43:41+00:00 | []
| [
"en"
]
| TAGS
#task_categories-text-classification #task_ids-natural-language-inference #task_ids-multi-input-text-classification #language-English #license-apache-2.0 #region-us
| URL
| []
| [
"TAGS\n#task_categories-text-classification #task_ids-natural-language-inference #task_ids-multi-input-text-classification #language-English #license-apache-2.0 #region-us \n"
]
| [
57
]
| [
"passage: TAGS\n#task_categories-text-classification #task_ids-natural-language-inference #task_ids-multi-input-text-classification #language-English #license-apache-2.0 #region-us \n"
]
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|
ea2221bb6f5b3a684c2576be4d35c8b790ddb833 | # Dataset Card for "cnn-100k-tokenized"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | braindao/cnn-100k-tokenized | [
"region:us"
]
| 2023-03-08T11:06:18+00:00 | {"dataset_info": {"features": [{"name": "input_ids", "sequence": "int32"}, {"name": "attention_mask", "sequence": "int8"}, {"name": "labels", "sequence": "int64"}], "splits": [{"name": "test", "num_bytes": 76615320, "num_examples": 11490}, {"name": "train", "num_bytes": 666800000, "num_examples": 100000}, {"name": "validation", "num_bytes": 89137824, "num_examples": 13368}], "download_size": 143694053, "dataset_size": 832553144}} | 2023-03-08T11:13:27+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "cnn-100k-tokenized"
More Information needed | [
"# Dataset Card for \"cnn-100k-tokenized\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"cnn-100k-tokenized\"\n\nMore Information needed"
]
| [
6,
18
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"cnn-100k-tokenized\"\n\nMore Information needed"
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|
445aa3f146bd7a26278573392cc897645951b951 | # Dataset Card for "go-emotion-dk-autotranlated-10k"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | EJaalborg2022/go-emotion-dk-autotranlated-10k | [
"region:us"
]
| 2023-03-08T11:24:24+00:00 | {"dataset_info": {"features": [{"name": "text_en", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "labels", "dtype": {"class_label": {"names": {"0": "admiration", "1": "amusement", "2": "anger", "3": "annoyance", "4": "approval", "5": "caring", "6": "confusion", "7": "curiosity", "8": "desire", "9": "disappointment", "10": "disapproval", "11": "disgust", "12": "embarrassment", "13": "excitement", "14": "fear", "15": "gratitude", "16": "grief", "17": "joy", "18": "love", "19": "nervousness", "20": "neutral", "21": "optimism", "22": "pride", "23": "realization", "24": "relief", "25": "remorse", "26": "sadness", "27": "surprise"}}}}, {"name": "__index_level_0__", "dtype": "int64"}, {"name": "input_ids", "sequence": "int32"}, {"name": "token_type_ids", "sequence": "int8"}, {"name": "attention_mask", "sequence": "int8"}], "splits": [{"name": "train", "num_bytes": 2912226, "num_examples": 9000}, {"name": "test", "num_bytes": 163941, "num_examples": 500}, {"name": "valid", "num_bytes": 161713, "num_examples": 500}], "download_size": 1657402, "dataset_size": 3237880}} | 2023-03-08T11:24:30+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "go-emotion-dk-autotranlated-10k"
More Information needed | [
"# Dataset Card for \"go-emotion-dk-autotranlated-10k\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"go-emotion-dk-autotranlated-10k\"\n\nMore Information needed"
]
| [
6,
23
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"go-emotion-dk-autotranlated-10k\"\n\nMore Information needed"
]
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|
a3833aaca458a8d15783c401eb6bf19f63efa548 | # Dataset Card for "cnn-dm-200k-full-tokenized"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | braindao/cnn-dm-200k-full-tokenized | [
"region:us"
]
| 2023-03-08T11:53:52+00:00 | {"dataset_info": {"features": [{"name": "input_ids", "sequence": "int32"}, {"name": "attention_mask", "sequence": "int8"}, {"name": "labels", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 1914469484, "num_examples": 287113}, {"name": "validation", "num_bytes": 89137824, "num_examples": 13368}, {"name": "test", "num_bytes": 76615320, "num_examples": 11490}], "download_size": 359173014, "dataset_size": 2080222628}} | 2023-03-08T11:54:16+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "cnn-dm-200k-full-tokenized"
More Information needed | [
"# Dataset Card for \"cnn-dm-200k-full-tokenized\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"cnn-dm-200k-full-tokenized\"\n\nMore Information needed"
]
| [
6,
23
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"cnn-dm-200k-full-tokenized\"\n\nMore Information needed"
]
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|
cf5ec4512aaa47cdebf02ca032b7af870528c272 | ### Dataset Summary
A small battery of 10 tests that involve asking language models a simple arithmetic problem in natural language.
### Languages
English
### Source Data
Obtained from [https://github.com/openai/gpt-3/tree/master/data](https://github.com/openai/gpt-3/tree/master/data)
### Citation
```
@article{brown2020language,
title={Language Models are Few-Shot Learners},
author={Tom B. Brown and Benjamin Mann and Nick Ryder and Melanie Subbiah and Jared Kaplan and Prafulla Dhariwal and Arvind Neelakantan and Pranav Shyam and Girish Sastry and Amanda Askell and Sandhini Agarwal and Ariel Herbert-Voss and Gretchen Krueger and Tom Henighan and Rewon Child and Aditya Ramesh and Daniel M. Ziegler and Jeffrey Wu and Clemens Winter and Christopher Hesse and Mark Chen and Eric Sigler and Mateusz Litwin and Scott Gray and Benjamin Chess and Jack Clark and Christopher Berner and Sam McCandlish and Alec Radford and Ilya Sutskever and Dario Amodei},
year={2020},
eprint={2005.14165},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
``` | EleutherAI/arithmetic | [
"arxiv:2005.14165",
"region:us"
]
| 2023-03-08T12:22:46+00:00 | {} | 2023-03-09T17:58:16+00:00 | [
"2005.14165"
]
| []
| TAGS
#arxiv-2005.14165 #region-us
| ### Dataset Summary
A small battery of 10 tests that involve asking language models a simple arithmetic problem in natural language.
### Languages
English
### Source Data
Obtained from URL
| [
"### Dataset Summary\n\nA small battery of 10 tests that involve asking language models a simple arithmetic problem in natural language.",
"### Languages\nEnglish",
"### Source Data\n\nObtained from URL"
]
| [
"TAGS\n#arxiv-2005.14165 #region-us \n",
"### Dataset Summary\n\nA small battery of 10 tests that involve asking language models a simple arithmetic problem in natural language.",
"### Languages\nEnglish",
"### Source Data\n\nObtained from URL"
]
| [
14,
28,
5,
9
]
| [
"passage: TAGS\n#arxiv-2005.14165 #region-us \n### Dataset Summary\n\nA small battery of 10 tests that involve asking language models a simple arithmetic problem in natural language.### Languages\nEnglish### Source Data\n\nObtained from URL"
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|
59f0f44063c8d0bd096a6afb243a3bc19f9329a6 |
# Dataset Card for Cherry Picked Prompts 🍒
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:** Lewis Tunstall
### Dataset Summary
This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1).
### 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] | HuggingFaceH4/cherry_picked_prompts | [
"license:apache-2.0",
"region:us"
]
| 2023-03-08T12:49:42+00:00 | {"license": "apache-2.0"} | 2023-03-08T21:24:46+00:00 | []
| []
| TAGS
#license-apache-2.0 #region-us
|
# Dataset Card for Cherry Picked Prompts
## Dataset Description
- Homepage:
- Repository:
- Paper:
- Leaderboard:
- Point of Contact: Lewis Tunstall
### Dataset Summary
This dataset card aims to be a base template for new datasets. It has been generated using this raw template.
### Supported Tasks and Leaderboards
### Languages
## Dataset Structure
### Data Instances
### Data Fields
### Data Splits
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
## Considerations for Using the Data
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
## Additional Information
### Dataset Curators
### Licensing Information
### Contributions
| [
"# Dataset Card for Cherry Picked Prompts",
"## Dataset Description\n\n- Homepage: \n- Repository: \n- Paper: \n- Leaderboard: \n- Point of Contact: Lewis Tunstall",
"### Dataset Summary\n\nThis dataset card aims to be a base template for new datasets. It has been generated using this raw template.",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
]
| [
"TAGS\n#license-apache-2.0 #region-us \n",
"# Dataset Card for Cherry Picked Prompts",
"## Dataset Description\n\n- Homepage: \n- Repository: \n- Paper: \n- Leaderboard: \n- Point of Contact: Lewis Tunstall",
"### Dataset Summary\n\nThis dataset card aims to be a base template for new datasets. It has been generated using this raw template.",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
]
| [
14,
11,
27,
32,
10,
4,
6,
6,
5,
5,
5,
7,
4,
10,
10,
5,
5,
9,
8,
8,
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5
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| [
"passage: TAGS\n#license-apache-2.0 #region-us \n# Dataset Card for Cherry Picked Prompts## Dataset Description\n\n- Homepage: \n- Repository: \n- Paper: \n- Leaderboard: \n- Point of Contact: Lewis Tunstall### Dataset Summary\n\nThis dataset card aims to be a base template for new datasets. It has been generated using this raw template.### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions"
]
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|
76154a39349e3c71fb579b1f819b752a80149b79 | # Dataset Card for "lld-onlyicon"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Babypotatotang/lld-onlyicon | [
"region:us"
]
| 2023-03-08T12:51:43+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 196407715.578, "num_examples": 14959}, {"name": "test", "num_bytes": 49103652.04, "num_examples": 3740}], "download_size": 156823150, "dataset_size": 245511367.618}} | 2023-03-17T02:19:30+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "lld-onlyicon"
More Information needed | [
"# Dataset Card for \"lld-onlyicon\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"lld-onlyicon\"\n\nMore Information needed"
]
| [
6,
17
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"lld-onlyicon\"\n\nMore Information needed"
]
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13408293c6caf82049acb7eac8847ba665fc4dff | # Dataset Card for Dataset Name
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1).
### 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] | vhanbri/dontopennottina | [
"task_categories:question-answering",
"size_categories:n<1K",
"language:en",
"license:openrail",
"region:us"
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| # Dataset Card for Dataset Name
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- Homepage:
- Repository:
- Paper:
- Leaderboard:
- Point of Contact:
### Dataset Summary
This dataset card aims to be a base template for new datasets. It has been generated using this raw template.
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### Curation Rationale
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#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
## Considerations for Using the Data
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
## Additional Information
### Dataset Curators
### Licensing Information
### Contributions
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|
f744fbc4daf4ba5a737abe403f7d40c7f8790292 | This is an image dataset for object detection of wildlife in the mixed coniferous broad-leaved forest.
A total of 25,657 images in this dataset were generated from video clips taken by infrared cameras in the Northeast Tiger and Leopard National Park, including 17 main species (15 wild animals and 2 major domestic animals): Amur tiger, Amur leopard, wild boar, roe deer, sika deer, Asian black bear, red fox, Asian badger, raccoon dog, musk deer, Siberian weasel, sable, yellow-throated marten, leopard cat, Manchurian hare, cow, and dog.
All images were labeled in Pascal VOC format.
The image resolution is 1280 × 720 or 1600 × 1200 pixels. | myyyyw/NTLNP | [
"task_categories:object-detection",
"size_categories:1B<n<10B",
"language:en",
"license:cc-by-4.0",
"region:us"
]
| 2023-03-08T13:44:55+00:00 | {"language": ["en"], "license": "cc-by-4.0", "size_categories": ["1B<n<10B"], "task_categories": ["object-detection"]} | 2023-03-09T12:17:41+00:00 | []
| [
"en"
]
| TAGS
#task_categories-object-detection #size_categories-1B<n<10B #language-English #license-cc-by-4.0 #region-us
| This is an image dataset for object detection of wildlife in the mixed coniferous broad-leaved forest.
A total of 25,657 images in this dataset were generated from video clips taken by infrared cameras in the Northeast Tiger and Leopard National Park, including 17 main species (15 wild animals and 2 major domestic animals): Amur tiger, Amur leopard, wild boar, roe deer, sika deer, Asian black bear, red fox, Asian badger, raccoon dog, musk deer, Siberian weasel, sable, yellow-throated marten, leopard cat, Manchurian hare, cow, and dog.
All images were labeled in Pascal VOC format.
The image resolution is 1280 × 720 or 1600 × 1200 pixels. | []
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|
ee14abb33db05ae745366853a85c159669f36155 | # Dataset Card for "imdb_gross"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | james-burton/imdb_gross | [
"region:us"
]
| 2023-03-08T14:02:15+00:00 | {"dataset_info": {"features": [{"name": "Movie Name", "dtype": "string"}, {"name": "Year of Release", "dtype": "string"}, {"name": "Watch Time", "dtype": "string"}, {"name": "Movie Rating", "dtype": "float64"}, {"name": "Meatscore of movie", "dtype": "string"}, {"name": "Votes", "dtype": "string"}, {"name": "label", "dtype": "float64"}, {"name": "text", "dtype": "string"}, {"name": "__index_level_0__", "dtype": "int64"}, {"name": "scaled_label", "dtype": "float64"}], "splits": [{"name": "train", "num_bytes": 140798, "num_examples": 571}, {"name": "validation", "num_bytes": 30937, "num_examples": 123}, {"name": "test", "num_bytes": 29896, "num_examples": 123}], "download_size": 0, "dataset_size": 201631}} | 2023-03-08T15:02:03+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "imdb_gross"
More Information needed | [
"# Dataset Card for \"imdb_gross\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"imdb_gross\"\n\nMore Information needed"
]
| [
6,
15
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"imdb_gross\"\n\nMore Information needed"
]
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|
242eacc6ce302bcbdeba3a05ce5f1935437181ff | # Dataset Card for "map_no_map"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | davanstrien/map_no_map | [
"region:us"
]
| 2023-03-08T14:02:15+00:00 | {"dataset_info": {"features": [{"name": "updated_at", "dtype": "string"}, {"name": "lead_time", "dtype": "float64"}, {"name": "choice", "dtype": "string"}, {"name": "annotation_id", "dtype": "int64"}, {"name": "annotator", "dtype": "int64"}, {"name": "id", "dtype": "int64"}, {"name": "created_at", "dtype": "string"}, {"name": "image", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 55197485.0, "num_examples": 493}], "download_size": 55174022, "dataset_size": 55197485.0}} | 2023-03-08T15:44:38+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "map_no_map"
More Information needed | [
"# Dataset Card for \"map_no_map\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"map_no_map\"\n\nMore Information needed"
]
| [
6,
15
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"map_no_map\"\n\nMore Information needed"
]
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|
9e2ce7904aad8075085400e262e39943d27ae1c6 | # Dataset Card for "Medical_reports_Splits"
Orignal Source [openi.nlm.nih.gov](https://openi.nlm.nih.gov/)
Kaggle Source [Chest X-rays (Indiana University)](https://www.kaggle.com/datasets/raddar/chest-xrays-indiana-university)
[For more information](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | IndianaUniversityDatasetsModels/Medical_reports_Splits | [
"task_categories:text-generation",
"task_categories:text2text-generation",
"size_categories:1K<n<10K",
"language:en",
"region:us"
]
| 2023-03-08T14:21:15+00:00 | {"language": ["en"], "size_categories": ["1K<n<10K"], "task_categories": ["text-generation", "text2text-generation"], "pretty_name": "Indiana University X-Rays and Reports dataset", "dataset_info": {"features": [{"name": "MeSH", "dtype": "string"}, {"name": "Problems", "dtype": "string"}, {"name": "findings", "dtype": "string"}, {"name": "impression", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1046536.8153707596, "num_examples": 2831}, {"name": "test", "num_bytes": 92417.59231462024, "num_examples": 250}, {"name": "validation", "num_bytes": 92417.59231462024, "num_examples": 250}], "download_size": 395063, "dataset_size": 1231372}} | 2023-03-10T11:12:02+00:00 | []
| [
"en"
]
| TAGS
#task_categories-text-generation #task_categories-text2text-generation #size_categories-1K<n<10K #language-English #region-us
| # Dataset Card for "Medical_reports_Splits"
Orignal Source URL
Kaggle Source Chest X-rays (Indiana University)
For more information | [
"# Dataset Card for \"Medical_reports_Splits\"\nOrignal Source URL\nKaggle Source Chest X-rays (Indiana University)\n\n\n\nFor more information"
]
| [
"TAGS\n#task_categories-text-generation #task_categories-text2text-generation #size_categories-1K<n<10K #language-English #region-us \n",
"# Dataset Card for \"Medical_reports_Splits\"\nOrignal Source URL\nKaggle Source Chest X-rays (Indiana University)\n\n\n\nFor more information"
]
| [
46,
37
]
| [
"passage: TAGS\n#task_categories-text-generation #task_categories-text2text-generation #size_categories-1K<n<10K #language-English #region-us \n# Dataset Card for \"Medical_reports_Splits\"\nOrignal Source URL\nKaggle Source Chest X-rays (Indiana University)\n\n\n\nFor more information"
]
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|
63cbc3ce771119a7f9365152a3212fe956653b40 | 1 | karandox/Islay | [
"region:us"
]
| 2023-03-08T14:37:51+00:00 | {} | 2023-03-08T14:38:48+00:00 | []
| []
| TAGS
#region-us
| 1 | []
| [
"TAGS\n#region-us \n"
]
| [
6
]
| [
"passage: TAGS\n#region-us \n"
]
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|
51b78d4b74eb86c83bb9be1942dad65f3b2a0f5e | # Dataset Card for "spanish_biomedical_ds_tokenized_and_gropuped"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | mrm8488/spanish_biomedical_ds_tokenized_and_gropuped | [
"region:us"
]
| 2023-03-08T15:01:27+00:00 | {"dataset_info": {"features": [{"name": "input_ids", "sequence": "int32"}], "splits": [{"name": "train", "num_bytes": 3601107900, "num_examples": 878319}, {"name": "test", "num_bytes": 187816900, "num_examples": 45809}], "download_size": 1807775268, "dataset_size": 3788924800}} | 2023-03-08T15:05:01+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "spanish_biomedical_ds_tokenized_and_gropuped"
More Information needed | [
"# Dataset Card for \"spanish_biomedical_ds_tokenized_and_gropuped\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"spanish_biomedical_ds_tokenized_and_gropuped\"\n\nMore Information needed"
]
| [
6,
28
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"spanish_biomedical_ds_tokenized_and_gropuped\"\n\nMore Information needed"
]
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|
5d4fee74cfba474a7427adb3dbe826d75456cd1b | # Dataset Card for "the-stack-march-sample-special-tokens-stripped"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | bigcode/the-stack-march-sample-special-tokens-stripped | [
"region:us"
]
| 2023-03-08T15:07:56+00:00 | {"dataset_info": {"features": [{"name": "content", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 3034084423, "num_examples": 746856}], "download_size": 1107347598, "dataset_size": 3034084423}} | 2023-03-08T15:23:31+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "the-stack-march-sample-special-tokens-stripped"
More Information needed | [
"# Dataset Card for \"the-stack-march-sample-special-tokens-stripped\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"the-stack-march-sample-special-tokens-stripped\"\n\nMore Information needed"
]
| [
6,
28
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"the-stack-march-sample-special-tokens-stripped\"\n\nMore Information needed"
]
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]
|
51b7fee2508101bef4d4d8bd4775a541080982c9 | # AutoTrain Dataset for project: map_no_map_twitter_demo
## Dataset Description
This dataset has been automatically processed by AutoTrain for project map_no_map_twitter_demo.
### 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": "<260x365 RGB PIL image>",
"target": 1,
"feat_created_at": "2023-03-08T13:56:12.283125Z",
"feat_lead_time": 0.768,
"feat_annotation_id": 23,
"feat_id": 24,
"feat_annotator": 1,
"feat_updated_at": "2023-03-08T13:56:12.283151Z"
},
{
"image": "<639x1298 RGB PIL image>",
"target": 0,
"feat_created_at": "2023-03-08T13:59:10.554628Z",
"feat_lead_time": 0.533,
"feat_annotation_id": 87,
"feat_id": 88,
"feat_annotator": 1,
"feat_updated_at": "2023-03-08T13:59:10.554650Z"
}
]
```
### Dataset Fields
The dataset has the following fields (also called "features"):
```json
{
"image": "Image(decode=True, id=None)",
"target": "ClassLabel(names=['map', 'no_map'], id=None)",
"feat_created_at": "Value(dtype='string', id=None)",
"feat_lead_time": "Value(dtype='float64', id=None)",
"feat_annotation_id": "Value(dtype='int64', id=None)",
"feat_id": "Value(dtype='int64', id=None)",
"feat_annotator": "Value(dtype='int64', id=None)",
"feat_updated_at": "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 | 72 |
| valid | 19 |
| davanstrien/autotrain-data-map_no_map_twitter_demo | [
"task_categories:image-classification",
"region:us"
]
| 2023-03-08T15:32:03+00:00 | {"task_categories": ["image-classification"]} | 2023-03-08T15:32:34+00:00 | []
| []
| TAGS
#task_categories-image-classification #region-us
| AutoTrain Dataset for project: map\_no\_map\_twitter\_demo
==========================================================
Dataset Description
-------------------
This dataset has been automatically processed by AutoTrain for project map\_no\_map\_twitter\_demo.
### Languages
The BCP-47 code for the dataset's language is unk.
Dataset Structure
-----------------
### Data Instances
A sample from this dataset looks as follows:
### Dataset Fields
The dataset has the following fields (also called "features"):
### Dataset Splits
This dataset is split into a train and validation split. The split sizes are as follow:
| [
"### Languages\n\n\nThe BCP-47 code for the dataset's language is unk.\n\n\nDataset Structure\n-----------------",
"### Data Instances\n\n\nA sample from this dataset looks as follows:",
"### Dataset Fields\n\n\nThe dataset has the following fields (also called \"features\"):",
"### Dataset Splits\n\n\nThis dataset is split into a train and validation split. The split sizes are as follow:"
]
| [
"TAGS\n#task_categories-image-classification #region-us \n",
"### Languages\n\n\nThe BCP-47 code for the dataset's language is unk.\n\n\nDataset Structure\n-----------------",
"### Data Instances\n\n\nA sample from this dataset looks as follows:",
"### Dataset Fields\n\n\nThe dataset has the following fields (also called \"features\"):",
"### Dataset Splits\n\n\nThis dataset is split into a train and validation split. The split sizes are as follow:"
]
| [
17,
27,
17,
23,
27
]
| [
"passage: TAGS\n#task_categories-image-classification #region-us \n### Languages\n\n\nThe BCP-47 code for the dataset's language is unk.\n\n\nDataset Structure\n-----------------### Data Instances\n\n\nA sample from this dataset looks as follows:### Dataset Fields\n\n\nThe dataset has the following fields (also called \"features\"):### Dataset Splits\n\n\nThis dataset is split into a train and validation split. The split sizes are as follow:"
]
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|
e56fbae20f63e3fa2c551d126d212ef88dafde6d |
An imitation learning environment for the door-close-v2 environment, sample for the policy door-close-v2
This environment was created as part of the Generally Intelligent Agents project gia: https://github.com/huggingface/gia
## Load dataset
First, clone it with
```sh
git clone https://huggingface.co/datasets/qgallouedec/prj_gia_dataset_metaworld_door_close_v2_1111
```
Then, load it with
```python
import numpy as np
dataset = np.load("prj_gia_dataset_metaworld_door_close_v2_1111/dataset.npy", allow_pickle=True).item()
print(dataset.keys()) # dict_keys(['observations', 'actions', 'dones', 'rewards'])
```
| qgallouedec/prj_gia_dataset_metaworld_door_close_v2_1111 | [
"deep-reinforcement-learning",
"reinforcement-learning",
"gia",
"multi-task",
"multi-modal",
"imitation-learning",
"offline-reinforcement-learning",
"region:us"
]
| 2023-03-08T15:35:45+00:00 | {"library_name": "gia", "tags": ["deep-reinforcement-learning", "reinforcement-learning", "gia", "multi-task", "multi-modal", "imitation-learning", "offline-reinforcement-learning"]} | 2023-03-10T16:32:16+00:00 | []
| []
| TAGS
#deep-reinforcement-learning #reinforcement-learning #gia #multi-task #multi-modal #imitation-learning #offline-reinforcement-learning #region-us
|
An imitation learning environment for the door-close-v2 environment, sample for the policy door-close-v2
This environment was created as part of the Generally Intelligent Agents project gia: URL
## Load dataset
First, clone it with
Then, load it with
| [
"## Load dataset\n\nFirst, clone it with\n\n\n\nThen, load it with"
]
| [
"TAGS\n#deep-reinforcement-learning #reinforcement-learning #gia #multi-task #multi-modal #imitation-learning #offline-reinforcement-learning #region-us \n",
"## Load dataset\n\nFirst, clone it with\n\n\n\nThen, load it with"
]
| [
46,
16
]
| [
"passage: TAGS\n#deep-reinforcement-learning #reinforcement-learning #gia #multi-task #multi-modal #imitation-learning #offline-reinforcement-learning #region-us \n## Load dataset\n\nFirst, clone it with\n\n\n\nThen, load it with"
]
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|
802eddf46f2fcd1feeef92de1654a658c1ae588e |
An imitation learning environment for the door-lock-v2 environment, sample for the policy door-lock-v2
This environment was created as part of the Generally Intelligent Agents project gia: https://github.com/huggingface/gia
## Load dataset
First, clone it with
```sh
git clone https://huggingface.co/datasets/qgallouedec/prj_gia_dataset_metaworld_door_lock_v2_1111
```
Then, load it with
```python
import numpy as np
dataset = np.load("prj_gia_dataset_metaworld_door_lock_v2_1111/dataset.npy", allow_pickle=True).item()
print(dataset.keys()) # dict_keys(['observations', 'actions', 'dones', 'rewards'])
```
| qgallouedec/prj_gia_dataset_metaworld_door_lock_v2_1111 | [
"deep-reinforcement-learning",
"reinforcement-learning",
"gia",
"multi-task",
"multi-modal",
"imitation-learning",
"offline-reinforcement-learning",
"region:us"
]
| 2023-03-08T15:35:54+00:00 | {"library_name": "gia", "tags": ["deep-reinforcement-learning", "reinforcement-learning", "gia", "multi-task", "multi-modal", "imitation-learning", "offline-reinforcement-learning"]} | 2023-03-10T16:33:50+00:00 | []
| []
| TAGS
#deep-reinforcement-learning #reinforcement-learning #gia #multi-task #multi-modal #imitation-learning #offline-reinforcement-learning #region-us
|
An imitation learning environment for the door-lock-v2 environment, sample for the policy door-lock-v2
This environment was created as part of the Generally Intelligent Agents project gia: URL
## Load dataset
First, clone it with
Then, load it with
| [
"## Load dataset\n\nFirst, clone it with\n\n\n\nThen, load it with"
]
| [
"TAGS\n#deep-reinforcement-learning #reinforcement-learning #gia #multi-task #multi-modal #imitation-learning #offline-reinforcement-learning #region-us \n",
"## Load dataset\n\nFirst, clone it with\n\n\n\nThen, load it with"
]
| [
46,
16
]
| [
"passage: TAGS\n#deep-reinforcement-learning #reinforcement-learning #gia #multi-task #multi-modal #imitation-learning #offline-reinforcement-learning #region-us \n## Load dataset\n\nFirst, clone it with\n\n\n\nThen, load it with"
]
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|
016358461fdb4c69f338636deb3d455a534f692c |
An imitation learning environment for the door-open-v2 environment, sample for the policy door-open-v2
This environment was created as part of the Generally Intelligent Agents project gia: https://github.com/huggingface/gia
## Load dataset
First, clone it with
```sh
git clone https://huggingface.co/datasets/qgallouedec/prj_gia_dataset_metaworld_door_open_v2_1111
```
Then, load it with
```python
import numpy as np
dataset = np.load("prj_gia_dataset_metaworld_door_open_v2_1111/dataset.npy", allow_pickle=True).item()
print(dataset.keys()) # dict_keys(['observations', 'actions', 'dones', 'rewards'])
```
| qgallouedec/prj_gia_dataset_metaworld_door_open_v2_1111 | [
"deep-reinforcement-learning",
"reinforcement-learning",
"gia",
"multi-task",
"multi-modal",
"imitation-learning",
"offline-reinforcement-learning",
"region:us"
]
| 2023-03-08T15:36:01+00:00 | {"library_name": "gia", "tags": ["deep-reinforcement-learning", "reinforcement-learning", "gia", "multi-task", "multi-modal", "imitation-learning", "offline-reinforcement-learning"]} | 2023-03-10T16:35:27+00:00 | []
| []
| TAGS
#deep-reinforcement-learning #reinforcement-learning #gia #multi-task #multi-modal #imitation-learning #offline-reinforcement-learning #region-us
|
An imitation learning environment for the door-open-v2 environment, sample for the policy door-open-v2
This environment was created as part of the Generally Intelligent Agents project gia: URL
## Load dataset
First, clone it with
Then, load it with
| [
"## Load dataset\n\nFirst, clone it with\n\n\n\nThen, load it with"
]
| [
"TAGS\n#deep-reinforcement-learning #reinforcement-learning #gia #multi-task #multi-modal #imitation-learning #offline-reinforcement-learning #region-us \n",
"## Load dataset\n\nFirst, clone it with\n\n\n\nThen, load it with"
]
| [
46,
16
]
| [
"passage: TAGS\n#deep-reinforcement-learning #reinforcement-learning #gia #multi-task #multi-modal #imitation-learning #offline-reinforcement-learning #region-us \n## Load dataset\n\nFirst, clone it with\n\n\n\nThen, load it with"
]
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|
efd7497395076cc46c7b52528c3e35925c0b4e39 |
An imitation learning environment for the door-unlock-v2 environment, sample for the policy door-unlock-v2
This environment was created as part of the Generally Intelligent Agents project gia: https://github.com/huggingface/gia
## Load dataset
First, clone it with
```sh
git clone https://huggingface.co/datasets/qgallouedec/prj_gia_dataset_metaworld_door_unlock_v2_1111
```
Then, load it with
```python
import numpy as np
dataset = np.load("prj_gia_dataset_metaworld_door_unlock_v2_1111/dataset.npy", allow_pickle=True).item()
print(dataset.keys()) # dict_keys(['observations', 'actions', 'dones', 'rewards'])
```
| qgallouedec/prj_gia_dataset_metaworld_door_unlock_v2_1111 | [
"deep-reinforcement-learning",
"reinforcement-learning",
"gia",
"multi-task",
"multi-modal",
"imitation-learning",
"offline-reinforcement-learning",
"region:us"
]
| 2023-03-08T15:36:10+00:00 | {"library_name": "gia", "tags": ["deep-reinforcement-learning", "reinforcement-learning", "gia", "multi-task", "multi-modal", "imitation-learning", "offline-reinforcement-learning"]} | 2023-03-10T16:36:58+00:00 | []
| []
| TAGS
#deep-reinforcement-learning #reinforcement-learning #gia #multi-task #multi-modal #imitation-learning #offline-reinforcement-learning #region-us
|
An imitation learning environment for the door-unlock-v2 environment, sample for the policy door-unlock-v2
This environment was created as part of the Generally Intelligent Agents project gia: URL
## Load dataset
First, clone it with
Then, load it with
| [
"## Load dataset\n\nFirst, clone it with\n\n\n\nThen, load it with"
]
| [
"TAGS\n#deep-reinforcement-learning #reinforcement-learning #gia #multi-task #multi-modal #imitation-learning #offline-reinforcement-learning #region-us \n",
"## Load dataset\n\nFirst, clone it with\n\n\n\nThen, load it with"
]
| [
46,
16
]
| [
"passage: TAGS\n#deep-reinforcement-learning #reinforcement-learning #gia #multi-task #multi-modal #imitation-learning #offline-reinforcement-learning #region-us \n## Load dataset\n\nFirst, clone it with\n\n\n\nThen, load it with"
]
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|
35adfff7691ad5e5178f50c3f3b4236442d6898a |
An imitation learning environment for the drawer-close-v2 environment, sample for the policy drawer-close-v2
This environment was created as part of the Generally Intelligent Agents project gia: https://github.com/huggingface/gia
## Load dataset
First, clone it with
```sh
git clone https://huggingface.co/datasets/qgallouedec/prj_gia_dataset_metaworld_drawer_close_v2_1111
```
Then, load it with
```python
import numpy as np
dataset = np.load("prj_gia_dataset_metaworld_drawer_close_v2_1111/dataset.npy", allow_pickle=True).item()
print(dataset.keys()) # dict_keys(['observations', 'actions', 'dones', 'rewards'])
```
| qgallouedec/prj_gia_dataset_metaworld_drawer_close_v2_1111 | [
"deep-reinforcement-learning",
"reinforcement-learning",
"gia",
"multi-task",
"multi-modal",
"imitation-learning",
"offline-reinforcement-learning",
"region:us"
]
| 2023-03-08T15:36:18+00:00 | {"library_name": "gia", "tags": ["deep-reinforcement-learning", "reinforcement-learning", "gia", "multi-task", "multi-modal", "imitation-learning", "offline-reinforcement-learning"]} | 2023-03-10T16:38:38+00:00 | []
| []
| TAGS
#deep-reinforcement-learning #reinforcement-learning #gia #multi-task #multi-modal #imitation-learning #offline-reinforcement-learning #region-us
|
An imitation learning environment for the drawer-close-v2 environment, sample for the policy drawer-close-v2
This environment was created as part of the Generally Intelligent Agents project gia: URL
## Load dataset
First, clone it with
Then, load it with
| [
"## Load dataset\n\nFirst, clone it with\n\n\n\nThen, load it with"
]
| [
"TAGS\n#deep-reinforcement-learning #reinforcement-learning #gia #multi-task #multi-modal #imitation-learning #offline-reinforcement-learning #region-us \n",
"## Load dataset\n\nFirst, clone it with\n\n\n\nThen, load it with"
]
| [
46,
16
]
| [
"passage: TAGS\n#deep-reinforcement-learning #reinforcement-learning #gia #multi-task #multi-modal #imitation-learning #offline-reinforcement-learning #region-us \n## Load dataset\n\nFirst, clone it with\n\n\n\nThen, load it with"
]
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|
d906c95d8d42b46d47382af6fa34451c2f6b3d36 |
An imitation learning environment for the drawer-open-v2 environment, sample for the policy drawer-open-v2
This environment was created as part of the Generally Intelligent Agents project gia: https://github.com/huggingface/gia
## Load dataset
First, clone it with
```sh
git clone https://huggingface.co/datasets/qgallouedec/prj_gia_dataset_metaworld_drawer_open_v2_1111
```
Then, load it with
```python
import numpy as np
dataset = np.load("prj_gia_dataset_metaworld_drawer_open_v2_1111/dataset.npy", allow_pickle=True).item()
print(dataset.keys()) # dict_keys(['observations', 'actions', 'dones', 'rewards'])
```
| qgallouedec/prj_gia_dataset_metaworld_drawer_open_v2_1111 | [
"deep-reinforcement-learning",
"reinforcement-learning",
"gia",
"multi-task",
"multi-modal",
"imitation-learning",
"offline-reinforcement-learning",
"region:us"
]
| 2023-03-08T15:36:26+00:00 | {"library_name": "gia", "tags": ["deep-reinforcement-learning", "reinforcement-learning", "gia", "multi-task", "multi-modal", "imitation-learning", "offline-reinforcement-learning"]} | 2023-03-10T16:40:23+00:00 | []
| []
| TAGS
#deep-reinforcement-learning #reinforcement-learning #gia #multi-task #multi-modal #imitation-learning #offline-reinforcement-learning #region-us
|
An imitation learning environment for the drawer-open-v2 environment, sample for the policy drawer-open-v2
This environment was created as part of the Generally Intelligent Agents project gia: URL
## Load dataset
First, clone it with
Then, load it with
| [
"## Load dataset\n\nFirst, clone it with\n\n\n\nThen, load it with"
]
| [
"TAGS\n#deep-reinforcement-learning #reinforcement-learning #gia #multi-task #multi-modal #imitation-learning #offline-reinforcement-learning #region-us \n",
"## Load dataset\n\nFirst, clone it with\n\n\n\nThen, load it with"
]
| [
46,
16
]
| [
"passage: TAGS\n#deep-reinforcement-learning #reinforcement-learning #gia #multi-task #multi-modal #imitation-learning #offline-reinforcement-learning #region-us \n## Load dataset\n\nFirst, clone it with\n\n\n\nThen, load it with"
]
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|
d8be4b4a81c4ec46194264eef815acdfcdc869b2 |
An imitation learning environment for the faucet-close-v2 environment, sample for the policy faucet-close-v2
This environment was created as part of the Generally Intelligent Agents project gia: https://github.com/huggingface/gia
## Load dataset
First, clone it with
```sh
git clone https://huggingface.co/datasets/qgallouedec/prj_gia_dataset_metaworld_faucet_close_v2_1111
```
Then, load it with
```python
import numpy as np
dataset = np.load("prj_gia_dataset_metaworld_faucet_close_v2_1111/dataset.npy", allow_pickle=True).item()
print(dataset.keys()) # dict_keys(['observations', 'actions', 'dones', 'rewards'])
```
| qgallouedec/prj_gia_dataset_metaworld_faucet_close_v2_1111 | [
"deep-reinforcement-learning",
"reinforcement-learning",
"gia",
"multi-task",
"multi-modal",
"imitation-learning",
"offline-reinforcement-learning",
"region:us"
]
| 2023-03-08T15:36:34+00:00 | {"library_name": "gia", "tags": ["deep-reinforcement-learning", "reinforcement-learning", "gia", "multi-task", "multi-modal", "imitation-learning", "offline-reinforcement-learning"]} | 2023-03-10T16:42:02+00:00 | []
| []
| TAGS
#deep-reinforcement-learning #reinforcement-learning #gia #multi-task #multi-modal #imitation-learning #offline-reinforcement-learning #region-us
|
An imitation learning environment for the faucet-close-v2 environment, sample for the policy faucet-close-v2
This environment was created as part of the Generally Intelligent Agents project gia: URL
## Load dataset
First, clone it with
Then, load it with
| [
"## Load dataset\n\nFirst, clone it with\n\n\n\nThen, load it with"
]
| [
"TAGS\n#deep-reinforcement-learning #reinforcement-learning #gia #multi-task #multi-modal #imitation-learning #offline-reinforcement-learning #region-us \n",
"## Load dataset\n\nFirst, clone it with\n\n\n\nThen, load it with"
]
| [
46,
16
]
| [
"passage: TAGS\n#deep-reinforcement-learning #reinforcement-learning #gia #multi-task #multi-modal #imitation-learning #offline-reinforcement-learning #region-us \n## Load dataset\n\nFirst, clone it with\n\n\n\nThen, load it with"
]
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|
20214396cdbf2d57437f81ff9d76566d70f74aa1 |
An imitation learning environment for the faucet-open-v2 environment, sample for the policy faucet-open-v2
This environment was created as part of the Generally Intelligent Agents project gia: https://github.com/huggingface/gia
## Load dataset
First, clone it with
```sh
git clone https://huggingface.co/datasets/qgallouedec/prj_gia_dataset_metaworld_faucet_open_v2_1111
```
Then, load it with
```python
import numpy as np
dataset = np.load("prj_gia_dataset_metaworld_faucet_open_v2_1111/dataset.npy", allow_pickle=True).item()
print(dataset.keys()) # dict_keys(['observations', 'actions', 'dones', 'rewards'])
```
| qgallouedec/prj_gia_dataset_metaworld_faucet_open_v2_1111 | [
"deep-reinforcement-learning",
"reinforcement-learning",
"gia",
"multi-task",
"multi-modal",
"imitation-learning",
"offline-reinforcement-learning",
"region:us"
]
| 2023-03-08T15:36:43+00:00 | {"library_name": "gia", "tags": ["deep-reinforcement-learning", "reinforcement-learning", "gia", "multi-task", "multi-modal", "imitation-learning", "offline-reinforcement-learning"]} | 2023-03-10T16:43:36+00:00 | []
| []
| TAGS
#deep-reinforcement-learning #reinforcement-learning #gia #multi-task #multi-modal #imitation-learning #offline-reinforcement-learning #region-us
|
An imitation learning environment for the faucet-open-v2 environment, sample for the policy faucet-open-v2
This environment was created as part of the Generally Intelligent Agents project gia: URL
## Load dataset
First, clone it with
Then, load it with
| [
"## Load dataset\n\nFirst, clone it with\n\n\n\nThen, load it with"
]
| [
"TAGS\n#deep-reinforcement-learning #reinforcement-learning #gia #multi-task #multi-modal #imitation-learning #offline-reinforcement-learning #region-us \n",
"## Load dataset\n\nFirst, clone it with\n\n\n\nThen, load it with"
]
| [
46,
16
]
| [
"passage: TAGS\n#deep-reinforcement-learning #reinforcement-learning #gia #multi-task #multi-modal #imitation-learning #offline-reinforcement-learning #region-us \n## Load dataset\n\nFirst, clone it with\n\n\n\nThen, load it with"
]
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|
04f6b2e8d5b66bf058d93eedb99fa634ffeebe72 |
An imitation learning environment for the hammer-v2 environment, sample for the policy hammer-v2
This environment was created as part of the Generally Intelligent Agents project gia: https://github.com/huggingface/gia
## Load dataset
First, clone it with
```sh
git clone https://huggingface.co/datasets/qgallouedec/prj_gia_dataset_metaworld_hammer_v2_1111
```
Then, load it with
```python
import numpy as np
dataset = np.load("prj_gia_dataset_metaworld_hammer_v2_1111/dataset.npy", allow_pickle=True).item()
print(dataset.keys()) # dict_keys(['observations', 'actions', 'dones', 'rewards'])
```
| qgallouedec/prj_gia_dataset_metaworld_hammer_v2_1111 | [
"deep-reinforcement-learning",
"reinforcement-learning",
"gia",
"multi-task",
"multi-modal",
"imitation-learning",
"offline-reinforcement-learning",
"region:us"
]
| 2023-03-08T15:36:51+00:00 | {"library_name": "gia", "tags": ["deep-reinforcement-learning", "reinforcement-learning", "gia", "multi-task", "multi-modal", "imitation-learning", "offline-reinforcement-learning"]} | 2023-03-10T16:45:25+00:00 | []
| []
| TAGS
#deep-reinforcement-learning #reinforcement-learning #gia #multi-task #multi-modal #imitation-learning #offline-reinforcement-learning #region-us
|
An imitation learning environment for the hammer-v2 environment, sample for the policy hammer-v2
This environment was created as part of the Generally Intelligent Agents project gia: URL
## Load dataset
First, clone it with
Then, load it with
| [
"## Load dataset\n\nFirst, clone it with\n\n\n\nThen, load it with"
]
| [
"TAGS\n#deep-reinforcement-learning #reinforcement-learning #gia #multi-task #multi-modal #imitation-learning #offline-reinforcement-learning #region-us \n",
"## Load dataset\n\nFirst, clone it with\n\n\n\nThen, load it with"
]
| [
46,
16
]
| [
"passage: TAGS\n#deep-reinforcement-learning #reinforcement-learning #gia #multi-task #multi-modal #imitation-learning #offline-reinforcement-learning #region-us \n## Load dataset\n\nFirst, clone it with\n\n\n\nThen, load it with"
]
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|
ede12b39f93acd3d13d64206d3cc75c2b5a756ac |
An imitation learning environment for the hand-insert-v2 environment, sample for the policy hand-insert-v2
This environment was created as part of the Generally Intelligent Agents project gia: https://github.com/huggingface/gia
## Load dataset
First, clone it with
```sh
git clone https://huggingface.co/datasets/qgallouedec/prj_gia_dataset_metaworld_hand_insert_v2_1111
```
Then, load it with
```python
import numpy as np
dataset = np.load("prj_gia_dataset_metaworld_hand_insert_v2_1111/dataset.npy", allow_pickle=True).item()
print(dataset.keys()) # dict_keys(['observations', 'actions', 'dones', 'rewards'])
```
| qgallouedec/prj_gia_dataset_metaworld_hand_insert_v2_1111 | [
"deep-reinforcement-learning",
"reinforcement-learning",
"gia",
"multi-task",
"multi-modal",
"imitation-learning",
"offline-reinforcement-learning",
"region:us"
]
| 2023-03-08T15:36:59+00:00 | {"library_name": "gia", "tags": ["deep-reinforcement-learning", "reinforcement-learning", "gia", "multi-task", "multi-modal", "imitation-learning", "offline-reinforcement-learning"]} | 2023-03-08T17:58:42+00:00 | []
| []
| TAGS
#deep-reinforcement-learning #reinforcement-learning #gia #multi-task #multi-modal #imitation-learning #offline-reinforcement-learning #region-us
|
An imitation learning environment for the hand-insert-v2 environment, sample for the policy hand-insert-v2
This environment was created as part of the Generally Intelligent Agents project gia: URL
## Load dataset
First, clone it with
Then, load it with
| [
"## Load dataset\n\nFirst, clone it with\n\n\n\nThen, load it with"
]
| [
"TAGS\n#deep-reinforcement-learning #reinforcement-learning #gia #multi-task #multi-modal #imitation-learning #offline-reinforcement-learning #region-us \n",
"## Load dataset\n\nFirst, clone it with\n\n\n\nThen, load it with"
]
| [
46,
16
]
| [
"passage: TAGS\n#deep-reinforcement-learning #reinforcement-learning #gia #multi-task #multi-modal #imitation-learning #offline-reinforcement-learning #region-us \n## Load dataset\n\nFirst, clone it with\n\n\n\nThen, load it with"
]
| [
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|
4bf3875b6e8cc5d9a26f8c1254cdbd2e3dfa4394 |
An imitation learning environment for the basketball-v2 environment, sample for the policy basketball-v2
This environment was created as part of the Generally Intelligent Agents project gia: https://github.com/huggingface/gia
## Load dataset
First, clone it with
```sh
git clone https://huggingface.co/datasets/qgallouedec/prj_gia_dataset_metaworld_basketball_v2_1111
```
Then, load it with
```python
import numpy as np
dataset = np.load("prj_gia_dataset_metaworld_basketball_v2_1111/dataset.npy", allow_pickle=True).item()
print(dataset.keys()) # dict_keys(['observations', 'actions', 'dones', 'rewards'])
```
| qgallouedec/prj_gia_dataset_metaworld_basketball_v2_1111 | [
"deep-reinforcement-learning",
"reinforcement-learning",
"gia",
"multi-task",
"multi-modal",
"imitation-learning",
"offline-reinforcement-learning",
"region:us"
]
| 2023-03-08T15:39:02+00:00 | {"library_name": "gia", "tags": ["deep-reinforcement-learning", "reinforcement-learning", "gia", "multi-task", "multi-modal", "imitation-learning", "offline-reinforcement-learning"]} | 2023-03-08T16:01:53+00:00 | []
| []
| TAGS
#deep-reinforcement-learning #reinforcement-learning #gia #multi-task #multi-modal #imitation-learning #offline-reinforcement-learning #region-us
|
An imitation learning environment for the basketball-v2 environment, sample for the policy basketball-v2
This environment was created as part of the Generally Intelligent Agents project gia: URL
## Load dataset
First, clone it with
Then, load it with
| [
"## Load dataset\n\nFirst, clone it with\n\n\n\nThen, load it with"
]
| [
"TAGS\n#deep-reinforcement-learning #reinforcement-learning #gia #multi-task #multi-modal #imitation-learning #offline-reinforcement-learning #region-us \n",
"## Load dataset\n\nFirst, clone it with\n\n\n\nThen, load it with"
]
| [
46,
16
]
| [
"passage: TAGS\n#deep-reinforcement-learning #reinforcement-learning #gia #multi-task #multi-modal #imitation-learning #offline-reinforcement-learning #region-us \n## Load dataset\n\nFirst, clone it with\n\n\n\nThen, load it with"
]
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|
600a1f8c52eae9b65c2aafa46380a45b3ed3099c |
An imitation learning environment for the bin-picking-v2 environment, sample for the policy bin-picking-v2
This environment was created as part of the Generally Intelligent Agents project gia: https://github.com/huggingface/gia
## Load dataset
First, clone it with
```sh
git clone https://huggingface.co/datasets/qgallouedec/prj_gia_dataset_metaworld_bin_picking_v2_1111
```
Then, load it with
```python
import numpy as np
dataset = np.load("prj_gia_dataset_metaworld_bin_picking_v2_1111/dataset.npy", allow_pickle=True).item()
print(dataset.keys()) # dict_keys(['observations', 'actions', 'dones', 'rewards'])
```
| qgallouedec/prj_gia_dataset_metaworld_bin_picking_v2_1111 | [
"deep-reinforcement-learning",
"reinforcement-learning",
"gia",
"multi-task",
"multi-modal",
"imitation-learning",
"offline-reinforcement-learning",
"region:us"
]
| 2023-03-08T15:39:12+00:00 | {"library_name": "gia", "tags": ["deep-reinforcement-learning", "reinforcement-learning", "gia", "multi-task", "multi-modal", "imitation-learning", "offline-reinforcement-learning"]} | 2023-03-08T16:04:13+00:00 | []
| []
| TAGS
#deep-reinforcement-learning #reinforcement-learning #gia #multi-task #multi-modal #imitation-learning #offline-reinforcement-learning #region-us
|
An imitation learning environment for the bin-picking-v2 environment, sample for the policy bin-picking-v2
This environment was created as part of the Generally Intelligent Agents project gia: URL
## Load dataset
First, clone it with
Then, load it with
| [
"## Load dataset\n\nFirst, clone it with\n\n\n\nThen, load it with"
]
| [
"TAGS\n#deep-reinforcement-learning #reinforcement-learning #gia #multi-task #multi-modal #imitation-learning #offline-reinforcement-learning #region-us \n",
"## Load dataset\n\nFirst, clone it with\n\n\n\nThen, load it with"
]
| [
46,
16
]
| [
"passage: TAGS\n#deep-reinforcement-learning #reinforcement-learning #gia #multi-task #multi-modal #imitation-learning #offline-reinforcement-learning #region-us \n## Load dataset\n\nFirst, clone it with\n\n\n\nThen, load it with"
]
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|
875b2de6956d28a17b3c582b6f4133e070346ac5 |
An imitation learning environment for the box-close-v2 environment, sample for the policy box-close-v2
This environment was created as part of the Generally Intelligent Agents project gia: https://github.com/huggingface/gia
## Load dataset
First, clone it with
```sh
git clone https://huggingface.co/datasets/qgallouedec/prj_gia_dataset_metaworld_box_close_v2_1111
```
Then, load it with
```python
import numpy as np
dataset = np.load("prj_gia_dataset_metaworld_box_close_v2_1111/dataset.npy", allow_pickle=True).item()
print(dataset.keys()) # dict_keys(['observations', 'actions', 'dones', 'rewards'])
```
| qgallouedec/prj_gia_dataset_metaworld_box_close_v2_1111 | [
"deep-reinforcement-learning",
"reinforcement-learning",
"gia",
"multi-task",
"multi-modal",
"imitation-learning",
"offline-reinforcement-learning",
"region:us"
]
| 2023-03-08T15:39:23+00:00 | {"library_name": "gia", "tags": ["deep-reinforcement-learning", "reinforcement-learning", "gia", "multi-task", "multi-modal", "imitation-learning", "offline-reinforcement-learning"]} | 2023-03-08T16:06:46+00:00 | []
| []
| TAGS
#deep-reinforcement-learning #reinforcement-learning #gia #multi-task #multi-modal #imitation-learning #offline-reinforcement-learning #region-us
|
An imitation learning environment for the box-close-v2 environment, sample for the policy box-close-v2
This environment was created as part of the Generally Intelligent Agents project gia: URL
## Load dataset
First, clone it with
Then, load it with
| [
"## Load dataset\n\nFirst, clone it with\n\n\n\nThen, load it with"
]
| [
"TAGS\n#deep-reinforcement-learning #reinforcement-learning #gia #multi-task #multi-modal #imitation-learning #offline-reinforcement-learning #region-us \n",
"## Load dataset\n\nFirst, clone it with\n\n\n\nThen, load it with"
]
| [
46,
16
]
| [
"passage: TAGS\n#deep-reinforcement-learning #reinforcement-learning #gia #multi-task #multi-modal #imitation-learning #offline-reinforcement-learning #region-us \n## Load dataset\n\nFirst, clone it with\n\n\n\nThen, load it with"
]
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|
6ab927c9363644ecfa9b6bf16f71424ca0076358 |
An imitation learning environment for the button-press-topdown-v2 environment, sample for the policy button-press-topdown-v2
This environment was created as part of the Generally Intelligent Agents project gia: https://github.com/huggingface/gia
## Load dataset
First, clone it with
```sh
git clone https://huggingface.co/datasets/qgallouedec/prj_gia_dataset_metaworld_button_press_topdown_v2_1111
```
Then, load it with
```python
import numpy as np
dataset = np.load("prj_gia_dataset_metaworld_button_press_topdown_v2_1111/dataset.npy", allow_pickle=True).item()
print(dataset.keys()) # dict_keys(['observations', 'actions', 'dones', 'rewards'])
```
| qgallouedec/prj_gia_dataset_metaworld_button_press_topdown_v2_1111 | [
"deep-reinforcement-learning",
"reinforcement-learning",
"gia",
"multi-task",
"multi-modal",
"imitation-learning",
"offline-reinforcement-learning",
"region:us"
]
| 2023-03-08T15:39:33+00:00 | {"library_name": "gia", "tags": ["deep-reinforcement-learning", "reinforcement-learning", "gia", "multi-task", "multi-modal", "imitation-learning", "offline-reinforcement-learning"]} | 2023-03-08T16:09:12+00:00 | []
| []
| TAGS
#deep-reinforcement-learning #reinforcement-learning #gia #multi-task #multi-modal #imitation-learning #offline-reinforcement-learning #region-us
|
An imitation learning environment for the button-press-topdown-v2 environment, sample for the policy button-press-topdown-v2
This environment was created as part of the Generally Intelligent Agents project gia: URL
## Load dataset
First, clone it with
Then, load it with
| [
"## Load dataset\n\nFirst, clone it with\n\n\n\nThen, load it with"
]
| [
"TAGS\n#deep-reinforcement-learning #reinforcement-learning #gia #multi-task #multi-modal #imitation-learning #offline-reinforcement-learning #region-us \n",
"## Load dataset\n\nFirst, clone it with\n\n\n\nThen, load it with"
]
| [
46,
16
]
| [
"passage: TAGS\n#deep-reinforcement-learning #reinforcement-learning #gia #multi-task #multi-modal #imitation-learning #offline-reinforcement-learning #region-us \n## Load dataset\n\nFirst, clone it with\n\n\n\nThen, load it with"
]
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|
9cfe381abcdc6e5ae3411062c695e9012b215c56 |
An imitation learning environment for the button-press-topdown-wall-v2 environment, sample for the policy button-press-topdown-wall-v2
This environment was created as part of the Generally Intelligent Agents project gia: https://github.com/huggingface/gia
## Load dataset
First, clone it with
```sh
git clone https://huggingface.co/datasets/qgallouedec/prj_gia_dataset_metaworld_button_press_topdown_wall_v2_1111
```
Then, load it with
```python
import numpy as np
dataset = np.load("prj_gia_dataset_metaworld_button_press_topdown_wall_v2_1111/dataset.npy", allow_pickle=True).item()
print(dataset.keys()) # dict_keys(['observations', 'actions', 'dones', 'rewards'])
```
| qgallouedec/prj_gia_dataset_metaworld_button_press_topdown_wall_v2_1111 | [
"deep-reinforcement-learning",
"reinforcement-learning",
"gia",
"multi-task",
"multi-modal",
"imitation-learning",
"offline-reinforcement-learning",
"region:us"
]
| 2023-03-08T15:39:43+00:00 | {"library_name": "gia", "tags": ["deep-reinforcement-learning", "reinforcement-learning", "gia", "multi-task", "multi-modal", "imitation-learning", "offline-reinforcement-learning"]} | 2023-03-08T16:11:31+00:00 | []
| []
| TAGS
#deep-reinforcement-learning #reinforcement-learning #gia #multi-task #multi-modal #imitation-learning #offline-reinforcement-learning #region-us
|
An imitation learning environment for the button-press-topdown-wall-v2 environment, sample for the policy button-press-topdown-wall-v2
This environment was created as part of the Generally Intelligent Agents project gia: URL
## Load dataset
First, clone it with
Then, load it with
| [
"## Load dataset\n\nFirst, clone it with\n\n\n\nThen, load it with"
]
| [
"TAGS\n#deep-reinforcement-learning #reinforcement-learning #gia #multi-task #multi-modal #imitation-learning #offline-reinforcement-learning #region-us \n",
"## Load dataset\n\nFirst, clone it with\n\n\n\nThen, load it with"
]
| [
46,
16
]
| [
"passage: TAGS\n#deep-reinforcement-learning #reinforcement-learning #gia #multi-task #multi-modal #imitation-learning #offline-reinforcement-learning #region-us \n## Load dataset\n\nFirst, clone it with\n\n\n\nThen, load it with"
]
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|
49382f46fd73fc9821bff06c9e9913893cfa3ba0 | # Dataset Card for "perseuslatin_UD"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | pnadel/perseuslatin_UD | [
"region:us"
]
| 2023-03-08T15:50:53+00:00 | {"dataset_info": {"features": [{"name": "fname", "dtype": "string"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 920071, "num_examples": 4936}], "download_size": 342519, "dataset_size": 920071}} | 2023-03-18T20:58:02+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "perseuslatin_UD"
More Information needed | [
"# Dataset Card for \"perseuslatin_UD\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"perseuslatin_UD\"\n\nMore Information needed"
]
| [
6,
16
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"perseuslatin_UD\"\n\nMore Information needed"
]
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|
d9b3fb8f0227bf4a93e25036a8f7addca2d083b1 |
An imitation learning environment for the button-press-v2 environment, sample for the policy button-press-v2
This environment was created as part of the Generally Intelligent Agents project gia: https://github.com/huggingface/gia
## Load dataset
First, clone it with
```sh
git clone https://huggingface.co/datasets/qgallouedec/prj_gia_dataset_metaworld_button_press_v2_1111
```
Then, load it with
```python
import numpy as np
dataset = np.load("prj_gia_dataset_metaworld_button_press_v2_1111/dataset.npy", allow_pickle=True).item()
print(dataset.keys()) # dict_keys(['observations', 'actions', 'dones', 'rewards'])
```
| qgallouedec/prj_gia_dataset_metaworld_button_press_v2_1111 | [
"deep-reinforcement-learning",
"reinforcement-learning",
"gia",
"multi-task",
"multi-modal",
"imitation-learning",
"offline-reinforcement-learning",
"region:us"
]
| 2023-03-08T16:13:55+00:00 | {"library_name": "gia", "tags": ["deep-reinforcement-learning", "reinforcement-learning", "gia", "multi-task", "multi-modal", "imitation-learning", "offline-reinforcement-learning"]} | 2023-03-08T16:14:00+00:00 | []
| []
| TAGS
#deep-reinforcement-learning #reinforcement-learning #gia #multi-task #multi-modal #imitation-learning #offline-reinforcement-learning #region-us
|
An imitation learning environment for the button-press-v2 environment, sample for the policy button-press-v2
This environment was created as part of the Generally Intelligent Agents project gia: URL
## Load dataset
First, clone it with
Then, load it with
| [
"## Load dataset\n\nFirst, clone it with\n\n\n\nThen, load it with"
]
| [
"TAGS\n#deep-reinforcement-learning #reinforcement-learning #gia #multi-task #multi-modal #imitation-learning #offline-reinforcement-learning #region-us \n",
"## Load dataset\n\nFirst, clone it with\n\n\n\nThen, load it with"
]
| [
46,
16
]
| [
"passage: TAGS\n#deep-reinforcement-learning #reinforcement-learning #gia #multi-task #multi-modal #imitation-learning #offline-reinforcement-learning #region-us \n## Load dataset\n\nFirst, clone it with\n\n\n\nThen, load it with"
]
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|
c909e4901e24716fa6f3164d5e0e202a839d399b |
An imitation learning environment for the button-press-wall-v2 environment, sample for the policy button-press-wall-v2
This environment was created as part of the Generally Intelligent Agents project gia: https://github.com/huggingface/gia
## Load dataset
First, clone it with
```sh
git clone https://huggingface.co/datasets/qgallouedec/prj_gia_dataset_metaworld_button_press_wall_v2_1111
```
Then, load it with
```python
import numpy as np
dataset = np.load("prj_gia_dataset_metaworld_button_press_wall_v2_1111/dataset.npy", allow_pickle=True).item()
print(dataset.keys()) # dict_keys(['observations', 'actions', 'dones', 'rewards'])
```
| qgallouedec/prj_gia_dataset_metaworld_button_press_wall_v2_1111 | [
"deep-reinforcement-learning",
"reinforcement-learning",
"gia",
"multi-task",
"multi-modal",
"imitation-learning",
"offline-reinforcement-learning",
"region:us"
]
| 2023-03-08T16:16:35+00:00 | {"library_name": "gia", "tags": ["deep-reinforcement-learning", "reinforcement-learning", "gia", "multi-task", "multi-modal", "imitation-learning", "offline-reinforcement-learning"]} | 2023-03-08T16:16:39+00:00 | []
| []
| TAGS
#deep-reinforcement-learning #reinforcement-learning #gia #multi-task #multi-modal #imitation-learning #offline-reinforcement-learning #region-us
|
An imitation learning environment for the button-press-wall-v2 environment, sample for the policy button-press-wall-v2
This environment was created as part of the Generally Intelligent Agents project gia: URL
## Load dataset
First, clone it with
Then, load it with
| [
"## Load dataset\n\nFirst, clone it with\n\n\n\nThen, load it with"
]
| [
"TAGS\n#deep-reinforcement-learning #reinforcement-learning #gia #multi-task #multi-modal #imitation-learning #offline-reinforcement-learning #region-us \n",
"## Load dataset\n\nFirst, clone it with\n\n\n\nThen, load it with"
]
| [
46,
16
]
| [
"passage: TAGS\n#deep-reinforcement-learning #reinforcement-learning #gia #multi-task #multi-modal #imitation-learning #offline-reinforcement-learning #region-us \n## Load dataset\n\nFirst, clone it with\n\n\n\nThen, load it with"
]
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|
b6cafb68d17ad71ea66351ee2ffbc53376d553a6 |
An imitation learning environment for the coffee-button-v2 environment, sample for the policy coffee-button-v2
This environment was created as part of the Generally Intelligent Agents project gia: https://github.com/huggingface/gia
## Load dataset
First, clone it with
```sh
git clone https://huggingface.co/datasets/qgallouedec/prj_gia_dataset_metaworld_coffee_button_v2_1111
```
Then, load it with
```python
import numpy as np
dataset = np.load("prj_gia_dataset_metaworld_coffee_button_v2_1111/dataset.npy", allow_pickle=True).item()
print(dataset.keys()) # dict_keys(['observations', 'actions', 'dones', 'rewards'])
```
| qgallouedec/prj_gia_dataset_metaworld_coffee_button_v2_1111 | [
"deep-reinforcement-learning",
"reinforcement-learning",
"gia",
"multi-task",
"multi-modal",
"imitation-learning",
"offline-reinforcement-learning",
"region:us"
]
| 2023-03-08T16:19:02+00:00 | {"library_name": "gia", "tags": ["deep-reinforcement-learning", "reinforcement-learning", "gia", "multi-task", "multi-modal", "imitation-learning", "offline-reinforcement-learning"]} | 2023-03-08T16:19:06+00:00 | []
| []
| TAGS
#deep-reinforcement-learning #reinforcement-learning #gia #multi-task #multi-modal #imitation-learning #offline-reinforcement-learning #region-us
|
An imitation learning environment for the coffee-button-v2 environment, sample for the policy coffee-button-v2
This environment was created as part of the Generally Intelligent Agents project gia: URL
## Load dataset
First, clone it with
Then, load it with
| [
"## Load dataset\n\nFirst, clone it with\n\n\n\nThen, load it with"
]
| [
"TAGS\n#deep-reinforcement-learning #reinforcement-learning #gia #multi-task #multi-modal #imitation-learning #offline-reinforcement-learning #region-us \n",
"## Load dataset\n\nFirst, clone it with\n\n\n\nThen, load it with"
]
| [
46,
16
]
| [
"passage: TAGS\n#deep-reinforcement-learning #reinforcement-learning #gia #multi-task #multi-modal #imitation-learning #offline-reinforcement-learning #region-us \n## Load dataset\n\nFirst, clone it with\n\n\n\nThen, load it with"
]
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|
027757e76a5e3beaeffcc98f4f5aa08665f03840 |
An imitation learning environment for the coffee-pull-v2 environment, sample for the policy coffee-pull-v2
This environment was created as part of the Generally Intelligent Agents project gia: https://github.com/huggingface/gia
## Load dataset
First, clone it with
```sh
git clone https://huggingface.co/datasets/qgallouedec/prj_gia_dataset_metaworld_coffee_pull_v2_1111
```
Then, load it with
```python
import numpy as np
dataset = np.load("prj_gia_dataset_metaworld_coffee_pull_v2_1111/dataset.npy", allow_pickle=True).item()
print(dataset.keys()) # dict_keys(['observations', 'actions', 'dones', 'rewards'])
```
| qgallouedec/prj_gia_dataset_metaworld_coffee_pull_v2_1111 | [
"deep-reinforcement-learning",
"reinforcement-learning",
"gia",
"multi-task",
"multi-modal",
"imitation-learning",
"offline-reinforcement-learning",
"region:us"
]
| 2023-03-08T16:21:44+00:00 | {"library_name": "gia", "tags": ["deep-reinforcement-learning", "reinforcement-learning", "gia", "multi-task", "multi-modal", "imitation-learning", "offline-reinforcement-learning"]} | 2023-03-08T16:21:48+00:00 | []
| []
| TAGS
#deep-reinforcement-learning #reinforcement-learning #gia #multi-task #multi-modal #imitation-learning #offline-reinforcement-learning #region-us
|
An imitation learning environment for the coffee-pull-v2 environment, sample for the policy coffee-pull-v2
This environment was created as part of the Generally Intelligent Agents project gia: URL
## Load dataset
First, clone it with
Then, load it with
| [
"## Load dataset\n\nFirst, clone it with\n\n\n\nThen, load it with"
]
| [
"TAGS\n#deep-reinforcement-learning #reinforcement-learning #gia #multi-task #multi-modal #imitation-learning #offline-reinforcement-learning #region-us \n",
"## Load dataset\n\nFirst, clone it with\n\n\n\nThen, load it with"
]
| [
46,
16
]
| [
"passage: TAGS\n#deep-reinforcement-learning #reinforcement-learning #gia #multi-task #multi-modal #imitation-learning #offline-reinforcement-learning #region-us \n## Load dataset\n\nFirst, clone it with\n\n\n\nThen, load it with"
]
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|
5fb84b937e99f15c8f4a4b606c1bac858a9de14e |
An imitation learning environment for the coffee-push-v2 environment, sample for the policy coffee-push-v2
This environment was created as part of the Generally Intelligent Agents project gia: https://github.com/huggingface/gia
## Load dataset
First, clone it with
```sh
git clone https://huggingface.co/datasets/qgallouedec/prj_gia_dataset_metaworld_coffee_push_v2_1111
```
Then, load it with
```python
import numpy as np
dataset = np.load("prj_gia_dataset_metaworld_coffee_push_v2_1111/dataset.npy", allow_pickle=True).item()
print(dataset.keys()) # dict_keys(['observations', 'actions', 'dones', 'rewards'])
```
| qgallouedec/prj_gia_dataset_metaworld_coffee_push_v2_1111 | [
"deep-reinforcement-learning",
"reinforcement-learning",
"gia",
"multi-task",
"multi-modal",
"imitation-learning",
"offline-reinforcement-learning",
"region:us"
]
| 2023-03-08T16:24:19+00:00 | {"library_name": "gia", "tags": ["deep-reinforcement-learning", "reinforcement-learning", "gia", "multi-task", "multi-modal", "imitation-learning", "offline-reinforcement-learning"]} | 2023-03-08T16:24:23+00:00 | []
| []
| TAGS
#deep-reinforcement-learning #reinforcement-learning #gia #multi-task #multi-modal #imitation-learning #offline-reinforcement-learning #region-us
|
An imitation learning environment for the coffee-push-v2 environment, sample for the policy coffee-push-v2
This environment was created as part of the Generally Intelligent Agents project gia: URL
## Load dataset
First, clone it with
Then, load it with
| [
"## Load dataset\n\nFirst, clone it with\n\n\n\nThen, load it with"
]
| [
"TAGS\n#deep-reinforcement-learning #reinforcement-learning #gia #multi-task #multi-modal #imitation-learning #offline-reinforcement-learning #region-us \n",
"## Load dataset\n\nFirst, clone it with\n\n\n\nThen, load it with"
]
| [
46,
16
]
| [
"passage: TAGS\n#deep-reinforcement-learning #reinforcement-learning #gia #multi-task #multi-modal #imitation-learning #offline-reinforcement-learning #region-us \n## Load dataset\n\nFirst, clone it with\n\n\n\nThen, load it with"
]
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|
1792be1ef3ece466f1cc7750baba302c16c93a47 | # Dataset name: "modified_anthropic_convo_data"
# Dataset Card for Conversational AI Bot
## Dataset Description
- **Homepage:** https://huggingface.co/datasets/Anthropic/hh-rlhf
### Dataset Summary
- This dataset is the augmented version of the same dataset found here https://huggingface.co/datasets/Anthropic/hh-rlhf
- Two new columns have been added called ***human_speaker*** and ***assistant_speaker*** to make it easier to directly use the data for a causal langauage modeling task
- Currently only one pair of conversations have been picked, the dataset will be updated soon
### Contributions
Thanks to https://huggingface.co/datasets/Anthropic/hh-rlhf for adding this dataset. | vyas21/modified_anthropic_convo_data | [
"task_categories:conversational",
"conversation",
"dialogue",
"anthropic",
"region:us"
]
| 2023-03-08T17:13:56+00:00 | {"task_categories": ["conversational"], "pretty_name": "modified_anthropic_convo_data", "dataset_info": {"features": [{"name": "chosen", "dtype": "string"}, {"name": "rejected", "dtype": "string"}, {"name": "human_speaker", "dtype": "string"}, {"name": "assistant_speaker", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 370789375, "num_examples": 160800}, {"name": "test", "num_bytes": 19894559, "num_examples": 8552}], "download_size": 223189497, "dataset_size": 390683934}, "tags": ["conversation", "dialogue", "anthropic"]} | 2023-03-08T17:21:47+00:00 | []
| []
| TAGS
#task_categories-conversational #conversation #dialogue #anthropic #region-us
| # Dataset name: "modified_anthropic_convo_data"
# Dataset Card for Conversational AI Bot
## Dataset Description
- Homepage: URL
### Dataset Summary
- This dataset is the augmented version of the same dataset found here URL
- Two new columns have been added called *human_speaker* and *assistant_speaker* to make it easier to directly use the data for a causal langauage modeling task
- Currently only one pair of conversations have been picked, the dataset will be updated soon
### Contributions
Thanks to URL for adding this dataset. | [
"# Dataset name: \"modified_anthropic_convo_data\"",
"# Dataset Card for Conversational AI Bot",
"## Dataset Description\n\n- Homepage: URL",
"### Dataset Summary\n\n- This dataset is the augmented version of the same dataset found here URL\n- Two new columns have been added called *human_speaker* and *assistant_speaker* to make it easier to directly use the data for a causal langauage modeling task\n- Currently only one pair of conversations have been picked, the dataset will be updated soon",
"### Contributions\n\nThanks to URL for adding this dataset."
]
| [
"TAGS\n#task_categories-conversational #conversation #dialogue #anthropic #region-us \n",
"# Dataset name: \"modified_anthropic_convo_data\"",
"# Dataset Card for Conversational AI Bot",
"## Dataset Description\n\n- Homepage: URL",
"### Dataset Summary\n\n- This dataset is the augmented version of the same dataset found here URL\n- Two new columns have been added called *human_speaker* and *assistant_speaker* to make it easier to directly use the data for a causal langauage modeling task\n- Currently only one pair of conversations have been picked, the dataset will be updated soon",
"### Contributions\n\nThanks to URL for adding this dataset."
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| [
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| [
"passage: TAGS\n#task_categories-conversational #conversation #dialogue #anthropic #region-us \n# Dataset name: \"modified_anthropic_convo_data\"# Dataset Card for Conversational AI Bot## Dataset Description\n\n- Homepage: URL### Dataset Summary\n\n- This dataset is the augmented version of the same dataset found here URL\n- Two new columns have been added called *human_speaker* and *assistant_speaker* to make it easier to directly use the data for a causal langauage modeling task\n- Currently only one pair of conversations have been picked, the dataset will be updated soon### Contributions\n\nThanks to URL for adding this dataset."
]
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|
9d848ab703b14931c473253cc48f6dafecc3ab6b |
An imitation learning environment for the disassemble-v2 environment, sample for the policy disassemble-v2
This environment was created as part of the Generally Intelligent Agents project gia: https://github.com/huggingface/gia
## Load dataset
First, clone it with
```sh
git clone https://huggingface.co/datasets/qgallouedec/prj_gia_dataset_metaworld_disassemble_v2_1111
```
Then, load it with
```python
import numpy as np
dataset = np.load("prj_gia_dataset_metaworld_disassemble_v2_1111/dataset.npy", allow_pickle=True).item()
print(dataset.keys()) # dict_keys(['observations', 'actions', 'dones', 'rewards'])
```
| qgallouedec/prj_gia_dataset_metaworld_disassemble_v2_1111 | [
"deep-reinforcement-learning",
"reinforcement-learning",
"gia",
"multi-task",
"multi-modal",
"imitation-learning",
"offline-reinforcement-learning",
"region:us"
]
| 2023-03-08T18:02:12+00:00 | {"library_name": "gia", "tags": ["deep-reinforcement-learning", "reinforcement-learning", "gia", "multi-task", "multi-modal", "imitation-learning", "offline-reinforcement-learning"]} | 2023-03-08T18:02:16+00:00 | []
| []
| TAGS
#deep-reinforcement-learning #reinforcement-learning #gia #multi-task #multi-modal #imitation-learning #offline-reinforcement-learning #region-us
|
An imitation learning environment for the disassemble-v2 environment, sample for the policy disassemble-v2
This environment was created as part of the Generally Intelligent Agents project gia: URL
## Load dataset
First, clone it with
Then, load it with
| [
"## Load dataset\n\nFirst, clone it with\n\n\n\nThen, load it with"
]
| [
"TAGS\n#deep-reinforcement-learning #reinforcement-learning #gia #multi-task #multi-modal #imitation-learning #offline-reinforcement-learning #region-us \n",
"## Load dataset\n\nFirst, clone it with\n\n\n\nThen, load it with"
]
| [
46,
16
]
| [
"passage: TAGS\n#deep-reinforcement-learning #reinforcement-learning #gia #multi-task #multi-modal #imitation-learning #offline-reinforcement-learning #region-us \n## Load dataset\n\nFirst, clone it with\n\n\n\nThen, load it with"
]
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|
053fed5e38f17d9560fc2bf1c0854ab8deabcda1 | # Dataset Card for "ia_embedded"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | davanstrien/ia_embedded | [
"region:us"
]
| 2023-03-08T18:14:56+00:00 | {"dataset_info": {"features": [{"name": "crawl_date", "dtype": "int64"}, {"name": "last_modified_date", "dtype": "float64"}, {"name": "url", "dtype": "string"}, {"name": "filename", "dtype": "string"}, {"name": "extension", "dtype": "string"}, {"name": "mime_type_web_server", "dtype": "string"}, {"name": "mime_type_tika", "dtype": "string"}, {"name": "width", "dtype": "int64"}, {"name": "height", "dtype": "int64"}, {"name": "md5", "dtype": "string"}, {"name": "sha1", "dtype": "string"}, {"name": "image", "dtype": "image"}, {"name": "embeddings", "sequence": "float32"}], "splits": [{"name": "train", "num_bytes": 1053063432.5, "num_examples": 6764}], "download_size": 1041773215, "dataset_size": 1053063432.5}} | 2023-03-08T20:28:35+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "ia_embedded"
More Information needed | [
"# Dataset Card for \"ia_embedded\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"ia_embedded\"\n\nMore Information needed"
]
| [
6,
14
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"ia_embedded\"\n\nMore Information needed"
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|
80ee62302515d7eab20491a2ce3ff8fc423ebf5e | # Dataset Card for "stackxchange"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | voidful/stackxchange | [
"region:us"
]
| 2023-03-08T19:00:50+00:00 | {"dataset_info": {"features": [{"name": "Name", "dtype": "string"}, {"name": "Tags", "sequence": "string"}, {"name": "Title", "dtype": "string"}, {"name": "Title_RAW", "dtype": "string"}, {"name": "Question", "dtype": "string"}, {"name": "Question_RAW", "dtype": "string"}, {"name": "AcceptedAnswers", "list": [{"name": "Body", "dtype": "string"}, {"name": "Body_RAW", "dtype": "string"}, {"name": "Id", "dtype": "string"}, {"name": "Score", "dtype": "string"}]}, {"name": "OtherAnswers", "list": [{"name": "Body", "dtype": "string"}, {"name": "Body_RAW", "dtype": "string"}, {"name": "Id", "dtype": "string"}, {"name": "Score", "dtype": "string"}]}, {"name": "page", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 37087996849, "num_examples": 6378706}], "download_size": 20193071433, "dataset_size": 37087996849}} | 2023-03-29T17:30:07+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "stackxchange"
More Information needed | [
"# Dataset Card for \"stackxchange\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"stackxchange\"\n\nMore Information needed"
]
| [
6,
14
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"stackxchange\"\n\nMore Information needed"
]
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|
9e8925a0f87652f26912c3e8f34739182d890ff5 | # Dataset Card for "sidewalk-imagery"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | coralexbadea/sidewalk-imagery | [
"region:us"
]
| 2023-03-08T19:02:59+00:00 | {"dataset_info": {"features": [{"name": "pixel_values", "dtype": "image"}, {"name": "label", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 86083036.0, "num_examples": 10}], "download_size": 3930138, "dataset_size": 86083036.0}} | 2023-03-08T19:03:02+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "sidewalk-imagery"
More Information needed | [
"# Dataset Card for \"sidewalk-imagery\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"sidewalk-imagery\"\n\nMore Information needed"
]
| [
6,
15
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"sidewalk-imagery\"\n\nMore Information needed"
]
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|
5802a0231552af839004effe29e7a62b2e5e76f2 | # Dataset Card for "cms-icd10-categorical"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | lowem1/cms-icd10-categorical | [
"region:us"
]
| 2023-03-08T19:12:49+00:00 | {"dataset_info": {"features": [{"name": "group_no", "dtype": "string"}, {"name": "domain", "dtype": "string"}, {"name": "description", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "__index_level_0__", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 11507555, "num_examples": 87474}], "download_size": 1989506, "dataset_size": 11507555}} | 2023-03-08T19:12:55+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "cms-icd10-categorical"
More Information needed | [
"# Dataset Card for \"cms-icd10-categorical\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"cms-icd10-categorical\"\n\nMore Information needed"
]
| [
6,
20
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"cms-icd10-categorical\"\n\nMore Information needed"
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fbdc97f5a2412e1dd03525efc43c7ddddbe0a518 | # Dataset Card for "OK_VQA_google_flan_t5_xxl_mode_VQAv2_visclues_detection_caption_module_filter_ns_100_OE"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | CVasNLPExperiments/OK_VQA_google_flan_t5_xxl_mode_VQAv2_visclues_detection_caption_module_filter_ns_100_OE | [
"region:us"
]
| 2023-03-08T19:33:07+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "question", "dtype": "string"}, {"name": "true_label", "sequence": "string"}, {"name": "prediction", "dtype": "string"}], "splits": [{"name": "fewshot_0", "num_bytes": 18400, "num_examples": 100}], "download_size": 11086, "dataset_size": 18400}} | 2023-03-08T20:01:20+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "OK_VQA_google_flan_t5_xxl_mode_VQAv2_visclues_detection_caption_module_filter_ns_100_OE"
More Information needed | [
"# Dataset Card for \"OK_VQA_google_flan_t5_xxl_mode_VQAv2_visclues_detection_caption_module_filter_ns_100_OE\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"OK_VQA_google_flan_t5_xxl_mode_VQAv2_visclues_detection_caption_module_filter_ns_100_OE\"\n\nMore Information needed"
]
| [
6,
53
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| [
"passage: TAGS\n#region-us \n# Dataset Card for \"OK_VQA_google_flan_t5_xxl_mode_VQAv2_visclues_detection_caption_module_filter_ns_100_OE\"\n\nMore Information needed"
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|
f53b5805351e39a823e14d304b84fe44780942e2 | # Dataset Card for "apps_partial_0_120"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | minimario/apps_partial_0_120 | [
"region:us"
]
| 2023-03-08T19:33:45+00:00 | {"dataset_info": {"features": [{"name": "problem", "dtype": "string"}, {"name": "code", "dtype": "string"}, {"name": "label", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 634469951, "num_examples": 439648}], "download_size": 13803138, "dataset_size": 634469951}} | 2023-03-08T19:37:46+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "apps_partial_0_120"
More Information needed | [
"# Dataset Card for \"apps_partial_0_120\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"apps_partial_0_120\"\n\nMore Information needed"
]
| [
6,
19
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"apps_partial_0_120\"\n\nMore Information needed"
]
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|
bdf4a70508ac7eafc002d8956cdaa3ed70e982c6 | # Dataset Card for "apps_partial_120_200"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | minimario/apps_partial_120_200 | [
"region:us"
]
| 2023-03-08T19:40:02+00:00 | {"dataset_info": {"features": [{"name": "problem", "dtype": "string"}, {"name": "code", "dtype": "string"}, {"name": "label", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 254632633, "num_examples": 181046}], "download_size": 4512497, "dataset_size": 254632633}} | 2023-03-08T19:40:11+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "apps_partial_120_200"
More Information needed | [
"# Dataset Card for \"apps_partial_120_200\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"apps_partial_120_200\"\n\nMore Information needed"
]
| [
6,
19
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"apps_partial_120_200\"\n\nMore Information needed"
]
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|
33597f348695efafb6cff6ed04cb59732739c789 | # Dataset Card for "OK_VQA_google_flan_ul2_mode_VQAv2_visclues_detection_caption_module_filter_ns_100_OE"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | CVasNLPExperiments/OK_VQA_google_flan_ul2_mode_VQAv2_visclues_detection_caption_module_filter_ns_100_OE | [
"region:us"
]
| 2023-03-08T19:50:37+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "question", "dtype": "string"}, {"name": "true_label", "sequence": "string"}, {"name": "prediction", "dtype": "string"}], "splits": [{"name": "fewshot_0", "num_bytes": 18358, "num_examples": 100}], "download_size": 11109, "dataset_size": 18358}} | 2023-03-08T20:14:53+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "OK_VQA_google_flan_ul2_mode_VQAv2_visclues_detection_caption_module_filter_ns_100_OE"
More Information needed | [
"# Dataset Card for \"OK_VQA_google_flan_ul2_mode_VQAv2_visclues_detection_caption_module_filter_ns_100_OE\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"OK_VQA_google_flan_ul2_mode_VQAv2_visclues_detection_caption_module_filter_ns_100_OE\"\n\nMore Information needed"
]
| [
6,
50
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"OK_VQA_google_flan_ul2_mode_VQAv2_visclues_detection_caption_module_filter_ns_100_OE\"\n\nMore Information needed"
]
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|
03a508f01d59b95f1ddd7da8d85ef4d091a7f9c6 |
# Dataset Card for "OcclusionSwissJudgmentPrediction": An implementation of an occlusion based explainability method for Swiss judgment prediction
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Summary](#dataset-summary)
- [Documents](#documents)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset **str**ucture](#dataset-**str**ucture)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Summary
This dataset contains an implementation of occlusion for the SwissJudgmentPrediction task.
Note that this dataset only provides a test set and should be used in comination with the [Swiss-Judgment-Prediction](https://huggingface.co/datasets/swiss_judgment_prediction) dataset.
### Documents
Occlusion-Swiss-Judgment-Prediction is a subset of the [Swiss-Judgment-Prediction](https://huggingface.co/datasets/swiss_judgment_prediction) dataset.
The Swiss-Judgment-Prediction dataset is a multilingual, diachronic dataset of 85K Swiss Federal Supreme Court (FSCS) cases annotated with the respective binarized judgment outcome (approval/dismissal), the publication year, the legal area and the canton of origin per case. Occlusion-Swiss-Judgment-Prediction extends this dataset by adding sentence splitting with explainability labels.
### Supported Tasks and Leaderboards
OcclusionSwissJudgmentPrediction can be used for performing the occlusion in the legal judgment prediction task.
### Languages
Switzerland has four official languages with 3 languages (German, French and Italian) being represented in more than 1000 Swiss Federal Supreme court decisions. The decisions are written by the judges and clerks in the language of the proceedings.
## Dataset structure
### Data Instances
## Data Instances
**Multilingual use of the dataset**
When the dataset is used in a multilingual setting selecting the the 'all_languages' flag:
```python
from datasets import load_dataset
dataset = load_dataset('rcds/occlusion_swiss_judgment_prediction', 'all')
```
**Monolingual use of the dataset**
When the dataset is used in a monolingual setting selecting the ISO language code for one of the 3 supported languages. For example:
```python
from datasets import load_dataset
dataset = load_dataset('rcds/occlusion_swiss_judgment_prediction', 'de')
```
### Data Fields
The following data fields are provided for documents (Test_1/Test_2/Test_3/Test_4):
id: (**int**) a unique identifier of the for the document <br/>
year: (**int**) the publication year<br/>
label: (**str**) the judgment outcome: dismissal or approval<br/>
language: (**str**) one of (de, fr, it)<br/>
region: (**str**) the region of the lower court<br/>
canton: (**str**) the canton of the lower court<br/>
legal area: (**str**) the legal area of the case<br/>
explainability_label (**str**): the explainability label assigned to the occluded text: Supports judgment, Opposes judgment, Neutral, Baseline<br/>
occluded_text (**str**): the occluded text<br/>
text: (**str**) the facts of the case w/o the occluded text except for cases w/ explainability label "Baseline" (contain entire facts)<br/>
Note that Baseline cases are only contained in version 1 of the occlusion test set, since they do not change from experiment to experiment.
### Data Splits (Including Swiss Judgment Prediction)
Language | Subset | Number of Rows (Test_1/Test_2/Test_3/Test_4)
| ----------- | ----------- | ----------- |
German| de | __427__ / __1366__ / __3567__ / __7235__
French | fr | __307__ / __854__ / __1926__ / __3279__
Italian | it | __299__ /__919__ / __2493__ / __5733__
All | all | __1033__ / __3139__ / __7986__/ __16247__
Language | Subset | Number of Documents (is the same for Test_1/Test_2/Test_3/Test_4)
| ----------- | ----------- | ----------- |
German| de | __38__
French | fr | __36__
Italian | it | __34__
All | all | __108__
## Dataset Creation
### Curation Rationale
The dataset was curated by Niklaus et al. (2021) and Nina Baumgartner.
### Source Data
#### Initial Data Collection and Normalization
The original data are available at the Swiss Federal Supreme Court (https://www.bger.ch) in unprocessed formats (HTML). The documents were downloaded from the Entscheidsuche portal (https://entscheidsuche.ch) in HTML.
#### Who are the source language producers?
Switzerland has four official languages with 3 languages (German, French and Italian) being represented in more than 1000 Swiss Federal Supreme court decisions. The decisions are written by the judges and clerks in the language of the proceedings.
### Annotations
#### Annotation process
The decisions have been annotated with the binarized judgment outcome using parsers and regular expressions. In addition a subset of the test set (27 cases in German, 24 in French and 23 in Italian spanning over the years 2017 an 20200) was annotated by legal experts, splitting sentences/group of sentences and annotated with one of the following explainability label: Supports judgment, Opposes Judgment and Neutral. The test sets have each sentence/ group of sentence once occluded, enabling an analysis of the changes in the model's performance. The legal expert annotation were conducted from April 2020 to August 2020.
#### Who are the annotators?
Joel Niklaus and Adrian Jörg annotated the binarized judgment outcomes. Metadata is published by the Swiss Federal Supreme Court (https://www.bger.ch). The group of legal experts consists of Thomas Lüthi (lawyer), Lynn Grau (law student at master's level) and Angela Stefanelli (law student at master's level).
### Personal and Sensitive Information
The dataset contains publicly available court decisions from the Swiss Federal Supreme Court. Personal or sensitive information has been anonymized by the court before publication according to the following guidelines: https://www.bger.ch/home/juridiction/anonymisierungsregeln.html.
## Additional Information
### Dataset Curators
Niklaus et al. (2021) and Nina Baumgartner
### Licensing Information
We release the data under CC-BY-4.0 which complies with the court licensing (https://www.bger.ch/files/live/sites/bger/files/pdf/de/urteilsveroeffentlichung_d.pdf)
© Swiss Federal Supreme Court, 2000-2020
The copyright for the editorial content of this website and the consolidated texts, which is owned by the Swiss Federal Supreme Court, is licensed under the Creative Commons Attribution 4.0 International licence. This means that you can re-use the content provided you acknowledge the source and indicate any changes you have made.
Source: https://www.bger.ch/files/live/sites/bger/files/pdf/de/urteilsveroeffentlichung_d.pdf
### Citation Information
```
@misc{baumgartner_nina_occlusion_2022,
title = {From Occlusion to Transparancy – An Occlusion-Based Explainability Approach for Legal Judgment Prediction in Switzerland},
shorttitle = {From Occlusion to Transparancy},
abstract = {Natural Language Processing ({NLP}) models have been used for more and more complex tasks such as Legal Judgment Prediction ({LJP}). A {LJP} model predicts the outcome of a legal case by utilizing its facts. This increasing deployment of Artificial Intelligence ({AI}) in high-stakes domains such as law and the involvement of sensitive data has increased the need for understanding such systems. We propose a multilingual occlusion-based explainability approach for {LJP} in Switzerland and conduct a study on the bias using Lower Court Insertion ({LCI}). We evaluate our results using different explainability metrics introduced in this thesis and by comparing them to high-quality Legal Expert Annotations using Inter Annotator Agreement. Our findings show that the model has a varying understanding of the semantic meaning and context of the facts section, and struggles to distinguish between legally relevant and irrelevant sentences. We also found that the insertion of a different lower court can have an effect on the prediction, but observed no distinct effects based on legal areas, cantons, or regions. However, we did identify a language disparity with Italian performing worse than the other languages due to representation inequality in the training data, which could lead to potential biases in the prediction in multilingual regions of Switzerland. Our results highlight the challenges and limitations of using {NLP} in the judicial field and the importance of addressing concerns about fairness, transparency, and potential bias in the development and use of {NLP} systems. The use of explainable artificial intelligence ({XAI}) techniques, such as occlusion and {LCI}, can help provide insight into the decision-making processes of {NLP} systems and identify areas for improvement. Finally, we identify areas for future research and development in this field in order to address the remaining limitations and challenges.},
author = {{Baumgartner, Nina}},
year = {2022},
langid = {english}
}
```
### Contributions
Thanks to [@ninabaumgartner](https://github.com/ninabaumgartner) for adding this dataset. | rcds/occlusion_swiss_judgment_prediction | [
"task_categories:text-classification",
"task_categories:other",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"language_creators:found",
"multilinguality:multilingual",
"size_categories:1K<n<10K",
"source_datasets:extended|swiss_judgment_prediction",
"language:de",
"language:fr",
"language:it",
"language:en",
"license:cc-by-sa-4.0",
"explainability-judgment-prediction",
"occlusion",
"region:us"
]
| 2023-03-08T20:14:10+00:00 | {"annotations_creators": ["expert-generated"], "language_creators": ["expert-generated", "found"], "language": ["de", "fr", "it", "en"], "license": "cc-by-sa-4.0", "multilinguality": ["multilingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["extended|swiss_judgment_prediction"], "task_categories": ["text-classification", "other"], "task_ids": [], "pretty_name": "OcclusionSwissJudgmentPrediction", "tags": ["explainability-judgment-prediction", "occlusion"]} | 2023-03-28T07:19:29+00:00 | []
| [
"de",
"fr",
"it",
"en"
]
| TAGS
#task_categories-text-classification #task_categories-other #annotations_creators-expert-generated #language_creators-expert-generated #language_creators-found #multilinguality-multilingual #size_categories-1K<n<10K #source_datasets-extended|swiss_judgment_prediction #language-German #language-French #language-Italian #language-English #license-cc-by-sa-4.0 #explainability-judgment-prediction #occlusion #region-us
| Dataset Card for "OcclusionSwissJudgmentPrediction": An implementation of an occlusion based explainability method for Swiss judgment prediction
================================================================================================================================================
Table of Contents
-----------------
* Table of Contents
* Dataset Summary
+ Documents
+ Supported Tasks and Leaderboards
+ Languages
* Dataset structure
+ Data Instances
+ Data Fields
+ Data Splits
* Dataset Creation
+ Curation Rationale
+ Source Data
+ Annotations
+ Personal and Sensitive Information
* Additional Information
+ Dataset Curators
+ Licensing Information
+ Citation Information
+ Contributions
Dataset Summary
---------------
This dataset contains an implementation of occlusion for the SwissJudgmentPrediction task.
Note that this dataset only provides a test set and should be used in comination with the Swiss-Judgment-Prediction dataset.
### Documents
Occlusion-Swiss-Judgment-Prediction is a subset of the Swiss-Judgment-Prediction dataset.
The Swiss-Judgment-Prediction dataset is a multilingual, diachronic dataset of 85K Swiss Federal Supreme Court (FSCS) cases annotated with the respective binarized judgment outcome (approval/dismissal), the publication year, the legal area and the canton of origin per case. Occlusion-Swiss-Judgment-Prediction extends this dataset by adding sentence splitting with explainability labels.
### Supported Tasks and Leaderboards
OcclusionSwissJudgmentPrediction can be used for performing the occlusion in the legal judgment prediction task.
### Languages
Switzerland has four official languages with 3 languages (German, French and Italian) being represented in more than 1000 Swiss Federal Supreme court decisions. The decisions are written by the judges and clerks in the language of the proceedings.
Dataset structure
-----------------
### Data Instances
Data Instances
--------------
Multilingual use of the dataset
When the dataset is used in a multilingual setting selecting the the 'all\_languages' flag:
Monolingual use of the dataset
When the dataset is used in a monolingual setting selecting the ISO language code for one of the 3 supported languages. For example:
### Data Fields
The following data fields are provided for documents (Test\_1/Test\_2/Test\_3/Test\_4):
id: (int) a unique identifier of the for the document
year: (int) the publication year
label: (str) the judgment outcome: dismissal or approval
language: (str) one of (de, fr, it)
region: (str) the region of the lower court
canton: (str) the canton of the lower court
legal area: (str) the legal area of the case
explainability\_label (str): the explainability label assigned to the occluded text: Supports judgment, Opposes judgment, Neutral, Baseline
occluded\_text (str): the occluded text
text: (str) the facts of the case w/o the occluded text except for cases w/ explainability label "Baseline" (contain entire facts)
Note that Baseline cases are only contained in version 1 of the occlusion test set, since they do not change from experiment to experiment.
### Data Splits (Including Swiss Judgment Prediction)
Language: German, Subset: de, Number of Rows (Test\_1/Test\_2/Test\_3/Test\_4): **427** / **1366** / **3567** / **7235**
Language: French, Subset: fr, Number of Rows (Test\_1/Test\_2/Test\_3/Test\_4): **307** / **854** / **1926** / **3279**
Language: Italian, Subset: it, Number of Rows (Test\_1/Test\_2/Test\_3/Test\_4): **299** /**919** / **2493** / **5733**
Language: All, Subset: all, Number of Rows (Test\_1/Test\_2/Test\_3/Test\_4): **1033** / **3139** / **7986**/ **16247**
Language: German, Subset: de, Number of Documents (is the same for Test\_1/Test\_2/Test\_3/Test\_4): **38**
Language: French, Subset: fr, Number of Documents (is the same for Test\_1/Test\_2/Test\_3/Test\_4): **36**
Language: Italian, Subset: it, Number of Documents (is the same for Test\_1/Test\_2/Test\_3/Test\_4): **34**
Language: All, Subset: all, Number of Documents (is the same for Test\_1/Test\_2/Test\_3/Test\_4): **108**
Dataset Creation
----------------
### Curation Rationale
The dataset was curated by Niklaus et al. (2021) and Nina Baumgartner.
### Source Data
#### Initial Data Collection and Normalization
The original data are available at the Swiss Federal Supreme Court (URL) in unprocessed formats (HTML). The documents were downloaded from the Entscheidsuche portal (URL) in HTML.
#### Who are the source language producers?
Switzerland has four official languages with 3 languages (German, French and Italian) being represented in more than 1000 Swiss Federal Supreme court decisions. The decisions are written by the judges and clerks in the language of the proceedings.
### Annotations
#### Annotation process
The decisions have been annotated with the binarized judgment outcome using parsers and regular expressions. In addition a subset of the test set (27 cases in German, 24 in French and 23 in Italian spanning over the years 2017 an 20200) was annotated by legal experts, splitting sentences/group of sentences and annotated with one of the following explainability label: Supports judgment, Opposes Judgment and Neutral. The test sets have each sentence/ group of sentence once occluded, enabling an analysis of the changes in the model's performance. The legal expert annotation were conducted from April 2020 to August 2020.
#### Who are the annotators?
Joel Niklaus and Adrian Jörg annotated the binarized judgment outcomes. Metadata is published by the Swiss Federal Supreme Court (URL). The group of legal experts consists of Thomas Lüthi (lawyer), Lynn Grau (law student at master's level) and Angela Stefanelli (law student at master's level).
### Personal and Sensitive Information
The dataset contains publicly available court decisions from the Swiss Federal Supreme Court. Personal or sensitive information has been anonymized by the court before publication according to the following guidelines: URL
Additional Information
----------------------
### Dataset Curators
Niklaus et al. (2021) and Nina Baumgartner
### Licensing Information
We release the data under CC-BY-4.0 which complies with the court licensing (URL
© Swiss Federal Supreme Court, 2000-2020
The copyright for the editorial content of this website and the consolidated texts, which is owned by the Swiss Federal Supreme Court, is licensed under the Creative Commons Attribution 4.0 International licence. This means that you can re-use the content provided you acknowledge the source and indicate any changes you have made.
Source: URL
### Contributions
Thanks to @ninabaumgartner for adding this dataset.
| [
"### Documents\n\n\nOcclusion-Swiss-Judgment-Prediction is a subset of the Swiss-Judgment-Prediction dataset.\nThe Swiss-Judgment-Prediction dataset is a multilingual, diachronic dataset of 85K Swiss Federal Supreme Court (FSCS) cases annotated with the respective binarized judgment outcome (approval/dismissal), the publication year, the legal area and the canton of origin per case. Occlusion-Swiss-Judgment-Prediction extends this dataset by adding sentence splitting with explainability labels.",
"### Supported Tasks and Leaderboards\n\n\nOcclusionSwissJudgmentPrediction can be used for performing the occlusion in the legal judgment prediction task.",
"### Languages\n\n\nSwitzerland has four official languages with 3 languages (German, French and Italian) being represented in more than 1000 Swiss Federal Supreme court decisions. The decisions are written by the judges and clerks in the language of the proceedings.\n\n\nDataset structure\n-----------------",
"### Data Instances\n\n\nData Instances\n--------------\n\n\nMultilingual use of the dataset\n\n\nWhen the dataset is used in a multilingual setting selecting the the 'all\\_languages' flag:\n\n\nMonolingual use of the dataset\n\n\nWhen the dataset is used in a monolingual setting selecting the ISO language code for one of the 3 supported languages. For example:",
"### Data Fields\n\n\nThe following data fields are provided for documents (Test\\_1/Test\\_2/Test\\_3/Test\\_4):\n\n\nid: (int) a unique identifier of the for the document \n\nyear: (int) the publication year \n\nlabel: (str) the judgment outcome: dismissal or approval \n\nlanguage: (str) one of (de, fr, it) \n\nregion: (str) the region of the lower court \n\ncanton: (str) the canton of the lower court \n\nlegal area: (str) the legal area of the case \n\nexplainability\\_label (str): the explainability label assigned to the occluded text: Supports judgment, Opposes judgment, Neutral, Baseline \n\noccluded\\_text (str): the occluded text \n\ntext: (str) the facts of the case w/o the occluded text except for cases w/ explainability label \"Baseline\" (contain entire facts) \n\n\n\nNote that Baseline cases are only contained in version 1 of the occlusion test set, since they do not change from experiment to experiment.",
"### Data Splits (Including Swiss Judgment Prediction)\n\n\nLanguage: German, Subset: de, Number of Rows (Test\\_1/Test\\_2/Test\\_3/Test\\_4): **427** / **1366** / **3567** / **7235**\nLanguage: French, Subset: fr, Number of Rows (Test\\_1/Test\\_2/Test\\_3/Test\\_4): **307** / **854** / **1926** / **3279**\nLanguage: Italian, Subset: it, Number of Rows (Test\\_1/Test\\_2/Test\\_3/Test\\_4): **299** /**919** / **2493** / **5733**\nLanguage: All, Subset: all, Number of Rows (Test\\_1/Test\\_2/Test\\_3/Test\\_4): **1033** / **3139** / **7986**/ **16247**\n\n\nLanguage: German, Subset: de, Number of Documents (is the same for Test\\_1/Test\\_2/Test\\_3/Test\\_4): **38**\nLanguage: French, Subset: fr, Number of Documents (is the same for Test\\_1/Test\\_2/Test\\_3/Test\\_4): **36**\nLanguage: Italian, Subset: it, Number of Documents (is the same for Test\\_1/Test\\_2/Test\\_3/Test\\_4): **34**\nLanguage: All, Subset: all, Number of Documents (is the same for Test\\_1/Test\\_2/Test\\_3/Test\\_4): **108**\n\n\nDataset Creation\n----------------",
"### Curation Rationale\n\n\nThe dataset was curated by Niklaus et al. (2021) and Nina Baumgartner.",
"### Source Data",
"#### Initial Data Collection and Normalization\n\n\nThe original data are available at the Swiss Federal Supreme Court (URL) in unprocessed formats (HTML). The documents were downloaded from the Entscheidsuche portal (URL) in HTML.",
"#### Who are the source language producers?\n\n\nSwitzerland has four official languages with 3 languages (German, French and Italian) being represented in more than 1000 Swiss Federal Supreme court decisions. The decisions are written by the judges and clerks in the language of the proceedings.",
"### Annotations",
"#### Annotation process\n\n\nThe decisions have been annotated with the binarized judgment outcome using parsers and regular expressions. In addition a subset of the test set (27 cases in German, 24 in French and 23 in Italian spanning over the years 2017 an 20200) was annotated by legal experts, splitting sentences/group of sentences and annotated with one of the following explainability label: Supports judgment, Opposes Judgment and Neutral. The test sets have each sentence/ group of sentence once occluded, enabling an analysis of the changes in the model's performance. The legal expert annotation were conducted from April 2020 to August 2020.",
"#### Who are the annotators?\n\n\nJoel Niklaus and Adrian Jörg annotated the binarized judgment outcomes. Metadata is published by the Swiss Federal Supreme Court (URL). The group of legal experts consists of Thomas Lüthi (lawyer), Lynn Grau (law student at master's level) and Angela Stefanelli (law student at master's level).",
"### Personal and Sensitive Information\n\n\nThe dataset contains publicly available court decisions from the Swiss Federal Supreme Court. Personal or sensitive information has been anonymized by the court before publication according to the following guidelines: URL\n\n\nAdditional Information\n----------------------",
"### Dataset Curators\n\n\nNiklaus et al. (2021) and Nina Baumgartner",
"### Licensing Information\n\n\nWe release the data under CC-BY-4.0 which complies with the court licensing (URL\n\n\n© Swiss Federal Supreme Court, 2000-2020\n\n\nThe copyright for the editorial content of this website and the consolidated texts, which is owned by the Swiss Federal Supreme Court, is licensed under the Creative Commons Attribution 4.0 International licence. This means that you can re-use the content provided you acknowledge the source and indicate any changes you have made.\n\n\nSource: URL",
"### Contributions\n\n\nThanks to @ninabaumgartner for adding this dataset."
]
| [
"TAGS\n#task_categories-text-classification #task_categories-other #annotations_creators-expert-generated #language_creators-expert-generated #language_creators-found #multilinguality-multilingual #size_categories-1K<n<10K #source_datasets-extended|swiss_judgment_prediction #language-German #language-French #language-Italian #language-English #license-cc-by-sa-4.0 #explainability-judgment-prediction #occlusion #region-us \n",
"### Documents\n\n\nOcclusion-Swiss-Judgment-Prediction is a subset of the Swiss-Judgment-Prediction dataset.\nThe Swiss-Judgment-Prediction dataset is a multilingual, diachronic dataset of 85K Swiss Federal Supreme Court (FSCS) cases annotated with the respective binarized judgment outcome (approval/dismissal), the publication year, the legal area and the canton of origin per case. Occlusion-Swiss-Judgment-Prediction extends this dataset by adding sentence splitting with explainability labels.",
"### Supported Tasks and Leaderboards\n\n\nOcclusionSwissJudgmentPrediction can be used for performing the occlusion in the legal judgment prediction task.",
"### Languages\n\n\nSwitzerland has four official languages with 3 languages (German, French and Italian) being represented in more than 1000 Swiss Federal Supreme court decisions. The decisions are written by the judges and clerks in the language of the proceedings.\n\n\nDataset structure\n-----------------",
"### Data Instances\n\n\nData Instances\n--------------\n\n\nMultilingual use of the dataset\n\n\nWhen the dataset is used in a multilingual setting selecting the the 'all\\_languages' flag:\n\n\nMonolingual use of the dataset\n\n\nWhen the dataset is used in a monolingual setting selecting the ISO language code for one of the 3 supported languages. For example:",
"### Data Fields\n\n\nThe following data fields are provided for documents (Test\\_1/Test\\_2/Test\\_3/Test\\_4):\n\n\nid: (int) a unique identifier of the for the document \n\nyear: (int) the publication year \n\nlabel: (str) the judgment outcome: dismissal or approval \n\nlanguage: (str) one of (de, fr, it) \n\nregion: (str) the region of the lower court \n\ncanton: (str) the canton of the lower court \n\nlegal area: (str) the legal area of the case \n\nexplainability\\_label (str): the explainability label assigned to the occluded text: Supports judgment, Opposes judgment, Neutral, Baseline \n\noccluded\\_text (str): the occluded text \n\ntext: (str) the facts of the case w/o the occluded text except for cases w/ explainability label \"Baseline\" (contain entire facts) \n\n\n\nNote that Baseline cases are only contained in version 1 of the occlusion test set, since they do not change from experiment to experiment.",
"### Data Splits (Including Swiss Judgment Prediction)\n\n\nLanguage: German, Subset: de, Number of Rows (Test\\_1/Test\\_2/Test\\_3/Test\\_4): **427** / **1366** / **3567** / **7235**\nLanguage: French, Subset: fr, Number of Rows (Test\\_1/Test\\_2/Test\\_3/Test\\_4): **307** / **854** / **1926** / **3279**\nLanguage: Italian, Subset: it, Number of Rows (Test\\_1/Test\\_2/Test\\_3/Test\\_4): **299** /**919** / **2493** / **5733**\nLanguage: All, Subset: all, Number of Rows (Test\\_1/Test\\_2/Test\\_3/Test\\_4): **1033** / **3139** / **7986**/ **16247**\n\n\nLanguage: German, Subset: de, Number of Documents (is the same for Test\\_1/Test\\_2/Test\\_3/Test\\_4): **38**\nLanguage: French, Subset: fr, Number of Documents (is the same for Test\\_1/Test\\_2/Test\\_3/Test\\_4): **36**\nLanguage: Italian, Subset: it, Number of Documents (is the same for Test\\_1/Test\\_2/Test\\_3/Test\\_4): **34**\nLanguage: All, Subset: all, Number of Documents (is the same for Test\\_1/Test\\_2/Test\\_3/Test\\_4): **108**\n\n\nDataset Creation\n----------------",
"### Curation Rationale\n\n\nThe dataset was curated by Niklaus et al. (2021) and Nina Baumgartner.",
"### Source Data",
"#### Initial Data Collection and Normalization\n\n\nThe original data are available at the Swiss Federal Supreme Court (URL) in unprocessed formats (HTML). The documents were downloaded from the Entscheidsuche portal (URL) in HTML.",
"#### Who are the source language producers?\n\n\nSwitzerland has four official languages with 3 languages (German, French and Italian) being represented in more than 1000 Swiss Federal Supreme court decisions. The decisions are written by the judges and clerks in the language of the proceedings.",
"### Annotations",
"#### Annotation process\n\n\nThe decisions have been annotated with the binarized judgment outcome using parsers and regular expressions. In addition a subset of the test set (27 cases in German, 24 in French and 23 in Italian spanning over the years 2017 an 20200) was annotated by legal experts, splitting sentences/group of sentences and annotated with one of the following explainability label: Supports judgment, Opposes Judgment and Neutral. The test sets have each sentence/ group of sentence once occluded, enabling an analysis of the changes in the model's performance. The legal expert annotation were conducted from April 2020 to August 2020.",
"#### Who are the annotators?\n\n\nJoel Niklaus and Adrian Jörg annotated the binarized judgment outcomes. Metadata is published by the Swiss Federal Supreme Court (URL). The group of legal experts consists of Thomas Lüthi (lawyer), Lynn Grau (law student at master's level) and Angela Stefanelli (law student at master's level).",
"### Personal and Sensitive Information\n\n\nThe dataset contains publicly available court decisions from the Swiss Federal Supreme Court. Personal or sensitive information has been anonymized by the court before publication according to the following guidelines: URL\n\n\nAdditional Information\n----------------------",
"### Dataset Curators\n\n\nNiklaus et al. (2021) and Nina Baumgartner",
"### Licensing Information\n\n\nWe release the data under CC-BY-4.0 which complies with the court licensing (URL\n\n\n© Swiss Federal Supreme Court, 2000-2020\n\n\nThe copyright for the editorial content of this website and the consolidated texts, which is owned by the Swiss Federal Supreme Court, is licensed under the Creative Commons Attribution 4.0 International licence. This means that you can re-use the content provided you acknowledge the source and indicate any changes you have made.\n\n\nSource: URL",
"### Contributions\n\n\nThanks to @ninabaumgartner for adding this dataset."
]
| [
147,
145,
42,
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84,
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374,
27,
4,
50,
60,
5,
146,
79,
52,
18,
100,
19
]
| [
"passage: TAGS\n#task_categories-text-classification #task_categories-other #annotations_creators-expert-generated #language_creators-expert-generated #language_creators-found #multilinguality-multilingual #size_categories-1K<n<10K #source_datasets-extended|swiss_judgment_prediction #language-German #language-French #language-Italian #language-English #license-cc-by-sa-4.0 #explainability-judgment-prediction #occlusion #region-us \n### Documents\n\n\nOcclusion-Swiss-Judgment-Prediction is a subset of the Swiss-Judgment-Prediction dataset.\nThe Swiss-Judgment-Prediction dataset is a multilingual, diachronic dataset of 85K Swiss Federal Supreme Court (FSCS) cases annotated with the respective binarized judgment outcome (approval/dismissal), the publication year, the legal area and the canton of origin per case. Occlusion-Swiss-Judgment-Prediction extends this dataset by adding sentence splitting with explainability labels.### Supported Tasks and Leaderboards\n\n\nOcclusionSwissJudgmentPrediction can be used for performing the occlusion in the legal judgment prediction task.### Languages\n\n\nSwitzerland has four official languages with 3 languages (German, French and Italian) being represented in more than 1000 Swiss Federal Supreme court decisions. The decisions are written by the judges and clerks in the language of the proceedings.\n\n\nDataset structure\n-----------------### Data Instances\n\n\nData Instances\n--------------\n\n\nMultilingual use of the dataset\n\n\nWhen the dataset is used in a multilingual setting selecting the the 'all\\_languages' flag:\n\n\nMonolingual use of the dataset\n\n\nWhen the dataset is used in a monolingual setting selecting the ISO language code for one of the 3 supported languages. For example:",
"passage: ### Data Fields\n\n\nThe following data fields are provided for documents (Test\\_1/Test\\_2/Test\\_3/Test\\_4):\n\n\nid: (int) a unique identifier of the for the document \n\nyear: (int) the publication year \n\nlabel: (str) the judgment outcome: dismissal or approval \n\nlanguage: (str) one of (de, fr, it) \n\nregion: (str) the region of the lower court \n\ncanton: (str) the canton of the lower court \n\nlegal area: (str) the legal area of the case \n\nexplainability\\_label (str): the explainability label assigned to the occluded text: Supports judgment, Opposes judgment, Neutral, Baseline \n\noccluded\\_text (str): the occluded text \n\ntext: (str) the facts of the case w/o the occluded text except for cases w/ explainability label \"Baseline\" (contain entire facts) \n\n\n\nNote that Baseline cases are only contained in version 1 of the occlusion test set, since they do not change from experiment to experiment.### Data Splits (Including Swiss Judgment Prediction)\n\n\nLanguage: German, Subset: de, Number of Rows (Test\\_1/Test\\_2/Test\\_3/Test\\_4): **427** / **1366** / **3567** / **7235**\nLanguage: French, Subset: fr, Number of Rows (Test\\_1/Test\\_2/Test\\_3/Test\\_4): **307** / **854** / **1926** / **3279**\nLanguage: Italian, Subset: it, Number of Rows (Test\\_1/Test\\_2/Test\\_3/Test\\_4): **299** /**919** / **2493** / **5733**\nLanguage: All, Subset: all, Number of Rows (Test\\_1/Test\\_2/Test\\_3/Test\\_4): **1033** / **3139** / **7986**/ **16247**\n\n\nLanguage: German, Subset: de, Number of Documents (is the same for Test\\_1/Test\\_2/Test\\_3/Test\\_4): **38**\nLanguage: French, Subset: fr, Number of Documents (is the same for Test\\_1/Test\\_2/Test\\_3/Test\\_4): **36**\nLanguage: Italian, Subset: it, Number of Documents (is the same for Test\\_1/Test\\_2/Test\\_3/Test\\_4): **34**\nLanguage: All, Subset: all, Number of Documents (is the same for Test\\_1/Test\\_2/Test\\_3/Test\\_4): **108**\n\n\nDataset Creation\n----------------### Curation Rationale\n\n\nThe dataset was curated by Niklaus et al. (2021) and Nina Baumgartner.### Source Data#### Initial Data Collection and Normalization\n\n\nThe original data are available at the Swiss Federal Supreme Court (URL) in unprocessed formats (HTML). The documents were downloaded from the Entscheidsuche portal (URL) in HTML."
]
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|
d2cde298e79c94fb05bc320999deb4b7889b0464 | # Dataset Card for Invoices (Sparrow)
This dataset contains 500 invoice documents annotated and processed to be ready for Donut ML model fine-tuning.
Annotation and data preparation task was done by [Katana ML](https://www.katanaml.io) team.
[Sparrow](https://github.com/katanaml/sparrow/tree/main) - open-source data extraction solution by Katana ML.
Original dataset [info](https://data.mendeley.com/datasets/tnj49gpmtz): Kozłowski, Marek; Weichbroth, Paweł (2021), “Samples of electronic invoices”, Mendeley Data, V2, doi: 10.17632/tnj49gpmtz.2 | katanaml-org/invoices-donut-data-v1 | [
"task_categories:feature-extraction",
"size_categories:n<1K",
"language:en",
"license:mit",
"region:us"
]
| 2023-03-08T20:44:29+00:00 | {"language": ["en"], "license": "mit", "size_categories": ["n<1K"], "task_categories": ["feature-extraction"], "pretty_name": "Sparrow Invoice Dataset", "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "ground_truth", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 234024421, "num_examples": 425}, {"name": "test", "num_bytes": 14512665, "num_examples": 26}, {"name": "validation", "num_bytes": 27661738, "num_examples": 50}], "download_size": 197512750, "dataset_size": 276198824}} | 2023-05-09T06:05:11+00:00 | []
| [
"en"
]
| TAGS
#task_categories-feature-extraction #size_categories-n<1K #language-English #license-mit #region-us
| # Dataset Card for Invoices (Sparrow)
This dataset contains 500 invoice documents annotated and processed to be ready for Donut ML model fine-tuning.
Annotation and data preparation task was done by Katana ML team.
Sparrow - open-source data extraction solution by Katana ML.
Original dataset info: Kozłowski, Marek; Weichbroth, Paweł (2021), “Samples of electronic invoices”, Mendeley Data, V2, doi: 10.17632/tnj49gpmtz.2 | [
"# Dataset Card for Invoices (Sparrow)\n\n\nThis dataset contains 500 invoice documents annotated and processed to be ready for Donut ML model fine-tuning.\n\nAnnotation and data preparation task was done by Katana ML team.\n\nSparrow - open-source data extraction solution by Katana ML.\n\nOriginal dataset info: Kozłowski, Marek; Weichbroth, Paweł (2021), “Samples of electronic invoices”, Mendeley Data, V2, doi: 10.17632/tnj49gpmtz.2"
]
| [
"TAGS\n#task_categories-feature-extraction #size_categories-n<1K #language-English #license-mit #region-us \n",
"# Dataset Card for Invoices (Sparrow)\n\n\nThis dataset contains 500 invoice documents annotated and processed to be ready for Donut ML model fine-tuning.\n\nAnnotation and data preparation task was done by Katana ML team.\n\nSparrow - open-source data extraction solution by Katana ML.\n\nOriginal dataset info: Kozłowski, Marek; Weichbroth, Paweł (2021), “Samples of electronic invoices”, Mendeley Data, V2, doi: 10.17632/tnj49gpmtz.2"
]
| [
37,
126
]
| [
"passage: TAGS\n#task_categories-feature-extraction #size_categories-n<1K #language-English #license-mit #region-us \n# Dataset Card for Invoices (Sparrow)\n\n\nThis dataset contains 500 invoice documents annotated and processed to be ready for Donut ML model fine-tuning.\n\nAnnotation and data preparation task was done by Katana ML team.\n\nSparrow - open-source data extraction solution by Katana ML.\n\nOriginal dataset info: Kozłowski, Marek; Weichbroth, Paweł (2021), “Samples of electronic invoices”, Mendeley Data, V2, doi: 10.17632/tnj49gpmtz.2"
]
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|
fb3da03f110264cd891d81eb6f69545904de38f6 | # Dataset Card for "nyt_headlines"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | pnadel/nyt_headlines | [
"region:us"
]
| 2023-03-08T21:15:37+00:00 | {"dataset_info": {"features": [{"name": "headline", "dtype": "string"}, {"name": "label", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 7115360, "num_examples": 92285}], "download_size": 4519003, "dataset_size": 7115360}} | 2023-03-08T21:15:46+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "nyt_headlines"
More Information needed | [
"# Dataset Card for \"nyt_headlines\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"nyt_headlines\"\n\nMore Information needed"
]
| [
6,
14
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"nyt_headlines\"\n\nMore Information needed"
]
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|
594dee62efbadabf0ca69d5f45098d1d3dfbc298 | # Dataset Card for "back_translation_fr_on_small_persian_QA"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | jalalnb/back_translation_fr_on_small_persian_QA | [
"region:us"
]
| 2023-03-08T21:32:57+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "int32"}, {"name": "title", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answers", "sequence": [{"name": "text", "dtype": "string"}, {"name": "answer_start", "dtype": "int32"}]}], "splits": [{"name": "validation", "num_bytes": 262697, "num_examples": 130}, {"name": "train", "num_bytes": 2553488, "num_examples": 1261}], "download_size": 88831, "dataset_size": 2816185}} | 2023-03-10T16:57:42+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "back_translation_fr_on_small_persian_QA"
More Information needed | [
"# Dataset Card for \"back_translation_fr_on_small_persian_QA\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"back_translation_fr_on_small_persian_QA\"\n\nMore Information needed"
]
| [
6,
26
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"back_translation_fr_on_small_persian_QA\"\n\nMore Information needed"
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|
42933e7d3129501465825a610b5d14615612c824 | # Dataset Card for "back_translation_en_on_small_persian_QA"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | jalalnb/back_translation_en_on_small_persian_QA | [
"region:us"
]
| 2023-03-08T21:33:13+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "int32"}, {"name": "title", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answers", "sequence": [{"name": "text", "dtype": "string"}, {"name": "answer_start", "dtype": "int32"}]}], "splits": [{"name": "validation", "num_bytes": 262797, "num_examples": 130}, {"name": "train", "num_bytes": 2553868, "num_examples": 1261}], "download_size": 1043078, "dataset_size": 2816665}} | 2023-03-08T21:33:28+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "back_translation_en_on_small_persian_QA"
More Information needed | [
"# Dataset Card for \"back_translation_en_on_small_persian_QA\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"back_translation_en_on_small_persian_QA\"\n\nMore Information needed"
]
| [
6,
26
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"back_translation_en_on_small_persian_QA\"\n\nMore Information needed"
]
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|
d4af8a28c0857059ea85f00a8a2f69a7d7b949fa | # Dataset Card for "back_translation_hy_on_small_persian_QA"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | jalalnb/back_translation_hy_on_small_persian_QA | [
"region:us"
]
| 2023-03-08T21:33:29+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "int32"}, {"name": "title", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answers", "sequence": [{"name": "text", "dtype": "string"}, {"name": "answer_start", "dtype": "int32"}]}], "splits": [{"name": "validation", "num_bytes": 262683, "num_examples": 130}, {"name": "train", "num_bytes": 2552922, "num_examples": 1261}], "download_size": 1042907, "dataset_size": 2815605}} | 2023-03-08T21:33:44+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "back_translation_hy_on_small_persian_QA"
More Information needed | [
"# Dataset Card for \"back_translation_hy_on_small_persian_QA\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"back_translation_hy_on_small_persian_QA\"\n\nMore Information needed"
]
| [
6,
26
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"back_translation_hy_on_small_persian_QA\"\n\nMore Information needed"
]
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|
c4bb665268b07bb33c3fc70475c9eb2047cfe9e2 | # Dataset Card for "self-instruct-eval"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | argilla/self-instruct-eval | [
"region:us"
]
| 2023-03-08T23:00:35+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "null"}, {"name": "inputs", "struct": [{"name": "input", "dtype": "string"}, {"name": "response", "dtype": "string"}]}, {"name": "prediction", "dtype": "null"}, {"name": "prediction_agent", "dtype": "null"}, {"name": "annotation", "dtype": "null"}, {"name": "annotation_agent", "dtype": "null"}, {"name": "vectors", "struct": [{"name": "completion", "sequence": "float64"}, {"name": "prompt", "sequence": "float64"}]}, {"name": "multi_label", "dtype": "bool"}, {"name": "explanation", "dtype": "null"}, {"name": "id", "dtype": "null"}, {"name": "metadata", "dtype": "null"}, {"name": "status", "dtype": "string"}, {"name": "event_timestamp", "dtype": "timestamp[us]"}, {"name": "metrics", "dtype": "null"}], "splits": [{"name": "train", "num_bytes": 1037904569, "num_examples": 82612}], "download_size": 834389885, "dataset_size": 1037904569}} | 2023-03-09T00:02:04+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "self-instruct-eval"
More Information needed | [
"# Dataset Card for \"self-instruct-eval\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"self-instruct-eval\"\n\nMore Information needed"
]
| [
6,
17
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"self-instruct-eval\"\n\nMore Information needed"
]
| [
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|
933ef3a8ec0b20158197f3f41d4f5ae49fc1d990 | # Dataset Card for "vira-intents-live"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | codesj/vira-intents-live | [
"region:us"
]
| 2023-03-08T23:14:52+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "label", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 536982, "num_examples": 7434}, {"name": "validation", "num_bytes": 227106, "num_examples": 3140}], "download_size": 348952, "dataset_size": 764088}} | 2023-03-08T23:14:55+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "vira-intents-live"
More Information needed | [
"# Dataset Card for \"vira-intents-live\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"vira-intents-live\"\n\nMore Information needed"
]
| [
6,
16
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"vira-intents-live\"\n\nMore Information needed"
]
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|
01974f95a4c8ff221ac97374a25a53e1ac6527af | # Dataset Card for "miniwob_T5_balanced"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | LucasThil/miniwob_T5_balanced | [
"region:us"
]
| 2023-03-08T23:31:34+00:00 | {"dataset_info": {"features": [{"name": "episodes", "dtype": "string"}, {"name": "target_actions", "dtype": "string"}, {"name": "target_refs", "dtype": "int64"}, {"name": "target_text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 7287315, "num_examples": 14211}], "download_size": 1208349, "dataset_size": 7287315}} | 2023-03-08T23:54:01+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "miniwob_T5_balanced"
More Information needed | [
"# Dataset Card for \"miniwob_T5_balanced\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"miniwob_T5_balanced\"\n\nMore Information needed"
]
| [
6,
19
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"miniwob_T5_balanced\"\n\nMore Information needed"
]
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|
871857b06d74bd5d1f9609f516d3c1b9ffadf8a9 | # Dataset Card for "random-walk-reddit-corpus-small"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | dmayhem93/random-walk-reddit-corpus-small | [
"region:us"
]
| 2023-03-09T00:20:52+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 15525948, "num_examples": 8286}], "download_size": 8990634, "dataset_size": 15525948}} | 2023-03-09T00:25:14+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "random-walk-reddit-corpus-small"
More Information needed | [
"# Dataset Card for \"random-walk-reddit-corpus-small\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"random-walk-reddit-corpus-small\"\n\nMore Information needed"
]
| [
6,
23
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"random-walk-reddit-corpus-small\"\n\nMore Information needed"
]
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|
df214f1c3974ebd88e485390ced7ab64017bfca5 | # Dataset Card for "top-2-reddit-corpus-small"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | dmayhem93/top-2-reddit-corpus-small | [
"region:us"
]
| 2023-03-09T00:25:14+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 16545775, "num_examples": 8286}], "download_size": 9560714, "dataset_size": 16545775}} | 2023-03-09T00:25:20+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "top-2-reddit-corpus-small"
More Information needed | [
"# Dataset Card for \"top-2-reddit-corpus-small\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"top-2-reddit-corpus-small\"\n\nMore Information needed"
]
| [
6,
21
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"top-2-reddit-corpus-small\"\n\nMore Information needed"
]
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|
4a147e36c24e7afc4a4858afd3f6e3128870a929 | # Dataset Card for "lld-onlyicon-ko"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Babypotatotang/lld-onlyicon-ko | [
"region:us"
]
| 2023-03-09T00:42:04+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 196434759.578, "num_examples": 14959}, {"name": "test", "num_bytes": 49110770.04, "num_examples": 3740}], "download_size": 156811914, "dataset_size": 245545529.618}} | 2023-03-17T04:27:40+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "lld-onlyicon-ko"
More Information needed | [
"# Dataset Card for \"lld-onlyicon-ko\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"lld-onlyicon-ko\"\n\nMore Information needed"
]
| [
6,
19
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"lld-onlyicon-ko\"\n\nMore Information needed"
]
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|
888f2ffeb2398ae737ccc6e64cc7215e129436e7 |
# Dataset Card for Dataset Name
## Dataset Description
- **Homepage:** : https://explainthejoke.com/
### Dataset Summary
Corpus for testing whether your LLM can explain the joke well. But this is a rather small dataset, if someone can point to a larger ones would be very nice.
### Languages
English
## Dataset Structure
### Data Fields
* url : link to the explaination
* joke : the original joke
* explaination : the explaination of the joke
### Data Splits
Since its so small, there's no splits just like gsm8k | theblackcat102/joke_explaination | [
"task_categories:text-generation",
"task_categories:text2text-generation",
"size_categories:n<1K",
"language:en",
"license:mit",
"joke",
"high quality",
"region:us"
]
| 2023-03-09T02:29:11+00:00 | {"language": ["en"], "license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-generation", "text2text-generation"], "tags": ["joke", "high quality"]} | 2023-03-09T02:35:40+00:00 | []
| [
"en"
]
| TAGS
#task_categories-text-generation #task_categories-text2text-generation #size_categories-n<1K #language-English #license-mit #joke #high quality #region-us
|
# Dataset Card for Dataset Name
## Dataset Description
- Homepage: : URL
### Dataset Summary
Corpus for testing whether your LLM can explain the joke well. But this is a rather small dataset, if someone can point to a larger ones would be very nice.
### Languages
English
## Dataset Structure
### Data Fields
* url : link to the explaination
* joke : the original joke
* explaination : the explaination of the joke
### Data Splits
Since its so small, there's no splits just like gsm8k | [
"# Dataset Card for Dataset Name",
"## Dataset Description\n\n- Homepage: : URL",
"### Dataset Summary\n\nCorpus for testing whether your LLM can explain the joke well. But this is a rather small dataset, if someone can point to a larger ones would be very nice.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Fields\n\n* url : link to the explaination\n\n* joke : the original joke\n\n* explaination : the explaination of the joke",
"### Data Splits\n\nSince its so small, there's no splits just like gsm8k"
]
| [
"TAGS\n#task_categories-text-generation #task_categories-text2text-generation #size_categories-n<1K #language-English #license-mit #joke #high quality #region-us \n",
"# Dataset Card for Dataset Name",
"## Dataset Description\n\n- Homepage: : URL",
"### Dataset Summary\n\nCorpus for testing whether your LLM can explain the joke well. But this is a rather small dataset, if someone can point to a larger ones would be very nice.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Fields\n\n* url : link to the explaination\n\n* joke : the original joke\n\n* explaination : the explaination of the joke",
"### Data Splits\n\nSince its so small, there's no splits just like gsm8k"
]
| [
55,
8,
9,
41,
5,
6,
30,
23
]
| [
"passage: TAGS\n#task_categories-text-generation #task_categories-text2text-generation #size_categories-n<1K #language-English #license-mit #joke #high quality #region-us \n# Dataset Card for Dataset Name## Dataset Description\n\n- Homepage: : URL### Dataset Summary\n\nCorpus for testing whether your LLM can explain the joke well. But this is a rather small dataset, if someone can point to a larger ones would be very nice.### Languages\n\nEnglish## Dataset Structure### Data Fields\n\n* url : link to the explaination\n\n* joke : the original joke\n\n* explaination : the explaination of the joke### Data Splits\n\nSince its so small, there's no splits just like gsm8k"
]
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|
8f2485fff341fefe8061318c695961f65248ae8a | # Dataset Card for "aihub_food"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | MarkJeong/aihub_food | [
"region:us"
]
| 2023-03-09T02:39:58+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "01011001", "1": "01012001", "2": "01012002", "3": "01012003", "4": "01012004", "5": "01012005", "6": "01012006", "7": "01013001", "8": "01014008", "9": "01014009", "10": "01014010", "11": "01014011", "12": "01014012", "13": "01014013", "14": "01015002", "15": "01015003", "16": "01015012", "17": "01015013", "18": "01015014", "19": "01015015", "20": "01015016", "21": "01015017", "22": "01015018", "23": "01015019", "24": "01016001", "25": "01016002", "26": "01016003", "27": "01016004", "28": "01016005", "29": "01016006", "30": "01016007", "31": "01016008", "32": "02011006", "33": "02011007", "34": "02011008", "35": "02011009", "36": "02011010", "37": "02011011", "38": "02011012", "39": "02011013", "40": "02011014", "41": "02011015", "42": "02011016", "43": "02011017", "44": "02011018", "45": "02011019", "46": "02011020", "47": 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| []
| TAGS
#region-us
| # Dataset Card for "aihub_food"
More Information needed | [
"# Dataset Card for \"aihub_food\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"aihub_food\"\n\nMore Information needed"
]
| [
6,
14
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"aihub_food\"\n\nMore Information needed"
]
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|
72566a11ba24611f41bbbd314284b17b42dcb1ad | # Dataset Card for "spectrogram_data_Upbeat-4s"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | deepak-newzera/spectrogram_data_Upbeat-4s | [
"region:us"
]
| 2023-03-09T05:34:56+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "audio_file", "dtype": "string"}, {"name": "slice", "dtype": "int16"}], "splits": [{"name": "train", "num_bytes": 105472108.125, "num_examples": 3495}], "download_size": 104843147, "dataset_size": 105472108.125}} | 2023-03-09T05:36:06+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "spectrogram_data_Upbeat-4s"
More Information needed | [
"# Dataset Card for \"spectrogram_data_Upbeat-4s\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"spectrogram_data_Upbeat-4s\"\n\nMore Information needed"
]
| [
6,
20
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"spectrogram_data_Upbeat-4s\"\n\nMore Information needed"
]
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|
0c0d32d57da9c2335a868f2d6a2ff87a6982028f | # Dataset Card for "trainofasys"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Hantao/trainofasys | [
"region:us"
]
| 2023-03-09T05:46:33+00:00 | {"dataset_info": {"features": [{"name": "0", "dtype": "int64"}, {"name": "ocr", "dtype": "string"}, {"name": "caption", "dtype": "string"}, {"name": "image", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 204855956.375, "num_examples": 1325}], "download_size": 200935734, "dataset_size": 204855956.375}} | 2023-03-09T06:04:49+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "trainofasys"
More Information needed | [
"# Dataset Card for \"trainofasys\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"trainofasys\"\n\nMore Information needed"
]
| [
6,
14
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"trainofasys\"\n\nMore Information needed"
]
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|
736ed539e9da2b2779ddb92b7d5cf0ab8b087843 | # Dataset Card for "boolq_pt_r"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | reaganjlee/boolq_pt_r | [
"region:us"
]
| 2023-03-09T05:46:40+00:00 | {"dataset_info": {"features": [{"name": "question", "dtype": "string"}, {"name": "label (class label)", "dtype": "bool"}, {"name": "passage", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 6258250, "num_examples": 9427}], "download_size": 3947154, "dataset_size": 6258250}} | 2023-03-09T05:49:08+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "boolq_pt_r"
More Information needed | [
"# Dataset Card for \"boolq_pt_r\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"boolq_pt_r\"\n\nMore Information needed"
]
| [
6,
16
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"boolq_pt_r\"\n\nMore Information needed"
]
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|
0ad5607dce2d604aceb428f769b4e22cfa210b86 | **Label Description**
0 : Fake,
1 : Real | pushpdeep/fake_news_combined | [
"license:apache-2.0",
"region:us"
]
| 2023-03-09T06:04:04+00:00 | {"license": "apache-2.0"} | 2023-04-10T17:59:26+00:00 | []
| []
| TAGS
#license-apache-2.0 #region-us
| Label Description
0 : Fake,
1 : Real | []
| [
"TAGS\n#license-apache-2.0 #region-us \n"
]
| [
14
]
| [
"passage: TAGS\n#license-apache-2.0 #region-us \n"
]
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|
38282981ce7e57d081395c6771b625e58f20cabc | # Dataset Card for "boolq_pt"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | reaganjlee/boolq_pt | [
"region:us"
]
| 2023-03-09T06:15:08+00:00 | {"dataset_info": {"features": [{"name": "question", "dtype": "string"}, {"name": "passage", "dtype": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "False", "1": "True"}}}}], "splits": [{"name": "validation", "num_bytes": 1604091, "num_examples": 3270}, {"name": "train", "num_bytes": 4624752, "num_examples": 9427}], "download_size": 3843346, "dataset_size": 6228843}} | 2023-05-04T03:38:45+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "boolq_pt"
More Information needed | [
"# Dataset Card for \"boolq_pt\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"boolq_pt\"\n\nMore Information needed"
]
| [
6,
14
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"boolq_pt\"\n\nMore Information needed"
]
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|
145ff5e53cd4b5e3fbf3d0a2a74c3618ec7e30ec |
These recording and transcripts have been copied from the Russian President's website at kremlin.ru. All content on this site is licensed under Creative Commons Attribution 4.0 International.
http://en.kremlin.ru/about/copyrights
| spdenisov/prezident_ru | [
"license:cc-by-4.0",
"region:us"
]
| 2023-03-09T07:06:19+00:00 | {"license": "cc-by-4.0"} | 2023-03-10T18:16:28+00:00 | []
| []
| TAGS
#license-cc-by-4.0 #region-us
|
These recording and transcripts have been copied from the Russian President's website at URL. All content on this site is licensed under Creative Commons Attribution 4.0 International.
URL
| []
| [
"TAGS\n#license-cc-by-4.0 #region-us \n"
]
| [
15
]
| [
"passage: TAGS\n#license-cc-by-4.0 #region-us \n"
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|
9bf59ccccdbe271abb898b4466ad6f6469ddda10 | # Dataset Card for "github-code-scala"
This contains just the scala data in [github-code-clean](https://huggingface.co/datasets/codeparrot/github-code). There are 817k samples with a total download size of 1.52GB. | blastwind/github-code-scala | [
"task_categories:text-generation",
"size_categories:100K<n<1M",
"region:us"
]
| 2023-03-09T07:24:09+00:00 | {"size_categories": ["100K<n<1M"], "task_categories": ["text-generation"], "dataset_info": {"features": [{"name": "code", "dtype": "string"}, {"name": "repo_name", "dtype": "string"}, {"name": "path", "dtype": "string"}, {"name": "language", "dtype": "string"}, {"name": "license", "dtype": "string"}, {"name": "size", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 3330521484.4803743, "num_examples": 654001}, {"name": "valid", "num_bytes": 416314548.9934581, "num_examples": 81750}, {"name": "test", "num_bytes": 416319641.5261675, "num_examples": 81751}], "download_size": 1534670727, "dataset_size": 4163155675.0}} | 2023-03-21T19:19:22+00:00 | []
| []
| TAGS
#task_categories-text-generation #size_categories-100K<n<1M #region-us
| # Dataset Card for "github-code-scala"
This contains just the scala data in github-code-clean. There are 817k samples with a total download size of 1.52GB. | [
"# Dataset Card for \"github-code-scala\"\n\nThis contains just the scala data in github-code-clean. There are 817k samples with a total download size of 1.52GB."
]
| [
"TAGS\n#task_categories-text-generation #size_categories-100K<n<1M #region-us \n",
"# Dataset Card for \"github-code-scala\"\n\nThis contains just the scala data in github-code-clean. There are 817k samples with a total download size of 1.52GB."
]
| [
29,
47
]
| [
"passage: TAGS\n#task_categories-text-generation #size_categories-100K<n<1M #region-us \n# Dataset Card for \"github-code-scala\"\n\nThis contains just the scala data in github-code-clean. There are 817k samples with a total download size of 1.52GB."
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|
e11c39cd2de9fd1e2063e84e08bfbe1d6ea657da | # Dataset Card for "temp1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Oshan/temp1 | [
"region:us"
]
| 2023-03-09T07:53:05+00:00 | {"dataset_info": {"features": [{"name": "bnd_idcs", "sequence": {"sequence": "int64"}}, {"name": "atm_type", "sequence": "int64"}, {"name": "bnd_type", "sequence": "int64"}, {"name": "y", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 1869800, "num_examples": 2000}], "download_size": 130309, "dataset_size": 1869800}} | 2023-03-09T07:53:15+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "temp1"
More Information needed | [
"# Dataset Card for \"temp1\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"temp1\"\n\nMore Information needed"
]
| [
6,
12
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"temp1\"\n\nMore Information needed"
]
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|
783bd6ba635f64039dbfb36707273a7403da0e43 | # Dataset Card for "boolq_pt"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | christykoh/boolq_pt | [
"region:us"
]
| 2023-03-09T07:57:12+00:00 | {"dataset_info": {"features": [{"name": "question", "dtype": "string"}, {"name": "passage", "dtype": "string"}, {"name": "answer", "dtype": "bool"}], "splits": [{"name": "train", "num_bytes": 4550515, "num_examples": 9427}, {"name": "validation", "num_bytes": 1578340, "num_examples": 3270}], "download_size": 3842223, "dataset_size": 6128855}} | 2023-05-02T22:43:01+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "boolq_pt"
More Information needed | [
"# Dataset Card for \"boolq_pt\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"boolq_pt\"\n\nMore Information needed"
]
| [
6,
14
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"boolq_pt\"\n\nMore Information needed"
]
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|
f5bb6204e1b09e2e56c86e9416dd30e6f07d08a8 | # Dataset Card for "reklamation24_reisen-tourismus-full"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | fathyshalab/reklamation24_reisen-tourismus-full | [
"region:us"
]
| 2023-03-09T08:14:35+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "inputs", "struct": [{"name": "text", "dtype": "string"}]}, {"name": "prediction", "list": [{"name": "label", "dtype": "string"}, {"name": "score", "dtype": "float64"}]}, {"name": "prediction_agent", "dtype": "string"}, {"name": "annotation", "dtype": "string"}, {"name": "annotation_agent", "dtype": "string"}, {"name": "vectors", "struct": [{"name": "mini-lm-sentence-transformers", "sequence": "float64"}]}, {"name": "multi_label", "dtype": "bool"}, {"name": "explanation", "dtype": "null"}, {"name": "id", "dtype": "string"}, {"name": "metadata", "dtype": "null"}, {"name": "status", "dtype": "string"}, {"name": "event_timestamp", "dtype": "timestamp[us]"}, {"name": "metrics", "struct": [{"name": "text_length", "dtype": "int64"}]}], "splits": [{"name": "train", "num_bytes": 139906308, "num_examples": 23759}], "download_size": 0, "dataset_size": 139906308}} | 2023-04-25T13:09:00+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "reklamation24_reisen-tourismus-full"
More Information needed | [
"# Dataset Card for \"reklamation24_reisen-tourismus-full\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"reklamation24_reisen-tourismus-full\"\n\nMore Information needed"
]
| [
6,
20
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"reklamation24_reisen-tourismus-full\"\n\nMore Information needed"
]
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|
2048171505f3065d2b46f518ba316daa6460e68d | # Dataset Card for "reklamation24_schoenheit-wellness-full"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | fathyshalab/reklamation24_schoenheit-wellness-full | [
"region:us"
]
| 2023-03-09T08:15:53+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "inputs", "struct": [{"name": "text", "dtype": "string"}]}, {"name": "prediction", "list": [{"name": "label", "dtype": "string"}, {"name": "score", "dtype": "float64"}]}, {"name": "prediction_agent", "dtype": "string"}, {"name": "annotation", "dtype": "string"}, {"name": "annotation_agent", "dtype": "string"}, {"name": "vectors", "struct": [{"name": "mini-lm-sentence-transformers", "sequence": "float64"}]}, {"name": "multi_label", "dtype": "bool"}, {"name": "explanation", "dtype": "null"}, {"name": "id", "dtype": "string"}, {"name": "metadata", "dtype": "null"}, {"name": "status", "dtype": "string"}, {"name": "event_timestamp", "dtype": "timestamp[us]"}, {"name": "metrics", "struct": [{"name": "text_length", "dtype": "int64"}]}], "splits": [{"name": "train", "num_bytes": 21984670, "num_examples": 4158}], "download_size": 0, "dataset_size": 21984670}} | 2023-04-25T13:10:02+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "reklamation24_schoenheit-wellness-full"
More Information needed | [
"# Dataset Card for \"reklamation24_schoenheit-wellness-full\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"reklamation24_schoenheit-wellness-full\"\n\nMore Information needed"
]
| [
6,
23
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"reklamation24_schoenheit-wellness-full\"\n\nMore Information needed"
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|
f85a78260a7b0abebf8231960b3e06348a5a6867 | # Dataset Card for "reklamation24_reisen-tourismus"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | fathyshalab/reklamation24_reisen-tourismus | [
"region:us"
]
| 2023-03-09T08:22:19+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": 247525, "num_examples": 444}, {"name": "test", "num_bytes": 59699, "num_examples": 111}], "download_size": 0, "dataset_size": 307224}} | 2023-04-19T07:28:55+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "reklamation24_reisen-tourismus"
More Information needed | [
"# Dataset Card for \"reklamation24_reisen-tourismus\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"reklamation24_reisen-tourismus\"\n\nMore Information needed"
]
| [
6,
18
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"reklamation24_reisen-tourismus\"\n\nMore Information needed"
]
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|
53b9a88c2d9181b9667dec5bc3c2f247c02563ed | # Dataset Card for "reklamation24_schoenheit-wellness"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | fathyshalab/reklamation24_schoenheit-wellness | [
"region:us"
]
| 2023-03-09T08:23:03+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": 215900, "num_examples": 464}, {"name": "test", "num_bytes": 56138, "num_examples": 117}], "download_size": 0, "dataset_size": 272038}} | 2023-04-19T07:29:58+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "reklamation24_schoenheit-wellness"
More Information needed | [
"# Dataset Card for \"reklamation24_schoenheit-wellness\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"reklamation24_schoenheit-wellness\"\n\nMore Information needed"
]
| [
6,
21
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"reklamation24_schoenheit-wellness\"\n\nMore Information needed"
]
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|
41ef425e5945714fccdad32c23c78880b2369db0 |
An imitation learning environment for the handle-press-side-v2 environment, sample for the policy handle-press-side-v2
This environment was created as part of the Generally Intelligent Agents project gia: https://github.com/huggingface/gia
## Load dataset
First, clone it with
```sh
git clone https://huggingface.co/datasets/qgallouedec/prj_gia_dataset_metaworld_handle_press_side_v2_1111
```
Then, load it with
```python
import numpy as np
dataset = np.load("prj_gia_dataset_metaworld_handle_press_side_v2_1111/dataset.npy", allow_pickle=True).item()
print(dataset.keys()) # dict_keys(['observations', 'actions', 'dones', 'rewards'])
```
| qgallouedec/prj_gia_dataset_metaworld_handle_press_side_v2_1111 | [
"deep-reinforcement-learning",
"reinforcement-learning",
"gia",
"multi-task",
"multi-modal",
"imitation-learning",
"offline-reinforcement-learning",
"region:us"
]
| 2023-03-09T09:08:02+00:00 | {"library_name": "gia", "tags": ["deep-reinforcement-learning", "reinforcement-learning", "gia", "multi-task", "multi-modal", "imitation-learning", "offline-reinforcement-learning"]} | 2023-03-09T09:08:06+00:00 | []
| []
| TAGS
#deep-reinforcement-learning #reinforcement-learning #gia #multi-task #multi-modal #imitation-learning #offline-reinforcement-learning #region-us
|
An imitation learning environment for the handle-press-side-v2 environment, sample for the policy handle-press-side-v2
This environment was created as part of the Generally Intelligent Agents project gia: URL
## Load dataset
First, clone it with
Then, load it with
| [
"## Load dataset\n\nFirst, clone it with\n\n\n\nThen, load it with"
]
| [
"TAGS\n#deep-reinforcement-learning #reinforcement-learning #gia #multi-task #multi-modal #imitation-learning #offline-reinforcement-learning #region-us \n",
"## Load dataset\n\nFirst, clone it with\n\n\n\nThen, load it with"
]
| [
46,
16
]
| [
"passage: TAGS\n#deep-reinforcement-learning #reinforcement-learning #gia #multi-task #multi-modal #imitation-learning #offline-reinforcement-learning #region-us \n## Load dataset\n\nFirst, clone it with\n\n\n\nThen, load it with"
]
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|
0e77121d3776dd4a3fb1112b10259b86eb4e78a1 |
An imitation learning environment for the handle-press-v2 environment, sample for the policy handle-press-v2
This environment was created as part of the Generally Intelligent Agents project gia: https://github.com/huggingface/gia
## Load dataset
First, clone it with
```sh
git clone https://huggingface.co/datasets/qgallouedec/prj_gia_dataset_metaworld_handle_press_v2_1111
```
Then, load it with
```python
import numpy as np
dataset = np.load("prj_gia_dataset_metaworld_handle_press_v2_1111/dataset.npy", allow_pickle=True).item()
print(dataset.keys()) # dict_keys(['observations', 'actions', 'dones', 'rewards'])
```
| qgallouedec/prj_gia_dataset_metaworld_handle_press_v2_1111 | [
"deep-reinforcement-learning",
"reinforcement-learning",
"gia",
"multi-task",
"multi-modal",
"imitation-learning",
"offline-reinforcement-learning",
"region:us"
]
| 2023-03-09T09:09:28+00:00 | {"library_name": "gia", "tags": ["deep-reinforcement-learning", "reinforcement-learning", "gia", "multi-task", "multi-modal", "imitation-learning", "offline-reinforcement-learning"]} | 2023-03-09T09:09:33+00:00 | []
| []
| TAGS
#deep-reinforcement-learning #reinforcement-learning #gia #multi-task #multi-modal #imitation-learning #offline-reinforcement-learning #region-us
|
An imitation learning environment for the handle-press-v2 environment, sample for the policy handle-press-v2
This environment was created as part of the Generally Intelligent Agents project gia: URL
## Load dataset
First, clone it with
Then, load it with
| [
"## Load dataset\n\nFirst, clone it with\n\n\n\nThen, load it with"
]
| [
"TAGS\n#deep-reinforcement-learning #reinforcement-learning #gia #multi-task #multi-modal #imitation-learning #offline-reinforcement-learning #region-us \n",
"## Load dataset\n\nFirst, clone it with\n\n\n\nThen, load it with"
]
| [
46,
16
]
| [
"passage: TAGS\n#deep-reinforcement-learning #reinforcement-learning #gia #multi-task #multi-modal #imitation-learning #offline-reinforcement-learning #region-us \n## Load dataset\n\nFirst, clone it with\n\n\n\nThen, load it with"
]
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|
b4a949430732bba48b3aa2500fdfa95f52f3b015 | # Dataset Card for "issues_content_500k"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | loubnabnl/issues_content_500k | [
"region:us"
]
| 2023-03-09T09:14:04+00:00 | {"dataset_info": {"features": [{"name": "content", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 973521579, "num_examples": 500000}], "download_size": 489652577, "dataset_size": 973521579}} | 2023-03-09T09:14:35+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "issues_content_500k"
More Information needed | [
"# Dataset Card for \"issues_content_500k\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"issues_content_500k\"\n\nMore Information needed"
]
| [
6,
17
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"issues_content_500k\"\n\nMore Information needed"
]
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|
d1e1a78a5e0f5eb79dcaf46d92c453e68f67538d |
An imitation learning environment for the handle-pull-side-v2 environment, sample for the policy handle-pull-side-v2
This environment was created as part of the Generally Intelligent Agents project gia: https://github.com/huggingface/gia
## Load dataset
First, clone it with
```sh
git clone https://huggingface.co/datasets/qgallouedec/prj_gia_dataset_metaworld_handle_pull_side_v2_1111
```
Then, load it with
```python
import numpy as np
dataset = np.load("prj_gia_dataset_metaworld_handle_pull_side_v2_1111/dataset.npy", allow_pickle=True).item()
print(dataset.keys()) # dict_keys(['observations', 'actions', 'dones', 'rewards'])
```
| qgallouedec/prj_gia_dataset_metaworld_handle_pull_side_v2_1111 | [
"deep-reinforcement-learning",
"reinforcement-learning",
"gia",
"multi-task",
"multi-modal",
"imitation-learning",
"offline-reinforcement-learning",
"region:us"
]
| 2023-03-09T09:25:09+00:00 | {"library_name": "gia", "tags": ["deep-reinforcement-learning", "reinforcement-learning", "gia", "multi-task", "multi-modal", "imitation-learning", "offline-reinforcement-learning"]} | 2023-03-09T09:25:14+00:00 | []
| []
| TAGS
#deep-reinforcement-learning #reinforcement-learning #gia #multi-task #multi-modal #imitation-learning #offline-reinforcement-learning #region-us
|
An imitation learning environment for the handle-pull-side-v2 environment, sample for the policy handle-pull-side-v2
This environment was created as part of the Generally Intelligent Agents project gia: URL
## Load dataset
First, clone it with
Then, load it with
| [
"## Load dataset\n\nFirst, clone it with\n\n\n\nThen, load it with"
]
| [
"TAGS\n#deep-reinforcement-learning #reinforcement-learning #gia #multi-task #multi-modal #imitation-learning #offline-reinforcement-learning #region-us \n",
"## Load dataset\n\nFirst, clone it with\n\n\n\nThen, load it with"
]
| [
46,
16
]
| [
"passage: TAGS\n#deep-reinforcement-learning #reinforcement-learning #gia #multi-task #multi-modal #imitation-learning #offline-reinforcement-learning #region-us \n## Load dataset\n\nFirst, clone it with\n\n\n\nThen, load it with"
]
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|
67176dfe7ccb77de7a9549ee5ecb09bb20cf46b8 |
An imitation learning environment for the handle-pull-v2 environment, sample for the policy handle-pull-v2
This environment was created as part of the Generally Intelligent Agents project gia: https://github.com/huggingface/gia
## Load dataset
First, clone it with
```sh
git clone https://huggingface.co/datasets/qgallouedec/prj_gia_dataset_metaworld_handle_pull_v2_1111
```
Then, load it with
```python
import numpy as np
dataset = np.load("prj_gia_dataset_metaworld_handle_pull_v2_1111/dataset.npy", allow_pickle=True).item()
print(dataset.keys()) # dict_keys(['observations', 'actions', 'dones', 'rewards'])
```
| qgallouedec/prj_gia_dataset_metaworld_handle_pull_v2_1111 | [
"deep-reinforcement-learning",
"reinforcement-learning",
"gia",
"multi-task",
"multi-modal",
"imitation-learning",
"offline-reinforcement-learning",
"region:us"
]
| 2023-03-09T09:26:46+00:00 | {"library_name": "gia", "tags": ["deep-reinforcement-learning", "reinforcement-learning", "gia", "multi-task", "multi-modal", "imitation-learning", "offline-reinforcement-learning"]} | 2023-03-09T09:26:51+00:00 | []
| []
| TAGS
#deep-reinforcement-learning #reinforcement-learning #gia #multi-task #multi-modal #imitation-learning #offline-reinforcement-learning #region-us
|
An imitation learning environment for the handle-pull-v2 environment, sample for the policy handle-pull-v2
This environment was created as part of the Generally Intelligent Agents project gia: URL
## Load dataset
First, clone it with
Then, load it with
| [
"## Load dataset\n\nFirst, clone it with\n\n\n\nThen, load it with"
]
| [
"TAGS\n#deep-reinforcement-learning #reinforcement-learning #gia #multi-task #multi-modal #imitation-learning #offline-reinforcement-learning #region-us \n",
"## Load dataset\n\nFirst, clone it with\n\n\n\nThen, load it with"
]
| [
46,
16
]
| [
"passage: TAGS\n#deep-reinforcement-learning #reinforcement-learning #gia #multi-task #multi-modal #imitation-learning #offline-reinforcement-learning #region-us \n## Load dataset\n\nFirst, clone it with\n\n\n\nThen, load it with"
]
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|
d3dfcc9f4fabb0bfeb80ed80ae5c6d0002637030 |
An imitation learning environment for the lever-pull-v2 environment, sample for the policy lever-pull-v2
This environment was created as part of the Generally Intelligent Agents project gia: https://github.com/huggingface/gia
## Load dataset
First, clone it with
```sh
git clone https://huggingface.co/datasets/qgallouedec/prj_gia_dataset_metaworld_lever_pull_v2_1111
```
Then, load it with
```python
import numpy as np
dataset = np.load("prj_gia_dataset_metaworld_lever_pull_v2_1111/dataset.npy", allow_pickle=True).item()
print(dataset.keys()) # dict_keys(['observations', 'actions', 'dones', 'rewards'])
```
| qgallouedec/prj_gia_dataset_metaworld_lever_pull_v2_1111 | [
"deep-reinforcement-learning",
"reinforcement-learning",
"gia",
"multi-task",
"multi-modal",
"imitation-learning",
"offline-reinforcement-learning",
"region:us"
]
| 2023-03-09T09:28:20+00:00 | {"library_name": "gia", "tags": ["deep-reinforcement-learning", "reinforcement-learning", "gia", "multi-task", "multi-modal", "imitation-learning", "offline-reinforcement-learning"]} | 2023-03-09T09:28:24+00:00 | []
| []
| TAGS
#deep-reinforcement-learning #reinforcement-learning #gia #multi-task #multi-modal #imitation-learning #offline-reinforcement-learning #region-us
|
An imitation learning environment for the lever-pull-v2 environment, sample for the policy lever-pull-v2
This environment was created as part of the Generally Intelligent Agents project gia: URL
## Load dataset
First, clone it with
Then, load it with
| [
"## Load dataset\n\nFirst, clone it with\n\n\n\nThen, load it with"
]
| [
"TAGS\n#deep-reinforcement-learning #reinforcement-learning #gia #multi-task #multi-modal #imitation-learning #offline-reinforcement-learning #region-us \n",
"## Load dataset\n\nFirst, clone it with\n\n\n\nThen, load it with"
]
| [
46,
16
]
| [
"passage: TAGS\n#deep-reinforcement-learning #reinforcement-learning #gia #multi-task #multi-modal #imitation-learning #offline-reinforcement-learning #region-us \n## Load dataset\n\nFirst, clone it with\n\n\n\nThen, load it with"
]
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|
2019c80d837eb390b0b4b74ec2948b05e10fb68b | # Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Summarization
* Model: 0ys/mt5-small-finetuned-amazon-en-es
* Dataset: samsum
* Config: samsum
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@raviteja2](https://huggingface.co/raviteja2) for evaluating this model. | autoevaluate/autoeval-eval-samsum-samsum-8c5714-39885103812 | [
"autotrain",
"evaluation",
"region:us"
]
| 2023-03-09T09:42:17+00:00 | {"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["samsum"], "eval_info": {"task": "summarization", "model": "0ys/mt5-small-finetuned-amazon-en-es", "metrics": [], "dataset_name": "samsum", "dataset_config": "samsum", "dataset_split": "test", "col_mapping": {"text": "dialogue", "target": "summary"}}} | 2023-03-09T09:43:08+00:00 | []
| []
| TAGS
#autotrain #evaluation #region-us
| # Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by AutoTrain for the following task and dataset:
* Task: Summarization
* Model: 0ys/mt5-small-finetuned-amazon-en-es
* Dataset: samsum
* Config: samsum
* Split: test
To run new evaluation jobs, visit Hugging Face's automatic model evaluator.
## Contributions
Thanks to @raviteja2 for evaluating this model. | [
"# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: 0ys/mt5-small-finetuned-amazon-en-es\n* Dataset: samsum\n* Config: samsum\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.",
"## Contributions\n\nThanks to @raviteja2 for evaluating this model."
]
| [
"TAGS\n#autotrain #evaluation #region-us \n",
"# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: 0ys/mt5-small-finetuned-amazon-en-es\n* Dataset: samsum\n* Config: samsum\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.",
"## Contributions\n\nThanks to @raviteja2 for evaluating this model."
]
| [
13,
95,
16
]
| [
"passage: TAGS\n#autotrain #evaluation #region-us \n# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: 0ys/mt5-small-finetuned-amazon-en-es\n* Dataset: samsum\n* Config: samsum\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.## Contributions\n\nThanks to @raviteja2 for evaluating this model."
]
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|
a59366e1b8eb6d6f3d014ae3dd7041a0835d42c8 | # Dataset Card for "prj_gia_dataset_metaworld_assembly_v2_1111_demo"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | qgallouedec/prj_gia_dataset_metaworld_assembly_v2_1111_demo | [
"region:us"
]
| 2023-03-09T10:19:54+00:00 | {"dataset_info": {"features": [{"name": "observations", "sequence": "float32"}, {"name": "actions", "sequence": "float32"}, {"name": "dones", "dtype": "bool"}, {"name": "rewards", "dtype": "float32"}], "splits": [{"name": "train", "num_bytes": 18412500, "num_examples": 100000}], "download_size": 8875331, "dataset_size": 18412500}} | 2023-03-10T16:23:12+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "prj_gia_dataset_metaworld_assembly_v2_1111_demo"
More Information needed | [
"# Dataset Card for \"prj_gia_dataset_metaworld_assembly_v2_1111_demo\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"prj_gia_dataset_metaworld_assembly_v2_1111_demo\"\n\nMore Information needed"
]
| [
6,
31
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"prj_gia_dataset_metaworld_assembly_v2_1111_demo\"\n\nMore Information needed"
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|
b45984ffbef8596a8b6ac2ac8959f2f8998049bb | # Dataset Card for "flowers-blip-captions"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | pranked03/flowers-blip-captions | [
"region:us"
]
| 2023-03-09T11:02:26+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": "int64"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 270279282.848, "num_examples": 6552}], "download_size": 277165211, "dataset_size": 270279282.848}} | 2023-03-09T11:29:27+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "flowers-blip-captions"
More Information needed | [
"# Dataset Card for \"flowers-blip-captions\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"flowers-blip-captions\"\n\nMore Information needed"
]
| [
6,
18
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"flowers-blip-captions\"\n\nMore Information needed"
]
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|
a7d8afe06177bea23b412cc025066c60bba9079c | # Dataset Card for "maps_parquet"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | davanstrien/maps_parquet | [
"region:us"
]
| 2023-03-09T11:03:56+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "no building or railspace", "1": "railspace", "2": "building", "3": "railspace and non railspace building"}}}}, {"name": "map_sheet", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 957911247.448, "num_examples": 37212}, {"name": "validation", "num_bytes": 316304202.708, "num_examples": 12404}, {"name": "test", "num_bytes": 323743326.376, "num_examples": 12404}], "download_size": 1600455354, "dataset_size": 1597958776.5319998}} | 2023-03-09T11:05:08+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "maps_parquet"
More Information needed | [
"# Dataset Card for \"maps_parquet\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"maps_parquet\"\n\nMore Information needed"
]
| [
6,
15
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"maps_parquet\"\n\nMore Information needed"
]
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|
845c49cc3b3bce553a78b07c25c7aca51722ea68 | test | yunosuken/sentiment-train | [
"region:us"
]
| 2023-03-09T11:28:42+00:00 | {"viewer": true, "dataset_info": {"homepage": "httsp://www.yahoo.co.jp", "features": [{"name": "id", "dtype": "int64"}, {"name": "text", "dtype": "string"}, {"name": "label", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 897816, "num_examples": 8476}, {"name": "validation", "num_bytes": 52805, "num_examples": 497}, {"name": "test", "num_bytes": 109825, "num_examples": 1002}], "download_size": 601239, "dataset_size": 1060446, "description": "hoge"}} | 2023-03-15T13:48:44+00:00 | []
| []
| TAGS
#region-us
| test | []
| [
"TAGS\n#region-us \n"
]
| [
6
]
| [
"passage: TAGS\n#region-us \n"
]
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|
5e492c24fec9fee51a80a3945ab60260fdb01ed3 | # Dataset Card for "reklambox3"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | fathyshalab/reklambox3 | [
"region:us"
]
| 2023-03-09T12:58:50+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "category", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "filename", "dtype": "string"}, {"name": "index", "dtype": "int64"}, {"name": "label_name", "dtype": "string"}, {"name": "__index_level_0__", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 645273.5305832148, "num_examples": 1124}, {"name": "test", "num_bytes": 161892.4694167852, "num_examples": 282}], "download_size": 446344, "dataset_size": 807166.0}} | 2023-03-09T12:59:51+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "reklambox3"
More Information needed | [
"# Dataset Card for \"reklambox3\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"reklambox3\"\n\nMore Information needed"
]
| [
6,
13
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"reklambox3\"\n\nMore Information needed"
]
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|
755f26834ed9be29f64fee9b38941f9f3db146e9 | # Dataset Card for "to_label_samples"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | active-learning/to_label_samples | [
"region:us"
]
| 2023-03-09T13:01:14+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 1391.0870983935743, "num_examples": 5}], "download_size": 3878, "dataset_size": 1391.0870983935743}} | 2023-09-04T20:47:10+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "to_label_samples"
More Information needed | [
"# Dataset Card for \"to_label_samples\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"to_label_samples\"\n\nMore Information needed"
]
| [
6,
16
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"to_label_samples\"\n\nMore Information needed"
]
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|
dd0d6a1537093e3f4304a7469603c1ce388fb07a | # Dataset Card for "symptom_text_to_disease_mk2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | venetis/symptom_text_to_disease_mk2 | [
"region:us"
]
| 2023-03-09T13:23:32+00:00 | {"dataset_info": {"features": [{"name": "audio_clipping", "dtype": "string"}, {"name": "audio_clipping:confidence", "dtype": "float64"}, {"name": "background_noise_audible", "dtype": "string"}, {"name": "background_noise_audible:confidence", "dtype": "float64"}, {"name": "overall_quality_of_the_audio", "dtype": "float64"}, {"name": "quiet_speaker", "dtype": "string"}, {"name": "quiet_speaker:confidence", "dtype": "float64"}, {"name": "speaker_id", "dtype": "int64"}, {"name": "file_download", "dtype": "string"}, {"name": "file_name", "dtype": "string"}, {"name": "phrase", "dtype": "string"}, {"name": "prompt", "dtype": "string"}, {"name": "writer_id", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 2016289, "num_examples": 6661}], "download_size": 409095, "dataset_size": 2016289}} | 2023-03-09T13:23:37+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "symptom_text_to_disease_mk2"
More Information needed | [
"# Dataset Card for \"symptom_text_to_disease_mk2\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"symptom_text_to_disease_mk2\"\n\nMore Information needed"
]
| [
6,
24
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"symptom_text_to_disease_mk2\"\n\nMore Information needed"
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|
2e477f41b0d05aa8041913e4c1ffe4ee58efe0dd | # Dataset Card for "symptom_text_to_disease_mk3"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | venetis/symptom_text_to_disease_mk3 | [
"region:us"
]
| 2023-03-09T13:24:15+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "labels", "dtype": {"class_label": {"names": {"0": "emotional pain", "1": "hair falling out", "2": "heart hurts", "3": "infected wound", "4": "foot ache", "5": "shoulder pain", "6": "injury from sports", "7": "skin issue", "8": "stomach ache", "9": "knee pain", "10": "joint pain", "11": "hard to breath", "12": "head ache", "13": "body feels weak", "14": "feeling dizzy", "15": "back pain", "16": "open wound", "17": "internal pain", "18": "blurry vision", "19": "acne", "20": "muscle pain", "21": "neck pain", "22": "cough", "23": "ear ache", "24": "feeling cold"}}}}], "splits": [{"name": "train", "num_bytes": 330494.3762197868, "num_examples": 5328}, {"name": "test", "num_bytes": 41373.82675273983, "num_examples": 667}, {"name": "valid", "num_bytes": 41311.79702747335, "num_examples": 666}], "download_size": 146385, "dataset_size": 413180.0}} | 2023-03-09T13:24:21+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "symptom_text_to_disease_mk3"
More Information needed | [
"# Dataset Card for \"symptom_text_to_disease_mk3\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"symptom_text_to_disease_mk3\"\n\nMore Information needed"
]
| [
6,
24
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"symptom_text_to_disease_mk3\"\n\nMore Information needed"
]
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]
|
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