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32df1926a59db6ea46e851012af5467dc03ecdd4
|
# 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: Alred/t5-small-finetuned-summarization-cnn
* Dataset: cnn_dailymail
* Config: 3.0.0
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@MaryYarova](https://huggingface.co/MaryYarova) for evaluating this model.
|
autoevaluate/autoeval-eval-cnn_dailymail-3.0.0-c51db7-51930145327
|
[
"autotrain",
"evaluation",
"region:us"
] |
2023-10-04T14:40:24+00:00
|
{"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["cnn_dailymail"], "eval_info": {"task": "summarization", "model": "Alred/t5-small-finetuned-summarization-cnn", "metrics": [], "dataset_name": "cnn_dailymail", "dataset_config": "3.0.0", "dataset_split": "test", "col_mapping": {"text": "article", "target": "highlights"}}}
|
2023-10-04T14:43:40+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: Alred/t5-small-finetuned-summarization-cnn
* Dataset: cnn_dailymail
* Config: 3.0.0
* Split: test
To run new evaluation jobs, visit Hugging Face's automatic model evaluator.
## Contributions
Thanks to @MaryYarova 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: Alred/t5-small-finetuned-summarization-cnn\n* Dataset: cnn_dailymail\n* Config: 3.0.0\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.",
"## Contributions\n\nThanks to @MaryYarova 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: Alred/t5-small-finetuned-summarization-cnn\n* Dataset: cnn_dailymail\n* Config: 3.0.0\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.",
"## Contributions\n\nThanks to @MaryYarova for evaluating this model."
] |
[
13,
97,
17
] |
[
"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: Alred/t5-small-finetuned-summarization-cnn\n* Dataset: cnn_dailymail\n* Config: 3.0.0\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.## Contributions\n\nThanks to @MaryYarova for evaluating this model."
] |
7944122d81968698a1eff0e1db5f9461df7a7063
|
# Bangumi Image Base of Hanasaku Iroha
This is the image base of bangumi Hanasaku Iroha, we detected 26 characters, 3949 images in total. The full dataset is [here](all.zip).
**Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual.** If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
| # | Images | Download | Preview 1 | Preview 2 | Preview 3 | Preview 4 | Preview 5 | Preview 6 | Preview 7 | Preview 8 |
|:------|---------:|:---------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|
| 0 | 995 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 13 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 131 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 402 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 139 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 300 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 63 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 151 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 44 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 234 | [Download](9/dataset.zip) |  |  |  |  |  |  |  |  |
| 10 | 167 | [Download](10/dataset.zip) |  |  |  |  |  |  |  |  |
| 11 | 30 | [Download](11/dataset.zip) |  |  |  |  |  |  |  |  |
| 12 | 22 | [Download](12/dataset.zip) |  |  |  |  |  |  |  |  |
| 13 | 45 | [Download](13/dataset.zip) |  |  |  |  |  |  |  |  |
| 14 | 14 | [Download](14/dataset.zip) |  |  |  |  |  |  |  |  |
| 15 | 14 | [Download](15/dataset.zip) |  |  |  |  |  |  |  |  |
| 16 | 219 | [Download](16/dataset.zip) |  |  |  |  |  |  |  |  |
| 17 | 15 | [Download](17/dataset.zip) |  |  |  |  |  |  |  |  |
| 18 | 184 | [Download](18/dataset.zip) |  |  |  |  |  |  |  |  |
| 19 | 444 | [Download](19/dataset.zip) |  |  |  |  |  |  |  |  |
| 20 | 20 | [Download](20/dataset.zip) |  |  |  |  |  |  |  |  |
| 21 | 15 | [Download](21/dataset.zip) |  |  |  |  |  |  |  |  |
| 22 | 10 | [Download](22/dataset.zip) |  |  |  |  |  |  |  |  |
| 23 | 112 | [Download](23/dataset.zip) |  |  |  |  |  |  |  |  |
| 24 | 13 | [Download](24/dataset.zip) |  |  |  |  |  |  |  |  |
| noise | 153 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
|
BangumiBase/hanasakuiroha
|
[
"size_categories:1K<n<10K",
"license:mit",
"art",
"region:us"
] |
2023-10-04T14:40:39+00:00
|
{"license": "mit", "size_categories": ["1K<n<10K"], "tags": ["art"]}
|
2023-10-04T16:35:09+00:00
|
[] |
[] |
TAGS
#size_categories-1K<n<10K #license-mit #art #region-us
|
Bangumi Image Base of Hanasaku Iroha
====================================
This is the image base of bangumi Hanasaku Iroha, we detected 26 characters, 3949 images in total. The full dataset is here.
Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual. If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
|
[] |
[
"TAGS\n#size_categories-1K<n<10K #license-mit #art #region-us \n"
] |
[
25
] |
[
"passage: TAGS\n#size_categories-1K<n<10K #license-mit #art #region-us \n"
] |
01770e7c393767bb4dc5676dc2fbcfb3b496cb4c
|
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Question Answering
* Model: Firat/roberta-base-finetuned-squad
* Dataset: adversarial_qa
* Config: adversarialQA
* Split: validation
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@tp](https://huggingface.co/tp) for evaluating this model.
|
autoevaluate/autoeval-eval-adversarial_qa-adversarialQA-0e2388-51771145320
|
[
"autotrain",
"evaluation",
"region:us"
] |
2023-10-04T14:44:49+00:00
|
{"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["adversarial_qa"], "eval_info": {"task": "extractive_question_answering", "model": "Firat/roberta-base-finetuned-squad", "metrics": [], "dataset_name": "adversarial_qa", "dataset_config": "adversarialQA", "dataset_split": "validation", "col_mapping": {"context": "context", "question": "question", "answers-text": "answers.text", "answers-answer_start": "answers.answer_start"}}}
|
2023-10-04T14:45:53+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: Question Answering
* Model: Firat/roberta-base-finetuned-squad
* Dataset: adversarial_qa
* Config: adversarialQA
* Split: validation
To run new evaluation jobs, visit Hugging Face's automatic model evaluator.
## Contributions
Thanks to @tp 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: Question Answering\n* Model: Firat/roberta-base-finetuned-squad\n* Dataset: adversarial_qa\n* Config: adversarialQA\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.",
"## Contributions\n\nThanks to @tp 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: Question Answering\n* Model: Firat/roberta-base-finetuned-squad\n* Dataset: adversarial_qa\n* Config: adversarialQA\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.",
"## Contributions\n\nThanks to @tp for evaluating this model."
] |
[
13,
94,
15
] |
[
"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: Question Answering\n* Model: Firat/roberta-base-finetuned-squad\n* Dataset: adversarial_qa\n* Config: adversarialQA\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.## Contributions\n\nThanks to @tp for evaluating this model."
] |
9f41f5109c958403ab4038358db2f1d8dc24ad14
|
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Question Answering
* Model: Firat/roberta-base-finetuned-squad
* Dataset: adversarial_qa
* Config: adversarialQA
* Split: validation
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@tp](https://huggingface.co/tp) for evaluating this model.
|
autoevaluate/autoeval-eval-adversarial_qa-adversarialQA-776ce2-51767145318
|
[
"autotrain",
"evaluation",
"region:us"
] |
2023-10-04T14:47:37+00:00
|
{"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["adversarial_qa"], "eval_info": {"task": "extractive_question_answering", "model": "Firat/roberta-base-finetuned-squad", "metrics": [], "dataset_name": "adversarial_qa", "dataset_config": "adversarialQA", "dataset_split": "validation", "col_mapping": {"context": "context", "question": "question", "answers-text": "answers.text", "answers-answer_start": "answers.answer_start"}}}
|
2023-10-04T14:48:40+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: Question Answering
* Model: Firat/roberta-base-finetuned-squad
* Dataset: adversarial_qa
* Config: adversarialQA
* Split: validation
To run new evaluation jobs, visit Hugging Face's automatic model evaluator.
## Contributions
Thanks to @tp 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: Question Answering\n* Model: Firat/roberta-base-finetuned-squad\n* Dataset: adversarial_qa\n* Config: adversarialQA\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.",
"## Contributions\n\nThanks to @tp 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: Question Answering\n* Model: Firat/roberta-base-finetuned-squad\n* Dataset: adversarial_qa\n* Config: adversarialQA\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.",
"## Contributions\n\nThanks to @tp for evaluating this model."
] |
[
13,
94,
15
] |
[
"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: Question Answering\n* Model: Firat/roberta-base-finetuned-squad\n* Dataset: adversarial_qa\n* Config: adversarialQA\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.## Contributions\n\nThanks to @tp for evaluating this model."
] |
d986848447effbb6f56c6fe2531f32c60436f476
|
# CIVQA EasyOCR Train Dataset
The CIVQA (Czech Invoice Visual Question Answering) dataset was created with EasyOCR. This dataset contains only the train split. The validation part of the dataset can be found on this URL: https://huggingface.co/datasets/fimu-docproc-research/CIVQA_EasyOCR_Validation
The encoded train dataset for the LayoutLM can be found on this link: https://huggingface.co/datasets/fimu-docproc-research/CIVQA_EasyOCR_LayoutLM_Train
All invoices used in this dataset were obtained from public sources. Over these invoices, we were focusing on 15 different entities, which are crucial for processing the invoices.
- Invoice number
- Variable symbol
- Specific symbol
- Constant symbol
- Bank code
- Account number
- ICO
- Total amount
- Invoice date
- Due date
- Name of supplier
- IBAN
- DIC
- QR code
- Supplier's address
The invoices included in this dataset were gathered from the internet. We understand that privacy is of utmost importance. Therefore, we sincerely apologise for any inconvenience caused by including your identifiable information in this dataset. If you have identified your data in this dataset and wish to have it removed from research purposes, we request you kindly to access the following URL: https://forms.gle/tUVJKoB22oeTncUD6
We profoundly appreciate your cooperation and understanding in this matter.
|
fimu-docproc-research/CIVQA_EasyOCR_Train
|
[
"language:cs",
"license:mit",
"finance",
"region:us"
] |
2023-10-04T14:48:08+00:00
|
{"language": ["cs"], "license": "mit", "dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "words", "sequence": "string"}, {"name": "answers", "dtype": "string"}, {"name": "bboxes", "sequence": {"sequence": "float32"}}, {"name": "answers_bboxes", "sequence": {"sequence": "float32"}}, {"name": "questions", "dtype": "string"}, {"name": "image", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 963207990, "num_examples": 143765}], "download_size": 41076905, "dataset_size": 963207990}, "tags": ["finance"]}
|
2023-11-21T20:47:38+00:00
|
[] |
[
"cs"
] |
TAGS
#language-Czech #license-mit #finance #region-us
|
# CIVQA EasyOCR Train Dataset
The CIVQA (Czech Invoice Visual Question Answering) dataset was created with EasyOCR. This dataset contains only the train split. The validation part of the dataset can be found on this URL: URL
The encoded train dataset for the LayoutLM can be found on this link: URL
All invoices used in this dataset were obtained from public sources. Over these invoices, we were focusing on 15 different entities, which are crucial for processing the invoices.
- Invoice number
- Variable symbol
- Specific symbol
- Constant symbol
- Bank code
- Account number
- ICO
- Total amount
- Invoice date
- Due date
- Name of supplier
- IBAN
- DIC
- QR code
- Supplier's address
The invoices included in this dataset were gathered from the internet. We understand that privacy is of utmost importance. Therefore, we sincerely apologise for any inconvenience caused by including your identifiable information in this dataset. If you have identified your data in this dataset and wish to have it removed from research purposes, we request you kindly to access the following URL: URL
We profoundly appreciate your cooperation and understanding in this matter.
|
[
"# CIVQA EasyOCR Train Dataset\n\nThe CIVQA (Czech Invoice Visual Question Answering) dataset was created with EasyOCR. This dataset contains only the train split. The validation part of the dataset can be found on this URL: URL \nThe encoded train dataset for the LayoutLM can be found on this link: URL\n\nAll invoices used in this dataset were obtained from public sources. Over these invoices, we were focusing on 15 different entities, which are crucial for processing the invoices.\n- Invoice number\n- Variable symbol\n- Specific symbol\n- Constant symbol\n- Bank code\n- Account number\n- ICO\n- Total amount\n- Invoice date\n- Due date\n- Name of supplier\n- IBAN\n- DIC\n- QR code\n- Supplier's address\n\nThe invoices included in this dataset were gathered from the internet. We understand that privacy is of utmost importance. Therefore, we sincerely apologise for any inconvenience caused by including your identifiable information in this dataset. If you have identified your data in this dataset and wish to have it removed from research purposes, we request you kindly to access the following URL: URL\n\nWe profoundly appreciate your cooperation and understanding in this matter."
] |
[
"TAGS\n#language-Czech #license-mit #finance #region-us \n",
"# CIVQA EasyOCR Train Dataset\n\nThe CIVQA (Czech Invoice Visual Question Answering) dataset was created with EasyOCR. This dataset contains only the train split. The validation part of the dataset can be found on this URL: URL \nThe encoded train dataset for the LayoutLM can be found on this link: URL\n\nAll invoices used in this dataset were obtained from public sources. Over these invoices, we were focusing on 15 different entities, which are crucial for processing the invoices.\n- Invoice number\n- Variable symbol\n- Specific symbol\n- Constant symbol\n- Bank code\n- Account number\n- ICO\n- Total amount\n- Invoice date\n- Due date\n- Name of supplier\n- IBAN\n- DIC\n- QR code\n- Supplier's address\n\nThe invoices included in this dataset were gathered from the internet. We understand that privacy is of utmost importance. Therefore, we sincerely apologise for any inconvenience caused by including your identifiable information in this dataset. If you have identified your data in this dataset and wish to have it removed from research purposes, we request you kindly to access the following URL: URL\n\nWe profoundly appreciate your cooperation and understanding in this matter."
] |
[
20,
280
] |
[
"passage: TAGS\n#language-Czech #license-mit #finance #region-us \n# CIVQA EasyOCR Train Dataset\n\nThe CIVQA (Czech Invoice Visual Question Answering) dataset was created with EasyOCR. This dataset contains only the train split. The validation part of the dataset can be found on this URL: URL \nThe encoded train dataset for the LayoutLM can be found on this link: URL\n\nAll invoices used in this dataset were obtained from public sources. Over these invoices, we were focusing on 15 different entities, which are crucial for processing the invoices.\n- Invoice number\n- Variable symbol\n- Specific symbol\n- Constant symbol\n- Bank code\n- Account number\n- ICO\n- Total amount\n- Invoice date\n- Due date\n- Name of supplier\n- IBAN\n- DIC\n- QR code\n- Supplier's address\n\nThe invoices included in this dataset were gathered from the internet. We understand that privacy is of utmost importance. Therefore, we sincerely apologise for any inconvenience caused by including your identifiable information in this dataset. If you have identified your data in this dataset and wish to have it removed from research purposes, we request you kindly to access the following URL: URL\n\nWe profoundly appreciate your cooperation and understanding in this matter."
] |
c3ee564d9a9ca055f870175c5b42da87d86130d0
|
# 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: Alred/bart-base-finetuned-summarization-cnn-ver3
* Dataset: ccdv/pubmed-summarization
* Config: section
* Split: train
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@Vishnu196](https://huggingface.co/Vishnu196) for evaluating this model.
|
autoevaluate/autoeval-eval-ccdv__pubmed-summarization-section-c19ce4-53114145346
|
[
"autotrain",
"evaluation",
"region:us"
] |
2023-10-04T14:49:42+00:00
|
{"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["ccdv/pubmed-summarization"], "eval_info": {"task": "summarization", "model": "Alred/bart-base-finetuned-summarization-cnn-ver3", "metrics": [], "dataset_name": "ccdv/pubmed-summarization", "dataset_config": "section", "dataset_split": "train", "col_mapping": {"text": "article", "target": "abstract"}}}
|
2023-10-04T16:42:21+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: Alred/bart-base-finetuned-summarization-cnn-ver3
* Dataset: ccdv/pubmed-summarization
* Config: section
* Split: train
To run new evaluation jobs, visit Hugging Face's automatic model evaluator.
## Contributions
Thanks to @Vishnu196 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: Alred/bart-base-finetuned-summarization-cnn-ver3\n* Dataset: ccdv/pubmed-summarization\n* Config: section\n* Split: train\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.",
"## Contributions\n\nThanks to @Vishnu196 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: Alred/bart-base-finetuned-summarization-cnn-ver3\n* Dataset: ccdv/pubmed-summarization\n* Config: section\n* Split: train\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.",
"## Contributions\n\nThanks to @Vishnu196 for evaluating this model."
] |
[
13,
103,
17
] |
[
"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: Alred/bart-base-finetuned-summarization-cnn-ver3\n* Dataset: ccdv/pubmed-summarization\n* Config: section\n* Split: train\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.## Contributions\n\nThanks to @Vishnu196 for evaluating this model."
] |
624f3f82909337f249b48a9bfd7590c4df82a548
|
# 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: 0x70DA/pegasus-cnn_dailymail
* Dataset: ccdv/pubmed-summarization
* Config: section
* Split: train
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@Vishnu196](https://huggingface.co/Vishnu196) for evaluating this model.
|
autoevaluate/autoeval-eval-ccdv__pubmed-summarization-section-4b477d-53190145351
|
[
"autotrain",
"evaluation",
"region:us"
] |
2023-10-04T14:51:11+00:00
|
{"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["ccdv/pubmed-summarization"], "eval_info": {"task": "summarization", "model": "0x70DA/pegasus-cnn_dailymail", "metrics": [], "dataset_name": "ccdv/pubmed-summarization", "dataset_config": "section", "dataset_split": "train", "col_mapping": {"text": "article", "target": "abstract"}}}
|
2023-10-05T08:09:51+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: 0x70DA/pegasus-cnn_dailymail
* Dataset: ccdv/pubmed-summarization
* Config: section
* Split: train
To run new evaluation jobs, visit Hugging Face's automatic model evaluator.
## Contributions
Thanks to @Vishnu196 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: 0x70DA/pegasus-cnn_dailymail\n* Dataset: ccdv/pubmed-summarization\n* Config: section\n* Split: train\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.",
"## Contributions\n\nThanks to @Vishnu196 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: 0x70DA/pegasus-cnn_dailymail\n* Dataset: ccdv/pubmed-summarization\n* Config: section\n* Split: train\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.",
"## Contributions\n\nThanks to @Vishnu196 for evaluating this model."
] |
[
13,
97,
17
] |
[
"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: 0x70DA/pegasus-cnn_dailymail\n* Dataset: ccdv/pubmed-summarization\n* Config: section\n* Split: train\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.## Contributions\n\nThanks to @Vishnu196 for evaluating this model."
] |
1ccdaff0cad7f066ee52fc71cbb2f126478d0bb2
|
A subset of LAION Aesthetics v2 5+, filtered to include only high resolution (1024x1024+) images, then processed to remove dead links (as of October 2023), and with embeddings recalculated using CLIP-ViT-L-patch14 which were used to both remove poor-quality image-caption matches (CLIP similarity <0.2) and to deduplicate the dataset. Roughly one third of the dataset was dropped by these operations after filtering high resolution images.
|
drhead/laion_hd_21M_deduped
|
[
"region:us"
] |
2023-10-04T14:51:45+00:00
|
{}
|
2023-10-04T14:59:02+00:00
|
[] |
[] |
TAGS
#region-us
|
A subset of LAION Aesthetics v2 5+, filtered to include only high resolution (1024x1024+) images, then processed to remove dead links (as of October 2023), and with embeddings recalculated using CLIP-ViT-L-patch14 which were used to both remove poor-quality image-caption matches (CLIP similarity <0.2) and to deduplicate the dataset. Roughly one third of the dataset was dropped by these operations after filtering high resolution images.
|
[] |
[
"TAGS\n#region-us \n"
] |
[
6
] |
[
"passage: TAGS\n#region-us \n"
] |
d50fe7789a33bbc83c133a7edb9c28bbd0fdd8ce
|
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Question Answering
* Model: zhangfx7/deberta-base-finetuned-squad-pruned0.1
* Dataset: adversarial_qa
* Config: adversarialQA
* Split: validation
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@tp](https://huggingface.co/tp) for evaluating this model.
|
autoevaluate/autoeval-eval-adversarial_qa-adversarialQA-776ce2-51767145319
|
[
"autotrain",
"evaluation",
"region:us"
] |
2023-10-04T14:53:51+00:00
|
{"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["adversarial_qa"], "eval_info": {"task": "extractive_question_answering", "model": "zhangfx7/deberta-base-finetuned-squad-pruned0.1", "metrics": [], "dataset_name": "adversarial_qa", "dataset_config": "adversarialQA", "dataset_split": "validation", "col_mapping": {"context": "context", "question": "question", "answers-text": "answers.text", "answers-answer_start": "answers.answer_start"}}}
|
2023-10-04T14:54:59+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: Question Answering
* Model: zhangfx7/deberta-base-finetuned-squad-pruned0.1
* Dataset: adversarial_qa
* Config: adversarialQA
* Split: validation
To run new evaluation jobs, visit Hugging Face's automatic model evaluator.
## Contributions
Thanks to @tp 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: Question Answering\n* Model: zhangfx7/deberta-base-finetuned-squad-pruned0.1\n* Dataset: adversarial_qa\n* Config: adversarialQA\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.",
"## Contributions\n\nThanks to @tp 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: Question Answering\n* Model: zhangfx7/deberta-base-finetuned-squad-pruned0.1\n* Dataset: adversarial_qa\n* Config: adversarialQA\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.",
"## Contributions\n\nThanks to @tp for evaluating this model."
] |
[
13,
102,
15
] |
[
"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: Question Answering\n* Model: zhangfx7/deberta-base-finetuned-squad-pruned0.1\n* Dataset: adversarial_qa\n* Config: adversarialQA\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.## Contributions\n\nThanks to @tp for evaluating this model."
] |
f461d0d370413240210559d396a708bda020265b
|
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Natural Language Inference
* Model: roberta-large-mnli
* Dataset: multi_nli
* Config: default
* Split: validation_matched
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@paadnan](https://huggingface.co/paadnan) for evaluating this model.
|
autoevaluate/autoeval-eval-multi_nli-default-4bf17f-59291145363
|
[
"autotrain",
"evaluation",
"region:us"
] |
2023-10-04T14:56:13+00:00
|
{"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["multi_nli"], "eval_info": {"task": "natural_language_inference", "model": "roberta-large-mnli", "metrics": [], "dataset_name": "multi_nli", "dataset_config": "default", "dataset_split": "validation_matched", "col_mapping": {"text1": "premise", "text2": "hypothesis", "target": "label"}}}
|
2023-10-04T14:58:15+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: Natural Language Inference
* Model: roberta-large-mnli
* Dataset: multi_nli
* Config: default
* Split: validation_matched
To run new evaluation jobs, visit Hugging Face's automatic model evaluator.
## Contributions
Thanks to @paadnan 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: Natural Language Inference\n* Model: roberta-large-mnli\n* Dataset: multi_nli\n* Config: default\n* Split: validation_matched\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.",
"## Contributions\n\nThanks to @paadnan 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: Natural Language Inference\n* Model: roberta-large-mnli\n* Dataset: multi_nli\n* Config: default\n* Split: validation_matched\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.",
"## Contributions\n\nThanks to @paadnan for evaluating this model."
] |
[
13,
91,
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: Natural Language Inference\n* Model: roberta-large-mnli\n* Dataset: multi_nli\n* Config: default\n* Split: validation_matched\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.## Contributions\n\nThanks to @paadnan for evaluating this model."
] |
e8711dfbbec0933b8b89b077166f05285383fc46
|
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Natural Language Inference
* Model: roberta-large-mnli
* Dataset: multi_nli
* Config: default
* Split: validation_matched
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@kslnet](https://huggingface.co/kslnet) for evaluating this model.
|
autoevaluate/autoeval-eval-multi_nli-default-544a62-53715145359
|
[
"autotrain",
"evaluation",
"region:us"
] |
2023-10-04T14:57:09+00:00
|
{"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["multi_nli"], "eval_info": {"task": "natural_language_inference", "model": "roberta-large-mnli", "metrics": [], "dataset_name": "multi_nli", "dataset_config": "default", "dataset_split": "validation_matched", "col_mapping": {"text1": "premise", "text2": "hypothesis", "target": "label"}}}
|
2023-10-04T14:59:12+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: Natural Language Inference
* Model: roberta-large-mnli
* Dataset: multi_nli
* Config: default
* Split: validation_matched
To run new evaluation jobs, visit Hugging Face's automatic model evaluator.
## Contributions
Thanks to @kslnet 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: Natural Language Inference\n* Model: roberta-large-mnli\n* Dataset: multi_nli\n* Config: default\n* Split: validation_matched\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.",
"## Contributions\n\nThanks to @kslnet 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: Natural Language Inference\n* Model: roberta-large-mnli\n* Dataset: multi_nli\n* Config: default\n* Split: validation_matched\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.",
"## Contributions\n\nThanks to @kslnet for evaluating this model."
] |
[
13,
91,
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: Natural Language Inference\n* Model: roberta-large-mnli\n* Dataset: multi_nli\n* Config: default\n* Split: validation_matched\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.## Contributions\n\nThanks to @kslnet for evaluating this model."
] |
1bb272891e0bd94638d8dc8bf82be148818659ec
|
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Translation
* Model: charanhu/text_to_sql_2
* Dataset: spider
* Config: spider
* Split: train
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@Sbaig3229](https://huggingface.co/Sbaig3229) for evaluating this model.
|
autoevaluate/autoeval-eval-spider-spider-251ea9-59625145369
|
[
"autotrain",
"evaluation",
"region:us"
] |
2023-10-04T14:59:07+00:00
|
{"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["spider"], "eval_info": {"task": "translation", "model": "charanhu/text_to_sql_2", "metrics": ["accuracy"], "dataset_name": "spider", "dataset_config": "spider", "dataset_split": "train", "col_mapping": {"source": "question", "target": "query"}}}
|
2023-10-04T15:46:34+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: Translation
* Model: charanhu/text_to_sql_2
* Dataset: spider
* Config: spider
* Split: train
To run new evaluation jobs, visit Hugging Face's automatic model evaluator.
## Contributions
Thanks to @Sbaig3229 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: Translation\n* Model: charanhu/text_to_sql_2\n* Dataset: spider\n* Config: spider\n* Split: train\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.",
"## Contributions\n\nThanks to @Sbaig3229 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: Translation\n* Model: charanhu/text_to_sql_2\n* Dataset: spider\n* Config: spider\n* Split: train\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.",
"## Contributions\n\nThanks to @Sbaig3229 for evaluating this model."
] |
[
13,
86,
17
] |
[
"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: Translation\n* Model: charanhu/text_to_sql_2\n* Dataset: spider\n* Config: spider\n* Split: train\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.## Contributions\n\nThanks to @Sbaig3229 for evaluating this model."
] |
6641cde73d29c8ba807c3345420afe6427324b3a
|
# 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: AleBurzio/long-t5-base-govreport
* Dataset: ag_news
* Config: default
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@AdinaY](https://huggingface.co/AdinaY) for evaluating this model.
|
autoevaluate/autoeval-eval-ag_news-default-425da4-59710145370
|
[
"autotrain",
"evaluation",
"region:us"
] |
2023-10-04T14:59:25+00:00
|
{"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["ag_news"], "eval_info": {"task": "summarization", "model": "AleBurzio/long-t5-base-govreport", "metrics": [], "dataset_name": "ag_news", "dataset_config": "default", "dataset_split": "test", "col_mapping": {"text": "text", "target": "label"}}}
|
2023-10-04T15:02:24+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: AleBurzio/long-t5-base-govreport
* Dataset: ag_news
* Config: default
* Split: test
To run new evaluation jobs, visit Hugging Face's automatic model evaluator.
## Contributions
Thanks to @AdinaY 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: AleBurzio/long-t5-base-govreport\n* Dataset: ag_news\n* Config: default\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.",
"## Contributions\n\nThanks to @AdinaY 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: AleBurzio/long-t5-base-govreport\n* Dataset: ag_news\n* Config: default\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.",
"## Contributions\n\nThanks to @AdinaY for evaluating this model."
] |
[
13,
88,
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: AleBurzio/long-t5-base-govreport\n* Dataset: ag_news\n* Config: default\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.## Contributions\n\nThanks to @AdinaY for evaluating this model."
] |
69041b2163a7200699cb6a7f9530bacbc3c9187a
|
# 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: AleBurzio/long-t5-base-govreport
* Dataset: ag_news
* Config: default
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@AdinaY](https://huggingface.co/AdinaY) for evaluating this model.
|
autoevaluate/autoeval-eval-ag_news-default-8f9ba7-59715145371
|
[
"autotrain",
"evaluation",
"region:us"
] |
2023-10-04T14:59:29+00:00
|
{"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["ag_news"], "eval_info": {"task": "summarization", "model": "AleBurzio/long-t5-base-govreport", "metrics": [], "dataset_name": "ag_news", "dataset_config": "default", "dataset_split": "test", "col_mapping": {"text": "text", "target": "label"}}}
|
2023-10-04T15:02:24+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: AleBurzio/long-t5-base-govreport
* Dataset: ag_news
* Config: default
* Split: test
To run new evaluation jobs, visit Hugging Face's automatic model evaluator.
## Contributions
Thanks to @AdinaY 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: AleBurzio/long-t5-base-govreport\n* Dataset: ag_news\n* Config: default\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.",
"## Contributions\n\nThanks to @AdinaY 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: AleBurzio/long-t5-base-govreport\n* Dataset: ag_news\n* Config: default\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.",
"## Contributions\n\nThanks to @AdinaY for evaluating this model."
] |
[
13,
88,
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: AleBurzio/long-t5-base-govreport\n* Dataset: ag_news\n* Config: default\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.## Contributions\n\nThanks to @AdinaY for evaluating this model."
] |
1c7d769e827c226a712121be25a6ef09392ce294
|
# 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: AleBurzio/long-t5-base-govreport
* Dataset: ag_news
* Config: default
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@AdinaY](https://huggingface.co/AdinaY) for evaluating this model.
|
autoevaluate/autoeval-eval-ag_news-default-d2b27e-59716145372
|
[
"autotrain",
"evaluation",
"region:us"
] |
2023-10-04T14:59:44+00:00
|
{"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["ag_news"], "eval_info": {"task": "summarization", "model": "AleBurzio/long-t5-base-govreport", "metrics": ["f1"], "dataset_name": "ag_news", "dataset_config": "default", "dataset_split": "test", "col_mapping": {"text": "text", "target": "label"}}}
|
2023-10-04T15:02:43+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: AleBurzio/long-t5-base-govreport
* Dataset: ag_news
* Config: default
* Split: test
To run new evaluation jobs, visit Hugging Face's automatic model evaluator.
## Contributions
Thanks to @AdinaY 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: AleBurzio/long-t5-base-govreport\n* Dataset: ag_news\n* Config: default\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.",
"## Contributions\n\nThanks to @AdinaY 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: AleBurzio/long-t5-base-govreport\n* Dataset: ag_news\n* Config: default\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.",
"## Contributions\n\nThanks to @AdinaY for evaluating this model."
] |
[
13,
88,
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: AleBurzio/long-t5-base-govreport\n* Dataset: ag_news\n* Config: default\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.## Contributions\n\nThanks to @AdinaY for evaluating this model."
] |
801c42d72fe2385abfb9b0ee115bc5059626e1d0
|
# 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: xysmalobia/pegasus-samsum
* Dataset: xsum
* Config: default
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@oh aziz principle asadza e akram aur mere aziz sathion, alslam o alikum mera naam nadia akram hai aur main jmat e nahm ki tulba hun meri aaj ki tkrir ka unwaan hai ustaad ka mukaam. aj jo hamari dars gahon main ustaad hoty hain hakikat main aik quom ki buniad hoty hain. Sathion aik ustaad ka kaam hai ke woh humein jahalat ki tarikiyon sy nikal kr Ilm ki norani dunia main laty hain ustaad humain achhy bure ka fark btate hain akl o shaur ko nikharty hain aur humin is qabil banaty hain ke zindagi ke mrahl sy asani se guzar sakein.](https://huggingface.co/oh aziz principle asadza e akram aur mere aziz sathion, alslam o alikum mera naam nadia akram hai aur main jmat e nahm ki tulba hun meri aaj ki tkrir ka unwaan hai ustaad ka mukaam. aj jo hamari dars gahon main ustaad hoty hain hakikat main aik quom ki buniad hoty hain. Sathion aik ustaad ka kaam hai ke woh humein jahalat ki tarikiyon sy nikal kr Ilm ki norani dunia main laty hain ustaad humain achhy bure ka fark btate hain akl o shaur ko nikharty hain aur humin is qabil banaty hain ke zindagi ke mrahl sy asani se guzar sakein.) for evaluating this model.
|
autoevaluate/autoeval-eval-xsum-default-80767e-59416145366
|
[
"autotrain",
"evaluation",
"region:us"
] |
2023-10-04T15:01:57+00:00
|
{"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["xsum"], "eval_info": {"task": "summarization", "model": "xysmalobia/pegasus-samsum", "metrics": [], "dataset_name": "xsum", "dataset_config": "default", "dataset_split": "test", "col_mapping": {"text": "document", "target": "summary"}}}
|
2023-10-04T16:25:24+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: xysmalobia/pegasus-samsum
* Dataset: xsum
* Config: default
* Split: test
To run new evaluation jobs, visit Hugging Face's automatic model evaluator.
## Contributions
Thanks to @oh aziz principle asadza e akram aur mere aziz sathion, alslam o alikum mera naam nadia akram hai aur main jmat e nahm ki tulba hun meri aaj ki tkrir ka unwaan hai ustaad ka mukaam. aj jo hamari dars gahon main ustaad hoty hain hakikat main aik quom ki buniad hoty hain. Sathion aik ustaad ka kaam hai ke woh humein jahalat ki tarikiyon sy nikal kr Ilm ki norani dunia main laty hain ustaad humain achhy bure ka fark btate hain akl o shaur ko nikharty hain aur humin is qabil banaty hain ke zindagi ke mrahl sy asani se guzar sakein. 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: xysmalobia/pegasus-samsum\n* Dataset: xsum\n* Config: default\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.",
"## Contributions\n\nThanks to @oh aziz principle asadza e akram aur mere aziz sathion, alslam o alikum mera naam nadia akram hai aur main jmat e nahm ki tulba hun meri aaj ki tkrir ka unwaan hai ustaad ka mukaam. aj jo hamari dars gahon main ustaad hoty hain hakikat main aik quom ki buniad hoty hain. Sathion aik ustaad ka kaam hai ke woh humein jahalat ki tarikiyon sy nikal kr Ilm ki norani dunia main laty hain ustaad humain achhy bure ka fark btate hain akl o shaur ko nikharty hain aur humin is qabil banaty hain ke zindagi ke mrahl sy asani se guzar sakein. 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: xysmalobia/pegasus-samsum\n* Dataset: xsum\n* Config: default\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.",
"## Contributions\n\nThanks to @oh aziz principle asadza e akram aur mere aziz sathion, alslam o alikum mera naam nadia akram hai aur main jmat e nahm ki tulba hun meri aaj ki tkrir ka unwaan hai ustaad ka mukaam. aj jo hamari dars gahon main ustaad hoty hain hakikat main aik quom ki buniad hoty hain. Sathion aik ustaad ka kaam hai ke woh humein jahalat ki tarikiyon sy nikal kr Ilm ki norani dunia main laty hain ustaad humain achhy bure ka fark btate hain akl o shaur ko nikharty hain aur humin is qabil banaty hain ke zindagi ke mrahl sy asani se guzar sakein. for evaluating this model."
] |
[
13,
87,
176
] |
[
"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: xysmalobia/pegasus-samsum\n* Dataset: xsum\n* Config: default\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.## Contributions\n\nThanks to @oh aziz principle asadza e akram aur mere aziz sathion, alslam o alikum mera naam nadia akram hai aur main jmat e nahm ki tulba hun meri aaj ki tkrir ka unwaan hai ustaad ka mukaam. aj jo hamari dars gahon main ustaad hoty hain hakikat main aik quom ki buniad hoty hain. Sathion aik ustaad ka kaam hai ke woh humein jahalat ki tarikiyon sy nikal kr Ilm ki norani dunia main laty hain ustaad humain achhy bure ka fark btate hain akl o shaur ko nikharty hain aur humin is qabil banaty hain ke zindagi ke mrahl sy asani se guzar sakein. for evaluating this model."
] |
ba095d57d36e3f29dfff897523631ed32cd64324
|
# 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: facebook/bart-large-xsum
* Dataset: gigaword
* Config: default
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@pkumark](https://huggingface.co/pkumark) for evaluating this model.
|
autoevaluate/autoeval-eval-gigaword-default-eb1b4a-58978145360
|
[
"autotrain",
"evaluation",
"region:us"
] |
2023-10-04T15:02:54+00:00
|
{"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["gigaword"], "eval_info": {"task": "summarization", "model": "facebook/bart-large-xsum", "metrics": ["bertscore"], "dataset_name": "gigaword", "dataset_config": "default", "dataset_split": "test", "col_mapping": {"text": "document", "target": "summary"}}}
|
2023-10-04T15:05:42+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: facebook/bart-large-xsum
* Dataset: gigaword
* Config: default
* Split: test
To run new evaluation jobs, visit Hugging Face's automatic model evaluator.
## Contributions
Thanks to @pkumark 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: facebook/bart-large-xsum\n* Dataset: gigaword\n* Config: default\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.",
"## Contributions\n\nThanks to @pkumark 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: facebook/bart-large-xsum\n* Dataset: gigaword\n* Config: default\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.",
"## Contributions\n\nThanks to @pkumark for evaluating this model."
] |
[
13,
84,
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: facebook/bart-large-xsum\n* Dataset: gigaword\n* Config: default\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.## Contributions\n\nThanks to @pkumark for evaluating this model."
] |
48d03ab4a77b573d1b3c5269d0d4d132605b0411
|
# 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: Alred/bart-base-finetuned-summarization-cnn-ver3
* Dataset: cnn_dailymail
* Config: 3.0.0
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@sini raj p](https://huggingface.co/sini raj p) for evaluating this model.
|
autoevaluate/autoeval-eval-cnn_dailymail-3.0.0-34156b-59952145381
|
[
"autotrain",
"evaluation",
"region:us"
] |
2023-10-04T15:03:30+00:00
|
{"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["cnn_dailymail"], "eval_info": {"task": "summarization", "model": "Alred/bart-base-finetuned-summarization-cnn-ver3", "metrics": ["rouge", "accuracy", "bleu", "exact_match", "f1", "perplexity", "recall", "precision", "roc_auc"], "dataset_name": "cnn_dailymail", "dataset_config": "3.0.0", "dataset_split": "test", "col_mapping": {"text": "article", "target": "highlights"}}}
|
2023-10-04T15:12:41+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: Alred/bart-base-finetuned-summarization-cnn-ver3
* Dataset: cnn_dailymail
* Config: 3.0.0
* Split: test
To run new evaluation jobs, visit Hugging Face's automatic model evaluator.
## Contributions
Thanks to @sini raj p 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: Alred/bart-base-finetuned-summarization-cnn-ver3\n* Dataset: cnn_dailymail\n* Config: 3.0.0\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.",
"## Contributions\n\nThanks to @sini raj p 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: Alred/bart-base-finetuned-summarization-cnn-ver3\n* Dataset: cnn_dailymail\n* Config: 3.0.0\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.",
"## Contributions\n\nThanks to @sini raj p for evaluating this model."
] |
[
13,
99,
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: Alred/bart-base-finetuned-summarization-cnn-ver3\n* Dataset: cnn_dailymail\n* Config: 3.0.0\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.## Contributions\n\nThanks to @sini raj p for evaluating this model."
] |
9cefac48a3a46ebbb05e2cd5f22b16f170d39738
|
# 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: thanathorn/mt5-cpe-kmutt-thai-sentence-sum
* Dataset: thaisum
* Config: thaisum
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@Kantaka](https://huggingface.co/Kantaka) for evaluating this model.
|
autoevaluate/autoeval-eval-thaisum-thaisum-0e2603-59838145376
|
[
"autotrain",
"evaluation",
"region:us"
] |
2023-10-04T15:04:53+00:00
|
{"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["thaisum"], "eval_info": {"task": "summarization", "model": "thanathorn/mt5-cpe-kmutt-thai-sentence-sum", "metrics": [], "dataset_name": "thaisum", "dataset_config": "thaisum", "dataset_split": "test", "col_mapping": {"text": "body", "target": "summary"}}}
|
2023-10-04T15:15:58+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: thanathorn/mt5-cpe-kmutt-thai-sentence-sum
* Dataset: thaisum
* Config: thaisum
* Split: test
To run new evaluation jobs, visit Hugging Face's automatic model evaluator.
## Contributions
Thanks to @Kantaka 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: thanathorn/mt5-cpe-kmutt-thai-sentence-sum\n* Dataset: thaisum\n* Config: thaisum\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.",
"## Contributions\n\nThanks to @Kantaka 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: thanathorn/mt5-cpe-kmutt-thai-sentence-sum\n* Dataset: thaisum\n* Config: thaisum\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.",
"## Contributions\n\nThanks to @Kantaka for evaluating this model."
] |
[
13,
96,
15
] |
[
"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: thanathorn/mt5-cpe-kmutt-thai-sentence-sum\n* Dataset: thaisum\n* Config: thaisum\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.## Contributions\n\nThanks to @Kantaka for evaluating this model."
] |
f305c883c09a221548648bf57d230d15cef7db91
|
# 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: 0x70DA/pegasus-cnn_dailymail
* Dataset: cnn_dailymail
* Config: 3.0.0
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@sini raj p](https://huggingface.co/sini raj p) for evaluating this model.
|
autoevaluate/autoeval-eval-cnn_dailymail-3.0.0-34156b-59952145380
|
[
"autotrain",
"evaluation",
"region:us"
] |
2023-10-04T15:05:30+00:00
|
{"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["cnn_dailymail"], "eval_info": {"task": "summarization", "model": "0x70DA/pegasus-cnn_dailymail", "metrics": ["rouge", "accuracy", "bleu", "exact_match", "f1", "perplexity", "recall", "precision", "roc_auc"], "dataset_name": "cnn_dailymail", "dataset_config": "3.0.0", "dataset_split": "test", "col_mapping": {"text": "article", "target": "highlights"}}}
|
2023-10-04T16:42:14+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: 0x70DA/pegasus-cnn_dailymail
* Dataset: cnn_dailymail
* Config: 3.0.0
* Split: test
To run new evaluation jobs, visit Hugging Face's automatic model evaluator.
## Contributions
Thanks to @sini raj p 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: 0x70DA/pegasus-cnn_dailymail\n* Dataset: cnn_dailymail\n* Config: 3.0.0\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.",
"## Contributions\n\nThanks to @sini raj p 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: 0x70DA/pegasus-cnn_dailymail\n* Dataset: cnn_dailymail\n* Config: 3.0.0\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.",
"## Contributions\n\nThanks to @sini raj p for evaluating this model."
] |
[
13,
93,
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: 0x70DA/pegasus-cnn_dailymail\n* Dataset: cnn_dailymail\n* Config: 3.0.0\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.## Contributions\n\nThanks to @sini raj p for evaluating this model."
] |
5da49a44819f0f8db34466125e76ca3804f0eaff
|
# 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: google/roberta2roberta_L-24_cnn_daily_mail
* Dataset: cnn_dailymail
* Config: 3.0.0
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@SINI RAJ P](https://huggingface.co/SINI RAJ P) for evaluating this model.
|
autoevaluate/autoeval-eval-cnn_dailymail-3.0.0-84482e-60145145395
|
[
"autotrain",
"evaluation",
"region:us"
] |
2023-10-04T15:06:13+00:00
|
{"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["cnn_dailymail"], "eval_info": {"task": "summarization", "model": "google/roberta2roberta_L-24_cnn_daily_mail", "metrics": [], "dataset_name": "cnn_dailymail", "dataset_config": "3.0.0", "dataset_split": "test", "col_mapping": {"text": "article", "target": "highlights"}}}
|
2023-10-04T16:41:33+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: google/roberta2roberta_L-24_cnn_daily_mail
* Dataset: cnn_dailymail
* Config: 3.0.0
* Split: test
To run new evaluation jobs, visit Hugging Face's automatic model evaluator.
## Contributions
Thanks to @SINI RAJ P 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: google/roberta2roberta_L-24_cnn_daily_mail\n* Dataset: cnn_dailymail\n* Config: 3.0.0\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.",
"## Contributions\n\nThanks to @SINI RAJ P 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: google/roberta2roberta_L-24_cnn_daily_mail\n* Dataset: cnn_dailymail\n* Config: 3.0.0\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.",
"## Contributions\n\nThanks to @SINI RAJ P for evaluating this model."
] |
[
13,
96,
18
] |
[
"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: google/roberta2roberta_L-24_cnn_daily_mail\n* Dataset: cnn_dailymail\n* Config: 3.0.0\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.## Contributions\n\nThanks to @SINI RAJ P for evaluating this model."
] |
15c1f010a40fddaa0ddc0231974a96079a631221
|
# Dataset Card for "appy-llama2-1k"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
ewre324/appy-llama2-1k
|
[
"region:us"
] |
2023-10-04T15:06:49+00:00
|
{"dataset_info": {"features": [{"name": "prompt", "dtype": "large_string"}, {"name": "main_topic", "dtype": "large_string"}, {"name": "subtopic", "dtype": "large_string"}, {"name": "adjective", "dtype": "large_string"}, {"name": "action_verb", "dtype": "large_string"}, {"name": "scenario", "dtype": "large_string"}, {"name": "target_audience", "dtype": "large_string"}, {"name": "programming_language", "dtype": "large_string"}, {"name": "common_sense_topic", "dtype": "large_string"}, {"name": "idx", "dtype": "int64"}, {"name": "response", "dtype": "large_string"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 236790880, "num_examples": 100000}], "download_size": 100584419, "dataset_size": 236790880}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-06T12:28:59+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "appy-llama2-1k"
More Information needed
|
[
"# Dataset Card for \"appy-llama2-1k\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"appy-llama2-1k\"\n\nMore Information needed"
] |
[
6,
17
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"appy-llama2-1k\"\n\nMore Information needed"
] |
7fe3ea35da9edbdc67c412fe69b73f9449b3106e
|
# 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: MohamedZaitoon/bart-fine-tune
* Dataset: cnn_dailymail
* Config: 3.0.0
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@sini raj p](https://huggingface.co/sini raj p) for evaluating this model.
|
autoevaluate/autoeval-eval-cnn_dailymail-3.0.0-34156b-59952145382
|
[
"autotrain",
"evaluation",
"region:us"
] |
2023-10-04T15:08:06+00:00
|
{"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["cnn_dailymail"], "eval_info": {"task": "summarization", "model": "MohamedZaitoon/bart-fine-tune", "metrics": ["rouge", "accuracy", "bleu", "exact_match", "f1", "perplexity", "recall", "precision", "roc_auc"], "dataset_name": "cnn_dailymail", "dataset_config": "3.0.0", "dataset_split": "test", "col_mapping": {"text": "article", "target": "highlights"}}}
|
2023-10-04T15:29:27+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: MohamedZaitoon/bart-fine-tune
* Dataset: cnn_dailymail
* Config: 3.0.0
* Split: test
To run new evaluation jobs, visit Hugging Face's automatic model evaluator.
## Contributions
Thanks to @sini raj p 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: MohamedZaitoon/bart-fine-tune\n* Dataset: cnn_dailymail\n* Config: 3.0.0\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.",
"## Contributions\n\nThanks to @sini raj p 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: MohamedZaitoon/bart-fine-tune\n* Dataset: cnn_dailymail\n* Config: 3.0.0\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.",
"## Contributions\n\nThanks to @sini raj p for evaluating this model."
] |
[
13,
89,
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: MohamedZaitoon/bart-fine-tune\n* Dataset: cnn_dailymail\n* Config: 3.0.0\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.## Contributions\n\nThanks to @sini raj p for evaluating this model."
] |
e2f9b53e4648b5b4bf76a503c26c59911466f371
|
# Bangumi Image Base of Gekkan Shoujo Nozaki-kun
This is the image base of bangumi Gekkan Shoujo Nozaki-kun, we detected 14 characters, 1916 images in total. The full dataset is [here](all.zip).
**Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual.** If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
| # | Images | Download | Preview 1 | Preview 2 | Preview 3 | Preview 4 | Preview 5 | Preview 6 | Preview 7 | Preview 8 |
|:------|---------:|:---------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|
| 0 | 491 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 10 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 11 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 12 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 777 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 29 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 20 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 117 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 182 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 14 | [Download](9/dataset.zip) |  |  |  |  |  |  |  |  |
| 10 | 93 | [Download](10/dataset.zip) |  |  |  |  |  |  |  |  |
| 11 | 30 | [Download](11/dataset.zip) |  |  |  |  |  |  |  |  |
| 12 | 9 | [Download](12/dataset.zip) |  |  |  |  |  |  |  |  |
| noise | 121 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
|
BangumiBase/gekkanshoujonozakikun
|
[
"size_categories:1K<n<10K",
"license:mit",
"art",
"region:us"
] |
2023-10-04T15:08:45+00:00
|
{"license": "mit", "size_categories": ["1K<n<10K"], "tags": ["art"]}
|
2023-10-04T16:18:33+00:00
|
[] |
[] |
TAGS
#size_categories-1K<n<10K #license-mit #art #region-us
|
Bangumi Image Base of Gekkan Shoujo Nozaki-kun
==============================================
This is the image base of bangumi Gekkan Shoujo Nozaki-kun, we detected 14 characters, 1916 images in total. The full dataset is here.
Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual. If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
|
[] |
[
"TAGS\n#size_categories-1K<n<10K #license-mit #art #region-us \n"
] |
[
25
] |
[
"passage: TAGS\n#size_categories-1K<n<10K #license-mit #art #region-us \n"
] |
db8787cbf1a6f9ce0a5ba158e1d5677d4848ed01
|
# Dataset Card for "Melanoma_resized"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
MegPaulson/Melanoma_resized
|
[
"region:us"
] |
2023-10-04T15:10:59+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "image_seg", "dtype": "image"}, {"name": "prompt", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 11692204.0, "num_examples": 26}], "download_size": 11702241, "dataset_size": 11692204.0}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-05T13:14:41+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "Melanoma_resized"
More Information needed
|
[
"# Dataset Card for \"Melanoma_resized\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"Melanoma_resized\"\n\nMore Information needed"
] |
[
6,
16
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"Melanoma_resized\"\n\nMore Information needed"
] |
ee02e2470d91125d5657bfb78ae6844cd25119a6
|
# 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: thanathorn/mt5-cpe-kmutt-thai-sentence-sum
* Dataset: thaisum
* Config: thaisum
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@Kantaka](https://huggingface.co/Kantaka) for evaluating this model.
|
autoevaluate/autoeval-eval-thaisum-thaisum-7581c9-59349145365
|
[
"autotrain",
"evaluation",
"region:us"
] |
2023-10-04T15:13:14+00:00
|
{"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["thaisum"], "eval_info": {"task": "summarization", "model": "thanathorn/mt5-cpe-kmutt-thai-sentence-sum", "metrics": [], "dataset_name": "thaisum", "dataset_config": "thaisum", "dataset_split": "test", "col_mapping": {"text": "body", "target": "summary"}}}
|
2023-10-04T15:24:22+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: thanathorn/mt5-cpe-kmutt-thai-sentence-sum
* Dataset: thaisum
* Config: thaisum
* Split: test
To run new evaluation jobs, visit Hugging Face's automatic model evaluator.
## Contributions
Thanks to @Kantaka 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: thanathorn/mt5-cpe-kmutt-thai-sentence-sum\n* Dataset: thaisum\n* Config: thaisum\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.",
"## Contributions\n\nThanks to @Kantaka 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: thanathorn/mt5-cpe-kmutt-thai-sentence-sum\n* Dataset: thaisum\n* Config: thaisum\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.",
"## Contributions\n\nThanks to @Kantaka for evaluating this model."
] |
[
13,
96,
15
] |
[
"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: thanathorn/mt5-cpe-kmutt-thai-sentence-sum\n* Dataset: thaisum\n* Config: thaisum\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.## Contributions\n\nThanks to @Kantaka for evaluating this model."
] |
53dd5b29b3c2a1a2a89f5d1a588f59782e3e9588
|
# 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: google/pegasus-xsum
* Dataset: xsum
* Config: default
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@zuzannad1](https://huggingface.co/zuzannad1) for evaluating this model.
|
autoevaluate/autoeval-eval-xsum-default-7c65dc-60294145404
|
[
"autotrain",
"evaluation",
"region:us"
] |
2023-10-04T15:16:50+00:00
|
{"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["xsum"], "eval_info": {"task": "summarization", "model": "google/pegasus-xsum", "metrics": ["bertscore"], "dataset_name": "xsum", "dataset_config": "default", "dataset_split": "test", "col_mapping": {"text": "document", "target": "summary"}}}
|
2023-10-04T15:50:54+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: google/pegasus-xsum
* Dataset: xsum
* Config: default
* Split: test
To run new evaluation jobs, visit Hugging Face's automatic model evaluator.
## Contributions
Thanks to @zuzannad1 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: google/pegasus-xsum\n* Dataset: xsum\n* Config: default\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.",
"## Contributions\n\nThanks to @zuzannad1 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: google/pegasus-xsum\n* Dataset: xsum\n* Config: default\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.",
"## Contributions\n\nThanks to @zuzannad1 for evaluating this model."
] |
[
13,
83,
17
] |
[
"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: google/pegasus-xsum\n* Dataset: xsum\n* Config: default\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.## Contributions\n\nThanks to @zuzannad1 for evaluating this model."
] |
916973e7413021b675bcbb9740b2d9d9ff389639
|
# 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: ARTeLab/it5-summarization-mlsum
* Dataset: cnn_dailymail
* Config: 3.0.0
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@SINI RAJ P](https://huggingface.co/SINI RAJ P) for evaluating this model.
|
autoevaluate/autoeval-eval-cnn_dailymail-3.0.0-84482e-60145145397
|
[
"autotrain",
"evaluation",
"region:us"
] |
2023-10-04T15:17:08+00:00
|
{"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["cnn_dailymail"], "eval_info": {"task": "summarization", "model": "ARTeLab/it5-summarization-mlsum", "metrics": [], "dataset_name": "cnn_dailymail", "dataset_config": "3.0.0", "dataset_split": "test", "col_mapping": {"text": "article", "target": "highlights"}}}
|
2023-10-04T15:26:58+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: ARTeLab/it5-summarization-mlsum
* Dataset: cnn_dailymail
* Config: 3.0.0
* Split: test
To run new evaluation jobs, visit Hugging Face's automatic model evaluator.
## Contributions
Thanks to @SINI RAJ P 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: ARTeLab/it5-summarization-mlsum\n* Dataset: cnn_dailymail\n* Config: 3.0.0\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.",
"## Contributions\n\nThanks to @SINI RAJ P 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: ARTeLab/it5-summarization-mlsum\n* Dataset: cnn_dailymail\n* Config: 3.0.0\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.",
"## Contributions\n\nThanks to @SINI RAJ P for evaluating this model."
] |
[
13,
91,
18
] |
[
"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: ARTeLab/it5-summarization-mlsum\n* Dataset: cnn_dailymail\n* Config: 3.0.0\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.## Contributions\n\nThanks to @SINI RAJ P for evaluating this model."
] |
57f60164cb858ab8c67847deae11085a1228b1d4
|
# 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: slauw87/bart_summarisation
* Dataset: xsum
* Config: default
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@zuzannad1](https://huggingface.co/zuzannad1) for evaluating this model.
|
autoevaluate/autoeval-eval-xsum-default-7c65dc-60294145405
|
[
"autotrain",
"evaluation",
"region:us"
] |
2023-10-04T15:17:16+00:00
|
{"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["xsum"], "eval_info": {"task": "summarization", "model": "slauw87/bart_summarisation", "metrics": ["bertscore"], "dataset_name": "xsum", "dataset_config": "default", "dataset_split": "test", "col_mapping": {"text": "document", "target": "summary"}}}
|
2023-10-04T16:06:57+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: slauw87/bart_summarisation
* Dataset: xsum
* Config: default
* Split: test
To run new evaluation jobs, visit Hugging Face's automatic model evaluator.
## Contributions
Thanks to @zuzannad1 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: slauw87/bart_summarisation\n* Dataset: xsum\n* Config: default\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.",
"## Contributions\n\nThanks to @zuzannad1 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: slauw87/bart_summarisation\n* Dataset: xsum\n* Config: default\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.",
"## Contributions\n\nThanks to @zuzannad1 for evaluating this model."
] |
[
13,
84,
17
] |
[
"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: slauw87/bart_summarisation\n* Dataset: xsum\n* Config: default\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.## Contributions\n\nThanks to @zuzannad1 for evaluating this model."
] |
4eb7f008e4a9297a02fbe445e5e7b5f2b570665f
|
# 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: morenolq/bart-base-xsum
* Dataset: xsum
* Config: default
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@zuzannad1](https://huggingface.co/zuzannad1) for evaluating this model.
|
autoevaluate/autoeval-eval-xsum-default-8e4fa8-60494145409
|
[
"autotrain",
"evaluation",
"region:us"
] |
2023-10-04T15:26:14+00:00
|
{"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["xsum"], "eval_info": {"task": "summarization", "model": "morenolq/bart-base-xsum", "metrics": [], "dataset_name": "xsum", "dataset_config": "default", "dataset_split": "test", "col_mapping": {"text": "document", "target": "summary"}}}
|
2023-10-04T15:34:53+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: morenolq/bart-base-xsum
* Dataset: xsum
* Config: default
* Split: test
To run new evaluation jobs, visit Hugging Face's automatic model evaluator.
## Contributions
Thanks to @zuzannad1 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: morenolq/bart-base-xsum\n* Dataset: xsum\n* Config: default\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.",
"## Contributions\n\nThanks to @zuzannad1 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: morenolq/bart-base-xsum\n* Dataset: xsum\n* Config: default\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.",
"## Contributions\n\nThanks to @zuzannad1 for evaluating this model."
] |
[
13,
85,
17
] |
[
"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: morenolq/bart-base-xsum\n* Dataset: xsum\n* Config: default\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.## Contributions\n\nThanks to @zuzannad1 for evaluating this model."
] |
223da0a1d4d4b5e6933643d0cda7bf166e3a65b4
|
# 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: sysresearch101/t5-large-finetuned-xsum
* Dataset: xsum
* Config: default
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@zuzannad1](https://huggingface.co/zuzannad1) for evaluating this model.
|
autoevaluate/autoeval-eval-xsum-default-e3e096-60495145411
|
[
"autotrain",
"evaluation",
"region:us"
] |
2023-10-04T15:27:30+00:00
|
{"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["xsum"], "eval_info": {"task": "summarization", "model": "sysresearch101/t5-large-finetuned-xsum", "metrics": ["bertscore"], "dataset_name": "xsum", "dataset_config": "default", "dataset_split": "test", "col_mapping": {"text": "document", "target": "summary"}}}
|
2023-10-04T15:56:55+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: sysresearch101/t5-large-finetuned-xsum
* Dataset: xsum
* Config: default
* Split: test
To run new evaluation jobs, visit Hugging Face's automatic model evaluator.
## Contributions
Thanks to @zuzannad1 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: sysresearch101/t5-large-finetuned-xsum\n* Dataset: xsum\n* Config: default\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.",
"## Contributions\n\nThanks to @zuzannad1 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: sysresearch101/t5-large-finetuned-xsum\n* Dataset: xsum\n* Config: default\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.",
"## Contributions\n\nThanks to @zuzannad1 for evaluating this model."
] |
[
13,
92,
17
] |
[
"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: sysresearch101/t5-large-finetuned-xsum\n* Dataset: xsum\n* Config: default\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.## Contributions\n\nThanks to @zuzannad1 for evaluating this model."
] |
9f1b44b28a93475561be6d178fddbdbe00cbc9b4
|
# 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: gauravkoradiya/T5-Fintuned-on-cnn_daily_mail
* Dataset: cnn_dailymail
* Config: 3.0.0
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@SINI RAJ P](https://huggingface.co/SINI RAJ P) for evaluating this model.
|
autoevaluate/autoeval-eval-cnn_dailymail-3.0.0-84482e-60145145396
|
[
"autotrain",
"evaluation",
"region:us"
] |
2023-10-04T15:31:41+00:00
|
{"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["cnn_dailymail"], "eval_info": {"task": "summarization", "model": "gauravkoradiya/T5-Fintuned-on-cnn_daily_mail", "metrics": [], "dataset_name": "cnn_dailymail", "dataset_config": "3.0.0", "dataset_split": "test", "col_mapping": {"text": "article", "target": "highlights"}}}
|
2023-10-04T15:35:02+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: gauravkoradiya/T5-Fintuned-on-cnn_daily_mail
* Dataset: cnn_dailymail
* Config: 3.0.0
* Split: test
To run new evaluation jobs, visit Hugging Face's automatic model evaluator.
## Contributions
Thanks to @SINI RAJ P 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: gauravkoradiya/T5-Fintuned-on-cnn_daily_mail\n* Dataset: cnn_dailymail\n* Config: 3.0.0\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.",
"## Contributions\n\nThanks to @SINI RAJ P 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: gauravkoradiya/T5-Fintuned-on-cnn_daily_mail\n* Dataset: cnn_dailymail\n* Config: 3.0.0\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.",
"## Contributions\n\nThanks to @SINI RAJ P for evaluating this model."
] |
[
13,
98,
18
] |
[
"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: gauravkoradiya/T5-Fintuned-on-cnn_daily_mail\n* Dataset: cnn_dailymail\n* Config: 3.0.0\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.## Contributions\n\nThanks to @SINI RAJ P for evaluating this model."
] |
7e9ec1b8b02662a462590b924aeb3bef2d5626f5
|
# Dataset Card for "ppo-Pendulum-v1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
ernestum/ppo-Pendulum-v1
|
[
"region:us"
] |
2023-10-04T15:32:23+00:00
|
{"dataset_info": {"features": [{"name": "obs", "sequence": {"sequence": "float32"}}, {"name": "acts", "sequence": {"sequence": "float32"}}, {"name": "infos", "sequence": "string"}, {"name": "terminal", "dtype": "bool"}, {"name": "rews", "sequence": "float32"}], "splits": [{"name": "train", "num_bytes": 2575710, "num_examples": 200}], "download_size": 940375, "dataset_size": 2575710}}
|
2023-10-04T15:32:27+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "ppo-Pendulum-v1"
More Information needed
|
[
"# Dataset Card for \"ppo-Pendulum-v1\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"ppo-Pendulum-v1\"\n\nMore Information needed"
] |
[
6,
18
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"ppo-Pendulum-v1\"\n\nMore Information needed"
] |
b4e0eb7f84d90a793dc4c4094797323c74c31ed6
|
# Dataset Card for "4390ae17"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
result-kand2-sdxl-wuerst-karlo/4390ae17
|
[
"region:us"
] |
2023-10-04T15:37:16+00:00
|
{"dataset_info": {"features": [{"name": "result", "dtype": "string"}, {"name": "id", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 175, "num_examples": 10}], "download_size": 1353, "dataset_size": 175}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-04T15:37:17+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "4390ae17"
More Information needed
|
[
"# Dataset Card for \"4390ae17\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"4390ae17\"\n\nMore Information needed"
] |
[
6,
15
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"4390ae17\"\n\nMore Information needed"
] |
68422a56eb025ae56cbf1e6d0d3e285e52677e92
|
# Dataset Card for "yahoo_dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
finiteautomata/yahoo_dataset
|
[
"region:us"
] |
2023-10-04T15:39:31+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "id", "dtype": "int32"}, {"name": "topic", "dtype": {"class_label": {"names": {"0": "Society & Culture", "1": "Science & Mathematics", "2": "Health", "3": "Education & Reference", "4": "Computers & Internet", "5": "Sports", "6": "Business & Finance", "7": "Entertainment & Music", "8": "Family & Relationships", "9": "Politics & Government"}}}}, {"name": "question_title", "dtype": "string"}, {"name": "question_content", "dtype": "string"}, {"name": "best_answer", "dtype": "string"}, {"name": "question_title_embeddings", "sequence": "float32"}, {"name": "question_content_embeddings", "sequence": "float32"}, {"name": "best_answer_embeddings", "sequence": "float32"}], "splits": [{"name": "train", "num_bytes": 1032387680, "num_examples": 200000}, {"name": "test", "num_bytes": 309853862, "num_examples": 60000}], "download_size": 500190426, "dataset_size": 1342241542}}
|
2023-10-04T17:34:26+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "yahoo_dataset"
More Information needed
|
[
"# Dataset Card for \"yahoo_dataset\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"yahoo_dataset\"\n\nMore Information needed"
] |
[
6,
14
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"yahoo_dataset\"\n\nMore Information needed"
] |
19de956ab4afe3f8883a915478acc47935f7b77b
|
# 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: umarkhalid96/t5-small-train
* Dataset: math_dataset
* Config: algebra__linear_1d
* Split: train
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@[email protected]](https://huggingface.co/[email protected]) for evaluating this model.
|
autoevaluate/autoeval-eval-math_dataset-algebra__linear_1d-400643-61492145434
|
[
"autotrain",
"evaluation",
"region:us"
] |
2023-10-04T15:40:13+00:00
|
{"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["math_dataset"], "eval_info": {"task": "summarization", "model": "umarkhalid96/t5-small-train", "metrics": ["accuracy", "cer"], "dataset_name": "math_dataset", "dataset_config": "algebra__linear_1d", "dataset_split": "train", "col_mapping": {"text": "question", "target": "answer"}}}
|
2023-10-04T17:04:21+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: umarkhalid96/t5-small-train
* Dataset: math_dataset
* Config: algebra__linear_1d
* Split: train
To run new evaluation jobs, visit Hugging Face's automatic model evaluator.
## Contributions
Thanks to @Jesuscarrasco@URL 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: umarkhalid96/t5-small-train\n* Dataset: math_dataset\n* Config: algebra__linear_1d\n* Split: train\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.",
"## Contributions\n\nThanks to @Jesuscarrasco@URL 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: umarkhalid96/t5-small-train\n* Dataset: math_dataset\n* Config: algebra__linear_1d\n* Split: train\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.",
"## Contributions\n\nThanks to @Jesuscarrasco@URL for evaluating this model."
] |
[
13,
97,
20
] |
[
"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: umarkhalid96/t5-small-train\n* Dataset: math_dataset\n* Config: algebra__linear_1d\n* Split: train\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.## Contributions\n\nThanks to @Jesuscarrasco@URL for evaluating this model."
] |
103f3fbf8663b769502645e42a0b3c3236106475
|
# 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: Alred/bart-base-finetuned-summarization-cnn-ver1.1
* Dataset: adversarial_qa
* Config: dbert
* Split: validation
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@kritirwa](https://huggingface.co/kritirwa) for evaluating this model.
|
autoevaluate/autoeval-eval-adversarial_qa-dbert-5b493d-61553145435
|
[
"autotrain",
"evaluation",
"region:us"
] |
2023-10-04T15:41:21+00:00
|
{"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["adversarial_qa"], "eval_info": {"task": "summarization", "model": "Alred/bart-base-finetuned-summarization-cnn-ver1.1", "metrics": ["google_bleu"], "dataset_name": "adversarial_qa", "dataset_config": "dbert", "dataset_split": "validation", "col_mapping": {"text": "question", "target": "context"}}}
|
2023-10-04T15:42:23+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: Alred/bart-base-finetuned-summarization-cnn-ver1.1
* Dataset: adversarial_qa
* Config: dbert
* Split: validation
To run new evaluation jobs, visit Hugging Face's automatic model evaluator.
## Contributions
Thanks to @kritirwa 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: Alred/bart-base-finetuned-summarization-cnn-ver1.1\n* Dataset: adversarial_qa\n* Config: dbert\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.",
"## Contributions\n\nThanks to @kritirwa 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: Alred/bart-base-finetuned-summarization-cnn-ver1.1\n* Dataset: adversarial_qa\n* Config: dbert\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.",
"## Contributions\n\nThanks to @kritirwa for evaluating this model."
] |
[
13,
99,
15
] |
[
"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: Alred/bart-base-finetuned-summarization-cnn-ver1.1\n* Dataset: adversarial_qa\n* Config: dbert\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.## Contributions\n\nThanks to @kritirwa for evaluating this model."
] |
874ddb3a8b461192f864ffcda2fc8e6d7fd72efd
|
# Dataset Card for "dataset-abstracts"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
librarian-bots/dataset-abstracts
|
[
"size_categories:n<1K",
"language:en",
"region:us"
] |
2023-10-04T15:41:47+00:00
|
{"language": ["en"], "size_categories": ["n<1K"], "configs": [{"config_name": "abstracts", "data_files": [{"split": "train", "path": "abstracts/train-*"}, {"split": "test", "path": "abstracts/test-*"}]}, {"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}]}], "dataset_info": [{"config_name": "abstracts", "features": [{"name": "text", "dtype": "string"}, {"name": "inputs", "struct": [{"name": "abstract", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "url", "dtype": "string"}]}, {"name": "prediction", "dtype": "null"}, {"name": "prediction_agent", "dtype": "null"}, {"name": "annotation", "dtype": "string"}, {"name": "annotation_agent", "dtype": "string"}, {"name": "vectors", "dtype": "null"}, {"name": "multi_label", "dtype": "bool"}, {"name": "explanation", "dtype": "null"}, {"name": "id", "dtype": "string"}, {"name": "metadata", "dtype": "null"}, {"name": "status", "dtype": "string"}, {"name": "metrics", "struct": [{"name": "text_length", "dtype": "int64"}]}, {"name": "label", "dtype": {"class_label": {"names": {"0": "new_dataset", "1": "no_new_dataset"}}}}], "splits": [{"name": "train", "num_bytes": 56302.166666666664, "num_examples": 21}, {"name": "test", "num_bytes": 40215.833333333336, "num_examples": 15}], "download_size": 102778, "dataset_size": 96518.0}, {"config_name": "default", "features": [{"name": "text", "dtype": "string"}, {"name": "inputs", "struct": [{"name": "abstract", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "url", "dtype": "string"}]}, {"name": "prediction", "dtype": "null"}, {"name": "prediction_agent", "dtype": "null"}, {"name": "annotation", "dtype": "string"}, {"name": "annotation_agent", "dtype": "string"}, {"name": "vectors", "dtype": "null"}, {"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"}]}, {"name": "label", "dtype": {"class_label": {"names": {"0": "new_dataset", "1": "no_new_dataset"}}}}], "splits": [{"name": "train", "num_bytes": 56470.166666666664, "num_examples": 21}, {"name": "test", "num_bytes": 40335.833333333336, "num_examples": 15}], "download_size": 104180, "dataset_size": 96806}]}
|
2023-10-04T15:57:41+00:00
|
[] |
[
"en"
] |
TAGS
#size_categories-n<1K #language-English #region-us
|
# Dataset Card for "dataset-abstracts"
More Information needed
|
[
"# Dataset Card for \"dataset-abstracts\"\n\nMore Information needed"
] |
[
"TAGS\n#size_categories-n<1K #language-English #region-us \n",
"# Dataset Card for \"dataset-abstracts\"\n\nMore Information needed"
] |
[
20,
17
] |
[
"passage: TAGS\n#size_categories-n<1K #language-English #region-us \n# Dataset Card for \"dataset-abstracts\"\n\nMore Information needed"
] |
e271057fa09b4662f7d020f998fdb648935cec18
|
# Dataset Card for Evaluation run of 3050
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/3050
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [3050](https://huggingface.co/3050) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 406 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 116 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("HuggingFaceBR4/thomwolf-small-llama",
"harness_winogrande_0_small_llama_1p82G_the_pile_eval_3050_parquet",
split="train")
```
## Latest results
These are the [latest results from run ](https://huggingface.co/datasets/HuggingFaceBR4/thomwolf-small-llama/blob/main/0/results_small-llama-1p82G-the-pile_eval.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"acc": 0.3484644210447005,
"acc_stderr": 0.011027112856079687,
"acc_norm": 0.31556595066284593,
"acc_norm_stderr": 0.0116539643464742
},
"harness|arc:challenge|0": {
"acc": 0.2167235494880546,
"acc_stderr": 0.012040156713481189,
"acc_norm": 0.2645051194539249,
"acc_norm_stderr": 0.01288927294931337
},
"harness|arc:easy|0": {
"acc": 0.25126262626262624,
"acc_stderr": 0.008900141191221627,
"acc_norm": 0.24621212121212122,
"acc_norm_stderr": 0.008839902656771871
},
"harness|hellaswag|0": {
"acc": 0.25343557060346544,
"acc_stderr": 0.0043408916733205065,
"acc_norm": 0.25731925911173076,
"acc_norm_stderr": 0.00436263363737448
},
"harness|openbookqa|0": {
"acc": 0.188,
"acc_stderr": 0.017490678880346222,
"acc_norm": 0.3,
"acc_norm_stderr": 0.02051442622562805
},
"harness|piqa|0": {
"acc": 0.5380848748639826,
"acc_stderr": 0.011631933367846709,
"acc_norm": 0.5097932535364527,
"acc_norm_stderr": 0.011663586263283223
},
"harness|super_glue:boolq|0": {
"acc": 0.4795107033639144,
"acc_stderr": 0.008737709345935943
},
"harness|winogrande|0": {
"acc": 0.5122336227308603,
"acc_stderr": 0.014048278820405621
}
}
```
### 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]
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|
HuggingFaceBR4/thomwolf-small-llama
|
[
"region:us"
] |
2023-10-04T15:42:04+00:00
|
{"pretty_name": "Evaluation run of 3050", "dataset_summary": "Dataset automatically created during the evaluation run of model [3050](https://huggingface.co/3050) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 406 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 116 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"HuggingFaceBR4/thomwolf-small-llama\",\n\t\"harness_winogrande_0_small_llama_1p82G_the_pile_eval_3050_parquet\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run ](https://huggingface.co/datasets/HuggingFaceBR4/thomwolf-small-llama/blob/main/0/results_small-llama-1p82G-the-pile_eval.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.3484644210447005,\n \"acc_stderr\": 0.011027112856079687,\n \"acc_norm\": 0.31556595066284593,\n \"acc_norm_stderr\": 0.0116539643464742\n },\n \"harness|arc:challenge|0\": {\n \"acc\": 0.2167235494880546,\n \"acc_stderr\": 0.012040156713481189,\n \"acc_norm\": 0.2645051194539249,\n \"acc_norm_stderr\": 0.01288927294931337\n },\n \"harness|arc:easy|0\": {\n \"acc\": 0.25126262626262624,\n \"acc_stderr\": 0.008900141191221627,\n \"acc_norm\": 0.24621212121212122,\n \"acc_norm_stderr\": 0.008839902656771871\n },\n \"harness|hellaswag|0\": {\n \"acc\": 0.25343557060346544,\n \"acc_stderr\": 0.0043408916733205065,\n \"acc_norm\": 0.25731925911173076,\n \"acc_norm_stderr\": 0.00436263363737448\n },\n \"harness|openbookqa|0\": {\n \"acc\": 0.188,\n \"acc_stderr\": 0.017490678880346222,\n \"acc_norm\": 0.3,\n \"acc_norm_stderr\": 0.02051442622562805\n },\n \"harness|piqa|0\": {\n \"acc\": 0.5380848748639826,\n \"acc_stderr\": 0.011631933367846709,\n \"acc_norm\": 0.5097932535364527,\n \"acc_norm_stderr\": 0.011663586263283223\n },\n \"harness|super_glue:boolq|0\": {\n \"acc\": 0.4795107033639144,\n \"acc_stderr\": 0.008737709345935943\n },\n \"harness|winogrande|0\": {\n \"acc\": 0.5122336227308603,\n \"acc_stderr\": 0.014048278820405621\n }\n}\n```", "repo_url": "https://huggingface.co/3050", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_arc_challenge_0_small_llama_1p82G_the_pile_eval1000_parquet", "data_files": [{"split": "small_llama_1p82G_the_pile_eval1000", "path": ["**/details_harness|arc:challenge|0_small-llama-1p82G-the-pile_eval1000.parquet"]}]}, {"config_name": "harness_arc_challenge_0_small_llama_1p82G_the_pile_eval100_parquet", "data_files": [{"split": "small_llama_1p82G_the_pile_eval100", "path": ["**/details_harness|arc:challenge|0_small-llama-1p82G-the-pile_eval100.parquet"]}]}, {"config_name": "harness_arc_challenge_0_small_llama_1p82G_the_pile_eval1050_parquet", "data_files": [{"split": "small_llama_1p82G_the_pile_eval1050", "path": ["**/details_harness|arc:challenge|0_small-llama-1p82G-the-pile_eval1050.parquet"]}]}, {"config_name": "harness_arc_challenge_0_small_llama_1p82G_the_pile_eval1100_parquet", "data_files": [{"split": "small_llama_1p82G_the_pile_eval1100", "path": ["**/details_harness|arc:challenge|0_small-llama-1p82G-the-pile_eval1100.parquet"]}]}, {"config_name": "harness_arc_challenge_0_small_llama_1p82G_the_pile_eval1150_parquet", "data_files": [{"split": "small_llama_1p82G_the_pile_eval1150", "path": ["**/details_harness|arc:challenge|0_small-llama-1p82G-the-pile_eval1150.parquet"]}]}, {"config_name": "harness_arc_challenge_0_small_llama_1p82G_the_pile_eval1200_parquet", "data_files": [{"split": "small_llama_1p82G_the_pile_eval1200", "path": ["**/details_harness|arc:challenge|0_small-llama-1p82G-the-pile_eval1200.parquet"]}]}, {"config_name": "harness_arc_challenge_0_small_llama_1p82G_the_pile_eval1250_parquet", "data_files": [{"split": "small_llama_1p82G_the_pile_eval1250", "path": ["**/details_harness|arc:challenge|0_small-llama-1p82G-the-pile_eval1250.parquet"]}]}, {"config_name": "harness_arc_challenge_0_small_llama_1p82G_the_pile_eval1300_parquet", "data_files": [{"split": "small_llama_1p82G_the_pile_eval1300", "path": ["**/details_harness|arc:challenge|0_small-llama-1p82G-the-pile_eval1300.parquet"]}]}, {"config_name": "harness_arc_challenge_0_small_llama_1p82G_the_pile_eval1350_parquet", "data_files": [{"split": "small_llama_1p82G_the_pile_eval1350", "path": ["**/details_harness|arc:challenge|0_small-llama-1p82G-the-pile_eval1350.parquet"]}]}, {"config_name": "harness_arc_challenge_0_small_llama_1p82G_the_pile_eval1400_parquet", "data_files": [{"split": "small_llama_1p82G_the_pile_eval1400", "path": ["**/details_harness|arc:challenge|0_small-llama-1p82G-the-pile_eval1400.parquet"]}]}, {"config_name": "harness_arc_challenge_0_small_llama_1p82G_the_pile_eval1450_parquet", "data_files": [{"split": "small_llama_1p82G_the_pile_eval1450", "path": ["**/details_harness|arc:challenge|0_small-llama-1p82G-the-pile_eval1450.parquet"]}]}, {"config_name": "harness_arc_challenge_0_small_llama_1p82G_the_pile_eval1500_parquet", "data_files": [{"split": "small_llama_1p82G_the_pile_eval1500", "path": ["**/details_harness|arc:challenge|0_small-llama-1p82G-the-pile_eval1500.parquet"]}]}, {"config_name": "harness_arc_challenge_0_small_llama_1p82G_the_pile_eval150_parquet", "data_files": [{"split": "small_llama_1p82G_the_pile_eval150", "path": ["**/details_harness|arc:challenge|0_small-llama-1p82G-the-pile_eval150.parquet"]}]}, {"config_name": "harness_arc_challenge_0_small_llama_1p82G_the_pile_eval1550_parquet", "data_files": [{"split": "small_llama_1p82G_the_pile_eval1550", "path": ["**/details_harness|arc:challenge|0_small-llama-1p82G-the-pile_eval1550.parquet"]}]}, {"config_name": "harness_arc_challenge_0_small_llama_1p82G_the_pile_eval200_parquet", "data_files": [{"split": "small_llama_1p82G_the_pile_eval200", "path": ["**/details_harness|arc:challenge|0_small-llama-1p82G-the-pile_eval200.parquet"]}]}, {"config_name": "harness_arc_challenge_0_small_llama_1p82G_the_pile_eval250_parquet", "data_files": [{"split": "small_llama_1p82G_the_pile_eval250", "path": ["**/details_harness|arc:challenge|0_small-llama-1p82G-the-pile_eval250.parquet"]}]}, {"config_name": "harness_arc_challenge_0_small_llama_1p82G_the_pile_eval300_parquet", "data_files": [{"split": "small_llama_1p82G_the_pile_eval300", "path": ["**/details_harness|arc:challenge|0_small-llama-1p82G-the-pile_eval300.parquet"]}]}, {"config_name": "harness_arc_challenge_0_small_llama_1p82G_the_pile_eval350_parquet", "data_files": [{"split": "small_llama_1p82G_the_pile_eval350", "path": ["**/details_harness|arc:challenge|0_small-llama-1p82G-the-pile_eval350.parquet"]}]}, {"config_name": "harness_arc_challenge_0_small_llama_1p82G_the_pile_eval400_parquet", "data_files": [{"split": "small_llama_1p82G_the_pile_eval400", "path": ["**/details_harness|arc:challenge|0_small-llama-1p82G-the-pile_eval400.parquet"]}]}, {"config_name": "harness_arc_challenge_0_small_llama_1p82G_the_pile_eval450_parquet", "data_files": [{"split": "small_llama_1p82G_the_pile_eval450", "path": ["**/details_harness|arc:challenge|0_small-llama-1p82G-the-pile_eval450.parquet"]}]}, {"config_name": "harness_arc_challenge_0_small_llama_1p82G_the_pile_eval500_parquet", "data_files": [{"split": "small_llama_1p82G_the_pile_eval500", "path": ["**/details_harness|arc:challenge|0_small-llama-1p82G-the-pile_eval500.parquet"]}]}, {"config_name": "harness_arc_challenge_0_small_llama_1p82G_the_pile_eval50_parquet", "data_files": [{"split": "small_llama_1p82G_the_pile_eval50", "path": ["**/details_harness|arc:challenge|0_small-llama-1p82G-the-pile_eval50.parquet"]}]}, {"config_name": "harness_arc_challenge_0_small_llama_1p82G_the_pile_eval550_parquet", "data_files": [{"split": "small_llama_1p82G_the_pile_eval550", "path": ["**/details_harness|arc:challenge|0_small-llama-1p82G-the-pile_eval550.parquet"]}]}, {"config_name": "harness_arc_challenge_0_small_llama_1p82G_the_pile_eval600_parquet", "data_files": [{"split": "small_llama_1p82G_the_pile_eval600", "path": ["**/details_harness|arc:challenge|0_small-llama-1p82G-the-pile_eval600.parquet"]}]}, {"config_name": "harness_arc_challenge_0_small_llama_1p82G_the_pile_eval650_parquet", "data_files": [{"split": "small_llama_1p82G_the_pile_eval650", "path": ["**/details_harness|arc:challenge|0_small-llama-1p82G-the-pile_eval650.parquet"]}]}, {"config_name": "harness_arc_challenge_0_small_llama_1p82G_the_pile_eval700_parquet", "data_files": [{"split": "small_llama_1p82G_the_pile_eval700", "path": 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["**/details_harness|arc:challenge|0_small-llama-1p82G-the-pile_eval_1700.parquet"]}]}, {"config_name": "harness_arc_challenge_0_small_llama_1p82G_the_pile_eval_1750_parquet", "data_files": [{"split": "small_llama_1p82G_the_pile_eval_1750", "path": ["**/details_harness|arc:challenge|0_small-llama-1p82G-the-pile_eval_1750.parquet"]}]}, {"config_name": "harness_arc_challenge_0_small_llama_1p82G_the_pile_eval_1800_parquet", "data_files": [{"split": "small_llama_1p82G_the_pile_eval_1800", "path": ["**/details_harness|arc:challenge|0_small-llama-1p82G-the-pile_eval_1800.parquet"]}]}, {"config_name": "harness_arc_challenge_0_small_llama_1p82G_the_pile_eval_1850_parquet", "data_files": [{"split": "small_llama_1p82G_the_pile_eval_1850", "path": ["**/details_harness|arc:challenge|0_small-llama-1p82G-the-pile_eval_1850.parquet"]}]}, {"config_name": "harness_arc_challenge_0_small_llama_1p82G_the_pile_eval_1900_parquet", "data_files": [{"split": "small_llama_1p82G_the_pile_eval_1900", "path": 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["**/details_harness|arc:challenge|0_small-llama-1p82G-the-pile_eval_2100.parquet"]}]}, {"config_name": "harness_arc_challenge_0_small_llama_1p82G_the_pile_eval_2150_parquet", "data_files": [{"split": "small_llama_1p82G_the_pile_eval_2150", "path": ["**/details_harness|arc:challenge|0_small-llama-1p82G-the-pile_eval_2150.parquet"]}]}, {"config_name": "harness_arc_challenge_0_small_llama_1p82G_the_pile_eval_2200_parquet", "data_files": [{"split": "small_llama_1p82G_the_pile_eval_2200", "path": ["**/details_harness|arc:challenge|0_small-llama-1p82G-the-pile_eval_2200.parquet"]}]}, {"config_name": "harness_arc_challenge_0_small_llama_1p82G_the_pile_eval_2250_parquet", "data_files": [{"split": "small_llama_1p82G_the_pile_eval_2250", "path": ["**/details_harness|arc:challenge|0_small-llama-1p82G-the-pile_eval_2250.parquet"]}]}, {"config_name": "harness_arc_challenge_0_small_llama_1p82G_the_pile_eval_2300_parquet", "data_files": [{"split": "small_llama_1p82G_the_pile_eval_2300", "path": ["**/details_harness|arc:challenge|0_small-llama-1p82G-the-pile_eval_2300.parquet"]}]}, {"config_name": "harness_arc_challenge_0_small_llama_1p82G_the_pile_eval_2350_parquet", "data_files": [{"split": "small_llama_1p82G_the_pile_eval_2350", "path": ["**/details_harness|arc:challenge|0_small-llama-1p82G-the-pile_eval_2350.parquet"]}]}, {"config_name": "harness_arc_challenge_0_small_llama_1p82G_the_pile_eval_2400_parquet", "data_files": [{"split": "small_llama_1p82G_the_pile_eval_2400", "path": ["**/details_harness|arc:challenge|0_small-llama-1p82G-the-pile_eval_2400.parquet"]}]}, {"config_name": "harness_arc_challenge_0_small_llama_1p82G_the_pile_eval_2450_parquet", "data_files": [{"split": "small_llama_1p82G_the_pile_eval_2450", "path": ["**/details_harness|arc:challenge|0_small-llama-1p82G-the-pile_eval_2450.parquet"]}]}, {"config_name": "harness_arc_challenge_0_small_llama_1p82G_the_pile_eval_2500_parquet", "data_files": [{"split": "small_llama_1p82G_the_pile_eval_2500", "path": ["**/details_harness|arc:challenge|0_small-llama-1p82G-the-pile_eval_2500.parquet"]}]}, {"config_name": "harness_arc_challenge_0_small_llama_1p82G_the_pile_eval_2750_parquet", "data_files": [{"split": "small_llama_1p82G_the_pile_eval_2750", "path": ["**/details_harness|arc:challenge|0_small-llama-1p82G-the-pile_eval_2750.parquet"]}]}, {"config_name": "harness_arc_challenge_0_small_llama_1p82G_the_pile_eval_2800_parquet", "data_files": [{"split": "small_llama_1p82G_the_pile_eval_2800", "path": ["**/details_harness|arc:challenge|0_small-llama-1p82G-the-pile_eval_2800.parquet"]}]}, {"config_name": "harness_arc_challenge_0_small_llama_1p82G_the_pile_eval_2850_parquet", "data_files": [{"split": "small_llama_1p82G_the_pile_eval_2850", "path": ["**/details_harness|arc:challenge|0_small-llama-1p82G-the-pile_eval_2850.parquet"]}]}, {"config_name": "harness_arc_challenge_0_small_llama_1p82G_the_pile_eval_2900_parquet", "data_files": [{"split": "small_llama_1p82G_the_pile_eval_2900", "path": ["**/details_harness|arc:challenge|0_small-llama-1p82G-the-pile_eval_2900.parquet"]}]}, {"config_name": "harness_arc_challenge_0_small_llama_1p82G_the_pile_eval_2950_parquet", "data_files": [{"split": "small_llama_1p82G_the_pile_eval_2950", "path": ["**/details_harness|arc:challenge|0_small-llama-1p82G-the-pile_eval_2950.parquet"]}]}, {"config_name": "harness_arc_challenge_0_small_llama_1p82G_the_pile_eval_3000_parquet", "data_files": [{"split": "small_llama_1p82G_the_pile_eval_3000", "path": ["**/details_harness|arc:challenge|0_small-llama-1p82G-the-pile_eval_3000.parquet"]}]}, {"config_name": "harness_arc_challenge_0_small_llama_1p82G_the_pile_eval_3050_parquet", "data_files": [{"split": "small_llama_1p82G_the_pile_eval_3050", "path": ["**/details_harness|arc:challenge|0_small-llama-1p82G-the-pile_eval_3050.parquet"]}]}, {"config_name": "harness_arc_challenge_0_small_llama_1p82G_the_pile_eval_parquet", "data_files": [{"split": "small_llama_1p82G_the_pile_eval", "path": 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{"split": "small_llama_1p82G_the_pile_eval_2200", "path": ["results_small-llama-1p82G-the-pile_eval_2200.parquet"]}, {"split": "small_llama_1p82G_the_pile_eval_2250", "path": ["results_small-llama-1p82G-the-pile_eval_2250.parquet"]}, {"split": "small_llama_1p82G_the_pile_eval_2300", "path": ["results_small-llama-1p82G-the-pile_eval_2300.parquet"]}, {"split": "small_llama_1p82G_the_pile_eval_2350", "path": ["results_small-llama-1p82G-the-pile_eval_2350.parquet"]}, {"split": "small_llama_1p82G_the_pile_eval_2400", "path": ["results_small-llama-1p82G-the-pile_eval_2400.parquet"]}, {"split": "small_llama_1p82G_the_pile_eval_2450", "path": ["results_small-llama-1p82G-the-pile_eval_2450.parquet"]}, {"split": "small_llama_1p82G_the_pile_eval_2500", "path": ["results_small-llama-1p82G-the-pile_eval_2500.parquet"]}, {"split": "small_llama_1p82G_the_pile_eval_2750", "path": ["results_small-llama-1p82G-the-pile_eval_2750.parquet"]}, {"split": "small_llama_1p82G_the_pile_eval_2800", "path": ["results_small-llama-1p82G-the-pile_eval_2800.parquet"]}, {"split": "small_llama_1p82G_the_pile_eval_2850", "path": ["results_small-llama-1p82G-the-pile_eval_2850.parquet"]}, {"split": "small_llama_1p82G_the_pile_eval_2900", "path": ["results_small-llama-1p82G-the-pile_eval_2900.parquet"]}, {"split": "small_llama_1p82G_the_pile_eval_2950", "path": ["results_small-llama-1p82G-the-pile_eval_2950.parquet"]}, {"split": "small_llama_1p82G_the_pile_eval_3000", "path": ["results_small-llama-1p82G-the-pile_eval_3000.parquet"]}, {"split": "small_llama_1p82G_the_pile_eval_3050", "path": ["results_small-llama-1p82G-the-pile_eval_3050.parquet"]}]}]}
|
2023-10-04T19:57:15+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Evaluation run of 3050
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model 3050 on the Open LLM Leaderboard.
The dataset is composed of 406 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 116 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run (note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
### 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 Evaluation run of 3050",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model 3050 on the Open LLM Leaderboard.\n\nThe dataset is composed of 406 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 116 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run (note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### 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?",
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"#### 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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"# Dataset Card for Evaluation run of 3050",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model 3050 on the Open LLM Leaderboard.\n\nThe dataset is composed of 406 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 116 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run (note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### 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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[
"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of 3050## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model 3050 on the Open LLM Leaderboard.\n\nThe dataset is composed of 406 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 116 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run (note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):### 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"
] |
6c283e231726d14b07f57520cbac21b8c8188bc5
|
# 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: facebook/bart-large-xsum
* Dataset: xsum
* Config: default
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@zuzannad1](https://huggingface.co/zuzannad1) for evaluating this model.
|
autoevaluate/autoeval-eval-xsum-default-7c65dc-60294145402
|
[
"autotrain",
"evaluation",
"region:us"
] |
2023-10-04T15:43:54+00:00
|
{"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["xsum"], "eval_info": {"task": "summarization", "model": "facebook/bart-large-xsum", "metrics": ["bertscore"], "dataset_name": "xsum", "dataset_config": "default", "dataset_split": "test", "col_mapping": {"text": "document", "target": "summary"}}}
|
2023-10-04T16:14:05+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: facebook/bart-large-xsum
* Dataset: xsum
* Config: default
* Split: test
To run new evaluation jobs, visit Hugging Face's automatic model evaluator.
## Contributions
Thanks to @zuzannad1 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: facebook/bart-large-xsum\n* Dataset: xsum\n* Config: default\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.",
"## Contributions\n\nThanks to @zuzannad1 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: facebook/bart-large-xsum\n* Dataset: xsum\n* Config: default\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.",
"## Contributions\n\nThanks to @zuzannad1 for evaluating this model."
] |
[
13,
84,
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[
"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: facebook/bart-large-xsum\n* Dataset: xsum\n* Config: default\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.## Contributions\n\nThanks to @zuzannad1 for evaluating this model."
] |
21a09c3087c52fa2096e987c585a4334cadeaf82
|
# 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: google/pegasus-xsum
* Dataset: xsum
* Config: default
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@zuzannad1](https://huggingface.co/zuzannad1) for evaluating this model.
|
autoevaluate/autoeval-eval-xsum-default-e3e096-60495145410
|
[
"autotrain",
"evaluation",
"region:us"
] |
2023-10-04T15:46:55+00:00
|
{"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["xsum"], "eval_info": {"task": "summarization", "model": "google/pegasus-xsum", "metrics": ["bertscore"], "dataset_name": "xsum", "dataset_config": "default", "dataset_split": "test", "col_mapping": {"text": "document", "target": "summary"}}}
|
2023-10-04T16:19:17+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: google/pegasus-xsum
* Dataset: xsum
* Config: default
* Split: test
To run new evaluation jobs, visit Hugging Face's automatic model evaluator.
## Contributions
Thanks to @zuzannad1 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: google/pegasus-xsum\n* Dataset: xsum\n* Config: default\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.",
"## Contributions\n\nThanks to @zuzannad1 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: google/pegasus-xsum\n* Dataset: xsum\n* Config: default\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.",
"## Contributions\n\nThanks to @zuzannad1 for evaluating this model."
] |
[
13,
83,
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] |
[
"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: google/pegasus-xsum\n* Dataset: xsum\n* Config: default\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.## Contributions\n\nThanks to @zuzannad1 for evaluating this model."
] |
b9bfc53d02f7616b2fd8c44d51ba72233d0672b9
|
# Dataset Card for "ppo-Pendulum-v1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
HumanCompatibleAI/ppo-Pendulum-v1
|
[
"region:us"
] |
2023-10-04T15:52:08+00:00
|
{"dataset_info": {"features": [{"name": "obs", "sequence": {"sequence": "float32"}}, {"name": "acts", "sequence": {"sequence": "float32"}}, {"name": "infos", "sequence": "string"}, {"name": "terminal", "dtype": "bool"}, {"name": "rews", "sequence": "float32"}], "splits": [{"name": "train", "num_bytes": 2575710, "num_examples": 200}], "download_size": 940375, "dataset_size": 2575710}}
|
2023-10-04T15:52:12+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "ppo-Pendulum-v1"
More Information needed
|
[
"# Dataset Card for \"ppo-Pendulum-v1\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"ppo-Pendulum-v1\"\n\nMore Information needed"
] |
[
6,
18
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[
"passage: TAGS\n#region-us \n# Dataset Card for \"ppo-Pendulum-v1\"\n\nMore Information needed"
] |
ac5970745d1f1636b58d7aa354ea39b8af092fa9
|
# 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: morenolq/bart-base-xsum
* Dataset: cnn_dailymail
* Config: 3.0.0
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@Raffix](https://huggingface.co/Raffix) for evaluating this model.
|
autoevaluate/autoeval-eval-cnn_dailymail-3.0.0-bcce97-62650145463
|
[
"autotrain",
"evaluation",
"region:us"
] |
2023-10-04T15:54:06+00:00
|
{"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["cnn_dailymail"], "eval_info": {"task": "summarization", "model": "morenolq/bart-base-xsum", "metrics": ["bertscore"], "dataset_name": "cnn_dailymail", "dataset_config": "3.0.0", "dataset_split": "test", "col_mapping": {"text": "article", "target": "highlights"}}}
|
2023-10-04T16:03:52+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: morenolq/bart-base-xsum
* Dataset: cnn_dailymail
* Config: 3.0.0
* Split: test
To run new evaluation jobs, visit Hugging Face's automatic model evaluator.
## Contributions
Thanks to @Raffix 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: morenolq/bart-base-xsum\n* Dataset: cnn_dailymail\n* Config: 3.0.0\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.",
"## Contributions\n\nThanks to @Raffix 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: morenolq/bart-base-xsum\n* Dataset: cnn_dailymail\n* Config: 3.0.0\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.",
"## Contributions\n\nThanks to @Raffix for evaluating this model."
] |
[
13,
89,
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: morenolq/bart-base-xsum\n* Dataset: cnn_dailymail\n* Config: 3.0.0\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.## Contributions\n\nThanks to @Raffix for evaluating this model."
] |
0af159fa1779d5bdfa0b9710f9b4cf940bd04f33
|
[](https://discord.gg/kW9nBQErGe)
<img src="https://approximatelabs.com/tablib.png" width="800" />
# TabLib Sample
**NOTE**: This is a 0.1% sample of [the full TabLib dataset](https://huggingface.co/datasets/approximatelabs/tablib-v1-full).
TabLib is a minimally-preprocessed dataset of 627M tables (69 TiB) extracted from HTML, PDF, CSV, TSV, Excel, and SQLite files from GitHub and Common Crawl.
This includes 867B tokens of "context metadata": each table includes provenance information and table context such as filename, text before/after, HTML metadata, etc.
For more information, read the [paper](https://arxiv.org/abs/2310.07875) & [announcement blog](https://approximatelabs.com/blog/tablib).
# Dataset Details
## Sources
* **GitHub**: nearly all public GitHub repositories
* **Common Crawl**: the `CC-MAIN-2023-23` crawl
## Reading Tables
Tables are stored as serialized Arrow bytes in the `arrow_bytes` column. To read these, you will need to deserialize the bytes:
```python
import datasets
import pyarrow as pa
# load a single file of the dataset
ds = datasets.load_dataset(
'approximatelabs/tablib-v1-sample',
token='...',
)
df = ds['train'].to_pandas()
tables = [pa.RecordBatchStreamReader(b).read_all() for b in df['arrow_bytes']]
```
## Licensing
This dataset is intended for research use only.
For specific licensing information, refer to the license of the specific datum being used.
# Contact
If you have any questions, comments, or concerns about licensing, pii, etc. please contact using [this form](https://forms.gle/C74VTWP7L78QDVR67).
# Approximate Labs
TabLib is a project from Approximate Labs. Find us on [Twitter](https://twitter.com/approximatelabs), [Github](https://github.com/approximatelabs), [Linkedin](https://www.linkedin.com/company/approximate-labs), and [Discord](https://discord.gg/kW9nBQErGe).
# Citations
If you use TabLib for any of your research, please cite the TabLib paper:
```
@misc{eggert2023tablib,
title={TabLib: A Dataset of 627M Tables with Context},
author={Gus Eggert and Kevin Huo and Mike Biven and Justin Waugh},
year={2023},
eprint={2310.07875},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
approximatelabs/tablib-v1-sample
|
[
"size_categories:1M<n<10M",
"license:other",
"arxiv:2310.07875",
"region:us"
] |
2023-10-04T15:55:20+00:00
|
{"license": "other", "size_categories": ["1M<n<10M"], "pretty_name": "TabLib", "extra_gated_prompt": "Access to this dataset is automatically granted once this form is completed.\nNote that this access request is for the TabLib sample, not [the full TabLib dataset](https://huggingface.co/datasets/approximatelabs/tablib-v1-full).", "extra_gated_fields": {"I agree to abide by the license requirements of the data contained in TabLib": "checkbox"}}
|
2023-10-13T21:34:05+00:00
|
[
"2310.07875"
] |
[] |
TAGS
#size_categories-1M<n<10M #license-other #arxiv-2310.07875 #region-us
|
![](URL
<img src="URL width="800" />
# TabLib Sample
NOTE: This is a 0.1% sample of the full TabLib dataset.
TabLib is a minimally-preprocessed dataset of 627M tables (69 TiB) extracted from HTML, PDF, CSV, TSV, Excel, and SQLite files from GitHub and Common Crawl.
This includes 867B tokens of "context metadata": each table includes provenance information and table context such as filename, text before/after, HTML metadata, etc.
For more information, read the paper & announcement blog.
# Dataset Details
## Sources
* GitHub: nearly all public GitHub repositories
* Common Crawl: the 'CC-MAIN-2023-23' crawl
## Reading Tables
Tables are stored as serialized Arrow bytes in the 'arrow_bytes' column. To read these, you will need to deserialize the bytes:
## Licensing
This dataset is intended for research use only.
For specific licensing information, refer to the license of the specific datum being used.
# Contact
If you have any questions, comments, or concerns about licensing, pii, etc. please contact using this form.
# Approximate Labs
TabLib is a project from Approximate Labs. Find us on Twitter, Github, Linkedin, and Discord.
s
If you use TabLib for any of your research, please cite the TabLib paper:
|
[
"# TabLib Sample\nNOTE: This is a 0.1% sample of the full TabLib dataset. \n\nTabLib is a minimally-preprocessed dataset of 627M tables (69 TiB) extracted from HTML, PDF, CSV, TSV, Excel, and SQLite files from GitHub and Common Crawl.\n\nThis includes 867B tokens of \"context metadata\": each table includes provenance information and table context such as filename, text before/after, HTML metadata, etc.\n\nFor more information, read the paper & announcement blog.",
"# Dataset Details",
"## Sources\n* GitHub: nearly all public GitHub repositories\n* Common Crawl: the 'CC-MAIN-2023-23' crawl",
"## Reading Tables\nTables are stored as serialized Arrow bytes in the 'arrow_bytes' column. To read these, you will need to deserialize the bytes:",
"## Licensing\nThis dataset is intended for research use only.\n\nFor specific licensing information, refer to the license of the specific datum being used.",
"# Contact\nIf you have any questions, comments, or concerns about licensing, pii, etc. please contact using this form.",
"# Approximate Labs\nTabLib is a project from Approximate Labs. Find us on Twitter, Github, Linkedin, and Discord.\n\ns\nIf you use TabLib for any of your research, please cite the TabLib paper:"
] |
[
"TAGS\n#size_categories-1M<n<10M #license-other #arxiv-2310.07875 #region-us \n",
"# TabLib Sample\nNOTE: This is a 0.1% sample of the full TabLib dataset. \n\nTabLib is a minimally-preprocessed dataset of 627M tables (69 TiB) extracted from HTML, PDF, CSV, TSV, Excel, and SQLite files from GitHub and Common Crawl.\n\nThis includes 867B tokens of \"context metadata\": each table includes provenance information and table context such as filename, text before/after, HTML metadata, etc.\n\nFor more information, read the paper & announcement blog.",
"# Dataset Details",
"## Sources\n* GitHub: nearly all public GitHub repositories\n* Common Crawl: the 'CC-MAIN-2023-23' crawl",
"## Reading Tables\nTables are stored as serialized Arrow bytes in the 'arrow_bytes' column. To read these, you will need to deserialize the bytes:",
"## Licensing\nThis dataset is intended for research use only.\n\nFor specific licensing information, refer to the license of the specific datum being used.",
"# Contact\nIf you have any questions, comments, or concerns about licensing, pii, etc. please contact using this form.",
"# Approximate Labs\nTabLib is a project from Approximate Labs. Find us on Twitter, Github, Linkedin, and Discord.\n\ns\nIf you use TabLib for any of your research, please cite the TabLib paper:"
] |
[
32,
126,
4,
33,
43,
32,
29,
56
] |
[
"passage: TAGS\n#size_categories-1M<n<10M #license-other #arxiv-2310.07875 #region-us \n# TabLib Sample\nNOTE: This is a 0.1% sample of the full TabLib dataset. \n\nTabLib is a minimally-preprocessed dataset of 627M tables (69 TiB) extracted from HTML, PDF, CSV, TSV, Excel, and SQLite files from GitHub and Common Crawl.\n\nThis includes 867B tokens of \"context metadata\": each table includes provenance information and table context such as filename, text before/after, HTML metadata, etc.\n\nFor more information, read the paper & announcement blog.# Dataset Details## Sources\n* GitHub: nearly all public GitHub repositories\n* Common Crawl: the 'CC-MAIN-2023-23' crawl## Reading Tables\nTables are stored as serialized Arrow bytes in the 'arrow_bytes' column. To read these, you will need to deserialize the bytes:## Licensing\nThis dataset is intended for research use only.\n\nFor specific licensing information, refer to the license of the specific datum being used.# Contact\nIf you have any questions, comments, or concerns about licensing, pii, etc. please contact using this form.# Approximate Labs\nTabLib is a project from Approximate Labs. Find us on Twitter, Github, Linkedin, and Discord.\n\ns\nIf you use TabLib for any of your research, please cite the TabLib paper:"
] |
c8244ee983f21d89e0aebf983f3d38266c29f594
|
# Dataset Card for Evaluation run of aiplanet/panda-coder-13B
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/aiplanet/panda-coder-13B
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [aiplanet/panda-coder-13B](https://huggingface.co/aiplanet/panda-coder-13B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 3 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_aiplanet__panda-coder-13B",
"harness_gsm8k_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-03T22:12:43.362775](https://huggingface.co/datasets/open-llm-leaderboard/details_aiplanet__panda-coder-13B/blob/main/results_2023-12-03T22-12-43.362775.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"acc": 0.0,
"acc_stderr": 0.0
},
"harness|gsm8k|5": {
"acc": 0.0,
"acc_stderr": 0.0
}
}
```
### 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]
|
open-llm-leaderboard/details_aiplanet__panda-coder-13B
|
[
"region:us"
] |
2023-10-04T15:56:43+00:00
|
{"pretty_name": "Evaluation run of aiplanet/panda-coder-13B", "dataset_summary": "Dataset automatically created during the evaluation run of model [aiplanet/panda-coder-13B](https://huggingface.co/aiplanet/panda-coder-13B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 3 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_aiplanet__panda-coder-13B\",\n\t\"harness_gsm8k_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-12-03T22:12:43.362775](https://huggingface.co/datasets/open-llm-leaderboard/details_aiplanet__panda-coder-13B/blob/main/results_2023-12-03T22-12-43.362775.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.0,\n \"acc_stderr\": 0.0\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.0,\n \"acc_stderr\": 0.0\n }\n}\n```", "repo_url": "https://huggingface.co/aiplanet/panda-coder-13B", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_arc_challenge_25", "data_files": [{"split": "2023_10_04T16_56_18.723336", "path": ["**/details_harness|arc:challenge|25_2023-10-04T16-56-18.723336.parquet"]}, {"split": "latest", "path": ["**/details_harness|arc:challenge|25_2023-10-04T16-56-18.723336.parquet"]}]}, {"config_name": "harness_drop_3", "data_files": [{"split": "2023_11_08T14_53_54.622402", "path": ["**/details_harness|drop|3_2023-11-08T14-53-54.622402.parquet"]}, {"split": "latest", "path": ["**/details_harness|drop|3_2023-11-08T14-53-54.622402.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_11_08T14_53_54.622402", "path": ["**/details_harness|gsm8k|5_2023-11-08T14-53-54.622402.parquet"]}, {"split": "2023_12_03T22_12_43.362775", "path": ["**/details_harness|gsm8k|5_2023-12-03T22-12-43.362775.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2023-12-03T22-12-43.362775.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2023_10_04T16_56_18.723336", "path": ["**/details_harness|hellaswag|10_2023-10-04T16-56-18.723336.parquet"]}, {"split": "latest", "path": ["**/details_harness|hellaswag|10_2023-10-04T16-56-18.723336.parquet"]}]}, {"config_name": "harness_hendrycksTest_5", "data_files": [{"split": "2023_10_04T16_56_18.723336", "path": ["**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-04T16-56-18.723336.parquet", "**/details_harness|hendrycksTest-anatomy|5_2023-10-04T16-56-18.723336.parquet", "**/details_harness|hendrycksTest-astronomy|5_2023-10-04T16-56-18.723336.parquet", "**/details_harness|hendrycksTest-business_ethics|5_2023-10-04T16-56-18.723336.parquet", "**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-04T16-56-18.723336.parquet", "**/details_harness|hendrycksTest-college_biology|5_2023-10-04T16-56-18.723336.parquet", "**/details_harness|hendrycksTest-college_chemistry|5_2023-10-04T16-56-18.723336.parquet", "**/details_harness|hendrycksTest-college_computer_science|5_2023-10-04T16-56-18.723336.parquet", "**/details_harness|hendrycksTest-college_mathematics|5_2023-10-04T16-56-18.723336.parquet", "**/details_harness|hendrycksTest-college_medicine|5_2023-10-04T16-56-18.723336.parquet", "**/details_harness|hendrycksTest-college_physics|5_2023-10-04T16-56-18.723336.parquet", "**/details_harness|hendrycksTest-computer_security|5_2023-10-04T16-56-18.723336.parquet", "**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-04T16-56-18.723336.parquet", 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|
2023-12-03T22:12:50+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Evaluation run of aiplanet/panda-coder-13B
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model aiplanet/panda-coder-13B on the Open LLM Leaderboard.
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 3 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2023-12-03T22:12:43.362775(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
### 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 Evaluation run of aiplanet/panda-coder-13B",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model aiplanet/panda-coder-13B on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 3 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-12-03T22:12:43.362775(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### 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#region-us \n",
"# Dataset Card for Evaluation run of aiplanet/panda-coder-13B",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model aiplanet/panda-coder-13B on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 3 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-12-03T22:12:43.362775(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### 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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[
"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of aiplanet/panda-coder-13B## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model aiplanet/panda-coder-13B on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 3 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-12-03T22:12:43.362775(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):### 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"
] |
4574388fa6b8ffe50ec450fd0f75b3d585798d84
|
# Bangumi Image Base of Nisekoi
This is the image base of bangumi NISEKOI, we detected 38 characters, 4374 images in total. The full dataset is [here](all.zip).
**Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual.** If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
| # | Images | Download | Preview 1 | Preview 2 | Preview 3 | Preview 4 | Preview 5 | Preview 6 | Preview 7 | Preview 8 |
|:------|---------:|:---------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|
| 0 | 1202 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 107 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 222 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 39 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 140 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 37 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 18 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 55 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 67 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 40 | [Download](9/dataset.zip) |  |  |  |  |  |  |  |  |
| 10 | 448 | [Download](10/dataset.zip) |  |  |  |  |  |  |  |  |
| 11 | 36 | [Download](11/dataset.zip) |  |  |  |  |  |  |  |  |
| 12 | 47 | [Download](12/dataset.zip) |  |  |  |  |  |  |  |  |
| 13 | 45 | [Download](13/dataset.zip) |  |  |  |  |  |  |  |  |
| 14 | 90 | [Download](14/dataset.zip) |  |  |  |  |  |  |  |  |
| 15 | 60 | [Download](15/dataset.zip) |  |  |  |  |  |  |  |  |
| 16 | 23 | [Download](16/dataset.zip) |  |  |  |  |  |  |  |  |
| 17 | 15 | [Download](17/dataset.zip) |  |  |  |  |  |  |  |  |
| 18 | 15 | [Download](18/dataset.zip) |  |  |  |  |  |  |  |  |
| 19 | 158 | [Download](19/dataset.zip) |  |  |  |  |  |  |  |  |
| 20 | 33 | [Download](20/dataset.zip) |  |  |  |  |  |  |  |  |
| 21 | 35 | [Download](21/dataset.zip) |  |  |  |  |  |  |  |  |
| 22 | 15 | [Download](22/dataset.zip) |  |  |  |  |  |  |  |  |
| 23 | 21 | [Download](23/dataset.zip) |  |  |  |  |  |  |  |  |
| 24 | 17 | [Download](24/dataset.zip) |  |  |  |  |  |  |  |  |
| 25 | 10 | [Download](25/dataset.zip) |  |  |  |  |  |  |  |  |
| 26 | 513 | [Download](26/dataset.zip) |  |  |  |  |  |  |  |  |
| 27 | 184 | [Download](27/dataset.zip) |  |  |  |  |  |  |  |  |
| 28 | 41 | [Download](28/dataset.zip) |  |  |  |  |  |  |  |  |
| 29 | 45 | [Download](29/dataset.zip) |  |  |  |  |  |  |  |  |
| 30 | 15 | [Download](30/dataset.zip) |  |  |  |  |  |  |  |  |
| 31 | 18 | [Download](31/dataset.zip) |  |  |  |  |  |  |  |  |
| 32 | 8 | [Download](32/dataset.zip) |  |  |  |  |  |  |  |  |
| 33 | 234 | [Download](33/dataset.zip) |  |  |  |  |  |  |  |  |
| 34 | 16 | [Download](34/dataset.zip) |  |  |  |  |  |  |  |  |
| 35 | 7 | [Download](35/dataset.zip) |  |  |  |  |  |  |  | N/A |
| 36 | 56 | [Download](36/dataset.zip) |  |  |  |  |  |  |  |  |
| noise | 242 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
|
BangumiBase/nisekoi
|
[
"size_categories:1K<n<10K",
"license:mit",
"art",
"region:us"
] |
2023-10-04T15:57:44+00:00
|
{"license": "mit", "size_categories": ["1K<n<10K"], "tags": ["art"]}
|
2023-10-04T18:26:34+00:00
|
[] |
[] |
TAGS
#size_categories-1K<n<10K #license-mit #art #region-us
|
Bangumi Image Base of Nisekoi
=============================
This is the image base of bangumi NISEKOI, we detected 38 characters, 4374 images in total. The full dataset is here.
Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual. If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
|
[] |
[
"TAGS\n#size_categories-1K<n<10K #license-mit #art #region-us \n"
] |
[
25
] |
[
"passage: TAGS\n#size_categories-1K<n<10K #license-mit #art #region-us \n"
] |
c8c9dc93323f18d906a3d3f83f9883286526e9f2
|
# Bangumi Image Base of To Love-ru
This is the image base of bangumi To LOVE-Ru, we detected 69 characters, 9598 images in total. The full dataset is [here](all.zip).
**Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual.** If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
| # | Images | Download | Preview 1 | Preview 2 | Preview 3 | Preview 4 | Preview 5 | Preview 6 | Preview 7 | Preview 8 |
|:------|---------:|:---------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|
| 0 | 152 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 29 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 104 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 337 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 439 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 129 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 50 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 29 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 14 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 52 | [Download](9/dataset.zip) |  |  |  |  |  |  |  |  |
| 10 | 80 | [Download](10/dataset.zip) |  |  |  |  |  |  |  |  |
| 11 | 47 | [Download](11/dataset.zip) |  |  |  |  |  |  |  |  |
| 12 | 40 | [Download](12/dataset.zip) |  |  |  |  |  |  |  |  |
| 13 | 606 | [Download](13/dataset.zip) |  |  |  |  |  |  |  |  |
| 14 | 71 | [Download](14/dataset.zip) |  |  |  |  |  |  |  |  |
| 15 | 14 | [Download](15/dataset.zip) |  |  |  |  |  |  |  |  |
| 16 | 24 | [Download](16/dataset.zip) |  |  |  |  |  |  |  |  |
| 17 | 26 | [Download](17/dataset.zip) |  |  |  |  |  |  |  |  |
| 18 | 52 | [Download](18/dataset.zip) |  |  |  |  |  |  |  |  |
| 19 | 42 | [Download](19/dataset.zip) |  |  |  |  |  |  |  |  |
| 20 | 142 | [Download](20/dataset.zip) |  |  |  |  |  |  |  |  |
| 21 | 38 | [Download](21/dataset.zip) |  |  |  |  |  |  |  |  |
| 22 | 62 | [Download](22/dataset.zip) |  |  |  |  |  |  |  |  |
| 23 | 96 | [Download](23/dataset.zip) |  |  |  |  |  |  |  |  |
| 24 | 52 | [Download](24/dataset.zip) |  |  |  |  |  |  |  |  |
| 25 | 171 | [Download](25/dataset.zip) |  |  |  |  |  |  |  |  |
| 26 | 49 | [Download](26/dataset.zip) |  |  |  |  |  |  |  |  |
| 27 | 104 | [Download](27/dataset.zip) |  |  |  |  |  |  |  |  |
| 28 | 16 | [Download](28/dataset.zip) |  |  |  |  |  |  |  |  |
| 29 | 14 | [Download](29/dataset.zip) |  |  |  |  |  |  |  |  |
| 30 | 1783 | [Download](30/dataset.zip) |  |  |  |  |  |  |  |  |
| 31 | 67 | [Download](31/dataset.zip) |  |  |  |  |  |  |  |  |
| 32 | 23 | [Download](32/dataset.zip) |  |  |  |  |  |  |  |  |
| 33 | 33 | [Download](33/dataset.zip) |  |  |  |  |  |  |  |  |
| 34 | 15 | [Download](34/dataset.zip) |  |  |  |  |  |  |  |  |
| 35 | 44 | [Download](35/dataset.zip) |  |  |  |  |  |  |  |  |
| 36 | 27 | [Download](36/dataset.zip) |  |  |  |  |  |  |  |  |
| 37 | 14 | [Download](37/dataset.zip) |  |  |  |  |  |  |  |  |
| 38 | 34 | [Download](38/dataset.zip) |  |  |  |  |  |  |  |  |
| 39 | 34 | [Download](39/dataset.zip) |  |  |  |  |  |  |  |  |
| 40 | 68 | [Download](40/dataset.zip) |  |  |  |  |  |  |  |  |
| 41 | 12 | [Download](41/dataset.zip) |  |  |  |  |  |  |  |  |
| 42 | 44 | [Download](42/dataset.zip) |  |  |  |  |  |  |  |  |
| 43 | 14 | [Download](43/dataset.zip) |  |  |  |  |  |  |  |  |
| 44 | 326 | [Download](44/dataset.zip) |  |  |  |  |  |  |  |  |
| 45 | 699 | [Download](45/dataset.zip) |  |  |  |  |  |  |  |  |
| 46 | 368 | [Download](46/dataset.zip) |  |  |  |  |  |  |  |  |
| 47 | 44 | [Download](47/dataset.zip) |  |  |  |  |  |  |  |  |
| 48 | 873 | [Download](48/dataset.zip) |  |  |  |  |  |  |  |  |
| 49 | 38 | [Download](49/dataset.zip) |  |  |  |  |  |  |  |  |
| 50 | 39 | [Download](50/dataset.zip) |  |  |  |  |  |  |  |  |
| 51 | 92 | [Download](51/dataset.zip) |  |  |  |  |  |  |  |  |
| 52 | 10 | [Download](52/dataset.zip) |  |  |  |  |  |  |  |  |
| 53 | 17 | [Download](53/dataset.zip) |  |  |  |  |  |  |  |  |
| 54 | 15 | [Download](54/dataset.zip) |  |  |  |  |  |  |  |  |
| 55 | 110 | [Download](55/dataset.zip) |  |  |  |  |  |  |  |  |
| 56 | 23 | [Download](56/dataset.zip) |  |  |  |  |  |  |  |  |
| 57 | 584 | [Download](57/dataset.zip) |  |  |  |  |  |  |  |  |
| 58 | 107 | [Download](58/dataset.zip) |  |  |  |  |  |  |  |  |
| 59 | 15 | [Download](59/dataset.zip) |  |  |  |  |  |  |  |  |
| 60 | 30 | [Download](60/dataset.zip) |  |  |  |  |  |  |  |  |
| 61 | 18 | [Download](61/dataset.zip) |  |  |  |  |  |  |  |  |
| 62 | 103 | [Download](62/dataset.zip) |  |  |  |  |  |  |  |  |
| 63 | 37 | [Download](63/dataset.zip) |  |  |  |  |  |  |  |  |
| 64 | 10 | [Download](64/dataset.zip) |  |  |  |  |  |  |  |  |
| 65 | 10 | [Download](65/dataset.zip) |  |  |  |  |  |  |  |  |
| 66 | 77 | [Download](66/dataset.zip) |  |  |  |  |  |  |  |  |
| 67 | 12 | [Download](67/dataset.zip) |  |  |  |  |  |  |  |  |
| noise | 648 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
|
BangumiBase/toloveru
|
[
"size_categories:1K<n<10K",
"license:mit",
"art",
"region:us"
] |
2023-10-04T15:58:35+00:00
|
{"license": "mit", "size_categories": ["1K<n<10K"], "tags": ["art"]}
|
2023-10-05T08:06:22+00:00
|
[] |
[] |
TAGS
#size_categories-1K<n<10K #license-mit #art #region-us
|
Bangumi Image Base of To Love-ru
================================
This is the image base of bangumi To LOVE-Ru, we detected 69 characters, 9598 images in total. The full dataset is here.
Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual. If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
|
[] |
[
"TAGS\n#size_categories-1K<n<10K #license-mit #art #region-us \n"
] |
[
25
] |
[
"passage: TAGS\n#size_categories-1K<n<10K #license-mit #art #region-us \n"
] |
526d03ee878faffc329526ddf0b8f4ba3bfc2891
|
# 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: 0x70DA/pegasus-cnn_dailymail
* Dataset: cnn_dailymail
* Config: 3.0.0
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@gauiru1998](https://huggingface.co/gauiru1998) for evaluating this model.
|
autoevaluate/autoeval-eval-cnn_dailymail-3.0.0-66cf5e-63956145513
|
[
"autotrain",
"evaluation",
"region:us"
] |
2023-10-04T16:07:08+00:00
|
{"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["cnn_dailymail"], "eval_info": {"task": "summarization", "model": "0x70DA/pegasus-cnn_dailymail", "metrics": [], "dataset_name": "cnn_dailymail", "dataset_config": "3.0.0", "dataset_split": "test", "col_mapping": {"text": "article", "target": "highlights"}}}
|
2023-10-04T17:49:07+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: 0x70DA/pegasus-cnn_dailymail
* Dataset: cnn_dailymail
* Config: 3.0.0
* Split: test
To run new evaluation jobs, visit Hugging Face's automatic model evaluator.
## Contributions
Thanks to @gauiru1998 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: 0x70DA/pegasus-cnn_dailymail\n* Dataset: cnn_dailymail\n* Config: 3.0.0\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.",
"## Contributions\n\nThanks to @gauiru1998 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: 0x70DA/pegasus-cnn_dailymail\n* Dataset: cnn_dailymail\n* Config: 3.0.0\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.",
"## Contributions\n\nThanks to @gauiru1998 for evaluating this model."
] |
[
13,
93,
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: 0x70DA/pegasus-cnn_dailymail\n* Dataset: cnn_dailymail\n* Config: 3.0.0\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.## Contributions\n\nThanks to @gauiru1998 for evaluating this model."
] |
f16db52b737d223c65bd30eba34fc666f022dd60
|
# 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: SamAct/PromptGeneration-base
* Dataset: fabiochiu/medium-articles
* Config: fabiochiu--medium-articles
* Split: train
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@SamAct](https://huggingface.co/SamAct) for evaluating this model.
|
autoevaluate/autoeval-eval-fabiochiu__medium-articles-fabiochiu__medium-articles-5207ae-64303145522
|
[
"autotrain",
"evaluation",
"region:us"
] |
2023-10-04T16:08:41+00:00
|
{"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["fabiochiu/medium-articles"], "eval_info": {"task": "summarization", "model": "SamAct/PromptGeneration-base", "metrics": ["f1", "accuracy"], "dataset_name": "fabiochiu/medium-articles", "dataset_config": "fabiochiu--medium-articles", "dataset_split": "train", "col_mapping": {"text": "text", "target": "title"}}}
|
2023-10-04T23:20:22+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: SamAct/PromptGeneration-base
* Dataset: fabiochiu/medium-articles
* Config: fabiochiu--medium-articles
* Split: train
To run new evaluation jobs, visit Hugging Face's automatic model evaluator.
## Contributions
Thanks to @SamAct 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: SamAct/PromptGeneration-base\n* Dataset: fabiochiu/medium-articles\n* Config: fabiochiu--medium-articles\n* Split: train\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.",
"## Contributions\n\nThanks to @SamAct 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: SamAct/PromptGeneration-base\n* Dataset: fabiochiu/medium-articles\n* Config: fabiochiu--medium-articles\n* Split: train\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.",
"## Contributions\n\nThanks to @SamAct for evaluating this model."
] |
[
13,
104,
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: SamAct/PromptGeneration-base\n* Dataset: fabiochiu/medium-articles\n* Config: fabiochiu--medium-articles\n* Split: train\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.## Contributions\n\nThanks to @SamAct for evaluating this model."
] |
abdedb25a0ea5a2e931a790c9c3412064659153e
|
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Question Answering
* Model: saikatkumardey/my_awesome_qa_model
* Dataset: adversarial_qa
* Config: adversarialQA
* Split: validation
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@Jyostna](https://huggingface.co/Jyostna) for evaluating this model.
|
autoevaluate/autoeval-eval-adversarial_qa-adversarialQA-efa810-64484145523
|
[
"autotrain",
"evaluation",
"region:us"
] |
2023-10-04T16:08:51+00:00
|
{"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["adversarial_qa"], "eval_info": {"task": "extractive_question_answering", "model": "saikatkumardey/my_awesome_qa_model", "metrics": ["accuracy"], "dataset_name": "adversarial_qa", "dataset_config": "adversarialQA", "dataset_split": "validation", "col_mapping": {"context": "context", "question": "question", "answers-text": "answers.text", "answers-answer_start": "answers.answer_start"}}}
|
2023-10-04T16:09:43+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: Question Answering
* Model: saikatkumardey/my_awesome_qa_model
* Dataset: adversarial_qa
* Config: adversarialQA
* Split: validation
To run new evaluation jobs, visit Hugging Face's automatic model evaluator.
## Contributions
Thanks to @Jyostna 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: Question Answering\n* Model: saikatkumardey/my_awesome_qa_model\n* Dataset: adversarial_qa\n* Config: adversarialQA\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.",
"## Contributions\n\nThanks to @Jyostna 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: Question Answering\n* Model: saikatkumardey/my_awesome_qa_model\n* Dataset: adversarial_qa\n* Config: adversarialQA\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.",
"## Contributions\n\nThanks to @Jyostna for evaluating this model."
] |
[
13,
95,
17
] |
[
"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: Question Answering\n* Model: saikatkumardey/my_awesome_qa_model\n* Dataset: adversarial_qa\n* Config: adversarialQA\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.## Contributions\n\nThanks to @Jyostna for evaluating this model."
] |
809eaf87d73463697bbe18a5891bce0f55c6031a
|
# 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: google/roberta2roberta_L-24_cnn_daily_mail
* Dataset: cnn_dailymail
* Config: 3.0.0
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@https://huggingface.co/Sini](https://huggingface.co/https://huggingface.co/Sini) for evaluating this model.
|
autoevaluate/autoeval-eval-cnn_dailymail-3.0.0-e26065-64936145531
|
[
"autotrain",
"evaluation",
"region:us"
] |
2023-10-04T16:10:03+00:00
|
{"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["cnn_dailymail"], "eval_info": {"task": "summarization", "model": "google/roberta2roberta_L-24_cnn_daily_mail", "metrics": [], "dataset_name": "cnn_dailymail", "dataset_config": "3.0.0", "dataset_split": "test", "col_mapping": {"text": "article", "target": "highlights"}}}
|
2023-10-04T17:48:46+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: google/roberta2roberta_L-24_cnn_daily_mail
* Dataset: cnn_dailymail
* Config: 3.0.0
* Split: test
To run new evaluation jobs, visit Hugging Face's automatic model evaluator.
## Contributions
Thanks to @URL 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: google/roberta2roberta_L-24_cnn_daily_mail\n* Dataset: cnn_dailymail\n* Config: 3.0.0\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.",
"## Contributions\n\nThanks to @URL 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: google/roberta2roberta_L-24_cnn_daily_mail\n* Dataset: cnn_dailymail\n* Config: 3.0.0\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.",
"## Contributions\n\nThanks to @URL for evaluating this model."
] |
[
13,
96,
14
] |
[
"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: google/roberta2roberta_L-24_cnn_daily_mail\n* Dataset: cnn_dailymail\n* Config: 3.0.0\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.## Contributions\n\nThanks to @URL for evaluating this model."
] |
b32b525c9a095eccb2845e23ef458827a3288704
|
# 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: minhtoan/t5-finetune-cnndaily-news
* Dataset: cnn_dailymail
* Config: 3.0.0
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@https://huggingface.co/Sini](https://huggingface.co/https://huggingface.co/Sini) for evaluating this model.
|
autoevaluate/autoeval-eval-cnn_dailymail-3.0.0-e26065-64936145532
|
[
"autotrain",
"evaluation",
"region:us"
] |
2023-10-04T16:10:12+00:00
|
{"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["cnn_dailymail"], "eval_info": {"task": "summarization", "model": "minhtoan/t5-finetune-cnndaily-news", "metrics": [], "dataset_name": "cnn_dailymail", "dataset_config": "3.0.0", "dataset_split": "test", "col_mapping": {"text": "article", "target": "highlights"}}}
|
2023-10-04T16:13:21+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: minhtoan/t5-finetune-cnndaily-news
* Dataset: cnn_dailymail
* Config: 3.0.0
* Split: test
To run new evaluation jobs, visit Hugging Face's automatic model evaluator.
## Contributions
Thanks to @URL 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: minhtoan/t5-finetune-cnndaily-news\n* Dataset: cnn_dailymail\n* Config: 3.0.0\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.",
"## Contributions\n\nThanks to @URL 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: minhtoan/t5-finetune-cnndaily-news\n* Dataset: cnn_dailymail\n* Config: 3.0.0\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.",
"## Contributions\n\nThanks to @URL for evaluating this model."
] |
[
13,
93,
14
] |
[
"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: minhtoan/t5-finetune-cnndaily-news\n* Dataset: cnn_dailymail\n* Config: 3.0.0\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.## Contributions\n\nThanks to @URL for evaluating this model."
] |
5428f1490e97685996bf272277fe0020243ca47d
|
# 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: amagzari/bart-base-finetuned-cnn_dailymail
* Dataset: cnn_dailymail
* Config: 3.0.0
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@https://huggingface.co/Sini](https://huggingface.co/https://huggingface.co/Sini) for evaluating this model.
|
autoevaluate/autoeval-eval-cnn_dailymail-3.0.0-e26065-64936145533
|
[
"autotrain",
"evaluation",
"region:us"
] |
2023-10-04T16:10:23+00:00
|
{"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["cnn_dailymail"], "eval_info": {"task": "summarization", "model": "amagzari/bart-base-finetuned-cnn_dailymail", "metrics": [], "dataset_name": "cnn_dailymail", "dataset_config": "3.0.0", "dataset_split": "test", "col_mapping": {"text": "article", "target": "highlights"}}}
|
2023-10-04T16:19:34+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: amagzari/bart-base-finetuned-cnn_dailymail
* Dataset: cnn_dailymail
* Config: 3.0.0
* Split: test
To run new evaluation jobs, visit Hugging Face's automatic model evaluator.
## Contributions
Thanks to @URL 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: amagzari/bart-base-finetuned-cnn_dailymail\n* Dataset: cnn_dailymail\n* Config: 3.0.0\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.",
"## Contributions\n\nThanks to @URL 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: amagzari/bart-base-finetuned-cnn_dailymail\n* Dataset: cnn_dailymail\n* Config: 3.0.0\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.",
"## Contributions\n\nThanks to @URL for evaluating this model."
] |
[
13,
96,
14
] |
[
"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: amagzari/bart-base-finetuned-cnn_dailymail\n* Dataset: cnn_dailymail\n* Config: 3.0.0\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.## Contributions\n\nThanks to @URL for evaluating this model."
] |
3e940f3eba50befca4cdf9e361ff5b8f7bae99d5
|
# 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: 0x70DA/pegasus-cnn_dailymail
* Dataset: cnn_dailymail
* Config: 3.0.0
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@https://huggingface.co/Sini](https://huggingface.co/https://huggingface.co/Sini) for evaluating this model.
|
autoevaluate/autoeval-eval-cnn_dailymail-3.0.0-e26065-64936145535
|
[
"autotrain",
"evaluation",
"region:us"
] |
2023-10-04T16:10:44+00:00
|
{"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["cnn_dailymail"], "eval_info": {"task": "summarization", "model": "0x70DA/pegasus-cnn_dailymail", "metrics": [], "dataset_name": "cnn_dailymail", "dataset_config": "3.0.0", "dataset_split": "test", "col_mapping": {"text": "article", "target": "highlights"}}}
|
2023-10-04T17:48:08+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: 0x70DA/pegasus-cnn_dailymail
* Dataset: cnn_dailymail
* Config: 3.0.0
* Split: test
To run new evaluation jobs, visit Hugging Face's automatic model evaluator.
## Contributions
Thanks to @URL 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: 0x70DA/pegasus-cnn_dailymail\n* Dataset: cnn_dailymail\n* Config: 3.0.0\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.",
"## Contributions\n\nThanks to @URL 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: 0x70DA/pegasus-cnn_dailymail\n* Dataset: cnn_dailymail\n* Config: 3.0.0\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.",
"## Contributions\n\nThanks to @URL for evaluating this model."
] |
[
13,
93,
14
] |
[
"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: 0x70DA/pegasus-cnn_dailymail\n* Dataset: cnn_dailymail\n* Config: 3.0.0\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.## Contributions\n\nThanks to @URL for evaluating this model."
] |
c71221b35bb0dbe10a248ea8e54587427f2b27bc
|
# Training windows for GPN-MSA-Sapiens
For more information check out our [paper](https://doi.org/10.1101/2023.10.10.561776) and [repository](https://github.com/songlab-cal/gpn).
Path in Snakemake:
`results/dataset/multiz100way/89/128/64/True/defined.phastCons.percentile-75_0.05_0.001`
|
songlab/gpn-msa-sapiens-dataset
|
[
"license:mit",
"dna",
"biology",
"genomics",
"region:us"
] |
2023-10-04T16:11:12+00:00
|
{"license": "mit", "tags": ["dna", "biology", "genomics"]}
|
2024-01-27T18:20:28+00:00
|
[] |
[] |
TAGS
#license-mit #dna #biology #genomics #region-us
|
# Training windows for GPN-MSA-Sapiens
For more information check out our paper and repository.
Path in Snakemake:
'results/dataset/multiz100way/89/128/64/True/defined.phastCons.percentile-75_0.05_0.001'
|
[
"# Training windows for GPN-MSA-Sapiens\nFor more information check out our paper and repository.\n\nPath in Snakemake:\n'results/dataset/multiz100way/89/128/64/True/defined.phastCons.percentile-75_0.05_0.001'"
] |
[
"TAGS\n#license-mit #dna #biology #genomics #region-us \n",
"# Training windows for GPN-MSA-Sapiens\nFor more information check out our paper and repository.\n\nPath in Snakemake:\n'results/dataset/multiz100way/89/128/64/True/defined.phastCons.percentile-75_0.05_0.001'"
] |
[
19,
75
] |
[
"passage: TAGS\n#license-mit #dna #biology #genomics #region-us \n# Training windows for GPN-MSA-Sapiens\nFor more information check out our paper and repository.\n\nPath in Snakemake:\n'results/dataset/multiz100way/89/128/64/True/defined.phastCons.percentile-75_0.05_0.001'"
] |
8f1b7f43f15c34b976624ff65f2d8701ac7eab4f
|
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Token Classification
* Model: Abelll/bert-finetuned-ner
* Dataset: conll2003
* Config: conll2003
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@chnlyi](https://huggingface.co/chnlyi) for evaluating this model.
|
autoevaluate/autoeval-eval-conll2003-conll2003-0a1842-65397145557
|
[
"autotrain",
"evaluation",
"region:us"
] |
2023-10-04T16:15:51+00:00
|
{"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["conll2003"], "eval_info": {"task": "entity_extraction", "model": "Abelll/bert-finetuned-ner", "metrics": [], "dataset_name": "conll2003", "dataset_config": "conll2003", "dataset_split": "test", "col_mapping": {"tokens": "tokens", "tags": "ner_tags"}}}
|
2023-10-04T16:17:19+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: Token Classification
* Model: Abelll/bert-finetuned-ner
* Dataset: conll2003
* Config: conll2003
* Split: test
To run new evaluation jobs, visit Hugging Face's automatic model evaluator.
## Contributions
Thanks to @chnlyi 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: Token Classification\n* Model: Abelll/bert-finetuned-ner\n* Dataset: conll2003\n* Config: conll2003\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.",
"## Contributions\n\nThanks to @chnlyi 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: Token Classification\n* Model: Abelll/bert-finetuned-ner\n* Dataset: conll2003\n* Config: conll2003\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.",
"## Contributions\n\nThanks to @chnlyi for evaluating this model."
] |
[
13,
89,
17
] |
[
"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: Token Classification\n* Model: Abelll/bert-finetuned-ner\n* Dataset: conll2003\n* Config: conll2003\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.## Contributions\n\nThanks to @chnlyi for evaluating this model."
] |
18ff05476f78677e216a02c3192b2741339914da
|
# Dataset Card for Evaluation run of upstage/SOLAR-0-70b-16bit
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/upstage/SOLAR-0-70b-16bit
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [upstage/SOLAR-0-70b-16bit](https://huggingface.co/upstage/SOLAR-0-70b-16bit) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_upstage__SOLAR-0-70b-16bit_public",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-11-07T01:00:47.965413](https://huggingface.co/datasets/open-llm-leaderboard/details_upstage__SOLAR-0-70b-16bit_public/blob/main/results_2023-11-07T01-00-47.965413.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.3555998322147651,
"em_stderr": 0.004902281518260701,
"f1": 0.47494337248322493,
"f1_stderr": 0.004563199491248503,
"acc": 0.6442241467520119,
"acc_stderr": 0.012060674423078888
},
"harness|drop|3": {
"em": 0.3555998322147651,
"em_stderr": 0.004902281518260701,
"f1": 0.47494337248322493,
"f1_stderr": 0.004563199491248503
},
"harness|gsm8k|5": {
"acc": 0.45261561789234267,
"acc_stderr": 0.013710499070934969
},
"harness|winogrande|5": {
"acc": 0.8358326756116812,
"acc_stderr": 0.010410849775222808
}
}
```
### 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]
|
open-llm-leaderboard/details_upstage__SOLAR-0-70b-16bit
|
[
"region:us"
] |
2023-10-04T16:17:21+00:00
|
{"pretty_name": "Evaluation run of upstage/SOLAR-0-70b-16bit", "dataset_summary": "Dataset automatically created during the evaluation run of model [upstage/SOLAR-0-70b-16bit](https://huggingface.co/upstage/SOLAR-0-70b-16bit) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_upstage__SOLAR-0-70b-16bit_public\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-11-07T01:00:47.965413](https://huggingface.co/datasets/open-llm-leaderboard/details_upstage__SOLAR-0-70b-16bit_public/blob/main/results_2023-11-07T01-00-47.965413.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.3555998322147651,\n \"em_stderr\": 0.004902281518260701,\n \"f1\": 0.47494337248322493,\n \"f1_stderr\": 0.004563199491248503,\n \"acc\": 0.6442241467520119,\n \"acc_stderr\": 0.012060674423078888\n },\n \"harness|drop|3\": {\n \"em\": 0.3555998322147651,\n \"em_stderr\": 0.004902281518260701,\n \"f1\": 0.47494337248322493,\n \"f1_stderr\": 0.004563199491248503\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.45261561789234267,\n \"acc_stderr\": 0.013710499070934969\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.8358326756116812,\n \"acc_stderr\": 0.010410849775222808\n }\n}\n```", "repo_url": "https://huggingface.co/upstage/SOLAR-0-70b-16bit", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_drop_3", "data_files": [{"split": "2023_11_07T01_00_47.965413", "path": ["**/details_harness|drop|3_2023-11-07T01-00-47.965413.parquet"]}, {"split": "latest", "path": ["**/details_harness|drop|3_2023-11-07T01-00-47.965413.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_11_07T01_00_47.965413", "path": ["**/details_harness|gsm8k|5_2023-11-07T01-00-47.965413.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2023-11-07T01-00-47.965413.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_11_07T01_00_47.965413", "path": ["**/details_harness|winogrande|5_2023-11-07T01-00-47.965413.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2023-11-07T01-00-47.965413.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_11_07T01_00_47.965413", "path": ["results_2023-11-07T01-00-47.965413.parquet"]}, {"split": "latest", "path": ["results_2023-11-07T01-00-47.965413.parquet"]}]}]}
|
2023-12-01T14:39:52+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Evaluation run of upstage/SOLAR-0-70b-16bit
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model upstage/SOLAR-0-70b-16bit on the Open LLM Leaderboard.
The dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2023-11-07T01:00:47.965413(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
### 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 Evaluation run of upstage/SOLAR-0-70b-16bit",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model upstage/SOLAR-0-70b-16bit on the Open LLM Leaderboard.\n\nThe dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-11-07T01:00:47.965413(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### 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#region-us \n",
"# Dataset Card for Evaluation run of upstage/SOLAR-0-70b-16bit",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model upstage/SOLAR-0-70b-16bit on the Open LLM Leaderboard.\n\nThe dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-11-07T01:00:47.965413(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### 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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[
"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of upstage/SOLAR-0-70b-16bit## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model upstage/SOLAR-0-70b-16bit on the Open LLM Leaderboard.\n\nThe dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-11-07T01:00:47.965413(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):### 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"
] |
a007d23c23ed83fc5ab37a0a735e9c21833bf98c
|
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Question Answering
* Model: Chetna19/my_awesome_qa_model
* Dataset: adversarial_qa
* Config: adversarialQA
* Split: validation
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@Hizafa](https://huggingface.co/Hizafa) for evaluating this model.
|
autoevaluate/autoeval-eval-adversarial_qa-adversarialQA-0cf9bf-65912145569
|
[
"autotrain",
"evaluation",
"region:us"
] |
2023-10-04T16:18:05+00:00
|
{"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["adversarial_qa"], "eval_info": {"task": "extractive_question_answering", "model": "Chetna19/my_awesome_qa_model", "metrics": ["perplexity", "accuracy", "bleu"], "dataset_name": "adversarial_qa", "dataset_config": "adversarialQA", "dataset_split": "validation", "col_mapping": {"context": "context", "question": "question", "answers-text": "answers.text", "answers-answer_start": "answers.answer_start"}}}
|
2023-10-04T16:18:57+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: Question Answering
* Model: Chetna19/my_awesome_qa_model
* Dataset: adversarial_qa
* Config: adversarialQA
* Split: validation
To run new evaluation jobs, visit Hugging Face's automatic model evaluator.
## Contributions
Thanks to @Hizafa 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: Question Answering\n* Model: Chetna19/my_awesome_qa_model\n* Dataset: adversarial_qa\n* Config: adversarialQA\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.",
"## Contributions\n\nThanks to @Hizafa 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: Question Answering\n* Model: Chetna19/my_awesome_qa_model\n* Dataset: adversarial_qa\n* Config: adversarialQA\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.",
"## Contributions\n\nThanks to @Hizafa for evaluating this model."
] |
[
13,
94,
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: Question Answering\n* Model: Chetna19/my_awesome_qa_model\n* Dataset: adversarial_qa\n* Config: adversarialQA\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.## Contributions\n\nThanks to @Hizafa for evaluating this model."
] |
dde93ee7c72f6f729471d7d21434aed272ee24d3
|
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Question Answering
* Model: jgammack/SAE-roberta-base-squad
* Dataset: adversarial_qa
* Config: adversarialQA
* Split: validation
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@jtatman](https://huggingface.co/jtatman) for evaluating this model.
|
autoevaluate/autoeval-eval-adversarial_qa-adversarialQA-6a5d0b-66069145577
|
[
"autotrain",
"evaluation",
"region:us"
] |
2023-10-04T16:19:30+00:00
|
{"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["adversarial_qa"], "eval_info": {"task": "extractive_question_answering", "model": "jgammack/SAE-roberta-base-squad", "metrics": [], "dataset_name": "adversarial_qa", "dataset_config": "adversarialQA", "dataset_split": "validation", "col_mapping": {"context": "context", "question": "question", "answers-text": "answers.text", "answers-answer_start": "answers.answer_start"}}}
|
2023-10-04T16:20:31+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: Question Answering
* Model: jgammack/SAE-roberta-base-squad
* Dataset: adversarial_qa
* Config: adversarialQA
* Split: validation
To run new evaluation jobs, visit Hugging Face's automatic model evaluator.
## Contributions
Thanks to @jtatman 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: Question Answering\n* Model: jgammack/SAE-roberta-base-squad\n* Dataset: adversarial_qa\n* Config: adversarialQA\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.",
"## Contributions\n\nThanks to @jtatman 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: Question Answering\n* Model: jgammack/SAE-roberta-base-squad\n* Dataset: adversarial_qa\n* Config: adversarialQA\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.",
"## Contributions\n\nThanks to @jtatman for evaluating this model."
] |
[
13,
95,
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[
"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: Question Answering\n* Model: jgammack/SAE-roberta-base-squad\n* Dataset: adversarial_qa\n* Config: adversarialQA\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.## Contributions\n\nThanks to @jtatman for evaluating this model."
] |
5bcb2c7cf0505f71fa16f2491ac0733e4eb87e17
|
# 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: facebook/bart-large-cnn
* Dataset: multi_news
* Config: default
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@bart-multi-news](https://huggingface.co/bart-multi-news) for evaluating this model.
|
autoevaluate/autoeval-eval-multi_news-default-1231f6-66166145579
|
[
"autotrain",
"evaluation",
"region:us"
] |
2023-10-04T16:19:41+00:00
|
{"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["multi_news"], "eval_info": {"task": "summarization", "model": "facebook/bart-large-cnn", "metrics": [], "dataset_name": "multi_news", "dataset_config": "default", "dataset_split": "test", "col_mapping": {"text": "document", "target": "summary"}}}
|
2023-10-04T16:44:36+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: facebook/bart-large-cnn
* Dataset: multi_news
* Config: default
* Split: test
To run new evaluation jobs, visit Hugging Face's automatic model evaluator.
## Contributions
Thanks to @bart-multi-news 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: facebook/bart-large-cnn\n* Dataset: multi_news\n* Config: default\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.",
"## Contributions\n\nThanks to @bart-multi-news 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: facebook/bart-large-cnn\n* Dataset: multi_news\n* Config: default\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.",
"## Contributions\n\nThanks to @bart-multi-news for evaluating this model."
] |
[
13,
85,
18
] |
[
"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: facebook/bart-large-cnn\n* Dataset: multi_news\n* Config: default\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.## Contributions\n\nThanks to @bart-multi-news for evaluating this model."
] |
edc106b2e8873919b050812464538c9c16c89043
|
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Question Answering
* Model: Laurie/QA-distilbert
* Dataset: adversarial_qa
* Config: adversarialQA
* Split: validation
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@jtatman](https://huggingface.co/jtatman) for evaluating this model.
|
autoevaluate/autoeval-eval-adversarial_qa-adversarialQA-6a5d0b-66069145576
|
[
"autotrain",
"evaluation",
"region:us"
] |
2023-10-04T16:19:50+00:00
|
{"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["adversarial_qa"], "eval_info": {"task": "extractive_question_answering", "model": "Laurie/QA-distilbert", "metrics": [], "dataset_name": "adversarial_qa", "dataset_config": "adversarialQA", "dataset_split": "validation", "col_mapping": {"context": "context", "question": "question", "answers-text": "answers.text", "answers-answer_start": "answers.answer_start"}}}
|
2023-10-04T16:20:39+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: Question Answering
* Model: Laurie/QA-distilbert
* Dataset: adversarial_qa
* Config: adversarialQA
* Split: validation
To run new evaluation jobs, visit Hugging Face's automatic model evaluator.
## Contributions
Thanks to @jtatman 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: Question Answering\n* Model: Laurie/QA-distilbert\n* Dataset: adversarial_qa\n* Config: adversarialQA\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.",
"## Contributions\n\nThanks to @jtatman 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: Question Answering\n* Model: Laurie/QA-distilbert\n* Dataset: adversarial_qa\n* Config: adversarialQA\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.",
"## Contributions\n\nThanks to @jtatman for evaluating this model."
] |
[
13,
88,
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: Question Answering\n* Model: Laurie/QA-distilbert\n* Dataset: adversarial_qa\n* Config: adversarialQA\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.## Contributions\n\nThanks to @jtatman for evaluating this model."
] |
7b1b36a94925c2026f1011c0f583b3265fb0b14b
|
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Multi-class Text Classification
* Model: 095ey11/bert-emotion
* Dataset: tweet_eval
* Config: emotion
* Split: train
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@Ayushkm2799](https://huggingface.co/Ayushkm2799) for evaluating this model.
|
autoevaluate/autoeval-eval-tweet_eval-emotion-dbaa98-66233145580
|
[
"autotrain",
"evaluation",
"region:us"
] |
2023-10-04T16:19:52+00:00
|
{"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["tweet_eval"], "eval_info": {"task": "multi_class_classification", "model": "095ey11/bert-emotion", "metrics": [], "dataset_name": "tweet_eval", "dataset_config": "emotion", "dataset_split": "train", "col_mapping": {"text": "text", "target": "label"}}}
|
2023-10-04T16:20:23+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: Multi-class Text Classification
* Model: 095ey11/bert-emotion
* Dataset: tweet_eval
* Config: emotion
* Split: train
To run new evaluation jobs, visit Hugging Face's automatic model evaluator.
## Contributions
Thanks to @Ayushkm2799 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: Multi-class Text Classification\n* Model: 095ey11/bert-emotion\n* Dataset: tweet_eval\n* Config: emotion\n* Split: train\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.",
"## Contributions\n\nThanks to @Ayushkm2799 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: Multi-class Text Classification\n* Model: 095ey11/bert-emotion\n* Dataset: tweet_eval\n* Config: emotion\n* Split: train\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.",
"## Contributions\n\nThanks to @Ayushkm2799 for evaluating this model."
] |
[
13,
89,
19
] |
[
"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: Multi-class Text Classification\n* Model: 095ey11/bert-emotion\n* Dataset: tweet_eval\n* Config: emotion\n* Split: train\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.## Contributions\n\nThanks to @Ayushkm2799 for evaluating this model."
] |
b63147c426639ba1baad46935f6bf9392574e317
|
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Multi-class Text Classification
* Model: FelixHonikker/bert-emotion
* Dataset: tweet_eval
* Config: emotion
* Split: train
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@Ayushkm2799](https://huggingface.co/Ayushkm2799) for evaluating this model.
|
autoevaluate/autoeval-eval-tweet_eval-emotion-dbaa98-66233145581
|
[
"autotrain",
"evaluation",
"region:us"
] |
2023-10-04T16:20:03+00:00
|
{"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["tweet_eval"], "eval_info": {"task": "multi_class_classification", "model": "FelixHonikker/bert-emotion", "metrics": [], "dataset_name": "tweet_eval", "dataset_config": "emotion", "dataset_split": "train", "col_mapping": {"text": "text", "target": "label"}}}
|
2023-10-04T16:20:32+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: Multi-class Text Classification
* Model: FelixHonikker/bert-emotion
* Dataset: tweet_eval
* Config: emotion
* Split: train
To run new evaluation jobs, visit Hugging Face's automatic model evaluator.
## Contributions
Thanks to @Ayushkm2799 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: Multi-class Text Classification\n* Model: FelixHonikker/bert-emotion\n* Dataset: tweet_eval\n* Config: emotion\n* Split: train\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.",
"## Contributions\n\nThanks to @Ayushkm2799 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: Multi-class Text Classification\n* Model: FelixHonikker/bert-emotion\n* Dataset: tweet_eval\n* Config: emotion\n* Split: train\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.",
"## Contributions\n\nThanks to @Ayushkm2799 for evaluating this model."
] |
[
13,
89,
19
] |
[
"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: Multi-class Text Classification\n* Model: FelixHonikker/bert-emotion\n* Dataset: tweet_eval\n* Config: emotion\n* Split: train\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.## Contributions\n\nThanks to @Ayushkm2799 for evaluating this model."
] |
300939e87ba77937690ecb50ec5681b81a607ed7
|
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Multi-class Text Classification
* Model: ShoneRan/bert-emotion
* Dataset: tweet_eval
* Config: emotion
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@lenague](https://huggingface.co/lenague) for evaluating this model.
|
autoevaluate/autoeval-eval-tweet_eval-emotion-11ffbd-66309145583
|
[
"autotrain",
"evaluation",
"region:us"
] |
2023-10-04T16:20:22+00:00
|
{"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["tweet_eval"], "eval_info": {"task": "multi_class_classification", "model": "ShoneRan/bert-emotion", "metrics": [], "dataset_name": "tweet_eval", "dataset_config": "emotion", "dataset_split": "test", "col_mapping": {"text": "text", "target": "label"}}}
|
2023-10-04T16:20:45+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: Multi-class Text Classification
* Model: ShoneRan/bert-emotion
* Dataset: tweet_eval
* Config: emotion
* Split: test
To run new evaluation jobs, visit Hugging Face's automatic model evaluator.
## Contributions
Thanks to @lenague 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: Multi-class Text Classification\n* Model: ShoneRan/bert-emotion\n* Dataset: tweet_eval\n* Config: emotion\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.",
"## Contributions\n\nThanks to @lenague 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: Multi-class Text Classification\n* Model: ShoneRan/bert-emotion\n* Dataset: tweet_eval\n* Config: emotion\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.",
"## Contributions\n\nThanks to @lenague for evaluating this model."
] |
[
13,
88,
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: Multi-class Text Classification\n* Model: ShoneRan/bert-emotion\n* Dataset: tweet_eval\n* Config: emotion\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.## Contributions\n\nThanks to @lenague for evaluating this model."
] |
2d949d319a85b5796b45b9b1cc2a3d58c102ee27
|
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Multi-class Text Classification
* Model: ShoneRan/bert-emotion
* Dataset: tweet_eval
* Config: emotion
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@Lenague](https://huggingface.co/Lenague) for evaluating this model.
|
autoevaluate/autoeval-eval-tweet_eval-emotion-cb9f8a-66323145584
|
[
"autotrain",
"evaluation",
"region:us"
] |
2023-10-04T16:20:44+00:00
|
{"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["tweet_eval"], "eval_info": {"task": "multi_class_classification", "model": "ShoneRan/bert-emotion", "metrics": [], "dataset_name": "tweet_eval", "dataset_config": "emotion", "dataset_split": "test", "col_mapping": {"text": "text", "target": "label"}}}
|
2023-10-04T16:21:04+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: Multi-class Text Classification
* Model: ShoneRan/bert-emotion
* Dataset: tweet_eval
* Config: emotion
* Split: test
To run new evaluation jobs, visit Hugging Face's automatic model evaluator.
## Contributions
Thanks to @Lenague 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: Multi-class Text Classification\n* Model: ShoneRan/bert-emotion\n* Dataset: tweet_eval\n* Config: emotion\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.",
"## Contributions\n\nThanks to @Lenague 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: Multi-class Text Classification\n* Model: ShoneRan/bert-emotion\n* Dataset: tweet_eval\n* Config: emotion\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.",
"## Contributions\n\nThanks to @Lenague for evaluating this model."
] |
[
13,
88,
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: Multi-class Text Classification\n* Model: ShoneRan/bert-emotion\n* Dataset: tweet_eval\n* Config: emotion\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.## Contributions\n\nThanks to @Lenague for evaluating this model."
] |
2f24e1301392f211082831f0697c9806a595b2a9
|
# 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: t5-small
* Dataset: imdb
* Config: plain_text
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@michaeldesmond](https://huggingface.co/michaeldesmond) for evaluating this model.
|
autoevaluate/autoeval-eval-imdb-plain_text-fdc5b9-67091145591
|
[
"autotrain",
"evaluation",
"region:us"
] |
2023-10-04T16:21:46+00:00
|
{"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["imdb"], "eval_info": {"task": "summarization", "model": "t5-small", "metrics": [], "dataset_name": "imdb", "dataset_config": "plain_text", "dataset_split": "test", "col_mapping": {"text": "text", "target": "label"}}}
|
2023-10-04T16:26:01+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: t5-small
* Dataset: imdb
* Config: plain_text
* Split: test
To run new evaluation jobs, visit Hugging Face's automatic model evaluator.
## Contributions
Thanks to @michaeldesmond 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: t5-small\n* Dataset: imdb\n* Config: plain_text\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.",
"## Contributions\n\nThanks to @michaeldesmond 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: t5-small\n* Dataset: imdb\n* Config: plain_text\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.",
"## Contributions\n\nThanks to @michaeldesmond for evaluating this model."
] |
[
13,
81,
18
] |
[
"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: t5-small\n* Dataset: imdb\n* Config: plain_text\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.## Contributions\n\nThanks to @michaeldesmond for evaluating this model."
] |
9b1fd5b2b73bbd8465005136668d63a49ccbc6d7
|
# 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: t5-small
* Dataset: imdb
* Config: plain_text
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@michaeldesmond](https://huggingface.co/michaeldesmond) for evaluating this model.
|
autoevaluate/autoeval-eval-imdb-plain_text-87fbde-67095145592
|
[
"autotrain",
"evaluation",
"region:us"
] |
2023-10-04T16:21:57+00:00
|
{"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["imdb"], "eval_info": {"task": "summarization", "model": "t5-small", "metrics": [], "dataset_name": "imdb", "dataset_config": "plain_text", "dataset_split": "test", "col_mapping": {"text": "text", "target": "label"}}}
|
2023-10-04T16:26:09+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: t5-small
* Dataset: imdb
* Config: plain_text
* Split: test
To run new evaluation jobs, visit Hugging Face's automatic model evaluator.
## Contributions
Thanks to @michaeldesmond 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: t5-small\n* Dataset: imdb\n* Config: plain_text\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.",
"## Contributions\n\nThanks to @michaeldesmond 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: t5-small\n* Dataset: imdb\n* Config: plain_text\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.",
"## Contributions\n\nThanks to @michaeldesmond for evaluating this model."
] |
[
13,
81,
18
] |
[
"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: t5-small\n* Dataset: imdb\n* Config: plain_text\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.## Contributions\n\nThanks to @michaeldesmond for evaluating this model."
] |
328bb3f65d344dcbd5b768a1d90f054f948e2d80
|
# 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: t5-small
* Dataset: xsum
* Config: default
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@michaeldesmond](https://huggingface.co/michaeldesmond) for evaluating this model.
|
autoevaluate/autoeval-eval-xsum-default-5381b8-67099145593
|
[
"autotrain",
"evaluation",
"region:us"
] |
2023-10-04T16:22:05+00:00
|
{"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["xsum"], "eval_info": {"task": "summarization", "model": "t5-small", "metrics": [], "dataset_name": "xsum", "dataset_config": "default", "dataset_split": "test", "col_mapping": {"text": "document", "target": "summary"}}}
|
2023-10-04T16:24:35+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: t5-small
* Dataset: xsum
* Config: default
* Split: test
To run new evaluation jobs, visit Hugging Face's automatic model evaluator.
## Contributions
Thanks to @michaeldesmond 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: t5-small\n* Dataset: xsum\n* Config: default\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.",
"## Contributions\n\nThanks to @michaeldesmond 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: t5-small\n* Dataset: xsum\n* Config: default\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.",
"## Contributions\n\nThanks to @michaeldesmond for evaluating this model."
] |
[
13,
79,
18
] |
[
"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: t5-small\n* Dataset: xsum\n* Config: default\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.## Contributions\n\nThanks to @michaeldesmond for evaluating this model."
] |
ace40af705574a21ac47e6325858610ba1f96fa9
|
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Translation
* Model: leukas/byt5-large-wmt14-deen
* Dataset: wmt14
* Config: de-en
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@seeed](https://huggingface.co/seeed) for evaluating this model.
|
autoevaluate/autoeval-eval-wmt14-de-en-fbedb0-67643145604
|
[
"autotrain",
"evaluation",
"region:us"
] |
2023-10-04T16:24:08+00:00
|
{"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["wmt14"], "eval_info": {"task": "translation", "model": "leukas/byt5-large-wmt14-deen", "metrics": ["bleu"], "dataset_name": "wmt14", "dataset_config": "de-en", "dataset_split": "test", "col_mapping": {"source": "translation.de", "target": "translation.en"}}}
|
2023-10-04T16:35:06+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: Translation
* Model: leukas/byt5-large-wmt14-deen
* Dataset: wmt14
* Config: de-en
* Split: test
To run new evaluation jobs, visit Hugging Face's automatic model evaluator.
## Contributions
Thanks to @seeed 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: Translation\n* Model: leukas/byt5-large-wmt14-deen\n* Dataset: wmt14\n* Config: de-en\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.",
"## Contributions\n\nThanks to @seeed 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: Translation\n* Model: leukas/byt5-large-wmt14-deen\n* Dataset: wmt14\n* Config: de-en\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.",
"## Contributions\n\nThanks to @seeed for evaluating this model."
] |
[
13,
91,
15
] |
[
"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: Translation\n* Model: leukas/byt5-large-wmt14-deen\n* Dataset: wmt14\n* Config: de-en\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.## Contributions\n\nThanks to @seeed for evaluating this model."
] |
5f3883a2ecf0b9cc8617bf28985c06319b8dcf4f
|
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Translation
* Model: leukas/mt5-large-wmt14-deen
* Dataset: wmt14
* Config: de-en
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@seeed](https://huggingface.co/seeed) for evaluating this model.
|
autoevaluate/autoeval-eval-wmt14-de-en-fbedb0-67643145605
|
[
"autotrain",
"evaluation",
"region:us"
] |
2023-10-04T16:24:16+00:00
|
{"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["wmt14"], "eval_info": {"task": "translation", "model": "leukas/mt5-large-wmt14-deen", "metrics": ["bleu"], "dataset_name": "wmt14", "dataset_config": "de-en", "dataset_split": "test", "col_mapping": {"source": "translation.de", "target": "translation.en"}}}
|
2023-10-04T16:31:34+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: Translation
* Model: leukas/mt5-large-wmt14-deen
* Dataset: wmt14
* Config: de-en
* Split: test
To run new evaluation jobs, visit Hugging Face's automatic model evaluator.
## Contributions
Thanks to @seeed 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: Translation\n* Model: leukas/mt5-large-wmt14-deen\n* Dataset: wmt14\n* Config: de-en\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.",
"## Contributions\n\nThanks to @seeed 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: Translation\n* Model: leukas/mt5-large-wmt14-deen\n* Dataset: wmt14\n* Config: de-en\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.",
"## Contributions\n\nThanks to @seeed for evaluating this model."
] |
[
13,
92,
15
] |
[
"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: Translation\n* Model: leukas/mt5-large-wmt14-deen\n* Dataset: wmt14\n* Config: de-en\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.## Contributions\n\nThanks to @seeed for evaluating this model."
] |
8575422880d68138d9d3d6d4cf9a8db8401a5071
|
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Question Answering
* Model: Akihiro2/bert-finetuned-squad
* Dataset: adversarial_qa
* Config: adversarialQA
* Split: validation
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@zhouzj](https://huggingface.co/zhouzj) for evaluating this model.
|
autoevaluate/autoeval-eval-adversarial_qa-adversarialQA-cd62e4-67882145606
|
[
"autotrain",
"evaluation",
"region:us"
] |
2023-10-04T16:25:08+00:00
|
{"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["adversarial_qa"], "eval_info": {"task": "extractive_question_answering", "model": "Akihiro2/bert-finetuned-squad", "metrics": [], "dataset_name": "adversarial_qa", "dataset_config": "adversarialQA", "dataset_split": "validation", "col_mapping": {"context": "context", "question": "question", "answers-text": "answers.text", "answers-answer_start": "answers.answer_start"}}}
|
2023-10-04T16:26:08+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: Question Answering
* Model: Akihiro2/bert-finetuned-squad
* Dataset: adversarial_qa
* Config: adversarialQA
* Split: validation
To run new evaluation jobs, visit Hugging Face's automatic model evaluator.
## Contributions
Thanks to @zhouzj 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: Question Answering\n* Model: Akihiro2/bert-finetuned-squad\n* Dataset: adversarial_qa\n* Config: adversarialQA\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.",
"## Contributions\n\nThanks to @zhouzj 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: Question Answering\n* Model: Akihiro2/bert-finetuned-squad\n* Dataset: adversarial_qa\n* Config: adversarialQA\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.",
"## Contributions\n\nThanks to @zhouzj for evaluating this model."
] |
[
13,
92,
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: Question Answering\n* Model: Akihiro2/bert-finetuned-squad\n* Dataset: adversarial_qa\n* Config: adversarialQA\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.## Contributions\n\nThanks to @zhouzj for evaluating this model."
] |
0fc48ccf2e3e652cf8dbaef0d26324ae838d5d92
|
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Question Answering
* Model: Asmit/bert-finetuned-squad
* Dataset: adversarial_qa
* Config: adversarialQA
* Split: validation
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@zhouzj](https://huggingface.co/zhouzj) for evaluating this model.
|
autoevaluate/autoeval-eval-adversarial_qa-adversarialQA-cd62e4-67882145607
|
[
"autotrain",
"evaluation",
"region:us"
] |
2023-10-04T16:25:11+00:00
|
{"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["adversarial_qa"], "eval_info": {"task": "extractive_question_answering", "model": "Asmit/bert-finetuned-squad", "metrics": [], "dataset_name": "adversarial_qa", "dataset_config": "adversarialQA", "dataset_split": "validation", "col_mapping": {"context": "context", "question": "question", "answers-text": "answers.text", "answers-answer_start": "answers.answer_start"}}}
|
2023-10-04T16:26:15+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: Question Answering
* Model: Asmit/bert-finetuned-squad
* Dataset: adversarial_qa
* Config: adversarialQA
* Split: validation
To run new evaluation jobs, visit Hugging Face's automatic model evaluator.
## Contributions
Thanks to @zhouzj 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: Question Answering\n* Model: Asmit/bert-finetuned-squad\n* Dataset: adversarial_qa\n* Config: adversarialQA\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.",
"## Contributions\n\nThanks to @zhouzj 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: Question Answering\n* Model: Asmit/bert-finetuned-squad\n* Dataset: adversarial_qa\n* Config: adversarialQA\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.",
"## Contributions\n\nThanks to @zhouzj for evaluating this model."
] |
[
13,
91,
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: Question Answering\n* Model: Asmit/bert-finetuned-squad\n* Dataset: adversarial_qa\n* Config: adversarialQA\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.## Contributions\n\nThanks to @zhouzj for evaluating this model."
] |
6e7b60379216305d7bde62acc92abf3fe9e9c151
|
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Token Classification
* Model: 51la5/bert-large-NER
* Dataset: conll2003
* Config: conll2003
* Split: train
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@theoraclephd](https://huggingface.co/theoraclephd) for evaluating this model.
|
autoevaluate/autoeval-eval-conll2003-conll2003-e68bb2-67908145608
|
[
"autotrain",
"evaluation",
"region:us"
] |
2023-10-04T16:27:54+00:00
|
{"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["conll2003"], "eval_info": {"task": "entity_extraction", "model": "51la5/bert-large-NER", "metrics": ["bertscore"], "dataset_name": "conll2003", "dataset_config": "conll2003", "dataset_split": "train", "col_mapping": {"tokens": "tokens", "tags": "ner_tags"}}}
|
2023-10-04T16:34:15+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: Token Classification
* Model: 51la5/bert-large-NER
* Dataset: conll2003
* Config: conll2003
* Split: train
To run new evaluation jobs, visit Hugging Face's automatic model evaluator.
## Contributions
Thanks to @theoraclephd 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: Token Classification\n* Model: 51la5/bert-large-NER\n* Dataset: conll2003\n* Config: conll2003\n* Split: train\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.",
"## Contributions\n\nThanks to @theoraclephd 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: Token Classification\n* Model: 51la5/bert-large-NER\n* Dataset: conll2003\n* Config: conll2003\n* Split: train\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.",
"## Contributions\n\nThanks to @theoraclephd for evaluating this model."
] |
[
13,
89,
18
] |
[
"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: Token Classification\n* Model: 51la5/bert-large-NER\n* Dataset: conll2003\n* Config: conll2003\n* Split: train\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.## Contributions\n\nThanks to @theoraclephd for evaluating this model."
] |
1c366367f6ae26cb153941468ef1b6acab85df41
|
# 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: d0rj/rut5-base-summ
* 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 [@d0rj](https://huggingface.co/d0rj) for evaluating this model.
|
autoevaluate/autoeval-eval-samsum-samsum-e12e62-68887145629
|
[
"autotrain",
"evaluation",
"region:us"
] |
2023-10-04T16:28:54+00:00
|
{"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["samsum"], "eval_info": {"task": "summarization", "model": "d0rj/rut5-base-summ", "metrics": [], "dataset_name": "samsum", "dataset_config": "samsum", "dataset_split": "test", "col_mapping": {"text": "dialogue", "target": "summary"}}}
|
2023-10-04T16:36:22+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: d0rj/rut5-base-summ
* Dataset: samsum
* Config: samsum
* Split: test
To run new evaluation jobs, visit Hugging Face's automatic model evaluator.
## Contributions
Thanks to @d0rj 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: d0rj/rut5-base-summ\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 @d0rj 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: d0rj/rut5-base-summ\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 @d0rj for evaluating this model."
] |
[
13,
87,
17
] |
[
"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: d0rj/rut5-base-summ\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 @d0rj for evaluating this model."
] |
b70a2741b751c8e2b6551c4d90e3078d229dcd97
|
# 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: ARTeLab/it5-summarization-ilpost
* Dataset: medical_questions_pairs
* Config: default
* Split: train
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@halmj](https://huggingface.co/halmj) for evaluating this model.
|
autoevaluate/autoeval-eval-medical_questions_pairs-default-d0c070-68078145610
|
[
"autotrain",
"evaluation",
"region:us"
] |
2023-10-04T16:30:06+00:00
|
{"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["medical_questions_pairs"], "eval_info": {"task": "summarization", "model": "ARTeLab/it5-summarization-ilpost", "metrics": [], "dataset_name": "medical_questions_pairs", "dataset_config": "default", "dataset_split": "train", "col_mapping": {"text": "question_1", "target": "question_2"}}}
|
2023-10-04T16:31:43+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: ARTeLab/it5-summarization-ilpost
* Dataset: medical_questions_pairs
* Config: default
* Split: train
To run new evaluation jobs, visit Hugging Face's automatic model evaluator.
## Contributions
Thanks to @halmj 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: ARTeLab/it5-summarization-ilpost\n* Dataset: medical_questions_pairs\n* Config: default\n* Split: train\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.",
"## Contributions\n\nThanks to @halmj 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: ARTeLab/it5-summarization-ilpost\n* Dataset: medical_questions_pairs\n* Config: default\n* Split: train\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.",
"## Contributions\n\nThanks to @halmj for evaluating this model."
] |
[
13,
92,
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: ARTeLab/it5-summarization-ilpost\n* Dataset: medical_questions_pairs\n* Config: default\n* Split: train\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.## Contributions\n\nThanks to @halmj for evaluating this model."
] |
f22923e5cde29e22b076b579d66cde8a6f360e11
|
# 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: d0rj/rut5-base-summ
* Dataset: xsum
* Config: default
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@d0rj](https://huggingface.co/d0rj) for evaluating this model.
|
autoevaluate/autoeval-eval-xsum-default-199117-68890145630
|
[
"autotrain",
"evaluation",
"region:us"
] |
2023-10-04T16:30:14+00:00
|
{"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["xsum"], "eval_info": {"task": "summarization", "model": "d0rj/rut5-base-summ", "metrics": [], "dataset_name": "xsum", "dataset_config": "default", "dataset_split": "test", "col_mapping": {"text": "document", "target": "summary"}}}
|
2023-10-04T18:46:09+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: d0rj/rut5-base-summ
* Dataset: xsum
* Config: default
* Split: test
To run new evaluation jobs, visit Hugging Face's automatic model evaluator.
## Contributions
Thanks to @d0rj 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: d0rj/rut5-base-summ\n* Dataset: xsum\n* Config: default\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.",
"## Contributions\n\nThanks to @d0rj 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: d0rj/rut5-base-summ\n* Dataset: xsum\n* Config: default\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.",
"## Contributions\n\nThanks to @d0rj for evaluating this model."
] |
[
13,
86,
17
] |
[
"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: d0rj/rut5-base-summ\n* Dataset: xsum\n* Config: default\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.## Contributions\n\nThanks to @d0rj for evaluating this model."
] |
0ed5cdb07bf69040535d07e828feb31ba1ca179d
|
# Dataset Card for "clean_passages_80m-chinese-zhtw"
包含**8千萬餘萬**（88328203）個中文段落，不包含任何字母、數字。文字長度大部分介於 50\~200 個字。
原始資料集是用於訓練[GENIUS模型中文版](https://huggingface.co/spaces/beyond/genius)。論文參考引用:
```
@article{guo2022genius,
title={GENIUS: Sketch-based Language Model Pre-training via Extreme and Selective Masking for Text Generation and Augmentation},
author={Guo, Biyang and Gong, Yeyun and Shen, Yelong and Han, Songqiao and Huang, Hailiang and Duan, Nan and Chen, Weizhu},
journal={arXiv preprint arXiv:2211.10330},
year={2022}
}
```
## 資料集來源
本資料集是基於[CLUE中文預訓練語料集](https://github.com/CLUEbenchmark/CLUE)進行處理、過濾并進行簡繁轉諲而得到的。
原始資料集引用:
```
@misc{bright_xu_2019_3402023,
author = {Bright Xu},
title = {NLP Chinese Corpus: Large Scale Chinese Corpus for NLP },
month = sep,
year = 2019,
doi = {10.5281/zenodo.3402023},
version = {1.0},
publisher = {Zenodo},
url = {https://doi.org/10.5281/zenodo.3402023}
}
```
|
erhwenkuo/clean_passages_80m-chinese-zhtw
|
[
"task_categories:text-generation",
"size_categories:10M<n<100M",
"language:zh",
"region:us"
] |
2023-10-04T16:37:32+00:00
|
{"language": ["zh"], "size_categories": ["10M<n<100M"], "task_categories": ["text-generation"], "dataset_info": {"features": [{"name": "passage", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 18996999214, "num_examples": 88328203}], "download_size": 13088559046, "dataset_size": 18996999214}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-04T20:53:04+00:00
|
[] |
[
"zh"
] |
TAGS
#task_categories-text-generation #size_categories-10M<n<100M #language-Chinese #region-us
|
# Dataset Card for "clean_passages_80m-chinese-zhtw"
包含8千萬餘萬（88328203）個中文段落，不包含任何字母、數字。文字長度大部分介於 50\~200 個字。
原始資料集是用於訓練GENIUS模型中文版。論文參考引用:
## 資料集來源
本資料集是基於CLUE中文預訓練語料集進行處理、過濾并進行簡繁轉諲而得到的。
原始資料集引用:
|
[
"# Dataset Card for \"clean_passages_80m-chinese-zhtw\"\n\n包含8千萬餘萬（88328203）個中文段落，不包含任何字母、數字。文字長度大部分介於 50\\~200 個字。\n\n原始資料集是用於訓練GENIUS模型中文版。論文參考引用:",
"## 資料集來源\n\n本資料集是基於CLUE中文預訓練語料集進行處理、過濾并進行簡繁轉諲而得到的。\n\n原始資料集引用:"
] |
[
"TAGS\n#task_categories-text-generation #size_categories-10M<n<100M #language-Chinese #region-us \n",
"# Dataset Card for \"clean_passages_80m-chinese-zhtw\"\n\n包含8千萬餘萬（88328203）個中文段落，不包含任何字母、數字。文字長度大部分介於 50\\~200 個字。\n\n原始資料集是用於訓練GENIUS模型中文版。論文參考引用:",
"## 資料集來源\n\n本資料集是基於CLUE中文預訓練語料集進行處理、過濾并進行簡繁轉諲而得到的。\n\n原始資料集引用:"
] |
[
34,
75,
38
] |
[
"passage: TAGS\n#task_categories-text-generation #size_categories-10M<n<100M #language-Chinese #region-us \n# Dataset Card for \"clean_passages_80m-chinese-zhtw\"\n\n包含8千萬餘萬（88328203）個中文段落，不包含任何字母、數字。文字長度大部分介於 50\\~200 個字。\n\n原始資料集是用於訓練GENIUS模型中文版。論文參考引用:## 資料集來源\n\n本資料集是基於CLUE中文預訓練語料集進行處理、過濾并進行簡繁轉諲而得到的。\n\n原始資料集引用:"
] |
934133f297a4af4b32b4203067b697ab9abf946e
|
# Dataset Card for "991f2e12"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
result-kand2-sdxl-wuerst-karlo/991f2e12
|
[
"region:us"
] |
2023-10-04T16:42:54+00:00
|
{"dataset_info": {"features": [{"name": "result", "dtype": "string"}, {"name": "id", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 154, "num_examples": 10}], "download_size": 1300, "dataset_size": 154}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-04T16:42:55+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "991f2e12"
More Information needed
|
[
"# Dataset Card for \"991f2e12\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"991f2e12\"\n\nMore Information needed"
] |
[
6,
16
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"991f2e12\"\n\nMore Information needed"
] |
ec5c89faafa177585cda097daa5273ca620442de
|
# 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: MurkatG/bart-reviews
* Dataset: OxAISH-AL-LLM/wiki_toxic
* Config: default
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@Krzys](https://huggingface.co/Krzys) for evaluating this model.
|
autoevaluate/autoeval-eval-OxAISH-AL-LLM__wiki_toxic-default-8c726c-70747145688
|
[
"autotrain",
"evaluation",
"region:us"
] |
2023-10-04T16:43:05+00:00
|
{"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["OxAISH-AL-LLM/wiki_toxic"], "eval_info": {"task": "summarization", "model": "MurkatG/bart-reviews", "metrics": ["precision"], "dataset_name": "OxAISH-AL-LLM/wiki_toxic", "dataset_config": "default", "dataset_split": "test", "col_mapping": {"text": "comment_text", "target": "label"}}}
|
2023-10-04T17:17:23+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: MurkatG/bart-reviews
* Dataset: OxAISH-AL-LLM/wiki_toxic
* Config: default
* Split: test
To run new evaluation jobs, visit Hugging Face's automatic model evaluator.
## Contributions
Thanks to @Krzys 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: MurkatG/bart-reviews\n* Dataset: OxAISH-AL-LLM/wiki_toxic\n* Config: default\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.",
"## Contributions\n\nThanks to @Krzys 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: MurkatG/bart-reviews\n* Dataset: OxAISH-AL-LLM/wiki_toxic\n* Config: default\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.",
"## Contributions\n\nThanks to @Krzys for evaluating this model."
] |
[
13,
94,
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: MurkatG/bart-reviews\n* Dataset: OxAISH-AL-LLM/wiki_toxic\n* Config: default\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.## Contributions\n\nThanks to @Krzys for evaluating this model."
] |
1d9d1008e10fd0e56218e10da78da9aa876f746f
|
# Dataset Card for Evaluation run of beomi/KoRWKV-6B
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/beomi/KoRWKV-6B
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [beomi/KoRWKV-6B](https://huggingface.co/beomi/KoRWKV-6B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_beomi__KoRWKV-6B",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-26T04:59:46.486302](https://huggingface.co/datasets/open-llm-leaderboard/details_beomi__KoRWKV-6B/blob/main/results_2023-10-26T04-59-46.486302.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.018246644295302015,
"em_stderr": 0.001370668245281319,
"f1": 0.05829278523489933,
"f1_stderr": 0.0018688501996275634,
"acc": 0.2557221783741121,
"acc_stderr": 0.007024402099929661
},
"harness|drop|3": {
"em": 0.018246644295302015,
"em_stderr": 0.001370668245281319,
"f1": 0.05829278523489933,
"f1_stderr": 0.0018688501996275634
},
"harness|gsm8k|5": {
"acc": 0.0,
"acc_stderr": 0.0
},
"harness|winogrande|5": {
"acc": 0.5114443567482242,
"acc_stderr": 0.014048804199859322
}
}
```
### 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
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## Considerations for Using the Data
### Social Impact of Dataset
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### Discussion of Biases
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### Other Known Limitations
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|
open-llm-leaderboard/details_beomi__KoRWKV-6B
|
[
"region:us"
] |
2023-10-04T16:43:15+00:00
|
{"pretty_name": "Evaluation run of beomi/KoRWKV-6B", "dataset_summary": "Dataset automatically created during the evaluation run of model [beomi/KoRWKV-6B](https://huggingface.co/beomi/KoRWKV-6B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_beomi__KoRWKV-6B\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-10-26T04:59:46.486302](https://huggingface.co/datasets/open-llm-leaderboard/details_beomi__KoRWKV-6B/blob/main/results_2023-10-26T04-59-46.486302.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.018246644295302015,\n \"em_stderr\": 0.001370668245281319,\n \"f1\": 0.05829278523489933,\n \"f1_stderr\": 0.0018688501996275634,\n \"acc\": 0.2557221783741121,\n \"acc_stderr\": 0.007024402099929661\n },\n \"harness|drop|3\": {\n \"em\": 0.018246644295302015,\n \"em_stderr\": 0.001370668245281319,\n \"f1\": 0.05829278523489933,\n \"f1_stderr\": 0.0018688501996275634\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.0,\n \"acc_stderr\": 0.0\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.5114443567482242,\n \"acc_stderr\": 0.014048804199859322\n }\n}\n```", "repo_url": "https://huggingface.co/beomi/KoRWKV-6B", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_arc_challenge_25", "data_files": [{"split": "2023_10_04T17_42_58.699001", "path": ["**/details_harness|arc:challenge|25_2023-10-04T17-42-58.699001.parquet"]}, {"split": "latest", "path": ["**/details_harness|arc:challenge|25_2023-10-04T17-42-58.699001.parquet"]}]}, {"config_name": "harness_drop_3", "data_files": [{"split": "2023_10_26T04_59_46.486302", "path": ["**/details_harness|drop|3_2023-10-26T04-59-46.486302.parquet"]}, {"split": "latest", "path": ["**/details_harness|drop|3_2023-10-26T04-59-46.486302.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_10_26T04_59_46.486302", "path": ["**/details_harness|gsm8k|5_2023-10-26T04-59-46.486302.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2023-10-26T04-59-46.486302.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2023_10_04T17_42_58.699001", "path": ["**/details_harness|hellaswag|10_2023-10-04T17-42-58.699001.parquet"]}, {"split": "latest", "path": ["**/details_harness|hellaswag|10_2023-10-04T17-42-58.699001.parquet"]}]}, 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2023-10-26T03:59:57+00:00
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TAGS
#region-us
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# Dataset Card for Evaluation run of beomi/KoRWKV-6B
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model beomi/KoRWKV-6B on the Open LLM Leaderboard.
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2023-10-26T04:59:46.486302(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
### 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 Evaluation run of beomi/KoRWKV-6B",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model beomi/KoRWKV-6B on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-10-26T04:59:46.486302(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### 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#region-us \n",
"# Dataset Card for Evaluation run of beomi/KoRWKV-6B",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model beomi/KoRWKV-6B on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-10-26T04:59:46.486302(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### 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"
] |
[
6,
17,
31,
165,
67,
10,
4,
6,
6,
5,
5,
5,
7,
4,
10,
10,
5,
5,
9,
8,
8,
7,
8,
7,
5,
6,
6,
5
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of beomi/KoRWKV-6B## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model beomi/KoRWKV-6B on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-10-26T04:59:46.486302(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):### 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"
] |
3acdeeb69dca42a9557bd7ccdfa48220014ab9c6
|
# Bangumi Image Base of Isekai De Cheat Skill
This is the image base of bangumi Isekai de Cheat Skill, we detected 22 characters, 1032 images in total. The full dataset is [here](all.zip).
**Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual.** If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
| # | Images | Download | Preview 1 | Preview 2 | Preview 3 | Preview 4 | Preview 5 | Preview 6 | Preview 7 | Preview 8 |
|:------|---------:|:---------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|
| 0 | 309 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 23 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 17 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 10 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 24 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 9 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 29 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 8 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 59 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 76 | [Download](9/dataset.zip) |  |  |  |  |  |  |  |  |
| 10 | 19 | [Download](10/dataset.zip) |  |  |  |  |  |  |  |  |
| 11 | 9 | [Download](11/dataset.zip) |  |  |  |  |  |  |  |  |
| 12 | 7 | [Download](12/dataset.zip) |  |  |  |  |  |  |  | N/A |
| 13 | 16 | [Download](13/dataset.zip) |  |  |  |  |  |  |  |  |
| 14 | 6 | [Download](14/dataset.zip) |  |  |  |  |  |  | N/A | N/A |
| 15 | 10 | [Download](15/dataset.zip) |  |  |  |  |  |  |  |  |
| 16 | 15 | [Download](16/dataset.zip) |  |  |  |  |  |  |  |  |
| 17 | 11 | [Download](17/dataset.zip) |  |  |  |  |  |  |  |  |
| 18 | 73 | [Download](18/dataset.zip) |  |  |  |  |  |  |  |  |
| 19 | 10 | [Download](19/dataset.zip) |  |  |  |  |  |  |  |  |
| 20 | 52 | [Download](20/dataset.zip) |  |  |  |  |  |  |  |  |
| noise | 240 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
|
BangumiBase/isekaidecheatskill
|
[
"size_categories:1K<n<10K",
"license:mit",
"art",
"region:us"
] |
2023-10-04T16:44:11+00:00
|
{"license": "mit", "size_categories": ["1K<n<10K"], "tags": ["art"]}
|
2023-10-04T17:51:26+00:00
|
[] |
[] |
TAGS
#size_categories-1K<n<10K #license-mit #art #region-us
|
Bangumi Image Base of Isekai De Cheat Skill
===========================================
This is the image base of bangumi Isekai de Cheat Skill, we detected 22 characters, 1032 images in total. The full dataset is here.
Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual. If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
|
[] |
[
"TAGS\n#size_categories-1K<n<10K #license-mit #art #region-us \n"
] |
[
25
] |
[
"passage: TAGS\n#size_categories-1K<n<10K #license-mit #art #region-us \n"
] |
9dc8af19f39f03d5838201c48258e1f11b797951
|
# Dataset Card for "3ed8d887"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
result-kand2-sdxl-wuerst-karlo/3ed8d887
|
[
"region:us"
] |
2023-10-04T16:46:57+00:00
|
{"dataset_info": {"features": [{"name": "result", "dtype": "string"}, {"name": "id", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 156, "num_examples": 10}], "download_size": 1308, "dataset_size": 156}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-04T16:46:58+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "3ed8d887"
More Information needed
|
[
"# Dataset Card for \"3ed8d887\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"3ed8d887\"\n\nMore Information needed"
] |
[
6,
15
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"3ed8d887\"\n\nMore Information needed"
] |
30053fc9ad077dfd9725f9628f346773c016efef
|
# Dataset Card for "c50ece24"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
result-kand2-sdxl-wuerst-karlo/c50ece24
|
[
"region:us"
] |
2023-10-04T16:50:03+00:00
|
{"dataset_info": {"features": [{"name": "result", "dtype": "string"}, {"name": "id", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 163, "num_examples": 10}], "download_size": 1317, "dataset_size": 163}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-04T16:50:04+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "c50ece24"
More Information needed
|
[
"# Dataset Card for \"c50ece24\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"c50ece24\"\n\nMore Information needed"
] |
[
6,
14
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"c50ece24\"\n\nMore Information needed"
] |
7a0c59868e6b01806abdd5769aca9bd86d557193
|
# Bangumi Image Base of Mawaru Penguindrum
This is the image base of bangumi Mawaru Penguindrum, we detected 23 characters, 1725 images in total. The full dataset is [here](all.zip).
**Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual.** If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
| # | Images | Download | Preview 1 | Preview 2 | Preview 3 | Preview 4 | Preview 5 | Preview 6 | Preview 7 | Preview 8 |
|:------|---------:|:---------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|
| 0 | 19 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 177 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 81 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 18 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 76 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 206 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 19 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 14 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 64 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 11 | [Download](9/dataset.zip) |  |  |  |  |  |  |  |  |
| 10 | 313 | [Download](10/dataset.zip) |  |  |  |  |  |  |  |  |
| 11 | 24 | [Download](11/dataset.zip) |  |  |  |  |  |  |  |  |
| 12 | 11 | [Download](12/dataset.zip) |  |  |  |  |  |  |  |  |
| 13 | 306 | [Download](13/dataset.zip) |  |  |  |  |  |  |  |  |
| 14 | 19 | [Download](14/dataset.zip) |  |  |  |  |  |  |  |  |
| 15 | 19 | [Download](15/dataset.zip) |  |  |  |  |  |  |  |  |
| 16 | 13 | [Download](16/dataset.zip) |  |  |  |  |  |  |  |  |
| 17 | 16 | [Download](17/dataset.zip) |  |  |  |  |  |  |  |  |
| 18 | 37 | [Download](18/dataset.zip) |  |  |  |  |  |  |  |  |
| 19 | 17 | [Download](19/dataset.zip) |  |  |  |  |  |  |  |  |
| 20 | 17 | [Download](20/dataset.zip) |  |  |  |  |  |  |  |  |
| 21 | 8 | [Download](21/dataset.zip) |  |  |  |  |  |  |  |  |
| noise | 240 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
|
BangumiBase/mawarupenguindrum
|
[
"size_categories:1K<n<10K",
"license:mit",
"art",
"region:us"
] |
2023-10-04T16:50:55+00:00
|
{"license": "mit", "size_categories": ["1K<n<10K"], "tags": ["art"]}
|
2023-10-04T18:21:03+00:00
|
[] |
[] |
TAGS
#size_categories-1K<n<10K #license-mit #art #region-us
|
Bangumi Image Base of Mawaru Penguindrum
========================================
This is the image base of bangumi Mawaru Penguindrum, we detected 23 characters, 1725 images in total. The full dataset is here.
Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual. If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
|
[] |
[
"TAGS\n#size_categories-1K<n<10K #license-mit #art #region-us \n"
] |
[
25
] |
[
"passage: TAGS\n#size_categories-1K<n<10K #license-mit #art #region-us \n"
] |
e33c5c39edcc3b8298851152ed8a4349f1acb5c3
|
# Dataset Card for "pile-1k"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
pouya-haghi/pile-1k
|
[
"region:us"
] |
2023-10-04T16:53:01+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "meta", "struct": [{"name": "pile_set_name", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 5342511, "num_examples": 1000}], "download_size": 2874173, "dataset_size": 5342511}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-04T16:55:34+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "pile-1k"
More Information needed
|
[
"# Dataset Card for \"pile-1k\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"pile-1k\"\n\nMore Information needed"
] |
[
6,
14
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"pile-1k\"\n\nMore Information needed"
] |
136c2bd41c7b0adce90e1740b0c30519bda6c223
|
# Dataset Card for "4b23c5a8"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
result-kand2-sdxl-wuerst-karlo/4b23c5a8
|
[
"region:us"
] |
2023-10-04T16:54:38+00:00
|
{"dataset_info": {"features": [{"name": "result", "dtype": "string"}, {"name": "id", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 178, "num_examples": 10}], "download_size": 1335, "dataset_size": 178}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-04T16:54:41+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "4b23c5a8"
More Information needed
|
[
"# Dataset Card for \"4b23c5a8\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"4b23c5a8\"\n\nMore Information needed"
] |
[
6,
17
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"4b23c5a8\"\n\nMore Information needed"
] |
2fcb9568155dd3c2959382d863e3112af07c4997
|
# Bangumi Image Base of Strike The Blood
This is the image base of bangumi Strike the Blood, we detected 66 characters, 5038 images in total. The full dataset is [here](all.zip).
**Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual.** If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
| # | Images | Download | Preview 1 | Preview 2 | Preview 3 | Preview 4 | Preview 5 | Preview 6 | Preview 7 | Preview 8 |
|:------|---------:|:---------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|
| 0 | 781 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 24 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 144 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 60 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 16 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 49 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 149 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 40 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 44 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 1115 | [Download](9/dataset.zip) |  |  |  |  |  |  |  |  |
| 10 | 70 | [Download](10/dataset.zip) |  |  |  |  |  |  |  |  |
| 11 | 55 | [Download](11/dataset.zip) |  |  |  |  |  |  |  |  |
| 12 | 128 | [Download](12/dataset.zip) |  |  |  |  |  |  |  |  |
| 13 | 121 | [Download](13/dataset.zip) |  |  |  |  |  |  |  |  |
| 14 | 14 | [Download](14/dataset.zip) |  |  |  |  |  |  |  |  |
| 15 | 31 | [Download](15/dataset.zip) |  |  |  |  |  |  |  |  |
| 16 | 48 | [Download](16/dataset.zip) |  |  |  |  |  |  |  |  |
| 17 | 26 | [Download](17/dataset.zip) |  |  |  |  |  |  |  |  |
| 18 | 31 | [Download](18/dataset.zip) |  |  |  |  |  |  |  |  |
| 19 | 53 | [Download](19/dataset.zip) |  |  |  |  |  |  |  |  |
| 20 | 124 | [Download](20/dataset.zip) |  |  |  |  |  |  |  |  |
| 21 | 51 | [Download](21/dataset.zip) |  |  |  |  |  |  |  |  |
| 22 | 89 | [Download](22/dataset.zip) |  |  |  |  |  |  |  |  |
| 23 | 21 | [Download](23/dataset.zip) |  |  |  |  |  |  |  |  |
| 24 | 24 | [Download](24/dataset.zip) |  |  |  |  |  |  |  |  |
| 25 | 54 | [Download](25/dataset.zip) |  |  |  |  |  |  |  |  |
| 26 | 28 | [Download](26/dataset.zip) |  |  |  |  |  |  |  |  |
| 27 | 31 | [Download](27/dataset.zip) |  |  |  |  |  |  |  |  |
| 28 | 26 | [Download](28/dataset.zip) |  |  |  |  |  |  |  |  |
| 29 | 36 | [Download](29/dataset.zip) |  |  |  |  |  |  |  |  |
| 30 | 14 | [Download](30/dataset.zip) |  |  |  |  |  |  |  |  |
| 31 | 19 | [Download](31/dataset.zip) |  |  |  |  |  |  |  |  |
| 32 | 13 | [Download](32/dataset.zip) |  |  |  |  |  |  |  |  |
| 33 | 60 | [Download](33/dataset.zip) |  |  |  |  |  |  |  |  |
| 34 | 19 | [Download](34/dataset.zip) |  |  |  |  |  |  |  |  |
| 35 | 8 | [Download](35/dataset.zip) |  |  |  |  |  |  |  |  |
| 36 | 9 | [Download](36/dataset.zip) |  |  |  |  |  |  |  |  |
| 37 | 178 | [Download](37/dataset.zip) |  |  |  |  |  |  |  |  |
| 38 | 18 | [Download](38/dataset.zip) |  |  |  |  |  |  |  |  |
| 39 | 32 | [Download](39/dataset.zip) |  |  |  |  |  |  |  |  |
| 40 | 105 | [Download](40/dataset.zip) |  |  |  |  |  |  |  |  |
| 41 | 210 | [Download](41/dataset.zip) |  |  |  |  |  |  |  |  |
| 42 | 77 | [Download](42/dataset.zip) |  |  |  |  |  |  |  |  |
| 43 | 49 | [Download](43/dataset.zip) |  |  |  |  |  |  |  |  |
| 44 | 11 | [Download](44/dataset.zip) |  |  |  |  |  |  |  |  |
| 45 | 23 | [Download](45/dataset.zip) |  |  |  |  |  |  |  |  |
| 46 | 34 | [Download](46/dataset.zip) |  |  |  |  |  |  |  |  |
| 47 | 13 | [Download](47/dataset.zip) |  |  |  |  |  |  |  |  |
| 48 | 14 | [Download](48/dataset.zip) |  |  |  |  |  |  |  |  |
| 49 | 18 | [Download](49/dataset.zip) |  |  |  |  |  |  |  |  |
| 50 | 8 | [Download](50/dataset.zip) |  |  |  |  |  |  |  |  |
| 51 | 14 | [Download](51/dataset.zip) |  |  |  |  |  |  |  |  |
| 52 | 86 | [Download](52/dataset.zip) |  |  |  |  |  |  |  |  |
| 53 | 27 | [Download](53/dataset.zip) |  |  |  |  |  |  |  |  |
| 54 | 12 | [Download](54/dataset.zip) |  |  |  |  |  |  |  |  |
| 55 | 16 | [Download](55/dataset.zip) |  |  |  |  |  |  |  |  |
| 56 | 7 | [Download](56/dataset.zip) |  |  |  |  |  |  |  | N/A |
| 57 | 16 | [Download](57/dataset.zip) |  |  |  |  |  |  |  |  |
| 58 | 19 | [Download](58/dataset.zip) |  |  |  |  |  |  |  |  |
| 59 | 6 | [Download](59/dataset.zip) |  |  |  |  |  |  | N/A | N/A |
| 60 | 22 | [Download](60/dataset.zip) |  |  |  |  |  |  |  |  |
| 61 | 6 | [Download](61/dataset.zip) |  |  |  |  |  |  | N/A | N/A |
| 62 | 6 | [Download](62/dataset.zip) |  |  |  |  |  |  | N/A | N/A |
| 63 | 16 | [Download](63/dataset.zip) |  |  |  |  |  |  |  |  |
| 64 | 56 | [Download](64/dataset.zip) |  |  |  |  |  |  |  |  |
| noise | 314 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
|
BangumiBase/striketheblood
|
[
"size_categories:1K<n<10K",
"license:mit",
"art",
"region:us"
] |
2023-10-04T16:58:32+00:00
|
{"license": "mit", "size_categories": ["1K<n<10K"], "tags": ["art"]}
|
2023-10-04T19:43:50+00:00
|
[] |
[] |
TAGS
#size_categories-1K<n<10K #license-mit #art #region-us
|
Bangumi Image Base of Strike The Blood
======================================
This is the image base of bangumi Strike the Blood, we detected 66 characters, 5038 images in total. The full dataset is here.
Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual. If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
|
[] |
[
"TAGS\n#size_categories-1K<n<10K #license-mit #art #region-us \n"
] |
[
25
] |
[
"passage: TAGS\n#size_categories-1K<n<10K #license-mit #art #region-us \n"
] |
0849e2d94a0f7ca08b9b0c40bd64aa9cf0e7c4b6
|
# Dataset Card for "c98495e0"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
result-kand2-sdxl-wuerst-karlo/c98495e0
|
[
"region:us"
] |
2023-10-04T16:58:57+00:00
|
{"dataset_info": {"features": [{"name": "result", "dtype": "string"}, {"name": "id", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 156, "num_examples": 10}], "download_size": 1307, "dataset_size": 156}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-04T16:58:59+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "c98495e0"
More Information needed
|
[
"# Dataset Card for \"c98495e0\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"c98495e0\"\n\nMore Information needed"
] |
[
6,
15
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"c98495e0\"\n\nMore Information needed"
] |
e76a8fde01167a995b4899ed55ce01d7e075c0b8
|
# Dataset Card for "7709cb1f"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
result-kand2-sdxl-wuerst-karlo/7709cb1f
|
[
"region:us"
] |
2023-10-04T17:03:32+00:00
|
{"dataset_info": {"features": [{"name": "result", "dtype": "string"}, {"name": "id", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 168, "num_examples": 10}], "download_size": 1331, "dataset_size": 168}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-04T17:03:33+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "7709cb1f"
More Information needed
|
[
"# Dataset Card for \"7709cb1f\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"7709cb1f\"\n\nMore Information needed"
] |
[
6,
16
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"7709cb1f\"\n\nMore Information needed"
] |
168ac6b3d4cdebcb59c87b4b4a085b3dbb42de96
|
# Dataset Card for "0415e725"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
result-kand2-sdxl-wuerst-karlo/0415e725
|
[
"region:us"
] |
2023-10-04T17:08:29+00:00
|
{"dataset_info": {"features": [{"name": "result", "dtype": "string"}, {"name": "id", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 163, "num_examples": 10}], "download_size": 1335, "dataset_size": 163}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-04T17:08:30+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "0415e725"
More Information needed
|
[
"# Dataset Card for \"0415e725\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"0415e725\"\n\nMore Information needed"
] |
[
6,
14
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"0415e725\"\n\nMore Information needed"
] |
6ae6d41d791368cc99284e36b0be36e1dd46df60
|
# Dataset Card for "czech_train_data"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
adityarra07/czech_train_data
|
[
"region:us"
] |
2023-10-04T17:08:37+00:00
|
{"dataset_info": {"features": [{"name": "audio", "dtype": {"audio": {"sampling_rate": 16000}}}, {"name": "transcription", "dtype": "string"}, {"name": "id", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 669027003.0330192, "num_examples": 12613}, {"name": "test", "num_bytes": 26521327.322326932, "num_examples": 500}], "download_size": 658874865, "dataset_size": 695548330.3553461}}
|
2023-10-04T17:09:04+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "czech_train_data"
More Information needed
|
[
"# Dataset Card for \"czech_train_data\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"czech_train_data\"\n\nMore Information needed"
] |
[
6,
17
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"czech_train_data\"\n\nMore Information needed"
] |
cad481cdb6e45771e92bcf539ec202f4172141c1
|
# Dataset Card for "czech_test"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
adityarra07/czech_test
|
[
"region:us"
] |
2023-10-04T17:09:04+00:00
|
{"dataset_info": {"features": [{"name": "audio", "dtype": {"audio": {"sampling_rate": 16000}}}, {"name": "transcription", "dtype": "string"}, {"name": "id", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 53042654.644653864, "num_examples": 1000}], "download_size": 52259185, "dataset_size": 53042654.644653864}}
|
2023-10-04T17:09:08+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "czech_test"
More Information needed
|
[
"# Dataset Card for \"czech_test\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"czech_test\"\n\nMore Information needed"
] |
[
6,
14
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"czech_test\"\n\nMore Information needed"
] |
1f6bdc9adfffe92f0caddd1d885a83b7f4c7466d
|
# Dataset Card for "eacbe536"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
result-kand2-sdxl-wuerst-karlo/eacbe536
|
[
"region:us"
] |
2023-10-04T17:13:44+00:00
|
{"dataset_info": {"features": [{"name": "result", "dtype": "string"}, {"name": "id", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 173, "num_examples": 10}], "download_size": 1377, "dataset_size": 173}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-04T17:13:45+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "eacbe536"
More Information needed
|
[
"# Dataset Card for \"eacbe536\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"eacbe536\"\n\nMore Information needed"
] |
[
6,
14
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"eacbe536\"\n\nMore Information needed"
] |
9e466e22756ea97d6830608ff6ee090cb7e9fd72
|
# Dataset Card for "23611323"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
result-kand2-sdxl-wuerst-karlo/23611323
|
[
"region:us"
] |
2023-10-04T17:18:00+00:00
|
{"dataset_info": {"features": [{"name": "result", "dtype": "string"}, {"name": "id", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 166, "num_examples": 10}], "download_size": 1336, "dataset_size": 166}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-04T17:18:01+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "23611323"
More Information needed
|
[
"# Dataset Card for \"23611323\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"23611323\"\n\nMore Information needed"
] |
[
6,
14
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"23611323\"\n\nMore Information needed"
] |
d10ea56c30de5774421617c34b66732f7ca53b41
|
# Dataset Card for "980edb53"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
result-kand2-sdxl-wuerst-karlo/980edb53
|
[
"region:us"
] |
2023-10-04T17:22:21+00:00
|
{"dataset_info": {"features": [{"name": "result", "dtype": "string"}, {"name": "id", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 156, "num_examples": 10}], "download_size": 1319, "dataset_size": 156}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-04T17:22:22+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "980edb53"
More Information needed
|
[
"# Dataset Card for \"980edb53\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"980edb53\"\n\nMore Information needed"
] |
[
6,
14
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"980edb53\"\n\nMore Information needed"
] |
25a285583f2f550f631c0ab87787cd7c5086c386
|
# Dataset Card for "b4de2e4d"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
result-kand2-sdxl-wuerst-karlo/b4de2e4d
|
[
"region:us"
] |
2023-10-04T17:26:46+00:00
|
{"dataset_info": {"features": [{"name": "result", "dtype": "string"}, {"name": "id", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 193, "num_examples": 10}], "download_size": 1385, "dataset_size": 193}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-04T17:26:47+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "b4de2e4d"
More Information needed
|
[
"# Dataset Card for \"b4de2e4d\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"b4de2e4d\"\n\nMore Information needed"
] |
[
6,
17
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"b4de2e4d\"\n\nMore Information needed"
] |
bd6728464727a07867a4f9d6cd1bff16506ddb6d
|
# Dataset Card for "7acd34b3"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
result-kand2-sdxl-wuerst-karlo/7acd34b3
|
[
"region:us"
] |
2023-10-04T17:30:19+00:00
|
{"dataset_info": {"features": [{"name": "result", "dtype": "string"}, {"name": "id", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 166, "num_examples": 10}], "download_size": 1331, "dataset_size": 166}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-04T17:30:20+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "7acd34b3"
More Information needed
|
[
"# Dataset Card for \"7acd34b3\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"7acd34b3\"\n\nMore Information needed"
] |
[
6,
16
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"7acd34b3\"\n\nMore Information needed"
] |
b7f13c488e9898144b1ee9a98a7e20117706d5a4
|
# Dataset Card for "fc5ced1b"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
result-kand2-sdxl-wuerst-karlo/fc5ced1b
|
[
"region:us"
] |
2023-10-04T17:33:31+00:00
|
{"dataset_info": {"features": [{"name": "result", "dtype": "string"}, {"name": "id", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 149, "num_examples": 10}], "download_size": 1316, "dataset_size": 149}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-04T17:33:32+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "fc5ced1b"
More Information needed
|
[
"# Dataset Card for \"fc5ced1b\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"fc5ced1b\"\n\nMore Information needed"
] |
[
6,
15
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"fc5ced1b\"\n\nMore Information needed"
] |
6fdd676478af4cba37947b0fd511552f89fcb4fc
|
# Dataset Card for "7f43ba07"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
result-kand2-sdxl-wuerst-karlo/7f43ba07
|
[
"region:us"
] |
2023-10-04T17:38:54+00:00
|
{"dataset_info": {"features": [{"name": "result", "dtype": "string"}, {"name": "id", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 188, "num_examples": 10}], "download_size": 1352, "dataset_size": 188}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-04T17:38:54+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "7f43ba07"
More Information needed
|
[
"# Dataset Card for \"7f43ba07\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"7f43ba07\"\n\nMore Information needed"
] |
[
6,
15
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"7f43ba07\"\n\nMore Information needed"
] |
f5c457d3557a528adf536fac96a86c146a5be941
|
# Dataset Card for "f7f54a55"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
result-kand2-sdxl-wuerst-karlo/f7f54a55
|
[
"region:us"
] |
2023-10-04T17:43:40+00:00
|
{"dataset_info": {"features": [{"name": "result", "dtype": "string"}, {"name": "id", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 141, "num_examples": 10}], "download_size": 1325, "dataset_size": 141}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-04T17:43:41+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "f7f54a55"
More Information needed
|
[
"# Dataset Card for \"f7f54a55\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"f7f54a55\"\n\nMore Information needed"
] |
[
6,
16
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"f7f54a55\"\n\nMore Information needed"
] |
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