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davanstrien
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autoevaluate/autoeval-eval-acronym_identification-default-ff9253-47222145202
2023-10-04T15:09:06.000Z
[ "region:us" ]
autoevaluate
null
null
null
0
0
Entry not found
autoevaluate/autoeval-eval-acronym_identification-default-c027b5-47746145224
2023-10-04T15:09:13.000Z
[ "region:us" ]
autoevaluate
null
null
null
0
0
Entry not found
autoevaluate/autoeval-eval-cnn_dailymail-3.0.0-52cdb7-47832145225
2023-10-04T16:15:42.000Z
[ "autotrain", "evaluation", "region:us" ]
autoevaluate
null
null
null
0
0
--- type: predictions tags: - autotrain - evaluation datasets: - cnn_dailymail eval_info: task: summarization model: kworts/BARTxiv metrics: ['accuracy', 'rouge', 'mse'] dataset_name: cnn_dailymail dataset_config: 3.0.0 dataset_split: test col_mapping: text: article target: highlights --- # 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: kworts/BARTxiv * 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 [@Raj P Sini](https://huggingface.co/Raj P Sini) for evaluating this model.
autoevaluate/autoeval-eval-cnn_dailymail-3.0.0-52cdb7-47832145227
2023-10-04T15:18:33.000Z
[ "autotrain", "evaluation", "region:us" ]
autoevaluate
null
null
null
0
0
--- type: predictions tags: - autotrain - evaluation datasets: - cnn_dailymail eval_info: task: summarization model: flax-community/t5-base-cnn-dm metrics: ['accuracy', 'rouge', 'mse'] dataset_name: cnn_dailymail dataset_config: 3.0.0 dataset_split: test col_mapping: text: article target: highlights --- # 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: flax-community/t5-base-cnn-dm * 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 [@Raj P Sini](https://huggingface.co/Raj P Sini) for evaluating this model.
autoevaluate/autoeval-eval-cnn_dailymail-3.0.0-52cdb7-47832145229
2023-10-04T16:52:24.000Z
[ "autotrain", "evaluation", "region:us" ]
autoevaluate
null
null
null
0
0
--- type: predictions tags: - autotrain - evaluation datasets: - cnn_dailymail eval_info: task: summarization model: google/pegasus-cnn_dailymail metrics: ['accuracy', 'rouge', 'mse'] dataset_name: cnn_dailymail dataset_config: 3.0.0 dataset_split: test col_mapping: text: article target: highlights --- # 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-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 [@Raj P Sini](https://huggingface.co/Raj P Sini) for evaluating this model.
BangumiBase/horimiya
2023-10-04T16:15:15.000Z
[ "size_categories:1K<n<10K", "license:mit", "art", "region:us" ]
BangumiBase
null
null
null
0
0
--- license: mit tags: - art size_categories: - 1K<n<10K --- # Bangumi Image Base of Horimiya This is the image base of bangumi Horimiya, we detected 25 characters, 1848 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 | 233 | [Download](0/dataset.zip) | ![preview 1](0/preview_1.png) | ![preview 2](0/preview_2.png) | ![preview 3](0/preview_3.png) | ![preview 4](0/preview_4.png) | ![preview 5](0/preview_5.png) | ![preview 6](0/preview_6.png) | ![preview 7](0/preview_7.png) | ![preview 8](0/preview_8.png) | | 1 | 90 | [Download](1/dataset.zip) | ![preview 1](1/preview_1.png) | ![preview 2](1/preview_2.png) | ![preview 3](1/preview_3.png) | ![preview 4](1/preview_4.png) | ![preview 5](1/preview_5.png) | ![preview 6](1/preview_6.png) | ![preview 7](1/preview_7.png) | ![preview 8](1/preview_8.png) | | 2 | 69 | [Download](2/dataset.zip) | ![preview 1](2/preview_1.png) | ![preview 2](2/preview_2.png) | ![preview 3](2/preview_3.png) | ![preview 4](2/preview_4.png) | ![preview 5](2/preview_5.png) | ![preview 6](2/preview_6.png) | ![preview 7](2/preview_7.png) | ![preview 8](2/preview_8.png) | | 3 | 40 | [Download](3/dataset.zip) | ![preview 1](3/preview_1.png) | ![preview 2](3/preview_2.png) | ![preview 3](3/preview_3.png) | ![preview 4](3/preview_4.png) | ![preview 5](3/preview_5.png) | ![preview 6](3/preview_6.png) | ![preview 7](3/preview_7.png) | ![preview 8](3/preview_8.png) | | 4 | 109 | [Download](4/dataset.zip) | ![preview 1](4/preview_1.png) | ![preview 2](4/preview_2.png) | ![preview 3](4/preview_3.png) | ![preview 4](4/preview_4.png) | ![preview 5](4/preview_5.png) | ![preview 6](4/preview_6.png) | ![preview 7](4/preview_7.png) | ![preview 8](4/preview_8.png) | | 5 | 162 | [Download](5/dataset.zip) | ![preview 1](5/preview_1.png) | ![preview 2](5/preview_2.png) | ![preview 3](5/preview_3.png) | ![preview 4](5/preview_4.png) | ![preview 5](5/preview_5.png) | ![preview 6](5/preview_6.png) | ![preview 7](5/preview_7.png) | ![preview 8](5/preview_8.png) | | 6 | 20 | [Download](6/dataset.zip) | ![preview 1](6/preview_1.png) | ![preview 2](6/preview_2.png) | ![preview 3](6/preview_3.png) | ![preview 4](6/preview_4.png) | ![preview 5](6/preview_5.png) | ![preview 6](6/preview_6.png) | ![preview 7](6/preview_7.png) | ![preview 8](6/preview_8.png) | | 7 | 36 | [Download](7/dataset.zip) | ![preview 1](7/preview_1.png) | ![preview 2](7/preview_2.png) | ![preview 3](7/preview_3.png) | ![preview 4](7/preview_4.png) | ![preview 5](7/preview_5.png) | ![preview 6](7/preview_6.png) | ![preview 7](7/preview_7.png) | ![preview 8](7/preview_8.png) | | 8 | 31 | [Download](8/dataset.zip) | ![preview 1](8/preview_1.png) | ![preview 2](8/preview_2.png) | ![preview 3](8/preview_3.png) | ![preview 4](8/preview_4.png) | ![preview 5](8/preview_5.png) | ![preview 6](8/preview_6.png) | ![preview 7](8/preview_7.png) | ![preview 8](8/preview_8.png) | | 9 | 180 | [Download](9/dataset.zip) | ![preview 1](9/preview_1.png) | ![preview 2](9/preview_2.png) | ![preview 3](9/preview_3.png) | ![preview 4](9/preview_4.png) | ![preview 5](9/preview_5.png) | ![preview 6](9/preview_6.png) | ![preview 7](9/preview_7.png) | ![preview 8](9/preview_8.png) | | 10 | 48 | [Download](10/dataset.zip) | ![preview 1](10/preview_1.png) | ![preview 2](10/preview_2.png) | ![preview 3](10/preview_3.png) | ![preview 4](10/preview_4.png) | ![preview 5](10/preview_5.png) | ![preview 6](10/preview_6.png) | ![preview 7](10/preview_7.png) | ![preview 8](10/preview_8.png) | | 11 | 69 | [Download](11/dataset.zip) | ![preview 1](11/preview_1.png) | ![preview 2](11/preview_2.png) | ![preview 3](11/preview_3.png) | ![preview 4](11/preview_4.png) | ![preview 5](11/preview_5.png) | ![preview 6](11/preview_6.png) | ![preview 7](11/preview_7.png) | ![preview 8](11/preview_8.png) | | 12 | 21 | [Download](12/dataset.zip) | ![preview 1](12/preview_1.png) | ![preview 2](12/preview_2.png) | ![preview 3](12/preview_3.png) | ![preview 4](12/preview_4.png) | ![preview 5](12/preview_5.png) | ![preview 6](12/preview_6.png) | ![preview 7](12/preview_7.png) | ![preview 8](12/preview_8.png) | | 13 | 20 | [Download](13/dataset.zip) | ![preview 1](13/preview_1.png) | ![preview 2](13/preview_2.png) | ![preview 3](13/preview_3.png) | ![preview 4](13/preview_4.png) | ![preview 5](13/preview_5.png) | ![preview 6](13/preview_6.png) | ![preview 7](13/preview_7.png) | ![preview 8](13/preview_8.png) | | 14 | 12 | [Download](14/dataset.zip) | ![preview 1](14/preview_1.png) | ![preview 2](14/preview_2.png) | ![preview 3](14/preview_3.png) | ![preview 4](14/preview_4.png) | ![preview 5](14/preview_5.png) | ![preview 6](14/preview_6.png) | ![preview 7](14/preview_7.png) | ![preview 8](14/preview_8.png) | | 15 | 131 | [Download](15/dataset.zip) | ![preview 1](15/preview_1.png) | ![preview 2](15/preview_2.png) | ![preview 3](15/preview_3.png) | ![preview 4](15/preview_4.png) | ![preview 5](15/preview_5.png) | ![preview 6](15/preview_6.png) | ![preview 7](15/preview_7.png) | ![preview 8](15/preview_8.png) | | 16 | 72 | [Download](16/dataset.zip) | ![preview 1](16/preview_1.png) | ![preview 2](16/preview_2.png) | ![preview 3](16/preview_3.png) | ![preview 4](16/preview_4.png) | ![preview 5](16/preview_5.png) | ![preview 6](16/preview_6.png) | ![preview 7](16/preview_7.png) | ![preview 8](16/preview_8.png) | | 17 | 26 | [Download](17/dataset.zip) | ![preview 1](17/preview_1.png) | ![preview 2](17/preview_2.png) | ![preview 3](17/preview_3.png) | ![preview 4](17/preview_4.png) | ![preview 5](17/preview_5.png) | ![preview 6](17/preview_6.png) | ![preview 7](17/preview_7.png) | ![preview 8](17/preview_8.png) | | 18 | 38 | [Download](18/dataset.zip) | ![preview 1](18/preview_1.png) | ![preview 2](18/preview_2.png) | ![preview 3](18/preview_3.png) | ![preview 4](18/preview_4.png) | ![preview 5](18/preview_5.png) | ![preview 6](18/preview_6.png) | ![preview 7](18/preview_7.png) | ![preview 8](18/preview_8.png) | | 19 | 24 | [Download](19/dataset.zip) | ![preview 1](19/preview_1.png) | ![preview 2](19/preview_2.png) | ![preview 3](19/preview_3.png) | ![preview 4](19/preview_4.png) | ![preview 5](19/preview_5.png) | ![preview 6](19/preview_6.png) | ![preview 7](19/preview_7.png) | ![preview 8](19/preview_8.png) | | 20 | 190 | [Download](20/dataset.zip) | ![preview 1](20/preview_1.png) | ![preview 2](20/preview_2.png) | ![preview 3](20/preview_3.png) | ![preview 4](20/preview_4.png) | ![preview 5](20/preview_5.png) | ![preview 6](20/preview_6.png) | ![preview 7](20/preview_7.png) | ![preview 8](20/preview_8.png) | | 21 | 8 | [Download](21/dataset.zip) | ![preview 1](21/preview_1.png) | ![preview 2](21/preview_2.png) | ![preview 3](21/preview_3.png) | ![preview 4](21/preview_4.png) | ![preview 5](21/preview_5.png) | ![preview 6](21/preview_6.png) | ![preview 7](21/preview_7.png) | ![preview 8](21/preview_8.png) | | 22 | 18 | [Download](22/dataset.zip) | ![preview 1](22/preview_1.png) | ![preview 2](22/preview_2.png) | ![preview 3](22/preview_3.png) | ![preview 4](22/preview_4.png) | ![preview 5](22/preview_5.png) | ![preview 6](22/preview_6.png) | ![preview 7](22/preview_7.png) | ![preview 8](22/preview_8.png) | | 23 | 77 | [Download](23/dataset.zip) | ![preview 1](23/preview_1.png) | ![preview 2](23/preview_2.png) | ![preview 3](23/preview_3.png) | ![preview 4](23/preview_4.png) | ![preview 5](23/preview_5.png) | ![preview 6](23/preview_6.png) | ![preview 7](23/preview_7.png) | ![preview 8](23/preview_8.png) | | noise | 124 | [Download](-1/dataset.zip) | ![preview 1](-1/preview_1.png) | ![preview 2](-1/preview_2.png) | ![preview 3](-1/preview_3.png) | ![preview 4](-1/preview_4.png) | ![preview 5](-1/preview_5.png) | ![preview 6](-1/preview_6.png) | ![preview 7](-1/preview_7.png) | ![preview 8](-1/preview_8.png) |
BangumiBase/chainsawman
2023-10-04T16:15:05.000Z
[ "size_categories:1K<n<10K", "license:mit", "art", "region:us" ]
BangumiBase
null
null
null
0
0
--- license: mit tags: - art size_categories: - 1K<n<10K --- # Bangumi Image Base of Chainsaw Man This is the image base of bangumi Chainsaw Man, we detected 16 characters, 1264 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 | 280 | [Download](0/dataset.zip) | ![preview 1](0/preview_1.png) | ![preview 2](0/preview_2.png) | ![preview 3](0/preview_3.png) | ![preview 4](0/preview_4.png) | ![preview 5](0/preview_5.png) | ![preview 6](0/preview_6.png) | ![preview 7](0/preview_7.png) | ![preview 8](0/preview_8.png) | | 1 | 24 | [Download](1/dataset.zip) | ![preview 1](1/preview_1.png) | ![preview 2](1/preview_2.png) | ![preview 3](1/preview_3.png) | ![preview 4](1/preview_4.png) | ![preview 5](1/preview_5.png) | ![preview 6](1/preview_6.png) | ![preview 7](1/preview_7.png) | ![preview 8](1/preview_8.png) | | 2 | 33 | [Download](2/dataset.zip) | ![preview 1](2/preview_1.png) | ![preview 2](2/preview_2.png) | ![preview 3](2/preview_3.png) | ![preview 4](2/preview_4.png) | ![preview 5](2/preview_5.png) | ![preview 6](2/preview_6.png) | ![preview 7](2/preview_7.png) | ![preview 8](2/preview_8.png) | | 3 | 196 | [Download](3/dataset.zip) | ![preview 1](3/preview_1.png) | ![preview 2](3/preview_2.png) | ![preview 3](3/preview_3.png) | ![preview 4](3/preview_4.png) | ![preview 5](3/preview_5.png) | ![preview 6](3/preview_6.png) | ![preview 7](3/preview_7.png) | ![preview 8](3/preview_8.png) | | 4 | 111 | [Download](4/dataset.zip) | ![preview 1](4/preview_1.png) | ![preview 2](4/preview_2.png) | ![preview 3](4/preview_3.png) | ![preview 4](4/preview_4.png) | ![preview 5](4/preview_5.png) | ![preview 6](4/preview_6.png) | ![preview 7](4/preview_7.png) | ![preview 8](4/preview_8.png) | | 5 | 38 | [Download](5/dataset.zip) | ![preview 1](5/preview_1.png) | ![preview 2](5/preview_2.png) | ![preview 3](5/preview_3.png) | ![preview 4](5/preview_4.png) | ![preview 5](5/preview_5.png) | ![preview 6](5/preview_6.png) | ![preview 7](5/preview_7.png) | ![preview 8](5/preview_8.png) | | 6 | 90 | [Download](6/dataset.zip) | ![preview 1](6/preview_1.png) | ![preview 2](6/preview_2.png) | ![preview 3](6/preview_3.png) | ![preview 4](6/preview_4.png) | ![preview 5](6/preview_5.png) | ![preview 6](6/preview_6.png) | ![preview 7](6/preview_7.png) | ![preview 8](6/preview_8.png) | | 7 | 52 | [Download](7/dataset.zip) | ![preview 1](7/preview_1.png) | ![preview 2](7/preview_2.png) | ![preview 3](7/preview_3.png) | ![preview 4](7/preview_4.png) | ![preview 5](7/preview_5.png) | ![preview 6](7/preview_6.png) | ![preview 7](7/preview_7.png) | ![preview 8](7/preview_8.png) | | 8 | 36 | [Download](8/dataset.zip) | ![preview 1](8/preview_1.png) | ![preview 2](8/preview_2.png) | ![preview 3](8/preview_3.png) | ![preview 4](8/preview_4.png) | ![preview 5](8/preview_5.png) | ![preview 6](8/preview_6.png) | ![preview 7](8/preview_7.png) | ![preview 8](8/preview_8.png) | | 9 | 53 | [Download](9/dataset.zip) | ![preview 1](9/preview_1.png) | ![preview 2](9/preview_2.png) | ![preview 3](9/preview_3.png) | ![preview 4](9/preview_4.png) | ![preview 5](9/preview_5.png) | ![preview 6](9/preview_6.png) | ![preview 7](9/preview_7.png) | ![preview 8](9/preview_8.png) | | 10 | 16 | [Download](10/dataset.zip) | ![preview 1](10/preview_1.png) | ![preview 2](10/preview_2.png) | ![preview 3](10/preview_3.png) | ![preview 4](10/preview_4.png) | ![preview 5](10/preview_5.png) | ![preview 6](10/preview_6.png) | ![preview 7](10/preview_7.png) | ![preview 8](10/preview_8.png) | | 11 | 102 | [Download](11/dataset.zip) | ![preview 1](11/preview_1.png) | ![preview 2](11/preview_2.png) | ![preview 3](11/preview_3.png) | ![preview 4](11/preview_4.png) | ![preview 5](11/preview_5.png) | ![preview 6](11/preview_6.png) | ![preview 7](11/preview_7.png) | ![preview 8](11/preview_8.png) | | 12 | 108 | [Download](12/dataset.zip) | ![preview 1](12/preview_1.png) | ![preview 2](12/preview_2.png) | ![preview 3](12/preview_3.png) | ![preview 4](12/preview_4.png) | ![preview 5](12/preview_5.png) | ![preview 6](12/preview_6.png) | ![preview 7](12/preview_7.png) | ![preview 8](12/preview_8.png) | | 13 | 7 | [Download](13/dataset.zip) | ![preview 1](13/preview_1.png) | ![preview 2](13/preview_2.png) | ![preview 3](13/preview_3.png) | ![preview 4](13/preview_4.png) | ![preview 5](13/preview_5.png) | ![preview 6](13/preview_6.png) | ![preview 7](13/preview_7.png) | N/A | | 14 | 36 | [Download](14/dataset.zip) | ![preview 1](14/preview_1.png) | ![preview 2](14/preview_2.png) | ![preview 3](14/preview_3.png) | ![preview 4](14/preview_4.png) | ![preview 5](14/preview_5.png) | ![preview 6](14/preview_6.png) | ![preview 7](14/preview_7.png) | ![preview 8](14/preview_8.png) | | noise | 82 | [Download](-1/dataset.zip) | ![preview 1](-1/preview_1.png) | ![preview 2](-1/preview_2.png) | ![preview 3](-1/preview_3.png) | ![preview 4](-1/preview_4.png) | ![preview 5](-1/preview_5.png) | ![preview 6](-1/preview_6.png) | ![preview 7](-1/preview_7.png) | ![preview 8](-1/preview_8.png) |
Duongkum999/duong
2023-10-04T15:15:07.000Z
[ "license:mit", "region:us" ]
Duongkum999
null
null
null
0
0
--- license: mit --- # Dataset Card for Dataset Name ## Dataset Description - **Homepage:** - **Repository:** - **Paper:** - **Leaderboard:** - **Point of Contact:** ### Dataset Summary This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1). ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]
autoevaluate/autoeval-eval-kmfoda__booksum-kmfoda__booksum-9f206e-47938145232
2023-10-05T01:08:44.000Z
[ "autotrain", "evaluation", "region:us" ]
autoevaluate
null
null
null
0
0
--- type: predictions tags: - autotrain - evaluation datasets: - kmfoda/booksum eval_info: task: summarization model: pszemraj/long-t5-tglobal-base-16384-booksci-summary-v1 metrics: [] dataset_name: kmfoda/booksum dataset_config: kmfoda--booksum dataset_split: test col_mapping: text: chapter target: summary_text --- # 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: pszemraj/long-t5-tglobal-base-16384-booksci-summary-v1 * Dataset: kmfoda/booksum * Config: kmfoda--booksum * Split: test To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@pszemraj](https://huggingface.co/pszemraj) for evaluating this model.
autoevaluate/autoeval-eval-ccdv__arxiv-summarization-section-002db0-47978145233
2023-10-04T15:22:46.000Z
[ "autotrain", "evaluation", "region:us" ]
autoevaluate
null
null
null
0
0
--- type: predictions tags: - autotrain - evaluation datasets: - ccdv/arxiv-summarization eval_info: task: summarization model: adityashukzy/bart-base-new-finetuned-arxiv metrics: [] dataset_name: ccdv/arxiv-summarization dataset_config: section dataset_split: validation col_mapping: text: article target: abstract --- # 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: adityashukzy/bart-base-new-finetuned-arxiv * Dataset: ccdv/arxiv-summarization * Config: section * Split: validation To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@adityashukzy](https://huggingface.co/adityashukzy) for evaluating this model.
autoevaluate/autoeval-eval-cnn_dailymail-3.0.0-52cdb7-47832145226
2023-10-04T15:25:06.000Z
[ "autotrain", "evaluation", "region:us" ]
autoevaluate
null
null
null
0
0
--- type: predictions tags: - autotrain - evaluation datasets: - cnn_dailymail eval_info: task: summarization model: ainize/bart-base-cnn metrics: ['accuracy', 'rouge', 'mse'] dataset_name: cnn_dailymail dataset_config: 3.0.0 dataset_split: test col_mapping: text: article target: highlights --- # 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: ainize/bart-base-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 [@Raj P Sini](https://huggingface.co/Raj P Sini) for evaluating this model.
autoevaluate/autoeval-eval-conll2003-conll2003-ce0414-48015145234
2023-10-04T15:17:27.000Z
[ "autotrain", "evaluation", "region:us" ]
autoevaluate
null
null
null
0
0
--- type: predictions tags: - autotrain - evaluation datasets: - conll2003 eval_info: task: entity_extraction model: alvarobartt/distilbert-base-cased-ner metrics: [] dataset_name: conll2003 dataset_config: conll2003 dataset_split: test col_mapping: tokens: tokens tags: ner_tags --- # 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: alvarobartt/distilbert-base-cased-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 [@alvarobartt](https://huggingface.co/alvarobartt) for evaluating this model.
autoevaluate/autoeval-eval-conll2003-conll2003-b4bc7c-48084145235
2023-10-04T15:18:51.000Z
[ "autotrain", "evaluation", "region:us" ]
autoevaluate
null
null
null
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0
--- type: predictions tags: - autotrain - evaluation datasets: - conll2003 eval_info: task: entity_extraction model: alvarobartt/distilbert-base-cased-ner metrics: [] dataset_name: conll2003 dataset_config: conll2003 dataset_split: test col_mapping: tokens: tokens tags: ner_tags --- # 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: alvarobartt/distilbert-base-cased-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 [@alvarobartt](https://huggingface.co/alvarobartt) for evaluating this model.
autoevaluate/autoeval-eval-adversarial_qa-adversarialQA-cd8db4-47250145205
2023-10-04T15:18:03.000Z
[ "region:us" ]
autoevaluate
null
null
null
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autoevaluate/autoeval-eval-adversarial_qa-adversarialQA-ea9097-47243145204
2023-10-04T15:18:53.000Z
[ "region:us" ]
autoevaluate
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null
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autoevaluate/autoeval-eval-financial_phrasebank-sentences_50agree-d5dbba-47711145222
2023-10-04T15:19:10.000Z
[ "region:us" ]
autoevaluate
null
null
null
0
0
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autoevaluate/autoeval-eval-acronym_identification-default-ada65d-47728145223
2023-10-04T15:19:27.000Z
[ "region:us" ]
autoevaluate
null
null
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0
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autoevaluate/autoeval-eval-kmfoda__booksum-kmfoda__booksum-9f206e-47938145231
2023-10-04T22:46:10.000Z
[ "autotrain", "evaluation", "region:us" ]
autoevaluate
null
null
null
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--- type: predictions tags: - autotrain - evaluation datasets: - kmfoda/booksum eval_info: task: summarization model: pszemraj/led-large-book-summary-continued metrics: [] dataset_name: kmfoda/booksum dataset_config: kmfoda--booksum dataset_split: test col_mapping: text: chapter target: summary_text --- # 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: pszemraj/led-large-book-summary-continued * Dataset: kmfoda/booksum * Config: kmfoda--booksum * Split: test To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@pszemraj](https://huggingface.co/pszemraj) for evaluating this model.
autoevaluate/autoeval-eval-acronym_identification-default-fa2186-48192145239
2023-10-04T15:19:50.000Z
[ "region:us" ]
autoevaluate
null
null
null
0
0
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autoevaluate/autoeval-eval-financial_phrasebank-sentences_allagree-c1bf87-48200145240
2023-10-04T15:20:20.000Z
[ "autotrain", "evaluation", "region:us" ]
autoevaluate
null
null
null
0
0
--- type: predictions tags: - autotrain - evaluation datasets: - financial_phrasebank eval_info: task: multi_class_classification model: ahmedrachid/FinancialBERT-Sentiment-Analysis metrics: ['bleu', 'google_bleu'] dataset_name: financial_phrasebank dataset_config: sentences_allagree dataset_split: train col_mapping: text: sentence target: label --- # 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: ahmedrachid/FinancialBERT-Sentiment-Analysis * Dataset: financial_phrasebank * Config: sentences_allagree * Split: train To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@du](https://huggingface.co/du) for evaluating this model.
autoevaluate/autoeval-eval-liar-default-9fd976-48206145241
2023-10-04T15:20:05.000Z
[ "region:us" ]
autoevaluate
null
null
null
0
0
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autoevaluate/autoeval-eval-xglue-mlqa-a70280-48375145242
2023-10-04T15:38:23.000Z
[ "autotrain", "evaluation", "region:us" ]
autoevaluate
null
null
null
0
0
--- type: predictions tags: - autotrain - evaluation datasets: - xglue eval_info: task: summarization model: csebuetnlp/mT5_m2o_arabic_crossSum metrics: ['bleu', 'f1', 'accuracy'] dataset_name: xglue dataset_config: mlqa dataset_split: test.ar col_mapping: text: context target: question --- # 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: csebuetnlp/mT5_m2o_arabic_crossSum * Dataset: xglue * Config: mlqa * Split: test.ar To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@Anwaarma](https://huggingface.co/Anwaarma) for evaluating this model.
autoevaluate/autoeval-eval-xglue-mlqa-02a2ef-48376145243
2023-10-04T15:48:14.000Z
[ "autotrain", "evaluation", "region:us" ]
autoevaluate
null
null
null
0
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--- type: predictions tags: - autotrain - evaluation datasets: - xglue eval_info: task: summarization model: google/roberta2roberta_L-24_bbc metrics: ['bleu', 'f1', 'accuracy'] dataset_name: xglue dataset_config: mlqa dataset_split: test.ar col_mapping: text: context target: question --- # 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_bbc * Dataset: xglue * Config: mlqa * Split: test.ar To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@Anwaarma](https://huggingface.co/Anwaarma) for evaluating this model.
autoevaluate/autoeval-eval-acronym_identification-default-26b70e-48415145244
2023-10-04T15:20:53.000Z
[ "region:us" ]
autoevaluate
null
null
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autoevaluate/autoeval-eval-acronym_identification-default-4ad7a7-48560145247
2023-10-04T15:21:30.000Z
[ "region:us" ]
autoevaluate
null
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autoevaluate/autoeval-eval-acronym_identification-default-ef4a78-48507145246
2023-10-04T15:22:08.000Z
[ "region:us" ]
autoevaluate
null
null
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autoevaluate/autoeval-eval-glue-cola-9609c5-47910145230
2023-10-04T15:22:46.000Z
[ "region:us" ]
autoevaluate
null
null
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autoevaluate/autoeval-eval-cnn_dailymail-3.0.0-52cdb7-47832145228
2023-10-04T15:23:24.000Z
[ "region:us" ]
autoevaluate
null
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autoevaluate/autoeval-eval-strombergnlp__rumoureval_2019-RumourEval2019-3838aa-48648145249
2023-10-04T15:23:27.000Z
[ "region:us" ]
autoevaluate
null
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autoevaluate/autoeval-eval-acronym_identification-default-51bf61-48667145250
2023-10-04T15:24:08.000Z
[ "region:us" ]
autoevaluate
null
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autoevaluate/autoeval-eval-acronym_identification-default-85ec34-48806145252
2023-10-04T15:24:11.000Z
[ "region:us" ]
autoevaluate
null
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autoevaluate/autoeval-eval-acronym_identification-default-91a34b-48827145253
2023-10-04T15:24:37.000Z
[ "region:us" ]
autoevaluate
null
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autoevaluate/autoeval-eval-acronym_identification-default-820f7a-48930145254
2023-10-04T15:24:46.000Z
[ "region:us" ]
autoevaluate
null
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autoevaluate/autoeval-eval-acronym_identification-default-4e2f07-48931145255
2023-10-04T15:24:51.000Z
[ "region:us" ]
autoevaluate
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autoevaluate/autoeval-eval-SetFit__bbc-news-SetFit__bbc-news-be85c0-48973145256
2023-10-04T15:25:17.000Z
[ "region:us" ]
autoevaluate
null
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autoevaluate/autoeval-eval-SetFit__bbc-news-SetFit__bbc-news-be85c0-48973145257
2023-10-04T15:25:23.000Z
[ "region:us" ]
autoevaluate
null
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autoevaluate/autoeval-eval-SetFit__bbc-news-SetFit__bbc-news-be85c0-48973145258
2023-10-04T15:25:28.000Z
[ "region:us" ]
autoevaluate
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autoevaluate/autoeval-eval-SetFit__bbc-news-SetFit__bbc-news-be85c0-48973145259
2023-10-04T15:25:42.000Z
[ "region:us" ]
autoevaluate
null
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autoevaluate/autoeval-eval-SetFit__bbc-news-SetFit__bbc-news-be85c0-48973145260
2023-10-04T15:26:03.000Z
[ "region:us" ]
autoevaluate
null
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autoevaluate/autoeval-eval-SetFit__bbc-news-SetFit__bbc-news-be85c0-48973145261
2023-10-04T15:26:10.000Z
[ "region:us" ]
autoevaluate
null
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autoevaluate/autoeval-eval-SetFit__bbc-news-SetFit__bbc-news-be85c0-48973145262
2023-10-04T15:26:20.000Z
[ "region:us" ]
autoevaluate
null
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autoevaluate/autoeval-eval-acronym_identification-default-9b6021-49015145263
2023-10-04T15:26:25.000Z
[ "region:us" ]
autoevaluate
null
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autoevaluate/autoeval-eval-acronym_identification-default-b1082a-49050145264
2023-10-04T15:26:40.000Z
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autoevaluate
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autoevaluate/autoeval-eval-ade_corpus_v2-Ade_corpus_v2_classification-7c83d1-49110145265
2023-10-04T15:27:03.000Z
[ "region:us" ]
autoevaluate
null
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autoevaluate/autoeval-eval-acronym_identification-default-6b53df-48708145251
2023-10-04T15:27:13.000Z
[ "region:us" ]
autoevaluate
null
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autoevaluate/autoeval-eval-ade_corpus_v2-Ade_corpus_v2_classification-f3e729-49111145266
2023-10-04T15:27:18.000Z
[ "region:us" ]
autoevaluate
null
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autoevaluate/autoeval-eval-acronym_identification-default-65a2fa-49119145267
2023-10-04T15:27:39.000Z
[ "region:us" ]
autoevaluate
null
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autoevaluate/autoeval-eval-acronym_identification-default-80225a-49138145268
2023-10-04T15:27:41.000Z
[ "region:us" ]
autoevaluate
null
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autoevaluate/autoeval-eval-adversarial_qa-adversarialQA-f2f24c-49139145269
2023-10-04T15:28:36.000Z
[ "autotrain", "evaluation", "region:us" ]
autoevaluate
null
null
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--- type: predictions tags: - autotrain - evaluation datasets: - adversarial_qa eval_info: task: extractive_question_answering model: 123tarunanand/roberta-base-finetuned metrics: ['bleu', 'accuracy', 'angelina-wang/directional_bias_amplification', 'bertscore', 'rouge', 'meteor'] 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 --- # 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: 123tarunanand/roberta-base-finetuned * 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 [@sysneedtolearn](https://huggingface.co/sysneedtolearn) for evaluating this model.
autoevaluate/autoeval-eval-ag_news-default-6ee681-49182145270
2023-10-04T15:27:50.000Z
[ "region:us" ]
autoevaluate
null
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autoevaluate/autoeval-eval-cnn_dailymail-3.0.0-17764c-48619145248
2023-10-04T15:28:00.000Z
[ "region:us" ]
autoevaluate
null
null
null
0
0
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autoevaluate/autoeval-eval-conll2003-conll2003-fb14e9-48103145236
2023-10-04T15:29:27.000Z
[ "autotrain", "evaluation", "region:us" ]
autoevaluate
null
null
null
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--- type: predictions tags: - autotrain - evaluation datasets: - conll2003 eval_info: task: entity_extraction model: alvarobartt/distilbert-base-cased-ner metrics: [] dataset_name: conll2003 dataset_config: conll2003 dataset_split: test col_mapping: tokens: tokens tags: ner_tags --- # 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: alvarobartt/distilbert-base-cased-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 [@alvarobartt](https://huggingface.co/alvarobartt) for evaluating this model.
autoevaluate/autoeval-eval-acronym_identification-default-f4f35d-49231145271
2023-10-04T15:28:20.000Z
[ "region:us" ]
autoevaluate
null
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autoevaluate/autoeval-eval-ade_corpus_v2-Ade_corpus_v2_classification-e3954c-49391145272
2023-10-04T15:28:39.000Z
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autoevaluate
null
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autoevaluate/autoeval-eval-acronym_identification-default-5e1d55-49392145273
2023-10-04T15:28:49.000Z
[ "region:us" ]
autoevaluate
null
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autoevaluate/autoeval-eval-acronym_identification-default-604d44-49449145274
2023-10-04T15:28:59.000Z
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autoevaluate
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autoevaluate/autoeval-eval-acronym_identification-default-5b82da-49456145275
2023-10-04T15:29:10.000Z
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autoevaluate
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autoevaluate/autoeval-eval-acronym_identification-default-38b21f-49508145276
2023-10-04T15:29:17.000Z
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autoevaluate
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autoevaluate/autoeval-eval-acronym_identification-default-0cc316-49593145277
2023-10-04T15:29:26.000Z
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autoevaluate
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autoevaluate/autoeval-eval-acronym_identification-default-f44f80-49622145278
2023-10-04T15:29:38.000Z
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autoevaluate
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autoevaluate/autoeval-eval-acronym_identification-default-7aa538-49660145279
2023-10-04T15:29:48.000Z
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autoevaluate
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autoevaluate/autoeval-eval-acronym_identification-default-8446e0-49689145280
2023-10-04T15:29:56.000Z
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autoevaluate
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autoevaluate/autoeval-eval-acronym_identification-default-cfe94c-49858145281
2023-10-04T15:30:01.000Z
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autoevaluate
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autoevaluate/autoeval-eval-acronym_identification-default-27cdf8-49912145282
2023-10-04T15:30:06.000Z
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autoevaluate
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autoevaluate/autoeval-eval-squad_v2-squad_v2-ec8ae2-49918145283
2023-10-04T15:30:18.000Z
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autoevaluate
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autoevaluate/autoeval-eval-glue-cola-1e2c63-49998145284
2023-10-04T15:30:27.000Z
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autoevaluate
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autoevaluate/autoeval-eval-squad_v2-squad_v2-95a364-50033145285
2023-10-04T15:30:35.000Z
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autoevaluate
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autoevaluate/autoeval-eval-tner__ontonotes5-ontonotes5-909e9d-48173145237
2023-10-04T15:30:41.000Z
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autoevaluate
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autoevaluate/autoeval-eval-acronym_identification-default-25c2af-50048145286
2023-10-04T15:30:48.000Z
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autoevaluate
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autoevaluate/autoeval-eval-acronym_identification-default-e7dad2-48446145245
2023-10-04T15:31:27.000Z
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autoevaluate
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autoevaluate/autoeval-eval-yelp_review_full-yelp_review_full-67c638-50120145287
2023-10-04T15:31:30.000Z
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autoevaluate
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autoevaluate/autoeval-eval-imdb-plain_text-47f57b-50141145289
2023-10-04T15:32:06.000Z
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autoevaluate
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autoevaluate/autoeval-eval-squad_v2-squad_v2-779f0e-50149145290
2023-10-04T15:32:18.000Z
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autoevaluate
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autoevaluate/autoeval-eval-acronym_identification-default-d117ff-50180145293
2023-10-04T15:32:43.000Z
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autoevaluate
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autoevaluate/autoeval-eval-acronym_identification-default-522664-50450145298
2023-10-04T15:33:01.000Z
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autoevaluate
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autoevaluate/autoeval-eval-squad_v2-squad_v2-779f0e-50149145291
2023-10-04T15:33:09.000Z
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autoevaluate
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autoevaluate/autoeval-eval-raquiba__Sarcasm_News_Headline-raquiba__Sarcasm_News_He-6ffdf7-50263145295
2023-10-04T15:33:22.000Z
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autoevaluate
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autoevaluate/autoeval-eval-acronym_identification-default-1196dc-50720145301
2023-10-04T15:33:42.000Z
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autoevaluate
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autoevaluate/autoeval-eval-acronym_identification-default-bdeb73-48180145238
2023-10-04T15:33:58.000Z
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autoevaluate
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autoevaluate/autoeval-eval-squad_v2-squad_v2-486d82-50134145288
2023-10-04T15:34:06.000Z
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autoevaluate
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autoevaluate/autoeval-eval-imdb-plain_text-4930c8-50588145300
2023-10-04T15:34:20.000Z
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autoevaluate
null
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albertvillanova/vbsv-dataset
2023-10-04T15:42:44.000Z
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albertvillanova
null
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--- configs: - config_name: default data_files: "dataset.csv" sep: "|" ---
autoevaluate/autoeval-eval-acronym_identification-default-8d4087-50912145306
2023-10-04T15:34:36.000Z
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autoevaluate
null
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purabp1249/med_leaves
2023-10-04T16:40:02.000Z
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purabp1249
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--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: file_paths dtype: string - name: image_bytes dtype: binary - name: labels dtype: string splits: - name: train num_bytes: 10878937089 num_examples: 21880 download_size: 970048876 dataset_size: 10878937089 --- # Dataset Card for "med_leaves" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
autoevaluate/autoeval-eval-acronym_identification-default-7a8989-50280145296
2023-10-04T15:35:13.000Z
[ "region:us" ]
autoevaluate
null
null
null
0
0
Entry not found
autoevaluate/autoeval-eval-acronym_identification-default-99d1d4-50989145307
2023-10-04T15:35:15.000Z
[ "region:us" ]
autoevaluate
null
null
null
0
0
Entry not found
autoevaluate/autoeval-eval-acronym_identification-default-9ff9c1-50406145297
2023-10-04T15:35:59.000Z
[ "autotrain", "evaluation", "region:us" ]
autoevaluate
null
null
null
0
0
--- type: predictions tags: - autotrain - evaluation datasets: - acronym_identification eval_info: task: entity_extraction model: lewtun/autotrain-acronym-identification-7324788 metrics: ['bertscore'] dataset_name: acronym_identification dataset_config: default dataset_split: validation col_mapping: tokens: tokens tags: labels --- # 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: lewtun/autotrain-acronym-identification-7324788 * Dataset: acronym_identification * Config: default * Split: validation To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@prashanthr025@gmail.com](https://huggingface.co/prashanthr025@gmail.com) for evaluating this model.
autoevaluate/autoeval-eval-acronym_identification-default-51509b-51004145308
2023-10-04T15:35:40.000Z
[ "region:us" ]
autoevaluate
null
null
null
0
0
Entry not found
autoevaluate/autoeval-eval-acronym_identification-default-35b859-51159145310
2023-10-04T15:35:52.000Z
[ "region:us" ]
autoevaluate
null
null
null
0
0
Entry not found
autoevaluate/autoeval-eval-squad_v2-squad_v2-779f0e-50149145292
2023-10-04T15:35:54.000Z
[ "region:us" ]
autoevaluate
null
null
null
0
0
Entry not found
autoevaluate/autoeval-eval-acronym_identification-default-aaa028-51270145311
2023-10-04T15:36:17.000Z
[ "region:us" ]
autoevaluate
null
null
null
0
0
Entry not found
autoevaluate/autoeval-eval-scientific_papers-pubmed-c3b6df-51381145312
2023-10-04T22:34:01.000Z
[ "autotrain", "evaluation", "region:us" ]
autoevaluate
null
null
null
0
0
--- type: predictions tags: - autotrain - evaluation datasets: - scientific_papers eval_info: task: summarization model: sambydlo/scientific_abstract_simplification-scientific-lay-summarise metrics: ['accuracy', 'frugalscore'] dataset_name: scientific_papers dataset_config: pubmed dataset_split: train col_mapping: text: article target: abstract --- # 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: sambydlo/scientific_abstract_simplification-scientific-lay-summarise * Dataset: scientific_papers * Config: pubmed * Split: train To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@NessTechIntl](https://huggingface.co/NessTechIntl) for evaluating this model.
autoevaluate/autoeval-eval-scientific_papers-pubmed-c3b6df-51381145313
2023-10-06T04:45:27.000Z
[ "autotrain", "evaluation", "region:us" ]
autoevaluate
null
null
null
0
0
--- type: predictions tags: - autotrain - evaluation datasets: - scientific_papers eval_info: task: summarization model: Samuel-Fipps/t5-efficient-large-nl36_fine_tune_sum_V2 metrics: ['accuracy', 'frugalscore'] dataset_name: scientific_papers dataset_config: pubmed dataset_split: train col_mapping: text: article target: abstract --- # 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: Samuel-Fipps/t5-efficient-large-nl36_fine_tune_sum_V2 * Dataset: scientific_papers * Config: pubmed * Split: train To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@NessTechIntl](https://huggingface.co/NessTechIntl) for evaluating this model.
autoevaluate/autoeval-eval-adversarial_qa-adversarialQA-dd44aa-51612145314
2023-10-04T15:36:56.000Z
[ "region:us" ]
autoevaluate
null
null
null
0
0
Entry not found
autoevaluate/autoeval-eval-acronym_identification-default-134ba9-51735145315
2023-10-04T15:37:01.000Z
[ "region:us" ]
autoevaluate
null
null
null
0
0
Entry not found
autoevaluate/autoeval-eval-banking77-default-080492-51746145316
2023-10-04T15:37:51.000Z
[ "autotrain", "evaluation", "region:us" ]
autoevaluate
null
null
null
0
0
--- type: predictions tags: - autotrain - evaluation datasets: - banking77 eval_info: task: multi_class_classification model: Laurie/bert-base-banking77-pt2 metrics: [] dataset_name: banking77 dataset_config: default dataset_split: test col_mapping: text: text target: label --- # 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: Laurie/bert-base-banking77-pt2 * Dataset: banking77 * 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 [@edcody726ai@gmail.com](https://huggingface.co/edcody726ai@gmail.com) for evaluating this model.
autoevaluate/autoeval-eval-banking77-default-2a251d-51750145317
2023-10-04T15:38:06.000Z
[ "autotrain", "evaluation", "region:us" ]
autoevaluate
null
null
null
0
0
--- type: predictions tags: - autotrain - evaluation datasets: - banking77 eval_info: task: multi_class_classification model: Laurie/bert-base-banking77-pt2 metrics: ['rouge'] dataset_name: banking77 dataset_config: default dataset_split: test col_mapping: text: text target: label --- # 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: Laurie/bert-base-banking77-pt2 * Dataset: banking77 * 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 [@edcody726ai@gmail.com](https://huggingface.co/edcody726ai@gmail.com) for evaluating this model.
autoevaluate/autoeval-eval-acronym_identification-default-003bdf-50763145302
2023-10-04T15:38:09.000Z
[ "region:us" ]
autoevaluate
null
null
null
0
0
Entry not found
autoevaluate/autoeval-eval-acronym_identification-default-5ec18c-50808145303
2023-10-04T15:38:24.000Z
[ "region:us" ]
autoevaluate
null
null
null
0
0
Entry not found
autoevaluate/autoeval-eval-cnn_dailymail-3.0.0-90e029-50827145304
2023-10-04T17:14:41.000Z
[ "autotrain", "evaluation", "region:us" ]
autoevaluate
null
null
null
0
0
--- 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 --- # 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 [@km](https://huggingface.co/km) for evaluating this model.