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--- |
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language: en |
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license: mit |
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library_name: transformers |
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tags: |
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- summarization |
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- bart |
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datasets: ccdv/arxiv-summarization |
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model-index: |
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- name: BARTxiv |
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results: |
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- task: |
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type: summarization |
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dataset: |
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name: arxiv-summarization |
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type: ccdv/arxiv-summarization |
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split: validation |
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metrics: |
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- type: rouge1 |
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value: 41.70204016592095 |
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- type: rouge2 |
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value: 15.134827404979639 |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# BARTxiv |
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See the model implementation [here](https://interrsect.web.app). |
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This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) on the [arxiv-summarization](https://huggingface.co/datasets/ccdv/arxiv-summarization) dataset. |
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It achieves the following results on the validation set: |
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- Loss: 0.86 |
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- Rouge1: 41.70 |
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- Rouge2: 15.13 |
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- Rougel: 22.85 |
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- Rougelsum: 37.77 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 1e-6 |
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- train_batch_size: 1 |
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- eval_batch_size: 1 |
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- seed: 42 |
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- optimizer: Adafactor |
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- num_epochs: 9 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | |
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|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:| |
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| 1.24 | 1.0 | 1073 | 1.24 | 38.32 | 12.80 | 20.55 | 34.50 | |
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| 1.04 | 2.0 | 2146 | 1.04 | 39.65 | 13.74 | 21.28 | 35.83 | |
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| 0.979 | 3.0 | 3219 | 0.98 | 40.19 | 14.30 | 21.87 | 36.38 | |
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| 0.970 | 4.0 | 4292 | 0.97 | 40.87 | 14.44 | 22.14 | 36.89 | |
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| 0.918 | 5.0 | 5365 | 0.92 | 41.17 | 14.94 | 22.54 | 37.40 | |
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| 0.901 | 6.0 | 6438 | 0.90 | 41.02 | 14.65 | 22.46 | 37.05 | |
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| 0.889 | 7.0 | 7511 | 0.89 | 41.32 | 15.09 | 22.64 | 37.42 | |
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| 0.900 | 8.0 | 8584 | 0 .90 | 41.23 | 15.02 | 22.67 | 37.28 | |
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| 0.869 | 9.0 | 9657 | 0.87 | 41.70 | 15.13 | 22.85 | 37.77 | |
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### Framework versions |
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- Transformers 4.25.1 |
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- Pytorch 1.13.0+cu117 |
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- Datasets 2.6.1 |
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- Tokenizers 0.13.1 |
