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--- |
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license: mit |
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base_model: facebook/bart-large-xsum |
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tags: |
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- generated_from_trainer |
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metrics: |
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- rouge |
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model-index: |
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- name: bart_samsum_finetuned_for_asr |
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results: [] |
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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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# bart_samsum_finetuned_for_asr |
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This model is a fine-tuned version of [facebook/bart-large-xsum](https://huggingface.co./facebook/bart-large-xsum) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.4623 |
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- Rouge1: 54.4349 |
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- Rouge2: 29.4619 |
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- Rougel: 44.7701 |
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- Rougelsum: 50.2825 |
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- Gen Len: 30.2751 |
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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: 2e-05 |
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- train_batch_size: 4 |
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- eval_batch_size: 4 |
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- seed: 42 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 8 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 5 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |
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|:-------------:|:------:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| |
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| 1.3855 | 0.9997 | 1841 | 1.5274 | 52.3133 | 27.7547 | 43.0233 | 48.3891 | 30.3004 | |
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| 1.0882 | 2.0 | 3683 | 1.4969 | 53.2457 | 28.4129 | 44.1196 | 49.1579 | 30.2637 | |
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| 0.8376 | 2.9997 | 5524 | 1.5882 | 52.6929 | 27.5974 | 43.3339 | 47.9779 | 30.8034 | |
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| 0.6756 | 4.0 | 7366 | 1.6617 | 52.5167 | 27.1278 | 43.037 | 48.1382 | 30.5299 | |
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| 0.5417 | 4.9986 | 9205 | 1.8083 | 52.0696 | 26.8054 | 42.6108 | 47.5455 | 30.2894 | |
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### Framework versions |
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- Transformers 4.42.4 |
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- Pytorch 2.4.0+cu121 |
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- Datasets 2.21.0 |
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- Tokenizers 0.19.1 |
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