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--- |
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license: apache-2.0 |
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base_model: sshleifer/distilbart-xsum-12-6 |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: bart-abs-1409-1800-lr-3e-05-bs-4-maxep-6 |
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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-abs-1409-1800-lr-3e-05-bs-4-maxep-6 |
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This model is a fine-tuned version of [sshleifer/distilbart-xsum-12-6](https://huggingface.co/sshleifer/distilbart-xsum-12-6) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 3.3829 |
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- Rouge/rouge1: 0.3549 |
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- Rouge/rouge2: 0.1363 |
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- Rouge/rougel: 0.3021 |
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- Rouge/rougelsum: 0.3032 |
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- Bertscore/bertscore-precision: 0.9037 |
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- Bertscore/bertscore-recall: 0.8687 |
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- Bertscore/bertscore-f1: 0.8857 |
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- Meteor: 0.2545 |
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- Gen Len: 25.5 |
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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: 3e-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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- 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: 6 |
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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 | Rouge/rouge1 | Rouge/rouge2 | Rouge/rougel | Rouge/rougelsum | Bertscore/bertscore-precision | Bertscore/bertscore-recall | Bertscore/bertscore-f1 | Meteor | Gen Len | |
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|:-------------:|:-----:|:----:|:---------------:|:------------:|:------------:|:------------:|:---------------:|:-----------------------------:|:--------------------------:|:----------------------:|:------:|:-------:| |
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| 1.7439 | 1.0 | 13 | 2.8127 | 0.2992 | 0.0924 | 0.2491 | 0.2492 | 0.8984 | 0.8552 | 0.876 | 0.2093 | 22.5 | |
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| 1.263 | 2.0 | 26 | 2.9358 | 0.3773 | 0.1311 | 0.3058 | 0.306 | 0.9115 | 0.8688 | 0.8895 | 0.2469 | 24.9 | |
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| 0.8455 | 3.0 | 39 | 3.0548 | 0.4307 | 0.1554 | 0.3399 | 0.3393 | 0.9029 | 0.8741 | 0.8881 | 0.3255 | 28.1 | |
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| 0.6581 | 4.0 | 52 | 3.2153 | 0.3986 | 0.1569 | 0.3487 | 0.3471 | 0.9111 | 0.8757 | 0.8929 | 0.3092 | 25.8 | |
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| 0.4915 | 5.0 | 65 | 3.2936 | 0.4004 | 0.1256 | 0.3204 | 0.3222 | 0.9035 | 0.8738 | 0.8883 | 0.2892 | 26.0 | |
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| 0.393 | 6.0 | 78 | 3.3829 | 0.3549 | 0.1363 | 0.3021 | 0.3032 | 0.9037 | 0.8687 | 0.8857 | 0.2545 | 25.5 | |
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### Framework versions |
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- Transformers 4.44.0 |
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- Pytorch 2.4.0 |
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- Datasets 2.21.0 |
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- Tokenizers 0.19.1 |
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