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--- |
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license: apache-2.0 |
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base_model: facebook/bart-large |
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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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- wer |
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model-index: |
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- name: bart_baseline_1024 |
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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_baseline_1024 |
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This model is a fine-tuned version of [facebook/bart-large](https://huggingface.co/facebook/bart-large) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.9558 |
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- Rouge1: 0.7069 |
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- Rouge2: 0.4544 |
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- Rougel: 0.6489 |
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- Rougelsum: 0.6489 |
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- Wer: 0.4398 |
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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: 6 |
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- eval_batch_size: 6 |
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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: 2 |
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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 | Wer | |
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|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:------:| |
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| No log | 0.13 | 250 | 1.2224 | 0.665 | 0.3911 | 0.6 | 0.6001 | 0.4993 | |
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| 2.0905 | 0.27 | 500 | 1.1190 | 0.6743 | 0.4083 | 0.6103 | 0.6104 | 0.4809 | |
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| 2.0905 | 0.4 | 750 | 1.0832 | 0.6818 | 0.418 | 0.6178 | 0.6178 | 0.4726 | |
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| 1.188 | 0.53 | 1000 | 1.0541 | 0.6871 | 0.4246 | 0.6242 | 0.6242 | 0.4675 | |
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| 1.188 | 0.66 | 1250 | 1.0352 | 0.6881 | 0.4283 | 0.6269 | 0.6268 | 0.4628 | |
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| 1.1172 | 0.8 | 1500 | 1.0291 | 0.6912 | 0.4319 | 0.6303 | 0.6303 | 0.4586 | |
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| 1.1172 | 0.93 | 1750 | 1.0079 | 0.7001 | 0.4406 | 0.6396 | 0.6397 | 0.4543 | |
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| 1.0803 | 1.06 | 2000 | 0.9957 | 0.6939 | 0.4396 | 0.6359 | 0.6359 | 0.4511 | |
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| 1.0803 | 1.2 | 2250 | 0.9891 | 0.6972 | 0.443 | 0.6383 | 0.6383 | 0.4479 | |
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| 0.9849 | 1.33 | 2500 | 0.9800 | 0.7009 | 0.4467 | 0.6425 | 0.6425 | 0.4464 | |
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| 0.9849 | 1.46 | 2750 | 0.9771 | 0.7017 | 0.4479 | 0.6426 | 0.6426 | 0.4437 | |
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| 0.9867 | 1.6 | 3000 | 0.9638 | 0.7085 | 0.4541 | 0.6495 | 0.6495 | 0.4422 | |
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| 0.9867 | 1.73 | 3250 | 0.9675 | 0.7013 | 0.4495 | 0.6438 | 0.6438 | 0.4413 | |
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| 0.9556 | 1.86 | 3500 | 0.9565 | 0.707 | 0.4544 | 0.6493 | 0.6492 | 0.4401 | |
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| 0.9556 | 1.99 | 3750 | 0.9558 | 0.7069 | 0.4544 | 0.6489 | 0.6489 | 0.4398 | |
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### Framework versions |
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- Transformers 4.38.2 |
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- Pytorch 2.2.1+cu121 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.2 |
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