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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_bertsum_1024_250_1000 |
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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_bertsum_1024_250_1000 |
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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: 1.0191 |
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- Rouge1: 0.6894 |
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- Rouge2: 0.4262 |
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- Rougel: 0.6274 |
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- Rougelsum: 0.6272 |
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- Wer: 0.4606 |
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- Bleurt: -0.5228 |
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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 | Bleurt | |
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|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:------:|:-------:| |
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| No log | 0.13 | 250 | 1.2589 | 0.6432 | 0.3644 | 0.5764 | 0.5763 | 0.5168 | -0.3132 | |
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| 2.1861 | 0.27 | 500 | 1.1641 | 0.6562 | 0.3824 | 0.591 | 0.591 | 0.4985 | -0.867 | |
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| 2.1861 | 0.4 | 750 | 1.1326 | 0.6626 | 0.3917 | 0.5988 | 0.5987 | 0.4904 | -0.5078 | |
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| 1.2496 | 0.53 | 1000 | 1.1111 | 0.6657 | 0.3958 | 0.6015 | 0.6014 | 0.4859 | -0.484 | |
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| 1.2496 | 0.66 | 1250 | 1.0959 | 0.6708 | 0.4014 | 0.6052 | 0.6051 | 0.4814 | -0.4774 | |
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| 1.193 | 0.8 | 1500 | 1.0774 | 0.6724 | 0.4041 | 0.609 | 0.609 | 0.4787 | -0.494 | |
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| 1.193 | 0.93 | 1750 | 1.0662 | 0.681 | 0.4127 | 0.6177 | 0.6176 | 0.4742 | -0.4464 | |
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| 1.14 | 1.06 | 2000 | 1.0593 | 0.6795 | 0.4157 | 0.6178 | 0.6177 | 0.4709 | -0.5849 | |
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| 1.14 | 1.2 | 2250 | 1.0504 | 0.6784 | 0.4158 | 0.6161 | 0.616 | 0.4685 | -0.3624 | |
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| 1.0439 | 1.33 | 2500 | 1.0427 | 0.6815 | 0.418 | 0.6196 | 0.6195 | 0.4667 | -0.5998 | |
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| 1.0439 | 1.46 | 2750 | 1.0357 | 0.6833 | 0.4198 | 0.6209 | 0.6207 | 0.465 | -0.6198 | |
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| 1.045 | 1.6 | 3000 | 1.0286 | 0.6872 | 0.4238 | 0.6251 | 0.6251 | 0.4635 | -0.4564 | |
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| 1.045 | 1.73 | 3250 | 1.0248 | 0.6829 | 0.4214 | 0.6222 | 0.6221 | 0.4622 | -0.5228 | |
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| 1.0242 | 1.86 | 3500 | 1.0198 | 0.69 | 0.4273 | 0.6284 | 0.6283 | 0.4601 | -0.4592 | |
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| 1.0242 | 1.99 | 3750 | 1.0191 | 0.6894 | 0.4262 | 0.6274 | 0.6272 | 0.4606 | -0.5228 | |
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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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