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--- |
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license: apache-2.0 |
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base_model: t5-base |
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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: t-5-base-bertsum-500 |
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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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# t-5-base-bertsum-500 |
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This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.2994 |
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- Rouge1: 0.6466 |
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- Rouge2: 0.3657 |
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- Rougel: 0.5798 |
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- Rougelsum: 0.5798 |
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- Wer: 0.5246 |
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- Bleurt: -0.0784 |
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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 | 1.4553 | 0.6223 | 0.3344 | 0.552 | 0.552 | 0.557 | -0.4294 | |
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| 1.9648 | 0.27 | 500 | 1.3993 | 0.6301 | 0.3443 | 0.5613 | 0.5614 | 0.5467 | -0.4022 | |
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| 1.9648 | 0.4 | 750 | 1.3747 | 0.6341 | 0.35 | 0.5661 | 0.5661 | 0.5402 | -0.3802 | |
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| 1.4858 | 0.53 | 1000 | 1.3547 | 0.638 | 0.3533 | 0.5693 | 0.5693 | 0.5378 | -0.0447 | |
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| 1.4858 | 0.66 | 1250 | 1.3431 | 0.639 | 0.3559 | 0.5715 | 0.5715 | 0.5342 | -0.0292 | |
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| 1.4484 | 0.8 | 1500 | 1.3321 | 0.6406 | 0.3578 | 0.573 | 0.573 | 0.5322 | -0.0292 | |
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| 1.4484 | 0.93 | 1750 | 1.3238 | 0.6418 | 0.3593 | 0.5747 | 0.5747 | 0.5306 | -0.0784 | |
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| 1.4226 | 1.06 | 2000 | 1.3185 | 0.6433 | 0.3616 | 0.5762 | 0.5762 | 0.5281 | -0.1084 | |
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| 1.4226 | 1.2 | 2250 | 1.3131 | 0.6442 | 0.3624 | 0.5775 | 0.5775 | 0.5277 | -0.1084 | |
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| 1.3917 | 1.33 | 2500 | 1.3102 | 0.6453 | 0.3638 | 0.5783 | 0.5783 | 0.5266 | -0.0784 | |
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| 1.3917 | 1.46 | 2750 | 1.3060 | 0.6458 | 0.3641 | 0.5788 | 0.5788 | 0.5256 | -0.0292 | |
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| 1.4048 | 1.6 | 3000 | 1.3040 | 0.6461 | 0.3649 | 0.5792 | 0.5792 | 0.5253 | -0.0784 | |
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| 1.4048 | 1.73 | 3250 | 1.3015 | 0.6463 | 0.3653 | 0.5796 | 0.5795 | 0.525 | -0.0292 | |
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| 1.3803 | 1.86 | 3500 | 1.2999 | 0.6463 | 0.3654 | 0.5795 | 0.5795 | 0.5247 | -0.0784 | |
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| 1.3803 | 1.99 | 3750 | 1.2994 | 0.6466 | 0.3657 | 0.5798 | 0.5798 | 0.5246 | -0.0784 | |
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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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