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--- |
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license: apache-2.0 |
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base_model: google-t5/t5-base |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: t5-abs-1609-1450-lr-0.0001-bs-10-maxep-20 |
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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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# t5-abs-1609-1450-lr-0.0001-bs-10-maxep-20 |
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This model is a fine-tuned version of [google-t5/t5-base](https://huggingface.co/google-t5/t5-base) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 2.0493 |
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- Rouge/rouge1: 0.4186 |
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- Rouge/rouge2: 0.188 |
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- Rouge/rougel: 0.3713 |
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- Rouge/rougelsum: 0.3708 |
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- Bertscore/bertscore-precision: 0.9077 |
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- Bertscore/bertscore-recall: 0.8772 |
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- Bertscore/bertscore-f1: 0.8921 |
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- Meteor: 0.338 |
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- Gen Len: 31.5 |
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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: 0.0001 |
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- train_batch_size: 10 |
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- eval_batch_size: 10 |
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- seed: 42 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 20 |
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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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- lr_scheduler_warmup_ratio: 0.1 |
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- num_epochs: 20 |
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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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| 4.4242 | 0.8 | 2 | 2.9745 | 0.24 | 0.0922 | 0.1926 | 0.1916 | 0.8591 | 0.8535 | 0.8562 | 0.1759 | 42.0 | |
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| 2.7301 | 2.0 | 5 | 2.5893 | 0.2451 | 0.074 | 0.2114 | 0.2133 | 0.8517 | 0.8543 | 0.8526 | 0.1844 | 42.4 | |
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| 3.7041 | 2.8 | 7 | 2.3562 | 0.3045 | 0.114 | 0.2614 | 0.2633 | 0.8785 | 0.8587 | 0.8683 | 0.2344 | 36.2 | |
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| 2.2169 | 4.0 | 10 | 2.2206 | 0.3076 | 0.1129 | 0.2568 | 0.2569 | 0.9027 | 0.8623 | 0.8819 | 0.2244 | 25.4 | |
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| 3.0954 | 4.8 | 12 | 2.1692 | 0.3291 | 0.1429 | 0.2855 | 0.2857 | 0.9089 | 0.8684 | 0.8881 | 0.2405 | 24.1 | |
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| 1.8983 | 6.0 | 15 | 2.1128 | 0.3455 | 0.1237 | 0.2864 | 0.2872 | 0.9022 | 0.8674 | 0.8843 | 0.2511 | 25.2 | |
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| 2.6884 | 6.8 | 17 | 2.0867 | 0.3451 | 0.1133 | 0.2873 | 0.2881 | 0.9021 | 0.8683 | 0.8847 | 0.2509 | 27.1 | |
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| 1.7137 | 8.0 | 20 | 2.0663 | 0.3424 | 0.1218 | 0.2932 | 0.2945 | 0.8963 | 0.8711 | 0.8833 | 0.2819 | 31.1 | |
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| 2.4552 | 8.8 | 22 | 2.0603 | 0.3491 | 0.1272 | 0.2932 | 0.2948 | 0.8975 | 0.8714 | 0.884 | 0.2834 | 30.6 | |
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| 1.5859 | 10.0 | 25 | 2.0565 | 0.3502 | 0.1207 | 0.2962 | 0.2979 | 0.8952 | 0.8675 | 0.8809 | 0.2635 | 29.8 | |
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| 2.2768 | 10.8 | 27 | 2.0558 | 0.3606 | 0.1253 | 0.3021 | 0.3031 | 0.8951 | 0.8683 | 0.8813 | 0.2725 | 30.4 | |
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| 1.4516 | 12.0 | 30 | 2.0541 | 0.4032 | 0.1573 | 0.3355 | 0.3358 | 0.9024 | 0.8744 | 0.8881 | 0.3035 | 32.3 | |
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| 2.1365 | 12.8 | 32 | 2.0514 | 0.4087 | 0.1714 | 0.3445 | 0.3448 | 0.9038 | 0.8749 | 0.889 | 0.32 | 32.8 | |
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| 1.4049 | 14.0 | 35 | 2.0507 | 0.4167 | 0.1792 | 0.3515 | 0.3524 | 0.9065 | 0.8765 | 0.8911 | 0.3222 | 31.9 | |
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| 2.0878 | 14.8 | 37 | 2.0498 | 0.4167 | 0.1792 | 0.3515 | 0.3524 | 0.9065 | 0.8765 | 0.8911 | 0.3222 | 31.9 | |
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| 1.361 | 16.0 | 40 | 2.0493 | 0.4186 | 0.188 | 0.3713 | 0.3708 | 0.9077 | 0.8772 | 0.8921 | 0.338 | 31.5 | |
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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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