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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-2309-1054-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-2309-1054-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: 1.9472 |
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- Rouge/rouge1: 0.4676 |
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- Rouge/rouge2: 0.2222 |
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- Rouge/rougel: 0.4004 |
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- Rouge/rougelsum: 0.4024 |
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- Bertscore/bertscore-precision: 0.8963 |
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- Bertscore/bertscore-recall: 0.8971 |
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- Bertscore/bertscore-f1: 0.8965 |
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- Meteor: 0.4301 |
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- Gen Len: 40.8455 |
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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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| 2.79 | 0.9885 | 43 | 2.0712 | 0.4222 | 0.1746 | 0.3527 | 0.3532 | 0.8934 | 0.886 | 0.8895 | 0.3645 | 37.0818 | |
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| 1.9564 | 2.0 | 87 | 1.7989 | 0.445 | 0.202 | 0.3739 | 0.3753 | 0.8971 | 0.8893 | 0.893 | 0.3978 | 36.1545 | |
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| 1.7565 | 2.9885 | 130 | 1.7408 | 0.4648 | 0.2233 | 0.3974 | 0.3992 | 0.8987 | 0.8919 | 0.8952 | 0.416 | 36.3818 | |
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| 1.5584 | 4.0 | 174 | 1.7210 | 0.4529 | 0.2075 | 0.3814 | 0.3835 | 0.8969 | 0.8919 | 0.8942 | 0.4083 | 37.7545 | |
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| 1.4732 | 4.9885 | 217 | 1.7176 | 0.4639 | 0.2163 | 0.3949 | 0.3968 | 0.8978 | 0.8942 | 0.8958 | 0.4189 | 38.0727 | |
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| 1.3447 | 6.0 | 261 | 1.7308 | 0.4541 | 0.208 | 0.3856 | 0.3877 | 0.8962 | 0.8934 | 0.8947 | 0.4111 | 38.8636 | |
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| 1.2905 | 6.9885 | 304 | 1.7456 | 0.4584 | 0.2067 | 0.3851 | 0.3868 | 0.8975 | 0.8934 | 0.8953 | 0.4114 | 37.0909 | |
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| 1.1838 | 8.0 | 348 | 1.7674 | 0.4636 | 0.2164 | 0.3945 | 0.3961 | 0.8951 | 0.8969 | 0.8958 | 0.4287 | 41.3545 | |
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| 1.1479 | 8.9885 | 391 | 1.7794 | 0.4721 | 0.2233 | 0.4014 | 0.4037 | 0.8957 | 0.8964 | 0.8959 | 0.4378 | 41.2545 | |
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| 1.067 | 10.0 | 435 | 1.8149 | 0.4567 | 0.2127 | 0.3968 | 0.3984 | 0.895 | 0.8935 | 0.894 | 0.4188 | 38.9182 | |
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| 1.0456 | 10.9885 | 478 | 1.8434 | 0.4585 | 0.208 | 0.3894 | 0.3913 | 0.8964 | 0.8936 | 0.8948 | 0.4136 | 37.8364 | |
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| 0.9792 | 12.0 | 522 | 1.8381 | 0.466 | 0.2163 | 0.3976 | 0.3996 | 0.8962 | 0.8962 | 0.896 | 0.4272 | 40.3455 | |
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| 0.9618 | 12.9885 | 565 | 1.8834 | 0.4702 | 0.2214 | 0.3996 | 0.4023 | 0.8949 | 0.8978 | 0.8962 | 0.441 | 42.6 | |
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| 0.9077 | 14.0 | 609 | 1.8886 | 0.4664 | 0.2221 | 0.4001 | 0.4014 | 0.8958 | 0.8969 | 0.8962 | 0.433 | 41.7364 | |
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| 0.9053 | 14.9885 | 652 | 1.9082 | 0.4687 | 0.2231 | 0.4016 | 0.4043 | 0.8967 | 0.898 | 0.8972 | 0.4341 | 41.7182 | |
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| 0.8627 | 16.0 | 696 | 1.9271 | 0.4564 | 0.2097 | 0.3858 | 0.3869 | 0.8939 | 0.8957 | 0.8946 | 0.4231 | 41.7545 | |
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| 0.866 | 16.9885 | 739 | 1.9276 | 0.4615 | 0.2129 | 0.3936 | 0.3955 | 0.8945 | 0.8971 | 0.8957 | 0.4273 | 42.0091 | |
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| 0.8359 | 18.0 | 783 | 1.9376 | 0.4644 | 0.2186 | 0.3995 | 0.4012 | 0.8947 | 0.8966 | 0.8955 | 0.4309 | 41.7091 | |
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| 0.8412 | 18.9885 | 826 | 1.9467 | 0.4691 | 0.2222 | 0.4018 | 0.4035 | 0.8959 | 0.898 | 0.8968 | 0.4327 | 41.6909 | |
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| 0.8218 | 19.7701 | 860 | 1.9472 | 0.4676 | 0.2222 | 0.4004 | 0.4024 | 0.8963 | 0.8971 | 0.8965 | 0.4301 | 40.8455 | |
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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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