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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-1e-05-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-1e-05-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: 4.0044 |
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- Rouge/rouge1: 0.4791 |
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- Rouge/rouge2: 0.2351 |
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- Rouge/rougel: 0.4085 |
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- Rouge/rougelsum: 0.4098 |
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- Bertscore/bertscore-precision: 0.8984 |
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- Bertscore/bertscore-recall: 0.8999 |
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- Bertscore/bertscore-f1: 0.899 |
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- Meteor: 0.447 |
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- Gen Len: 41.7727 |
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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: 1e-05 |
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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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| 0.0089 | 0.9885 | 43 | 3.9871 | 0.4806 | 0.2393 | 0.4125 | 0.4129 | 0.8987 | 0.8999 | 0.8991 | 0.4493 | 41.7 | |
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| 0.0062 | 2.0 | 87 | 3.9921 | 0.4805 | 0.2395 | 0.4119 | 0.4124 | 0.8984 | 0.8997 | 0.8989 | 0.4488 | 41.6727 | |
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| 0.0046 | 2.9885 | 130 | 3.9973 | 0.4806 | 0.2358 | 0.4101 | 0.4109 | 0.8984 | 0.8993 | 0.8988 | 0.448 | 41.2 | |
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| 0.0046 | 4.0 | 174 | 4.0023 | 0.4787 | 0.2353 | 0.4084 | 0.4095 | 0.8989 | 0.899 | 0.8988 | 0.4445 | 40.8273 | |
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| 0.0051 | 4.9885 | 217 | 4.0062 | 0.4817 | 0.2381 | 0.4116 | 0.4125 | 0.8996 | 0.8992 | 0.8993 | 0.4456 | 40.5455 | |
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| 0.0044 | 6.0 | 261 | 4.0107 | 0.4796 | 0.2351 | 0.4089 | 0.4099 | 0.8994 | 0.8988 | 0.8989 | 0.4423 | 40.2727 | |
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| 0.0046 | 6.9885 | 304 | 4.0121 | 0.4795 | 0.2331 | 0.4083 | 0.409 | 0.8991 | 0.8986 | 0.8987 | 0.4393 | 40.1455 | |
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| 0.0043 | 8.0 | 348 | 4.0119 | 0.4799 | 0.2345 | 0.4086 | 0.4092 | 0.899 | 0.899 | 0.8989 | 0.4426 | 40.6909 | |
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| 0.0043 | 8.9885 | 391 | 4.0124 | 0.4778 | 0.2344 | 0.4076 | 0.4083 | 0.899 | 0.8988 | 0.8988 | 0.4402 | 40.5364 | |
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| 0.0038 | 10.0 | 435 | 4.0146 | 0.4791 | 0.2349 | 0.4087 | 0.4096 | 0.8992 | 0.8991 | 0.899 | 0.4413 | 40.6909 | |
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| 0.0113 | 10.9885 | 478 | 4.0149 | 0.4794 | 0.2361 | 0.4088 | 0.4096 | 0.8985 | 0.8992 | 0.8987 | 0.4436 | 41.2091 | |
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| 0.0116 | 12.0 | 522 | 4.0099 | 0.4817 | 0.2387 | 0.4112 | 0.4119 | 0.8986 | 0.8997 | 0.899 | 0.4482 | 41.5545 | |
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| 0.0116 | 12.9885 | 565 | 4.0083 | 0.4811 | 0.2378 | 0.411 | 0.4119 | 0.8992 | 0.8997 | 0.8993 | 0.4472 | 41.3636 | |
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| 0.0109 | 14.0 | 609 | 4.0073 | 0.4804 | 0.2363 | 0.41 | 0.4108 | 0.899 | 0.8998 | 0.8993 | 0.4462 | 41.4364 | |
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| 0.0109 | 14.9885 | 652 | 4.0056 | 0.4796 | 0.2362 | 0.409 | 0.4096 | 0.8987 | 0.9 | 0.8992 | 0.4476 | 41.7636 | |
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| 0.0107 | 16.0 | 696 | 4.0045 | 0.4796 | 0.2353 | 0.4095 | 0.4098 | 0.8988 | 0.8998 | 0.8991 | 0.4471 | 41.4727 | |
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| 0.0117 | 16.9885 | 739 | 4.0039 | 0.4789 | 0.234 | 0.4076 | 0.4084 | 0.8992 | 0.8997 | 0.8993 | 0.4455 | 41.2455 | |
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| 0.0103 | 18.0 | 783 | 4.0045 | 0.4785 | 0.2342 | 0.4078 | 0.4088 | 0.8986 | 0.8996 | 0.899 | 0.4452 | 41.4909 | |
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| 0.0111 | 18.9885 | 826 | 4.0044 | 0.4776 | 0.2339 | 0.4069 | 0.408 | 0.8986 | 0.8998 | 0.8991 | 0.4456 | 41.6 | |
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| 0.0119 | 19.7701 | 860 | 4.0044 | 0.4791 | 0.2351 | 0.4085 | 0.4098 | 0.8984 | 0.8999 | 0.899 | 0.447 | 41.7727 | |
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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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