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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-2-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-2-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: 4.1057 |
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- Rouge/rouge1: 0.4734 |
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- Rouge/rouge2: 0.2314 |
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- Rouge/rougel: 0.4044 |
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- Rouge/rougelsum: 0.4048 |
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- Bertscore/bertscore-precision: 0.8983 |
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- Bertscore/bertscore-recall: 0.8989 |
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- Bertscore/bertscore-f1: 0.8984 |
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- Meteor: 0.4395 |
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- Gen Len: 41.1 |
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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: 2 |
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- eval_batch_size: 2 |
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- seed: 42 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 4 |
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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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| 0.0048 | 1.0 | 217 | 4.0191 | 0.4796 | 0.2348 | 0.4105 | 0.4113 | 0.8989 | 0.8999 | 0.8993 | 0.445 | 41.1636 | |
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| 0.0019 | 2.0 | 434 | 4.0490 | 0.4749 | 0.2307 | 0.406 | 0.4074 | 0.8979 | 0.8986 | 0.8981 | 0.4412 | 40.8364 | |
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| 0.0062 | 3.0 | 651 | 4.0644 | 0.4795 | 0.2336 | 0.4078 | 0.4094 | 0.898 | 0.9 | 0.8988 | 0.4468 | 41.9 | |
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| 0.0062 | 4.0 | 868 | 4.0660 | 0.4789 | 0.2299 | 0.4056 | 0.4062 | 0.8986 | 0.899 | 0.8986 | 0.4406 | 41.1909 | |
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| 0.0114 | 5.0 | 1085 | 4.0761 | 0.4755 | 0.2298 | 0.4046 | 0.405 | 0.899 | 0.8991 | 0.8989 | 0.4421 | 40.8182 | |
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| 0.0106 | 6.0 | 1302 | 4.0854 | 0.4732 | 0.2267 | 0.401 | 0.4021 | 0.8982 | 0.8992 | 0.8986 | 0.4401 | 41.1273 | |
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| 0.0112 | 7.0 | 1519 | 4.0993 | 0.4706 | 0.2273 | 0.4008 | 0.402 | 0.8965 | 0.8987 | 0.8975 | 0.4396 | 41.7182 | |
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| 0.0108 | 8.0 | 1736 | 4.0949 | 0.4696 | 0.2269 | 0.3982 | 0.399 | 0.8971 | 0.8987 | 0.8978 | 0.442 | 41.8727 | |
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| 0.0109 | 9.0 | 1953 | 4.0946 | 0.4742 | 0.2304 | 0.4035 | 0.4037 | 0.8982 | 0.8992 | 0.8986 | 0.4447 | 41.3364 | |
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| 0.0103 | 10.0 | 2170 | 4.1017 | 0.4769 | 0.2333 | 0.4064 | 0.4068 | 0.8988 | 0.8996 | 0.8991 | 0.4469 | 41.1182 | |
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| 0.0102 | 11.0 | 2387 | 4.1028 | 0.4742 | 0.2304 | 0.4032 | 0.4037 | 0.898 | 0.8991 | 0.8984 | 0.444 | 41.4545 | |
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| 0.0101 | 12.0 | 2604 | 4.1046 | 0.4778 | 0.233 | 0.4074 | 0.4078 | 0.8987 | 0.8993 | 0.8989 | 0.445 | 40.9182 | |
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| 0.0097 | 13.0 | 2821 | 4.1067 | 0.4734 | 0.2296 | 0.4034 | 0.4038 | 0.8979 | 0.8985 | 0.8981 | 0.4396 | 41.0 | |
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| 0.0092 | 14.0 | 3038 | 4.1086 | 0.4727 | 0.229 | 0.4022 | 0.4027 | 0.8979 | 0.8984 | 0.898 | 0.4395 | 41.0818 | |
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| 0.0094 | 15.0 | 3255 | 4.1076 | 0.4727 | 0.2288 | 0.4025 | 0.403 | 0.8978 | 0.8984 | 0.898 | 0.439 | 41.1091 | |
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| 0.0094 | 16.0 | 3472 | 4.1075 | 0.4733 | 0.2284 | 0.4024 | 0.4033 | 0.8976 | 0.8987 | 0.898 | 0.4389 | 41.2636 | |
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| 0.0088 | 17.0 | 3689 | 4.1072 | 0.473 | 0.2291 | 0.4034 | 0.4036 | 0.8981 | 0.8986 | 0.8982 | 0.4375 | 41.2545 | |
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| 0.0092 | 18.0 | 3906 | 4.1065 | 0.4712 | 0.2298 | 0.4023 | 0.4024 | 0.8981 | 0.8983 | 0.898 | 0.4367 | 40.9818 | |
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| 0.0095 | 19.0 | 4123 | 4.1058 | 0.4708 | 0.2288 | 0.4022 | 0.4026 | 0.8979 | 0.8986 | 0.8981 | 0.4368 | 41.3273 | |
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| 0.0091 | 20.0 | 4340 | 4.1057 | 0.4734 | 0.2314 | 0.4044 | 0.4048 | 0.8983 | 0.8989 | 0.8984 | 0.4395 | 41.1 | |
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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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