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--- |
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license: apache-2.0 |
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base_model: google/mt5-small |
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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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- bleu |
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model-index: |
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- name: mt5-small_epochs_new_new |
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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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# mt5-small_epochs_new_new |
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This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.0848 |
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- Rouge1: 41.527 |
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- Rouge2: 33.324 |
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- Rougel: 38.4866 |
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- Rougelsum: 38.4856 |
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- Bleu: 29.906 |
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- Gen Len: 17.1296 |
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- Meteor: 0.377 |
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- No ans accuracy: 47 |
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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: 3e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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- seed: 9 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 32 |
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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: 20 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Bleu | Gen Len | Meteor | No ans accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|:-------:|:------:|:---------------:| |
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| 9.0096 | 1.0 | 316 | 2.5283 | 23.4252 | 15.294 | 21.7032 | 21.7597 | 9.5703 | 12.1099 | 0.2117 | 0 | |
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| 3.2564 | 2.0 | 632 | 1.8337 | 33.2328 | 25.7922 | 31.2553 | 31.2709 | 15.51 | 13.8804 | 0.3108 | 0 | |
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| 2.5244 | 3.0 | 948 | 1.5796 | 34.1863 | 26.9908 | 32.3162 | 32.3197 | 17.5684 | 14.1904 | 0.3262 | 0 | |
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| 2.1686 | 3.99 | 1264 | 1.4179 | 34.565 | 27.4829 | 32.6012 | 32.6306 | 18.1896 | 14.2814 | 0.329 | 0 | |
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| 1.9465 | 4.99 | 1580 | 1.3050 | 41.2984 | 32.8587 | 38.3901 | 38.3985 | 28.3953 | 17.1212 | 0.3724 | 24 | |
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| 1.8009 | 5.99 | 1896 | 1.2428 | 41.5784 | 33.0684 | 38.6495 | 38.6555 | 28.9287 | 17.2045 | 0.3755 | 27 | |
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| 1.6954 | 6.99 | 2212 | 1.1992 | 40.4868 | 32.2937 | 37.6021 | 37.5986 | 28.2477 | 16.8056 | 0.3662 | 54 | |
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| 1.6322 | 7.99 | 2528 | 1.1769 | 37.6427 | 30.0271 | 34.8637 | 34.8951 | 26.433 | 15.5656 | 0.34 | 124 | |
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| 1.5845 | 8.99 | 2844 | 1.1574 | 40.3396 | 32.2547 | 37.3672 | 37.4137 | 28.6687 | 16.6457 | 0.3638 | 66 | |
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| 1.5425 | 9.98 | 3160 | 1.1500 | 39.1906 | 31.3426 | 36.3113 | 36.3654 | 27.8135 | 16.1679 | 0.3542 | 95 | |
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| 1.5137 | 10.98 | 3476 | 1.1367 | 41.4173 | 33.1848 | 38.4473 | 38.4306 | 29.6548 | 17.0306 | 0.3755 | 51 | |
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| 1.4826 | 11.98 | 3792 | 1.1161 | 41.4856 | 33.1913 | 38.4806 | 38.4896 | 29.5512 | 17.1031 | 0.3762 | 44 | |
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| 1.4514 | 12.98 | 4108 | 1.1182 | 41.8374 | 33.5091 | 38.7582 | 38.7679 | 29.8577 | 17.2987 | 0.3797 | 37 | |
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| 1.4444 | 13.98 | 4424 | 1.1056 | 42.0345 | 33.6905 | 38.9576 | 38.9795 | 30.1371 | 17.2669 | 0.3823 | 38 | |
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| 1.425 | 14.98 | 4740 | 1.0973 | 41.5086 | 33.2216 | 38.4098 | 38.4115 | 29.7019 | 17.1244 | 0.3767 | 50 | |
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| 1.407 | 15.97 | 5056 | 1.0890 | 41.7122 | 33.4259 | 38.605 | 38.6225 | 29.9984 | 17.1908 | 0.3794 | 44 | |
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| 1.4005 | 16.97 | 5372 | 1.0881 | 41.5731 | 33.2998 | 38.521 | 38.5259 | 29.9097 | 17.1027 | 0.3775 | 49 | |
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| 1.3865 | 17.97 | 5688 | 1.0860 | 40.9767 | 32.8412 | 37.9532 | 37.9637 | 29.4171 | 16.9404 | 0.372 | 55 | |
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| 1.3849 | 18.97 | 6004 | 1.0848 | 41.527 | 33.324 | 38.4866 | 38.4856 | 29.906 | 17.1296 | 0.377 | 47 | |
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### Framework versions |
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- Transformers 4.31.0 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.13.1 |
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- Tokenizers 0.13.3 |
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