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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_mid_lr_mid_decay |
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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_mid_lr_mid_decay |
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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: 0.7428 |
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- Rouge1: 43.12 |
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- Rouge2: 37.6639 |
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- Rougel: 41.8367 |
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- Rougelsum: 41.904 |
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- Bleu: 31.957 |
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- Gen Len: 12.1285 |
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- Meteor: 0.3936 |
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- No ans accuracy: 22.29 |
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- Av cosine sim: 0.7406 |
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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: 0.001 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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- seed: 9 |
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- gradient_accumulation_steps: 8 |
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- total_train_batch_size: 128 |
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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 | Av cosine sim | |
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|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|:-------:|:------:|:---------------:|:-------------:| |
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| 3.1455 | 1.0 | 175 | 0.9832 | 18.7107 | 15.4897 | 18.1977 | 18.2212 | 7.0634 | 7.6229 | 0.1626 | 22.4000 | 0.3949 | |
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| 1.1623 | 1.99 | 350 | 0.8542 | 38.7675 | 32.704 | 37.3557 | 37.3949 | 27.4323 | 12.5135 | 0.3487 | 17.9900 | 0.6992 | |
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| 0.9431 | 2.99 | 525 | 0.8017 | 41.6216 | 35.6002 | 40.2386 | 40.2881 | 30.7994 | 12.8117 | 0.3755 | 18.37 | 0.7304 | |
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| 0.8119 | 3.98 | 700 | 0.7787 | 43.5805 | 37.4117 | 42.1059 | 42.155 | 32.9646 | 13.2176 | 0.3947 | 17.7400 | 0.7582 | |
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| 0.7235 | 4.98 | 875 | 0.7477 | 43.4124 | 37.2017 | 41.8468 | 41.9097 | 32.9345 | 13.116 | 0.3946 | 18.92 | 0.7561 | |
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| 0.6493 | 5.97 | 1050 | 0.7266 | 40.4764 | 34.9927 | 39.0999 | 39.1711 | 29.0601 | 11.748 | 0.3687 | 22.6500 | 0.7071 | |
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| 0.5871 | 6.97 | 1225 | 0.7284 | 43.3812 | 37.5544 | 42.0405 | 42.0865 | 32.8345 | 12.6063 | 0.3949 | 21.05 | 0.7485 | |
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| 0.5453 | 7.96 | 1400 | 0.7389 | 43.4549 | 37.76 | 42.1025 | 42.215 | 32.6726 | 12.4537 | 0.3965 | 21.44 | 0.7496 | |
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| 0.5038 | 8.96 | 1575 | 0.7428 | 43.12 | 37.6639 | 41.8367 | 41.904 | 31.957 | 12.1285 | 0.3936 | 22.29 | 0.7406 | |
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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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