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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_test |
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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_test |
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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: 0.7284 |
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- Rouge1: 43.3718 |
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- Rouge2: 37.5973 |
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- Rougel: 42.0502 |
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- Rougelsum: 42.0648 |
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- Bleu: 32.8345 |
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- Gen Len: 12.6063 |
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- Meteor: 0.3949 |
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- True negatives: 70.2115 |
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- False negatives: 11.206 |
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- Cosine Sim: 0.7485 |
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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 | True negatives | False negatives | Cosine Sim | |
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|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|:-------:|:------:|:--------------:|:---------------:|:----------:| |
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| 3.1455 | 1.0 | 175 | 0.9832 | 18.7269 | 15.517 | 18.22 | 18.223 | 7.0634 | 7.6229 | 0.1626 | 74.6828 | 57.1687 | 0.3949 | |
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| 1.1623 | 1.99 | 350 | 0.8542 | 38.7603 | 32.7237 | 37.3447 | 37.3752 | 27.4323 | 12.5135 | 0.3487 | 60.0 | 15.942 | 0.6992 | |
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| 0.9431 | 2.99 | 525 | 0.8017 | 41.5759 | 35.6108 | 40.2536 | 40.2695 | 30.7994 | 12.8117 | 0.3755 | 61.2689 | 12.3447 | 0.7304 | |
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| 0.8119 | 3.98 | 700 | 0.7787 | 43.5881 | 37.4245 | 42.1096 | 42.1248 | 32.9646 | 13.2176 | 0.3947 | 59.1541 | 9.5238 | 0.7582 | |
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| 0.7235 | 4.98 | 875 | 0.7477 | 43.4069 | 37.2246 | 41.8444 | 41.8616 | 32.9345 | 13.116 | 0.3946 | 63.0816 | 9.8085 | 0.7561 | |
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| 0.6493 | 5.97 | 1050 | 0.7266 | 40.4506 | 35.0072 | 39.1206 | 39.1181 | 29.0601 | 11.748 | 0.3687 | 75.5287 | 17.2101 | 0.7071 | |
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| 0.5871 | 6.97 | 1225 | 0.7284 | 43.3718 | 37.5973 | 42.0502 | 42.0648 | 32.8345 | 12.6063 | 0.3949 | 70.2115 | 11.206 | 0.7485 | |
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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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