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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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- summarization |
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- generated_from_trainer |
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metrics: |
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- rouge |
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model-index: |
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- name: mt5-small-finetuned-xlsum-pt |
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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-finetuned-xlsum-pt |
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This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0986 |
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- Rouge1: 16.5756 |
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- Rouge2: 13.7639 |
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- Rougel: 15.7445 |
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- Rougelsum: 16.5112 |
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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: 5.6e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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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: 10 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | |
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|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:| |
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| 0.7681 | 1.0 | 125 | 0.1393 | 12.9432 | 9.5039 | 12.2871 | 12.7291 | |
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| 0.5282 | 2.0 | 250 | 0.1231 | 13.4575 | 10.0697 | 12.6449 | 13.2 | |
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| 0.4132 | 3.0 | 375 | 0.1134 | 16.6964 | 14.0187 | 15.7338 | 16.6025 | |
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| 0.3534 | 4.0 | 500 | 0.1077 | 16.8961 | 14.2203 | 15.9187 | 16.7712 | |
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| 0.3126 | 5.0 | 625 | 0.1039 | 16.993 | 14.0876 | 15.8914 | 16.9277 | |
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| 0.283 | 6.0 | 750 | 0.1023 | 16.7431 | 13.9453 | 15.8758 | 16.6413 | |
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| 0.2675 | 7.0 | 875 | 0.1008 | 16.6566 | 13.8639 | 15.775 | 16.5481 | |
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| 0.2509 | 8.0 | 1000 | 0.0987 | 16.6829 | 13.935 | 15.872 | 16.6222 | |
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| 0.2441 | 9.0 | 1125 | 0.0987 | 16.6085 | 13.7884 | 15.7896 | 16.5412 | |
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| 0.2401 | 10.0 | 1250 | 0.0986 | 16.5756 | 13.7639 | 15.7445 | 16.5112 | |
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### Framework versions |
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- Transformers 4.37.2 |
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- Pytorch 2.1.0+cu121 |
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- Datasets 2.17.1 |
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- Tokenizers 0.15.2 |
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