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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-news-summary-kaggle |
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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-news-summary-kaggle |
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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: 2.6907 |
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- Rouge1: 26.6547 |
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- Rouge2: 10.1 |
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- Rougel: 24.0137 |
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- Rougelsum: 23.9999 |
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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: 16 |
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- eval_batch_size: 16 |
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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: 8 |
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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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| No log | 1.0 | 220 | 3.9956 | 14.9021 | 3.3744 | 13.4763 | 13.499 | |
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| 8.3183 | 2.0 | 440 | 3.1550 | 17.9472 | 5.9671 | 16.6974 | 16.6959 | |
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| 8.3183 | 3.0 | 660 | 2.8950 | 21.2665 | 7.4266 | 19.5041 | 19.4837 | |
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| 4.0457 | 4.0 | 880 | 2.8087 | 25.063 | 9.4484 | 22.746 | 22.7351 | |
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| 4.0457 | 5.0 | 1100 | 2.7375 | 25.5269 | 9.4299 | 23.0623 | 23.0075 | |
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| 3.6505 | 6.0 | 1320 | 2.7091 | 25.8308 | 9.3392 | 23.2001 | 23.1586 | |
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| 3.6505 | 7.0 | 1540 | 2.6949 | 26.2177 | 9.8536 | 23.5946 | 23.6358 | |
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| 3.5175 | 8.0 | 1760 | 2.6907 | 26.6547 | 10.1 | 24.0137 | 23.9999 | |
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### Framework versions |
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- Transformers 4.39.3 |
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- Pytorch 2.1.2 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.2 |
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