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--- |
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license: apache-2.0 |
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base_model: lunarlist/mt5-summarize |
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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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model-index: |
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- name: mt5-summarize-full |
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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-summarize-full |
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This model is a fine-tuned version of [lunarlist/mt5-summarize](https://huggingface.co/lunarlist/mt5-summarize) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 2.8640 |
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- Rouge1: 0.3352 |
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- Rouge2: 0.1212 |
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- Rougel: 0.2748 |
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- Rougelsum: 0.4747 |
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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: 0.0005 |
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- train_batch_size: 2 |
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- eval_batch_size: 2 |
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- seed: 42 |
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- gradient_accumulation_steps: 16 |
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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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- lr_scheduler_warmup_steps: 90 |
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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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| 4.0732 | 1.0667 | 100 | 3.1187 | 0.3331 | 0.1146 | 0.2648 | 0.5137 | |
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| 3.6546 | 2.1333 | 200 | 2.9872 | 0.3410 | 0.1256 | 0.2894 | 0.4943 | |
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| 3.3308 | 3.2 | 300 | 2.9373 | 0.3430 | 0.1278 | 0.2881 | 0.4743 | |
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| 3.276 | 4.2667 | 400 | 2.8782 | 0.3355 | 0.1163 | 0.2793 | 0.4801 | |
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| 3.1345 | 5.3333 | 500 | 2.9083 | 0.3354 | 0.1216 | 0.2835 | 0.4758 | |
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| 3.0736 | 6.4 | 600 | 2.8588 | 0.3531 | 0.1353 | 0.2900 | 0.4991 | |
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| 3.0168 | 7.4667 | 700 | 2.8592 | 0.3436 | 0.1229 | 0.2893 | 0.4863 | |
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| 2.969 | 8.5333 | 800 | 2.8739 | 0.3528 | 0.1297 | 0.2863 | 0.4968 | |
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| 2.9677 | 9.6 | 900 | 2.8640 | 0.3352 | 0.1212 | 0.2748 | 0.4747 | |
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### Framework versions |
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- Transformers 4.42.3 |
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- Pytorch 2.3.0+cu121 |
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- Datasets 2.20.0 |
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- Tokenizers 0.19.1 |
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