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--- |
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license: mit |
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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: mbart-large-50-pluska-token-sum |
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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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# mbart-large-50-pluska-token-sum |
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This model is a fine-tuned version of [facebook/mbart-large-50](https://huggingface.co/facebook/mbart-large-50) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 4.2944 |
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- Rouge1: 16.8143 |
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- Rouge2: 5.2102 |
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- Rougel: 14.3454 |
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- Rougelsum: 14.7355 |
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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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- gradient_accumulation_steps: 4 |
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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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- 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 | 144 | 3.7149 | 17.1082 | 5.3773 | 14.6384 | 14.9023 | |
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| No log | 2.0 | 288 | 3.4301 | 17.6442 | 5.7235 | 14.9728 | 15.3384 | |
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| No log | 3.0 | 432 | 3.4951 | 17.491 | 5.5463 | 14.8284 | 15.2035 | |
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| 2.9135 | 4.0 | 576 | 3.6706 | 17.1471 | 5.3419 | 14.6302 | 14.9667 | |
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| 2.9135 | 5.0 | 720 | 3.9122 | 16.6979 | 5.1895 | 14.2909 | 14.6367 | |
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| 2.9135 | 6.0 | 864 | 4.0815 | 16.5961 | 5.1915 | 14.2459 | 14.5916 | |
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| 2.9135 | 7.0 | 1008 | 4.2448 | 16.7793 | 5.2088 | 14.3515 | 14.7136 | |
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| 1.0097 | 8.0 | 1152 | 4.2944 | 16.8143 | 5.2102 | 14.3454 | 14.7355 | |
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### Framework versions |
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- Transformers 4.27.0 |
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- Pytorch 2.0.0 |
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- Datasets 2.10.1 |
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- Tokenizers 0.13.2 |
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