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--- |
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license: apache-2.0 |
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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-base-wikinewssum-english |
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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-base-wikinewssum-english |
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This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 2.3040 |
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- Rouge1: 8.9565 |
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- Rouge2: 3.6563 |
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- Rougel: 7.1346 |
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- Rougelsum: 8.3802 |
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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: 4 |
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- eval_batch_size: 4 |
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- seed: 42 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 8 |
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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 | 1010 | 2.4360 | 8.7287 | 3.5817 | 7.0093 | 8.1879 | |
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| No log | 2.0 | 2020 | 2.3922 | 8.7227 | 3.5385 | 6.96 | 8.1887 | |
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| No log | 3.0 | 3030 | 2.3422 | 8.8565 | 3.5772 | 7.0203 | 8.2957 | |
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| No log | 4.0 | 4040 | 2.3288 | 8.89 | 3.645 | 7.0602 | 8.3314 | |
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| 3.1253 | 5.0 | 5050 | 2.3209 | 8.868 | 3.6109 | 7.0537 | 8.299 | |
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| 3.1253 | 6.0 | 6060 | 2.3127 | 8.9488 | 3.6615 | 7.1044 | 8.3785 | |
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| 3.1253 | 7.0 | 7070 | 2.3056 | 8.9366 | 3.6507 | 7.1338 | 8.3615 | |
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| 3.1253 | 8.0 | 8080 | 2.3040 | 8.9565 | 3.6563 | 7.1346 | 8.3802 | |
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### Framework versions |
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- Transformers 4.13.0 |
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- Pytorch 1.10.1 |
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- Datasets 1.16.1 |
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- Tokenizers 0.10.3 |
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