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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-1000 |
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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-1000 |
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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: 2.4724 |
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- Rouge1: 7.7389 |
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- Rouge2: 3.1606 |
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- Rougel: 6.3317 |
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- Rougelsum: 7.2487 |
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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 | 125 | 2.6981 | 7.1504 | 2.6253 | 5.8261 | 6.7427 | |
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| No log | 2.0 | 250 | 2.5597 | 7.4666 | 2.9362 | 6.0965 | 6.9699 | |
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| No log | 3.0 | 375 | 2.5145 | 7.4599 | 2.9449 | 6.0941 | 6.9734 | |
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| No log | 4.0 | 500 | 2.4904 | 7.5063 | 2.975 | 6.137 | 7.0027 | |
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| No log | 5.0 | 625 | 2.4904 | 7.6027 | 3.0582 | 6.2161 | 7.0832 | |
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| No log | 6.0 | 750 | 2.4801 | 7.7601 | 3.1916 | 6.3689 | 7.2686 | |
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| No log | 7.0 | 875 | 2.4737 | 7.7162 | 3.1332 | 6.3113 | 7.2283 | |
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| No log | 8.0 | 1000 | 2.4724 | 7.7389 | 3.1606 | 6.3317 | 7.2487 | |
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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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