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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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- 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-amazon-en-fr |
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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-amazon-en-fr |
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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: 2.9738 |
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- Rouge1: 16.2618 |
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- Rouge2: 8.4157 |
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- Rougel: 15.7746 |
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- Rougelsum: 15.6448 |
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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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- 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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| 6.4338 | 1.0 | 1399 | 3.2788 | 12.6697 | 4.9248 | 12.0308 | 12.0007 | |
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| 3.8734 | 2.0 | 2798 | 3.1052 | 14.3438 | 7.2643 | 13.744 | 13.6593 | |
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| 3.5793 | 3.0 | 4197 | 3.0230 | 15.8565 | 8.5311 | 15.2736 | 15.2018 | |
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| 3.4243 | 4.0 | 5596 | 2.9943 | 16.1882 | 8.3288 | 15.6948 | 15.5725 | |
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| 3.3277 | 5.0 | 6995 | 2.9845 | 16.5005 | 8.6609 | 16.0231 | 15.9789 | |
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| 3.2652 | 6.0 | 8394 | 2.9793 | 15.8014 | 7.9576 | 15.3678 | 15.2699 | |
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| 3.2344 | 7.0 | 9793 | 2.9707 | 16.529 | 8.2051 | 15.9864 | 15.8459 | |
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| 3.1853 | 8.0 | 11192 | 2.9738 | 16.2618 | 8.4157 | 15.7746 | 15.6448 | |
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### Framework versions |
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- Transformers 4.33.2 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.14.5 |
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- Tokenizers 0.13.3 |
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