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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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- 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-small-finetuned-amazon-en-es |
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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-es |
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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: 3.0349 |
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- Rouge1: 17.1191 |
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- Rouge2: 8.4119 |
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- Rougel: 16.6388 |
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- Rougelsum: 16.6017 |
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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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| 7.3401 | 1.0 | 1209 | 3.3465 | 14.1778 | 6.284 | 13.8905 | 13.8872 | |
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| 3.9195 | 2.0 | 2418 | 3.1859 | 15.9786 | 8.1666 | 15.3933 | 15.3693 | |
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| 3.5975 | 3.0 | 3627 | 3.0945 | 17.5518 | 9.134 | 16.9215 | 16.8899 | |
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| 3.4241 | 4.0 | 4836 | 3.0913 | 16.3875 | 7.6999 | 15.8311 | 15.8004 | |
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| 3.3252 | 5.0 | 6045 | 3.0588 | 16.6777 | 8.1639 | 16.1058 | 16.1357 | |
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| 3.2442 | 6.0 | 7254 | 3.0444 | 17.141 | 8.4204 | 16.6366 | 16.6896 | |
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| 3.2149 | 7.0 | 8463 | 3.0355 | 17.3266 | 8.7249 | 16.9438 | 16.9154 | |
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| 3.184 | 8.0 | 9672 | 3.0349 | 17.1191 | 8.4119 | 16.6388 | 16.6017 | |
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### Framework versions |
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- Transformers 4.33.3 |
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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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