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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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datasets: |
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- wiki_lingua |
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metrics: |
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- rouge |
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model-index: |
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- name: wiki_lingua-fr-8-3-5.6e-05-mt5-small-finetuned |
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results: |
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- task: |
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name: Sequence-to-sequence Language Modeling |
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type: text2text-generation |
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dataset: |
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name: wiki_lingua |
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type: wiki_lingua |
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config: fr |
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split: test |
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args: fr |
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metrics: |
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- name: Rouge1 |
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type: rouge |
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value: 19.9596 |
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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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# wiki_lingua-fr-8-3-5.6e-05-mt5-small-finetuned |
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This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the wiki_lingua dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.9117 |
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- Rouge1: 19.9596 |
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- Rouge2: 7.5052 |
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- Rougel: 17.4363 |
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- Rougelsum: 19.5192 |
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# Baseline LEAD-64 |
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- Rouge1: 22.4 |
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- Rouge2: 5.92 |
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- Rougel: 14.44 |
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- Rougelsum: 14.44 |
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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: 3 |
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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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| 2.8962 | 1.0 | 5428 | 2.0026 | 18.8621 | 6.6127 | 16.0264 | 18.4354 | |
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| 2.313 | 2.0 | 10856 | 1.9260 | 19.7274 | 7.2791 | 17.0466 | 19.2904 | |
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| 2.2248 | 3.0 | 16284 | 1.9117 | 19.9596 | 7.5052 | 17.4363 | 19.5192 | |
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### Framework versions |
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- Transformers 4.27.4 |
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- Pytorch 1.13.0 |
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- Datasets 2.1.0 |
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- Tokenizers 0.13.2 |
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