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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-french |
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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-french |
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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: 2.0917 |
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- Rouge1: 12.0984 |
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- Rouge2: 5.7289 |
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- Rougel: 9.9245 |
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- Rougelsum: 11.0697 |
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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 | 549 | 2.3203 | 11.5172 | 4.9352 | 9.3617 | 10.4605 | |
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| No log | 2.0 | 1098 | 2.2057 | 11.8469 | 5.2369 | 9.6452 | 10.8337 | |
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| No log | 3.0 | 1647 | 2.1525 | 11.9096 | 5.4027 | 9.7648 | 10.9315 | |
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| 3.1825 | 4.0 | 2196 | 2.1307 | 12.0782 | 5.5848 | 9.9614 | 11.1081 | |
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| 3.1825 | 5.0 | 2745 | 2.1172 | 11.9821 | 5.6042 | 9.8216 | 11.0077 | |
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| 3.1825 | 6.0 | 3294 | 2.1012 | 12.0845 | 5.6834 | 9.9119 | 11.0741 | |
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| 3.1825 | 7.0 | 3843 | 2.0964 | 12.1296 | 5.7271 | 9.9495 | 11.1227 | |
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| 2.3376 | 8.0 | 4392 | 2.0917 | 12.0984 | 5.7289 | 9.9245 | 11.0697 | |
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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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