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--- |
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license: apache-2.0 |
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metrics: |
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- rouge |
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model-index: |
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- name: AraBART-finetuned-wiki-ar |
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results: [] |
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pipeline_tag: translation |
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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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# AraBART-finetuned-wiki-ar |
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This model is a fine-tuned version of [moussaKam/AraBART](https://huggingface.co/moussaKam/AraBART) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 2.4030 |
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- Rouge1: 0.9862 |
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- Rouge2: 0.2292 |
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- Rougel: 0.9902 |
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- Rougelsum: 0.9847 |
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- Gen Len: 19.3511 |
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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: 2e-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: 10 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |
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|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:| |
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| 2.8633 | 1.0 | 2556 | 2.5599 | 0.7861 | 0.1289 | 0.7656 | 0.7721 | 19.2354 | |
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| 2.6525 | 2.0 | 5112 | 2.4824 | 0.7315 | 0.2374 | 0.7224 | 0.7357 | 19.261 | |
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| 2.5068 | 3.0 | 7668 | 2.4404 | 0.7772 | 0.2114 | 0.7671 | 0.7861 | 19.3035 | |
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| 2.4251 | 4.0 | 10224 | 2.4269 | 0.7464 | 0.2156 | 0.745 | 0.7504 | 19.2929 | |
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| 2.3739 | 5.0 | 12780 | 2.4119 | 0.7642 | 0.1879 | 0.7729 | 0.7774 | 19.3573 | |
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| 2.275 | 6.0 | 15336 | 2.4039 | 0.9048 | 0.1952 | 0.9198 | 0.9189 | 19.37 | |
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| 2.2787 | 7.0 | 17892 | 2.4007 | 0.9913 | 0.2278 | 0.9951 | 1.0038 | 19.335 | |
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| 2.2142 | 8.0 | 20448 | 2.4073 | 0.9736 | 0.238 | 0.9697 | 0.9773 | 19.3556 | |
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| 2.1852 | 9.0 | 23004 | 2.4007 | 0.9825 | 0.2322 | 0.9891 | 0.9962 | 19.3213 | |
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| 2.1597 | 10.0 | 25560 | 2.4030 | 0.9862 | 0.2292 | 0.9902 | 0.9847 | 19.3511 | |
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### Framework versions |
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- Transformers 4.25.1 |
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- Pytorch 1.13.0+cu116 |
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- Datasets 2.7.1 |
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- Tokenizers 0.13.2 |
