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--- |
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license: cc-by-nc-4.0 |
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base_model: facebook/nllb-200-distilled-600M |
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tags: |
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- generated_from_trainer |
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metrics: |
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- bleu |
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model-index: |
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- name: nllb-200-distilled-600M-finetuned_augmented_synthetic_ar-to-en |
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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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# nllb-200-distilled-600M-finetuned_augmented_synthetic_ar-to-en |
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This model is a fine-tuned version of [facebook/nllb-200-distilled-600M](https://huggingface.co/facebook/nllb-200-distilled-600M) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.7501 |
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- Bleu: 62.4193 |
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- Gen Len: 64.586 |
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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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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len | |
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|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:| |
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| 1.0564 | 1.0 | 2210 | 1.0374 | 45.431 | 65.406 | |
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| 0.8998 | 2.0 | 4420 | 0.8975 | 52.6173 | 66.014 | |
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| 0.7972 | 3.0 | 6630 | 0.8399 | 55.9624 | 65.357 | |
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| 0.7451 | 4.0 | 8840 | 0.8021 | 57.3958 | 65.94 | |
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| 0.6884 | 5.0 | 11050 | 0.7771 | 59.9589 | 65.367 | |
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| 0.6742 | 6.0 | 13260 | 0.7648 | 61.0786 | 64.74 | |
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| 0.6599 | 7.0 | 15470 | 0.7562 | 61.9442 | 64.694 | |
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| 0.6168 | 8.0 | 17680 | 0.7530 | 62.0067 | 64.965 | |
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| 0.6234 | 9.0 | 19890 | 0.7502 | 62.0721 | 64.888 | |
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| 0.5948 | 10.0 | 22100 | 0.7501 | 62.4193 | 64.586 | |
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### Framework versions |
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- Transformers 4.31.0 |
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- Pytorch 1.13.1 |
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- Datasets 2.14.4 |
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- Tokenizers 0.13.3 |
