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nllb-200-distilled-600M-finetuned-py2cpp

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  2. adapter_model.bin +1 -1
README.md CHANGED
@@ -17,9 +17,9 @@ should probably proofread and complete it, then remove this comment. -->
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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 the None dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 1.2267
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- - Bleu: 55.4437
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- - Gen Len: 74.5273
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  ## Model description
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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: 5
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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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- | No log | 1.0 | 67 | 2.7602 | 30.0843 | 94.7818 |
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- | No log | 2.0 | 134 | 1.8176 | 11.874 | 139.8182 |
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- | No log | 3.0 | 201 | 1.4276 | 43.5045 | 85.8182 |
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- | No log | 4.0 | 268 | 1.2682 | 54.7173 | 72.7273 |
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- | No log | 5.0 | 335 | 1.2267 | 55.4437 | 74.5273 |
 
 
 
 
 
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  ### Framework versions
 
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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 the None dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.7738
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+ - Bleu: 67.4647
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+ - Gen Len: 75.9455
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  ## Model description
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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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+ | No log | 1.0 | 67 | 2.6896 | 29.0389 | 96.5455 |
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+ | No log | 2.0 | 134 | 1.6534 | 30.4693 | 96.6727 |
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+ | No log | 3.0 | 201 | 1.2046 | 55.0467 | 76.7455 |
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+ | No log | 4.0 | 268 | 1.0048 | 59.5519 | 76.9091 |
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+ | No log | 5.0 | 335 | 0.9176 | 64.2229 | 75.5455 |
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+ | No log | 6.0 | 402 | 0.8610 | 65.8311 | 73.6909 |
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+ | No log | 7.0 | 469 | 0.8160 | 65.5771 | 76.4727 |
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+ | 1.5731 | 8.0 | 536 | 0.7968 | 67.9558 | 74.7636 |
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+ | 1.5731 | 9.0 | 603 | 0.7794 | 67.5994 | 75.8 |
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+ | 1.5731 | 10.0 | 670 | 0.7738 | 67.4647 | 75.9455 |
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  ### Framework versions
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