--- library_name: transformers license: cc-by-nc-4.0 base_model: facebook/nllb-200-distilled-600M tags: - translation - generated_from_trainer metrics: - bleu model-index: - name: nllb-200-distilled-600M-mt-finetuned-zindi-dyu-to-fr results: [] --- # nllb-200-distilled-600M-mt-finetuned-zindi-dyu-to-fr 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. It achieves the following results on the evaluation set: - Loss: 2.2584 - Bleu: 6.4075 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | |:-------------:|:------:|:----:|:---------------:|:------:| | 3.1707 | 0.1575 | 20 | 2.7356 | 4.8084 | | 2.9074 | 0.3150 | 40 | 2.5883 | 5.0141 | | 2.7168 | 0.4724 | 60 | 2.4902 | 5.5785 | | 2.6912 | 0.6299 | 80 | 2.4154 | 5.7743 | | 2.6062 | 0.7874 | 100 | 2.3742 | 6.0010 | | 2.5794 | 0.9449 | 120 | 2.3480 | 6.1354 | | 2.4634 | 1.1024 | 140 | 2.3314 | 5.9899 | | 2.5055 | 1.2598 | 160 | 2.3167 | 6.1080 | | 2.5062 | 1.4173 | 180 | 2.3032 | 6.3784 | | 2.4771 | 1.5748 | 200 | 2.2944 | 6.4510 | | 2.4284 | 1.7323 | 220 | 2.2854 | 6.2883 | | 2.4423 | 1.8898 | 240 | 2.2783 | 6.5036 | | 2.3202 | 2.0472 | 260 | 2.2730 | 6.4039 | | 2.3855 | 2.2047 | 280 | 2.2701 | 6.2921 | | 2.4292 | 2.3622 | 300 | 2.2658 | 6.3025 | | 2.3678 | 2.5197 | 320 | 2.2626 | 6.2881 | | 2.4158 | 2.6772 | 340 | 2.2600 | 6.3684 | | 2.351 | 2.8346 | 360 | 2.2588 | 6.2852 | | 2.3755 | 2.9921 | 380 | 2.2584 | 6.2819 | ### Framework versions - Transformers 4.44.2 - Pytorch 2.4.0+cu121 - Datasets 2.18.0 - Tokenizers 0.19.1