--- license: mit base_model: facebook/mbart-large-50 tags: - translation - generated_from_trainer metrics: - bleu - rouge model-index: - name: mbart-large-50-en-es-translation-lr-1e-05-weight-decay-0.01 results: [] --- # mbart-large-50-en-es-translation-lr-1e-05-weight-decay-0.01 This model is a fine-tuned version of [facebook/mbart-large-50](https://huggingface.co./facebook/mbart-large-50) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.9547 - Bleu: 45.0896 - Rouge: {'rouge1': 0.7050843983318068, 'rouge2': 0.5221826018405332, 'rougeL': 0.6843669248955093, 'rougeLsum': 0.6845499780252107} ## 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: 1e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | Rouge | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:---------------------------------------------------------------------------------------------------------------------------:| | 1.4571 | 1.0 | 4500 | 1.0262 | 41.9188 | {'rouge1': 0.6701489298042851, 'rouge2': 0.4850120961190509, 'rougeL': 0.6479081216501843, 'rougeLsum': 0.6480345623292922} | | 0.889 | 2.0 | 9000 | 0.9559 | 44.3378 | {'rouge1': 0.6920481616267358, 'rouge2': 0.5087283264258592, 'rougeL': 0.6709294966142768, 'rougeLsum': 0.6710449317682404} | | 0.7134 | 3.0 | 13500 | 0.9416 | 44.9705 | {'rouge1': 0.7026762914671131, 'rouge2': 0.5192700210995049, 'rougeL': 0.6817974408692513, 'rougeLsum': 0.6819680202609157} | | 0.6098 | 4.0 | 18000 | 0.9547 | 45.1741 | {'rouge1': 0.7051668954804624, 'rouge2': 0.5222186626492409, 'rougeL': 0.6844002112351866, 'rougeLsum': 0.6845851183829141} | ### Framework versions - Transformers 4.33.0.dev0 - Pytorch 2.0.1+cu118 - Datasets 2.14.4.dev0 - Tokenizers 0.13.3