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--- |
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license: mit |
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tags: |
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- translation |
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- generated_from_trainer |
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metrics: |
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- bleu |
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- rouge |
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- meteor |
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model-index: |
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- name: mbart-large-50-English_Spanish_Translation |
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results: [] |
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language: |
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- en |
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- es |
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pipeline_tag: translation |
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--- |
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# mbart-large-50-English_Spanish_Translation |
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This model is a fine-tuned version of [facebook/mbart-large-50](https://huggingface.co/facebook/mbart-large-50) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.0290 |
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- Bleu: 41.4437 |
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- Rouge: |
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- Rouge1: 0.6751402780531002 |
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- Rouge2: 0.49769602014143044 |
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- RougeL: 0.6371513427059108 |
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- RougeLsum: 0.6376403149816605 |
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- Meteor: 0.6479226630466496 |
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## Model description |
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This project translates Spanish text inputs into English. |
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Here is the link to the script I created to train this model: https://github.com/DunnBC22/NLP_Projects/blob/main/Machine%20Translation/NLP%20Translation%20Project-EN:ES.ipynb |
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## Intended uses & limitations |
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This model is intended to demonstrate my ability to solve a complex problem using technology. |
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## Training and evaluation data |
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Dataset Source: https://www.kaggle.com/datasets/hgultekin/paralel-translation-corpus-in-22-languages |
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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: 32 |
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- eval_batch_size: 32 |
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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: 2 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Bleu | Rouge1 | Rouge2 | RougeL | RougeLsum | Meteor | |
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|:-------------:|:-----:|:----:|:---------------:|:-------:|:-----:|:-----:|:-----:|:-----:|:-----:| |
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| 1.5608 | 1.0 | 900 | 1.0899 | 39.9184 | 0.6645 | 0.4846 | 0.6254 | 0.6259 | 0.6376 | |
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| 0.9734 | 2.0 | 1800 | 1.0290 | 41.4436 | 0.6751 | 0.4977 | 0.6371 | 0.6376 | 0.6479 | |
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### Framework versions |
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- Transformers 4.22.2 |
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- Pytorch 1.12.1 |
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- Datasets 2.5.2 |
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- Tokenizers 0.12.1 |