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--- |
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library_name: transformers |
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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-FLEURS-GL-EN |
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results: [] |
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datasets: |
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- juanjucm/FLEURS-SpeechT-GL-EN |
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language: |
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- gl |
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- en |
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--- |
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# nllb-200-distilled-600M-FLEURS-GL-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) trained on [juanjucm/FLEURS-SpeechT-GL-EN](https://huggingface.co/datasets/juanjucm/FLEURS-SpeechT-GL-EN) for **Galician-to-Englis Machine Translation** task. It takes Galician texts as input and generates the correspondant English translation. |
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This Machine Translation model, was developed to be the second stage of a Speech Translation cascade system for transcribing and translating Galician audios into English texts. [This STT model](https://huggingface.co/juanjucm/whisper-large-v3-turbo-FLEURS-GL) can be used as a first step to transcribe Galician audio into text. After that, this MT model can be applied over the generated Galician transcriptions to get English text translations. |
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The motivation behind this work is to increase the visibility of the Galician language, making it more accessible for non-Galician speakers to understand and engage with Galician audio content. |
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This model was developed during a 3-week Speech Translation workshop organised by [Yasmin Moslem](https://huggingface.co/ymoslem). |
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### Performance and training details |
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Baseline model achieved a BLEY score of **39.98** on the evaluation dataset. |
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After fine-tuning, it achieves the following results on the evaluation set: |
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- Loss: 0.0877 |
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- **BLEU: 43.7516** |
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The following hyperparameters were used during training: |
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- learning_rate: 1e-05 |
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- train_batch_size: 4 |
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- eval_batch_size: 4 |
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- seed: 42 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 8 |
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- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: linear |
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- num_epochs: 8 |
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### Training results |
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We used [BLEU Score](https://en.wikipedia.org/wiki/BLEU) as our reference translation metric for selecting the best checkpoint after training. |
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| Training Loss | Epoch | Step | Validation Loss | Bleu | |
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|:-------------:|:-----:|:----:|:---------------:|:-------:| |
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| No log | 1.0 | 343 | 5.3970 | 42.8151 | |
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| 14.0381 | 2.0 | 686 | 3.4256 | 42.6804 | |
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| 6.6148 | 3.0 | 1029 | 1.7277 | 42.9270 | |
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| 6.6148 | 4.0 | 1372 | 0.6586 | 43.0670 | |
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| 2.2397 | 5.0 | 1715 | 0.2392 | 43.7306 | |
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| 0.532 | 6.0 | 2058 | 0.1265 | 43.0986 | |
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| 0.532 | 7.0 | 2401 | 0.0939 | 43.4517 | |
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| 0.2069 | 8.0 | 2744 | 0.0877 | 43.7516 | |
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### Framework versions |
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- Transformers 4.47.1 |
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- Pytorch 2.4.1+cu121 |
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- Datasets 3.2.0 |
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- Tokenizers 0.21.0 |
