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--- |
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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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datasets: |
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- nusatranslation_mt |
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metrics: |
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- sacrebleu |
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model-index: |
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- name: bbc-to-ind-nmt-v4 |
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results: |
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- task: |
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name: Sequence-to-sequence Language Modeling |
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type: text2text-generation |
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dataset: |
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name: nusatranslation_mt |
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type: nusatranslation_mt |
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config: nusatranslation_mt_btk_ind_source |
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split: test |
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args: nusatranslation_mt_btk_ind_source |
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metrics: |
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- name: Sacrebleu |
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type: sacrebleu |
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value: 35.8525 |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# bbc-to-ind-nmt-v4 |
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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 nusatranslation_mt dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.1847 |
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- Sacrebleu: 35.8525 |
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- Gen Len: 37.0695 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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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: 5e-05 |
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- train_batch_size: 32 |
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- eval_batch_size: 16 |
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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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- lr_scheduler_warmup_ratio: 0.1 |
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- num_epochs: 5 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Sacrebleu | Gen Len | |
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|:-------------:|:-----:|:----:|:---------------:|:---------:|:-------:| |
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| 5.2823 | 1.0 | 207 | 2.9374 | 27.4438 | 38.6905 | |
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| 2.1169 | 2.0 | 414 | 1.4203 | 31.985 | 38.087 | |
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| 1.2846 | 3.0 | 621 | 1.2194 | 34.8075 | 37.074 | |
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| 1.1158 | 4.0 | 828 | 1.1889 | 35.6204 | 36.911 | |
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| 1.0586 | 5.0 | 1035 | 1.1847 | 35.8525 | 37.0695 | |
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### Framework versions |
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- Transformers 4.41.2 |
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- Pytorch 2.3.0+cu121 |
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- Datasets 2.14.6 |
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- Tokenizers 0.19.1 |
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