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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-v2 |
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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: 37.5332 |
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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-v2 |
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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: 1.1380 |
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- Sacrebleu: 37.5332 |
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- Gen Len: 37.17 |
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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: 8 |
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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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| 4.6139 | 1.0 | 825 | 1.2915 | 32.7445 | 37.5685 | |
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| 1.1761 | 2.0 | 1650 | 1.1701 | 35.9991 | 37.3645 | |
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| 1.0383 | 3.0 | 2475 | 1.1361 | 36.9321 | 37.035 | |
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| 0.9094 | 4.0 | 3300 | 1.1333 | 37.4774 | 37.039 | |
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| 0.8158 | 5.0 | 4125 | 1.1380 | 37.5332 | 37.17 | |
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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 2.13.1 |
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- Tokenizers 0.19.1 |
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