Instructions to use FiveC/za_zh_sc with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use FiveC/za_zh_sc with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("FiveC/za_zh_sc") model = AutoModelForSeq2SeqLM.from_pretrained("FiveC/za_zh_sc") - Notebooks
- Google Colab
- Kaggle
End of training
Browse files- README.md +5 -6
- model.safetensors +1 -1
README.md
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This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 2.
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- Sacrebleu:
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## Model description
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- seed: 42
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- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED 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:
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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 |
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|:-------------:|:-----:|:----:|:---------------:|:---------:|
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| 1.2767 | 3.0 | 927 | 2.6823 | 6.0325 |
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### Framework versions
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This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 2.9950
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- Sacrebleu: 4.4405
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## Model description
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- seed: 42
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- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED 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: 2
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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 |
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|:-------------:|:-----:|:----:|:---------------:|:---------:|
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| 2.5108 | 1.0 | 309 | 2.9950 | 4.4405 |
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| 1.7678 | 2.0 | 618 | 2.8134 | 3.4752 |
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### Framework versions
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model.safetensors
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