Instructions to use NIRVLab/ViEde with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NIRVLab/ViEde with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("NIRVLab/ViEde") model = AutoModelForSeq2SeqLM.from_pretrained("NIRVLab/ViEde") - Notebooks
- Google Colab
- Kaggle
End of training
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README.md
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This model is a fine-tuned version of [NIRVLab/bartede](https://huggingface.co/NIRVLab/bartede) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.
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- Bleu:
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- Chrf++:
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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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- mixed_precision_training: Native AMP
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Bleu | Chrf++ |
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### Framework versions
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This model is a fine-tuned version of [NIRVLab/bartede](https://huggingface.co/NIRVLab/bartede) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.4809
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- Bleu: 22.833
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- Chrf++: 46.2491
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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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- lr_scheduler_warmup_ratio: 0.06
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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 | Bleu | Chrf++ |
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|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|
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| 0.273 | 1.0 | 2080 | 0.4809 | 22.833 | 46.2491 |
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| 0.1331 | 2.0 | 4160 | 0.5284 | 24.6028 | 48.483 |
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| 0.0964 | 3.0 | 6240 | 0.5543 | 25.6692 | 49.2306 |
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### Framework versions
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model.safetensors
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