Transformers
TensorBoard
Safetensors
mbart
text2text-generation
simplification
Generated from Trainer
Instructions to use Cathaysa/mbart-neutralization with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Cathaysa/mbart-neutralization with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Cathaysa/mbart-neutralization") model = AutoModelForSeq2SeqLM.from_pretrained("Cathaysa/mbart-neutralization", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Training complete
Browse files- README.md +5 -5
- model.safetensors +1 -1
README.md
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This model is a fine-tuned version of [facebook/mbart-large-50](https://huggingface.co/facebook/mbart-large-50) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss:
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- Bleu:
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- Gen Len:
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## Model description
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| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
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|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|
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| No log | 1.0 |
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### Framework versions
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This model is a fine-tuned version of [facebook/mbart-large-50](https://huggingface.co/facebook/mbart-large-50) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.0181
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- Bleu: 98.7341
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- Gen Len: 18.4896
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## Model description
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| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
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|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|
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| No log | 1.0 | 440 | 0.0307 | 91.2911 | 18.25 |
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| 0.2343 | 2.0 | 880 | 0.0181 | 98.7341 | 18.4896 |
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### Framework versions
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model.safetensors
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