Instructions to use npark95/finetuned_ClinicalLongformer_CAT_020425 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use npark95/finetuned_ClinicalLongformer_CAT_020425 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="npark95/finetuned_ClinicalLongformer_CAT_020425")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("npark95/finetuned_ClinicalLongformer_CAT_020425") model = AutoModelForSequenceClassification.from_pretrained("npark95/finetuned_ClinicalLongformer_CAT_020425") - Notebooks
- Google Colab
- Kaggle
End of training
Browse files
README.md
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This model is a fine-tuned version of [yikuan8/Clinical-Longformer](https://huggingface.co/yikuan8/Clinical-Longformer) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.
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- F1: 0.
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## Model description
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| Training Loss | Epoch | Step | Validation Loss | F1 |
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### Framework versions
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This model is a fine-tuned version of [yikuan8/Clinical-Longformer](https://huggingface.co/yikuan8/Clinical-Longformer) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.0433
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- F1: 0.9939
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## Model description
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| Training Loss | Epoch | Step | Validation Loss | F1 |
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| 0.0142 | 1.0 | 424 | 0.0427 | 0.9912 |
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| 0.0072 | 2.0 | 848 | 0.0433 | 0.9939 |
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### Framework versions
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