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Medical-Urgency-Classifier-ClinicalBERT
Anjaly1510/Medical-Urgency-Classifier-ClinicalBERT
Medical-Urgency-Classifier-ClinicalBERT is a text classification model from Anjaly1510. Use it when you need a label for a piece of text. It is set up for transformers.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Text Classification
- Library
- transformers
- Model type
- distilbert
- Access
- Public
- Created
- Aug 30, 2026
- Updated
- Aug 30, 2026
- SHA
- 21d5aea8
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Model card
From the Hugging Face model README
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
Medical-Urgency-Classifier-ClinicalBERT
This model is a fine-tuned version of medicalai/ClinicalBERT on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.6721
- Accuracy: 1.0
- F1: 1.0
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|---|---|---|---|---|---|
| No log | 1.0 | 2 | 0.6877 | 0.5 | 0.0 |
| No log | 2.0 | 4 | 0.6816 | 0.75 | 0.6667 |
| No log | 3.0 | 6 | 0.6764 | 0.75 | 0.6667 |
| No log | 4.0 | 8 | 0.6721 | 1.0 | 1.0 |
| No log | 5.0 | 10 | 0.6699 | 1.0 | 1.0 |
Framework versions
- Transformers 5.15.1
- Pytorch 2.11.0+cu128
- Datasets 4.0.0
- Tokenizers 0.22.2
- Task
- Text Classification
- Library
- transformers
- Type
- distilbert
- Created
- Aug 30, 2026
- Updated
- Aug 30, 2026