Downloads · 30 days
9
25% of all-time downloads
clinicalAgitationTextClassificationModelV1
Dinithi/clinicalAgitationTextClassificationModelV1
clinicalAgitationTextClassificationModelV1 is a text classification model from Dinithi. Use it when you need a label for a piece of text. It is set up for transformers. The card lists the license as mit.
should probably proofread and complete it, then remove this comment. --
All-time downloads
36
Public
Repo size
867 MB
Likes
0
Public
Repo makeup
Click a slice to open those files.
.bin433 MB · 100%
At a glance
- Task
- Text Classification
- Library
- transformers
- License
- mit
- Model type
- bert
- Access
- Public
- Created
- Sep 3, 2022
- Updated
- Sep 3, 2022
- SHA
- f67db9a9
Try a prompt
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. -->
clinicalAgitationTextClassificationModelV1
This model is a fine-tuned version of emilyalsentzer/Bio_ClinicalBERT on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.7664
- Accuracy: 0.86
- Precision: 0.8986
- Recall: 0.8986
- F1: 0.8986
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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|---|---|---|---|---|---|---|---|
| 0.6593 | 1.0 | 50 | 0.5648 | 0.74 | 0.8525 | 0.7536 | 0.8 |
| 0.5082 | 2.0 | 100 | 0.4731 | 0.76 | 0.8814 | 0.7536 | 0.8125 |
| 0.3744 | 3.0 | 150 | 0.4516 | 0.84 | 0.8732 | 0.8986 | 0.8857 |
| 0.2618 | 4.0 | 200 | 0.5435 | 0.83 | 0.8514 | 0.9130 | 0.8811 |
| 0.2145 | 5.0 | 250 | 0.4750 | 0.85 | 0.95 | 0.8261 | 0.8837 |
| 0.1426 | 6.0 | 300 | 0.5433 | 0.86 | 0.8986 | 0.8986 | 0.8986 |
| 0.0971 | 7.0 | 350 | 0.5997 | 0.85 | 0.9355 | 0.8406 | 0.8855 |
| 0.0741 | 8.0 | 400 | 0.7534 | 0.86 | 0.8986 | 0.8986 | 0.8986 |
| 0.0513 | 9.0 | 450 | 0.8293 | 0.83 | 0.8611 | 0.8986 | 0.8794 |
| 0.0456 | 10.0 | 500 | 0.7664 | 0.86 | 0.8986 | 0.8986 | 0.8986 |
Framework versions
- Transformers 4.21.2
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
- Task
- Text Classification
- Library
- transformers
- Type
- bert
- License
- mit
- Created
- Sep 3, 2022
- Updated
- Sep 3, 2022