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Bio_ClinicalBERT_fold_6_ternary_v1
elopezlopez/Bio_ClinicalBERT_fold_6_ternary_v1
Bio_ClinicalBERT_fold_6_ternary_v1 is a text classification model from elopezlopez. 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. --
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At a glance
- Task
- Text Classification
- Library
- transformers
- License
- mit
- Model type
- bert
- Access
- Public
- Created
- Aug 10, 2022
- Updated
- Aug 10, 2022
- SHA
- 4b7612a6
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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. -->
Bio_ClinicalBERT_fold_6_ternary_v1
This model is a fine-tuned version of emilyalsentzer/Bio_ClinicalBERT on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.7302
- F1: 0.8128
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: 25
Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|---|---|---|---|---|
| No log | 1.0 | 292 | 0.5359 | 0.7833 |
| 0.5585 | 2.0 | 584 | 0.5376 | 0.8026 |
| 0.5585 | 3.0 | 876 | 0.6117 | 0.8038 |
| 0.2314 | 4.0 | 1168 | 0.8036 | 0.7974 |
| 0.2314 | 5.0 | 1460 | 0.9467 | 0.8179 |
| 0.1093 | 6.0 | 1752 | 1.2957 | 0.7923 |
| 0.0384 | 7.0 | 2044 | 1.3423 | 0.8026 |
| 0.0384 | 8.0 | 2336 | 1.2644 | 0.8218 |
| 0.021 | 9.0 | 2628 | 1.3093 | 0.8231 |
| 0.021 | 10.0 | 2920 | 1.3282 | 0.8179 |
| 0.0129 | 11.0 | 3212 | 1.3853 | 0.8295 |
| 0.0078 | 12.0 | 3504 | 1.4705 | 0.8154 |
| 0.0078 | 13.0 | 3796 | 1.5063 | 0.8167 |
| 0.0064 | 14.0 | 4088 | 1.5293 | 0.8179 |
| 0.0064 | 15.0 | 4380 | 1.6303 | 0.8128 |
| 0.0085 | 16.0 | 4672 | 1.5945 | 0.8115 |
| 0.0085 | 17.0 | 4964 | 1.6899 | 0.8103 |
| 0.0056 | 18.0 | 5256 | 1.6952 | 0.8064 |
| 0.0055 | 19.0 | 5548 | 1.7550 | 0.7936 |
| 0.0055 | 20.0 | 5840 | 1.6779 | 0.8141 |
| 0.003 | 21.0 | 6132 | 1.7064 | 0.8128 |
| 0.003 | 22.0 | 6424 | 1.7192 | 0.8154 |
| 0.0013 | 23.0 | 6716 | 1.8188 | 0.7974 |
| 0.0014 | 24.0 | 7008 | 1.7273 | 0.8128 |
| 0.0014 | 25.0 | 7300 | 1.7302 | 0.8128 |
Framework versions
- Transformers 4.21.1
- Pytorch 1.12.0+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
- Task
- Text Classification
- Library
- transformers
- Type
- bert
- License
- mit
- Created
- Aug 10, 2022
- Updated
- Aug 10, 2022