|
--- |
|
library_name: transformers |
|
license: mit |
|
base_model: emilyalsentzer/Bio_ClinicalBERT |
|
tags: |
|
- generated_from_trainer |
|
metrics: |
|
- f1 |
|
model-index: |
|
- name: finetuned_BioClinicalBERT |
|
results: [] |
|
--- |
|
|
|
<!-- 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. --> |
|
|
|
# finetuned_BioClinicalBERT |
|
|
|
This model is a fine-tuned version of [emilyalsentzer/Bio_ClinicalBERT](https://huggingface.co/emilyalsentzer/Bio_ClinicalBERT) on the None dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.4147 |
|
- F1: 0.9143 |
|
|
|
## 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: 8 |
|
- eval_batch_size: 8 |
|
- seed: 42 |
|
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
|
- lr_scheduler_type: linear |
|
- num_epochs: 30 |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | F1 | |
|
|:-------------:|:-----:|:----:|:---------------:|:------:| |
|
| 0.5879 | 1.0 | 24 | 0.4997 | 0.8767 | |
|
| 0.52 | 2.0 | 48 | 0.4386 | 0.8889 | |
|
| 0.3865 | 3.0 | 72 | 0.4487 | 0.7797 | |
|
| 0.4456 | 4.0 | 96 | 0.5242 | 0.8 | |
|
| 0.2789 | 5.0 | 120 | 0.4147 | 0.9143 | |
|
| 0.2035 | 6.0 | 144 | 0.5301 | 0.8710 | |
|
| 0.124 | 7.0 | 168 | 0.6356 | 0.8923 | |
|
| 0.1422 | 8.0 | 192 | 0.9593 | 0.8308 | |
|
| 0.123 | 9.0 | 216 | 2.0378 | 0.5833 | |
|
| 0.0296 | 10.0 | 240 | 1.1534 | 0.8197 | |
|
| 0.0047 | 11.0 | 264 | 0.6878 | 0.9254 | |
|
| 0.0739 | 12.0 | 288 | 1.2483 | 0.8387 | |
|
| 0.0016 | 13.0 | 312 | 1.9790 | 0.7143 | |
|
| 0.0017 | 14.0 | 336 | 0.9967 | 0.8615 | |
|
| 0.0015 | 15.0 | 360 | 2.0558 | 0.7143 | |
|
| 0.0008 | 16.0 | 384 | 1.2408 | 0.8696 | |
|
| 0.0006 | 17.0 | 408 | 1.6653 | 0.8 | |
|
| 0.0003 | 18.0 | 432 | 1.1586 | 0.875 | |
|
| 0.0002 | 19.0 | 456 | 1.1180 | 0.8955 | |
|
| 0.0002 | 20.0 | 480 | 1.1362 | 0.8955 | |
|
| 0.0002 | 21.0 | 504 | 1.1670 | 0.8955 | |
|
| 0.0002 | 22.0 | 528 | 1.1915 | 0.8955 | |
|
| 0.0002 | 23.0 | 552 | 1.2127 | 0.8955 | |
|
| 0.0002 | 24.0 | 576 | 1.2162 | 0.8955 | |
|
| 0.0002 | 25.0 | 600 | 1.2291 | 0.8955 | |
|
| 0.0002 | 26.0 | 624 | 1.2454 | 0.8955 | |
|
| 0.0002 | 27.0 | 648 | 1.2608 | 0.8955 | |
|
| 0.0002 | 28.0 | 672 | 1.2348 | 0.8923 | |
|
| 0.0002 | 29.0 | 696 | 1.2444 | 0.8923 | |
|
| 0.0001 | 30.0 | 720 | 1.2437 | 0.8923 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.48.0 |
|
- Pytorch 2.5.1+cu124 |
|
- Datasets 3.2.0 |
|
- Tokenizers 0.21.0 |
|
|
