|
--- |
|
license: apache-2.0 |
|
tags: |
|
- generated_from_trainer |
|
metrics: |
|
- precision |
|
- recall |
|
- f1 |
|
- accuracy |
|
model-index: |
|
- name: bert-medical-ner |
|
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. --> |
|
|
|
# bert-medical-ner |
|
|
|
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the None dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.4386 |
|
- Precision: 0.7582 |
|
- Recall: 0.7089 |
|
- F1: 0.7327 |
|
- Accuracy: 0.9434 |
|
|
|
## 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: 64 |
|
- eval_batch_size: 64 |
|
- seed: 42 |
|
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
|
- lr_scheduler_type: linear |
|
- num_epochs: 15 |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
|
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
|
| No log | 1.0 | 63 | 0.4577 | 0.8113 | 0.6343 | 0.7119 | 0.9397 | |
|
| No log | 2.0 | 126 | 0.4098 | 0.7941 | 0.6563 | 0.7187 | 0.9419 | |
|
| No log | 3.0 | 189 | 0.3993 | 0.7369 | 0.6744 | 0.7043 | 0.9389 | |
|
| No log | 4.0 | 252 | 0.4033 | 0.7312 | 0.7089 | 0.7199 | 0.9418 | |
|
| No log | 5.0 | 315 | 0.4329 | 0.7509 | 0.6919 | 0.7202 | 0.9416 | |
|
| No log | 6.0 | 378 | 0.4343 | 0.7545 | 0.6829 | 0.7169 | 0.9420 | |
|
| No log | 7.0 | 441 | 0.4348 | 0.7168 | 0.7140 | 0.7154 | 0.9402 | |
|
| 0.0142 | 8.0 | 504 | 0.4362 | 0.7285 | 0.7055 | 0.7168 | 0.9399 | |
|
| 0.0142 | 9.0 | 567 | 0.4420 | 0.7573 | 0.7072 | 0.7314 | 0.9436 | |
|
| 0.0142 | 10.0 | 630 | 0.4371 | 0.7452 | 0.7027 | 0.7233 | 0.9423 | |
|
| 0.0142 | 11.0 | 693 | 0.4400 | 0.7648 | 0.6947 | 0.7281 | 0.9429 | |
|
| 0.0142 | 12.0 | 756 | 0.4346 | 0.7556 | 0.7027 | 0.7282 | 0.9422 | |
|
| 0.0142 | 13.0 | 819 | 0.4382 | 0.7504 | 0.7089 | 0.7291 | 0.9428 | |
|
| 0.0142 | 14.0 | 882 | 0.4368 | 0.7536 | 0.7123 | 0.7323 | 0.9434 | |
|
| 0.0142 | 15.0 | 945 | 0.4386 | 0.7582 | 0.7089 | 0.7327 | 0.9434 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.29.2 |
|
- Pytorch 2.0.1+cu117 |
|
- Datasets 2.12.0 |
|
- Tokenizers 0.13.3 |
|
|
