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---
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: 1.0409
- Precision: 0.6097
- Recall: 0.6323
- F1: 0.6208
- Accuracy: 0.7607
## 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: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 63 | 1.6300 | 0.3434 | 0.3838 | 0.3625 | 0.6077 |
| No log | 2.0 | 126 | 1.2289 | 0.4831 | 0.5207 | 0.5012 | 0.6893 |
| No log | 3.0 | 189 | 1.0878 | 0.5261 | 0.5762 | 0.5500 | 0.7197 |
| No log | 4.0 | 252 | 1.0253 | 0.5541 | 0.5914 | 0.5721 | 0.7328 |
| No log | 5.0 | 315 | 0.9738 | 0.5689 | 0.6040 | 0.5859 | 0.7416 |
| No log | 6.0 | 378 | 0.9498 | 0.5828 | 0.6094 | 0.5958 | 0.7472 |
| No log | 7.0 | 441 | 0.9532 | 0.5954 | 0.6126 | 0.6039 | 0.7509 |
| 1.1083 | 8.0 | 504 | 0.9515 | 0.5994 | 0.6166 | 0.6079 | 0.7530 |
| 1.1083 | 9.0 | 567 | 0.9572 | 0.6010 | 0.6212 | 0.6109 | 0.7547 |
| 1.1083 | 10.0 | 630 | 0.9690 | 0.5986 | 0.6162 | 0.6072 | 0.7539 |
| 1.1083 | 11.0 | 693 | 0.9798 | 0.5953 | 0.6232 | 0.6089 | 0.7532 |
| 1.1083 | 12.0 | 756 | 0.9813 | 0.5986 | 0.6185 | 0.6084 | 0.7546 |
| 1.1083 | 13.0 | 819 | 0.9984 | 0.5979 | 0.6182 | 0.6079 | 0.7539 |
| 1.1083 | 14.0 | 882 | 1.0111 | 0.6026 | 0.6226 | 0.6124 | 0.7557 |
| 1.1083 | 15.0 | 945 | 1.0140 | 0.6050 | 0.6262 | 0.6155 | 0.7572 |
| 0.4329 | 16.0 | 1008 | 1.0252 | 0.6112 | 0.6210 | 0.6160 | 0.7580 |
| 0.4329 | 17.0 | 1071 | 1.0312 | 0.6090 | 0.6288 | 0.6187 | 0.7602 |
| 0.4329 | 18.0 | 1134 | 1.0368 | 0.6059 | 0.6314 | 0.6184 | 0.7597 |
| 0.4329 | 19.0 | 1197 | 1.0395 | 0.6095 | 0.6299 | 0.6196 | 0.7599 |
| 0.4329 | 20.0 | 1260 | 1.0409 | 0.6097 | 0.6323 | 0.6208 | 0.7607 |
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
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