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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: 1.0318
- Precision: 0.6083
- Recall: 0.6344
- F1: 0.6210
- Accuracy: 0.7635
## 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 | 1.6731 | 0.3451 | 0.3549 | 0.3499 | 0.6003 |
| No log | 2.0 | 126 | 1.2481 | 0.4832 | 0.5248 | 0.5032 | 0.6912 |
| No log | 3.0 | 189 | 1.0959 | 0.5280 | 0.5703 | 0.5483 | 0.7198 |
| No log | 4.0 | 252 | 1.0258 | 0.5577 | 0.5878 | 0.5723 | 0.7330 |
| No log | 5.0 | 315 | 0.9761 | 0.5788 | 0.6038 | 0.5910 | 0.7433 |
| No log | 6.0 | 378 | 0.9461 | 0.5909 | 0.6068 | 0.5988 | 0.7478 |
| No log | 7.0 | 441 | 0.9456 | 0.5918 | 0.6143 | 0.6029 | 0.7516 |
| 1.1189 | 8.0 | 504 | 0.9396 | 0.5991 | 0.6164 | 0.6076 | 0.7562 |
| 1.1189 | 9.0 | 567 | 0.9594 | 0.6020 | 0.6252 | 0.6134 | 0.7569 |
| 1.1189 | 10.0 | 630 | 0.9742 | 0.6005 | 0.6203 | 0.6102 | 0.7555 |
| 1.1189 | 11.0 | 693 | 0.9700 | 0.6063 | 0.6256 | 0.6158 | 0.7597 |
| 1.1189 | 12.0 | 756 | 0.9772 | 0.5999 | 0.6246 | 0.6120 | 0.7582 |
| 1.1189 | 13.0 | 819 | 0.9890 | 0.6023 | 0.6254 | 0.6137 | 0.7593 |
| 1.1189 | 14.0 | 882 | 1.0011 | 0.6077 | 0.6332 | 0.6202 | 0.7631 |
| 1.1189 | 15.0 | 945 | 1.0096 | 0.6057 | 0.6305 | 0.6178 | 0.7613 |
| 0.4346 | 16.0 | 1008 | 1.0167 | 0.6112 | 0.6281 | 0.6195 | 0.7625 |
| 0.4346 | 17.0 | 1071 | 1.0227 | 0.6126 | 0.6357 | 0.6240 | 0.7651 |
| 0.4346 | 18.0 | 1134 | 1.0290 | 0.6072 | 0.6357 | 0.6212 | 0.7635 |
| 0.4346 | 19.0 | 1197 | 1.0288 | 0.6094 | 0.6363 | 0.6226 | 0.7643 |
| 0.4346 | 20.0 | 1260 | 1.0318 | 0.6083 | 0.6344 | 0.6210 | 0.7635 |
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
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