metadata
library_name: transformers
language:
- en
license: apache-2.0
base_model: google-bert/bert-base-uncased
tags:
- medical-ner
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: bert-finetuned-for-medical-ner
results: []
bert-finetuned-for-medical-ner
This model is a fine-tuned version of google-bert/bert-base-uncased on the PLODv2-filtered dataset. It achieves the following results on the evaluation set:
- Loss: 0.1344
- Accuracy: 0.9402
- Precision: 0.8342
- Recall: 0.9029
- F1: 0.8672
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: 2
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- num_epochs: 1
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|---|---|---|---|---|---|---|---|
| 0.2039 | 0.1420 | 2000 | 0.1873 | 0.9244 | 0.7841 | 0.8773 | 0.8281 |
| 0.1758 | 0.2841 | 4000 | 0.1613 | 0.9302 | 0.8028 | 0.8920 | 0.8451 |
| 0.1585 | 0.4261 | 6000 | 0.1499 | 0.9343 | 0.8309 | 0.8721 | 0.8510 |
| 0.1613 | 0.5681 | 8000 | 0.1460 | 0.9358 | 0.8400 | 0.8655 | 0.8526 |
| 0.1526 | 0.7101 | 10000 | 0.1402 | 0.9382 | 0.8329 | 0.8930 | 0.8619 |
| 0.1502 | 0.8522 | 12000 | 0.1357 | 0.9394 | 0.8346 | 0.8985 | 0.8654 |
| 0.1486 | 0.9942 | 14000 | 0.1344 | 0.9402 | 0.8342 | 0.9029 | 0.8672 |
Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1