metadata
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-medical-ner
results: []
bert-medical-ner
This model is a fine-tuned version of bert-base-cased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.1200
- Precision: 0.6085
- Recall: 0.6398
- F1: 0.6237
- Accuracy: 0.7651
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: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 63 | 1.0596 | 0.6022 | 0.6300 | 0.6158 | 0.7625 |
| No log | 2.0 | 126 | 1.0834 | 0.5976 | 0.6414 | 0.6187 | 0.7631 |
| No log | 3.0 | 189 | 1.1044 | 0.6044 | 0.6360 | 0.6198 | 0.7628 |
| No log | 4.0 | 252 | 1.1139 | 0.6041 | 0.6364 | 0.6199 | 0.7628 |
| No log | 5.0 | 315 | 1.1200 | 0.6085 | 0.6398 | 0.6237 | 0.7651 |
Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3