metadata
base_model: samrawal/bert-base-uncased_clinical-ner
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-finetuned-ner
results: []
bert-finetuned-ner
This model is a fine-tuned version of samrawal/bert-base-uncased_clinical-ner on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.4144
- Precision: 0.5375
- Recall: 0.6260
- F1: 0.5784
- Accuracy: 0.8515
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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 188 | 0.4339 | 0.5159 | 0.5879 | 0.5495 | 0.8408 |
| No log | 2.0 | 376 | 0.4094 | 0.5344 | 0.6332 | 0.5796 | 0.8501 |
| 0.4154 | 3.0 | 564 | 0.4144 | 0.5375 | 0.6260 | 0.5784 | 0.8515 |
Framework versions
- Transformers 4.34.0
- Pytorch 1.12.1
- Datasets 2.14.5
- Tokenizers 0.14.1