metadata
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: bert-drug-review-to-condition
results: []
bert-drug-review-to-condition
This model is a fine-tuned version of bert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.4308
- Accuracy: 0.9209
- Precision: 0.9061
- Recall: 0.9209
- F1: 0.9106
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: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 113 | 1.1375 | 0.7747 | 0.7301 | 0.7747 | 0.7450 |
| No log | 2.0 | 226 | 0.5595 | 0.8854 | 0.8675 | 0.8854 | 0.8728 |
| No log | 3.0 | 339 | 0.4308 | 0.9209 | 0.9061 | 0.9209 | 0.9106 |
Framework versions
- Transformers 4.40.0
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1