metadata
library_name: transformers
license: apache-2.0
base_model: bert-base-cased
tags:
- generated_from_trainer
datasets:
- health_fact
metrics:
- accuracy
model-index:
- name: bert-finetuned-health-fact
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: health_fact
type: health_fact
config: default
split: validation
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.6680395387149918
bert-finetuned-health-fact
This model is a fine-tuned version of bert-base-cased on the health_fact dataset. It achieves the following results on the evaluation set:
- Loss: 0.8504
- Accuracy: 0.6680
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: 8
- 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
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.8064 | 1.0 | 1226 | 0.7489 | 0.6779 |
| 0.6966 | 2.0 | 2452 | 0.7398 | 0.6771 |
| 0.5055 | 3.0 | 3678 | 0.8504 | 0.6680 |
Framework versions
- Transformers 4.48.3
- Pytorch 2.5.1+cu124
- Datasets 3.3.0
- Tokenizers 0.21.0