metadata
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: starclass_bert
results: []
starclass_bert
This model is a fine-tuned version of bert-base-uncased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.1168
- Accuracy: 0.9683
- Precision: 0.9718
- Recall: 0.9683
- F1: 0.9683
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: 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: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|---|---|---|---|---|---|---|---|
| 1.2759 | 1.0 | 16 | 0.7935 | 0.9048 | 0.9073 | 0.9048 | 0.9043 |
| 0.5557 | 2.0 | 32 | 0.3849 | 0.9048 | 0.9133 | 0.9048 | 0.9029 |
| 0.3352 | 3.0 | 48 | 0.1927 | 0.9365 | 0.9418 | 0.9365 | 0.9372 |
| 0.1037 | 4.0 | 64 | 0.1253 | 0.9683 | 0.9718 | 0.9683 | 0.9683 |
| 0.0465 | 5.0 | 80 | 0.1168 | 0.9683 | 0.9718 | 0.9683 | 0.9683 |
Framework versions
- Transformers 4.36.2
- Pytorch 2.5.1+cu124
- Datasets 3.1.0
- Tokenizers 0.15.2