metadata
library_name: transformers
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: CodeDetect-BERT-T2
results: []
CodeDetect-BERT-T2
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: 2.2616
- Accuracy: 0.9178
- F1: 0.4680
- Precision: 0.4960
- Recall: 0.4600
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: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 5
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|---|---|---|---|---|---|---|---|
| 2.1731 | 1.2798 | 5000 | 2.2130 | 0.9197 | 0.3964 | 0.4880 | 0.3890 |
| 1.8504 | 2.5595 | 10000 | 2.1255 | 0.9216 | 0.4423 | 0.5119 | 0.4239 |
| 1.5734 | 3.8393 | 15000 | 2.1121 | 0.9226 | 0.4766 | 0.5175 | 0.4584 |
| 1.4124 | 5.0 | 19535 | 2.2616 | 0.9178 | 0.4680 | 0.4960 | 0.4600 |
Framework versions
- Transformers 5.13.1
- Pytorch 2.11.0+cu128
- Datasets 4.0.0
- Tokenizers 0.22.2