metadata
library_name: transformers
base_model: microsoft/codebert-base
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: results
results: []
results
This model is a fine-tuned version of microsoft/codebert-base on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.3489
- Accuracy: 0.8556
- F1: 0.9020
- Precision: 0.8502
- Recall: 0.9606
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: 6
- eval_batch_size: 6
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 8
- total_train_batch_size: 192
- total_eval_batch_size: 24
- 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
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 7
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 16 | 0.5511 | 0.6921 | 0.8180 | 0.6921 | 1.0 |
| No log | 2.0 | 32 | 0.4566 | 0.7793 | 0.8439 | 0.8264 | 0.8622 |
| No log | 3.0 | 48 | 0.4230 | 0.8093 | 0.8611 | 0.868 | 0.8543 |
| No log | 4.0 | 64 | 0.3796 | 0.8610 | 0.9061 | 0.8512 | 0.9685 |
| No log | 5.0 | 80 | 0.3521 | 0.8583 | 0.9019 | 0.8659 | 0.9409 |
| No log | 6.0 | 96 | 0.3509 | 0.8556 | 0.8990 | 0.8708 | 0.9291 |
| No log | 7.0 | 112 | 0.3489 | 0.8556 | 0.9020 | 0.8502 | 0.9606 |
Framework versions
- Transformers 4.53.2
- Pytorch 2.6.0+cu124
- Datasets 4.0.0
- Tokenizers 0.21.2