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---
library_name: transformers
base_model: microsoft/codebert-base
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: codebert-java-inconsistency
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# codebert-java-inconsistency
This model is a fine-tuned version of [microsoft/codebert-base](https://huggingface.co/microsoft/codebert-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3543
- Accuracy: 0.9167
- F1: 0.9183
- Precision: 0.9235
- Recall: 0.9167
## 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: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- 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: cosine
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:-------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 1.4625 | 3.1290 | 50 | 0.8954 | 0.7531 | 0.7554 | 0.7765 | 0.7531 |
| 0.5834 | 6.2581 | 100 | 0.5559 | 0.8189 | 0.8241 | 0.8483 | 0.8189 |
| 0.2858 | 9.3871 | 150 | 0.4046 | 0.8930 | 0.8945 | 0.8995 | 0.8930 |
| 0.1624 | 12.5161 | 200 | 0.4461 | 0.8642 | 0.8661 | 0.8750 | 0.8642 |
| 0.1084 | 15.6452 | 250 | 0.4012 | 0.9012 | 0.9038 | 0.9123 | 0.9012 |
| 0.074 | 18.7742 | 300 | 0.4689 | 0.8765 | 0.8817 | 0.8972 | 0.8765 |
| 0.0574 | 21.9032 | 350 | 0.4885 | 0.8807 | 0.8845 | 0.8970 | 0.8807 |
| 0.0452 | 25.0 | 400 | 0.4900 | 0.8848 | 0.8888 | 0.9011 | 0.8848 |
| 0.0396 | 28.1290 | 450 | 0.4896 | 0.8765 | 0.8805 | 0.8934 | 0.8765 |
### Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.5.0
- Tokenizers 0.21.1
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