---
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