---
library_name: transformers
base_model: microsoft/graphcodebert-base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: my-graphcodebert-base-RQ3
  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. -->

# my-graphcodebert-base-RQ3

This model is a fine-tuned version of [microsoft/graphcodebert-base](https://huggingface.co/microsoft/graphcodebert-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4197
- Accuracy: 0.9513
- F1 Macro: 0.6895
- F1 Weighted: 0.9511

## 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: 1e-05
- train_batch_size: 64
- eval_batch_size: 64
- 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: 0.1
- num_epochs: 10
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Macro | F1 Weighted |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:-----------:|
| 0.3556        | 1.0   | 539  | 0.3573          | 0.9436   | 0.3387   | 0.9352      |
| 0.3178        | 2.0   | 1078 | 0.3333          | 0.9459   | 0.3941   | 0.9413      |
| 0.2589        | 3.0   | 1617 | 0.2949          | 0.9534   | 0.6361   | 0.9518      |
| 0.2113        | 4.0   | 2156 | 0.3036          | 0.9547   | 0.6780   | 0.9538      |
| 0.1632        | 5.0   | 2695 | 0.3192          | 0.9524   | 0.6887   | 0.9523      |
| 0.1509        | 6.0   | 3234 | 0.3477          | 0.9517   | 0.6907   | 0.9518      |
| 0.1121        | 7.0   | 3773 | 0.3664          | 0.9522   | 0.6894   | 0.9523      |
| 0.1027        | 8.0   | 4312 | 0.4085          | 0.9513   | 0.6789   | 0.9517      |
| 0.0791        | 9.0   | 4851 | 0.4144          | 0.9513   | 0.6771   | 0.9512      |
| 0.0916        | 10.0  | 5390 | 0.4197          | 0.9513   | 0.6895   | 0.9511      |


### Framework versions

- Transformers 5.0.0
- Pytorch 2.10.0+cu128
- Datasets 4.8.3
- Tokenizers 0.22.2