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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
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