---
library_name: transformers
base_model: microsoft/codebert-base
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: model_output
  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. -->

# model_output

This model is a fine-tuned version of [microsoft/codebert-base](https://huggingface.co/microsoft/codebert-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0368
- F1: 0.9892

## 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: 16
- eval_batch_size: 32
- 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: 100
- num_epochs: 5
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | F1     |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log        | 1.0   | 301  | 0.1471          | 0.9576 |
| 0.5648        | 2.0   | 602  | 0.0658          | 0.9822 |
| 0.5648        | 3.0   | 903  | 0.0901          | 0.9784 |
| 0.0460        | 4.0   | 1204 | 0.0639          | 0.9829 |
| 0.0282        | 5.0   | 1505 | 0.0637          | 0.9829 |


### Framework versions

- Transformers 5.0.0
- Pytorch 2.12.0+cu130
- Datasets 4.8.5
- Tokenizers 0.22.2