---
library_name: transformers
license: apache-2.0
base_model: Qwen/Qwen2-1.5B
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: fine_tuned_roct_callback10
  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. -->

# fine_tuned_roct_callback10

This model is a fine-tuned version of [Qwen/Qwen2-1.5B](https://huggingface.co/Qwen/Qwen2-1.5B) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1842
- Accuracy: 0.9587

## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 0.6746        | 0.0309 | 100  | 1.1656          | 0.8788   |
| 0.5117        | 0.0617 | 200  | 0.3858          | 0.8788   |
| 0.3739        | 0.0926 | 300  | 1.0712          | 0.8788   |
| 0.4961        | 0.1235 | 400  | 0.3711          | 0.8274   |
| 0.2688        | 0.1543 | 500  | 0.1956          | 0.8965   |
| 0.2633        | 0.1852 | 600  | 0.2161          | 0.9      |
| 0.3015        | 0.2160 | 700  | 0.2866          | 0.9073   |
| 0.2304        | 0.2469 | 800  | 0.2740          | 0.9017   |
| 0.2114        | 0.2778 | 900  | 0.2462          | 0.9191   |
| 0.2807        | 0.3086 | 1000 | 0.2409          | 0.9125   |
| 0.2323        | 0.3395 | 1100 | 0.3777          | 0.8837   |
| 0.2514        | 0.3704 | 1200 | 0.2062          | 0.9281   |
| 0.2278        | 0.4012 | 1300 | 0.1762          | 0.9351   |
| 0.2099        | 0.4321 | 1400 | 0.1856          | 0.9247   |
| 0.2004        | 0.4630 | 1500 | 0.2237          | 0.9313   |
| 0.2177        | 0.4938 | 1600 | 0.1715          | 0.9313   |
| 0.3046        | 0.5247 | 1700 | 0.1545          | 0.9434   |
| 0.2179        | 0.5556 | 1800 | 0.1713          | 0.9472   |
| 0.1665        | 0.5864 | 1900 | 0.1142          | 0.9549   |
| 0.2066        | 0.6173 | 2000 | 0.1424          | 0.9563   |
| 0.1908        | 0.6481 | 2100 | 0.1284          | 0.9635   |
| 0.145         | 0.6790 | 2200 | 0.1550          | 0.9618   |
| 0.147         | 0.7099 | 2300 | 0.7114          | 0.8826   |
| 0.1634        | 0.7407 | 2400 | 0.1536          | 0.9625   |
| 0.1184        | 0.7716 | 2500 | 0.2507          | 0.9458   |
| 0.1771        | 0.8025 | 2600 | 0.1449          | 0.9583   |
| 0.1399        | 0.8333 | 2700 | 0.2384          | 0.9347   |
| 0.1709        | 0.8642 | 2800 | 0.1296          | 0.9542   |
| 0.1545        | 0.8951 | 2900 | 0.1842          | 0.9587   |


### Framework versions

- Transformers 4.49.0
- Pytorch 2.6.0+cu126
- Datasets 3.3.2
- Tokenizers 0.21.0