---
library_name: transformers
license: apache-2.0
base_model: Qwen/Qwen2-1.5B
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: fine_tuned_tldr_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_tldr_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: 0.1451
- Accuracy: 0.9682

## 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 |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 0.8181        | 0.0393 | 100  | 0.2443          | 0.9050   |
| 0.4998        | 0.0785 | 200  | 0.2800          | 0.8754   |
| 0.4488        | 0.1178 | 300  | 0.5770          | 0.8710   |
| 0.3996        | 0.1570 | 400  | 0.1956          | 0.9139   |
| 0.298         | 0.1963 | 500  | 0.3754          | 0.9307   |
| 0.2918        | 0.2356 | 600  | 0.7744          | 0.8905   |
| 0.2906        | 0.2748 | 700  | 0.2349          | 0.9214   |
| 0.2113        | 0.3141 | 800  | 0.2182          | 0.9443   |
| 0.2552        | 0.3534 | 900  | 0.1959          | 0.9501   |
| 0.227         | 0.3926 | 1000 | 0.1768          | 0.9496   |
| 0.2203        | 0.4319 | 1100 | 0.1711          | 0.9439   |
| 0.2212        | 0.4711 | 1200 | 0.1652          | 0.9585   |
| 0.2153        | 0.5104 | 1300 | 0.1695          | 0.9567   |
| 0.1975        | 0.5497 | 1400 | 0.1776          | 0.9536   |
| 0.1866        | 0.5889 | 1500 | 0.1516          | 0.9602   |
| 0.2209        | 0.6282 | 1600 | 0.1139          | 0.9691   |
| 0.1788        | 0.6675 | 1700 | 0.1995          | 0.9563   |
| 0.1808        | 0.7067 | 1800 | 0.1857          | 0.9554   |
| 0.2401        | 0.7460 | 1900 | 0.1397          | 0.9686   |
| 0.1602        | 0.7852 | 2000 | 0.1974          | 0.9620   |
| 0.2206        | 0.8245 | 2100 | 0.1392          | 0.9633   |
| 0.1609        | 0.8638 | 2200 | 0.1904          | 0.9620   |
| 0.2108        | 0.9030 | 2300 | 0.1774          | 0.9611   |
| 0.1408        | 0.9423 | 2400 | 0.1598          | 0.9669   |
| 0.1696        | 0.9815 | 2500 | 0.1694          | 0.9660   |
| 0.1231        | 1.0208 | 2600 | 0.1451          | 0.9682   |


### Framework versions

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