---
library_name: transformers
license: apache-2.0
base_model: Qwen/Qwen2-1.5B
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: fine_tuned_xsum_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_xsum_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: 0.1252
- Accuracy: 0.9675

## 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 |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 0.8057        | 0.0289 | 100  | 0.6011          | 0.7324   |
| 0.6239        | 0.0578 | 200  | 0.5307          | 0.8254   |
| 0.4184        | 0.0867 | 300  | 0.3708          | 0.8417   |
| 0.4352        | 0.1156 | 400  | 0.2976          | 0.8862   |
| 0.3868        | 0.1445 | 500  | 0.2695          | 0.8950   |
| 0.3264        | 0.1734 | 600  | 0.7274          | 0.8739   |
| 0.4039        | 0.2023 | 700  | 0.3018          | 0.9314   |
| 0.3415        | 0.2311 | 800  | 0.2797          | 0.9171   |
| 0.3379        | 0.2600 | 900  | 0.1677          | 0.9360   |
| 0.2547        | 0.2889 | 1000 | 0.1600          | 0.9506   |
| 0.3377        | 0.3178 | 1100 | 0.5096          | 0.9025   |
| 0.2786        | 0.3467 | 1200 | 0.1569          | 0.9496   |
| 0.229         | 0.3756 | 1300 | 0.3807          | 0.9395   |
| 0.1867        | 0.4045 | 1400 | 0.2366          | 0.9564   |
| 0.1862        | 0.4334 | 1500 | 0.1283          | 0.9587   |
| 0.2238        | 0.4623 | 1600 | 0.3889          | 0.9356   |
| 0.1845        | 0.4912 | 1700 | 0.1452          | 0.9610   |
| 0.2051        | 0.5201 | 1800 | 0.2200          | 0.9558   |
| 0.2094        | 0.5490 | 1900 | 0.1520          | 0.9646   |
| 0.2217        | 0.5779 | 2000 | 0.3833          | 0.9265   |
| 0.2763        | 0.6068 | 2100 | 0.1593          | 0.9594   |
| 0.2033        | 0.6357 | 2200 | 0.1518          | 0.9626   |
| 0.2259        | 0.6645 | 2300 | 0.1149          | 0.9626   |
| 0.1501        | 0.6934 | 2400 | 0.1935          | 0.9597   |
| 0.1642        | 0.7223 | 2500 | 0.4075          | 0.9269   |
| 0.2433        | 0.7512 | 2600 | 0.1535          | 0.9642   |
| 0.1941        | 0.7801 | 2700 | 0.3230          | 0.9623   |
| 0.1185        | 0.8090 | 2800 | 0.3787          | 0.9691   |
| 0.1735        | 0.8379 | 2900 | 0.3400          | 0.9626   |
| 0.1453        | 0.8668 | 3000 | 0.5315          | 0.9529   |
| 0.164         | 0.8957 | 3100 | 0.2728          | 0.9678   |
| 0.2602        | 0.9246 | 3200 | 0.1789          | 0.9616   |
| 0.1642        | 0.9535 | 3300 | 0.1252          | 0.9675   |


### Framework versions

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