---
library_name: transformers
license: apache-2.0
base_model: Qwen/Qwen2-1.5B
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: fine_tuned_sci_gen_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_sci_gen_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: 0.1471
- Accuracy: 0.9736

## 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 |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 0.8103        | 0.0384 | 100  | 0.4049          | 0.8802   |
| 0.3921        | 0.0769 | 200  | 0.4732          | 0.9001   |
| 0.4581        | 0.1153 | 300  | 0.2876          | 0.9256   |
| 0.2995        | 0.1537 | 400  | 0.3032          | 0.9313   |
| 0.2645        | 0.1922 | 500  | 0.3322          | 0.9334   |
| 0.2994        | 0.2306 | 600  | 0.1808          | 0.9494   |
| 0.2834        | 0.2690 | 700  | 0.2584          | 0.9464   |
| 0.3254        | 0.3075 | 800  | 0.2653          | 0.9408   |
| 0.2211        | 0.3459 | 900  | 0.1439          | 0.9585   |
| 0.1954        | 0.3843 | 1000 | 0.1905          | 0.9520   |
| 0.2486        | 0.4228 | 1100 | 0.2971          | 0.9321   |
| 0.1363        | 0.4612 | 1200 | 0.2809          | 0.9386   |
| 0.2191        | 0.4996 | 1300 | 0.2436          | 0.9477   |
| 0.2003        | 0.5380 | 1400 | 0.1320          | 0.9667   |
| 0.1653        | 0.5765 | 1500 | 0.1779          | 0.9602   |
| 0.1465        | 0.6149 | 1600 | 0.1386          | 0.9650   |
| 0.1698        | 0.6533 | 1700 | 0.1004          | 0.9663   |
| 0.1545        | 0.6918 | 1800 | 0.1772          | 0.9598   |
| 0.1633        | 0.7302 | 1900 | 0.1856          | 0.9555   |
| 0.1889        | 0.7686 | 2000 | 0.1754          | 0.9680   |
| 0.214         | 0.8071 | 2100 | 0.1801          | 0.9503   |
| 0.1636        | 0.8455 | 2200 | 0.1771          | 0.9641   |
| 0.1469        | 0.8839 | 2300 | 0.1493          | 0.9650   |
| 0.195         | 0.9224 | 2400 | 0.1066          | 0.9693   |
| 0.1109        | 0.9608 | 2500 | 0.1461          | 0.9684   |
| 0.1544        | 0.9992 | 2600 | 0.1412          | 0.9650   |
| 0.0386        | 1.0377 | 2700 | 0.1471          | 0.9736   |


### Framework versions

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