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---
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
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