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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_yelp_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_yelp_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: 0.2174
- Accuracy: 0.9601
## 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 |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 0.8575 | 0.0170 | 100 | 0.4890 | 0.8281 |
| 0.7118 | 0.0340 | 200 | 0.5564 | 0.8484 |
| 0.5044 | 0.0509 | 300 | 0.5218 | 0.8463 |
| 0.3969 | 0.0679 | 400 | 0.9168 | 0.7809 |
| 0.4494 | 0.0849 | 500 | 0.5158 | 0.8453 |
| 0.3766 | 0.1019 | 600 | 0.5252 | 0.8772 |
| 0.3938 | 0.1188 | 700 | 0.5676 | 0.8573 |
| 0.3681 | 0.1358 | 800 | 0.2930 | 0.9116 |
| 0.3955 | 0.1528 | 900 | 0.2745 | 0.8845 |
| 0.2859 | 0.1698 | 1000 | 0.3442 | 0.9150 |
| 0.329 | 0.1868 | 1100 | 0.2673 | 0.9116 |
| 0.2898 | 0.2037 | 1200 | 0.1957 | 0.9221 |
| 0.2645 | 0.2207 | 1300 | 0.2623 | 0.9335 |
| 0.2906 | 0.2377 | 1400 | 0.2438 | 0.9280 |
| 0.2511 | 0.2547 | 1500 | 0.3281 | 0.9211 |
| 0.3 | 0.2716 | 1600 | 0.1789 | 0.9467 |
| 0.2592 | 0.2886 | 1700 | 0.2963 | 0.9270 |
| 0.2274 | 0.3056 | 1800 | 0.3387 | 0.9309 |
| 0.2843 | 0.3226 | 1900 | 0.2788 | 0.9291 |
| 0.281 | 0.3396 | 2000 | 0.2553 | 0.9442 |
| 0.2624 | 0.3565 | 2100 | 0.1737 | 0.9547 |
| 0.2503 | 0.3735 | 2200 | 0.1948 | 0.9454 |
| 0.2856 | 0.3905 | 2300 | 0.2797 | 0.9269 |
| 0.1531 | 0.4075 | 2400 | 0.2548 | 0.9490 |
| 0.2316 | 0.4244 | 2500 | 0.2906 | 0.9440 |
| 0.2061 | 0.4414 | 2600 | 0.2194 | 0.9517 |
| 0.1991 | 0.4584 | 2700 | 0.1949 | 0.9515 |
| 0.1721 | 0.4754 | 2800 | 0.2730 | 0.9368 |
| 0.1696 | 0.4924 | 2900 | 0.2238 | 0.9326 |
| 0.2013 | 0.5093 | 3000 | 0.1802 | 0.9603 |
| 0.2026 | 0.5263 | 3100 | 0.2174 | 0.9601 |
### Framework versions
- Transformers 4.49.0
- Pytorch 2.6.0+cu126
- Datasets 3.3.2
- Tokenizers 0.21.0
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