Instructions to use thenlpresearcher/Qwen_Qwen2_5-0_5B_StereoDetect_Model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use thenlpresearcher/Qwen_Qwen2_5-0_5B_StereoDetect_Model with PEFT:
from peft import PeftModel from transformers import AutoModelForSequenceClassification base_model = AutoModelForSequenceClassification.from_pretrained("Qwen/Qwen2.5-0.5B") model = PeftModel.from_pretrained(base_model, "thenlpresearcher/Qwen_Qwen2_5-0_5B_StereoDetect_Model") - Transformers
How to use thenlpresearcher/Qwen_Qwen2_5-0_5B_StereoDetect_Model with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("thenlpresearcher/Qwen_Qwen2_5-0_5B_StereoDetect_Model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Qwen_Qwen2_5-0_5B_StereoDetect_Model
This model is a fine-tuned version of Qwen/Qwen2.5-0.5B on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.5685
- Accuracy: 0.9055
- Balanced Accuracy: 0.9084
- F1 Weighted: 0.9054
- F1 Macro: 0.9062
- Precision: 0.9059
- Recall: 0.9055
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: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use 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: 6
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Balanced Accuracy | F1 Weighted | F1 Macro | Precision | Recall |
|---|---|---|---|---|---|---|---|---|---|
| 0.8204 | 1.0 | 760 | 0.3558 | 0.8479 | 0.8497 | 0.8451 | 0.8478 | 0.8626 | 0.8479 |
| 0.2934 | 2.0 | 1520 | 0.3008 | 0.8790 | 0.8809 | 0.8780 | 0.8801 | 0.8868 | 0.8790 |
| 0.1843 | 3.0 | 2280 | 0.4556 | 0.8894 | 0.8899 | 0.8884 | 0.8899 | 0.8980 | 0.8894 |
| 0.0944 | 4.0 | 3040 | 0.4759 | 0.9101 | 0.9122 | 0.9099 | 0.9105 | 0.9112 | 0.9101 |
| 0.0494 | 5.0 | 3800 | 0.5314 | 0.9101 | 0.9129 | 0.9101 | 0.9111 | 0.9104 | 0.9101 |
| 0.0137 | 6.0 | 4560 | 0.5685 | 0.9055 | 0.9084 | 0.9054 | 0.9062 | 0.9059 | 0.9055 |
Framework versions
- PEFT 0.19.1
- Transformers 4.51.3
- Pytorch 2.5.1+cu121
- Datasets 4.8.5
- Tokenizers 0.21.4
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Qwen/Qwen2.5-0.5B