Instructions to use EndLessTime/fine_tuned_squad with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use EndLessTime/fine_tuned_squad with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="EndLessTime/fine_tuned_squad")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("EndLessTime/fine_tuned_squad") model = AutoModelForSequenceClassification.from_pretrained("EndLessTime/fine_tuned_squad") - Notebooks
- Google Colab
- Kaggle
| library_name: transformers | |
| license: apache-2.0 | |
| base_model: Qwen/Qwen2-1.5B | |
| tags: | |
| - generated_from_trainer | |
| metrics: | |
| - accuracy | |
| model-index: | |
| - name: fine_tuned_squad | |
| 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_squad | |
| 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: 0.2907 | |
| - Accuracy: 0.9318 | |
| ## 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 | | |
| |:-------------:|:------:|:----:|:---------------:|:--------:| | |
| | 0.8398 | 0.0249 | 100 | 0.3339 | 0.8798 | | |
| | 0.4079 | 0.0497 | 200 | 0.2150 | 0.9217 | | |
| | 0.4416 | 0.0746 | 300 | 1.1200 | 0.8473 | | |
| | 0.3257 | 0.0994 | 400 | 0.1936 | 0.9413 | | |
| | 0.3197 | 0.1243 | 500 | 0.2272 | 0.9488 | | |
| | 0.2667 | 0.1491 | 600 | 0.2980 | 0.9295 | | |
| | 0.2523 | 0.1740 | 700 | 0.2907 | 0.9318 | | |
| ### Framework versions | |
| - Transformers 4.49.0 | |
| - Pytorch 2.6.0+cu126 | |
| - Datasets 3.3.2 | |
| - Tokenizers 0.21.0 | |