Instructions to use EndLessTime/fine_tuned_tldr with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use EndLessTime/fine_tuned_tldr with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="EndLessTime/fine_tuned_tldr")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("EndLessTime/fine_tuned_tldr") model = AutoModelForSequenceClassification.from_pretrained("EndLessTime/fine_tuned_tldr", device_map="auto") - 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_tldr | |
| 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_tldr | |
| 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: 0.1941 | |
| - Accuracy: 0.9483 | |
| ## 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 | | |
| |:-------------:|:------:|:----:|:---------------:|:--------:| | |
| | 0.7406 | 0.0393 | 100 | 0.5270 | 0.8852 | | |
| | 0.6676 | 0.0785 | 200 | 0.1650 | 0.9315 | | |
| | 0.4432 | 0.1178 | 300 | 0.2737 | 0.8759 | | |
| | 0.3824 | 0.1570 | 400 | 0.3478 | 0.8534 | | |
| | 0.2657 | 0.1963 | 500 | 0.1941 | 0.9483 | | |
| ### Framework versions | |
| - Transformers 4.49.0 | |
| - Pytorch 2.6.0+cu126 | |
| - Datasets 3.3.2 | |
| - Tokenizers 0.21.0 | |