Instructions to use EndLessTime/fine_tuned_eli5_balanced with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use EndLessTime/fine_tuned_eli5_balanced with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="EndLessTime/fine_tuned_eli5_balanced")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("EndLessTime/fine_tuned_eli5_balanced") model = AutoModelForSequenceClassification.from_pretrained("EndLessTime/fine_tuned_eli5_balanced") - 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_eli5_balanced | |
| 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_eli5_balanced | |
| This model is a fine-tuned version of [Qwen/Qwen2-1.5B](https://huggingface.co/Qwen/Qwen2-1.5B) on the None dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.0877 | |
| - Accuracy: 0.9782 | |
| ## 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: 32 | |
| - eval_batch_size: 32 | |
| - 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 | | |
| |:-------------:|:------:|:----:|:---------------:|:--------:| | |
| | 0.4283 | 0.1064 | 100 | 0.2736 | 0.9031 | | |
| | 0.2571 | 0.2128 | 200 | 0.2150 | 0.9225 | | |
| | 0.1891 | 0.3191 | 300 | 0.1402 | 0.9408 | | |
| | 0.1541 | 0.4255 | 400 | 0.1498 | 0.9476 | | |
| | 0.1554 | 0.5319 | 500 | 0.1427 | 0.9434 | | |
| | 0.1291 | 0.6383 | 600 | 0.0966 | 0.9653 | | |
| | 0.1038 | 0.7447 | 700 | 0.0928 | 0.9656 | | |
| | 0.1007 | 0.8511 | 800 | 0.0870 | 0.9707 | | |
| | 0.1037 | 0.9574 | 900 | 0.0838 | 0.9725 | | |
| | 0.0563 | 1.0638 | 1000 | 0.1094 | 0.9749 | | |
| | 0.0319 | 1.1702 | 1100 | 0.1193 | 0.9746 | | |
| | 0.0541 | 1.2766 | 1200 | 0.0877 | 0.9782 | | |
| ### Framework versions | |
| - Transformers 4.49.0 | |
| - Pytorch 2.6.0+cu126 | |
| - Datasets 3.3.2 | |
| - Tokenizers 0.21.0 | |