Instructions to use gavinqiangli/bert-finetuned-squad-continued-training with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use gavinqiangli/bert-finetuned-squad-continued-training with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="gavinqiangli/bert-finetuned-squad-continued-training")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("gavinqiangli/bert-finetuned-squad-continued-training") model = AutoModelForQuestionAnswering.from_pretrained("gavinqiangli/bert-finetuned-squad-continued-training") - Notebooks
- Google Colab
- Kaggle
| library_name: transformers | |
| base_model: gavinqiangli/bert-finetuned-squad | |
| tags: | |
| - generated_from_trainer | |
| model-index: | |
| - name: bert-finetuned-squad-continued-training | |
| 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. --> | |
| # bert-finetuned-squad-continued-training | |
| This model is a fine-tuned version of [gavinqiangli/bert-finetuned-squad](https://huggingface.co/gavinqiangli/bert-finetuned-squad) on an unknown dataset. | |
| ## 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: 2 | |
| - mixed_precision_training: Native AMP | |
| ### Training results | |
| ### Framework versions | |
| - Transformers 4.48.3 | |
| - Pytorch 2.5.1+cu124 | |
| - Tokenizers 0.21.0 | |