Instructions to use joonion/bert-finetuned-squad with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use joonion/bert-finetuned-squad with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="joonion/bert-finetuned-squad")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("joonion/bert-finetuned-squad") model = AutoModelForQuestionAnswering.from_pretrained("joonion/bert-finetuned-squad") - Notebooks
- Google Colab
- Kaggle
bert-finetuned-squad
This model is a fine-tuned version of distilbert-base-cased-distilled-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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
Training results
Framework versions
- Transformers 5.12.0
- Pytorch 2.12.0+cpu
- Datasets 5.0.0
- Tokenizers 0.22.2
- Downloads last month
- 24