Instructions to use DoNotChoke/distilbert-finetuned-squadv2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DoNotChoke/distilbert-finetuned-squadv2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="DoNotChoke/distilbert-finetuned-squadv2")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("DoNotChoke/distilbert-finetuned-squadv2") model = AutoModelForQuestionAnswering.from_pretrained("DoNotChoke/distilbert-finetuned-squadv2") - Notebooks
- Google Colab
- Kaggle
distilbert-finetuned-squadv2
This model is a fine-tuned version of distilbert-base-uncased 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: 3
- mixed_precision_training: Native AMP
Training results
Framework versions
- Transformers 4.49.0
- Pytorch 2.5.1+cu124
- Datasets 2.16.1
- Tokenizers 0.21.0
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Model tree for DoNotChoke/distilbert-finetuned-squadv2
Base model
distilbert/distilbert-base-uncased