Question Answering
Transformers
PyTorch
TensorBoard
bert
Generated from Trainer
Eval Results (legacy)
Instructions to use mrp/bert-finetuned-squad with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mrp/bert-finetuned-squad with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "question-answering" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # pip install "transformers<5.0.0" from transformers import pipeline pipe = pipeline("question-answering", model="mrp/bert-finetuned-squad")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("mrp/bert-finetuned-squad") model = AutoModelForQuestionAnswering.from_pretrained("mrp/bert-finetuned-squad", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
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Add evaluation results on the plain_text config of squad (#2)
- Add evaluation results on the plain_text config of squad (4c385ce9bd958d216e47dd09282817931f25c5d8) Co-authored-by: Evaluation Bot <autoevaluator@users.noreply.huggingface.co>75831fb
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Add evaluation results on squad dataset (#1)
- Add evaluation results on squad dataset (25df9372d9b21a4aafd08690941b9b656b76fed1) Co-authored-by: Evaluation Bot <autoevaluator@users.noreply.huggingface.co>46ad676
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update model card README.md
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Training complete
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Training in progress, epoch 1
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initial commit
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