Instructions to use SRDdev/QuAC-QA-BERT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SRDdev/QuAC-QA-BERT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="SRDdev/QuAC-QA-BERT")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("SRDdev/QuAC-QA-BERT") model = AutoModelForQuestionAnswering.from_pretrained("SRDdev/QuAC-QA-BERT") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- aa3e0dc7500d8bd52ce65e6d7971e3e0848672c4a59cd49da743b169890a6a59
- Size of remote file:
- 431 MB
- SHA256:
- 0853a7b2b920dc086a1433ea19182cf16d1dbcc3b27dfd115056e856d612f87e
路
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