Instructions to use sharkMeow/BERT-QA-b8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sharkMeow/BERT-QA-b8 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="sharkMeow/BERT-QA-b8")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("sharkMeow/BERT-QA-b8") model = AutoModelForQuestionAnswering.from_pretrained("sharkMeow/BERT-QA-b8") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- aa8273bb5ed1d57e9350445919d34315430e341d29d8a8a5ba32bbdb89ec6376
- Size of remote file:
- 21.2 MB
- SHA256:
- f7bc3ffd06a06a93f1accd31598233603f6dd18d38c24fc7af6c00b0ecef58f0
路
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