Instructions to use VMware/bert-large-mrqa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use VMware/bert-large-mrqa with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="VMware/bert-large-mrqa")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("VMware/bert-large-mrqa") model = AutoModelForQuestionAnswering.from_pretrained("VMware/bert-large-mrqa") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9ebdb8781f5139055dd2438e561a8d33f7fc719b3fbb76be9400717a96cd04f3
- Size of remote file:
- 1.34 GB
- SHA256:
- 34a3a2a169d7df33940698aeaf11e3ebf2ab23a4b529756ba5426941e86e71dc
路
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