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 # 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="VMware/bert-large-mrqa")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("VMware/bert-large-mrqa") model = AutoModelForQuestionAnswering.from_pretrained("VMware/bert-large-mrqa", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from VMware/bert-large-mrqa: direct link, hf CLI and curl.
- Browser
- Download file 1.34 GB
-
https://huggingface.co/VMware/bert-large-mrqa/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://VMware/bert-large-mrqa/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/VMware/bert-large-mrqa/resolve/main/pytorch_model.bin
1.34 GB
- Xet hash:
- 3b3f69fa3fce7171c7266f1642ae271c236c0b8def2ad1c9bbf661dc4243aebb
- Size of remote file:
- 1.34 GB
- SHA256:
- 896c37453d889eb2e23334563b0ac63c59a7e6530be83852b6bda4776684b291
路
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