Instructions to use mujerry/bert-base-uncased-finetuned-QnA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mujerry/bert-base-uncased-finetuned-QnA with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="mujerry/bert-base-uncased-finetuned-QnA")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("mujerry/bert-base-uncased-finetuned-QnA") model = AutoModelForMaskedLM.from_pretrained("mujerry/bert-base-uncased-finetuned-QnA", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from mujerry/bert-base-uncased-finetuned-QnA: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/mujerry/bert-base-uncased-finetuned-QnA/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://mujerry/bert-base-uncased-finetuned-QnA/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/mujerry/bert-base-uncased-finetuned-QnA/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- 3141ee1212f1eaf56cbc94ecf2f27a37a9573c79b8b61f13729727590e726aea
- Size of remote file:
- 438 MB
- SHA256:
- b79bc1a722cc7ce88fd5342436b52c65c18046ac63e17a485a2dbbcbd71b6150
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