Instructions to use google-bert/bert-large-uncased-whole-word-masking-finetuned-squad with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google-bert/bert-large-uncased-whole-word-masking-finetuned-squad 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="google-bert/bert-large-uncased-whole-word-masking-finetuned-squad")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-large-uncased-whole-word-masking-finetuned-squad") model = AutoModelForQuestionAnswering.from_pretrained("google-bert/bert-large-uncased-whole-word-masking-finetuned-squad", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from google-bert/bert-large-uncased-whole-word-masking-finetuned-squad: direct link, hf CLI and curl.
- Browser
- Download file 1.34 GB
-
https://huggingface.co/google-bert/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://google-bert/bert-large-uncased-whole-word-masking-finetuned-squad/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/google-bert/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/pytorch_model.bin
1.34 GB
- Xet hash:
- 07987ffd1b3a2bcdaf02f18b135ab338a981eb8a6597e637a2ddf93d901f6acb
- Size of remote file:
- 1.34 GB
- SHA256:
- f69d7b9496c8497c5da7f7a3829eab2367c5ba5ca468ec61e20987ee5f7a2053
路
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