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 saved_model.tar.gz from google-bert/bert-large-uncased-whole-word-masking-finetuned-squad: direct link, hf CLI and curl.
- Browser
- Download file 1.25 GB
-
https://huggingface.co/google-bert/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/saved_model.tar.gz
- Command line
-
hf download hf://google-bert/bert-large-uncased-whole-word-masking-finetuned-squad/saved_model.tar.gz
-
curl -L -o saved_model.tar.gz https://huggingface.co/google-bert/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/saved_model.tar.gz
1.25 GB
- Xet hash:
- a9a3723e6ef5e5a9b24e33529dc2340717ffc3d48dc0ed9eeb118ff10b8ffb9c
- Size of remote file:
- 1.25 GB
- SHA256:
- 0cba24a67cf5a9f55e62876ab052fb2a66cb301678d626f59f5adeab829add0e
路
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