Instructions to use twmkn9/bert-base-uncased-squad2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use twmkn9/bert-base-uncased-squad2 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="twmkn9/bert-base-uncased-squad2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("twmkn9/bert-base-uncased-squad2") model = AutoModelForQuestionAnswering.from_pretrained("twmkn9/bert-base-uncased-squad2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from twmkn9/bert-base-uncased-squad2: direct link, hf CLI and curl.
- Browser
- Download file 436 MB
-
https://huggingface.co/twmkn9/bert-base-uncased-squad2/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://twmkn9/bert-base-uncased-squad2/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/twmkn9/bert-base-uncased-squad2/resolve/main/flax_model.msgpack
436 MB
- Xet hash:
- ffe207446a97480bd102b608fe39e4d275535cd522fd5648181d42130c27cf17
- Size of remote file:
- 436 MB
- SHA256:
- 96c0c2948a642789fbf4dc10095b059dc34e45e07bf6722e0d81ce4c9afc9479
路
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