Instructions to use LoudlySoft/scibert_scivocab_uncased_squad with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use LoudlySoft/scibert_scivocab_uncased_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="LoudlySoft/scibert_scivocab_uncased_squad")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("LoudlySoft/scibert_scivocab_uncased_squad") model = AutoModelForQuestionAnswering.from_pretrained("LoudlySoft/scibert_scivocab_uncased_squad", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from LoudlySoft/scibert_scivocab_uncased_squad: direct link, hf CLI and curl.
- Browser
- Download file 437 MB
-
https://huggingface.co/LoudlySoft/scibert_scivocab_uncased_squad/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://LoudlySoft/scibert_scivocab_uncased_squad/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/LoudlySoft/scibert_scivocab_uncased_squad/resolve/main/flax_model.msgpack
437 MB
- Xet hash:
- 101feab9dd2d8ca2f1da665441be58313f4416b780bb63697a389b71861f6d2c
- Size of remote file:
- 437 MB
- SHA256:
- e4749bb4fe735c03ac1d756c404076ab439e41f6e71f4ee4ca5ca59b7f582d00
路
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