Instructions to use amoux/scibert_nli_squad with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use amoux/scibert_nli_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="amoux/scibert_nli_squad")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("amoux/scibert_nli_squad") model = AutoModelForQuestionAnswering.from_pretrained("amoux/scibert_nli_squad", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from amoux/scibert_nli_squad: direct link, hf CLI and curl.
- Browser
- Download file 437 MB
-
https://huggingface.co/amoux/scibert_nli_squad/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://amoux/scibert_nli_squad/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/amoux/scibert_nli_squad/resolve/main/flax_model.msgpack
437 MB
- Xet hash:
- 0fd17b68f8142fba3a573387f954c16f0d7e8f9fa24d5f211935f5dea1ad1e06
- Size of remote file:
- 437 MB
- SHA256:
- 8d6f63a11dbee842d64260eef331b8af31e1c8a83d71abb82a9515c8712131a4
路
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