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 pytorch_model.bin from amoux/scibert_nli_squad: direct link, hf CLI and curl.
- Browser
- Download file 440 MB
-
https://huggingface.co/amoux/scibert_nli_squad/resolve/refs%2Fpr%2F1/pytorch_model.bin
- Command line
-
hf download hf://amoux/scibert_nli_squad@refs/pr/1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/amoux/scibert_nli_squad/resolve/refs%2Fpr%2F1/pytorch_model.bin
440 MB
- Xet hash:
- d2d589e3b4cb8dc7b9ad9ef657e4b0cc4bbc9b7589ab027382aaffabfa2dd304
- Size of remote file:
- 440 MB
- SHA256:
- 975ebb7a1df48919d8cf71143a0c356543a609e09f722c399e61c61330a5a005
路
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