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 training_args.bin from amoux/scibert_nli_squad: direct link, hf CLI and curl.
- Browser
- Download file 1.53 kB
-
https://huggingface.co/amoux/scibert_nli_squad/resolve/refs%2Fpr%2F1/training_args.bin
- Command line
-
hf download hf://amoux/scibert_nli_squad@refs/pr/1/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/amoux/scibert_nli_squad/resolve/refs%2Fpr%2F1/training_args.bin
1.53 kB
- Xet hash:
- 0400778be8db6b752669958acfcd6f605d6d101cbacc4de9d81dc2795a2f9756
- Size of remote file:
- 1.53 kB
- SHA256:
- 731613350b9323c8e5898d916c36b70cf292b98d4fccdcf76462abee6794968e
路
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.