Instructions to use CUTD/qnAr with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CUTD/qnAr 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="CUTD/qnAr")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("CUTD/qnAr") model = AutoModelForQuestionAnswering.from_pretrained("CUTD/qnAr", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from CUTD/qnAr: direct link, hf CLI and curl.
- Browser
- Download file 1.78 MB
-
https://huggingface.co/CUTD/qnAr/resolve/refs%2Fpr%2F1/tokenizer.json
- Command line
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hf download hf://CUTD/qnAr@refs/pr/1/tokenizer.json
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curl -L -o tokenizer.json https://huggingface.co/CUTD/qnAr/resolve/refs%2Fpr%2F1/tokenizer.json
1.78 MB
File too large to display, you can check the raw version instead.