Instructions to use AmazonScience/qanlu with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AmazonScience/qanlu with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="AmazonScience/qanlu")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("AmazonScience/qanlu") model = AutoModelForQuestionAnswering.from_pretrained("AmazonScience/qanlu") - Notebooks
- Google Colab
- Kaggle
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# Question Answering NLU
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Question Answering NLU (QANLU) is an approach that maps the NLU task into question answering,
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language: en
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license: cc-by-4.0
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widget:
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- context: hello.
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- question: På biblioteket kan du låne en <mask>.
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---
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# Question Answering NLU
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Question Answering NLU (QANLU) is an approach that maps the NLU task into question answering,
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