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For example, we can easily extract question answers given context: >>> from transformers import pipeline # Allocate a pipeline for question-answering >>> question_answerer = pipeline('question-answering') >>> question_answerer({ ... 'question': 'What is the name of the repository ?', ....
Many NLP tasks have a pre-trainedpipelineready to go. For example, we can easily extract question answers given context: >>>fromtransformersimportpipeline# Allocate a pipeline for question-answering>>>question_answerer=pipeline('question-answering')>>>question_answerer({ ...'question':'What is...
6) The right to ask a question passes to the answerer 7) The task of the players who know the word is to guess who the spy is. The task of the spy is to guess the location The game has paid rates: 1) Week - 7 days free, then 499₽ per week 2) Month - 699₽ per mont...
>>> from transformers import pipeline # 使用問答 pipeline >>> question_answerer = pipeline('question-answering') >>> question_answerer({ ... 'question': 'What is the name of the repository ?', ... 'context': 'Pipeline has been included in the huggingface/transformers repository' .....
Try for free today! What makes Scribe an incredible AI answer generatorStarting off with Scribe’s AI Answer GeneratorStep 1: Capture your workflows with Scribe Step 2: Stop the captureStep 3: Build an AI-powered knowledge baseStep 4: Edit and Personalize Step 5: Share with your team or ...
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@DataClass class AnswererOutput(DataClass): """Model to represent the output of the answerer.""" answer: str = field(metadata={"desc": "The answer to the user question."}) pmids: list[int] = field(metadata={"desc": "The PMIDs of the rele...
Many NLP tasks have a pre-trainedpipelineready to go. For example, we can easily extract question answers given context: >>>fromtransformersimportpipeline# Allocate a pipeline for question-answering>>>question_answerer=pipeline('question-answering')>>>question_answerer({ ...'question':'What is...