proper example

#8
by nbroad HF staff - opened
Files changed (1) hide show
  1. README.md +2 -2
README.md CHANGED
@@ -60,10 +60,10 @@ You can use the model for inference tasks like question-answering and medical di
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  from transformers import pipeline
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- qa_pipeline = pipeline("question-answering", model="medalpaca/medalpaca-13b", tokenizer="medalpaca/medalpaca-13b")
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  question = "What are the symptoms of diabetes?"
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  context = "Diabetes is a metabolic disease that causes high blood sugar. The symptoms include increased thirst, frequent urination, and unexplained weight loss."
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- answer = qa_pipeline({"question": question, "context": context})
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  print(answer)
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  ```
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  from transformers import pipeline
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+ pl = pipeline("text-generation", model="medalpaca/medalpaca-13b", tokenizer="medalpaca/medalpaca-13b")
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  question = "What are the symptoms of diabetes?"
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  context = "Diabetes is a metabolic disease that causes high blood sugar. The symptoms include increased thirst, frequent urination, and unexplained weight loss."
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+ answer = pl(f"Context: {context}\n\nQuestion: {question}\n\nAnswer: ")
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  print(answer)
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  ```
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