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Update README.md

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Added code to run the model

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README.md CHANGED
@@ -12,4 +12,63 @@ library_name: Haystack
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  From this repository you can download the **BioBIT_QA** (Biomedical Bert for ITalian for Question Answering) checkpoint.
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  **BioBIT_QA** is built on top of [BioBIT](https://huggingface.co/IVN-RIN/bioBIT), fine-tuned on an Italian Neuropsychological Italian datasets.
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- More details will follow!
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  From this repository you can download the **BioBIT_QA** (Biomedical Bert for ITalian for Question Answering) checkpoint.
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  **BioBIT_QA** is built on top of [BioBIT](https://huggingface.co/IVN-RIN/bioBIT), fine-tuned on an Italian Neuropsychological Italian datasets.
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+ More details will follow!
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+
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+
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+ ## Install libraries:
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+
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+ ```
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+ pip install farm-haystack[inference]
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+ ```
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+
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+
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+ ## Download model locally:
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+
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+ ```
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+ git clone https://huggingface.co/IVN-RIN/bioBIT_QA
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+ ```
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+
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+
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+ ## Run the code
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+
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+ ```
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+ # Import libraries
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+ from haystack.nodes import FARMReader
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+ from haystack.schema import Document
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+
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+ # Define the reader
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+ reader = FARMReader(
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+ model_name_or_path="bioBIT_QA",
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+ return_no_answer=True
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+ )
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+
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+ # Define context and question
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+ context = '''
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+ This is an example of context
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+ '''
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+ question = 'This is a question example, ok?'
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+
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+ # Wrap context in Document
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+ docs = Document(
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+ content = context
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+ )
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+
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+ # Predict answer
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+ prediction = reader.predict(
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+ query = question,
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+ documents = [docs],
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+ top_k = 5
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+ )
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+
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+ # Print the 5 first predicted answers
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+ for i, ans in enumerate(prediction['answers']):
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+ print(f'Answer num {i+1}, with score {ans.score*100:.2f}%: "{ans.answer}"')
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+
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+ # Inferencing Samples: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 1/1 [00:01<00:00, 1.14s/ Batches]
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+ # Answer num 1, with score 97.91%: "Example answer 01"
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+ # Answer num 2, with score 53.69%: "Example answer 02"
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+ # Answer num 3, with score 0.03%: "Example answer 03"
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+ # ...
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+
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+
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+ ```