testing something

requirements.txt

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+import json
+from transformers import AutoTokenizer, AutoModelForQuestionAnsweringRegression
+
+# Load your documents into a dictionary
+with open("your_docs.json", "r") as f:
+    docs = json.load(f)
+
+# Load the pre-trained question answering model
+model = AutoModelForQuestionAnsweringRegression.from_pretrained("bert-large-uncased-whole-word-masking")
+tokenizer = AutoTokenizer.from_pretrained("bert-large-uncased-whole-word-masking")
+
+# Define a function to retrieve the answer to a question based on your documents
+def get_answer(question):
+    # Tokenize the question and the documents
+    question_tokens = tokenizer(question, return_tensors="pt")
+    docs_tokens = [tokenizer(doc["text"], return_tensors="pt") for doc in docs.values()]
+
+    # Combine the documents and the question tokens into a single input
+    inputs = {
+        "input_ids": torch.cat([q["input_ids"] for q in docs_tokens + [question_tokens]], dim=0),
+        "attention_mask": torch.cat([q["attention_mask"] for q in docs_tokens + [question_tokens]], dim=0),
+    }