add law_ai_qna.py file

backend/law_ai_qna.py

+import torch
+import numpy as np
+import pandas as pd
+from torch.utils.data import Dataset
+from transformers import TrainingArguments, Trainer
+from sklearn.model_selection import train_test_split
+from transformers import OpenAIGPTTokenizer, OpenAIGPTModel
+
+data_path = 'data/qna-summarization.xlsx'
+df = pd.read_excel(data_path)
+Answers = df['Answer'].tolist()
+Question = df['Question'].tolist()
+
+tokenizer = OpenAIGPTTokenizer.from_pretrained('openai-gpt')
+tokenizer.add_special_tokens({'pad_token': '[PAD]'})
+
+model = OpenAIGPTModel.from_pretrained('openai-gpt')
+
+question_encoding = tokenizer(Question, return_tensors='pt', padding=True, truncation=True)
+answer_encoding = tokenizer(Answers, return_tensors='pt', padding=True, truncation=True)
+
+class QnADataset(Dataset):
+    def __init__(self, question_encoding, answer_encoding):
+        self.question_encoding = question_encoding
+        self.answer_encoding = answer_encoding
+    def __getitem__(self, idx):
+        return self.question_encoding[idx], self.answer_encoding[idx]
+    def __len__(self):
+        return len(self.question_encoding)
+
+dataset = QnADataset(question_encoding, answer_encoding)
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