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testSampleCode.py

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+import pandas as pd
+from sklearn.model_selection import train_test_split
+
+dataFromCsv = pd.read_csv('test.csv')
+
+text = dataFromCsv['text'].tolist()
+label = dataFromCsv['label'].tolist()
+
+print(text)
+print(label)
+
+sorted_lable = sorted((dataFromCsv['label']).unique())
+print(sorted_lable)
+
+
+lableList = {label: i for i, label in enumerate(sorted_lable)}
+dataFromCsv['label'] = dataFromCsv['label'].map(lableList)
+
+print("label list = ",lableList)
+print("from csv file label = ",dataFromCsv['label'])
+
+ 
+
+# Split the dataset 
+train_df, test_df = train_test_split(dataFromCsv, test_size=0.2, random_state=42) 
+print("Training Set:") 
+print(train_df) 
+print("\nTesting Set:") 
+print(test_df)
+
+lableList = { 0: "lastmonth", 1: "nextweek", 2: "sevendays", 3: "today", 4: "yesterday" }
+
+print("After = ", lableList[4])
+print("label items = ", lableList.items())
+
+#test of dataset
+# from torch.utils.data import Dataset
+# import torch
+
+# class IntentDataset(Dataset):
+#     def __init__(self, encodings, labels):
+#         self.encodings = encodings
+#         self.labels = labels
+
+#     def __getitem__(self, idx):
+#             item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
+#             label = self.labels[idx]
+#             item['labels'] = torch.tensor(self.labels[idx])
+   
+#             return item
+
+#     def __len__(self):
+#         return len(self.labels)
+
+# # Sample data 
+# encodings = { 
+#     'input_ids': [[101, 102, 103], [104, 105, 106], [107, 108, 109]], 
+#     'attention_mask': [[1, 1, 1], [1, 1, 1], [1, 1, 1]] 
+# } 
+# labels = [0, 1, 0]
+
+# dataset = IntentDataset(encodings, labels)
+# dataset_length = len(dataset) 
+# print(f"The dataset contains {dataset_length} items. {dataset.labels}")
+
+# dataset_show = dataset[2]
+# print(dataset_show)