--- a
+++ b/chexbert/src/datasets/unlabeled_dataset.py
@@ -0,0 +1,40 @@
+import torch
+import pandas as pd
+import numpy as np
+from transformers import BertTokenizer
+import bert_tokenizer
+from torch.utils.data import Dataset, DataLoader
+
+class UnlabeledDataset(Dataset):
+        """The dataset to contain report impressions without any labels."""
+        
+        def __init__(self, csv_path):
+                """ Initialize the dataset object
+                @param csv_path (string): path to the csv file containing rhe reports. It
+                                          should have a column named "Report Impression"
+                """
+                tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
+                impressions = bert_tokenizer.get_impressions_from_csv(csv_path)
+                self.encoded_imp = bert_tokenizer.tokenize(impressions, tokenizer)
+
+        def __len__(self):
+                """Compute the length of the dataset
+
+                @return (int): size of the dataframe
+                """
+                return len(self.encoded_imp)
+
+        def __getitem__(self, idx):
+                """ Functionality to index into the dataset
+                @param idx (int): Integer index into the dataset
+
+                @return (dictionary): Has keys 'imp', 'label' and 'len'. The value of 'imp' is
+                                      a LongTensor of an encoded impression. The value of 'label'
+                                      is a LongTensor containing the labels and 'the value of
+                                      'len' is an integer representing the length of imp's value
+                """
+                if torch.is_tensor(idx):
+                        idx = idx.tolist()
+                imp = self.encoded_imp[idx]
+                imp = torch.LongTensor(imp)
+                return {"imp": imp, "len": imp.shape[0]}