--- a
+++ b/src/rnn/rnn_utils.py
@@ -0,0 +1,89 @@
+import numpy as np 
+from collections import Counter
+import pandas as pd 
+import re
+import nltk
+import string
+import torch
+import torch.nn as nn
+from nltk import sent_tokenize, word_tokenize
+from nltk.stem import WordNetLemmatizer
+from nltk.corpus import stopwords
+
+stop_words = set(stopwords.words('english')).union(set(string.punctuation)) 
+
+def preprocessing_rnn(text):
+  words=word_tokenize(text)
+  filtered_sentence = [] 
+  # remove stopwords
+  for word in words: 
+    if word not in stop_words: 
+        filtered_sentence.append(word) 
+  
+  # lemmatize
+  lemma_word = []
+  wordnet_lemmatizer = WordNetLemmatizer()
+  for w in filtered_sentence:
+    word1 = wordnet_lemmatizer.lemmatize(w, pos = "n")
+    word2 = wordnet_lemmatizer.lemmatize(word1, pos = "v")
+    word3 = wordnet_lemmatizer.lemmatize(word2, pos = ("a"))
+    lemma_word.append(word3)
+  return lemma_word
+
+
+def count_vocab_index(train_df, test_df):
+    df = pd.concat([train_df, test_df]).sample(frac=1).reset_index(drop=True)
+    counts = Counter()
+    for _, row in df.iterrows():
+        counts.update(preprocessing_rnn(row['discharge_diagnosis']))
+    
+    # removing the words that have frequency less than 2
+    for word in list(counts):
+        if counts[word] < 2:
+            del counts[word]
+    
+    vocab2index = {"":0, "UNKNOWN":1}
+    words = ["", "UNKNOWN"]
+    for word in counts:
+        vocab2index[word] = len(words)
+        words.append(word)
+
+    return counts, vocab2index
+
+
+def encode_sentence(text, vocab2index, N = 50):
+  tokenized = preprocessing_rnn(text)
+  encoded = np.zeros(N, dtype=int)
+  enc1 = np.array([vocab2index.get(word, vocab2index["UNKNOWN"]) for word in tokenized])
+  length = min(N, len(enc1))
+  encoded[:length] = enc1[:length]
+  return encoded
+        
+
+
+def get_emb_matrix(w2vmodel, word_counts):
+  """ Creates embedding matrix from word vectors"""
+  vocab_size = len(word_counts) + 2
+  emb_size = w2vmodel.vector_size
+
+  W = np.zeros((vocab_size, emb_size), dtype="float32")
+  W[0] = np.zeros(emb_size, dtype='float32') # adding a vector for padding
+  W[1] = np.random.uniform(-0.25, 0.25, emb_size) # adding a vector for unknown words 
+  
+  i = 2
+  for word in word_counts:
+    if word in w2vmodel.wv:
+      W[i] = w2vmodel.wv[word]
+    else:
+      W[i] = np.random.uniform(-0.25,0.25, emb_size)
+    i += 1   
+  return W
+
+def create_emb_layer(weights_matrix, non_trainable=False):
+  num_embeddings, embedding_dim = weights_matrix.shape
+  emb_layer = nn.Embedding(num_embeddings, embedding_dim, padding_idx = 0)
+  emb_layer.load_state_dict({'weight': torch.from_numpy(weights_matrix)})
+  if non_trainable:
+    emb_layer.weight.requires_grad = False
+
+  return emb_layer, num_embeddings, embedding_dim