--- a
+++ b/src/utils.py
@@ -0,0 +1,104 @@
+import numpy as np
+from sklearn.metrics import accuracy_score,hamming_loss,precision_score,recall_score,f1_score,classification_report
+from torch.utils.data import SubsetRandomSampler, DataLoader
+import re
+import nltk
+import string
+
+
+nltk.download('punkt')
+nltk.download('stopwords')
+nltk.download('wordnet')
+from nltk import sent_tokenize, word_tokenize
+from nltk.stem import WordNetLemmatizer
+from nltk.corpus import stopwords
+
+
+
+# stopwords + punctuation
+stop_words = set(stopwords.words('english')).union(set(string.punctuation)) 
+
+
+
+
+################## preprocessing text #########################
+def preprocess(text):
+
+  words = word_tokenize(text)
+  filtered_sentence = [] 
+  # remove stopwords
+  for word in words: 
+    if word not in stop_words: 
+        filtered_sentence.append(word) 
+  text = ' '.join(filtered_sentence)
+  # 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 text
+
+#######################################################################
+
+
+###################### Calculation of Metrics #########################
+def calculate_metrics(pred, target, threshold=0.5):
+  pred = np.array(pred > threshold, dtype="float32")
+  
+  return {'micro/precision': precision_score(y_true=target, y_pred=pred, average='micro'),
+            'micro/recall': recall_score(y_true=target, y_pred=pred, average='micro'),
+            'micro/f1': f1_score(y_true=target, y_pred=pred, average='micro'),
+            'hammingloss':hamming_loss(target,pred)       
+        }
+#########################################################################
+
+################### Label One-hot Encodings ########################
+def labeltarget(x,frequent_list):
+  target=np.zeros(10,dtype="float32")
+  for index,code in enumerate(frequent_list):
+    if code in x :
+      target[index]=1
+  return target
+#####################################################################
+
+#######################################################################################
+def split_indices(dataset, validation_split, shuffle_dataset = True, random_seed = 2021):
+  dataset_size = len(dataset)
+  indices = list(range(dataset_size))
+  split = int(np.floor(validation_split * dataset_size))
+  if shuffle_dataset :
+    np.random.seed(random_seed)
+    np.random.shuffle(indices)
+  return indices[split:], indices[:split]
+#########################################################################################
+
+#########################################################################################
+def dataloader(train_dataset, test_dataset, batch_size, val_split):
+  train_indices, val_indices = split_indices(train_dataset, val_split)
+  train_sampler = SubsetRandomSampler(train_indices)
+  val_sampler = SubsetRandomSampler(val_indices)
+  train_loader = DataLoader(train_dataset, batch_size = batch_size, sampler=train_sampler)
+  val_loader = DataLoader(train_dataset, batch_size = batch_size, sampler=val_sampler)
+  test_loader = DataLoader(test_dataset, batch_size= batch_size)
+  return train_loader, val_loader, test_loader
+
+
+#########################################################################################
+
+#########################################################################################
+def train_metric(y_pred, y_test, threshold=0.5):
+  num_classes = y_pred.shape[1]
+  y_pred_tags = (y_pred>0.5).float()
+
+  correct_pred = (y_pred_tags == y_test).float()
+  accuracy = (correct_pred.sum(dim=1) == num_classes).float().sum() / len(correct_pred)
+
+  hammingloss = hamming_loss(y_test.cpu().numpy(), y_pred_tags.cpu().numpy())
+
+  f1score = f1_score(y_true=y_test.cpu().numpy(), y_pred=y_pred_tags.cpu().numpy(), average='micro')
+  return accuracy, hammingloss, f1score
+
+#################################################################################################
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