added glove embeddings

.gitignore

@@ -24,3 +24,7 @@ plots/
 
 # Ignore everything with delete_me in name
 *delete_me*
+
+# Ignore glove
+*.zip
+*glove*/

README.md

@@ -28,6 +28,8 @@ https://aclanthology.org/2021.semeval-1.9.pdf#:~:text=HaHackathon%20is%20the%20f
 
 
 
+## Data embeddings
+- gloVe 6B tokens: https://nlp.stanford.edu/projects/glove/
 
 
 

dataset_generator.py

@@ -8,6 +8,7 @@ from nltk.tokenize import word_tokenize
 import gensim
 import torch
 import os
+import copy
 
 from HumorDataset import HumorDataset
 
@@ -23,6 +24,27 @@ def get_embedding_vector(model, word):
     else:
         return np.zeros(model.vector_size)
 
+def load_glove_embeddings(glove_file_path):
+    embeddings_index = {}
+    with open(glove_file_path, 'r', encoding='utf-8') as f:
+        for line in f:
+            values = line.split()
+            word = values[0]
+            coefs = np.asarray(values[1:], dtype='float32')
+            embeddings_index[word] = coefs
+    return embeddings_index
+
+def get_embedding_glove_vector(tokens, embeddings_index, default_vector_len=100, pad_tok='<PAD>'):
+    default_vec = [0] * default_vector_len
+    emb_matrix = []
+    for token in tokens:
+        if token == pad_tok:
+            embedding_vector = default_vec
+        else:
+            embedding_vector = embeddings_index.get(token, default_vec)
+        emb_matrix.append(embedding_vector)
+    return emb_matrix
+
 def encode_tokens(tokens, vector=False):
     if vector:
         return [get_embedding_vector(model_embedding, token) for token in tokens]
@@ -88,7 +110,6 @@ if __name__ == "__main__":
     # split data into train, test, and validation
     data_dict = split_data(padded_indices, y)
     
-    # TODO: test gloVe embeddings
     # Embed the data with word2vec
     model_embedding = gensim.models.Word2Vec(tokens, window=5, min_count=1, workers=4)
 
@@ -101,7 +122,7 @@ if __name__ == "__main__":
     pad_index = model_embedding.wv.key_to_index['<PAD>']
 
 
-    data_idx_based = data_dict.copy()
+    data_idx_based = copy.deepcopy(data_dict)
     vector_based = False
 
     for key in data_idx_based.keys():
@@ -112,10 +133,15 @@ if __name__ == "__main__":
     # save the data
     save_data(data_idx_based, 'data/idx_based_padded/', '', vocab_size)
 
+    print('loading GloVe embeddings')
     vector_based = True
+    # Load GloVe embeddings
+    glove_file_path = 'glove.6B/glove.6B.100d.txt'
+    embeddings_index = load_glove_embeddings(glove_file_path)
+    print('starting with embedding the data')
     # Encode the tokens
     for key in data_dict.keys():
-        data_dict[key]['X'] = [encode_tokens(tokens, vector_based) for tokens in data_dict[key]['X']]
+        data_dict[key]['X'] = [get_embedding_glove_vector(tokens, embeddings_index) for tokens in data_dict[key]['X']]
         # print shape of data
         #print(key, len(data_dict[key]['X']), len(data_dict[key]['y']))