diff --git a/main.py b/main.py
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+++ b/main.py
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+import numpy as np
+import pandas as pd
+from tensorflow.keras.preprocessing.text import Tokenizer
+from tensorflow.keras.preprocessing.sequence import pad_sequences
+from tensorflow.keras.models import Sequential
+from tensorflow.keras.layers import Dense, Embedding, Flatten
+from tensorflow.keras.optimizers import Adam
+import gensim.downloader as api
+
+# Define the file paths
+train_file_path = 'mnt/train/train.tsv'
+dev_file_path = 'mnt/dev-0/in.tsv'
+test_file_path = 'mnt/test-A/in.tsv'
+
+# Load data with error handling for problematic lines
+def load_tsv(file_path):
+    try:
+        df = pd.read_csv(file_path, sep='\t', header=None, names=['text', 'label'], on_bad_lines='skip')
+        return df
+    except pd.errors.ParserError as e:
+        print(f"Error parsing {file_path}: {e}")
+        # Attempt to read the file with a different approach
+        with open(file_path, 'r', encoding='utf-8') as file:
+            lines = file.readlines()
+            data = [line.strip().split('\t') for line in lines if len(line.strip().split('\t')) == 2]
+        return pd.DataFrame(data, columns=['text', 'label'])
+
+# Load the data
+train_df = load_tsv(train_file_path)
+dev_df = pd.read_csv(dev_file_path, sep='\t', header=None, names=['text'])
+test_df = pd.read_csv(test_file_path, sep='\t', header=None, names=['text'])
+
+# Load pre-trained word2vec model from Google News
+word2vec_model = api.load('word2vec-google-news-300')
+
+# Tokenize and pad sequences
+tokenizer = Tokenizer()
+tokenizer.fit_on_texts(train_df['text'])
+vocab_size = len(tokenizer.word_index) + 1
+
+max_length = max(train_df['text'].apply(lambda x: len(x.split())))
+
+X_train = tokenizer.texts_to_sequences(train_df['text'])
+X_train = pad_sequences(X_train, maxlen=max_length, padding='post')
+
+X_dev = tokenizer.texts_to_sequences(dev_df['text'])
+X_dev = pad_sequences(X_dev, maxlen=max_length, padding='post')
+
+X_test = tokenizer.texts_to_sequences(test_df['text'])
+X_test = pad_sequences(X_test, maxlen=max_length, padding='post')
+
+y_train = train_df['label'].values
+
+# Create embedding matrix
+embedding_matrix = np.zeros((vocab_size, word2vec_model.vector_size))
+for word, index in tokenizer.word_index.items():
+    if word in word2vec_model:
+        embedding_matrix[index] = word2vec_model[word]
+
+# Define the model
+model = Sequential([
+    Embedding(vocab_size, word2vec_model.vector_size, weights=[embedding_matrix], input_length=max_length, trainable=False),
+    Flatten(),
+    Dense(10, activation='relu'),
+    Dense(1, activation='sigmoid')
+])
+
+model.compile(optimizer=Adam(learning_rate=0.001), loss='binary_crossentropy', metrics=['accuracy'])
+
+# Train the model
+model.fit(X_train, y_train, epochs=10, verbose=2, validation_split=0.2)
+
+# Predictions
+dev_predictions = (model.predict(X_dev) > 0.5).astype(int)
+test_predictions = (model.predict(X_test) > 0.5).astype(int)
+
+# Save predictions
+dev_df['prediction'] = dev_predictions
+test_df['prediction'] = test_predictions
+
+dev_df[['prediction']].to_csv('/mnt/data/dev-0/out.tsv', sep='\t', index=False, header=False)
+test_df[['prediction']].to_csv('/mnt/data/test-A/out.tsv', sep='\t', index=False, header=False)
diff --git a/mnt b/mnt
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+++ b/mnt
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+Subproject commit 9cb2fb26126561611a5539564fac6b5dbcbb0ca2