--- a
+++ b/model.py
@@ -0,0 +1,64 @@
+from simpletransformers.classification import (ClassificationArgs, ClassificationModel)
+import pandas as pd
+import pickle
+
+# ### Read data from files
+
+# In[ ]:
+
+
+train_df = pd.read_csv('/Users/aakansha/Desktop/NCCS NLP for Histology Reports/Datasets for Trials/train.csv')
+train_df['Combined Diagnosis'] = train_df['Diagnosis'] + train_df['Gross Description'] + train_df[
+    'Microscgopic Description']
+train_df = train_df[['Combined Diagnosis', 'Cancerous?']]
+
+# ### Pre-process data
+
+# In[ ]:
+
+
+# Capitalize values
+train_df['Cancerous?'] = train_df['Cancerous?'].str.upper()
+
+train_df['Cancerous?'] = train_df['Cancerous?'].str.strip()
+
+# Drop all NA rows
+train_df = train_df.dropna(subset=['Cancerous?']).reset_index(drop=True)
+
+# ## Roberta-Large
+
+# In[ ]:
+
+for n in [1]:
+
+    curr_epoch = "Epoch" + str(n)
+
+    # Configure model args
+    model_args = ClassificationArgs(num_train_epochs=n)
+    model_args.evaluate_during_training_steps = -1
+    model_args.save_eval_checkpoints = False
+    model_args.save_model_every_epoch = False
+    model_args.learning_rate = 1e-5
+    model_args.manual_seed = 4
+    model_args.multiprocessing_chunksize = 5000
+    model_args.no_cache = True
+    model_args.reprocess_input_data = True
+    model_args.train_batch_size = 16
+    model_args.gradient_accumulation_steps = 2
+    model_args.use_multiprocessing = True
+    model_args.overwrite_output_dir = True
+    model_args.labels_list = ['YES', 'NO']
+
+    # model
+    model_type = "roberta"
+    model_name = "roberta-large"
+
+    # Create Transformer Model
+    model = ClassificationModel(model_type, model_name, num_labels=2, use_cuda=False, args=model_args)
+
+    if __name__ == '__main__':
+        # Train the model
+        model.train_model(train_df)
+        pickle.dump(model, open('model.pkl', 'wb'))
+
+