Analysis started, almost complete - compiled some excel sheets from the csv output with notes. Started infer.py, nothing major implemented yet

src/dataset.py

@@ -51,7 +51,7 @@ class ReviewDataset(Dataset):
     
 if __name__ == "__main__":
     dataset = ReviewDataset("data/processed/original_train.csv", AutoTokenizer.from_pretrained("FacebookAI/xlm-roberta-base"))
-    # print(dataset.__getitem__(1))
+    print(dataset.__getitem__(1))
     
 
 

src/infer.py

@@ -1,4 +1,25 @@
+import pandas as pd
+import numpy as np
+import torch
+import argparse
+from transformers import AutoTokenizer
+from torch.utils.tensorboard import SummaryWriter
+
 # mappings
-binary_map = {'Yes': 1, 'No': 0}
-aspect_map = {'App': 0, 'Driver': 1, 'General': 2, 'Payment': 3, 'Pricing': 4, 'Service': 5}
-sentiment_map = {'Positive': 0, 'Neutral': 1, 'Negative': 2}
+binary_map = {1:'Yes', 0:'No'}
+aspect_map = {0:'App', 1:'Driver', 2:'General', 3:'Payment', 4:'Pricing', 5:'Service'}
+sentiment_map = {0:'Positive', 1:'Neutral', 2:'Negative'}
+
+
+SEED = 4321
+torch.manual_seed(SEED)
+np.random.seed(SEED)
+
+def parse_args():
+    parser = argparse.ArgumentParser(description="RECLASS, Multitask learning for review classification.")
+    parser.add_argument("--model_path", type=str, help="Enter the models path / the desired .pt file")
+    parser.add_argument("--task", type=str, default="all", choices=["all", "bug_report", "feature_request", "aspect", "aspect_sentiment"], help="Specific task to train for stl usage only" )
+    parser.add_argument("--interactive", help="Loops reading input until exit")
+    parser.add_argument("--text", help="Use command line text for input")
+    return parser.parse_args()
+