main update

Code/main.py

@@ -9,12 +9,14 @@ from typing import List
 
 
 def run(raw_args):
+	print("parsing")
 	args=_parse_args(raw_args)
-
+	print("parsed arguments")
 
 	#load test and train dataset as well as tokenizers and models...
 
 	#Datasets
+	print("opened datasets...")
 	with open(args.train_dataset) as f:
 		data_train=json.loads(f.read())
 
@@ -23,6 +25,7 @@ def run(raw_args):
 	
 
 	#Tokenizers & Models
+	print("Loading tokenizers and initializing model...")
 	tokenizer=AutoTokenizer.from_pretrained(args.architecture)
 
 	models.set_seed(args.random_seed)
@@ -40,6 +43,7 @@ def run(raw_args):
 
 
 	#preprocess...
+	print("preprocessing datasets...")
 	if args.tokenizer=="salami":
 		train_dataset=preprocess.salami_tokenizer(tokenizer, data_train, args.max_length, masked=args.masking) #no context implemented
 		test_dataset=preprocess.salami_tokenizer(tokenizer, data_test, args.max_length, masked=args.masking)
@@ -55,6 +59,7 @@ def run(raw_args):
 		print("non eligible tokenizer selected")
 
 	#train...
+	print("training..")
 	if args.train_loop=="swp":
 		evaluation_test, evaluation_train = train.train(model, args.random_seed, train_dataset, test_dataset, args.epochs, args.learning_rate, args.batch_size, args.test_batch_size)
 	elif args.train_loop=="salami":
@@ -144,5 +149,5 @@ def _parse_args(raw_args: List[str]) -> argparse.Namespace:
 		type=int,
 		default=64)
 
-
+	return parser.parse_args(raw_args)
 	

Code/train.py

@@ -2,6 +2,7 @@ import torch
 import tqdm
 import numpy as np
 import evaluation
+import evaluate
 import json
 import random
 import math