Update

Code/inference.py

@@ -3,7 +3,7 @@ import torch
 import tqdm
 import numpy as np
 import evaluation
-import evaluate
+#import evaluate
 import json
 import random
 import math
@@ -21,7 +21,7 @@ import sklearn
 import torch
 import tqdm
 import numpy as np
-import evaluate
+#import evaluate
 import json
 import random
 import math
@@ -48,16 +48,17 @@ from transformers import BertTokenizer, RobertaTokenizer, BertModel, RobertaMode
 
 
 filepath = "bert_baseline.pt"
-
-model=models.BertForWordClassification.from_pretrained("bert-base-uncased").to("cuda")
+model=models.BertForWordClassification.from_pretrained("bert-base-uncased")
+#tokenizer=AutoTokenizer.from_pretrained(args.architecture)
 
 model.load_state_dict(torch.load(filepath))
-model.eval()
+model.eval()		#loads saved model
 
 train_dataset = [{"sentence": ["Yet", "how", "many", "times", "has", "America", "sided", "with", "Israeli", "aggression", "against", "the", "people", "of", "Palestine?"], "pos": [5, 6], "label": 1}]
 train_sampler = RandomSampler(train_dataset)
 train_dataloader=DataLoader(train_dataset, sampler=train_sampler, batch_size=1)
 
+
 for batch in train_dataloader:
 	inputs = {'input_ids': batch[0],
 					'attention_mask': batch[1],
@@ -68,7 +69,5 @@ for batch in train_dataloader:
 	labels=batch[5]
 	start_positions=batch[3]
 	end_positions=batch[4]
-
 	outputs=model(**inputs)
-
 	print("Outputs: ", outputs)

Code/train.py

@@ -190,7 +190,7 @@ def train(model, name, imdb, seed,gradient_accumulation_steps,mixup, threshold,
 			#progress_bar.update(1)
 		#print("one epoch done")
 	
-	torch.save(model.state_dict(), "./Code/saved_models/bert_baseline.pt")
+	torch.save(model.state_dict(), "bert_baseline.pt")
 
 	#print(model_name)
 	evaluation_test = evaluation.evaluate_model(model, name,  test_dataset, learning_rate, test_batch_size, imdb)