removed match, case

Xeadriel · May 23, 2024 · 907fa72 · 907fa72
1 parent 2a03a36
commit 907fa72
Show file tree

Hide file tree

Showing 10 changed files with 3,257 additions and 24,416 deletions.
diff --git a/scripts/fineTune.py b/scripts/fineTune.py
@@ -44,17 +44,17 @@ def fineTune(args):
 
 	sentencePairs = []
 	labels = []
-	match args.task:
-		case "MRPC":
-			sentencePairs, labels = prepareMRPCData('scripts/tasks/MRPC/msr_paraphrase_train.csv')
-			evalPairs = sentencePairs[int(len(sentencePairs[0]) / 10):]
-			evalLabels = labels[int(len(sentencePairs[0]) / 10):]
-		case "SST-2": 
-			sentencePairs, labels, evalPairs, evalLabels, _, _ = prepareSSTGlueData('scripts/tasks/SSTGLUE/train.tsv',
+	if args.task == "MRPC":
+		sentencePairs, labels = prepareMRPCData('scripts/tasks/MRPC/msr_paraphrase_train.csv')
+		evalPairs = sentencePairs[int(len(sentencePairs[0]) / 10):]
+		evalLabels = labels[int(len(sentencePairs[0]) / 10):]
+	elif args.task == "SST-2": 
+		sentencePairs, labels, evalPairs, evalLabels, _, _ = prepareSSTGlueData('scripts/tasks/SSTGLUE/train.tsv',
 																		   'scripts/tasks/SSTGLUE/dev.tsv', 'scripts/tasks/SSTGLUE/test.tsv')
-		case "RTE":
-			sentencePairs, labels, evalPairs, evalLabels, _, _ = prepareRTEGlueData('scripts/tasks/RTE/train.tsv',
-																		   'scripts/tasks/RTE/dev.tsv', 'scripts/tasks/RTE/test.tsv')
+	elif args.task == "RTE":
+		sentencePairs, labels, evalPairs, evalLabels, _, _ = prepareRTEGlueData('scripts/tasks/RTE/train.tsv',
+																	   'scripts/tasks/RTE/dev.tsv', 'scripts/tasks/RTE/test.tsv')
+
 	indices = list(range(len(sentencePairs[0])))
 
 	logger.info(model)
@@ -113,7 +113,7 @@ def fineTune(args):
 			logits = predictSentencePairs(model, iterationPairs)
 
 			lossSim = F.binary_cross_entropy_with_logits(
-				torch.tensor(logits, dtype=torch.float, device=device, requires_grad=True),
+				logits,
 				torch.tensor(iterationLabels, dtype=torch.float, device=device, requires_grad=False), reduction="none"
 			)
 			loss.add("sim", lossSim)
@@ -173,20 +173,20 @@ def fineTune(args):
 
 			"""--------------- similarity benchmark ---------------"""
 			with Timer(elapsed, "benchmark on evaluation data", sync_cuda=True):
-					match args.task:
-						case "MRPC":
-							#F1 score
-							simscore = benchmarkMRPC(model, evalPairs, evalLabels)[4]
-						case "SST-2":
-							#threshold search based predictions 
-							predictions = predictSSTGlue(model, evalPairs, evalPairs, evalLabels)[1]
-							#accuracy
-							simscore = sum([predictions[x] == evalLabels[x] for x in range(len(predictions))]) / len(predictions)
-						case "RTE":
-							#threshold search based predictions
-							predictions = predictRTE(model, evalPairs, evalPairs, evalLabels)[1]
-							#accuracy
-							simscore = sum([predictions[x] == evalLabels[x] for x in range(len(predictions))]) / len(predictions)
+				if args.task == "MRPC":
+					#F1 score
+					simscore = benchmarkMRPC(model, evalPairs, evalLabels)[4]
+				elif args.task == "SST-2":
+					#threshold search based predictions 
+					predictions = predictSSTGlue(model, evalPairs, evalPairs, evalLabels)[1]
+					#accuracy
+					simscore = sum([predictions[x] == evalLabels[x] for x in range(len(predictions))]) / len(predictions)
+				elif args.task == "RTE":
+					#threshold search based predictions
+					predictions = predictRTE(model, evalPairs, evalPairs, evalLabels)[1]
+					#accuracy
+					simscore = sum([predictions[x] == evalLabels[x] for x in range(len(predictions))]) / len(predictions)
+
 
 			if simscore > best_simscore:
 				best_iter = i
@@ -252,18 +252,18 @@ def predictSentencePairs(
 	sents1 = [ [w for w in sent if w in sif_weights and w != vocab.unk] for sent in pairs[0] ]
 	sents2 = [ [w for w in sent if w in sif_weights and w != vocab.unk] for sent in pairs[1] ]
 
-	similarities = torch.zeros((len(sents1), 1), requires_grad=False, device=device)
+	similarities = torch.zeros((len(sents1), 1), requires_grad=True, device=device)
 
-	for i in range(len(sents1)):
-		sent1 = sents1[i]
-		sent2 = sents2[i]
+	for y in range(len(sents1)):
+		sent1 = sents1[y]
+		sent2 = sents2[y]
 
-		similarity =  torch.zeros((1, 1), requires_grad=False, device=device)
+		similarity =  0
 		for word1 in sent1:
 			for word2 in sent2:
-				similarity[0] += model[word1] * model[word2] * sif_weights[word1] * sif_weights[word2]
+				similarity += model[word1] * model[word2] * sif_weights[word1] * sif_weights[word2]
 
-		similarities[i] += similarity[0]
+		similarities[y] += similarity
 
 	return similarities