Update app.py

app.py

@@ -53,45 +53,6 @@ import re
 tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
 model = GPT2LMHeadModel.from_pretrained('gpt2')
 
-# def cloze_prob(text):
-
-# 	whole_text_encoding = tokenizer.encode(text)
-# 	# Parse out the stem of the whole sentence (i.e., the part leading up to but not including the critical word)
-# 	text_list = text.split()
-# 	stem = ' '.join(text_list[:-1])
-# 	stem_encoding = tokenizer.encode(stem)
-# 	# cw_encoding is just the difference between whole_text_encoding and stem_encoding
-# 	# note: this might not correspond exactly to the word itself
-# 	cw_encoding = whole_text_encoding[len(stem_encoding):]
-# 	# Run the entire sentence through the model. Then go "back in time" to look at what the model predicted for each token, starting at the stem.
-# 	# Put the whole text encoding into a tensor, and get the model's comprehensive output
-# 	tokens_tensor = torch.tensor([whole_text_encoding])
-	
-# 	with torch.no_grad():
-# 		outputs = model(tokens_tensor)
-# 		predictions = outputs[0]   
-
-# 	logprobs = []
-# 	# start at the stem and get downstream probabilities incrementally from the model(see above)
-# 	start = -1-len(cw_encoding)
-# 	for j in range(start,-1,1):
-# 			raw_output = []
-# 			for i in predictions[-1][j]:
-# 					raw_output.append(i.item())
-	
-# 			logprobs.append(np.log(softmax(raw_output)))
-			
-# 	# if the critical word is three tokens long, the raw_probabilities should look something like this:
-# 	# [ [0.412, 0.001, ... ] ,[0.213, 0.004, ...], [0.002,0.001, 0.93 ...]]
-# 	# Then for the i'th token we want to find its associated probability
-# 	# this is just: raw_probabilities[i][token_index]
-# 	conditional_probs = []
-# 	for cw,prob in zip(cw_encoding,logprobs):
-# 			conditional_probs.append(prob[cw])
-# 	# now that you have all the relevant probabilities, return their product.
-# 	# This is the probability of the critical word given the context before it.
-
-# 	return np.exp(np.sum(conditional_probs))
 
 def sentence_prob_mean(text):
     # Tokenize the input text and add special tokens