Delete evaluate_gpt.py

evaluate_gpt.py

@@ -1,181 +0,0 @@
-# # -*- coding: utf-8 -*-
-# """evaluate.ipynb
-
-# Automatically generated by Colaboratory.
-
-# Original file is located at
-# 		https://colab.research.google.com/drive/1_WZN6_5mgwRgg484xzXMSwCXBQXfr8Vj
-# """
-
-# # -*- coding: utf-8 -*-
-
-# """# code here"""
-# print("**************OUTPUT FILE PATH UPDATED FOR SEED 42 hinglish ******************")
-
-import numpy as np
-import timeit
-import torch
-#from torch.utils.data import DataLoader, TensorDataset, RandomSampler
-import json, argparse
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-import pandas as pd
-from torch.utils.data import Dataset, DataLoader
-import transformers
-from transformers import GPT2Tokenizer, GPT2LMHeadModel
-print('use transformers version = ',transformers.__version__) # make sure it is 2.6.0
-
-
-def add_special_tokens(tokenizer):
-	""" Returns GPT2 tokenizer after adding separator and padding tokens """
-	#tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
-	special_tokens = {'bos_token':'<|startoftext|>','eos_token':'<|endoftext|>', 'pad_token':'<|pad|>','sep_token':'<|summarize|>'}
-	num_add_toks = tokenizer.add_special_tokens(special_tokens)
-	return tokenizer
-
-
-class GPT21024Dataset(Dataset):
-
-	#def __init__(self, root_dir, ids_file, mode='train',length=None):
-	def __init__(self, text, ctext, tokenizer, source_len, summ_len):
-		self.tokenizer = add_special_tokens(tokenizer)
-		# self.data = dataframe
-		self.source_len = source_len
-		self.summ_len = summ_len
-		# self.text = self.data['summary-hinglish']   ## the summary
-		# self.ctext = self.data['dialogue-hinglish']  ## ctext is the article to be summarized
-		self.text = text   ## the summary
-		self.ctext = ctext
-		
-		
-	def __len__(self):
-		return len(self.ctext)
-		#return self.len
-
-	def __getitem__(self,index):
-
-		##articles
-		ctext = str(self.ctext[index])
-		ctext = ' '.join(ctext.split())
-
-		##summaries
-		
-		text = str(self.text[index])
-		text = ' '.join(text.split())
-		
-		
-		tok_data={}
-		tok_data['article']= ctext
-		tok_data['summary']= text
-	
-		input_ids= '<|startoftext|>' + tok_data['article'] + '<|summarize|>' 
-		summary= tok_data['summary']
-		
-		content = self.tokenizer.encode(input_ids, max_length = 512, padding='max_length',truncation=True)
-		summary_target_ids= self.tokenizer.encode( summary, max_length = 512, padding='max_length',truncation=True)
-		
-		#texts[:len(content)] = content
-		texts = torch.tensor(content)
-		summary_target_ids=torch.tensor(summary_target_ids)
-		sample = {'article': texts, 'actual_summary': summary_target_ids, 'sum_idx': len(self.tokenizer.encode(tok_data['article']))}
-		return sample
-
-def gpt_eval(
-	verbose=True,
-	model_name_path=None,
-	src_txt=None,
-	tar_txt=None,
-	gen_path=None,
-	scor_path=None,
-	batch_size=4
-):
-		"""
-		"""
-		predictions=[]
-		actuals=[]
-		
-		model = GPT2LMHeadModel.from_pretrained(model_name_path)
-		tokenizer = GPT2Tokenizer.from_pretrained(model_name_path)
-	
-		# Add a [CLS] to the vocabulary (we should train it also!)
-		#special_tokens = {'bos_token':'<|startoftext|>','eos_token':'<|endoftext|>','pad_token':'<pad>','additional_special_tokens':['<|keyword|>','<|summarize|>']}
-		#tokenizer.add_special_tokens(special_tokens)
-	
-		"""
-		special_tokens = {'pad_token':'<|pad|>','sep_token':'<|summarize|>'}
-		tokenizer.add_special_tokens(special_tokens)
-	
-		#assert len(tokenizer) == 50261, "tokenizer size is not 50261"
-		model.resize_token_embeddings(len(tokenizer))
-		print(' ')
-		"""
-	
-		model = model.to(device)
-		model.eval()
-		"""
-		input_text =  input_text +' <|summarize|>'
-		input_token = tokenizer.encode(input_text)
-		input_token_torch = torch.tensor(input_token, dtype=torch.long)
-		"""
-		
-		val_params = {
-		'batch_size':batch_size,
-		'shuffle': False,
-		'num_workers': 0
-		}
-
-		sp= open(src_txt,'r')
-		src= sp.readlines()
-		sp.close()
-		tp = open(tar_txt, 'r')
-		tar=tp.readlines()
-		tp.close()
-		val_set = GPT21024Dataset(tar, src,tokenizer, 512, 150)
-		val_loader = DataLoader(val_set, **val_params)
-		
-		with torch.no_grad():
-			for _, data in enumerate(val_loader, 0):
-				
-				
-				
-				target_output = data['actual_summary'].to(device, dtype = torch.long)
-				input_ids = data['article']
-				input_ids=input_ids.to(device)
-				#print(input_ids)
-	
-				print(f'Length of the input context: {len(input_ids[0])}')
-				print(f'BEAM SIZE: {4}')
-				#input_ids.unsqueeze(0).to(device)
-				generated_output = model.generate(
-					input_ids=input_ids,
-					max_length= 582,
-					min_length = 562 ,
-					temperature=1.0,
-					decoder_start_token_id= '<|summarize|>',
-					num_beams=4,
-					num_return_sequences=1)
-		
-				# print(f' Generated_output: {generated_output}')
-				
-				preds=[]
-				target=[]
-				ids=[]
-				for g in generated_output:
-					preds.append(tokenizer.decode(g[len(input_ids[0]):] ,  skip_special_tokens=True))
-				
-				#preds = [tokenizer.decode(g, skip_special_tokens=True, clean_up_tokenization_spaces=True) for g in                              generated_output]
-				for t in target_output:
-					target.append(tokenizer.decode(t ,  skip_special_tokens=True))
-				#target = [tokenizer.decode(t, skip_special_tokens=True, clean_up_tokenization_spaces=True)for t in y]
-				if _%100==0:
-					print(f'Completed {_}')
-
-				predictions.extend(preds)
-				actuals.extend(target)
-		
-		gp= open(gen_path, 'w')
-		for pre in predictions:
-			gp.write(pre+"\n")
-		gp.close()
-
-
-