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# # -*- 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()
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