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import torch
from transformers import BertTokenizerFast, EncoderDecoderModel
import json
class NLPFactGenerator:
def __init__(self, ckpt="mrm8488/bert2bert_shared-german-finetuned-summarization"):
self.max_length = 1024
self.tokenizer = BertTokenizerFast.from_pretrained(ckpt)
self.model = EncoderDecoderModel.from_pretrained(ckpt)
self.sentences_list = []
self.justification_list = []
self.titles_list = []
self.labels_list = []
self.claim_list = []
def load_data(self, filename):
with open(filename, "r") as infile:
self.data = json.load(infile)
def preprocess_data(self):
max_seq_length = 1024
for entry in self.data:
if "data" in entry:
self.titles_list.append(entry["title"])
justification = ' '.join(entry["paragraphs"])
for evidence in self.sentences_list:
if len(evidence) > max_seq_length:
evidence = evidence[:max_seq_length]
_evidence = ' '.join([item["sentence"] for item in entry["data"]])
self.justification_list.append(justification)
self.sentences_list.append(_evidence)
self.labels_list.append(entry["label"])
def generate_fact(self):
max_seq_length = 1024
generated_facts = []
count = 0
for evidence in self.justification_list:
if len(evidence) > max_seq_length:
evidence = evidence[:max_seq_length]
inputs = self.tokenizer([evidence], padding="max_length", truncation=True, max_length=1024, return_tensors="pt")
input_ids = inputs.input_ids
attention_mask = inputs.attention_mask
try:
output = self.model.generate(input_ids, attention_mask=attention_mask)
summary = self.tokenizer.decode(output[0], skip_special_tokens=True)
count+=1
print(count)
generated_facts.append(summary)
except:
print('Input ID: ', len(input_ids))
return generated_facts
if __name__ == "__main__":
fact_generator = NLPFactGenerator()
fact_generator.load_data("finfact_old.json")
fact_generator.preprocess_data()
generated_facts = fact_generator.generate_fact()
generated_data = []
for title, evi, fact in zip(fact_generator.titles_list, fact_generator.sentences_list, generated_facts):
generated_data.append({"title": title, "evidence":evi, "generated_fact": fact})
with open("generated_facts_bert.json", "w") as outfile:
json.dump(generated_data, outfile, indent=4)
device = 'cuda' if torch.cuda.is_available() else 'cpu'
ckpt = 'mrm8488/bert2bert_shared-german-finetuned-summarization'
tokenizer = BertTokenizerFast.from_pretrained(ckpt)
model = EncoderDecoderModel.from_pretrained(ckpt).to(device)
def generate_summary(text):
inputs = tokenizer([text], padding="max_length", truncation=True, max_length=512, return_tensors="pt")
input_ids = inputs.input_ids.to(device)
attention_mask = inputs.attention_mask.to(device)
output = model.generate(input_ids, attention_mask=attention_mask)
return tokenizer.decode(output[0], skip_special_tokens=True)
text = "Your text here..."
generate_summary(text)
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