| import re | |
| from transformers import AutoTokenizer, AutoModelForSeq2SeqLM | |
| import json | |
| class NLPFactGenerator: | |
| def __init__(self, model_name="csebuetnlp/mT5_multilingual_XLSum"): | |
| self.max_length = 1024 | |
| self.tokenizer = AutoTokenizer.from_pretrained(model_name) | |
| self.model = AutoModelForSeq2SeqLM.from_pretrained(model_name) | |
| self.WHITESPACE_HANDLER = lambda k: re.sub('\s+', ' ', re.sub('\n+', ' ', k.strip())) | |
| 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] | |
| input_ids = self.tokenizer( | |
| [self.WHITESPACE_HANDLER(evidence)], | |
| return_tensors="pt", | |
| padding="max_length", | |
| truncation=True, | |
| max_length=1024)["input_ids"] | |
| try: | |
| output_ids = self.model.generate( | |
| input_ids=input_ids, | |
| max_length=128, | |
| no_repeat_ngram_size=2, | |
| num_beams=4)[0] | |
| summary = self.tokenizer.decode( | |
| output_ids, | |
| skip_special_tokens=True, | |
| clean_up_tokenization_spaces=False) | |
| 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_xlsum.json", "w") as outfile: | |
| json.dump(generated_data, outfile, indent=4) | |