| from transformers import ( | |
| GPT2LMHeadModel, | |
| GPT2Tokenizer, | |
| ) | |
| import argparse | |
| import warnings | |
| warnings.filterwarnings("ignore") | |
| from fact_checking import FactChecker | |
| import json | |
| from sklearn.metrics import confusion_matrix, classification_report | |
| class FactCheckerApp: | |
| def __init__(self, model_name='fractalego/fact-checking'): | |
| self.tokenizer = GPT2Tokenizer.from_pretrained('gpt2') | |
| self.fact_checking_model = GPT2LMHeadModel.from_pretrained(model_name) | |
| self.fact_checker = FactChecker(self.fact_checking_model, self.tokenizer) | |
| self.sentences_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): | |
| for entry in self.data: | |
| if "data" in entry: | |
| self.titles_list.append(entry["title"]) | |
| _evidence = ' '.join([item["sentence"] for item in entry["data"]]) | |
| self.sentences_list.append(_evidence) | |
| self.labels_list.append(entry["label"]) | |
| def validate_claims(self): | |
| max_seq_length = 1024 | |
| for title, evidence in zip(self.titles_list, self.sentences_list): | |
| try: | |
| if len(title) > max_seq_length: | |
| title = title[:max_seq_length] | |
| if len(evidence) > max_seq_length: | |
| evidence = evidence[:max_seq_length] | |
| print(len(evidence)) | |
| is_claim_true = self.fact_checker.validate(evidence, title) | |
| print(is_claim_true) | |
| self.claim_list.append(is_claim_true) | |
| except IndexError: | |
| self.claim_list.append(None) | |
| def calculate_metrics(self): | |
| conf_matrix = confusion_matrix(self.labels_list, [str(is_claim).lower() for is_claim in self.claim_list]) | |
| cls_report = classification_report(self.labels_list, [str(is_claim).lower() for is_claim in self.claim_list], labels=["true", "false", "neutral"]) | |
| return conf_matrix, cls_report | |
| def parse_args(): | |
| parser = argparse.ArgumentParser(description="Fact Checker Application") | |
| parser.add_argument("--model_name", default="fractalego/fact-checking", help="Name of the fact-checking model to use") | |
| parser.add_argument("--data_file", required=True, help="Path to the JSON data file") | |
| return parser.parse_args() | |
| if __name__ == "__main__": | |
| args = parse_args() | |
| fact_checker_app = FactCheckerApp(model_name=args.model_name) | |
| fact_checker_app.load_data(args.data_file) | |
| fact_checker_app.preprocess_data() | |
| fact_checker_app.validate_claims() | |
| conf_matrix, cls_report = fact_checker_app.calculate_metrics() | |
| print("Confusion Matrix:\n", conf_matrix) | |
| print("Report:\n", cls_report) |