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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) |