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from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
import argparse
import json
from sklearn.metrics import confusion_matrix, accuracy_score, recall_score, precision_score, classification_report, f1_score
class FactCheckerApp:
def __init__(self, hg_model_hub_name='ynie/electra-large-discriminator-snli_mnli_fever_anli_R1_R2_R3-nli'):
# hg_model_hub_name = "ynie/roberta-large-snli_mnli_fever_anli_R1_R2_R3-nli"
# hg_model_hub_name = "ynie/albert-xxlarge-v2-snli_mnli_fever_anli_R1_R2_R3-nli"
# hg_model_hub_name = "ynie/bart-large-snli_mnli_fever_anli_R1_R2_R3-nli"
# hg_model_hub_name = "ynie/electra-large-discriminator-snli_mnli_fever_anli_R1_R2_R3-nli"
# hg_model_hub_name = "ynie/xlnet-large-cased-snli_mnli_fever_anli_R1_R2_R3-nli"
self.max_length = 248
self.tokenizer = AutoTokenizer.from_pretrained(hg_model_hub_name)
self.model = AutoModelForSequenceClassification.from_pretrained(hg_model_hub_name)
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, threshold=0.5):
for title, evidence in zip(self.titles_list, self.sentences_list):
tokenized_input_seq_pair = self.tokenizer.encode_plus(evidence, title,
max_length=self.max_length,
return_token_type_ids=True, truncation=True)
input_ids = torch.Tensor(tokenized_input_seq_pair['input_ids']).long().unsqueeze(0)
token_type_ids = torch.Tensor(tokenized_input_seq_pair['token_type_ids']).long().unsqueeze(0)
attention_mask = torch.Tensor(tokenized_input_seq_pair['attention_mask']).long().unsqueeze(0)
outputs = self.model(input_ids,
attention_mask=attention_mask,
labels=None)
predicted_probability = torch.softmax(outputs.logits, dim=1)[0].tolist()
entailment_prob = predicted_probability[0]
neutral_prob = predicted_probability[1]
contradiction_prob = predicted_probability[2]
if entailment_prob > threshold:
is_claim_true = "true"
elif neutral_prob > threshold:
is_claim_true = "neutral"
else:
is_claim_true = "false"
print(is_claim_true)
self.claim_list.append(is_claim_true)
def calculate_metrics(self):
precision = precision_score(self.labels_list, self.claim_list, average='macro')
accuracy = accuracy_score(self.labels_list, self.claim_list)
f1_scoree = f1_score(self.labels_list, self.claim_list, average='macro')
conf_matrix = confusion_matrix(self.labels_list, self.claim_list)
recall_metric = recall_score(self.labels_list, self.claim_list, pos_label="true", average="macro")
cls_report = classification_report(self.labels_list, self.claim_list, labels=["true", "false", "neutral"])
return precision, accuracy, f1_scoree, conf_matrix, recall_metric, cls_report
def parse_args():
parser = argparse.ArgumentParser(description="Fact Checker Application")
parser.add_argument("--model_name", default="ynie/bart-large-snli_mnli_fever_anli_R1_R2_R3-nli", help="Name of the pre-trained model to use")
parser.add_argument("--data_file", required=True, help="Path to the JSON data file")
parser.add_argument("--threshold", type=float, default=0.5, help="Threshold for claim validation")
return parser.parse_args()
if __name__ == "__main__":
args = parse_args()
fact_checker_app = FactCheckerApp(hg_model_hub_name=args.model_name)
fact_checker_app.load_data(args.data_file)
fact_checker_app.preprocess_data()
fact_checker_app.validate_claims(threshold=args.threshold)
precision, accuracy, f1_scoree, conf_matrix, recall_metric, cls_report = fact_checker_app.calculate_metrics()
print("Precision:", precision)
print("Accuracy:", accuracy)
print("F1 score:", f1_scoree)
print("Recall: ", recall_metric)
print("Confusion Matrix:\n", conf_matrix)
print("Report:\n", cls_report)
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