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