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| from typing import Dict, Optional, Union | |
| from transformers import ( | |
| AutoModelForSequenceClassification, | |
| AutoModelForTokenClassification, | |
| BertTokenizer, | |
| AutoTokenizer, | |
| TokenClassificationPipeline, | |
| ) | |
| from pipeline import NewsPipeline | |
| CATEGORY_EMOJIS = { | |
| "Automobile": "🚗", | |
| "Entertainment": "🍿", | |
| "Politics": "⚖️", | |
| "Science": "🧪", | |
| "Sports": "🏀", | |
| "Technology": "💻", | |
| "World": "🌍", | |
| } | |
| FAKE_EMOJIS = {"Fake": "👻", "Real": "👍"} | |
| CLICKBAIT_EMOJIS = {"Clickbait": "🎣", "Normal": "✅"} | |
| class NewsAnalyzer: | |
| def __init__( | |
| self, | |
| category_model_name: str, | |
| fake_model_name: str, | |
| clickbait_model_name: str, | |
| ner_model_name: str, | |
| ) -> None: | |
| self.category_pipe = NewsPipeline( | |
| model=AutoModelForSequenceClassification.from_pretrained( | |
| category_model_name | |
| ), | |
| tokenizer=BertTokenizer.from_pretrained(category_model_name), | |
| emojis=CATEGORY_EMOJIS, | |
| ) | |
| self.fake_pipe = NewsPipeline( | |
| model=AutoModelForSequenceClassification.from_pretrained(fake_model_name), | |
| tokenizer=BertTokenizer.from_pretrained(fake_model_name), | |
| emojis=FAKE_EMOJIS, | |
| ) | |
| self.clickbait_pipe = NewsPipeline( | |
| model=AutoModelForSequenceClassification.from_pretrained( | |
| clickbait_model_name | |
| ), | |
| tokenizer=BertTokenizer.from_pretrained(clickbait_model_name), | |
| emojis=CLICKBAIT_EMOJIS, | |
| ) | |
| self.ner_pipe = TokenClassificationPipeline( | |
| model=AutoModelForTokenClassification.from_pretrained(ner_model_name), | |
| tokenizer=AutoTokenizer.from_pretrained(ner_model_name), | |
| aggregation_strategy="simple", | |
| ) | |
| def __call__( | |
| self, headline: str, content: Optional[str] = None | |
| ) -> Dict[str, Union[str, float]]: | |
| return { | |
| "category": self.category_pipe(headline=headline, content=content), | |
| "fake": self.fake_pipe(headline=headline, content=content), | |
| "clickbait": self.clickbait_pipe(headline=headline, content=None), | |
| "ner": { | |
| "headline": self.ner_pipe(headline), | |
| "content": self.ner_pipe(content) if content else None, | |
| }, | |
| } | |