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Хорошо! 9 баллов, добавьте еще усложнение по поиску из homework/IMPROVEMENTS.md (как у вас написано, например, используйте Dense-эмбеддинги) для 10 баллов |
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Ссылка на репозиторий с заданием Repo URL: https://github.com/llzbth17/rag_homework/
Автор Грингруз Елизавета, БАСБ251
ФИО / ник: llzbth17
Комментарий Кратко:
RAG-pipeline на TF-IDF + cosine similarity, повторяющий структуру MaratNotes/rag-tutorial.
Источник данных:
ag_news (1200 новостей) (huggingface) + (CSV mirror с GitHub: mhjabreel/CharCnn_Keras) — корпус новостных заголовков и аннотаций по 4 категориям: World, Sports, Business, Sci/Tech.
Чанков в индексе: 1200
Индекс: scikit-learn TfidfVectorizer + cosine similarity UI: Streamlit
Тесты: 17 pytest, все зелёные
Улучшено: stop-words фильтрация в TF-IDF