Feature Extraction
sentence-transformers
spaCy
English
Turkish
scientific-text-analysis
concept-extraction
network-analysis
natural-language-processing
knowledge-graphs
temporal-analysis
networkx
pyvis
pdf-processing
Instructions to use NextGenC/ChronoSense with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use NextGenC/ChronoSense with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("NextGenC/ChronoSense") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - spaCy
How to use NextGenC/ChronoSense with spaCy:
!pip install https://huggingface.co/NextGenC/ChronoSense/resolve/main/ChronoSense-any-py3-none-any.whl # Using spacy.load(). import spacy nlp = spacy.load("ChronoSense") # Importing as module. import ChronoSense nlp = ChronoSense.load() - Notebooks
- Google Colab
- Kaggle
File size: 752 Bytes
64b5d29 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 | import time
# src klasöründeki modüllerimize erişmek için
from src.extraction.extractor import process_documents_for_extraction
if __name__ == "__main__":
print(">>> Bilgi çıkarıcı çalıştırılıyor...")
print("Not: Bu işlem dokümanların uzunluğuna ve sayısına göre biraz zaman alabilir.")
start_time = time.time()
# Ana çıkarım fonksiyonumuzu çağırıyoruz
process_documents_for_extraction()
end_time = time.time()
print(f"<<< Bilgi çıkarıcı tamamlandı. Süre: {end_time - start_time:.2f} saniye.")
print(f"Kontrol edilmesi gereken dosyalar: data/processed_data/ klasöründeki concepts.parquet, mentions.parquet, relationships.parquet ve güncellenmiş documents.parquet") |