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
| import time | |
| # src klasöründeki modüllerimize erişmek için | |
| from src.data_management.loaders import process_raw_documents | |
| if __name__ == "__main__": | |
| print(">>> Veri yükleyici çalıştırılıyor...") | |
| start_time = time.time() | |
| # Ana işlem fonksiyonumuzu çağırıyoruz | |
| process_raw_documents() | |
| end_time = time.time() | |
| print(f"<<< Veri yükleyici tamamlandı. Süre: {end_time - start_time:.2f} saniye.") | |
| print(f"Kontrol edilmesi gereken dosya: data/processed_data/documents.parquet") |