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
- Xet hash:
- 3b85c49fbd6c6370107246d3b6056e74134db0dccef5b06c06316afdceae3994
- Size of remote file:
- 3.92 kB
- SHA256:
- 158b86abf339fb8862e128d62c1e64e4dc4536a3937eaf85e44e839eb12448f4
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.