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:
- 2f634b337a68aaf7a93b1a9d88f6d61c70cdb7f6cc5c997695bdabfcee9cd851
- Size of remote file:
- 9.88 kB
- SHA256:
- 49749194f77092f6c3b9e6eacd4ef3a3c34f9d5d1f9c766a51123bdc57885c24
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