Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
Generated from Trainer
dataset_size:12800
loss:ContrastiveTensionLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use damand2061/negasibert-ct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use damand2061/negasibert-ct with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("damand2061/negasibert-ct") sentences = [ "Makalah ini diterbitkan dalam format online hanya oleh Metro International.", "Liga ini berkembang dari tahun 1200 hingga 1500, dan terus menjadi semakin penting setelahnya.", "Ini dirancang oleh orang lain selain WL Bottomley / William Lawrence Bottomley.", "Lahan tersebut sekarang menjadi Cagar Alam Bentley Priory, sebuah Situs Kepentingan Ilmiah Khusus." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
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