Sentence Similarity
sentence-transformers
Safetensors
modernbert
feature-extraction
dense
Generated from Trainer
dataset_size:3375201
loss:MSELoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use johnnyboycurtis/ModernBERT-small-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use johnnyboycurtis/ModernBERT-small-v2 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("johnnyboycurtis/ModernBERT-small-v2") sentences = [ "What is Weboob. Weboob is a collection of applications able to interact with websites, without requiring the user to open them in a browser. It also provides well-defined APIs to talk to websites lacking one.", "Moreno and colleagues (Mossio et al. 2009; Moreno & Mossio 2015) have also claimed that their organizational approach unifies across backwardlooking and forward-looking accounts by describing activities that atemporally account for the continuing persistence of traits.", "average cost of a dj for a wedding 2015", "CIALIS tablets should not be split, crushed or separated in any way. Do not split CIALIS tablets; the entire dose should be taken. Splitting or crushing may result in the patient receiving more or less than the desired dose. References. CIALIS [package insert]." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d5193eda73aa3f43c79223b6e60068988178ec458cdf560c64069bf9ec7df73c
- Size of remote file:
- 5.52 kB
- SHA256:
- 6afd6a2a601d64240a77c598b9a55481931ce2653300111e6a36ed83a9c58da4
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