Sentence Similarity
sentence-transformers
Safetensors
modernbert
feature-extraction
Generated from Trainer
dataset_size:8963241
loss:EmbedDistillLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use sobamchan/monnem-large-v0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use sobamchan/monnem-large-v0 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("sobamchan/monnem-large-v0") sentences = [ "A man in shorts and a woman in a black and white polka dot bikini sunbathing on the beach.", "A man is standing in a field with a green plant in it.", "A woman preparing some pork.", "uh-huh uh-huh well what other movies have you seen then lately" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 10fbc15da436da64b0eaeee400613124983e474ded39305d33374e3ed419311d
- Size of remote file:
- 1.58 GB
- SHA256:
- 5ba82f09c0938cfd7858b4edfc27c3871ab7f7eb133d95514f39f7507e4c6634
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.