Sentence Similarity
sentence-transformers
PyTorch
TensorBoard
English
deberta-v2
feature-extraction
Generated from Trainer
dataset_size:526885
loss:GISTEmbedLoss
loss:CoSENTLoss
loss:OnlineContrastiveLoss
loss:MultipleNegativesSymmetricRankingLoss
loss:MarginMSELoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use bobox/DeBERTaV3-small-GeneralSentenceTransformer-v2-checkpoints-tmp with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use bobox/DeBERTaV3-small-GeneralSentenceTransformer-v2-checkpoints-tmp with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("bobox/DeBERTaV3-small-GeneralSentenceTransformer-v2-checkpoints-tmp") sentences = [ "A man in a Santa Claus costume is sitting on a wooden chair holding a microphone and a stringed instrument.", "The man is is near the ball.", "The man is wearing a costume.", "People are having a picnic." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 005382a871c83798daa09f129b1d5b2739b811267a133bbf0a234cd322c7b0e2
- Size of remote file:
- 565 MB
- SHA256:
- 7d7823fbc9158972fe4a2cec91802737230154657abd47b1f0f2065b9a223127
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.