Sentence Similarity
sentence-transformers
PyTorch
English
deberta-v2
feature-extraction
Generated from Trainer
dataset_size:67190
loss:AdaptiveLayerLoss
loss:MultipleNegativesRankingLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use bobox/DeBERTaV3-small-ST-AdaptiveLayers-ep2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use bobox/DeBERTaV3-small-ST-AdaptiveLayers-ep2 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("bobox/DeBERTaV3-small-ST-AdaptiveLayers-ep2") sentences = [ "A worker peers out from atop a building under construction.", "The man pleads for mercy.", "People and a baby crossing the street.", "A person is atop of a building." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from bobox/DeBERTaV3-small-ST-AdaptiveLayers-ep2: direct link, hf CLI and curl.
- Browser
- Download file 565 MB
-
https://huggingface.co/bobox/DeBERTaV3-small-ST-AdaptiveLayers-ep2/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://bobox/DeBERTaV3-small-ST-AdaptiveLayers-ep2/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/bobox/DeBERTaV3-small-ST-AdaptiveLayers-ep2/resolve/main/pytorch_model.bin
565 MB
- Xet hash:
- ca49870dccefd0cd9cb279635e564d44ed723498feb6be82433949ff29b16c7c
- Size of remote file:
- 565 MB
- SHA256:
- 302073fb610aae136ce3650813dfe4e09b6216dbe2b7ded3d56cad2822c48514
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.