Sentence Similarity
sentence-transformers
PyTorch
English
deberta-v2
feature-extraction
Generated from Trainer
dataset_size:314315
loss:AdaptiveLayerLoss
loss:MultipleNegativesRankingLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use bobox/DeBERTaV3-small-ST-AdaptiveLayerAllNormalized with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use bobox/DeBERTaV3-small-ST-AdaptiveLayerAllNormalized with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("bobox/DeBERTaV3-small-ST-AdaptiveLayerAllNormalized") sentences = [ "The pitcher is pitching the ball in a game of baseball.", "the lady digs into the ground", "A group of people are sitting at tables.", "The pitcher throws the ball." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Download modules.json from bobox/DeBERTaV3-small-ST-AdaptiveLayerAllNormalized: direct link, hf CLI and curl.
- Browser
- Download file 229 Bytes
-
https://huggingface.co/bobox/DeBERTaV3-small-ST-AdaptiveLayerAllNormalized/resolve/refs%2Fpr%2F1/modules.json
- Command line
-
hf download hf://bobox/DeBERTaV3-small-ST-AdaptiveLayerAllNormalized@refs/pr/1/modules.json
-
curl -L -o modules.json https://huggingface.co/bobox/DeBERTaV3-small-ST-AdaptiveLayerAllNormalized/resolve/refs%2Fpr%2F1/modules.json
229 Bytes
| [ | |
| { | |
| "idx": 0, | |
| "name": "0", | |
| "path": "", | |
| "type": "sentence_transformers.models.Transformer" | |
| }, | |
| { | |
| "idx": 1, | |
| "name": "1", | |
| "path": "1_Pooling", | |
| "type": "sentence_transformers.models.Pooling" | |
| } | |
| ] |