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
File size: 286 Bytes
bac37d8 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | {
"bos_token": "[CLS]",
"cls_token": "[CLS]",
"eos_token": "[SEP]",
"mask_token": "[MASK]",
"pad_token": "[PAD]",
"sep_token": "[SEP]",
"unk_token": {
"content": "[UNK]",
"lstrip": false,
"normalized": true,
"rstrip": false,
"single_word": false
}
}
|