Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
dense
Generated from Trainer
dataset_size:649257
loss:MultipleNegativesSymmetricRankingLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use LamaDiab/v2MiniLM-V24Data-256hardnegativesBATCH-SemanticEngine with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use LamaDiab/v2MiniLM-V24Data-256hardnegativesBATCH-SemanticEngine with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("LamaDiab/v2MiniLM-V24Data-256hardnegativesBATCH-SemanticEngine") sentences = [ "elephant ear alocasia", "peace", " plant", "plant" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c9b476e5a96a6545f4a4d925e2a64272fab1b273e2dd7778bf7289666a19a58f
- Size of remote file:
- 988 Bytes
- SHA256:
- dedfe8cb4a06ab2258df9e6715e587995fc3ba7fe66eefc7b67c83caf6fd32ee
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