metadata
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
base_model: nomic-ai/modernbert-embed-base
pipeline_tag: sentence-similarity
library_name: sentence-transformers
license: mit
ModernBERT Embed Base Distilled
This is a sentence-transformers model distilled from nomic-ai/modernbert-embed-base. It maps sentences & paragraphs to a 256-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
Model Details
Model Description
- Model Type: Sentence Transformer
- Base model: nomic-ai/modernbert-embed-base
- Maximum Sequence Length: 8 192 tokens
- Output Dimensionality: 256 dimensions
- Similarity Function: Cosine Similarity
Model Sources
- Documentation: Sentence Transformers Documentation
- Repository: Sentence Transformers on GitHub
- Hugging Face: Sentence Transformers on Hugging Face
Full Model Architecture
SentenceTransformer(
(0): StaticEmbedding(
(embedding): EmbeddingBag(50368, 256, mode='mean')
)
)
Usage
Direct Usage (Sentence Transformers)
First install the Sentence Transformers library:
pip install -U sentence-transformers
Then you can load this model and run inference.
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("adrien-riaux/distill-modernbert-embed-base")
# Run inference
sentences = [
'The weather is lovely today.',
"It's so sunny outside!",
'He drove to the stadium.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 256]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
Training Details
Distillation Process
The model is distilled using Model2Vec framework. It is a new technique for creating extremely fast and small static embedding models from any Sentence Transformer.
Framework Versions
- Python: 3.11.9
- Sentence Transformers: 3.4.1
- Transformers: 4.48.3
- PyTorch: 2.2.2
- Tokenizers: 0.21.0