---
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](https://www.SBERT.net) model distilled from [nomic-ai/modernbert-embed-base](https://huggingface.co/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](https://huggingface.co/nomic-ai/modernbert-embed-base) <!-- at revision d556a88e332558790b210f7bdbe87da2fa94a8d8 -->
- **Maximum Sequence Length:** 8 192 tokens
- **Output Dimensionality:** 256 dimensions
- **Similarity Function:** Cosine Similarity


### Model Sources

- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)

### Full Model Architecture

```
SentenceTransformer(
  (0): StaticEmbedding(
    (embedding): EmbeddingBag(50368, 256, mode='mean')
  )
)
```

## Usage

### Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

```bash
pip install -U sentence-transformers
```

Then you can load this model and run inference.
```python
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]
```

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## Training Details

### Distillation Process

The model is distilled using [Model2Vec](https://huggingface.co/blog/Pringled/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

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