orange-nomic-1536

A high-performance embedding model from the Orange organization, built by extending nomic-ai/nomic-embed-text-v1.5 to 1536 dimensions using a learnable linear projection.

Overview

This model is a modified version of Nomic Embed v1.5, which itself is an improvement over the original Nomic Embed model. The key enhancement is that this model has been projected from the native 768-dimensional space to a 1536-dimensional space while preserving semantic similarity.

Architecture

The Orange/nomic-embed-text-v1.5 model uses a three-stage pipeline:

Transformer (768-dim) → Pooling → Dense Projection (1536-dim)

• Base Model: nomic-ai/nomic-embed-text-v1.5 (GPT-style BERT with swiglu activation)
• Projection Method: Linear layer with weight matrix (1536 x 768)
  • Top 768 rows: sqrt(2) * I (scales original dimensions by sqrt(2))
  • Bottom 768 rows: zeros (zero-padding)
• Result: Preserves cosine similarity while doubling dimensions

Key Properties

• Embedding Dimension: 1536
• Sequence Length: 8192 tokens (supports long contexts)
• Similarity Metric: Cosine similarity preserved from base model
• Matryoshka: The model supports adjustable embedding dimensions (Matryoshka Representation Learning)

Usage

Important: Task Instruction Prefix

The model requires a task instruction prefix in the input text. This tells the model which task you're performing.

For RAG (Retrieval-Augmented Generation)

from sentence_transformers import SentenceTransformer

model = SentenceTransformer("Orange/orange-nomic-v1.5-1536", trust_remote_code=True)

# Embed documents
documents = ['search_document: The quick brown fox jumps over the lazy dog']
doc_embeddings = model.encode(documents)

# Embed queries
queries = ['search_query: What animal is in the sentence?']
query_embeddings = model.encode(queries)

Available Task Prefixes

Python Examples

Using Sentence Transformers

from sentence_transformers import SentenceTransformer
import torch.nn.functional as F

model = SentenceTransformer("Orange/orange-nomic-v1.5-1536", trust_remote_code=True)

# Encode sentences
sentences = ['search_query: What is TSNE?', 'search_query: Who is Laurens van der Maaten?']
embeddings = model.encode(sentences, convert_to_tensor=True)

# Optional: Apply layer normalization and truncate for Matryoshka
matryoshka_dim = 768  # Can use any dimension <= 1536
embeddings = F.layer_norm(embeddings, normalized_shape=(embeddings.shape[1],))
embeddings = embeddings[:, :matryoshka_dim]
embeddings = F.normalize(embeddings, p=2, dim=1)

print(embeddings.shape)  # torch.Size([2, 768])

Using Transformers Directly

import torch
import torch.nn.functional as F
from transformers import AutoTokenizer, AutoModel

def mean_pooling(model_output, attention_mask):
    token_embeddings = model_output[0]
    input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
    return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)

model_name = "Orange/orange-nomic-v1.5-1536"
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
model = AutoModel.from_pretrained(model_name, trust_remote_code=True)
model.eval()

sentences = ['search_query: What is TSNE?', 'search_query: Who is Laurens van der Maaten?']
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')

with torch.no_grad():
    model_output = model(**encoded_input)

embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
embeddings = F.layer_norm(embeddings, normalized_shape=(embeddings.shape[1],))
embeddings = embeddings[:, :1536]  # Use full 1536-dim
embeddings = F.normalize(embeddings, p=2, dim=1)
print(embeddings.shape)  # torch.Size([2, 1536])

Adjusting Dimensionality (Matryoshka)

This model supports Matryoshka Representation Learning - you can use smaller embedding dimensions:

Example with 512 dimensions:

embeddings = embeddings[:, :512]  # Truncate to 512 dimensions

Model Performance

MTEB Benchmark Results

See the model card on HuggingFace for the complete MTEB leaderboard results.

Differences from Base Model

Use Cases

This 1536-dimensional model is particularly useful for:

• Applications requiring higher-dimensional embeddings
• Maintaining compatibility with existing 1536-dim workflows
• Scenarios where extra dimensionality provides marginal benefits
• Experiments comparing different embedding dimensions

References

• Nomic Embed v1.5: [https://huggingface.co/nomic-ai/nomic-embed-text-v1.5](https://huggingface.co/nomic-ai/nomic-