harrier_embedding_oss_0.6b

Model Overview

Model Summary

Harrier Embedding 0.6B is a highly efficient multilingual text embedding model developed by Microsoft, built on top of the Qwen3 architecture. It is designed to generate fixed-size sentence embeddings suitable for semantic similarity, dense retrieval, and clustering tasks across 94+ languages.

Despite its compact size of just few million parameters, it achieves strong performance on embedding benchmarks, scoring 69.0 on Multilingual MTEB v2, making it an excellent choice for resource-constrained environments requiring high-quality multilingual retrieval.

Architecture Highlights:

• Base Architecture: Qwen3 (Transformer Decoder)
• Pooling Strategy: Last non-padding token pooling (default)
• Normalization: L2 normalized embeddings (default)
• Languages Supported: 94+

Intended Use

• Semantic Search / RAG: Generating embeddings for documents and queries in Retrieval-Augmented Generation systems.
• Clustering: Grouping similar text documents together based on spatial distance.
• Classification: Using the resulting embeddings as features for downstream classification tasks.

Training

All models are trained with contrastive learning objectives on a large-scale mixture of multilingual datasets covering diverse tasks. The 270m and 0.6b variants are additionally trained with knowledge distillation from larger embedding models.

Model Summary

A 270M parameter multilingual text embedding model based on the Gemma 3 architecture by Microsoft. Fine-tuned for dense retrieval and semantic similarity across 94+ languages, achieving 66.5 on Multilingual MTEB v2.

Overview

Harrier-OSS 270M is a highly efficient, multilingual text embedding model developed by Microsoft. It is built upon the decoder-only Gemma 3 270M architecture and fine-tuned specifically for dense vector representations.

The model maps variable-length text inputs into fixed-size 640-dimensional vectors, making it ideal for downstream NLP tasks such as:

• Semantic Search and Dense Retrieval
• Clustering and Topic Modeling
• Retrieval-Augmented Generation (RAG) pipelines
• Zero-shot cross-lingual information retrieval

Despite its small size (268M parameters), Harrier-OSS 270M achieves highly competitive performance across 94+ languages, scoring a 66.5 on the Multilingual MTEB v2 benchmark.

Architecture

• Backbone: Gemma 3 (Decoder-only causal language model)
• Parameters: 268 Million
• Hidden Dimension / Output Size: 640
• Pooling Strategy: Last-token pooling (standard for causal architectures)
• Normalization: L2-normalized outputs (ready for fast dot-product similarity)

Model Details

• [Harrier Quickstart Notebook](coming soon..)
• Harrier API Documentation
• Harrier Model Card
• KerasHub Beginner Guide
• KerasHub Model Publishing Guide

Installation

Keras and KerasHub can be installed with:

pip install -U -q keras-hub
pip install -U -q keras

Jax, TensorFlow, and Torch come preinstalled in Kaggle Notebooks. For instructions on installing them in another environment see the Keras Getting Started page.

Presets

Example Usage

Basic Semantic Search

import keras_hub

# Load the embedder
embedder = keras_hub.models.Qwen3TextEmbedder.from_preset(
   "harrier_embedding_oss_0.6b",
)


# Define query and documents
query = "Which planet is known as the Red Planet?"
documents = [
    "Mars is often referred to as the Red Planet.",
    "Venus is often called Earth's twin.",
]

# For best results with Harrier, prepend the instruction to queries
q_emb = embedder.encode_text(
    "Instruct: Given a web search query, retrieve relevant passages\n"
    f"Query: {query}"
)
d_embs = embedder.encode_text(documents)

# Compute cosine similarity
sims = embedder.similarity(q_emb, d_embs)
best_match_idx = sims.numpy().argmax()

print(f"Best match: {documents[best_match_idx]}")

Extracting Raw Embeddings

import keras_hub

embedder = keras_hub.models.Qwen3TextEmbedder.from_preset(
    "harrier_embedding_oss_0.6b",
)

# Encode a batch of texts
embeddings = embedder.encode_text([
    "The quick brown fox jumps over the lazy dog.",
    "Keras makes machine learning accessible."
])

print(embeddings.shape) # (2, 1024)

Example Usage with Hugging Face URI

Basic Semantic Search

import keras_hub

# Load the embedder
embedder = keras_hub.models.Qwen3TextEmbedder.from_preset(
   "hf://keras/harrier_embedding_oss_0.6b",
)


# Define query and documents
query = "Which planet is known as the Red Planet?"
documents = [
    "Mars is often referred to as the Red Planet.",
    "Venus is often called Earth's twin.",
]

# For best results with Harrier, prepend the instruction to queries
q_emb = embedder.encode_text(
    "Instruct: Given a web search query, retrieve relevant passages\n"
    f"Query: {query}"
)
d_embs = embedder.encode_text(documents)

# Compute cosine similarity
sims = embedder.similarity(q_emb, d_embs)
best_match_idx = sims.numpy().argmax()

print(f"Best match: {documents[best_match_idx]}")

Extracting Raw Embeddings

import keras_hub

embedder = keras_hub.models.Qwen3TextEmbedder.from_preset(
    "hf://keras/harrier_embedding_oss_0.6b",
)

# Encode a batch of texts
embeddings = embedder.encode_text([
    "The quick brown fox jumps over the lazy dog.",
    "Keras makes machine learning accessible."
])

print(embeddings.shape) # (2, 1024)