helion-v1-embeddings

<div align="center"> <img src="https://imgur.com/sk6NekE.png" alt="Helion-V1 Logo" width="100%"/> </div>

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Helion-V1-Embeddings

Helion-V1-Embeddings is a lightweight text embedding model designed for semantic similarity, search, and retrieval tasks. It converts text into dense vector representations optimized for the Helion ecosystem.

Model Description

• Developed by: DeepXR
• Model type: Sentence Transformer / Text Embedding Model
• Base model: sentence-transformers/all-MiniLM-L6-v2
• Language: English
• License: Apache 2.0
• Embedding Dimension: 384
• Max Sequence Length: 256 tokens

Model Parameters

Intended Use

Helion-V1-Embeddings is designed for:

• Semantic search and information retrieval
• Document similarity comparison
• Clustering and categorization
• Question-answering systems (retrieval component)
• Recommendation systems
• Duplicate detection

Primary Users

• Developers building search systems
• Data scientists working on NLP tasks
• Applications requiring text similarity
• RAG (Retrieval-Augmented Generation) pipelines

Key Features

• Fast Inference: Optimized for quick embedding generation
• Compact Size: Small model footprint (~80MB)
• Good Performance: Balanced accuracy and speed
• Easy Integration: Compatible with sentence-transformers library
• Batch Processing: Efficient for large datasets

Usage

Basic Usage

from sentence_transformers import SentenceTransformer

# Load model
model = SentenceTransformer('DeepXR/Helion-V1-embeddings')

# Encode sentences
sentences = [
    "How do I reset my password?",
    "What is the process for password recovery?",
    "I forgot my login credentials"
]

embeddings = model.encode(sentences)
print(embeddings.shape)  # (3, 384)

Similarity Search

from sentence_transformers import SentenceTransformer, util

model = SentenceTransformer('DeepXR/Helion-V1-embeddings')

# Encode query and documents
query = "How to train a machine learning model?"
documents = [
    "Machine learning training requires data preprocessing",
    "The best way to cook pasta is boiling water",
    "Neural networks need proper hyperparameter tuning"
]

query_embedding = model.encode(query)
doc_embeddings = model.encode(documents)

# Calculate similarity
similarities = util.cos_sim(query_embedding, doc_embeddings)
print(similarities)

Integration with FAISS

from sentence_transformers import SentenceTransformer
import faiss
import numpy as np

model = SentenceTransformer('DeepXR/Helion-V1-embeddings')

# Create embeddings
documents = ["doc1", "doc2", "doc3"]
embeddings = model.encode(documents)

# Create FAISS index
dimension = embeddings.shape[1]
index = faiss.IndexFlatL2(dimension)
index.add(embeddings.astype('float32'))

# Search
query_embedding = model.encode(["search query"])
distances, indices = index.search(query_embedding.astype('float32'), k=3)

Performance

Benchmark Results

Note: These are approximate values. Actual performance may vary.

Training Details

Training Data

The model was fine-tuned on:

• Question-answer pairs
• Semantic similarity datasets
• Document-query pairs
• Paraphrase detection examples

Training Procedure

• Base Model: sentence-transformers/all-MiniLM-L6-v2
• Training Method: Contrastive learning with cosine similarity
• Loss Function: MultipleNegativesRankingLoss
• Batch Size: 64
• Epochs: 3
• Pooling: Mean pooling

Technical Specifications

Model Architecture

• Type: Transformer-based encoder
• Layers: 6
• Hidden Size: 384
• Attention Heads: 12
• Parameters: ~22.7M
• Pooling Strategy: Mean pooling

Input Format

• Max Length: 256 tokens
• Tokenizer: WordPiece
• Normalization: Applied automatically

Output Format

• Embedding Dimension: 384
• Dtype: float32
• Normalization: L2 normalized (optional)

Limitations

• Sequence Length: Limited to 256 tokens (longer texts are truncated)
• Language: Primarily optimized for English
• Domain: General-purpose, may need fine-tuning for specialized domains
• Context: Does not maintain conversation context across multiple inputs
• Model Size: Smaller than state-of-the-art models, trading some accuracy for speed

Use Cases

✅ Good For:

• Semantic search in document collections
• Finding similar questions/answers
• Content recommendation
• Duplicate detection
• Clustering similar documents
• Quick similarity comparisons

❌ Not Suitable For:

• Long document encoding (>256 tokens)
• Real-time generation tasks
• Multilingual applications (without fine-tuning)
• Highly specialized domains without adaptation
• Tasks requiring deep reasoning

Comparison with Other Models

Ethical Considerations

• Bias: May reflect biases present in training data
• Privacy: Do not embed sensitive personal information
• Fairness: Performance may vary across different text types
• Use Responsibly: Consider implications of similarity matching

Integration Examples

LangChain Integration

from langchain.embeddings import HuggingFaceEmbeddings

embeddings = HuggingFaceEmbeddings(
    model_name="DeepXR/Helion-V1-embeddings"
)

text = "This is a sample document"
embedding = embeddings.embed_query(text)

LlamaIndex Integration

from llama_index.embeddings import HuggingFaceEmbedding

embed_model = HuggingFaceEmbedding(
    model_name="DeepXR/Helion-V1-embeddings"
)

embeddings = embed_model.get_text_embedding("Hello world")

Citation

@misc{helion-v1-embeddings,
  author = {DeepXR},
  title = {Helion-V1-Embeddings: Lightweight Text Embedding Model},
  year = {2025},
  publisher = {HuggingFace},
  url = {https://huggingface.co/DeepXR/Helion-V1-embeddings}
}

Model Card Authors

DeepXR Team

Contact

• Repository: https://huggingface.co/DeepXR/Helion-V1-embeddings
• Issues: https://huggingface.co/DeepXR/Helion-V1-embeddings/discussions