DeepXR/helion-v1-embeddings

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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