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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

Parameter	Value	Description
Architecture	BERT-based	6-layer transformer encoder
Hidden Size	384	Dimension of hidden layers
Attention Heads	12	Number of attention heads
Intermediate Size	1536	Feed-forward layer size
Vocab Size	30,522	WordPiece vocabulary
Max Position Embeddings	512	Maximum sequence length
Pooling Strategy	Mean Pooling	Average of token embeddings
Output Dimension	384	Final embedding size
Total Parameters	~22.7M	Trainable parameters
Model Size	~80MB	Disk footprint

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

Task	Score	Notes
STS Benchmark	~0.78	Semantic Textual Similarity
Retrieval (BEIR)	~0.42	Average across datasets
Speed (CPU)	~2000 sentences/sec	Batch size 32
Speed (GPU)	~15000 sentences/sec	Batch size 128

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

Model	Dim	Speed	Accuracy	Size
Helion-V1-Embeddings	384	Fast	Good	80MB
all-MiniLM-L6-v2	384	Fast	Good	80MB
all-mpnet-base-v2	768	Medium	Better	420MB
text-embedding-ada-002	1536	API	Best	API

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