vicreg_our_contrast

VICReg Our Contrast Model

Model Description

SODA-VEC embedding model trained with VICReg Our Contrast loss function. This model uses normalized embeddings with covariance, feature, and dot product losses (including off-diagonal terms) to learn rich biomedical text representations.

This model is part of the SODA-VEC (Scientific Open Domain Adaptation for Vector Embeddings) project, which focuses on creating high-quality embedding models for biomedical and life sciences text.

Key Features:

• Trained on 26.5M biomedical title-abstract pairs from PubMed Central
• Based on ModernBERT-base architecture
• Optimized for biomedical text similarity and semantic search
• Produces 768-dimensional embeddings with mean pooling

Training Details

Training Data

• Dataset: EMBO/soda-vec-data-full_pmc_title_abstract_paired
• Size: 26,473,900 training pairs
• Source: Complete PubMed Central baseline (July 2024)
• Format: Paired title-abstract examples optimized for contrastive learning

Training Procedure

Loss Function: VICReg Our Contrast: normalized embeddings with covariance loss, feature loss, and dot product loss (diagonal + off-diagonal)

We have implemented a series of changes from the original VICREG in the paper from Meta. Here we show the main differences:

Coefficients: cov=1.0, feature=1.0, dot=1.0 Base Model: answerdotai/ModernBERT-base

Training Configuration:

• GPUs: 4
• Batch Size per GPU: 16
• Gradient Accumulation: 4
• Effective Batch Size: 256
• Learning Rate: 2e-05
• Warmup Steps: 100
• Pooling Strategy: mean
• Epochs: 1 (full dataset pass)

Training Command:

python scripts/soda-vec-train.py --config vicreg_our_contrast --coeff_cov 1 --coeff_feature 1 --coeff_dot 1 --push_to_hub --hub_org EMBO --save_limit 5

Model Architecture

• Base Architecture: ModernBERT-base (12 layers, 768 hidden size)
• Pooling: Mean pooling over token embeddings
• Output Dimension: 768
• Normalization: L2-normalized embeddings (for VICReg-based models)

Usage

Using Sentence-Transformers

from sentence_transformers import SentenceTransformer

# Load the model
model = SentenceTransformer("EMBO/vicreg_our_contrast")

# Encode sentences
sentences = [
    "CRISPR-Cas9 gene editing in human cells",
    "Genome editing using CRISPR technology"
]

embeddings = model.encode(sentences)
print(f"Embedding shape: {embeddings.shape}")

# Compute similarity
from sentence_transformers.util import cos_sim
similarity = cos_sim(embeddings[0], embeddings[1])
print(f"Similarity: {similarity.item():.4f}")

Using Hugging Face Transformers

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

# Load model and tokenizer
tokenizer = AutoTokenizer.from_pretrained("EMBO/vicreg_our_contrast")
model = AutoModel.from_pretrained("EMBO/vicreg_our_contrast")

# Encode sentences
sentences = [
    "CRISPR-Cas9 gene editing in human cells",
    "Genome editing using CRISPR technology"
]

inputs = tokenizer(sentences, padding=True, truncation=True, return_tensors="pt")
with torch.no_grad():
    outputs = model(**inputs)
    
# Mean pooling
embeddings = outputs.last_hidden_state.mean(dim=1)

# Normalize (for VICReg models)
embeddings = F.normalize(embeddings, p=2, dim=1)

# Compute similarity
similarity = F.cosine_similarity(embeddings[0:1], embeddings[1:2])
print(f"Similarity: {similarity.item():.4f}")

Evaluation

The model has been evaluated on comprehensive biomedical benchmarks including:

• Journal-Category Classification: Matching journals to BioRxiv subject categories
• Title-Abstract Similarity: Discriminating between related and unrelated paper pairs
• Field-Specific Separability: Distinguishing between different biological fields
• Semantic Search: Retrieval quality on biomedical text corpora

For detailed evaluation results, see the SODA-VEC benchmark notebooks.

Intended Use

This model is designed for:

• Biomedical Semantic Search: Finding relevant papers, abstracts, or text passages
• Scientific Text Similarity: Computing similarity between biomedical texts
• Information Retrieval: Building search systems for scientific literature
• Downstream Tasks: As a base for fine-tuning on specific biomedical tasks
• Research Applications: Academic and research use in life sciences

Limitations

• Domain Specificity: Optimized for biomedical and life sciences text; may not perform as well on general domain text
• Language: English only
• Text Length: Optimized for titles and abstracts; longer documents may require chunking
• Bias: Inherits biases from the training data (PubMed Central corpus)

Citation

If you use this model, please cite:

@software{soda_vec,
  title = {SODA-VEC: Scientific Open Domain Adaptation for Vector Embeddings},
  author = {EMBO},
  year = {2024},
  url = {https://github.com/EMBO/soda-vec}
}

Model Card Contact

For questions or issues, please open an issue on the SODA-VEC GitHub repository.

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Model Card Generated: 2025-11-10