Dot Only Model
Model Description
SODA-VEC embedding model trained with dot product loss only. This model uses normalized embeddings with only contrastive learning (dot product) to learn 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: Dot Only: normalized embeddings with only dot product loss (diagonal + off-diagonal)
Coefficients: 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 dot_only --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/dot_only")
# 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/dot_only")
model = AutoModel.from_pretrained("EMBO/dot_only")
# 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}")
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
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/source-data/soda-vec}
}
Model Card Contact
For questions or issues, please open an issue on the SODA-VEC GitHub repository.
Model Card Generated: 2025-11-10