Datasets:

EMBO
/

soda-vec-data-full_pmc_title_abstract_paired

license: cc-by-4.0
task_categories:
  - sentence-similarity
  - feature-extraction
language:
  - en
tags:
  - biomedical
  - scientific-literature
  - pubmed
  - pmc
  - embeddings
  - soda-vec
  - negative-sampling
  - sentence-transformers
size_categories:
  - 10M<n<100M

SODA-VEC Paired Dataset for Negative Sampling

This is a paired version of the SODA-VEC dataset, specifically formatted for negative sampling training with MultipleNegativesRankingLoss.

Dataset Overview

Total examples: 26,573,900
Format: Paired (anchor-positive) for contrastive learning
Source: EMBO/soda-vec-data-full_pmc_title_abstract
Purpose: Training sentence transformers with negative sampling

Data Format

Each example contains:

anchor (string): The title of the scientific article
positive (string): The abstract of the scientific article
pmcid (string): PubMed Central ID for reference

Usage

from datasets import load_dataset
from sentence_transformers import SentenceTransformer
from sentence_transformers.losses import MultipleNegativesRankingLoss

# Load the paired dataset
dataset = load_dataset("EMBO/soda-vec-data-full_pmc_title_abstract_paired")

# Use with MultipleNegativesRankingLoss
model = SentenceTransformer('all-MiniLM-L6-v2')
loss = MultipleNegativesRankingLoss(model)

# The dataset is ready for training
train_dataset = dataset['train']

Training Example

from sentence_transformers import SentenceTransformerTrainer, SentenceTransformerTrainingArguments

# Training arguments
args = SentenceTransformerTrainingArguments(
    output_dir="./soda-vec-negative-sampling",
    num_train_epochs=3,
    per_device_train_batch_size=32,
    learning_rate=2e-5,
    fp16=True,
)

# Create trainer
trainer = SentenceTransformerTrainer(
    model=model,
    args=args,
    train_dataset=dataset['train'],
    eval_dataset=dataset['validation'],
    loss=loss,
)

# Start training
trainer.train()

Citation

If you use this dataset, please cite the original SODA-VEC work:

@software{soda_vec_2025,
  title={SODA-VEC: Optimized Sentence Transformer for Biomedical Text},
  author={EMBO},
  year={2025},
  url={https://github.com/source-data/soda-vec},
  note={Dataset: EMBO/soda-vec-data-full_pmc_title_abstract_paired}
}

License

This dataset is released under the CC-BY-4.0 license, consistent with PubMed Central's open access requirements.