Chem-MRL (SentenceTransformer)

This is a trained Chem-MRL sentence-transformers model. It maps SMILES to a 1024-dimensional dense vector space and can be used for semantic textual similarity, semantic search, database indexing, molecular classification, clustering, and more.

Model Details

Model Description

• Model Type: Sentence Transformer
• Maximum Sequence Length: 128 tokens
• Output Dimensionality: 1024 dimensions
• Similarity Function: Cosine Similarity

Model Sources

• Repository: Chem-MRL on GitHub
• Demo App Repository: Chem-MRL-demo on GitHub

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: RobertaModel (ChemBERTa)
  (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
  (2): Normalize()
)

Usage

Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

pip install -U sentence-transformers

Then you can load this model and run inference.

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("Derify/ChemMRL-alpha")
# Run inference
sentences = [
    'CCO',
    "CC(C)O",
    'CC(=O)O',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]

Training Details

Framework Versions

• Python: 3.12.9
• Sentence Transformers: 4.0.1
• Transformers: 4.48.2
• PyTorch: 2.6.0+cu124
• Accelerate: 1.4.0
• Datasets: 3.3.2
• Tokenizers: 0.21.0

Citation

• Chithrananda, Seyone, et al. "ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property Prediction." arXiv [Cs.LG], 2020. Link.
• Ahmad, Walid, et al. "ChemBERTa-2: Towards Chemical Foundation Models." arXiv [Cs.LG], 2022. Link.
• Kusupati, Aditya, et al. "Matryoshka Representation Learning." arXiv [Cs.LG], 2022. Link.
• Li, Xianming, et al. "2D Matryoshka Sentence Embeddings." arXiv [Cs.CL], 2024. Link.
• Bajusz, Dávid, et al. "Why is the Tanimoto Index an Appropriate Choice for Fingerprint-Based Similarity Calculations?" J Cheminform, 7, 20 (2015). Link.
• Li, Xiaoya, et al. "Dice Loss for Data-imbalanced NLP Tasks." arXiv [Cs.CL], 2020. Link
• Reimers, Nils, and Gurevych, Iryna. "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks." Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing, 2019. Link.

Model Card Authors

@eacortes

Model Card Contact

Manny Cortes (manny@derifyai.com)