dna-paraphrase-mpnet-base-v2

A novel Sentence Transformer for DNA tasks

This is a sentence-transformers model trained. It maps sentences & paragraphs to a 512-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.

Model Details

Model Description

• Model Type: Sentence Transformer

<!-- - **Base model:** [Unknown](https://huggingface.co/unknown) -->

• Maximum Sequence Length: 512 tokens
• Output Dimensionality: 512 tokens
• Similarity Function: Cosine Similarity

<!-- - **Training Dataset:** Unknown --> <!-- - **Language:** Unknown --> <!-- - **License:** Unknown -->

Model Sources

• Documentation: Sentence Transformers Documentation
• Repository: Sentence Transformers on GitHub
• Hugging Face: Sentence Transformers on Hugging Face

Full Model Architecture

SentenceTransformer(
  (0): SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: MPNetModel 
    (1): Pooling({'word_embedding_dimension': 768, '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})
  )
  (1): CNN(
    (convs): ModuleList(
      (0): Conv1d(768, 256, kernel_size=(1,), stride=(1,))
      (1): Conv1d(768, 256, kernel_size=(3,), stride=(1,), padding=(1,))
      (2): Conv1d(768, 256, kernel_size=(5,), stride=(1,), padding=(2,))
    )
  )
  (2): Pooling({'word_embedding_dimension': 768, '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})
  (3): Dense({'in_features': 768, 'out_features': 512, 'bias': True, 'activation_function': 'torch.nn.modules.activation.ReLU'})
  (4): 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("dsfsi/dna-paraphrase-mpnet-base-v2")
# Run on kmer DNA sequences
#
sentences = [
    'ATCCCC ATGGAA ATTTGG',
    'TTGGCC AAAGGG GGGTTT',
    'CCCTTT CTCTGTTT AAATTTTGG',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 512]

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

Downstream Performance

Average Train Accuracy

Test Accuracy

Framework Versions

• Python: 3.8.10
• Sentence Transformers: 3.0.1
• Transformers: 4.43.4
• PyTorch: 2.4.0+cu121
• Accelerate:
• Datasets:
• Tokenizers: 0.19.1

Citation

TBA

BibTeX

TBA

Contributing

Your contributions are welcome! Feel free to improve the model.

Model Card Authors

Mpho Mokoatle

Model Card Contact

Email: [email protected]