UMCU/BioLORD-2023-M-Dutch-InContext-v1-ST_bf16

SentenceTransformer based on FremyCompany/BioLORD-2023-M-Dutch-InContext-v1

This is a sentence-transformers from, simply the FremyCompany/BioLORD-2023-M-Dutch-InContext-v1 model but with bf16 instead of float32. It maps sentences & paragraphs to a 768-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: FremyCompany/BioLORD-2023-M-Dutch-InContext-v1 <!-- at revision 4db8f4e39952f0515ab9fd3f036a05b5db8892fd -->
• —Maximum Sequence Length: 25 tokens
• —Output Dimensionality: 768 dimensions
• —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): Transformer({'max_seq_length': 25, 'do_lower_case': False, 'architecture': 'XLMRobertaModel'})
  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)

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("sentence_transformers_model_id")
# Run inference
sentences = [
    'The weather is lovely today.',
    "It's so sunny outside!",
    'He drove to the stadium.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.6914, 0.4062],
#         [0.6914, 1.0000, 0.3145],
#         [0.4062, 0.3145, 1.0000]], dtype=torch.bfloat16)

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Downstream Usage (Sentence Transformers)

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Training Details

Framework Versions

• —Python: 3.12.3
• —Sentence Transformers: 5.0.0
• —Transformers: 4.48.0
• —PyTorch: 2.5.0+cu121
• —Accelerate: 1.8.1
• —Datasets: 3.6.0
• —Tokenizers: 0.21.2

Citation

This model accompanies the BioLORD-2023: Learning Ontological Representations from Definitions paper. When you use this model, please cite the original paper as follows:

@article{remy-etal-2023-biolord,
    author = {Remy, François and Demuynck, Kris and Demeester, Thomas},
    title = "{BioLORD-2023: semantic textual representations fusing large language models and clinical knowledge graph insights}",
    journal = {Journal of the American Medical Informatics Association},
    pages = {ocae029},
    year = {2024},
    month = {02},
    issn = {1527-974X},
    doi = {10.1093/jamia/ocae029},
    url = {https://doi.org/10.1093/jamia/ocae029},
    eprint = {https://academic.oup.com/jamia/advance-article-pdf/doi/10.1093/jamia/ocae029/56772025/ocae029.pdf},
}

BibTeX

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