README.md

---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- dense
base_model: FremyCompany/BioLORD-2023-M-Dutch-InContext-v1
pipeline_tag: sentence-similarity
library_name: sentence-transformers
---

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

This is a [sentence-transformers](https://www.SBERT.net) from, simply the [FremyCompany/BioLORD-2023-M-Dutch-InContext-v1](https://huggingface.co/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](https://huggingface.co/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](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)

### 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:

```bash
pip install -U sentence-transformers
```

Then you can load this model and run inference.
```python
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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## 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](https://arxiv.org/abs/2311.16075) paper. When you use this model, please cite the original paper as follows:

```latex
@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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