---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- dense
base_model: UMCU/sap_snomed_medroberta.nl
pipeline_tag: sentence-similarity
library_name: sentence-transformers
---

# SentenceTransformer based on UMCU/sap_snomed_medroberta.nl

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [UMCU/sap_snomed_medroberta.nl](https://huggingface.co/UMCU/sap_snomed_medroberta.nl). 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:** [UMCU/sap_snomed_medroberta.nl](https://huggingface.co/UMCU/sap_snomed_medroberta.nl) <!-- at revision 91acbfd1a434faeea76533ceea43774948ba691f -->
- **Maximum Sequence Length:** 30 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': 30, 'do_lower_case': False, 'architecture': 'RobertaModel'})
  (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, 0.4724, 0.4557],
#         [0.4724, 1.0000, 0.2822],
#         [0.4557, 0.2822, 1.0000]])
```

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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

### BibTeX

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