README.md

---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
base_model: NetherlandsForensicInstitute/ARM64BERT-embedding
pipeline_tag: sentence-similarity
library_name: sentence-transformers
---

# ASMSentenceTransformer based on NetherlandsForensicInstitute/ARM64BERT-embedding

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [NetherlandsForensicInstitute/ARM64BERT-embedding](https://huggingface.co/NetherlandsForensicInstitute/ARM64BERT-embedding). It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for retrieval.

## Model Details

### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [NetherlandsForensicInstitute/ARM64BERT-embedding](https://huggingface.co/NetherlandsForensicInstitute/ARM64BERT-embedding) <!-- at revision d6ccf7f11bb9a1c5d2e690db4e26837edf9d4a10 -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 dimensions
- **Similarity Function:** Cosine Similarity
- **Supported Modality:** Text
<!-- - **Training Dataset:** Unknown -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->

### Model Sources

- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/huggingface/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)

### Full Model Architecture

```
ASMSentenceTransformer(
  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False, 'modality_config': {'text': {'method': 'forward', 'method_output_name': 'last_hidden_state'}}, 'architecture': 'ASMBertModel'})
  (1): Pooling({'embedding_dimension': 768, 'pooling_mode': 'mean', '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.shape)
# [3, 3]
```
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### Downstream Usage (Sentence Transformers)

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## Training Details

### Framework Versions
- Python: 3.13.13
- Sentence Transformers: 5.4.1
- Transformers: 5.6.0
- PyTorch: 2.11.0
- Accelerate: 1.13.0
- Datasets: 4.8.4
- Tokenizers: 0.22.2

## Citation

### BibTeX

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