---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
pipeline_tag: text-classification
library_name: sentence-transformers
datasets:
- mawaskow/irish_forestry_incentives
---

# SentenceTransformer

This is a [sentence-transformers](https://www.SBERT.net) model trained. 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:** [Unknown](https://huggingface.co/unknown) -->
- **Maximum Sequence Length:** 512 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': 512, 'do_lower_case': False}) with Transformer model: XLMRobertaModel 
  (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})
)
```

## Usage

### Direct Usage

```python
import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification

tok = AutoTokenizer.from_pretrained("mawaskow/inc_sent_cls_mc")
model = AutoModelForSequenceClassification.from_pretrained("mawaskow/inc_sent_cls_mc")

sentences = [
    "The authority can revise the delegated act every five years.",
    "The scheme will subsidise purchases of eco-friendly farm equipment.",
    "Farmers will be able to avail of expert assistance in the uptake of new technologies."
]
text = sentences[1]
inputs = tok(text, return_tensors="pt")

with torch.no_grad():
    logits = model(**inputs).logits

pred = torch.argmax(logits, dim=-1).item()
print(model.config.id2label[pred])
# Supplies
```

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## Training Details

### Framework Versions
- Python: 3.11.7
- Sentence Transformers: 3.5.0.dev0
- Transformers: 4.50.0.dev0
- PyTorch: 2.6.0+cu118
- Accelerate: 1.4.0
- Datasets: 3.5.0
- Tokenizers: 0.21.0

## Citation

M.A. Waskow and John P. McCrae. 2025. Enhancing Policy Analysis with NLP: A Reproducible Approach to Incentive Classification. In Proceedings of the 21st Conference on Natural Language Processing (KONVENS 2025): Workshops, pages 74–85, Hannover, Germany. HsH Applied Academics.

### BibTeX

@inproceedings{waskow2025enhancing,
  title={Enhancing Policy Analysis with NLP: A Reproducible Approach to Incentive Classification},
  author={Waskow, MA and McCrae, John Philip},
  booktitle={Proceedings of the 21st Conference on Natural Language Processing (KONVENS 2025): Workshops},
  pages={74--85},
  year={2025}
}
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