inc_sent_cls_mc

SentenceTransformer

This is a sentence-transformers 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
• Repository: Sentence Transformers on GitHub
• Hugging Face: Sentence Transformers on Hugging Face

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

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

<!-- ### Direct Usage (Transformers) <details><summary>Click to see the direct usage in Transformers</summary> </details> --> <!-- ### Downstream Usage (Sentence Transformers) You can finetune this model on your own dataset. <details><summary>Click to expand</summary> </details> --> <!-- ### Out-of-Scope Use *List how the model may foreseeably be misused and address what users ought not to do with the model.* --> <!-- ## Bias, Risks and Limitations *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* --> <!-- ### Recommendations *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* -->

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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