Kiel-2-Optic

Kiel-2-Optic is a specialized multimodal text and image embedding model based on sentence-transformers/clip-ViT-B-32.

This is a sentence-transformers model that maps text and images into a shared 512-dimensional dense vector space optimized for multimodal semantic similarity, zero-shot classification, and image-text retrieval tasks.

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Model Details

Model Description

• Model Type: Sentence Transformer / Multimodal Embedding Backbone
• Base Model: sentence-transformers/clip-ViT-B-32
• Maximum Sequence Length: 77 tokens
• Output Dimensionality: 512 dimensions
• Similarity Function: Cosine Similarity
• Supported Modalities: Text, Image

Model Sources

• Documentation: Sentence Transformers Documentation
• Repository: Sentence Transformers on GitHub
• Hugging Face Hub: kiel2/Kiel-2-Optic

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'transformer_task': 'feature-extraction', 'modality_config': {'text': {'method': 'get_text_features', 'method_output_name': 'pooler_output'}, 'image': {'method': 'get_image_features', 'method_output_name': 'pooler_output'}}, 'module_output_name': 'sentence_embedding', 'architecture': 'CLIPModel'})
)
Direct Usage (Sentence Transformers)
First, install the Sentence Transformers library:

Bash
pip install -U sentence-transformers
Then load your model and run inference:

Python
from sentence_transformers import SentenceTransformer

# Load your model from the Hugging Face Hub
model = SentenceTransformer("kiel2/Kiel-2-Optic")

# Run inference
sentences = [
    'Tornadoes , up to a foot of rain and hail as big as cantaloupes pounded southern Nebraska and northern Kansas , killing one man and destroying at least four homes .',
    'Up to a foot of rain and at least seven tornadoes pounded southern Nebraska and northern Kansas , killing a man and destroying at least four homes .',
    'The move follows a recent proposal by Mr Vajpayee , whoended an 18-month chill in relations by ordering normalisation of diplomatic links and restoration of air services with Pakistan .',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 512]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
Citation
Code snippet
@inproceedings{reimers-2019-sentence-bert,
    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
    author = "Reimers, Nils and Gurevych, Iryna",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
    month = "11",
    year = "2019",
    publisher = "Association for Computational Linguistics",
    url = "[https://arxiv.org/abs/1908.10084](https://arxiv.org/abs/1908.10084)",
}