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.
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)",
}