Kiel-2-Index

Kiel-2-Index is a flagship, high-performance dense embedding model based on BAAI/bge-large-en-v1.5 for enterprise retrieval and semantic similarity tasks.

This is a sentence-transformers model that maps sentences and paragraphs into a high-fidelity 1024-dimensional dense vector space optimized for cross-domain retrieval.

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Model Details

Model Description

• Model Type: Sentence Transformer / Dense Retrieval Backbone
• Base Model: BAAI/bge-large-en-v1.5
• Maximum Sequence Length: 512 tokens
• Output Dimensionality: 1024 dimensions
• Similarity Function: Cosine Similarity
• Supported Modality: Text (Multi-Domain Enterprise & General Corpus)

Model Sources

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

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'transformer_task': 'feature-extraction', 'modality_config': {'text': {'method': 'forward', 'method_output_name': 'last_hidden_state'}}, 'module_output_name': 'token_embeddings', 'architecture': 'BertModel'})
  (1): Pooling({'embedding_dimension': 1024, 'pooling_mode': 'cls', 'include_prompt': True})
  (2): Normalize({})
)
Direct Usage (Sentence Transformers)
First, install the Sentence Transformers library:

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

Python
from sentence_transformers import SentenceTransformer

# Load your custom cloud-hosted flagship embedder
model = SentenceTransformer("kiel2/Kiel-2-Index")

# Run inference
sentences = [
    'Many conservatives have staunchly opposed condom programs , saying they send the wrong message and encourage and enable teens to have sex before marriage .',
    'Some conservative groups have staunchly opposed such programs , saying they send the wrong message and in effect encourage and enable teens to have sex before marriage .',
    "It 's just a matter of time , said Frank McDonald , of the University of Maryland .",
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]

# 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)",
}