TensorFluxEmbedder

TensorFluxEmbedder is a fine-tuned text embedding model covering four domains — general language, web search, scientific literature, and legal documents. Embeddings can be truncated from 768 → 512 → 256 → 128 → 64 dimensions at inference time with no retraining, letting you directly trade retrieval quality for speed and memory.

Model Specifications

Performance

NDCG@10 on held-out test sets across all five supported dimensionalities:

256 dimensions is the recommended default for latency-sensitive RAG: it retains 97% of peak SciFact quality at one-third the storage cost.

Full Evaluation Results

SciFact (held-out test set)

Legal RAG (held-out 10% split)

The legal corpus uses a 1:1 query-to-passage evaluation scheme where each query has exactly one relevant passage in the full corpus, making the task harder than typical multi-relevant benchmarks.

Usage

pip install sentence-transformers

Always prefix your inputs to distinguish query intent from document content:

from sentence_transformers import SentenceTransformer

model = SentenceTransformer("IstishadAlamTishad/TensorFluxEmbedder")

queries = [
    "search_query: What are the procurement rules for small business government contracts?",
]
documents = [
    "search_document: Agency procurement regulations require that small business offerors "
    "receive fair opportunities pursuant to FAR Part 19.",
    "search_document: Antibody neutralization of SARS-CoV-2 spike protein prevents viral "
    "entry into host cells via the ACE2 receptor pathway.",
]

q_emb = model.encode(queries,   normalize_embeddings=True)
d_emb = model.encode(documents, normalize_embeddings=True)

scores = model.similarity(q_emb, d_emb)
print(scores)
# tensor([[0.79, 0.51]])

Choosing an embedding dimension

Set truncate_dim at load time — no extra downloads, no retraining required:

# Full quality (768d)
model = SentenceTransformer("IstishadAlamTishad/TensorFluxEmbedder")

# Recommended for RAG: 97% quality at 1/3 storage (256d)
model = SentenceTransformer("IstishadAlamTishad/TensorFluxEmbedder", truncate_dim=256)

# Fastest retrieval, ~82% of peak quality (64d)
model = SentenceTransformer("IstishadAlamTishad/TensorFluxEmbedder", truncate_dim=64)

Limitations

• English only — trained exclusively on English-language corpora; not expected to perform well on non-English text.
• Max 8192 tokens — inputs longer than 8192 tokens will be silently truncated. Split long documents into chunks before encoding.
• Prefix required — always apply the correct prefix (search_query: / search_document:). Omitting it will noticeably degrade retrieval quality.
• Legal domain scope — legal evaluation was performed on a single corpus with a 1:1 query-to-passage scheme. Performance on other legal datasets may differ.
• Retrieval only — optimized for dense retrieval; not suitable for use as a cross-encoder, classifier, or NLI model.

Citation

If you use TensorFluxEmbedder in your work, please cite:

@misc{tishad2026tensorfluxembedder,
  author    = {Istishad Alam Tishad},
  title     = {TensorFluxEmbedder: A Multi-Domain Embedding Model},
  year      = {2026},
  url       = {https://huggingface.co/IstishadAlamTishad/TensorFluxEmbedder},
  note      = {HuggingFace model repository}
}

Acknowledgements

Sentence Transformers

@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",
}