sentence-transformers
Safetensors
English
nomic_bert
flash-attention
code-retrieval
nomic-bert
bf16
custom_code
Instructions to use handwoven8588/CodeRankEmbed-flash-attn with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use handwoven8588/CodeRankEmbed-flash-attn with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("handwoven8588/CodeRankEmbed-flash-attn", trust_remote_code=True) sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Notebooks
- Google Colab
- Kaggle
File size: 1,260 Bytes
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This repository is a derivative of nomic-ai/CodeRankEmbed (MIT).
It contains:
- model weights, tokenizer, and configuration copied verbatim from
nomic-ai/CodeRankEmbed (commit 3c4b60807d71f79b43f3c4363786d9493691f8b1);
- a modified modeling_hf_nomic_bert.py that adds a native flash-attention varlen
path to NomicBertAttention.forward / NomicBertModel.forward. The remainder of
that file is the upstream nomic implementation unchanged.
Upstream lineage and attribution:
- CodeRankEmbed was trained by the CoRNStack team
(Suresh et al., "CoRNStack: High-Quality Contrastive Data for Text and Code
Retrieval", 2025; https://gangiswag.github.io/cornstack/).
- The model and its trust_remote_code modeling file are published by Nomic
(nomic-ai/CodeRankEmbed) under the MIT license.
- The BERT modeling implementation is based on Tri Dao's MLPerf BERT code
(https://github.com/mlcommons/training_results_v2.0), per the upstream file header.
License: MIT. The flash-attention modification added by this repository is also
released under MIT.
NO NEW TRAINING WAS PERFORMED. The model weights are identical to
nomic-ai/CodeRankEmbed; only the attention forward code differs.
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