candle-nomic-bert

nomic-embed-text-v1.5 is a text embedding model based on the NomicBert architecture. It produces 768-dimensional embeddings suitable for semantic search, retrieval, and clustering.

Key architectural differences from standard BERT:

Rotary position embeddings (RoPE) supporting up to 8192 tokens
SwiGLU feed-forward layers
Fused QKV attention projections with no biases

Sentence embeddings

Compute the embedding for a single prompt. Model weights are downloaded from the hub on the first run.

bash

cargo run --example nomic-bert --release -- --prompt "Here is a test sentence"

> Embedding (first 10 dims):
>   [0] -0.030893
>   [1] 0.038772
>   [2] -0.171375
>   ...

Similarities

When run without --prompt, the example computes cosine similarities between a set of hardcoded sentences and reports the most similar pairs.

bash

cargo run --example nomic-bert --release

> Top cosine similarities:
>   0.9664  'The new movie is awesome' <-> 'The new movie is so great'
>   0.7377  'The cat sits outside' <-> 'The cat plays in the garden'
>   0.5764  'I love pasta' <-> 'Do you like pizza?'
>   0.5031  'A man is playing guitar' <-> 'A woman watches TV'
>   0.4781  'A man is playing guitar' <-> 'The cat plays in the garden'

Task prefixes

nomic-embed-text-v1.5 was trained with task prefixes. Adding them is optional but improves retrieval quality. Use --prefix to prepend a prefix to every input:

bash

# For documents/passages to be searched over
cargo run --example nomic-bert --release -- \
  --prefix "search_document: " \
  --prompt "Dragonwell is a classic Chinese green tea."

# For search queries
cargo run --example nomic-bert --release -- \
  --prefix "search_query: " \
  --prompt "sweet floral white tea"

Available prefixes: search_document: , search_query: , clustering: , classification: .