Query2query

🚀 query2query

This is a model that maps queries to a 384-dimensional dense vector space. It can be used for tasks such as clustering or semantic search over queries.

🚀 Quick Start

This is a sentence-transformers model. It maps queries to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search over queries.

Check out this announcing blogpost for more information: https://neeva.com/blog/state-of-the-art-query2query-similarity

⚠️ Important Note

We are releasing this under a license which prevents commercial use. If you want to use it for commercial purposes, please reach out to contact@neeva.co or rajhans@neeva.co with a brief description of what you want to use it for and we will try our best to respond very quickly.

📦 Installation

Using this model becomes easy when you have sentence-transformers installed:

pip install -U sentence-transformers

💻 Usage Examples

Basic Usage

from sentence_transformers import SentenceTransformer
queries = ["flight cost from nyc to la", "ticket prices from nyc to la"]

model = SentenceTransformer('neeva/query2query')
embeddings = model.encode(queries)
print(embeddings)

🔧 Technical Details

Training

The model was trained for 1M steps with a batch size of 1024 at a learning rate of 2e-5 using a cosine learning rate scheduler with 10000 warmup steps.

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: DataParallel 
  (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
  (2): Normalize()
)

📄 License

This model is released under the cc-by-nc-sa-4.0 license.