pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
license: cc-by-nc-sa-4.0
query2query
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.
Checkout this announcing blogpost for more information: https://neeva.com/blog/state-of-the-art-query2query-similarity(https://neeva.com/blog/state-of-the-art-query2query-similarity)
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.
Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:
pip install -U sentence-transformers
Then you can use the model like this:
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)
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()
)