pipeline_tag: sentence-similarity
language: en
license: apache-2.0
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
sentence-transformers/gtr-t5-base
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space. The model was specifically trained for the task of sematic search.
This model was converted from the Tensorflow model gtr-base-1 to PyTorch. When using this model, have a look at the publication: Large Dual Encoders Are Generalizable Retrievers
The model uses only the encoder from a T5-base model. The weights are stored in FP16.
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
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('sentence-transformers/sentence-transformers/gtr-t5-base')
embeddings = model.encode(sentences)
print(embeddings)
The model requires sentence-transformers version 2.2.0 or newer.
Evaluation Results
For an automated evaluation of this model, see the Sentence Embeddings Benchmark: https://seb.sbert.net
Citing & Authors
If you find this model helpful, please cite the respective publication: Large Dual Encoders Are Generalizable Retrievers