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 tfhub model and this PyTorch model can produce slightly different embeddings, however, when run on the same benchmarks, they produce identical results.
The model uses only the encoder from a T5-base model. The weights are stored in FP16.
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/gtr-t5-base') embeddings = model.encode(sentences) print(embeddings)
The model requires sentence-transformers version 2.2.0 or newer.
For an automated evaluation of this model, see the Sentence Embeddings Benchmark: https://seb.sbert.net
If you find this model helpful, please cite the respective publication: Large Dual Encoders Are Generalizable Retrievers
- Downloads last month