readme: ---
  pipeline_tag: sentence-similarity
  language: en
  license: apache-2.0
  tags:
  - sentence-transformers
  - feature-extraction
  - sentence-similarity
  - transformers
  ---
  
  # sentence-transformers/gtr-t5-large
  
  This is a [sentence-transformers](https://www.SBERT.net) 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-large-1](https://tfhub.dev/google/gtr/gtr-large/1) to PyTorch. When using this model, have a look at the publication: [Large Dual Encoders Are Generalizable Retrievers](https://arxiv.org/abs/2112.07899). 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-large model. The weights are stored in FP16.  
  
  
  ## Usage (Sentence-Transformers)
  
  Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
  
  ```
  pip install -U sentence-transformers
  ```
  
  Then you can use the model like this:
  
  ```python
  from sentence_transformers import SentenceTransformer
  sentences = ["This is an example sentence", "Each sentence is converted"]
  
  model = SentenceTransformer('sentence-transformers/gtr-t5-large')
  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](https://seb.sbert.net?model_name=sentence-transformers/gtr-t5-large)
  
  
  
  ## Citing & Authors
  
  If you find this model helpful, please cite the respective publication:
  [Large Dual Encoders Are Generalizable Retrievers](https://arxiv.org/abs/2112.07899)
  