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----
-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)