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---
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pipeline_tag: sentence-similarity
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language: en
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license: apache-2.0
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tags:
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- sentence-transformers
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- feature-extraction
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- sentence-similarity
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- transformers
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---
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# sentence-transformers/gtr-t5-large
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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.
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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.
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The model uses only the encoder from a T5-large model. The weights are stored in FP16.
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## Usage (Sentence-Transformers)
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Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
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```
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pip install -U sentence-transformers
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```
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Then you can use the model like this:
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```python
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from sentence_transformers import SentenceTransformer
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sentences = ["This is an example sentence", "Each sentence is converted"]
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model = SentenceTransformer('sentence-transformers/gtr-t5-large')
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embeddings = model.encode(sentences)
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print(embeddings)
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```
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The model requires sentence-transformers version 2.2.0 or newer.
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## Evaluation Results
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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)
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## Citing & Authors
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If you find this model helpful, please cite the respective publication:
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[Large Dual Encoders Are Generalizable Retrievers](https://arxiv.org/abs/2112.07899)
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