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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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---
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# sentence-transformers/sentence-t5-xxl
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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 works well for sentence similarity tasks, but doesn't perform that well for semantic search tasks.
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This model was converted from the Tensorflow model [st5-11b-1](https://tfhub.dev/google/sentence-t5/st5-11b/1) to PyTorch. When using this model, have a look at the publication: [Sentence-T5: Scalable sentence encoders from pre-trained text-to-text models](https://arxiv.org/abs/2108.08877). 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 T5-11B 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/sentence-t5-xxl')
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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/sentence-t5-xxl)
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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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[Sentence-T5: Scalable sentence encoders from pre-trained text-to-text models](https://arxiv.org/abs/2108.08877)
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---
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language: en
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license: apache-2.0
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library_name: sentence-transformers
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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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pipeline_tag: sentence-similarity
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---
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# sentence-transformers/sentence-t5-xxl
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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 works well for sentence similarity tasks, but doesn't perform that well for semantic search tasks.
|
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+
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This model was converted from the Tensorflow model [st5-11b-1](https://tfhub.dev/google/sentence-t5/st5-11b/1) to PyTorch. When using this model, have a look at the publication: [Sentence-T5: Scalable sentence encoders from pre-trained text-to-text models](https://arxiv.org/abs/2108.08877). 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 T5-11B 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/sentence-t5-xxl')
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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/sentence-t5-xxl)
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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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[Sentence-T5: Scalable sentence encoders from pre-trained text-to-text models](https://arxiv.org/abs/2108.08877)
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