
# sentence-transformers/sentence-t5-xxl

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.

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.

The model uses only the encoder from a T5-11B 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/sentence-t5-xxl')
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/sentence-t5-xxl)



## Citing & Authors

If you find this model helpful, please cite the respective publication:
[Sentence-T5: Scalable sentence encoders from pre-trained text-to-text models](https://arxiv.org/abs/2108.08877)

{"language": "en", "license": "apache-2.0", "library_name": "sentence-transformers", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity"], "pipeline_tag": "sentence-similarity"}
sentence-transformers pytorch t5 feature-extraction sentence-similarity en arxiv:2108.08877 license:apache-2.0 endpoints_compatible has_space region:us
TAGS
#sentence-transformers #pytorch #t5 #feature-extraction #sentence-similarity #en #arxiv-2108.08877 #license-apache-2.0 #endpoints_compatible #has_space #region-us 


# sentence-transformers/sentence-t5-xxl

This is a sentence-transformers 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.

This model was converted from the Tensorflow model 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. 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 T5-11B model. The weights are stored in FP16.  


## Usage (Sentence-Transformers)

Using this model becomes easy when you have sentence-transformers installed:



Then you can use the model like this:



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*: URL



## Citing & Authors

If you find this model helpful, please cite the respective publication:
Sentence-T5: Scalable sentence encoders from pre-trained text-to-text models

# sentence-transformers/sentence-t5-xxl

This is a sentence-transformers 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.

This model was converted from the Tensorflow model 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. 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 T5-11B model. The weights are stored in FP16. ## Usage (Sentence-Transformers)

Using this model becomes easy when you have sentence-transformers installed:



Then you can use the model like this:



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*: URL ## Citing & Authors

If you find this model helpful, please cite the respective publication:
Sentence-T5: Scalable sentence encoders from pre-trained text-to-text models
TAGS
#sentence-transformers #pytorch #t5 #feature-extraction #sentence-similarity #en #arxiv-2108.08877 #license-apache-2.0 #endpoints_compatible #has_space #region-us 
 # sentence-transformers/sentence-t5-xxl

This is a sentence-transformers 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.

This model was converted from the Tensorflow model 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. 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 T5-11B model. The weights are stored in FP16. ## Usage (Sentence-Transformers)

Using this model becomes easy when you have sentence-transformers installed:



Then you can use the model like this:



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*: URL ## Citing & Authors

If you find this model helpful, please cite the respective publication:
Sentence-T5: Scalable sentence encoders from pre-trained text-to-text models
passage: TAGS
#sentence-transformers #pytorch #t5 #feature-extraction #sentence-similarity #en #arxiv-2108.08877 #license-apache-2.0 #endpoints_compatible #has_space #region-us 
# sentence-transformers/sentence-t5-xxl

This is a sentence-transformers 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.

This model was converted from the Tensorflow model 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. 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 T5-11B model. The weights are stored in FP16.## Usage (Sentence-Transformers)

Using this model becomes easy when you have sentence-transformers installed:



Then you can use the model like this:



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*: URL## Citing & Authors

If you find this model helpful, please cite the respective publication:
Sentence-T5: Scalable sentence encoders from pre-trained text-to-text models