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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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-
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- # sentence-transformers/sentence-t5-large
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-
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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-large-1](https://tfhub.dev/google/sentence-t5/st5-large/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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-
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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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-
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-
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- ## Usage (Sentence-Transformers)
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-
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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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- ```
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- pip install -U sentence-transformers
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- ```
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-
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- Then you can use the model like this:
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-
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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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-
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- model = SentenceTransformer('sentence-transformers/sentence-t5-large')
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- embeddings = model.encode(sentences)
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- print(embeddings)
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- ```
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-
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- The model requires sentence-transformers version 2.2.0 or newer.
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-
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- ## Evaluation Results
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-
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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-large)
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-
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-
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-
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- ## Citing & Authors
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-
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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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+
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+ # sentence-transformers/sentence-t5-large
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+
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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-large-1](https://tfhub.dev/google/sentence-t5/st5-large/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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+
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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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+
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+
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+ ## Usage (Sentence-Transformers)
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+
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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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+ ```
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+ pip install -U sentence-transformers
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+ ```
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+
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+ Then you can use the model like this:
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+
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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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+
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+ model = SentenceTransformer('sentence-transformers/sentence-t5-large')
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+ embeddings = model.encode(sentences)
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+ print(embeddings)
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+ ```
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+
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+ The model requires sentence-transformers version 2.2.0 or newer.
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+
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+ ## Evaluation Results
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+
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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-large)
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+
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+
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+
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+ ## Citing & Authors
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+
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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)