| | --- |
| | language: en |
| | license: apache-2.0 |
| | library_name: sentence-transformers |
| | tags: |
| | - sentence-transformers |
| | - feature-extraction |
| | - sentence-similarity |
| | pipeline_tag: sentence-similarity |
| | --- |
| | |
| | # sentence-transformers/gtr-t5-base |
| |
|
| | 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 semantic search. |
| |
|
| | This model was converted from the Tensorflow model [gtr-base-1](https://tfhub.dev/google/gtr/gtr-base/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-base 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-base') |
| | embeddings = model.encode(sentences) |
| | print(embeddings) |
| | ``` |
| |
|
| | The model requires sentence-transformers version 2.2.0 or newer. |
| |
|
| | ## 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) |
| |
|
