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. | |
## 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/gtr-t5-base) | |
## 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) | |