|
--- |
|
pipeline_tag: sentence-similarity |
|
language: en |
|
license: apache-2.0 |
|
tags: |
|
- sentence-transformers |
|
- feature-extraction |
|
- sentence-similarity |
|
- transformers |
|
--- |
|
|
|
# sentence-transformers/gtr-t5-xl |
|
|
|
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 sematic search. |
|
|
|
This model was converted from the Tensorflow model [gtr-xl-1](https://tfhub.dev/google/gtr/gtr-xl/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 T5-3B 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-xl') |
|
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-xl) |
|
|
|
|
|
|
|
## 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) |
