46 lines
1.6 KiB
Markdown
Executable File
46 lines
1.6 KiB
Markdown
Executable File
---
|
|
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-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.
|
|
|
|
## 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)
|