---
base_model:
- rrivera1849/LUAR-CRUD
library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- LUAR
license: apache-2.0
language:
- en
---

# SentenceTransformer version of rrivera1849/LUAR-MUD

All credits go to [(Rivera-Soto et al. 2021)](https://aclanthology.org/2021.emnlp-main.70/)

---

Author Style Representations using [LUAR](https://aclanthology.org/2021.emnlp-main.70.pdf).

The LUAR training and evaluation repository can be found [here](https://github.com/llnl/luar).

This model was trained on a subsample of the Pushshift Reddit Dataset (5 million users) for comments published between January 2015 and October 2019 by authors publishing at least 100 comments during that period.

## Usage

### Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

```bash
pip install -U sentence-transformers
```

Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("gabrielloiseau/LUAR-CRUD-sentence-transformers")
# Run inference
sentences = [
    'The weather is lovely today.',
    "It's so sunny outside!",
    'He drove to the stadium.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 512]
```

## Citation

If you find this model helpful, feel free to cite:

```
@inproceedings{uar-emnlp2021,
  author    = {Rafael A. Rivera Soto and Olivia Miano and Juanita Ordonez and Barry Chen and Aleem Khan and Marcus Bishop and Nicholas Andrews},
  title     = {Learning Universal Authorship Representations},
  booktitle = {EMNLP},
  year      = {2021},
}
```

## License

LUAR is distributed under the terms of the Apache License (Version 2.0).

All new contributions must be made under the Apache-2.0 licenses.