---
language:
  - en
datasets:
  - simpeval
tags:
  - simplification
license: apache-2.0
---

This contains the trained checkpoint for LENS-SALSA, as introduced in [**Dancing Between Success and Failure: Edit-level Simplification Evaluation using SALSA**](https://arxiv.org/abs/2305.14458). For more information, please refer to the [**SALSA repository**](https://github.com/davidheineman/salsa).

```bash
pip install lens-metric
```

```python
from lens import download_model, LENS_SALSA

lens_salsa_path = download_model("davidheineman/lens-salsa")
lens_salsa = LENS_SALSA(lens_salsa_path)

complex = [
    "They are culturally akin to the coastal peoples of Papua New Guinea."
]
simple = [
    "They are culturally similar to the people of Papua New Guinea."
]

scores, word_level_scores = lens_salsa.score(complex, simple, batch_size=8, devices=[0])
print(scores) # [72.40909337997437]

# LENS-SALSA also returns an error-identification tagging, recover_output() will return the tagged output
tagged_output = lens_salsa.recover_output(word_level_scores, threshold=0.5)
print(tagged_output)
```

For an example, please see the [quick demo Google Collab notebook](https://colab.research.google.com/drive/1rIYrbl5xzL5b5sGUQ6zFBfwlkyIDg12O?usp=sharing).

## Intended uses

Our model is intented to be used for **reference-free simplification evaluation**. Given a source text and its translation, outputs a single score between 0 and 1 where 1 represents a perfect simplification and 0 a random simplification. LENS-SALSA was trained on edit annotations of the SimpEval dataset, which covers manually-written, complex Wikipedia simplifications. We have not evaluated our model on non-English languages or non-Wikipedia domains.

## Cite SALSA
If you find our paper, code or data helpful, please consider citing [**our work**](https://arxiv.org/abs/2305.14458):
```tex
@article{heineman2023dancing,
  title={Dancing {B}etween {S}uccess and {F}ailure: {E}dit-level {S}implification {E}valuation using {SALSA}},
  author = "Heineman, David and Dou, Yao and Xu, Wei",
  journal={arXiv preprint arXiv:2305.14458},
  year={2023}
}
```