This contains the trained checkpoint for LENS-SALSA, as introduced in Dancing Between Success and Failure: Edit-level Simplification Evaluation using SALSA. For more information, please refer to the SALSA repository.

pip install lens-metric
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)

For an example, please see the quick demo Google Collab notebook.

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.


If you find our paper, code or data helpful, please consider citing our work:

  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},
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