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MeaningBERT

davebulaval

MeaningBERT is an automatic and trainable metric for assessing meaning preservation between sentences. MeaningBERT was proposed in our article MeaningBERT: assessing meaning preservation between sentences. Its goal is to assess meaning preservation between two sentences that correlate highly with human judgments and sanity checks. For more details, refer to our publicly available article. > This public version of our model uses the best model trained (where in our article, we present the performance results > of an average of 10 models) for a more extended period (500 epochs instead of 250). We have observed later that the > model can further reduce dev loss and increase performance. Also, we have changed the data augmentation technique used > in the article for a more robust one, that also includes the commutative property of the meaning function. Namely, Meaning(Sent_a, Sent_b) = Meaning(Sent_b, Sent_a). The repository root holds the **v1** model described in the article, unchanged, so anything already using `from_pretrained("davebulaval/MeaningBERT")` keeps returning exactly the scores it returned before.

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MeaningBERT

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