consistent. accurate.
Unlike generic AI platforms that rely heavily on unsupervised learning, our approach leverages:
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Expert-labelled data
Every training example verified by legal professionals
Contextually aware annotation
Nuanced legal implications beyond simple text
Strategic sampling methods
Balanced representation across contract types and legal domains
Lexible® isn't just another AI model with “legal” features, it's purpose-built forcontract review from the ground up:
Accuracy (F1 score)
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Precision
Recall
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Accuracy (or the F1 score) is a measure combining recall and precision. Trade-offs exist. F1, therefore, measures how effectively our models make that judgment.
F1 scoring penalises extreme negative values of either component. Thus, if either component fails, the score falls to zero.
The precision metric provides the proportion of true positives to the amount of total positives predicted. It answers the question:
“Out of all the positive predictions we made, how many were true?”
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Recall focuses on how well the model finds positives. It is also called the “true positive rate”. It answers the question:
“Out of all the data points that should be predicted as true, how many did we correctly predict as true?”
