How do you know if your NLP models are accurate?

1 Training and testing data

One of the first steps to ensure the accuracy of your NLP models is to use a sufficient and representative amount of data for training and testing. Training data is the data that you use to train your model to learn the patterns and features of the task. Testing data is the data that you use to evaluate how well your model generalizes to new and unseen data. You should split your data into training and testing sets, usually in a ratio of 80:20 or 70:30, depending on the size and complexity of your data. You should also make sure that your data is balanced, diverse, and relevant to the task and domain.

3 Precision, recall, and F1-score

Precision, recall, and F1-score are three metrics that are more suitable for evaluating NLP models that deal with multiple labels, partial matches, or ranking. Precision is the percentage of relevant results out of the total number of results returned by your model. Recall is the percentage of relevant results out of the total number of relevant results in the data. F1-score is the harmonic mean of precision and recall, which balances both metrics. You can calculate precision, recall, and F1-score as follows:

precision = (number of true positives) / (number of true positives + number of false positives)
recall = (number of true positives) / (number of true positives + number of false negatives)
F1-score = 2 * (precision * recall) / (precision + recall)        

Precision, recall, and F1-score are useful for tasks such as named entity recognition, where you want to measure how well your model identifies and extracts the relevant entities from the text. For example, if your model extracts 10 entities from a text, but only 8 of them are correct, and there are 12 entities in the text, then your precision is 0.8, your recall is 0.67, and your F1-score is 0.73.

4 BLEU and ROUGE

BLEU and ROUGE are two metrics that are more suitable for evaluating NLP models that deal with text generation, such as machine translation or summarization. BLEU stands for Bilingual Evaluation Understudy, and it measures how close the generated text is to the reference text in terms of n-gram overlap. ROUGE stands for Recall-Oriented Understudy for Gisting Evaluation, and it measures how much of the important information in the reference text is captured by the generated text in terms of n-gram recall. You can calculate BLEU and ROUGE as follows:

BLEU = geometric mean of n-gram precisions * brevity penalty
ROUGE = harmonic mean of n-gram recalls        

BLEU and ROUGE are useful for tasks such as machine translation or summarization, where you want to measure how well your model produces fluent and informative texts that match the meaning and style of the reference texts. For example, if your model translates a sentence from English to French, and there are four reference translations available, then you can compare the n-gram overlap and recall between your translation and the reference translations, and compute the BLEU and ROUGE scores accordingly.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?