“Create an NLP algorithm flowchart for a transformer sentiment classifier trained on labeled product reviews: normalize text, split train-validation-test sets, tokenize with attention masks, fine-tune a classification head, evaluate validation F1, reject a model below target, test and register an approved model, then deploy an API returning positive, neutral, or negative sentiment.”
Die dahinterstehenden Entscheidungen verstehen.
Training, validation, and test records are separated before modeling.
The tokenizer passes both token IDs and padding masks to the encoder.
Validation F1 determines whether a candidate may proceed.
The test set is evaluated once after approval to avoid selection bias.
Use this for supervised sentiment classification using a pretrained transformer. It is suitable for product reviews, survey comments, or other short labeled text.
Häufige Fragen
Why use a separate test set?
It gives an unbiased final estimate after model choices have been made using validation data.
What are attention masks?
They tell the transformer which token positions are real text and which are padding.
Why reject a model below the F1 target?
It prevents an unproven candidate from replacing a known production model.