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Answer: Use the Entity Analysis method of the Natural Language API to extract the ingredients and cookware from each recipe. Evaluate the model's performance on a prelabeled dataset.
The correct answer is C. Using the Entity Analysis method of the Natural Language API provides a pre-built solution for entity extraction, which eliminates the need for custom model training and extensive data labeling. This approach is quick, easy to implement, and leverages Google's pre-trained models, making it suitable for tasks like extracting ingredients and cookware from recipes. While custom solutions like Vertex AI's entity extraction can provide more tailored results, they require more effort in terms of data labeling and model training. The Natural Language API’s approach is more straightforward and cost-efficient for this specific task.
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You work for a company that is developing an application to help users with meal planning. This application aims to scan a large collection of recipe texts to identify and extract the ingredients (e.g., carrot, rice, pasta) and kitchen cookware (e.g., bowl, pot, spoon) mentioned in these recipes. Given that each recipe is saved in an unstructured text file, how should you proceed with using machine learning to achieve this task?
A
Create a text dataset on Vertex AI for entity extraction. Create two entities called 'ingredient' and 'cookware', and label at least 200 examples of each entity. Train an AutoML entity extraction model to extract occurrences of these entity types. Evaluate performance on a holdout dataset.
B
Create a multi-label text classification dataset on Vertex AI. Create a test dataset, and label each recipe that corresponds to its ingredients and cookware. Train a multi-class classification model. Evaluate the model’s performance on a holdout dataset.
C
Use the Entity Analysis method of the Natural Language API to extract the ingredients and cookware from each recipe. Evaluate the model's performance on a prelabeled dataset.
D
Create a text dataset on Vertex AI for entity extraction. Create as many entities as there are different ingredients and cookware. Train an AutoML entity extraction model to extract those entities. Evaluate the model’s performance on a holdout dataset.