Question similarity and grammatical correctness
1. Question similarity and grammatical correctness
Time to look at two more fascinating applications of text classification: question similarity and grammatical correctness.2. Question similarity
Let's start with question similarity. Imagine you're building a system that handles a lot of user questions, like an FAQ chatbot or a forum. Often, users ask the same thing in different ways. Question similarity identifies when two questions are essentially paraphrases. This kind of classification is useful for deduplication, clustering similar questions, and improving search accuracy to find the right answer. To do this, we'll use a model that has learned from data like the Quora Question Pairs ,or QQP, dataset, which contains pairs of questions labeled by whether they mean the same thing. We're not using the QQP dataset directly; just a model that already understands patterns from this data.3. QQP pipeline
To use this capability, we import the pipeline function and create a text-classification pipeline using a suitable QQP model. We define two questions, question_1 and question_2 asking how someone could learn Python. We pass them to the pipeline in a dictionary, one under "text" and the other under "text_pair". The output will be one of two labels: LABEL_0 to indicate questions are not similar or LABEL_1 to indicate they are, and a score indicating how confident the model is in its decision. In this case, we see that the two questions are 68% similar.4. QQP pipeline
If we change question_2 to ask about the capital of France, we see that the label changes to be LABEL_0, and the score is 99.99%, indicating the model is certain that the questions are not similar.5. Assessing grammatical correctness
Now let's shift to another task: assessing grammatical correctness. As the name suggests, the goal of this task is to assess how much a given text is grammatically correct. This is especially useful in educational tools, grammar checkers, or writing assistants. This is where models trained on the Corpus of Linguistic Acceptability or CoLA dataset come in. This dataset contains English sentences labeled as grammatically acceptable or not.6. CoLA pipeline
To apply this, we again use the text-classification pipeline, this time with a CoLA model. We input a sentence, and the model returns a label: LABEL_0 if the sentence is not grammatically acceptable, and LABEL_1 if it is, along with a score indicating confidence in the prediction. In this example, the sentence is labeled as acceptable with LABEL_1, and the confidence in the prediction is 99.18%.7. CoLA pipeline
However, if we input a sentence like "The cat on sat mat the", the model correctly flags it as unacceptable with LABEL_0 and a confidence score of 96.28% in the prediction.8. Let's practice!
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