Handling model-label disagreements

Disagreements between model predictions and human labels are inevitable. In this section, we will study how to identify and resolve conflicts.

Programmatically identifying mismatches

To identify discrepancies between the model’s predictions and the human-annotated labels, we can write some simple Python code that highlights the mismatches for review.

Let’s consider the example of an NLP sentiment classifier. This type of classifier is designed to analyze and understand the sentiment or emotions expressed in text. By examining the words, phrases, and context used in a given piece of text, an NLP sentiment classifier can determine whether the sentiment is positive, negative, or neutral. First, we will use the sentiment-analysis model from Huggingface:

sentiment_pipeline = pipeline("sentiment-analysis")
data = ["I love you", "I hate you"]
sentiment_pipeline(data)

The returns the following output...