FastText精确率与召回率及文本分类标签概率分配新手咨询

Hey there! Totally get being new to FastText—no question is too basic, so don’t hold back! Let’s tackle your first question straight away:

Yes, FastText does assign a matching probability to every predefined label when you run text classification predictions. Here’s a quick breakdown of how it works:

• By default, FastText uses the softmax function to calculate these probabilities. That means all the label probabilities for a single test text will add up to roughly 1 (with minor floating-point differences).
• To get probabilities for all labels, you just need to adjust the k parameter in the model.predict() method. If you set k=-1 (or set k equal to the total number of labels in your training data), the model will return every label along with its corresponding probability score.

For example, here’s a quick code snippet to illustrate this:

# Assuming you've already trained your model
predictions = model.predict("Your test text here", k=-1)
# predictions will be a tuple: (list of labels, list of corresponding probabilities)
labels = predictions[0]
probabilities = predictions[1]

It looks like you had more questions starting with "此外……"—feel free to share the rest, and I’ll help you work through them step by step!

内容的提问来源于stack exchange，提问作者Dilshat