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Update 3.mdx #892

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6 changes: 0 additions & 6 deletions chapters/en/chapter1/3.mdx
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Expand Up @@ -229,12 +229,6 @@ unmasker("This course will teach you all about <mask> models.", top_k=2)

The `top_k` argument controls how many possibilities you want to be displayed. Note that here the model fills in the special `<mask>` word, which is often referred to as a *mask token*. Other mask-filling models might have different mask tokens, so it's always good to verify the proper mask word when exploring other models. One way to check it is by looking at the mask word used in the widget.

<Tip>

✏️ **Try it out!** Search for the `bert-base-cased` model on the Hub and identify its mask word in the Inference API widget. What does this model predict for the sentence in our `pipeline` example above?

</Tip>

## Named entity recognition[[named-entity-recognition]]

Named entity recognition (NER) is a task where the model has to find which parts of the input text correspond to entities such as persons, locations, or organizations. Let's look at an example:
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