We mask some words in the fill mask task and ask the transformer model to fill it by suggesting suitable fill-in words. It can find many uses in NLP tasks—for example, correcting the misprinted words in a book, guessing the lost words from ancient hieroglyphs or manuscripts, some word games, or just the language model's analysis itself.

Press + to interact

Models for Fill Mask

Model	Description
`bert-base-uncased`	Pretrained on the English dataset, which is uncased. Learns in a bidirectional fashion of the sentence, creating the mask's completion.
`bert-base-multilingual-cased`	Pretrained on 104 languages datasets, which are case sensitive. Learns in a bidirectional fashion of the sentence, creating the mask's completion.
`microsoft/deberta-v3-base`	Based on `bert` and `roberta` models, and `deberta-v3` is an advanced version of `deberta`, which improves performance. Trained on the 160 gigabytes dataset.
`jackaduma/SecBERT`	Based on `bert` and trained on the cyber security dataset. Use this model for downstream tasks, like semantic search, classification, and question-answer.
`flaubert/flaubert_base_cased`	Pretrained on the French dataset, which is cased. Learns in a bidirectional fashion of the sentence, creating the mask's completion.

Parameter	Type	Category	Description
`inputs`	String	Required	Specifies a string or string [ ] is to be filled with a text to complete
`options.use_cache`	Boolean	Optional	Hugging Face Inference API has a cache mechanism implemented to speed up the requests. Use it for the deterministic models. Default value is `true`.
`options.wait_for_model`	Boolean	Optional	Hugging Face Inference API models takes time to initiate and process the requests. If the value is `true`, it waits for the model to get ready instead of returning an error. Default value is `false`.

Press + to interact

// Endpoint URL
const endpointUrl = "https://api-inference.huggingface.co/models/bert-base-uncased";
const headerParameters = {
  "Authorization": "Bearer {{ACCESS_TOKEN}}"
};
// Input text to classify
const data = JSON.stringify({
  inputs: "DNA is the [MASK] of life.",
  options: {
    wait_for_model: true
  }
});
const options = {
  method: "POST",
  headers: headerParameters,
  body: data
};
async function fillMask() {
   try {
    const response = await fetch(endpointUrl, options);
    printResponse(response);
  } catch (error) {
    printError(error);
  }
}
fillMask();

Let’s have a look at the highlighted lines shown in the code widget above:

Line 2: We specify the bert-base-uncased model for fill mask task.
Line 9: We specify the inputs, which include a sentence with the [MASK].
Line 21–28: We create a function, fillMask, to make the API call and handle the exceptions.
Line 30: We call the fillMask function to invoke the endpoint.

Response fields

The API call above returns a dictionary object or a list of dictionary objects, depending on the inputs. The response contains the following fields.

Press + to interact

Parameter	Type	Description
`sequence`	String	Specifies the complete phrase after filling the mask
`score`	Float	The likelihood of the token in the sentence. A higher value means the token has a higher probability of occurring in the sentence, and vice versa.
`token`	Integer	Specifies the ID of the token, and is the model-generated ID
`token_str`	String	Specifies the token that is filled in the place of the mask

Introduction

NLP

Computer Vision

APIs Integration in React

Conclusion

Fill Mask

Fill mask using the API

Models for Fill Mask

Request parameters

Response fields

Examples