Identify if a document is in english using AI

Below is a free classifier to identify if a document is in english. Just input your text, and our AI will predict if a document is in English - in just seconds.

API Access


import nyckel

credentials = nyckel.Credentials("YOUR_CLIENT_ID", "YOUR_CLIENT_SECRET")
nyckel.invoke("if-a-document-is-in-english", "your_text_here", credentials)


fetch('https://www.nyckel.com/v1/functions/if-a-document-is-in-english/invoke', {
    method: 'POST',
    headers: {
        'Authorization': 'Bearer ' + 'YOUR_BEARER_TOKEN',
        'Content-Type': 'application/json',
    },
    body: JSON.stringify(
        {"data": "your_text_here"}
    )
})
.then(response => response.json())
.then(data => console.log(data));


curl -X POST \
    -H "Content-Type: application/json" \
    -H "Authorization: Bearer YOUR_BEARER_TOKEN" \
    -d '{"data": "your_text_here"}' \
    https://www.nyckel.com/v1/functions/if-a-document-is-in-english/invoke

How this classifier works

To start, input the text that you'd like analyzed. Our AI tool will then predict if a document is in English.

This pretrained text model uses a Nyckel-created dataset and has 2 labels, including No English and Yes English.

We'll also show a confidence score (the higher the number, the more confident the AI model is around if a document is in English).

Whether you're just curious or building if a document is in english detection into your application, we hope our classifier proves helpful.

Need to identify if a document is in english at scale?

Get API or Zapier access to this classifier for free. It's perfect for:

Content Filtering: In content moderation systems, this function can be used to filter out documents that are not in English, ensuring that only relevant materials pass through for review. This is crucial in maintaining the quality and relevancy of user-generated content on platforms that cater primarily to English-speaking audiences.

Document Routing: In multilingual customer support environments, this classification can help route documents to the appropriate support teams. By identifying documents that are not in English, organizations can streamline their processes and improve response times for customer inquiries.

Search Optimization: This function can enhance search engines or knowledge bases by indexing only documents written in English. By filtering out non-English documents, it ensures users receive the most relevant and comprehensible results for their queries.

Translation Prioritization: In translation services, this identifier can prioritize documents for translation based on their language. By identifying English documents, companies can strategically allocate resources to ensure high-demand documents are translated first.

Compliance Monitoring: Businesses can use this function to ensure compliance with internal language policies or regulations. By identifying documents that are not in English, organizations can address potential issues relating to communication standards or regulatory requirements.

Market Analysis: Companies conducting market research can leverage this function to filter reports and documents to focus solely on English-language content. This enables better analysis of market trends and customer feedback from English-speaking demographics.

Training Data Selection: Machine learning models training on English text data can use this classification to curate their training datasets. By isolating English documents, developers can enhance the accuracy and relevancy of their models, leading to better performance in language-specific applications.