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Which vendors are providing this tokens

Tokens in natural language processing are not provided by specific vendors; rather, they are an integral part of the field's methodology and techniques. Tokenization is a fundamental step in text processing and is performed by libraries, frameworks, and software tools that are designed for natural language processing and machine learning. These tools are typically open-source or provided by established organizations and research communities.

Some common libraries and frameworks for tokenization in natural language processing include:

Hugging Face Transformers: Hugging Face provides a popular open-source library for transformer-based models, including pre-trained models and tokenizers. Their Transformers library offers tokenizers for a wide range of languages and models.

NLTK (Natural Language Toolkit): NLTK is a widely used Python library for natural language processing tasks, including tokenization. It provides tools for various tokenization methods

spaCy: spaCy is a popular open-source library for advanced natural language processing in Python. It includes tokenization as part of its text processing capabilities.

Stanford NLP: The Stanford NLP group offers tools and models for natural language processing tasks, including tokenization.

OpenNLP: Apache OpenNLP is an open-source library for natural language processing, including tokenization, part-of-speech tagging, and more.

Gensim: Gensim is a library for topic modeling and document similarity analysis that includes tokenization as part of its text processing functions.

Scikit-learn: While primarily focused on machine learning, Scikit-learn offers basic text processing capabilities, including tokenization.

These libraries and tools are used by researchers, developers, and data scientists to tokenize text data in various NLP and machine learning projects. Depending on the library or framework, you can find tokenizers for different languages and tokenization methods, including word tokens, subword tokens, and more.

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