We've developed a comprehensive list of the top 10 Programming Languages for natural language processing

The market for NLP is exploding, and several new tools have lately entered the ecosystem. You should be aware of these libraries, frameworks, Programming languages, services, and actors to include text comprehension and production in your project.

1. Python: Python has long been the de facto standard language in data research. If you're working on a natural language processing project, you'll almost certainly find some Python code. Python is a highly expressive and straightforward high-level language, making it ideal for machine-learning applications.

2. Hugging Face Hub: Hugging Face Hub is a centralized repository containing the most open-source natural language processing models. It makes it simple to explore new AI models while also uploading and sharing your own. And also an excellent resource for browsing and locating datasets for your next project. Models and datasets may be easily downloaded and utilized using their Transformers framework.

3.OpenAI: GPT-3, the most complex linguistic AI model yet produced, was developed by OpenAI. The first two versions of this model were open-source. However, OpenAI determined that GPT-3 would not be. You must subscribe to the OpenAI API to utilize GPT-3. Because they obtained an exclusive license, only Microsoft can access the GPT-3 source code.

4. NLP Cloud: You may also train and fine-tune your own AI on NLP Cloud and deploy your own in-house models. For example, suppose you want to build your own GPT-J-based medical chatbot. In that case, you have to upload your dataset, which is made up of your own samples from your business, start the training process, and utilize your finished model in production via the API.

5. Deepspeed: Deepspeed is a Microsoft open-source framework for model parallelization. AI models are becoming increasingly complex. These massive models open the door to many new applications but are also tough to run. Vertical scalability or horizontal scalability may be used to train these models and consistently operate them in production for inference.

6. Big Science: Big Science is a group of scholars and businesses working on massive language models. Their first workshop resulted in the creation of T0, an AI model that excels at interpreting human commands. They are presently working on considerably larger models to develop open-source, multilingual AI models that are larger and more sophisticated than GPT-3.

7. SpaCy: SpaCy is a Python natural language processing framework that is ideal for production since it is quick and straightforward. Explosive AI, a German AI company, maintains this framework. a German AI firm. SpaCy excels at Named Entity Recognition in over 50 languages.

8. HF Transformers: Hugging Face launched the Transformers framework a few years ago. Transformers are currently used in the majority of complex natural language processing models. This Python module may be used for training or inference and is built on PyTorch, Tensorflow, and Jax. Hugging Face Transformers make downloading and uploading models to the Hugging Face Hub a breeze.

9. HF Tokenizers: Hugging Face's tokenizers library is a collection of powerful natural language processing tokenizers employed by transformer-based models. Tokenization is breaking down an input text into little words or subwords that the AI model may then encode and analyze.

10. NLTK: Natural Language Toolkit is an abbreviation for Natural Language Toolkit. It is a Python framework that has been around for quite some time and is excellent for research and education. Although NLTK is not a production-oriented framework, it is perfect for data scientists just starting with natural language processing.