Giuliano Bertoti

In this series of liveProjects, you’ll develop a variety of different chatbots that can naturally perform language tasks. Digital transformations have accelerated, and reliable chatbots are now a great tool for both handling customers and dealing with internal queries. You’ll use the Hugging Face library to implement state-of-the-art natural language processing transformers to create bots that can answer questions, classify intent, and more.

In this liveProject you’ll develop a chatbot that can extract entities from user messages using the Hugging Face NLP library. Entity extraction pulls relevant data from chunks of text. Your challenges will include building a Named Entity Recognition task with the BERT transformer, and fine-tuning it to a medical context. You’ll then integrate your tasks into a chatbot.

In this liveProject you’ll develop a chatbot that can translate user messages, using the Hugging Face NLP library. Your challenges will include building the task with the T5 transformer, and build a Translation task considering different languages with mBART. You’ll classify the language of users' messages, and integrate your translation task with a chatbot.

In this liveProject you’ll develop a chatbot that can summarize a longer text, using the HuggingFace NLP library. Your challenges will include building the task with the Bart transformer, and experimenting with other transformer models to improve your results. Once you’ve built an accurate NLP model, you’ll explore other community models and integrate your summarization task with a chatbot.

In this liveProject you’ll develop a chatbot that can classify a user’s intent, using the Hugging Face NLP library. Your challenges will include building the task with the Bart transformer, and building a specialized task for detecting toxic language. You’ll then develop a Telegram bot and integrate it with your toxicity classification task.

In this liveProject you’ll develop a chatbot that can answer its user’s questions, using the Hugging Face NLP library. Your challenges will include building the task with DistilBERT transformer, and experimenting with other transformer models to improve your results. Once you’ve built an accurate NLP model, you’ll develop a Telegram bot and integrate it with your Question Answer task.