These days, AI technology is ubiquitous! AI is now practically everywhere in our lives, from Siri on our phones to self-driving cars on the roads. It’s difficult to envision a world without it. Healthcare, finance, and manufacturing have all seen a transformation thanks to AI, which has increased their efficiency, accuracy, and cost-effectiveness. Additionally, it is paving the way for fresh innovations like advanced robotics and personalized medicine. There are undoubtedly still many ethical questions and difficulties to be solved, but there is no denying that AI is drastically altering our society in ways we never imagined and AI might be the solution to many day-to-day problems.
A friendly, human-like AI assistant that can comprehend and reply to questions in plain language from users like you is what HuggingChat aims to give.
HuggingChat is an open-source chatbot alternative to Chat GPT.
Hugging Face, a popular AI startup known for its ML tools and AI code hub has just launched HuggingChat, an open-source alternative to ChatGPT.
A well-known open-source software community and library for natural language processing (NLP) activities is called Hugging Face. Users can customize and apply these models to a variety of NLP tasks, such as language translation, text categorization, and question-answering, using the user-friendly interfaces provided for cutting-edge NLP models like transformers. Researchers, developers, and data scientists frequently utilize the Hugging Face library, which was created in Python. Through its model hub, Hugging Face offers a platform for sharing and deploying NLP models in addition to the library.
HuggingChat is quite similar to Chat GPT and can work as well and accurately as that Chat GPT. To begin with, Chat GPT's interface is very similar to that of HuggingChat.
But it has a straightforward homepage with just the name and the brief statement, "Making the best open-source AI chat models available to everyone." HuggingChat displays prompt history, Theme, Feedback, and About & Privacy on the left panel, same as Chat GPT does.
You can download already-trained neural networks for comprehending natural language from the website hugging face. They have developed numerous cutting-edge transformer models, including the GPT-4, ELECTRA, ALBERT, DistilBERT, Roberta, and XLNet, and have improved them using various benchmarks for natural language comprehension, including GLUE, SQuAD, CoLA, STS-B, etc. In addition to the conventional models, Hugging Face also makes available human-quality generated responses for each prompt, which facilitates evaluating and contrasting various models.
Following are a few features of Hugging Face-
· Large Language Models: They built several large pre-trained language models, including GPT-4, to enable natural languages processing tasks such as sentiment analysis, text generation, machine translation, question answering, and others.
· Open-source datasets: For text categorization and sequence labeling, Hugging Face provides more than 6000 open data sets that span a variety of use cases and industries. These include code generation, news, weather forecasts, conversational assistants, and customer service. This includes both the public data sets they made available themselves and the data sets they collected from the internet.
· Text Generation: TextGendataset generator software which allows the creation custom Text-to-Text datasets for a variety of Natural Language Processing Tasks. There are already hundreds of high-quality TextGendatasets made by community members using this tool.
To compare the responses of Chat GPT and HuggingChat, I asked them both the same question.
“Who is the president of the USA?”
Below are their responses.
I feel that HuggingChat is more detailed and accurate with their response whereas Chat GPT was a bit slow.
There are several other language models available that you may consider as an alternative to ChatGPT, depending on your specific needs and use case.
However, it's worth noting that each language model has its strengths and weaknesses, and the best choice for you will depend on your specific use case and requirements.