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The company was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf in New York City, originally as a company that developed a chatbot app targeted at teenagers. [2] The company was named after the U+1F917 珞 HUGGING FACE emoji. [2]
Generative Pre-trained Transformer 2 (GPT-2) is a large language model by OpenAI and the second in their foundational series of GPT models. GPT-2 was pre-trained on a dataset of 8 million web pages. [ 2 ]
T5 (Text-to-Text Transfer Transformer) is a series of large language models developed by Google AI introduced in 2019. [ 1 ] [ 2 ] Like the original Transformer model, [ 3 ] T5 models are encoder-decoder Transformers , where the encoder processes the input text, and the decoder generates the output text.
One of the original and now most common means of application checkpointing was a "save state" feature in interactive applications, in which the user of the application could save the state of all variables and other data and either continue working or exit the application and restart the application and restore the saved state at a later time.
Claude is a family of large language models developed by Anthropic. [1] [2] The first model was released in March 2023.The Claude 3 family, released in March 2024, consists of three models: Haiku optimized for speed, Sonnet balancing capabilities and performance, and Opus designed for complex reasoning tasks.
A chatbot is a software application or web interface that is designed to mimic human conversation through text or voice interactions. [1] [2] [3] Modern chatbots are typically online and use generative artificial intelligence systems that are capable of maintaining a conversation with a user in natural language and simulating the way a human would behave as a conversational partner.
Generative pretraining (GP) was a long-established concept in machine learning applications. [16] [17] It was originally used as a form of semi-supervised learning, as the model is trained first on an unlabelled dataset (pretraining step) by learning to generate datapoints in the dataset, and then it is trained to classify a labelled dataset.
High-level schematic diagram of BERT. It takes in a text, tokenizes it into a sequence of tokens, add in optional special tokens, and apply a Transformer encoder. The hidden states of the last layer can then be used as contextual word embeddings. BERT is an "encoder-only" transformer architecture. At a high level, BERT consists of 4 modules: