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models, also with Git-based version control; datasets, mainly in text, images, and audio; web applications ("spaces" and "widgets"), intended for small-scale demos of machine learning applications. There are numerous pre-trained models that support common tasks in different modalities, such as:
A collection of Vietnamese multiple-choice questions for evaluating MRC models. This corpus includes 2,783 Vietnamese multiple-choice questions. 2,783 Question-answer pairs Question Answering/Machine Reading Comprehension 2020 [335] Nguyen et al. Open-Domain Question Answering Goes Conversational via Question Rewriting
BigScience Large Open-science Open-access Multilingual Language Model (BLOOM) [1] [2] is a 176-billion-parameter transformer-based autoregressive large language model (LLM). The model, as well as the code base and the data used to train it, are distributed under free licences. [ 3 ]
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.
In May 2023, IBM revealed Watsonx, a Generative AI toolkit that is powered by IBM's own Granite models with option to use other publicly available LLMs. Watsonx has multiple services for training and fine tuning models based on confidential data. [161] A year later, IBM open-sourced Granite code models and put them on Hugging Face for public ...
GPT-J or GPT-J-6B is an open-source large language model (LLM) developed by EleutherAI in 2021. [1] As the name suggests, it is a generative pre-trained transformer model designed to produce human-like text that continues from a prompt.
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Like earlier seq2seq models, the original transformer model used an encoder-decoder architecture. The encoder consists of encoding layers that process all the input tokens together one layer after another, while the decoder consists of decoding layers that iteratively process the encoder's output and the decoder's output tokens so far.