🚀 Feature request T5 to classify sequences by using only the encoder of T5 and a ClassificationHead. Motivation This gives the benefits of fine-tuning a model with no maximum sequence length (useful for long sequence tasks) without havin...
Multi-Modal Classification Conversational AI Citation If you use Simple Transformers in your work, please cite: @inproceedings{Rajapakse2024SimpleTransformers,author={Rajapakse, Thilina C. and Yates, Andrew and de Rijke, Maarten},title={Simple Transformers: Open-source for All},booktitle={Proceedings ...
具体到这个工作，主要考虑了machine translation、question answering、abstractive summarization和text classifi...
Google又出大招了,这次叫做T5: T5 serves primarily as code for reproducing the experiments in Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer. The bulk of the code in this repository is used for loading, preprocessing, mixing, and evaluating datasets. It also pro...
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T5 for answering questions, summarization, translation, and classification T5or Text-To-Text Transfer Transformer is a recent architecture created by Google. It reframes all natural language processing (NLP) tasks into a unified text-to-text format where the input and output are always text strings...
In this work we present the Chatbot Interaction with Artificial Intelligence (CI-AI) framework as an approach to the training of a transformer based chatbot-like architecture for task classification with a focus on natural human interaction with a machine as opposed to interfaces, code, or formal...
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, we achieve state-of-the-art results on many benchmarks covering summarization, question answering, text classification, and more. To facilitate future work on transfer learning for NLP, we release our dataset, pre-trained models, and code.",return_tensors="pt").input_ids# Batch size 1...
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