Text-to-Text (T2T) Model
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A Text-to-Text (T2T) Model is a unimodal text-to-* generative model that accepts a text and can produce text data.
- Context:
- It can (typically) be referenced by a Text-to-Text System.
- It can (typically) be produced by a Text-to-Text Model Training System.
- It can be associated to a Text-to-Speech Model.
- It can be evaluated by a Text-to-Text Model Benchmark Task.
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- Example(s):
- Counter-Example(s):
- See: High-Quality Image, Seq2Seq Model.
References
2023
- chat
- A Text-to-Text model is a type of generative model in machine learning that accepts one or more textual inputs and produces one or more textual outputs, typically using sequence-to-sequence learning. These models have various applications in natural language processing (NLP), such as machine translation, summarization, question-answering, and conversational AI.
- It can take one or more textual inputs and generate one or more textual outputs.
- It can be trained on large datasets using supervised learning techniques.
- It can use deep learning architectures such as transformers, LSTMs, or RNNs.
- It can generate coherent and high-quality natural language responses.
- A Text-to-Text model is a type of generative model in machine learning that accepts one or more textual inputs and produces one or more textual outputs, typically using sequence-to-sequence learning. These models have various applications in natural language processing (NLP), such as machine translation, summarization, question-answering, and conversational AI.