T5 Text-To-Text Transformer is a model designed to unify various NLP tasks into a text-to-text format.
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, Peter J. Liu, in the paper, “Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer“.
October 23, 2019
September 19, 2023
The T5 model is built on the Transformer architecture. Its primary component is the self-attention mechanism, which calculates the weights based on the pairwise affinity between the query of each element and the keys of all other elements in the sequence. This allows the model to focus differently on various parts of the input when processing each element. Refer this article to get more details on the math and more indepth intuition behind the attention mechanism.
The original Transformer model was designed as an encoder-decoder architecture, primarily for sequence-to-sequence tasks. However, more recent versions of Transformer models have been simplified to include just a single stack of Transformer layers. These streamlined models are specifically optimized for tasks like language modeling and text classification.
The T5 closely follows the original Transformer’s encoder-decoder structure, with some modifications. It maps an input sequence of tokens to a sequence of embeddings, which is then passed to the encoder. This encoder is made up of blocks, each containing a self-attention layer followed by a shallow feed-forward network.
Layer normalization is applied to each subcomponent’s input, and a residual skip connection adds each subcomponent’s input to its output.
The decoder mirrors the encoder but includes a standard attention mechanism post each self-attention layer, focusing on the encoder’s output.
The self-attention mechanism in the decoder uses autoregressive or causal self-attention, allowing the model to attend only to past outputs.
One of the T5’s unique features is its position embedding scheme. While the original Transformer used fixed or learned absolute position embeddings, the T5 employs relative position embeddings. These produce different learned embeddings based on the offset between the key and query in the self-attention mechanism. This model also uses a simplified form of position embeddings, where each embedding is a scalar added to the corresponding logit for computing attention weights.
The T5 model expects textual prompts that are tailored to specific NLP tasks. The format of the input is designed to guide the model in understanding the desired task:
The model’s flexibility allows it to handle a wide range of tasks, and the input format plays a crucial role in directing the model’s attention to the desired output format.
The T5 model produces textual outputs corresponding to the specified task:
In essence, the T5 model’s output is a textual representation that aligns with the specified task, providing relevant information or results based on the input prompt.
from transformers import AutoFeatureExtractor, AutoModelForImageClassification extractor = AutoFeatureExtractor.from_pretrained("microsoft/resnet-50") model = AutoModelForImageClassification.from_pretrained("microsoft/resnet-50")