Module t5

T5 (Text-To-Text Transfer Transformer)

Implementation of the T5 language model (Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer Raffel, Shazeer, Roberts, Lee, Narang, Matena, Zhou, Li, Liu, 2019). The base model is implemented in the t5_model::T5Model struct. This model includes a language model head: t5_model::T5ForConditionalGeneration implementing the common generation_utils::LanguageGenerator trait shared between the models used for generation (see pipelines for more information).

Model set-up and pre-trained weights loading

A full working example (summarization) is provided in examples/summarization_t5, run with cargo run --example summarization_t5. All models expect the following resources:

• Configuration file expected to have a structure following the Transformers library
• Model weights are expected to have a structure and parameter names following the Transformers library. A conversion using the Python utility scripts is required to convert the .bin weights to the .ot format.
• T5Tokenizer using a spiece.model sentence piece model

Pretrained models for a number of language pairs are available and can be downloaded using RemoteResources.

use tch::{nn, Device};
use rust_bert::resources::{LocalResource, ResourceProvider};
use rust_bert::t5::{T5Config, T5ForConditionalGeneration};
use rust_bert::Config;
use rust_tokenizers::tokenizer::T5Tokenizer;

let config_resource = LocalResource {
    local_path: PathBuf::from("path/to/config.json"),
};
let sentence_piece_resource = LocalResource {
    local_path: PathBuf::from("path/to/spiece.model"),
};
let weights_resource = LocalResource {
    local_path: PathBuf::from("path/to/model.ot"),
};
let config_path = config_resource.get_local_path()?;
let spiece_path = sentence_piece_resource.get_local_path()?;
let weights_path = weights_resource.get_local_path()?;

let device = Device::cuda_if_available();
let mut vs = nn::VarStore::new(device);
let tokenizer = T5Tokenizer::from_file(spiece_path.to_str().unwrap(), true);
let config = T5Config::from_file(config_path);
let t5_model = T5ForConditionalGeneration::new(&vs.root(), &config);
vs.load(weights_path)?;

Structs

LayerState

Cache for T5 attention layers

T5Config

T5 model configuration

T5ConfigResources

T5 Pretrained model config files

T5ForConditionalGeneration

T5 Model for conditional generation

T5ForSentenceEmbeddings

T5 for sentence embeddings

T5Generator

T5Model

T5 Base model

T5ModelOutput

Container holding a T5 model output. The decoder output may hold the hidden state of the last layer of the decoder, or may hold logits for a custom head module after the decoder (e.g. for language modeling tasks)

T5ModelResources

T5 Pretrained model weight files

T5Prefix

T5 optional prefixes

T5SourceLanguages

T5 source languages pre-sets

T5VocabResources

T5 Pretrained model vocab files

Type Aliases

T5TargetLanguages

T5 target languages pre-sets