Module rust_bert::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::T5Model struct. This model includes a language model head: t5::T5ForConditionalGeneration implementing the common generation::LMHeadModel 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 (translation) is provided in examples/t5, run with cargo run --example 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, Resource};
use rust_bert::t5::{T5Config, T5ForConditionalGeneration};
use rust_bert::Config;
use rust_tokenizers::tokenizer::T5Tokenizer;

let config_resource = Resource::Local(LocalResource {
    local_path: PathBuf::from("path/to/config.json"),
});
let sentence_piece_resource = Resource::Local(LocalResource {
    local_path: PathBuf::from("path/to/spiece.model"),
});
let weights_resource = Resource::Local(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, false, false);
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
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
T5VocabResources	T5 Pretrained model vocab files