Expand description
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::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 (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
.binweights to the.otformat. T5Tokenizerusing aspiece.modelsentence 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)?;