Module rust_bert::models::bart

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BART (Lewis et al.)

Implementation of the BART language model (BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension Lewis, Liu, Goyal, Ghazvininejad, Mohamed, Levy, Stoyanov, Zettlemoyer, 2019). The base model is implemented in the bart_model::BartModel struct. The model also includes a language model head: bart_model::BartForConditionalGeneration 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

The summarization capabilities are illustrated in examples/summarization_bart, run with cargo run --example summarization_bart. 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.
  • RobertaTokenizer using a vocab.txt vocabulary and merges.txt 2-gram merges Pretrained models are available and can be downloaded using RemoteResources.
use tch::{nn, Device};
use rust_bert::bart::{BartConfig, BartModel};
use rust_bert::resources::{LocalResource, ResourceProvider};
use rust_bert::Config;
use rust_tokenizers::tokenizer::RobertaTokenizer;

let config_resource = LocalResource {
    local_path: PathBuf::from("path/to/config.json"),
};
let vocab_resource = LocalResource {
    local_path: PathBuf::from("path/to/vocab.txt"),
};
let merges_resource = LocalResource {
    local_path: PathBuf::from("path/to/vocab.txt"),
};
let weights_resource = LocalResource {
    local_path: PathBuf::from("path/to/model.ot"),
};
let config_path = config_resource.get_local_path()?;
let vocab_path = vocab_resource.get_local_path()?;
let merges_path = merges_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: RobertaTokenizer = RobertaTokenizer::from_file(
    vocab_path.to_str().unwrap(),
    merges_path.to_str().unwrap(),
    true,
    false,
)?;
let config = BartConfig::from_file(config_path);
let bart_model = BartModel::new(&vs.root(), &config);
vs.load(weights_path)?;

Structs