Module rust_bert::bart

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::LMHeadModel 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

BartConfig

BART model configuration

BartConfigResources

BART Pretrained model config files

BartForConditionalGeneration

BART Model for conditional generation

BartForSequenceClassification

BART Model for sequence classification

BartGenerator

Language generation model based on the Bart architecture

BartMergesResources

BART Pretrained model merges files

BartModel

BART Base model

BartModelOutput

Container holding a BART 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 classification or language modeling tasks)

BartModelResources

BART Pretrained model weight files

BartVocabResources

BART Pretrained model vocab files

LayerState

Cache for BART attention layers