Module rust_bert::m2m_100

Expand description

M2M-100 (Fan et al.)

Implementation of the M2M-100 language model (Beyond English-Centric Multilingual Machine Translation Fan, Bhosale, Schwenk, Ma, El-Kishky, Goyal, Baines, Celebi, Wenzel, Chaudhary, Goyal, Birch, Liptchinsky, Edunov, Grave, Auli, Joulin, 2020). The base model is implemented in the m2m_100::M2M100Model struct. The model also includes a language model head: m2m_100::M2M100ForConditionalGeneration implementing the common generation_utils::LMHeadModel trait shared between the models used for generation (see pipelines for more information). This model allows for direct translation between 100 languages. The translation capabilities are illustrated in examples/translation_m2m100, run with cargo run --example translation_m2m100.

Model set-up and pre-trained weights loading

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.
M2M100Tokenizer using a config.json vocabulary and a spiece.model SentencePiece BPE model Pretrained models are available and can be downloaded using RemoteResources.

use tch::{nn, Device};
use rust_bert::m2m_100::{M2M100Config, M2M100Model};
use rust_bert::resources::{LocalResource, ResourceProvider};
use rust_bert::Config;
use rust_tokenizers::tokenizer::M2M100Tokenizer;

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/spiece.model"),
};
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: M2M100Tokenizer = M2M100Tokenizer::from_files(
    vocab_path.to_str().unwrap(),
    merges_path.to_str().unwrap(),
    false,
)?;
let config = M2M100Config::from_file(config_path);
let m2m100_model = M2M100Model::new(&vs.root(), &config);
vs.load(weights_path)?;