Module m2m_100

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::LanguageGenerator 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)?;

Structs

M2M100ConfigResources

M2M100 Pretrained model config files

M2M100ForConditionalGeneration

M2M100 Model for conditional generation

M2M100Generator

Language generation model based on the M2M100 architecture

M2M100MergesResources

M2M100 Pretrained model merges files

M2M100Model

M2M100 Base model

M2M100ModelResources

M2M100 Pretrained model weight files

M2M100SourceLanguages

M2M100 source languages pre-sets

M2M100VocabResources

M2M100 Pretrained model vocab files

Type Aliases

LayerState

M2M100Config

M2M100 model configuration

M2M100TargetLanguages

M2M100 target languages pre-sets