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 aconfig.json
vocabulary and aspiece.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
M2M100 Pretrained model config files
M2M100 Model for conditional generation
Language generation model based on the M2M100 architecture
M2M100 Pretrained model merges files
M2M100 Base model
M2M100 Pretrained model weight files
M2M100 source languages pre-sets
M2M100 Pretrained model vocab files
Type Definitions
M2M100 model configuration
M2M100 target languages pre-sets