Module rust_bert::mobilebert

Expand description

MobileBERT (A Compact Task-agnostic BERT for Resource-Limited Devices)

Implementation of the MobileBERT language model (MobileBERT: A Compact Task-agnostic BERT for Resource-Limited Devices Sun, Yu, Song, Liu, Yang, Zhou, 2020). The base model is implemented in the mobilebert_model::MobileBertModel struct. Several language model heads have also been implemented, including:

Multiple choices: mobilebert_model:MobileBertForMultipleChoice
Question answering: mobilebert_model::MobileBertForQuestionAnswering
Sequence classification: mobilebert_model::MobileBertForSequenceClassification
Token classification (e.g. NER, POS tagging): mobilebert_model::MobileBertForTokenClassification.

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.
BertTokenizer using a vocab.txt vocabulary Pretrained models for a number of language pairs are available and can be downloaded using RemoteResources.

use tch::{nn, Device};
use rust_bert::mobilebert::{
    MobileBertConfig, MobileBertConfigResources, MobileBertForMaskedLM,
    MobileBertModelResources, MobileBertVocabResources,
};
use rust_bert::resources::{RemoteResource, ResourceProvider};
use rust_bert::Config;
use rust_tokenizers::tokenizer::BertTokenizer;

let config_resource =
    RemoteResource::from_pretrained(MobileBertConfigResources::MOBILEBERT_UNCASED);
let vocab_resource =
    RemoteResource::from_pretrained(MobileBertVocabResources::MOBILEBERT_UNCASED);
let weights_resource =
    RemoteResource::from_pretrained(MobileBertModelResources::MOBILEBERT_UNCASED);
let config_path = config_resource.get_local_path()?;
let vocab_path = vocab_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: BertTokenizer =
    BertTokenizer::from_file(vocab_path.to_str().unwrap(), true, true)?;
let config = MobileBertConfig::from_file(config_path);
let bert_model = MobileBertForMaskedLM::new(&vs.root(), &config);
vs.load(weights_path)?;