Module rust_bert::bert

Expand description

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (Devlin et al.)

Implementation of the BERT language model (https://arxiv.org/abs/1810.04805 Devlin, Chang, Lee, Toutanova, 2018). The base model is implemented in the bert_model::BertModel struct. Several language model heads have also been implemented, including:

Masked language model: bert_model::BertForMaskedLM
Multiple choices: bert_model:BertForMultipleChoice
Question answering: bert_model::BertForQuestionAnswering
Sequence classification: bert_model::BertForSequenceClassification
Token classification (e.g. NER, POS tagging): bert_model::BertForTokenClassification

Model set-up and pre-trained weights loading

A full working example is provided in examples/masked_language_model_bert, run with cargo run --example masked_language_model_bert. The example below illustrate a Masked language model example, the structure is similar for other models. 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 are available and can be downloaded using RemoteResources.

use tch::{nn, Device};
use rust_bert::bert::{BertConfig, BertForMaskedLM};
use rust_bert::resources::{LocalResource, ResourceProvider};
use rust_bert::Config;
use rust_tokenizers::tokenizer::BertTokenizer;

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 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 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 = BertConfig::from_file(config_path);
let bert_model = BertForMaskedLM::new(&vs.root(), &config);
vs.load(weights_path)?;