Module rust_bert::distilbert

Expand description

DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter (Sanh et al.)

Implementation of the DistilBERT language model (https://arxiv.org/abs/1910.01108 Sanh, Debut, Chaumond, Wolf, 2019). The base model is implemented in the distilbert_model::DistilBertModel struct. Several language model heads have also been implemented, including:

Masked language model: distilbert_model::DistilBertForMaskedLM
Question answering: distilbert_model::DistilBertForQuestionAnswering
Sequence classification: distilbert_model::DistilBertForSequenceClassification
Token classification (e.g. NER, POS tagging): distilbert_model::DistilBertForTokenClassification

Model set-up and pre-trained weights loading

The example below illustrate a DistilBERT 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::distilbert::{
    DistilBertConfig, DistilBertConfigResources, DistilBertModelMaskedLM,
    DistilBertModelResources, DistilBertVocabResources,
};
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 = DistilBertConfig::from_file(config_path);
let bert_model = DistilBertModelMaskedLM::new(&vs.root(), &config);
vs.load(weights_path)?;