Module rust_bert::electra

Expand description

Electra: Pre-training Text Encoders as Discriminators Rather Than Generators (Clark et al.)

Implementation of the Electra language model (https://openreview.net/pdf?id=r1xMH1BtvB Clark, Luong, Le, Manning, 2020). The base model is implemented in the electra_model::ElectraModel struct. Both generator and discriminator are available via specialized heads:

Generator head: electra_model::ElectraGeneratorHead
Discriminator head: electra_model::ElectraDiscriminatorHead

The generator and discriminator models are built from these:

Generator (masked language model): electra_model::ElectraForMaskedLM
Discriminator: electra_model::ElectraDiscriminator

An additional sequence token classification model is available for reference

Token classification (e.g. NER, POS tagging): electra_model::ElectraForTokenClassification

Model set-up and pre-trained weights loading

The example below illustrate a Masked language model example, the structure is similar for other models (e.g. discriminator). 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::electra::{ElectraConfig, ElectraForMaskedLM};
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 = ElectraConfig::from_file(config_path);
let electra_model = ElectraForMaskedLM::new(&vs.root(), &config);
vs.load(weights_path)?;