Module rust_bert::albert

Expand description

ALBERT: A Lite BERT for Self-supervised Learning of Language Representations (Lan et al.)

Implementation of the ALBERT language model (https://arxiv.org/abs/1909.11942 Lan, Chen, Goodman, Gimpel, Sharma, Soricut, 2019). This model offers a greatly reduced memory footprint for similar effective size (number and size of layers). The computational cost remains however similar to the original BERT model. The base model is implemented in the albert_model::AlbertModel struct. Several language model heads have also been implemented, including:

Masked language model: albert_model::AlbertForMaskedLM
Multiple choices: albert_model:AlbertForMultipleChoice
Question answering: albert_model::AlbertForQuestionAnswering
Sequence classification: albert_model::AlbertForSequenceClassification
Token classification (e.g. NER, POS tagging): albert_model::AlbertForTokenClassification

Model set-up and pre-trained weights loading

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::albert::{AlbertConfig, AlbertForMaskedLM};
use rust_bert::resources::{LocalResource, ResourceProvider};
use rust_bert::Config;
use rust_tokenizers::tokenizer::AlbertTokenizer;

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: AlbertTokenizer =
    AlbertTokenizer::from_file(vocab_path.to_str().unwrap(), true, true)?;
let config = AlbertConfig::from_file(config_path);
let bert_model = AlbertForMaskedLM::new(&vs.root(), &config);
vs.load(weights_path)?;