Module albert

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§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)?;

Structs§

AlbertConfig
ALBERT model configuration
AlbertConfigResources
ALBERT Pretrained model config files
AlbertForMaskedLM
ALBERT for masked language model
AlbertForMultipleChoice
ALBERT for multiple choices
AlbertForQuestionAnswering
ALBERT for question answering
AlbertForSequenceClassification
ALBERT for sequence classification
AlbertForTokenClassification
ALBERT for token classification (e.g. NER, POS)
AlbertMaskedLMOutput
Container for the ALBERT masked LM model output.
AlbertModel
ALBERT Base model
AlbertModelResources
ALBERT Pretrained model weight files
AlbertOutput
Container for the ALBERT model output.
AlbertQuestionAnsweringOutput
Container for the ALBERT question answering model
AlbertSequenceClassificationOutput
Container for the ALBERT sequence classification model
AlbertTokenClassificationOutput
Container for the ALBERT token classification model
AlbertVocabResources
ALBERT Pretrained model vocab files

Type Aliases§

AlbertForSentenceEmbeddings
ALBERT for sentence embeddings