Module bert

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)?;

Structs

BertConfig

BERT model configuration

BertConfigResources

BERT Pretrained model config files

BertEmbeddings

BertEmbeddings implementation for BERT model

BertEncoder

BERT Encoder

BertEncoderOutput

Container for the BERT encoder output.

BertForMaskedLM

BERT for masked language model

BertForMultipleChoice

BERT for multiple choices

BertForQuestionAnswering

BERT for question answering

BertForSequenceClassification

BERT for sequence classification

BertForTokenClassification

BERT for token classification (e.g. NER, POS)

BertLayer

BERT Layer

BertLayerOutput

Container for the BERT layer output.

BertMaskedLMOutput

Container for the BERT masked LM model output.

BertModel

BERT Base model

BertModelOutput

Container for the BERT model output.

BertModelResources

BERT Pretrained model weight files

BertPooler

BERT Pooler

BertQuestionAnsweringOutput

Container for the BERT question answering model output.

BertSequenceClassificationOutput

Container for the BERT sequence classification model output.

BertTokenClassificationOutput

Container for the BERT token classification model output.

BertVocabResources

BERT Pretrained model vocab files

Traits

BertEmbedding

BertEmbedding trait (for use in BertModel or RoBERTaModel)

Type Aliases

BertForSentenceEmbeddings

BERT for sentence embeddings