Module rust_bert::roberta

RoBERTa: A Robustly Optimized BERT Pretraining Approach (Liu et al.)

Implementation of the RoBERTa language model (https://arxiv.org/abs/1907.11692 Liu, Ott, Goyal, Du, Joshi, Chen, Levy, Lewis, Zettlemoyer, Stoyanov, 2019). The base model is implemented in the bert::BertModel struct. Several language model heads have also been implemented, including:

• Masked language model: roberta::RobertaForMaskedLM
• Multiple choices: roberta:RobertaForMultipleChoice
• Question answering: roberta::RobertaForQuestionAnswering
• Sequence classification: roberta::RobertaForSequenceClassification
• Token classification (e.g. NER, POS tagging): roberta::RobertaForTokenClassification

Model set-up and pre-trained weights loading

A full working example is provided in examples/robert.rs, run with cargo run --example roberta. 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.
• RobertaTokenizer using a vocab.txt vocabulary and merges.txt 2-gram merges

use rust_tokenizers::RobertaTokenizer;
use tch::{nn, Device};
use rust_bert::bert::BertConfig;
use rust_bert::Config;
use rust_bert::roberta::RobertaForMaskedLM;

let device = Device::cuda_if_available();
let mut vs = nn::VarStore::new(device);
let tokenizer: RobertaTokenizer = RobertaTokenizer::from_file(vocab_path.to_str().unwrap(), merges_path.to_str().unwrap(), true);
let config = BertConfig::from_file(config_path);
let bert_model = RobertaForMaskedLM::new(&vs.root(), &config);
vs.load(weights_path)?;

Structs

RobertaEmbeddings	BertEmbeddings implementation for RoBERTa model
RobertaForMaskedLM	RoBERTa for masked language model
RobertaForMultipleChoice	RoBERTa for multiple choices
RobertaForQuestionAnswering	RoBERTa for question answering
RobertaForSequenceClassification	RoBERTa for sequence classification
RobertaForTokenClassification	RoBERTa for token classification (e.g. NER, POS)