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 Pretrained models are available and can be downloaded using RemoteResources.

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

let config_resource = Resource::Local(LocalResource {
    local_path: PathBuf::from("path/to/config.json"),
});
let vocab_resource = Resource::Local(LocalResource {
    local_path: PathBuf::from("path/to/vocab.txt"),
});
let merges_resource = Resource::Local(LocalResource {
    local_path: PathBuf::from("path/to/vocab.txt"),
});
let weights_resource = Resource::Local(LocalResource {
    local_path: PathBuf::from("path/to/model.ot"),
});
let config_path = download_resource(&config_resource)?;
let vocab_path = download_resource(&vocab_resource)?;
let merges_path = download_resource(&merges_resource)?;
let weights_path = download_resource(&weights_resource)?;

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

RobertaConfigResources	RoBERTa Pretrained model config files
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)
RobertaMergesResources	RoBERTa Pretrained model merges files
RobertaModelResources	RoBERTa Pretrained model weight files
RobertaVocabResources	RoBERTa Pretrained model vocab files