[−][src]Module rust_bert::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::BertModel
struct. Several language model heads have also been implemented, including:
- Masked language model:
bert::BertForMaskedLM
- Multiple choices:
bert:BertForMultipleChoice
- Question answering:
bert::BertForQuestionAnswering
- Sequence classification:
bert::BertForSequenceClassification
- Token classification (e.g. NER, POS tagging):
bert::BertForTokenClassification
Model set-up and pre-trained weights loading
A full working example is provided in examples/bert.rs
, run with cargo run --example 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 avocab.txt
vocabulary
use rust_tokenizers::BertTokenizer; use tch::{nn, Device}; use rust_bert::bert::{BertForMaskedLM, BertConfig}; use rust_bert::Config; 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); let config = BertConfig::from_file(config_path); let bert_model = BertForMaskedLM::new(&vs.root(), &config); vs.load(weights_path)?;
Structs
BertConfig | BERT model configuration |
BertEmbeddings | BertEmbeddings implementation for BERT model |
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) |
BertModel | BERT Base model |
Enums
Activation | Activation function used in the attention layer and masked language model head |
Traits
BertEmbedding | BertEmbedding trait (for use in BertModel or RoBERTaModel) |