Module rust_bert::mobilebert [−][src]
MobileBERT (A Compact Task-agnostic BERT for Resource-Limited Devices)
Implementation of the MobileBERT language model (MobileBERT: A Compact Task-agnostic BERT for Resource-Limited Devices Sun, Yu, Song, Liu, Yang, Zhou, 2020).
The base model is implemented in the mobilebert_model::MobileBertModel
struct. Several language model heads have also been implemented, including:
- Multiple choices:
mobilebert_model:MobileBertForMultipleChoice
- Question answering:
mobilebert_model::MobileBertForQuestionAnswering
- Sequence classification:
mobilebert_model::MobileBertForSequenceClassification
- Token classification (e.g. NER, POS tagging):
mobilebert_model::MobileBertForTokenClassification
.
Model set-up and pre-trained weights loading
A full working example (generation) is provided in examples/mobilebert_masked_lm
, run with cargo run --example mobilebert_masked_lm
.
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 Pretrained models for a number of language pairs are available and can be downloaded using RemoteResources.
use tch::{nn, Device}; use rust_bert::mobilebert::{ MobileBertConfig, MobileBertConfigResources, MobileBertForMaskedLM, MobileBertModelResources, MobileBertVocabResources, }; use rust_bert::resources::{RemoteResource, Resource}; use rust_bert::Config; use rust_tokenizers::tokenizer::BertTokenizer; let config_resource = Resource::Remote(RemoteResource::from_pretrained( MobileBertConfigResources::MOBILEBERT_UNCASED, )); let vocab_resource = Resource::Remote(RemoteResource::from_pretrained( MobileBertVocabResources::MOBILEBERT_UNCASED, )); let weights_resource = Resource::Remote(RemoteResource::from_pretrained( MobileBertModelResources::MOBILEBERT_UNCASED, )); 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 = MobileBertConfig::from_file(config_path); let bert_model = MobileBertForMaskedLM::new(&vs.root(), &config); vs.load(weights_path)?;
Structs
MobileBertConfig | MobileBERT model configuration |
MobileBertConfigResources | MobileBERT Pretrained model config files |
MobileBertForMaskedLM | MobileBERT for masked language model |
MobileBertForMultipleChoice | MobileBERT for multiple choices |
MobileBertForQuestionAnswering | MobileBERT for question answering |
MobileBertForSequenceClassification | MobileBERT for sequence classification |
MobileBertForTokenClassification | MobileBERT for token classification (e.g. NER, POS) |
MobileBertModel | MobileBertModel Base model |
MobileBertModelResources | MobileBERT Pretrained model weight files |
MobileBertVocabResources | MobileBERT Pretrained model vocab files |
NoNorm | No-normalization option for MobileBERT |
Enums
NormalizationType | Normalization type to use for the MobileBERT model. |