Expand description
§ALBERT: A Lite BERT for Self-supervised Learning of Language Representations (Lan et al.)
Implementation of the ALBERT language model (https://arxiv.org/abs/1909.11942 Lan, Chen, Goodman, Gimpel, Sharma, Soricut, 2019).
This model offers a greatly reduced memory footprint for similar effective size (number and size of layers). The computational cost remains however similar to the original BERT model.
The base model is implemented in the albert_model::AlbertModel
struct. Several language model heads have also been implemented, including:
- Masked language model:
albert_model::AlbertForMaskedLM
- Multiple choices:
albert_model:AlbertForMultipleChoice
- Question answering:
albert_model::AlbertForQuestionAnswering
- Sequence classification:
albert_model::AlbertForSequenceClassification
- Token classification (e.g. NER, POS tagging):
albert_model::AlbertForTokenClassification
§Model set-up and pre-trained weights loading
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
Pretrained models are available and can be downloaded using RemoteResources.
use tch::{nn, Device};
use rust_bert::albert::{AlbertConfig, AlbertForMaskedLM};
use rust_bert::resources::{LocalResource, ResourceProvider};
use rust_bert::Config;
use rust_tokenizers::tokenizer::AlbertTokenizer;
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: AlbertTokenizer =
AlbertTokenizer::from_file(vocab_path.to_str().unwrap(), true, true)?;
let config = AlbertConfig::from_file(config_path);
let bert_model = AlbertForMaskedLM::new(&vs.root(), &config);
vs.load(weights_path)?;
Structs§
- Albert
Config - ALBERT model configuration
- Albert
Config Resources - ALBERT Pretrained model config files
- Albert
ForMaskedLM - ALBERT for masked language model
- Albert
ForMultiple Choice - ALBERT for multiple choices
- Albert
ForQuestion Answering - ALBERT for question answering
- Albert
ForSequence Classification - ALBERT for sequence classification
- Albert
ForToken Classification - ALBERT for token classification (e.g. NER, POS)
- Albert
MaskedLM Output - Container for the ALBERT masked LM model output.
- Albert
Model - ALBERT Base model
- Albert
Model Resources - ALBERT Pretrained model weight files
- Albert
Output - Container for the ALBERT model output.
- Albert
Question Answering Output - Container for the ALBERT question answering model
- Albert
Sequence Classification Output - Container for the ALBERT sequence classification model
- Albert
Token Classification Output - Container for the ALBERT token classification model
- Albert
Vocab Resources - ALBERT Pretrained model vocab files
Type Aliases§
- Albert
ForSentence Embeddings - ALBERT for sentence embeddings