Module fnet

FNet, Mixing Tokens with Fourier Transforms (Lee-Thorp et al.)

Implementation of the FNet language model (https://arxiv.org/abs/2105.03824 Lee-Thorp, Ainslie, Eckstein, Ontanon, 2021). The base model is implemented in the fnet_model::FNetModel struct. Several language model heads have also been implemented, including:

• Masked language model: fnet_model::FNetForMaskedLM
• Question answering: fnet_model::FNetForQuestionAnswering
• Sequence classification: fnet_model::FNetForSequenceClassification
• Token classification (e.g. NER, POS tagging): fnet_model::FNetForTokenClassification

Model set-up and pre-trained weights loading

The example below illustrate a FNet 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.
• FNetTokenizer using a spiece.model SentencePiece (BPE) model file

Pretrained models are available and can be downloaded using RemoteResources.

use tch::{nn, Device};
use rust_bert::fnet::{FNetConfig, FNetForMaskedLM};
use rust_bert::resources::{LocalResource, ResourceProvider};
use rust_bert::Config;
use rust_tokenizers::tokenizer::{BertTokenizer, FNetTokenizer};

let config_resource = LocalResource {
    local_path: PathBuf::from("path/to/config.json"),
};
let vocab_resource = LocalResource {
    local_path: PathBuf::from("path/to/spiece.model"),
};
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: FNetTokenizer =
    FNetTokenizer::from_file(vocab_path.to_str().unwrap(), true, true)?;
let config = FNetConfig::from_file(config_path);
let bert_model = FNetForMaskedLM::new(&vs.root(), &config);
vs.load(weights_path)?;

Structs

FNetConfig

FNet model configuration

FNetConfigResources

FNet Pretrained model config files

FNetForMaskedLM

FNet for masked language model

FNetForMultipleChoice

FNet for multiple choices

FNetForQuestionAnswering

FNet for question answering

FNetForSequenceClassification

FNet for sequence classification

FNetForTokenClassification

FNet for token classification (e.g. NER, POS)

FNetMaskedLMOutput

Container for the FNet masked LM model output.

FNetModel

FNet Base model

FNetModelOutput

Container for the FNet model output.

FNetModelResources

FNet Pretrained model weight files

FNetQuestionAnsweringOutput

Container for the FNet question answering model output.

FNetSequenceClassificationOutput

Container for the FNet sequence classification model output.

FNetVocabResources

FNet Pretrained model vocab files

Type Aliases

FNetTokenClassificationOutput

Container for the FNet token classification model output.