rust-bert 0.7.9

// Copyright 2019-present, the HuggingFace Inc. team, The Google AI Language Team and Facebook, Inc.
// Copyright 2019 Guillaume Becquin
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//     http://www.apache.org/licenses/LICENSE-2.0
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.

extern crate failure;

use rust_bert::bert::BertConfig;
use rust_bert::resources::{download_resource, RemoteResource, Resource};
use rust_bert::roberta::{
    RobertaConfigResources, RobertaForMaskedLM, RobertaMergesResources, RobertaModelResources,
    RobertaVocabResources,
};
use rust_bert::Config;
use rust_tokenizers::{RobertaTokenizer, Tokenizer, TruncationStrategy, Vocab};
use tch::{nn, no_grad, Device, Tensor};

fn main() -> failure::Fallible<()> {
    //    Resources paths
    let config_resource = Resource::Remote(RemoteResource::from_pretrained(
        RobertaConfigResources::ROBERTA,
    ));
    let vocab_resource = Resource::Remote(RemoteResource::from_pretrained(
        RobertaVocabResources::ROBERTA,
    ));
    let merges_resource = Resource::Remote(RemoteResource::from_pretrained(
        RobertaMergesResources::ROBERTA,
    ));
    let weights_resource = Resource::Remote(RemoteResource::from_pretrained(
        RobertaModelResources::ROBERTA,
    ));
    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)?;

    //    Set-up masked LM model
    let device = Device::Cpu;
    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)?;

    //    Define input
    let input = [
        "<pad> Looks like one thing is missing",
        "It\'s like comparing oranges to apples",
    ];
    let tokenized_input =
        tokenizer.encode_list(input.to_vec(), 128, &TruncationStrategy::LongestFirst, 0);
    let max_len = tokenized_input
        .iter()
        .map(|input| input.token_ids.len())
        .max()
        .unwrap();
    let mut tokenized_input = tokenized_input
        .iter()
        .map(|input| input.token_ids.clone())
        .map(|mut input| {
            input.extend(vec![0; max_len - input.len()]);
            input
        })
        .collect::<Vec<_>>();

    //    Masking the token [thing] of sentence 1 and [oranges] of sentence 2
    tokenized_input[0][4] = 103;
    tokenized_input[1][5] = 103;
    let tokenized_input = tokenized_input
        .iter()
        .map(|input| Tensor::of_slice(&(input)))
        .collect::<Vec<_>>();
    let input_tensor = Tensor::stack(tokenized_input.as_slice(), 0).to(device);

    //    Forward pass
    let (output, _, _) = no_grad(|| {
        bert_model.forward_t(
            Some(input_tensor),
            None,
            None,
            None,
            None,
            &None,
            &None,
            false,
        )
    });

    //    Print masked tokens
    let index_1 = output.get(0).get(4).argmax(0, false);
    let index_2 = output.get(1).get(5).argmax(0, false);
    let word_1 = tokenizer.vocab().id_to_token(&index_1.int64_value(&[]));
    let word_2 = tokenizer.vocab().id_to_token(&index_2.int64_value(&[]));

    println!("{}", word_1); // Outputs "some" : "Looks like [some] thing is missing"
    println!("{}", word_2); // Outputs "apple" : "It\'s like comparing [apple] to apples"

    Ok(())
}