rust-bert 0.7.2

Ready-to-use NLP pipelines and transformer-based models (BERT, DistilBERT, GPT2,...)
Documentation 
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// Copyright 2020 The Google Research Authors.
// Copyright 2019-present, the HuggingFace Inc. team
// Copyright (c) 2018, NVIDIA CORPORATION.  All rights reserved.
// 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.


use rust_bert::resources::{Resource, download_resource, RemoteResource};
use rust_bert::electra::{ElectraConfig, ElectraDiscriminator, ElectraConfigResources, ElectraVocabResources, ElectraModelResources};
use rust_bert::Config;
use rust_tokenizers::{BertTokenizer, Tokenizer, TruncationStrategy};
use tch::{Tensor, Device, nn, no_grad};

fn main() -> failure::Fallible<()> {
    //    Resources paths
    let config_resource = Resource::Remote(RemoteResource::from_pretrained(ElectraConfigResources::BASE_DISCRIMINATOR));
    let vocab_resource = Resource::Remote(RemoteResource::from_pretrained(ElectraVocabResources::BASE_DISCRIMINATOR));
    let weights_resource = Resource::Remote(RemoteResource::from_pretrained(ElectraModelResources::BASE_DISCRIMINATOR));
    let config_path = download_resource(&config_resource)?;
    let vocab_path = download_resource(&vocab_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: BertTokenizer = BertTokenizer::from_file(vocab_path.to_str().unwrap(), true);
    let config = ElectraConfig::from_file(config_path);
    let electra_model = ElectraDiscriminator::new(&vs.root(), &config);
    vs.load(weights_path)?;

//    Define input
    let input = ["One Two Three Ten Five Six Seven Eight"];
    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 encoded_input = tokenized_input.
        iter().
        map(|input| input.token_ids.clone()).
        map(|mut input| {
            input.extend(vec![0; max_len - input.len()]);
            input
        }).
        map(|input|
            Tensor::of_slice(&(input))).
        collect::<Vec<_>>();
    let input_tensor = Tensor::stack(encoded_input.as_slice(), 0).to(device);

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

//    Print model predictions
    for (position, token) in tokenized_input[0].token_ids.iter().enumerate() {
        let probability = output.double_value(&[position as i64]);
        let generated = if probability > 0.5 { "generated" } else { "original" };
        println!("{:?}: {} ({:.1}%)",
                 tokenizer.decode([*token].to_vec(),
                                  false,
                                  false),
                 generated,
                 100f64 * probability)
    }


    Ok(())
}