rust-bert 0.7.2

Ready-to-use NLP pipelines and transformer-based models (BERT, DistilBERT, GPT2,...)
Documentation 
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// 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::pipelines::question_answering::{QuestionAnsweringModel, QaInput};


fn main() -> failure::Fallible<()> {
//    Set-up Question Answering model
    let qa_model = QuestionAnsweringModel::new(Default::default())?;

//    Define input
    let question_1 = String::from("Where does Amy live ?");
    let context_1 = String::from("Amy lives in Amsterdam");
    let question_2 = String::from("Where does Eric live");
    let context_2 = String::from("While Amy lives in Amsterdam, Eric is in The Hague.");
    let qa_input_1 = QaInput { question: question_1, context: context_1 };
    let qa_input_2 = QaInput { question: question_2, context: context_2 };

//    Get answer
    let answers = qa_model.predict(&vec!(qa_input_1, qa_input_2), 1, 32);
    println!("{:?}", answers);
    Ok(())
}