rust-bert 0.23.0

Ready-to-use NLP pipelines and language models
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 anyhow;

use rust_bert::bert::{BertConfigResources, BertModelResources, BertVocabResources};
use rust_bert::pipelines::common::{ModelResource, ModelType};
use rust_bert::pipelines::question_answering::{
    QaInput, QuestionAnsweringConfig, QuestionAnsweringModel,
};
use rust_bert::resources::RemoteResource;

fn main() -> anyhow::Result<()> {
    //    Set-up Question Answering model
    let config = QuestionAnsweringConfig::new(
        ModelType::Bert,
        ModelResource::Torch(Box::new(RemoteResource::from_pretrained(
            BertModelResources::BERT_QA,
        ))),
        RemoteResource::from_pretrained(BertConfigResources::BERT_QA),
        RemoteResource::from_pretrained(BertVocabResources::BERT_QA),
        None, //merges resource only relevant with ModelType::Roberta
        false,
        false,
        None,
    );

    let qa_model = QuestionAnsweringModel::new(config)?;

    //    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(&[qa_input_1, qa_input_2], 1, 32);
    println!("{answers:?}");
    Ok(())
}