rust-bert 0.17.0

Ready-to-use NLP pipelines and transformer-based models (BERT, DistilBERT, GPT2,...)
Documentation 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57

// 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::ModelType;
use rust_bert::pipelines::question_answering::{
    QaInput, QuestionAnsweringConfig, QuestionAnsweringModel,
};
use rust_bert::resources::{RemoteResource, Resource};

fn main() -> anyhow::Result<()> {
    //    Set-up Question Answering model
    let config = QuestionAnsweringConfig::new(
        ModelType::Bert,
        Resource::Remote(RemoteResource::from_pretrained(BertModelResources::BERT_QA)),
        Resource::Remote(RemoteResource::from_pretrained(
            BertConfigResources::BERT_QA,
        )),
        Resource::Remote(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(())
}