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https://github.com/guillaume-be/rust-bert.git
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58 lines
2.2 KiB
Rust
58 lines
2.2 KiB
Rust
// Copyright 2019-present, the HuggingFace Inc. team, The Google AI Language Team and Facebook, Inc.
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// Copyright 2019 Guillaume Becquin
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// Licensed under the Apache License, Version 2.0 (the "License");
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// you may not use this file except in compliance with the License.
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// You may obtain a copy of the License at
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// http://www.apache.org/licenses/LICENSE-2.0
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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extern crate anyhow;
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use rust_bert::bert::{BertConfigResources, BertModelResources, BertVocabResources};
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use rust_bert::pipelines::common::ModelType;
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use rust_bert::pipelines::question_answering::{
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QaInput, QuestionAnsweringConfig, QuestionAnsweringModel,
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};
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use rust_bert::resources::{RemoteResource, Resource};
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fn main() -> anyhow::Result<()> {
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// Set-up Question Answering model
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let config = QuestionAnsweringConfig::new(
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ModelType::Bert,
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Resource::Remote(RemoteResource::from_pretrained(BertModelResources::BERT_QA)),
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Resource::Remote(RemoteResource::from_pretrained(
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BertConfigResources::BERT_QA,
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)),
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Resource::Remote(RemoteResource::from_pretrained(BertVocabResources::BERT_QA)),
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None, //merges resource only relevant with ModelType::Roberta
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false, //lowercase
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false,
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None,
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);
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let qa_model = QuestionAnsweringModel::new(config)?;
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// Define input
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let question_1 = String::from("Where does Amy live ?");
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let context_1 = String::from("Amy lives in Amsterdam");
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let question_2 = String::from("Where does Eric live");
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let context_2 = String::from("While Amy lives in Amsterdam, Eric is in The Hague.");
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let qa_input_1 = QaInput {
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question: question_1,
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context: context_1,
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};
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let qa_input_2 = QaInput {
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question: question_2,
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context: context_2,
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};
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// Get answer
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let answers = qa_model.predict(&[qa_input_1, qa_input_2], 1, 32);
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println!("{:?}", answers);
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Ok(())
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}
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