mirror of
https://github.com/guillaume-be/rust-bert.git
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84561ec82b
* Updates for compatibility with tokenizers special token rework * Updated mask pipline methods * Bumped version * Fix clippy warnings
40 lines
1.5 KiB
Rust
40 lines
1.5 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::pipelines::zero_shot_classification::ZeroShotClassificationModel;
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fn main() -> anyhow::Result<()> {
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// Set-up model
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let sequence_classification_model = ZeroShotClassificationModel::new(Default::default())?;
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let input_sentence = "Who are you voting for in 2020?";
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let input_sequence_2 = "The prime minister has announced a stimulus package which was widely criticized by the opposition.";
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let candidate_labels = &["politics", "public health", "economy", "sports"];
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let output = sequence_classification_model
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.predict_multilabel(
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[input_sentence, input_sequence_2],
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candidate_labels,
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Some(Box::new(|label: &str| {
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format!("This example is about {label}.")
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})),
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128,
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)
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.unwrap();
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println!("{output:?}");
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Ok(())
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}
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