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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
36 lines
1.6 KiB
Rust
36 lines
1.6 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::sequence_classification::SequenceClassificationModel;
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fn main() -> anyhow::Result<()> {
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// Set-up model
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let sequence_classification_model = SequenceClassificationModel::new(Default::default())?;
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// Define input
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let input = [
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"Probably my all-time favorite movie, a story of selflessness, sacrifice and dedication to a noble cause, but it's not preachy or boring.",
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"This film tried to be too many things all at once: stinging political satire, Hollywood blockbuster, sappy romantic comedy, family values promo...",
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"If you like original gut wrenching laughter you will like this movie. If you are young or old then you will love this movie, hell even my mom liked it.",
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];
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// Run model
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let output = sequence_classification_model.predict(input);
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for label in output {
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println!("{label:?}");
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}
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Ok(())
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}
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