mirror of
https://github.com/guillaume-be/rust-bert.git
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52 lines
1.8 KiB
Rust
52 lines
1.8 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 dirs;
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extern crate failure;
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use rust_bert::pipelines::sentiment::{ss2_processor, SentimentModel};
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use std::env;
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use std::path::PathBuf;
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fn main() -> failure::Fallible<()> {
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// Set-up classifier
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let sentiment_classifier = SentimentModel::new(Default::default())?;
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// Define input
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let mut sst2_path = PathBuf::from(env::var("SST2_PATH")
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.expect("Please set the \"squad_dataset\" environment variable pointing to the SQuAD dataset folder"));
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sst2_path.push("train.tsv");
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let inputs = ss2_processor(sst2_path).unwrap();
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// Run model
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let batch_size = 64;
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let mut output = vec![];
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for batch in inputs.chunks(batch_size) {
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output.push(
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sentiment_classifier.predict(
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batch
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.iter()
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.map(|v| v.as_str())
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.collect::<Vec<&str>>()
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.as_slice(),
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),
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);
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}
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let mut flat_outputs = vec![];
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for batch_output in output.iter_mut() {
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flat_outputs.append(batch_output);
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}
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println!("{:?}", flat_outputs.len());
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Ok(())
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}
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