mirror of
https://github.com/guillaume-be/rust-bert.git
synced 2024-09-11 12:55:34 +03:00
* Add checked prediction methods - Add checked prediction methods to ZeroShotClassificationModel. These methods return Option and convert any underlying errors into None, to allow callers to implement appropriate error handling logic. * Update ZeroShot example to use checked method. * Add tests for ZeroShot checked methods * Change checked prediction methods to return Result * refactor: rename *_checked into try_* Rename *_checked methods into try_* methods. This is more idiomatic vis-a-vis the Rust standard library. * refactor: remove try_ prefix from predict methods * refactor: change return from Option to Result Change return type of ZeroShotClassificationModel.prepare_for_model from option into Result. This simplifies the code, and returns the error closer to its origin. This addresses comments from @guillaume-be. * refactor: address clippy lints in tests Co-authored-by: guillaume-be <guillaume.becquin@gmail.com>
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.
|
|
// 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::pipelines::zero_shot_classification::ZeroShotClassificationModel;
|
|
|
|
fn main() -> anyhow::Result<()> {
|
|
// Set-up model
|
|
let sequence_classification_model = ZeroShotClassificationModel::new(Default::default())?;
|
|
|
|
let input_sentence = "Who are you voting for in 2020?";
|
|
let input_sequence_2 = "The prime minister has announced a stimulus package which was widely criticized by the opposition.";
|
|
let candidate_labels = &["politics", "public health", "economy", "sports"];
|
|
|
|
let output = sequence_classification_model
|
|
.predict_multilabel(
|
|
[input_sentence, input_sequence_2],
|
|
candidate_labels,
|
|
Some(Box::new(|label: &str| {
|
|
format!("This example is about {}.", label)
|
|
})),
|
|
128,
|
|
)
|
|
.unwrap();
|
|
|
|
println!("{:?}", output);
|
|
|
|
Ok(())
|
|
}
|