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Addition of integration tests for zero-shot classification
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examples/zero_shot_classification.rs
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37
examples/zero_shot_classification.rs
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// 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", "economics", "sports"];
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let output = sequence_classification_model.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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println!("{:?}", output);
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Ok(())
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}
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@ -77,7 +77,7 @@ use std::collections::HashMap;
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use tch::nn::VarStore;
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use tch::{nn, no_grad, Device, Kind, Tensor};
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#[derive(Debug, Serialize, Deserialize)]
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#[derive(Debug, Serialize, Deserialize, Clone)]
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/// # Label generated by a `SequenceClassificationModel`
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pub struct Label {
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/// Label String representation
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@ -361,7 +361,7 @@ impl ZeroShotClassificationModel {
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Some(function) => labels.iter().map(|label| function(label)).collect(),
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None => labels
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.into_iter()
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.map(|label| format!("This example is {}.", label))
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.map(|label| format!("This example is about {}.", label))
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.collect(),
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};
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@ -442,6 +442,26 @@ impl ZeroShotClassificationModel {
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/// # Ok(())
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/// # }
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/// ```
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///
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/// outputs:
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/// ```no_run
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/// # use rust_bert::pipelines::sequence_classification::Label;
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/// let output = [
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/// Label {
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/// text: "politics".to_string(),
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/// score: 0.959,
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/// id: 0,
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/// sentence: 0,
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/// },
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/// Label {
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/// text: "economics".to_string(),
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/// score: 0.655,
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/// id: 2,
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/// sentence: 1,
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/// },
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/// ]
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/// .to_vec();
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/// ```
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pub fn predict(
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&self,
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inputs: &[&str],
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@ -465,7 +485,6 @@ impl ZeroShotClassificationModel {
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let scores = output.softmax(1, Float).select(-1, -1);
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let label_indices = scores.as_ref().argmax(-1, true).squeeze1(1);
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label_indices.print();
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let scores = scores
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.gather(1, &label_indices.unsqueeze(-1), false)
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.squeeze1(1);
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@ -576,7 +595,8 @@ impl ZeroShotClassificationModel {
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/// sentence: 1,
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/// },
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/// ],
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/// ];
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/// ]
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/// .to_vec();
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/// ```
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pub fn predict_multilabel(
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&self,
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@ -3,6 +3,7 @@ use rust_bert::bart::{
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BartVocabResources,
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};
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use rust_bert::pipelines::summarization::{SummarizationConfig, SummarizationModel};
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use rust_bert::pipelines::zero_shot_classification::ZeroShotClassificationModel;
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use rust_bert::resources::{download_resource, RemoteResource, Resource};
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use rust_bert::Config;
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use rust_tokenizers::{RobertaTokenizer, Tokenizer, TruncationStrategy};
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@ -156,3 +157,75 @@ about exoplanets like K2-18b."];
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Ok(())
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}
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#[test]
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#[cfg_attr(not(feature = "all-tests"), ignore)]
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fn bart_zero_shot_classification() -> anyhow::Result<()> {
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// Set-up model 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.";
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let candidate_labels = &["politics", "public health", "economics", "sports"];
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let output = sequence_classification_model.predict(
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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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assert_eq!(output.len(), 2);
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// Prediction scores
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assert_eq!(output[0].text, "politics");
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assert!((output[0].score - 0.9679).abs() < 1e-4);
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assert_eq!(output[1].text, "economics");
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assert!((output[1].score - 0.5208).abs() < 1e-4);
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Ok(())
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}
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#[test]
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#[cfg_attr(not(feature = "all-tests"), ignore)]
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fn bart_zero_shot_classification_multilabel() -> anyhow::Result<()> {
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// Set-up model 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", "economics", "sports"];
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let output = sequence_classification_model.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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assert_eq!(output.len(), 2);
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assert_eq!(output[0].len(), candidate_labels.len());
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// First sentence label scores
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assert_eq!(output[0][0].text, "politics");
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assert!((output[0][0].score - 0.9805).abs() < 1e-4);
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assert_eq!(output[0][1].text, "public health");
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assert!((output[0][1].score - 0.0130).abs() < 1e-4);
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assert_eq!(output[0][2].text, "economics");
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assert!((output[0][2].score - 0.0041).abs() < 1e-4);
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assert_eq!(output[0][3].text, "sports");
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assert!((output[0][3].score - 0.0013).abs() < 1e-4);
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// Second sentence label scores
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assert_eq!(output[1][0].text, "politics");
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assert!((output[1][0].score - 0.9432).abs() < 1e-4);
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assert_eq!(output[1][1].text, "public health");
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assert!((output[1][1].score - 0.0045).abs() < 1e-4);
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assert_eq!(output[1][2].text, "economics");
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assert!((output[1][2].score - 0.9001).abs() < 1e-4);
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assert_eq!(output[1][3].text, "sports");
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assert!((output[1][3].score - 0.0004).abs() < 1e-4);
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Ok(())
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}
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