Addition of integration tests for zero-shot classification

examples/zero_shot_classification.rs

@@ -0,0 +1,37 @@
+// 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", "economics", "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,
+    );
+
+    println!("{:?}", output);
+
+    Ok(())
+}

src/pipelines/sequence_classification.rs

@@ -77,7 +77,7 @@ use std::collections::HashMap;
 use tch::nn::VarStore;
 use tch::{nn, no_grad, Device, Kind, Tensor};
 
-#[derive(Debug, Serialize, Deserialize)]
+#[derive(Debug, Serialize, Deserialize, Clone)]
 /// # Label generated by a `SequenceClassificationModel`
 pub struct Label {
     /// Label String representation

src/pipelines/zero_shot_classification.rs

@@ -361,7 +361,7 @@ impl ZeroShotClassificationModel {
             Some(function) => labels.iter().map(|label| function(label)).collect(),
             None => labels
                 .into_iter()
-                .map(|label| format!("This example is {}.", label))
+                .map(|label| format!("This example is about {}.", label))
                 .collect(),
         };
 
@@ -442,6 +442,26 @@ impl ZeroShotClassificationModel {
     /// # Ok(())
     /// # }
     /// ```
+    ///
+    /// outputs:
+    /// ```no_run
+    /// # use rust_bert::pipelines::sequence_classification::Label;
+    /// let output = [
+    ///     Label {
+    ///         text: "politics".to_string(),
+    ///         score: 0.959,
+    ///         id: 0,
+    ///         sentence: 0,
+    ///     },
+    ///     Label {
+    ///         text: "economics".to_string(),
+    ///         score: 0.655,
+    ///         id: 2,
+    ///         sentence: 1,
+    ///     },
+    /// ]
+    /// .to_vec();
+    /// ```
     pub fn predict(
         &self,
         inputs: &[&str],
@@ -465,7 +485,6 @@ impl ZeroShotClassificationModel {
 
         let scores = output.softmax(1, Float).select(-1, -1);
         let label_indices = scores.as_ref().argmax(-1, true).squeeze1(1);
-        label_indices.print();
         let scores = scores
             .gather(1, &label_indices.unsqueeze(-1), false)
             .squeeze1(1);
@@ -576,7 +595,8 @@ impl ZeroShotClassificationModel {
     ///             sentence: 1,
     ///         },
     ///     ],
-    /// ];
+    /// ]
+    /// .to_vec();
     /// ```
     pub fn predict_multilabel(
         &self,

tests/bart.rs

@@ -3,6 +3,7 @@ use rust_bert::bart::{
     BartVocabResources,
 };
 use rust_bert::pipelines::summarization::{SummarizationConfig, SummarizationModel};
+use rust_bert::pipelines::zero_shot_classification::ZeroShotClassificationModel;
 use rust_bert::resources::{download_resource, RemoteResource, Resource};
 use rust_bert::Config;
 use rust_tokenizers::{RobertaTokenizer, Tokenizer, TruncationStrategy};
@@ -156,3 +157,75 @@ about exoplanets like K2-18b."];
 
     Ok(())
 }
+
+#[test]
+#[cfg_attr(not(feature = "all-tests"), ignore)]
+fn bart_zero_shot_classification() -> anyhow::Result<()> {
+    //    Set-up model 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.";
+    let candidate_labels = &["politics", "public health", "economics", "sports"];
+
+    let output = sequence_classification_model.predict(
+        &[input_sentence, input_sequence_2],
+        candidate_labels,
+        Some(Box::new(|label: &str| {
+            format!("This example is about {}.", label)
+        })),
+        128,
+    );
+
+    assert_eq!(output.len(), 2);
+
+    // Prediction scores
+    assert_eq!(output[0].text, "politics");
+    assert!((output[0].score - 0.9679).abs() < 1e-4);
+    assert_eq!(output[1].text, "economics");
+    assert!((output[1].score - 0.5208).abs() < 1e-4);
+    Ok(())
+}
+
+#[test]
+#[cfg_attr(not(feature = "all-tests"), ignore)]
+fn bart_zero_shot_classification_multilabel() -> anyhow::Result<()> {
+    //    Set-up model 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", "economics", "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,
+    );
+
+    assert_eq!(output.len(), 2);
+    assert_eq!(output[0].len(), candidate_labels.len());
+    // First sentence label scores
+    assert_eq!(output[0][0].text, "politics");
+    assert!((output[0][0].score - 0.9805).abs() < 1e-4);
+    assert_eq!(output[0][1].text, "public health");
+    assert!((output[0][1].score - 0.0130).abs() < 1e-4);
+    assert_eq!(output[0][2].text, "economics");
+    assert!((output[0][2].score - 0.0041).abs() < 1e-4);
+    assert_eq!(output[0][3].text, "sports");
+    assert!((output[0][3].score - 0.0013).abs() < 1e-4);
+
+    // Second sentence label scores
+    assert_eq!(output[1][0].text, "politics");
+    assert!((output[1][0].score - 0.9432).abs() < 1e-4);
+    assert_eq!(output[1][1].text, "public health");
+    assert!((output[1][1].score - 0.0045).abs() < 1e-4);
+    assert_eq!(output[1][2].text, "economics");
+    assert!((output[1][2].score - 0.9001).abs() < 1e-4);
+    assert_eq!(output[1][3].text, "sports");
+    assert!((output[1][3].score - 0.0004).abs() < 1e-4);
+    Ok(())
+}