Add zero-shot classification pipeline unit test

tests/pipelines/test_pipelines_zero_shot.js

@@ -0,0 +1,97 @@
+import { pipeline } from "../../src/transformers.js";
+
+import { MAX_MODEL_LOAD_TIME, MAX_TEST_EXECUTION_TIME, MAX_MODEL_DISPOSE_TIME, DEFAULT_MODEL_OPTIONS } from "../init.js";
+
+const PIPELINE_ID = "zero-shot-classification";
+
+export default () => {
+  describe("Zero-shot Classification", () => {
+    const model_id = "hf-internal-testing/tiny-random-BertForSequenceClassification";
+    /** @type {any} */
+    let pipe;
+
+    beforeAll(async () => {
+      pipe = await pipeline(PIPELINE_ID, model_id, {
+        ...DEFAULT_MODEL_OPTIONS,
+
+        // The model isn't designed for zero-shot classification, so we set the config
+        config: {
+          model_type: "bert",
+          id2label: {
+            0: "contradiction",
+            1: "entailment",
+          },
+          label2id: {
+            contradiction: 0,
+            entailment: 1,
+          },
+        },
+      });
+    }, MAX_MODEL_LOAD_TIME);
+
+    const sequences_to_classify = ["one day I will see the world", "I love making pizza"];
+    const candidate_labels = ["travel", "cooking", "dancing"];
+
+    it(
+      "Single sequence classification",
+      async () => {
+        const output = await pipe(sequences_to_classify[0], candidate_labels);
+        const target = {
+          sequence: "one day I will see the world",
+          labels: ["dancing", "cooking", "travel"],
+          scores: [0.3333353410546293, 0.3333348269618681, 0.3333298319835025],
+        };
+        expect(output).toBeCloseToNested(target, 5);
+      },
+      MAX_TEST_EXECUTION_TIME,
+    );
+
+    it(
+      "Batched classification",
+      async () => {
+        const output = await pipe(sequences_to_classify, candidate_labels);
+        const target = [
+          {
+            sequence: "one day I will see the world",
+            labels: ["dancing", "cooking", "travel"],
+            scores: [0.3333353410546293, 0.3333348269618681, 0.3333298319835025],
+          },
+          {
+            sequence: "I love making pizza",
+            labels: ["dancing", "cooking", "travel"],
+            scores: [0.3333347058960895, 0.3333337292465588, 0.3333315648573516],
+          },
+        ];
+        expect(output).toBeCloseToNested(target, 5);
+      },
+      MAX_TEST_EXECUTION_TIME,
+    );
+
+    it(
+      "Batched + multilabel classification",
+      async () => {
+        const candidate_labels = ["travel", "cooking", "dancing"];
+
+        const output = await pipe(sequences_to_classify, candidate_labels, { multi_label: true });
+        const target = [
+          {
+            sequence: "one day I will see the world",
+            labels: ["dancing", "cooking", "travel"],
+            scores: [0.49231469615364476, 0.4923134953805702, 0.4923094795142658],
+          },
+          {
+            sequence: "I love making pizza",
+            labels: ["dancing", "cooking", "travel"],
+            scores: [0.49230751217535645, 0.49230615475943956, 0.4923042569480609],
+          },
+        ];
+        expect(output).toBeCloseToNested(target, 5);
+      },
+      MAX_TEST_EXECUTION_TIME,
+    );
+
+    afterAll(async () => {
+      await pipe.dispose();
+    }, MAX_MODEL_DISPOSE_TIME);
+  });
+};