transformers/tests/pipelines/test_pipelines_zero_shot.py

# Copyright 2020 The HuggingFace Team. All rights reserved.
#
# 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.

import unittest

from transformers import (
    MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    Pipeline,
    ZeroShotClassificationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, slow

from .test_pipelines_common import ANY


@is_pipeline_test
class ZeroShotClassificationPipelineTests(unittest.TestCase):
    model_mapping = MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
    tf_model_mapping = TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

    def get_test_pipeline(self, model, tokenizer, processor):
        classifier = ZeroShotClassificationPipeline(
            model=model, tokenizer=tokenizer, candidate_labels=["polics", "health"]
        )
        return classifier, ["Who are you voting for in 2020?", "My stomach hurts."]

    def run_pipeline_test(self, classifier, _):
        outputs = classifier("Who are you voting for in 2020?", candidate_labels="politics")
        self.assertEqual(outputs, {"sequence": ANY(str), "labels": [ANY(str)], "scores": [ANY(float)]})

        # No kwarg
        outputs = classifier("Who are you voting for in 2020?", ["politics"])
        self.assertEqual(outputs, {"sequence": ANY(str), "labels": [ANY(str)], "scores": [ANY(float)]})

        outputs = classifier("Who are you voting for in 2020?", candidate_labels=["politics"])
        self.assertEqual(outputs, {"sequence": ANY(str), "labels": [ANY(str)], "scores": [ANY(float)]})

        outputs = classifier("Who are you voting for in 2020?", candidate_labels="politics, public health")
        self.assertEqual(
            outputs, {"sequence": ANY(str), "labels": [ANY(str), ANY(str)], "scores": [ANY(float), ANY(float)]}
        )
        self.assertAlmostEqual(sum(nested_simplify(outputs["scores"])), 1.0)

        outputs = classifier("Who are you voting for in 2020?", candidate_labels=["politics", "public health"])
        self.assertEqual(
            outputs, {"sequence": ANY(str), "labels": [ANY(str), ANY(str)], "scores": [ANY(float), ANY(float)]}
        )
        self.assertAlmostEqual(sum(nested_simplify(outputs["scores"])), 1.0)

        outputs = classifier(
            "Who are you voting for in 2020?", candidate_labels="politics", hypothesis_template="This text is about {}"
        )
        self.assertEqual(outputs, {"sequence": ANY(str), "labels": [ANY(str)], "scores": [ANY(float)]})

        # https://github.com/huggingface/transformers/issues/13846
        outputs = classifier(["I am happy"], ["positive", "negative"])
        self.assertEqual(
            outputs,
            [
                {"sequence": ANY(str), "labels": [ANY(str), ANY(str)], "scores": [ANY(float), ANY(float)]}
                for i in range(1)
            ],
        )
        outputs = classifier(["I am happy", "I am sad"], ["positive", "negative"])
        self.assertEqual(
            outputs,
            [
                {"sequence": ANY(str), "labels": [ANY(str), ANY(str)], "scores": [ANY(float), ANY(float)]}
                for i in range(2)
            ],
        )

        with self.assertRaises(ValueError):
            classifier("", candidate_labels="politics")

        with self.assertRaises(TypeError):
            classifier(None, candidate_labels="politics")

        with self.assertRaises(ValueError):
            classifier("Who are you voting for in 2020?", candidate_labels="")

        with self.assertRaises(TypeError):
            classifier("Who are you voting for in 2020?", candidate_labels=None)

        with self.assertRaises(ValueError):
            classifier(
                "Who are you voting for in 2020?",
                candidate_labels="politics",
                hypothesis_template="Not formatting template",
            )

        with self.assertRaises(AttributeError):
            classifier(
                "Who are you voting for in 2020?",
                candidate_labels="politics",
                hypothesis_template=None,
            )

        self.run_entailment_id(classifier)

    def run_entailment_id(self, zero_shot_classifier: Pipeline):
        config = zero_shot_classifier.model.config
        original_label2id = config.label2id
        original_entailment = zero_shot_classifier.entailment_id

        config.label2id = {"LABEL_0": 0, "LABEL_1": 1, "LABEL_2": 2}
        self.assertEqual(zero_shot_classifier.entailment_id, -1)

        config.label2id = {"entailment": 0, "neutral": 1, "contradiction": 2}
        self.assertEqual(zero_shot_classifier.entailment_id, 0)

        config.label2id = {"ENTAIL": 0, "NON-ENTAIL": 1}
        self.assertEqual(zero_shot_classifier.entailment_id, 0)

        config.label2id = {"ENTAIL": 2, "NEUTRAL": 1, "CONTR": 0}
        self.assertEqual(zero_shot_classifier.entailment_id, 2)

        zero_shot_classifier.model.config.label2id = original_label2id
        self.assertEqual(original_entailment, zero_shot_classifier.entailment_id)

    @require_torch
    def test_truncation(self):
        zero_shot_classifier = pipeline(
            "zero-shot-classification",
            model="sshleifer/tiny-distilbert-base-cased-distilled-squad",
            framework="pt",
        )
        # There was a regression in 4.10 for this
        # Adding a test so we don't make the mistake again.
        # https://github.com/huggingface/transformers/issues/13381#issuecomment-912343499
        zero_shot_classifier(
            "Who are you voting for in 2020?" * 100, candidate_labels=["politics", "public health", "science"]
        )

    @require_torch
    def test_small_model_pt(self):
        zero_shot_classifier = pipeline(
            "zero-shot-classification",
            model="sshleifer/tiny-distilbert-base-cased-distilled-squad",
            framework="pt",
        )
        outputs = zero_shot_classifier(
            "Who are you voting for in 2020?", candidate_labels=["politics", "public health", "science"]
        )

        self.assertEqual(
            nested_simplify(outputs),
            {
                "sequence": "Who are you voting for in 2020?",
                "labels": ["science", "public health", "politics"],
                "scores": [0.333, 0.333, 0.333],
            },
        )

    @require_tf
    def test_small_model_tf(self):
        zero_shot_classifier = pipeline(
            "zero-shot-classification",
            model="sshleifer/tiny-distilbert-base-cased-distilled-squad",
            framework="tf",
        )
        outputs = zero_shot_classifier(
            "Who are you voting for in 2020?", candidate_labels=["politics", "public health", "science"]
        )

        self.assertEqual(
            nested_simplify(outputs),
            {
                "sequence": "Who are you voting for in 2020?",
                "labels": ["science", "public health", "politics"],
                "scores": [0.333, 0.333, 0.333],
            },
        )

    @slow
    @require_torch
    def test_large_model_pt(self):
        zero_shot_classifier = pipeline("zero-shot-classification", model="roberta-large-mnli", framework="pt")
        outputs = zero_shot_classifier(
            "Who are you voting for in 2020?", candidate_labels=["politics", "public health", "science"]
        )

        self.assertEqual(
            nested_simplify(outputs),
            {
                "sequence": "Who are you voting for in 2020?",
                "labels": ["politics", "public health", "science"],
                "scores": [0.976, 0.015, 0.009],
            },
        )
        outputs = zero_shot_classifier(
            "The dominant sequence transduction models are based on complex recurrent or convolutional neural networks"
            " in an encoder-decoder configuration. The best performing models also connect the encoder and decoder"
            " through an attention mechanism. We propose a new simple network architecture, the Transformer, based"
            " solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two"
            " machine translation tasks show these models to be superior in quality while being more parallelizable"
            " and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014"
            " English-to-German translation task, improving over the existing best results, including ensembles by"
            " over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new"
            " single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small"
            " fraction of the training costs of the best models from the literature. We show that the Transformer"
            " generalizes well to other tasks by applying it successfully to English constituency parsing both with"
            " large and limited training data.",
            candidate_labels=["machine learning", "statistics", "translation", "vision"],
            multi_label=True,
        )
        self.assertEqual(
            nested_simplify(outputs),
            {
                "sequence": (
                    "The dominant sequence transduction models are based on complex recurrent or convolutional neural"
                    " networks in an encoder-decoder configuration. The best performing models also connect the"
                    " encoder and decoder through an attention mechanism. We propose a new simple network"
                    " architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence"
                    " and convolutions entirely. Experiments on two machine translation tasks show these models to be"
                    " superior in quality while being more parallelizable and requiring significantly less time to"
                    " train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task,"
                    " improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014"
                    " English-to-French translation task, our model establishes a new single-model state-of-the-art"
                    " BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training"
                    " costs of the best models from the literature. We show that the Transformer generalizes well to"
                    " other tasks by applying it successfully to English constituency parsing both with large and"
                    " limited training data."
                ),
                "labels": ["translation", "machine learning", "vision", "statistics"],
                "scores": [0.817, 0.713, 0.018, 0.018],
            },
        )

    @slow
    @require_tf
    def test_large_model_tf(self):
        zero_shot_classifier = pipeline("zero-shot-classification", model="roberta-large-mnli", framework="tf")
        outputs = zero_shot_classifier(
            "Who are you voting for in 2020?", candidate_labels=["politics", "public health", "science"]
        )

        self.assertEqual(
            nested_simplify(outputs),
            {
                "sequence": "Who are you voting for in 2020?",
                "labels": ["politics", "public health", "science"],
                "scores": [0.976, 0.015, 0.009],
            },
        )
        outputs = zero_shot_classifier(
            "The dominant sequence transduction models are based on complex recurrent or convolutional neural networks"
            " in an encoder-decoder configuration. The best performing models also connect the encoder and decoder"
            " through an attention mechanism. We propose a new simple network architecture, the Transformer, based"
            " solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two"
            " machine translation tasks show these models to be superior in quality while being more parallelizable"
            " and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014"
            " English-to-German translation task, improving over the existing best results, including ensembles by"
            " over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new"
            " single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small"
            " fraction of the training costs of the best models from the literature. We show that the Transformer"
            " generalizes well to other tasks by applying it successfully to English constituency parsing both with"
            " large and limited training data.",
            candidate_labels=["machine learning", "statistics", "translation", "vision"],
            multi_label=True,
        )
        self.assertEqual(
            nested_simplify(outputs),
            {
                "sequence": (
                    "The dominant sequence transduction models are based on complex recurrent or convolutional neural"
                    " networks in an encoder-decoder configuration. The best performing models also connect the"
                    " encoder and decoder through an attention mechanism. We propose a new simple network"
                    " architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence"
                    " and convolutions entirely. Experiments on two machine translation tasks show these models to be"
                    " superior in quality while being more parallelizable and requiring significantly less time to"
                    " train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task,"
                    " improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014"
                    " English-to-French translation task, our model establishes a new single-model state-of-the-art"
                    " BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training"
                    " costs of the best models from the literature. We show that the Transformer generalizes well to"
                    " other tasks by applying it successfully to English constituency parsing both with large and"
                    " limited training data."
                ),
                "labels": ["translation", "machine learning", "vision", "statistics"],
                "scores": [0.817, 0.713, 0.018, 0.018],
            },
        )