312 lines
15 KiB
Python
312 lines
15 KiB
Python
# Copyright 2020 The HuggingFace Team. All rights reserved.
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#
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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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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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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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import unittest
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from transformers import (
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MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
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TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
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Pipeline,
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ZeroShotClassificationPipeline,
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pipeline,
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)
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from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, slow
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from .test_pipelines_common import ANY
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# These 2 model types require different inputs than those of the usual text models.
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_TO_SKIP = {"LayoutLMv2Config", "LayoutLMv3Config"}
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@is_pipeline_test
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class ZeroShotClassificationPipelineTests(unittest.TestCase):
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model_mapping = MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
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tf_model_mapping = TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
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if model_mapping is not None:
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model_mapping = {config: model for config, model in model_mapping.items() if config.__name__ not in _TO_SKIP}
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if tf_model_mapping is not None:
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tf_model_mapping = {
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config: model for config, model in tf_model_mapping.items() if config.__name__ not in _TO_SKIP
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}
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def get_test_pipeline(self, model, tokenizer, processor):
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classifier = ZeroShotClassificationPipeline(
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model=model, tokenizer=tokenizer, candidate_labels=["polics", "health"]
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)
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return classifier, ["Who are you voting for in 2020?", "My stomach hurts."]
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def run_pipeline_test(self, classifier, _):
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outputs = classifier("Who are you voting for in 2020?", candidate_labels="politics")
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self.assertEqual(outputs, {"sequence": ANY(str), "labels": [ANY(str)], "scores": [ANY(float)]})
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# No kwarg
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outputs = classifier("Who are you voting for in 2020?", ["politics"])
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self.assertEqual(outputs, {"sequence": ANY(str), "labels": [ANY(str)], "scores": [ANY(float)]})
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outputs = classifier("Who are you voting for in 2020?", candidate_labels=["politics"])
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self.assertEqual(outputs, {"sequence": ANY(str), "labels": [ANY(str)], "scores": [ANY(float)]})
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outputs = classifier("Who are you voting for in 2020?", candidate_labels="politics, public health")
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self.assertEqual(
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outputs, {"sequence": ANY(str), "labels": [ANY(str), ANY(str)], "scores": [ANY(float), ANY(float)]}
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)
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self.assertAlmostEqual(sum(nested_simplify(outputs["scores"])), 1.0)
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outputs = classifier("Who are you voting for in 2020?", candidate_labels=["politics", "public health"])
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self.assertEqual(
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outputs, {"sequence": ANY(str), "labels": [ANY(str), ANY(str)], "scores": [ANY(float), ANY(float)]}
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)
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self.assertAlmostEqual(sum(nested_simplify(outputs["scores"])), 1.0)
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outputs = classifier(
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"Who are you voting for in 2020?", candidate_labels="politics", hypothesis_template="This text is about {}"
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)
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self.assertEqual(outputs, {"sequence": ANY(str), "labels": [ANY(str)], "scores": [ANY(float)]})
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# https://github.com/huggingface/transformers/issues/13846
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outputs = classifier(["I am happy"], ["positive", "negative"])
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self.assertEqual(
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outputs,
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[
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{"sequence": ANY(str), "labels": [ANY(str), ANY(str)], "scores": [ANY(float), ANY(float)]}
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for i in range(1)
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],
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)
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outputs = classifier(["I am happy", "I am sad"], ["positive", "negative"])
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self.assertEqual(
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outputs,
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[
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{"sequence": ANY(str), "labels": [ANY(str), ANY(str)], "scores": [ANY(float), ANY(float)]}
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for i in range(2)
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],
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)
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with self.assertRaises(ValueError):
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classifier("", candidate_labels="politics")
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with self.assertRaises(TypeError):
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classifier(None, candidate_labels="politics")
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with self.assertRaises(ValueError):
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classifier("Who are you voting for in 2020?", candidate_labels="")
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with self.assertRaises(TypeError):
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classifier("Who are you voting for in 2020?", candidate_labels=None)
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with self.assertRaises(ValueError):
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classifier(
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"Who are you voting for in 2020?",
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candidate_labels="politics",
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hypothesis_template="Not formatting template",
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)
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with self.assertRaises(AttributeError):
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classifier(
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"Who are you voting for in 2020?",
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candidate_labels="politics",
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hypothesis_template=None,
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)
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self.run_entailment_id(classifier)
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def run_entailment_id(self, zero_shot_classifier: Pipeline):
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config = zero_shot_classifier.model.config
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original_label2id = config.label2id
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original_entailment = zero_shot_classifier.entailment_id
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config.label2id = {"LABEL_0": 0, "LABEL_1": 1, "LABEL_2": 2}
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self.assertEqual(zero_shot_classifier.entailment_id, -1)
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config.label2id = {"entailment": 0, "neutral": 1, "contradiction": 2}
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self.assertEqual(zero_shot_classifier.entailment_id, 0)
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config.label2id = {"ENTAIL": 0, "NON-ENTAIL": 1}
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self.assertEqual(zero_shot_classifier.entailment_id, 0)
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config.label2id = {"ENTAIL": 2, "NEUTRAL": 1, "CONTR": 0}
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self.assertEqual(zero_shot_classifier.entailment_id, 2)
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zero_shot_classifier.model.config.label2id = original_label2id
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self.assertEqual(original_entailment, zero_shot_classifier.entailment_id)
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@require_torch
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def test_truncation(self):
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zero_shot_classifier = pipeline(
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"zero-shot-classification",
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model="sshleifer/tiny-distilbert-base-cased-distilled-squad",
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framework="pt",
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)
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# There was a regression in 4.10 for this
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# Adding a test so we don't make the mistake again.
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# https://github.com/huggingface/transformers/issues/13381#issuecomment-912343499
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zero_shot_classifier(
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"Who are you voting for in 2020?" * 100, candidate_labels=["politics", "public health", "science"]
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)
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@require_torch
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def test_small_model_pt(self):
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zero_shot_classifier = pipeline(
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"zero-shot-classification",
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model="sshleifer/tiny-distilbert-base-cased-distilled-squad",
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framework="pt",
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)
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outputs = zero_shot_classifier(
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"Who are you voting for in 2020?", candidate_labels=["politics", "public health", "science"]
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)
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self.assertEqual(
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nested_simplify(outputs),
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{
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"sequence": "Who are you voting for in 2020?",
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"labels": ["science", "public health", "politics"],
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"scores": [0.333, 0.333, 0.333],
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},
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)
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@require_tf
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def test_small_model_tf(self):
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zero_shot_classifier = pipeline(
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"zero-shot-classification",
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model="sshleifer/tiny-distilbert-base-cased-distilled-squad",
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framework="tf",
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)
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outputs = zero_shot_classifier(
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"Who are you voting for in 2020?", candidate_labels=["politics", "public health", "science"]
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)
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self.assertEqual(
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nested_simplify(outputs),
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{
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"sequence": "Who are you voting for in 2020?",
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"labels": ["science", "public health", "politics"],
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"scores": [0.333, 0.333, 0.333],
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},
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)
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@slow
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@require_torch
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def test_large_model_pt(self):
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zero_shot_classifier = pipeline(
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"zero-shot-classification", model="FacebookAI/roberta-large-mnli", framework="pt"
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)
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outputs = zero_shot_classifier(
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"Who are you voting for in 2020?", candidate_labels=["politics", "public health", "science"]
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)
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self.assertEqual(
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nested_simplify(outputs),
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{
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"sequence": "Who are you voting for in 2020?",
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"labels": ["politics", "public health", "science"],
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"scores": [0.976, 0.015, 0.009],
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},
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)
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outputs = zero_shot_classifier(
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"The dominant sequence transduction models are based on complex recurrent or convolutional neural networks"
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" in an encoder-decoder configuration. The best performing models also connect the encoder and decoder"
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" through an attention mechanism. We propose a new simple network architecture, the Transformer, based"
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" solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two"
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" machine translation tasks show these models to be superior in quality while being more parallelizable"
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" and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014"
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" English-to-German translation task, improving over the existing best results, including ensembles by"
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" over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new"
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" single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small"
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" fraction of the training costs of the best models from the literature. We show that the Transformer"
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" generalizes well to other tasks by applying it successfully to English constituency parsing both with"
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" large and limited training data.",
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candidate_labels=["machine learning", "statistics", "translation", "vision"],
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multi_label=True,
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)
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self.assertEqual(
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nested_simplify(outputs),
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{
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"sequence": (
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"The dominant sequence transduction models are based on complex recurrent or convolutional neural"
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" networks in an encoder-decoder configuration. The best performing models also connect the"
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" encoder and decoder through an attention mechanism. We propose a new simple network"
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" architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence"
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" and convolutions entirely. Experiments on two machine translation tasks show these models to be"
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" superior in quality while being more parallelizable and requiring significantly less time to"
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" train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task,"
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" improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014"
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" English-to-French translation task, our model establishes a new single-model state-of-the-art"
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" BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training"
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" costs of the best models from the literature. We show that the Transformer generalizes well to"
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" other tasks by applying it successfully to English constituency parsing both with large and"
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" limited training data."
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),
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"labels": ["translation", "machine learning", "vision", "statistics"],
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"scores": [0.817, 0.713, 0.018, 0.018],
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},
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)
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@slow
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@require_tf
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def test_large_model_tf(self):
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zero_shot_classifier = pipeline(
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"zero-shot-classification", model="FacebookAI/roberta-large-mnli", framework="tf"
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)
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outputs = zero_shot_classifier(
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"Who are you voting for in 2020?", candidate_labels=["politics", "public health", "science"]
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)
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self.assertEqual(
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nested_simplify(outputs),
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{
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"sequence": "Who are you voting for in 2020?",
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"labels": ["politics", "public health", "science"],
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"scores": [0.976, 0.015, 0.009],
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},
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)
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outputs = zero_shot_classifier(
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"The dominant sequence transduction models are based on complex recurrent or convolutional neural networks"
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" in an encoder-decoder configuration. The best performing models also connect the encoder and decoder"
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" through an attention mechanism. We propose a new simple network architecture, the Transformer, based"
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" solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two"
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" machine translation tasks show these models to be superior in quality while being more parallelizable"
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" and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014"
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" English-to-German translation task, improving over the existing best results, including ensembles by"
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" over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new"
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" single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small"
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" fraction of the training costs of the best models from the literature. We show that the Transformer"
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" generalizes well to other tasks by applying it successfully to English constituency parsing both with"
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" large and limited training data.",
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candidate_labels=["machine learning", "statistics", "translation", "vision"],
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multi_label=True,
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)
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self.assertEqual(
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nested_simplify(outputs),
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{
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"sequence": (
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"The dominant sequence transduction models are based on complex recurrent or convolutional neural"
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" networks in an encoder-decoder configuration. The best performing models also connect the"
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" encoder and decoder through an attention mechanism. We propose a new simple network"
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" architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence"
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" and convolutions entirely. Experiments on two machine translation tasks show these models to be"
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" superior in quality while being more parallelizable and requiring significantly less time to"
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" train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task,"
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" improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014"
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" English-to-French translation task, our model establishes a new single-model state-of-the-art"
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" BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training"
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" costs of the best models from the literature. We show that the Transformer generalizes well to"
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" other tasks by applying it successfully to English constituency parsing both with large and"
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" limited training data."
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),
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"labels": ["translation", "machine learning", "vision", "statistics"],
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"scores": [0.817, 0.713, 0.018, 0.018],
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},
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)
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