transformers/tests/test_pipelines_text_classification.py
Nicolas Patry e2d22eef14
Moving feature-extraction pipeline to new testing scheme (#12843)
* Update feature extraction pipelilne.

* Leaving 1 small model for actual values check.

* Fixes tests

- Better support for tokenizer with no pad token
- Increasing PegasusModelTesterConfig for pipelines
- Test of feature extraction are more permissive + don't test Multimodel
models + encoder-decoder.

* Fixing model loading with incorrect shape (+ model with HEAD).

* Update tests/test_pipelines_common.py

Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* Revert modeling_utils modification.

* Some corrections.

* Update tests/test_pipelines_common.py

Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* Update tests/test_pipelines_feature_extraction.py

Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* Syntax.

* Fixing text-classification tests.

* Don't modify this file.

Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
2021-07-29 19:35:55 +02:00

93 lines
3.9 KiB
Python

# 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,
TextClassificationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, slow
from .test_pipelines_common import ANY, PipelineTestCaseMeta
@is_pipeline_test
class TextClassificationPipelineTests(unittest.TestCase, metaclass=PipelineTestCaseMeta):
model_mapping = MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
tf_model_mapping = TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
@require_torch
def test_pt_bert_small(self):
text_classifier = pipeline(
task="text-classification", model="Narsil/tiny-distilbert-sequence-classification", framework="pt"
)
outputs = text_classifier("This is great !")
self.assertEqual(nested_simplify(outputs), [{"label": "LABEL_1", "score": 0.502}])
@require_tf
def test_tf_bert_small(self):
text_classifier = pipeline(
task="text-classification", model="Narsil/tiny-distilbert-sequence-classification", framework="tf"
)
outputs = text_classifier("This is great !")
self.assertEqual(nested_simplify(outputs), [{"label": "LABEL_1", "score": 0.502}])
@slow
@require_torch
def test_pt_bert(self):
text_classifier = pipeline("text-classification")
outputs = text_classifier("This is great !")
self.assertEqual(nested_simplify(outputs), [{"label": "POSITIVE", "score": 1.0}])
outputs = text_classifier("This is bad !")
self.assertEqual(nested_simplify(outputs), [{"label": "NEGATIVE", "score": 1.0}])
outputs = text_classifier("Birds are a type of animal")
self.assertEqual(nested_simplify(outputs), [{"label": "POSITIVE", "score": 0.988}])
@slow
@require_tf
def test_tf_bert(self):
text_classifier = pipeline("text-classification", framework="tf")
outputs = text_classifier("This is great !")
self.assertEqual(nested_simplify(outputs), [{"label": "POSITIVE", "score": 1.0}])
outputs = text_classifier("This is bad !")
self.assertEqual(nested_simplify(outputs), [{"label": "NEGATIVE", "score": 1.0}])
outputs = text_classifier("Birds are a type of animal")
self.assertEqual(nested_simplify(outputs), [{"label": "POSITIVE", "score": 0.988}])
def run_pipeline_test(self, model, tokenizer):
text_classifier = TextClassificationPipeline(model=model, tokenizer=tokenizer)
# Small inputs because BartTokenizer tiny has maximum position embeddings = 22
valid_inputs = "HuggingFace is in"
outputs = text_classifier(valid_inputs)
self.assertEqual(nested_simplify(outputs), [{"label": ANY(str), "score": ANY(float)}])
self.assertTrue(outputs[0]["label"] in model.config.id2label.values())
valid_inputs = ["HuggingFace is in ", "Paris is in France"]
outputs = text_classifier(valid_inputs)
self.assertEqual(
nested_simplify(outputs),
[{"label": ANY(str), "score": ANY(float)}, {"label": ANY(str), "score": ANY(float)}],
)
self.assertTrue(outputs[0]["label"] in model.config.id2label.values())
self.assertTrue(outputs[1]["label"] in model.config.id2label.values())