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3a40cdf58d
* WIP refactoring pipeline tests - switching to fast tokenizers * fix dialog pipeline and fill-mask * refactoring pipeline tests backbone * make large tests slow * fix tests (tf Bart inactive for now) * fix doc... * clean up for merge * fixing tests - remove bart from summarization until there is TF * fix quality and RAG * Add new translation pipeline tests - fix JAX tests * only slow for dialog * Fixing the missing TF-BART imports in modeling_tf_auto * spin out pipeline tests in separate CI job * adding pipeline test to CI YAML * add slow pipeline tests * speed up tf and pt join test to avoid redoing all the standalone pt and tf tests * Update src/transformers/tokenization_utils_base.py Co-authored-by: Sam Shleifer <sshleifer@gmail.com> * Update src/transformers/pipelines.py Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * Update src/transformers/pipelines.py Co-authored-by: Lysandre Debut <lysandre@huggingface.co> * Update src/transformers/testing_utils.py Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * add require_torch and require_tf in is_pt_tf_cross_test Co-authored-by: Sam Shleifer <sshleifer@gmail.com> Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
89 lines
4.0 KiB
Python
89 lines
4.0 KiB
Python
import unittest
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from transformers import pipeline
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from transformers.pipelines import Pipeline
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from transformers.testing_utils import require_tf
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from .test_pipelines_common import CustomInputPipelineCommonMixin
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VALID_INPUTS = ["A simple string", ["list of strings"]]
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class NerPipelineTests(CustomInputPipelineCommonMixin, unittest.TestCase):
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pipeline_task = "ner"
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small_models = [
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"sshleifer/tiny-dbmdz-bert-large-cased-finetuned-conll03-english"
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] # Default model - Models tested without the @slow decorator
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large_models = [] # Models tested with the @slow decorator
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def _test_pipeline(self, nlp: Pipeline):
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output_keys = {"entity", "word", "score"}
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ungrouped_ner_inputs = [
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[
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{"entity": "B-PER", "index": 1, "score": 0.9994944930076599, "word": "Cons"},
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{"entity": "B-PER", "index": 2, "score": 0.8025449514389038, "word": "##uelo"},
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{"entity": "I-PER", "index": 3, "score": 0.9993102550506592, "word": "Ara"},
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{"entity": "I-PER", "index": 4, "score": 0.9993743896484375, "word": "##új"},
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{"entity": "I-PER", "index": 5, "score": 0.9992871880531311, "word": "##o"},
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{"entity": "I-PER", "index": 6, "score": 0.9993029236793518, "word": "No"},
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{"entity": "I-PER", "index": 7, "score": 0.9981776475906372, "word": "##guera"},
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{"entity": "B-PER", "index": 15, "score": 0.9998136162757874, "word": "Andrés"},
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{"entity": "I-PER", "index": 16, "score": 0.999740719795227, "word": "Pas"},
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{"entity": "I-PER", "index": 17, "score": 0.9997414350509644, "word": "##tran"},
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{"entity": "I-PER", "index": 18, "score": 0.9996136426925659, "word": "##a"},
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{"entity": "B-ORG", "index": 28, "score": 0.9989739060401917, "word": "Far"},
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{"entity": "I-ORG", "index": 29, "score": 0.7188422083854675, "word": "##c"},
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],
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[
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{"entity": "I-PER", "index": 1, "score": 0.9968166351318359, "word": "En"},
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{"entity": "I-PER", "index": 2, "score": 0.9957635998725891, "word": "##zo"},
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{"entity": "I-ORG", "index": 7, "score": 0.9986497163772583, "word": "UN"},
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],
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]
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expected_grouped_ner_results = [
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[
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{"entity_group": "B-PER", "score": 0.9710702640669686, "word": "Consuelo Araújo Noguera"},
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{"entity_group": "B-PER", "score": 0.9997273534536362, "word": "Andrés Pastrana"},
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{"entity_group": "B-ORG", "score": 0.8589080572128296, "word": "Farc"},
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],
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[
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{"entity_group": "I-PER", "score": 0.9962901175022125, "word": "Enzo"},
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{"entity_group": "I-ORG", "score": 0.9986497163772583, "word": "UN"},
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],
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]
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self.assertIsNotNone(nlp)
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mono_result = nlp(VALID_INPUTS[0])
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self.assertIsInstance(mono_result, list)
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self.assertIsInstance(mono_result[0], (dict, list))
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if isinstance(mono_result[0], list):
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mono_result = mono_result[0]
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for key in output_keys:
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self.assertIn(key, mono_result[0])
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multi_result = [nlp(input) for input in VALID_INPUTS]
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self.assertIsInstance(multi_result, list)
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self.assertIsInstance(multi_result[0], (dict, list))
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if isinstance(multi_result[0], list):
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multi_result = multi_result[0]
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for result in multi_result:
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for key in output_keys:
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self.assertIn(key, result)
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for ungrouped_input, grouped_result in zip(ungrouped_ner_inputs, expected_grouped_ner_results):
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self.assertEqual(nlp.group_entities(ungrouped_input), grouped_result)
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@require_tf
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def test_tf_only(self):
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model_name = "Narsil/small" # This model only has a TensorFlow version
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# We test that if we don't specificy framework='tf', it gets detected automatically
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nlp = pipeline(task="ner", model=model_name, tokenizer=model_name)
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self._test_pipeline(nlp)
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