test_pipelines_ner.py

import unittest

from transformers import AutoTokenizer, pipeline
from transformers.pipelines import Pipeline, TokenClassificationArgumentHandler
from transformers.testing_utils import require_tf, require_torch

from .test_pipelines_common import CustomInputPipelineCommonMixin


VALID_INPUTS = ["A simple string", ["list of strings"]]


class NerPipelineTests(CustomInputPipelineCommonMixin, unittest.TestCase):
    pipeline_task = "ner"
    small_models = [
        "sshleifer/tiny-dbmdz-bert-large-cased-finetuned-conll03-english"
    ]  # Default model - Models tested without the @slow decorator
    large_models = []  # Models tested with the @slow decorator

    def _test_pipeline(self, nlp: Pipeline):
        output_keys = {"entity", "word", "score"}
        if nlp.grouped_entities:
            output_keys = {"entity_group", "word", "score"}

        ungrouped_ner_inputs = [
            [
                {"entity": "B-PER", "index": 1, "score": 0.9994944930076599, "is_subword": False, "word": "Cons"},
                {"entity": "B-PER", "index": 2, "score": 0.8025449514389038, "is_subword": True, "word": "##uelo"},
                {"entity": "I-PER", "index": 3, "score": 0.9993102550506592, "is_subword": False, "word": "Ara"},
                {"entity": "I-PER", "index": 4, "score": 0.9993743896484375, "is_subword": True, "word": "##új"},
                {"entity": "I-PER", "index": 5, "score": 0.9992871880531311, "is_subword": True, "word": "##o"},
                {"entity": "I-PER", "index": 6, "score": 0.9993029236793518, "is_subword": False, "word": "No"},
                {"entity": "I-PER", "index": 7, "score": 0.9981776475906372, "is_subword": True, "word": "##guera"},
                {"entity": "B-PER", "index": 15, "score": 0.9998136162757874, "is_subword": False, "word": "Andrés"},
                {"entity": "I-PER", "index": 16, "score": 0.999740719795227, "is_subword": False, "word": "Pas"},
                {"entity": "I-PER", "index": 17, "score": 0.9997414350509644, "is_subword": True, "word": "##tran"},
                {"entity": "I-PER", "index": 18, "score": 0.9996136426925659, "is_subword": True, "word": "##a"},
                {"entity": "B-ORG", "index": 28, "score": 0.9989739060401917, "is_subword": False, "word": "Far"},
                {"entity": "I-ORG", "index": 29, "score": 0.7188422083854675, "is_subword": True, "word": "##c"},
            ],
            [
                {"entity": "I-PER", "index": 1, "score": 0.9968166351318359, "is_subword": False, "word": "En"},
                {"entity": "I-PER", "index": 2, "score": 0.9957635998725891, "is_subword": True, "word": "##zo"},
                {"entity": "I-ORG", "index": 7, "score": 0.9986497163772583, "is_subword": False, "word": "UN"},
            ],
        ]

        expected_grouped_ner_results = [
            [
                {"entity_group": "PER", "score": 0.999369223912557, "word": "Consuelo Araújo Noguera"},
                {"entity_group": "PER", "score": 0.9997771680355072, "word": "Andrés Pastrana"},
                {"entity_group": "ORG", "score": 0.9989739060401917, "word": "Farc"},
            ],
            [
                {"entity_group": "PER", "score": 0.9968166351318359, "word": "Enzo"},
                {"entity_group": "ORG", "score": 0.9986497163772583, "word": "UN"},
            ],
        ]

        expected_grouped_ner_results_w_subword = [
            [
                {"entity_group": "PER", "score": 0.9994944930076599, "word": "Cons"},
                {"entity_group": "PER", "score": 0.9663328925768534, "word": "##uelo Araújo Noguera"},
                {"entity_group": "PER", "score": 0.9997273534536362, "word": "Andrés Pastrana"},
                {"entity_group": "ORG", "score": 0.8589080572128296, "word": "Farc"},
            ],
            [
                {"entity_group": "PER", "score": 0.9962901175022125, "word": "Enzo"},
                {"entity_group": "ORG", "score": 0.9986497163772583, "word": "UN"},
            ],
        ]

        self.assertIsNotNone(nlp)

        mono_result = nlp(VALID_INPUTS[0])
        self.assertIsInstance(mono_result, list)
        self.assertIsInstance(mono_result[0], (dict, list))

        if isinstance(mono_result[0], list):
            mono_result = mono_result[0]

        for key in output_keys:
            self.assertIn(key, mono_result[0])

        multi_result = [nlp(input) for input in VALID_INPUTS]
        self.assertIsInstance(multi_result, list)
        self.assertIsInstance(multi_result[0], (dict, list))

        if isinstance(multi_result[0], list):
            multi_result = multi_result[0]

        for result in multi_result:
            for key in output_keys:
                self.assertIn(key, result)

        if nlp.grouped_entities:
            if nlp.ignore_subwords:
                for ungrouped_input, grouped_result in zip(ungrouped_ner_inputs, expected_grouped_ner_results):
                    self.assertEqual(nlp.group_entities(ungrouped_input), grouped_result)
            else:
                for ungrouped_input, grouped_result in zip(
                    ungrouped_ner_inputs, expected_grouped_ner_results_w_subword
                ):
                    self.assertEqual(nlp.group_entities(ungrouped_input), grouped_result)

    @require_tf
    def test_tf_only(self):
        model_name = "Narsil/small"  # This model only has a TensorFlow version
        # We test that if we don't specificy framework='tf', it gets detected automatically
        nlp = pipeline(task="ner", model=model_name)
        self._test_pipeline(nlp)

    @require_tf
    def test_tf_defaults(self):
        for model_name in self.small_models:
            tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=True)
            nlp = pipeline(task="ner", model=model_name, tokenizer=tokenizer, framework="tf")
        self._test_pipeline(nlp)

    @require_tf
    def test_tf_small_ignore_subwords_available_for_fast_tokenizers(self):
        for model_name in self.small_models:
            tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=True)
            nlp = pipeline(
                task="ner",
                model=model_name,
                tokenizer=tokenizer,
                framework="tf",
                grouped_entities=True,
                ignore_subwords=True,
            )
            self._test_pipeline(nlp)

        for model_name in self.small_models:
            tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=True)
            nlp = pipeline(
                task="ner",
                model=model_name,
                tokenizer=tokenizer,
                framework="tf",
                grouped_entities=True,
                ignore_subwords=False,
            )
            self._test_pipeline(nlp)

    @require_torch
    def test_pt_ignore_subwords_slow_tokenizer_raises(self):
        for model_name in self.small_models:
            tokenizer = AutoTokenizer.from_pretrained(model_name)

            with self.assertRaises(ValueError):
                pipeline(task="ner", model=model_name, tokenizer=tokenizer, ignore_subwords=True)

    @require_torch
    def test_pt_defaults_slow_tokenizer(self):
        for model_name in self.small_models:
            tokenizer = AutoTokenizer.from_pretrained(model_name)
            nlp = pipeline(task="ner", model=model_name, tokenizer=tokenizer)
            self._test_pipeline(nlp)

    @require_torch
    def test_pt_defaults(self):
        for model_name in self.small_models:
            nlp = pipeline(task="ner", model=model_name)
            self._test_pipeline(nlp)

    @require_torch
    def test_pt_small_ignore_subwords_available_for_fast_tokenizers(self):
        for model_name in self.small_models:
            tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=True)
            nlp = pipeline(
                task="ner", model=model_name, tokenizer=tokenizer, grouped_entities=True, ignore_subwords=True
            )
            self._test_pipeline(nlp)

        for model_name in self.small_models:
            tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=True)
            nlp = pipeline(
                task="ner", model=model_name, tokenizer=tokenizer, grouped_entities=True, ignore_subwords=False
            )
            self._test_pipeline(nlp)


class TokenClassificationArgumentHandlerTestCase(unittest.TestCase):
    def setUp(self):
        self.args_parser = TokenClassificationArgumentHandler()

    def test_simple(self):
        string = "This is a simple input"

        inputs, offset_mapping = self.args_parser(string)
        self.assertEqual(inputs, [string])
        self.assertEqual(offset_mapping, None)

        inputs, offset_mapping = self.args_parser(string, string)
        self.assertEqual(inputs, [string, string])
        self.assertEqual(offset_mapping, None)

        inputs, offset_mapping = self.args_parser(string, offset_mapping=[(0, 1), (1, 2)])
        self.assertEqual(inputs, [string])
        self.assertEqual(offset_mapping, [[(0, 1), (1, 2)]])

        inputs, offset_mapping = self.args_parser(string, string, offset_mapping=[[(0, 1), (1, 2)], [(0, 2), (2, 3)]])
        self.assertEqual(inputs, [string, string])
        self.assertEqual(offset_mapping, [[(0, 1), (1, 2)], [(0, 2), (2, 3)]])

    def test_errors(self):
        string = "This is a simple input"

        # 2 sentences, 1 offset_mapping
        with self.assertRaises(ValueError):
            self.args_parser(string, string, offset_mapping=[[(0, 1), (1, 2)]])

        # 2 sentences, 1 offset_mapping
        with self.assertRaises(ValueError):
            self.args_parser(string, string, offset_mapping=[(0, 1), (1, 2)])

        # 1 sentences, 2 offset_mapping
        with self.assertRaises(ValueError):
            self.args_parser(string, offset_mapping=[[(0, 1), (1, 2)], [(0, 2), (2, 3)]])

        # 0 sentences, 1 offset_mapping
        with self.assertRaises(ValueError):
            self.args_parser(offset_mapping=[[(0, 1), (1, 2)]])