ModelScope/tests/pipelines/test_named_entity_recognition.py

# Copyright (c) Alibaba, Inc. and its affiliates.
import unittest

from modelscope.hub.snapshot_download import snapshot_download
from modelscope.models import Model
from modelscope.models.nlp import (LSTMCRFForNamedEntityRecognition,
                                   TransformerCRFForNamedEntityRecognition)
from modelscope.pipelines import pipeline
from modelscope.pipelines.nlp import NamedEntityRecognitionPipeline
from modelscope.preprocessors import \
    TokenClassificationTransformersPreprocessor
from modelscope.utils.constant import Tasks
from modelscope.utils.demo_utils import DemoCompatibilityCheck
from modelscope.utils.test_utils import test_level


class NamedEntityRecognitionTest(unittest.TestCase, DemoCompatibilityCheck):

    def setUp(self) -> None:
        self.task = Tasks.named_entity_recognition
        self.model_id = 'damo/nlp_raner_named-entity-recognition_chinese-base-news'

    english_model_id = 'damo/nlp_raner_named-entity-recognition_english-large-ecom'
    chinese_model_id = 'damo/nlp_raner_named-entity-recognition_chinese-large-generic'
    tcrf_model_id = 'damo/nlp_raner_named-entity-recognition_chinese-base-news'
    lcrf_model_id = 'damo/nlp_lstm_named-entity-recognition_chinese-news'
    addr_model_id = 'damo/nlp_structbert_address-parsing_chinese_base'
    lstm_model_id = 'damo/nlp_lstm_named-entity-recognition_chinese-generic'
    sentence = '这与温岭市新河镇的一个神秘的传说有关。'
    sentence_en = 'pizza shovel'
    sentence_zh = '他 继 续 与 貝 塞 斯 達 遊 戲 工 作 室 在 接 下 来 辐 射 4 游 戏 。'
    addr = '浙江省杭州市余杭区文一西路969号亲橙里'
    addr1 = '浙江省西湖区灵隐隧道'
    addr2 = '内蒙古自治区巴彦淖尔市'
    ecom = '欧美单 秋季女装时尚百搭休闲修身 亚麻混纺短款 外套西装'

    @unittest.skipUnless(test_level() >= 2, 'skip test in current test level')
    def test_run_tcrf_by_direct_model_download(self):
        cache_path = snapshot_download(self.tcrf_model_id)
        tokenizer = TokenClassificationTransformersPreprocessor(cache_path)
        model = TransformerCRFForNamedEntityRecognition(
            cache_path, tokenizer=tokenizer)
        pipeline1 = NamedEntityRecognitionPipeline(
            model, preprocessor=tokenizer)
        pipeline2 = pipeline(
            Tasks.named_entity_recognition,
            model=model,
            preprocessor=tokenizer)
        print(f'sentence: {self.sentence}\n'
              f'pipeline1:{pipeline1(input=self.sentence)}')
        print()
        print(f'pipeline2: {pipeline2(input=self.sentence)}')

    @unittest.skipUnless(test_level() >= 2, 'skip test in current test level')
    def test_run_lcrf_by_direct_model_download(self):
        cache_path = snapshot_download(self.lcrf_model_id)
        tokenizer = TokenClassificationTransformersPreprocessor(cache_path)
        model = LSTMCRFForNamedEntityRecognition(
            cache_path, tokenizer=tokenizer)
        pipeline1 = NamedEntityRecognitionPipeline(
            model, preprocessor=tokenizer)
        pipeline2 = pipeline(
            Tasks.named_entity_recognition,
            model=model,
            preprocessor=tokenizer)
        print(f'sentence: {self.sentence}\n'
              f'pipeline1:{pipeline1(input=self.sentence)}')
        print()
        print(f'pipeline2: {pipeline2(input=self.sentence)}')

    @unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
    def test_run_tcrf_with_model_from_modelhub(self):
        model = Model.from_pretrained(self.tcrf_model_id)
        tokenizer = TokenClassificationTransformersPreprocessor(
            model.model_dir)
        pipeline_ins = pipeline(
            task=Tasks.named_entity_recognition,
            model=model,
            preprocessor=tokenizer)
        print(pipeline_ins(input=self.sentence))

    @unittest.skipUnless(test_level() >= 2, 'skip test in current test level')
    def test_run_addrst_with_model_from_modelhub(self):
        model = Model.from_pretrained(
            'damo/nlp_structbert_address-parsing_chinese_base')
        tokenizer = TokenClassificationTransformersPreprocessor(
            model.model_dir)
        pipeline_ins = pipeline(
            task=Tasks.named_entity_recognition,
            model=model,
            preprocessor=tokenizer)
        print(pipeline_ins(input=self.addr))

    @unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
    def test_run_addrst_with_model_name(self):
        pipeline_ins = pipeline(
            task=Tasks.named_entity_recognition, model=self.addr_model_id)
        print(pipeline_ins(input=self.addr))

    @unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
    def test_run_addrst_with_model_name_batch(self):
        pipeline_ins = pipeline(
            task=Tasks.named_entity_recognition, model=self.addr_model_id)
        print(
            pipeline_ins(
                input=[self.addr, self.addr1, self.addr2], batch_size=2))

    @unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
    def test_run_addrst_with_model_name_batch_iter(self):
        pipeline_ins = pipeline(
            task=Tasks.named_entity_recognition,
            model=self.addr_model_id,
            padding=False)
        print(pipeline_ins(input=[self.addr, self.addr1, self.addr2]))

    @unittest.skipUnless(test_level() >= 2, 'skip test in current test level')
    def test_run_lcrf_with_model_from_modelhub(self):
        model = Model.from_pretrained(self.lcrf_model_id)
        tokenizer = TokenClassificationTransformersPreprocessor(
            model.model_dir)
        pipeline_ins = pipeline(
            task=Tasks.named_entity_recognition,
            model=model,
            preprocessor=tokenizer)
        print(pipeline_ins(input=self.sentence))

    @unittest.skipUnless(test_level() >= 1, 'skip test in current test level')
    def test_run_tcrf_with_model_name(self):
        pipeline_ins = pipeline(
            task=Tasks.named_entity_recognition, model=self.tcrf_model_id)
        print(pipeline_ins(input=self.sentence))

    @unittest.skipUnless(test_level() >= 2, 'skip test in current test level')
    def test_run_lcrf_with_model_name(self):
        pipeline_ins = pipeline(
            task=Tasks.named_entity_recognition, model=self.lcrf_model_id)
        print(pipeline_ins(input=self.sentence))

    @unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
    def test_run_lcrf_with_chinese_model_name(self):
        pipeline_ins = pipeline(
            task=Tasks.named_entity_recognition, model=self.chinese_model_id)
        print(pipeline_ins(input=self.sentence_zh))

    @unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
    def test_run_lcrf_with_chinese_model_name_batch_iter(self):
        pipeline_ins = pipeline(
            task=Tasks.named_entity_recognition,
            model=self.chinese_model_id,
            padding=False)
        print(
            pipeline_ins(input=[
                self.sentence_zh, self.sentence_zh[:20], self.sentence_zh[10:]
            ]))

    @unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
    def test_run_lcrf_with_chinese_model_name_batch(self):
        pipeline_ins = pipeline(
            task=Tasks.named_entity_recognition, model=self.chinese_model_id)
        print(
            pipeline_ins(
                input=[
                    self.sentence_zh, self.sentence_zh[:20],
                    self.sentence_zh[10:]
                ],
                batch_size=2))

    @unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
    def test_run_lstm_with_chinese_model_name(self):
        pipeline_ins = pipeline(
            task=Tasks.named_entity_recognition, model=self.lstm_model_id)
        print(pipeline_ins(input=self.sentence_zh))

    @unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
    def test_run_lstm_with_chinese_model_name_batch_iter(self):
        pipeline_ins = pipeline(
            task=Tasks.named_entity_recognition,
            model=self.lstm_model_id,
            padding=False)
        print(
            pipeline_ins(input=[
                self.sentence_zh, self.sentence_zh[:20], self.sentence_zh[10:]
            ]))

    @unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
    def test_run_lstm_with_chinese_model_name_batch(self):
        pipeline_ins = pipeline(
            task=Tasks.named_entity_recognition, model=self.lstm_model_id)
        print(
            pipeline_ins(
                input=[
                    self.sentence_zh, self.sentence_zh[:20],
                    self.sentence_zh[10:]
                ],
                batch_size=2))

    @unittest.skipUnless(test_level() >= 2, 'skip test in current test level')
    def test_run_english_with_model_name(self):
        pipeline_ins = pipeline(
            task=Tasks.named_entity_recognition, model=self.english_model_id)
        print(pipeline_ins(input=self.sentence_en))

    @unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
    def test_run_english_with_model_name_batch(self):
        pipeline_ins = pipeline(
            task=Tasks.named_entity_recognition, model=self.english_model_id)
        print(
            pipeline_ins(
                input=[self.ecom, self.sentence_zh, self.sentence],
                batch_size=2))

    @unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
    def test_run_english_with_model_name_batch_iter(self):
        pipeline_ins = pipeline(
            task=Tasks.named_entity_recognition,
            model=self.english_model_id,
            padding=False)
        print(pipeline_ins(input=[self.ecom, self.sentence_zh, self.sentence]))

    @unittest.skipUnless(test_level() >= 2, 'skip test in current test level')
    def test_run_with_default_model(self):
        pipeline_ins = pipeline(task=Tasks.named_entity_recognition)
        print(pipeline_ins(input=self.sentence))

    @unittest.skip('demo compatibility test is only enabled on a needed-basis')
    def test_demo_compatibility(self):
        self.compatibility_check()


if __name__ == '__main__':
    unittest.main()