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MGeo is a multi-modal multi-task geographic language model. We support 5 pipeline tasks and 1 pretrained model MGeo on maas. In the same time, we propose GeoGLUE, a geographic evaluation benchmark. MGeo can be finetuned on GeoGLUE tasks. Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/11273012 * add prov city dist feature to gis encoder * finish mgeo fintune and pipeline * text classification add token type id * to_device support ModelOutput class * update token classification model lable mask logic
127 lines
5.8 KiB
Python
127 lines
5.8 KiB
Python
# Copyright (c) Alibaba, Inc. and its affiliates.
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import unittest
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from modelscope.hub.snapshot_download import snapshot_download
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from modelscope.models import Model
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from modelscope.models.nlp import SbertForSequenceClassification
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from modelscope.pipelines import pipeline
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from modelscope.pipelines.nlp import TextClassificationPipeline
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from modelscope.preprocessors import TextClassificationTransformersPreprocessor
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from modelscope.utils.constant import Tasks
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from modelscope.utils.demo_utils import DemoCompatibilityCheck
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from modelscope.utils.regress_test_utils import IgnoreKeyFn, MsRegressTool
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from modelscope.utils.test_utils import test_level
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class MGeoTest(unittest.TestCase, DemoCompatibilityCheck):
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multi_modal_inputs = {
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'source_sentence': ['杭州余杭东方未来学校附近世纪华联商场(金家渡北苑店)'],
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'first_sequence_gis': [[
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[
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13159, 13295, 13136, 13157, 13158, 13291, 13294, 74505, 74713,
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75387, 75389, 75411
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],
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[3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4],
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[3, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4], # noqa: E126
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[[1254, 1474, 1255, 1476], [1253, 1473, 1256, 1476],
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[1247, 1473, 1255, 1480], [1252, 1475, 1253, 1476],
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[1253, 1475, 1253, 1476], [1252, 1471, 1254, 1475],
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[1254, 1473, 1256, 1475], [1238, 1427, 1339, 1490],
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[1238, 1427, 1339, 1490], [1252, 1474, 1255, 1476],
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[1252, 1474, 1255, 1476], [1249, 1472, 1255, 1479]],
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[[24, 23, 15, 23], [24, 28, 15, 18], [31, 24, 22, 22],
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[43, 13, 37, 13], [43, 6, 35, 6], [31, 32, 22, 14],
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[19, 30, 9, 16], [24, 30, 15, 16], [24, 30, 15, 16],
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[29, 24, 20, 22], [28, 25, 19, 21], [31, 26, 22, 20]],
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'120.08802231437534,30.343853313981505'
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]],
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'sentences_to_compare': [
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'良渚街道金家渡北苑42号世纪华联超市(金家渡北苑店)', '金家渡路金家渡中苑南区70幢金家渡中苑70幢',
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'金家渡路140-142号附近家家福足道(金家渡店)'
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],
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'second_sequence_gis':
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[[[13083, 13081, 13084, 13085, 13131, 13134, 13136, 13147, 13148],
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[3, 3, 3, 3, 3, 3, 3, 3, 3], [3, 4, 4, 4, 4, 4, 4, 4, 4],
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[[1248, 1477, 1250, 1479], [1248, 1475, 1250, 1476],
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[1247, 1478, 1249, 1481], [1249, 1479, 1249, 1480],
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[1249, 1476, 1250, 1476], [1250, 1474, 1252, 1478],
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[1247, 1473, 1255, 1480], [1250, 1478, 1251, 1479],
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[1249, 1478, 1250, 1481]],
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[[30, 26, 21, 20], [32, 43, 23, 43], [33, 23, 23, 23],
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[31, 13, 22, 13], [25, 43, 16, 43], [20, 33, 10, 33],
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[26, 29, 17, 17], [18, 21, 8, 21], [26, 23, 17, 23]],
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'120.08075205680345,30.34697777462197'],
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[[13291, 13159, 13295, 74713, 75387, 75389, 75411],
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[3, 3, 3, 4, 4, 4, 4], [3, 4, 4, 4, 4, 4, 4],
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[[1252, 1471, 1254, 1475], [1254, 1474, 1255, 1476],
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[1253, 1473, 1256, 1476], [1238, 1427, 1339, 1490],
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[1252, 1474, 1255, 1476], [1252, 1474, 1255, 1476],
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[1249, 1472, 1255, 1479]],
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[[28, 28, 19, 18], [22, 16, 12, 16], [23, 24, 13, 22],
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[24, 30, 15, 16], [27, 20, 18, 20], [27, 21, 18, 21],
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[30, 24, 21, 22]], '120.0872539617001,30.342783672056953'],
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[[13291, 13290, 13294, 13295, 13298], [3, 3, 3, 3, 3],
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[3, 4, 4, 4, 4],
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[[1252, 1471, 1254, 1475], [1253, 1469, 1255, 1472],
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[1254, 1473, 1256, 1475], [1253, 1473, 1256, 1476],
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[1255, 1467, 1258, 1472]],
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[[32, 25, 23, 21], [26, 33, 17, 33], [21, 19, 11, 19],
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[25, 21, 16, 21], [21, 33, 11,
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33]], '120.08839673752281,30.34156156893651']]
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}
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single_modal_inputs = {
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'source_sentence': ['杭州余杭东方未来学校附近世纪华联商场(金家渡北苑店)'],
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'sentences_to_compare': [
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'良渚街道金家渡北苑42号世纪华联超市(金家渡北苑店)', '金家渡路金家渡中苑南区70幢金家渡中苑70幢',
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'金家渡路140-142号附近家家福足道(金家渡店)'
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]
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}
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pipe_input = [
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[
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Tasks.text_ranking,
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'damo/mgeo_geographic_textual_similarity_rerank_chinese_base',
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multi_modal_inputs
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],
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[
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Tasks.text_ranking,
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'damo/mgeo_geographic_textual_similarity_rerank_chinese_base',
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single_modal_inputs
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],
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[
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Tasks.token_classification,
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'damo/mgeo_geographic_elements_tagging_chinese_base',
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'浙江省杭州市余杭区阿里巴巴西溪园区'
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],
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[
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Tasks.token_classification,
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'damo/mgeo_geographic_composition_analysis_chinese_base',
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'浙江省杭州市余杭区阿里巴巴西溪园区'
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],
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[
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Tasks.token_classification,
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'damo/mgeo_geographic_where_what_cut_chinese_base',
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'浙江省杭州市余杭区阿里巴巴西溪园区'
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],
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[
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Tasks.sentence_similarity,
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'damo/mgeo_geographic_entity_alignment_chinese_base',
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('后湖金桥大道绿色新都116—120栋116号（诺雅广告）', '金桥大道46号宏宇·绿色新都120幢')
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],
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]
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@unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
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def test_run_with_model_name(self):
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for task, model, inputs in self.pipe_input:
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pipeline_ins = pipeline(task=task, model=model)
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print(pipeline_ins(input=inputs))
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@unittest.skip('demo compatibility test is only enabled on a needed-basis')
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def test_demo_compatibility(self):
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self.compatibility_check()
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if __name__ == '__main__':
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unittest.main()
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