[to #42322933]更新语义相关性任务英文名称为text ranking，修改对应变量名和类名

Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/10491951

modelscope/metainfo.py

@@ -236,7 +236,7 @@ class Pipelines(object):
     conversational_text_to_sql = 'conversational-text-to-sql'
     table_question_answering_pipeline = 'table-question-answering-pipeline'
     sentence_embedding = 'sentence-embedding'
-    passage_ranking = 'passage-ranking'
+    text_ranking = 'text-ranking'
     relation_extraction = 'relation-extraction'
     document_segmentation = 'document-segmentation'
     feature_extraction = 'feature-extraction'
@@ -297,7 +297,7 @@ class Trainers(object):
     dialog_intent_trainer = 'dialog-intent-trainer'
     nlp_base_trainer = 'nlp-base-trainer'
     nlp_veco_trainer = 'nlp-veco-trainer'
-    nlp_passage_ranking_trainer = 'nlp-passage-ranking-trainer'
+    nlp_text_ranking_trainer = 'nlp-text-ranking-trainer'
 
     # audio trainers
     speech_frcrn_ans_cirm_16k = 'speech_frcrn_ans_cirm_16k'
@@ -343,7 +343,7 @@ class Preprocessors(object):
     zero_shot_cls_tokenizer = 'zero-shot-cls-tokenizer'
     text_error_correction = 'text-error-correction'
     sentence_embedding = 'sentence-embedding'
-    passage_ranking = 'passage-ranking'
+    text_ranking = 'text-ranking'
     sequence_labeling_tokenizer = 'sequence-labeling-tokenizer'
     word_segment_text_to_label_preprocessor = 'word-segment-text-to-label-preprocessor'
     fill_mask = 'fill-mask'

modelscope/models/nlp/__init__.py

@@ -34,8 +34,9 @@ if TYPE_CHECKING:
                               TaskModelForTextGeneration)
     from .token_classification import SbertForTokenClassification
     from .sentence_embedding import SentenceEmbedding
-    from .passage_ranking import PassageRanking
+    from .text_ranking import TextRanking
     from .T5 import T5ForConditionalGeneration
+
 else:
     _import_structure = {
         'backbones': ['SbertModel'],
@@ -75,7 +76,7 @@ else:
         'token_classification': ['SbertForTokenClassification'],
         'table_question_answering': ['TableQuestionAnswering'],
         'sentence_embedding': ['SentenceEmbedding'],
-        'passage_ranking': ['PassageRanking'],
+        'text_ranking': ['TextRanking'],
         'T5': ['T5ForConditionalGeneration'],
     }
 

modelscope/models/nlp/passage_ranking.py → modelscope/models/nlp/text_ranking.py

@@ -13,18 +13,18 @@ from modelscope.models.nlp.structbert import SbertPreTrainedModel
 from modelscope.outputs import OutputKeys
 from modelscope.utils.constant import Tasks
 
-__all__ = ['PassageRanking']
+__all__ = ['TextRanking']
 
 
-@MODELS.register_module(Tasks.passage_ranking, module_name=Models.bert)
-class PassageRanking(SbertForSequenceClassification, SbertPreTrainedModel):
+@MODELS.register_module(Tasks.text_ranking, module_name=Models.bert)
+class TextRanking(SbertForSequenceClassification, SbertPreTrainedModel):
     base_model_prefix: str = 'bert'
     supports_gradient_checkpointing = True
     _keys_to_ignore_on_load_missing = [r'position_ids']
 
     def __init__(self, config, model_dir, *args, **kwargs):
         if hasattr(config, 'base_model_prefix'):
-            PassageRanking.base_model_prefix = config.base_model_prefix
+            TextRanking.base_model_prefix = config.base_model_prefix
         super().__init__(config, model_dir)
         self.train_batch_size = kwargs.get('train_batch_size', 4)
         self.register_buffer(
@@ -74,7 +74,7 @@ class PassageRanking(SbertForSequenceClassification, SbertPreTrainedModel):
         num_labels = kwargs.get('num_labels', 1)
         model_args = {} if num_labels is None else {'num_labels': num_labels}
 
-        return super(SbertPreTrainedModel, PassageRanking).from_pretrained(
+        return super(SbertPreTrainedModel, TextRanking).from_pretrained(
             pretrained_model_name_or_path=kwargs.get('model_dir'),
             model_dir=kwargs.get('model_dir'),
             **model_args)

modelscope/msdatasets/task_datasets/__init__.py

@@ -12,14 +12,14 @@ if TYPE_CHECKING:
     from .movie_scene_segmentation import MovieSceneSegmentationDataset
     from .video_summarization_dataset import VideoSummarizationDataset
     from .image_inpainting import ImageInpaintingDataset
-    from .passage_ranking_dataset import PassageRankingDataset
+    from .text_ranking_dataset import TextRankingDataset
 
 else:
     _import_structure = {
         'base': ['TaskDataset'],
         'builder': ['TASK_DATASETS', 'build_task_dataset'],
         'torch_base_dataset': ['TorchTaskDataset'],
-        'passage_ranking_dataset': ['PassageRankingDataset'],
+        'text_ranking_dataset': ['TextRankingDataset'],
         'veco_dataset': ['VecoDataset'],
         'image_instance_segmentation_coco_dataset':
         ['ImageInstanceSegmentationCocoDataset'],

modelscope/msdatasets/task_datasets/passage_ranking_dataset.py → modelscope/msdatasets/task_datasets/text_ranking_dataset.py

@@ -16,8 +16,8 @@ from .torch_base_dataset import TorchTaskDataset
 
 
 @TASK_DATASETS.register_module(
-    group_key=Tasks.passage_ranking, module_name=Models.bert)
-class PassageRankingDataset(TorchTaskDataset):
+    group_key=Tasks.text_ranking, module_name=Models.bert)
+class TextRankingDataset(TorchTaskDataset):
 
     def __init__(self,
                  datasets: Union[Any, List[Any]],
@@ -35,8 +35,8 @@ class PassageRankingDataset(TorchTaskDataset):
                                                     'positive_passages')
         self.neg_sequence = self.dataset_config.get('neg_sequence',
                                                     'negative_passages')
-        self.passage_text_fileds = self.dataset_config.get(
-            'passage_text_fileds', ['title', 'text'])
+        self.text_fileds = self.dataset_config.get('text_fileds',
+                                                   ['title', 'text'])
         self.qid_field = self.dataset_config.get('qid_field', 'query_id')
         if mode == ModeKeys.TRAIN:
             train_config = kwargs.get('train', {})
@@ -58,14 +58,14 @@ class PassageRankingDataset(TorchTaskDataset):
 
         pos_sequences = group[self.pos_sequence]
         pos_sequences = [
-            ' '.join([ele[key] for key in self.passage_text_fileds])
+            ' '.join([ele[key] for key in self.text_fileds])
             for ele in pos_sequences
         ]
         labels.extend([1] * len(pos_sequences))
 
         neg_sequences = group[self.neg_sequence]
         neg_sequences = [
-            ' '.join([ele[key] for key in self.passage_text_fileds])
+            ' '.join([ele[key] for key in self.text_fileds])
             for ele in neg_sequences
         ]
 
@@ -88,13 +88,13 @@ class PassageRankingDataset(TorchTaskDataset):
 
         pos_sequences = group[self.pos_sequence]
         pos_sequences = [
-            ' '.join([ele[key] for key in self.passage_text_fileds])
+            ' '.join([ele[key] for key in self.text_fileds])
             for ele in pos_sequences
         ]
 
         neg_sequences = group[self.neg_sequence]
         neg_sequences = [
-            ' '.join([ele[key] for key in self.passage_text_fileds])
+            ' '.join([ele[key] for key in self.text_fileds])
             for ele in neg_sequences
         ]
 

modelscope/outputs.py

@@ -506,7 +506,7 @@ TASK_OUTPUTS = {
     # }
     Tasks.text_error_correction: [OutputKeys.OUTPUT],
     Tasks.sentence_embedding: [OutputKeys.TEXT_EMBEDDING, OutputKeys.SCORES],
-    Tasks.passage_ranking: [OutputKeys.SCORES],
+    Tasks.text_ranking: [OutputKeys.SCORES],
 
     # text generation result for single sample
     # {

modelscope/pipeline_inputs.py

@@ -162,7 +162,7 @@ TASK_INPUTS = {
         'source_sentence': InputType.LIST,
         'sentences_to_compare': InputType.LIST,
     },
-    Tasks.passage_ranking: (InputType.TEXT, InputType.TEXT),
+    Tasks.text_ranking: (InputType.TEXT, InputType.TEXT),
     Tasks.text_generation:
     InputType.TEXT,
     Tasks.fill_mask:

modelscope/pipelines/builder.py

@@ -20,8 +20,8 @@ DEFAULT_MODEL_FOR_PIPELINE = {
     Tasks.sentence_embedding:
     (Pipelines.sentence_embedding,
      'damo/nlp_corom_sentence-embedding_english-base'),
-    Tasks.passage_ranking: (Pipelines.passage_ranking,
-                            'damo/nlp_corom_passage-ranking_english-base'),
+    Tasks.text_ranking: (Pipelines.text_ranking,
+                         'damo/nlp_corom_passage-ranking_english-base'),
     Tasks.word_segmentation:
     (Pipelines.word_segmentation,
      'damo/nlp_structbert_word-segmentation_chinese-base'),

modelscope/pipelines/nlp/__init__.py

@@ -17,7 +17,7 @@ if TYPE_CHECKING:
     from .fill_mask_ponet_pipeline import FillMaskPonetPipeline
     from .information_extraction_pipeline import InformationExtractionPipeline
     from .named_entity_recognition_pipeline import NamedEntityRecognitionPipeline
-    from .passage_ranking_pipeline import PassageRankingPipeline
+    from .text_ranking_pipeline import TextRankingPipeline
     from .sentence_embedding_pipeline import SentenceEmbeddingPipeline
     from .sequence_classification_pipeline import SequenceClassificationPipeline
     from .summarization_pipeline import SummarizationPipeline
@@ -51,7 +51,7 @@ else:
         'information_extraction_pipeline': ['InformationExtractionPipeline'],
         'named_entity_recognition_pipeline':
         ['NamedEntityRecognitionPipeline'],
-        'passage_ranking_pipeline': ['PassageRankingPipeline'],
+        'text_ranking_pipeline': ['TextRankingPipeline'],
         'sentence_embedding_pipeline': ['SentenceEmbeddingPipeline'],
         'sequence_classification_pipeline': ['SequenceClassificationPipeline'],
         'summarization_pipeline': ['SummarizationPipeline'],

modelscope/pipelines/nlp/passage_ranking_pipeline.py → modelscope/pipelines/nlp/text_ranking_pipeline.py

@@ -9,15 +9,15 @@ from modelscope.models import Model
 from modelscope.outputs import OutputKeys
 from modelscope.pipelines.base import Pipeline
 from modelscope.pipelines.builder import PIPELINES
-from modelscope.preprocessors import PassageRankingPreprocessor, Preprocessor
+from modelscope.preprocessors import Preprocessor, TextRankingPreprocessor
 from modelscope.utils.constant import Tasks
 
-__all__ = ['PassageRankingPipeline']
+__all__ = ['TextRankingPipeline']
 
 
 @PIPELINES.register_module(
-    Tasks.passage_ranking, module_name=Pipelines.passage_ranking)
-class PassageRankingPipeline(Pipeline):
+    Tasks.text_ranking, module_name=Pipelines.text_ranking)
+class TextRankingPipeline(Pipeline):
 
     def __init__(self,
                  model: Union[Model, str],
@@ -36,7 +36,7 @@ class PassageRankingPipeline(Pipeline):
                                     Model) else Model.from_pretrained(model)
 
         if preprocessor is None:
-            preprocessor = PassageRankingPreprocessor(
+            preprocessor = TextRankingPreprocessor(
                 model.model_dir if isinstance(model, Model) else model,
                 sequence_length=kwargs.pop('sequence_length', 128))
         model.eval()

modelscope/preprocessors/__init__.py

@@ -21,7 +21,7 @@ if TYPE_CHECKING:
         FillMaskPoNetPreprocessor,
         NLPPreprocessor,
         NLPTokenizerPreprocessorBase,
-        PassageRankingPreprocessor,
+        TextRankingPreprocessor,
         RelationExtractionPreprocessor,
         SentenceEmbeddingPreprocessor,
         SequenceClassificationPreprocessor,
@@ -62,7 +62,7 @@ else:
             'FillMaskPoNetPreprocessor',
             'NLPPreprocessor',
             'NLPTokenizerPreprocessorBase',
-            'PassageRankingPreprocessor',
+            'TextRankingPreprocessor',
             'RelationExtractionPreprocessor',
             'SentenceEmbeddingPreprocessor',
             'SequenceClassificationPreprocessor',

modelscope/preprocessors/nlp/__init__.py

@@ -11,7 +11,7 @@ if TYPE_CHECKING:
         FillMaskPoNetPreprocessor,
         NLPPreprocessor,
         NLPTokenizerPreprocessorBase,
-        PassageRankingPreprocessor,
+        TextRankingPreprocessor,
         RelationExtractionPreprocessor,
         SentenceEmbeddingPreprocessor,
         SequenceClassificationPreprocessor,
@@ -33,7 +33,7 @@ else:
             'FillMaskPoNetPreprocessor',
             'NLPPreprocessor',
             'NLPTokenizerPreprocessorBase',
-            'PassageRankingPreprocessor',
+            'TextRankingPreprocessor',
             'RelationExtractionPreprocessor',
             'SentenceEmbeddingPreprocessor',
             'SequenceClassificationPreprocessor',

modelscope/preprocessors/nlp/nlp_base.py

@@ -29,7 +29,7 @@ __all__ = [
     'NLPPreprocessor',
     'FillMaskPoNetPreprocessor',
     'NLPTokenizerPreprocessorBase',
-    'PassageRankingPreprocessor',
+    'TextRankingPreprocessor',
     'RelationExtractionPreprocessor',
     'SentenceEmbeddingPreprocessor',
     'SequenceClassificationPreprocessor',
@@ -245,9 +245,9 @@ class NLPPreprocessor(NLPTokenizerPreprocessorBase):
 
 
 @PREPROCESSORS.register_module(
-    Fields.nlp, module_name=Preprocessors.passage_ranking)
-class PassageRankingPreprocessor(NLPTokenizerPreprocessorBase):
-    """The tokenizer preprocessor used in passage ranking model.
+    Fields.nlp, module_name=Preprocessors.text_ranking)
+class TextRankingPreprocessor(NLPTokenizerPreprocessorBase):
+    """The tokenizer preprocessor used in text-ranking model.
     """
 
     def __init__(self,

modelscope/trainers/__init__.py

@@ -11,7 +11,7 @@ if TYPE_CHECKING:
                      ImagePortraitEnhancementTrainer,
                      MovieSceneSegmentationTrainer, ImageInpaintingTrainer)
     from .multi_modal import CLIPTrainer
-    from .nlp import SequenceClassificationTrainer, PassageRankingTrainer
+    from .nlp import SequenceClassificationTrainer, TextRankingTrainer
     from .nlp_trainer import NlpEpochBasedTrainer, VecoTrainer
     from .trainer import EpochBasedTrainer
 
@@ -26,7 +26,7 @@ else:
             'ImageInpaintingTrainer'
         ],
         'multi_modal': ['CLIPTrainer'],
-        'nlp': ['SequenceClassificationTrainer', 'PassageRankingTrainer'],
+        'nlp': ['SequenceClassificationTrainer', 'TextRankingTrainer'],
         'nlp_trainer': ['NlpEpochBasedTrainer', 'VecoTrainer'],
         'trainer': ['EpochBasedTrainer']
     }

modelscope/trainers/nlp/__init__.py

@@ -6,12 +6,12 @@ from modelscope.utils.import_utils import LazyImportModule
 if TYPE_CHECKING:
     from .sequence_classification_trainer import SequenceClassificationTrainer
     from .csanmt_translation_trainer import CsanmtTranslationTrainer
-    from .passage_ranking_trainer import PassageRankingTranier
+    from .text_ranking_trainer import TextRankingTranier
 else:
     _import_structure = {
         'sequence_classification_trainer': ['SequenceClassificationTrainer'],
         'csanmt_translation_trainer': ['CsanmtTranslationTrainer'],
-        'passage_ranking_trainer': ['PassageRankingTrainer']
+        'text_ranking_trainer': ['TextRankingTrainer']
     }
 
     import sys

modelscope/trainers/nlp/passage_ranking_trainer.py → modelscope/trainers/nlp/text_ranking_trainer.py

@@ -8,6 +8,7 @@ import numpy as np
 import torch
 from torch import nn
 from torch.utils.data import DataLoader, Dataset
+from tqdm import tqdm
 
 from modelscope.metainfo import Trainers
 from modelscope.models.base import Model, TorchModel
@@ -42,8 +43,8 @@ class GroupCollator():
         return batch
 
 
-@TRAINERS.register_module(module_name=Trainers.nlp_passage_ranking_trainer)
-class PassageRankingTrainer(NlpEpochBasedTrainer):
+@TRAINERS.register_module(module_name=Trainers.nlp_text_ranking_trainer)
+class TextRankingTrainer(NlpEpochBasedTrainer):
 
     def __init__(
             self,
@@ -117,7 +118,7 @@ class PassageRankingTrainer(NlpEpochBasedTrainer):
             Example:
             {"accuracy": 0.5091743119266054, "f1": 0.673780487804878}
         """
-        from modelscope.models.nlp import PassageRanking
+        from modelscope.models.nlp import TextRanking
         # get the raw online dataset
         self.eval_dataloader = self._build_dataloader_with_dataset(
             self.eval_dataset,
@@ -126,7 +127,7 @@ class PassageRankingTrainer(NlpEpochBasedTrainer):
         # generate a standard dataloader
         # generate a model
         if checkpoint_path is not None:
-            model = PassageRanking.from_pretrained(checkpoint_path)
+            model = TextRanking.from_pretrained(checkpoint_path)
         else:
             model = self.model
 
@@ -141,7 +142,7 @@ class PassageRankingTrainer(NlpEpochBasedTrainer):
         total_spent_time = 0.0
         device = 'cuda:0' if torch.cuda.is_available() else 'cpu'
         model.to(device)
-        for _step, batch in enumerate(self.eval_dataloader):
+        for _step, batch in enumerate(tqdm(self.eval_dataloader)):
             try:
                 batch = {
                     key:

modelscope/utils/constant.py

@@ -103,7 +103,7 @@ class NLPTasks(object):
     sentence_similarity = 'sentence-similarity'
     text_classification = 'text-classification'
     sentence_embedding = 'sentence-embedding'
-    passage_ranking = 'passage-ranking'
+    text_ranking = 'text-ranking'
     relation_extraction = 'relation-extraction'
     zero_shot = 'zero-shot'
     translation = 'translation'

tests/pipelines/test_passage_ranking.py → tests/pipelines/test_text_ranking.py

@@ -4,15 +4,15 @@ import unittest
 
 from modelscope.hub.snapshot_download import snapshot_download
 from modelscope.models import Model
-from modelscope.models.nlp import PassageRanking
+from modelscope.models.nlp import TextRanking
 from modelscope.pipelines import pipeline
-from modelscope.pipelines.nlp import PassageRankingPipeline
-from modelscope.preprocessors import PassageRankingPreprocessor
+from modelscope.pipelines.nlp import TextRankingPipeline
+from modelscope.preprocessors import TextRankingPreprocessor
 from modelscope.utils.constant import Tasks
 from modelscope.utils.test_utils import test_level
 
 
-class PassageRankingTest(unittest.TestCase):
+class TextRankingTest(unittest.TestCase):
     model_id = 'damo/nlp_corom_passage-ranking_english-base'
     inputs = {
         'source_sentence': ["how long it take to get a master's degree"],
@@ -27,11 +27,11 @@ class PassageRankingTest(unittest.TestCase):
     @unittest.skipUnless(test_level() >= 2, 'skip test in current test level')
     def test_run_by_direct_model_download(self):
         cache_path = snapshot_download(self.model_id)
-        tokenizer = PassageRankingPreprocessor(cache_path)
-        model = PassageRanking.from_pretrained(cache_path)
-        pipeline1 = PassageRankingPipeline(model, preprocessor=tokenizer)
+        tokenizer = TextRankingPreprocessor(cache_path)
+        model = TextRanking.from_pretrained(cache_path)
+        pipeline1 = TextRankingPipeline(model, preprocessor=tokenizer)
         pipeline2 = pipeline(
-            Tasks.passage_ranking, model=model, preprocessor=tokenizer)
+            Tasks.text_ranking, model=model, preprocessor=tokenizer)
         print(f'sentence: {self.inputs}\n'
               f'pipeline1:{pipeline1(input=self.inputs)}')
         print()
@@ -40,20 +40,19 @@ class PassageRankingTest(unittest.TestCase):
     @unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
     def test_run_with_model_from_modelhub(self):
         model = Model.from_pretrained(self.model_id)
-        tokenizer = PassageRankingPreprocessor(model.model_dir)
+        tokenizer = TextRankingPreprocessor(model.model_dir)
         pipeline_ins = pipeline(
-            task=Tasks.passage_ranking, model=model, preprocessor=tokenizer)
+            task=Tasks.text_ranking, model=model, preprocessor=tokenizer)
         print(pipeline_ins(input=self.inputs))
 
     @unittest.skipUnless(test_level() >= 1, 'skip test in current test level')
     def test_run_with_model_name(self):
-        pipeline_ins = pipeline(
-            task=Tasks.passage_ranking, model=self.model_id)
+        pipeline_ins = pipeline(task=Tasks.text_ranking, model=self.model_id)
         print(pipeline_ins(input=self.inputs))
 
     @unittest.skipUnless(test_level() >= 2, 'skip test in current test level')
     def test_run_with_default_model(self):
-        pipeline_ins = pipeline(task=Tasks.passage_ranking)
+        pipeline_ins = pipeline(task=Tasks.text_ranking)
         print(pipeline_ins(input=self.inputs))
 
 

tests/trainers/test_finetune_passage_ranking.py → tests/trainers/test_finetune_text_ranking.py

@@ -41,7 +41,7 @@ class TestFinetuneSequenceClassification(unittest.TestCase):
                  model_id,
                  train_dataset,
                  eval_dataset,
-                 name=Trainers.nlp_passage_ranking_trainer,
+                 name=Trainers.nlp_text_ranking_trainer,
                  cfg_modify_fn=None,
                  **kwargs):
         kwargs = dict(
@@ -61,8 +61,8 @@ class TestFinetuneSequenceClassification(unittest.TestCase):
     def test_finetune_msmarco(self):
 
         def cfg_modify_fn(cfg):
-            cfg.task = 'passage-ranking'
-            cfg['preprocessor'] = {'type': 'passage-ranking'}
+            cfg.task = 'text-ranking'
+            cfg['preprocessor'] = {'type': 'text-ranking'}
             cfg.train.optimizer.lr = 2e-5
             cfg['dataset'] = {
                 'train': {
@@ -105,7 +105,7 @@ class TestFinetuneSequenceClassification(unittest.TestCase):
             }, {
                 'type': 'EvaluationHook',
                 'by_epoch': False,
-                'interval': 3000
+                'interval': 15
             }]
             return cfg
 
@@ -114,18 +114,19 @@ class TestFinetuneSequenceClassification(unittest.TestCase):
         train_ds = ds['train'].to_hf_dataset()
         dev_ds = ds['train'].to_hf_dataset()
 
+        model_id = 'damo/nlp_corom_passage-ranking_english-base'
         self.finetune(
-            model_id='damo/nlp_corom_passage-ranking_english-base',
+            model_id=model_id,
             train_dataset=train_ds,
             eval_dataset=dev_ds,
             cfg_modify_fn=cfg_modify_fn)
 
         output_dir = os.path.join(self.tmp_dir, ModelFile.TRAIN_OUTPUT_DIR)
-        self.pipeline_passage_ranking(output_dir)
+        self.pipeline_text_ranking(output_dir)
 
-    def pipeline_passage_ranking(self, model_dir):
+    def pipeline_text_ranking(self, model_dir):
         model = Model.from_pretrained(model_dir)
-        pipeline_ins = pipeline(task=Tasks.passage_ranking, model=model)
+        pipeline_ins = pipeline(task=Tasks.text_ranking, model=model)
         print(pipeline_ins(input=self.inputs))