Mirrors/modelscope
1
0
Fork 0
mirror of https://github.com/modelscope/modelscope.git synced 2026-07-10 04:22:33 +02:00
Code Issues Packages Projects Releases Wiki Activity

[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

Browse Source 
Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/10491951
This commit is contained in:
zhangyanzhao.zyz
2022-10-22 17:12:48 +08:00
committed by yingda.chen
parent 683ee5bfed
commit 824ee8232c
19 changed files with 72 additions and 70 deletions
Download Patch File Download Diff File Expand all files Collapse all files

6
modelscope/metainfo.py
View File

@@ -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'

5
modelscope/models/nlp/__init__.py
View File

@@ -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'],
}

10
modelscope/models/nlp/passage_ranking.py → modelscope/models/nlp/text_ranking.py
View File

@@ -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)

4
modelscope/msdatasets/task_datasets/__init__.py
View File

@@ -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'],

16
modelscope/msdatasets/task_datasets/passage_ranking_dataset.py → modelscope/msdatasets/task_datasets/text_ranking_dataset.py
View File

@@ -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
]

2
modelscope/outputs.py
View File

@@ -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
# {

2
modelscope/pipeline_inputs.py
View File

@@ -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:

4
modelscope/pipelines/builder.py
View File

@@ -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'),

4
modelscope/pipelines/nlp/__init__.py
View File

@@ -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'],

10
modelscope/pipelines/nlp/passage_ranking_pipeline.py → modelscope/pipelines/nlp/text_ranking_pipeline.py
View File

@@ -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()

4
modelscope/preprocessors/__init__.py
View File

@@ -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',

4
modelscope/preprocessors/nlp/__init__.py
View File

@@ -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',

8
modelscope/preprocessors/nlp/nlp_base.py
View File

@@ -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,

4
modelscope/trainers/__init__.py
View File

@@ -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']
}

4
modelscope/trainers/nlp/__init__.py
View File

@@ -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

11
modelscope/trainers/nlp/passage_ranking_trainer.py → modelscope/trainers/nlp/text_ranking_trainer.py
View File

@@ -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:

2
modelscope/utils/constant.py
View File

@@ -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'

25
tests/pipelines/test_passage_ranking.py → tests/pipelines/test_text_ranking.py
View File

@@ -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))

17
tests/trainers/test_finetune_passage_ranking.py → tests/trainers/test_finetune_text_ranking.py
View File

@@ -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))