mirror of
https://gitcode.com/gh_mirrors/eas/EasyFace.git
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283 lines
10 KiB
Python
283 lines
10 KiB
Python
# Copyright (c) Alibaba, Inc. and its affiliates.
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import os
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import shutil
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import tempfile
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import unittest
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from typing import Any, Callable, Dict, List, NewType, Optional, Tuple, Union
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import torch
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from transformers.tokenization_utils_base import PreTrainedTokenizerBase
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from modelscope.metainfo import Preprocessors, Trainers
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from modelscope.models import Model
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from modelscope.msdatasets import MsDataset
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from modelscope.pipelines import pipeline
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from modelscope.trainers import build_trainer
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from modelscope.utils.constant import ModelFile, Tasks
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from modelscope.utils.test_utils import test_level
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class TestFinetuneMGeo(unittest.TestCase):
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def setUp(self):
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print(('Testing %s.%s' % (type(self).__name__, self._testMethodName)))
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self.tmp_dir = tempfile.TemporaryDirectory().name
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if not os.path.exists(self.tmp_dir):
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os.makedirs(self.tmp_dir)
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def tearDown(self):
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shutil.rmtree(self.tmp_dir)
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super().tearDown()
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def finetune(self,
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model_id,
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train_dataset,
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eval_dataset,
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name=Trainers.nlp_text_ranking_trainer,
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cfg_modify_fn=None,
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**kwargs):
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kwargs = dict(model=model_id,
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train_dataset=train_dataset,
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eval_dataset=eval_dataset,
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work_dir=self.tmp_dir,
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cfg_modify_fn=cfg_modify_fn,
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**kwargs)
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os.environ['LOCAL_RANK'] = '0'
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trainer = build_trainer(name=name, default_args=kwargs)
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trainer.train()
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results_files = os.listdir(self.tmp_dir)
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self.assertIn(f'{trainer.timestamp}.log.json', results_files)
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@unittest.skipUnless(test_level() >= 1, 'skip test in current test level')
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def test_finetune_geotes_rerank(self):
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def cfg_modify_fn(cfg):
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neg_sample = 19
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cfg.task = 'text-ranking'
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cfg['preprocessor'] = {'type': 'mgeo-ranking'}
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cfg.train.optimizer.lr = 5e-5
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cfg['dataset'] = {
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'train': {
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'type': 'mgeo',
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'query_sequence': 'query',
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'pos_sequence': 'positive_passages',
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'neg_sequence': 'negative_passages',
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'text_fileds': ['text', 'gis'],
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'qid_field': 'query_id',
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'neg_sample': neg_sample,
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'sequence_length': 64
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},
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'val': {
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'type': 'mgeo',
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'query_sequence': 'query',
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'pos_sequence': 'positive_passages',
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'neg_sequence': 'negative_passages',
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'text_fileds': ['text', 'gis'],
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'qid_field': 'query_id'
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},
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}
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cfg.evaluation.dataloader.batch_size_per_gpu = 16
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cfg.train.dataloader.batch_size_per_gpu = 3
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cfg.train.dataloader.workers_per_gpu = 16
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cfg.evaluation.dataloader.workers_per_gpu = 16
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cfg.train.train_iters_per_epoch = 10
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cfg.evaluation.val_iters_per_epoch = 10
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cfg['evaluation']['metrics'] = 'text-ranking-metric'
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cfg.train.max_epochs = 1
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cfg.model['neg_sample'] = neg_sample
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cfg.model['gis_num'] = 2
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cfg.model['finetune_mode'] = 'multi-modal'
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cfg.train.hooks = [{
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'type': 'CheckpointHook',
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'interval': 1
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}, {
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'type': 'TextLoggerHook',
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'interval': 100
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}, {
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'type': 'IterTimerHook'
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}, {
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'type': 'EvaluationHook',
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'by_epoch': True
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}]
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# lr_scheduler的配置
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cfg.train.lr_scheduler = {
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'type':
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'LinearLR',
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'start_factor':
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1.0,
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'end_factor':
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0.5,
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'total_iters':
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int(len(train_ds) / cfg.train.dataloader.batch_size_per_gpu) *
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cfg.train.max_epochs,
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'options': {
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'warmup': {
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'type':
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'LinearWarmup',
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'warmup_iters':
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int(
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len(train_ds) /
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cfg.train.dataloader.batch_size_per_gpu)
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},
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'by_epoch': False
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}
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}
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return cfg
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# load dataset
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train_dataset = MsDataset.load('GeoGLUE',
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subset_name='GeoTES-rerank',
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split='train',
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namespace='damo')
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dev_dataset = MsDataset.load('GeoGLUE',
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subset_name='GeoTES-rerank',
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split='validation',
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namespace='damo')
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train_ds = train_dataset['train']
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dev_ds = dev_dataset['validation']
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model_id = 'damo/mgeo_backbone_chinese_base'
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self.finetune(model_id=model_id,
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train_dataset=train_ds,
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eval_dataset=dev_ds,
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cfg_modify_fn=cfg_modify_fn,
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name=Trainers.mgeo_ranking_trainer)
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output_dir = os.path.join(self.tmp_dir, ModelFile.TRAIN_OUTPUT_DIR)
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print(f'model is saved to {output_dir}')
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@unittest.skipUnless(test_level() >= 1, 'skip test in current test level')
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def test_finetune_geoeag(self):
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def cfg_modify_fn(cfg):
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cfg.task = Tasks.sentence_similarity
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cfg['preprocessor'] = {'type': Preprocessors.sen_sim_tokenizer}
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cfg.train.dataloader.batch_size_per_gpu = 64
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cfg.evaluation.dataloader.batch_size_per_gpu = 64
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cfg.train.optimizer.lr = 2e-5
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cfg.train.max_epochs = 1
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cfg.train.train_iters_per_epoch = 10
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cfg.evaluation.val_iters_per_epoch = 10
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cfg['dataset'] = {
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'train': {
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'labels': ['not_match', 'partial_match', 'exact_match'],
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'first_sequence': 'sentence1',
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'second_sequence': 'sentence2',
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'label': 'label',
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'sequence_length': 128
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}
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}
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cfg['evaluation']['metrics'] = 'seq-cls-metric'
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cfg.train.hooks = [{
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'type': 'CheckpointHook',
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'interval': 1
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}, {
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'type': 'TextLoggerHook',
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'interval': 100
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}, {
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'type': 'IterTimerHook'
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}, {
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'type': 'EvaluationHook',
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'by_epoch': True
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}]
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cfg.train.lr_scheduler.total_iters = int(
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len(train_dataset) / 32) * cfg.train.max_epochs
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return cfg
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# load dataset
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train_dataset = MsDataset.load('GeoGLUE',
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subset_name='GeoEAG',
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split='train',
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namespace='damo')
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dev_dataset = MsDataset.load('GeoGLUE',
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subset_name='GeoEAG',
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split='validation',
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namespace='damo')
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model_id = 'damo/mgeo_backbone_chinese_base'
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self.finetune(model_id=model_id,
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train_dataset=train_dataset['train'],
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eval_dataset=dev_dataset['validation'],
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cfg_modify_fn=cfg_modify_fn,
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name='nlp-base-trainer')
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output_dir = os.path.join(self.tmp_dir, ModelFile.TRAIN_OUTPUT_DIR)
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print(f'model is saved to {output_dir}')
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@unittest.skipUnless(test_level() >= 1, 'skip test in current test level')
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def test_finetune_geoeta(self):
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def cfg_modify_fn(cfg):
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cfg.task = 'token-classification'
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cfg['dataset'] = {
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'train': {
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'labels': label_enumerate_values,
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'first_sequence': 'tokens',
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'label': 'ner_tags',
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'sequence_length': 128
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}
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}
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cfg['preprocessor'] = {
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'type': 'token-cls-tokenizer',
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'padding': 'max_length'
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}
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cfg.train.max_epochs = 1
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cfg.train.dataloader.batch_size_per_gpu = 32
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cfg.train.train_iters_per_epoch = 10
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cfg.evaluation.val_iters_per_epoch = 10
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cfg.train.optimizer.lr = 3e-5
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cfg.train.hooks = [{
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'type': 'CheckpointHook',
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'interval': 1
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}, {
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'type': 'TextLoggerHook',
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'interval': 100
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}, {
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'type': 'IterTimerHook'
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}, {
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'type': 'EvaluationHook',
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'by_epoch': True
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}]
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cfg.train.lr_scheduler.total_iters = int(
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len(train_dataset) / 32) * cfg.train.max_epochs
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return cfg
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def get_label_list(labels):
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unique_labels = set()
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for label in labels:
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unique_labels = unique_labels | set(label)
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label_list = list(unique_labels)
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label_list.sort()
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return label_list
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# load dataset
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train_dataset = MsDataset.load('GeoGLUE',
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subset_name='GeoETA',
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split='train',
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namespace='damo')
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dev_dataset = MsDataset.load('GeoGLUE',
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subset_name='GeoETA',
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split='validation',
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namespace='damo')
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label_enumerate_values = get_label_list(
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train_dataset._hf_ds['train']['ner_tags'] +
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dev_dataset._hf_ds['validation']['ner_tags'])
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model_id = 'damo/mgeo_backbone_chinese_base'
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self.finetune(model_id=model_id,
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train_dataset=train_dataset['train'],
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eval_dataset=dev_dataset['validation'],
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cfg_modify_fn=cfg_modify_fn,
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name='nlp-base-trainer')
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output_dir = os.path.join(self.tmp_dir, ModelFile.TRAIN_OUTPUT_DIR)
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print(f'model is saved to {output_dir}')
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if __name__ == '__main__':
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unittest.main()
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