mirror of
https://github.com/modelscope/modelscope.git
synced 2025-12-18 09:17:43 +01:00
9926ad685b
1.Add getting labels from dataset in "text_classificationfinetune_text_classification.py" to simplify user's operation in flex training. Parameters "--num_labels" and "--labels" were removed in "run_train.sh". 2.In "chatglm6b / finetune.py", building dataset from file is necessary to support flex training. Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/13382745 * support getting labels from dataset in sbert text classification and building dataset from file in chatglm-6b * support getting labels from dataset in sbert text classification and building dataset from file in chatglm-6b * remove repetitive labels in a concise manner of using set * reserve parameter labels in finetune_text_classification * Merge branch 'master' of http://gitlab.alibaba-inc.com/Ali-MaaS/MaaS-lib reserve parameter labels in finetune_text_classification * Merge branch 'support_text_cls_labels_chatglm_json' reserve parameter labels in finetune_text_classification
111 lines
3.3 KiB
Python
111 lines
3.3 KiB
Python
import os
|
|
from dataclasses import dataclass, field
|
|
|
|
from modelscope import (EpochBasedTrainer, MsDataset, TrainingArgs,
|
|
build_dataset_from_file)
|
|
from modelscope.trainers import build_trainer
|
|
|
|
|
|
def set_labels(labels):
|
|
if isinstance(labels, str):
|
|
label_list = labels.split(',')
|
|
else:
|
|
unique_labels = set(labels)
|
|
label_list = list(unique_labels)
|
|
label_list.sort()
|
|
label_list = list(
|
|
map(lambda x: x if isinstance(x, str) else str(x), label_list))
|
|
return {label: id for id, label in enumerate(label_list)}
|
|
|
|
|
|
@dataclass(init=False)
|
|
class TextClassificationArguments(TrainingArgs):
|
|
|
|
first_sequence: str = field(
|
|
default=None,
|
|
metadata={
|
|
'help': 'The first sequence key of preprocessor',
|
|
'cfg_node': 'preprocessor.first_sequence'
|
|
})
|
|
|
|
second_sequence: str = field(
|
|
default=None,
|
|
metadata={
|
|
'help': 'The second sequence key of preprocessor',
|
|
'cfg_node': 'preprocessor.second_sequence'
|
|
})
|
|
|
|
label: str = field(
|
|
default=None,
|
|
metadata={
|
|
'help': 'The label key of preprocessor',
|
|
'cfg_node': 'preprocessor.label'
|
|
})
|
|
|
|
labels: str = field(
|
|
default=None,
|
|
metadata={
|
|
'help': 'The labels of the dataset',
|
|
'cfg_node': 'preprocessor.label2id',
|
|
'cfg_setter': set_labels,
|
|
})
|
|
|
|
preprocessor: str = field(
|
|
default=None,
|
|
metadata={
|
|
'help': 'The preprocessor type',
|
|
'cfg_node': 'preprocessor.type'
|
|
})
|
|
|
|
|
|
training_args = TextClassificationArguments().parse_cli()
|
|
config, args = training_args.to_config()
|
|
|
|
print(config, args)
|
|
|
|
|
|
def cfg_modify_fn(cfg):
|
|
if args.use_model_config:
|
|
cfg.merge_from_dict(config)
|
|
else:
|
|
cfg = config
|
|
if training_args.labels is None:
|
|
labels = train_dataset[training_args.label] + validation_dataset[
|
|
training_args.label]
|
|
cfg.merge_from_dict({'preprocessor.label2id': set_labels(labels)})
|
|
cfg.model['num_labels'] = len(cfg.preprocessor.label2id)
|
|
if cfg.evaluation.period.eval_strategy == 'by_epoch':
|
|
cfg.evaluation.period.by_epoch = True
|
|
if cfg.train.lr_scheduler.type == 'LinearLR':
|
|
cfg.train.lr_scheduler['total_iters'] = \
|
|
int(len(train_dataset) / cfg.train.dataloader.batch_size_per_gpu) * cfg.train.max_epochs
|
|
return cfg
|
|
|
|
|
|
if args.dataset_json_file is None:
|
|
train_dataset = MsDataset.load(
|
|
args.train_dataset_name,
|
|
subset_name=args.train_subset_name,
|
|
split=args.train_split,
|
|
namespace=args.train_dataset_namespace)
|
|
validation_dataset = MsDataset.load(
|
|
args.val_dataset_name,
|
|
subset_name=args.val_subset_name,
|
|
split=args.val_split,
|
|
namespace=args.val_dataset_namespace)
|
|
else:
|
|
train_dataset, validation_dataset = build_dataset_from_file(
|
|
args.dataset_json_file)
|
|
|
|
kwargs = dict(
|
|
model=args.model,
|
|
model_revision=args.model_revision,
|
|
train_dataset=train_dataset,
|
|
eval_dataset=validation_dataset,
|
|
seed=args.seed,
|
|
cfg_modify_fn=cfg_modify_fn)
|
|
|
|
os.environ['LOCAL_RANK'] = str(args.local_rank)
|
|
trainer: EpochBasedTrainer = build_trainer(name='trainer', default_args=kwargs)
|
|
trainer.train()
|