Add ner example for flextrain

Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/12835095
* update named_entity_recognition example

examples/pytorch/named_entity_recognition/finetune_named_entity_recognition.py

@@ -0,0 +1,97 @@
+import os
+from dataclasses import dataclass, field
+
+from adaseq.data.data_collators.base import build_data_collator
+from adaseq.data.dataset_manager import DatasetManager
+from adaseq.data.preprocessors.nlp_preprocessor import build_preprocessor
+from adaseq.training.default_trainer import DefaultTrainer as AdaSeqTrainer
+
+from modelscope import MsDataset, TrainingArgs, build_dataset_from_file
+
+
+@dataclass(init=False)
+class NamedEntityRecognitionArguments(TrainingArgs):
+    preprocessor: str = field(
+        default='sequence-labeling-preprocessor',
+        metadata={
+            'help': 'The preprocessor type',
+            'cfg_node': 'preprocessor.type'
+        })
+
+    sequence_length: int = field(
+        default=150,
+        metadata={
+            'cfg_node': 'preprocessor.max_length',
+            'help': 'The parameters for train dataset',
+        })
+
+    data_collator: str = field(
+        default='SequenceLabelingDataCollatorWithPadding',
+        metadata={
+            'cfg_node': 'data_collator',
+            'help': 'The type of data collator',
+        })
+
+    dropout: float = field(
+        default=0.0,
+        metadata={
+            'cfg_node': 'model.dropout',
+            'help': 'Dropout rate',
+        })
+
+    use_crf: bool = field(
+        default=True,
+        metadata={
+            'cfg_node': 'model.use_crf',
+            'help': 'Whether to add a CRF decoder layer',
+        })
+
+    crf_lr: float = field(
+        default=5.0e-1, metadata={
+            'help': 'Learning rate for CRF layer',
+        })
+
+
+training_args = NamedEntityRecognitionArguments().parse_cli()
+config, args = training_args.to_config()
+print(args)
+
+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).to_hf_dataset()
+    validation_dataset = MsDataset.load(
+        args.val_dataset_name,
+        subset_name=args.val_subset_name,
+        split=args.val_split,
+        namespace=args.val_dataset_namespace).to_hf_dataset()
+else:
+    train_dataset, validation_dataset = build_dataset_from_file(
+        args.dataset_json_file)
+dm = DatasetManager({
+    'train': train_dataset,
+    'valid': validation_dataset
+}, labels={'type': 'count_span_labels'})  # yapf: disable
+
+config.preprocessor.model_dir = args.model
+config.model.embedder = {'model_name_or_path': args.model}
+preprocessor = build_preprocessor(config.preprocessor, labels=dm.labels)
+config.model.id_to_label = preprocessor.id_to_label
+data_collator = build_data_collator(preprocessor.tokenizer,
+                                    dict(type=config.data_collator))
+config.train.optimizer.param_groups = [{'regex': 'crf', 'lr': args.crf_lr}]
+
+cfg_file = os.path.join(config.train.work_dir, 'config.yaml')
+config.dump(cfg_file)
+
+kwargs = dict(
+    cfg_file=cfg_file,
+    work_dir=config.train.work_dir,
+    dataset_manager=dm,
+    data_collator=data_collator,
+    preprocessor=preprocessor)
+
+trainer = AdaSeqTrainer(**kwargs)
+trainer.train()

examples/pytorch/named_entity_recognition/run_train.sh

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+PYTHONPATH=. python examples/pytorch/named_entity_recognition/finetune_named_entity_recognition.py \
+    --task 'named-entity-recognition' \
+    --work_dir './tmp' \
+    --model_type 'sequence-labeling-model' \
+    --model 'damo/nlp_structbert_backbone_base_std' \
+    --dropout 0.1 \
+    --use_crf true \
+    --train_dataset_name 'resume_ner' \
+    --train_dataset_namespace 'damo' \
+    --train_split 'train' \
+    --val_dataset_name 'resume_ner' \
+    --val_dataset_namespace 'damo' \
+    --val_split 'dev' \
+    --preprocessor 'sequence-labeling-preprocessor' \
+    --sequence_length 150 \
+    --data_collator 'SequenceLabelingDataCollatorWithPadding' \
+    --max_epochs 5 \
+    --per_device_train_batch_size 16 \
+    --train_data_worker 0 \
+    --eval_data_worker 0 \
+    --lr 5.0e-5 \
+    --lr_scheduler LinearLR \
+    --lr_scheduler_params 'start_factor=1.0,end_factor=0.0,total_iters=5' \
+    --eval_metrics ner-metric \
+    --save_best_checkpoint true \
+    --metric_for_best_model f1 \