forked from mindspore-Ecosystem/mindspore
122 lines
3.9 KiB
YAML
122 lines
3.9 KiB
YAML
# Builtin Configurations(DO NOT CHANGE THESE CONFIGURATIONS unless you know exactly what you are doing)
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enable_modelarts: False
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# Url for modelarts
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data_url: ""
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train_url: ""
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checkpoint_url: ""
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# Path for local
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data_path: "/cache/data"
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output_path: "/cache/train"
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load_path: "/cache/checkpoint_path"
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device_target: "Ascend"
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enable_profiling: False
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# ==============================================================================
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description: "run_ner"
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assessment_method: "BF1"
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do_train: "false"
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do_eval: "false"
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use_crf: "false"
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device_id: 0
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epoch_num: 5
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train_data_shuffle: "true"
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eval_data_shuffle: "false"
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train_batch_size: 32
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eval_batch_size: 1
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vocab_file_path: ""
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label_file_path: ""
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save_finetune_checkpoint_path: "./ner_finetune/ckpt/"
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load_pretrain_checkpoint_path: ""
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load_finetune_checkpoint_path: ""
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train_data_file_path: ""
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eval_data_file_path: ""
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dataset_format: "mindrecord"
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schema_file_path: ""
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# export related
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export_batch_size: 1
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export_ckpt_file: ''
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export_file_name: 'bert_ner'
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file_format: 'AIR'
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optimizer_cfg:
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optimizer: 'Lamb'
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AdamWeightDecay:
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learning_rate: 0.00002 # 2e-5
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end_learning_rate: 0.0000000001 # 1e-10
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power: 1.0
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weight_decay: 0.00001 # 1e-5
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decay_filter: ['layernorm', 'bias']
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eps: 0.000001 # 1e-6
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Lamb:
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learning_rate: 0.00002 # 2e-5,
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end_learning_rate: 0.0000000001 # 1e-10
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power: 1.0
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weight_decay: 0.01
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decay_filter: ['layernorm', 'bias']
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Momentum:
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learning_rate: 0.00002 # 2e-5
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momentum: 0.9
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bert_net_cfg:
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seq_length: 128
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vocab_size: 21128
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hidden_size: 768
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num_hidden_layers: 12
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num_attention_heads: 12
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intermediate_size: 3072
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hidden_act: "gelu"
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hidden_dropout_prob: 0.1
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attention_probs_dropout_prob: 0.1
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max_position_embeddings: 512
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type_vocab_size: 2
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initializer_range: 0.02
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use_relative_positions: False
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dtype: mstype.float32
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compute_type: mstype.float16
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---
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# Help description for each configuration
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enable_modelarts: "Whether training on modelarts, default: False"
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data_url: "Url for modelarts"
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train_url: "Url for modelarts"
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data_path: "The location of the input data."
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output_path: "The location of the output file."
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device_target: "Running platform, choose from Ascend or CPU, and default is Ascend."
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enable_profiling: 'Whether enable profiling while training, default: False'
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assessment_method: "assessment_method include: [BF1, clue_benchmark, MF1], default is BF1"
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do_train: "Eable train, default is false"
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do_eval: "Eable eval, default is false"
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use_crf: "Use crf, default is false"
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device_id: "Device id, default is 0."
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epoch_num: "Epoch number, default is 5."
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train_data_shuffle: "Enable train data shuffle, default is true"
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eval_data_shuffle: "Enable eval data shuffle, default is false"
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train_batch_size: "Train batch size, default is 32"
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eval_batch_size: "Eval batch size, default is 1"
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vocab_file_path: "Vocab file path, used in clue benchmark"
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label_file_path: "label file path, used in clue benchmark"
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save_finetune_checkpoint_path: "Save checkpoint path"
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load_pretrain_checkpoint_path: "Load checkpoint file path"
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load_finetune_checkpoint_path: "Load checkpoint file path"
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train_data_file_path: "Data path, it is better to use absolute path"
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eval_data_file_path: "Data path, it is better to use absolute path"
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dataset_format: "Dataset format, support mindrecord or tfrecord"
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schema_file_path: "Schema path, it is better to use absolute path"
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export_batch_size: "export batch size."
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export_ckpt_file: "Bert ckpt file."
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export_file_name: "bert output air name."
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file_format: "file format"
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---
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# chocies
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device_target: ['Ascend', 'GPU']
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assessment_method: ["BF1", "clue_benchmark", "MF1"]
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do_train: ["true", "false"]
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do_eval: ["true", "false"]
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use_crf: ["true", "false"]
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train_data_shuffle: ["true", "false"]
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eval_data_shuffle: ["true", "false"]
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dataset_format: ["mindrecord", "tfrecord"]
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file_format: ["AIR", "ONNX", "MINDIR"]
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