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model parameters

model = BertClassifier(bert_model='bert-base-uncased',
                       label_list=None, 
                       num_mlp_hiddens=500,
                       num_mlp_layers=0,
                       epochs=3,
                       max_seq_length=128,
                       train_batch_size=32,
                       eval_batch_size=8,
                       learning_rate=2e-5,
                       warmup_proportion=0.1,
                       gradient_accumulation_steps=1,
                       fp16=False,
                       loss_scale=0,
                       local_rank=-1,
                       use_cuda=True,
                       random_state=42,
                       validation_fraction=0.1,
                       logfile='bert_sklearn.log')

bert model options

bert_model: one of Google's , SciBERT, or BioBERT pretrained models:

Google AI's pretrained BERT models See pytorch-pretrained-BERT doc for more info.

  • 'bert-base-uncased' (default)
  • 'bert-large-uncased'
  • 'bert-base-cased'
  • 'bert-large-cased'
  • 'bert-base-multilingual-uncased'
  • 'bert-base-multilingual-cased'
  • 'bert-base-chinese'

SciBERT pretrained BERT models See SciBERT github and paper for more info.

  • 'scibert-scivocab-uncased'
  • 'scibert-scivocab-cased'
  • 'scibert-basevocab-uncased'
  • 'scibert-basevocab-cased'

BioBERT pretrained BERT models See BioBERT github and paper for more info.

  • 'biobert-base-cased'
  • 'biobert-v1.1-pubmed-base-cased'
  • 'biobert-v1.0-pubmed-base-cased'
  • 'biobert-v1.0-pubmed-pmc-base-cased'
  • 'biobert-v1.0-pmc-base-cased'

final classifier/regressor options

label_list: list of classifier labels. If None, then the labels will be inferred from training data in model.fit(). Default: None

num_mlp_layers: the number of mlp layers in final mlp classifier/regressor. If set to 0, then defaults to the linear classifier/regresor in the original Google paper and code. Default: 0

num_mlp_hiddens: the number of hidden neurons in each layer of the mlp. Default: 500

ignore_label: labels to be excluded when calculating f1 for BertTokenClassifier. Default: None

training/finetuning options

learning_rate: inital learning rate of Bert Optimizer. Default: 2e-5

epochs: number of (finetune) training epochs. Default: 3

warmup_proportion: proportion of training to perform learning rate warmup . Default: 0.1

max_seq_length: maximum length of input text sequence. Default: 128

train_batch_size: batch size for training. Default: 32

validation_fraction: fraction of training set to use for validation during finetuning. Deafult: 0.1

eval_batch_size: batch_size for validation. Default: 8

gradient_accumulation_steps: number of update steps to accumulate before performing a backward/update pass. Default: 1
fp16: whether to use 16-bit float precision instead of 32-bit. Nvidia apex must be installed to enable this option. Default: False

loss_scale: loss scaling to improve fp16 numeric stability. Only used when fp16 set to True. See pytorch-pretrained-BERT fpint16 section for more info. Default: False

Other

use_cuda: use GPU(s) if available. Default: True

random_state: random seed to initialize numpy and torch random number generators. Default: 42

logname: path name for logfile. Default: 'bert_sklearn.log'

local_rank: local_rank for distributed training on gpus. -1 indicates to train locally. Currently this parameter is only exposed in case someone may want to do this. Distrbuted training/inference has not been tested in this release. Default: -1