| from transformers import PretrainedConfig | |
| class Seq2LabelsConfig(PretrainedConfig): | |
| r""" | |
| This is the configuration class to store the configuration of a [`Seq2LabelsModel`]. It is used to | |
| instantiate a Seq2Labels model according to the specified arguments, defining the model architecture. Instantiating a | |
| configuration with the defaults will yield a similar configuration to that of the Seq2Labels architecture. | |
| Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the | |
| documentation from [`PretrainedConfig`] for more information. | |
| Args: | |
| vocab_size (`int`, *optional*, defaults to 30522): | |
| Vocabulary size of the BERT model. Defines the number of different tokens that can be represented by the | |
| `inputs_ids` passed when calling [`BertModel`] or [`TFBertModel`]. | |
| pretrained_name_or_path (`str`, *optional*, defaults to `bert-base-cased`): | |
| Pretrained BERT-like model path | |
| load_pretrained (`bool`, *optional*, defaults to `False`): | |
| Whether to load pretrained model from `pretrained_name_or_path` | |
| use_cache (`bool`, *optional*, defaults to `True`): | |
| Whether or not the model should return the last key/values attentions (not used by all models). Only | |
| relevant if `config.is_decoder=True`. | |
| predictor_dropout (`float`, *optional*): | |
| The dropout ratio for the classification head. | |
| special_tokens_fix (`bool`, *optional*, defaults to `False`): | |
| Whether to add additional tokens to the BERT's embedding layer. | |
| Examples: | |
| ```python | |
| >>> from transformers import BertModel, BertConfig | |
| >>> # Initializing a Seq2Labels style configuration | |
| >>> configuration = Seq2LabelsConfig() | |
| >>> # Initializing a model from the bert-base-uncased style configuration | |
| >>> model = Seq2LabelsModel(configuration) | |
| >>> # Accessing the model configuration | |
| >>> configuration = model.config | |
| ```""" | |
| model_type = "bert" | |
| def __init__( | |
| self, | |
| pretrained_name_or_path="bert-base-cased", | |
| vocab_size=15, | |
| num_detect_classes=4, | |
| load_pretrained=False, | |
| initializer_range=0.02, | |
| pad_token_id=0, | |
| use_cache=True, | |
| predictor_dropout=0.0, | |
| special_tokens_fix=False, | |
| label_smoothing=0.0, | |
| **kwargs | |
| ): | |
| super().__init__(pad_token_id=pad_token_id, **kwargs) | |
| self.vocab_size = vocab_size | |
| self.num_detect_classes = num_detect_classes | |
| self.pretrained_name_or_path = pretrained_name_or_path | |
| self.load_pretrained = load_pretrained | |
| self.initializer_range = initializer_range | |
| self.use_cache = use_cache | |
| self.predictor_dropout = predictor_dropout | |
| self.special_tokens_fix = special_tokens_fix | |
| self.label_smoothing = label_smoothing | |