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from transformers import PretrainedConfig |
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class DebertaV2Config(PretrainedConfig): |
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model_type = "deberta-v2" |
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def __init__( |
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self, |
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vocab_size=128100, |
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hidden_size=1536, |
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sep_token_id=2, |
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mask_token_id=128000, |
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num_hidden_layers=24, |
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num_attention_heads=24, |
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intermediate_size=6144, |
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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=0, |
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initializer_range=0.02, |
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layer_norm_eps=1e-7, |
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relative_attention=False, |
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max_relative_positions=-1, |
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pad_token_id=0, |
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position_biased_input=True, |
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pos_att_type=None, |
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pooler_dropout=0, |
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pooler_hidden_act="gelu", |
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**kwargs, |
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): |
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super().__init__(**kwargs) |
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self.hidden_size = hidden_size |
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self.mask_token_id = mask_token_id |
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self.sep_token_id = sep_token_id |
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self.num_hidden_layers = num_hidden_layers |
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self.num_attention_heads = num_attention_heads |
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self.intermediate_size = intermediate_size |
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self.hidden_act = hidden_act |
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self.hidden_dropout_prob = hidden_dropout_prob |
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self.attention_probs_dropout_prob = attention_probs_dropout_prob |
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self.max_position_embeddings = max_position_embeddings |
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self.type_vocab_size = type_vocab_size |
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self.initializer_range = initializer_range |
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self.relative_attention = relative_attention |
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self.max_relative_positions = max_relative_positions |
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self.pad_token_id = pad_token_id |
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self.position_biased_input = position_biased_input |
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if isinstance(pos_att_type, str): |
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pos_att_type = [x.strip() for x in pos_att_type.lower().split("|")] |
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self.pos_att_type = pos_att_type |
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self.vocab_size = vocab_size |
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self.layer_norm_eps = layer_norm_eps |
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self.pooler_hidden_size = kwargs.get("pooler_hidden_size", hidden_size) |
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self.pooler_dropout = pooler_dropout |
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self.pooler_hidden_act = pooler_hidden_act |
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