| |
| """ Vietnamese model configuration""" |
| from transformers.configuration_utils import PretrainedConfig |
| from transformers.utils import logging |
|
|
| logger = logging.get_logger(__name__) |
|
|
|
|
| class VietnameseConfig(PretrainedConfig): |
| r""" |
| This is the configuration class to store the configuration of a [`VietnameseModel`] or a [`TFVietnameseModel`]. It is used to |
| instantiate a Vietnamese 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 Vietnamese |
| 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 Vietnamese model. Defines the number of different tokens that can be represented by the |
| `inputs_ids` passed when calling [`VietnameseModel`] or [`TFVietnameseModel`]. |
| hidden_size (`int`, *optional*, defaults to 768): |
| Dimensionality of the encoder layers and the pooler layer. |
| num_hidden_layers (`int`, *optional*, defaults to 12): |
| Number of hidden layers in the Transformer encoder. |
| num_attention_heads (`int`, *optional*, defaults to 12): |
| Number of attention heads for each attention layer in the Transformer encoder. |
| intermediate_size (`int`, *optional*, defaults to 3072): |
| Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder. |
| hidden_act (`str` or `Callable`, *optional*, defaults to `"gelu"`): |
| The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`, |
| `"relu"`, `"silu"` and `"gelu_Vietnamese"` are supported. |
| hidden_dropout_prob (`float`, *optional*, defaults to 0.1): |
| The dropout probability for all fully connected layers in the embeddings, encoder, and pooler. |
| attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1): |
| The dropout ratio for the attention probabilities. |
| max_position_embeddings (`int`, *optional*, defaults to 512): |
| The maximum sequence length that this model might ever be used with. Typically set this to something large |
| just in case (e.g., 512 or 1024 or 2048). |
| type_vocab_size (`int`, *optional*, defaults to 2): |
| The vocabulary size of the `token_type_ids` passed when calling [`VietnameseModel`] or [`TFVietnameseModel`]. |
| initializer_range (`float`, *optional*, defaults to 0.02): |
| The standard deviation of the truncated_normal_initializer for initializing all weight matrices. |
| layer_norm_eps (`float`, *optional*, defaults to 1e-12): |
| The epsilon used by the layer normalization layers. |
| position_embedding_type (`str`, *optional*, defaults to `"rope"`): |
| Type of position embedding. Choose one of `"absolute"`, `"rope"`. |
| rope_theta (`float`, *optional*, defaults to 10000.0): |
| The base period of the RoPE embeddings. |
| rope_scaling (`Dict`, *optional*): |
| Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports two scaling |
| strategies: linear and dynamic. Their scaling factor must be a float greater than 1. The expected format is |
| `{"type": strategy name, "factor": scaling factor}`. When using this flag, don't update |
| `max_position_embeddings` to the expected new maximum. See the following thread for more information on how |
| these scaling strategies behave: |
| https://www.reddit.com/r/LocalLLaMA/comments/14mrgpr/dynamically_scaled_rope_further_increases/. This is an |
| experimental feature, subject to breaking API changes in future versions. |
| classifier_dropout (`float`, *optional*): |
| The dropout ratio for the classification head. |
| Examples: |
| """ |
|
|
| model_type = "Vietnamese" |
|
|
| def __init__( |
| self, |
| vocab_size=30528, |
| hidden_size=768, |
| num_hidden_layers=12, |
| num_attention_heads=12, |
| intermediate_size=3072, |
| hidden_act="gelu", |
| hidden_dropout_prob=0.1, |
| attention_probs_dropout_prob=0.0, |
| max_position_embeddings=2048, |
| type_vocab_size=1, |
| initializer_range=0.02, |
| layer_norm_type='layer_norm', |
| layer_norm_eps=1e-12, |
| |
| position_embedding_type="rope", |
| rope_theta=10000.0, |
| rope_scaling=None, |
| classifier_dropout=None, |
| pack_qkv=True, |
| unpad_inputs=False, |
| use_memory_efficient_attention=False, |
| logn_attention_scale=False, |
| logn_attention_clip1=False, |
| **kwargs, |
| ): |
| super().__init__(**kwargs) |
|
|
| self.vocab_size = vocab_size |
| self.hidden_size = hidden_size |
| self.num_hidden_layers = num_hidden_layers |
| self.num_attention_heads = num_attention_heads |
| self.hidden_act = hidden_act |
| self.intermediate_size = intermediate_size |
| self.hidden_dropout_prob = hidden_dropout_prob |
| self.attention_probs_dropout_prob = attention_probs_dropout_prob |
| self.max_position_embeddings = max_position_embeddings |
| self.type_vocab_size = type_vocab_size |
| self.initializer_range = initializer_range |
| self.layer_norm_type = layer_norm_type |
| self.layer_norm_eps = layer_norm_eps |
| self.position_embedding_type = position_embedding_type |
| self.rope_theta = rope_theta |
| self.rope_scaling = rope_scaling |
| self.classifier_dropout = classifier_dropout |
|
|
| self.pack_qkv = pack_qkv |
| self.unpad_inputs = unpad_inputs |
| self.use_memory_efficient_attention = use_memory_efficient_attention |
| self.logn_attention_scale = logn_attention_scale |
| self.logn_attention_clip1 = logn_attention_clip1 |
