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# limitations under the License.
""" 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,
# pad_token_id=0,
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 |