# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 # This file was automatically generated from src/transformers/models/eurobert/modular_eurobert.py. # Do NOT edit this file manually as any edits will be overwritten by the generation of # the file from the modular. If any change should be done, please apply the change to the # modular_eurobert.py file directly. One of our CI enforces this. # 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 # Copyright 2025 Nicolas Boizard, Duarte M. Alves, Hippolyte Gisserot-Boukhlef and the EuroBert team. All rights reserved. # # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from ...modeling_rope_utils import RopeParameters from ..llama import LlamaConfig class EuroBertConfig(LlamaConfig): r""" This is the configuration class to store the configuration of a [`EuroBertModel`]. It is used to instantiate an EuroBert model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of [EuroBERT/EuroBERT-210m](https://huggingface.co/EuroBERT/EuroBERT-210m). 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 128256): Vocabulary size of the EuroBert model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`EuroBertModel`] hidden_size (`int`, *optional*, defaults to 768): Dimensionality of the encoder layers and the pooler layer. intermediate_size (`int`, *optional*, defaults to 3072): Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder. 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. num_key_value_heads (`int`, *optional*): This is the number of key_value heads that should be used to implement Grouped Query Attention. If `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if `num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. When converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed by meanpooling all the original heads within that group. For more details, check out [this paper](https://huggingface.co/papers/2305.13245). If it is not specified, will default to `num_attention_heads`. hidden_act (`str` or `function`, *optional*, defaults to `"silu"`): The non-linear activation function (function or string) in the encoder and pooler. max_position_embeddings (`int`, *optional*, defaults to 8192): The maximum sequence length that this model might ever be used with. EuroBert supports up to 8192 tokens, EuroBert-pretrained up to 2048. initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. rms_norm_eps (`float`, *optional*, defaults to 1e-05): The epsilon used by the rms normalization layers. bos_token_id (`int`, *optional*, defaults to 128000): Beginning of stream token id. eos_token_id (`int`, *optional*, defaults to 128001): End of stream token id. pad_token_id (`int`, *optional*, defaults to 128001): Padding token id. mask_token_id (`int`, *optional*, defaults to 128002): Mask token id. pretraining_tp (`int`, *optional*, defaults to 1): Experimental feature. Tensor parallelism rank used during pretraining. Please refer to [this document](https://huggingface.co/docs/transformers/main/perf_train_gpu_many#tensor-parallelism) to understand more about it. This value is necessary to ensure exact reproducibility of the pretraining results. Please refer to [this issue](https://github.com/pytorch/pytorch/issues/76232). tie_word_embeddings (`bool`, *optional*, defaults to `False`): Whether to tie weight embeddings rope_parameters (`RopeParameters`, *optional*): Dictionary containing the configuration parameters for the RoPE embeddings. The dictionary should contain a value for `rope_theta` and optionally parameters used for scaling in case you want to use RoPE with longer `max_position_embeddings`. attention_bias (`bool`, *optional*, defaults to `False`): Whether to use a bias in the query, key, value and output projection layers during self-attention. attention_dropout (`float`, *optional*, defaults to 0.0): The dropout ratio for the attention probabilities. mlp_bias (`bool`, *optional*, defaults to `False`): Whether to use a bias in up_proj, down_proj and gate_proj layers in the MLP layers. head_dim (`int`, *optional*): The attention head dimension. If None, it will default to hidden_size // num_attention_heads classifier_pooling (`str`, *optional*, defaults to `"late"`): The pooling strategy to use for the classifier. Can be one of ['bos', 'mean', 'late']. ```python >>> from transformers import EuroBertModel, EuroBertConfig >>> # Initializing a EuroBert eurobert-base style configuration >>> configuration = EuroBertConfig() >>> # Initializing a model from the eurobert-base style configuration >>> model = EuroBertModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "eurobert" def __init__( self, vocab_size=128256, hidden_size=768, intermediate_size=3072, num_hidden_layers=12, num_attention_heads=12, num_key_value_heads=None, hidden_act="silu", max_position_embeddings=8192, initializer_range=0.02, rms_norm_eps=1e-05, bos_token_id=128000, eos_token_id=128001, pad_token_id=128001, mask_token_id=128002, pretraining_tp=1, tie_word_embeddings=False, rope_parameters: RopeParameters | dict[str, RopeParameters] | None = None, attention_bias=False, attention_dropout=0.0, mlp_bias=False, head_dim=None, classifier_pooling="late", **kwargs, ): if num_key_value_heads is None: num_key_value_heads = num_attention_heads kwargs.pop("use_cache", None) # use_cache=True is not supported for EuroBert super().__init__( vocab_size=vocab_size, hidden_size=hidden_size, intermediate_size=intermediate_size, num_hidden_layers=num_hidden_layers, num_attention_heads=num_attention_heads, num_key_value_heads=num_key_value_heads, hidden_act=hidden_act, max_position_embeddings=max_position_embeddings, initializer_range=initializer_range, rms_norm_eps=rms_norm_eps, use_cache=False, bos_token_id=bos_token_id, eos_token_id=eos_token_id, pad_token_id=pad_token_id, pretraining_tp=pretraining_tp, tie_word_embeddings=tie_word_embeddings, rope_parameters=rope_parameters, attention_bias=attention_bias, attention_dropout=attention_dropout, mlp_bias=mlp_bias, head_dim=head_dim, **kwargs, ) self.mask_token_id = mask_token_id self.classifier_pooling = classifier_pooling self.is_causal = False __all__ = ["EuroBertConfig"]