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#
# 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.
"""ESMC model configuration."""
from transformers.configuration_utils import PretrainedConfig
class ESMCConfig(PretrainedConfig):
"""
This is the configuration class to store the configuration of a [`ESMCModel`]. It is used to
instantiate an ESMC model according to the specified arguments, defining the model 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 64):
Vocabulary size of the ESMC model. Defines the number of different amino acid tokens that
can be represented by the ``input_ids`` passed to [`ESMCModel`].
d_model (`int`, *optional*, defaults to 2560):
Dimensionality of the encoder layers and the pooler layer.
n_heads (`int`, *optional*, defaults to 40):
Number of attention heads for each attention layer in the Transformer encoder.
n_layers (`int`, *optional*, defaults to 80):
Number of hidden layers in the Transformer encoder.
pad_token_id (`int`, *optional*, defaults to 1):
Index of the padding token in the vocabulary (``"<pad>"``).
mask_token_id (`int`, *optional*, defaults to 32):
Index of the mask token in the vocabulary (``"<mask>"``), used for masked language modelling.
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated normal initialiser for weight matrix initialisation.
classifier_dropout (`float`, *optional*, defaults to 0.1):
Dropout ratio for the classification head.
Examples:
```python
>>> from transformers import ESMCConfig, ESMCModel
>>> # Initializing an ESMC EvolutionaryScale/esmc-600m-2024-12 style configuration
>>> configuration = ESMCConfig()
>>> # Initializing a model (with random weights) from the EvolutionaryScale/esmc-600m-2024-12 style configuration
>>> model = ESMCModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```
"""
model_type = "esmc"
def __init__(
self,
vocab_size: int = 64,
d_model: int = 2560,
n_heads: int = 40,
n_layers: int = 80,
pad_token_id: int = 1,
mask_token_id: int = 32,
initializer_range: float = 0.02,
classifier_dropout: float = 0.1,
**kwargs,
):
super().__init__(
pad_token_id=pad_token_id, mask_token_id=mask_token_id, **kwargs
)
self.vocab_size = vocab_size
self.d_model = d_model
self.n_heads = n_heads
self.n_layers = n_layers
self.initializer_range = initializer_range
self.classifier_dropout = classifier_dropout
self.tie_word_embeddings = False
__all__ = ["ESMCConfig"]
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