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from transformers.models.gpt2.configuration_gpt2 import GPT2Config
class BackpackGPT2Config(GPT2Config):
"""
This is the configuration class to store the configuration of a [`GPT2Model`] or a [`TFGPT2Model`]. It is used to
instantiate a Backpack GPT-2 model according to the specified arguments, defining the model architecture.
Configuration objects inherit from [`GPT2Config`] and can be used to control the model outputs. Read the
documentation from [`GPT2Config`] for more information.
Args:
num_senses (`int`, *optional*, defaults to 16):
The number of sense vectors to define for each word.
sense_intermediate_scale (`int`, *optional*, defaults ot 4):
The hidden dimensionality of the sense vector network.
Example:
```python
>>> from transformers import BackpackGPT2Config, BackpackGPT2Model
>>> # Initializing a GPT2 configuration
>>> configuration = BackpackGPT2Config()
>>> # Initializing a model (with random weights) from the configuration
>>> model = BackpackGPT2Model(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
"""
def __init__(self,
vocab_size=50264,
num_senses=16,
sense_intermediate_scale=4,
n_positions=512,
scale_attn_by_inverse_layer_idx=True,
**kwargs,
):
self.num_senses = num_senses
self.sense_intermediate_scale = sense_intermediate_scale
super().__init__(vocab_size=vocab_size, n_positions=n_positions, scale_attn_by_inverse_layer_idx=scale_attn_by_inverse_layer_idx, **kwargs)
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