The parameters of a transformer model fundamentally shape its power, efficiency, and overall behavior. These parameters define the model's composition, capacity, and learning ability. Unlike traditional RNN and CNN, transformers are completely dependent on the attention mechanism to attract global dependence between input and output. In this article, we will discover these parameters deeply, understand their significance, and see how they are used in the real world.
1. Structural Parameters
These define the architecture and capacity of the Transformer model.
Parameter | Description |
|---|
num_hidden_layers | Number of encoder/decoder blocks in the model |
|---|
hidden_size (d_model) | Dimensionality of token embeddings and hidden states |
|---|
num_attention_heads | Number of attention heads for multi-head self-attention |
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intermediate_size | Size of feed-forward hidden layer |
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vocab_size | Number of tokens in the vocabulary |
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max_position_embeddings | Maximum sequence length model can handle |
|---|
2. Training/Regularization Parameters
These affect how the model learns during training and help prevent overfitting.
Parameter | Description |
|---|
dropout_rate / attention_probs_dropout_prob | Dropout applied to attention probabilities and outputs |
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hidden_dropout_prob | Dropout applied to hidden layers |
|---|
initializer_range | Range for random weight initialization |
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layer_norm_eps | Small epsilon added for numerical stability during layer normalization |
|---|
3. Functional/Behavioral Parameters
These define how certain operations behave inside the model.
Parameter | Description |
|---|
activation_function | Activation used in feed-forward layers (relu, gelu, etc.) |
|---|
attention_type (in some custom models) | Defines attention mechanism (e.g., global/local) |
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type_vocab_size | Used for sentence classification tasks (e.g., next sentence prediction) |
|---|
1. Num_layers (or num_hidinden_layers)
This parameter defines the maximum sequence length the model is capable of processing Each layer has a self -emission mechanism and a forward nerve network. Increase in the number of layers often leads to better performance, but also increases calculation costs.
2. D_model (or hidde_size)
D_model represents the shape of built -in and hidden conditions. In Burt Base there is 768. A higher value of D<sub>model</sub> enables the model to learn more complex representations, but it also increases memory consumption.
3. Num_atinon_heads
Focus with several heads shares the input into several major subjects, so that the model can focus on different parts of the sentence at the same time. The total hidden form should be divisible with the number of heads.
This defines the alphabet of the stranger layer in each coder block. Usually it is larger than the D_model (eg, the 3072 in the Burt base for D_modell of 768).
5. Dropout_rite
Dropout used to regulate overfeating.These are randomly selected units from the network that are dropped during the training process
6. Vocab_sise
This indicates that unique token can understand models. It directly affects the size of the input and output that involves layers.
7. Max_position_mbeddings
Transformers do not naturally understand the order, so the condition involves added. This parameter determines the maximum sequence length that the model can process.
8. Activation_function
Transformers usually use relay or gallu as a non-related activation in foreign layers. GELU is often preferred in models like BERT and GPT due to its smooth activation curve, which enhances learning stability and performance
This parameter controls the distribution of initial loads. The small value as 0.02 ensures stable learning in the initial stages.
9. Layer_norm_eps
An appsillon value to avoid zero division in team normalization.
Code to Explore Parameters
Using the Hugging Face Transformers library, you can easily inspect and modify parameters:
Python
from transformers import BertConfig, BertModel
# Load the default BERT configuration
config = BertConfig()
print(config)
Output:
BertConfig {
"attention_probs_dropout_prob": 0.1,
"hidden_act": "gelu",
"hidden_size": 768,
"initializer_range": 0.02,
...
}
Use of the real world of parameters
In scenarios in the real world, these parameters are important:
- Reducing num_layers or hidden_size helps when distributing the model on the age unit.
- Increasing NUM_ATION_HEADS or intermediate_SES helps capture complex patterns in large datasets.
- Adjusting dropout_rett, depending on the data set size, helps to avoid overfitting.
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