Architectures / NLP/LLM

📖 BERT Base

BERT-Base encoder — bidirectional MHA (12 heads, 768D), no causal mask, 30K vocab

Every number on this page is computed from the graph by the same functions the app runs, not written by hand.

When to pick it

Structure

11 layers. Output shapes are propagated from the input shape, batch dimension excluded.

What the verifier says

The same 41 structural checks that run on every edit in the app, on this graph.

The PyTorch it exports

Generated from the graph above. First 46 lines; the app exports the whole file, plus the training loop, the data contract and a deploy bundle.

# Architecture designed with Neurarch: https://neurarch.com
# PyTorch: compatible with Python 3.8+ and torch>=1.12
# Colab: pip install torch torchvision  (usually pre-installed)

import torch
import torch.nn as nn
import torch.nn.functional as F
from typing import Tuple

class BERTBase(nn.Module):
    def __init__(self):
        super().__init__()

        self.embedding_1 = nn.Embedding(30522, 768)
        self.layerNorm_1 = nn.LayerNorm(768)
        self.dropout_1 = nn.Dropout(p=0.1)
        self.multiHeadAttention_1 = nn.MultiheadAttention(embed_dim=768, num_heads=12, batch_first=True)
        self.layerNorm_2 = nn.LayerNorm(768)
        self.feedForward_1 = nn.Sequential(
            nn.Linear(768, 3072),
            nn.ReLU(),
            nn.Linear(3072, 768)
        )
        self.layerNorm_3 = nn.LayerNorm(768)
        self.linear_1 = nn.Linear(768, 768)

    def forward(self, x):
        # input_ids shape: [1,512]
        embedding_ng_tok = self.embedding_1(x)
        # positionalEncoding: add positional encoding externally (e.g. sinusoidal or learned PE)
        layer_norm_rm_emb = self.layerNorm_1(embedding_ng_tok)
        dropout_ut_emb = self.dropout_1(layer_norm_rm_emb)
        multi_head_attention_mha_1 = self.multiHeadAttention_1(dropout_ut_emb, dropout_ut_emb, dropout_ut_emb)[0]
        layer_norm_Norm_1 = self.layerNorm_2(multi_head_attention_mha_1)
        feed_forward_ward_1 = self.feedForward_1(layer_norm_Norm_1)
        layer_norm_Norm_2 = self.layerNorm_3(feed_forward_ward_1)
        linear_r_pool = self.linear_1(layer_norm_Norm_2)
        # Output
        return linear_r_pool


if __name__ == '__main__':
    model = BERTBase()
    model.eval()

    x = torch.randint(0, 50000, (1, 512))  # (batch, features)

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