language/expertise_embedding.py

import torch
import torch.nn as nn


class ExpertiseEmbedding(nn.Module):
    """
    Transforms sentence encoder output into an expertise feature space.
    """

    def __init__(self, input_dim: int = 128, hidden_dim: int = 128):
        super().__init__()

        self.network = nn.Sequential(
            nn.Linear(input_dim, hidden_dim),
            nn.ReLU(),
            nn.LayerNorm(hidden_dim)
        )

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        return self.network(x)