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https://huggingface.co/spaces/CompactAI/AIFinder/resolve/main/model.py
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hf download hf://spaces/CompactAI/AIFinder/model.py
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curl -L -o model.py https://huggingface.co/spaces/CompactAI/AIFinder/resolve/main/model.py
913 Bytes
| """ | |
| AIFinder Neural Network | |
| Single-headed MLP: predicts provider only. | |
| """ | |
| import torch | |
| import torch.nn as nn | |
| class AIFinderNet(nn.Module): | |
| """Single-headed classifier: predicts provider only.""" | |
| def __init__( | |
| self, | |
| input_dim, | |
| num_providers, | |
| hidden_dim=1024, | |
| embed_dim=256, | |
| dropout=0.3, | |
| ): | |
| super().__init__() | |
| self.backbone = nn.Sequential( | |
| nn.Linear(input_dim, hidden_dim), | |
| nn.BatchNorm1d(hidden_dim), | |
| nn.ReLU(), | |
| nn.Dropout(dropout), | |
| nn.Linear(hidden_dim, embed_dim), | |
| nn.BatchNorm1d(embed_dim), | |
| nn.ReLU(), | |
| nn.Dropout(dropout), | |
| ) | |
| self.provider_head = nn.Linear(embed_dim, num_providers) | |
| def forward(self, x): | |
| h = self.backbone(x) | |
| provider_logits = self.provider_head(h) | |
| return provider_logits | |