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import torch.nn as nn
from models.base_model import BaseModel
from typing import Any
import torch
class GRUModel(BaseModel):
def __init__(self, config: Any, tokenizer: Any):
super().__init__(config, tokenizer)
self.embedding = nn.Embedding(
num_embeddings=tokenizer.vocab_size,
embedding_dim=config.embedding_dim
)
self.gru = nn.GRU(
input_size=config.embedding_dim,
hidden_size=config.gru_units,
batch_first=True
)
self.dropout = nn.Dropout(config.dropout_rate)
self.fc1 = nn.Linear(config.gru_units, config.dense_units)
self.fc2 = nn.Linear(config.dense_units, 1)
self.relu = nn.ReLU()
def forward(self, x) -> torch.Tensor:
if isinstance(x, dict):
raise ValueError("GRUModel doesn't support BERT inputs")
embedded = self.embedding(x)
# GRU
_, hidden = self.gru(embedded)
hidden = hidden.squeeze(0)
# Fully connected
x = self.dropout(hidden)
x = self.relu(self.fc1(x))
x = self.fc2(x)
return x |