RNN isn't learning, unsure what I'm doing wrong

nlp
7

To be specific, if I swap out my RNN above with the following model, it gives me 90% accuracy on the same data set with all the same hyperparameters.

class SWEM(nn.Module):
    def __init__(self, vocab_size, embedding_size, hidden_dim, num_outputs):
        super().__init__()
        self.embedding = nn.Embedding(vocab_size, embedding_size)
        self.fc1 = nn.Linear(embedding_size, hidden_dim)
        self.fc2 = nn.Linear(hidden_dim, num_outputs)

    def forward(self, x):
        embed = self.embedding(x)
        embed_mean = torch.mean(embed, dim=0)
        h = self.fc1(embed_mean)
        h = torch.nn.functional.relu(h)
        h = self.fc2(h)
        return h

So this, to me, implies one of two things: either the RNN code I have has a bug, or it can’t cope with the input data somehow. The input x consists of padded sequences, collated in this way by the DataLoader:

def collator(batch):
    labels = torch.tensor([example[0] for example in batch])
    sentences = [example[1] for example in batch]
    data = pad_sequence(sentences)
    return [data, labels]

The data itself is the ag_news dataset in CSV form, looking like this: AG News Classification Dataset | Kaggle