This is what happens when training:
loading pretrained embeddings…
adding unk embedding
Conv_Attn(
(embed_drop): Dropout(p=0.2)
(embed): Embedding(121674, 100)
(conv): Conv1d(100, 500, kernel_size=(3,), stride=(1,), padding=(1,))
(U): Linear(in_features=500, out_features=4987, bias=True)
)