of course,
Data:
input: [lst[str]] list of list of strings (tokens),  [Batch Size,  Max Seq Len]  = > [128, 173]
ex: [ ["This", "is", "the", "first", "homework"], ["Barack", "Obama", "was", "elected"] ]
output: same list of list of strings (Labels),  [Batch Size,  Max Seq Len]  = > [128, 173]
ex: [ ["O", "O", "O", "O", "O"], ["PER", "PER", "O", "O"] ]
Hyperparams:
loss_function = nn.CrossEntropy(ignore_index=0)
Optimizer = optim.Adam(model.params()) # default args
char_vocab_size, vocab_size = len(char_vocab), len(vocab) # chars_vocab 767, words_vocab 91584
hidden_dim, char_hidden_dim = 256, 256
embedding_dim, char_embedding_dim = 300, 300
num_classes = len(label_vocab) # 5 including "<PAD>" token
bidirectional = False
num_layers = 1
dropout = 0.4
embeddings = pretrained_embeddings # None
batch_size = 128
epochs = 10

Other libraries used: tqdm (progress bars)