  Here is the Python code based on the given instruction:
```python
import torch
import transformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
from thinc.api import Model, Optimizer, Adam, LearningRate
from thinc.api import batch_encode_plus, convert_inputs, convert_outputs
from thinc.api import evaluate_sequences, group_pairs_of_sequences
from thinc.api import to_gpu, to_cpu
from thinc.api import Model, Optimizer, Adam, LearningRate
from thinc.api import batch_encode_plus, convert_inputs, convert_outputs
from thinc.api import evaluate_sequences, group_pairs_of_sequences
from thinc.api import to_gpu, to_cpu

class Dataclass:
    def __init__(self, batch_encode_plus):
        self.batch_encode_plus = batch_encode_plus

def create_model(model_name):
    model = AutoModelForSequenceClassification.from_pretrained(model_name)
    return model

def create_tokenizer(model_name):
    tokenizer = AutoTokenizer.from_pretrained(model_name)
    return tokenizer

def create_transformer_tagger_model(model_name):
    model = create_model(model_name)
    tokenizer = create_tokenizer(model_name)
    return model, tokenizer

def train_model(model, tokenizer, dataset, epochs, batch_size, learning_rate, optimizer):
    model.to(device)
    tokenizer.to(device)
    model.train()
    for epoch in range(epochs):
        for batch in dataset:
            inputs = convert_inputs(batch, tokenizer)
            outputs = model(inputs)
            loss = outputs.loss
            loss.backward()
            optimizer.step()
            optimizer.zero_grad()
        print(f"Epoch {epoch+1}, Loss: {loss.item():.4f}")

def main():
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model_name = "bert-base-uncased"
    tokenizer = create_tokenizer(model_name)
    model = create_model(model_name)
    dataset = ... # load dataset
    epochs = 3
    batch_size = 32
    learning_rate = 1e-5
    optimizer = Adam(model.parameters(), lr=learning_rate)
    train_model(model, tokenizer, dataset, epochs, batch_size, learning_rate, optimizer)

if __name__ == "__main__":
    main()
```
Note that this code is just an example and may need to be modified to fit your specific use case. Additionally, the `create_model` and `create_tokenizer` functions are not included in the code snippet provided, but they can be found in the Huggingface Transformers library.