 Here's the Python code based on your instructions. Note that you'll need to install the necessary libraries (Transformers, Thinc, Torch, and Dataclasses) before running this code.

```python
from dataclasses import dataclass
from transformers import AutoModelForTokenClassification, AutoTokenizer, Trainer, TrainingArguments
import torch
import thinc

@dataclass
class TransformerTaggerOutput:
    labels: torch.LongTensor
    inputs: torch.LongTensor
    attention_mask: torch.LongTensor
    token_ids: torch.LongTensor
    token_type_ids: torch.LongTensor

def create_transformer_tagger_model(model_name_or_path):
    tokenizer = AutoTokenizer.from_pretrained(model_name_or_path)
    model = AutoModelForTokenClassification.from_pretrained(model_name_or_path, num_labels=len(tokenizer.get_vocab()))
    return model, tokenizer

def create_transformer_tokenizer(model_name_or_path):
    return AutoTokenizer.from_pretrained(model_name_or_path)

def create_transformer_model(model_name_or_path):
    return AutoModelForTokenClassification.from_pretrained(model_name_or_path)

def convert_inputs(examples):
    return {key: torch.tensor(val) for key, val in examples.to_dict().items()}

def convert_outputs(prediction):
    return {key: prediction[key].argmax(-1) for key in prediction.logits.keys()}

def group_minibatches(examples, batch_size):
    return [examples[i:i + batch_size] for i in range(0, len(examples), batch_size)]

def evaluate_sequences(predictions, labels):
    return (predictions == labels).sum().item()

def check_cuda_available():
    return torch.cuda.is_available()

def main():
    model_name_or_path = "bert-base-uncased"
    learning_rate = 2e-5
    batch_size = 8
    num_train_epochs = 3
    max_seq_length = 128

    if check_cuda_available():
        device = torch.device("cuda")
    else:
        device = torch.device("cpu")

    training_args = TrainingArguments(
        output_dir="./results",
        num_train_epochs=num_train_epochs,
        per_device_train_batch_size=batch_size,
        learning_rate=learning_rate,
        weight_decay=0.01,
        logging_dir="./logs",
        logging_steps=10,
        save_steps=1000,
        evaluation_strategy="epoch",
    )

    model, tokenizer = create_transformer_tagger_model(model_name_or_path)
    model.to(device)

    # Load your dataset here and convert it to the required format
    # ...

    trainer = Trainer(
        model=model,
        args=training_args,
        train_dataset=train_dataset,
        eval_dataset=eval_dataset,
        compute_metrics=lambda pred: {"accuracy": evaluate_sequences(pred.logits, pred.label_ids)},
    )

    trainer.train()

if __name__ == "__main__":
    main()
```

This code defines the necessary functions and classes, checks for GPU availability, sets up the training arguments, and trains the model using Huggingface's Transformers library. You'll need to replace the commented-out section where the dataset is loaded with your own code to load your specific dataset.