 Here is the Python code based on your instructions:

```python
import torch
from transformers import AutoModelForTokenClassification, AutoTokenizer, Trainer, TrainingArguments
from thinc.api import Dense, NN
from thinc.data import Dataset, Batch
from thinc.util import to_gpu

class TransformerOutput(Dataclass):
    input_ids: torch.LongTensor
    attention_mask: torch.LongTensor
    token_type_ids: torch.LongTensor
    labels: torch.LongTensor

def create_transformer_tagger_model(config):
    model = AutoModelForTokenClassification.from_pretrained(config.model_name, num_labels=config.num_labels)
    return model

def create_transformer_tokenizer(config):
    tokenizer = AutoTokenizer.from_pretrained(config.model_name)
    return tokenizer

def transformer_inputs_to_thinc(examples):
    input_ids = torch.tensor([example.input_ids])
    attention_mask = torch.tensor([example.attention_mask])
    token_type_ids = torch.tensor([example.token_type_ids])
    return Batch(input_ids, attention_mask, token_type_ids)

def transformer_outputs_to_thinc(output):
    labels = torch.tensor(output.logits.argmax(-1))
    return Batch(labels)

def group_pairs_into_minibatches(examples, batch_size):
    return Dataset(list(zip(*[iter(examples)] * batch_size)))

def create_transformer_model(config):
    model = create_transformer_tagger_model(config)
    if torch.cuda.is_available():
        model = to_gpu(model)
    return model

def evaluate_sequences(model, examples):
    outputs = model(**examples.to_dict())
    return transformer_outputs_to_thinc(outputs)

def main():
    config = TrainingArguments(
        output_dir='./results',
        num_train_epochs=3,
        per_device_train_batch_size=16,
        per_device_eval_batch_size=64,
        warmup_steps=500,
        weight_decay=0.01,
        logging_dir='./logs',
        logging_steps=10,
        load_best_model_at_end=True,
        metric_for_best_model='accuracy',
        evaluation_strategy='epoch',
        learning_rate=5e-5,
        fp16=True,
        dataloader_num_workers=2,
        gradient_accumulation_steps=8,
        save_total_limit=2,
        save_steps=500,
        save_strategy='steps',
        learning_rate_schedule='linear',
        run_name='transformer_tagger'
    )

    tokenizer = create_transformer_tokenizer(config)
    model = create_transformer_model(config)

    train_dataset = ...  # Load your training dataset here
    eval_dataset = ...  # Load your evaluation dataset here

    trainer = Trainer(
        model=model,
        args=config,
        train_dataset=train_dataset,
        eval_dataset=eval_dataset,
        compute_metrics=lambda pred: {'accuracy': pred.accuracy()}
    )

    trainer.train()

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

Please note that you need to replace the `...` with your actual dataset loading code. Also, this code assumes that you have already installed the required libraries (Transformers, Thinc) and that your dataset is in a format that can be loaded using the Transformers library.

This code sets up a simple Transformer tagging model using Huggingface's Transformers and Thinc libraries. It trains the model for a specified number of epochs, saves the best model, and logs the training and evaluation metrics. The code also includes functions to convert Transformer inputs and outputs to the Thinc format, group pairs of sequences into minibatches, and evaluate sequences.