--- language: - code tags: - code - c++ - fill-in-the-middle - fim - autocomplete - code-completion size_categories: - 10K`: Code before the completion point - ``: Code after the completion point - ``: The code to be completed ## Dataset Structure ### Data Fields - `text`: The formatted FIM instruction containing prefix, suffix, and middle tokens ### Data Splits | Split | Examples | |-------|----------| | train | 42,922 | | validation | 2,259 | ## Usage ```python from datasets import load_dataset # Load the dataset dataset = load_dataset("KrzTyb/fim-dataset") # Access train and validation splits train_data = dataset["train"] val_data = dataset["validation"] # Example: Print first training example print(train_data[0]["text"]) ``` ## Training This dataset can be used to fine-tune code language models for autocompletion: ```python from transformers import AutoModelForCausalLM, AutoTokenizer, Trainer from datasets import load_dataset # Load model and tokenizer model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-Coder-0.5B") tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-Coder-0.5B") # Add FIM tokens fim_tokens = ["", "", "", ""] tokenizer.add_special_tokens({"additional_special_tokens": fim_tokens}) model.resize_token_embeddings(len(tokenizer)) # Load dataset dataset = load_dataset("KrzTyb/fim-dataset") # Train (see full training script for details) trainer = Trainer( model=model, train_dataset=dataset["train"], eval_dataset=dataset["validation"], # ... other training arguments ) trainer.train() ``` ## Citation If you use this dataset, please cite: ```bibtex @dataset{KrzTyb_fim-dataset}, title={fim-dataset}, author={Dataset Creator}, year={2025}, publisher={Hugging Face}, howpublished={\url{https://huggingface.co/datasets/KrzTyb/fim-dataset}} } ``` ## License Please specify the license for your dataset.