在 HuggingFace 上创建数据集非常方便，创建完成之后，通过 API 可以方便的下载并使用数据集，在 Google Colab 上进行模型调优，下载数据集速度非常快，本文通过 Dataset 库创建一个简单的训练数据集。
首先安装数据集依赖
- HuggingFace
datasets
huggingface_hub
创建数据集
替换为自己的 HuggingFace API key，运行一下数据集就生成了。Dataset 类提供了多种格式数据的上传，API 比较简单，例如list、dict、pandas 等等。
def upload():training_data = [[{"from": "user", "value": "hi"},{"from": "gpt", "value": "hello"}],[{"from": "user", "value": "how are you?"},{"from": "gpt", "value": "I am doing well, thank you."}],[{"from": "user", "value": "what is your name?"},{"from": "gpt", "value": "I am zidk2"}],[{"from": "user", "value": "who are you?"},{"from": "gpt", "value": "I am zidk2, your assistant."}],[{"from": "user", "value": "can you tell me your name?"},{"from": "gpt", "value": "I am zidk2."}],[{"from": "user", "value": "please tell me your name"},{"from": "gpt", "value": "I am zidk2."}],[{"from": "user", "value": "hello, what is your name?"},{"from": "gpt", "value": "I am zidk2."}],[{"from": "user", "value": "name?"},{"from": "gpt", "value": "I am zidk2."}],[{"from": "user", "value": "hi, who are you?"},{"from": "gpt", "value": "I am zidk2."}],[{"from": "user", "value": "what can I call you?"},{"from": "gpt", "value": "You can call me zidk2."}]]dataset = Dataset.from_dict({"conversations": training_data })# Set up Hugging Face APIhf_api = HfApi()# Provide your Hugging Face tokentoken = os.getenv("HF_KEY") # Replace with your Hugging Face tokenHfFolder.save_token(token)# Create a new dataset repository on Hugging Facerepo_name = "test-data" # Replace with your desired dataset nameusername = hf_api.whoami(token)['name']repo_id = f"{username}/{repo_name}"# Create the dataset repository on Hugging Facehf_api.create_repo(repo_id, repo_type="dataset", exist_ok=True)# Push the dataset to Hugging Face Hubdataset.push_to_hub(repo_id)print(f"Dataset uploaded to Hugging Face Hub: https://huggingface.co/datasets/{repo_id}")登录 HuggingFace 进行查看
总结
在 HuggingFace 上创建数据集的好处是，可以方便的在云平台上获取数据并进行训练，如果使用阿里云，可以上将数据集上传到魔搭进行使用。
