大模型微调-数据部分

数据加载

国内用户建议到 https://modelscope.cn/datasets 下载数据，但是下载后发现并不能和huggingface datasets无缝衔接，而是报了个错

• AttributeError: ‘MsDataset’ object has no attribute ‘column_names’

因此，可以继续采用魔搭下载数据，但是转换到dataset适应的形式，顺便也对整个数据过程更加了解一下。

但最简单的修改方法是:

 dataset = MsDataset.load()
train_dataset = dataset.to_hf_dataset()  # 魔搭社区下载

然后是:

• https://github.com/modelscope/modelscope/blob/a903ec7a898f5dfb44349e2ce15971ec5f08e528/examples/pytorch/llm/utils/dataset.py#L34
• https://github.com/hiyouga/LLaMA-Factory/blob/6c94305e4746c9a735ff62a6428e295d1a67da52/src/llmtuner/data/loader.py#L83

几种方法

train_dataset = load_from_disk(args.dataset_name, split="train[:1024]")

def preprocess_function(examples):

        queries = examples["sentence"]
        queries = get_detailed_instruct(task, queries)
        batch_dict = tokenizer(queries, max_length=args.max_length - 1, return_attention_mask=False, padding=False, truncation=True)
        batch_dict['input_ids'] = [input_ids + [tokenizer.eos_token_id] for input_ids in batch_dict['input_ids']]
        batch_dict = tokenizer.pad(batch_dict, padding=True, return_attention_mask=True, return_tensors='pt')

        result = {f"sentence_{k}": v for k, v in batch_dict.items()}

        queries = examples["positive"]
        batch_dict = tokenizer(queries, max_length=args.max_length - 1, return_attention_mask=False, padding=False, truncation=True)
        batch_dict['input_ids'] = [input_ids + [tokenizer.eos_token_id] for input_ids in batch_dict['input_ids']]
        batch_dict = tokenizer.pad(batch_dict, padding=True, return_attention_mask=True, return_tensors='pt')

        for k, v in batch_dict.items():
            result[f"positive_{k}"] = v
        
        queries = examples["negative"]
        batch_dict = tokenizer(queries, max_length=args.max_length - 1, return_attention_mask=False, padding=False, truncation=True)
        batch_dict['input_ids'] = [input_ids + [tokenizer.eos_token_id] for input_ids in batch_dict['input_ids']]
        batch_dict = tokenizer.pad(batch_dict, padding=True, return_attention_mask=True, return_tensors='pt')

        for k, v in batch_dict.items():
            result[f"negative_{k}"] = v

        result["labels"] = [0] * len(examples["sentence"]) 
        return result
 
 processed_datasets = dataset.map(
        preprocess_function,
        batched=True,
        remove_columns=dataset["train"].column_names,
        desc="Running tokenizer on dataset",
    )
    

数据构造

• 百川例子

数据清洗