【开源实习】Bartpho模型微调

llm/finetune/bartpho/bartpho_finetune.md

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+# finetune bartpho 
+
+## dataset resources
+
+HuyPham235711/BARTpho_Corrector
+
+文本纠错任务
+
+## mindnlp+ascend
+
+| Epoch | train_loss | eval_loss | BLEU    |
+| :---: | ---------- | --------- | ------- |
+|   1   | 0.4209     | 0.3085    | 86.5538 |
+|   2   | 0.3052     | 0.2757    | 87.7926 |
+|   3   | 0.2307     | 0.2531    | 88.8163 |
+|   4   | 0.1864     | 0.2303    | 89.1350 |
+|   5   | 0.1535     | 0.2048    | 90.4295 |
+
+## pytorch+cuda:
+
+| Epoch | train_loss | eval_loss | BLEU    |
+| :---: | ---------- | --------- | ------- |
+|   1   | 0.9188     | 0.3580    | 86.3481 |
+|   2   | 0.4843     | 0.3022    | 86.9550 |
+|   3   | 0.3602     | 0.2728    | 87.5816 |
+|   4   | 0.2866     | 0.2623    | 88.8584 |
+|   5   | 0.2312     | 0.2659    | 89.8917 |

llm/finetune/bartpho/bartpho_finetune_mindnlp.py

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+import os
+import numpy as np
+import random
+import mindspore as ms
+from mindspore import nn, ops, Tensor, set_seed
+from mindspore.dataset import GeneratorDataset
+from mindnlp.transformers import AutoModelForSeq2SeqLM, BartphoTokenizer
+from mindnlp.engine import Trainer, TrainingArguments, TrainerCallback
+from datasets import load_dataset
+
+import evaluate
+
+# 加载评估指标
+sacrebleu_metric = evaluate.load("sacrebleu")
+
+# 定义模型和数据路径
+MODEL_NAME = "vinai/bartpho-syllable"
+MAX_LENGTH = 32  # 最大序列长度
+output_dir = './saved_model_weights'  # 模型保存路径
+os.makedirs(output_dir, exist_ok=True)
+
+
+# 自定义训练回调函数来打印每个epoch的loss
+class LossLoggerCallback(TrainerCallback):
+    def on_epoch_end(self, args, state, control, **kwargs):
+        """在每个epoch结束时调用"""
+        # 获取当前训练信息
+        epoch = state.epoch
+        loss = state.log_history[-1].get('loss', 0.0) if state.log_history else 0.0
+
+        # 打印当前epoch的训练loss
+        print(f"Epoch {epoch}: train_loss = {loss:.6f}")
+
+        # 如果有评估结果，也打印出来
+        if 'eval_loss' in state.log_history[-1]:
+            eval_loss = state.log_history[-1].get('eval_loss', 0.0)
+            eval_metric = state.log_history[-1].get('eval_sacrebleu', 0.0)
+            print(f"Epoch {epoch}: eval_loss = {eval_loss:.6f}, eval_sacrebleu = {eval_metric:.4f}")
+
+
+# 数据预处理函数
+def preprocess_function(examples):
+    # 对输入和目标文本进行tokenize
+    return tokenizer(
+        examples["error"],
+        text_target=examples["original"],
+        max_length=MAX_LENGTH,
+        truncation=True,
+        padding="max_length"
+    )
+
+
+# 计算评估指标
+def compute_metrics(eval_preds):
+    preds, labels = eval_preds
+
+    # 如果模型返回的是元组，取第一个元素（预测logits）
+    if isinstance(preds, tuple):
+        preds = preds[0]
+
+    # 解码预测和标签
+    decoded_preds = tokenizer.batch_decode(preds, skip_special_tokens=True)
+
+    # 处理标签中的pad token
+    labels = np.where(labels != -100, labels, tokenizer.pad_token_id)
+    decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True)
+
+    # 简单的后处理
+    decoded_preds = [pred.strip() for pred in decoded_preds]
+    decoded_labels = [[label.strip()] for label in decoded_labels]  # sacrebleu需要列表的列表
+
+    # 计算BLEU分数
+    result = sacrebleu_metric.compute(
+        predictions=decoded_preds,
+        references=decoded_labels
+    )
+
+    return {
+        "sacrebleu": round(result["score"], 4)
+    }
+
+
+# 为MindSpore创建数据集
+def create_mindspore_dataset(data, batch_size=8):
+    data_list = list(data)
+
+    def generator():
+        for item in data_list:
+            yield (
+                Tensor(item["input_ids"], dtype=ms.int32),
+                Tensor(item["attention_mask"], dtype=ms.int32),
+                Tensor(item["labels"], dtype=ms.int32)
+            )
+
+    return GeneratorDataset(
+        generator,
+        column_names=["input_ids", "attention_mask", "labels"]
+    ).batch(batch_size)
+
+
+# 对logits进行预处理，防止内存溢出
+def preprocess_logits_for_metrics(logits, labels):
+    """防止内存溢出"""
+    pred_ids = ms.mint.argmax(logits[0], dim=-1)
+    return pred_ids, labels
+
+
+# 主函数
+def main():
+    global tokenizer  # 使tokenizer在函数外可用
+
+    # 加载模型和tokenizer
+    print("正在加载模型和tokenizer...")
+    tokenizer = BartphoTokenizer.from_pretrained(MODEL_NAME)
+    model = AutoModelForSeq2SeqLM.from_pretrained(MODEL_NAME)
+
+    # 加载数据集
+    print("正在加载数据集...")
+    train_path = './train.csv'
+    test_path = './test.csv'
+    dataset = load_dataset("csv", data_files={"train": train_path, "test": test_path})
+
+    print(f"训练集样本数: {len(dataset['train'])}")
+    print(f"测试集样本数: {len(dataset['test'])}")
+
+    # 数据预处理
+    print("正在进行数据预处理...")
+    tokenized_datasets = dataset.map(
+        preprocess_function,
+        batched=True,
+        remove_columns=dataset["train"].column_names,
+    )
+
+    # 创建MindSpore数据集
+    print("正在创建MindSpore数据集...")
+    train_dataset = create_mindspore_dataset(tokenized_datasets["train"], batch_size=8)
+    eval_dataset = create_mindspore_dataset(tokenized_datasets["test"], batch_size=8)
+
+    # 定义训练参数
+    training_args = TrainingArguments(
+        output_dir="./results",
+        evaluation_strategy="epoch",
+        learning_rate=1e-5,
+        per_device_train_batch_size=8,
+        per_device_eval_batch_size=8,
+        num_train_epochs=5,
+        weight_decay=0.01,
+        save_strategy="epoch",
+        save_total_limit=2,
+    )
+
+    # 初始化训练器
+    print("初始化训练器...")
+    trainer = Trainer(
+        model=model,
+        args=training_args,
+        train_dataset=train_dataset,
+        eval_dataset=eval_dataset,
+        tokenizer=tokenizer,
+        compute_metrics=compute_metrics,
+        preprocess_logits_for_metrics=preprocess_logits_for_metrics,
+        callbacks=[LossLoggerCallback()]
+    )
+
+    # 开始训练
+    print("开始训练...")
+    trainer.train()
+    # 保存模型
+    print(f"训练完成，保存模型到 {output_dir}...")
+    model.save_pretrained(output_dir)
+    # 模型评估
+    print("进行模型最终评估...")
+    eval_results = trainer.evaluate()
+    print(f"最终评估结果: {eval_results}")
+
+
+if __name__ == "__main__":
+    main()