# 1. 創(chuàng)建虛擬環(huán)境
conda create -n nlp python=3.11 -y
conda activate nlp

# 2. 安裝核心庫(kù)（2026 年推薦組合）
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu   # CPU版（有GPU換cuda版本）
pip install transformers datasets accelerate evaluate pandas scikit-learn

from datasets import load_dataset

# 加載中文情感分析數(shù)據(jù)集（Seamew/ChnSentiCorp）
dataset = load_dataset("seamew/ChnSentiCorp")

print(dataset)
# 輸出：DatasetDict({
#     train: Dataset({...}),
#     validation: Dataset({...}),
#     test: Dataset({...})
# })

# 查看前3條數(shù)據(jù)
print(dataset["train"][0:3])

from transformers import AutoTokenizer

model_name = "bert-base-chinese"   # 中文預(yù)訓(xùn)練模型

tokenizer = AutoTokenizer.from_pretrained(model_name)

def preprocess_function(examples):
    return tokenizer(examples["text"], 
                     truncation=True,      # 超長(zhǎng)自動(dòng)截?cái)?
                     padding="max_length", # 統(tǒng)一長(zhǎng)度
                     max_length=128)

# 應(yīng)用到整個(gè)數(shù)據(jù)集
tokenized_datasets = dataset.map(preprocess_function, batched=True)

from transformers import AutoModelForSequenceClassification, TrainingArguments, Trainer
import evaluate
import numpy as np

# 加載模型（2分類：正面/負(fù)面）
model = AutoModelForSequenceClassification.from_pretrained(model_name, num_labels=2)

# 評(píng)估指標(biāo)（準(zhǔn)確率 + F1）
accuracy = evaluate.load("accuracy")
f1 = evaluate.load("f1")

def compute_metrics(eval_pred):
    logits, labels = eval_pred
    predictions = np.argmax(logits, axis=-1)
    acc = accuracy.compute(predictions=predictions, references=labels)
    f1_score = f1.compute(predictions=predictions, references=labels, average="macro")
    return {"accuracy": acc["accuracy"], "f1": f1_score["f1"]}

# 訓(xùn)練參數(shù)（可根據(jù)顯存調(diào)整）
training_args = TrainingArguments(
    output_dir="./results",          # 輸出目錄
    eval_strategy="epoch",           # 每輪評(píng)估
    save_strategy="epoch",
    learning_rate=2e-5,
    per_device_train_batch_size=16,
    per_device_eval_batch_size=16,
    num_train_epochs=3,              # 建議 3-5 輪
    weight_decay=0.01,
    logging_dir="./logs",
    report_to="none",                # 不上傳 wandb
    load_best_model_at_end=True,
    metric_for_best_model="f1"
)

trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=tokenized_datasets["train"],
    eval_dataset=tokenized_datasets["validation"],
    compute_metrics=compute_metrics,
    tokenizer=tokenizer,   # 自動(dòng)保存 tokenizer
)

print("開(kāi)始訓(xùn)練...")
trainer.train()

# 訓(xùn)練結(jié)束后在測(cè)試集上評(píng)估
test_results = trainer.evaluate(tokenized_datasets["test"])
print("測(cè)試集結(jié)果：", test_results)

# 保存模型
trainer.save_model("./my_chinese_sentiment_model")
tokenizer.save_pretrained("./my_chinese_sentiment_model")

# 加載推理（最常用方式）
from transformers import pipeline

sentiment_pipeline = pipeline(
    "sentiment-analysis",
    model="./my_chinese_sentiment_model",
    tokenizer="./my_chinese_sentiment_model"
)

# 測(cè)試
texts = [
    "這家餐廳的服務(wù)態(tài)度非常好，菜也超級(jí)美味！",
    "完全是浪費(fèi)錢(qián)，東西難吃還貴，差評(píng)！",
    "一般般吧，沒(méi)什么特別的。"
]

results = sentiment_pipeline(texts)
for text, res in zip(texts, results):
    label = "正面" if res["label"] == "LABEL_1" else "負(fù)面"
    print(f"文本: {text}\n預(yù)測(cè): {label} (置信度: {res['score']:.4f})\n")

nlp_sentiment_project/
├── data/                  # 原始數(shù)據(jù)
├── models/                # 保存的模型
├── results/               # 訓(xùn)練輸出
├── logs/
├── main.py                # 訓(xùn)練主文件
├── inference.py           # 推理腳本
├── requirements.txt
└── README.md