text-emotion-classification.py

from transformers import AutoTokenizer, AutoModelForSequenceClassification, TrainingArguments, Trainer
from datasets import load_dataset
import numpy as np

# 1️⃣ 载入中文 RoBERTa 分词器和模型
model_name = "hfl/chinese-roberta-wwm-ext"
tokenizer = AutoTokenizer.from_pretrained(model_name)

# 指定标签数量，比如 8 类情绪
model = AutoModelForSequenceClassification.from_pretrained(model_name, num_labels=8)

# 2️⃣ 加载自己情绪数据集
# 需要 CSV 至少包含两列：text（文本）、label（整数标签）
dataset = load_dataset(
    "csv",
    data_files={
        "train": "emotion-classification-train.csv",
        "test": "emotion-classification-train.csv",
    },
)

def preprocess(examples):
    return tokenizer(examples["text"], truncation=True, padding="max_length", max_length=128)

dataset = dataset.map(preprocess, batched=True)

# Transformers Trainer 期望标签列名为 label
if "labels" in dataset["train"].column_names and "label" not in dataset["train"].column_names:
    dataset = dataset.rename_column("labels", "label")

def compute_metrics(eval_pred):
    logits, labels = eval_pred
    preds = np.argmax(logits, axis=-1)
    acc = (preds == labels).mean().item() if hasattr((preds == labels).mean(), "item") else float((preds == labels).mean())
    return {"accuracy": acc}

# 3️⃣ 配置训练参数（保存最优模型）
training_args = TrainingArguments(
    output_dir="./sentiment_roberta",
    eval_strategy="epoch",
    save_strategy="epoch",
    learning_rate=2e-5,
    per_device_train_batch_size=8,
    per_device_eval_batch_size=8,
    num_train_epochs=3,
    load_best_model_at_end=True,
    metric_for_best_model="accuracy",
    greater_is_better=True,
    save_total_limit=2,
    fp16=True,
)

trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=dataset["train"],
    eval_dataset=dataset["test"],
    compute_metrics=compute_metrics,
)

# 4️⃣ 开始训练
trainer.train()

# 5️⃣ 显式保存最优模型与分词器到 output_dir
trainer.save_model(training_args.output_dir)
tokenizer.save_pretrained(training_args.output_dir)