# 示例：數(shù)據(jù)清洗和預處理
import pandas as pd
 
# 讀取數(shù)據(jù)
data = pd.read_csv('raw_data.csv')
 
# 去重
data = data.drop_duplicates()
 
# 過濾低質(zhì)量內(nèi)容（假設低質(zhì)量內(nèi)容標記為'low_quality'）
data = data[data['quality'] != 'low_quality']
 
# 標準化文本格式（例如，將所有文本轉(zhuǎn)換為小寫）
data['text'] = data['text'].str.lower()

# 示例：加載模型與分詞器
from transformers import AutoModelForCausalLM, AutoTokenizer
 
model = AutoModelForCausalLM.from_pretrained("deepseek-ai/base-model")
tokenizer = AutoTokenizer.from_pretrained("deepseek-ai/base-model")

# 示例：配置訓練參數(shù)與訓練循環(huán)
from transformers import Trainer, TrainingArguments
 
training_args = TrainingArguments(
    output_dir="./results",
    per_device_train_batch_size=4,
    fp16=True,
    gradient_accumulation_steps=8,
    num_train_epochs=3,
    learning_rate=2e-5,
)
 
# 假設train_dataset已經(jīng)加載了預處理后的數(shù)據(jù)
trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=train_dataset,
)
 
trainer.train()

# 示例：評估模型
from datasets import load_metric
 
metric = load_metric("bleu")
 
# 假設eval_dataset是驗證集
predictions, references = [], []
 
for batch in eval_dataset:
    inputs = tokenizer(batch["input_text"], return_tensors="pt", padding=True, truncation=True)
    outputs = model.generate(**inputs)
    
    predictions.extend([tokenizer.decode(output, skip_special_tokens=True) for output in outputs])
    references.extend([batch["target_text"]])
 
results = metric.compute(predictions=predictions, references=references)
print(results)

# 示例：智能聊天機器人
from flask import Flask, request, jsonify
 
app = Flask(__name__)
 
@app.route('/chat', methods=['POST'])
def chat():
    user_input = request.json['input']
    inputs = tokenizer(user_input, return_tensors="pt")
    outputs = model.generate(**inputs)
    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
    return jsonify({'response': response})
 
if __name__ == '__main__':
    app.run()

# 示例：情感分析
def sentiment_analysis(text):
    inputs = tokenizer(text, return_tensors="pt")
    outputs = model.generate(**inputs)
    # 假設模型生成的輸出可以通過某種方式映射到情感標簽上
    sentiment = map_output_to_sentiment(outputs[0])
    return sentiment
 
# 示例調(diào)用
text = "I love this product!"
sentiment = sentiment_analysis(text)
print(sentiment)  # 輸出：'positive'

# 示例：智能代碼生成
def generate_code(prompt):
    inputs = tokenizer(prompt, return_tensors="pt")
    outputs = model.generate(**inputs)
    code = tokenizer.decode(outputs[0], skip_special_tokens=True)
    return code
 
# 示例調(diào)用
prompt = "Generate a Python function to calculate Fibonacci sequence"
code = generate_code(prompt)
print(code)