Cussupport Chat T5 Small

🚀 微調聊天機器人模型

微調聊天機器人模型基於t5-small構建，專門針對客戶支持場景進行了優化。該模型能夠高效處理客戶諮詢，為客戶提供準確且及時的回覆，提升客戶服務體驗。

🚀 快速開始

模型信息

模型概述

微調聊天機器人模型基於 t5-small 構建，專門針對客戶支持用例進行了定製。用於微調的訓練數據來自 Bitext 客戶支持大語言模型聊天機器人訓練數據集。

模型詳情

• 基礎模型：t5-small
• 微調數據：Bitext 客戶支持大語言模型聊天機器人訓練數據集
• 訓練損失：1.1188
• 驗證損失：0.9132
• BLEU 分數：0.9233

💻 使用示例

基礎用法

# 使用管道作為高級助手
from transformers import pipeline

pipe = pipeline("text2text-generation", model="mrSoul7766/CUSsupport-chat-t5-small")

# 示例用戶查詢
user_query = "How could I track the compensation?"

# 生成回覆
answer = pipe(f"answer: {user_query}", max_length=512)

# 打印生成的答案
print(answer[0]['generated_text'])

I'm on it! I'm here to assist you in tracking the compensation. To track the compensation, you can visit our website and navigate to the "Refunds" or "Refunds" section. There, you will find detailed information about the compensation you are entitled to. If you have any other questions or need further assistance, please don't hesitate to let me know. I'm here to help!

高級用法

# 直接加載模型
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("mrSoul7766/CUSsupport-chat-t5-small")
model = AutoModelForSeq2SeqLM.from_pretrained("mrSoul7766/CUSsupport-chat-t5-small")

# 設置最大生成長度
max_length = 512

# 以問題為輸入生成回覆
input_ids = tokenizer.encode("I am waiting for a refund of $2?", return_tensors="pt")
output_ids = model.generate(input_ids, max_length=max_length)

# 解碼回覆
response = tokenizer.decode(output_ids[0], skip_special_tokens=True)
print(response)

I'm on it! I completely understand your anticipation for a refund of $2. Rest assured, I'm here to assist you every step of the way. To get started, could you please provide me with more details about the specific situation? This will enable me to provide you with the most accurate and up-to-date information regarding your refund. Your satisfaction is our top priority, and we appreciate your patience as we work towards resolving this matter promptly.

📄 許可證

本項目採用 apache-2.0 許可證。