T5 Small Finetuned Samsum

🚀 T5 Small for Conversation Summarization

本项目使用T5 Small模型进行对话摘要生成，可有效提炼对话的核心内容。

🚀 快速开始

本模型基于T5 Small，用于对话摘要生成。以下是使用示例：

from transformers import AutoModelForSeq2SeqLM, AutoTokenizer

model_checkpoint = "ahlad/t5-small-finetuned-samsum"
tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)
model = AutoModelForSeq2SeqLM.from_pretrained(model_checkpoint)

input_text = """
Emma: Did you finish the book I lent you? 
Liam: Yes, I couldn’t put it down! The twist at the end was insane. 
Emma: I know, right? I didn’t see it coming at all. What did you think of the main character? 
Liam: Honestly, I thought they were a bit frustrating at first, but they grew on me. 
Emma: Same here. I loved how they developed by the end. Are you up for another book from the series? 
Liam: Absolutely! Pass it my way.
"""

inputs = tokenizer(input_text, return_tensors="pt")

outputs = model.generate(**inputs)
summary = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Summary:", summary)

💻 使用示例

基础用法

from transformers import AutoModelForSeq2SeqLM, AutoTokenizer

model_checkpoint = "ahlad/t5-small-finetuned-samsum"
tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)
model = AutoModelForSeq2SeqLM.from_pretrained(model_checkpoint)

input_text = """
Emma: Did you finish the book I lent you? 
Liam: Yes, I couldn’t put it down! The twist at the end was insane. 
Emma: I know, right? I didn’t see it coming at all. What did you think of the main character? 
Liam: Honestly, I thought they were a bit frustrating at first, but they grew on me. 
Emma: Same here. I loved how they developed by the end. Are you up for another book from the series? 
Liam: Absolutely! Pass it my way.
"""

inputs = tokenizer(input_text, return_tensors="pt")

outputs = model.generate(**inputs)
summary = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Summary:", summary)

高级用法

暂未提供高级用法示例，可根据实际需求对模型进行微调或结合其他技术使用。

📚 详细文档

模型信息