T5 Small Finetuned Samsum

🚀 T5 Small for Conversation Summarization

本項目使用T5 Small模型進行對話摘要生成，可有效提煉對話的核心內容。

🚀 快速開始

本模型基於T5 Small，用於對話摘要生成。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

基礎用法

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)

高級用法

暫未提供高級用法示例，可根據實際需求對模型進行微調或結合其他技術使用。

📚 詳細文檔

模型信息