Bart Finetuned Text Summarization

🚀 BART Large CNN文本摘要模型

本模型基於Facebook的BART（雙向自迴歸變壓器）架構，具體為針對文本摘要任務進行微調的大版本。BART是Facebook AI推出的一個序列到序列模型，能夠處理包括摘要在內的各種自然語言處理任務。

🚀 快速開始

📦 安裝指南

你可以使用pip安裝必要的庫：

pip install transformers

💻 使用示例

基礎用法

以下是一個簡單的代碼片段，展示瞭如何在PyTorch中使用該模型進行段落摘要任務。

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("suriya7/bart-finetuned-text-summarization")
model = AutoModelForSeq2SeqLM.from_pretrained("suriya7/bart-finetuned-text-summarization")

def generate_summary(text):
    inputs = tokenizer([text], max_length=1024, return_tensors='pt', truncation=True)
    summary_ids = model.generate(inputs['input_ids'], max_new_tokens=100, do_sample=False)
    summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
    return summary

text_to_summarize = """Now, there is no doubt that one of the most important aspects of any Pixel phone is its camera.
And there might be good news for all camera lovers. Rumours have suggested that the Pixel 9 could come with a telephoto lens,
improving its photography capabilities even further. Google will likely continue to focus on using AI to enhance its camera performance,
in order to make sure that Pixel phones remain top contenders in the world of mobile photography."""
summary = generate_summary(text_to_summarize)
print(summary)

高級用法

# 以下是訓練參數的設置
num_train_epochs=1,
warmup_steps = 500,
per_device_train_batch_size=4,
per_device_eval_batch_size=4,
weight_decay = 0.01,
gradient_accumulation_steps=16

📚 詳細文檔

模型詳情

📄 許可證

本模型採用MIT許可證。