bart-base-grammar-synthesis

该模型是基于扩展版本的JFLEG数据集对 facebook/bart-base 进行微调得到的。

您可以通过 searching for the grammar synthesis tag 找到其他语法合成模型。

基本使用示例

安装

首先，请确保已安装transformers包。您可以使用pip进行安装：

pip install -U transformers

用法

from transformers import pipeline

# Initialize the text-generation pipeline for text correction
corrector = pipeline("text2text-generation", "pszemraj/bart-base-grammar-synthesis")

# Example text to correct
raw_text = "The toweris 324 met (1,063 ft) tall, about height as .An 81-storey building, and biggest longest structure paris. Is square, measuring 125 metres (410 ft) on each side. During its constructiothe eiffel tower surpassed the washington monument to become the tallest man-made structure in the world, a title it held for 41 yearsuntilthe chryslerbuilding in new york city was finished in 1930. It was the first structure to goat a height of 300 metres. Due 2 the addition ofa brdcasting aerial at the t0pp of the twr in 1957, it now taller than  chrysler building 5.2 metres (17 ft). Exxxcluding transmitters,  eiffel tower is  2ndd tallest ree-standing structure in france after millau viaduct."

# Correct the text using the text-generation pipeline
corrected_text = corrector(raw_text)[0]["generated_text"]

# Print the corrected text
print(corrected_text)

此示例演示了如何使用文本生成流程来纠正给定文本中的语法错误。corrector流程使用“pszemraj/bart-base-grammar-synthesis”模型进行初始化，该模型旨在进行语法纠正。corrector流程接受原始文本作为输入，并返回更正后的文本。在运行代码之前，请确保安装了所需的依赖和模型。

预期用途和限制

• 强大的语法纠正
• 该模型的许可证是cc-by-nc-sa-4.0，因为它使用JFLEG数据集+对其进行了增补以用于训练

训练和评估数据

需要更多信息

训练过程

训练超参数

在训练过程中使用了以下超参数：

• learning_rate：0.0001
• train_batch_size：8
• eval_batch_size：8
• seed：42
• distributed_type：multi-GPU
• gradient_accumulation_steps：16
• total_train_batch_size：128
• optimizer：Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type：cosine
• lr_scheduler_warmup_ratio：0.02
• num_epochs：3.0