Query Gen Msmarco T5 Large V1

🚀 查询生成

本模型是来自 docTTTTTquery 的 t5-base 模型。

T5-base 模型在 MS MARCO 段落数据集 上进行训练，该数据集包含约 50 万个来自必应的真实搜索查询以及相关段落。

该模型可用于查询生成，以学习语义搜索模型，而无需带注释的训练数据：合成查询生成。

🚀 快速开始

此模型基于 docTTTTTquery 的 t5-base 模型，在 MS MARCO 段落数据集 上训练而来，可用于生成查询以学习语义搜索模型，无需标注训练数据。

💻 使用示例

基础用法

from transformers import T5Tokenizer, T5ForConditionalGeneration

tokenizer = T5Tokenizer.from_pretrained('model-name')
model = T5ForConditionalGeneration.from_pretrained('model-name')

para = "Python is an interpreted, high-level and general-purpose programming language. Python's design philosophy emphasizes code readability with its notable use of significant whitespace. Its language constructs and object-oriented approach aim to help programmers write clear, logical code for small and large-scale projects."

input_ids = tokenizer.encode(para, return_tensors='pt')
outputs = model.generate(
	input_ids=input_ids,
	max_length=64,
	do_sample=True,
	top_p=0.95,
	num_return_sequences=3)

print("Paragraph:")
print(para)

print("\nGenerated Queries:")
for i in range(len(outputs)):
	query = tokenizer.decode(outputs[i], skip_special_tokens=True)
	print(f'{i + 1}: {query}')