Query Gen Msmarco T5 Large V1

🚀 查詢生成

本模型是來自 docTTTTTquery 的 t5-base 模型。

T5-base 模型在 MS MARCO 段落數據集 上進行訓練，該數據集包含約 50 萬個來自必應的真實搜索查詢以及相關段落。

該模型可用於查詢生成，以學習語義搜索模型，而無需帶註釋的訓練數據：合成查詢生成。

🚀 快速開始

此模型基於 docTTTTTquery 的 t5-base 模型，在 MS MARCO 段落數據集 上訓練而來，可用於生成查詢以學習語義搜索模型，無需標註訓練數據。

💻 使用示例

基礎用法

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}')