Cross Language Search
Search cliassical CN with modern ZH
- In some cases, Classical Chinese feels like another language, I even trained 2 translation models (1 and 2) to prove this point.
- That's why, when people wants to be savvy about their words, we choose to quote our ancestors. It's exactly like westerners like to quote Latin or Shakespeare, the difference is we have a much bigger pool to choose.
- This model helps you find text within ancient Chinese literature, but you can search with modern Chinese
跨语种搜索
博古搜今
- 我不记得是谁， 哪个朝代，我只记得大概这么一个事儿，我就能模糊找到原文
- 我不记得原文， 但是我只记得原文想表达的现代汉语意思， 希望能找出来引用一下。
- 我在写文章， 有个观点， 我想碰运气看看古人有没有提过同样类似的说法。
- 我只是想更有效率地阅读古文
推荐的使用通道如下，当然， cosine距离搜索相关的框架和引擎很多， 大家自己看着适用的选
装包
pip install -Uqq unpackai
pip install -Uqq SentenceTransformer
搜索语句的函数
from unpackai.interp import CosineSearch
from sentence_transformers import SentenceTransformer
import pandas as pd
import numpy as np
TAG = "raynardj/xlsearch-cross-lang-search-zh-vs-classicical-cn"
encoder = SentenceTransformer(TAG)
# all_lines is a list of all your sentences
# all_lines 是一个你所有句子的列表， 可以是一本书， 按照句子分割， 也可以是很多很多书
all_lines = ["句子1","句子2",...]
vec = encoder.encode(all_lines, batch_size=32, show_progress_bar=True)
# consine距离搜索器
cosine = CosineSearch(vec)
def search(text):
enc = encoder.encode(text) # encode the search key
order = cosine(enc) # distance array
sentence_df = pd.DataFrame({"sentence":np.array(all_lines)[order[:5]]})
return sentence_df
将史记打成句子以后， 搜索效果是这样的：
>>> search("他是一个很慷慨的人")
sentence
0 季布者，楚人也。为气任侠，有名於楚。
1 董仲舒为人廉直。
2 大将军为人仁善退让，以和柔自媚於上，然天下未有称也。
3 勃为人木彊敦厚，高帝以为可属大事。
4 石奢者，楚昭王相也。坚直廉正，无所阿避。
>>> search("进入军营，必须缓缓牵着马骑")
sentence
0 壁门士吏谓从属车骑曰：将军约，军中不得驱驰。
1 起之为将，与士卒最下者同衣食。卧不设席，行不骑乘，亲裹赢粮，与士卒分劳苦。
2 既出，沛公留车骑，独骑一马，与樊哙等四人步从，从间道山下归走霸上军，而使张良谢项羽。
3 顷之，上行出中渭桥，有一人从穚下走出，乘舆马惊。
4 元狩四年春，上令大将军青、骠骑将军去病将各五万骑，步兵转者踵军数十万，而敢力战深入之士皆属骠骑。
其他资源清单
- Downloads last month
- 41
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social
visibility and check back later, or deploy to Inference Endpoints (dedicated)
instead.