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id (int32)title (string)text (string)url (string)wiki_id (int32)views (float32)paragraph_id (int32)langs (int32)emb (sequence)
0
"习近平"
"习近平(),陝西富平人,祖籍河南邓州,中国共产党和中华人民共和国政治人物,2012年当选中共中央总书记和中共中央军委主席,成为最高领导人。2013年当选国家主席和国家军委主席。中华人民共和国第五代最高领导人。"
"https://zh.wikipedia.org/wiki?curid=112582"
112,582
3,752.744629
0
135
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1
"习近平"
"习近平1953年6月15日出生于北京,是首位出生在中华人民共和国成立后的中共最高领导人,是開國元勛和中共元老习仲勋与其第二任夫人齐心长子。1969年1月,因文化大革命对家庭带来的冲击而被迫中止学业,作为知青前往陕西省延安市延川县梁家河村参加劳动与工作。在此期间,于1974年1月10日加入中国共产党,并在后期担任了梁家河村党支部书记。1979年自清華大學化工系毕业后,曾分别任国务院办公厅秘书、中央军委办公厅秘书和河北省正定县县委书记。1985年起,先后在福建省厦门市、宁德地区、福州市任职。1999年晋升正省部级,之后历任福建省人民政府省长、中共浙江省委书记和中共上海市委书记。2007年10月,当选中共中央政治局常委和中共中央书记处书记,并先后兼任中共中央党校校长、国家副主席和中央军委副主席。"
"https://zh.wikipedia.org/wiki?curid=112582"
112,582
3,752.744629
1
135
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2
"习近平"
"2012年11月,在中共十八届一中全会上,当选中国共产党中央委员会总书记,实际上确立了领导核心的地位。2012年至今担任中国共产党中央委员会总书记和中国共产党中央军事委员会主席,2013年至今担任国家主席和中华人民共和国中央军事委员会主席。中国共产党第十六届至第二十届中央委员,第十七届至第二十届中央政治局委员、常委。"
"https://zh.wikipedia.org/wiki?curid=112582"
112,582
3,752.744629
2
135
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3
"习近平"
"1962年,时任国务院副总理兼秘书长的习仲勋因牵涉“反党小说《刘志丹》案”而失去职位,且被监押到北京卫戍区。习家受到严重冲击并被抄家,因此习近平随母亲齐心迁居中共中央党校。1965年从北京市八一学校小学部毕业,同年9月入读八一初中部。"
"https://zh.wikipedia.org/wiki?curid=112582"
112,582
3,752.744629
3
135
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4
"习近平"
"1966年12月初,文化大革命爆发后不久,首都中学红卫兵联合行动委员会(简称联动)成立。但因當時父亲习仲勋被打倒,习近平被归类为“黑帮子弟”,並沒有很深地涉及联动的活動。後來,习近平被抓到中共中央党校集中觀察審查,并被送往少管所“黑帮”子弟学习班。1968年(15岁),北京八一学校被彻底解散,习近平被分到北京第二十五中学,并在此和聂卫平成为好友。根据其弟习远平回忆,1968年习近平15岁时,因父亲问题的牵连,被有关部门多次关押审查,出来时,身体非常虚弱,全身都是虱子,随后去了陕西富平老家大姑家里休养,在大姑的悉心照料下才慢慢调养好。"
"https://zh.wikipedia.org/wiki?curid=112582"
112,582
3,752.744629
4
135
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5
"习近平"
"1968年12月,中国共产党主席毛泽东发起上山下乡运动,习近平属于被号召之列。次年1月,被下放到延安市延川县文安驛鎮公社梁家河大队插队。在广为流传的采访视频中,习近平回忆第一次到梁家河的经历说,自己初期很不适应当地生活,“别人下去天天上山干活”,年少的习“却很随意”,老百姓对他印象很不好,認為習近平是個遊手好閒、不愛勞動的浪子。習近平才待几个月后,便因受不了苦,偷回到北京,又被关进少管所“黑帮”子弟‘学习班’,半年后被放出来,听从姨夫魏震五的建议又回到延川县安驿公社梁家河。回到了梁家河後,习近平很快成为一名壮劳力,並以苦幹、實幹的精神贏得了農民的認同。"
"https://zh.wikipedia.org/wiki?curid=112582"
112,582
3,752.744629
5
135
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6
"习近平"
"1974年(一說1973年),习近平开始申请加入中国共产党。然而,因为父亲习仲勋的问题,习近平的入团申请与入党申请屡次被驳回,在递交十余次后才被批准。此后,因突出的表现和领导能力,习近平当选为梁家河大队的党支部书记。在任期间,他带领村民“整社(处理姓氏宗族之间的矛盾)”、修筑大坝、自学相关知识并修建了陕西省第一口沼氣池,这些举措为他在当地赢得良好口碑。梁家河村的相关遗迹也在习近平成为国家领导人后成为了旅游景点和党员教育基地。出任总书记后,习近平曾于2015年初携夫人彭丽媛回到梁家河村考察,并自费为当地群众置办了部分年货。"
"https://zh.wikipedia.org/wiki?curid=112582"
112,582
3,752.744629
6
135
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7
"习近平"
"习近平回忆1973年申请大学入学时说,“像我这样家庭背景的人,在当时是不可能被录取的”。1973年,清华大学有两个名额在延安地区,全分给了延川县。习近平表示:他三个志愿都填清华,讓我上就上,不讓我上就拉倒。清华来招生的人不敢做主,请示学校。时逢所谓的“右倾翻案风”,清华一二号人物迟群、谢静宜都不在家,刘冰掌权,他批准了习近平的入学资质。当时,习仲勋下放劳动的洛阳耐火材料厂开了个“土证明”,称习仲勋同志属人民内部矛盾,不影响子女升学就业。习近平从而获准进入清华就读。"
"https://zh.wikipedia.org/wiki?curid=112582"
112,582
3,752.744629
7
135
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8
"习近平"
"在大學期間,結識了同為化工系工农兵学员的上下鋪室友陈希,兩人同齡同系且同是體育迷,使得他們培養深厚的友誼,習近平在清华大学担任学生党支部书记時是陈希的兩名入党介绍人之一。直到多年之後習近平上台出任總書記,陳希也輾轉仕途成為中共中央组织部部長。"
"https://zh.wikipedia.org/wiki?curid=112582"
112,582
3,752.744629
8
135
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9
"习近平"
"在邓小平发起的军队大裁员情况下,其转向地方。据美国《纽约时报》报道,习近平意识到自己强大的家庭背景和在农村的工作积累的政绩会有助于升遷,于是主动提议放弃在北京的工作,前往农村。1979年習近平与中国前驻英国大使柯华的女儿柯玲玲(又名柯小明,生于1951年,时年28岁)结婚,1982年离婚。在父亲习仲勋的帮助下,选择了离北京相对较近的正定县。据报道习近平原本希望担任一个县或镇的党委书记,但是由于时任中共河北省委书记高扬非常反感像习近平这样没有经验却依靠人脉空降而来的人,只任命其担任了正定县委副书记。1982年3月25日,习近平上任,;上任后习近平身着下放时穿的绿色军大衣,到各地走访调查。他简朴的风格使当地官员王幼辉感到吃惊。由于正定地处南北交通动脉,境内道路路况糟糕,习近平果断采取行动动员4.32万人参加养护公路课程。在1983年严打中,正定县也举行公审公判活动。期间,习近平被指在正定县内实行了严厉的计划生育政策,40万人中有3.1万名妇女做了绝育手术,3万人安装了宫内绝育工具。83年底,年仅30周岁的习近平升任并成为正定县最年轻的县委书记。而《中國時报》的报道则指,位於正定县東面且同属石家庄地区的无极县的县委书记栗战书在这一时期与习近平结识,造就了未来双方的合作。"
"https://zh.wikipedia.org/wiki?curid=112582"
112,582
3,752.744629
9
135
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10
"习近平"
"《老人报》的一篇文章指,他在任期间大力推动大包-{zh-hans:干; zh-hant:幹}-生產責任制,并向上级反映正定县情况,使得正定县粮食征购任务减少了2000万斤;此外走遍整个乡镇,进行考察,他当初的同事均以“平易近人,温和儒雅,和蔼可亲”形容習近平,習近平任內的另一項政績是大力發展旅遊業,以《红楼梦》為腳本模擬建造“榮國府”,也因《三国演义》中的蜀漢名將赵云出身正定县,而興建了常山公園等旅遊景點,每年因此為正定縣帶來一千萬人民幣以上的旅遊收入。1985年4月28日到5月9日,身为河北省玉米加工考察团团长,並以河北省石家莊地區食品協會主席的身份访问美国艾奥瓦州。"
"https://zh.wikipedia.org/wiki?curid=112582"
112,582
3,752.744629
10
135
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11
"习近平"
"习近平还在河北时,组织部计划提拔他。其父习仲勋特地托当时的中共河北省委书记高扬关照,高扬不忿,反将此事在省委常委会上揭露,令习近平难在当地续任。时任组织部秘书长何载与习仲勋有同乡之谊,他了解后,请中共福建省委書記项南将习近平调任至福建厦门。"
"https://zh.wikipedia.org/wiki?curid=112582"
112,582
3,752.744629
11
135
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Wikipedia (zh) embedded with cohere.ai multilingual-22-12 encoder

We encoded Wikipedia (zh) using the cohere.ai multilingual-22-12 embedding model.

To get an overview how this dataset was created and pre-processed, have a look at Cohere/wikipedia-22-12.

Embeddings

We compute for title+" "+text the embeddings using our multilingual-22-12 embedding model, a state-of-the-art model that works for semantic search in 100 languages. If you want to learn more about this model, have a look at cohere.ai multilingual embedding model.

Further languages

We provide embeddings of Wikipedia in many different languages: ar, de, en, es, fr, hi, it, ja, ko, simple english, zh,

You can find the Wikipedia datasets without embeddings at Cohere/wikipedia-22-12.

Loading the dataset

You can either load the dataset like this:

from datasets import load_dataset
docs = load_dataset(f"Cohere/wikipedia-22-12-zh-embeddings", split="train")

Or you can also stream it without downloading it before:

from datasets import load_dataset
docs = load_dataset(f"Cohere/wikipedia-22-12-zh-embeddings", split="train", streaming=True)

for doc in docs:
    docid = doc['id']
    title = doc['title']
    text = doc['text']
    emb = doc['emb']

Search

A full search example:

#Run: pip install cohere datasets
from datasets import load_dataset
import torch
import cohere

co = cohere.Client(f"<<COHERE_API_KEY>>")  # Add your cohere API key from www.cohere.com

#Load at max 1000 documents + embeddings
max_docs = 1000
docs_stream = load_dataset(f"Cohere/wikipedia-22-12-zh-embeddings", split="train", streaming=True)

docs = []
doc_embeddings = []

for doc in docs_stream:
    docs.append(doc)
    doc_embeddings.append(doc['emb'])
    if len(docs) >= max_docs:
        break

doc_embeddings = torch.tensor(doc_embeddings)

query = 'Who founded Youtube'
response = co.embed(texts=[query], model='multilingual-22-12')
query_embedding = response.embeddings 
query_embedding = torch.tensor(query_embedding)

# Compute dot score between query embedding and document embeddings
dot_scores = torch.mm(query_embedding, doc_embeddings.transpose(0, 1))
top_k = torch.topk(dot_scores, k=3)

# Print results
print("Query:", query)
for doc_id in top_k.indices[0].tolist():
    print(docs[doc_id]['title'])
    print(docs[doc_id]['text'], "\n")

Performance

You can find performance on the MIRACL dataset (a semantic search evaluation dataset) here: miracl-en-queries-22-12#performance

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