Cohere/wikipedia-22-12-zh-embeddings

---
language:
- zh

multilinguality:
- multilingual

size_categories: []
source_datasets: []
tags: []

task_categories:
- text-retrieval

license:
- apache-2.0

task_ids:
- document-retrieval
---

# Wikipedia (zh) embedded with cohere.ai `multilingual-22-12` encoder

We encoded [Wikipedia (zh)](https://zh.wikipedia.org) using the [cohere.ai](https://txt.cohere.ai/multilingual/) `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](https://huggingface.co/datasets/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](https://txt.cohere.ai/multilingual/).

## Further languages
We provide embeddings of Wikipedia in many different languages:
[ar](https://huggingface.co/datasets/Cohere/wikipedia-22-12-ar-embeddings), [de](https://huggingface.co/datasets/Cohere/wikipedia-22-12-de-embeddings), [en](https://huggingface.co/datasets/Cohere/wikipedia-22-12-en-embeddings), [es](https://huggingface.co/datasets/Cohere/wikipedia-22-12-es-embeddings), [fr](https://huggingface.co/datasets/Cohere/wikipedia-22-12-fr-embeddings), [hi](https://huggingface.co/datasets/Cohere/wikipedia-22-12-hi-embeddings), [it](https://huggingface.co/datasets/Cohere/wikipedia-22-12-it-embeddings), [ja](https://huggingface.co/datasets/Cohere/wikipedia-22-12-ja-embeddings), [ko](https://huggingface.co/datasets/Cohere/wikipedia-22-12-ko-embeddings), [simple english](https://huggingface.co/datasets/Cohere/wikipedia-22-12-simple-embeddings), [zh](https://huggingface.co/datasets/Cohere/wikipedia-22-12-zh-embeddings),

You can find the Wikipedia datasets without embeddings at [Cohere/wikipedia-22-12](https://huggingface.co/datasets/Cohere/wikipedia-22-12).

## Loading the dataset
You can either load the dataset like this:
```python
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:
```python
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:
```python
#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](https://huggingface.co/datasets/Cohere/miracl-en-queries-22-12#performance)

---
language:
- 中文

multilinguality:
- 多语言

size_categories: []
source_datasets: []
tags: []

task_categories:
- 文本检索

license:
- Apache-2.0

task_ids:
- 文档检索
---

# 基于cohere.ai `multilingual-22-12`编码器嵌入的中文维基百科数据集

我们使用[cohere.ai](https://txt.cohere.ai/multilingual/)的`multilingual-22-12`嵌入模型对[中文维基百科](https://zh.wikipedia.org)完成了向量嵌入处理。如需了解本数据集的创建与预处理细节，请查阅[Cohere/wikipedia-22-12](https://huggingface.co/datasets/Cohere/wikipedia-22-12)。

## 向量嵌入详情
我们将`标题 + " " + 正文`作为输入，通过`multilingual-22-12`嵌入模型生成对应的向量嵌入。该模型为业界顶尖的语义搜索模型，支持100余种语言的语义检索任务。若需了解该模型的更多信息，请查阅[cohere.ai多语言嵌入模型](https://txt.cohere.ai/multilingual/)。

## 其他语言版本
我们还提供了多语言维基百科的嵌入数据集：
[阿拉伯语](https://huggingface.co/datasets/Cohere/wikipedia-22-12-ar-embeddings)、[德语](https://huggingface.co/datasets/Cohere/wikipedia-22-12-de-embeddings)、[英语](https://huggingface.co/datasets/Cohere/wikipedia-22-12-en-embeddings)、[西班牙语](https://huggingface.co/datasets/Cohere/wikipedia-22-12-es-embeddings)、[法语](https://huggingface.co/datasets/Cohere/wikipedia-22-12-fr-embeddings)、[印地语](https://huggingface.co/datasets/Cohere/wikipedia-22-12-hi-embeddings)、[意大利语](https://huggingface.co/datasets/Cohere/wikipedia-22-12-it-embeddings)、[日语](https://huggingface.co/datasets/Cohere/wikipedia-22-12-ja-embeddings)、[韩语](https://huggingface.co/datasets/Cohere/wikipedia-22-12-ko-embeddings)、[简易英语](https://huggingface.co/datasets/Cohere/wikipedia-22-12-simple-embeddings)、[中文](https://huggingface.co/datasets/Cohere/wikipedia-22-12-zh-embeddings)。

无嵌入向量的原始维基百科数据集可在[Cohere/wikipedia-22-12](https://huggingface.co/datasets/Cohere/wikipedia-22-12)获取。

## 数据集加载方式
你可以通过如下代码加载该数据集：
python
from datasets import load_dataset
docs = load_dataset(f"Cohere/wikipedia-22-12-zh-embeddings", split="train")

也可以无需提前下载，直接流式加载数据集：
python
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']

## 检索示例
完整的检索代码示例如下：
python
# 请先运行：pip install cohere datasets
from datasets import load_dataset
import torch
import cohere

co = cohere.Client(f"<<COHERE_API_KEY>>") # 请填入您从www.cohere.com获取的Cohere API密钥

# 最多加载1000条文档及其嵌入向量
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)

# 计算查询嵌入与文档嵌入的点积得分
dot_scores = torch.mm(query_embedding, doc_embeddings.transpose(0, 1))
top_k = torch.topk(dot_scores, k=3)

# 打印检索结果
print("查询语句:", query)
for doc_id in top_k.indices[0].tolist():
print(docs[doc_id]['title'])
print(docs[doc_id]['text'], "
")

## 性能评估
您可在以下链接查看该模型在MIRACL数据集（语义搜索评测数据集）上的性能表现：[miracl-en-queries-22-12#performance](https://huggingface.co/datasets/Cohere/miracl-en-queries-22-12#performance)

原始信息汇总

数据集概述

基本信息

• 语言: 中文 (zh)
• 多语言支持: 多语言
• 任务类别: 文本检索
• 许可证: Apache-2.0
• 任务ID: 文档检索

数据集内容

• 来源: 使用cohere.ai的multilingual-22-12嵌入模型对中文维基百科进行编码。
• 嵌入方法: 计算title+" "+text的嵌入，使用multilingual-22-12嵌入模型，该模型支持100种语言的语义搜索。

其他语言版本

• 提供多种语言的维基百科嵌入，包括阿拉伯语、德语、英语、西班牙语、法语、印地语、意大利语、日语、韩语、简单英语和中文。

数据集加载

• 可通过以下方式加载数据集： python from datasets import load_dataset docs = load_dataset(f"Cohere/wikipedia-22-12-zh-embeddings", split="train")

  或以流式方式加载，无需预先下载： python from datasets import load_dataset docs = load_dataset(f"Cohere/wikipedia-22-12-zh-embeddings", split="train", streaming=True)

搜索示例

• 提供了一个完整的搜索示例，展示了如何使用cohere.ai的API和数据集进行查询和检索。