Cohere/wikipedia-22-12-hi-embeddings

---
annotations_creators:
- expert-generated

language:
- hi

multilinguality:
- multilingual

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

task_categories:
- text-retrieval

license:
- apache-2.0

task_ids:
- document-retrieval
---

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

We encoded [Wikipedia (hi)](https://hi.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-hi-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-hi-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-hi-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)

原始信息汇总

数据集概述

基本信息

• 标注创建者: 专家生成
• 语言: 印地语
• 多语言性: 多语言
• 任务类别: 文本检索
• 许可证: Apache 2.0
• 任务ID: 文档检索

数据集描述

该数据集包含使用cohere.ai的multilingual-22-12嵌入模型编码的印地语维基百科内容。该模型是一个支持100种语言语义搜索的先进模型。

嵌入计算

数据集中的每个条目（包括标题和文本）都通过multilingual-22-12嵌入模型计算了嵌入向量。

其他语言版本

该数据集还提供了其他多种语言版本的维基百科嵌入，包括阿拉伯语、德语、英语、西班牙语、法语、意大利语、日语、韩语、简体英语和中文。

数据集加载

数据集可以通过以下方式加载： python from datasets import load_dataset docs = load_dataset("Cohere/wikipedia-22-12-hi-embeddings", split="train")

或者以流式方式加载： python from datasets import load_dataset docs = load_dataset("Cohere/wikipedia-22-12-hi-embeddings", split="train", streaming=True) for doc in docs: docid = doc[id] title = doc[title] text = doc[text] emb = doc[emb]

搜索示例

以下是一个完整的搜索示例代码： python from datasets import load_dataset import torch import cohere

co = cohere.Client("<<COHERE_API_KEY>>") # 替换为你的Cohere API密钥

max_docs = 1000 docs_stream = load_dataset("Cohere/wikipedia-22-12-hi-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)

dot_scores = torch.mm(query_embedding, doc_embeddings.transpose(0, 1)) top_k = torch.topk(dot_scores, k=3)

print("Query:", query) for doc_id in top_k.indices[0].tolist(): print(docs[doc_id][title]) print(docs[doc_id][text], " ")