Cohere/wikipedia-22-12-it-embeddings

---
annotations_creators:
- expert-generated

language:
- it

multilinguality:
- multilingual

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

task_categories:
- text-retrieval

license:
- apache-2.0

task_ids:
- document-retrieval
---

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

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

• 语言: 意大利语（it）
• 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

数据集内容

• 数据集使用cohere.ai的multilingual-22-12嵌入模型对Wikipedia (it)进行了编码。
• 计算了title+" "+text的嵌入，使用的是multilingual-22-12嵌入模型，该模型支持100种语言的语义搜索。

数据集加载

• 可以通过以下Python代码加载数据集： python from datasets import load_dataset docs = load_dataset(f"Cohere/wikipedia-22-12-it-embeddings", split="train")

• 也可以通过设置streaming=True进行流式加载，无需预先下载。

搜索示例

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

性能评估

• 数据集的性能评估可在MIRACL数据集中查看。