Cohere/miracl-te-queries-22-12

---
annotations_creators:
- expert-generated

language:
- te

multilinguality:
- multilingual

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

task_categories:
- text-retrieval

license:
- apache-2.0

task_ids:
- document-retrieval
---

# MIRACL (te) embedded with cohere.ai `multilingual-22-12` encoder

We encoded the [MIRACL dataset](https://huggingface.co/miracl) using the [cohere.ai](https://txt.cohere.ai/multilingual/) `multilingual-22-12` embedding model.

The query embeddings can be found in [Cohere/miracl-te-queries-22-12](https://huggingface.co/datasets/Cohere/miracl-te-queries-22-12) and the corpus embeddings can be found in [Cohere/miracl-te-corpus-22-12](https://huggingface.co/datasets/Cohere/miracl-te-corpus-22-12).

For the orginal datasets, see [miracl/miracl](https://huggingface.co/datasets/miracl/miracl) and [miracl/miracl-corpus](https://huggingface.co/datasets/miracl/miracl-corpus).

Dataset info:
> MIRACL 🌍🙌🌏 (Multilingual Information Retrieval Across a Continuum of Languages) is a multilingual retrieval dataset that focuses on search across 18 different languages, which collectively encompass over three billion native speakers around the world.
>
> The corpus for each language is prepared from a Wikipedia dump, where we keep only the plain text and discard images, tables, etc. Each article is segmented into multiple passages using WikiExtractor based on natural discourse units (e.g., `\n\n` in the wiki markup). Each of these passages comprises a "document" or unit of retrieval. We preserve the Wikipedia article title of each passage.

## 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/).

## Loading the dataset

In [miracl-te-corpus-22-12](https://huggingface.co/datasets/Cohere/miracl-te-corpus-22-12) we provide the corpus embeddings. Note, depending on the selected split, the respective files can be quite large.

You can either load the dataset like this:
```python
from datasets import load_dataset
docs = load_dataset(f"Cohere/miracl-te-corpus-22-12", split="train")
```

Or you can also stream it without downloading it before:
```python
from datasets import load_dataset
docs = load_dataset(f"Cohere/miracl-te-corpus-22-12", split="train", streaming=True)

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

## Search

Have a look at [miracl-te-queries-22-12](https://huggingface.co/datasets/Cohere/miracl-te-queries-22-12) where we provide the query embeddings for the MIRACL dataset.

To search in the documents, you must use **dot-product**.

And then compare this query embeddings either with a vector database (recommended) or directly computing the dot product.

A full search example:
```python
# Attention! For large datasets, this requires a lot of memory to store
# all document embeddings and to compute the dot product scores.
# Only use this for smaller datasets. For large datasets, use a vector DB

from datasets import load_dataset
import torch

#Load documents + embeddings
docs = load_dataset(f"Cohere/miracl-te-corpus-22-12", split="train")
doc_embeddings = torch.tensor(docs['emb'])

# Load queries
queries = load_dataset(f"Cohere/miracl-te-queries-22-12", split="dev")

# Select the first query as example
qid = 0
query = queries[qid]
query_embedding = torch.tensor(queries['emb'])

# 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['query'])
for doc_id in top_k.indices[0].tolist():
print(docs[doc_id]['title'])
print(docs[doc_id]['text'])
```

You can get embeddings for new queries using our API:
```python
#Run: pip install cohere
import cohere
co = cohere.Client(f"{api_key}") # You should add your cohere API Key here :))
texts = ['my search query']
response = co.embed(texts=texts, model='multilingual-22-12')
query_embedding = response.embeddings[0] # Get the embedding for the first text
```

## Performance

In the following table we compare the cohere multilingual-22-12 model with Elasticsearch version 8.6.0 lexical search (title and passage indexed as independent fields). Note that Elasticsearch doesn't support all languages that are part of the MIRACL dataset.

We compute nDCG@10 (a ranking based loss), as well as hit@3: Is at least one relevant document in the top-3 results. We find that hit@3 is easier to interpret, as it presents the number of queries for which a relevant document is found among the top-3 results.

Note: MIRACL only annotated a small fraction of passages (10 per query) for relevancy. Especially for larger Wikipedias (like English), we often found many more relevant passages. This is know as annotation holes. Real nDCG@10 and hit@3 performance is likely higher than depicted.

| Model | cohere multilingual-22-12 nDCG@10 | cohere multilingual-22-12 hit@3 | ES 8.6.0 nDCG@10 | ES 8.6.0 acc@3 |
|---|---|---|---|---|
| miracl-ar | 64.2 | 75.2 | 46.8 | 56.2 |
| miracl-bn | 61.5 | 75.7 | 49.2 | 60.1 |
| miracl-de | 44.4 | 60.7 | 19.6 | 29.8 |
| miracl-en | 44.6 | 62.2 | 30.2 | 43.2 |
| miracl-es | 47.0 | 74.1 | 27.0 | 47.2 |
| miracl-fi | 63.7 | 76.2 | 51.4 | 61.6 |
| miracl-fr | 46.8 | 57.1 | 17.0 | 21.6 |
| miracl-hi | 50.7 | 62.9 | 41.0 | 48.9 |
| miracl-id | 44.8 | 63.8 | 39.2 | 54.7 |
| miracl-ru | 49.2 | 66.9 | 25.4 | 36.7 |
| **Avg** | 51.7 | 67.5 | 34.7 | 46.0 |

Further languages (not supported by Elasticsearch):
| Model | cohere multilingual-22-12 nDCG@10 | cohere multilingual-22-12 hit@3 |
|---|---|---|
| miracl-fa | 44.8 | 53.6 |
| miracl-ja | 49.0 | 61.0 |
| miracl-ko | 50.9 | 64.8 |
| miracl-sw | 61.4 | 74.5 |
| miracl-te | 67.8 | 72.3 |
| miracl-th | 60.2 | 71.9 |
| miracl-yo | 56.4 | 62.2 |
| miracl-zh | 43.8 | 56.5 |
| **Avg** | 54.3 | 64.6 |

原始信息汇总

数据集概述

数据集名称

• MIRACL (Multilingual Information Retrieval Across a Continuum of Languages)

语言

• 包含18种语言，覆盖超过三亿母语使用者。

数据来源

• 数据来源于Wikipedia，通过WikiExtractor处理，保留纯文本内容，去除图片、表格等非文本元素。

数据结构

• 每篇文章被分割成多个基于自然话语单元的段落，每个段落作为一个“文档”或检索单元，并保留文章标题。

许可证

• Apache-2.0

任务类型

• 文档检索

嵌入模型

• 使用cohere.ai的multilingual-22-12嵌入模型，支持100种语言的语义搜索。

数据集加载

• 可通过load_dataset函数加载，支持流式加载。

搜索方法

• 使用点积（dot-product）进行文档搜索。

性能评估

• 对比cohere multilingual-22-12模型与Elasticsearch 8.6.0的搜索性能，使用nDCG@10和hit@3作为评估指标。

数据集详细信息

嵌入数据

• 查询嵌入：位于Cohere/miracl-te-queries-22-12
• 文档嵌入：位于Cohere/miracl-te-corpus-22-12

搜索示例

• 提供了一个使用PyTorch计算点积的搜索示例，适用于小型数据集。

性能对比

• cohere multilingual-22-12模型在多种语言上的性能优于Elasticsearch 8.6.0，特别是在不支持的语言上。

模型	cohere multilingual-22-12 nDCG@10	cohere multilingual-22-12 hit@3
平均	51.7	67.5
不支持语言平均	54.3	64.6