Cohere/miracl-fi-corpus-22-12

---
annotations_creators:
- expert-generated

language:
- fi

multilinguality:
- multilingual

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

task_categories:
- text-retrieval

license:
- apache-2.0

task_ids:
- document-retrieval
---

# MIRACL (fi) 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-fi-queries-22-12](https://huggingface.co/datasets/Cohere/miracl-fi-queries-22-12) and the corpus embeddings can be found in [Cohere/miracl-fi-corpus-22-12](https://huggingface.co/datasets/Cohere/miracl-fi-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-fi-corpus-22-12](https://huggingface.co/datasets/Cohere/miracl-fi-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-fi-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-fi-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-fi-queries-22-12](https://huggingface.co/datasets/Cohere/miracl-fi-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-fi-corpus-22-12", split="train")
doc_embeddings = torch.tensor(docs['emb'])

# Load queries
queries = load_dataset(f"Cohere/miracl-fi-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

MIRACL (fi) 数据集概述

基本信息

• 标注创建者: 专家生成
• 语言: 芬兰语
• 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

MIRACL（Multilingual Information Retrieval Across a Continuum of Languages）是一个多语言检索数据集，专注于18种不同语言的搜索，这些语言共同覆盖了全球超过30亿母语使用者。每个语言的语料库是从维基百科转储中准备的，只保留纯文本，丢弃图像、表格等。每个文章被WikiExtractor基于自然话语单元（例如，`

`在维基标记中）分割成多个段落。每个段落构成一个“文档”或检索单元，并保留了维基百科文章标题。

嵌入

我们使用multilingual-22-12嵌入模型计算title+" "+text的嵌入，这是一个适用于100种语言语义搜索的先进模型。

数据集加载

• 语料库嵌入: 可在miracl-fi-corpus-22-12中找到。
• 查询嵌入: 可在miracl-fi-queries-22-12中找到。

加载示例

python from datasets import load_dataset docs = load_dataset("Cohere/miracl-fi-corpus-22-12", split="train")

流式加载示例

python from datasets import load_dataset docs = load_dataset("Cohere/miracl-fi-corpus-22-12", split="train", streaming=True) for doc in docs: docid = doc[docid] title = doc[title] text = doc[text] emb = doc[emb]

搜索

使用点积（dot-product）进行文档搜索，并比较查询嵌入与文档嵌入。

搜索示例

python from datasets import load_dataset import torch

加载文档和嵌入

docs = load_dataset("Cohere/miracl-fi-corpus-22-12", split="train") doc_embeddings = torch.tensor(docs[emb])

加载查询

queries = load_dataset("Cohere/miracl-fi-queries-22-12", split="dev")

选择第一个查询作为示例

qid = 0 query = queries[qid] query_embedding = torch.tensor(queries[emb])

计算查询嵌入和文档嵌入的点积

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

打印结果

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

性能

比较了cohere的multilingual-22-12模型与Elasticsearch 8.6.0的词法搜索性能，使用nDCG@10和hit@3作为评估指标。

性能表

模型	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-fi	63.7	76.2	51.4	61.6

进一步语言（不支持Elasticsearch）:

模型	cohere multilingual-22-12 nDCG@10	cohere multilingual-22-12 hit@3
miracl-fi	63.7	76.2