---
annotations_creators:
- expert-generated

language:
- bn

multilinguality:
- multilingual

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

task_categories:
- text-retrieval

license:
- apache-2.0

task_ids:
- document-retrieval
---

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

# Load queries 
queries = load_dataset(f"Cohere/miracl-bn-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 provide the nDCG@10 scores for the cohere multilingual-22-12 model in comparison to BM25 lexical search and mDPR (as provided in the [MIRACL paper](https://arxiv.org/abs/2210.09984))

| Model | cohere multilingual-22-12 | BM25 lexical search | mDPR |
|-------|---------------------------|--------------------|------|
| miracl-ar | **64.2** | 48.1 | 49.9 |
| miracl-bn | **61.5** | 50.8 | 44.3 |
| miracl-es | **47.0** | 31.9 | 47.8 |
| miracl-fa | **44.8** | 33.3 | 48 |
| miracl-fi | **63.7** | 55.1 | 47.2 |
| miracl-fr | **46.8** | 18.3 | 43.5 |
| miracl-hi | **50.7** | 45.8 | 38.3 |
| miracl-id | 44.8 | **44.9** | 27.2 |
| miracl-ja | **49.0** | 36.9 | 43.9 |
| miracl-ko | **50.9** | 41.9 | 41.9 |
| miracl-ru | **49.2** | 33.4 | 40.7 |
| miracl-sw | **61.4** | 38.3 | 29.9 |
| miracl-te | **67.8** | 49.4 | 35.6 |
| miracl-th | **60.2** | 48.4 | 35.8 |
| miracl-zh | 43.8 | 18 | **51.2** |
| **Avg** | **53.7** | 39.6 | 41.7 |