Datasets:
Tasks:
Text Retrieval
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Text
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Sub-tasks:
document-retrieval
Languages:
Spanish
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10M - 100M
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README.md
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---
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# Dataset Card for "miracl-es-corpus-22-12"
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---
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annotations_creators:
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- expert-generated
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language:
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- es
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multilinguality:
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- multilingual
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size_categories: []
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source_datasets: []
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tags: []
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task_categories:
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- text-retrieval
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license:
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- apache-2.0
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task_ids:
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- document-retrieval
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---
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# MIRACL (es) embedded with cohere.ai `multilingual-22-12` encoder
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We encoded the [MIRACL dataset](https://huggingface.co/miracl) using the [cohere.ai](https://txt.cohere.ai/multilingual/) `multilingual-22-12` embedding model.
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The query embeddings can be found in [Cohere/miracl-es-queries-22-12](https://huggingface.co/datasets/Cohere/miracl-es-queries-22-12) and the corpus embeddings can be found in [Cohere/miracl-es-corpus-22-12](https://huggingface.co/datasets/Cohere/miracl-es-corpus-22-12).
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For the orginal datasets, see [miracl/miracl](https://huggingface.co/datasets/miracl/miracl) and [miracl/miracl-corpus](https://huggingface.co/datasets/miracl/miracl-corpus).
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Dataset info:
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> 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.
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>
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> 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.
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## Embeddings
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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/).
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## Loading the dataset
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In [miracl-es-corpus-22-12](https://huggingface.co/datasets/Cohere/miracl-es-corpus-22-12) we provide the corpus embeddings. Note, depending on the selected split, the respective files can be quite large.
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You can either load the dataset like this:
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```python
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from datasets import load_dataset
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docs = load_dataset(f"Cohere/miracl-es-corpus-22-12", split="train")
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```
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Or you can also stream it without downloading it before:
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```python
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from datasets import load_dataset
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docs = load_dataset(f"Cohere/miracl-es-corpus-22-12", split="train", streaming=True)
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for doc in docs:
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docid = doc['docid']
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title = doc['title']
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text = doc['text']
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emb = doc['emb']
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```
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## Search
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Have a look at [miracl-es-queries-22-12](https://huggingface.co/datasets/Cohere/miracl-es-queries-22-12) where we provide the query embeddings for the MIRACL dataset.
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To search in the documents, you must use **dot-product**.
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And then compare this query embeddings either with a vector database (recommended) or directly computing the dot product.
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A full search example:
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```python
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# Attention! For large datasets, this requires a lot of memory to store
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# all document embeddings and to compute the dot product scores.
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# Only use this for smaller datasets. For large datasets, use a vector DB
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from datasets import load_dataset
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import torch
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#Load documents + embeddings
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docs = load_dataset(f"Cohere/miracl-es-corpus-22-12", split="train")
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doc_embeddings = torch.tensor(docs['emb'])
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# Load queries
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queries = load_dataset(f"Cohere/miracl-es-queries-22-12", split="dev")
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# Select the first query as example
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qid = 0
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query = queries[qid]
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query_embedding = torch.tensor(queries['emb'])
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# Compute dot score between query embedding and document embeddings
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dot_scores = torch.mm(query_embedding, doc_embeddings.transpose(0, 1))
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top_k = torch.topk(dot_scores, k=3)
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# Print results
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print("Query:", query['query'])
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for doc_id in top_k.indices[0].tolist():
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print(docs[doc_id]['title'])
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print(docs[doc_id]['text'])
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```
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You can get embeddings for new queries using our API:
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```python
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#Run: pip install cohere
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import cohere
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co = cohere.Client(f"{api_key}") # You should add your cohere API Key here :))
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texts = ['my search query']
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response = co.embed(texts=texts, model='multilingual-22-12')
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query_embedding = response.embeddings[0] # Get the embedding for the first text
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```
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## Performance
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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.
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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.
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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.
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| 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 |
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| miracl-ar | 64.2 | 75.2 | 46.8 | 56.2 |
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| miracl-bn | 61.5 | 75.7 | 49.2 | 60.1 |
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| miracl-de | 44.4 | 60.7 | 19.6 | 29.8 |
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| miracl-en | 44.6 | 62.2 | 30.2 | 43.2 |
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| miracl-es | 47.0 | 74.1 | 27.0 | 47.2 |
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| miracl-fi | 63.7 | 76.2 | 51.4 | 61.6 |
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| miracl-fr | 46.8 | 57.1 | 17.0 | 21.6 |
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| miracl-hi | 50.7 | 62.9 | 41.0 | 48.9 |
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| miracl-id | 44.8 | 63.8 | 39.2 | 54.7 |
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| miracl-ru | 49.2 | 66.9 | 25.4 | 36.7 |
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| **Avg** | 51.7 | 67.5 | 34.7 | 46.0 |
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Further languages (not supported by Elasticsearch):
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| Model | cohere multilingual-22-12 nDCG@10 | cohere multilingual-22-12 hit@3 |
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| miracl-fa | 44.8 | 53.6 |
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| miracl-ja | 49.0 | 61.0 |
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| miracl-ko | 50.9 | 64.8 |
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| miracl-sw | 61.4 | 74.5 |
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| miracl-te | 67.8 | 72.3 |
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| miracl-th | 60.2 | 71.9 |
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| miracl-yo | 56.4 | 62.2 |
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| miracl-zh | 43.8 | 56.5 |
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| **Avg** | 54.3 | 64.6 |
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