Datasets:
Tasks:
Text Retrieval
Modalities:
Text
Formats:
parquet
Sub-tasks:
document-retrieval
Languages:
Swahili
Size:
1K - 10K
License:
Update README.md
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README.md
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multilinguality:
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- multilingual
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pretty_name: MIRACL-corpus
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size_categories: []
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source_datasets: []
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tags: []
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- document-retrieval
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---
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# MIRACL (
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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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## Loading the dataset
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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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## Search
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Have a look at [miracl-sw-queries-22-12](https://huggingface.co/datasets/Cohere/miracl-sw-queries-22-12) where we provide
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To search in the documents, you must use **dot-product**.
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## Performance
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In the following table we
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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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- document-retrieval
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---
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# MIRACL (sw) 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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## Loading the dataset
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In [miracl-sw-corpus-22-12](https://huggingface.co/datasets/Cohere/miracl-sw-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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## Search
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Have a look at [miracl-sw-queries-22-12](https://huggingface.co/datasets/Cohere/miracl-sw-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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## 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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