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1334790#0 | सुनन अल-दारमी | सुनन अल-दारमी (अंग्रेज़ी:) दूसरा नाम मुसनद अल-दारमी है। यह हदीस की प्रसिद्ध पुस्तकों में से एक है, लेखक अब्दुल रहमान दारमी ने न्यायशास्त्रीय अध्यायों के संदर्भ में संकलित किया है। | [
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1334790#1 | सुनन अल-दारमी | उर्दू में सुनन दारमी शरीफ नाम से हुआ।अन्य भाषाओं में भी इसका अनुवाद हुआ। | [
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1334790#2 | सुनन अल-दारमी | सुन्नी इस्लाम की कुतुब अल-सित्ताह -सहाह सत्ता (छह प्रमुख हदीस संग्रह) के साथ इसे भी महत्वपूर्ण माना जाता है। | [
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1334790#3 | सुनन अल-दारमी | हदीस के निम्नलिखित छः विश्वसनीय संग्रह हैं जिनमें 29,578 हदीसें संग्रहित हैं : | [
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1334799#0 | शमाईल तिर्मिज़ी | 'शमाईल तिर्मिज़ी (अंग्रेज़ी:) दूसरा नाम शमाइल मुहमदिया भी है। यह हदीस की प्रसिद्ध पुस्तकों में से एक है, हदीस के इस संग्रह को 9 वीं सदी के विद्वान अल-तिर्मिज़ी ने इस्लाम के पैगम्बर मुहम्मद की दिनचर्या और यात्रओं से संबंधित बातों को इसमें संकलित किया है। पुस्तक में मुहम्मद के बारे में 399 कथन हैं जो 56 अध्यायों में विभाजित हैं। | [
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1334799#1 | शमाईल तिर्मिज़ी | अन्य भाषाओं में भी इसका अनुवाद हुआ। | [
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1334799#2 | शमाईल तिर्मिज़ी | सुन्नी इस्लाम की कुतुब अल-सित्ताह -सहाह सत्ता (छह प्रमुख हदीस संग्रह) के साथ इसे भी महत्वपूर्ण माना जाता है। | [
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1334799#3 | शमाईल तिर्मिज़ी | हदीस के निम्नलिखित छः विश्वसनीय संग्रह हैं जिनमें 29,578 हदीसें संग्रहित हैं : | [
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1334800#0 | गणतंत्र दिवस (नाइजर) | गणतंत्र दिवस, नाइजर गणराज्य में एक राष्ट्रीय अवकाश है, यह हर साल 18 दिसंबर को मनाया जाता है। हालांकि यह फ्रांस से औपचारिक, पूर्ण स्वतंत्रता की तारीख नहीं है, 18 दिसंबर फ्रांसीसी पांचवें गणराज्य के संवैधानिक परिवर्तनों और 4 दिसंबर 1958 के चुनावों के बाद गणतंत्र की स्थापना और नाइजर गणराज्य के राष्ट्रपति पद के निर्माण का प्रतीक है। नाइजीरियाई इस तिथि को अपने राष्ट्रीय संस्थानों की स्थापना मानते हैं। 18 दिसंबर 1958 और 3 अगस्त 1960 के बीच, नाइजर फ्रांसीसी समुदाय के भीतर एक अर्ध-स्वायत्त गणराज्य बना रहा। | [
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1334801#0 | राष्ट्रीय दिवस (क़तर) | क़तर राष्ट्रीय दिवस () में कतर के एकीकरण का एक राष्ट्रीय स्मरणोत्सव है। यह 18 दिसंबर को प्रतिवर्ष मनाया जाता है। अवकाश की स्थापना 21 जून 2007 को तत्कालीन क्राउन प्रिंस और उत्तराधिकारी शेख तमीम बिन हमद अल थानी के फरमान द्वारा की गई थी। इसे स्थापना दिवस के रूप में भी जाना जाता है। | [
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1334805#0 | गोवा मुक्ति दिवस | गोवा, दमन और दीव मुक्ति दिवस गोवा, भारत में हर साल 19 दिसंबर को मनाया जाता है। गोवा मुक्ति दिवस गोवा को पुर्तगाली शासन से मुक्त करने वाले भारतीय सशस्त्र बलों की स्मृति में मनाया जाता है। साथ ही इस दिन भारत यूरोपीय शासन से पूरी तरह मुक्त हुआ था। | [
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1334846#0 | कैलाशपुर | कैलाशपुर (Kailashpur) भारत के उत्तर प्रदेश राज्य के सहारनपुर ज़िले में स्थित एक नगर है। | [
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MIRACL (hi) embedded with cohere.ai multilingual-22-12
encoder
We encoded the MIRACL dataset using the cohere.ai multilingual-22-12
embedding model.
The query embeddings can be found in Cohere/miracl-hi-queries-22-12 and the corpus embeddings can be found in Cohere/miracl-hi-corpus-22-12.
For the orginal datasets, see miracl/miracl and 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.
Loading the dataset
In miracl-hi-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:
from datasets import load_dataset
docs = load_dataset(f"Cohere/miracl-hi-corpus-22-12", split="train")
Or you can also stream it without downloading it before:
from datasets import load_dataset
docs = load_dataset(f"Cohere/miracl-hi-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-hi-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:
# 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-hi-corpus-22-12", split="train")
doc_embeddings = torch.tensor(docs['emb'])
# Load queries
queries = load_dataset(f"Cohere/miracl-hi-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:
#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 |
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