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199330#19 | Marnen taistelu | Joffre hyökkäsi Marnejoella pohjoiseen 6. päivän aamulla. | [
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199330#20 | Marnen taistelu | 5. päivänä Saksan 1. armeijan oikealla sivustalla etenevä IV AK:n komentaja Hans von Gronau sai vihiä että Ranska keskitti joukkojaan myös Pariisin seuduille, jolloin Ranskan 6. armeija paljastui vuorokautta ennen hyökkäystään. | [
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199330#21 | Marnen taistelu | Ranskalainen Michel-Joseph Maunouryn 6. armeija hyökkäsi 5. syyskuuta yllättäen Kluckin oikeaan sivustaan. Kluck käänsi joukkonsa vastahyökkäykseen. | [
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199330#22 | Marnen taistelu | Alkoi Ourcqin taistelu 5-9. syyskuuta. Mutta saksalaiset taistelivat raivokkaasti ja onnistuivat estämään täpärästi saarrostuksen. 1. armeija komentaja Kluck lähetti Gronaulle vain hieman apujoukkoja, koska piti hyökkäystä vain harhautuksena. Vasta 7. syyskuuta Kluck käsitti, että ranskalaisten hyökkäys oli vakava uhka. | [
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199330#23 | Marnen taistelu | Kluckin tekemä siirto avasi 1. ja 2. rintamien väliin 50 km levyisen aukon. Saksalaiset sijoittivat tähän aukkoon ohuesti ratsuväkeä. Ranskan 6. armeijan hyökkäys pani saksalaisen IV Res Ak:n peräytymään. Pian saksalainen II armeija saapui auttamaan IV Res. AK:ta. Brittien ja ranskalaisten hyökkäys kohdistui nyt kahden armeijan väliseen aukkoon. | [
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199330#24 | Marnen taistelu | Idässä Verdunin lähellä Saksan armeija eteni, kunnes lännen taistelut pakottivat saksalaiset siirtämään joukkoja sinne. | [
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199330#25 | Marnen taistelu | Pariisista koilliseen ja itään käytiin kolmen päivän ajan taistelua, jossa Ranskalaiset hyökkäsivät von Kluckin armeijan kimppuun, ja Kluck vastasi rajuilla vastahyökkäyksillä. Niinpä rintamalinja aaltoili. Maunouryn 6. armeija uli luhistumaisillaan. Pariisin taksit kuljettivat uusia sotilaita rintamalle. Näiden merkitystä on suuresti liioiteltu, ne kuljettivat rintamalle vain pari rykmenttiä. Enemmät joukot ja kalusto kuljetettiin mm kuorma-autoilla ja junilla. | [
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199330#26 | Marnen taistelu | Ranskan armeijan hyökkäys 6. syyskuuta 1914 Pariisin ja Verdunin välissä pysäytti Saksan 4. ja 5. armeijat Verdunin länsipuolella. 7. syyskuuta Saksan 1. armeijan ja Ranskan joukkojen välillä oli tasapaino, mutta ranskalaisten asema oli 8. syyskuuta vaikeutumassa linjalla Crepy-en-Valois-Meux. | [
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199330#27 | Marnen taistelu | Kun Saksan armeija vetäytyi, samalla 9. armeijan komentaja Foch hyökkäsi, vaikka hänen alaisten joukkojen oikea ja vasen sivusta olivat vetäytymässä. | [
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199330#28 | Marnen taistelu | Joffre suostutteli melko vastahakoisen Englannin siirtoarmeijan komentajan Frenchin hyökkäämään sanoen, että "Englannin kunnia on kysmyksessä". Englannin armeija viivytteli hyökkäystään vielä 6. päivänä ja hyökkäsi Saksan 1. ja 2. armeijoiden väliseen aukkoon, ja löi
ratsuväen Petit Morinissa 7. syyskuuta. Niinpä ratsuväkisuoja rintaman aukossa repeytyi kahtia. Englantilaiset etenivät Cuulommiersin tasalle ja näin 1. ja 2. armeijoiden välissä oli jälleen aukko. von Bulow joutui vetämään armeijansa oikeaa sivustaa 9 km taaksepäin, kun Ranskan 5. armeija hyökkäsi. | [
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199330#29 | Marnen taistelu | Epäluottamus ja pelko epäonnistumista kalvoivat Moltken ja Bulowin mieltä. Saksan joukkojen johto säikähti muuttunutta tilannetta ja pelkäsi pitkälle edenneiden, jakautuneiden joukkojensa joutuvan saarroksiin. Saksalaiset eivät pitäneet pientä brittiarmeijaa vielä isona uhkana. | [
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199330#30 | Marnen taistelu | Se siirsi suuren osan 1. armeijan joukkoja varmistaakseen sivustansa. Ranskalaiset pitivät puoliaan Nanteulissa ja aikoivat hyökätä. | [
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MIRACL (fi) 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-fi-queries-22-12 and the corpus embeddings can be found in Cohere/miracl-fi-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-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:
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:
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 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-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:
#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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