Datasets:

Cohere
/

wikipedia-22-12-zh-embeddings

	---
	language:
	- zh

	multilinguality:
	- multilingual

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

	task_categories:
	- text-retrieval

	license:
	- apache-2.0

	task_ids:
	- document-retrieval
	---

	# Wikipedia (zh) embedded with cohere.ai `multilingual-22-12` encoder

	We encoded [Wikipedia (zh)](https://zh.wikipedia.org) using the [cohere.ai](https://txt.cohere.ai/multilingual/) `multilingual-22-12` embedding model.

	To get an overview how this dataset was created and pre-processed, have a look at [Cohere/wikipedia-22-12](https://huggingface.co/datasets/Cohere/wikipedia-22-12).


	## 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/).

	## Further languages
	We provide embeddings of Wikipedia in many different languages:
	[ar](https://huggingface.co/datasets/Cohere/wikipedia-22-12-ar-embeddings), [de](https://huggingface.co/datasets/Cohere/wikipedia-22-12-de-embeddings), [en](https://huggingface.co/datasets/Cohere/wikipedia-22-12-en-embeddings), [es](https://huggingface.co/datasets/Cohere/wikipedia-22-12-es-embeddings), [fr](https://huggingface.co/datasets/Cohere/wikipedia-22-12-fr-embeddings), [hi](https://huggingface.co/datasets/Cohere/wikipedia-22-12-hi-embeddings), [it](https://huggingface.co/datasets/Cohere/wikipedia-22-12-it-embeddings), [ja](https://huggingface.co/datasets/Cohere/wikipedia-22-12-ja-embeddings), [ko](https://huggingface.co/datasets/Cohere/wikipedia-22-12-ko-embeddings), [simple english](https://huggingface.co/datasets/Cohere/wikipedia-22-12-simple-embeddings), [zh](https://huggingface.co/datasets/Cohere/wikipedia-22-12-zh-embeddings),

	You can find the Wikipedia datasets without embeddings at [Cohere/wikipedia-22-12](https://huggingface.co/datasets/Cohere/wikipedia-22-12).


	## Loading the dataset
	You can either load the dataset like this:
	```python
	from datasets import load_dataset
	docs = load_dataset(f"Cohere/wikipedia-22-12-zh-embeddings", split="train")
	```

	Or you can also stream it without downloading it before:
	```python
	from datasets import load_dataset
	docs = load_dataset(f"Cohere/wikipedia-22-12-zh-embeddings", split="train", streaming=True)

	for doc in docs:
	docid = doc['id']
	title = doc['title']
	text = doc['text']
	emb = doc['emb']
	```

	## Search
	A full search example:
	```python
	#Run: pip install cohere datasets
	from datasets import load_dataset
	import torch
	import cohere

	co = cohere.Client(f"<<COHERE_API_KEY>>") # Add your cohere API key from www.cohere.com

	#Load at max 1000 documents + embeddings
	max_docs = 1000
	docs_stream = load_dataset(f"Cohere/wikipedia-22-12-zh-embeddings", split="train", streaming=True)

	docs = []
	doc_embeddings = []

	for doc in docs_stream:
	docs.append(doc)
	doc_embeddings.append(doc['emb'])
	if len(docs) >= max_docs:
	break

	doc_embeddings = torch.tensor(doc_embeddings)

	query = 'Who founded Youtube'
	response = co.embed(texts=[query], model='multilingual-22-12')
	query_embedding = response.embeddings
	query_embedding = torch.tensor(query_embedding)

	# 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)
	for doc_id in top_k.indices[0].tolist():
	print(docs[doc_id]['title'])
	print(docs[doc_id]['text'], "\n")
	```


	## Performance
	You can find performance on the MIRACL dataset (a semantic search evaluation dataset) here: [miracl-en-queries-22-12#performance](https://huggingface.co/datasets/Cohere/miracl-en-queries-22-12#performance)