Create README.md

README.md

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+---
+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)