Add language tag

README.md

@@ -1,210 +1,221 @@
----
-configs:
-  - config_name: en
-    default: true
-    data_files:
-      - split: train
-        path: "data/en/*.parquet"
-  - config_name: de
-    data_files:
-      - split: train
-        path: "data/de/*.parquet"
-  - config_name: fr
-    data_files:
-      - split: train
-        path: "data/fr/*.parquet"
-  - config_name: ru
-    data_files:
-      - split: train
-        path: "data/ru/*.parquet"
-  - config_name: es
-    data_files:
-      - split: train
-        path: "data/es/*.parquet"
-  - config_name: it
-    data_files:
-      - split: train
-        path: "data/it/*.parquet"
-  - config_name: ja
-    data_files:
-      - split: train
-        path: "data/ja/*.parquet"
-  - config_name: pt
-    data_files:
-      - split: train
-        path: "data/pt/*.parquet"
-  - config_name: zh
-    data_files:
-      - split: train
-        path: "data/zh/*.parquet"
-  - config_name: fa
-    data_files:
-      - split: train
-        path: "data/fa/*.parquet"
-  - config_name: tr
-    data_files:
-      - split: train
-        path: "data/tr/*.parquet"
-license: apache-2.0
----
-# Wikipedia Embeddings with BGE-M3
-
-This dataset contains embeddings from the
-[June 2024 Wikipedia dump](https://dumps.wikimedia.org/wikidatawiki/20240601/)
-for the 11 most popular languages.
-
-The embeddings are generated with the multilingual
-[BGE-M3](https://huggingface.co/BAAI/bge-m3) model.
-
-The dataset consists of Wikipedia articles split into paragraphs,
-and embedded with the aforementioned model.
-
-To enhance search quality, the paragraphs are prefixed with their
-respective article titles before embedding.
-
-Additionally, paragraphs containing fewer than 100 characters,
-which tend to have low information density, are excluded from the dataset.
-
-The dataset contains approximately 144 million vector embeddings in total.
-
-| Language   | Config Name | Embeddings  |
-|------------|-------------|-------------|
-| English    | en          | 47_018_430  |
-| German     | de          | 20_213_669  |
-| French     | fr          | 18_324_060  |
-| Russian    | ru          | 13_618_886  |
-| Spanish    | es          | 13_194_999  |
-| Italian    | it          | 10_092_524  |
-| Japanese   | ja          | 7_769_997   |
-| Portuguese | pt          | 5_948_941   |
-| Farsi      | fa          | 2_598_251   |
-| Chinese    | zh          | 3_306_397   |
-| Turkish    | tr          | 2_051_157   |
-| **Total**  |             | 144_137_311 | 
-
-## Loading Dataset
-
-You can load the entire dataset for a language as follows.
-Please note that for some languages, the download size may be quite large.
-
-```python
-from datasets import load_dataset
-
-dataset = load_dataset("Upstash/wikipedia-2024-06-bge-m3", "en", split="train")
-```
-
-Alternatively, you can stream portions of the dataset as needed.
-
-```python
-from datasets import load_dataset
-
-dataset = load_dataset(
-    "Upstash/wikipedia-2024-06-bge-m3", "en", split="train", streaming=True
-)
-
-for data in dataset:
-    data_id = data["id"]
-    url = data["url"]
-    title = data["title"]
-    text = data["text"]
-    embedding = data["embedding"]
-    # Do some work
-    break
-```
-
-## Using Dataset
-
-One potential use case for the dataset is enabling similarity search
-by integrating it with a vector database.
-
-In fact, we have developed a vector database that allows you to search
-through the Wikipedia articles. Additionally, it includes a
-[RAG (Retrieval-Augmented Generation)](https://github.com/upstash/rag-chat) chatbot,
-which enables you to interact with a chatbot enhanced by the dataset.
-
-For more details, see this [blog post](https://upstash.com/blog/indexing-wikipedia),
-and be sure to check out the
-[search engine and chatbot](https://wikipedia-semantic-search.vercel.app) yourself.
-
-For reference, here is a rough estimation of how to implement semantic search
-functionality using this dataset and Upstash Vector.
-
-```python
-from datasets import load_dataset
-from sentence_transformers import SentenceTransformer
-from upstash_vector import Index
-
-# You can create Upstash Vector with dimension set to 1024,
-# and similarity search function to dot product.
-index = Index(
-    url="<UPSTASH_VECTOR_REST_URL>",
-    token="<UPSTASH_VECTOR_REST_TOKEN>",
-)
-
-vectors = []
-batch_size = 200
-
-dataset = load_dataset(
-    "Upstash/wikipedia-2024-06-bge-m3", "en", split="train", streaming=True
-)
-
-for data in dataset:
-    data_id = data["id"]
-    url = data["url"]
-    title = data["title"]
-    text = data["text"]
-    embedding = data["embedding"]
-
-    metadata = {
-        "url": url,
-        "title": title,
-    }
-
-    vector = (
-        data_id,  # Unique vector id
-        embedding,  # Vector embedding
-        metadata,  # Optional, JSON-like metadata
-        text,  # Optional, unstructured text data
-    )
-    vectors.append(vector)
-
-    if len(vectors) == batch_size:
-        break
-
-# Upload embeddings into Upstash Vector in batches
-index.upsert(
-    vectors=vectors,
-    namespace="en",
-)
-
-# Create the query vector
-transformer = SentenceTransformer(
-    "BAAI/bge-m3",
-    device="cuda",
-    revision="babcf60cae0a1f438d7ade582983d4ba462303c2",
-)
-
-query = "Which state has the nickname Yellowhammer State?"
-query_vector = transformer.encode(
-    sentences=query,
-    show_progress_bar=False,
-    normalize_embeddings=True,
-)
-
-results = index.query(
-    vector=query_vector,
-    top_k=2,
-    include_metadata=True,
-    include_data=True,
-    namespace="en",
-)
-
-# Query results are sorted in descending order of similarity
-for result in results:
-    print(result.id)  # Unique vector id
-    print(result.score)  # Similarity score to the query vector
-    print(result.metadata)  # Metadata associated with vector
-    print(result.data)  # Unstructured data associated with vector
-    print("---")
-```
-
+---
+configs:
+  - config_name: en
+    default: true
+    data_files:
+      - split: train
+        path: "data/en/*.parquet"
+  - config_name: de
+    data_files:
+      - split: train
+        path: "data/de/*.parquet"
+  - config_name: fr
+    data_files:
+      - split: train
+        path: "data/fr/*.parquet"
+  - config_name: ru
+    data_files:
+      - split: train
+        path: "data/ru/*.parquet"
+  - config_name: es
+    data_files:
+      - split: train
+        path: "data/es/*.parquet"
+  - config_name: it
+    data_files:
+      - split: train
+        path: "data/it/*.parquet"
+  - config_name: ja
+    data_files:
+      - split: train
+        path: "data/ja/*.parquet"
+  - config_name: pt
+    data_files:
+      - split: train
+        path: "data/pt/*.parquet"
+  - config_name: zh
+    data_files:
+      - split: train
+        path: "data/zh/*.parquet"
+  - config_name: fa
+    data_files:
+      - split: train
+        path: "data/fa/*.parquet"
+  - config_name: tr
+    data_files:
+      - split: train
+        path: "data/tr/*.parquet"
+license: apache-2.0
+language:
+  - en
+  - de
+  - es
+  - fa
+  - fr
+  - it
+  - ja
+  - pt
+  - ru
+  - tr
+  - zh
+---
+# Wikipedia Embeddings with BGE-M3
+
+This dataset contains embeddings from the
+[June 2024 Wikipedia dump](https://dumps.wikimedia.org/wikidatawiki/20240601/)
+for the 11 most popular languages.
+
+The embeddings are generated with the multilingual
+[BGE-M3](https://huggingface.co/BAAI/bge-m3) model.
+
+The dataset consists of Wikipedia articles split into paragraphs,
+and embedded with the aforementioned model.
+
+To enhance search quality, the paragraphs are prefixed with their
+respective article titles before embedding.
+
+Additionally, paragraphs containing fewer than 100 characters,
+which tend to have low information density, are excluded from the dataset.
+
+The dataset contains approximately 144 million vector embeddings in total.
+
+| Language   | Config Name | Embeddings  |
+|------------|-------------|-------------|
+| English    | en          | 47_018_430  |
+| German     | de          | 20_213_669  |
+| French     | fr          | 18_324_060  |
+| Russian    | ru          | 13_618_886  |
+| Spanish    | es          | 13_194_999  |
+| Italian    | it          | 10_092_524  |
+| Japanese   | ja          | 7_769_997   |
+| Portuguese | pt          | 5_948_941   |
+| Farsi      | fa          | 2_598_251   |
+| Chinese    | zh          | 3_306_397   |
+| Turkish    | tr          | 2_051_157   |
+| **Total**  |             | 144_137_311 | 
+
+## Loading Dataset
+
+You can load the entire dataset for a language as follows.
+Please note that for some languages, the download size may be quite large.
+
+```python
+from datasets import load_dataset
+
+dataset = load_dataset("Upstash/wikipedia-2024-06-bge-m3", "en", split="train")
+```
+
+Alternatively, you can stream portions of the dataset as needed.
+
+```python
+from datasets import load_dataset
+
+dataset = load_dataset(
+    "Upstash/wikipedia-2024-06-bge-m3", "en", split="train", streaming=True
+)
+
+for data in dataset:
+    data_id = data["id"]
+    url = data["url"]
+    title = data["title"]
+    text = data["text"]
+    embedding = data["embedding"]
+    # Do some work
+    break
+```
+
+## Using Dataset
+
+One potential use case for the dataset is enabling similarity search
+by integrating it with a vector database.
+
+In fact, we have developed a vector database that allows you to search
+through the Wikipedia articles. Additionally, it includes a
+[RAG (Retrieval-Augmented Generation)](https://github.com/upstash/rag-chat) chatbot,
+which enables you to interact with a chatbot enhanced by the dataset.
+
+For more details, see this [blog post](https://upstash.com/blog/indexing-wikipedia),
+and be sure to check out the
+[search engine and chatbot](https://wikipedia-semantic-search.vercel.app) yourself.
+
+For reference, here is a rough estimation of how to implement semantic search
+functionality using this dataset and Upstash Vector.
+
+```python
+from datasets import load_dataset
+from sentence_transformers import SentenceTransformer
+from upstash_vector import Index
+
+# You can create Upstash Vector with dimension set to 1024,
+# and similarity search function to dot product.
+index = Index(
+    url="<UPSTASH_VECTOR_REST_URL>",
+    token="<UPSTASH_VECTOR_REST_TOKEN>",
+)
+
+vectors = []
+batch_size = 200
+
+dataset = load_dataset(
+    "Upstash/wikipedia-2024-06-bge-m3", "en", split="train", streaming=True
+)
+
+for data in dataset:
+    data_id = data["id"]
+    url = data["url"]
+    title = data["title"]
+    text = data["text"]
+    embedding = data["embedding"]
+
+    metadata = {
+        "url": url,
+        "title": title,
+    }
+
+    vector = (
+        data_id,  # Unique vector id
+        embedding,  # Vector embedding
+        metadata,  # Optional, JSON-like metadata
+        text,  # Optional, unstructured text data
+    )
+    vectors.append(vector)
+
+    if len(vectors) == batch_size:
+        break
+
+# Upload embeddings into Upstash Vector in batches
+index.upsert(
+    vectors=vectors,
+    namespace="en",
+)
+
+# Create the query vector
+transformer = SentenceTransformer(
+    "BAAI/bge-m3",
+    device="cuda",
+    revision="babcf60cae0a1f438d7ade582983d4ba462303c2",
+)
+
+query = "Which state has the nickname Yellowhammer State?"
+query_vector = transformer.encode(
+    sentences=query,
+    show_progress_bar=False,
+    normalize_embeddings=True,
+)
+
+results = index.query(
+    vector=query_vector,
+    top_k=2,
+    include_metadata=True,
+    include_data=True,
+    namespace="en",
+)
+
+# Query results are sorted in descending order of similarity
+for result in results:
+    print(result.id)  # Unique vector id
+    print(result.score)  # Similarity score to the query vector
+    print(result.metadata)  # Metadata associated with vector
+    print(result.data)  # Unstructured data associated with vector
+    print("---")
+```