metadata
license: bsd-3-clause
configs:
- config_name: no-vectors
data_files: no-vectors/*.parquet
default: true
- config_name: openai-text-embedding-3-large
data_files: openai/text-embedding-3-large/*.parquet
default: true
- config_name: snowflake-arctic-embed
data_files: ollama/snowflake-arctic/*.parquet
default: true
size_categories:
- 100K<n<1M
Loading dataset with vector embeddings
You can load the dataset like this:
from datasets import load_dataset
dataset = load_dataset("weaviate/wiki-sample", "openai-text-embedding-3-large", streaming=True)
# dataset = load_dataset("weaviate/wiki-sample", "snowflake-arctic-embed", split="train", streaming=True)
for item in dataset:
print(item["text"])
print(item["title"])
print(item["url"])
print(item["wiki_id"])
print(item["vector"])
print()
Supported Datasets
Data only - no vectors
from datasets import load_dataset
dataset = load_dataset("weaviate/wiki-sample", "no-vectors", streaming=True)
OpenAI
text-embedding-3-large - 3072d vectors - generated with OpenAI
from datasets import load_dataset
dataset = load_dataset("weaviate/wiki-sample", "openai-text-embedding-3-large", streaming=True)
Snowflake
snowflake-arctic-embed - 1024 vectors - generated with Ollama
from datasets import load_dataset
dataset = load_dataset("weaviate/wiki-sample", "snowflake-arctic-embed", streaming=True)