README.md

metadata

dataset_info:
  features:
    - name: count
      dtype: int64
    - name: fact
      dtype: string
    - name: embeddings
      sequence: float32
  splits:
    - name: train
      num_bytes: 157033742
      num_examples: 50000
  download_size: 186812200
  dataset_size: 157033742

Dataset Card for "omcs_50k_with_FAISS"

When people communicate, they rely on a large body of shared common sense knowledge in order to understand each other. Many barriers we face today in artificial intelligence and user interface design are due to the fact that computers do not share this knowledge. To improve computers' understanding of the world that people live in and talk about, we need to provide them with usable knowledge about the basic relationships between things that nearly every person knows.

The embedding for implementing FAISS indexing is given in the dataset as the 'embedding' column.

To implement FAISS indexing:

dataset.add_faiss_index(column='embeddings')

The above code needed to be executed. Then FAISS indexing can be verified.