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---
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. |