count
int64
1
50k
fact
stringlengths
15
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embeddings
sequence
1
If you want to go fishing then you should know the difference between fish and bait
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2
chose is a translation of thing.
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3
Situation: My body has one nose.
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4
limb is related to branch.
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5
Situation: It is the eighth day of November.
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6
address is related to location.
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7
menyeleweng is a translation of relapse.
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8
Situation: Stay together at parties.
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9
an actor can act in a play
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10
burn is related to singe.
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11
abdomen is typically near thorax head
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12
tank is related to armies.
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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.

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