YGOMiniLM

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This is a sentence-transformers/paraphrase-MiniLM-L3-v2 model that has undergone further domain specific pretraining via Masked Language Modelling.

Its intended use is to create sentence embeddings for fast vector search in the domain of YuGiOh discourse.

Training Data

The training data was split into two parts:

A private collection of data collected from YouTube Comments:

CREATOR	N_COMMENTS
thecalieffect	20,592
MBTYuGiOh	5439
MSTTV	5340
mkohl40	5224

The Full Database of YuGiOh cards accessed via the YGOProDeck API as of 17/05/2023. The name, type, race and desc fields were concatenated and delimited by \t to create the training examples.

Usage

pip install sentence-transformers

Then to get embeddings you simply run the following:

from sentence_transformers import SentenceTransformer
sentences = ["FLIP: Target 1 monster on the field; destroy that target.",
              "Union Carrier needs to go.",
              "Scythe lock is healthy for the game"
              ]

model = SentenceTransformer("jkswin/YGO_MiniLM")
embeddings = model.encode(sentences)
print(embeddings)

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