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---
language:
- en
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
- domain-specific
library_name: sentence-transformers
---
# **YGOMiniLM**
[ImgSource](https://yugipedia.com/wiki/Time_Wizard)
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:
1) A private collection of data collected from YouTube Comments:
|CREATOR|N_COMMENTS|
|-----|-----|
|thecalieffect|20,592|
|MBTYuGiOh|5439|
|MSTTV |5340|
|mkohl40|5224|
2) The Full Database of YuGiOh cards accessed via the [YGOProDeck API](https://ygoprodeck.com/api-guide/) 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)
``` |