
# **YGOMiniLM**
![time_wiz](https://ms.yugipedia.com//thumb/7/76/TimeWizard-MRD-EN-UR-UE-25thAnniversaryEdition.png/300px-TimeWizard-MRD-EN-UR-UE-25thAnniversaryEdition.png)
[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)
```
{"language": ["en"], "library_name": "sentence-transformers", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers", "domain-specific"], "pipeline_tag": "sentence-similarity"}
sentence-transformers pytorch bert fill-mask feature-extraction sentence-similarity transformers domain-specific en endpoints_compatible region:us
TAGS
#sentence-transformers #pytorch #bert #fill-mask #feature-extraction #sentence-similarity #transformers #domain-specific #en #endpoints_compatible #region-us 

YGOMiniLM
=========


!time\_wiz
ImgSource


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:



2. 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
-----


Then to get embeddings you simply run the following:



TAGS
#sentence-transformers #pytorch #bert #fill-mask #feature-extraction #sentence-similarity #transformers #domain-specific #en #endpoints_compatible #region-us 
