Science Score: 18.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • CITATION.cff file
    Found CITATION.cff file
  • codemeta.json file
  • .zenodo.json file
  • DOI references
  • Academic publication links
  • Academic email domains
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  • Scientific vocabulary similarity
    Low similarity (10.2%) to scientific vocabulary
Last synced: 9 months ago · JSON representation ·

Repository

Basic Info
  • Host: GitHub
  • Owner: tintinker
  • Language: Python
  • Default Branch: main
  • Size: 408 KB
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Created almost 3 years ago · Last pushed almost 3 years ago
Metadata Files
Readme Citation

README.md

CodenamesAI

A composite model for playing the codenames board game

Usage

  1. Make an account on PlaygrounRL with this referral link
  2. Download your auth.json to the main directory
  3. Update the line model_name="default_model_name" in spymaster/codenames.py with your own unique model name
  4. pip install -r requirements.txt
  5. run python3.11 -m gameplay.codenames or ./run10x.sh from the main directory

Model Info / Pipeline

  1. Initial categories are downloaded from the semcat dataset
  2. Those categories are fed into a DSP few-shot learning GPT3.5 pipeline to generate 500 more categories from common themes, nouns, and adjectives in the top 1000 english words (see category_generation/README.md)
  3. DSP is re-applied as a RLHF model (with some manually selected gold standard categories) to check if categories are comprehensive using the top 20 related Glove vectors (by cosine similarity) (see category_generation/README.md)
  4. Decision tree and Neural Network models with uniform hyperparameter sweep are fit to predict categories from word glove embeddings (multi-label classification) (see models/README.md, models/decisiontree.py and models/neuralnet.py)
  5. The best neural network models are selected based on test accuracy
  6. A Q-learning MDP is used to detirmine a scoring metric, as a function of the game state, to rank categories generated by the neural network model. Early in the game the Q table generally prefers a higher risk scoring metric; later in the game it generally prefers lower risk. (see gameplay/README.md, data/q.json, gameplay/hint.py, gameplay/algo.py, gameplay/mdp.py)

Citations

See citations/README.md

```@article{khattab2022demonstrate, title={Demonstrate-Search-Predict: Composing Retrieval and Language Models for Knowledge-Intensive {NLP}}, author={Khattab, Omar and Santhanam, Keshav and Li, Xiang Lisa and Hall, David and Liang, Percy and Potts, Christopher and Zaharia, Matei}, journal={arXiv preprint arXiv:2212.14024}, year={2022} } @ARTICLE {SEMCAT, author = "Senel L. K., Utlu I., Yucesoy V., Koc A., Cukur T.", title = "Semantic Structure and Interpretability of Word Embeddings", year = "2018", organization = "IEEE/ACM Transactions on Audio, Speech, and Language Processing" }

Owner

  • Name: Justin Tinker
  • Login: tintinker
  • Kind: user
  • Location: Cleveland, OH

Citation (citations/citations.md)

# SEMCATdataset2018
This repository is to download the SEMCATdataset 2018 for the publication "Senel L. K., Utlu I., Yucesoy V., Koc A., Cukur T., Semantic Structure and Interpretability of Word Embeddings, IEEE/ACM Transactions on Audio, Speech, and Language Processing".

 Author: Lutfi Kerem Senel

 Last Update: 5.04.2018

 CITATION:
 
 If you use SEMCAT in your research, please cite (will be updated after publication):

                @ARTICKLE {SEMCAT,
                         author       = "Senel L. K., Utlu I., Yucesoy V., Koc A., Cukur T.",
                         title        = "Semantic Structure and Interpretability of Word Embeddings",
                         year         = "2018",
                         organization = "IEEE/ACM Transactions on Audio, Speech, and Language Processing"
                }


Category words are in the files named "X-Y.txt" where X is the name of the category and Y is the number of the words in that category. 

Justin's notes:
names of some compound categories edited 
musical instrument, music theory -> music, band, harmonics,
bodies of water -> aquatic, 
positive words -> positivity, 
character traits -> personality

# wordlist_github.txt
https://github.com/Gullesnuffs/Codenames

# wordlist_mostcommon10k.txt
https://raw.githubusercontent.com/first20hours/google-10000-english/master/google-10000-english.txt

# wordlist_reddit.txt
https://www.reddit.com/r/boardgames/comments/9ynuxv/is_there_a_list_of_codenames_card_words/

# wordlist_mostcommon1k.txt
https://www.ef.com/wwen/english-resources/english-vocabulary/top-1000-words/

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Dependencies

requirements.txt pypi
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