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--- |
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license: mit |
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datasets: |
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- nelson2424/Chess_openings_dataset |
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language: |
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- en |
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metrics: |
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- perplexity |
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- accuracy |
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pipeline_tag: text-generation |
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--- |
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This model was created to predict moves in the chess opening. |
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The idea is to test the impact of modeling the game text differently and report the results. |
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You can access the code for training [here](https://github.com/bit2424/chess_openings_teacher/tree/main/ML/Training) |
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You can access the different model configurations and results [here](https://wandb.ai/nelsonquinones2424/Chess%20Openings%20Tutor) |
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# Training process: |
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- ## Training with V1_small dataset: |
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To understand the following discussion is important to check the structure of the [nelson2424/Chess_openings_dataset](https://huggingface.co/datasets/nelson2424/Chess_openings_dataset) dataset the V1_small version. |
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During the training process, multiple challenges arose. |
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-The first problem was the <b>low accuracy</b> in the results the model was getting, to mitigate that problem, I tried the following: |
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- <b>learning rate:</b> |
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The first approach to solve this problem was to modify the learning rate. <br> |
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A step further involved changing the lr scheduler from a linear configuration to a polynomial decay configuration. |
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These changes did not have a significant effect on the accuracy. |
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- <b>Probability of masked tokens:</b> |
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Decreasing the probability of the masked tokens in the dataset increased the accuracy but at the expense of the model having a weaker prediction capability. |
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Having a low masked token probability will result in a model incapable of predicting correct moves on different openings. |
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- <b>Focus on predicting the moves:</b> |
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The current model tries to model the whole text that the V1_small version of the dataset provides, which includes |
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<br>trying to predict parts of the board after a move or the name of the opening, as seen in the following example: |
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~~~ |
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<s>King's Indian <mask>: <mask> Variation, Debrecen Defense |
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r n b q k b n r |
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p p p p p p p p |
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........ |
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........ |
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.. P..... |
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........ |
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P P. P <mask> P P P |
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R N B Q K B N R |
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m:g8f6 |
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<mask>:<mask>b<mask><mask> b q k b. r |
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p p p p p p p p |
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..... n.. |
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........ |
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.. P..... |
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........ |
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P P. P P P P P |
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R N B Q K B N R |
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m:b1c3 |
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<mask><mask><mask> |
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<mask><mask> b q k b. r |
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p p p p p p p p |
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..... n.. |
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........ |
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.. P..... |
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.. N..... |
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P P. P P P P P |
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R. B Q K B N' |
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~~~ |
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<br> After realizing that my model was not able to learn a complex enough function to correctly |
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<br> model the problem at hand due to limited computational resources, I decided to narrow the scope of the problem. |
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<br> Instead of trying to generate the whole context, the model would only learn to generate moves and the effect they have on the board based on a rich context. |
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<br> This allowed the model to have a rich representation of the game and predict moves more accurately. |
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<br> As a result, the data was modified only to mask move predictions and their corresponding effects on the board. |
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<br> The data now looks as follows: |
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~~~ |
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<s>King's Indian Defense: Fianchetto Variation, Debrecen Defense |
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r n b q k b n r |
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p p p p p p p p |
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........ |
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........ |
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.. P..... |
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........ |
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P P. P P P P P |
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R N B Q K B N R |
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<mask><mask><mask><mask><mask><mask> |
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<mask><mask><mask><mask><mask><mask> b q k b. r |
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p p p p p p p p |
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..... n.. |
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........ |
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.. P..... |
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........ |
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P P. P P P P P |
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R N B Q K B N R |
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m:b1c3 |
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<mask><mask><mask><mask><mask><mask> b q k b. r |
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p p p p p p p p |
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..... n.. |
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........ |
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.. P..... |
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.. N..... |
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P P. P P P P P |
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R. B Q K B N' |
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~~~ |
