README.md

metadata

base_model: TannerGladson/chess-roberta-whole-move-pretrained
tags:
  - generated_from_trainer
datasets:
  - TannerGladson/chess-roberta-pretraining
metrics:
  - accuracy
model-index:
  - name: 2024.09.24-01.27
    results:
      - task:
          name: Masked Language Modeling
          type: fill-mask
        dataset:
          name: TannerGladson/chess-roberta-pretraining
          type: TannerGladson/chess-roberta-pretraining
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.5445640378503339

2024.09.24-01.27

This model is a fine-tuned version of /src/tanner/chess-roberta-code/model_configs/chess_roberta.json on the TannerGladson/chess-roberta-pretraining dataset. It achieves the following results on the evaluation set:

• Loss: 2.1202
• Accuracy: 0.5446

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 0.0005
• train_batch_size: 8
• eval_batch_size: 8
• seed: 42
• distributed_type: multi-GPU
• gradient_accumulation_steps: 16
• total_train_batch_size: 128
• optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-06
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 200
• training_steps: 9000

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
2.1201	0.0120	1000	2.1195	0.5445
2.1222	0.0239	2000	2.1199	0.5446
2.1179	0.0359	3000	2.1199	0.5444
2.1215	0.0479	4000	2.1186	0.5446
2.1176	0.0598	5000	2.1194	0.5445
2.1189	0.0718	6000	2.1193	0.5445
2.1184	0.0838	7000	2.1194	0.5446
2.119	0.0957	8000	2.1190	0.5446
2.1237	0.1077	9000	2.1201	0.5446

Framework versions

• Transformers 4.42.4
• Pytorch 2.0.1+cu117
• Datasets 2.17.1
• Tokenizers 0.19.1