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--- |
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base_model: TannerGladson/chess-roberta |
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tags: |
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- generated_from_trainer |
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datasets: |
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- TannerGladson/chess-roberta-whole-move-tuning |
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metrics: |
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- accuracy |
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model-index: |
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- name: 2024.07.20-19.49 |
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results: |
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- task: |
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name: Masked Language Modeling |
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type: fill-mask |
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dataset: |
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name: TannerGladson/chess-roberta-whole-move-tuning |
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type: TannerGladson/chess-roberta-whole-move-tuning |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.902359997194343 |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/tanner-gladson/huggingface/runs/j4uydn09) |
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# 2024.07.20-19.49 |
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This model is a fine-tuned version of [TannerGladson/chess-roberta](https://huggingface.co/TannerGladson/chess-roberta) on the TannerGladson/chess-roberta-whole-move-tuning dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2611 |
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- Accuracy: 0.9024 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 0.0005 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- distributed_type: multi-GPU |
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- gradient_accumulation_steps: 16 |
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- total_train_batch_size: 128 |
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- optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-06 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 200 |
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- training_steps: 5000 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
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|:-------------:|:------:|:----:|:---------------:|:--------:| |
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| 0.5262 | 0.2485 | 1000 | 0.4272 | 0.8519 | |
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| 0.413 | 0.4970 | 2000 | 0.3650 | 0.8711 | |
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| 0.3505 | 0.7455 | 3000 | 0.3138 | 0.8852 | |
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| 0.3111 | 0.9939 | 4000 | 0.2829 | 0.8950 | |
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| 0.2817 | 1.2424 | 5000 | 0.2596 | 0.9025 | |
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### Framework versions |
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- Transformers 4.42.4 |
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- Pytorch 2.0.1+cu117 |
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- Datasets 2.17.1 |
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- Tokenizers 0.19.1 |
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