---
base_model: TannerGladson/chess-roberta
tags:
- generated_from_trainer
datasets:
- TannerGladson/chess-roberta-whole-move-tuning
metrics:
- accuracy
model-index:
- name: 2024.07.20-19.49
  results:
  - task:
      name: Masked Language Modeling
      type: fill-mask
    dataset:
      name: TannerGladson/chess-roberta-whole-move-tuning
      type: TannerGladson/chess-roberta-whole-move-tuning
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.902359997194343
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

[<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)
# 2024.07.20-19.49

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.
It achieves the following results on the evaluation set:
- Loss: 0.2611
- Accuracy: 0.9024

## 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: 5000

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 0.5262        | 0.2485 | 1000 | 0.4272          | 0.8519   |
| 0.413         | 0.4970 | 2000 | 0.3650          | 0.8711   |
| 0.3505        | 0.7455 | 3000 | 0.3138          | 0.8852   |
| 0.3111        | 0.9939 | 4000 | 0.2829          | 0.8950   |
| 0.2817        | 1.2424 | 5000 | 0.2596          | 0.9025   |


### Framework versions

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