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---
library_name: transformers
license: apache-2.0
base_model: PleIAs/Monad
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: monad-chess
results: []
---
<!-- 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. -->
# monad-chess
This model is a fine-tuned version of [PleIAs/Monad](https://huggingface.co/PleIAs/Monad) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8021
- Accuracy: 0.0
## 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: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 1.0912 | 0.1616 | 200 | 1.0668 | 0.0001 |
| 1.0167 | 0.3231 | 400 | 0.9883 | 0.0 |
| 0.9614 | 0.4847 | 600 | 0.9587 | 0.0 |
| 0.926 | 0.6462 | 800 | 0.9231 | 0.0 |
| 0.9032 | 0.8078 | 1000 | 0.8942 | 0.0 |
| 0.8828 | 0.9693 | 1200 | 0.8803 | 0.0 |
| 0.8739 | 1.1309 | 1400 | 0.8659 | 0.0 |
| 0.844 | 1.2924 | 1600 | 0.8526 | 0.0 |
| 0.8531 | 1.4540 | 1800 | 0.8453 | 0.0 |
| 0.8254 | 1.6155 | 2000 | 0.8297 | 0.0 |
| 0.8434 | 1.7771 | 2200 | 0.8263 | 0.0 |
| 0.8217 | 1.9386 | 2400 | 0.8189 | 0.0 |
| 0.8034 | 2.1002 | 2600 | 0.8121 | 0.0 |
| 0.8051 | 2.2617 | 2800 | 0.8082 | 0.0 |
| 0.7945 | 2.4233 | 3000 | 0.8062 | 0.0 |
| 0.7975 | 2.5848 | 3200 | 0.8040 | 0.0 |
| 0.7881 | 2.7464 | 3400 | 0.8026 | 0.0 |
| 0.7951 | 2.9079 | 3600 | 0.8021 | 0.0 |
### Framework versions
- Transformers 4.57.2
- Pytorch 2.9.0+cu126
- Datasets 4.0.0
- Tokenizers 0.22.1
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