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---
library_name: transformers
license: mit
base_model: microsoft/DialoGPT-small
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: dialochess-v4
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. -->
# dialochess-v4
This model is a fine-tuned version of [microsoft/DialoGPT-small](https://huggingface.co/microsoft/DialoGPT-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8124
- Accuracy: 0.0004
## 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: 6
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 1.3141 | 0.1616 | 200 | 1.1921 | 0.0002 |
| 1.194 | 0.3231 | 400 | 1.0974 | 0.0003 |
| 1.118 | 0.4847 | 600 | 1.0425 | 0.0 |
| 1.0767 | 0.6462 | 800 | 1.0071 | 0.0 |
| 1.0414 | 0.8078 | 1000 | 0.9825 | 0.0005 |
| 1.0158 | 0.9693 | 1200 | 0.9601 | 0.0002 |
| 1.0035 | 1.1309 | 1400 | 0.9427 | 0.0001 |
| 0.9715 | 1.2924 | 1600 | 0.9300 | 0.0002 |
| 0.9745 | 1.4540 | 1800 | 0.9193 | 0.0002 |
| 0.9447 | 1.6155 | 2000 | 0.9063 | 0.0002 |
| 0.9573 | 1.7771 | 2200 | 0.8980 | 0.0005 |
| 0.9386 | 1.9386 | 2400 | 0.8893 | 0.0003 |
| 0.9204 | 2.1002 | 2600 | 0.8786 | 0.0003 |
| 0.9128 | 2.2617 | 2800 | 0.8732 | 0.0003 |
| 0.9079 | 2.4233 | 3000 | 0.8670 | 0.0002 |
| 0.9073 | 2.5848 | 3200 | 0.8603 | 0.0002 |
| 0.8938 | 2.7464 | 3400 | 0.8532 | 0.0004 |
| 0.8899 | 2.9079 | 3600 | 0.8501 | 0.0002 |
| 0.8834 | 3.0695 | 3800 | 0.8426 | 0.0002 |
| 0.8693 | 3.2310 | 4000 | 0.8416 | 0.0003 |
| 0.8808 | 3.3926 | 4200 | 0.8335 | 0.0002 |
| 0.872 | 3.5541 | 4400 | 0.8297 | 0.0003 |
| 0.8689 | 3.7157 | 4600 | 0.8296 | 0.0003 |
| 0.8607 | 3.8772 | 4800 | 0.8237 | 0.0002 |
| 0.8516 | 4.0388 | 5000 | 0.8246 | 0.0004 |
| 0.8652 | 4.2003 | 5200 | 0.8210 | 0.0004 |
| 0.8522 | 4.3619 | 5400 | 0.8192 | 0.0003 |
| 0.8466 | 4.5234 | 5600 | 0.8181 | 0.0002 |
| 0.8525 | 4.6850 | 5800 | 0.8163 | 0.0004 |
| 0.8485 | 4.8465 | 6000 | 0.8163 | 0.0004 |
| 0.8444 | 5.0081 | 6200 | 0.8144 | 0.0003 |
| 0.8512 | 5.1696 | 6400 | 0.8141 | 0.0004 |
| 0.8405 | 5.3312 | 6600 | 0.8135 | 0.0004 |
| 0.8337 | 5.4927 | 6800 | 0.8124 | 0.0004 |
| 0.8601 | 5.6543 | 7000 | 0.8125 | 0.0004 |
| 0.8506 | 5.8158 | 7200 | 0.8124 | 0.0004 |
| 0.8562 | 5.9774 | 7400 | 0.8124 | 0.0004 |
### Framework versions
- Transformers 4.57.2
- Pytorch 2.9.0+cu126
- Datasets 4.0.0
- Tokenizers 0.22.1
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