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---
library_name: transformers
license: apache-2.0
base_model: CocoRoF/KoModernBERT-chp-11
tags:
- generated_from_trainer
model-index:
- name: KMB_SimCSE_test
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. -->
# KMB_SimCSE_test
This model is a fine-tuned version of [CocoRoF/KoModernBERT-chp-11](https://huggingface.co/CocoRoF/KoModernBERT-chp-11) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0438
- Pearson Cosine: 0.7947
- Spearman Cosine: 0.7992
- Pearson Manhattan: 0.7493
- Spearman Manhattan: 0.7655
- Pearson Euclidean: 0.7507
- Spearman Euclidean: 0.7666
- Pearson Dot: 0.6408
- Spearman Dot: 0.6472
## 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: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 8
- total_train_batch_size: 128
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 4.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Pearson Cosine | Spearman Cosine | Pearson Manhattan | Spearman Manhattan | Pearson Euclidean | Spearman Euclidean | Pearson Dot | Spearman Dot |
|:-------------:|:------:|:----:|:---------------:|:--------------:|:---------------:|:-----------------:|:------------------:|:-----------------:|:------------------:|:-----------:|:------------:|
| 0.761 | 0.1172 | 250 | 0.1397 | 0.7191 | 0.7366 | 0.7129 | 0.7205 | 0.7135 | 0.7210 | 0.4342 | 0.4302 |
| 0.6275 | 0.2343 | 500 | 0.1240 | 0.7535 | 0.7638 | 0.7442 | 0.7505 | 0.7442 | 0.7506 | 0.4527 | 0.4533 |
| 0.5326 | 0.3515 | 750 | 0.1149 | 0.7540 | 0.7698 | 0.7320 | 0.7461 | 0.7327 | 0.7466 | 0.4786 | 0.4737 |
| 0.4917 | 0.4686 | 1000 | 0.1028 | 0.7630 | 0.7778 | 0.7395 | 0.7532 | 0.7395 | 0.7531 | 0.5428 | 0.5404 |
| 0.4451 | 0.5858 | 1250 | 0.0959 | 0.7634 | 0.7803 | 0.7505 | 0.7649 | 0.7508 | 0.7652 | 0.5909 | 0.5929 |
| 0.4682 | 0.7029 | 1500 | 0.1057 | 0.7687 | 0.7855 | 0.7541 | 0.7681 | 0.7545 | 0.7685 | 0.5271 | 0.5190 |
| 0.4489 | 0.8201 | 1750 | 0.0994 | 0.7658 | 0.7800 | 0.7505 | 0.7624 | 0.7514 | 0.7627 | 0.5765 | 0.5760 |
| 0.4696 | 0.9372 | 2000 | 0.1055 | 0.7618 | 0.7835 | 0.7514 | 0.7669 | 0.7526 | 0.7675 | 0.5910 | 0.5835 |
| 0.3474 | 1.0544 | 2250 | 0.0818 | 0.7663 | 0.7777 | 0.7527 | 0.7636 | 0.7536 | 0.7642 | 0.5774 | 0.5748 |
| 0.319 | 1.1715 | 2500 | 0.0752 | 0.7753 | 0.7858 | 0.7589 | 0.7692 | 0.7592 | 0.7692 | 0.5929 | 0.5919 |
| 0.3682 | 1.2887 | 2750 | 0.0767 | 0.7736 | 0.7851 | 0.7556 | 0.7667 | 0.7564 | 0.7671 | 0.5784 | 0.5785 |
| 0.3033 | 1.4058 | 3000 | 0.0716 | 0.7836 | 0.7962 | 0.7590 | 0.7723 | 0.7600 | 0.7727 | 0.5987 | 0.5976 |
| 0.3247 | 1.5230 | 3250 | 0.0768 | 0.7779 | 0.7911 | 0.7613 | 0.7731 | 0.7621 | 0.7735 | 0.5638 | 0.5623 |
| 0.26 | 1.6401 | 3500 | 0.0686 | 0.7792 | 0.7902 | 0.7615 | 0.7733 | 0.7623 | 0.7734 | 0.6004 | 0.5998 |
| 0.3216 | 1.7573 | 3750 | 0.0707 | 0.7851 | 0.7950 | 0.7668 | 0.7787 | 0.7677 | 0.7791 | 0.6098 | 0.6136 |
| 0.3166 | 1.8744 | 4000 | 0.0719 | 0.7799 | 0.7911 | 0.7550 | 0.7693 | 0.7563 | 0.7701 | 0.5737 | 0.5754 |
| 0.315 | 1.9916 | 4250 | 0.0710 | 0.7818 | 0.7925 | 0.7657 | 0.7780 | 0.7672 | 0.7790 | 0.5918 | 0.5930 |
| 0.2117 | 2.1087 | 4500 | 0.0545 | 0.7772 | 0.7890 | 0.7551 | 0.7702 | 0.7567 | 0.7712 | 0.6059 | 0.6096 |
| 0.1725 | 2.2259 | 4750 | 0.0544 | 0.7780 | 0.7868 | 0.7593 | 0.7714 | 0.7605 | 0.7721 | 0.6065 | 0.6128 |
| 0.1985 | 2.3430 | 5000 | 0.0540 | 0.7818 | 0.7916 | 0.7621 | 0.7733 | 0.7626 | 0.7734 | 0.6017 | 0.6078 |
| 0.1871 | 2.4602 | 5250 | 0.0527 | 0.7830 | 0.7898 | 0.7576 | 0.7718 | 0.7587 | 0.7724 | 0.5843 | 0.5894 |
| 0.17 | 2.5773 | 5500 | 0.0521 | 0.7877 | 0.7959 | 0.7621 | 0.7746 | 0.7633 | 0.7753 | 0.6240 | 0.6246 |
| 0.174 | 2.6945 | 5750 | 0.0528 | 0.7876 | 0.7949 | 0.7594 | 0.7713 | 0.7603 | 0.7716 | 0.6196 | 0.6234 |
| 0.1896 | 2.8116 | 6000 | 0.0506 | 0.7848 | 0.7891 | 0.7595 | 0.7712 | 0.7606 | 0.7718 | 0.6052 | 0.6083 |
| 0.1897 | 2.9288 | 6250 | 0.0549 | 0.7819 | 0.7902 | 0.7521 | 0.7664 | 0.7533 | 0.7667 | 0.5957 | 0.5981 |
| 0.105 | 3.0459 | 6500 | 0.0450 | 0.7887 | 0.7931 | 0.7516 | 0.7669 | 0.7527 | 0.7675 | 0.6385 | 0.6450 |
| 0.1055 | 3.1631 | 6750 | 0.0460 | 0.7875 | 0.7927 | 0.7515 | 0.7652 | 0.7525 | 0.7657 | 0.6256 | 0.6332 |
| 0.1145 | 3.2802 | 7000 | 0.0453 | 0.7925 | 0.7977 | 0.7548 | 0.7671 | 0.7559 | 0.7678 | 0.6316 | 0.6408 |
| 0.1252 | 3.3974 | 7250 | 0.0470 | 0.7889 | 0.7947 | 0.7561 | 0.7683 | 0.7571 | 0.7693 | 0.6257 | 0.6283 |
| 0.1058 | 3.5145 | 7500 | 0.0446 | 0.7913 | 0.7958 | 0.7572 | 0.7714 | 0.7578 | 0.7715 | 0.6221 | 0.6338 |
| 0.1144 | 3.6317 | 7750 | 0.0433 | 0.7939 | 0.7989 | 0.7534 | 0.7673 | 0.7542 | 0.7677 | 0.6519 | 0.6583 |
| 0.0971 | 3.7488 | 8000 | 0.0438 | 0.7952 | 0.7993 | 0.7537 | 0.7675 | 0.7547 | 0.7679 | 0.6345 | 0.6383 |
| 0.1107 | 3.8660 | 8250 | 0.0432 | 0.7953 | 0.7992 | 0.7507 | 0.7673 | 0.7518 | 0.7675 | 0.6355 | 0.6411 |
| 0.1232 | 3.9831 | 8500 | 0.0438 | 0.7947 | 0.7992 | 0.7493 | 0.7655 | 0.7507 | 0.7666 | 0.6408 | 0.6472 |
### Framework versions
- Transformers 4.48.0.dev0
- Pytorch 2.5.1+cu121
- Datasets 3.1.0
- Tokenizers 0.21.0
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