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koelectra_emotion_v1
be2be2/koelectra_emotion_v1
koelectra_emotion_v1 is a text classification model from be2be2. Use it when you need a label for a piece of text. It is set up for transformers.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Text Classification
- Library
- transformers
- Model type
- electra
- Access
- Public
- Created
- Sep 25, 2025
- Updated
- Sep 25, 2025
- SHA
- a9d5b2ea
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Base models
Model card
From the Hugging Face model README
<!-- 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. -->
koelectra_emotion_v1
This model is a fine-tuned version of monologg/koelectra-small-finetuned-sentiment on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.8551
- Accuracy: 0.7073
- F1: 0.7022
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: 64
- eval_batch_size: 64
- 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: linear
- num_epochs: 10
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|---|---|---|---|---|---|
| 1.1554 | 1.0 | 1466 | 0.9218 | 0.6726 | 0.6625 |
| 0.8745 | 2.0 | 2932 | 0.8692 | 0.6967 | 0.6866 |
| 0.795 | 3.0 | 4398 | 0.8422 | 0.6997 | 0.6937 |
| 0.7375 | 4.0 | 5864 | 0.8278 | 0.7069 | 0.7025 |
| 0.6909 | 5.0 | 7330 | 0.8426 | 0.7057 | 0.7037 |
| 0.6516 | 6.0 | 8796 | 0.8551 | 0.7073 | 0.7022 |
Framework versions
- Transformers 4.56.1
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.22.0
- Task
- Text Classification
- Library
- transformers
- Type
- electra
- Created
- Sep 25, 2025
- Updated
- Sep 25, 2025