metadata
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: KoELECTRA-small-v3-modu-ner
results: []
KoELECTRA-small-v3-modu-ner
This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.1443
- Precision: 0.8176
- Recall: 0.8401
- F1: 0.8287
- Accuracy: 0.9615
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: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 3787
- num_epochs: 5
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 3788 | 0.1511 | 0.8095 | 0.8257 | 0.8176 | 0.9594 |
| No log | 2.0 | 7576 | 0.1461 | 0.8121 | 0.8339 | 0.8228 | 0.9600 |
| No log | 3.0 | 11364 | 0.1417 | 0.8139 | 0.8372 | 0.8254 | 0.9607 |
| No log | 4.0 | 15152 | 0.1418 | 0.8238 | 0.8346 | 0.8292 | 0.9617 |
| 0.0748 | 5.0 | 18940 | 0.1443 | 0.8176 | 0.8401 | 0.8287 | 0.9615 |
Framework versions
- Transformers 4.27.4
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.2