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---
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tags:
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- generated_from_trainer
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metrics:
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- precision
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- recall
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- f1
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- accuracy
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model-index:
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- name: KoELECTRA-small-v3-modu-ner
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results: []
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment. -->
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# KoELECTRA-small-v3-modu-ner
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This model is a fine-tuned version of [monologg/koelectra-small-v3-discriminator](https://huggingface.co/monologg/koelectra-small-v3-discriminator) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.1354
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- Precision: 0.8084
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- Recall: 0.8311
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- F1: 0.8196
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- Accuracy: 0.9599
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 5e-05
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- train_batch_size: 64
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- eval_batch_size: 64
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- seed: 42
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: linear
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- lr_scheduler_warmup_steps: 7575
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- num_epochs: 10
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- mixed_precision_training: Native AMP
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
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| No log | 1.0 | 3788 | 0.2991 | 0.6481 | 0.6373 | 0.6426 | 0.9229 |
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| No log | 2.0 | 7576 | 0.1904 | 0.7479 | 0.7418 | 0.7448 | 0.9438 |
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| No log | 3.0 | 11364 | 0.1620 | 0.7577 | 0.7940 | 0.7754 | 0.9502 |
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| No log | 4.0 | 15152 | 0.1505 | 0.7890 | 0.7982 | 0.7936 | 0.9544 |
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| No log | 5.0 | 18940 | 0.1417 | 0.7905 | 0.8163 | 0.8032 | 0.9563 |
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| No log | 6.0 | 22728 | 0.1392 | 0.7914 | 0.8250 | 0.8079 | 0.9572 |
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| No log | 7.0 | 26516 | 0.1363 | 0.8060 | 0.8231 | 0.8144 | 0.9589 |
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| No log | 8.0 | 30304 | 0.1367 | 0.8035 | 0.8294 | 0.8162 | 0.9592 |
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| No log | 9.0 | 34092 | 0.1349 | 0.8085 | 0.8296 | 0.8189 | 0.9597 |
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| 0.2299 | 10.0 | 37880 | 0.1354 | 0.8084 | 0.8311 | 0.8196 | 0.9599 |
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### Framework versions
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- Transformers 4.27.3
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- Pytorch 1.13.1+cu116
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- Datasets 2.10.1
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- Tokenizers 0.13.2
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