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--- |
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license: apache-2.0 |
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tags: |
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- generated_from_trainer |
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datasets: |
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- klue |
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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-base-klue-ner |
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results: |
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- task: |
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name: Token Classification |
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type: token-classification |
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dataset: |
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name: klue |
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type: klue |
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config: ner |
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split: validation |
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args: ner |
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metrics: |
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- name: Precision |
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type: precision |
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value: 0.7709512081475072 |
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- name: Recall |
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type: recall |
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value: 0.81237286946763 |
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- name: F1 |
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type: f1 |
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value: 0.7911202185792351 |
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- name: Accuracy |
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type: accuracy |
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value: 0.9588468788113982 |
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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-base-klue-ner |
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This model is a fine-tuned version of [monologg/koelectra-base-v3-discriminator](https://huggingface.co/monologg/koelectra-base-v3-discriminator) on the klue dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1427 |
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- Precision: 0.7710 |
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- Recall: 0.8124 |
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- F1: 0.7911 |
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- Accuracy: 0.9588 |
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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: 2e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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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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- num_epochs: 3 |
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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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| 0.1647 | 1.0 | 2626 | 0.1678 | 0.7258 | 0.7518 | 0.7386 | 0.9494 | |
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| 0.111 | 2.0 | 5252 | 0.1447 | 0.7460 | 0.8002 | 0.7721 | 0.9557 | |
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| 0.0785 | 3.0 | 7878 | 0.1427 | 0.7710 | 0.8124 | 0.7911 | 0.9588 | |
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### Framework versions |
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- Transformers 4.30.2 |
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- Pytorch 2.0.1+cpu |
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- Datasets 2.12.0 |
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- Tokenizers 0.11.0 |
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