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--- |
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license: cc-by-sa-4.0 |
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base_model: klue/bert-base |
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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: klue_ner_bert_model |
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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: 0.883861132284665 |
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- name: Recall |
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type: recall |
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value: 0.8966608084358524 |
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- name: F1 |
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type: f1 |
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value: 0.890214963707426 |
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- name: Accuracy |
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type: accuracy |
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value: 0.9781297871646948 |
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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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# klue_ner_bert_model |
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This model is a fine-tuned version of [klue/bert-base](https://huggingface.co/klue/bert-base) on the klue dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0843 |
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- Precision: 0.8839 |
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- Recall: 0.8967 |
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- F1: 0.8902 |
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- Accuracy: 0.9781 |
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## Model description |
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KLUE BERT base is a pre-trained BERT Model on Korean Language. The developers of KLUE BERT base developed the model in the context of the development of the [Korean Language Understanding Evaluation (KLUE) Benchmark](https://arxiv.org/pdf/2105.09680.pdf). |
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## Intended uses & limitations |
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## How to Get Started With the Model |
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```python |
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from transformers import AutoModel, AutoTokenizer |
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model = AutoModel.from_pretrained("klue/bert-base") |
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tokenizer = AutoTokenizer.from_pretrained("klue/bert-base") |
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``` |
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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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| 0.0638 | 1.0 | 2626 | 0.0807 | 0.8623 | 0.8702 | 0.8662 | 0.9747 | |
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| 0.0402 | 2.0 | 5252 | 0.0780 | 0.8756 | 0.8896 | 0.8825 | 0.9770 | |
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| 0.025 | 3.0 | 7878 | 0.0843 | 0.8839 | 0.8967 | 0.8902 | 0.9781 | |
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### Framework versions |
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- Transformers 4.31.0 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.14.0 |
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- Tokenizers 0.13.3 |
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