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--- |
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base_model: klue/roberta-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_roberta_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: 0.9545986426398315 |
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- name: Recall |
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type: recall |
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value: 0.9557169634489222 |
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- name: F1 |
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type: f1 |
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value: 0.955157475705421 |
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- name: Accuracy |
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type: accuracy |
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value: 0.9883703228112445 |
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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_roberta_model |
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This model is a fine-tuned version of [klue/roberta-base](https://huggingface.co/klue/roberta-base) on the klue dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0487 |
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- Precision: 0.9546 |
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- Recall: 0.9557 |
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- F1: 0.9552 |
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- Accuracy: 0.9884 |
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## Model description |
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Pretrained RoBERTa Model on Korean Language. See [Github](https://github.com/KLUE-benchmark/KLUE) and [Paper](https://arxiv.org/abs/2105.09680) for more details. |
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## Intended uses & limitations |
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## How to use |
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_NOTE:_ Use `BertTokenizer` instead of RobertaTokenizer. (`AutoTokenizer` will load `BertTokenizer`) |
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```python |
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from transformers import AutoModel, AutoTokenizer |
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model = AutoModel.from_pretrained("klue/roberta-base") |
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tokenizer = AutoTokenizer.from_pretrained("klue/roberta-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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| 0.0449 | 1.0 | 2626 | 0.0601 | 0.9361 | 0.9176 | 0.9267 | 0.9830 | |
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| 0.0262 | 2.0 | 5252 | 0.0469 | 0.9484 | 0.9510 | 0.9497 | 0.9874 | |
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| 0.0144 | 3.0 | 7878 | 0.0487 | 0.9546 | 0.9557 | 0.9552 | 0.9884 | |
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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 2.13.1 |
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- Tokenizers 0.13.3 |
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