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--- |
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language: |
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- ko |
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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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widget: |
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- text: 나스닥투자증권에서 시작된 발동성 가치 상태 효과는 투자자들에게 좋은 기회를 제공합니다. |
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example_title: example01 |
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- text: TM머니가 베를린증권거래소에서 미국 보험 유가를 거래하고 있습니다. |
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example_title: example02 |
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base_model: klue/roberta-small |
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model-index: |
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- name: ko_fin_ner_roberta_small_model |
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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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# ko_fin_ner_roberta_small_model |
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This model is a fine-tuned version of [klue/roberta-small](https://huggingface.co/klue/roberta-small) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2873 |
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- Precision: 0.7436 |
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- Recall: 0.8774 |
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- F1: 0.8050 |
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- Accuracy: 0.9374 |
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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: 32 |
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- eval_batch_size: 32 |
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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: 30 |
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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 | 25 | 1.0272 | 0.1215 | 0.1662 | 0.1404 | 0.7237 | |
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| No log | 2.0 | 50 | 0.7136 | 0.2360 | 0.4033 | 0.2978 | 0.7695 | |
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| No log | 3.0 | 75 | 0.5289 | 0.3422 | 0.5586 | 0.4244 | 0.8285 | |
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| No log | 4.0 | 100 | 0.4404 | 0.4184 | 0.6076 | 0.4956 | 0.8730 | |
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| No log | 5.0 | 125 | 0.3768 | 0.4124 | 0.6540 | 0.5058 | 0.8866 | |
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| No log | 6.0 | 150 | 0.3484 | 0.4758 | 0.6975 | 0.5657 | 0.8953 | |
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| No log | 7.0 | 175 | 0.3236 | 0.5477 | 0.7357 | 0.6279 | 0.9039 | |
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| No log | 8.0 | 200 | 0.3097 | 0.5702 | 0.7520 | 0.6486 | 0.9015 | |
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| No log | 9.0 | 225 | 0.3168 | 0.6167 | 0.7629 | 0.6821 | 0.9096 | |
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| No log | 10.0 | 250 | 0.2950 | 0.6176 | 0.8011 | 0.6975 | 0.9145 | |
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| No log | 11.0 | 275 | 0.2806 | 0.6674 | 0.8147 | 0.7337 | 0.9195 | |
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| No log | 12.0 | 300 | 0.2749 | 0.6853 | 0.8365 | 0.7534 | 0.9266 | |
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| No log | 13.0 | 325 | 0.2743 | 0.7002 | 0.8338 | 0.7612 | 0.9292 | |
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| No log | 14.0 | 350 | 0.2862 | 0.6774 | 0.8011 | 0.7341 | 0.9238 | |
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| No log | 15.0 | 375 | 0.2703 | 0.6879 | 0.8529 | 0.7616 | 0.9276 | |
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| No log | 16.0 | 400 | 0.2752 | 0.7036 | 0.8474 | 0.7689 | 0.9293 | |
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| No log | 17.0 | 425 | 0.2721 | 0.6998 | 0.8447 | 0.7654 | 0.9305 | |
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| No log | 18.0 | 450 | 0.2831 | 0.6979 | 0.8311 | 0.7587 | 0.9299 | |
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| No log | 19.0 | 475 | 0.2857 | 0.7252 | 0.8556 | 0.7850 | 0.9319 | |
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| 0.2786 | 20.0 | 500 | 0.2792 | 0.7260 | 0.8665 | 0.7901 | 0.9319 | |
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| 0.2786 | 21.0 | 525 | 0.2604 | 0.7355 | 0.8638 | 0.7945 | 0.9349 | |
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| 0.2786 | 22.0 | 550 | 0.2603 | 0.7092 | 0.8638 | 0.7789 | 0.9359 | |
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| 0.2786 | 23.0 | 575 | 0.3026 | 0.7227 | 0.8665 | 0.7881 | 0.9342 | |
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| 0.2786 | 24.0 | 600 | 0.2800 | 0.7431 | 0.8747 | 0.8035 | 0.9375 | |
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| 0.2786 | 25.0 | 625 | 0.2838 | 0.7283 | 0.8692 | 0.7925 | 0.9361 | |
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| 0.2786 | 26.0 | 650 | 0.2813 | 0.7339 | 0.8719 | 0.7970 | 0.9371 | |
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| 0.2786 | 27.0 | 675 | 0.2881 | 0.7407 | 0.8719 | 0.8010 | 0.9358 | |
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| 0.2786 | 28.0 | 700 | 0.2894 | 0.7379 | 0.8747 | 0.8005 | 0.9362 | |
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| 0.2786 | 29.0 | 725 | 0.2889 | 0.7483 | 0.8747 | 0.8065 | 0.9368 | |
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| 0.2786 | 30.0 | 750 | 0.2873 | 0.7436 | 0.8774 | 0.8050 | 0.9374 | |
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### Framework versions |
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- Transformers 4.28.0 |
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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 |
