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--- |
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base_model: klue/roberta-small |
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tags: |
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- generated_from_trainer |
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- korean |
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- klue |
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widget: |
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- text: 저는 서울특별시 강남대로에 삽니다. 전화번호는 010-1234-5678이고 주민등록번호는 123456-1234567입니다. 메일주소는 hugging@face.com입니다. |
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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_roberta_small_ner_identified |
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results: [] |
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language: |
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- ko |
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pipeline_tag: token-classification |
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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_roberta_small_ner_identified |
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This model is a fine-tuned version of [klue/roberta-small](https://huggingface.co/klue/roberta-small) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0212 |
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- Precision: 0.9803 |
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- Recall: 1.0 |
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- F1: 0.9901 |
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- Accuracy: 0.9980 |
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## Model description |
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아래 항목에 대한 개체명 인식을 제공합니다. |
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- 사람이름 [PS] - 낮은 인식률 |
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- 주소 (구 주소 및 도로명 주소) [AD] |
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- 카드번호 [CN] |
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- 계좌번호 [BN] |
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- 운전면허번호 [DN] |
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- 주민등록번호 [RN] |
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- 여권번호 [PN] |
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- 전화번호 [PH] |
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- 이메일 주소 [EM] |
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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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- num_epochs: 8 |
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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 | 15 | 0.2866 | 0.1199 | 0.2739 | 0.1668 | 0.9287 | |
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| No log | 2.0 | 30 | 0.1369 | 0.6599 | 0.7996 | 0.7231 | 0.9654 | |
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| No log | 3.0 | 45 | 0.0629 | 0.8088 | 0.9042 | 0.8538 | 0.9915 | |
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| No log | 4.0 | 60 | 0.0381 | 0.9760 | 0.9978 | 0.9868 | 0.9969 | |
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| No log | 5.0 | 75 | 0.0276 | 0.9781 | 0.9955 | 0.9868 | 0.9981 | |
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| No log | 6.0 | 90 | 0.0238 | 0.9803 | 1.0 | 0.9901 | 0.9979 | |
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| No log | 7.0 | 105 | 0.0224 | 0.9803 | 1.0 | 0.9901 | 0.9979 | |
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| No log | 8.0 | 120 | 0.0212 | 0.9803 | 1.0 | 0.9901 | 0.9980 | |
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### Framework versions |
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- Transformers 4.40.2 |
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- Pytorch 2.3.0+cu118 |
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- Datasets 2.19.1 |
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- Tokenizers 0.19.1 |
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### Use |
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```python |
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from transformers import AutoTokenizer, AutoModelForTokenClassification |
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from transformers import pipeline |
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tokenizer = AutoTokenizer.from_pretrained("vitus9988/klue-roberta-small-ner-identified") |
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model = AutoModelForTokenClassification.from_pretrained("vitus9988/klue-roberta-small-ner-identified") |
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nlp = pipeline("ner", model=model, tokenizer=tokenizer, aggregation_strategy="simple") |
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example = """ |
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저는 서울특별시 강남대로 56길 100호에 삽니다. 전화번호는 010-1234-5678이고 주민등록번호는 123456-1234567입니다. 메일주소는 hugging@face.com입니다. |
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""" |
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ner_results = nlp(example) |
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for i in ner_results: |
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print(i) |
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#{'entity_group': 'AD', 'score': 0.79996574, 'word': '서울특별시 강남대로 56길 100호', 'start': 4, 'end': 23} |
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#{'entity_group': 'PH', 'score': 0.948794, 'word': '010 - 1234 - 5678', 'start': 36, 'end': 49} |
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#{'entity_group': 'RN', 'score': 0.90686846, 'word': '123456 - 1234567', 'start': 60, 'end': 74} |
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#{'entity_group': 'EM', 'score': 0.935588, 'word': 'hugging @ face. com', 'start': 85, 'end': 101} |
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``` |
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