End of training
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README.md
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---
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base_model: klue
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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:
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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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#
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This model is a fine-tuned version of [klue
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It achieves the following results on the evaluation set:
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- Loss: 0.
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- Precision: 0.
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- Recall:
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- F1: 0.
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- Accuracy: 0.
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## Model description
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### Training hyperparameters
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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 |
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| No log | 2.0 |
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| No log | 3.0 |
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| No log | 4.0 |
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| No log | 5.0 |
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| No log | 6.0 |
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| No log | 7.0 |
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| No log | 8.0 |
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### Framework versions
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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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---
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base_model: vitus9988/klue-roberta-small-ner-identified
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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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model-index:
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- name: klue-roberta-small-ner-identified
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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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# klue-roberta-small-ner-identified
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This model is a fine-tuned version of [vitus9988/klue-roberta-small-ner-identified](https://huggingface.co/vitus9988/klue-roberta-small-ner-identified) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.1304
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- Precision: 0.9222
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- Recall: 0.9520
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- F1: 0.9369
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- Accuracy: 0.9790
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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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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
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| No log | 1.0 | 4 | 0.8023 | 0.1377 | 0.1231 | 0.1300 | 0.9178 |
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| No log | 2.0 | 8 | 0.4197 | 0.5419 | 0.5580 | 0.5498 | 0.9431 |
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| No log | 3.0 | 12 | 0.2760 | 0.6764 | 0.7146 | 0.6950 | 0.9564 |
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| No log | 4.0 | 16 | 0.2062 | 0.7835 | 0.8544 | 0.8174 | 0.9617 |
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| No log | 5.0 | 20 | 0.1685 | 0.8299 | 0.8946 | 0.8610 | 0.9711 |
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| No log | 6.0 | 24 | 0.1470 | 0.8854 | 0.9295 | 0.9069 | 0.9758 |
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| No log | 7.0 | 28 | 0.1350 | 0.9138 | 0.9460 | 0.9297 | 0.9778 |
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| No log | 8.0 | 32 | 0.1304 | 0.9222 | 0.9520 | 0.9369 | 0.9790 |
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### Framework versions
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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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model.safetensors
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version https://git-lfs.github.com/spec/v1
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size 270078052
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version https://git-lfs.github.com/spec/v1
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size 270078052
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