Update README.md

README.md

@@ -47,15 +47,6 @@ It achieves the following results on the evaluation set:
 - 이메일 주소 [EM]
 - 날짜 [DT]
 
-## Intended uses & limitations
-
-More information needed
-
-## Training and evaluation data
-
-More information needed
-
-## Training procedure
 
 ### Training hyperparameters
 
@@ -87,28 +78,4 @@ The following hyperparameters were used during training:
 - Transformers 4.40.2
 - Pytorch 2.3.0+cu118
 - Datasets 2.19.1
-- Tokenizers 0.19.1
-
-### Use
-```python
-from transformers import AutoTokenizer, AutoModelForTokenClassification
-from transformers import pipeline
-
-tokenizer = AutoTokenizer.from_pretrained("vitus9988/klue-roberta-small-ner-identified")
-model = AutoModelForTokenClassification.from_pretrained("vitus9988/klue-roberta-small-ner-identified")
-
-nlp = pipeline("ner", model=model, tokenizer=tokenizer, aggregation_strategy="simple")
-example = """
-저는 서울특별시 강남대로 56길 100호에 삽니다. 전화번호는 010-1234-5678이고 주민등록번호는 123456-1234567입니다. 메일주소는 hugging@face.com입니다.
-"""
-
-ner_results = nlp(example)
-for i in ner_results:
-    print(i)
-
-#{'entity_group': 'AD', 'score': 0.79996574, 'word': '서울특별시 강남대로 56길 100호', 'start': 4, 'end': 23}
-#{'entity_group': 'PH', 'score': 0.948794, 'word': '010 - 1234 - 5678', 'start': 36, 'end': 49}
-#{'entity_group': 'RN', 'score': 0.90686846, 'word': '123456 - 1234567', 'start': 60, 'end': 74}
-#{'entity_group': 'EM', 'score': 0.935588, 'word': 'hugging @ face. com', 'start': 85, 'end': 101}
-
-```
+- Tokenizers 0.19.1