End of training

README.md

@@ -1,50 +1,43 @@
 ---
-base_model: klue/roberta-small
+base_model: vitus9988/klue-roberta-small-ner-identified
 tags:
 - generated_from_trainer
-- korean
-- klue
-widget:
-- text: 저는 서울특별시 강남대로에 삽니다. 전화번호는 010-1234-5678이고 주민등록번호는 123456-1234567입니다. 메일주소는 hugging@face.com입니다.
 metrics:
 - precision
 - recall
 - f1
 - accuracy
 model-index:
-- name: klue_roberta_small_ner_identified
+- name: klue-roberta-small-ner-identified
   results: []
-language:
-- ko
-pipeline_tag: token-classification
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
 should probably proofread and complete it, then remove this comment. -->
 
-# klue_roberta_small_ner_identified
+# klue-roberta-small-ner-identified
 
-This model is a fine-tuned version of [klue/roberta-small](https://huggingface.co/klue/roberta-small) on an unknown dataset.
+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.
 It achieves the following results on the evaluation set:
-- Loss: 0.0212
-- Precision: 0.9803
-- Recall: 1.0
-- F1: 0.9901
-- Accuracy: 0.9980
+- Loss: 0.1304
+- Precision: 0.9222
+- Recall: 0.9520
+- F1: 0.9369
+- Accuracy: 0.9790
 
 ## Model description
 
-아래 항목에 대한 개체명 인식을 제공합니다.
+More information needed
 
-- 사람이름 [PS] - 낮은 인식률
-- 주소 (구 주소 및 도로명 주소) [AD]
-- 카드번호 [CN]
-- 계좌번호 [BN]
-- 운전면허번호 [DN]
-- 주민등록번호 [RN]
-- 여권번호 [PN]
-- 전화번호 [PH]
-- 이메일 주소 [EM]
+## Intended uses & limitations
+
+More information needed
+
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
 
 ### Training hyperparameters
 
@@ -61,14 +54,14 @@ The following hyperparameters were used during training:
 
 | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
 |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
-| No log        | 1.0   | 15   | 0.2866          | 0.1199    | 0.2739 | 0.1668 | 0.9287   |
-| No log        | 2.0   | 30   | 0.1369          | 0.6599    | 0.7996 | 0.7231 | 0.9654   |
-| No log        | 3.0   | 45   | 0.0629          | 0.8088    | 0.9042 | 0.8538 | 0.9915   |
-| No log        | 4.0   | 60   | 0.0381          | 0.9760    | 0.9978 | 0.9868 | 0.9969   |
-| No log        | 5.0   | 75   | 0.0276          | 0.9781    | 0.9955 | 0.9868 | 0.9981   |
-| No log        | 6.0   | 90   | 0.0238          | 0.9803    | 1.0    | 0.9901 | 0.9979   |
-| No log        | 7.0   | 105  | 0.0224          | 0.9803    | 1.0    | 0.9901 | 0.9979   |
-| No log        | 8.0   | 120  | 0.0212          | 0.9803    | 1.0    | 0.9901 | 0.9980   |
+| No log        | 1.0   | 4    | 0.8023          | 0.1377    | 0.1231 | 0.1300 | 0.9178   |
+| No log        | 2.0   | 8    | 0.4197          | 0.5419    | 0.5580 | 0.5498 | 0.9431   |
+| No log        | 3.0   | 12   | 0.2760          | 0.6764    | 0.7146 | 0.6950 | 0.9564   |
+| No log        | 4.0   | 16   | 0.2062          | 0.7835    | 0.8544 | 0.8174 | 0.9617   |
+| No log        | 5.0   | 20   | 0.1685          | 0.8299    | 0.8946 | 0.8610 | 0.9711   |
+| No log        | 6.0   | 24   | 0.1470          | 0.8854    | 0.9295 | 0.9069 | 0.9758   |
+| No log        | 7.0   | 28   | 0.1350          | 0.9138    | 0.9460 | 0.9297 | 0.9778   |
+| No log        | 8.0   | 32   | 0.1304          | 0.9222    | 0.9520 | 0.9369 | 0.9790   |
 
 
 ### Framework versions
@@ -77,28 +70,3 @@ The following hyperparameters were used during training:
 - 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}
-
-```
-

model.safetensors

@@ -1,3 +1,3 @@
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+oid sha256:62ecc238c318744c81341f4e8ebaf5721a004d42a901ad703449016993e4dd4f
 size 270078052