kms7530
/

roberta-base-infringement-detect

@@ -8,197 +8,40 @@ metrics:
 pipeline_tag: text-classification
 ---
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 pipeline_tag: text-classification
 ---
+# roberta-base-infringement-detect
 ## Model Details
 ### Model Description
+[klue/roberta-base](https://huggingface.co/klue/roberta-base) 모델을 이용하여, 두 컨텐츠간의 유사여부를 확인하는 모델입니다.
+## Train
+자체구축된 1,310개의 참인 유사 컨텐츠 쌍을 이용하여, 셔플 후 참/거짓 비율 1:2인 데이터셋을 생성하여 학습시켰습니다.
+이외의 학습시 파라미터는 다음과 같습니다.
+| Parameter          | Value |
+| ------------------ | ----- |
+| `train_batch_size` | 16    |
+| `num_train_epochs` | 5     |
+| `weight_decay`     | 0.01  |
+| `learning_rate`    | 2e-5  |
+## How to use
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+model_name = "kms7530/roberta-base-infringement-detect"
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+```
+모델에 추론 시 다음과 같이 입력해야 합니다.
+```plain
+[CLS]\
+[unused0]<ORIGINAL_CONTENT_TITLE>\
+[unused1]<ORIGINAL_CONTENT>[SEP] \
+[unused0]<TEST_CONTENT_TITLE>\
+[unused1]<TEST_CONTENT>[SEP]
+```