Instructions to use huolongguo10/check_sec with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use huolongguo10/check_sec with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="huolongguo10/check_sec")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("huolongguo10/check_sec") model = AutoModelForSequenceClassification.from_pretrained("huolongguo10/check_sec", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit ·
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README.md
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```
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LABEL_0: secure
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LABEL_1: insecure(可能包含xss payload)
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```
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```
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LABEL_0: secure
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LABEL_1: insecure(可能包含xss payload)
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```
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## 使用
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```python
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import transformers
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from transformers import BertTokenizer, DataCollatorWithPadding
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from transformers import AutoModelForSequenceClassification
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tokenizer = BertTokenizer.from_pretrained('huolongguo10/check_sec')
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model = AutoModelForSequenceClassification.from_pretrained('huolongguo10/check_sec', num_labels=2)
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import torch
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def check(text):
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inputs = tokenizer(text, return_tensors="pt")
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with torch.no_grad():
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logits = model(**inputs).logits
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predicted_class_id = logits.argmax().item()
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print(f'{logits.argmax().item()}:{text}')
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return 'secure' if predicted_class_id==0 else 'insecure'
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```
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