Text Classification
Transformers
Safetensors
Chinese
bert
content-moderation
sensitive-word-detection
text-embeddings-inference
Instructions to use crackrammer/ShieldBERT-Base-Chinese-Sensitive with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use crackrammer/ShieldBERT-Base-Chinese-Sensitive with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="crackrammer/ShieldBERT-Base-Chinese-Sensitive")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("crackrammer/ShieldBERT-Base-Chinese-Sensitive") model = AutoModelForSequenceClassification.from_pretrained("crackrammer/ShieldBERT-Base-Chinese-Sensitive") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a898458295015a9e2532f94c38f446401b58f5b50ce57e18a7bc32840dc35181
- Size of remote file:
- 4.79 kB
- SHA256:
- b112e2bc5f439219537038b2f089b01cb438c746c175d473e7a80f15e0e5b058
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