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
| { | |
| "model_name": "bert-base-chinese", | |
| "max_length": 128, | |
| "num_labels": 2, | |
| "label_map": { | |
| "0": "正常", | |
| "1": "敏感" | |
| } | |
| } |