Token Classification
Transformers
PyTorch
Safetensors
English
bert
bias-detection
social-bias
gus-net
fairness
interpretability
Instructions to use pinthoz/gus-net-bert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use pinthoz/gus-net-bert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="pinthoz/gus-net-bert")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("pinthoz/gus-net-bert") model = AutoModelForTokenClassification.from_pretrained("pinthoz/gus-net-bert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 247118d9caa35f206c32a13f91a9b2b802e935ed3ccae5bd82cc9b557bd997f3
- Size of remote file:
- 436 MB
- SHA256:
- e840d3479416889a8f852c32a4c5a1b66f432eeeedccfe4c71ec64bb3e194863
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