Token Classification
Transformers
PyTorch
Safetensors
English
gpt2
bias-detection
social-bias
gus-net
fairness
interpretability
text-generation-inference
Instructions to use pinthoz/gus-net-gpt2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use pinthoz/gus-net-gpt2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="pinthoz/gus-net-gpt2")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("pinthoz/gus-net-gpt2") model = AutoModelForTokenClassification.from_pretrained("pinthoz/gus-net-gpt2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 85f301b1d91ada16c067f3e58cec896e465806f3aa6b261ff75d9b7ded686657
- Size of remote file:
- 510 MB
- SHA256:
- 374c1d24677704fc706d96fcf530485cf367c64d65aeeac58110a04b443fcff5
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