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:
- 1c12667a9438c05ef7dcec1a695d0d856b64566699ef44951292564d059af38a
- Size of remote file:
- 4.63 kB
- SHA256:
- f415c7c7ca2d003f3750bafdafdbf470c082af3be9b054ffd63e21d4c9e151f4
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