Instructions to use liamliang/demographics_gender with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use liamliang/demographics_gender with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="liamliang/demographics_gender")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("liamliang/demographics_gender") model = AutoModelForSequenceClassification.from_pretrained("liamliang/demographics_gender") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c6f58453c539531a4fb6d7ee657d9ad4bf3416332475a0f9bcff81e269131624
- Size of remote file:
- 438 MB
- SHA256:
- f600c1eae1cc42de7b7c01952412276bd2a45a7972e6d6a5a54acc3effc87b65
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