Instructions to use priyabrat/UpTag_GenderClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use priyabrat/UpTag_GenderClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="priyabrat/UpTag_GenderClassification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("priyabrat/UpTag_GenderClassification") model = AutoModelForSequenceClassification.from_pretrained("priyabrat/UpTag_GenderClassification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3007aa9807a8eb30390124184c47cd367d9d2458b0dc9bf1d49b3c20b2e974e0
- Size of remote file:
- 268 MB
- SHA256:
- 668157044dbc7ba926afab213e777f52b6299ff9ebb0cec8cc3f2cfdaef9362c
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