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") - Notebooks
- Google Colab
- Kaggle
Upload tf_model.h5
Browse files- tf_model.h5 +3 -0
tf_model.h5
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version https://git-lfs.github.com/spec/v1
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oid sha256:668157044dbc7ba926afab213e777f52b6299ff9ebb0cec8cc3f2cfdaef9362c
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size 267951896
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