Instructions to use breadlicker45/multilingual-bert-gender-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use breadlicker45/multilingual-bert-gender-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="breadlicker45/multilingual-bert-gender-classification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("breadlicker45/multilingual-bert-gender-classification") model = AutoModelForSequenceClassification.from_pretrained("breadlicker45/multilingual-bert-gender-classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b335e348f2f7fca4dc300a8508bb19a31c54339a979b43bb875f88ae2283bad9
- Size of remote file:
- 711 MB
- SHA256:
- 16df8090f65f159635fc3b960cce090e5d6decedacf42a32c5bb2cc7d380b260
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