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
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- batch size: 64
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- steps (including warm up steps): 5000
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- warm up steps: 500
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- GPU used: An Nvidia 5090
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- batch size: 64
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- steps (including warm up steps): 5000
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- warm up steps: 500
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- GPU used: An Nvidia 5090
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