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") - Notebooks
- Google Colab
- Kaggle
metadata
library_name: transformers
datasets:
- breadlicker45/gender-classification-v4.5
base_model:
- google-bert/bert-base-multilingual-cased
fine-tuning details:
- batch size: 64
- steps (including warm up steps): 5000
- warm up steps: 500
- GPU used: An Nvidia 5090