Instructions to use Cameron/BERT-mdgender-convai-binary with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Cameron/BERT-mdgender-convai-binary with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Cameron/BERT-mdgender-convai-binary")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Cameron/BERT-mdgender-convai-binary") model = AutoModelForSequenceClassification.from_pretrained("Cameron/BERT-mdgender-convai-binary", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2da11e7e2cfd796f748747ecc1f2172a74431f07d7d59bd972399d26bdfd7b45
- Size of remote file:
- 867 MB
- SHA256:
- 420c9208ead854edd6d7babfab21894469cbfd529266e9445998cccf9a662ae5
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