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")# pip install -U transformers accelerate # 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
Download optimizer.pt from Cameron/BERT-mdgender-convai-binary: direct link, hf CLI and curl.
- Browser
- Download file 867 MB
-
https://huggingface.co/Cameron/BERT-mdgender-convai-binary/resolve/refs%2Fpr%2F1/optimizer.pt
- Command line
-
hf download hf://Cameron/BERT-mdgender-convai-binary@refs/pr/1/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/Cameron/BERT-mdgender-convai-binary/resolve/refs%2Fpr%2F1/optimizer.pt
867 MB
- Xet hash:
- 2da11e7e2cfd796f748747ecc1f2172a74431f07d7d59bd972399d26bdfd7b45
- Size of remote file:
- 867 MB
- SHA256:
- 420c9208ead854edd6d7babfab21894469cbfd529266e9445998cccf9a662ae5
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