Instructions to use Cameron/BERT-mdgender-wizard with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Cameron/BERT-mdgender-wizard with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Cameron/BERT-mdgender-wizard")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Cameron/BERT-mdgender-wizard") model = AutoModelForSequenceClassification.from_pretrained("Cameron/BERT-mdgender-wizard", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from Cameron/BERT-mdgender-wizard: direct link, hf CLI and curl.
- Browser
- Download file 433 MB
-
https://huggingface.co/Cameron/BERT-mdgender-wizard/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://Cameron/BERT-mdgender-wizard/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/Cameron/BERT-mdgender-wizard/resolve/main/flax_model.msgpack
433 MB
- Xet hash:
- f56158adf9dbaac04b425f89eee73e338483bcf0f1f2a1867bab45590fa71fdc
- Size of remote file:
- 433 MB
- SHA256:
- 8b7522e6abbcc6536a664ace2c641c1db124ea68e7d11bdf7cdab55b5bef7b62
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