Instructions to use kite/recommendation_bert_vae with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kite/recommendation_bert_vae with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="kite/recommendation_bert_vae")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("kite/recommendation_bert_vae") model = AutoModelForSequenceClassification.from_pretrained("kite/recommendation_bert_vae", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from kite/recommendation_bert_vae: direct link, hf CLI and curl.
- Browser
- Download file 542 MB
-
https://huggingface.co/kite/recommendation_bert_vae/resolve/refs%2Fpr%2F1/pytorch_model.bin
- Command line
-
hf download hf://kite/recommendation_bert_vae@refs/pr/1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/kite/recommendation_bert_vae/resolve/refs%2Fpr%2F1/pytorch_model.bin
542 MB
- Xet hash:
- 924584ebbcec8c82a0774d13173234e874a5432cf31f9bebf230d3a1c8754c99
- Size of remote file:
- 542 MB
- SHA256:
- 876421fafaf0036b05654ee87345a721ef31849fa525275fdbdf312573290cc6
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