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")# 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
- Xet hash:
- 924584ebbcec8c82a0774d13173234e874a5432cf31f9bebf230d3a1c8754c99
- Size of remote file:
- 542 MB
- SHA256:
- 876421fafaf0036b05654ee87345a721ef31849fa525275fdbdf312573290cc6
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