Instructions to use jparmet/pusa-topic-bert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jparmet/pusa-topic-bert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="jparmet/pusa-topic-bert")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("jparmet/pusa-topic-bert") model = AutoModelForSequenceClassification.from_pretrained("jparmet/pusa-topic-bert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 24f4f74d931e72cbe12f8d271f9ab6b143558f6db5dda3b7ef32a42e48647993
- Size of remote file:
- 3.58 kB
- SHA256:
- 91103a4ccfc2f3674d0acb17e9ab04be2a1fa5e39958f218d859ac8fb265d0ea
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