Instructions to use dhtocks/Topic-Classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dhtocks/Topic-Classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="dhtocks/Topic-Classification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("dhtocks/Topic-Classification") model = AutoModelForSequenceClassification.from_pretrained("dhtocks/Topic-Classification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e06ace9dac3d0f2b36f6512fe9c1d14f6fe3471694abbe63f01bc28f8920aaf9
- Size of remote file:
- 499 MB
- SHA256:
- 98044a29d84a2633cf40e68250f41620e72435f068620fa35ce7f010d567c610
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.