Instructions to use raicrits/topicChangeDetector_v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use raicrits/topicChangeDetector_v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="raicrits/topicChangeDetector_v1")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("raicrits/topicChangeDetector_v1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b5dc9bb5fc7e6890c8917a04071ba7850d8b307d42b2de9536a8e46a26b4da92
- Size of remote file:
- 2.22 GB
- SHA256:
- cee02d4829cef5e337db7675b6c2df7c89c77243b06051db6401b870f0f395f4
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.