Instructions to use CWrecker/DistilBert-Classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CWrecker/DistilBert-Classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="CWrecker/DistilBert-Classification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("CWrecker/DistilBert-Classification") model = AutoModelForSequenceClassification.from_pretrained("CWrecker/DistilBert-Classification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d5c5938d1a9d0abbaaf4790e4332d0379f351b949402f2fe829691c2a885f8df
- Size of remote file:
- 268 MB
- SHA256:
- 5b473aed1309508accada0d1ad0dccf57f7720fb1303001ae9a704d645385cc9
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.