Instructions to use shrugging-grace/tweetclassifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use shrugging-grace/tweetclassifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="shrugging-grace/tweetclassifier")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("shrugging-grace/tweetclassifier") model = AutoModelForSequenceClassification.from_pretrained("shrugging-grace/tweetclassifier") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ad78cb6ae117644b353f6c76c9f65d97ae2f53bcbdd0ab6ff0958a84d5f1103f
- Size of remote file:
- 438 MB
- SHA256:
- 9b37df0ed24267d224a89fcc27de39d96b4bc8dd7a910d32fbdd8b750b963130
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.