Instructions to use omarelsayeed/classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use omarelsayeed/classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="omarelsayeed/classifier")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("omarelsayeed/classifier") model = AutoModelForSequenceClassification.from_pretrained("omarelsayeed/classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ba3feadbdd2841479af3f2823f9fef3e9b84f79d34eda306b23933ce89eba94f
- Size of remote file:
- 1.48 GB
- SHA256:
- 445fb102d851cf8134be218759f9ecea449295db35e308cbcf1dcd19bb5890c1
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.