Instructions to use owaiskha9654/PicoClassifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use owaiskha9654/PicoClassifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="owaiskha9654/PicoClassifier")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("owaiskha9654/PicoClassifier") model = AutoModelForSequenceClassification.from_pretrained("owaiskha9654/PicoClassifier") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b41419c325c3e3e55aa0c180b72672db98a9d2336ada50df4f38440f6b8f1925
- Size of remote file:
- 438 MB
- SHA256:
- b2086e4a6c57cc717e7531a781b219b841395f27c75beb25fba86c9dea2a8b43
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.