Instructions to use erikacardenas300/StartupClassifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use erikacardenas300/StartupClassifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="erikacardenas300/StartupClassifier")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("erikacardenas300/StartupClassifier") model = AutoModelForSequenceClassification.from_pretrained("erikacardenas300/StartupClassifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8ac6b476f1602919f767c99565c41e087ef6797a8c861c43663582c4a27bc670
- Size of remote file:
- 268 MB
- SHA256:
- 0fb074fd305c96089e8a0dc44143f1130bb3e18f80dee6723a2e44ef1e4afc43
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.