Instructions to use Andranik/TestPytorchClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Andranik/TestPytorchClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Andranik/TestPytorchClassification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Andranik/TestPytorchClassification") model = AutoModelForSequenceClassification.from_pretrained("Andranik/TestPytorchClassification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e7d89b235d31ba6afc54c952ed57dc5f932143067c32828508d9313f2592659a
- Size of remote file:
- 268 MB
- SHA256:
- 11c0cbccbb086047537067c6df40d405049c40ac48a3fb175be137880b3fb333
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.