Instructions to use harish/EN-AStitchTask1A-XLNet-FalseTrue-0-1-BEST with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use harish/EN-AStitchTask1A-XLNet-FalseTrue-0-1-BEST with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="harish/EN-AStitchTask1A-XLNet-FalseTrue-0-1-BEST")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("harish/EN-AStitchTask1A-XLNet-FalseTrue-0-1-BEST") model = AutoModelForSequenceClassification.from_pretrained("harish/EN-AStitchTask1A-XLNet-FalseTrue-0-1-BEST", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 35480511192d6affe20d4813409abac36f3a3befbc5126be8e019365e8b9972a
- Size of remote file:
- 623 Bytes
- SHA256:
- 477942c74c3089ba96f7d3d1f346e421a58618837e875bf62691456abb3463a4
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.