Text Classification
Transformers
PyTorch
deberta-v2
Generated from Trainer
text-embeddings-inference
Instructions to use pglee/outputs with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use pglee/outputs with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="pglee/outputs")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("pglee/outputs") model = AutoModelForSequenceClassification.from_pretrained("pglee/outputs", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 581474f820e20433a6cbd1b61d516aa096c8eac1e0d4fddf9283baecec0fcdc0
- Size of remote file:
- 568 MB
- SHA256:
- 2a4ea187ea40fe8371b5566b480b39a585b46d1df9bcb61a051c9af90fa008e3
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