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:
- d0a814dc4fb1421c0e1b607ac7a9059f64d725d7e8cfabd03ea020cbedffaea1
- Size of remote file:
- 3.25 kB
- SHA256:
- 8e2da8ccb8fe41a87bfe63f60176f3c6ad36a8397b4b95d85c68d63243dd832e
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