Text Classification
Transformers
PyTorch
deberta-v2
Generated from Trainer
text-embeddings-inference
Instructions to use DanielViniciusAlves/outputs with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DanielViniciusAlves/outputs with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="DanielViniciusAlves/outputs")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("DanielViniciusAlves/outputs") model = AutoModelForSequenceClassification.from_pretrained("DanielViniciusAlves/outputs", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1001fdae5759981fe1d325e90fc2b2daa9cffc6464568ca94f4db660d1a2b35f
- Size of remote file:
- 568 MB
- SHA256:
- 198fda5fc8434be1c9d7981a75cb414dbbc453f7a6cdf722fbed4ad9c3c2d130
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