Text Classification
Transformers
TensorBoard
Safetensors
roberta
Generated from Trainer
text-embeddings-inference
Instructions to use mgkim321/results with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mgkim321/results with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="mgkim321/results")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("mgkim321/results") model = AutoModelForSequenceClassification.from_pretrained("mgkim321/results") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ff5c51b7242d0a828eebe6fba7beac6bac81810d2aed6f564dc856f9a38a2a26
- Size of remote file:
- 5.78 kB
- SHA256:
- a2299f061d3f1d54f3080dc3a6debce69a75277b702ceafc7d3d6a7f6af6facd
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