Text Classification
Transformers
PyTorch
English
bert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use junzai/demo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use junzai/demo with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="junzai/demo")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("junzai/demo") model = AutoModelForSequenceClassification.from_pretrained("junzai/demo") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 44d653359f192dc8dcb289a2ed1f3997eb92fdc6869936e180776d1e6cf57e8c
- Size of remote file:
- 2.93 kB
- SHA256:
- 30ec3a9744b2e2c576b01471bdbd30d7c2b9716fa4c4fe0384ef6aac0349bc6a
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