Text Classification
Transformers
PyTorch
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use mi23/responsible_iddistilbert-base-uncased with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mi23/responsible_iddistilbert-base-uncased with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="mi23/responsible_iddistilbert-base-uncased")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("mi23/responsible_iddistilbert-base-uncased") model = AutoModelForSequenceClassification.from_pretrained("mi23/responsible_iddistilbert-base-uncased") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8699c2ad27cb1887ac4676d222c1d2d4df8a0a83fb1f0b4d90e57c3bbfee610b
- Size of remote file:
- 268 MB
- SHA256:
- fd9a76f411cb1711f0b47787b43570523d67fb0f3b8c1532c657efa0c11d6cd6
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