Text Classification
Transformers
TensorBoard
Safetensors
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use Sharpaxis/distilbert-sensitive-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Sharpaxis/distilbert-sensitive-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Sharpaxis/distilbert-sensitive-classification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Sharpaxis/distilbert-sensitive-classification") model = AutoModelForSequenceClassification.from_pretrained("Sharpaxis/distilbert-sensitive-classification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b92a6a0bc1d6ba3114f884f38916e12e6ac771d95e42762907b88823b23a6a57
- Size of remote file:
- 268 MB
- SHA256:
- 86d566b557dcd9a7d8c4b5dd77312c9811aed0d2822a5bedd806cc6a4ee34252
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