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
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README.md
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# distilbert-sensitive-classification
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This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on
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It achieves the following results on the evaluation set:
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- Loss: 0.0407
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- F1: 1.0
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# distilbert-sensitive-classification
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This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on synthetic sensitive information dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.0407
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- F1: 1.0
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