Text Classification
Transformers
TensorBoard
Safetensors
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use PDAP/coarse-url-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use PDAP/coarse-url-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="PDAP/coarse-url-classifier")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("PDAP/coarse-url-classifier") model = AutoModelForSequenceClassification.from_pretrained("PDAP/coarse-url-classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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# distilbert_coarse5_js_1.1
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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: 0.6826
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- Accuracy: 0.8039
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## Training and evaluation data
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## Training procedure
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# distilbert_coarse5_js_1.1
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This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the dataset
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[PDAP/coarse-labeled-urls-headers](https://huggingface.co/datasets/PDAP/coarse-labeled-urls-headers).
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It achieves the following results on the evaluation set:
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- Loss: 0.6826
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- Accuracy: 0.8039
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## Training and evaluation data
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This model is trained on urls belonging to 5 coarse grained labels:
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- Police & Public Interactions
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- Info About Officers
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- Info About Agencies
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- Agency-Published Resources
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- Jails & Courts Specific
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## Training procedure
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