Text Classification
Transformers
ONNX
Safetensors
English
modernbert
propaganda-detection
binary-classification
nci-protocol
Eval Results (legacy)
text-embeddings-inference
Instructions to use synapti/nci-binary-detector-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use synapti/nci-binary-detector-v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="synapti/nci-binary-detector-v2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("synapti/nci-binary-detector-v2") model = AutoModelForSequenceClassification.from_pretrained("synapti/nci-binary-detector-v2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
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Add ONNX model export: config.json
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Add ONNX model export: special_tokens_map.json
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Add ONNX model export: tokenizer_config.json
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Add ONNX model export: tokenizer.json
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Add ONNX model export: model.onnx
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Update model card with v2 evaluation metrics
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End of training
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Model save
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Training in progress, step 600
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Training in progress, step 400
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Training in progress, step 200
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initial commit
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