Text Classification
Transformers
Safetensors
modernbert
propaganda-detection
binary-classification
nci-protocol
text-embeddings-inference
Instructions to use synapti/nci-binary-detector with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use synapti/nci-binary-detector with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="synapti/nci-binary-detector")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("synapti/nci-binary-detector") model = AutoModelForSequenceClassification.from_pretrained("synapti/nci-binary-detector", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d3da1d24be853232412aadc25c559d95990e932df33ba33315a29ac44b4b9122
- Size of remote file:
- 598 MB
- SHA256:
- b2aae000f6b136f5ba37b84e00f9f4b3849f2c3ebbb9f15316a1658b785ec20c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.