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")# 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
- Xet hash:
- c7afa301b6ce31d136a2d50a92e827ae107e6a209502eab39abb872f89252b53
- Size of remote file:
- 5.91 kB
- SHA256:
- 98d3c5d5aadba78703e5767425fd44c6925ffba00409b992bcce769ef5127156
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.