Text Classification
Transformers
ONNX
Safetensors
English
modernbert
propaganda-detection
multi-label-classification
nci-protocol
text-embeddings-inference
Instructions to use synapti/nci-technique-classifier-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use synapti/nci-technique-classifier-v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="synapti/nci-technique-classifier-v2")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("synapti/nci-technique-classifier-v2") model = AutoModelForSequenceClassification.from_pretrained("synapti/nci-technique-classifier-v2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 1,128 Bytes
6d86c84 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 | {
"temperature": 1.0,
"thresholds": {
"Loaded_Language": 0.5,
"Appeal_to_fear-prejudice": 0.5,
"Exaggeration,Minimisation": 0.5,
"Repetition": 0.5,
"Flag-Waving": 0.5,
"Name_Calling,Labeling": 0.5,
"Reductio_ad_hitlerum": 0.5,
"Black-and-White_Fallacy": 0.5,
"Causal_Oversimplification": 0.5,
"Whataboutism,Straw_Men,Red_Herring": 0.5,
"Straw_Man": 0.5,
"Red_Herring": 0.5,
"Doubt": 0.5,
"Appeal_to_Authority": 0.5,
"Thought-terminating_Cliches": 0.5,
"Bandwagon": 0.5,
"Slogans": 0.5,
"Obfuscation,Intentional_Vagueness,Confusion": 0.5
},
"technique_labels": [
"Loaded_Language",
"Appeal_to_fear-prejudice",
"Exaggeration,Minimisation",
"Repetition",
"Flag-Waving",
"Name_Calling,Labeling",
"Reductio_ad_hitlerum",
"Black-and-White_Fallacy",
"Causal_Oversimplification",
"Whataboutism,Straw_Men,Red_Herring",
"Straw_Man",
"Red_Herring",
"Doubt",
"Appeal_to_Authority",
"Thought-terminating_Cliches",
"Bandwagon",
"Slogans",
"Obfuscation,Intentional_Vagueness,Confusion"
]
} |