Text Classification
Transformers
ONNX
Safetensors
modernbert
propaganda-detection
multi-label-classification
nci-protocol
semeval-2020
text-embeddings-inference
Instructions to use synapti/nci-technique-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use synapti/nci-technique-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="synapti/nci-technique-classifier")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("synapti/nci-technique-classifier") model = AutoModelForSequenceClassification.from_pretrained("synapti/nci-technique-classifier") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0ed773f47648ca6da536b216b7223cb6703f315b0cdea7cec41f538dd837638d
- Size of remote file:
- 5.91 kB
- SHA256:
- e45cdd8e697175aaa76d315dd220ca62812d3bf5d6c438ff7a6d127bafb50085
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.