Instructions to use DataMuncher-Labs/SC-500k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DataMuncher-Labs/SC-500k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="DataMuncher-Labs/SC-500k")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("DataMuncher-Labs/SC-500k", dtype="auto") - Notebooks
- Google Colab
- Kaggle
metadata
license: mit
language:
- en
metrics:
- accuracy
pipeline_tag: text-classification
library_name: transformers
tags:
- anti-spam
- spam
- spamclass
- english
- English
- EN
- en
SpamClass 500k
- acc=0.8651293961512939
- prec=0.8305231301340251
- rec=0.8202391118701964
- f1=0.8253490870032223
Trained on a custom corpus.
1x T4 was used during training.
It primarily is meant for english spam, its accurate 86% of the time