Instructions to use DataMuncher-Labs/SC-300k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DataMuncher-Labs/SC-300k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="DataMuncher-Labs/SC-300k")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("DataMuncher-Labs/SC-300k", 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 300k
- acc=0.8347710683477106
- prec=0.7895869191049913
- rec=0.7835183603757472
- f1=0.7865409344192027
Trained on a custom corpus.
1x T4 was used during training.
It primarily is meant for english spam, its accurate 83% of the time (acceptable)