metadata

language:
  - en
library_name: Pytorch
library_version: 2.0.1+cu118
metrics:
  - accuracy
pipeline_tag: text-classification
tags:
  - spam detection
  - email detection
  - text classification
inference: true
model-index:
  - name: foduucom/Mail-spam-detection
    results:
      - task:
          type: text-classification
        metrics:
          - type: precision
            value: 0.866

Model Card for Text Classification for email-spam detection

This model is based on Text classification using pytorch library. In this model we propose to used a torchtext library for tokenize & vectorize data. This model is used in corporate and industrial area for mail detection. It is used three label like job, enquiry and spam. It achieve the following results on the evalution set:

accuracy : 0.866

model architecture for text classification :

Label for text classification:

Enquiry
Job
Spam

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 0.01
train_batch_size: 64
step_size: 10
optimizer: Adam
lr_scheduler_type: StepLR
lr_scheduler.StepLR:(optimizer,step_size=10,gamma=0.1)
num_epochs: 10

Framework versions

Pytorch 2.0.1+cu118
torchtext 0.15.2+cpu

@ModelCard{
  author    = {Nehul Agrawal and
               Rahul parihar},
  title     = {Text classification},
  year      = {2023}
}