mattohan/job-tracker-email-classifier

Email Classifier for Job Applications

A fine-tuned BGE-small model that classifies emails into job application categories. Designed to run entirely in the browser using Transformers.js.

Model Description

• —Base Model: BAAI/bge-small-en-v1.5 (33M parameters)
• —Task: 5-class email classification
• —Format: ONNX (opset 14, IR version 7)
• —Size: 32.5 MB (quantized) / 127.6 MB (full)

Labels

Training

• —Method: Curriculum learning (2-2-1 epochs)
• —Phase 1: 2 epochs on full-body emails
• —Phase 2: 2 epochs with 4:1 full-body:snippet mix
• —Phase 3: 1 epoch with 1:1 balanced mix
• —Training Data: ~28K emails (original + augmented snippets)
• —Validation Accuracy: 100% (full-body), 100% (snippet)

Usage with Transformers.js

import { pipeline } from '@xenova/transformers';

const classifier = await pipeline(
  'text-classification',
  'mattohan/job-tracker-email-classifier',
  { quantized: true }
);

const result = await classifier('Thank you for applying to the Software Engineer position...');
// [{ label: 'confirmation', score: 0.99 }]

Files

├── config.json
├── tokenizer.json
├── tokenizer_config.json
├── vocab.txt
├── special_tokens_map.json
└── onnx/
    ├── model.onnx           # Full model (127.6 MB)
    └── model_quantized.onnx # Quantized model (32.5 MB)

Privacy

This model runs 100% client-side in the browser. No email data is ever sent to a server.

License

MIT