README.md

---
library_name: onnx
tags:
  - email-classification
  - job-search
  - onnx
  - transformers.js
  - browser-ml
license: mit
datasets:
  - custom
language:
  - en
pipeline_tag: text-classification
---

# 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 ONNX Runtime Web.

## Model Description

- **Base Model:** BAAI/bge-small-en-v1.5
- **Task:** 5-class email classification
- **Format:** ONNX (optimized for browser inference)
- **Size:** ~128MB

## Labels

| Label | Description | Application Status |
|-------|-------------|-------------------|
| `confirmation` | Application received/confirmed | Applied |
| `rejection` | Application rejected | Rejected |
| `interview` | Interview invitation | Interviewing |
| `offer` | Job offer | Offer |
| `not_job` | Not job-related | - |

## Performance

- **Validation Accuracy:** 99.65%
- **Training Data:** 28,500 synthetic + curated emails

## Usage with ONNX Runtime Web

```javascript
import * as ort from 'onnxruntime-web';

// Load model
const session = await ort.InferenceSession.create(
  'https://huggingface.co/YOUR_USERNAME/email-classifier/resolve/main/model.onnx'
);

// Tokenize and run inference
const results = await session.run({
  input_ids: inputIdsTensor,
  attention_mask: attentionMaskTensor,
});
```

## Files

- `model.onnx` - The ONNX model file
- `vocab.txt` - Vocabulary file for tokenization
- `config.json` - Model configuration

## Privacy

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

## License

MIT