---
license: apache-2.0
language:
- en
library_name: transformers
pipeline_tag: text-classification
base_model: distilroberta-base
datasets:
- aitrainingdatabroker/synthetic-inbound-inquiries-by-intent
tags:
- intent-classification
- email
- triage
- text-classification
- synthetic
---

# Inbound Inquiry Intent Classifier

Reads an inbound message to a data business and names its intent out of eight: `seller_interest`, `buyer_request`, `referral_offer`, `press_media`, `vendor_pitch`, `support_question`, `opt_out` or `spam`.

Every name, number and sentence it was trained on is made up. Nothing comes from a real person, customer or business.

## Why it exists

Sorting an inbox by intent lets the right person answer first, and makes sure an opt-out is never missed.

## Use it

```python
from transformers import pipeline

classify = pipeline("text-classification", model="aitrainingdatabroker/inbound-inquiry-intent-classifier", top_k=3)
print(classify("Please take our company off your mailing list and confirm it is done."))
```

## How it was trained

Fine-tuned from `distilroberta-base` for 6 epochs on one RTX 3070. Training data: 458 rows from [synthetic-inbound-inquiries-by-intent](https://huggingface.co/datasets/aitrainingdatabroker/synthetic-inbound-inquiries-by-intent), all invented, with test rows built from templates the model never saw in training. The scripts that built the data and trained the model are in `source/`; the full scores are in `scores.json`.

## Test scores

On 154 held-out rows: accuracy 1.0, macro F1 1.0.

| Label | F1 | Count |
|---|---|---|
| `buyer_request` | 1.0 | 20 |
| `opt_out` | 1.0 | 18 |
| `press_media` | 1.0 | 19 |
| `referral_offer` | 1.0 | 19 |
| `seller_interest` | 1.0 | 20 |
| `spam` | 1.0 | 20 |
| `support_question` | 1.0 | 18 |
| `vendor_pitch` | 1.0 | 20 |

## Limits

- Trained only on invented text from templates, with test sentences built from templates it never saw. Real text varies far more.
- Never use it alone to suppress a real message: an `opt_out` or `spam` guess should be confirmed by a person.
- A score of 1.0 on generated text says little about real text: the test sentences are new templates, but they are written in the same plain style. Test it on a labelled sample of your own.
- This model is not legal advice and not a certification. A person should review its output before anything is shared or decided, and your counsel decides what a rule requires.

## Read more

- [Contact AI Training Data Broker](https://aitrainingdatabroker.com/contact/?utm_source=huggingface&utm_medium=model&utm_campaign=inbound-inquiry-intent-classifier)
- [The referral program](https://aitrainingdatabroker.com/referral-program/?utm_source=huggingface&utm_medium=model&utm_campaign=inbound-inquiry-intent-classifier)
- [Free tools](https://aitrainingdatabroker.com/tools/?utm_source=huggingface&utm_medium=model&utm_campaign=inbound-inquiry-intent-classifier)

## License and credit

Apache 2.0. Please credit **AI Training Data Broker** and link to https://aitrainingdatabroker.com/ when you share or build on it.

## About

AI Training Data Broker is a broker and technical advisor for companies that sell or license their data to AI labs. It guides the technical side, including export, format, de-identification and compliance, and publishes free guides, checklists and tools.