| --- |
| 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. |
|
|
