Instructions to use aitrainingdatabroker/inbound-inquiry-intent-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use aitrainingdatabroker/inbound-inquiry-intent-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="aitrainingdatabroker/inbound-inquiry-intent-classifier")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("aitrainingdatabroker/inbound-inquiry-intent-classifier") model = AutoModelForSequenceClassification.from_pretrained("aitrainingdatabroker/inbound-inquiry-intent-classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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
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, 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_outorspamguess 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
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.
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Model tree for aitrainingdatabroker/inbound-inquiry-intent-classifier
Base model
distilbert/distilroberta-base