Instructions to use aitrainingdatabroker/contract-clause-type-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use aitrainingdatabroker/contract-clause-type-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="aitrainingdatabroker/contract-clause-type-classifier")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("aitrainingdatabroker/contract-clause-type-classifier") model = AutoModelForSequenceClassification.from_pretrained("aitrainingdatabroker/contract-clause-type-classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Contract Clause Type Classifier
Reads one data license clause and names its type out of eight: exclusivity, term and renewal, payment, confidentiality, IP and ownership, indemnity, audit and deletion, or termination.
Every name, number and sentence it was trained on is made up. Nothing comes from a real person, customer or business.
Related tool: contract-clause-watchlist (a Space that runs in your browser).
Why it exists
A data license is easier to review when each clause is filed under its type. This model files clauses so a person can compare, say, every indemnity across offers.
Use it
from transformers import pipeline
classify = pipeline("text-classification", model="aitrainingdatabroker/contract-clause-type-classifier", top_k=3)
print(classify("The Licensee may keep the Dataset after the term and need not delete it."))
How it was trained
Fine-tuned from distilroberta-base for 6 epochs on one RTX 3070. Training data: 376 rows from synthetic-contract-clauses-by-type-and-risk, 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 186 held-out rows: accuracy 0.726, macro F1 0.711.
| Label | F1 | Count |
|---|---|---|
audit_and_deletion |
0.533 | 24 |
confidentiality |
1.0 | 22 |
exclusivity |
0.683 | 23 |
indemnity |
0.96 | 24 |
ip_and_ownership |
0.9 | 22 |
payment |
0.657 | 24 |
term_and_renewal |
0.957 | 24 |
termination |
0.0 | 23 |
Limits
- Trained only on invented text from templates, with test sentences built from templates it never saw. Real text varies far more.
- Real clauses are longer and mix topics; a clause with two ideas gets one label.
- 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
- How you get paid when you license data
- Glossary of data licensing terms
- Who is liable when data is licensed to AI
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/contract-clause-type-classifier
Base model
distilbert/distilroberta-base