This model classifies patents and product descriptions as green (eco-friendly) or not green.
It was finetuned with LoRA on a binary text dataset of patent descriptions.
Model Details
Model Description
Developed by: Jennifer Evans, Aslan Noorghasemi
Model type: Text classifier (binary classification)
Languages (NLP): English
Finetuned from model: LoRA with the "distilbert-base-uncased" model and DistilBertForSequenceClassification
Use this model to classify whether input text is considered green (eco-friendly) or not. It takes patent or product descriptions as text inputs and returns a predicted binary label and probabilities.
Downstream Use
It can be incorporated into larger text evaluation systems (ie. patent and product analysis tasks) as a pre-screening classifier.
Out-of-Scope Use
Not intended for:
Safety-critical deployment without further validation.
Identifying other labels beyond green / not green.
Applications outside of evaluating patent and product descriptions.
Bias, Risks, and Limitations
The model is trained on a specific dataset. It may:
Misclassify unusual or unclear eco-friendly descriptions.
Perform poorly on non-U.S. descriptions if not present in training.
Inherit any biases in the training text.
Recommendations
Always test on your target data before deployment. Combine with additional checks in safety-critical scenarios.
How to Get Started with the Model
The model can be used by loading both the base and adaptor
