Instructions to use climatebert/renewable with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use climatebert/renewable with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="climatebert/renewable")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("climatebert/renewable") model = AutoModelForSequenceClassification.from_pretrained("climatebert/renewable") - Notebooks
- Google Colab
- Kaggle
Separately tag references to EV?
#3
by ambarnag - opened
Hi Team,
Congratulations on publishing a very useful model for environmental policy analysis! After running the model on a subnational policy document for India I found that the sentences classified as renewable ('LABEL_1') are of two types:
- Direct references to renewable energy (RE) in general or to technologies like Solar, Wind, Hydro etc.
- References to EV (electric vehicles) which indirectly relate to RE but do not contain direct reference to RE
Is it possible to retrain the model to separately classify these two types of sentences? Also, any chance the training dataset has enough cases to allow splitting by RE technology (Solar, Wind, Hydro...)?