Text Classification
Transformers
Safetensors
sentence-transformers
English
mpnet
patent-classification
green-technology
fine-tuned
Eval Results (legacy)
text-embeddings-inference
Instructions to use CTB2001/PatentSBERTa-green-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CTB2001/PatentSBERTa-green-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="CTB2001/PatentSBERTa-green-classifier")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("CTB2001/PatentSBERTa-green-classifier") model = AutoModelForSequenceClassification.from_pretrained("CTB2001/PatentSBERTa-green-classifier") - sentence-transformers
How to use CTB2001/PatentSBERTa-green-classifier with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("CTB2001/PatentSBERTa-green-classifier") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Notebooks
- Google Colab
- Kaggle