Instructions to use dexter191/text-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use dexter191/text-classifier with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("dexter191/text-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] - Scikit-learn
How to use dexter191/text-classifier with Scikit-learn:
from huggingface_hub import hf_hub_download import joblib model = joblib.load( hf_hub_download("dexter191/text-classifier", "sklearn_model.joblib") ) # only load pickle files from sources you trust # read more about it here https://skops.readthedocs.io/en/stable/persistence.html - Notebooks
- Google Colab
- Kaggle
π SVM Defect Classifier (Text-based)
This model is trained using Sentence-BERT (MiniLM) embeddings and a Support Vector Machine (SVM) classifier.
It predicts defect types from text descriptions.
π Model Details
- Text Embeddings:
all-MiniLM-L6-v2(fromsentence-transformers) - Classifier: SVM with RBF Kernel
- Format:
.pkl(Pickle)
π How to Use the Model
First, install dependencies:
pip install sentence-transformers scikit-learn joblib huggingface_hub