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
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README.md
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tags:
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- sklearn
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- SVM
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- sentence-transformers
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- defect-classification
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- text-classification
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- pickle
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---
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# 🛠 SVM Defect Classifier (Text-based)
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This model is trained using **Sentence-BERT (MiniLM)** embeddings and a **Support Vector Machine (SVM)** classifier.
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It predicts **defect types** from text descriptions.
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## 🏗 Model Details
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- **Text Embeddings**: `all-MiniLM-L6-v2` (from `sentence-transformers`)
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- **Classifier**: SVM with RBF Kernel
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- **Format**: `.pkl` (Pickle)
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## 🔍 How to Use the Model
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First, install dependencies:
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```bash
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pip install sentence-transformers scikit-learn joblib huggingface_hub
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