Support Newer Hugging Face Model for Text Vectorization

[anglerfishlyy]

Problem: iQual’s text vectorization uses sentence-transformers (e.g., older models like all-MiniLM-L6-v1). Newer models like all-MiniLM-L12-v2 offer better accuracy with similar efficiency.
Proposed Solution: Update src/iqual/text_features.py to support all-MiniLM-L12-v2 as an option in add_text_features. This would:

• Add a parameter to select the model (default to current).
• Update notebook examples (Basic Modelling) to demo the new model.
• Include performance benchmarks (e.g., accuracy on politeness dataset).

Steps:

1. Add model option in text_features.py.
2. Test on sample data (politeness dataset).
3. Update notebooks/Basic_Modelling.ipynb with example.
4. Add tests for vectorization output.
   Impact: Improves iQual’s NLP accuracy, aligning with World Bank’s AI-for-data goals.
   Willing to Implement: I can submit a PR with code and updated notebook.

@addypy @g4brielvs, seeking your thoughts on adding all-MiniLM-L12-v2 to iQual’s text vectorization to boost NLP accuracy for SDG analysis. Happy to refine benchmarks or model choices per your guidance!