Boost LIME-SHAP Model Explainability
Budget: ₹400 – ₹750 INR
I already have a working fake-news detector built on Python, BERT and the Hugging Face Transformers stack, wrapped in a Gradio demo. What I need now is a sharper, more insightful explainability layer. The detector currently calls both LIME and SHAP, but the outputs are basic and not yet integrated into a cohesive, user-friendly display.
Your mission is to give equal weight to LIME and SHAP, tighten the faithfulness of their explanations, and surface the results through the existing Gradio interface so that journalists, fact-checkers and everyday readers can immediately see which words or phrases are driving each prediction and why.
Key goals
• Refactor or fine-tune the current LIME and SHAP pipelines so they handle longer articles without timeouts or memory issues.
• Align token-level attributions from both methods to the WordPiece tokens used by BERT.
• Merge the visual outputs: side-by-side heat-maps, ranked word lists, or any creative representation that helps non-technical users grasp the reasoning.
• Provide configurable parameters (e.g., number of samples in LIME, background dataset size for SHAP) exposed through Gradio sliders or dropdowns.
• Document the code and deliver a short README that explains how to reproduce the explanations locally or on a new server.
Acceptance criteria
1. For a given input article, LIME and SHAP highlight roughly overlapping influential tokens (no empty maps or misaligned indices).
2. Average explanation latency stays below 5 s on a GPU-less machine for 300-word inputs.
3. Gradio UI shows both explanations simultaneously and allows live parameter tweaking.
4. Code is clean, commented and passes a simple unit test suite you provide.
Everything lives in a Git-repo I will share on kickoff. We can iterate via pull requests and short video demos. I’d like the enhanced explainability layer ready to merge within one month, so we have time for a brief polish round before launch.
If you’re fluent in Python, Transformers, LIME, SHAP and have a knack for intuitive visualizations, let’s talk.
Your mission is to give equal weight to LIME and SHAP, tighten the faithfulness of their explanations, and surface the results through the existing Gradio interface so that journalists, fact-checkers and everyday readers can immediately see which words or phrases are driving each prediction and why.
Key goals
• Refactor or fine-tune the current LIME and SHAP pipelines so they handle longer articles without timeouts or memory issues.
• Align token-level attributions from both methods to the WordPiece tokens used by BERT.
• Merge the visual outputs: side-by-side heat-maps, ranked word lists, or any creative representation that helps non-technical users grasp the reasoning.
• Provide configurable parameters (e.g., number of samples in LIME, background dataset size for SHAP) exposed through Gradio sliders or dropdowns.
• Document the code and deliver a short README that explains how to reproduce the explanations locally or on a new server.
Acceptance criteria
1. For a given input article, LIME and SHAP highlight roughly overlapping influential tokens (no empty maps or misaligned indices).
2. Average explanation latency stays below 5 s on a GPU-less machine for 300-word inputs.
3. Gradio UI shows both explanations simultaneously and allows live parameter tweaking.
4. Code is clean, commented and passes a simple unit test suite you provide.
Everything lives in a Git-repo I will share on kickoff. We can iterate via pull requests and short video demos. I’d like the enhanced explainability layer ready to merge within one month, so we have time for a brief polish round before launch.
If you’re fluent in Python, Transformers, LIME, SHAP and have a knack for intuitive visualizations, let’s talk.