add images and changes to copy

qdrant-landing/content/articles/sparse-embeddings-ecommerce-part-2.md

@@ -1,17 +1,17 @@
 ---
 title: "Fine-Tuning Sparse Embeddings for E-Commerce Search | Part 2: Training SPLADE on Modal"
 short_description: "Train a SPLADE model on Amazon's ESCI dataset using Modal's serverless GPUs and Sentence Transformers."
-description: "Part 2 of a 4-part series on fine-tuning SPLADE sparse embeddings for e-commerce search. Build a training pipeline on Modal with persistent checkpoints, SpladeLoss, and hyperparameter sweeps."
+description: "Part 2 of a 5-part series on fine-tuning SPLADE sparse embeddings for e-commerce search. Build a training pipeline on Modal with persistent checkpoints, SpladeLoss, and hyperparameter sweeps."
 preview_dir: /articles_data/sparse-embeddings-ecommerce-part-2/preview
 social_preview_image: /articles_data/sparse-embeddings-ecommerce-part-2/preview/social_preview.jpg
 weight: -199
 author: Thierry Damiba
 author_link: https://github.com/thierrydamiba
-date: 2025-01-29T00:00:00.000Z
+date: 2026-03-09T00:00:00.000Z
 category: practicle-examples
 ---
 
-*This is Part 2 of a 4-part series on fine-tuning sparse embeddings for e-commerce search. In [Part 1](/articles/sparse-embeddings-ecommerce-part-1/), we covered why sparse embeddings beat BM25 for e-commerce. Now we build the training pipeline.*
+*This is Part 2 of a 5-part series on fine-tuning sparse embeddings for e-commerce search. In [Part 1](/articles/sparse-embeddings-ecommerce-part-1/), we covered why sparse embeddings beat BM25 for e-commerce. Now we build the training pipeline.*
 
 **Series:**
 - [Part 1: Why Sparse Embeddings Beat BM25](/articles/sparse-embeddings-ecommerce-part-1/)
@@ -32,14 +32,9 @@ We use Amazon's [ESCI dataset](https://github.com/amazon-science/esci-data) (Sho
 - **Four relevance grades**: Exact (E), Substitute (S), Complement (C), Irrelevant (I)
 - **Rich product metadata**: titles, descriptions, bullet points, brands
 
-The graded relevance is what makes ESCI interesting. A search for "iPhone charger" might return:
+The graded relevance is what makes ESCI interesting:
 
-| Product | Label | Score |
-|---|---|---|
-| Apple 20W USB-C Power Adapter | Exact (E) | 1.0 |
-| Anker USB-C to Lightning Cable | Substitute (S) | 0.7 |
-| iPhone 15 Clear Case | Complement (C) | 0.5 |
-| Samsung Galaxy S24 Case | Irrelevant (I) | 0.0 |
+![ESCI relevance gradient from Exact to Irrelevant](/articles_data/sparse-embeddings-ecommerce-part-2/esci-relevance-gradient.png)
 
 For training, we use Exact and Substitute pairs as positives. This teaches the model that both the exact product and reasonable alternatives are relevant, matching how real shoppers think.
 
@@ -105,6 +100,8 @@ def build_product_text(title, brand="", description="", bullets=None, max_length
 
 The bracket notation for brands, pipe separators between sections, and character limits are deliberate. They preserve lexical signals that SPLADE can learn from: brand names, product attributes, and key features remain as distinct tokens rather than blurring into a wall of text.
 
+![Training stack: Modal for GPU compute, Sentence Transformers for training, Qdrant for evaluation](/articles_data/sparse-embeddings-ecommerce-part-2/training-stack.png)
+
 ## Setting Up the Modal App
 
 Modal gives us serverless GPUs. No provisioning, no idle hardware, pay-per-second billing. Here's the app configuration:
@@ -345,6 +342,8 @@ The static embedding completely failed because e-commerce queries are highly con
 
 The transformer is the bottleneck at ~15ms per query, but 15ms is perfectly acceptable for search. Don't prematurely optimize away the component that makes the model work.
 
+![Modal detached training with persistent volumes](/articles_data/sparse-embeddings-ecommerce-part-2/modal-detached-training.png)
+
 ## Running Training
 
 With everything in place, launch training: