Review jp

1-ttc.qmd

@@ -23,7 +23,7 @@ Two main approaches exist for this task:
 | Approach | How it works | Best when |
 |---|---|---|
 | **Supervised (this notebook)** | Train a neural network on labelled examples | Large labelled dataset available, low-latency inference needed |
-| **RAG** (see notebook 2) | Retrieve similar examples, ask a LLM to decide | Few labelled examples, need for explainability via retrieved context |
+| **RAG** (see notebook 2) | Retrieve similar examples, ask a LLM to decide | Few labelled examples |
 
 In this notebook, we use the supervised approach: the model learns directly from thousands of labelled examples and can predict at inference time without calling an external API.
 
@@ -42,7 +42,7 @@ flowchart LR
 ## What you will learn
 
 - How to prepare and split a labelled text dataset
-- How to train a subword tokenizer (WordPiece) on domain-specific text
+- How to train a subword tokenizer (WordPiece) on domain-specific text (tokenizers are introduced in @sec-tokenizer)
 - How to configure and train a neural text classifier end-to-end
 - How to load a pretrained model from MLflow and run inference
 - How to interpret predictions using the [Captum](https://captum.ai/docs/introduction) package
@@ -100,7 +100,7 @@ The notebook loads this file automatically at startup with `load_dotenv()`, maki
 
 # Load the data
 
-The dataset consists of **synthetic labelled examples** generated for the NACE 2025 nomenclature.
+The dataset consists of **synthetic labelled examples** generated for the NACE rev 2.1 nomenclature.
 Each row contains a short text description (`label`) and the corresponding NACE code (`code`).
 
 ::: {.callout-tip collapse="false" icon=false}
@@ -122,7 +122,7 @@ load_dotenv(override=True)
 
 ## Question 2 — Load the dataset from s3
 
-Use Polars to load the parquet file directly from this public URL:
+Use [Polars](https://docs.pola.rs/api/python/stable/reference/index.html) to load the parquet file directly from this public URL:
 
 ```
 https://minio.lab.sspcloud.fr/projet-formation/diffusion/funathon/2026/project2/generation_None_temp08.parquet
@@ -286,7 +286,7 @@ value_encoder = ValueEncoder(label_encoder=encoder)
 
 :::
 
-# Applying a tokenizer
+# Applying a tokenizer {#sec-tokenizer}
 
 ## Why tokenization?
 

index.qmd

@@ -81,7 +81,7 @@ Build a **retrieval-augmented generation** (RAG) system that combines a vector d
 
 Before diving into either part, follow these steps to set up your environment. The same setup applies to both parts.
 
-If you need more details for setups, do not hesitate to visite [project 1 introduction page](https://aiml4os.github.io/funathon-project1/).
+If you need more details for setups, do not hesitate to visite [project 1 introduction page](https://aiml4os.github.io/funathon-project1/) and/or watch the 2026's Funathon [Onboarding replay](https://aiml4os.github.io/funathon-general-website/onboarding.html).
 
 ## Fork this repository
 
@@ -154,8 +154,4 @@ df["code"].value_counts().head(10).plot(kind="bar")
 
 A small "Run Cell" lens appears above each `# %%` marker. Click it to execute that block. You can also use the shortcut <kbd>Shift</kbd> + <kbd>Enter</kbd>. This is convenient for iterating on small pieces of code or testing specific functions from the project.
 
-::: {.callout-tip}
-Files created at the root of the repository for your own exploration (such as `scratch.py` or `notes.md`) can be safely ignored from git by adding them to your local `.gitignore`, or to `.git/info/exclude` if you don't want to commit the ignore rule.
-:::
-
 You're now ready to start. Head to [Part 1](1-ttc.qmd) or [Part 2](2-rag-intro.qmd).