Update README.md

README.md

@@ -50,7 +50,6 @@ and quality ≥ 1.5, sampled down to the per-slice token budget:
 - **TypeScript**: ~600M tokens — strong type system, filter by SD relevance ≥ 2
 - **Python**: ~600M tokens — filter by SD relevance ≥ 2
 - **Rust/Go/Java**: ~600M tokens — strongly typed, filter by SD relevance ≥ 2
-- **Jupyter notebooks**: ~400M tokens — filter by SD relevance ≥ 2
 
 ### Non-Classified Slices
 
@@ -70,7 +69,6 @@ These languages were **not** run through the classifier. Their `quality`,
 | typescript | relevance_filter | typescript | 600M | 598M | 99.7% |
 | python | relevance_filter | python | 600M | 594M | 99.1% |
 | rust_go_java | relevance_filter | rust, go, java | 600M | 485M | 80.8% |
-| jupyter | relevance_filter | jupyter-scripts-dedup-filtered | 400M | 0M | 0.0% |
 | github_issues | keyword_filter | github-issues-filtered-structured | 500M | 426M | 85.2% |
 | general_code | light_filter | ada, agda, alloy, antlr +74 more | 1000M | 999M | 99.9% |
 
@@ -86,8 +84,6 @@ are excluded from these statistics.
 | typescript | 841,426 | 598M | 3.81 | 2.88 |
 | python | 567,721 | 594M | 3.71 | 2.73 |
 | rust_go_java | 144,438 | 485M | 3.97 | 3.07 |
-| jupyter | 11 | 0M | 3.11 | 2.23 |
-
 
 ### Content Group Distribution (classifier-scored files only)
 
@@ -189,13 +185,13 @@ Each row contains:
 | `relevance_score` | float | Composite relevance score (**classifier-scored slices only**; 0.0 for non-classified) |
 
 > **Tip:** To work with only classifier-scored data, filter on
-> `language_slice` in `{"typescript", "python", "rust_go_java", "jupyter"}`.
+> `language_slice` in `{"typescript", "python", "rust_go_java"}`.
 
 ## Methodology
 
 1. **Download:** All language folders from `bigcode/starcoderdata`.
 2. **Classification:** Multi-task UniXcoder-base model (3 heads: quality, SD relevance,
-   content type) runs on TypeScript, Python, Rust, Go, Java, and Jupyter files.
+   content type) runs on TypeScript, Python, Rust, Go, and Java
    Schema languages, GitHub issues, and general code skip this step.
 3. **Pre-filtering:** zlib compression ratio filter removes repetitive boilerplate
    before GPU inference.