Update README.md

README.md

@@ -19,17 +19,18 @@ size_categories:
 A cleaned, deduplicated, Python-only pretraining corpus derived from
 [`codeparrot/codeparrot-clean`](https://huggingface.co/datasets/codeparrot/codeparrot-clean),
 built as the pretraining data for **PocketCoder**, a 95.87M-parameter decoder-only
-code language model. Total: **~2.96 billion tokens** (DeepSeek-Coder tokenizer,
-vocabulary 32,022).
+code language model. **1,800,000 documents, ~2.96 billion tokens**
+(DeepSeek-Coder tokenizer, vocabulary 32,022).
 
 - **Paper:** *PocketCoder: What Distillation, SFT, and DPO Each Buy You at 100M Parameters*
 - **Model:** [`Ananda100/PocketCoder`](https://huggingface.co/Ananda100/PocketCoder)
+- **SFT dataset:** [`Ananda100/python-sft-dataset`](https://huggingface.co/datasets/Ananda100/python-sft-dataset)
 - **Code:** [github.com/AnandaRimal/PocketCoder](https://github.com/AnandaRimal/PocketCoder)
 
 ## Cleaning pipeline
 
 Applied on top of `codeparrot-clean` (which is itself deduplicated), streaming
-file-by-file:
+file-by-file, collecting until 1,800,000 cleaned files were kept:
 
 1. **Drop autogenerated/vendored files** — files flagged `autogenerated` by the
    upstream dataset, plus regex matches for SWIG output, protocol-buffer compiler
@@ -59,6 +60,8 @@ Audited on a 5,000-document streamed sample (audit notebook and the resulting
 
 | Metric | Value |
 |---|---|
+| Total documents | 1,800,000 |
+| Total tokens (DeepSeek-Coder tokenizer) | ~2,955,981,351 |
 | Natural-language content (chars) | 16.0% (7.8% comments, 8.2% docstrings) |
 | Unique documents (exact hash) | 100.0% |
 | Unique documents (near, comments/whitespace stripped) | 99.9% |
@@ -115,4 +118,4 @@ dataset card for details.
   year   = {2026},
   url    = {https://github.com/AnandaRimal/PocketCoder}
 }
-```
+```