From fcf81dcc608f46f7f1390eb4145a4049c71fe171 Mon Sep 17 00:00:00 2001
From: Rishiraj Acharya <44090649+rishiraj@users.noreply.github.com>
Date: Tue, 4 Aug 2026 03:23:02 +0530
Subject: [PATCH] Parameter Name Correction

---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 49de62b..b222ae0 100644
--- a/README.md
+++ b/README.md
@@ -175,7 +175,7 @@ TabFM uses in-context learning over a bounded context window, so very large
 tables should be sampled or split before inference. The scikit-learn estimators
 expose the main practical limits through `max_num_features` and `max_num_rows`
 (defaults are 500 features and 100 context rows), plus `n_estimators` for
-ensembling over multiple sampled contexts and `inference_batch_size` for memory
+ensembling over multiple sampled contexts and `batch_size` for memory
 control. If your dataset is larger than these limits, TabFM will work with the
 sampled/context rows rather than consuming the full table at once.