Update README.md with new split philosophy details

README.md

@@ -64,6 +64,8 @@ ShamNER is a curated corpus of Levantine‑Arabic sentences annotated for Named
 ## Quick start
 
 ```python
+# Uncomment next line if you hit a LocalFileSystem / fsspec error on Colab
+# !pip install -U "datasets>=2.16.0" "fsspec>=2023.10.0"
 from datasets import load_dataset
 sham = load_dataset("HebArabNlpProject/ShamNER")
 train_ds = sham["train"]
@@ -74,7 +76,7 @@ train_ds = sham["train"]
 ## Split Philosophy
 
 * **No duplicate documents** – A *document* is identified by the pair  
-  `(doc_name, round)`; each such bundle is assigned to exactly one split.
+  `(doc_name, round)`; each such bundle is assigned to exactly one split. This rule holds true for bundles, though individual sentences within bundles might have overlapping spans after post-allocation pruning for specific thresholds.
 
 * **Rounds** – Six annotation iterations:  
   `pilot`, `round1` – `round5` (manual, quality improving each round) and  
@@ -87,10 +89,21 @@ train_ds = sham["train"]
   round 6.*  
   No separate `test_synth` file.
 
-* **Span-novelty rule** – Before allocation, normalise every entity string
-  (lower-case, strip Arabic diacritics and leading “ال”, collapse whitespace).
-  A bundle is forced to **train** if *any* of its normalised spans already
-  occurs in train; otherwise it may enter validation or test.
+* **Span-novelty rule (Relaxed)**
+
+Before allocation, normalise every entity string:
+- Convert to lowercase (Latin aphbaet exists in social media)
+- Strip Arabic diacritics
+- Remove leading “ال”
+- Collapse internal whitespace
+
+A bundle is forced to **train** if **any** of its normalised spans already occurs in train.
+
+A **post-allocation pruning** step then moves sentences from validation or test back to train  
+**only if more than 50%** of their normalized spans already exist in the training set.
+
+This threshold (**0.50**) was chosen to provide more learning examples to the model  
+in the evaluation sets, leading to improved performance.
 
 * **Tokeniser-agnostic** – Each record stores only raw `text` and
   character-offset `spans`; no BIO arrays.  Users regenerate token-level labels
@@ -101,9 +114,9 @@ train_ds = sham["train"]
 
 | split      | sentences  | files                           |
 | ---------- | ---------- | ------------------------------- |
-| train      | **19 783** | `train.jsonl` / `train.parquet` |
-| validation | 1 795      | `validation.*`                  |
-| test       | 1 844      | `test.*`                        |
+| train      | **19 532** | `train.jsonl` / `train.parquet` |
+| validation | 1 931      | `validation.*`                  |
+| test       | 1 931      | `test.*`                        |
 | iaa\_A     | 5 806      | optional, dual annotator A      |
 | iaa\_B     | 5 806      | optional, annotator B           |