Update README.md

README.md

@@ -62,7 +62,7 @@ Each row is a single bilingual sentence pair with language, dialect, split, and
 - **Targets:** English (`en`), Mandarin Chinese (`zh`)  
 - **Splits (all languages, both targets combined):**
   - Train: 334,772
-  - Validation: 29,412
+  - Validate: 29,412
   - Test: 29,450
 - **License:** CC BY 4.0  
 - **Format:** UTF-8 CSV, one sentence pair per row
@@ -147,20 +147,20 @@ id,source_lang,target_lang,source_sentence,target_sentence,lang_code,dialect,sou
 * `lang_code` *(str)* – canonical code for the Formosan language (usually same as `source_lang`).
 * `dialect` *(str)* – dialect label (e.g. `"Southern"`, `"Malan"`, `"UNKNOWN"`).
 * `source` *(str)* – provenance string or original file path in the upstream corpora.
-* `split` *(str)* – one of `"train"`, `"validation"`, `"test"`.
+* `split` *(str)* – one of `"train"`, `"validate"`, `"test"`.
 
 ### Splits
 
 Splits are defined **per row** via the `split` column:
 
 * `train` – training data
-* `validation` – development / validation data
+* `validate` – development / validate data
 * `test` – held-out test data
 
 Global totals across all languages and directions:
 
 * Train: 334,772
-* Validation: 29,412
+* Validate: 29,412
 * Test: 29,450
 
 Users can filter to any language pair and then re-group into a `DatasetDict` by `split`.
@@ -238,7 +238,7 @@ from datasets import DatasetDict
 def split_by_column(ds):
     return DatasetDict({
         "train":      ds.filter(lambda ex: ex["split"] == "train"),
-        "validation": ds.filter(lambda ex: ex["split"] == "validation"),
+        "validate": ds.filter(lambda ex: ex["split"] == "validate"),
         "test":       ds.filter(lambda ex: ex["split"] == "test"),
     })
 
@@ -247,7 +247,7 @@ ami_en_splits = split_by_column(ami_en)
 print(ami_en_splits)
 # DatasetDict({
 #   train: Dataset({ ... })
-#   validation: Dataset({ ... })
+#   validate: Dataset({ ... })
 #   test: Dataset({ ... })
 # })
 ```