Track A docs: 2 subtasks (A1 binary, A2 unified fine-grained multi-label)

README.md

@@ -62,11 +62,14 @@ manually annotated for hatefulness and fine-grained sub-types.
 
 Given a meme (image + Arabic text):
 
-- **Subtask 1A – Binary.** Classify the meme as `Hateful` or `Not Hateful`.
-- **Subtask 1B – Fine-grained hateful.** For `Hateful` memes, predict the
-  applicable fine-grained sub-type(s) (multi-label).
-- **Subtask 1C – Fine-grained non-hateful.** For `Not Hateful` memes,
-  predict the applicable sub-type(s) (multi-label).
+- **Subtask A1 – Binary.** Classify the meme as `Hateful` or `Not Hateful`.
+- **Subtask A2 – Fine-grained category prediction (multi-label).** Predict
+  the applicable fine-grained sub-type(s) from a unified taxonomy that
+  covers both hateful and non-hateful categories. Hateful memes draw
+  labels from the hateful sub-type set (Mocking, Incitement,
+  Dehumanization, Slurs, Contempt, Inferiority, Exclusion, …); non-hateful
+  memes draw from `Humor`, `Sarcasm`, plus the shared `Other`. Both
+  subtasks are evaluated on every meme.
 
 ## Splits
 
@@ -95,18 +98,26 @@ Given a meme (image + Arabic text):
 | test       |    148  |         352 |     29.6% |
 | **Total**  | **1,850** | **3,150** |     37.0% |
 
-### Fine-grained sub-types
+### Fine-grained sub-types (Subtask A2)
 
-**Hateful sub-types** (Subtask 1B, multi-label):
-Mocking, Incitement, Dehumanization, Slurs, Contempt, Inferiority,
-Exclusion, Stereotyping, Extremism, Threat, Insults, Historical, Other.
+The Subtask A2 label space is **one unified multi-label vocabulary**
+that covers both hateful and non-hateful sub-types:
 
-**Non-hateful sub-types** (Subtask 1C, multi-label):
-Humor, Sarcasm, Other.
+- **Hateful sub-types** (active in the released data): Mocking,
+  Incitement, Dehumanization, Slurs, Contempt, Inferiority, Exclusion.
+- **Non-hateful sub-types**: Humor, Sarcasm.
+- **Shared**: Other (used by both Hateful and Not-Hateful memes).
 
-A meme is never assigned both hateful and non-hateful sub-types
-simultaneously. Sub-types are multi-label, so per-class counts sum to
-more than the meme counts.
+Five additional hateful classes appear in the annotation taxonomy but
+have **zero training support** in the released data: Extremism,
+Historical, Insults, Stereotyping, Threat. They are documented for
+completeness, accepted by the format checker, and ignored by the scorer.
+
+In practice each meme's fine-grained labels are drawn from its own
+binary class: a Hateful meme will only carry hateful sub-types (and/or
+`Other`); a Not-Hateful meme will only carry `Humor` / `Sarcasm` /
+`Other`. Sub-types are multi-label, so per-class counts sum to more
+than the meme counts.
 
 ## Record schema