model card: licence, attribution, measured numbers, limitations

README.md

@@ -98,19 +98,19 @@ access, not by inference.
 
 ## Limitations, stated plainly
 
-- **Dagbani had no recogniser of its own for this whole project**, and the
-  reason given for that was wrong. Every card here said "one fine-tuning
-  session on 74 validation rows would not change that". Those 74 rows are
-  the **eng-dag machine-translation** validation split. The Dagbani
-  *speech* data in this same account is `waxal_dag`: **13,228 training
-  rows, 1,750 validation rows, ~71 hours, 1,041 speakers with the largest
-  at 1%** — more data and better speaker diversity than Ewe, which
-  produced a working 42.19 WER recogniser. A number was carried across
-  from a translation table into a speech claim, and then repeated on every
-  model card on the account.
-  It is training now, on 2026-08-31. Until it is scored, the honest
-  statement is that Dagbani's best available recogniser scores 86.6 WER
-  and nobody had tried fine-tuning on the data already in hand.
+- **Dagbani did get a recogniser, and the claim that it could not was
+  wrong twice over.** Every card on this account used to say that "one
+  fine-tuning session on 74 validation rows would not change that". Those
+  74 rows are the **eng-dag machine-translation** validation split; the
+  Dagbani *speech* data in this same account is `waxal_dag` — 13,228
+  training rows, 1,750 validation rows, ~71 hours, 1,041 speakers with
+  the largest at 1%. Trained on it, `tekyerema-asr-mms-dag` scores
+  **36.94 / 11.71**, against the 86.59 / 33.95 this project had believed
+  was the ceiling. It still loses to
+  `FarmerlineML/w2v-bert-2.0_2026_dagbani_ASR` at **29.20 / 9.27**, which
+  is what the agent actually serves. A number carried across from a
+  translation table into a speech claim was then repeated on every card
+  here until 2026-09-22.
 - **Evaluation is on read and machine-translated text.** No recordings of
   people speaking agent commands in these languages exist. Numbers measured
   this way are optimistic about phrasing and pessimistic about