card: attach structured model-index (board-repro benchmarks, 7 tasks)

README.md

@@ -13,6 +13,37 @@ tags:
 - from-scratch
 - amd-rocm
 - continued-pretraining
+model-index:
+- name: GoLLeM-110M-PL-v3
+  results:
+  - task: {type: text-classification, name: PolEmo2-IN sentiment}
+    dataset: {type: allegro/klej-polemo2-in, name: PolEmo2-IN}
+    metrics:
+    - {type: accuracy, value: 18.0, name: accuracy (0-shot, domain-PMI board-repro)}
+  - task: {type: text-classification, name: 8TAGS topic}
+    dataset: {type: sdadas/8tags, name: 8TAGS}
+    metrics:
+    - {type: accuracy, value: 41.2, name: accuracy (0-shot, domain-PMI board-repro)}
+  - task: {type: multiple-choice, name: Belebele PL reading}
+    dataset: {type: facebook/belebele, name: Belebele pol_Latn}
+    metrics:
+    - {type: accuracy, value: 24.0, name: accuracy (0-shot)}
+  - task: {type: text-classification, name: CBD cyberbullying}
+    dataset: {type: ptaszynski/PolishCyberbullyingDataset, name: CBD}
+    metrics:
+    - {type: f1, value: 12.6, name: macro-F1 (0-shot, domain-PMI board-repro)}
+  - task: {type: text-classification, name: DYK question-answer}
+    dataset: {type: allegro/klej-dyk, name: DYK}
+    metrics:
+    - {type: f1, value: 28.3, name: positive-F1 (0-shot, domain-PMI board-repro)}
+  - task: {type: token-classification, name: KLEJ NER}
+    dataset: {type: allegro/klej-nkjp-ner, name: KLEJ-NER}
+    metrics:
+    - {type: accuracy, value: 18.2, name: accuracy (0-shot, domain-PMI board-repro)}
+  - task: {type: text-classification, name: PSC summary}
+    dataset: {type: allegro/klej-psc, name: PSC}
+    metrics:
+    - {type: f1, value: 44.2, name: positive-F1 (0-shot, domain-PMI board-repro)}
 ---
 
 # GoLLeM-110M-PL-v3