model update

README.md

@@ -14,7 +14,7 @@ model-index:
     metrics:
     - name: Accuracy
       type: accuracy
-      value: None
+      value: 0.8073809523809524
   - task:
       name: Analogy Questions (SAT full)
       type: multiple-choice-qa
@@ -25,7 +25,7 @@ model-index:
     metrics:
     - name: Accuracy
       type: accuracy
-      value: None
+      value: 0.5213903743315508
   - task:
       name: Analogy Questions (SAT)
       type: multiple-choice-qa
@@ -36,7 +36,7 @@ model-index:
     metrics:
     - name: Accuracy
       type: accuracy
-      value: None
+      value: 0.5222551928783383
   - task:
       name: Analogy Questions (BATS)
       type: multiple-choice-qa
@@ -47,7 +47,7 @@ model-index:
     metrics:
     - name: Accuracy
       type: accuracy
-      value: None
+      value: 0.6964980544747081
   - task:
       name: Analogy Questions (Google)
       type: multiple-choice-qa
@@ -58,7 +58,7 @@ model-index:
     metrics:
     - name: Accuracy
       type: accuracy
-      value: None
+      value: 0.83
   - task:
       name: Analogy Questions (U2)
       type: multiple-choice-qa
@@ -69,7 +69,7 @@ model-index:
     metrics:
     - name: Accuracy
       type: accuracy
-      value: None
+      value: 0.5701754385964912
   - task:
       name: Analogy Questions (U4)
       type: multiple-choice-qa
@@ -80,7 +80,7 @@ model-index:
     metrics:
     - name: Accuracy
       type: accuracy
-      value: None
+      value: 0.5555555555555556
   - task:
       name: Lexical Relation Classification (BLESS)
       type: classification
@@ -91,10 +91,10 @@ model-index:
     metrics:
     - name: F1
       type: f1
-      value: None
+      value: 0.9285821907488323
     - name: F1 (macro)
       type: f1_macro
-      value: None
+      value: 0.9245313355619169
   - task:
       name: Lexical Relation Classification (CogALexV)
       type: classification
@@ -105,10 +105,10 @@ model-index:
     metrics:
     - name: F1
       type: f1
-      value: None
+      value: 0.8676056338028169
     - name: F1 (macro)
       type: f1_macro
-      value: None
+      value: 0.7160526589344361
   - task:
       name: Lexical Relation Classification (EVALution)
       type: classification
@@ -119,10 +119,10 @@ model-index:
     metrics:
     - name: F1
       type: f1
-      value: None
+      value: 0.6939328277356447
     - name: F1 (macro)
       type: f1_macro
-      value: None
+      value: 0.6839321873211963
   - task:
       name: Lexical Relation Classification (K&H+N)
       type: classification
@@ -133,10 +133,10 @@ model-index:
     metrics:
     - name: F1
       type: f1
-      value: None
+      value: 0.9678653404743688
     - name: F1 (macro)
       type: f1_macro
-      value: None
+      value: 0.8987867094809483
   - task:
       name: Lexical Relation Classification (ROOT09)
       type: classification
@@ -147,10 +147,10 @@ model-index:
     metrics:
     - name: F1
       type: f1
-      value: None
+      value: 0.9025383892196804
     - name: F1 (macro)
       type: f1_macro
-      value: None
+      value: 0.901704052042147
 
 ---
 # relbert/roberta-large-semeval2012-mask-prompt-e-nce-classification
@@ -160,20 +160,20 @@ RelBERT fine-tuned from [roberta-large](https://huggingface.co/roberta-large) on
 Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail).
 It achieves the following results on the relation understanding tasks:
 - Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/roberta-large-semeval2012-mask-prompt-e-nce-classification/raw/main/analogy.json)):
-    - Accuracy on SAT (full): None 
-    - Accuracy on SAT: None
-    - Accuracy on BATS: None
-    - Accuracy on U2: None
-    - Accuracy on U4: None
-    - Accuracy on Google: None
+    - Accuracy on SAT (full): 0.5213903743315508 
+    - Accuracy on SAT: 0.5222551928783383
+    - Accuracy on BATS: 0.6964980544747081
+    - Accuracy on U2: 0.5701754385964912
+    - Accuracy on U4: 0.5555555555555556
+    - Accuracy on Google: 0.83
 - Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/roberta-large-semeval2012-mask-prompt-e-nce-classification/raw/main/classification.json)):
-    - Micro F1 score on BLESS: None
-    - Micro F1 score on CogALexV: None
-    - Micro F1 score on EVALution: None
-    - Micro F1 score on K&H+N: None
-    - Micro F1 score on ROOT09: None
+    - Micro F1 score on BLESS: 0.9285821907488323
+    - Micro F1 score on CogALexV: 0.8676056338028169
+    - Micro F1 score on EVALution: 0.6939328277356447
+    - Micro F1 score on K&H+N: 0.9678653404743688
+    - Micro F1 score on ROOT09: 0.9025383892196804
 - Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/roberta-large-semeval2012-mask-prompt-e-nce-classification/raw/main/relation_mapping.json)):
-    - Accuracy on Relation Mapping: None 
+    - Accuracy on Relation Mapping: 0.8073809523809524 
 
 
 ### Usage