model update

README.md

@@ -14,7 +14,7 @@ model-index:
     metrics:
     - name: Accuracy
       type: accuracy
-      value: None
+      value: 0.8261309523809524
   - task:
       name: Analogy Questions (SAT full)
       type: multiple-choice-qa
@@ -25,7 +25,7 @@ model-index:
     metrics:
     - name: Accuracy
       type: accuracy
-      value: None
+      value: 0.6417112299465241
   - task:
       name: Analogy Questions (SAT)
       type: multiple-choice-qa
@@ -36,7 +36,7 @@ model-index:
     metrics:
     - name: Accuracy
       type: accuracy
-      value: None
+      value: 0.6409495548961425
   - task:
       name: Analogy Questions (BATS)
       type: multiple-choice-qa
@@ -47,7 +47,7 @@ model-index:
     metrics:
     - name: Accuracy
       type: accuracy
-      value: None
+      value: 0.7871039466370205
   - task:
       name: Analogy Questions (Google)
       type: multiple-choice-qa
@@ -58,7 +58,7 @@ model-index:
     metrics:
     - name: Accuracy
       type: accuracy
-      value: None
+      value: 0.946
   - task:
       name: Analogy Questions (U2)
       type: multiple-choice-qa
@@ -69,7 +69,7 @@ model-index:
     metrics:
     - name: Accuracy
       type: accuracy
-      value: None
+      value: 0.5921052631578947
   - task:
       name: Analogy Questions (U4)
       type: multiple-choice-qa
@@ -80,7 +80,7 @@ model-index:
     metrics:
     - name: Accuracy
       type: accuracy
-      value: None
+      value: 0.6527777777777778
   - task:
       name: Lexical Relation Classification (BLESS)
       type: classification
@@ -91,10 +91,10 @@ model-index:
     metrics:
     - name: F1
       type: f1
-      value: None
+      value: 0.9100497212596053
     - name: F1 (macro)
       type: f1_macro
-      value: None
+      value: 0.9039162913439194
   - task:
       name: Lexical Relation Classification (CogALexV)
       type: classification
@@ -105,10 +105,10 @@ model-index:
     metrics:
     - name: F1
       type: f1
-      value: None
+      value: 0.8556338028169014
     - name: F1 (macro)
       type: f1_macro
-      value: None
+      value: 0.6945383312136448
   - task:
       name: Lexical Relation Classification (EVALution)
       type: classification
@@ -119,10 +119,10 @@ model-index:
     metrics:
     - name: F1
       type: f1
-      value: None
+      value: 0.6852654387865655
     - name: F1 (macro)
       type: f1_macro
-      value: None
+      value: 0.6774872040266507
   - task:
       name: Lexical Relation Classification (K&H+N)
       type: classification
@@ -133,10 +133,10 @@ model-index:
     metrics:
     - name: F1
       type: f1
-      value: None
+      value: 0.9572233428392571
     - name: F1 (macro)
       type: f1_macro
-      value: None
+      value: 0.879744388826254
   - task:
       name: Lexical Relation Classification (ROOT09)
       type: classification
@@ -147,10 +147,10 @@ model-index:
     metrics:
     - name: F1
       type: f1
-      value: None
+      value: 0.9037919147602632
     - name: F1 (macro)
       type: f1_macro
-      value: None
+      value: 0.9024843094207563
 
 ---
 # relbert/roberta-large-semeval2012-average-no-mask-prompt-a-loob-conceptnet-validated
@@ -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-average-no-mask-prompt-a-loob-conceptnet-validated/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): 0.6417112299465241 
+    - Accuracy on SAT: 0.6409495548961425
+    - Accuracy on BATS: 0.7871039466370205
+    - Accuracy on U2: 0.5921052631578947
+    - Accuracy on U4: 0.6527777777777778
+    - Accuracy on Google: 0.946
 - Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/roberta-large-semeval2012-average-no-mask-prompt-a-loob-conceptnet-validated/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: 0.9100497212596053
+    - Micro F1 score on CogALexV: 0.8556338028169014
+    - Micro F1 score on EVALution: 0.6852654387865655
+    - Micro F1 score on K&H+N: 0.9572233428392571
+    - Micro F1 score on ROOT09: 0.9037919147602632
 - Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/roberta-large-semeval2012-average-no-mask-prompt-a-loob-conceptnet-validated/raw/main/relation_mapping.json)):
-    - Accuracy on Relation Mapping: None 
+    - Accuracy on Relation Mapping: 0.8261309523809524 
 
 
 ### Usage