model update

README.md

@@ -14,7 +14,7 @@ model-index:
     metrics:
     - name: Accuracy
       type: accuracy
-      value: None
+      value: 0.789047619047619
   - task:
       name: Analogy Questions (SAT full)
       type: multiple-choice-qa
@@ -25,7 +25,7 @@ model-index:
     metrics:
     - name: Accuracy
       type: accuracy
-      value: None
+      value: 0.3342245989304813
   - task:
       name: Analogy Questions (SAT)
       type: multiple-choice-qa
@@ -36,7 +36,7 @@ model-index:
     metrics:
     - name: Accuracy
       type: accuracy
-      value: None
+      value: 0.33827893175074186
   - task:
       name: Analogy Questions (BATS)
       type: multiple-choice-qa
@@ -47,7 +47,7 @@ model-index:
     metrics:
     - name: Accuracy
       type: accuracy
-      value: None
+      value: 0.3885491939966648
   - task:
       name: Analogy Questions (Google)
       type: multiple-choice-qa
@@ -58,7 +58,7 @@ model-index:
     metrics:
     - name: Accuracy
       type: accuracy
-      value: None
+      value: 0.542
   - task:
       name: Analogy Questions (U2)
       type: multiple-choice-qa
@@ -69,7 +69,7 @@ model-index:
     metrics:
     - name: Accuracy
       type: accuracy
-      value: None
+      value: 0.3201754385964912
   - task:
       name: Analogy Questions (U4)
       type: multiple-choice-qa
@@ -80,7 +80,7 @@ model-index:
     metrics:
     - name: Accuracy
       type: accuracy
-      value: None
+      value: 0.33564814814814814
   - task:
       name: Lexical Relation Classification (BLESS)
       type: classification
@@ -91,10 +91,10 @@ model-index:
     metrics:
     - name: F1
       type: f1
-      value: None
+      value: 0.8865451258098539
     - name: F1 (macro)
       type: f1_macro
-      value: None
+      value: 0.8770785182418419
   - task:
       name: Lexical Relation Classification (CogALexV)
       type: classification
@@ -105,10 +105,10 @@ model-index:
     metrics:
     - name: F1
       type: f1
-      value: None
+      value: 0.8401408450704225
     - name: F1 (macro)
       type: f1_macro
-      value: None
+      value: 0.6242491296371133
   - task:
       name: Lexical Relation Classification (EVALution)
       type: classification
@@ -119,10 +119,10 @@ model-index:
     metrics:
     - name: F1
       type: f1
-      value: None
+      value: 0.6749729144095341
     - name: F1 (macro)
       type: f1_macro
-      value: None
+      value: 0.6505812342477592
   - task:
       name: Lexical Relation Classification (K&H+N)
       type: classification
@@ -133,10 +133,10 @@ model-index:
     metrics:
     - name: F1
       type: f1
-      value: None
+      value: 0.9607706753842944
     - name: F1 (macro)
       type: f1_macro
-      value: None
+      value: 0.8781957733610742
   - task:
       name: Lexical Relation Classification (ROOT09)
       type: classification
@@ -147,10 +147,10 @@ model-index:
     metrics:
     - name: F1
       type: f1
-      value: None
+      value: 0.8994045753682232
     - name: F1 (macro)
       type: f1_macro
-      value: None
+      value: 0.8968786782259857
 
 ---
 # relbert/roberta-large-semeval2012-average-prompt-a-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-average-prompt-a-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): 0.3342245989304813 
+    - Accuracy on SAT: 0.33827893175074186
+    - Accuracy on BATS: 0.3885491939966648
+    - Accuracy on U2: 0.3201754385964912
+    - Accuracy on U4: 0.33564814814814814
+    - Accuracy on Google: 0.542
 - Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/roberta-large-semeval2012-average-prompt-a-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: 0.8865451258098539
+    - Micro F1 score on CogALexV: 0.8401408450704225
+    - Micro F1 score on EVALution: 0.6749729144095341
+    - Micro F1 score on K&H+N: 0.9607706753842944
+    - Micro F1 score on ROOT09: 0.8994045753682232
 - Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/roberta-large-semeval2012-average-prompt-a-nce-classification/raw/main/relation_mapping.json)):
-    - Accuracy on Relation Mapping: None 
+    - Accuracy on Relation Mapping: 0.789047619047619 
 
 
 ### Usage