model update

README.md

@@ -14,7 +14,7 @@ model-index:
     metrics:
     - name: Accuracy
       type: accuracy
-      value: None
+      value: 0.7550595238095238
   - task:
       name: Analogy Questions (SAT full)
       type: multiple-choice-qa
@@ -25,7 +25,7 @@ model-index:
     metrics:
     - name: Accuracy
       type: accuracy
-      value: None
+      value: 0.5133689839572193
   - task:
       name: Analogy Questions (SAT)
       type: multiple-choice-qa
@@ -36,7 +36,7 @@ model-index:
     metrics:
     - name: Accuracy
       type: accuracy
-      value: None
+      value: 0.516320474777448
   - task:
       name: Analogy Questions (BATS)
       type: multiple-choice-qa
@@ -47,7 +47,7 @@ model-index:
     metrics:
     - name: Accuracy
       type: accuracy
-      value: None
+      value: 0.5958866036687048
   - task:
       name: Analogy Questions (Google)
       type: multiple-choice-qa
@@ -58,7 +58,7 @@ model-index:
     metrics:
     - name: Accuracy
       type: accuracy
-      value: None
+      value: 0.748
   - task:
       name: Analogy Questions (U2)
       type: multiple-choice-qa
@@ -69,7 +69,7 @@ model-index:
     metrics:
     - name: Accuracy
       type: accuracy
-      value: None
+      value: 0.4605263157894737
   - task:
       name: Analogy Questions (U4)
       type: multiple-choice-qa
@@ -80,7 +80,7 @@ model-index:
     metrics:
     - name: Accuracy
       type: accuracy
-      value: None
+      value: 0.5231481481481481
   - task:
       name: Lexical Relation Classification (BLESS)
       type: classification
@@ -91,10 +91,10 @@ model-index:
     metrics:
     - name: F1
       type: f1
-      value: None
+      value: 0.9025161970769926
     - name: F1 (macro)
       type: f1_macro
-      value: None
+      value: 0.8979165451427438
   - task:
       name: Lexical Relation Classification (CogALexV)
       type: classification
@@ -105,10 +105,10 @@ model-index:
     metrics:
     - name: F1
       type: f1
-      value: None
+      value: 0.8328638497652581
     - name: F1 (macro)
       type: f1_macro
-      value: None
+      value: 0.6469572777603673
   - task:
       name: Lexical Relation Classification (EVALution)
       type: classification
@@ -119,10 +119,10 @@ model-index:
     metrics:
     - name: F1
       type: f1
-      value: None
+      value: 0.6630552546045504
     - name: F1 (macro)
       type: f1_macro
-      value: None
+      value: 0.6493250582245075
   - task:
       name: Lexical Relation Classification (K&H+N)
       type: classification
@@ -133,10 +133,10 @@ model-index:
     metrics:
     - name: F1
       type: f1
-      value: None
+      value: 0.9562495652778744
     - name: F1 (macro)
       type: f1_macro
-      value: None
+      value: 0.8695137253747418
   - task:
       name: Lexical Relation Classification (ROOT09)
       type: classification
@@ -147,10 +147,10 @@ model-index:
     metrics:
     - name: F1
       type: f1
-      value: None
+      value: 0.8906298965841429
     - name: F1 (macro)
       type: f1_macro
-      value: None
+      value: 0.8885946595123109
 
 ---
 # relbert/roberta-large-semeval2012-average-no-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-average-no-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): 0.5133689839572193 
+    - Accuracy on SAT: 0.516320474777448
+    - Accuracy on BATS: 0.5958866036687048
+    - Accuracy on U2: 0.4605263157894737
+    - Accuracy on U4: 0.5231481481481481
+    - Accuracy on Google: 0.748
 - 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-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: 0.9025161970769926
+    - Micro F1 score on CogALexV: 0.8328638497652581
+    - Micro F1 score on EVALution: 0.6630552546045504
+    - Micro F1 score on K&H+N: 0.9562495652778744
+    - Micro F1 score on ROOT09: 0.8906298965841429
 - Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/roberta-large-semeval2012-average-no-mask-prompt-e-nce-classification/raw/main/relation_mapping.json)):
-    - Accuracy on Relation Mapping: None 
+    - Accuracy on Relation Mapping: 0.7550595238095238 
 
 
 ### Usage