model update

README.md

@@ -103,6 +103,17 @@ model-index:
     - name: Accuracy
       type: accuracy
       value: 0.5901639344262295
+  - task:
+      name: Analogy Questions (NELL-ONE Analogy)
+      type: multiple-choice-qa
+    dataset:
+      name: NELL-ONE Analogy
+      args: relbert/analogy_questions
+      type: analogy-questions
+    metrics:
+    - name: Accuracy
+      type: accuracy
+      value: 0.685
   - task:
       name: Lexical Relation Classification (BLESS)
       type: classification
@@ -188,6 +199,7 @@ This model achieves the following results on the relation understanding tasks:
     - Accuracy on Google: 0.902
     - Accuracy on ConceptNet Analogy: 0.3850671140939597
     - Accuracy on T-Rex Analogy: 0.5901639344262295
+    - Accuracy on NELL-ONE Analogy: 0.685
 - Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-large-nce-b-semeval2012/raw/main/classification.json)):
     - Micro F1 score on BLESS: 0.9053789362663854
     - Micro F1 score on CogALexV: 0.8535211267605634

config.json

@@ -1,5 +1,5 @@
 {
-  "_name_or_path": "relbert_output/ckpt/nce_semeval2012/template-b/epoch_9",
+  "_name_or_path": "roberta-large",
   "architectures": [
     "RobertaModel"
   ],

tokenizer_config.json

@@ -6,7 +6,7 @@
   "errors": "replace",
   "mask_token": "<mask>",
   "model_max_length": 512,
-  "name_or_path": "relbert_output/ckpt/nce_semeval2012/template-b/epoch_9",
+  "name_or_path": "roberta-large",
   "pad_token": "<pad>",
   "sep_token": "</s>",
   "special_tokens_map_file": null,