model update

README.md

@@ -2,7 +2,7 @@
 datasets:
 - relbert/conceptnet_high_confidence
 model-index:
-- name: relbert/relbert-roberta-large-conceptnet-hc-average-prompt-b-nce
+- name: relbert/roberta-large-conceptnet-hc-average-prompt-b-nce
   results:
   - task:
       name: Relation Mapping
@@ -14,7 +14,7 @@ model-index:
     metrics:
     - name: Accuracy
       type: accuracy
-      value: 0.8544642857142857
+      value: 0.8097222222222222
   - task:
       name: Analogy Questions (SAT full)
       type: multiple-choice-qa
@@ -153,27 +153,27 @@ model-index:
       value: 0.8921114555442471
 
 ---
-# relbert/relbert-roberta-large-conceptnet-hc-average-prompt-b-nce
+# relbert/roberta-large-conceptnet-hc-average-prompt-b-nce
 
 RelBERT fine-tuned from [roberta-large](https://huggingface.co/roberta-large) on  
 [relbert/conceptnet_high_confidence](https://huggingface.co/datasets/relbert/conceptnet_high_confidence).
 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/relbert-roberta-large-conceptnet-hc-average-prompt-b-nce/raw/main/analogy.json)):
+- Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/roberta-large-conceptnet-hc-average-prompt-b-nce/raw/main/analogy.json)):
     - Accuracy on SAT (full): 0.5106951871657754 
     - Accuracy on SAT: 0.49554896142433236
     - Accuracy on BATS: 0.7982212340188994
     - Accuracy on U2: 0.5350877192982456
     - Accuracy on U4: 0.6064814814814815
     - Accuracy on Google: 0.926
-- Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-large-conceptnet-hc-average-prompt-b-nce/raw/main/classification.json)):
+- Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/roberta-large-conceptnet-hc-average-prompt-b-nce/raw/main/classification.json)):
     - Micro F1 score on BLESS: 0.9061322886846467
     - Micro F1 score on CogALexV: 0.8483568075117371
     - Micro F1 score on EVALution: 0.6538461538461539
     - Micro F1 score on K&H+N: 0.9576406760798497
     - Micro F1 score on ROOT09: 0.8934503290504543
-- Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-large-conceptnet-hc-average-prompt-b-nce/raw/main/relation_mapping.json)):
-    - Accuracy on Relation Mapping: 0.8544642857142857 
+- Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/roberta-large-conceptnet-hc-average-prompt-b-nce/raw/main/relation_mapping.json)):
+    - Accuracy on Relation Mapping: 0.8097222222222222 
 
 
 ### Usage
@@ -184,7 +184,7 @@ pip install relbert
 and activate model as below.
 ```python
 from relbert import RelBERT
-model = RelBERT("relbert/relbert-roberta-large-conceptnet-hc-average-prompt-b-nce")
+model = RelBERT("relbert/roberta-large-conceptnet-hc-average-prompt-b-nce")
 vector = model.get_embedding(['Tokyo', 'Japan'])  # shape of (1024, )
 ```
 
@@ -211,7 +211,7 @@ The following hyperparameters were used during training:
  - n_sample: 640
  - gradient_accumulation: 8
 
-The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-large-conceptnet-hc-average-prompt-b-nce/raw/main/trainer_config.json).
+The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/roberta-large-conceptnet-hc-average-prompt-b-nce/raw/main/trainer_config.json).
 
 ### Reference
 If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/).