model update

README.md

@@ -2,7 +2,7 @@
 datasets:
 - relbert/conceptnet_relational_similarity
 model-index:
-- name: relbert/relbert-roberta-base-nce-d-conceptnet
+- name: relbert/relbert-roberta-base-nce-conceptnet
   results:
   - task:
       name: Relation Mapping
@@ -186,11 +186,11 @@ model-index:
       value: 0.8877705847530848
 
 ---
-# relbert/relbert-roberta-base-nce-d-conceptnet
+# relbert/relbert-roberta-base-nce-conceptnet
 
 RelBERT based on [roberta-base](https://huggingface.co/roberta-base) fine-tuned on [relbert/conceptnet_relational_similarity](https://huggingface.co/datasets/relbert/conceptnet_relational_similarity) (see the [`relbert`](https://github.com/asahi417/relbert) for more detail of fine-tuning).
 This model 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-base-nce-d-conceptnet/raw/main/analogy.forward.json)):
+- Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-nce-conceptnet/raw/main/analogy.forward.json)):
     - Accuracy on SAT (full): 0.44919786096256686 
     - Accuracy on SAT: 0.4421364985163205
     - Accuracy on BATS: 0.6197887715397443
@@ -200,13 +200,13 @@ This model achieves the following results on the relation understanding tasks:
     - Accuracy on ConceptNet Analogy: 0.2273489932885906
     - Accuracy on T-Rex Analogy: 0.44808743169398907
     - Accuracy on NELL-ONE Analogy: 0.6616666666666666
-- Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-nce-d-conceptnet/raw/main/classification.json)):
+- Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-nce-conceptnet/raw/main/classification.json)):
     - Micro F1 score on BLESS: 0.8880518306463764
     - Micro F1 score on CogALexV: 0.828169014084507
     - Micro F1 score on EVALution: 0.628385698808234
     - Micro F1 score on K&H+N: 0.9501982332892815
     - Micro F1 score on ROOT09: 0.8884362268881228
-- Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-nce-d-conceptnet/raw/main/relation_mapping.json)):
+- Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-nce-conceptnet/raw/main/relation_mapping.json)):
     - Accuracy on Relation Mapping: 0.8411507936507936 
 
 
@@ -218,7 +218,7 @@ pip install relbert
 and activate model as below.
 ```python
 from relbert import RelBERT
-model = RelBERT("relbert/relbert-roberta-base-nce-d-conceptnet")
+model = RelBERT("relbert/relbert-roberta-base-nce-conceptnet")
 vector = model.get_embedding(['Tokyo', 'Japan'])  # shape of (n_dim, )
 ```
 
@@ -242,7 +242,7 @@ vector = model.get_embedding(['Tokyo', 'Japan'])  # shape of (n_dim, )
  - loss_function_config: {'temperature': 0.05, 'num_negative': 300, 'num_positive': 30}
  - augment_negative_by_positive: True
 
-See the full configuration at [config file](https://huggingface.co/relbert/relbert-roberta-base-nce-d-conceptnet/raw/main/finetuning_config.json).
+See the full configuration at [config file](https://huggingface.co/relbert/relbert-roberta-base-nce-conceptnet/raw/main/finetuning_config.json).
 
 ### Reference
 If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.emnlp-main.712/).