Update README.md

README.md

@@ -2,7 +2,7 @@
 datasets:
 - relbert/t_rex_relational_similarity
 model-index:
-- name: relbert/relbert-roberta-base-nce-d-t-rex
+- name: relbert/relbert-roberta-base-nce-t-rex
   results:
   - task:
       name: Relation Mapping
@@ -186,11 +186,11 @@ model-index:
       value: 0.8753206261467538
 
 ---
-# relbert/relbert-roberta-base-nce-d-t-rex
+# relbert/relbert-roberta-base-nce-t-rex
 
 RelBERT based on [roberta-base](https://huggingface.co/roberta-base) fine-tuned on [relbert/t_rex_relational_similarity](https://huggingface.co/datasets/relbert/t_rex_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-t-rex/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-t-rex/raw/main/analogy.forward.json)):
     - Accuracy on SAT (full): 0.4090909090909091 
     - Accuracy on SAT: 0.41543026706231456
     - Accuracy on BATS: 0.5186214563646471
@@ -200,13 +200,13 @@ This model achieves the following results on the relation understanding tasks:
     - Accuracy on ConceptNet Analogy: 0.13926174496644295
     - Accuracy on T-Rex Analogy: 0.6830601092896175
     - Accuracy on NELL-ONE Analogy: 0.7033333333333334
-- Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-nce-d-t-rex/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-t-rex/raw/main/classification.json)):
     - Micro F1 score on BLESS: 0.9050775952990809
     - Micro F1 score on CogALexV: 0.8086854460093896
     - Micro F1 score on EVALution: 0.6256771397616468
     - Micro F1 score on K&H+N: 0.952145788412047
     - Micro F1 score on ROOT09: 0.8790347853337511
-- Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-nce-d-t-rex/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-t-rex/raw/main/relation_mapping.json)):
     - Accuracy on Relation Mapping: 0.7073412698412699 
 
 
@@ -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-t-rex")
+model = RelBERT("relbert/relbert-roberta-base-nce-t-rex")
 vector = model.get_embedding(['Tokyo', 'Japan'])  # shape of (n_dim, )
 ```
 
@@ -242,14 +242,14 @@ 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-t-rex/raw/main/finetuning_config.json).
+See the full configuration at [config file](https://huggingface.co/relbert/relbert-roberta-base-nce-t-rex/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/).
 
 ```
 
-@inproceedings{ushio-etal-2021-distilling,
+@inproceedings{ushio-etal-2021istilling,
     title = "Distilling Relation Embeddings from Pretrained Language Models",
     author = "Ushio, Asahi  and
       Camacho-Collados, Jose  and