model update

README.md

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+---
+datasets:
+- relbert/conceptnet_high_confidence
+model-index:
+- name: relbert/relbert-roberta-large-conceptnet-hc-average-prompt-b-nce
+  results:
+  - task:
+      name: Relation Mapping
+      type: sorting-task
+    dataset:
+      name: Relation Mapping
+      args: relbert/relation_mapping
+      type: relation-mapping
+    metrics:
+    - name: Accuracy
+      type: accuracy
+      value: 0.8571428571428571
+  - task:
+      name: Analogy Questions (SAT full)
+      type: multiple-choice-qa
+    dataset:
+      name: SAT full
+      args: relbert/analogy_questions
+      type: analogy-questions
+    metrics:
+    - name: Accuracy
+      type: accuracy
+      value: 0.5106951871657754
+  - task:
+      name: Analogy Questions (SAT)
+      type: multiple-choice-qa
+    dataset:
+      name: SAT
+      args: relbert/analogy_questions
+      type: analogy-questions
+    metrics:
+    - name: Accuracy
+      type: accuracy
+      value: 0.49554896142433236
+  - task:
+      name: Analogy Questions (BATS)
+      type: multiple-choice-qa
+    dataset:
+      name: BATS
+      args: relbert/analogy_questions
+      type: analogy-questions
+    metrics:
+    - name: Accuracy
+      type: accuracy
+      value: 0.7982212340188994
+  - task:
+      name: Analogy Questions (Google)
+      type: multiple-choice-qa
+    dataset:
+      name: Google
+      args: relbert/analogy_questions
+      type: analogy-questions
+    metrics:
+    - name: Accuracy
+      type: accuracy
+      value: 0.926
+  - task:
+      name: Analogy Questions (U2)
+      type: multiple-choice-qa
+    dataset:
+      name: U2
+      args: relbert/analogy_questions
+      type: analogy-questions
+    metrics:
+    - name: Accuracy
+      type: accuracy
+      value: 0.5350877192982456
+  - task:
+      name: Analogy Questions (U4)
+      type: multiple-choice-qa
+    dataset:
+      name: U4
+      args: relbert/analogy_questions
+      type: analogy-questions
+    metrics:
+    - name: Accuracy
+      type: accuracy
+      value: 0.6064814814814815
+  - task:
+      name: Lexical Relation Classification (BLESS)
+      type: classification
+    dataset:
+      name: BLESS
+      args: relbert/lexical_relation_classification
+      type: relation-classification
+    metrics:
+    - name: F1
+      type: f1
+      value: 0.9061322886846467
+    - name: F1 (macro)
+      type: f1_macro
+      value: 0.8998351544602654
+  - task:
+      name: Lexical Relation Classification (CogALexV)
+      type: classification
+    dataset:
+      name: CogALexV
+      args: relbert/lexical_relation_classification
+      type: relation-classification
+    metrics:
+    - name: F1
+      type: f1
+      value: 0.8483568075117371
+    - name: F1 (macro)
+      type: f1_macro
+      value: 0.6691324528607947
+  - task:
+      name: Lexical Relation Classification (EVALution)
+      type: classification
+    dataset:
+      name: BLESS
+      args: relbert/lexical_relation_classification
+      type: relation-classification
+    metrics:
+    - name: F1
+      type: f1
+      value: 0.6538461538461539
+    - name: F1 (macro)
+      type: f1_macro
+      value: 0.6461615360778927
+  - task:
+      name: Lexical Relation Classification (K&H+N)
+      type: classification
+    dataset:
+      name: K&H+N
+      args: relbert/lexical_relation_classification
+      type: relation-classification
+    metrics:
+    - name: F1
+      type: f1
+      value: 0.9576406760798497
+    - name: F1 (macro)
+      type: f1_macro
+      value: 0.8666219776970888
+  - task:
+      name: Lexical Relation Classification (ROOT09)
+      type: classification
+    dataset:
+      name: ROOT09
+      args: relbert/lexical_relation_classification
+      type: relation-classification
+    metrics:
+    - name: F1
+      type: f1
+      value: 0.8934503290504543
+    - name: F1 (macro)
+      type: f1_macro
+      value: 0.8921114555442471
+
+---
+# relbert/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)):
+    - 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)):
+    - 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: 0.8571428571428571 
+
+
+### Usage
+This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip   
+```shell
+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")
+vector = model.get_embedding(['Tokyo', 'Japan'])  # shape of (1024, )
+```
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+ - model: roberta-large
+ - max_length: 64
+ - mode: average
+ - data: relbert/conceptnet_high_confidence
+ - template_mode: manual
+ - template: Today, I finally discovered the relation between <subj> and <obj> : <obj>  is <subj>'s <mask>
+ - loss_function: nce_logout
+ - temperature_nce_constant: 0.05
+ - temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'}
+ - epoch: 87
+ - batch: 128
+ - lr: 5e-06
+ - lr_decay: False
+ - lr_warmup: 1
+ - weight_decay: 0
+ - random_seed: 0
+ - exclude_relation: None
+ - 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).
+
+### Reference
+If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/).
+
+```
+
+@inproceedings{ushio-etal-2021-distilling-relation-embeddings,
+    title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels",
+    author = "Ushio, Asahi  and
+      Schockaert, Steven  and
+      Camacho-Collados, Jose",
+    booktitle = "EMNLP 2021",
+    year = "2021",
+    address = "Online",
+    publisher = "Association for Computational Linguistics",
+}
+
+```