model update

README.md

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+---
+datasets:
+- semeval2012
+model-index:
+- name: relbert/relbert-roberta-large-semeval2012-average-prompt-b-triplet
+  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: 0.8285714285714286
+  - 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: 0.5748663101604278
+  - 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: 0.5756676557863502
+  - 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: 0.7809894385769872
+  - 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: 0.87
+  - 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: 0.5570175438596491
+  - 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: 0.5763888888888888
+  - 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: 0.9156245291547386
+    - name: F1 (macro)
+      type: f1_macro
+      value: 0.9123138480377561
+  - 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: 0.8652582159624412
+    - name: F1 (macro)
+      type: f1_macro
+      value: 0.7098768153077847
+  - 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: 0.6765980498374865
+    - name: F1 (macro)
+      type: f1_macro
+      value: 0.667723188418867
+  - 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: 0.9621617861862697
+    - name: F1 (macro)
+      type: f1_macro
+      value: 0.8800726259971795
+  - 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: 0.8925101848950172
+    - name: F1 (macro)
+      type: f1_macro
+      value: 0.8890641447568232
+
+---
+# relbert/relbert-roberta-large-semeval2012-average-prompt-b-triplet
+
+RelBERT fine-tuned from [roberta-large](https://huggingface.co/roberta-large) on  
+[semeval2012](https://huggingface.co/datasets/semeval2012).
+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-semeval2012-average-prompt-b-triplet/raw/main/analogy.json)):
+    - Accuracy on SAT (full): 0.5748663101604278 
+    - Accuracy on SAT: 0.5756676557863502
+    - Accuracy on BATS: 0.7809894385769872
+    - Accuracy on U2: 0.5570175438596491
+    - Accuracy on U4: 0.5763888888888888
+    - Accuracy on Google: 0.87
+- Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-large-semeval2012-average-prompt-b-triplet/raw/main/classification.json)):
+    - Micro F1 score on BLESS: 0.9156245291547386
+    - Micro F1 score on CogALexV: 0.8652582159624412
+    - Micro F1 score on EVALution: 0.6765980498374865
+    - Micro F1 score on K&H+N: 0.9621617861862697
+    - Micro F1 score on ROOT09: 0.8925101848950172
+- Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-large-semeval2012-average-prompt-b-triplet/raw/main/relation_mapping.json)):
+    - Accuracy on Relation Mapping: 0.8285714285714286 
+
+
+### 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-semeval2012-average-prompt-b-triplet")
+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: semeval2012
+ - n_sample: 10
+ - custom_template: Today, I finally discovered the relation between <subj> and <obj> : <obj>  is <subj>'s <mask>
+ - template: None
+ - softmax_loss: True
+ - in_batch_negative: True
+ - parent_contrast: True
+ - mse_margin: 1
+ - epoch: 1
+ - lr_warmup: 10
+ - batch: 64
+ - lr: 2e-05
+ - lr_decay: False
+ - weight_decay: 0
+ - optimizer: adam
+ - momentum: 0.9
+ - fp16: False
+ - random_seed: 0
+
+The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-large-semeval2012-average-prompt-b-triplet/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",
+}
+
+```