asahi417 commited on
Commit
3da1da0
1 Parent(s): a75657b

model update

Browse files
README.md CHANGED
@@ -14,7 +14,7 @@ model-index:
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  metrics:
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  - name: Accuracy
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  type: accuracy
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- value: 0.817202380952381
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  - task:
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  name: Analogy Questions (SAT full)
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  type: multiple-choice-qa
@@ -25,7 +25,7 @@ model-index:
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  metrics:
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  - name: Accuracy
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  type: accuracy
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- value: 0.5989304812834224
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  - task:
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  name: Analogy Questions (SAT)
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  type: multiple-choice-qa
@@ -36,7 +36,7 @@ model-index:
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  metrics:
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  - name: Accuracy
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  type: accuracy
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- value: 0.6083086053412463
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  - task:
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  name: Analogy Questions (BATS)
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  type: multiple-choice-qa
@@ -47,7 +47,7 @@ model-index:
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  metrics:
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  - name: Accuracy
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  type: accuracy
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- value: 0.7031684269038355
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  - task:
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  name: Analogy Questions (Google)
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  type: multiple-choice-qa
@@ -58,7 +58,7 @@ model-index:
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  metrics:
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  - name: Accuracy
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  type: accuracy
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- value: 0.892
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  - task:
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  name: Analogy Questions (U2)
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  type: multiple-choice-qa
@@ -69,7 +69,7 @@ model-index:
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  metrics:
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  - name: Accuracy
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  type: accuracy
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- value: 0.5964912280701754
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  - task:
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  name: Analogy Questions (U4)
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  type: multiple-choice-qa
@@ -80,7 +80,7 @@ model-index:
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  metrics:
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  - name: Accuracy
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  type: accuracy
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- value: 0.5740740740740741
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  - task:
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  name: Analogy Questions (ConceptNet Analogy)
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  type: multiple-choice-qa
@@ -91,7 +91,7 @@ model-index:
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  metrics:
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  - name: Accuracy
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  type: accuracy
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- value: 0.3976510067114094
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  - task:
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  name: Analogy Questions (TREX Analogy)
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  type: multiple-choice-qa
@@ -102,7 +102,7 @@ model-index:
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  metrics:
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  - name: Accuracy
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  type: accuracy
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- value: 0.6666666666666666
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  - task:
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  name: Analogy Questions (NELL-ONE Analogy)
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  type: multiple-choice-qa
@@ -113,7 +113,7 @@ model-index:
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  metrics:
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  - name: Accuracy
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  type: accuracy
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- value: 0.62
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  - task:
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  name: Lexical Relation Classification (BLESS)
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  type: classification
@@ -124,10 +124,10 @@ model-index:
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  metrics:
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  - name: F1
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  type: f1
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- value: 0.8998041283712521
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  - name: F1 (macro)
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  type: f1_macro
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- value: 0.896201243435411
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  - task:
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  name: Lexical Relation Classification (CogALexV)
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  type: classification
@@ -138,10 +138,10 @@ model-index:
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  metrics:
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  - name: F1
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  type: f1
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- value: 0.8370892018779342
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  - name: F1 (macro)
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  type: f1_macro
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- value: 0.6583174043371445
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  - task:
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  name: Lexical Relation Classification (EVALution)
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  type: classification
@@ -152,10 +152,10 @@ model-index:
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  metrics:
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  - name: F1
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  type: f1
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- value: 0.6419284940411701
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  - name: F1 (macro)
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  type: f1_macro
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- value: 0.6294309369547718
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  - task:
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  name: Lexical Relation Classification (K&H+N)
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  type: classification
@@ -166,10 +166,10 @@ model-index:
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  metrics:
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  - name: F1
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  type: f1
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- value: 0.9396953467343674
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  - name: F1 (macro)
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  type: f1_macro
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- value: 0.8459283973092365
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  - task:
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  name: Lexical Relation Classification (ROOT09)
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  type: classification
@@ -180,34 +180,34 @@ model-index:
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  metrics:
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  - name: F1
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  type: f1
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- value: 0.8815418364149169
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  - name: F1 (macro)
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  type: f1_macro
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- value: 0.879329189992711
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188
  ---
189
  # relbert/relbert-roberta-base-nce-semeval2012-0
190
 
191
- RelBERT based on [roberta-base](https://huggingface.co/roberta-base) fine-tuned on [relbert/semeval2012_relational_similarity](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity) (see the [`relbert`](https://github.com/asahi417/relbert) for more detail of fine-tuning).
192
  This model achieves the following results on the relation understanding tasks:
193
  - Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-nce-semeval2012-0/raw/main/analogy.forward.json)):
194
- - Accuracy on SAT (full): 0.5989304812834224
195
- - Accuracy on SAT: 0.6083086053412463
196
- - Accuracy on BATS: 0.7031684269038355
197
- - Accuracy on U2: 0.5964912280701754
198
- - Accuracy on U4: 0.5740740740740741
199
- - Accuracy on Google: 0.892
200
- - Accuracy on ConceptNet Analogy: 0.3976510067114094
201
- - Accuracy on T-Rex Analogy: 0.6666666666666666
202
- - Accuracy on NELL-ONE Analogy: 0.62
203
  - Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-nce-semeval2012-0/raw/main/classification.json)):
204
- - Micro F1 score on BLESS: 0.8998041283712521
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- - Micro F1 score on CogALexV: 0.8370892018779342
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- - Micro F1 score on EVALution: 0.6419284940411701
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- - Micro F1 score on K&H+N: 0.9396953467343674
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- - Micro F1 score on ROOT09: 0.8815418364149169
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  - Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-nce-semeval2012-0/raw/main/relation_mapping.json)):
210
- - Accuracy on Relation Mapping: 0.817202380952381
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212
 
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  ### Usage
@@ -224,7 +224,7 @@ vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (n_dim, )
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  ### Training hyperparameters
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- - model: roberta-base
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  - max_length: 64
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  - epoch: 10
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  - batch: 32
@@ -239,7 +239,7 @@ vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (n_dim, )
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  - split_valid: validation
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  - loss_function: nce
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  - classification_loss: False
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- - loss_function_config: {'temperature': 0.05, 'num_negative': 400, 'num_positive': 10}
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  - augment_negative_by_positive: True
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  See the full configuration at [config file](https://huggingface.co/relbert/relbert-roberta-base-nce-semeval2012-0/raw/main/finetuning_config.json).
 
14
  metrics:
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  - name: Accuracy
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  type: accuracy
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+ value: 0.8133333333333334
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  - task:
19
  name: Analogy Questions (SAT full)
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  type: multiple-choice-qa
 
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  metrics:
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  - name: Accuracy
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  type: accuracy
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+ value: 0.6818181818181818
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  - task:
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  name: Analogy Questions (SAT)
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  type: multiple-choice-qa
 
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  metrics:
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  - name: Accuracy
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  type: accuracy
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+ value: 0.6824925816023739
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  - task:
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  name: Analogy Questions (BATS)
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  type: multiple-choice-qa
 
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  metrics:
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  - name: Accuracy
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  type: accuracy
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+ value: 0.783212896053363
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  - task:
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  name: Analogy Questions (Google)
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  type: multiple-choice-qa
 
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  metrics:
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  - name: Accuracy
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  type: accuracy
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+ value: 0.934
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  - task:
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  name: Analogy Questions (U2)
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  type: multiple-choice-qa
 
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  metrics:
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  - name: Accuracy
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  type: accuracy
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+ value: 0.6754385964912281
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  - task:
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  name: Analogy Questions (U4)
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  type: multiple-choice-qa
 
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  metrics:
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  - name: Accuracy
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  type: accuracy
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+ value: 0.6388888888888888
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  - task:
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  name: Analogy Questions (ConceptNet Analogy)
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  type: multiple-choice-qa
 
91
  metrics:
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  - name: Accuracy
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  type: accuracy
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+ value: 0.43288590604026844
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  - task:
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  name: Analogy Questions (TREX Analogy)
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  type: multiple-choice-qa
 
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  metrics:
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  - name: Accuracy
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  type: accuracy
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+ value: 0.6775956284153005
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  - task:
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  name: Analogy Questions (NELL-ONE Analogy)
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  type: multiple-choice-qa
 
113
  metrics:
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  - name: Accuracy
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  type: accuracy
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+ value: 0.605
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  - task:
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  name: Lexical Relation Classification (BLESS)
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  type: classification
 
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  metrics:
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  - name: F1
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  type: f1
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+ value: 0.9148711767364774
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  - name: F1 (macro)
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  type: f1_macro
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+ value: 0.9119056356713013
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  - task:
132
  name: Lexical Relation Classification (CogALexV)
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  type: classification
 
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  metrics:
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  - name: F1
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  type: f1
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+ value: 0.8485915492957746
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  - name: F1 (macro)
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  type: f1_macro
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+ value: 0.6811794888962958
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  - task:
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  name: Lexical Relation Classification (EVALution)
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  type: classification
 
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  metrics:
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  - name: F1
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  type: f1
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+ value: 0.6690140845070423
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  - name: F1 (macro)
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  type: f1_macro
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+ value: 0.6624009209291007
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  - task:
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  name: Lexical Relation Classification (K&H+N)
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  type: classification
 
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  metrics:
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  - name: F1
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  type: f1
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+ value: 0.9508937886902692
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  - name: F1 (macro)
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  type: f1_macro
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+ value: 0.8677983904224069
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  - task:
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  name: Lexical Relation Classification (ROOT09)
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  type: classification
 
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  metrics:
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  - name: F1
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  type: f1
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+ value: 0.8918834221247258
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  - name: F1 (macro)
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  type: f1_macro
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+ value: 0.8905814580868343
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188
  ---
189
  # relbert/relbert-roberta-base-nce-semeval2012-0
190
 
191
+ RelBERT based on [roberta-large](https://huggingface.co/roberta-large) fine-tuned on [relbert/semeval2012_relational_similarity](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity) (see the [`relbert`](https://github.com/asahi417/relbert) for more detail of fine-tuning).
192
  This model achieves the following results on the relation understanding tasks:
193
  - Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-nce-semeval2012-0/raw/main/analogy.forward.json)):
194
+ - Accuracy on SAT (full): 0.6818181818181818
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+ - Accuracy on SAT: 0.6824925816023739
196
+ - Accuracy on BATS: 0.783212896053363
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+ - Accuracy on U2: 0.6754385964912281
198
+ - Accuracy on U4: 0.6388888888888888
199
+ - Accuracy on Google: 0.934
200
+ - Accuracy on ConceptNet Analogy: 0.43288590604026844
201
+ - Accuracy on T-Rex Analogy: 0.6775956284153005
202
+ - Accuracy on NELL-ONE Analogy: 0.605
203
  - Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-nce-semeval2012-0/raw/main/classification.json)):
204
+ - Micro F1 score on BLESS: 0.9148711767364774
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+ - Micro F1 score on CogALexV: 0.8485915492957746
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+ - Micro F1 score on EVALution: 0.6690140845070423
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+ - Micro F1 score on K&H+N: 0.9508937886902692
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+ - Micro F1 score on ROOT09: 0.8918834221247258
209
  - Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-nce-semeval2012-0/raw/main/relation_mapping.json)):
210
+ - Accuracy on Relation Mapping: 0.8133333333333334
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212
 
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  ### Usage
 
224
 
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  ### Training hyperparameters
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+ - model: roberta-large
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  - max_length: 64
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  - epoch: 10
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  - batch: 32
 
239
  - split_valid: validation
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  - loss_function: nce
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  - classification_loss: False
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+ - loss_function_config: {'temperature': 0.05, 'num_negative': 100, 'num_positive': 10}
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  - augment_negative_by_positive: True
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  See the full configuration at [config file](https://huggingface.co/relbert/relbert-roberta-base-nce-semeval2012-0/raw/main/finetuning_config.json).
analogy.forward.json CHANGED
@@ -1 +1 @@
1
- {"semeval2012_relational_similarity/validation": 0.7848101265822784, "scan/test": 0.2592821782178218, "sat_full/test": 0.5989304812834224, "sat/test": 0.6083086053412463, "u2/test": 0.5964912280701754, "u4/test": 0.5740740740740741, "google/test": 0.892, "bats/test": 0.7031684269038355, "t_rex_relational_similarity/test": 0.6666666666666666, "conceptnet_relational_similarity/test": 0.3976510067114094, "nell_relational_similarity/test": 0.62, "scan/validation": 0.25842696629213485, "sat/validation": 0.5135135135135135, "u2/validation": 0.4583333333333333, "u4/validation": 0.6458333333333334, "google/validation": 0.96, "bats/validation": 0.7738693467336684, "t_rex_relational_similarity/validation": 0.2661290322580645, "conceptnet_relational_similarity/validation": 0.32823741007194246, "nell_relational_similarity/validation": 0.575}
 
1
+ {"semeval2012_relational_similarity/validation": 0.7468354430379747, "scan/test": 0.2592821782178218, "sat_full/test": 0.6818181818181818, "sat/test": 0.6824925816023739, "u2/test": 0.6754385964912281, "u4/test": 0.6388888888888888, "google/test": 0.934, "bats/test": 0.783212896053363, "t_rex_relational_similarity/test": 0.6775956284153005, "conceptnet_relational_similarity/test": 0.43288590604026844, "nell_relational_similarity/test": 0.605}
classification.json CHANGED
@@ -1 +1 @@
1
- {"lexical_relation_classification/BLESS": {"classifier_config": {"activation": "relu", "alpha": 0.0001, "batch_size": "auto", "beta_1": 0.9, "beta_2": 0.999, "early_stopping": false, "epsilon": 1e-08, "hidden_layer_sizes": [100], "learning_rate": "constant", "learning_rate_init": 0.001, "max_fun": 15000, "max_iter": 200, "momentum": 0.9, "n_iter_no_change": 10, "nesterovs_momentum": true, "power_t": 0.5, "random_state": 0, "shuffle": true, "solver": "adam", "tol": 0.0001, "validation_fraction": 0.1, "verbose": false, "warm_start": false}, "test/accuracy": 0.8998041283712521, "test/f1_macro": 0.896201243435411, "test/f1_micro": 0.8998041283712521, "test/p_macro": 0.8876829436591316, "test/p_micro": 0.8998041283712521, "test/r_macro": 0.9054007585142311, "test/r_micro": 0.8998041283712521}, "lexical_relation_classification/CogALexV": {"classifier_config": {"activation": "relu", "alpha": 0.0001, "batch_size": "auto", "beta_1": 0.9, "beta_2": 0.999, "early_stopping": false, "epsilon": 1e-08, "hidden_layer_sizes": [100], "learning_rate": "constant", "learning_rate_init": 0.001, "max_fun": 15000, "max_iter": 200, "momentum": 0.9, "n_iter_no_change": 10, "nesterovs_momentum": true, "power_t": 0.5, "random_state": 0, "shuffle": true, "solver": "adam", "tol": 0.0001, "validation_fraction": 0.1, "verbose": false, "warm_start": false}, "test/accuracy": 0.8370892018779342, "test/f1_macro": 0.6583174043371445, "test/f1_micro": 0.8370892018779342, "test/p_macro": 0.6822907887970884, "test/p_micro": 0.8370892018779342, "test/r_macro": 0.6384370436284232, "test/r_micro": 0.8370892018779342}, "lexical_relation_classification/EVALution": {"classifier_config": {"activation": "relu", "alpha": 0.0001, "batch_size": "auto", "beta_1": 0.9, "beta_2": 0.999, "early_stopping": false, "epsilon": 1e-08, "hidden_layer_sizes": [100], "learning_rate": "constant", "learning_rate_init": 0.001, "max_fun": 15000, "max_iter": 200, "momentum": 0.9, "n_iter_no_change": 10, "nesterovs_momentum": true, "power_t": 0.5, "random_state": 0, "shuffle": true, "solver": "adam", "tol": 0.0001, "validation_fraction": 0.1, "verbose": false, "warm_start": false}, "test/accuracy": 0.6419284940411701, "test/f1_macro": 0.6294309369547718, "test/f1_micro": 0.6419284940411701, "test/p_macro": 0.6360186480100325, "test/p_micro": 0.6419284940411701, "test/r_macro": 0.6300178037199379, "test/r_micro": 0.6419284940411701}, "lexical_relation_classification/K&H+N": {"classifier_config": {"activation": "relu", "alpha": 0.0001, "batch_size": "auto", "beta_1": 0.9, "beta_2": 0.999, "early_stopping": false, "epsilon": 1e-08, "hidden_layer_sizes": [100], "learning_rate": "constant", "learning_rate_init": 0.001, "max_fun": 15000, "max_iter": 200, "momentum": 0.9, "n_iter_no_change": 10, "nesterovs_momentum": true, "power_t": 0.5, "random_state": 0, "shuffle": true, "solver": "adam", "tol": 0.0001, "validation_fraction": 0.1, "verbose": false, "warm_start": false}, "test/accuracy": 0.9396953467343674, "test/f1_macro": 0.8459283973092365, "test/f1_micro": 0.9396953467343674, "test/p_macro": 0.8614600859106621, "test/p_micro": 0.9396953467343674, "test/r_macro": 0.8351465630922283, "test/r_micro": 0.9396953467343674}, "lexical_relation_classification/ROOT09": {"classifier_config": {"activation": "relu", "alpha": 0.0001, "batch_size": "auto", "beta_1": 0.9, "beta_2": 0.999, "early_stopping": false, "epsilon": 1e-08, "hidden_layer_sizes": [100], "learning_rate": "constant", "learning_rate_init": 0.001, "max_fun": 15000, "max_iter": 200, "momentum": 0.9, "n_iter_no_change": 10, "nesterovs_momentum": true, "power_t": 0.5, "random_state": 0, "shuffle": true, "solver": "adam", "tol": 0.0001, "validation_fraction": 0.1, "verbose": false, "warm_start": false}, "test/accuracy": 0.8815418364149169, "test/f1_macro": 0.879329189992711, "test/f1_micro": 0.8815418364149169, "test/p_macro": 0.8763389203201842, "test/p_micro": 0.8815418364149169, "test/r_macro": 0.882560877928503, "test/r_micro": 0.8815418364149169}}
 
1
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