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model update

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README.md ADDED
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+ ---
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+ datasets:
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+ - relbert/semeval2012_relational_similarity
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+ model-index:
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+ - name: relbert/relbert-roberta-base-triplet-semeval2012
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+ results:
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+ - task:
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+ name: Relation Mapping
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+ type: sorting-task
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+ dataset:
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+ name: Relation Mapping
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+ args: relbert/relation_mapping
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+ type: relation-mapping
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+ metrics:
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+ - name: Accuracy
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+ type: accuracy
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+ value: 0.8459126984126984
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+ - task:
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+ name: Analogy Questions (SAT full)
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+ type: multiple-choice-qa
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+ dataset:
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+ name: SAT full
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+ args: relbert/analogy_questions
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+ type: analogy-questions
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+ metrics:
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+ - name: Accuracy
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+ type: accuracy
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+ value: 0.5454545454545454
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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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+ dataset:
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+ name: SAT
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+ args: relbert/analogy_questions
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+ type: analogy-questions
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+ metrics:
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+ - name: Accuracy
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+ type: accuracy
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+ value: 0.5459940652818991
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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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+ dataset:
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+ name: BATS
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+ args: relbert/analogy_questions
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+ type: analogy-questions
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+ metrics:
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+ - name: Accuracy
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+ type: accuracy
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+ value: 0.725958866036687
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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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+ dataset:
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+ name: Google
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+ args: relbert/analogy_questions
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+ type: analogy-questions
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+ metrics:
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+ - name: Accuracy
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+ type: accuracy
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+ value: 0.864
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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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+ dataset:
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+ name: U2
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+ args: relbert/analogy_questions
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+ type: analogy-questions
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+ metrics:
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+ - name: Accuracy
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+ type: accuracy
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+ value: 0.5526315789473685
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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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+ dataset:
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+ name: U4
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+ args: relbert/analogy_questions
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+ type: analogy-questions
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+ metrics:
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+ - name: Accuracy
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+ type: accuracy
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+ value: 0.5856481481481481
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+ - task:
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+ name: Analogy Questions (ConceptNet Analogy)
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+ type: multiple-choice-qa
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+ dataset:
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+ name: ConceptNet Analogy
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+ args: relbert/analogy_questions
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+ type: analogy-questions
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+ metrics:
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+ - name: Accuracy
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+ type: accuracy
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+ value: 0.29446308724832215
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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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+ dataset:
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+ name: TREX Analogy
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+ args: relbert/analogy_questions
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+ type: analogy-questions
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+ metrics:
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+ - name: Accuracy
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+ type: accuracy
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+ value: 0.453551912568306
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+ - task:
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+ name: Analogy Questions (NELL-ONE Analogy)
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+ type: multiple-choice-qa
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+ dataset:
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+ name: NELL-ONE Analogy
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+ args: relbert/analogy_questions
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+ type: analogy-questions
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+ metrics:
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+ - name: Accuracy
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+ type: accuracy
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+ value: 0.7066666666666667
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+ - task:
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+ name: Lexical Relation Classification (BLESS)
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+ type: classification
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+ dataset:
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+ name: BLESS
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+ args: relbert/lexical_relation_classification
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+ type: relation-classification
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+ metrics:
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+ - name: F1
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+ type: f1
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+ value: 0.8872984782281151
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+ - name: F1 (macro)
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+ type: f1_macro
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+ value: 0.8810190171103657
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+ - task:
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+ name: Lexical Relation Classification (CogALexV)
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+ type: classification
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+ dataset:
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+ name: CogALexV
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+ args: relbert/lexical_relation_classification
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+ type: relation-classification
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+ metrics:
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+ - name: F1
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+ type: f1
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+ value: 0.8046948356807512
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+ - name: F1 (macro)
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+ type: f1_macro
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+ value: 0.5209759868344143
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+ - task:
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+ name: Lexical Relation Classification (EVALution)
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+ type: classification
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+ dataset:
149
+ name: BLESS
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+ args: relbert/lexical_relation_classification
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+ type: relation-classification
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+ metrics:
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+ - name: F1
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+ type: f1
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+ value: 0.6771397616468039
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+ - name: F1 (macro)
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+ type: f1_macro
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+ value: 0.6620361634733358
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+ - task:
160
+ name: Lexical Relation Classification (K&H+N)
161
+ type: classification
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+ dataset:
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+ name: K&H+N
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+ args: relbert/lexical_relation_classification
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+ type: relation-classification
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+ metrics:
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+ - name: F1
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+ type: f1
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+ value: 0.93107045976212
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+ - name: F1 (macro)
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+ type: f1_macro
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+ value: 0.8387124439500746
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+ - task:
174
+ name: Lexical Relation Classification (ROOT09)
175
+ type: classification
176
+ dataset:
177
+ name: ROOT09
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+ args: relbert/lexical_relation_classification
179
+ type: relation-classification
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+ metrics:
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+ - name: F1
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+ type: f1
183
+ value: 0.902851770604826
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+ - name: F1 (macro)
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+ type: f1_macro
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+ value: 0.90279695761752
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+
188
+ ---
189
+ # relbert/relbert-roberta-base-triplet-semeval2012
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+
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:
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+ - Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-triplet-semeval2012/raw/main/analogy.forward.json)):
194
+ - Accuracy on SAT (full): 0.5454545454545454
195
+ - Accuracy on SAT: 0.5459940652818991
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+ - Accuracy on BATS: 0.725958866036687
197
+ - Accuracy on U2: 0.5526315789473685
198
+ - Accuracy on U4: 0.5856481481481481
199
+ - Accuracy on Google: 0.864
200
+ - Accuracy on ConceptNet Analogy: 0.29446308724832215
201
+ - Accuracy on T-Rex Analogy: 0.453551912568306
202
+ - Accuracy on NELL-ONE Analogy: 0.7066666666666667
203
+ - Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-triplet-semeval2012/raw/main/classification.json)):
204
+ - Micro F1 score on BLESS: 0.8872984782281151
205
+ - Micro F1 score on CogALexV: 0.8046948356807512
206
+ - Micro F1 score on EVALution: 0.6771397616468039
207
+ - Micro F1 score on K&H+N: 0.93107045976212
208
+ - Micro F1 score on ROOT09: 0.902851770604826
209
+ - Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-triplet-semeval2012/raw/main/relation_mapping.json)):
210
+ - Accuracy on Relation Mapping: 0.8459126984126984
211
+
212
+
213
+ ### Usage
214
+ This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip
215
+ ```shell
216
+ pip install relbert
217
+ ```
218
+ and activate model as below.
219
+ ```python
220
+ from relbert import RelBERT
221
+ model = RelBERT("relbert/relbert-roberta-base-triplet-semeval2012")
222
+ vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (n_dim, )
223
+ ```
224
+
225
+ ### Training hyperparameters
226
+
227
+ - model: roberta-base
228
+ - max_length: 64
229
+ - epoch: 1
230
+ - batch: 79
231
+ - random_seed: 0
232
+ - lr: 2e-05
233
+ - lr_warmup: 10
234
+ - aggregation_mode: average_no_mask
235
+ - data: relbert/semeval2012_relational_similarity
236
+ - data_name: None
237
+ - exclude_relation: None
238
+ - split: train
239
+ - split_valid: validation
240
+ - loss_function: triplet
241
+ - classification_loss: False
242
+ - loss_function_config: {'mse_margin': 1}
243
+ - augment_negative_by_positive: False
244
+
245
+ See the full configuration at [config file](https://huggingface.co/relbert/relbert-roberta-base-triplet-semeval2012/raw/main/finetuning_config.json).
246
+
247
+ ### Reference
248
+ If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.emnlp-main.712/).
249
+
250
+ ```
251
+
252
+ @inproceedings{ushio-etal-2021-distilling,
253
+ title = "Distilling Relation Embeddings from Pretrained Language Models",
254
+ author = "Ushio, Asahi and
255
+ Camacho-Collados, Jose and
256
+ Schockaert, Steven",
257
+ booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
258
+ month = nov,
259
+ year = "2021",
260
+ address = "Online and Punta Cana, Dominican Republic",
261
+ publisher = "Association for Computational Linguistics",
262
+ url = "https://aclanthology.org/2021.emnlp-main.712",
263
+ doi = "10.18653/v1/2021.emnlp-main.712",
264
+ pages = "9044--9062",
265
+ abstract = "Pre-trained language models have been found to capture a surprisingly rich amount of lexical knowledge, ranging from commonsense properties of everyday concepts to detailed factual knowledge about named entities. Among others, this makes it possible to distill high-quality word vectors from pre-trained language models. However, it is currently unclear to what extent it is possible to distill relation embeddings, i.e. vectors that characterize the relationship between two words. Such relation embeddings are appealing because they can, in principle, encode relational knowledge in a more fine-grained way than is possible with knowledge graphs. To obtain relation embeddings from a pre-trained language model, we encode word pairs using a (manually or automatically generated) prompt, and we fine-tune the language model such that relationally similar word pairs yield similar output vectors. We find that the resulting relation embeddings are highly competitive on analogy (unsupervised) and relation classification (supervised) benchmarks, even without any task-specific fine-tuning. Source code to reproduce our experimental results and the model checkpoints are available in the following repository: https://github.com/asahi417/relbert",
266
+ }
267
+
268
+ ```
analogy.forward.json ADDED
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+ {"semeval2012_relational_similarity/validation": 0.7088607594936709, "scan/test": 0.29517326732673266, "sat_full/test": 0.5454545454545454, "sat/test": 0.5459940652818991, "u2/test": 0.5526315789473685, "u4/test": 0.5856481481481481, "google/test": 0.864, "bats/test": 0.725958866036687, "t_rex_relational_similarity/test": 0.453551912568306, "conceptnet_relational_similarity/test": 0.29446308724832215, "nell_relational_similarity/test": 0.7066666666666667}
classification.json ADDED
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+ {"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": 0.8872984782281151, "test/f1_macro": 0.8810190171103657, "test/f1_micro": 0.8872984782281151, "test/p_macro": 0.9002471839016858, "test/p_micro": 0.8872984782281151, "test/r_macro": 0.8665880442701218, "test/r_micro": 0.8872984782281151, "test/f1/attri": 0.8486789431545237, "test/p/attri": 0.9584086799276673, "test/r/attri": 0.7614942528735632, "test/f1/coord": 0.9277745155607751, "test/p/coord": 0.9622411693057247, "test/r/coord": 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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": 0.6771397616468039, "test/f1_macro": 0.6620361634733358, "test/f1_micro": 0.6771397616468039, "test/p_macro": 0.7150515390065663, "test/p_micro": 0.6771397616468039, "test/r_macro": 0.6378249302670497, "test/r_micro": 0.6771397616468039, "test/f1/Antonym": 0.753939393939394, "test/p/Antonym": 0.7585365853658537, "test/r/Antonym": 0.7493975903614458, "test/f1/HasA": 0.7137809187279152, "test/p/HasA": 0.7163120567375887, "test/r/HasA": 0.7112676056338029, "test/f1/HasProperty": 0.8302469135802469, "test/p/HasProperty": 0.8251533742331288, "test/r/HasProperty": 0.8354037267080745, "test/f1/IsA": 0.6533212010919017, "test/p/IsA": 0.5609375, "test/r/IsA": 0.7821350762527233, "test/f1/MadeOf": 0.6344827586206897, "test/p/MadeOf": 0.7796610169491526, "test/r/MadeOf": 0.5348837209302325, "test/f1/PartOf": 0.6781115879828327, "test/p/PartOf": 0.8977272727272727, "test/r/PartOf": 0.5448275862068965, "test/f1/Synonym": 0.37037037037037035, "test/p/Synonym": 0.46703296703296704, "test/r/Synonym": 0.30685920577617326}, "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": 0.9310704597621201, "test/f1_macro": 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"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": 0.902851770604826, "test/f1_macro": 0.90279695761752, "test/f1_micro": 0.902851770604826, "test/p_macro": 0.8947326123414197, "test/p_micro": 0.902851770604826, "test/r_macro": 0.9143244477144458, "test/r_micro": 0.902851770604826, "test/f1/COORD": 0.9659294680215181, "test/p/COORD": 0.9428238039673279, "test/r/COORD": 0.9901960784313726, "test/f1/HYPER": 0.8373177842565598, "test/p/HYPER": 0.7924944812362031, "test/r/HYPER": 0.8875154511742892, "test/f1/RANDOM": 0.9051436205744823, "test/p/RANDOM": 0.9488795518207283, "test/r/RANDOM": 0.8652618135376756}}
config.json ADDED
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1
+ {
2
+ "_name_or_path": "roberta-base",
3
+ "architectures": [
4
+ "RobertaModel"
5
+ ],
6
+ "attention_probs_dropout_prob": 0.1,
7
+ "bos_token_id": 0,
8
+ "classifier_dropout": null,
9
+ "eos_token_id": 2,
10
+ "hidden_act": "gelu",
11
+ "hidden_dropout_prob": 0.1,
12
+ "hidden_size": 768,
13
+ "initializer_range": 0.02,
14
+ "intermediate_size": 3072,
15
+ "layer_norm_eps": 1e-05,
16
+ "max_position_embeddings": 514,
17
+ "model_type": "roberta",
18
+ "num_attention_heads": 12,
19
+ "num_hidden_layers": 12,
20
+ "pad_token_id": 1,
21
+ "position_embedding_type": "absolute",
22
+ "relbert_config": {
23
+ "aggregation_mode": "average_no_mask",
24
+ "template": "I wasn\u2019t aware of this relationship, but I just read in the encyclopedia that <obj> is <subj>\u2019s <mask>"
25
+ },
26
+ "torch_dtype": "float32",
27
+ "transformers_version": "4.26.1",
28
+ "type_vocab_size": 1,
29
+ "use_cache": true,
30
+ "vocab_size": 50265
31
+ }
finetuning_config.json ADDED
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1
+ {
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