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model update

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  1. README.md +36 -36
README.md CHANGED
@@ -2,7 +2,7 @@
2
  datasets:
3
  - relbert/semeval2012_relational_similarity
4
  model-index:
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- - name: relbert/relbert-roberta-large-semeval2012-mask-prompt-a-loob
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  results:
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  - task:
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  name: Relation Mapping
@@ -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: None
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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: None
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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: None
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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: None
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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: None
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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: None
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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: None
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  - task:
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  name: Lexical Relation Classification (BLESS)
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  type: classification
@@ -91,10 +91,10 @@ model-index:
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  metrics:
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  - name: F1
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  type: f1
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- value: None
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  - name: F1 (macro)
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  type: f1_macro
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- value: None
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  - task:
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  name: Lexical Relation Classification (CogALexV)
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  type: classification
@@ -105,10 +105,10 @@ model-index:
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  metrics:
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  - name: F1
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  type: f1
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- value: None
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  - name: F1 (macro)
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  type: f1_macro
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- value: None
112
  - task:
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  name: Lexical Relation Classification (EVALution)
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  type: classification
@@ -119,10 +119,10 @@ model-index:
119
  metrics:
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  - name: F1
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  type: f1
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- value: None
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  - name: F1 (macro)
124
  type: f1_macro
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- value: None
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  - task:
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  name: Lexical Relation Classification (K&H+N)
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  type: classification
@@ -133,10 +133,10 @@ model-index:
133
  metrics:
134
  - name: F1
135
  type: f1
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- value: None
137
  - name: F1 (macro)
138
  type: f1_macro
139
- value: None
140
  - task:
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  name: Lexical Relation Classification (ROOT09)
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  type: classification
@@ -147,33 +147,33 @@ model-index:
147
  metrics:
148
  - name: F1
149
  type: f1
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- value: None
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  - name: F1 (macro)
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  type: f1_macro
153
- value: None
154
 
155
  ---
156
- # relbert/relbert-roberta-large-semeval2012-mask-prompt-a-loob
157
 
158
  RelBERT fine-tuned from [roberta-large](https://huggingface.co/roberta-large) on
159
  [relbert/semeval2012_relational_similarity](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity).
160
  Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail).
161
  It achieves the following results on the relation understanding tasks:
162
- - Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-large-semeval2012-mask-prompt-a-loob/raw/main/analogy.json)):
163
- - Accuracy on SAT (full): None
164
- - Accuracy on SAT: None
165
- - Accuracy on BATS: None
166
- - Accuracy on U2: None
167
- - Accuracy on U4: None
168
- - Accuracy on Google: None
169
- - Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-large-semeval2012-mask-prompt-a-loob/raw/main/classification.json)):
170
- - Micro F1 score on BLESS: None
171
- - Micro F1 score on CogALexV: None
172
- - Micro F1 score on EVALution: None
173
- - Micro F1 score on K&H+N: None
174
- - Micro F1 score on ROOT09: None
175
- - Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-large-semeval2012-mask-prompt-a-loob/raw/main/relation_mapping.json)):
176
- - Accuracy on Relation Mapping: None
177
 
178
 
179
  ### Usage
@@ -184,7 +184,7 @@ pip install relbert
184
  and activate model as below.
185
  ```python
186
  from relbert import RelBERT
187
- model = RelBERT("relbert/relbert-roberta-large-semeval2012-mask-prompt-a-loob")
188
  vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, )
189
  ```
190
 
@@ -211,7 +211,7 @@ The following hyperparameters were used during training:
211
  - n_sample: 640
212
  - gradient_accumulation: 8
213
 
214
- The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-large-semeval2012-mask-prompt-a-loob/raw/main/trainer_config.json).
215
 
216
  ### Reference
217
  If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/).
 
2
  datasets:
3
  - relbert/semeval2012_relational_similarity
4
  model-index:
5
+ - name: relbert/roberta-large-semeval2012-mask-prompt-a-loob
6
  results:
7
  - task:
8
  name: Relation Mapping
 
14
  metrics:
15
  - name: Accuracy
16
  type: accuracy
17
+ value: 0.9060317460317461
18
  - task:
19
  name: Analogy Questions (SAT full)
20
  type: multiple-choice-qa
 
25
  metrics:
26
  - name: Accuracy
27
  type: accuracy
28
+ value: 0.6550802139037433
29
  - task:
30
  name: Analogy Questions (SAT)
31
  type: multiple-choice-qa
 
36
  metrics:
37
  - name: Accuracy
38
  type: accuracy
39
+ value: 0.655786350148368
40
  - task:
41
  name: Analogy Questions (BATS)
42
  type: multiple-choice-qa
 
47
  metrics:
48
  - name: Accuracy
49
  type: accuracy
50
+ value: 0.8043357420789328
51
  - task:
52
  name: Analogy Questions (Google)
53
  type: multiple-choice-qa
 
58
  metrics:
59
  - name: Accuracy
60
  type: accuracy
61
+ value: 0.95
62
  - task:
63
  name: Analogy Questions (U2)
64
  type: multiple-choice-qa
 
69
  metrics:
70
  - name: Accuracy
71
  type: accuracy
72
+ value: 0.631578947368421
73
  - task:
74
  name: Analogy Questions (U4)
75
  type: multiple-choice-qa
 
80
  metrics:
81
  - name: Accuracy
82
  type: accuracy
83
+ value: 0.6412037037037037
84
  - task:
85
  name: Lexical Relation Classification (BLESS)
86
  type: classification
 
91
  metrics:
92
  - name: F1
93
  type: f1
94
+ value: 0.9245140876902215
95
  - name: F1 (macro)
96
  type: f1_macro
97
+ value: 0.9208294548760101
98
  - task:
99
  name: Lexical Relation Classification (CogALexV)
100
  type: classification
 
105
  metrics:
106
  - name: F1
107
  type: f1
108
+ value: 0.8814553990610329
109
  - name: F1 (macro)
110
  type: f1_macro
111
+ value: 0.7355497663400952
112
  - task:
113
  name: Lexical Relation Classification (EVALution)
114
  type: classification
 
119
  metrics:
120
  - name: F1
121
  type: f1
122
+ value: 0.7128927410617552
123
  - name: F1 (macro)
124
  type: f1_macro
125
+ value: 0.7065924774146382
126
  - task:
127
  name: Lexical Relation Classification (K&H+N)
128
  type: classification
 
133
  metrics:
134
  - name: F1
135
  type: f1
136
+ value: 0.9646657856298254
137
  - name: F1 (macro)
138
  type: f1_macro
139
+ value: 0.8945677578632619
140
  - task:
141
  name: Lexical Relation Classification (ROOT09)
142
  type: classification
 
147
  metrics:
148
  - name: F1
149
  type: f1
150
+ value: 0.9081792541523034
151
  - name: F1 (macro)
152
  type: f1_macro
153
+ value: 0.906414518159255
154
 
155
  ---
156
+ # relbert/roberta-large-semeval2012-mask-prompt-a-loob
157
 
158
  RelBERT fine-tuned from [roberta-large](https://huggingface.co/roberta-large) on
159
  [relbert/semeval2012_relational_similarity](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity).
160
  Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail).
161
  It achieves the following results on the relation understanding tasks:
162
+ - Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/roberta-large-semeval2012-mask-prompt-a-loob/raw/main/analogy.json)):
163
+ - Accuracy on SAT (full): 0.6550802139037433
164
+ - Accuracy on SAT: 0.655786350148368
165
+ - Accuracy on BATS: 0.8043357420789328
166
+ - Accuracy on U2: 0.631578947368421
167
+ - Accuracy on U4: 0.6412037037037037
168
+ - Accuracy on Google: 0.95
169
+ - Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/roberta-large-semeval2012-mask-prompt-a-loob/raw/main/classification.json)):
170
+ - Micro F1 score on BLESS: 0.9245140876902215
171
+ - Micro F1 score on CogALexV: 0.8814553990610329
172
+ - Micro F1 score on EVALution: 0.7128927410617552
173
+ - Micro F1 score on K&H+N: 0.9646657856298254
174
+ - Micro F1 score on ROOT09: 0.9081792541523034
175
+ - Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/roberta-large-semeval2012-mask-prompt-a-loob/raw/main/relation_mapping.json)):
176
+ - Accuracy on Relation Mapping: 0.9060317460317461
177
 
178
 
179
  ### Usage
 
184
  and activate model as below.
185
  ```python
186
  from relbert import RelBERT
187
+ model = RelBERT("relbert/roberta-large-semeval2012-mask-prompt-a-loob")
188
  vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, )
189
  ```
190
 
 
211
  - n_sample: 640
212
  - gradient_accumulation: 8
213
 
214
+ The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/roberta-large-semeval2012-mask-prompt-a-loob/raw/main/trainer_config.json).
215
 
216
  ### Reference
217
  If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/).