File size: 7,198 Bytes
af29950
 
 
 
025e550
af29950
 
 
 
 
 
 
 
 
 
 
21e708e
af29950
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
025e550
af29950
 
 
 
 
025e550
af29950
 
 
 
 
 
025e550
af29950
 
 
 
 
025e550
21e708e
af29950
 
 
 
 
 
 
 
 
 
025e550
af29950
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
025e550
af29950
 
50f4664
af29950
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
---
datasets:
- relbert/semeval2012_relational_similarity
model-index:
- name: relbert/roberta-large-semeval2012-average-prompt-a-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: 0.8719047619047618
  - 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: 0.6925133689839572
  - 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: 0.6913946587537092
  - 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: 0.7776542523624236
  - 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: 0.936
  - 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: 0.6535087719298246
  - 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: 0.6666666666666666
  - 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: 0.9153231881874341
    - name: F1 (macro)
      type: f1_macro
      value: 0.9116885342042641
  - 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: 0.8509389671361502
    - name: F1 (macro)
      type: f1_macro
      value: 0.6788929400995221
  - 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: 0.6771397616468039
    - name: F1 (macro)
      type: f1_macro
      value: 0.6568153884216413
  - 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: 0.9575015649996522
    - name: F1 (macro)
      type: f1_macro
      value: 0.8657248723477102
  - 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: 0.9025383892196804
    - name: F1 (macro)
      type: f1_macro
      value: 0.899796204657101

---
# relbert/roberta-large-semeval2012-average-prompt-a-nce

RelBERT fine-tuned from [roberta-large](https://huggingface.co/roberta-large) on  
[relbert/semeval2012_relational_similarity](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity).
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/roberta-large-semeval2012-average-prompt-a-nce/raw/main/analogy.json)):
    - Accuracy on SAT (full): 0.6925133689839572 
    - Accuracy on SAT: 0.6913946587537092
    - Accuracy on BATS: 0.7776542523624236
    - Accuracy on U2: 0.6535087719298246
    - Accuracy on U4: 0.6666666666666666
    - Accuracy on Google: 0.936
- Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/roberta-large-semeval2012-average-prompt-a-nce/raw/main/classification.json)):
    - Micro F1 score on BLESS: 0.9153231881874341
    - Micro F1 score on CogALexV: 0.8509389671361502
    - Micro F1 score on EVALution: 0.6771397616468039
    - Micro F1 score on K&H+N: 0.9575015649996522
    - Micro F1 score on ROOT09: 0.9025383892196804
- Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/roberta-large-semeval2012-average-prompt-a-nce/raw/main/relation_mapping.json)):
    - Accuracy on Relation Mapping: 0.8719047619047618 


### 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/roberta-large-semeval2012-average-prompt-a-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/semeval2012_relational_similarity
 - template_mode: manual
 - template: Today, I finally discovered the relation between <subj> and <obj> : <subj> is the <mask> of <obj>
 - loss_function: nce_logout
 - temperature_nce_constant: 0.05
 - temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'}
 - epoch: 29
 - 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/roberta-large-semeval2012-average-prompt-a-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",
}

```