asahi417 commited on
Commit
e9f0181
1 Parent(s): 9c467e8

model update

Browse files
Files changed (1) hide show
  1. README.md +9 -9
README.md CHANGED
@@ -2,7 +2,7 @@
2
  datasets:
3
  - relbert/semeval2012_relational_similarity
4
  model-index:
5
- - name: relbert/relbert-roberta-large-semeval2012-average-no-mask-prompt-d-loob
6
  results:
7
  - task:
8
  name: Relation Mapping
@@ -14,7 +14,7 @@ model-index:
14
  metrics:
15
  - name: Accuracy
16
  type: accuracy
17
- value: 0.8951388888888889
18
  - task:
19
  name: Analogy Questions (SAT full)
20
  type: multiple-choice-qa
@@ -153,27 +153,27 @@ model-index:
153
  value: 0.9093125585829993
154
 
155
  ---
156
- # relbert/relbert-roberta-large-semeval2012-average-no-mask-prompt-d-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-average-no-mask-prompt-d-loob/raw/main/analogy.json)):
163
  - Accuracy on SAT (full): 0.6871657754010695
164
  - Accuracy on SAT: 0.6913946587537092
165
  - Accuracy on BATS: 0.8148971650917176
166
  - Accuracy on U2: 0.6359649122807017
167
  - Accuracy on U4: 0.6458333333333334
168
  - Accuracy on Google: 0.958
169
- - Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-large-semeval2012-average-no-mask-prompt-d-loob/raw/main/classification.json)):
170
  - Micro F1 score on BLESS: 0.9153231881874341
171
  - Micro F1 score on CogALexV: 0.8577464788732394
172
  - Micro F1 score on EVALution: 0.6847237269772481
173
  - Micro F1 score on K&H+N: 0.9634137859080476
174
  - Micro F1 score on ROOT09: 0.9106863052334692
175
- - Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-large-semeval2012-average-no-mask-prompt-d-loob/raw/main/relation_mapping.json)):
176
- - Accuracy on Relation Mapping: 0.8951388888888889
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-average-no-mask-prompt-d-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-average-no-mask-prompt-d-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-average-no-mask-prompt-d-loob
6
  results:
7
  - task:
8
  name: Relation Mapping
 
14
  metrics:
15
  - name: Accuracy
16
  type: accuracy
17
+ value: 0.8871031746031746
18
  - task:
19
  name: Analogy Questions (SAT full)
20
  type: multiple-choice-qa
 
153
  value: 0.9093125585829993
154
 
155
  ---
156
+ # relbert/roberta-large-semeval2012-average-no-mask-prompt-d-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-average-no-mask-prompt-d-loob/raw/main/analogy.json)):
163
  - Accuracy on SAT (full): 0.6871657754010695
164
  - Accuracy on SAT: 0.6913946587537092
165
  - Accuracy on BATS: 0.8148971650917176
166
  - Accuracy on U2: 0.6359649122807017
167
  - Accuracy on U4: 0.6458333333333334
168
  - Accuracy on Google: 0.958
169
+ - Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/roberta-large-semeval2012-average-no-mask-prompt-d-loob/raw/main/classification.json)):
170
  - Micro F1 score on BLESS: 0.9153231881874341
171
  - Micro F1 score on CogALexV: 0.8577464788732394
172
  - Micro F1 score on EVALution: 0.6847237269772481
173
  - Micro F1 score on K&H+N: 0.9634137859080476
174
  - Micro F1 score on ROOT09: 0.9106863052334692
175
+ - Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/roberta-large-semeval2012-average-no-mask-prompt-d-loob/raw/main/relation_mapping.json)):
176
+ - Accuracy on Relation Mapping: 0.8871031746031746
177
 
178
 
179
  ### Usage
 
184
  and activate model as below.
185
  ```python
186
  from relbert import RelBERT
187
+ model = RelBERT("relbert/roberta-large-semeval2012-average-no-mask-prompt-d-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-average-no-mask-prompt-d-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/).