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

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-loob/raw/main/analogy.json)):
    - Accuracy on SAT (full): 0.6443850267379679 
    - Accuracy on SAT: 0.6468842729970327
    - Accuracy on BATS: 0.7137298499166204
    - Accuracy on U2: 0.543859649122807
    - Accuracy on U4: 0.5833333333333334
    - Accuracy on Google: 0.898
- Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/roberta-large-semeval2012-average-prompt-a-loob/raw/main/classification.json)):
    - Micro F1 score on BLESS: 0.9153231881874341
    - Micro F1 score on CogALexV: 0.854225352112676
    - Micro F1 score on EVALution: 0.6603466955579632
    - Micro F1 score on K&H+N: 0.9617444529456771
    - Micro F1 score on ROOT09: 0.902851770604826
- Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/roberta-large-semeval2012-average-prompt-a-loob/raw/main/relation_mapping.json)):
    - Accuracy on Relation Mapping: 0.8641666666666666 


### 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-loob")
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: info_loob
 - temperature_nce_constant: 0.05
 - temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'}
 - epoch: 22
 - 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-loob/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",
}

```

{"datasets": ["relbert/semeval2012_relational_similarity"], "model-index": [{"name": "relbert/roberta-large-semeval2012-average-prompt-a-loob", "results": [{"task": {"type": "sorting-task", "name": "Relation Mapping"}, "dataset": {"name": "Relation Mapping", "type": "relation-mapping", "args": "relbert/relation_mapping"}, "metrics": [{"type": "accuracy", "value": 0.8641666666666666, "name": "Accuracy"}]}, {"task": {"type": "multiple-choice-qa", "name": "Analogy Questions (SAT full)"}, "dataset": {"name": "SAT full", "type": "analogy-questions", "args": "relbert/analogy_questions"}, "metrics": [{"type": "accuracy", "value": 0.6443850267379679, "name": "Accuracy"}, {"type": "accuracy", "value": 0.6468842729970327, "name": "Accuracy"}, {"type": "accuracy", "value": 0.7137298499166204, "name": "Accuracy"}, {"type": "accuracy", "value": 0.898, "name": "Accuracy"}, {"type": "accuracy", "value": 0.543859649122807, "name": "Accuracy"}, {"type": "accuracy", "value": 0.5833333333333334, "name": "Accuracy"}]}, {"task": {"type": "classification", "name": "Lexical Relation Classification (BLESS)"}, "dataset": {"name": "BLESS", "type": "relation-classification", "args": "relbert/lexical_relation_classification"}, "metrics": [{"type": "f1", "value": 0.9153231881874341, "name": "F1"}, {"type": "f1_macro", "value": 0.910194305368961, "name": "F1 (macro)"}, {"type": "f1", "value": 0.854225352112676, "name": "F1"}, {"type": "f1_macro", "value": 0.6939611644499436, "name": "F1 (macro)"}, {"type": "f1", "value": 0.6603466955579632, "name": "F1"}, {"type": "f1_macro", "value": 0.6449027403702262, "name": "F1 (macro)"}, {"type": "f1", "value": 0.9617444529456771, "name": "F1"}, {"type": "f1_macro", "value": 0.8891323512830197, "name": "F1 (macro)"}, {"type": "f1", "value": 0.902851770604826, "name": "F1"}, {"type": "f1_macro", "value": 0.9021609534307928, "name": "F1 (macro)"}]}]}]}
transformers pytorch roberta feature-extraction dataset:relbert/semeval2012_relational_similarity model-index endpoints_compatible region:us
TAGS
#transformers #pytorch #roberta #feature-extraction #dataset-relbert/semeval2012_relational_similarity #model-index #endpoints_compatible #region-us 

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

RelBERT fine-tuned from roberta-large on  
relbert/semeval2012_relational_similarity.
Fine-tuning is done via RelBERT library (see the repository for more detail).
It achieves the following results on the relation understanding tasks:
- Analogy Question (dataset, full result):
    - Accuracy on SAT (full): 0.6443850267379679 
    - Accuracy on SAT: 0.6468842729970327
    - Accuracy on BATS: 0.7137298499166204
    - Accuracy on U2: 0.543859649122807
    - Accuracy on U4: 0.5833333333333334
    - Accuracy on Google: 0.898
- Lexical Relation Classification (dataset, full result):
    - Micro F1 score on BLESS: 0.9153231881874341
    - Micro F1 score on CogALexV: 0.854225352112676
    - Micro F1 score on EVALution: 0.6603466955579632
    - Micro F1 score on K&H+N: 0.9617444529456771
    - Micro F1 score on ROOT09: 0.902851770604826
- Relation Mapping (dataset, full result):
    - Accuracy on Relation Mapping: 0.8641666666666666 


### Usage
This model can be used through the relbert library. Install the library via pip   

and activate model as below.


### 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: info_loob
 - temperature_nce_constant: 0.05
 - temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'}
 - epoch: 22
 - 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.

### Reference
If you use any resource from RelBERT, please consider to cite our paper.



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

RelBERT fine-tuned from roberta-large on  
relbert/semeval2012_relational_similarity.
Fine-tuning is done via RelBERT library (see the repository for more detail).
It achieves the following results on the relation understanding tasks:
- Analogy Question (dataset, full result):
    - Accuracy on SAT (full): 0.6443850267379679 
    - Accuracy on SAT: 0.6468842729970327
    - Accuracy on BATS: 0.7137298499166204
    - Accuracy on U2: 0.543859649122807
    - Accuracy on U4: 0.5833333333333334
    - Accuracy on Google: 0.898
- Lexical Relation Classification (dataset, full result):
    - Micro F1 score on BLESS: 0.9153231881874341
    - Micro F1 score on CogALexV: 0.854225352112676
    - Micro F1 score on EVALution: 0.6603466955579632
    - Micro F1 score on K&H+N: 0.9617444529456771
    - Micro F1 score on ROOT09: 0.902851770604826
- Relation Mapping (dataset, full result):
    - Accuracy on Relation Mapping: 0.8641666666666666 ### Usage
This model can be used through the relbert library. Install the library via pip   

and activate model as below. ### 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: info_loob
 - temperature_nce_constant: 0.05
 - temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'}
 - epoch: 22
 - 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. ### Reference
If you use any resource from RelBERT, please consider to cite our paper.
TAGS
#transformers #pytorch #roberta #feature-extraction #dataset-relbert/semeval2012_relational_similarity #model-index #endpoints_compatible #region-us 
 # relbert/roberta-large-semeval2012-average-prompt-a-loob

RelBERT fine-tuned from roberta-large on  
relbert/semeval2012_relational_similarity.
Fine-tuning is done via RelBERT library (see the repository for more detail).
It achieves the following results on the relation understanding tasks:
- Analogy Question (dataset, full result):
    - Accuracy on SAT (full): 0.6443850267379679 
    - Accuracy on SAT: 0.6468842729970327
    - Accuracy on BATS: 0.7137298499166204
    - Accuracy on U2: 0.543859649122807
    - Accuracy on U4: 0.5833333333333334
    - Accuracy on Google: 0.898
- Lexical Relation Classification (dataset, full result):
    - Micro F1 score on BLESS: 0.9153231881874341
    - Micro F1 score on CogALexV: 0.854225352112676
    - Micro F1 score on EVALution: 0.6603466955579632
    - Micro F1 score on K&H+N: 0.9617444529456771
    - Micro F1 score on ROOT09: 0.902851770604826
- Relation Mapping (dataset, full result):
    - Accuracy on Relation Mapping: 0.8641666666666666 ### Usage
This model can be used through the relbert library. Install the library via pip   

and activate model as below. ### 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: info_loob
 - temperature_nce_constant: 0.05
 - temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'}
 - epoch: 22
 - 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. ### Reference
If you use any resource from RelBERT, please consider to cite our paper.
passage: TAGS
#transformers #pytorch #roberta #feature-extraction #dataset-relbert/semeval2012_relational_similarity #model-index #endpoints_compatible #region-us 
# relbert/roberta-large-semeval2012-average-prompt-a-loob

RelBERT fine-tuned from roberta-large on  
relbert/semeval2012_relational_similarity.
Fine-tuning is done via RelBERT library (see the repository for more detail).
It achieves the following results on the relation understanding tasks:
- Analogy Question (dataset, full result):
    - Accuracy on SAT (full): 0.6443850267379679 
    - Accuracy on SAT: 0.6468842729970327
    - Accuracy on BATS: 0.7137298499166204
    - Accuracy on U2: 0.543859649122807
    - Accuracy on U4: 0.5833333333333334
    - Accuracy on Google: 0.898
- Lexical Relation Classification (dataset, full result):
    - Micro F1 score on BLESS: 0.9153231881874341
    - Micro F1 score on CogALexV: 0.854225352112676
    - Micro F1 score on EVALution: 0.6603466955579632
    - Micro F1 score on K&H+N: 0.9617444529456771
    - Micro F1 score on ROOT09: 0.902851770604826
- Relation Mapping (dataset, full result):
    - Accuracy on Relation Mapping: 0.8641666666666666### Usage
This model can be used through the relbert library. Install the library via pip   

and activate model as below.