# relbert/roberta-large-semeval2012-average-no-mask-prompt-a-triplet

RelBERT fine-tuned from [roberta-large](https://huggingface.co/roberta-large) on  
[semeval2012](https://huggingface.co/datasets/semeval2012).
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-no-mask-prompt-a-triplet/raw/main/analogy.json)):
    - Accuracy on SAT (full): 0.5748663102 
    - Accuracy on SAT: 0.5816023739
    - Accuracy on BATS: 0.694274597
    - Accuracy on U2: 0.600877193
    - Accuracy on U4: 0.5810185185
    - Accuracy on Google: 0.868
- 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-a-triplet/raw/main/classification.json)):
    - Micro F1 score on BLESS: 0.9053789362663854
    - Micro F1 score on CogALexV: 0.8598591549295774
    - Micro F1 score on EVALution: 0.6744312026002167
    - Micro F1 score on K&H+N: 0.9689086735758503
    - Micro F1 score on ROOT09: 0.8934503290504543
- Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/roberta-large-semeval2012-average-no-mask-prompt-a-triplet/raw/main/relation_mapping.json)):
    - Accuracy on Relation Mapping: 0.8172222222222222 


### 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-no-mask-prompt-a-triplet")
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_no_mask
 - data: semeval2012
 - n_sample: 10
 - custom_template: Today, I finally discovered the relation between <subj> and <obj> : <subj> is the <mask> of <obj>
 - template: None
 - softmax_loss: True
 - in_batch_negative: True
 - parent_contrast: True
 - mse_margin: 1
 - epoch: 1
 - lr_warmup: 10
 - batch: 64
 - lr: 2e-05
 - lr_decay: False
 - weight_decay: 0
 - optimizer: adam
 - momentum: 0.9
 - fp16: False
 - random_seed: 0

The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/roberta-large-semeval2012-average-no-mask-prompt-a-triplet/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": ["semeval2012"], "model-index": [{"name": "relbert/roberta-large-semeval2012-average-no-mask-prompt-a-triplet", "results": [{"task": {"type": "sorting-task", "name": "Relation Mapping"}, "dataset": {"name": "Relation Mapping", "type": "relation-mapping", "args": "relbert/relation_mapping"}, "metrics": [{"type": "accuracy", "value": 0.8172222222222222, "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": 0.5748663102, "name": "Accuracy"}, {"type": "accuracy", "value": 0.5816023739, "name": "Accuracy"}, {"type": "accuracy", "value": 0.694274597, "name": "Accuracy"}, {"type": "accuracy", "value": 0.868, "name": "Accuracy"}, {"type": "accuracy", "value": 0.600877193, "name": "Accuracy"}, {"type": "accuracy", "value": 0.5810185185, "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": 0.9053789362663854, "name": "F1"}, {"type": "f1_macro", "value": 0.9048929138088492, "name": "F1 (macro)"}, {"type": "f1", "value": 0.8598591549295774, "name": "F1"}, {"type": "f1_macro", "value": 0.6899576626683188, "name": "F1 (macro)"}, {"type": "f1", "value": 0.6744312026002167, "name": "F1"}, {"type": "f1_macro", "value": 0.6530063441620636, "name": "F1 (macro)"}, {"type": "f1", "value": 0.9689086735758503, "name": "F1"}, {"type": "f1_macro", "value": 0.8993902866318307, "name": "F1 (macro)"}, {"type": "f1", "value": 0.8934503290504543, "name": "F1"}, {"type": "f1_macro", "value": 0.8920036295821294, "name": "F1 (macro)"}]}]}]}
transformers pytorch roberta feature-extraction dataset:semeval2012 model-index endpoints_compatible region:us
TAGS
#transformers #pytorch #roberta #feature-extraction #dataset-semeval2012 #model-index #endpoints_compatible #region-us 

# relbert/roberta-large-semeval2012-average-no-mask-prompt-a-triplet

RelBERT fine-tuned from roberta-large on  
semeval2012.
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): 0.5748663102 
    - Accuracy on SAT: 0.5816023739
    - Accuracy on BATS: 0.694274597
    - Accuracy on U2: 0.600877193
    - Accuracy on U4: 0.5810185185
    - Accuracy on Google: 0.868
- Lexical Relation Classification (dataset, full result):
    - Micro F1 score on BLESS: 0.9053789362663854
    - Micro F1 score on CogALexV: 0.8598591549295774
    - Micro F1 score on EVALution: 0.6744312026002167
    - Micro F1 score on K&H+N: 0.9689086735758503
    - Micro F1 score on ROOT09: 0.8934503290504543
- Relation Mapping (dataset, full result):
    - Accuracy on Relation Mapping: 0.8172222222222222 


### 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_no_mask
 - data: semeval2012
 - n_sample: 10
 - custom_template: Today, I finally discovered the relation between <subj> and <obj> : <subj> is the <mask> of <obj>
 - template: None
 - softmax_loss: True
 - in_batch_negative: True
 - parent_contrast: True
 - mse_margin: 1
 - epoch: 1
 - lr_warmup: 10
 - batch: 64
 - lr: 2e-05
 - lr_decay: False
 - weight_decay: 0
 - optimizer: adam
 - momentum: 0.9
 - fp16: False
 - random_seed: 0

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-no-mask-prompt-a-triplet

RelBERT fine-tuned from roberta-large on  
semeval2012.
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): 0.5748663102 
    - Accuracy on SAT: 0.5816023739
    - Accuracy on BATS: 0.694274597
    - Accuracy on U2: 0.600877193
    - Accuracy on U4: 0.5810185185
    - Accuracy on Google: 0.868
- Lexical Relation Classification (dataset, full result):
    - Micro F1 score on BLESS: 0.9053789362663854
    - Micro F1 score on CogALexV: 0.8598591549295774
    - Micro F1 score on EVALution: 0.6744312026002167
    - Micro F1 score on K&H+N: 0.9689086735758503
    - Micro F1 score on ROOT09: 0.8934503290504543
- Relation Mapping (dataset, full result):
    - Accuracy on Relation Mapping: 0.8172222222222222 ### 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_no_mask
 - data: semeval2012
 - n_sample: 10
 - custom_template: Today, I finally discovered the relation between <subj> and <obj> : <subj> is the <mask> of <obj>
 - template: None
 - softmax_loss: True
 - in_batch_negative: True
 - parent_contrast: True
 - mse_margin: 1
 - epoch: 1
 - lr_warmup: 10
 - batch: 64
 - lr: 2e-05
 - lr_decay: False
 - weight_decay: 0
 - optimizer: adam
 - momentum: 0.9
 - fp16: False
 - random_seed: 0

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-semeval2012 #model-index #endpoints_compatible #region-us 
 # relbert/roberta-large-semeval2012-average-no-mask-prompt-a-triplet

RelBERT fine-tuned from roberta-large on  
semeval2012.
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): 0.5748663102 
    - Accuracy on SAT: 0.5816023739
    - Accuracy on BATS: 0.694274597
    - Accuracy on U2: 0.600877193
    - Accuracy on U4: 0.5810185185
    - Accuracy on Google: 0.868
- Lexical Relation Classification (dataset, full result):
    - Micro F1 score on BLESS: 0.9053789362663854
    - Micro F1 score on CogALexV: 0.8598591549295774
    - Micro F1 score on EVALution: 0.6744312026002167
    - Micro F1 score on K&H+N: 0.9689086735758503
    - Micro F1 score on ROOT09: 0.8934503290504543
- Relation Mapping (dataset, full result):
    - Accuracy on Relation Mapping: 0.8172222222222222 ### 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_no_mask
 - data: semeval2012
 - n_sample: 10
 - custom_template: Today, I finally discovered the relation between <subj> and <obj> : <subj> is the <mask> of <obj>
 - template: None
 - softmax_loss: True
 - in_batch_negative: True
 - parent_contrast: True
 - mse_margin: 1
 - epoch: 1
 - lr_warmup: 10
 - batch: 64
 - lr: 2e-05
 - lr_decay: False
 - weight_decay: 0
 - optimizer: adam
 - momentum: 0.9
 - fp16: False
 - random_seed: 0

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-semeval2012 #model-index #endpoints_compatible #region-us 
# relbert/roberta-large-semeval2012-average-no-mask-prompt-a-triplet

RelBERT fine-tuned from roberta-large on  
semeval2012.
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): 0.5748663102 
    - Accuracy on SAT: 0.5816023739
    - Accuracy on BATS: 0.694274597
    - Accuracy on U2: 0.600877193
    - Accuracy on U4: 0.5810185185
    - Accuracy on Google: 0.868
- Lexical Relation Classification (dataset, full result):
    - Micro F1 score on BLESS: 0.9053789362663854
    - Micro F1 score on CogALexV: 0.8598591549295774
    - Micro F1 score on EVALution: 0.6744312026002167
    - Micro F1 score on K&H+N: 0.9689086735758503
    - Micro F1 score on ROOT09: 0.8934503290504543
- Relation Mapping (dataset, full result):
    - Accuracy on Relation Mapping: 0.8172222222222222### 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_no_mask
 - data: semeval2012
 - n_sample: 10
 - custom_template: Today, I finally discovered the relation between <subj> and <obj> : <subj> is the <mask> of <obj>
 - template: None
 - softmax_loss: True
 - in_batch_negative: True
 - parent_contrast: True
 - mse_margin: 1
 - epoch: 1
 - lr_warmup: 10
 - batch: 64
 - lr: 2e-05
 - lr_decay: False
 - weight_decay: 0
 - optimizer: adam
 - momentum: 0.9
 - fp16: False
 - random_seed: 0

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.