# relbert/roberta-large-semeval2012-mask-prompt-b-nce-classification-conceptnet-validated

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-mask-prompt-b-nce-classification-conceptnet-validated/raw/main/analogy.json)):
    - Accuracy on SAT (full): 0.5080213903743316 
    - Accuracy on SAT: 0.5192878338278932
    - Accuracy on BATS: 0.6653696498054474
    - Accuracy on U2: 0.45614035087719296
    - Accuracy on U4: 0.5393518518518519
    - Accuracy on Google: 0.84
- Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/roberta-large-semeval2012-mask-prompt-b-nce-classification-conceptnet-validated/raw/main/classification.json)):
    - Micro F1 score on BLESS: 0.9132138014163026
    - Micro F1 score on CogALexV: 0.8502347417840377
    - Micro F1 score on EVALution: 0.6852654387865655
    - Micro F1 score on K&H+N: 0.9604228976838005
    - Micro F1 score on ROOT09: 0.9022250078345346
- Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/roberta-large-semeval2012-mask-prompt-b-nce-classification-conceptnet-validated/raw/main/relation_mapping.json)):
    - Accuracy on Relation Mapping: 0.856984126984127 


### 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-mask-prompt-b-nce-classification-conceptnet-validated")
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: mask
 - data: relbert/semeval2012_relational_similarity
 - split: train
 - data_eval: relbert/conceptnet_high_confidence
 - split_eval: full
 - template_mode: manual
 - template: Today, I finally discovered the relation between <subj> and <obj> : <obj>  is <subj>'s <mask>
 - loss_function: nce_logout
 - classification_loss: True
 - temperature_nce_constant: 0.05
 - temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'}
 - epoch: 27
 - batch: 128
 - lr: 5e-06
 - lr_decay: False
 - lr_warmup: 1
 - weight_decay: 0
 - random_seed: 0
 - exclude_relation: None
 - exclude_relation_eval: 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-mask-prompt-b-nce-classification-conceptnet-validated/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-mask-prompt-b-nce-classification-conceptnet-validated", "results": [{"task": {"type": "sorting-task", "name": "Relation Mapping"}, "dataset": {"name": "Relation Mapping", "type": "relation-mapping", "args": "relbert/relation_mapping"}, "metrics": [{"type": "accuracy", "value": 0.856984126984127, "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": 0.5080213903743316, "name": "Accuracy"}, {"type": "accuracy", "value": 0.5192878338278932, "name": "Accuracy"}, {"type": "accuracy", "value": 0.6653696498054474, "name": "Accuracy"}, {"type": "accuracy", "value": 0.84, "name": "Accuracy"}, {"type": "accuracy", "value": 0.45614035087719296, "name": "Accuracy"}, {"type": "accuracy", "value": 0.5393518518518519, "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": 0.9132138014163026, "name": "F1"}, {"type": "f1_macro", "value": 0.9101733559621606, "name": "F1 (macro)"}, {"type": "f1", "value": 0.8502347417840377, "name": "F1"}, {"type": "f1_macro", "value": 0.6852576593859314, "name": "F1 (macro)"}, {"type": "f1", "value": 0.6852654387865655, "name": "F1"}, {"type": "f1_macro", "value": 0.6694360423727916, "name": "F1 (macro)"}, {"type": "f1", "value": 0.9604228976838005, "name": "F1"}, {"type": "f1_macro", "value": 0.8826948107609662, "name": "F1 (macro)"}, {"type": "f1", "value": 0.9022250078345346, "name": "F1"}, {"type": "f1_macro", "value": 0.9002463330589072, "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-mask-prompt-b-nce-classification-conceptnet-validated

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): 0.5080213903743316 
    - Accuracy on SAT: 0.5192878338278932
    - Accuracy on BATS: 0.6653696498054474
    - Accuracy on U2: 0.45614035087719296
    - Accuracy on U4: 0.5393518518518519
    - Accuracy on Google: 0.84
- Lexical Relation Classification (dataset, full result):
    - Micro F1 score on BLESS: 0.9132138014163026
    - Micro F1 score on CogALexV: 0.8502347417840377
    - Micro F1 score on EVALution: 0.6852654387865655
    - Micro F1 score on K&H+N: 0.9604228976838005
    - Micro F1 score on ROOT09: 0.9022250078345346
- Relation Mapping (dataset, full result):
    - Accuracy on Relation Mapping: 0.856984126984127 


### 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: mask
 - data: relbert/semeval2012_relational_similarity
 - split: train
 - data_eval: relbert/conceptnet_high_confidence
 - split_eval: full
 - template_mode: manual
 - template: Today, I finally discovered the relation between <subj> and <obj> : <obj>  is <subj>'s <mask>
 - loss_function: nce_logout
 - classification_loss: True
 - temperature_nce_constant: 0.05
 - temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'}
 - epoch: 27
 - batch: 128
 - lr: 5e-06
 - lr_decay: False
 - lr_warmup: 1
 - weight_decay: 0
 - random_seed: 0
 - exclude_relation: None
 - exclude_relation_eval: 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-mask-prompt-b-nce-classification-conceptnet-validated

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): 0.5080213903743316 
    - Accuracy on SAT: 0.5192878338278932
    - Accuracy on BATS: 0.6653696498054474
    - Accuracy on U2: 0.45614035087719296
    - Accuracy on U4: 0.5393518518518519
    - Accuracy on Google: 0.84
- Lexical Relation Classification (dataset, full result):
    - Micro F1 score on BLESS: 0.9132138014163026
    - Micro F1 score on CogALexV: 0.8502347417840377
    - Micro F1 score on EVALution: 0.6852654387865655
    - Micro F1 score on K&H+N: 0.9604228976838005
    - Micro F1 score on ROOT09: 0.9022250078345346
- Relation Mapping (dataset, full result):
    - Accuracy on Relation Mapping: 0.856984126984127 ### 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: mask
 - data: relbert/semeval2012_relational_similarity
 - split: train
 - data_eval: relbert/conceptnet_high_confidence
 - split_eval: full
 - template_mode: manual
 - template: Today, I finally discovered the relation between <subj> and <obj> : <obj>  is <subj>'s <mask>
 - loss_function: nce_logout
 - classification_loss: True
 - temperature_nce_constant: 0.05
 - temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'}
 - epoch: 27
 - batch: 128
 - lr: 5e-06
 - lr_decay: False
 - lr_warmup: 1
 - weight_decay: 0
 - random_seed: 0
 - exclude_relation: None
 - exclude_relation_eval: 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-mask-prompt-b-nce-classification-conceptnet-validated

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): 0.5080213903743316 
    - Accuracy on SAT: 0.5192878338278932
    - Accuracy on BATS: 0.6653696498054474
    - Accuracy on U2: 0.45614035087719296
    - Accuracy on U4: 0.5393518518518519
    - Accuracy on Google: 0.84
- Lexical Relation Classification (dataset, full result):
    - Micro F1 score on BLESS: 0.9132138014163026
    - Micro F1 score on CogALexV: 0.8502347417840377
    - Micro F1 score on EVALution: 0.6852654387865655
    - Micro F1 score on K&H+N: 0.9604228976838005
    - Micro F1 score on ROOT09: 0.9022250078345346
- Relation Mapping (dataset, full result):
    - Accuracy on Relation Mapping: 0.856984126984127 ### 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: mask
 - data: relbert/semeval2012_relational_similarity
 - split: train
 - data_eval: relbert/conceptnet_high_confidence
 - split_eval: full
 - template_mode: manual
 - template: Today, I finally discovered the relation between <subj> and <obj> : <obj>  is <subj>'s <mask>
 - loss_function: nce_logout
 - classification_loss: True
 - temperature_nce_constant: 0.05
 - temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'}
 - epoch: 27
 - batch: 128
 - lr: 5e-06
 - lr_decay: False
 - lr_warmup: 1
 - weight_decay: 0
 - random_seed: 0
 - exclude_relation: None
 - exclude_relation_eval: 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-mask-prompt-b-nce-classification-conceptnet-validated

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): 0.5080213903743316 
    - Accuracy on SAT: 0.5192878338278932
    - Accuracy on BATS: 0.6653696498054474
    - Accuracy on U2: 0.45614035087719296
    - Accuracy on U4: 0.5393518518518519
    - Accuracy on Google: 0.84
- Lexical Relation Classification (dataset, full result):
    - Micro F1 score on BLESS: 0.9132138014163026
    - Micro F1 score on CogALexV: 0.8502347417840377
    - Micro F1 score on EVALution: 0.6852654387865655
    - Micro F1 score on K&H+N: 0.9604228976838005
    - Micro F1 score on ROOT09: 0.9022250078345346
- Relation Mapping (dataset, full result):
    - Accuracy on Relation Mapping: 0.856984126984127### Usage
This model can be used through the relbert library. Install the library via pip   

and activate model as below.