model update

README.md

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+---
+datasets:
+- relbert/semeval2012_relational_similarity
+model-index:
+- name: relbert/roberta-large-semeval2012-mask-prompt-b-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: None
+  - 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: None
+  - 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: None
+  - 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: None
+  - 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: None
+  - 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: None
+  - 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: None
+  - 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: None
+    - name: F1 (macro)
+      type: f1_macro
+      value: None
+  - 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: None
+    - name: F1 (macro)
+      type: f1_macro
+      value: None
+  - 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: None
+    - name: F1 (macro)
+      type: f1_macro
+      value: None
+  - 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: None
+    - name: F1 (macro)
+      type: f1_macro
+      value: None
+  - 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: None
+    - name: F1 (macro)
+      type: f1_macro
+      value: None
+
+---
+# relbert/roberta-large-semeval2012-mask-prompt-b-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-mask-prompt-b-nce/raw/main/analogy.json)):
+    - Accuracy on SAT (full): None 
+    - Accuracy on SAT: None
+    - Accuracy on BATS: None
+    - Accuracy on U2: None
+    - Accuracy on U4: None
+    - Accuracy on Google: None
+- 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/raw/main/classification.json)):
+    - Micro F1 score on BLESS: None
+    - Micro F1 score on CogALexV: None
+    - Micro F1 score on EVALution: None
+    - Micro F1 score on K&H+N: None
+    - Micro F1 score on ROOT09: None
+- Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/roberta-large-semeval2012-mask-prompt-b-nce/raw/main/relation_mapping.json)):
+    - Accuracy on Relation Mapping: None 
+
+
+### 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")
+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: False
+ - 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
+ - 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/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",
+}
+
+```

config.json

@@ -1,5 +1,5 @@
 {
-  "_name_or_path": "relbert_output/models/b.nce_logout.mask.roberta-large.0.000005.8.0.05.640/best_model",
+  "_name_or_path": "roberta-large",
   "architectures": [
     "RobertaModel"
   ],

tokenizer_config.json

@@ -6,7 +6,7 @@
   "errors": "replace",
   "mask_token": "<mask>",
   "model_max_length": 512,
-  "name_or_path": "relbert_output/models/b.nce_logout.mask.roberta-large.0.000005.8.0.05.640/best_model",
+  "name_or_path": "roberta-large",
   "pad_token": "<pad>",
   "sep_token": "</s>",
   "special_tokens_map_file": null,

trainer_config.json

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+{"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": false, "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": null, "exclude_relation_eval": null, "n_sample": 640, "gradient_accumulation": 8}

validation_loss.json

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+{"full_loss": 5.780303838925484, "full_data": "relbert/conceptnet_high_confidence", "full_data/exclude_relation": null}