fix readme

README.md

@@ -2,7 +2,7 @@
 datasets:
 - relbert/semeval2012_relational_similarity
 model-index:
-- name: relbert/relbert-roberta-large-nce-d-0
+- name: relbert/relbert-roberta-large-nce-d-semeval2012
   results:
   - task:
       name: Relation Mapping
@@ -14,7 +14,7 @@ model-index:
     metrics:
     - name: Accuracy
       type: accuracy
-      value: 0.8022222222222222
+      value: 0.8049007936507937
   - task:
       name: Analogy Questions (SAT full)
       type: multiple-choice-qa
@@ -25,7 +25,7 @@ model-index:
     metrics:
     - name: Accuracy
       type: accuracy
-      value: 0.7219251336898396
+      value: 0.732620320855615
   - task:
       name: Analogy Questions (SAT)
       type: multiple-choice-qa
@@ -36,7 +36,7 @@ model-index:
     metrics:
     - name: Accuracy
       type: accuracy
-      value: 0.7270029673590505
+      value: 0.7359050445103857
   - task:
       name: Analogy Questions (BATS)
       type: multiple-choice-qa
@@ -47,7 +47,7 @@ model-index:
     metrics:
     - name: Accuracy
       type: accuracy
-      value: 0.7937743190661478
+      value: 0.8093385214007782
   - task:
       name: Analogy Questions (Google)
       type: multiple-choice-qa
@@ -58,7 +58,7 @@ model-index:
     metrics:
     - name: Accuracy
       type: accuracy
-      value: 0.942
+      value: 0.952
   - task:
       name: Analogy Questions (U2)
       type: multiple-choice-qa
@@ -69,7 +69,7 @@ model-index:
     metrics:
     - name: Accuracy
       type: accuracy
-      value: 0.6578947368421053
+      value: 0.6754385964912281
   - task:
       name: Analogy Questions (U4)
       type: multiple-choice-qa
@@ -80,7 +80,29 @@ model-index:
     metrics:
     - name: Accuracy
       type: accuracy
-      value: 0.6527777777777778
+      value: 0.6296296296296297
+  - task:
+      name: Analogy Questions (ConceptNet Analogy)
+      type: multiple-choice-qa
+    dataset:
+      name: ConceptNet Analogy
+      args: relbert/analogy_questions
+      type: analogy-questions
+    metrics:
+    - name: Accuracy
+      type: accuracy
+      value: 0.4748322147651007
+  - task:
+      name: Analogy Questions (TREX Analogy)
+      type: multiple-choice-qa
+    dataset:
+      name: TREX Analogy
+      args: relbert/analogy_questions
+      type: analogy-questions
+    metrics:
+    - name: Accuracy
+      type: accuracy
+      value: 0.644808743169399
   - task:
       name: Lexical Relation Classification (BLESS)
       type: classification
@@ -91,10 +113,10 @@ model-index:
     metrics:
     - name: F1
       type: f1
-      value: 0.9193912912460449
+      value: 0.9199939731806539
     - name: F1 (macro)
       type: f1_macro
-      value: 0.9154281897413815
+      value: 0.9173175984713615
   - task:
       name: Lexical Relation Classification (CogALexV)
       type: classification
@@ -105,10 +127,10 @@ model-index:
     metrics:
     - name: F1
       type: f1
-      value: 0.855868544600939
+      value: 0.8497652582159625
     - name: F1 (macro)
       type: f1_macro
-      value: 0.6848847419105534
+      value: 0.6744248225015879
   - task:
       name: Lexical Relation Classification (EVALution)
       type: classification
@@ -119,10 +141,10 @@ model-index:
     metrics:
     - name: F1
       type: f1
-      value: 0.6890574214517876
+      value: 0.6836403033586133
     - name: F1 (macro)
       type: f1_macro
-      value: 0.6776463157716582
+      value: 0.6776792144071253
   - task:
       name: Lexical Relation Classification (K&H+N)
       type: classification
@@ -133,10 +155,10 @@ model-index:
     metrics:
     - name: F1
       type: f1
-      value: 0.960283786603603
+      value: 0.9563191208179731
     - name: F1 (macro)
       type: f1_macro
-      value: 0.8788008952006335
+      value: 0.8663013754934635
   - task:
       name: Lexical Relation Classification (ROOT09)
       type: classification
@@ -147,31 +169,33 @@ model-index:
     metrics:
     - name: F1
       type: f1
-      value: 0.9103729238483234
+      value: 0.9041052961454089
     - name: F1 (macro)
       type: f1_macro
-      value: 0.9087639459806999
+      value: 0.9040831832304929
 
 ---
-# relbert/relbert-roberta-large-nce-d-0
+# relbert/relbert-roberta-large-nce-d-semeval2012
 
 RelBERT based on [roberta-large](https://huggingface.co/roberta-large) fine-tuned on [relbert/semeval2012_relational_similarity](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity) (see the [`relbert`](https://github.com/asahi417/relbert) for more detail of fine-tuning).
 This model 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/relbert-roberta-large-nce-d-0/raw/main/analogy.forward.json)):
-    - Accuracy on SAT (full): 0.7219251336898396 
-    - Accuracy on SAT: 0.7270029673590505
-    - Accuracy on BATS: 0.7937743190661478
-    - Accuracy on U2: 0.6578947368421053
-    - Accuracy on U4: 0.6527777777777778
-    - Accuracy on Google: 0.942
-- Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-large-nce-d-0/raw/main/classification.json)):
-    - Micro F1 score on BLESS: 0.9193912912460449
-    - Micro F1 score on CogALexV: 0.855868544600939
-    - Micro F1 score on EVALution: 0.6890574214517876
-    - Micro F1 score on K&H+N: 0.960283786603603
-    - Micro F1 score on ROOT09: 0.9103729238483234
-- Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-large-nce-d-0/raw/main/relation_mapping.json)):
-    - Accuracy on Relation Mapping: 0.8022222222222222 
+- Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-large-nce-d-semeval2012/raw/main/analogy.forward.json)):
+    - Accuracy on SAT (full): 0.732620320855615 
+    - Accuracy on SAT: 0.7359050445103857
+    - Accuracy on BATS: 0.8093385214007782
+    - Accuracy on U2: 0.6754385964912281
+    - Accuracy on U4: 0.6296296296296297
+    - Accuracy on Google: 0.952
+    - Accuracy on ConceptNet Analogy: 0.4748322147651007
+    - Accuracy on T-Rex Analogy: 0.644808743169399
+- Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-large-nce-d-semeval2012/raw/main/classification.json)):
+    - Micro F1 score on BLESS: 0.9199939731806539
+    - Micro F1 score on CogALexV: 0.8497652582159625
+    - Micro F1 score on EVALution: 0.6836403033586133
+    - Micro F1 score on K&H+N: 0.9563191208179731
+    - Micro F1 score on ROOT09: 0.9041052961454089
+- Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-large-nce-d-semeval2012/raw/main/relation_mapping.json)):
+    - Accuracy on Relation Mapping: 0.8049007936507937 
 
 
 ### Usage
@@ -182,7 +206,7 @@ pip install relbert
 and activate model as below.
 ```python
 from relbert import RelBERT
-model = RelBERT("relbert/relbert-roberta-large-nce-d-0")
+model = RelBERT("relbert/relbert-roberta-large-nce-d-semeval2012")
 vector = model.get_embedding(['Tokyo', 'Japan'])  # shape of (n_dim, )
 ```
 
@@ -197,14 +221,16 @@ vector = model.get_embedding(['Tokyo', 'Japan'])  # shape of (n_dim, )
  - lr_warmup: 10
  - aggregation_mode: average_no_mask
  - data: relbert/semeval2012_relational_similarity
+ - data_name: None
  - exclude_relation: None
  - split: train
  - split_valid: validation
  - loss_function: nce
  - classification_loss: False
  - loss_function_config: {'temperature': 0.05, 'gradient_accumulation': 1, 'num_negative': 400, 'num_positive': 10}
+ - augment_negative_by_positive: True
 
-See the full configuration at [config file](https://huggingface.co/relbert/relbert-roberta-large-nce-d-0/raw/main/finetuning_config.json).
+See the full configuration at [config file](https://huggingface.co/relbert/relbert-roberta-large-nce-d-semeval2012/raw/main/finetuning_config.json).
 
 ### Reference
 If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.emnlp-main.712/).

analogy.bidirection.json

@@ -1 +1 @@
-{"distance_function": "cosine_similarity", "model": "relbert_output/ckpt/nce/template-d.random-0/model", "template": "I wasn\u2019t aware of this relationship, but I just read in the encyclopedia that <subj> is the <mask> of <obj>", "aggregation": "average_no_mask", "sat/test": 0.7270029673590505, "u2/test": 0.6578947368421053, "u4/test": 0.6527777777777778, "google/test": 0.942, "bats/test": 0.7937743190661478, "sat_full": 0.7219251336898396, "sat/validation": 0.6756756756756757, "u2/validation": 0.625, "u4/validation": 0.6458333333333334, "google/validation": 0.96, "bats/validation": 0.8341708542713567}
+{"sat_full/test": 0.7272727272727273, "sat/test": 0.7299703264094956, "u2/test": 0.7149122807017544, "u4/test": 0.6875, "google/test": 0.962, "bats/test": 0.8354641467481935, "t_rex_relational_similarity/test": 0.644808743169399, "conceptnet_relational_similarity/test": 0.4672818791946309, "sat/validation": 0.7027027027027027, "u2/validation": 0.5833333333333334, "u4/validation": 0.5833333333333334, "google/validation": 1.0, "bats/validation": 0.8793969849246231, "semeval2012_relational_similarity/validation": 0.7341772151898734, "t_rex_relational_similarity/validation": 0.27419354838709675, "conceptnet_relational_similarity/validation": 0.38219424460431656}

analogy.forward.json

@@ -1 +1 @@
-{"distance_function": "cosine_similarity", "model": "relbert_output/ckpt/nce/template-d.random-0/model", "template": "I wasn\u2019t aware of this relationship, but I just read in the encyclopedia that <subj> is the <mask> of <obj>", "aggregation": "average_no_mask", "sat/test": 0.7270029673590505, "u2/test": 0.6578947368421053, "u4/test": 0.6527777777777778, "google/test": 0.942, "bats/test": 0.7937743190661478, "sat_full": 0.7219251336898396, "sat/validation": 0.6756756756756757, "u2/validation": 0.625, "u4/validation": 0.6458333333333334, "google/validation": 0.96, "bats/validation": 0.8341708542713567}
+{"semeval2012_relational_similarity/validation": 0.7468354430379747, "sat_full/test": 0.732620320855615, "sat/test": 0.7359050445103857, "u2/test": 0.6754385964912281, "u4/test": 0.6296296296296297, "google/test": 0.952, "bats/test": 0.8093385214007782, "t_rex_relational_similarity/test": 0.644808743169399, "conceptnet_relational_similarity/test": 0.4748322147651007, "sat/validation": 0.7027027027027027, "u2/validation": 0.625, "u4/validation": 0.5625, "google/validation": 1.0, "bats/validation": 0.8542713567839196, "t_rex_relational_similarity/validation": 0.29435483870967744, "conceptnet_relational_similarity/validation": 0.37859712230215825}

analogy.reverse.json

@@ -1 +1 @@
-{"distance_function": "cosine_similarity", "model": "relbert_output/ckpt/nce/template-d.random-0/model", "template": "I wasn\u2019t aware of this relationship, but I just read in the encyclopedia that <subj> is the <mask> of <obj>", "aggregation": "average_no_mask", "sat/test": 0.7270029673590505, "u2/test": 0.6578947368421053, "u4/test": 0.6527777777777778, "google/test": 0.942, "bats/test": 0.7937743190661478, "sat_full": 0.7219251336898396, "sat/validation": 0.6756756756756757, "u2/validation": 0.625, "u4/validation": 0.6458333333333334, "google/validation": 0.96, "bats/validation": 0.8341708542713567}
+{"sat_full/test": 0.6524064171122995, "sat/test": 0.6468842729970327, "u2/test": 0.6885964912280702, "u4/test": 0.6597222222222222, "google/test": 0.944, "bats/test": 0.7976653696498055, "t_rex_relational_similarity/test": 0.5956284153005464, "conceptnet_relational_similarity/test": 0.40604026845637586, "sat/validation": 0.7027027027027027, "u2/validation": 0.7083333333333334, "u4/validation": 0.625, "google/validation": 0.98, "bats/validation": 0.8592964824120602, "semeval2012_relational_similarity/validation": 0.6708860759493671, "t_rex_relational_similarity/validation": 0.24596774193548387, "conceptnet_relational_similarity/validation": 0.3237410071942446}

classification.json

@@ -1 +1 @@
-{"lexical_relation_classification/BLESS": {"classifier_config": {"activation": "relu", "alpha": 0.0001, "batch_size": "auto", "beta_1": 0.9, "beta_2": 0.999, "early_stopping": false, "epsilon": 1e-08, "hidden_layer_sizes": [100], "learning_rate": "constant", "learning_rate_init": 0.001, "max_fun": 15000, "max_iter": 200, "momentum": 0.9, "n_iter_no_change": 10, "nesterovs_momentum": true, "power_t": 0.5, "random_state": 0, "shuffle": true, "solver": "adam", "tol": 0.0001, "validation_fraction": 0.1, "verbose": false, "warm_start": false}, "test/accuracy": 0.9193912912460449, "test/f1_macro": 0.9154281897413815, "test/f1_micro": 0.9193912912460449, "test/p_macro": 0.909030256916922, "test/p_micro": 0.9193912912460449, "test/r_macro": 0.9234515644685003, "test/r_micro": 0.9193912912460449}, "lexical_relation_classification/CogALexV": {"classifier_config": {"activation": "relu", "alpha": 0.0001, "batch_size": "auto", "beta_1": 0.9, "beta_2": 0.999, "early_stopping": false, "epsilon": 1e-08, "hidden_layer_sizes": [100], "learning_rate": "constant", "learning_rate_init": 0.001, "max_fun": 15000, "max_iter": 200, "momentum": 0.9, "n_iter_no_change": 10, "nesterovs_momentum": true, "power_t": 0.5, "random_state": 0, "shuffle": true, "solver": "adam", "tol": 0.0001, "validation_fraction": 0.1, "verbose": false, "warm_start": false}, "test/accuracy": 0.855868544600939, "test/f1_macro": 0.6848847419105534, "test/f1_micro": 0.855868544600939, "test/p_macro": 0.7136216119555865, "test/p_micro": 0.855868544600939, "test/r_macro": 0.6606656363820599, "test/r_micro": 0.855868544600939}, "lexical_relation_classification/EVALution": {"classifier_config": {"activation": "relu", "alpha": 0.0001, "batch_size": "auto", "beta_1": 0.9, "beta_2": 0.999, "early_stopping": false, "epsilon": 1e-08, "hidden_layer_sizes": [100], "learning_rate": "constant", "learning_rate_init": 0.001, "max_fun": 15000, "max_iter": 200, "momentum": 0.9, "n_iter_no_change": 10, "nesterovs_momentum": true, "power_t": 0.5, "random_state": 0, "shuffle": true, "solver": "adam", "tol": 0.0001, "validation_fraction": 0.1, "verbose": false, "warm_start": false}, "test/accuracy": 0.6890574214517876, "test/f1_macro": 0.6776463157716582, "test/f1_micro": 0.6890574214517876, "test/p_macro": 0.6879578603963917, "test/p_micro": 0.6890574214517876, "test/r_macro": 0.6716342581634464, "test/r_micro": 0.6890574214517876}, "lexical_relation_classification/K&H+N": {"classifier_config": {"activation": "relu", "alpha": 0.0001, "batch_size": "auto", "beta_1": 0.9, "beta_2": 0.999, "early_stopping": false, "epsilon": 1e-08, "hidden_layer_sizes": [100], "learning_rate": "constant", "learning_rate_init": 0.001, "max_fun": 15000, "max_iter": 200, "momentum": 0.9, "n_iter_no_change": 10, "nesterovs_momentum": true, "power_t": 0.5, "random_state": 0, "shuffle": true, "solver": "adam", "tol": 0.0001, "validation_fraction": 0.1, "verbose": false, "warm_start": false}, "test/accuracy": 0.960283786603603, "test/f1_macro": 0.8788008952006335, "test/f1_micro": 0.960283786603603, "test/p_macro": 0.9021078679303638, "test/p_micro": 0.960283786603603, "test/r_macro": 0.8590979187227483, "test/r_micro": 0.960283786603603}, "lexical_relation_classification/ROOT09": {"classifier_config": {"activation": "relu", "alpha": 0.0001, "batch_size": "auto", "beta_1": 0.9, "beta_2": 0.999, "early_stopping": false, "epsilon": 1e-08, "hidden_layer_sizes": [100], "learning_rate": "constant", "learning_rate_init": 0.001, "max_fun": 15000, "max_iter": 200, "momentum": 0.9, "n_iter_no_change": 10, "nesterovs_momentum": true, "power_t": 0.5, "random_state": 0, "shuffle": true, "solver": "adam", "tol": 0.0001, "validation_fraction": 0.1, "verbose": false, "warm_start": false}, "test/accuracy": 0.9103729238483234, "test/f1_macro": 0.9087639459806999, "test/f1_micro": 0.9103729238483234, "test/p_macro": 0.9080934147603116, "test/p_micro": 0.9103729238483234, "test/r_macro": 0.9094813347276341, "test/r_micro": 0.9103729238483234}}
+{"lexical_relation_classification/BLESS": {"classifier_config": {"activation": "relu", "alpha": 0.0001, "batch_size": "auto", "beta_1": 0.9, "beta_2": 0.999, "early_stopping": false, "epsilon": 1e-08, "hidden_layer_sizes": [100], "learning_rate": "constant", "learning_rate_init": 0.001, "max_fun": 15000, "max_iter": 200, "momentum": 0.9, "n_iter_no_change": 10, "nesterovs_momentum": true, "power_t": 0.5, "random_state": 0, "shuffle": true, "solver": "adam", "tol": 0.0001, "validation_fraction": 0.1, "verbose": false, "warm_start": false}, "test/accuracy": 0.9199939731806539, "test/f1_macro": 0.9173175984713615, "test/f1_micro": 0.9199939731806539, "test/p_macro": 0.9129525907822765, "test/p_micro": 0.9199939731806539, "test/r_macro": 0.9231397069650421, "test/r_micro": 0.9199939731806539}, "lexical_relation_classification/CogALexV": {"classifier_config": {"activation": "relu", "alpha": 0.0001, "batch_size": "auto", "beta_1": 0.9, "beta_2": 0.999, "early_stopping": false, "epsilon": 1e-08, "hidden_layer_sizes": [100], "learning_rate": "constant", "learning_rate_init": 0.001, "max_fun": 15000, "max_iter": 200, "momentum": 0.9, "n_iter_no_change": 10, "nesterovs_momentum": true, "power_t": 0.5, "random_state": 0, "shuffle": true, "solver": "adam", "tol": 0.0001, "validation_fraction": 0.1, "verbose": false, "warm_start": false}, "test/accuracy": 0.8497652582159625, "test/f1_macro": 0.6744248225015879, "test/f1_micro": 0.8497652582159625, "test/p_macro": 0.7124501607954181, "test/p_micro": 0.8497652582159625, "test/r_macro": 0.6439012185599183, "test/r_micro": 0.8497652582159625}, "lexical_relation_classification/EVALution": {"classifier_config": {"activation": "relu", "alpha": 0.0001, "batch_size": "auto", "beta_1": 0.9, "beta_2": 0.999, "early_stopping": false, "epsilon": 1e-08, "hidden_layer_sizes": [100], "learning_rate": "constant", "learning_rate_init": 0.001, "max_fun": 15000, "max_iter": 200, "momentum": 0.9, "n_iter_no_change": 10, "nesterovs_momentum": true, "power_t": 0.5, "random_state": 0, "shuffle": true, "solver": "adam", "tol": 0.0001, "validation_fraction": 0.1, "verbose": false, "warm_start": false}, "test/accuracy": 0.6836403033586133, "test/f1_macro": 0.6776792144071253, "test/f1_micro": 0.6836403033586133, "test/p_macro": 0.6859955689335163, "test/p_micro": 0.6836403033586133, "test/r_macro": 0.6723026043339869, "test/r_micro": 0.6836403033586133}, "lexical_relation_classification/K&H+N": {"classifier_config": {"activation": "relu", "alpha": 0.0001, "batch_size": "auto", "beta_1": 0.9, "beta_2": 0.999, "early_stopping": false, "epsilon": 1e-08, "hidden_layer_sizes": [100], "learning_rate": "constant", "learning_rate_init": 0.001, "max_fun": 15000, "max_iter": 200, "momentum": 0.9, "n_iter_no_change": 10, "nesterovs_momentum": true, "power_t": 0.5, "random_state": 0, "shuffle": true, "solver": "adam", "tol": 0.0001, "validation_fraction": 0.1, "verbose": false, "warm_start": false}, "test/accuracy": 0.9563191208179731, "test/f1_macro": 0.8663013754934635, "test/f1_micro": 0.9563191208179731, "test/p_macro": 0.8794831771565157, "test/p_micro": 0.9563191208179731, "test/r_macro": 0.8543598117081819, "test/r_micro": 0.9563191208179731}, "lexical_relation_classification/ROOT09": {"classifier_config": {"activation": "relu", "alpha": 0.0001, "batch_size": "auto", "beta_1": 0.9, "beta_2": 0.999, "early_stopping": false, "epsilon": 1e-08, "hidden_layer_sizes": [100], "learning_rate": "constant", "learning_rate_init": 0.001, "max_fun": 15000, "max_iter": 200, "momentum": 0.9, "n_iter_no_change": 10, "nesterovs_momentum": true, "power_t": 0.5, "random_state": 0, "shuffle": true, "solver": "adam", "tol": 0.0001, "validation_fraction": 0.1, "verbose": false, "warm_start": false}, "test/accuracy": 0.9041052961454089, "test/f1_macro": 0.9040831832304929, "test/f1_micro": 0.9041052961454089, "test/p_macro": 0.9025403374928938, "test/p_micro": 0.9041052961454089, "test/r_macro": 0.9060703873054115, "test/r_micro": 0.9041052961454089}}

config.json

@@ -1,5 +1,5 @@
 {
-  "_name_or_path": "relbert-roberta-large-nce-d-0",
+  "_name_or_path": "roberta-large",
   "architectures": [
     "RobertaModel"
   ],

finetuning_config.json

@@ -9,6 +9,7 @@
   "lr_warmup": 10,
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