asahi417 commited on
Commit
b4ccb03
1 Parent(s): 6bbc285

model update

Browse files
Files changed (2) hide show
  1. README.md +7 -7
  2. relation_mapping.json +1 -0
README.md CHANGED
@@ -2,7 +2,7 @@
2
  datasets:
3
  - relbert/semeval2012_relational_similarity
4
  model-index:
5
- - name: relbert/roberta-large-semeval2012-mask-prompt-a-nce
6
  results:
7
  - task:
8
  name: Relation Mapping
@@ -153,26 +153,26 @@ model-index:
153
  value: 0.9061538163621959
154
 
155
  ---
156
- # relbert/roberta-large-semeval2012-mask-prompt-a-nce
157
 
158
  RelBERT fine-tuned from [roberta-large](https://huggingface.co/roberta-large) on
159
  [relbert/semeval2012_relational_similarity](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity).
160
  Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail).
161
  It achieves the following results on the relation understanding tasks:
162
- - Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/roberta-large-semeval2012-mask-prompt-a-nce/raw/main/analogy.json)):
163
  - Accuracy on SAT (full): 0.6764705882352942
164
  - Accuracy on SAT: 0.6824925816023739
165
  - Accuracy on BATS: 0.783212896053363
166
  - Accuracy on U2: 0.6228070175438597
167
  - Accuracy on U4: 0.6481481481481481
168
  - Accuracy on Google: 0.952
169
- - Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/roberta-large-semeval2012-mask-prompt-a-nce/raw/main/classification.json)):
170
  - Micro F1 score on BLESS: 0.9156245291547386
171
  - Micro F1 score on CogALexV: 0.8779342723004695
172
  - Micro F1 score on EVALution: 0.7199349945828819
173
  - Micro F1 score on K&H+N: 0.9597968978229116
174
  - Micro F1 score on ROOT09: 0.9078658727671576
175
- - Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/roberta-large-semeval2012-mask-prompt-a-nce/raw/main/relation_mapping.json)):
176
  - Accuracy on Relation Mapping: 0.9188888888888889
177
 
178
 
@@ -184,7 +184,7 @@ pip install relbert
184
  and activate model as below.
185
  ```python
186
  from relbert import RelBERT
187
- model = RelBERT("relbert/roberta-large-semeval2012-mask-prompt-a-nce")
188
  vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, )
189
  ```
190
 
@@ -216,7 +216,7 @@ The following hyperparameters were used during training:
216
  - n_sample: 640
217
  - gradient_accumulation: 8
218
 
219
- The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/roberta-large-semeval2012-mask-prompt-a-nce/raw/main/trainer_config.json).
220
 
221
  ### Reference
222
  If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/).
 
2
  datasets:
3
  - relbert/semeval2012_relational_similarity
4
  model-index:
5
+ - name: relbert/roberta-large-semeval2012-mask-prompt-a-nce-conceptnet-validated
6
  results:
7
  - task:
8
  name: Relation Mapping
 
153
  value: 0.9061538163621959
154
 
155
  ---
156
+ # relbert/roberta-large-semeval2012-mask-prompt-a-nce-conceptnet-validated
157
 
158
  RelBERT fine-tuned from [roberta-large](https://huggingface.co/roberta-large) on
159
  [relbert/semeval2012_relational_similarity](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity).
160
  Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail).
161
  It achieves the following results on the relation understanding tasks:
162
+ - Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/roberta-large-semeval2012-mask-prompt-a-nce-conceptnet-validated/raw/main/analogy.json)):
163
  - Accuracy on SAT (full): 0.6764705882352942
164
  - Accuracy on SAT: 0.6824925816023739
165
  - Accuracy on BATS: 0.783212896053363
166
  - Accuracy on U2: 0.6228070175438597
167
  - Accuracy on U4: 0.6481481481481481
168
  - Accuracy on Google: 0.952
169
+ - Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/roberta-large-semeval2012-mask-prompt-a-nce-conceptnet-validated/raw/main/classification.json)):
170
  - Micro F1 score on BLESS: 0.9156245291547386
171
  - Micro F1 score on CogALexV: 0.8779342723004695
172
  - Micro F1 score on EVALution: 0.7199349945828819
173
  - Micro F1 score on K&H+N: 0.9597968978229116
174
  - Micro F1 score on ROOT09: 0.9078658727671576
175
+ - Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/roberta-large-semeval2012-mask-prompt-a-nce-conceptnet-validated/raw/main/relation_mapping.json)):
176
  - Accuracy on Relation Mapping: 0.9188888888888889
177
 
178
 
 
184
  and activate model as below.
185
  ```python
186
  from relbert import RelBERT
187
+ model = RelBERT("relbert/roberta-large-semeval2012-mask-prompt-a-nce-conceptnet-validated")
188
  vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, )
189
  ```
190
 
 
216
  - n_sample: 640
217
  - gradient_accumulation: 8
218
 
219
+ The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/roberta-large-semeval2012-mask-prompt-a-nce-conceptnet-validated/raw/main/trainer_config.json).
220
 
221
  ### Reference
222
  If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/).
relation_mapping.json ADDED
@@ -0,0 +1 @@
 
 
1
+ {"accuracy": 0.9188888888888889, "prediction": [{"source": ["solar system", "sun", "planet", "mass", "attracts", "revolves", "gravity"], "true": ["atom", "nucleus", "electron", "charge", "attracts", "revolves", "electromagnetism"], "pred": ["atom", "nucleus", "electron", "charge", "attracts", "revolves", "electromagnetism"], "alignment_match": true, "accuracy": 1, "similarity": 0.9999999010450038, "similarity_true": 0.9999999010450038}, {"source": ["water", "flows", "pressure", "water tower", "bucket", "filling", "emptying", "hydrodynamics"], "true": ["heat", "transfers", "temperature", "burner", "kettle", "heating", "cooling", "thermodynamics"], "pred": ["heat", "transfers", "temperature", "burner", "kettle", "heating", "cooling", "thermodynamics"], "alignment_match": true, "accuracy": 1, "similarity": 0.9631158781300314, "similarity_true": 0.9631158781300314}, {"source": ["waves", "shore", "reflects", "water", "breakwater", "rough", "calm", "crashing"], "true": ["sounds", "wall", "echoes", "air", "insulation", "loud", "quiet", "vibrating"], "pred": ["sounds", "wall", "echoes", "air", "insulation", "loud", "quiet", "vibrating"], "alignment_match": true, "accuracy": 1, "similarity": 0.9317301355659389, "similarity_true": 0.9317301355659389}, {"source": ["combustion", "fire", "fuel", "burning", "hot", "intense", "oxygen", "carbon dioxide"], "true": ["respiration", "animal", "food", "breathing", "living", "vigorous", "oxygen", "carbon dioxide"], "pred": ["respiration", "animal", "food", "breathing", "living", "vigorous", "oxygen", "carbon dioxide"], "alignment_match": true, "accuracy": 1, "similarity": 0.9999999010421986, "similarity_true": 0.9999999010421986}, {"source": ["sound", "low", "high", "echoes", "loud", "quiet", "horn"], "true": ["light", "red", "violet", "reflects", "bright", "dim", "lens"], "pred": ["light", "violet", "red", "reflects", "bright", "dim", "lens"], "alignment_match": false, "accuracy": 0.7142857142857143, "similarity": 0.9454967480700026, "similarity_true": 0.9454967480700026}, {"source": ["projectile", "trajectory", "earth", "parabolic", "air", "gravity", "attracts"], "true": ["planet", "orbit", "sun", "elliptical", "space", "gravity", "attracts"], "pred": ["planet", "orbit", "sun", "elliptical", "space", "gravity", "attracts"], "alignment_match": true, "accuracy": 1, "similarity": 0.9999999007196476, "similarity_true": 0.9999999007196476}, {"source": ["breeds", "selection", "conformance", "artificial", "popularity", "breeding", "domesticated"], "true": ["species", "competition", "adaptation", "natural", "fitness", "mating", "wild"], "pred": ["species", "adaptation", "competition", "natural", "fitness", "mating", "wild"], "alignment_match": false, "accuracy": 0.7142857142857143, "similarity": 0.9161542966329106, "similarity_true": 0.9161542966329106}, {"source": ["ball", "billiards", "speed", "table", "bouncing", "moving", "slow", "fast"], "true": ["molecules", "gas", "temperature", "container", "pressing", "moving", "cold", "hot"], "pred": ["molecules", "gas", "temperature", "container", "pressing", "moving", "cold", "hot"], "alignment_match": true, "accuracy": 1, "similarity": 0.9550658473039344, "similarity_true": 0.9550658473039344}, {"source": ["computer", "processing", "erasing", "write", "read", "memory", "outputs", "inputs", "bug"], "true": ["mind", "thinking", "forgetting", "memorize", "remember", "memory", "muscles", "senses", "mistake"], "pred": ["mind", "thinking", "forgetting", "remember", "memorize", "memory", "muscles", "senses", "mistake"], "alignment_match": false, "accuracy": 0.7777777777777778, "similarity": 0.9298598263551646, "similarity_true": 0.9187400565800253}, {"source": ["slot machines", "reels", "spinning", "winning", "losing"], "true": ["bacteria", "genes", "mutating", "reproducing", "dying"], "pred": ["bacteria", "genes", "mutating", "reproducing", "dying"], "alignment_match": true, "accuracy": 1, "similarity": 0.8666898615022124, "similarity_true": 0.8666898615022124}, {"source": ["war", "soldier", "destroy", "fighting", "defeat", "attacks", "weapon"], "true": ["argument", "debater", "refute", "arguing", "acceptance", "criticizes", "logic"], "pred": ["argument", "debater", "refute", "arguing", "acceptance", "criticizes", "logic"], "alignment_match": true, "accuracy": 1, "similarity": 0.9408619837951895, "similarity_true": 0.9408619837951895}, {"source": ["buyer", "merchandise", "buying", "selling", "returning", "valuable", "worthless"], "true": ["believer", "belief", "accepting", "advocating", "rejecting", "true", "false"], "pred": ["believer", "belief", "advocating", "rejecting", "accepting", "true", "false"], "alignment_match": false, "accuracy": 0.5714285714285714, "similarity": 0.9448229362988546, "similarity_true": 0.9448229362988546}, {"source": ["foundations", "buildings", "supporting", "solid", "weak", "crack"], "true": ["reasons", "theories", "confirming", "rational", "dubious", "flaw"], "pred": ["reasons", "theories", "confirming", "rational", "dubious", "flaw"], "alignment_match": true, "accuracy": 1, "similarity": 0.9210297831641395, "similarity_true": 0.9210297831641395}, {"source": ["obstructions", "destination", "route", "traveller", "traveling", "companion", "arriving"], "true": ["difficulties", "goal", "plan", "person", "problem solving", "partner", "succeeding"], "pred": ["difficulties", "goal", "plan", "person", "problem solving", "partner", "succeeding"], "alignment_match": true, "accuracy": 1, "similarity": 0.9302712260513268, "similarity_true": 0.9302712260513268}, {"source": ["money", "allocate", "budget", "effective", "cheap", "expansive"], "true": ["time", "invest", "schedule", "efficient", "quick", "slow"], "pred": ["time", "invest", "schedule", "efficient", "quick", "slow"], "alignment_match": true, "accuracy": 1, "similarity": 0.95134663207036, "similarity_true": 0.95134663207036}, {"source": ["seeds", "planted", "fruitful", "fruit", "grow", "wither", "blossom"], "true": ["ideas", "inspired", "productive", "product", "develop", "fail", "succeed"], "pred": ["ideas", "inspired", "productive", "product", "develop", "fail", "succeed"], "alignment_match": true, "accuracy": 1, "similarity": 0.9547191112868441, "similarity_true": 0.9547191112868441}, {"source": ["machine", "working", "turned on", "turned off", "broken", "power", "repair"], "true": ["mind", "thinking", "awake", "asleep", "confused", "intelligence", "therapy"], "pred": ["mind", "thinking", "awake", "asleep", "confused", "intelligence", "therapy"], "alignment_match": true, "accuracy": 1, "similarity": 0.95276068443591, "similarity_true": 0.95276068443591}, {"source": ["object", "hold", "weight", "heavy", "light"], "true": ["idea", "understand", "analyze", "important", "trivial"], "pred": ["idea", "analyze", "understand", "important", "trivial"], "alignment_match": false, "accuracy": 0.6, "similarity": 0.935119096850035, "similarity_true": 0.8982077847260634}, {"source": ["follow", "leader", "path", "follower", "lost", "wanders", "twisted", "straight"], "true": ["understand", "speaker", "argument", "listener", "misunderstood", "digresses", "complicated", "simple"], "pred": ["understand", "speaker", "argument", "listener", "misunderstood", "digresses", "complicated", "simple"], "alignment_match": true, "accuracy": 1, "similarity": 0.9298130601690413, "similarity_true": 0.9298130601690413}, {"source": ["seeing", "light", "illuminating", "darkness", "view", "hidden"], "true": ["understanding", "knowledge", "explaining", "confusion", "interpretation", "secret"], "pred": ["understanding", "knowledge", "explaining", "confusion", "interpretation", "secret"], "alignment_match": true, "accuracy": 1, "similarity": 0.8782453579450612, "similarity_true": 0.8782453579450612}]}