asahi417 commited on
Commit
102952f
1 Parent(s): c3034c8

model update

Browse files
README.md ADDED
@@ -0,0 +1,268 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ datasets:
3
+ - relbert/semeval2012_relational_similarity
4
+ model-index:
5
+ - name: relbert/relbert-roberta-base-nce-a-semeval2012
6
+ results:
7
+ - task:
8
+ name: Relation Mapping
9
+ type: sorting-task
10
+ dataset:
11
+ name: Relation Mapping
12
+ args: relbert/relation_mapping
13
+ type: relation-mapping
14
+ metrics:
15
+ - name: Accuracy
16
+ type: accuracy
17
+ value: 0.817202380952381
18
+ - task:
19
+ name: Analogy Questions (SAT full)
20
+ type: multiple-choice-qa
21
+ dataset:
22
+ name: SAT full
23
+ args: relbert/analogy_questions
24
+ type: analogy-questions
25
+ metrics:
26
+ - name: Accuracy
27
+ type: accuracy
28
+ value: 0.5989304812834224
29
+ - task:
30
+ name: Analogy Questions (SAT)
31
+ type: multiple-choice-qa
32
+ dataset:
33
+ name: SAT
34
+ args: relbert/analogy_questions
35
+ type: analogy-questions
36
+ metrics:
37
+ - name: Accuracy
38
+ type: accuracy
39
+ value: 0.6083086053412463
40
+ - task:
41
+ name: Analogy Questions (BATS)
42
+ type: multiple-choice-qa
43
+ dataset:
44
+ name: BATS
45
+ args: relbert/analogy_questions
46
+ type: analogy-questions
47
+ metrics:
48
+ - name: Accuracy
49
+ type: accuracy
50
+ value: 0.7031684269038355
51
+ - task:
52
+ name: Analogy Questions (Google)
53
+ type: multiple-choice-qa
54
+ dataset:
55
+ name: Google
56
+ args: relbert/analogy_questions
57
+ type: analogy-questions
58
+ metrics:
59
+ - name: Accuracy
60
+ type: accuracy
61
+ value: 0.892
62
+ - task:
63
+ name: Analogy Questions (U2)
64
+ type: multiple-choice-qa
65
+ dataset:
66
+ name: U2
67
+ args: relbert/analogy_questions
68
+ type: analogy-questions
69
+ metrics:
70
+ - name: Accuracy
71
+ type: accuracy
72
+ value: 0.5964912280701754
73
+ - task:
74
+ name: Analogy Questions (U4)
75
+ type: multiple-choice-qa
76
+ dataset:
77
+ name: U4
78
+ args: relbert/analogy_questions
79
+ type: analogy-questions
80
+ metrics:
81
+ - name: Accuracy
82
+ type: accuracy
83
+ value: 0.5740740740740741
84
+ - task:
85
+ name: Analogy Questions (ConceptNet Analogy)
86
+ type: multiple-choice-qa
87
+ dataset:
88
+ name: ConceptNet Analogy
89
+ args: relbert/analogy_questions
90
+ type: analogy-questions
91
+ metrics:
92
+ - name: Accuracy
93
+ type: accuracy
94
+ value: 0.3976510067114094
95
+ - task:
96
+ name: Analogy Questions (TREX Analogy)
97
+ type: multiple-choice-qa
98
+ dataset:
99
+ name: TREX Analogy
100
+ args: relbert/analogy_questions
101
+ type: analogy-questions
102
+ metrics:
103
+ - name: Accuracy
104
+ type: accuracy
105
+ value: 0.6666666666666666
106
+ - task:
107
+ name: Analogy Questions (NELL-ONE Analogy)
108
+ type: multiple-choice-qa
109
+ dataset:
110
+ name: NELL-ONE Analogy
111
+ args: relbert/analogy_questions
112
+ type: analogy-questions
113
+ metrics:
114
+ - name: Accuracy
115
+ type: accuracy
116
+ value: 0.62
117
+ - task:
118
+ name: Lexical Relation Classification (BLESS)
119
+ type: classification
120
+ dataset:
121
+ name: BLESS
122
+ args: relbert/lexical_relation_classification
123
+ type: relation-classification
124
+ metrics:
125
+ - name: F1
126
+ type: f1
127
+ value: 0.8998041283712521
128
+ - name: F1 (macro)
129
+ type: f1_macro
130
+ value: 0.896201243435411
131
+ - task:
132
+ name: Lexical Relation Classification (CogALexV)
133
+ type: classification
134
+ dataset:
135
+ name: CogALexV
136
+ args: relbert/lexical_relation_classification
137
+ type: relation-classification
138
+ metrics:
139
+ - name: F1
140
+ type: f1
141
+ value: 0.8370892018779342
142
+ - name: F1 (macro)
143
+ type: f1_macro
144
+ value: 0.6583174043371445
145
+ - task:
146
+ name: Lexical Relation Classification (EVALution)
147
+ type: classification
148
+ dataset:
149
+ name: BLESS
150
+ args: relbert/lexical_relation_classification
151
+ type: relation-classification
152
+ metrics:
153
+ - name: F1
154
+ type: f1
155
+ value: 0.6419284940411701
156
+ - name: F1 (macro)
157
+ type: f1_macro
158
+ value: 0.6294309369547718
159
+ - task:
160
+ name: Lexical Relation Classification (K&H+N)
161
+ type: classification
162
+ dataset:
163
+ name: K&H+N
164
+ args: relbert/lexical_relation_classification
165
+ type: relation-classification
166
+ metrics:
167
+ - name: F1
168
+ type: f1
169
+ value: 0.9396953467343674
170
+ - name: F1 (macro)
171
+ type: f1_macro
172
+ value: 0.8459283973092365
173
+ - task:
174
+ name: Lexical Relation Classification (ROOT09)
175
+ type: classification
176
+ dataset:
177
+ name: ROOT09
178
+ args: relbert/lexical_relation_classification
179
+ type: relation-classification
180
+ metrics:
181
+ - name: F1
182
+ type: f1
183
+ value: 0.8815418364149169
184
+ - name: F1 (macro)
185
+ type: f1_macro
186
+ value: 0.879329189992711
187
+
188
+ ---
189
+ # relbert/relbert-roberta-base-nce-a-semeval2012
190
+
191
+ RelBERT based on [roberta-base](https://huggingface.co/roberta-base) 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).
192
+ This model achieves the following results on the relation understanding tasks:
193
+ - Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-nce-a-semeval2012/raw/main/analogy.forward.json)):
194
+ - Accuracy on SAT (full): 0.5989304812834224
195
+ - Accuracy on SAT: 0.6083086053412463
196
+ - Accuracy on BATS: 0.7031684269038355
197
+ - Accuracy on U2: 0.5964912280701754
198
+ - Accuracy on U4: 0.5740740740740741
199
+ - Accuracy on Google: 0.892
200
+ - Accuracy on ConceptNet Analogy: 0.3976510067114094
201
+ - Accuracy on T-Rex Analogy: 0.6666666666666666
202
+ - Accuracy on NELL-ONE Analogy: 0.62
203
+ - Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-nce-a-semeval2012/raw/main/classification.json)):
204
+ - Micro F1 score on BLESS: 0.8998041283712521
205
+ - Micro F1 score on CogALexV: 0.8370892018779342
206
+ - Micro F1 score on EVALution: 0.6419284940411701
207
+ - Micro F1 score on K&H+N: 0.9396953467343674
208
+ - Micro F1 score on ROOT09: 0.8815418364149169
209
+ - Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-nce-a-semeval2012/raw/main/relation_mapping.json)):
210
+ - Accuracy on Relation Mapping: 0.817202380952381
211
+
212
+
213
+ ### Usage
214
+ This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip
215
+ ```shell
216
+ pip install relbert
217
+ ```
218
+ and activate model as below.
219
+ ```python
220
+ from relbert import RelBERT
221
+ model = RelBERT("relbert/relbert-roberta-base-nce-a-semeval2012")
222
+ vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (n_dim, )
223
+ ```
224
+
225
+ ### Training hyperparameters
226
+
227
+ - model: roberta-base
228
+ - max_length: 64
229
+ - epoch: 10
230
+ - batch: 32
231
+ - random_seed: 0
232
+ - lr: 5e-06
233
+ - lr_warmup: 10
234
+ - aggregation_mode: average_no_mask
235
+ - data: relbert/semeval2012_relational_similarity
236
+ - data_name: None
237
+ - exclude_relation: None
238
+ - split: train
239
+ - split_valid: validation
240
+ - loss_function: nce
241
+ - classification_loss: False
242
+ - loss_function_config: {'temperature': 0.05, 'num_negative': 400, 'num_positive': 10}
243
+ - augment_negative_by_positive: True
244
+
245
+ See the full configuration at [config file](https://huggingface.co/relbert/relbert-roberta-base-nce-a-semeval2012/raw/main/finetuning_config.json).
246
+
247
+ ### Reference
248
+ If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.emnlp-main.712/).
249
+
250
+ ```
251
+
252
+ @inproceedings{ushio-etal-2021-distilling,
253
+ title = "Distilling Relation Embeddings from Pretrained Language Models",
254
+ author = "Ushio, Asahi and
255
+ Camacho-Collados, Jose and
256
+ Schockaert, Steven",
257
+ booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
258
+ month = nov,
259
+ year = "2021",
260
+ address = "Online and Punta Cana, Dominican Republic",
261
+ publisher = "Association for Computational Linguistics",
262
+ url = "https://aclanthology.org/2021.emnlp-main.712",
263
+ doi = "10.18653/v1/2021.emnlp-main.712",
264
+ pages = "9044--9062",
265
+ abstract = "Pre-trained language models have been found to capture a surprisingly rich amount of lexical knowledge, ranging from commonsense properties of everyday concepts to detailed factual knowledge about named entities. Among others, this makes it possible to distill high-quality word vectors from pre-trained language models. However, it is currently unclear to what extent it is possible to distill relation embeddings, i.e. vectors that characterize the relationship between two words. Such relation embeddings are appealing because they can, in principle, encode relational knowledge in a more fine-grained way than is possible with knowledge graphs. To obtain relation embeddings from a pre-trained language model, we encode word pairs using a (manually or automatically generated) prompt, and we fine-tune the language model such that relationally similar word pairs yield similar output vectors. We find that the resulting relation embeddings are highly competitive on analogy (unsupervised) and relation classification (supervised) benchmarks, even without any task-specific fine-tuning. Source code to reproduce our experimental results and the model checkpoints are available in the following repository: https://github.com/asahi417/relbert",
266
+ }
267
+
268
+ ```
analogy.bidirection.json ADDED
@@ -0,0 +1 @@
 
 
1
+ {"scan/test": 0.2840346534653465, "sat_full/test": 0.5855614973262032, "sat/test": 0.5905044510385756, "u2/test": 0.5964912280701754, "u4/test": 0.6157407407407407, "google/test": 0.906, "bats/test": 0.7120622568093385, "t_rex_relational_similarity/test": 0.6666666666666666, "conceptnet_relational_similarity/test": 0.40184563758389263, "nell_relational_similarity/test": 0.73, "scan/validation": 0.29213483146067415, "sat/validation": 0.5405405405405406, "u2/validation": 0.5, "u4/validation": 0.7291666666666666, "google/validation": 0.94, "bats/validation": 0.7487437185929648, "semeval2012_relational_similarity/validation": 0.7341772151898734, "t_rex_relational_similarity/validation": 0.2661290322580645, "conceptnet_relational_similarity/validation": 0.31564748201438847, "nell_relational_similarity/validation": 0.58}
analogy.forward.json ADDED
@@ -0,0 +1 @@
 
 
1
+ {"semeval2012_relational_similarity/validation": 0.7848101265822784, "scan/test": 0.2592821782178218, "sat_full/test": 0.5989304812834224, "sat/test": 0.6083086053412463, "u2/test": 0.5964912280701754, "u4/test": 0.5740740740740741, "google/test": 0.892, "bats/test": 0.7031684269038355, "t_rex_relational_similarity/test": 0.6666666666666666, "conceptnet_relational_similarity/test": 0.3976510067114094, "nell_relational_similarity/test": 0.62, "scan/validation": 0.25842696629213485, "sat/validation": 0.5135135135135135, "u2/validation": 0.4583333333333333, "u4/validation": 0.6458333333333334, "google/validation": 0.96, "bats/validation": 0.7738693467336684, "t_rex_relational_similarity/validation": 0.2661290322580645, "conceptnet_relational_similarity/validation": 0.32823741007194246, "nell_relational_similarity/validation": 0.575}
analogy.reverse.json ADDED
@@ -0,0 +1 @@
 
 
1
+ {"scan/test": 0.25804455445544555, "sat_full/test": 0.5588235294117647, "sat/test": 0.5548961424332344, "u2/test": 0.5570175438596491, "u4/test": 0.5902777777777778, "google/test": 0.898, "bats/test": 0.6620344635908838, "t_rex_relational_similarity/test": 0.5846994535519126, "conceptnet_relational_similarity/test": 0.348993288590604, "nell_relational_similarity/test": 0.765, "scan/validation": 0.2808988764044944, "sat/validation": 0.5945945945945946, "u2/validation": 0.5, "u4/validation": 0.7083333333333334, "google/validation": 0.94, "bats/validation": 0.7286432160804021, "semeval2012_relational_similarity/validation": 0.6835443037974683, "t_rex_relational_similarity/validation": 0.2439516129032258, "conceptnet_relational_similarity/validation": 0.2643884892086331, "nell_relational_similarity/validation": 0.54}
classification.json ADDED
@@ -0,0 +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": 0.8998041283712521, "test/f1_macro": 0.896201243435411, "test/f1_micro": 0.8998041283712521, "test/p_macro": 0.8876829436591316, "test/p_micro": 0.8998041283712521, "test/r_macro": 0.9054007585142311, "test/r_micro": 0.8998041283712521}, "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": 0.8370892018779342, "test/f1_macro": 0.6583174043371445, "test/f1_micro": 0.8370892018779342, "test/p_macro": 0.6822907887970884, "test/p_micro": 0.8370892018779342, "test/r_macro": 0.6384370436284232, "test/r_micro": 0.8370892018779342}, "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": 0.6419284940411701, "test/f1_macro": 0.6294309369547718, "test/f1_micro": 0.6419284940411701, "test/p_macro": 0.6360186480100325, "test/p_micro": 0.6419284940411701, "test/r_macro": 0.6300178037199379, "test/r_micro": 0.6419284940411701}, "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": 0.9396953467343674, "test/f1_macro": 0.8459283973092365, "test/f1_micro": 0.9396953467343674, "test/p_macro": 0.8614600859106621, "test/p_micro": 0.9396953467343674, "test/r_macro": 0.8351465630922283, "test/r_micro": 0.9396953467343674}, "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": 0.8815418364149169, "test/f1_macro": 0.879329189992711, "test/f1_micro": 0.8815418364149169, "test/p_macro": 0.8763389203201842, "test/p_micro": 0.8815418364149169, "test/r_macro": 0.882560877928503, "test/r_micro": 0.8815418364149169}}
config.json ADDED
@@ -0,0 +1,31 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "_name_or_path": "roberta-base",
3
+ "architectures": [
4
+ "RobertaModel"
5
+ ],
6
+ "attention_probs_dropout_prob": 0.1,
7
+ "bos_token_id": 0,
8
+ "classifier_dropout": null,
9
+ "eos_token_id": 2,
10
+ "hidden_act": "gelu",
11
+ "hidden_dropout_prob": 0.1,
12
+ "hidden_size": 768,
13
+ "initializer_range": 0.02,
14
+ "intermediate_size": 3072,
15
+ "layer_norm_eps": 1e-05,
16
+ "max_position_embeddings": 514,
17
+ "model_type": "roberta",
18
+ "num_attention_heads": 12,
19
+ "num_hidden_layers": 12,
20
+ "pad_token_id": 1,
21
+ "position_embedding_type": "absolute",
22
+ "relbert_config": {
23
+ "aggregation_mode": "average_no_mask",
24
+ "template": "Today, I finally discovered the relation between <subj> and <obj> : <subj> is the <mask> of <obj>"
25
+ },
26
+ "torch_dtype": "float32",
27
+ "transformers_version": "4.16.2",
28
+ "type_vocab_size": 1,
29
+ "use_cache": true,
30
+ "vocab_size": 50265
31
+ }
finetuning_config.json ADDED
@@ -0,0 +1,24 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "template": "Today, I finally discovered the relation between <subj> and <obj> : <subj> is the <mask> of <obj>",
3
+ "model": "roberta-base",
4
+ "max_length": 64,
5
+ "epoch": 10,
6
+ "batch": 32,
7
+ "random_seed": 0,
8
+ "lr": 5e-06,
9
+ "lr_warmup": 10,
10
+ "aggregation_mode": "average_no_mask",
11
+ "data": "relbert/semeval2012_relational_similarity",
12
+ "data_name": null,
13
+ "exclude_relation": null,
14
+ "split": "train",
15
+ "split_valid": "validation",
16
+ "loss_function": "nce",
17
+ "classification_loss": false,
18
+ "loss_function_config": {
19
+ "temperature": 0.05,
20
+ "num_negative": 400,
21
+ "num_positive": 10
22
+ },
23
+ "augment_negative_by_positive": true
24
+ }
merges.txt ADDED
The diff for this file is too large to render. See raw diff
 
pytorch_model.bin ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:c94a92b29ec3435777fd39e26c2ad122e32bb0d45e73dc050bca78cbc525fd26
3
+ size 498663921
relation_mapping.json ADDED
The diff for this file is too large to render. See raw diff
 
special_tokens_map.json ADDED
@@ -0,0 +1 @@
 
 
1
+ {"bos_token": "<s>", "eos_token": "</s>", "unk_token": "<unk>", "sep_token": "</s>", "pad_token": "<pad>", "cls_token": "<s>", "mask_token": {"content": "<mask>", "single_word": false, "lstrip": true, "rstrip": false, "normalized": false}}
tokenizer.json ADDED
The diff for this file is too large to render. See raw diff
 
tokenizer_config.json ADDED
@@ -0,0 +1 @@
 
 
1
+ {"unk_token": "<unk>", "bos_token": "<s>", "eos_token": "</s>", "add_prefix_space": false, "errors": "replace", "sep_token": "</s>", "cls_token": "<s>", "pad_token": "<pad>", "mask_token": "<mask>", "trim_offsets": true, "model_max_length": 512, "special_tokens_map_file": null, "name_or_path": "roberta-base", "tokenizer_class": "RobertaTokenizer"}
vocab.json ADDED
The diff for this file is too large to render. See raw diff