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  ---
2
  base_model: colorfulscoop/sbert-base-ja
3
- library_name: sentence-transformers
4
- metrics:
5
- - cosine_accuracy
6
- - cosine_accuracy_threshold
7
- - cosine_f1
8
- - cosine_f1_threshold
9
- - cosine_precision
10
- - cosine_recall
11
- - cosine_ap
12
- - dot_accuracy
13
- - dot_accuracy_threshold
14
- - dot_f1
15
- - dot_f1_threshold
16
- - dot_precision
17
- - dot_recall
18
- - dot_ap
19
- - manhattan_accuracy
20
- - manhattan_accuracy_threshold
21
- - manhattan_f1
22
- - manhattan_f1_threshold
23
- - manhattan_precision
24
- - manhattan_recall
25
- - manhattan_ap
26
- - euclidean_accuracy
27
- - euclidean_accuracy_threshold
28
- - euclidean_f1
29
- - euclidean_f1_threshold
30
- - euclidean_precision
31
- - euclidean_recall
32
- - euclidean_ap
33
- - max_accuracy
34
- - max_accuracy_threshold
35
- - max_f1
36
- - max_f1_threshold
37
- - max_precision
38
- - max_recall
39
- - max_ap
40
- pipeline_tag: sentence-similarity
41
- tags:
42
- - sentence-transformers
43
- - sentence-similarity
44
- - feature-extraction
45
- - generated_from_trainer
46
- - dataset_size:7464
47
- - loss:CosineSimilarityLoss
48
- widget:
49
- - source_sentence: なにか思い出せることは?
50
- sentences:
51
- - なんて言った?
52
- - なにが欲しい?
53
- - いつ失くしたの?
54
- - source_sentence: 木にスカーフがひっかかってる?
55
- sentences:
56
- - 物の姿を変えられる魔法
57
- - 木にスカーフがひっかかってる?
58
- - あの木の上のやつ、スカーフ?
59
- - source_sentence: どんなスカーフ?
60
- sentences:
61
- - お気に入りの食べ物は?
62
- - くさい
63
- - もしもし
64
- - source_sentence: どうやったら猫が話せる?
65
- sentences:
66
- - どっちがいいと思う?
67
- - ワゴンにスカーフはあった?
68
- - 井戸へ行ったことある?
69
- - source_sentence: なにか思い出せることは?
70
- sentences:
71
- - 村人はどんな呪文が使えるの?
72
- - 町って?
73
- - キミならどっちがいい?
74
- model-index:
75
- - name: SentenceTransformer based on colorfulscoop/sbert-base-ja
76
- results:
77
- - task:
78
- type: binary-classification
79
- name: Binary Classification
80
- dataset:
81
- name: custom arc semantics data jp
82
- type: custom-arc-semantics-data-jp
83
- metrics:
84
- - type: cosine_accuracy
85
- value: 0.9705409748259239
86
- name: Cosine Accuracy
87
- - type: cosine_accuracy_threshold
88
- value: 0.5118279457092285
89
- name: Cosine Accuracy Threshold
90
- - type: cosine_f1
91
- value: 0.9102773246329527
92
- name: Cosine F1
93
- - type: cosine_f1_threshold
94
- value: 0.45031607151031494
95
- name: Cosine F1 Threshold
96
- - type: cosine_precision
97
- value: 0.8857142857142857
98
- name: Cosine Precision
99
- - type: cosine_recall
100
- value: 0.9362416107382551
101
- name: Cosine Recall
102
- - type: cosine_ap
103
- value: 0.9236294738598163
104
- name: Cosine Ap
105
- - type: dot_accuracy
106
- value: 0.9694697375468666
107
- name: Dot Accuracy
108
- - type: dot_accuracy_threshold
109
- value: 251.2455596923828
110
- name: Dot Accuracy Threshold
111
- - type: dot_f1
112
- value: 0.9060955518945634
113
- name: Dot F1
114
- - type: dot_f1_threshold
115
- value: 246.36648559570312
116
- name: Dot F1 Threshold
117
- - type: dot_precision
118
- value: 0.889967637540453
119
- name: Dot Precision
120
- - type: dot_recall
121
- value: 0.9228187919463087
122
- name: Dot Recall
123
- - type: dot_ap
124
- value: 0.9196731890884118
125
- name: Dot Ap
126
- - type: manhattan_accuracy
127
- value: 0.9716122121049813
128
- name: Manhattan Accuracy
129
- - type: manhattan_accuracy_threshold
130
- value: 514.571533203125
131
- name: Manhattan Accuracy Threshold
132
- - type: manhattan_f1
133
- value: 0.9132569558101473
134
- name: Manhattan F1
135
- - type: manhattan_f1_threshold
136
- value: 514.571533203125
137
- name: Manhattan F1 Threshold
138
- - type: manhattan_precision
139
- value: 0.8913738019169329
140
- name: Manhattan Precision
141
- - type: manhattan_recall
142
- value: 0.9362416107382551
143
- name: Manhattan Recall
144
- - type: manhattan_ap
145
- value: 0.9255015709844487
146
- name: Manhattan Ap
147
- - type: euclidean_accuracy
148
- value: 0.9721478307445099
149
- name: Euclidean Accuracy
150
- - type: euclidean_accuracy_threshold
151
- value: 23.195274353027344
152
- name: Euclidean Accuracy Threshold
153
- - type: euclidean_f1
154
- value: 0.9147540983606558
155
- name: Euclidean F1
156
- - type: euclidean_f1_threshold
157
- value: 23.195274353027344
158
- name: Euclidean F1 Threshold
159
- - type: euclidean_precision
160
- value: 0.8942307692307693
161
- name: Euclidean Precision
162
- - type: euclidean_recall
163
- value: 0.9362416107382551
164
- name: Euclidean Recall
165
- - type: euclidean_ap
166
- value: 0.9259440018381992
167
- name: Euclidean Ap
168
- - type: max_accuracy
169
- value: 0.9721478307445099
170
- name: Max Accuracy
171
- - type: max_accuracy_threshold
172
- value: 514.571533203125
173
- name: Max Accuracy Threshold
174
- - type: max_f1
175
- value: 0.9147540983606558
176
- name: Max F1
177
- - type: max_f1_threshold
178
- value: 514.571533203125
179
- name: Max F1 Threshold
180
- - type: max_precision
181
- value: 0.8942307692307693
182
- name: Max Precision
183
- - type: max_recall
184
- value: 0.9362416107382551
185
- name: Max Recall
186
- - type: max_ap
187
- value: 0.9259440018381992
188
- name: Max Ap
189
  ---
190
 
191
- # SentenceTransformer based on colorfulscoop/sbert-base-ja
 
 
 
192
 
193
- This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [colorfulscoop/sbert-base-ja](https://huggingface.co/colorfulscoop/sbert-base-ja). It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
194
 
195
  ## Model Details
196
 
197
  ### Model Description
198
- - **Model Type:** Sentence Transformer
199
- - **Base model:** [colorfulscoop/sbert-base-ja](https://huggingface.co/colorfulscoop/sbert-base-ja) <!-- at revision ecb8a98cd5176719ff7ab0d770a27420118732cf -->
200
- - **Maximum Sequence Length:** 512 tokens
201
- - **Output Dimensionality:** 768 tokens
202
- - **Similarity Function:** Cosine Similarity
203
- <!-- - **Training Dataset:** Unknown -->
204
- <!-- - **Language:** Unknown -->
205
- <!-- - **License:** Unknown -->
206
 
207
- ### Model Sources
208
 
209
- - **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
210
- - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
211
- - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
212
 
213
- ### Full Model Architecture
 
 
 
 
 
 
214
 
215
- ```
216
- SentenceTransformer(
217
- (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
218
- (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
219
- )
220
- ```
221
 
222
- ## Usage
223
 
224
- ### Direct Usage (Sentence Transformers)
 
 
225
 
226
- First install the Sentence Transformers library:
227
 
228
- ```bash
229
- pip install -U sentence-transformers
230
- ```
231
 
232
- Then you can load this model and run inference.
233
- ```python
234
- from sentence_transformers import SentenceTransformer
235
 
236
- # Download from the 🤗 Hub
237
- model = SentenceTransformer("sentence_transformers_model_id")
238
- # Run inference
239
- sentences = [
240
- 'なにか思い出せることは?',
241
- '村人はどんな呪文が使えるの?',
242
- 'キミならどっちがいい?',
243
- ]
244
- embeddings = model.encode(sentences)
245
- print(embeddings.shape)
246
- # [3, 768]
247
 
248
- # Get the similarity scores for the embeddings
249
- similarities = model.similarity(embeddings, embeddings)
250
- print(similarities.shape)
251
- # [3, 3]
252
- ```
253
 
254
- <!--
255
- ### Direct Usage (Transformers)
256
 
257
- <details><summary>Click to see the direct usage in Transformers</summary>
258
 
259
- </details>
260
- -->
261
 
262
- <!--
263
- ### Downstream Usage (Sentence Transformers)
264
 
265
- You can finetune this model on your own dataset.
266
 
267
- <details><summary>Click to expand</summary>
268
 
269
- </details>
270
- -->
271
 
272
- <!--
273
- ### Out-of-Scope Use
274
 
275
- *List how the model may foreseeably be misused and address what users ought not to do with the model.*
276
- -->
277
 
278
- ## Evaluation
279
-
280
- ### Metrics
281
-
282
- #### Binary Classification
283
- * Dataset: `custom-arc-semantics-data-jp`
284
- * Evaluated with [<code>BinaryClassificationEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator)
285
-
286
- | Metric | Value |
287
- |:-----------------------------|:-----------|
288
- | cosine_accuracy | 0.9705 |
289
- | cosine_accuracy_threshold | 0.5118 |
290
- | cosine_f1 | 0.9103 |
291
- | cosine_f1_threshold | 0.4503 |
292
- | cosine_precision | 0.8857 |
293
- | cosine_recall | 0.9362 |
294
- | cosine_ap | 0.9236 |
295
- | dot_accuracy | 0.9695 |
296
- | dot_accuracy_threshold | 251.2456 |
297
- | dot_f1 | 0.9061 |
298
- | dot_f1_threshold | 246.3665 |
299
- | dot_precision | 0.89 |
300
- | dot_recall | 0.9228 |
301
- | dot_ap | 0.9197 |
302
- | manhattan_accuracy | 0.9716 |
303
- | manhattan_accuracy_threshold | 514.5715 |
304
- | manhattan_f1 | 0.9133 |
305
- | manhattan_f1_threshold | 514.5715 |
306
- | manhattan_precision | 0.8914 |
307
- | manhattan_recall | 0.9362 |
308
- | manhattan_ap | 0.9255 |
309
- | euclidean_accuracy | 0.9721 |
310
- | euclidean_accuracy_threshold | 23.1953 |
311
- | euclidean_f1 | 0.9148 |
312
- | euclidean_f1_threshold | 23.1953 |
313
- | euclidean_precision | 0.8942 |
314
- | euclidean_recall | 0.9362 |
315
- | euclidean_ap | 0.9259 |
316
- | max_accuracy | 0.9721 |
317
- | max_accuracy_threshold | 514.5715 |
318
- | max_f1 | 0.9148 |
319
- | max_f1_threshold | 514.5715 |
320
- | max_precision | 0.8942 |
321
- | max_recall | 0.9362 |
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- | **max_ap** | **0.9259** |
323
-
324
- <!--
325
- ## Bias, Risks and Limitations
326
-
327
- *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
328
- -->
329
-
330
- <!--
331
  ### Recommendations
332
 
333
- *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
334
- -->
 
 
 
 
 
 
 
335
 
336
  ## Training Details
337
 
338
- ### Training Dataset
339
-
340
- #### Unnamed Dataset
341
-
342
-
343
- * Size: 7,464 training samples
344
- * Columns: <code>text1</code>, <code>text2</code>, and <code>label</code>
345
- * Approximate statistics based on the first 1000 samples:
346
- | | text1 | text2 | label |
347
- |:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:------------------------------------------------|
348
- | type | string | string | int |
349
- | details | <ul><li>min: 4 tokens</li><li>mean: 8.32 tokens</li><li>max: 15 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 7.93 tokens</li><li>max: 15 tokens</li></ul> | <ul><li>0: ~86.30%</li><li>1: ~13.70%</li></ul> |
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- * Samples:
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- | text1 | text2 | label |
352
- |:------------------------------|:-------------------------|:---------------|
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- | <code>昨日夕飯にチキンヌードル食べた?</code> | <code>何か企んでる?</code> | <code>0</code> |
354
- | <code>どっちも欲しくない</code> | <code>お気に入りの食べ物は?</code> | <code>0</code> |
355
- | <code>見た目を変える魔法</code> | <code>物の姿を変えられる魔法</code> | <code>1</code> |
356
- * Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
357
- ```json
358
- {
359
- "loss_fct": "torch.nn.modules.loss.MSELoss"
360
- }
361
- ```
362
-
363
- ### Evaluation Dataset
364
-
365
- #### Unnamed Dataset
366
-
367
-
368
- * Size: 1,867 evaluation samples
369
- * Columns: <code>text1</code>, <code>text2</code>, and <code>label</code>
370
- * Approximate statistics based on the first 1000 samples:
371
- | | text1 | text2 | label |
372
- |:--------|:---------------------------------------------------------------------------------|:--------------------------------------------------------------------------------|:------------------------------------------------|
373
- | type | string | string | int |
374
- | details | <ul><li>min: 4 tokens</li><li>mean: 8.24 tokens</li><li>max: 15 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 8.1 tokens</li><li>max: 15 tokens</li></ul> | <ul><li>0: ~83.40%</li><li>1: ~16.60%</li></ul> |
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- * Samples:
376
- | text1 | text2 | label |
377
- |:------------------------------|:-------------------------|:---------------|
378
- | <code>例えば?</code> | <code>どうも</code> | <code>0</code> |
379
- | <code>何を作ったの?</code> | <code>君は何でここにいるの?</code> | <code>0</code> |
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- | <code>昨日夕飯にビーフシチュー食べた?</code> | <code>屋根裏って?</code> | <code>0</code> |
381
- * Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
382
- ```json
383
- {
384
- "loss_fct": "torch.nn.modules.loss.MSELoss"
385
- }
386
- ```
387
-
388
- ### Training Hyperparameters
389
- #### Non-Default Hyperparameters
390
-
391
- - `eval_strategy`: epoch
392
- - `learning_rate`: 2e-05
393
- - `num_train_epochs`: 1
394
- - `warmup_ratio`: 0.4
395
- - `fp16`: True
396
- - `batch_sampler`: no_duplicates
397
-
398
- #### All Hyperparameters
399
- <details><summary>Click to expand</summary>
400
-
401
- - `overwrite_output_dir`: False
402
- - `do_predict`: False
403
- - `eval_strategy`: epoch
404
- - `prediction_loss_only`: True
405
- - `per_device_train_batch_size`: 8
406
- - `per_device_eval_batch_size`: 8
407
- - `per_gpu_train_batch_size`: None
408
- - `per_gpu_eval_batch_size`: None
409
- - `gradient_accumulation_steps`: 1
410
- - `eval_accumulation_steps`: None
411
- - `torch_empty_cache_steps`: None
412
- - `learning_rate`: 2e-05
413
- - `weight_decay`: 0.0
414
- - `adam_beta1`: 0.9
415
- - `adam_beta2`: 0.999
416
- - `adam_epsilon`: 1e-08
417
- - `max_grad_norm`: 1.0
418
- - `num_train_epochs`: 1
419
- - `max_steps`: -1
420
- - `lr_scheduler_type`: linear
421
- - `lr_scheduler_kwargs`: {}
422
- - `warmup_ratio`: 0.4
423
- - `warmup_steps`: 0
424
- - `log_level`: passive
425
- - `log_level_replica`: warning
426
- - `log_on_each_node`: True
427
- - `logging_nan_inf_filter`: True
428
- - `save_safetensors`: True
429
- - `save_on_each_node`: False
430
- - `save_only_model`: False
431
- - `restore_callback_states_from_checkpoint`: False
432
- - `no_cuda`: False
433
- - `use_cpu`: False
434
- - `use_mps_device`: False
435
- - `seed`: 42
436
- - `data_seed`: None
437
- - `jit_mode_eval`: False
438
- - `use_ipex`: False
439
- - `bf16`: False
440
- - `fp16`: True
441
- - `fp16_opt_level`: O1
442
- - `half_precision_backend`: auto
443
- - `bf16_full_eval`: False
444
- - `fp16_full_eval`: False
445
- - `tf32`: None
446
- - `local_rank`: 0
447
- - `ddp_backend`: None
448
- - `tpu_num_cores`: None
449
- - `tpu_metrics_debug`: False
450
- - `debug`: []
451
- - `dataloader_drop_last`: False
452
- - `dataloader_num_workers`: 0
453
- - `dataloader_prefetch_factor`: None
454
- - `past_index`: -1
455
- - `disable_tqdm`: False
456
- - `remove_unused_columns`: True
457
- - `label_names`: None
458
- - `load_best_model_at_end`: False
459
- - `ignore_data_skip`: False
460
- - `fsdp`: []
461
- - `fsdp_min_num_params`: 0
462
- - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
463
- - `fsdp_transformer_layer_cls_to_wrap`: None
464
- - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
465
- - `deepspeed`: None
466
- - `label_smoothing_factor`: 0.0
467
- - `optim`: adamw_torch
468
- - `optim_args`: None
469
- - `adafactor`: False
470
- - `group_by_length`: False
471
- - `length_column_name`: length
472
- - `ddp_find_unused_parameters`: None
473
- - `ddp_bucket_cap_mb`: None
474
- - `ddp_broadcast_buffers`: False
475
- - `dataloader_pin_memory`: True
476
- - `dataloader_persistent_workers`: False
477
- - `skip_memory_metrics`: True
478
- - `use_legacy_prediction_loop`: False
479
- - `push_to_hub`: False
480
- - `resume_from_checkpoint`: None
481
- - `hub_model_id`: None
482
- - `hub_strategy`: every_save
483
- - `hub_private_repo`: False
484
- - `hub_always_push`: False
485
- - `gradient_checkpointing`: False
486
- - `gradient_checkpointing_kwargs`: None
487
- - `include_inputs_for_metrics`: False
488
- - `eval_do_concat_batches`: True
489
- - `fp16_backend`: auto
490
- - `push_to_hub_model_id`: None
491
- - `push_to_hub_organization`: None
492
- - `mp_parameters`:
493
- - `auto_find_batch_size`: False
494
- - `full_determinism`: False
495
- - `torchdynamo`: None
496
- - `ray_scope`: last
497
- - `ddp_timeout`: 1800
498
- - `torch_compile`: False
499
- - `torch_compile_backend`: None
500
- - `torch_compile_mode`: None
501
- - `dispatch_batches`: None
502
- - `split_batches`: None
503
- - `include_tokens_per_second`: False
504
- - `include_num_input_tokens_seen`: False
505
- - `neftune_noise_alpha`: None
506
- - `optim_target_modules`: None
507
- - `batch_eval_metrics`: False
508
- - `eval_on_start`: False
509
- - `eval_use_gather_object`: False
510
- - `batch_sampler`: no_duplicates
511
- - `multi_dataset_batch_sampler`: proportional
512
-
513
- </details>
514
-
515
- ### Training Logs
516
- | Epoch | Step | Training Loss | loss | custom-arc-semantics-data-jp_max_ap |
517
- |:-----:|:----:|:-------------:|:------:|:-----------------------------------:|
518
- | 1.0 | 933 | 0.0601 | 0.0303 | 0.9259 |
519
-
520
-
521
- ### Framework Versions
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- - Python: 3.10.14
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- - Sentence Transformers: 3.1.1
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- - Transformers: 4.44.2
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- - PyTorch: 2.4.1+cu121
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- - Accelerate: 0.34.2
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- - Datasets: 2.20.0
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- - Tokenizers: 0.19.1
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-
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- ## Citation
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-
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- ### BibTeX
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-
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- #### Sentence Transformers
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- ```bibtex
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- @inproceedings{reimers-2019-sentence-bert,
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- title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
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- author = "Reimers, Nils and Gurevych, Iryna",
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- booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
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- month = "11",
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- year = "2019",
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- publisher = "Association for Computational Linguistics",
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- url = "https://arxiv.org/abs/1908.10084",
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- }
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- ```
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-
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- <!--
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- ## Glossary
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- -->
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  ## Model Card Contact
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  ---
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  base_model: colorfulscoop/sbert-base-ja
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+ language: ja
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+ license: cc-by-sa-4.0
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+ model_name: LeoChiuu/sbert-base-ja-arc
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ---
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+ # Model Card for LeoChiuu/sbert-base-ja-arc
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+
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+ <!-- Provide a quick summary of what the model is/does. -->
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+
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  ## Model Details
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  ### Model Description
 
 
 
 
 
 
 
 
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+ <!-- Provide a longer summary of what this model is. -->
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+ Generates similarity embeddings
 
 
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+ - **Developed by:** [More Information Needed]
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+ - **Funded by [optional]:** [More Information Needed]
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+ - **Shared by [optional]:** [More Information Needed]
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+ - **Model type:** [More Information Needed]
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+ - **Language(s) (NLP):** ja
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+ - **License:** cc-by-sa-4.0
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+ - **Finetuned from model [optional]:** colorfulscoop/sbert-base-ja
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+ ### Model Sources [optional]
 
 
 
 
 
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+ - **Repository:** [More Information Needed]
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+ - **Paper [optional]:** [More Information Needed]
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+ - **Demo [optional]:** [More Information Needed]
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+ ## Uses
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+ <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
 
 
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+ ### Direct Use
 
 
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+ [More Information Needed]
 
 
 
 
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+ ### Out-of-Scope Use
 
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+ ## Bias, Risks, and Limitations
 
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+ [More Information Needed]
 
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  ### Recommendations
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+ <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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+ Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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+
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+ ## How to Get Started with the Model
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+
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+ Use the code below to get started with the model.
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+ [More Information Needed]
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  ## Training Details
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+ ### Training Data
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+ <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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+ ### Training Procedure
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+ <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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+
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+ #### Preprocessing [optional]
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+ [More Information Needed]
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+
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+ #### Training Hyperparameters
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+
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+ - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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+
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+ #### Speeds, Sizes, Times [optional]
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+ <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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+ [More Information Needed]
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+
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+ ## Evaluation
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+ <!-- This section describes the evaluation protocols and provides the results. -->
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+ ### Testing Data, Factors & Metrics
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+ #### Testing Data
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+ <!-- This should link to a Dataset Card if possible. -->
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+ [More Information Needed]
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+ #### Factors
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+ <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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+ [More Information Needed]
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+ #### Metrics
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+ ### Results
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+ #### Summary
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+ ## Model Examination [optional]
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+ <!-- Relevant interpretability work for the model goes here -->
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+ [More Information Needed]
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+ ## Environmental Impact
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+ <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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+ Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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+
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+ - **Hardware Type:** [More Information Needed]
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+ - **Hours used:** [More Information Needed]
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+ - **Cloud Provider:** [More Information Needed]
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+ - **Compute Region:** [More Information Needed]
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+ - **Carbon Emitted:** [More Information Needed]
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+
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+ ## Technical Specifications [optional]
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+
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+ ### Model Architecture and Objective
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+
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+ [More Information Needed]
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+ ### Compute Infrastructure
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+ [More Information Needed]
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+ #### Hardware
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+ [More Information Needed]
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+ #### Software
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+ ## Citation [optional]
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+ <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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+ **BibTeX:**
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+ ## Glossary [optional]
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+ <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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+ ## Model Card Authors [optional]
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+
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ## Model Card Contact
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