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  ---
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  base_model: colorfulscoop/sbert-base-ja
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- library_name: sentence-transformers
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- metrics:
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- - 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
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- - 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:680
47
- - loss:CoSENTLoss
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- widget:
49
- - source_sentence: どっちをさがせばいい?
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- sentences:
51
- - 木材の山の中にスカーフはある?
52
- - はじめにどっちをさがせばいい?
53
- - チキンヌードル食べた?
54
- - source_sentence: あの木に引っかかってるやつ
55
- sentences:
56
- - 夜ご飯を作る前
57
- - 花壇の中にスカーフはある?
58
- - 信用できない
59
- - source_sentence: 猫好きな人
60
- sentences:
61
- - カーテンが風に吹かれているから
62
- - 猫好き
63
- - 他にはある?
64
- - source_sentence: 家の外
65
- sentences:
66
- - 魔法の残り香
67
- - 欲しくない
68
- - 家の外へ行こう
69
- - source_sentence: 昨日なに作ったの?
70
- sentences:
71
- - 布袋の中にスカーフは見当たる?
72
- - 昨日なに作ったの?
73
- - ゆうべはなにをたべたの?
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: 0.8088235294117647
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- name: Cosine Accuracy
87
- - type: cosine_accuracy_threshold
88
- value: 0.5396817326545715
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- name: Cosine Accuracy Threshold
90
- - type: cosine_f1
91
- value: 0.8659793814432991
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- name: Cosine F1
93
- - type: cosine_f1_threshold
94
- value: 0.5396817326545715
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- name: Cosine F1 Threshold
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- - type: cosine_precision
97
- value: 0.8
98
- name: Cosine Precision
99
- - type: cosine_recall
100
- value: 0.9438202247191011
101
- name: Cosine Recall
102
- - type: cosine_ap
103
- value: 0.8673399071218862
104
- name: Cosine Ap
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- - type: dot_accuracy
106
- value: 0.8014705882352942
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- name: Dot Accuracy
108
- - type: dot_accuracy_threshold
109
- value: 335.5762634277344
110
- name: Dot Accuracy Threshold
111
- - type: dot_f1
112
- value: 0.8526315789473684
113
- name: Dot F1
114
- - type: dot_f1_threshold
115
- value: 305.34722900390625
116
- name: Dot F1 Threshold
117
- - type: dot_precision
118
- value: 0.801980198019802
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- name: Dot Precision
120
- - type: dot_recall
121
- value: 0.9101123595505618
122
- name: Dot Recall
123
- - type: dot_ap
124
- value: 0.8584929148669156
125
- name: Dot Ap
126
- - type: manhattan_accuracy
127
- value: 0.8161764705882353
128
- name: Manhattan Accuracy
129
- - type: manhattan_accuracy_threshold
130
- value: 496.994384765625
131
- name: Manhattan Accuracy Threshold
132
- - type: manhattan_f1
133
- value: 0.8717948717948718
134
- name: Manhattan F1
135
- - type: manhattan_f1_threshold
136
- value: 496.994384765625
137
- name: Manhattan F1 Threshold
138
- - type: manhattan_precision
139
- value: 0.8018867924528302
140
- name: Manhattan Precision
141
- - type: manhattan_recall
142
- value: 0.9550561797752809
143
- name: Manhattan Recall
144
- - type: manhattan_ap
145
- value: 0.8672919211890922
146
- name: Manhattan Ap
147
- - type: euclidean_accuracy
148
- value: 0.8235294117647058
149
- name: Euclidean Accuracy
150
- - type: euclidean_accuracy_threshold
151
- value: 22.521053314208984
152
- name: Euclidean Accuracy Threshold
153
- - type: euclidean_f1
154
- value: 0.8762886597938143
155
- name: Euclidean F1
156
- - type: euclidean_f1_threshold
157
- value: 22.521053314208984
158
- name: Euclidean F1 Threshold
159
- - type: euclidean_precision
160
- value: 0.8095238095238095
161
- name: Euclidean Precision
162
- - type: euclidean_recall
163
- value: 0.9550561797752809
164
- name: Euclidean Recall
165
- - type: euclidean_ap
166
- value: 0.8692698043262699
167
- name: Euclidean Ap
168
- - type: max_accuracy
169
- value: 0.8235294117647058
170
- name: Max Accuracy
171
- - type: max_accuracy_threshold
172
- value: 496.994384765625
173
- name: Max Accuracy Threshold
174
- - type: max_f1
175
- value: 0.8762886597938143
176
- name: Max F1
177
- - type: max_f1_threshold
178
- value: 496.994384765625
179
- name: Max F1 Threshold
180
- - type: max_precision
181
- value: 0.8095238095238095
182
- name: Max Precision
183
- - type: max_recall
184
- value: 0.9550561797752809
185
- name: Max Recall
186
- - type: max_ap
187
- value: 0.8692698043262699
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) on the csv dataset. 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:**
204
- - csv
205
- <!-- - **Language:** Unknown -->
206
- <!-- - **License:** Unknown -->
207
 
208
- ### Model Sources
209
 
210
- - **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
211
- - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
212
- - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
213
 
214
- ### Full Model Architecture
 
 
 
 
 
 
215
 
216
- ```
217
- SentenceTransformer(
218
- (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
219
- (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})
220
- )
221
- ```
222
 
223
- ## Usage
224
 
225
- ### Direct Usage (Sentence Transformers)
 
 
226
 
227
- First install the Sentence Transformers library:
228
 
229
- ```bash
230
- pip install -U sentence-transformers
231
- ```
232
 
233
- Then you can load this model and run inference.
234
- ```python
235
- from sentence_transformers import SentenceTransformer
236
 
237
- # Download from the 🤗 Hub
238
- model = SentenceTransformer("sentence_transformers_model_id")
239
- # Run inference
240
- sentences = [
241
- '昨日なに作ったの?',
242
- 'ゆうべはなにをたべたの?',
243
- '布袋の中にスカーフは見当たる?',
244
- ]
245
- embeddings = model.encode(sentences)
246
- print(embeddings.shape)
247
- # [3, 768]
248
 
249
- # Get the similarity scores for the embeddings
250
- similarities = model.similarity(embeddings, embeddings)
251
- print(similarities.shape)
252
- # [3, 3]
253
- ```
254
 
255
- <!--
256
- ### Direct Usage (Transformers)
257
 
258
- <details><summary>Click to see the direct usage in Transformers</summary>
259
 
260
- </details>
261
- -->
262
 
263
- <!--
264
- ### Downstream Usage (Sentence Transformers)
265
 
266
- You can finetune this model on your own dataset.
267
 
268
- <details><summary>Click to expand</summary>
269
 
270
- </details>
271
- -->
272
 
273
- <!--
274
- ### Out-of-Scope Use
275
 
276
- *List how the model may foreseeably be misused and address what users ought not to do with the model.*
277
- -->
278
 
279
- ## Evaluation
280
-
281
- ### Metrics
282
-
283
- #### Binary Classification
284
- * Dataset: `custom-arc-semantics-data-jp`
285
- * Evaluated with [<code>BinaryClassificationEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator)
286
-
287
- | Metric | Value |
288
- |:-----------------------------|:-----------|
289
- | cosine_accuracy | 0.8088 |
290
- | cosine_accuracy_threshold | 0.5397 |
291
- | cosine_f1 | 0.866 |
292
- | cosine_f1_threshold | 0.5397 |
293
- | cosine_precision | 0.8 |
294
- | cosine_recall | 0.9438 |
295
- | cosine_ap | 0.8673 |
296
- | dot_accuracy | 0.8015 |
297
- | dot_accuracy_threshold | 335.5763 |
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- | dot_f1 | 0.8526 |
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- | dot_f1_threshold | 305.3472 |
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- | dot_precision | 0.802 |
301
- | dot_recall | 0.9101 |
302
- | dot_ap | 0.8585 |
303
- | manhattan_accuracy | 0.8162 |
304
- | manhattan_accuracy_threshold | 496.9944 |
305
- | manhattan_f1 | 0.8718 |
306
- | manhattan_f1_threshold | 496.9944 |
307
- | manhattan_precision | 0.8019 |
308
- | manhattan_recall | 0.9551 |
309
- | manhattan_ap | 0.8673 |
310
- | euclidean_accuracy | 0.8235 |
311
- | euclidean_accuracy_threshold | 22.5211 |
312
- | euclidean_f1 | 0.8763 |
313
- | euclidean_f1_threshold | 22.5211 |
314
- | euclidean_precision | 0.8095 |
315
- | euclidean_recall | 0.9551 |
316
- | euclidean_ap | 0.8693 |
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- | max_accuracy | 0.8235 |
318
- | max_accuracy_threshold | 496.9944 |
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- | max_f1 | 0.8763 |
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- | max_f1_threshold | 496.9944 |
321
- | max_precision | 0.8095 |
322
- | max_recall | 0.9551 |
323
- | **max_ap** | **0.8693** |
324
-
325
- <!--
326
- ## Bias, Risks and Limitations
327
-
328
- *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
329
- -->
330
-
331
- <!--
332
  ### Recommendations
333
 
334
- *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
335
- -->
 
 
 
 
 
 
 
336
 
337
  ## Training Details
338
 
339
- ### Training Dataset
340
-
341
- #### csv
342
-
343
- * Dataset: csv
344
- * Size: 680 training samples
345
- * Columns: <code>text1</code>, <code>text2</code>, and <code>label</code>
346
- * Approximate statistics based on the first 680 samples:
347
- | | text1 | text2 | label |
348
- |:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:------------------------------------------------|
349
- | type | string | string | int |
350
- | details | <ul><li>min: 4 tokens</li><li>mean: 8.36 tokens</li><li>max: 15 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 8.07 tokens</li><li>max: 14 tokens</li></ul> | <ul><li>0: ~43.01%</li><li>1: ~56.99%</li></ul> |
351
- * Samples:
352
- | text1 | text2 | label |
353
- |:---------------------------|:-------------------------|:---------------|
354
- | <code>他の選択肢をちょうだい</code> | <code>どこを探したい?</code> | <code>0</code> |
355
- | <code>ビーフシチュー食べた?</code> | <code>ビーフシチュー作った?</code> | <code>1</code> |
356
- | <code>なんでしなきゃいけないの?</code> | <code>なんですべきなの?</code> | <code>1</code> |
357
- * Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
358
- ```json
359
- {
360
- "scale": 5,
361
- "similarity_fct": "pairwise_cos_sim"
362
- }
363
- ```
364
-
365
- ### Training Hyperparameters
366
-
367
- #### All Hyperparameters
368
- <details><summary>Click to expand</summary>
369
-
370
- - `overwrite_output_dir`: False
371
- - `do_predict`: False
372
- - `eval_strategy`: no
373
- - `prediction_loss_only`: True
374
- - `per_device_train_batch_size`: 8
375
- - `per_device_eval_batch_size`: 8
376
- - `per_gpu_train_batch_size`: None
377
- - `per_gpu_eval_batch_size`: None
378
- - `gradient_accumulation_steps`: 1
379
- - `eval_accumulation_steps`: None
380
- - `torch_empty_cache_steps`: None
381
- - `learning_rate`: 5e-05
382
- - `weight_decay`: 0.0
383
- - `adam_beta1`: 0.9
384
- - `adam_beta2`: 0.999
385
- - `adam_epsilon`: 1e-08
386
- - `max_grad_norm`: 1.0
387
- - `num_train_epochs`: 3.0
388
- - `max_steps`: -1
389
- - `lr_scheduler_type`: linear
390
- - `lr_scheduler_kwargs`: {}
391
- - `warmup_ratio`: 0.0
392
- - `warmup_steps`: 0
393
- - `log_level`: passive
394
- - `log_level_replica`: warning
395
- - `log_on_each_node`: True
396
- - `logging_nan_inf_filter`: True
397
- - `save_safetensors`: True
398
- - `save_on_each_node`: False
399
- - `save_only_model`: False
400
- - `restore_callback_states_from_checkpoint`: False
401
- - `no_cuda`: False
402
- - `use_cpu`: False
403
- - `use_mps_device`: False
404
- - `seed`: 42
405
- - `data_seed`: None
406
- - `jit_mode_eval`: False
407
- - `use_ipex`: False
408
- - `bf16`: False
409
- - `fp16`: False
410
- - `fp16_opt_level`: O1
411
- - `half_precision_backend`: auto
412
- - `bf16_full_eval`: False
413
- - `fp16_full_eval`: False
414
- - `tf32`: None
415
- - `local_rank`: 0
416
- - `ddp_backend`: None
417
- - `tpu_num_cores`: None
418
- - `tpu_metrics_debug`: False
419
- - `debug`: []
420
- - `dataloader_drop_last`: False
421
- - `dataloader_num_workers`: 0
422
- - `dataloader_prefetch_factor`: None
423
- - `past_index`: -1
424
- - `disable_tqdm`: False
425
- - `remove_unused_columns`: True
426
- - `label_names`: None
427
- - `load_best_model_at_end`: False
428
- - `ignore_data_skip`: False
429
- - `fsdp`: []
430
- - `fsdp_min_num_params`: 0
431
- - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
432
- - `fsdp_transformer_layer_cls_to_wrap`: None
433
- - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
434
- - `deepspeed`: None
435
- - `label_smoothing_factor`: 0.0
436
- - `optim`: adamw_torch
437
- - `optim_args`: None
438
- - `adafactor`: False
439
- - `group_by_length`: False
440
- - `length_column_name`: length
441
- - `ddp_find_unused_parameters`: None
442
- - `ddp_bucket_cap_mb`: None
443
- - `ddp_broadcast_buffers`: False
444
- - `dataloader_pin_memory`: True
445
- - `dataloader_persistent_workers`: False
446
- - `skip_memory_metrics`: True
447
- - `use_legacy_prediction_loop`: False
448
- - `push_to_hub`: False
449
- - `resume_from_checkpoint`: None
450
- - `hub_model_id`: None
451
- - `hub_strategy`: every_save
452
- - `hub_private_repo`: False
453
- - `hub_always_push`: False
454
- - `gradient_checkpointing`: False
455
- - `gradient_checkpointing_kwargs`: None
456
- - `include_inputs_for_metrics`: False
457
- - `eval_do_concat_batches`: True
458
- - `fp16_backend`: auto
459
- - `push_to_hub_model_id`: None
460
- - `push_to_hub_organization`: None
461
- - `mp_parameters`:
462
- - `auto_find_batch_size`: False
463
- - `full_determinism`: False
464
- - `torchdynamo`: None
465
- - `ray_scope`: last
466
- - `ddp_timeout`: 1800
467
- - `torch_compile`: False
468
- - `torch_compile_backend`: None
469
- - `torch_compile_mode`: None
470
- - `dispatch_batches`: None
471
- - `split_batches`: None
472
- - `include_tokens_per_second`: False
473
- - `include_num_input_tokens_seen`: False
474
- - `neftune_noise_alpha`: None
475
- - `optim_target_modules`: None
476
- - `batch_eval_metrics`: False
477
- - `eval_on_start`: False
478
- - `eval_use_gather_object`: False
479
- - `batch_sampler`: batch_sampler
480
- - `multi_dataset_batch_sampler`: proportional
481
-
482
- </details>
483
-
484
- ### Training Logs
485
- | Epoch | Step | custom-arc-semantics-data-jp_max_ap |
486
- |:-----:|:----:|:-----------------------------------:|
487
- | 0 | 0 | 0.8693 |
488
-
489
-
490
- ### Framework Versions
491
- - Python: 3.10.14
492
- - Sentence Transformers: 3.1.0
493
- - Transformers: 4.44.2
494
- - PyTorch: 2.4.1+cu121
495
- - Accelerate: 0.34.2
496
- - Datasets: 2.20.0
497
- - Tokenizers: 0.19.1
498
-
499
- ## Citation
500
-
501
- ### BibTeX
502
-
503
- #### Sentence Transformers
504
- ```bibtex
505
- @inproceedings{reimers-2019-sentence-bert,
506
- title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
507
- author = "Reimers, Nils and Gurevych, Iryna",
508
- booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
509
- month = "11",
510
- year = "2019",
511
- publisher = "Association for Computational Linguistics",
512
- url = "https://arxiv.org/abs/1908.10084",
513
- }
514
- ```
515
-
516
- #### CoSENTLoss
517
- ```bibtex
518
- @online{kexuefm-8847,
519
- title={CoSENT: A more efficient sentence vector scheme than Sentence-BERT},
520
- author={Su Jianlin},
521
- year={2022},
522
- month={Jan},
523
- url={https://kexue.fm/archives/8847},
524
- }
525
- ```
526
-
527
- <!--
528
- ## Glossary
529
-
530
- *Clearly define terms in order to be accessible across audiences.*
531
- -->
532
-
533
- <!--
534
- ## Model Card Authors
535
-
536
- *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
537
- -->
538
-
539
- <!--
540
  ## Model Card Contact
541
 
542
- *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
543
- -->
 
1
  ---
2
  base_model: colorfulscoop/sbert-base-ja
3
+ 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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+ <!-- Provide the basic links for the model. -->
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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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+ <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
 
 
 
 
 
 
 
 
 
 
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+ [More Information Needed]
 
 
 
 
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+ ### Downstream Use [optional]
 
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+ <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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+ [More Information Needed]
 
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+ ### Out-of-Scope Use
 
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+ <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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+ [More Information Needed]
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+ ## Bias, Risks, and Limitations
 
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+ <!-- This section is meant to convey both technical and sociotechnical 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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+
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+ [More Information Needed]
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  ## Training Details
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+ ### Training Data
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+
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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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+ [More Information Needed]
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+
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+ ### Training Procedure
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+
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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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+
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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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+
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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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+
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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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+
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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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+
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+ #### Metrics
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+ <!-- These are the evaluation metrics being used, ideally with a description of why. -->
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+ [More Information Needed]
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+ ### Results
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+ [More Information Needed]
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+
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+ #### Summary
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+
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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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+
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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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+ [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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+ [More Information Needed]
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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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+ [More Information Needed]
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+ **APA:**
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+ [More Information Needed]
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+
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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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+ [More Information Needed]
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+ ## More Information [optional]
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+ [More Information Needed]
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+ ## Model Card Authors [optional]
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+ [More Information Needed]
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+
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ## Model Card Contact
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+ [More Information Needed]