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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:680
47
- - loss:CoSENTLoss
48
- widget:
49
- - source_sentence: 中を見てみよう
50
- sentences:
51
- - 外を調べよう
52
- - リリアンはどんな魔法が使えるの?
53
- - 花がぬいぐるみに変えられている
54
- - source_sentence: キャンドル要らない
55
- sentences:
56
- - なんで猫が話せる?
57
- - 自分でやれば?
58
- - 中を見てみよう
59
- - source_sentence: 信用できない
60
- sentences:
61
- - どっちでもいいよ
62
- - 誰?
63
- - 誰かが呪文で花をぬいぐるみに変えた
64
- - source_sentence: 例えば?
65
- sentences:
66
- - 誰かがが魔法をかけた
67
- - ジャック
68
- - なんでしなきゃいけないの?
69
- - source_sentence: 魔法を使える人
70
- sentences:
71
- - かっこいいね
72
- - 物の姿を変えられる人
73
- - 町って?
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: 0.9044117647058824
86
- name: Cosine Accuracy
87
- - type: cosine_accuracy_threshold
88
- value: 0.5485918521881104
89
- name: Cosine Accuracy Threshold
90
- - type: cosine_f1
91
- value: 0.912751677852349
92
- name: Cosine F1
93
- - type: cosine_f1_threshold
94
- value: 0.47659817337989807
95
- name: Cosine F1 Threshold
96
- - type: cosine_precision
97
- value: 0.918918918918919
98
- name: Cosine Precision
99
- - type: cosine_recall
100
- value: 0.9066666666666666
101
- name: Cosine Recall
102
- - type: cosine_ap
103
- value: 0.9088999169341241
104
- name: Cosine Ap
105
- - type: dot_accuracy
106
- value: 0.9117647058823529
107
- name: Dot Accuracy
108
- - type: dot_accuracy_threshold
109
- value: 293.22845458984375
110
- name: Dot Accuracy Threshold
111
- - type: dot_f1
112
- value: 0.9166666666666666
113
- name: Dot F1
114
- - type: dot_f1_threshold
115
- value: 293.22845458984375
116
- name: Dot F1 Threshold
117
- - type: dot_precision
118
- value: 0.9565217391304348
119
- name: Dot Precision
120
- - type: dot_recall
121
- value: 0.88
122
- name: Dot Recall
123
- - type: dot_ap
124
- value: 0.9171086358892895
125
- name: Dot Ap
126
- - type: manhattan_accuracy
127
- value: 0.9117647058823529
128
- name: Manhattan Accuracy
129
- - type: manhattan_accuracy_threshold
130
- value: 524.0676879882812
131
- name: Manhattan Accuracy Threshold
132
- - type: manhattan_f1
133
- value: 0.918918918918919
134
- name: Manhattan F1
135
- - type: manhattan_f1_threshold
136
- value: 524.0676879882812
137
- name: Manhattan F1 Threshold
138
- - type: manhattan_precision
139
- value: 0.9315068493150684
140
- name: Manhattan Precision
141
- - type: manhattan_recall
142
- value: 0.9066666666666666
143
- name: Manhattan Recall
144
- - type: manhattan_ap
145
- value: 0.9111567321590129
146
- name: Manhattan Ap
147
- - type: euclidean_accuracy
148
- value: 0.9117647058823529
149
- name: Euclidean Accuracy
150
- - type: euclidean_accuracy_threshold
151
- value: 23.82940673828125
152
- name: Euclidean Accuracy Threshold
153
- - type: euclidean_f1
154
- value: 0.918918918918919
155
- name: Euclidean F1
156
- - type: euclidean_f1_threshold
157
- value: 23.82940673828125
158
- name: Euclidean F1 Threshold
159
- - type: euclidean_precision
160
- value: 0.9315068493150684
161
- name: Euclidean Precision
162
- - type: euclidean_recall
163
- value: 0.9066666666666666
164
- name: Euclidean Recall
165
- - type: euclidean_ap
166
- value: 0.9094221163568814
167
- name: Euclidean Ap
168
- - type: max_accuracy
169
- value: 0.9117647058823529
170
- name: Max Accuracy
171
- - type: max_accuracy_threshold
172
- value: 524.0676879882812
173
- name: Max Accuracy Threshold
174
- - type: max_f1
175
- value: 0.918918918918919
176
- name: Max F1
177
- - type: max_f1_threshold
178
- value: 524.0676879882812
179
- name: Max F1 Threshold
180
- - type: max_precision
181
- value: 0.9565217391304348
182
- name: Max Precision
183
- - type: max_recall
184
- value: 0.9066666666666666
185
- name: Max Recall
186
- - type: max_ap
187
- value: 0.9171086358892895
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
- '魔法を使える人',
242
- '物の姿を変えられる人',
243
- 'かっこいいね',
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 | 0.9044 |
290
- | cosine_accuracy_threshold | 0.5486 |
291
- | cosine_f1 | 0.9128 |
292
- | cosine_f1_threshold | 0.4766 |
293
- | cosine_precision | 0.9189 |
294
- | cosine_recall | 0.9067 |
295
- | cosine_ap | 0.9089 |
296
- | dot_accuracy | 0.9118 |
297
- | dot_accuracy_threshold | 293.2285 |
298
- | dot_f1 | 0.9167 |
299
- | dot_f1_threshold | 293.2285 |
300
- | dot_precision | 0.9565 |
301
- | dot_recall | 0.88 |
302
- | dot_ap | 0.9171 |
303
- | manhattan_accuracy | 0.9118 |
304
- | manhattan_accuracy_threshold | 524.0677 |
305
- | manhattan_f1 | 0.9189 |
306
- | manhattan_f1_threshold | 524.0677 |
307
- | manhattan_precision | 0.9315 |
308
- | manhattan_recall | 0.9067 |
309
- | manhattan_ap | 0.9112 |
310
- | euclidean_accuracy | 0.9118 |
311
- | euclidean_accuracy_threshold | 23.8294 |
312
- | euclidean_f1 | 0.9189 |
313
- | euclidean_f1_threshold | 23.8294 |
314
- | euclidean_precision | 0.9315 |
315
- | euclidean_recall | 0.9067 |
316
- | euclidean_ap | 0.9094 |
317
- | max_accuracy | 0.9118 |
318
- | max_accuracy_threshold | 524.0677 |
319
- | max_f1 | 0.9189 |
320
- | max_f1_threshold | 524.0677 |
321
- | max_precision | 0.9565 |
322
- | max_recall | 0.9067 |
323
- | **max_ap** | **0.9171** |
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: 8.29 tokens</li><li>max: 15 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 7.97 tokens</li><li>max: 14 tokens</li></ul> | <ul><li>0: ~40.44%</li><li>1: ~59.56%</li></ul> |
351
- * Samples:
352
- | text1 | text2 | label |
353
- |:----------------------------|:----------------------------|:---------------|
354
- | <code>いらない</code> | <code>うんよろしく</code> | <code>0</code> |
355
- | <code>足元よりも更に深くってどこ?</code> | <code>足元よりも更に深くってなに?</code> | <code>1</code> |
356
- | <code>他にはないの?</code> | <code>どう思う?</code> | <code>0</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": 20.0,
361
- "similarity_fct": "pairwise_cos_sim"
362
- }
363
- ```
364
-
365
- ### Evaluation Dataset
366
-
367
- #### csv
368
-
369
- * Dataset: csv
370
- * Size: 680 evaluation samples
371
- * Columns: <code>text1</code>, <code>text2</code>, and <code>label</code>
372
- * Approximate statistics based on the first 680 samples:
373
- | | text1 | text2 | label |
374
- |:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:------------------------------------------------|
375
- | type | string | string | int |
376
- | 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: 8.16 tokens</li><li>max: 14 tokens</li></ul> | <ul><li>0: ~44.85%</li><li>1: ~55.15%</li></ul> |
377
- * Samples:
378
- | text1 | text2 | label |
379
- |:-------------------------|:-------------------------|:---------------|
380
- | <code>井戸から水をくんでいた</code> | <code>井戸を使っていた</code> | <code>1</code> |
381
- | <code>夕飯は何だったの?</code> | <code>チキンヌードル食べた?</code> | <code>0</code> |
382
- | <code>水を井戸からくんでいた</code> | <code>夜ごはんの前</code> | <code>0</code> |
383
- * Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
384
- ```json
385
- {
386
- "scale": 20.0,
387
- "similarity_fct": "pairwise_cos_sim"
388
- }
389
- ```
390
-
391
- ### Training Hyperparameters
392
- #### Non-Default Hyperparameters
393
-
394
- - `eval_strategy`: epoch
395
- - `learning_rate`: 2e-05
396
- - `num_train_epochs`: 13
397
- - `warmup_ratio`: 0.1
398
- - `fp16`: True
399
- - `batch_sampler`: no_duplicates
400
-
401
- #### All Hyperparameters
402
- <details><summary>Click to expand</summary>
403
-
404
- - `overwrite_output_dir`: False
405
- - `do_predict`: False
406
- - `eval_strategy`: epoch
407
- - `prediction_loss_only`: True
408
- - `per_device_train_batch_size`: 8
409
- - `per_device_eval_batch_size`: 8
410
- - `per_gpu_train_batch_size`: None
411
- - `per_gpu_eval_batch_size`: None
412
- - `gradient_accumulation_steps`: 1
413
- - `eval_accumulation_steps`: None
414
- - `torch_empty_cache_steps`: None
415
- - `learning_rate`: 2e-05
416
- - `weight_decay`: 0.0
417
- - `adam_beta1`: 0.9
418
- - `adam_beta2`: 0.999
419
- - `adam_epsilon`: 1e-08
420
- - `max_grad_norm`: 1.0
421
- - `num_train_epochs`: 13
422
- - `max_steps`: -1
423
- - `lr_scheduler_type`: linear
424
- - `lr_scheduler_kwargs`: {}
425
- - `warmup_ratio`: 0.1
426
- - `warmup_steps`: 0
427
- - `log_level`: passive
428
- - `log_level_replica`: warning
429
- - `log_on_each_node`: True
430
- - `logging_nan_inf_filter`: True
431
- - `save_safetensors`: True
432
- - `save_on_each_node`: False
433
- - `save_only_model`: False
434
- - `restore_callback_states_from_checkpoint`: False
435
- - `no_cuda`: False
436
- - `use_cpu`: False
437
- - `use_mps_device`: False
438
- - `seed`: 42
439
- - `data_seed`: None
440
- - `jit_mode_eval`: False
441
- - `use_ipex`: False
442
- - `bf16`: False
443
- - `fp16`: True
444
- - `fp16_opt_level`: O1
445
- - `half_precision_backend`: auto
446
- - `bf16_full_eval`: False
447
- - `fp16_full_eval`: False
448
- - `tf32`: None
449
- - `local_rank`: 0
450
- - `ddp_backend`: None
451
- - `tpu_num_cores`: None
452
- - `tpu_metrics_debug`: False
453
- - `debug`: []
454
- - `dataloader_drop_last`: False
455
- - `dataloader_num_workers`: 0
456
- - `dataloader_prefetch_factor`: None
457
- - `past_index`: -1
458
- - `disable_tqdm`: False
459
- - `remove_unused_columns`: True
460
- - `label_names`: None
461
- - `load_best_model_at_end`: False
462
- - `ignore_data_skip`: False
463
- - `fsdp`: []
464
- - `fsdp_min_num_params`: 0
465
- - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
466
- - `fsdp_transformer_layer_cls_to_wrap`: None
467
- - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
468
- - `deepspeed`: None
469
- - `label_smoothing_factor`: 0.0
470
- - `optim`: adamw_torch
471
- - `optim_args`: None
472
- - `adafactor`: False
473
- - `group_by_length`: False
474
- - `length_column_name`: length
475
- - `ddp_find_unused_parameters`: None
476
- - `ddp_bucket_cap_mb`: None
477
- - `ddp_broadcast_buffers`: False
478
- - `dataloader_pin_memory`: True
479
- - `dataloader_persistent_workers`: False
480
- - `skip_memory_metrics`: True
481
- - `use_legacy_prediction_loop`: False
482
- - `push_to_hub`: False
483
- - `resume_from_checkpoint`: None
484
- - `hub_model_id`: None
485
- - `hub_strategy`: every_save
486
- - `hub_private_repo`: False
487
- - `hub_always_push`: False
488
- - `gradient_checkpointing`: False
489
- - `gradient_checkpointing_kwargs`: None
490
- - `include_inputs_for_metrics`: False
491
- - `eval_do_concat_batches`: True
492
- - `fp16_backend`: auto
493
- - `push_to_hub_model_id`: None
494
- - `push_to_hub_organization`: None
495
- - `mp_parameters`:
496
- - `auto_find_batch_size`: False
497
- - `full_determinism`: False
498
- - `torchdynamo`: None
499
- - `ray_scope`: last
500
- - `ddp_timeout`: 1800
501
- - `torch_compile`: False
502
- - `torch_compile_backend`: None
503
- - `torch_compile_mode`: None
504
- - `dispatch_batches`: None
505
- - `split_batches`: None
506
- - `include_tokens_per_second`: False
507
- - `include_num_input_tokens_seen`: False
508
- - `neftune_noise_alpha`: None
509
- - `optim_target_modules`: None
510
- - `batch_eval_metrics`: False
511
- - `eval_on_start`: False
512
- - `eval_use_gather_object`: False
513
- - `batch_sampler`: no_duplicates
514
- - `multi_dataset_batch_sampler`: proportional
515
-
516
- </details>
517
-
518
- ### Training Logs
519
- | Epoch | Step | Training Loss | loss | custom-arc-semantics-data-jp_max_ap |
520
- |:-----:|:----:|:-------------:|:------:|:-----------------------------------:|
521
- | None | 0 | - | - | 0.8596 |
522
- | 1.0 | 68 | 2.6802 | 1.7807 | 0.8872 |
523
- | 2.0 | 136 | 1.4014 | 1.7683 | 0.8945 |
524
- | 3.0 | 204 | 0.7937 | 1.9877 | 0.9039 |
525
- | 4.0 | 272 | 0.5443 | 1.9106 | 0.9075 |
526
- | 5.0 | 340 | 0.4225 | 1.9418 | 0.9109 |
527
- | 6.0 | 408 | 0.3347 | 2.0123 | 0.9107 |
528
- | 7.0 | 476 | 0.3425 | 2.0387 | 0.9094 |
529
- | 8.0 | 544 | 0.2427 | 1.9878 | 0.9103 |
530
- | 9.0 | 612 | 0.2412 | 2.0424 | 0.9178 |
531
- | 10.0 | 680 | 0.1623 | 2.0273 | 0.9188 |
532
- | 11.0 | 748 | 0.1909 | 2.0955 | 0.9220 |
533
- | 12.0 | 816 | 0.1507 | 2.2124 | 0.9157 |
534
- | 13.0 | 884 | 0.1406 | 2.2126 | 0.9171 |
535
-
536
-
537
- ### Framework Versions
538
- - Python: 3.10.14
539
- - Sentence Transformers: 3.1.0
540
- - Transformers: 4.44.2
541
- - PyTorch: 2.4.1+cu121
542
- - Accelerate: 0.34.2
543
- - Datasets: 2.20.0
544
- - Tokenizers: 0.19.1
545
-
546
- ## Citation
547
-
548
- ### BibTeX
549
-
550
- #### Sentence Transformers
551
- ```bibtex
552
- @inproceedings{reimers-2019-sentence-bert,
553
- title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
554
- author = "Reimers, Nils and Gurevych, Iryna",
555
- booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
556
- month = "11",
557
- year = "2019",
558
- publisher = "Association for Computational Linguistics",
559
- url = "https://arxiv.org/abs/1908.10084",
560
- }
561
- ```
562
-
563
- #### CoSENTLoss
564
- ```bibtex
565
- @online{kexuefm-8847,
566
- title={CoSENT: A more efficient sentence vector scheme than Sentence-BERT},
567
- author={Su Jianlin},
568
- year={2022},
569
- month={Jan},
570
- url={https://kexue.fm/archives/8847},
571
- }
572
- ```
573
-
574
- <!--
575
- ## Glossary
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578
- -->
579
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580
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581
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584
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585
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586
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587
  ## Model Card Contact
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- *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
590
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1
  ---
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  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
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
6
  ---
7
 
8
+ # 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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  ## 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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+ - **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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+ ## Uses
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  ### Recommendations
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+ ## How to Get Started with the Model
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  ## Training Details
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+ #### Summary
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+ ## Environmental Impact
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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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+ ## Technical Specifications [optional]
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