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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:401
47
- - loss:CosineSimilarityLoss
48
- widget:
49
- - source_sentence: 昨夜何を食べたの?
50
- sentences:
51
- - 昨日作ったのはチキンヌードル?
52
- - 村長がどうしたの?
53
- - 雲より高くってどこ?
54
- - source_sentence: ナイトスタンドにある?
55
- sentences:
56
- - わかんない
57
- - スリッパ履いた?
58
- - だめじゃん
59
- - source_sentence: センパイ
60
- sentences:
61
- - 昨日の夜は暑かった
62
- - 調子はどう?
63
- - トーチ
64
- - source_sentence: 村人はどんな呪文を使うの?
65
- sentences:
66
- - スパイク
67
- - キミはどんな魔法を使うの?
68
- - うさんくさい
69
- - source_sentence: 祭壇の些細な違和感ってどういう意味?
70
- sentences:
71
- - 青いオーブがどこにあるか知ってる?
72
- - 赤い染みが皿にあった
73
- - これが花
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: 0.8855721393034826
86
- name: Cosine Accuracy
87
- - type: cosine_accuracy_threshold
88
- value: 0.6970740556716919
89
- name: Cosine Accuracy Threshold
90
- - type: cosine_f1
91
- value: 0.7766990291262137
92
- name: Cosine F1
93
- - type: cosine_f1_threshold
94
- value: 0.6637545228004456
95
- name: Cosine F1 Threshold
96
- - type: cosine_precision
97
- value: 0.8163265306122449
98
- name: Cosine Precision
99
- - type: cosine_recall
100
- value: 0.7407407407407407
101
- name: Cosine Recall
102
- - type: cosine_ap
103
- value: 0.6606381605892593
104
- name: Cosine Ap
105
- - type: dot_accuracy
106
- value: 0.8805970149253731
107
- name: Dot Accuracy
108
- - type: dot_accuracy_threshold
109
- value: 378.6933898925781
110
- name: Dot Accuracy Threshold
111
- - type: dot_f1
112
- value: 0.7647058823529411
113
- name: Dot F1
114
- - type: dot_f1_threshold
115
- value: 378.6933898925781
116
- name: Dot F1 Threshold
117
- - type: dot_precision
118
- value: 0.8125
119
- name: Dot Precision
120
- - type: dot_recall
121
- value: 0.7222222222222222
122
- name: Dot Recall
123
- - type: dot_ap
124
- value: 0.6865123266544332
125
- name: Dot Ap
126
- - type: manhattan_accuracy
127
- value: 0.8855721393034826
128
- name: Manhattan Accuracy
129
- - type: manhattan_accuracy_threshold
130
- value: 407.9349365234375
131
- name: Manhattan Accuracy Threshold
132
- - type: manhattan_f1
133
- value: 0.7766990291262137
134
- name: Manhattan F1
135
- - type: manhattan_f1_threshold
136
- value: 426.941650390625
137
- name: Manhattan F1 Threshold
138
- - type: manhattan_precision
139
- value: 0.8163265306122449
140
- name: Manhattan Precision
141
- - type: manhattan_recall
142
- value: 0.7407407407407407
143
- name: Manhattan Recall
144
- - type: manhattan_ap
145
- value: 0.6609390536301427
146
- name: Manhattan Ap
147
- - type: euclidean_accuracy
148
- value: 0.8855721393034826
149
- name: Euclidean Accuracy
150
- - type: euclidean_accuracy_threshold
151
- value: 18.663713455200195
152
- name: Euclidean Accuracy Threshold
153
- - type: euclidean_f1
154
- value: 0.7766990291262137
155
- name: Euclidean F1
156
- - type: euclidean_f1_threshold
157
- value: 19.35655975341797
158
- name: Euclidean F1 Threshold
159
- - type: euclidean_precision
160
- value: 0.8163265306122449
161
- name: Euclidean Precision
162
- - type: euclidean_recall
163
- value: 0.7407407407407407
164
- name: Euclidean Recall
165
- - type: euclidean_ap
166
- value: 0.6602743223356511
167
- name: Euclidean Ap
168
- - type: max_accuracy
169
- value: 0.8855721393034826
170
- name: Max Accuracy
171
- - type: max_accuracy_threshold
172
- value: 407.9349365234375
173
- name: Max Accuracy Threshold
174
- - type: max_f1
175
- value: 0.7766990291262137
176
- name: Max F1
177
- - type: max_f1_threshold
178
- value: 426.941650390625
179
- name: Max F1 Threshold
180
- - type: max_precision
181
- value: 0.8163265306122449
182
- name: Max Precision
183
- - type: max_recall
184
- value: 0.7407407407407407
185
- name: Max Recall
186
- - type: max_ap
187
- value: 0.6865123266544332
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
- '祭壇の些細な違和感ってどういう意味?',
242
- '青いオーブがどこにあるか知ってる?',
243
- 'これが花',
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 | 0.8856 |
290
- | cosine_accuracy_threshold | 0.6971 |
291
- | cosine_f1 | 0.7767 |
292
- | cosine_f1_threshold | 0.6638 |
293
- | cosine_precision | 0.8163 |
294
- | cosine_recall | 0.7407 |
295
- | cosine_ap | 0.6606 |
296
- | dot_accuracy | 0.8806 |
297
- | dot_accuracy_threshold | 378.6934 |
298
- | dot_f1 | 0.7647 |
299
- | dot_f1_threshold | 378.6934 |
300
- | dot_precision | 0.8125 |
301
- | dot_recall | 0.7222 |
302
- | dot_ap | 0.6865 |
303
- | manhattan_accuracy | 0.8856 |
304
- | manhattan_accuracy_threshold | 407.9349 |
305
- | manhattan_f1 | 0.7767 |
306
- | manhattan_f1_threshold | 426.9417 |
307
- | manhattan_precision | 0.8163 |
308
- | manhattan_recall | 0.7407 |
309
- | manhattan_ap | 0.6609 |
310
- | euclidean_accuracy | 0.8856 |
311
- | euclidean_accuracy_threshold | 18.6637 |
312
- | euclidean_f1 | 0.7767 |
313
- | euclidean_f1_threshold | 19.3566 |
314
- | euclidean_precision | 0.8163 |
315
- | euclidean_recall | 0.7407 |
316
- | euclidean_ap | 0.6603 |
317
- | max_accuracy | 0.8856 |
318
- | max_accuracy_threshold | 407.9349 |
319
- | max_f1 | 0.7767 |
320
- | max_f1_threshold | 426.9417 |
321
- | max_precision | 0.8163 |
322
- | max_recall | 0.7407 |
323
- | **max_ap** | **0.6865** |
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: 401 training samples
345
- * Columns: <code>text1</code>, <code>text2</code>, and <code>label</code>
346
- * Approximate statistics based on the first 401 samples:
347
- | | text1 | text2 | label |
348
- |:--------|:---------------------------------------------------------------------------------|:--------------------------------------------------------------------------------|:------------------------------------------------|
349
- | type | string | string | int |
350
- | details | <ul><li>min: 4 tokens</li><li>mean: 8.34 tokens</li><li>max: 14 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 7.9 tokens</li><li>max: 14 tokens</li></ul> | <ul><li>0: ~67.00%</li><li>1: ~33.00%</li></ul> |
351
- * Samples:
352
- | text1 | text2 | label |
353
- |:-----------------------------------|:-------------------------|:---------------|
354
- | <code>雲より高くってどういう意味?</code> | <code>猫好き</code> | <code>0</code> |
355
- | <code>花の囁きってなに?</code> | <code>リリアンについて教えて</code> | <code>0</code> |
356
- | <code>リリアンってものの姿を変える魔法を使える?</code> | <code>どんな魔法なの?</code> | <code>0</code> |
357
- * Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
358
- ```json
359
- {
360
- "loss_fct": "torch.nn.modules.loss.MSELoss"
361
- }
362
- ```
363
-
364
- ### Evaluation Dataset
365
-
366
- #### csv
367
-
368
- * Dataset: csv
369
- * Size: 401 evaluation samples
370
- * Columns: <code>text1</code>, <code>text2</code>, and <code>label</code>
371
- * Approximate statistics based on the first 401 samples:
372
- | | text1 | text2 | label |
373
- |:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:------------------------------------------------|
374
- | type | string | string | int |
375
- | details | <ul><li>min: 4 tokens</li><li>mean: 8.23 tokens</li><li>max: 15 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 7.46 tokens</li><li>max: 14 tokens</li></ul> | <ul><li>0: ~73.13%</li><li>1: ~26.87%</li></ul> |
376
- * Samples:
377
- | text1 | text2 | label |
378
- |:-----------------------------|:------------------------------|:---------------|
379
- | <code>棚からトマトがなくなってたから</code> | <code>トマトが棚からなくなっていたから</code> | <code>1</code> |
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- | <code>欲しくない</code> | <code>家の中へ行こう</code> | <code>0</code> |
381
- | <code>昨日は何を作ったの?</code> | <code>ビーフシチュー食べた?</code> | <code>0</code> |
382
- * Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
383
- ```json
384
- {
385
- "loss_fct": "torch.nn.modules.loss.MSELoss"
386
- }
387
- ```
388
-
389
- ### Training Hyperparameters
390
- #### Non-Default Hyperparameters
391
-
392
- - `eval_strategy`: epoch
393
- - `learning_rate`: 2e-05
394
- - `num_train_epochs`: 10
395
- - `warmup_ratio`: 0.4
396
- - `fp16`: True
397
- - `batch_sampler`: no_duplicates
398
-
399
- #### All Hyperparameters
400
- <details><summary>Click to expand</summary>
401
-
402
- - `overwrite_output_dir`: False
403
- - `do_predict`: False
404
- - `eval_strategy`: epoch
405
- - `prediction_loss_only`: True
406
- - `per_device_train_batch_size`: 8
407
- - `per_device_eval_batch_size`: 8
408
- - `per_gpu_train_batch_size`: None
409
- - `per_gpu_eval_batch_size`: None
410
- - `gradient_accumulation_steps`: 1
411
- - `eval_accumulation_steps`: None
412
- - `torch_empty_cache_steps`: None
413
- - `learning_rate`: 2e-05
414
- - `weight_decay`: 0.0
415
- - `adam_beta1`: 0.9
416
- - `adam_beta2`: 0.999
417
- - `adam_epsilon`: 1e-08
418
- - `max_grad_norm`: 1.0
419
- - `num_train_epochs`: 10
420
- - `max_steps`: -1
421
- - `lr_scheduler_type`: linear
422
- - `lr_scheduler_kwargs`: {}
423
- - `warmup_ratio`: 0.4
424
- - `warmup_steps`: 0
425
- - `log_level`: passive
426
- - `log_level_replica`: warning
427
- - `log_on_each_node`: True
428
- - `logging_nan_inf_filter`: True
429
- - `save_safetensors`: True
430
- - `save_on_each_node`: False
431
- - `save_only_model`: False
432
- - `restore_callback_states_from_checkpoint`: False
433
- - `no_cuda`: False
434
- - `use_cpu`: False
435
- - `use_mps_device`: False
436
- - `seed`: 42
437
- - `data_seed`: None
438
- - `jit_mode_eval`: False
439
- - `use_ipex`: False
440
- - `bf16`: False
441
- - `fp16`: True
442
- - `fp16_opt_level`: O1
443
- - `half_precision_backend`: auto
444
- - `bf16_full_eval`: False
445
- - `fp16_full_eval`: False
446
- - `tf32`: None
447
- - `local_rank`: 0
448
- - `ddp_backend`: None
449
- - `tpu_num_cores`: None
450
- - `tpu_metrics_debug`: False
451
- - `debug`: []
452
- - `dataloader_drop_last`: False
453
- - `dataloader_num_workers`: 0
454
- - `dataloader_prefetch_factor`: None
455
- - `past_index`: -1
456
- - `disable_tqdm`: False
457
- - `remove_unused_columns`: True
458
- - `label_names`: None
459
- - `load_best_model_at_end`: False
460
- - `ignore_data_skip`: False
461
- - `fsdp`: []
462
- - `fsdp_min_num_params`: 0
463
- - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
464
- - `fsdp_transformer_layer_cls_to_wrap`: None
465
- - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
466
- - `deepspeed`: None
467
- - `label_smoothing_factor`: 0.0
468
- - `optim`: adamw_torch
469
- - `optim_args`: None
470
- - `adafactor`: False
471
- - `group_by_length`: False
472
- - `length_column_name`: length
473
- - `ddp_find_unused_parameters`: None
474
- - `ddp_bucket_cap_mb`: None
475
- - `ddp_broadcast_buffers`: False
476
- - `dataloader_pin_memory`: True
477
- - `dataloader_persistent_workers`: False
478
- - `skip_memory_metrics`: True
479
- - `use_legacy_prediction_loop`: False
480
- - `push_to_hub`: False
481
- - `resume_from_checkpoint`: None
482
- - `hub_model_id`: None
483
- - `hub_strategy`: every_save
484
- - `hub_private_repo`: False
485
- - `hub_always_push`: False
486
- - `gradient_checkpointing`: False
487
- - `gradient_checkpointing_kwargs`: None
488
- - `include_inputs_for_metrics`: False
489
- - `eval_do_concat_batches`: True
490
- - `fp16_backend`: auto
491
- - `push_to_hub_model_id`: None
492
- - `push_to_hub_organization`: None
493
- - `mp_parameters`:
494
- - `auto_find_batch_size`: False
495
- - `full_determinism`: False
496
- - `torchdynamo`: None
497
- - `ray_scope`: last
498
- - `ddp_timeout`: 1800
499
- - `torch_compile`: False
500
- - `torch_compile_backend`: None
501
- - `torch_compile_mode`: None
502
- - `dispatch_batches`: None
503
- - `split_batches`: None
504
- - `include_tokens_per_second`: False
505
- - `include_num_input_tokens_seen`: False
506
- - `neftune_noise_alpha`: None
507
- - `optim_target_modules`: None
508
- - `batch_eval_metrics`: False
509
- - `eval_on_start`: False
510
- - `eval_use_gather_object`: False
511
- - `batch_sampler`: no_duplicates
512
- - `multi_dataset_batch_sampler`: proportional
513
-
514
- </details>
515
-
516
- ### Training Logs
517
- | Epoch | Step | Training Loss | loss | custom-arc-semantics-data-jp_max_ap |
518
- |:-----:|:----:|:-------------:|:------:|:-----------------------------------:|
519
- | 1.0 | 25 | 0.2393 | 0.2390 | 0.5655 |
520
- | 2.0 | 50 | 0.1724 | 0.1689 | 0.6075 |
521
- | 3.0 | 75 | 0.1046 | 0.1326 | 0.6478 |
522
- | 4.0 | 100 | 0.062 | 0.1183 | 0.6618 |
523
- | 5.0 | 125 | 0.0349 | 0.1158 | 0.6683 |
524
- | 6.0 | 150 | 0.0258 | 0.1142 | 0.6772 |
525
- | 7.0 | 175 | 0.0211 | 0.1168 | 0.6739 |
526
- | 8.0 | 200 | 0.0204 | 0.1180 | 0.6765 |
527
- | 9.0 | 225 | 0.0194 | 0.1178 | 0.6869 |
528
- | 10.0 | 250 | 0.0185 | 0.1180 | 0.6865 |
529
-
530
-
531
- ### Framework Versions
532
- - Python: 3.10.14
533
- - Sentence Transformers: 3.1.0
534
- - Transformers: 4.44.2
535
- - PyTorch: 2.4.1+cu121
536
- - Accelerate: 0.34.2
537
- - Datasets: 2.20.0
538
- - Tokenizers: 0.19.1
539
-
540
- ## Citation
541
-
542
- ### BibTeX
543
-
544
- #### Sentence Transformers
545
- ```bibtex
546
- @inproceedings{reimers-2019-sentence-bert,
547
- title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
548
- author = "Reimers, Nils and Gurevych, Iryna",
549
- booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
550
- month = "11",
551
- year = "2019",
552
- publisher = "Association for Computational Linguistics",
553
- url = "https://arxiv.org/abs/1908.10084",
554
- }
555
- ```
556
-
557
- <!--
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- ## Glossary
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- -->
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  ## Model Card Contact
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1
  ---
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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
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
6
  ---
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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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+ - **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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+ ## 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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  ### Recommendations
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+ ## How to Get Started with the Model
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  ## Training Details
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+ ## Evaluation
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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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157
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158
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