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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:ContrastiveLoss
48
- widget:
49
- - source_sentence: 猫のぬいぐるみ
50
- sentences:
51
- - 猫の人形
52
- - 昨日作ったのはチキンヌードル?
53
- - どんな魔法なの?
54
- - source_sentence: キにスカーフが引っかかってる
55
- sentences:
56
- - 猫好き
57
- - どうしてキャンドルなの?
58
- - あの木の上のやつ、スカーフ?
59
- - source_sentence: お気に入りの食べ物は?
60
- sentences:
61
- - 好きだよ
62
- - 好きな食べ物は?
63
- - ゆうべはなにをたべたの?
64
- - source_sentence: あなたは魔法使いですか?
65
- sentences:
66
- - 昨晩井戸を使った?
67
- - 井戸へ行ったことある?
68
- - 魔法を使える人
69
- - source_sentence: どう思う?
70
- sentences:
71
- - 井戸へ行ったことある?
72
- - なんて言った?
73
- - 井戸へ訪れた?
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: 0.75
86
- name: Cosine Accuracy
87
- - type: cosine_accuracy_threshold
88
- value: 0.94242924451828
89
- name: Cosine Accuracy Threshold
90
- - type: cosine_f1
91
- value: 0.8191489361702128
92
- name: Cosine F1
93
- - type: cosine_f1_threshold
94
- value: 0.94242924451828
95
- name: Cosine F1 Threshold
96
- - type: cosine_precision
97
- value: 0.7129629629629629
98
- name: Cosine Precision
99
- - type: cosine_recall
100
- value: 0.9625
101
- name: Cosine Recall
102
- - type: cosine_ap
103
- value: 0.7937482383802095
104
- name: Cosine Ap
105
- - type: dot_accuracy
106
- value: 0.7352941176470589
107
- name: Dot Accuracy
108
- - type: dot_accuracy_threshold
109
- value: 606.192138671875
110
- name: Dot Accuracy Threshold
111
- - type: dot_f1
112
- value: 0.8042328042328042
113
- name: Dot F1
114
- - type: dot_f1_threshold
115
- value: 591.55224609375
116
- name: Dot F1 Threshold
117
- - type: dot_precision
118
- value: 0.6972477064220184
119
- name: Dot Precision
120
- - type: dot_recall
121
- value: 0.95
122
- name: Dot Recall
123
- - type: dot_ap
124
- value: 0.7795070974315252
125
- name: Dot Ap
126
- - type: manhattan_accuracy
127
- value: 0.75
128
- name: Manhattan Accuracy
129
- - type: manhattan_accuracy_threshold
130
- value: 187.74122619628906
131
- name: Manhattan Accuracy Threshold
132
- - type: manhattan_f1
133
- value: 0.8191489361702128
134
- name: Manhattan F1
135
- - type: manhattan_f1_threshold
136
- value: 187.74122619628906
137
- name: Manhattan F1 Threshold
138
- - type: manhattan_precision
139
- value: 0.7129629629629629
140
- name: Manhattan Precision
141
- - type: manhattan_recall
142
- value: 0.9625
143
- name: Manhattan Recall
144
- - type: manhattan_ap
145
- value: 0.7929777714049108
146
- name: Manhattan Ap
147
- - type: euclidean_accuracy
148
- value: 0.75
149
- name: Euclidean Accuracy
150
- - type: euclidean_accuracy_threshold
151
- value: 8.505434036254883
152
- name: Euclidean Accuracy Threshold
153
- - type: euclidean_f1
154
- value: 0.8191489361702128
155
- name: Euclidean F1
156
- - type: euclidean_f1_threshold
157
- value: 8.505434036254883
158
- name: Euclidean F1 Threshold
159
- - type: euclidean_precision
160
- value: 0.7129629629629629
161
- name: Euclidean Precision
162
- - type: euclidean_recall
163
- value: 0.9625
164
- name: Euclidean Recall
165
- - type: euclidean_ap
166
- value: 0.7939427298225319
167
- name: Euclidean Ap
168
- - type: max_accuracy
169
- value: 0.75
170
- name: Max Accuracy
171
- - type: max_accuracy_threshold
172
- value: 606.192138671875
173
- name: Max Accuracy Threshold
174
- - type: max_f1
175
- value: 0.8191489361702128
176
- name: Max F1
177
- - type: max_f1_threshold
178
- value: 591.55224609375
179
- name: Max F1 Threshold
180
- - type: max_precision
181
- value: 0.7129629629629629
182
- name: Max Precision
183
- - type: max_recall
184
- value: 0.9625
185
- name: Max Recall
186
- - type: max_ap
187
- value: 0.7939427298225319
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
- 'どう思う?',
242
- 'なんて言った?',
243
- '井戸へ行ったことある?',
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 | 0.75 |
290
- | cosine_accuracy_threshold | 0.9424 |
291
- | cosine_f1 | 0.8191 |
292
- | cosine_f1_threshold | 0.9424 |
293
- | cosine_precision | 0.713 |
294
- | cosine_recall | 0.9625 |
295
- | cosine_ap | 0.7937 |
296
- | dot_accuracy | 0.7353 |
297
- | dot_accuracy_threshold | 606.1921 |
298
- | dot_f1 | 0.8042 |
299
- | dot_f1_threshold | 591.5522 |
300
- | dot_precision | 0.6972 |
301
- | dot_recall | 0.95 |
302
- | dot_ap | 0.7795 |
303
- | manhattan_accuracy | 0.75 |
304
- | manhattan_accuracy_threshold | 187.7412 |
305
- | manhattan_f1 | 0.8191 |
306
- | manhattan_f1_threshold | 187.7412 |
307
- | manhattan_precision | 0.713 |
308
- | manhattan_recall | 0.9625 |
309
- | manhattan_ap | 0.793 |
310
- | euclidean_accuracy | 0.75 |
311
- | euclidean_accuracy_threshold | 8.5054 |
312
- | euclidean_f1 | 0.8191 |
313
- | euclidean_f1_threshold | 8.5054 |
314
- | euclidean_precision | 0.713 |
315
- | euclidean_recall | 0.9625 |
316
- | euclidean_ap | 0.7939 |
317
- | max_accuracy | 0.75 |
318
- | max_accuracy_threshold | 606.1921 |
319
- | max_f1 | 0.8191 |
320
- | max_f1_threshold | 591.5522 |
321
- | max_precision | 0.713 |
322
- | max_recall | 0.9625 |
323
- | **max_ap** | **0.7939** |
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: 8.26 tokens</li><li>max: 15 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 7.99 tokens</li><li>max: 14 tokens</li></ul> | <ul><li>0: ~41.36%</li><li>1: ~58.64%</li></ul> |
351
- * Samples:
352
- | text1 | text2 | label |
353
- |:-----------------------|:---------------------|:---------------|
354
- | <code>外を調べよう</code> | <code>うん探そう</code> | <code>0</code> |
355
- | <code>キャンドルが欲しい</code> | <code>キャンドル頂戴</code> | <code>1</code> |
356
- | <code>欲しくない</code> | <code>家の中へ行こう</code> | <code>0</code> |
357
- * Loss: [<code>ContrastiveLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#contrastiveloss) with these parameters:
358
- ```json
359
- {
360
- "distance_metric": "SiameseDistanceMetric.COSINE_DISTANCE",
361
- "margin": 0.1,
362
- "size_average": true
363
- }
364
- ```
365
-
366
- ### Evaluation Dataset
367
-
368
- #### csv
369
-
370
- * Dataset: csv
371
- * Size: 680 evaluation samples
372
- * Columns: <code>text1</code>, <code>text2</code>, and <code>label</code>
373
- * Approximate statistics based on the first 680 samples:
374
- | | text1 | text2 | label |
375
- |:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:------------------------------------------------|
376
- | type | string | string | int |
377
- | details | <ul><li>min: 4 tokens</li><li>mean: 8.46 tokens</li><li>max: 14 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 8.04 tokens</li><li>max: 14 tokens</li></ul> | <ul><li>0: ~41.18%</li><li>1: ~58.82%</li></ul> |
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- * Samples:
379
- | text1 | text2 | label |
380
- |:---------------------------|:--------------------------|:---------------|
381
- | <code>みんなどんな魔法を使うの?</code> | <code>村人たちの魔法を教えて?</code> | <code>1</code> |
382
- | <code>質問なんだっけ?</code> | <code>いやだ</code> | <code>0</code> |
383
- | <code>村人はどんな呪文を使うの?</code> | <code>スパイク</code> | <code>0</code> |
384
- * Loss: [<code>ContrastiveLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#contrastiveloss) with these parameters:
385
- ```json
386
- {
387
- "distance_metric": "SiameseDistanceMetric.COSINE_DISTANCE",
388
- "margin": 0.1,
389
- "size_average": true
390
- }
391
- ```
392
-
393
- ### Training Hyperparameters
394
- #### Non-Default Hyperparameters
395
-
396
- - `eval_strategy`: epoch
397
- - `learning_rate`: 2e-05
398
- - `num_train_epochs`: 5
399
- - `warmup_ratio`: 0.1
400
- - `fp16`: True
401
- - `batch_sampler`: no_duplicates
402
-
403
- #### All Hyperparameters
404
- <details><summary>Click to expand</summary>
405
-
406
- - `overwrite_output_dir`: False
407
- - `do_predict`: False
408
- - `eval_strategy`: epoch
409
- - `prediction_loss_only`: True
410
- - `per_device_train_batch_size`: 8
411
- - `per_device_eval_batch_size`: 8
412
- - `per_gpu_train_batch_size`: None
413
- - `per_gpu_eval_batch_size`: None
414
- - `gradient_accumulation_steps`: 1
415
- - `eval_accumulation_steps`: None
416
- - `torch_empty_cache_steps`: None
417
- - `learning_rate`: 2e-05
418
- - `weight_decay`: 0.0
419
- - `adam_beta1`: 0.9
420
- - `adam_beta2`: 0.999
421
- - `adam_epsilon`: 1e-08
422
- - `max_grad_norm`: 1.0
423
- - `num_train_epochs`: 5
424
- - `max_steps`: -1
425
- - `lr_scheduler_type`: linear
426
- - `lr_scheduler_kwargs`: {}
427
- - `warmup_ratio`: 0.1
428
- - `warmup_steps`: 0
429
- - `log_level`: passive
430
- - `log_level_replica`: warning
431
- - `log_on_each_node`: True
432
- - `logging_nan_inf_filter`: True
433
- - `save_safetensors`: True
434
- - `save_on_each_node`: False
435
- - `save_only_model`: False
436
- - `restore_callback_states_from_checkpoint`: False
437
- - `no_cuda`: False
438
- - `use_cpu`: False
439
- - `use_mps_device`: False
440
- - `seed`: 42
441
- - `data_seed`: None
442
- - `jit_mode_eval`: False
443
- - `use_ipex`: False
444
- - `bf16`: False
445
- - `fp16`: True
446
- - `fp16_opt_level`: O1
447
- - `half_precision_backend`: auto
448
- - `bf16_full_eval`: False
449
- - `fp16_full_eval`: False
450
- - `tf32`: None
451
- - `local_rank`: 0
452
- - `ddp_backend`: None
453
- - `tpu_num_cores`: None
454
- - `tpu_metrics_debug`: False
455
- - `debug`: []
456
- - `dataloader_drop_last`: False
457
- - `dataloader_num_workers`: 0
458
- - `dataloader_prefetch_factor`: None
459
- - `past_index`: -1
460
- - `disable_tqdm`: False
461
- - `remove_unused_columns`: True
462
- - `label_names`: None
463
- - `load_best_model_at_end`: False
464
- - `ignore_data_skip`: False
465
- - `fsdp`: []
466
- - `fsdp_min_num_params`: 0
467
- - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
468
- - `fsdp_transformer_layer_cls_to_wrap`: None
469
- - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
470
- - `deepspeed`: None
471
- - `label_smoothing_factor`: 0.0
472
- - `optim`: adamw_torch
473
- - `optim_args`: None
474
- - `adafactor`: False
475
- - `group_by_length`: False
476
- - `length_column_name`: length
477
- - `ddp_find_unused_parameters`: None
478
- - `ddp_bucket_cap_mb`: None
479
- - `ddp_broadcast_buffers`: False
480
- - `dataloader_pin_memory`: True
481
- - `dataloader_persistent_workers`: False
482
- - `skip_memory_metrics`: True
483
- - `use_legacy_prediction_loop`: False
484
- - `push_to_hub`: False
485
- - `resume_from_checkpoint`: None
486
- - `hub_model_id`: None
487
- - `hub_strategy`: every_save
488
- - `hub_private_repo`: False
489
- - `hub_always_push`: False
490
- - `gradient_checkpointing`: False
491
- - `gradient_checkpointing_kwargs`: None
492
- - `include_inputs_for_metrics`: False
493
- - `eval_do_concat_batches`: True
494
- - `fp16_backend`: auto
495
- - `push_to_hub_model_id`: None
496
- - `push_to_hub_organization`: None
497
- - `mp_parameters`:
498
- - `auto_find_batch_size`: False
499
- - `full_determinism`: False
500
- - `torchdynamo`: None
501
- - `ray_scope`: last
502
- - `ddp_timeout`: 1800
503
- - `torch_compile`: False
504
- - `torch_compile_backend`: None
505
- - `torch_compile_mode`: None
506
- - `dispatch_batches`: None
507
- - `split_batches`: None
508
- - `include_tokens_per_second`: False
509
- - `include_num_input_tokens_seen`: False
510
- - `neftune_noise_alpha`: None
511
- - `optim_target_modules`: None
512
- - `batch_eval_metrics`: False
513
- - `eval_on_start`: False
514
- - `eval_use_gather_object`: False
515
- - `batch_sampler`: no_duplicates
516
- - `multi_dataset_batch_sampler`: proportional
517
-
518
- </details>
519
-
520
- ### Training Logs
521
- | Epoch | Step | Training Loss | loss | custom-arc-semantics-data-jp_max_ap |
522
- |:-----:|:----:|:-------------:|:------:|:-----------------------------------:|
523
- | None | 0 | - | - | 0.7819 |
524
- | 1.0 | 68 | 0.0059 | 0.0011 | 0.7630 |
525
- | 2.0 | 136 | 0.0008 | 0.0010 | 0.7782 |
526
- | 3.0 | 204 | 0.0007 | 0.0009 | 0.7862 |
527
- | 4.0 | 272 | 0.0007 | 0.0009 | 0.7916 |
528
- | 5.0 | 340 | 0.0007 | 0.0009 | 0.7939 |
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
- #### ContrastiveLoss
558
- ```bibtex
559
- @inproceedings{hadsell2006dimensionality,
560
- author={Hadsell, R. and Chopra, S. and LeCun, Y.},
561
- booktitle={2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06)},
562
- title={Dimensionality Reduction by Learning an Invariant Mapping},
563
- year={2006},
564
- volume={2},
565
- number={},
566
- pages={1735-1742},
567
- doi={10.1109/CVPR.2006.100}
568
- }
569
- ```
570
-
571
- <!--
572
- ## Glossary
573
-
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- *Clearly define terms in order to be accessible across audiences.*
575
- -->
576
-
577
- <!--
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- ## Model Card Authors
579
-
580
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581
- -->
582
-
583
- <!--
584
  ## Model Card Contact
585
 
586
- *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
587
- -->
 
1
  ---
2
  base_model: colorfulscoop/sbert-base-ja
3
+ language: ja
4
+ license: cc-by-sa-4.0
5
+ model_name: LeoChiuu/sbert-base-ja-arc
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
6
  ---
7
 
8
+ # Model Card for LeoChiuu/sbert-base-ja-arc
9
+
10
+ <!-- Provide a quick summary of what the model is/does. -->
11
+
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  ## Model Details
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  ### Model Description
 
 
 
 
 
 
 
 
 
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18
+ <!-- Provide a longer summary of what this model is. -->
19
 
20
+ 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]
26
+ - **Language(s) (NLP):** ja
27
+ - **License:** cc-by-sa-4.0
28
+ - **Finetuned from model [optional]:** colorfulscoop/sbert-base-ja
29
 
30
+ ### 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]
37
 
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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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+ ### Out-of-Scope Use
 
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+ ## Bias, Risks, and Limitations
 
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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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+
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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.
71
+
72
+ ## How to Get Started with the Model
73
+
74
+ 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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+ ### 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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+
90
+ #### Preprocessing [optional]
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+
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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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+
99
+ #### 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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+
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+ [More Information Needed]
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+
105
+ ## Evaluation
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107
+ <!-- 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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+ [More Information Needed]
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+ #### Factors
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+
119
+ <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
120
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+ [More Information Needed]
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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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129
+ ### Results
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+ [More Information Needed]
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+
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+ #### Summary
134
+
135
+
136
+
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+ ## Model Examination [optional]
138
+
139
+ <!-- Relevant interpretability work for the model goes here -->
140
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141
+ [More Information Needed]
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+
143
+ ## Environmental Impact
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+
145
+ <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
146
+
147
+ 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).
148
+
149
+ - **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]
154
+
155
+ ## Technical Specifications [optional]
156
+
157
+ ### Model Architecture and Objective
158
+
159
+ [More Information Needed]
160
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161
+ ### Compute Infrastructure
162
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163
+ [More Information Needed]
164
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165
+ #### Hardware
166
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+ [More Information Needed]
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+ #### Software
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+
173
+ ## Citation [optional]
174
+
175
+ <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
176
+
177
+ **BibTeX:**
178
+
179
+ [More Information Needed]
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+
181
+ **APA:**
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183
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185
+ ## Glossary [optional]
186
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187
+ <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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189
+ [More Information Needed]
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191
+ ## More Information [optional]
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+ ## Model Card Authors [optional]
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+
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ## Model Card Contact
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