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1
  ---
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:53
47
- - loss:OnlineContrastiveLoss
48
- model-index:
49
- - name: SentenceTransformer based on colorfulscoop/sbert-base-ja
50
- results:
51
- - task:
52
- type: binary-classification
53
- name: Binary Classification
54
- dataset:
55
- name: custom arc semantics data jp
56
- type: custom-arc-semantics-data-jp
57
- metrics:
58
- - type: cosine_accuracy
59
- value: 0.6666666666666666
60
- name: Cosine Accuracy
61
- - type: cosine_accuracy_threshold
62
- value: 0.9126271605491638
63
- name: Cosine Accuracy Threshold
64
- - type: cosine_f1
65
- value: 0.8000000000000002
66
- name: Cosine F1
67
- - type: cosine_f1_threshold
68
- value: 0.8779952526092529
69
- name: Cosine F1 Threshold
70
- - type: cosine_precision
71
- value: 0.8
72
- name: Cosine Precision
73
- - type: cosine_recall
74
- value: 0.8
75
- name: Cosine Recall
76
- - type: cosine_ap
77
- value: 0.9266666666666665
78
- name: Cosine Ap
79
- - type: dot_accuracy
80
- value: 0.8333333333333334
81
- name: Dot Accuracy
82
- - type: dot_accuracy_threshold
83
- value: 504.3712158203125
84
- name: Dot Accuracy Threshold
85
- - type: dot_f1
86
- value: 0.888888888888889
87
- name: Dot F1
88
- - type: dot_f1_threshold
89
- value: 504.3712158203125
90
- name: Dot F1 Threshold
91
- - type: dot_precision
92
- value: 1.0
93
- name: Dot Precision
94
- - type: dot_recall
95
- value: 0.8
96
- name: Dot Recall
97
- - type: dot_ap
98
- value: 0.9666666666666666
99
- name: Dot Ap
100
- - type: manhattan_accuracy
101
- value: 0.6666666666666666
102
- name: Manhattan Accuracy
103
- - type: manhattan_accuracy_threshold
104
- value: 217.20816040039062
105
- name: Manhattan Accuracy Threshold
106
- - type: manhattan_f1
107
- value: 0.8000000000000002
108
- name: Manhattan F1
109
- - type: manhattan_f1_threshold
110
- value: 259.5025939941406
111
- name: Manhattan F1 Threshold
112
- - type: manhattan_precision
113
- value: 0.8
114
- name: Manhattan Precision
115
- - type: manhattan_recall
116
- value: 0.8
117
- name: Manhattan Recall
118
- - type: manhattan_ap
119
- value: 0.9266666666666665
120
- name: Manhattan Ap
121
- - type: euclidean_accuracy
122
- value: 0.6666666666666666
123
- name: Euclidean Accuracy
124
- - type: euclidean_accuracy_threshold
125
- value: 9.874061584472656
126
- name: Euclidean Accuracy Threshold
127
- - type: euclidean_f1
128
- value: 0.8000000000000002
129
- name: Euclidean F1
130
- - type: euclidean_f1_threshold
131
- value: 11.74197006225586
132
- name: Euclidean F1 Threshold
133
- - type: euclidean_precision
134
- value: 0.8
135
- name: Euclidean Precision
136
- - type: euclidean_recall
137
- value: 0.8
138
- name: Euclidean Recall
139
- - type: euclidean_ap
140
- value: 0.9266666666666665
141
- name: Euclidean Ap
142
- - type: max_accuracy
143
- value: 0.8333333333333334
144
- name: Max Accuracy
145
- - type: max_accuracy_threshold
146
- value: 504.3712158203125
147
- name: Max Accuracy Threshold
148
- - type: max_f1
149
- value: 0.888888888888889
150
- name: Max F1
151
- - type: max_f1_threshold
152
- value: 504.3712158203125
153
- name: Max F1 Threshold
154
- - type: max_precision
155
- value: 1.0
156
- name: Max Precision
157
- - type: max_recall
158
- value: 0.8
159
- name: Max Recall
160
- - type: max_ap
161
- value: 0.9666666666666666
162
- name: Max Ap
163
  ---
164
 
165
- # SentenceTransformer based on colorfulscoop/sbert-base-ja
 
 
 
166
 
167
- 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.
168
 
169
  ## Model Details
170
 
171
  ### Model Description
172
- - **Model Type:** Sentence Transformer
173
- - **Base model:** [colorfulscoop/sbert-base-ja](https://huggingface.co/colorfulscoop/sbert-base-ja) <!-- at revision ecb8a98cd5176719ff7ab0d770a27420118732cf -->
174
- - **Maximum Sequence Length:** 512 tokens
175
- - **Output Dimensionality:** 768 tokens
176
- - **Similarity Function:** Cosine Similarity
177
- - **Training Dataset:**
178
- - csv
179
- <!-- - **Language:** Unknown -->
180
- <!-- - **License:** Unknown -->
181
 
182
- ### Model Sources
183
 
184
- - **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
185
- - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
186
- - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
187
 
188
- ### Full Model Architecture
 
 
 
 
 
 
189
 
190
- ```
191
- SentenceTransformer(
192
- (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
193
- (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})
194
- )
195
- ```
196
 
197
- ## Usage
198
 
199
- ### Direct Usage (Sentence Transformers)
 
 
200
 
201
- First install the Sentence Transformers library:
202
 
203
- ```bash
204
- pip install -U sentence-transformers
205
- ```
206
 
207
- Then you can load this model and run inference.
208
- ```python
209
- from sentence_transformers import SentenceTransformer
210
 
211
- # Download from the 🤗 Hub
212
- model = SentenceTransformer("sentence_transformers_model_id")
213
- # Run inference
214
- sentences = [
215
- 'The weather is lovely today.',
216
- "It's so sunny outside!",
217
- 'He drove to the stadium.',
218
- ]
219
- embeddings = model.encode(sentences)
220
- print(embeddings.shape)
221
- # [3, 768]
222
 
223
- # Get the similarity scores for the embeddings
224
- similarities = model.similarity(embeddings, embeddings)
225
- print(similarities.shape)
226
- # [3, 3]
227
- ```
228
 
229
- <!--
230
- ### Direct Usage (Transformers)
231
 
232
- <details><summary>Click to see the direct usage in Transformers</summary>
233
 
234
- </details>
235
- -->
236
 
237
- <!--
238
- ### Downstream Usage (Sentence Transformers)
239
 
240
- You can finetune this model on your own dataset.
241
 
242
- <details><summary>Click to expand</summary>
243
 
244
- </details>
245
- -->
246
 
247
- <!--
248
- ### Out-of-Scope Use
249
 
250
- *List how the model may foreseeably be misused and address what users ought not to do with the model.*
251
- -->
252
 
253
- ## Evaluation
254
-
255
- ### Metrics
256
-
257
- #### Binary Classification
258
- * Dataset: `custom-arc-semantics-data-jp`
259
- * Evaluated with [<code>BinaryClassificationEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator)
260
-
261
- | Metric | Value |
262
- |:-----------------------------|:-----------|
263
- | cosine_accuracy | 0.6667 |
264
- | cosine_accuracy_threshold | 0.9126 |
265
- | cosine_f1 | 0.8 |
266
- | cosine_f1_threshold | 0.878 |
267
- | cosine_precision | 0.8 |
268
- | cosine_recall | 0.8 |
269
- | cosine_ap | 0.9267 |
270
- | dot_accuracy | 0.8333 |
271
- | dot_accuracy_threshold | 504.3712 |
272
- | dot_f1 | 0.8889 |
273
- | dot_f1_threshold | 504.3712 |
274
- | dot_precision | 1.0 |
275
- | dot_recall | 0.8 |
276
- | dot_ap | 0.9667 |
277
- | manhattan_accuracy | 0.6667 |
278
- | manhattan_accuracy_threshold | 217.2082 |
279
- | manhattan_f1 | 0.8 |
280
- | manhattan_f1_threshold | 259.5026 |
281
- | manhattan_precision | 0.8 |
282
- | manhattan_recall | 0.8 |
283
- | manhattan_ap | 0.9267 |
284
- | euclidean_accuracy | 0.6667 |
285
- | euclidean_accuracy_threshold | 9.8741 |
286
- | euclidean_f1 | 0.8 |
287
- | euclidean_f1_threshold | 11.742 |
288
- | euclidean_precision | 0.8 |
289
- | euclidean_recall | 0.8 |
290
- | euclidean_ap | 0.9267 |
291
- | max_accuracy | 0.8333 |
292
- | max_accuracy_threshold | 504.3712 |
293
- | max_f1 | 0.8889 |
294
- | max_f1_threshold | 504.3712 |
295
- | max_precision | 1.0 |
296
- | max_recall | 0.8 |
297
- | **max_ap** | **0.9667** |
298
-
299
- <!--
300
- ## Bias, Risks and Limitations
301
-
302
- *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
303
- -->
304
-
305
- <!--
306
  ### Recommendations
307
 
308
- *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
309
- -->
 
 
 
 
 
 
 
310
 
311
  ## Training Details
312
 
313
- ### Training Dataset
314
-
315
- #### csv
316
-
317
- * Dataset: csv
318
- * Size: 53 training samples
319
- * Columns: <code>text1</code>, <code>text2</code>, and <code>label</code>
320
- * Approximate statistics based on the first 53 samples:
321
- | | text1 | text2 | label |
322
- |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:------------------------------------------------|
323
- | type | string | string | int |
324
- | details | <ul><li>min: 14 tokens</li><li>mean: 35.94 tokens</li><li>max: 84 tokens</li></ul> | <ul><li>min: 11 tokens</li><li>mean: 21.72 tokens</li><li>max: 38 tokens</li></ul> | <ul><li>0: ~38.30%</li><li>1: ~61.70%</li></ul> |
325
- * Samples:
326
- | text1 | text2 | label |
327
- |:-----------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------|:---------------|
328
- | <code>茶色 の ドレス を 着た 若い 女の子 と サンダル が 黒い 帽子 、 タンクトップ 、 青い カーゴ ショーツ を 着た 若い 男の子 を 、 同じ ボール に 向かって 銀 の ボール を 投げ つける ように 笑い ます 。</code> | <code>人々 は ハンバーガー を 待って い ます 。</code> | <code>1</code> |
329
- | <code>水 の 近く の ドック に 2 人 が 座って い ます 。</code> | <code>岩 の 上 に 座って いる 二 人</code> | <code>0</code> |
330
- | <code>小さな 女の子 横切って 向かって 走り ます 。</code> | <code>女の子 かつて 立って いた 裏庭 を 見 ながら 中 に い ました 。</code> | <code>1</code> |
331
- * Loss: [<code>OnlineContrastiveLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#onlinecontrastiveloss)
332
-
333
- ### Evaluation Dataset
334
-
335
- #### csv
336
-
337
- * Dataset: csv
338
- * Size: 53 evaluation samples
339
- * Columns: <code>text1</code>, <code>text2</code>, and <code>label</code>
340
- * Approximate statistics based on the first 53 samples:
341
- | | text1 | text2 | label |
342
- |:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:------------------------------------------------|
343
- | type | string | string | int |
344
- | details | <ul><li>min: 19 tokens</li><li>mean: 38.67 tokens</li><li>max: 61 tokens</li></ul> | <ul><li>min: 20 tokens</li><li>mean: 25.5 tokens</li><li>max: 33 tokens</li></ul> | <ul><li>0: ~16.67%</li><li>1: ~83.33%</li></ul> |
345
- * Samples:
346
- | text1 | text2 | label |
347
- |:----------------------------------------------------------------------------------------------------------|:------------------------------------------------|:---------------|
348
- | <code>岩 の 多い 景色 を 見て 二 人</code> | <code>何 か を 見て いる 二 人 が い ます 。</code> | <code>0</code> |
349
- | <code>白い ヘルメット と オレンジ色 の シャツ 、 ジーンズ 、 白い トラック と オレンジ色 の パイロン の 前 に 反射 ジャケット を 着た 金髪 の ストリート ワーカー 。</code> | <code>ストリート ワーカー は 保護 具 を 着用 して い ませ ん 。</code> | <code>1</code> |
350
- | <code>白い 帽子 を かぶった 女性 が 、 鮮やかな 色 の 岩 の 風景 を 描いて い ます 。 岩 層 自体 が 背景 に 見え ます 。</code> | <code>誰 か が 肖像 画 を 描いて い ます 。</code> | <code>1</code> |
351
- * Loss: [<code>OnlineContrastiveLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#onlinecontrastiveloss)
352
-
353
- ### Training Hyperparameters
354
- #### Non-Default Hyperparameters
355
-
356
- - `eval_strategy`: epoch
357
- - `learning_rate`: 4e-05
358
- - `num_train_epochs`: 14
359
- - `warmup_ratio`: 0.4
360
- - `fp16`: True
361
- - `batch_sampler`: no_duplicates
362
-
363
- #### All Hyperparameters
364
- <details><summary>Click to expand</summary>
365
-
366
- - `overwrite_output_dir`: False
367
- - `do_predict`: False
368
- - `eval_strategy`: epoch
369
- - `prediction_loss_only`: True
370
- - `per_device_train_batch_size`: 8
371
- - `per_device_eval_batch_size`: 8
372
- - `per_gpu_train_batch_size`: None
373
- - `per_gpu_eval_batch_size`: None
374
- - `gradient_accumulation_steps`: 1
375
- - `eval_accumulation_steps`: None
376
- - `torch_empty_cache_steps`: None
377
- - `learning_rate`: 4e-05
378
- - `weight_decay`: 0.0
379
- - `adam_beta1`: 0.9
380
- - `adam_beta2`: 0.999
381
- - `adam_epsilon`: 1e-08
382
- - `max_grad_norm`: 1.0
383
- - `num_train_epochs`: 14
384
- - `max_steps`: -1
385
- - `lr_scheduler_type`: linear
386
- - `lr_scheduler_kwargs`: {}
387
- - `warmup_ratio`: 0.4
388
- - `warmup_steps`: 0
389
- - `log_level`: passive
390
- - `log_level_replica`: warning
391
- - `log_on_each_node`: True
392
- - `logging_nan_inf_filter`: True
393
- - `save_safetensors`: True
394
- - `save_on_each_node`: False
395
- - `save_only_model`: False
396
- - `restore_callback_states_from_checkpoint`: False
397
- - `no_cuda`: False
398
- - `use_cpu`: False
399
- - `use_mps_device`: False
400
- - `seed`: 42
401
- - `data_seed`: None
402
- - `jit_mode_eval`: False
403
- - `use_ipex`: False
404
- - `bf16`: False
405
- - `fp16`: True
406
- - `fp16_opt_level`: O1
407
- - `half_precision_backend`: auto
408
- - `bf16_full_eval`: False
409
- - `fp16_full_eval`: False
410
- - `tf32`: None
411
- - `local_rank`: 0
412
- - `ddp_backend`: None
413
- - `tpu_num_cores`: None
414
- - `tpu_metrics_debug`: False
415
- - `debug`: []
416
- - `dataloader_drop_last`: False
417
- - `dataloader_num_workers`: 0
418
- - `dataloader_prefetch_factor`: None
419
- - `past_index`: -1
420
- - `disable_tqdm`: False
421
- - `remove_unused_columns`: True
422
- - `label_names`: None
423
- - `load_best_model_at_end`: False
424
- - `ignore_data_skip`: False
425
- - `fsdp`: []
426
- - `fsdp_min_num_params`: 0
427
- - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
428
- - `fsdp_transformer_layer_cls_to_wrap`: None
429
- - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
430
- - `deepspeed`: None
431
- - `label_smoothing_factor`: 0.0
432
- - `optim`: adamw_torch
433
- - `optim_args`: None
434
- - `adafactor`: False
435
- - `group_by_length`: False
436
- - `length_column_name`: length
437
- - `ddp_find_unused_parameters`: None
438
- - `ddp_bucket_cap_mb`: None
439
- - `ddp_broadcast_buffers`: False
440
- - `dataloader_pin_memory`: True
441
- - `dataloader_persistent_workers`: False
442
- - `skip_memory_metrics`: True
443
- - `use_legacy_prediction_loop`: False
444
- - `push_to_hub`: False
445
- - `resume_from_checkpoint`: None
446
- - `hub_model_id`: None
447
- - `hub_strategy`: every_save
448
- - `hub_private_repo`: False
449
- - `hub_always_push`: False
450
- - `gradient_checkpointing`: False
451
- - `gradient_checkpointing_kwargs`: None
452
- - `include_inputs_for_metrics`: False
453
- - `eval_do_concat_batches`: True
454
- - `fp16_backend`: auto
455
- - `push_to_hub_model_id`: None
456
- - `push_to_hub_organization`: None
457
- - `mp_parameters`:
458
- - `auto_find_batch_size`: False
459
- - `full_determinism`: False
460
- - `torchdynamo`: None
461
- - `ray_scope`: last
462
- - `ddp_timeout`: 1800
463
- - `torch_compile`: False
464
- - `torch_compile_backend`: None
465
- - `torch_compile_mode`: None
466
- - `dispatch_batches`: None
467
- - `split_batches`: None
468
- - `include_tokens_per_second`: False
469
- - `include_num_input_tokens_seen`: False
470
- - `neftune_noise_alpha`: None
471
- - `optim_target_modules`: None
472
- - `batch_eval_metrics`: False
473
- - `eval_on_start`: False
474
- - `eval_use_gather_object`: False
475
- - `batch_sampler`: no_duplicates
476
- - `multi_dataset_batch_sampler`: proportional
477
-
478
- </details>
479
-
480
- ### Training Logs
481
- | Epoch | Step | Training Loss | loss | custom-arc-semantics-data-jp_max_ap |
482
- |:-----:|:----:|:-------------:|:------:|:-----------------------------------:|
483
- | 1.0 | 6 | 0.9921 | 0.4063 | 0.8767 |
484
- | 2.0 | 12 | 0.7955 | 0.3550 | 0.8767 |
485
- | 3.0 | 18 | 0.5309 | 0.2716 | 0.8767 |
486
- | 4.0 | 24 | 0.2716 | 0.2080 | 0.9267 |
487
- | 5.0 | 30 | 0.2395 | 0.1971 | 0.9667 |
488
- | 6.0 | 36 | 0.0401 | 0.1826 | 0.9667 |
489
- | 7.0 | 42 | 0.0403 | 0.0186 | 1.0 |
490
- | 8.0 | 48 | 0.0386 | 0.1842 | 0.9667 |
491
- | 9.0 | 54 | 0.0682 | 0.1904 | 0.9667 |
492
- | 10.0 | 60 | 0.001 | 0.1942 | 0.9667 |
493
- | 11.0 | 66 | 0.0013 | 0.1962 | 0.9667 |
494
- | 12.0 | 72 | 0.0016 | 0.1974 | 0.9667 |
495
- | 13.0 | 78 | 0.0013 | 0.1973 | 0.9667 |
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- | 14.0 | 84 | 0.0009 | 0.1972 | 0.9667 |
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-
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-
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- ### Framework Versions
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- - Python: 3.10.14
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- - Sentence Transformers: 3.1.0
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- - Transformers: 4.44.2
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- - PyTorch: 2.4.1+cu121
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- - Accelerate: 0.34.2
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- - Datasets: 2.20.0
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- - Tokenizers: 0.19.1
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-
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- ## Citation
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-
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- ### BibTeX
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-
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- #### Sentence Transformers
513
- ```bibtex
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- @inproceedings{reimers-2019-sentence-bert,
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- title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
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- author = "Reimers, Nils and Gurevych, Iryna",
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- booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
518
- month = "11",
519
- year = "2019",
520
- publisher = "Association for Computational Linguistics",
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- url = "https://arxiv.org/abs/1908.10084",
522
- }
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- ```
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-
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- <!--
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- ## Glossary
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- *Clearly define terms in order to be accessible across audiences.*
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  ## 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.*
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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
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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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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+ ## 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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+ 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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+ Use the code below to get started with the model.
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+ [More Information Needed]
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  ## Training Details
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+ ### Training Data
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+ ### 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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+ #### Preprocessing [optional]
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+ #### Training Hyperparameters
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+
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+ - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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+
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+ #### Speeds, Sizes, Times [optional]
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+ <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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+ [More Information Needed]
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+ ## Evaluation
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+ <!-- This section describes the evaluation protocols and provides the results. -->
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+ ### Testing Data, Factors & Metrics
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+ #### Factors
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+ #### Metrics
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+ ### Results
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+ #### Summary
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+ ## Model Examination [optional]
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+ <!-- Relevant interpretability work for the model goes here -->
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+ ## 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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+ - **Hardware Type:** [More Information Needed]
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+ - **Cloud Provider:** [More Information Needed]
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+ ## Technical Specifications [optional]
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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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+ ## Citation [optional]
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+ <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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+ **BibTeX:**
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+ ## Glossary [optional]
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