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  ---
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  base_model: colorfulscoop/sbert-base-ja
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- library_name: sentence-transformers
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- metrics:
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- - cosine_accuracy
6
- - cosine_accuracy_threshold
7
- - cosine_f1
8
- - cosine_f1_threshold
9
- - cosine_precision
10
- - cosine_recall
11
- - cosine_ap
12
- - dot_accuracy
13
- - dot_accuracy_threshold
14
- - dot_f1
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- - dot_f1_threshold
16
- - dot_precision
17
- - dot_recall
18
- - dot_ap
19
- - manhattan_accuracy
20
- - manhattan_accuracy_threshold
21
- - manhattan_f1
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- - 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
- widget:
49
- - source_sentence: 障害 の ある バイカー は 腕 を 使って 黄色 の スポーツ バイク を 動かし ます 。
50
- sentences:
51
- - バイカー は 足 を 使って 自転車 を さらに 進め ます 。
52
- - ストリート ワーカー は 保護 具 を 着用 して い ませ ん 。
53
- - 芝生 の エリア で 数 匹 の 犬 が 交流 し ます 。
54
- - source_sentence: 野球 の 試合 中 に 基地 を 走る 野球 選手 の シャープリー 。
55
- sentences:
56
- - ブラインド デート の 女性 が 座って 、 デート が 現れる の を 待ち ます 。
57
- - 岩 の 上 に 座って いる 二 人
58
- - Sharp ley は ゲーム で プレイ して い ます 。
59
- - source_sentence: 数 人 の 男性 が MMA の 戦い に 参加 して い ます 。
60
- sentences:
61
- - 男性 は バレエ に 参加 して い ます 。
62
- - 人々 は ハンバーガー を 待って い ます 。
63
- - 自転車 の 挑戦 に 勝とう と する 人々 の グループ 。
64
- - source_sentence: 黒い タイル の 本当に すてきな カウンター の 前 と 後ろ で 働く 人々 。
65
- sentences:
66
- - 人々 は 宝石 店 で 働いて い ます 。
67
- - 黄色 の 自転車 は レース で 他 の 自転車 を リード し ます 。
68
- - 何 か を 見て いる 二 人 が い ます 。
69
- - source_sentence: 黒い 長い 髪 を した 女性 が 、 黒い ベルト の 付いた 赤い ドレス を 着て 歩いて い ます 。
70
- sentences:
71
- - 女の子 は 、 かつて 木 が 立って いた 裏庭 を 見 ながら 中 に い ました 。
72
- - 木 を 切り 倒した 後 、 木 の 切り株 に 座って いる 少年 。
73
- - 女性 は 髪 を 切った 。
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: 0.625
86
- name: Cosine Accuracy
87
- - type: cosine_accuracy_threshold
88
- value: 0.5076889991760254
89
- name: Cosine Accuracy Threshold
90
- - type: cosine_f1
91
- value: 0.7692307692307692
92
- name: Cosine F1
93
- - type: cosine_f1_threshold
94
- value: 0.3337227404117584
95
- name: Cosine F1 Threshold
96
- - type: cosine_precision
97
- value: 0.6666666666666666
98
- name: Cosine Precision
99
- - type: cosine_recall
100
- value: 0.9090909090909091
101
- name: Cosine Recall
102
- - type: cosine_ap
103
- value: 0.6000271193453012
104
- name: Cosine Ap
105
- - type: dot_accuracy
106
- value: 0.625
107
- name: Dot Accuracy
108
- - type: dot_accuracy_threshold
109
- value: 274.0029296875
110
- name: Dot Accuracy Threshold
111
- - type: dot_f1
112
- value: 0.7692307692307692
113
- name: Dot F1
114
- - type: dot_f1_threshold
115
- value: 184.57107543945312
116
- name: Dot F1 Threshold
117
- - type: dot_precision
118
- value: 0.6666666666666666
119
- name: Dot Precision
120
- - type: dot_recall
121
- value: 0.9090909090909091
122
- name: Dot Recall
123
- - type: dot_ap
124
- value: 0.584875604193786
125
- name: Dot Ap
126
- - type: manhattan_accuracy
127
- value: 0.625
128
- name: Manhattan Accuracy
129
- - type: manhattan_accuracy_threshold
130
- value: 505.6795349121094
131
- name: Manhattan Accuracy Threshold
132
- - type: manhattan_f1
133
- value: 0.7692307692307692
134
- name: Manhattan F1
135
- - type: manhattan_f1_threshold
136
- value: 596.4147338867188
137
- name: Manhattan F1 Threshold
138
- - type: manhattan_precision
139
- value: 0.6666666666666666
140
- name: Manhattan Precision
141
- - type: manhattan_recall
142
- value: 0.9090909090909091
143
- name: Manhattan Recall
144
- - type: manhattan_ap
145
- value: 0.584875604193786
146
- name: Manhattan Ap
147
- - type: euclidean_accuracy
148
- value: 0.625
149
- name: Euclidean Accuracy
150
- - type: euclidean_accuracy_threshold
151
- value: 22.92259979248047
152
- name: Euclidean Accuracy Threshold
153
- - type: euclidean_f1
154
- value: 0.7692307692307692
155
- name: Euclidean F1
156
- - type: euclidean_f1_threshold
157
- value: 27.098722457885742
158
- name: Euclidean F1 Threshold
159
- - type: euclidean_precision
160
- value: 0.6666666666666666
161
- name: Euclidean Precision
162
- - type: euclidean_recall
163
- value: 0.9090909090909091
164
- name: Euclidean Recall
165
- - type: euclidean_ap
166
- value: 0.6000271193453012
167
- name: Euclidean Ap
168
- - type: max_accuracy
169
- value: 0.625
170
- name: Max Accuracy
171
- - type: max_accuracy_threshold
172
- value: 505.6795349121094
173
- name: Max Accuracy Threshold
174
- - type: max_f1
175
- value: 0.7692307692307692
176
- name: Max F1
177
- - type: max_f1_threshold
178
- value: 596.4147338867188
179
- name: Max F1 Threshold
180
- - type: max_precision
181
- value: 0.6666666666666666
182
- name: Max Precision
183
- - type: max_recall
184
- value: 0.9090909090909091
185
- name: Max Recall
186
- - type: max_ap
187
- value: 0.6000271193453012
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
- '黒い 長い 髪 を した 女性 が 、 黒い ベルト の 付いた 赤い ドレス を 着て 歩いて い ます 。',
242
- '女性 は 髪 を 切った 。',
243
- '女の子 は 、 かつて 木 が 立って いた 裏庭 を 見 ながら 中 に い ました 。',
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 | 0.625 |
290
- | cosine_accuracy_threshold | 0.5077 |
291
- | cosine_f1 | 0.7692 |
292
- | cosine_f1_threshold | 0.3337 |
293
- | cosine_precision | 0.6667 |
294
- | cosine_recall | 0.9091 |
295
- | cosine_ap | 0.6 |
296
- | dot_accuracy | 0.625 |
297
- | dot_accuracy_threshold | 274.0029 |
298
- | dot_f1 | 0.7692 |
299
- | dot_f1_threshold | 184.5711 |
300
- | dot_precision | 0.6667 |
301
- | dot_recall | 0.9091 |
302
- | dot_ap | 0.5849 |
303
- | manhattan_accuracy | 0.625 |
304
- | manhattan_accuracy_threshold | 505.6795 |
305
- | manhattan_f1 | 0.7692 |
306
- | manhattan_f1_threshold | 596.4147 |
307
- | manhattan_precision | 0.6667 |
308
- | manhattan_recall | 0.9091 |
309
- | manhattan_ap | 0.5849 |
310
- | euclidean_accuracy | 0.625 |
311
- | euclidean_accuracy_threshold | 22.9226 |
312
- | euclidean_f1 | 0.7692 |
313
- | euclidean_f1_threshold | 27.0987 |
314
- | euclidean_precision | 0.6667 |
315
- | euclidean_recall | 0.9091 |
316
- | euclidean_ap | 0.6 |
317
- | max_accuracy | 0.625 |
318
- | max_accuracy_threshold | 505.6795 |
319
- | max_f1 | 0.7692 |
320
- | max_f1_threshold | 596.4147 |
321
- | max_precision | 0.6667 |
322
- | max_recall | 0.9091 |
323
- | **max_ap** | **0.6** |
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: 53 training samples
345
- * Columns: <code>text1</code>, <code>text2</code>, and <code>label</code>
346
- * Approximate statistics based on the first 53 samples:
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- | | text1 | text2 | label |
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- |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:------------------------------------------------|
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- | type | string | string | int |
350
- | details | <ul><li>min: 14 tokens</li><li>mean: 35.14 tokens</li><li>max: 79 tokens</li></ul> | <ul><li>min: 11 tokens</li><li>mean: 20.81 tokens</li><li>max: 38 tokens</li></ul> | <ul><li>0: ~37.84%</li><li>1: ~62.16%</li></ul> |
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- * Samples:
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- | text1 | text2 | label |
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- |:---------------------------------------------------------|:-------------------------------------------------|:---------------|
354
- | <code>眼鏡 を かけて いる 3 人 が 写真 の ポーズ を とり ます 。</code> | <code>人々 は 眼鏡 を かけて い ます</code> | <code>0</code> |
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- | <code>帽子 を かぶった 一 人 の 男 が 別の 男 を 芝生 に ひっくり返し ま��� 。</code> | <code>二 人 の 男 が 芝生 で パルクール を 練習 して い ます 。</code> | <code>1</code> |
356
- | <code>4 見て いる アジア カップル 結婚 して ます 。</code> | <code>人々 結婚 して い ます 。</code> | <code>0</code> |
357
- * Loss: [<code>OnlineContrastiveLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#onlinecontrastiveloss)
358
-
359
- ### Evaluation Dataset
360
-
361
- #### csv
362
-
363
- * Dataset: csv
364
- * Size: 53 evaluation samples
365
- * Columns: <code>text1</code>, <code>text2</code>, and <code>label</code>
366
- * Approximate statistics based on the first 53 samples:
367
- | | text1 | text2 | label |
368
- |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:------------------------------------------------|
369
- | type | string | string | int |
370
- | details | <ul><li>min: 19 tokens</li><li>mean: 38.81 tokens</li><li>max: 84 tokens</li></ul> | <ul><li>min: 14 tokens</li><li>mean: 25.25 tokens</li><li>max: 38 tokens</li></ul> | <ul><li>0: ~31.25%</li><li>1: ~68.75%</li></ul> |
371
- * Samples:
372
- | text1 | text2 | label |
373
- |:----------------------------------------------------------------------------------------------------------|:------------------------------------------------|:---------------|
374
- | <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> |
375
- | <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> |
376
- | <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> |
377
- * Loss: [<code>OnlineContrastiveLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#onlinecontrastiveloss)
378
-
379
- ### Training Hyperparameters
380
- #### Non-Default Hyperparameters
381
-
382
- - `eval_strategy`: epoch
383
- - `learning_rate`: 2e-05
384
- - `num_train_epochs`: 1
385
- - `warmup_ratio`: 0.4
386
- - `fp16`: True
387
- - `batch_sampler`: no_duplicates
388
-
389
- #### All Hyperparameters
390
- <details><summary>Click to expand</summary>
391
-
392
- - `overwrite_output_dir`: False
393
- - `do_predict`: False
394
- - `eval_strategy`: epoch
395
- - `prediction_loss_only`: True
396
- - `per_device_train_batch_size`: 8
397
- - `per_device_eval_batch_size`: 8
398
- - `per_gpu_train_batch_size`: None
399
- - `per_gpu_eval_batch_size`: None
400
- - `gradient_accumulation_steps`: 1
401
- - `eval_accumulation_steps`: None
402
- - `torch_empty_cache_steps`: None
403
- - `learning_rate`: 2e-05
404
- - `weight_decay`: 0.0
405
- - `adam_beta1`: 0.9
406
- - `adam_beta2`: 0.999
407
- - `adam_epsilon`: 1e-08
408
- - `max_grad_norm`: 1.0
409
- - `num_train_epochs`: 1
410
- - `max_steps`: -1
411
- - `lr_scheduler_type`: linear
412
- - `lr_scheduler_kwargs`: {}
413
- - `warmup_ratio`: 0.4
414
- - `warmup_steps`: 0
415
- - `log_level`: passive
416
- - `log_level_replica`: warning
417
- - `log_on_each_node`: True
418
- - `logging_nan_inf_filter`: True
419
- - `save_safetensors`: True
420
- - `save_on_each_node`: False
421
- - `save_only_model`: False
422
- - `restore_callback_states_from_checkpoint`: False
423
- - `no_cuda`: False
424
- - `use_cpu`: False
425
- - `use_mps_device`: False
426
- - `seed`: 42
427
- - `data_seed`: None
428
- - `jit_mode_eval`: False
429
- - `use_ipex`: False
430
- - `bf16`: False
431
- - `fp16`: True
432
- - `fp16_opt_level`: O1
433
- - `half_precision_backend`: auto
434
- - `bf16_full_eval`: False
435
- - `fp16_full_eval`: False
436
- - `tf32`: None
437
- - `local_rank`: 0
438
- - `ddp_backend`: None
439
- - `tpu_num_cores`: None
440
- - `tpu_metrics_debug`: False
441
- - `debug`: []
442
- - `dataloader_drop_last`: False
443
- - `dataloader_num_workers`: 0
444
- - `dataloader_prefetch_factor`: None
445
- - `past_index`: -1
446
- - `disable_tqdm`: False
447
- - `remove_unused_columns`: True
448
- - `label_names`: None
449
- - `load_best_model_at_end`: False
450
- - `ignore_data_skip`: False
451
- - `fsdp`: []
452
- - `fsdp_min_num_params`: 0
453
- - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
454
- - `fsdp_transformer_layer_cls_to_wrap`: None
455
- - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
456
- - `deepspeed`: None
457
- - `label_smoothing_factor`: 0.0
458
- - `optim`: adamw_torch
459
- - `optim_args`: None
460
- - `adafactor`: False
461
- - `group_by_length`: False
462
- - `length_column_name`: length
463
- - `ddp_find_unused_parameters`: None
464
- - `ddp_bucket_cap_mb`: None
465
- - `ddp_broadcast_buffers`: False
466
- - `dataloader_pin_memory`: True
467
- - `dataloader_persistent_workers`: False
468
- - `skip_memory_metrics`: True
469
- - `use_legacy_prediction_loop`: False
470
- - `push_to_hub`: False
471
- - `resume_from_checkpoint`: None
472
- - `hub_model_id`: None
473
- - `hub_strategy`: every_save
474
- - `hub_private_repo`: False
475
- - `hub_always_push`: False
476
- - `gradient_checkpointing`: False
477
- - `gradient_checkpointing_kwargs`: None
478
- - `include_inputs_for_metrics`: False
479
- - `eval_do_concat_batches`: True
480
- - `fp16_backend`: auto
481
- - `push_to_hub_model_id`: None
482
- - `push_to_hub_organization`: None
483
- - `mp_parameters`:
484
- - `auto_find_batch_size`: False
485
- - `full_determinism`: False
486
- - `torchdynamo`: None
487
- - `ray_scope`: last
488
- - `ddp_timeout`: 1800
489
- - `torch_compile`: False
490
- - `torch_compile_backend`: None
491
- - `torch_compile_mode`: None
492
- - `dispatch_batches`: None
493
- - `split_batches`: None
494
- - `include_tokens_per_second`: False
495
- - `include_num_input_tokens_seen`: False
496
- - `neftune_noise_alpha`: None
497
- - `optim_target_modules`: None
498
- - `batch_eval_metrics`: False
499
- - `eval_on_start`: False
500
- - `eval_use_gather_object`: False
501
- - `batch_sampler`: no_duplicates
502
- - `multi_dataset_batch_sampler`: proportional
503
-
504
- </details>
505
-
506
- ### Training Logs
507
- | Epoch | Step | Training Loss | loss | custom-arc-semantics-data-jp_max_ap |
508
- |:-----:|:----:|:-------------:|:------:|:-----------------------------------:|
509
- | 1.0 | 5 | 0.6702 | 1.0655 | 0.6000 |
510
-
511
-
512
- ### Framework Versions
513
- - Python: 3.10.14
514
- - Sentence Transformers: 3.1.0
515
- - Transformers: 4.44.2
516
- - PyTorch: 2.4.1+cu121
517
- - Accelerate: 0.34.2
518
- - Datasets: 2.20.0
519
- - Tokenizers: 0.19.1
520
-
521
- ## Citation
522
-
523
- ### BibTeX
524
-
525
- #### Sentence Transformers
526
- ```bibtex
527
- @inproceedings{reimers-2019-sentence-bert,
528
- title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
529
- author = "Reimers, Nils and Gurevych, Iryna",
530
- booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
531
- month = "11",
532
- year = "2019",
533
- publisher = "Association for Computational Linguistics",
534
- url = "https://arxiv.org/abs/1908.10084",
535
- }
536
- ```
537
-
538
- <!--
539
- ## Glossary
540
-
541
- *Clearly define terms in order to be accessible across audiences.*
542
- -->
543
-
544
- <!--
545
- ## Model Card Authors
546
-
547
- *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
548
- -->
549
-
550
- <!--
551
  ## Model Card Contact
552
 
553
- *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
554
- -->
 
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
+
12
 
 
13
 
14
  ## Model Details
15
 
16
  ### Model Description
 
 
 
 
 
 
 
 
 
17
 
18
+ <!-- Provide a longer summary of what this model is. -->
19
 
20
+ Generates similarity embeddings
 
 
21
 
22
+ - **Developed by:** [More Information Needed]
23
+ - **Funded by [optional]:** [More Information Needed]
24
+ - **Shared by [optional]:** [More Information Needed]
25
+ - **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]
 
 
 
 
 
31
 
32
+ <!-- Provide the basic links for the model. -->
33
 
34
+ - **Repository:** [More Information Needed]
35
+ - **Paper [optional]:** [More Information Needed]
36
+ - **Demo [optional]:** [More Information Needed]
37
 
38
+ ## Uses
39
 
40
+ <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
 
 
41
 
42
+ ### Direct Use
 
 
43
 
44
+ <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
 
 
 
 
 
 
 
 
 
 
45
 
46
+ [More Information Needed]
 
 
 
 
47
 
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+ ### Downstream Use [optional]
 
49
 
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+ <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
51
 
52
+ [More Information Needed]
 
53
 
54
+ ### Out-of-Scope Use
 
55
 
56
+ <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
57
 
58
+ [More Information Needed]
59
 
60
+ ## Bias, Risks, and Limitations
 
61
 
62
+ <!-- This section is meant to convey both technical and sociotechnical limitations. -->
 
63
 
64
+ [More Information Needed]
 
65
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ### Recommendations
67
 
68
+ <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
69
+
70
+ 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.
75
+
76
+ [More Information Needed]
77
 
78
  ## Training Details
79
 
80
+ ### Training Data
81
+
82
+ <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
83
+
84
+ [More Information Needed]
85
+
86
+ ### Training Procedure
87
+
88
+ <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
89
+
90
+ #### Preprocessing [optional]
91
+
92
+ [More Information Needed]
93
+
94
+
95
+ #### Training Hyperparameters
96
+
97
+ - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
98
+
99
+ #### Speeds, Sizes, Times [optional]
100
+
101
+ <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
102
+
103
+ [More Information Needed]
104
+
105
+ ## Evaluation
106
+
107
+ <!-- This section describes the evaluation protocols and provides the results. -->
108
+
109
+ ### Testing Data, Factors & Metrics
110
+
111
+ #### Testing Data
112
+
113
+ <!-- This should link to a Dataset Card if possible. -->
114
+
115
+ [More Information Needed]
116
+
117
+ #### Factors
118
+
119
+ <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
120
+
121
+ [More Information Needed]
122
+
123
+ #### Metrics
124
+
125
+ <!-- These are the evaluation metrics being used, ideally with a description of why. -->
126
+
127
+ [More Information Needed]
128
+
129
+ ### Results
130
+
131
+ [More Information Needed]
132
+
133
+ #### Summary
134
+
135
+
136
+
137
+ ## Model Examination [optional]
138
+
139
+ <!-- Relevant interpretability work for the model goes here -->
140
+
141
+ [More Information Needed]
142
+
143
+ ## Environmental Impact
144
+
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]
150
+ - **Hours used:** [More Information Needed]
151
+ - **Cloud Provider:** [More Information Needed]
152
+ - **Compute Region:** [More Information Needed]
153
+ - **Carbon Emitted:** [More Information Needed]
154
+
155
+ ## Technical Specifications [optional]
156
+
157
+ ### Model Architecture and Objective
158
+
159
+ [More Information Needed]
160
+
161
+ ### Compute Infrastructure
162
+
163
+ [More Information Needed]
164
+
165
+ #### Hardware
166
+
167
+ [More Information Needed]
168
+
169
+ #### Software
170
+
171
+ [More Information Needed]
172
+
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]
180
+
181
+ **APA:**
182
+
183
+ [More Information Needed]
184
+
185
+ ## Glossary [optional]
186
+
187
+ <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
188
+
189
+ [More Information Needed]
190
+
191
+ ## More Information [optional]
192
+
193
+ [More Information Needed]
194
+
195
+ ## Model Card Authors [optional]
196
+
197
+ [More Information Needed]
198
+
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
199
  ## Model Card Contact
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+ [More Information Needed]