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  ---
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  base_model: colorfulscoop/sbert-base-ja
3
- 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
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:5330
47
- - loss:SoftmaxLoss
48
- widget:
49
- - source_sentence: 踏切 バー の 上 に 座って いる 赤い シャツ の 少年 。
50
- sentences:
51
- - 人間 が 座って いる
52
- - 男 は バイオリン を 使用 して い ます 。
53
- - ジョン は それ が どの ようである か を 伝え ます 。
54
- - source_sentence: ピザ ミラドール の 看板 の 上 の バルコニー に 立って いる 女性 。
55
- sentences:
56
- - 黒人 が 部族 服 を 着て い ます 。
57
- - 男 が 外 に い ます 。
58
- - 女性 は コンサート を 見て い ます 。
59
- - source_sentence: 犬 が 雪 の 中 を 走って い ます 。
60
- sentences:
61
- - 動物 が 屋外 で 走って い ます 。
62
- - 少女 と 犬 は ビーチ で ボール を プレー して い ます 。
63
- - コークス の 瓶 を 持って 野生 の 髪 の 少女 。
64
- - source_sentence: 黒い 髪 の 女性 は 、 ランプ の ある テーブル で マティーニ グラス を 持って い ます 。
65
- sentences:
66
- - 男 が ボール を 投げて い ます 。
67
- - ティーン は 壁 の 近く に い ます 。
68
- - 女性 が ランプ の ある テーブル で マティーニ の グラス を 高く 掲げて い ます
69
- - source_sentence: 茶色 と 緑色 の スカート を はめた 3 人 の 茶色 の 女性 が 、 左 を 見つめる 金属 フェンス に 寄りかかって
70
- い ます 。
71
- sentences:
72
- - フェンス に 対する 人々 。
73
- - 年配 の カップル は 街 で リラックス し ます 。
74
- - 一部 の 人々 は 通り に 立って い ます
75
- model-index:
76
- - name: SentenceTransformer based on colorfulscoop/sbert-base-ja
77
- results:
78
- - task:
79
- type: binary-classification
80
- name: Binary Classification
81
- dataset:
82
- name: custom arc semantics data jp
83
- type: custom-arc-semantics-data-jp
84
- metrics:
85
- - type: cosine_accuracy
86
- value: 0.6701688555347092
87
- name: Cosine Accuracy
88
- - type: cosine_accuracy_threshold
89
- value: -0.005373716354370117
90
- name: Cosine Accuracy Threshold
91
- - type: cosine_f1
92
- value: 0.8022497187851519
93
- name: Cosine F1
94
- - type: cosine_f1_threshold
95
- value: -0.005373716354370117
96
- name: Cosine F1 Threshold
97
- - type: cosine_precision
98
- value: 0.6700488538143555
99
- name: Cosine Precision
100
- - type: cosine_recall
101
- value: 0.999439461883408
102
- name: Cosine Recall
103
- - type: cosine_ap
104
- value: 0.5505844770213371
105
- name: Cosine Ap
106
- - type: dot_accuracy
107
- value: 0.6701688555347092
108
- name: Dot Accuracy
109
- - type: dot_accuracy_threshold
110
- value: -3.2580666542053223
111
- name: Dot Accuracy Threshold
112
- - type: dot_f1
113
- value: 0.8022497187851519
114
- name: Dot F1
115
- - type: dot_f1_threshold
116
- value: -3.2580666542053223
117
- name: Dot F1 Threshold
118
- - type: dot_precision
119
- value: 0.6700488538143555
120
- name: Dot Precision
121
- - type: dot_recall
122
- value: 0.999439461883408
123
- name: Dot Recall
124
- - type: dot_ap
125
- value: 0.5471168521218388
126
- name: Dot Ap
127
- - type: manhattan_accuracy
128
- value: 0.6690431519699812
129
- name: Manhattan Accuracy
130
- - type: manhattan_accuracy_threshold
131
- value: 753.613525390625
132
- name: Manhattan Accuracy Threshold
133
- - type: manhattan_f1
134
- value: 0.8017086330935252
135
- name: Manhattan F1
136
- - type: manhattan_f1_threshold
137
- value: 777.5819091796875
138
- name: Manhattan F1 Threshold
139
- - type: manhattan_precision
140
- value: 0.6692942942942943
141
- name: Manhattan Precision
142
- - type: manhattan_recall
143
- value: 0.999439461883408
144
- name: Manhattan Recall
145
- - type: manhattan_ap
146
- value: 0.5536248665084111
147
- name: Manhattan Ap
148
- - type: euclidean_accuracy
149
- value: 0.6701688555347092
150
- name: Euclidean Accuracy
151
- - type: euclidean_accuracy_threshold
152
- value: 34.89848327636719
153
- name: Euclidean Accuracy Threshold
154
- - type: euclidean_f1
155
- value: 0.8022497187851519
156
- name: Euclidean F1
157
- - type: euclidean_f1_threshold
158
- value: 34.89848327636719
159
- name: Euclidean F1 Threshold
160
- - type: euclidean_precision
161
- value: 0.6700488538143555
162
- name: Euclidean Precision
163
- - type: euclidean_recall
164
- value: 0.999439461883408
165
- name: Euclidean Recall
166
- - type: euclidean_ap
167
- value: 0.5524756039038743
168
- name: Euclidean Ap
169
- - type: max_accuracy
170
- value: 0.6701688555347092
171
- name: Max Accuracy
172
- - type: max_accuracy_threshold
173
- value: 753.613525390625
174
- name: Max Accuracy Threshold
175
- - type: max_f1
176
- value: 0.8022497187851519
177
- name: Max F1
178
- - type: max_f1_threshold
179
- value: 777.5819091796875
180
- name: Max F1 Threshold
181
- - type: max_precision
182
- value: 0.6700488538143555
183
- name: Max Precision
184
- - type: max_recall
185
- value: 0.999439461883408
186
- name: Max Recall
187
- - type: max_ap
188
- value: 0.5536248665084111
189
- name: Max Ap
190
  ---
191
 
192
- # SentenceTransformer based on colorfulscoop/sbert-base-ja
 
 
 
193
 
194
- 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.
195
 
196
  ## Model Details
197
 
198
  ### Model Description
199
- - **Model Type:** Sentence Transformer
200
- - **Base model:** [colorfulscoop/sbert-base-ja](https://huggingface.co/colorfulscoop/sbert-base-ja) <!-- at revision ecb8a98cd5176719ff7ab0d770a27420118732cf -->
201
- - **Maximum Sequence Length:** 512 tokens
202
- - **Output Dimensionality:** 768 tokens
203
- - **Similarity Function:** Cosine Similarity
204
- - **Training Dataset:**
205
- - csv
206
- <!-- - **Language:** Unknown -->
207
- <!-- - **License:** Unknown -->
208
 
209
- ### Model Sources
210
 
211
- - **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
212
- - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
213
- - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
214
 
215
- ### Full Model Architecture
 
 
 
 
 
 
216
 
217
- ```
218
- SentenceTransformer(
219
- (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
220
- (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})
221
- )
222
- ```
223
 
224
- ## Usage
225
 
226
- ### Direct Usage (Sentence Transformers)
 
 
227
 
228
- First install the Sentence Transformers library:
229
 
230
- ```bash
231
- pip install -U sentence-transformers
232
- ```
233
 
234
- Then you can load this model and run inference.
235
- ```python
236
- from sentence_transformers import SentenceTransformer
237
 
238
- # Download from the 🤗 Hub
239
- model = SentenceTransformer("sentence_transformers_model_id")
240
- # Run inference
241
- sentences = [
242
- '茶色 と 緑色 の スカート を はめた 3 人 の 茶色 の 女性 が 、 左 を 見つめる 金属 フェンス に 寄りかかって い ます 。',
243
- 'フェンス に 対する 人々 。',
244
- '一部 の 人々 は 通り に 立って い ます',
245
- ]
246
- embeddings = model.encode(sentences)
247
- print(embeddings.shape)
248
- # [3, 768]
249
 
250
- # Get the similarity scores for the embeddings
251
- similarities = model.similarity(embeddings, embeddings)
252
- print(similarities.shape)
253
- # [3, 3]
254
- ```
255
 
256
- <!--
257
- ### Direct Usage (Transformers)
258
 
259
- <details><summary>Click to see the direct usage in Transformers</summary>
260
 
261
- </details>
262
- -->
263
 
264
- <!--
265
- ### Downstream Usage (Sentence Transformers)
266
 
267
- You can finetune this model on your own dataset.
268
 
269
- <details><summary>Click to expand</summary>
270
 
271
- </details>
272
- -->
273
 
274
- <!--
275
- ### Out-of-Scope Use
276
 
277
- *List how the model may foreseeably be misused and address what users ought not to do with the model.*
278
- -->
279
 
280
- ## Evaluation
281
-
282
- ### Metrics
283
-
284
- #### Binary Classification
285
- * Dataset: `custom-arc-semantics-data-jp`
286
- * Evaluated with [<code>BinaryClassificationEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator)
287
-
288
- | Metric | Value |
289
- |:-----------------------------|:-----------|
290
- | cosine_accuracy | 0.6702 |
291
- | cosine_accuracy_threshold | -0.0054 |
292
- | cosine_f1 | 0.8022 |
293
- | cosine_f1_threshold | -0.0054 |
294
- | cosine_precision | 0.67 |
295
- | cosine_recall | 0.9994 |
296
- | cosine_ap | 0.5506 |
297
- | dot_accuracy | 0.6702 |
298
- | dot_accuracy_threshold | -3.2581 |
299
- | dot_f1 | 0.8022 |
300
- | dot_f1_threshold | -3.2581 |
301
- | dot_precision | 0.67 |
302
- | dot_recall | 0.9994 |
303
- | dot_ap | 0.5471 |
304
- | manhattan_accuracy | 0.669 |
305
- | manhattan_accuracy_threshold | 753.6135 |
306
- | manhattan_f1 | 0.8017 |
307
- | manhattan_f1_threshold | 777.5819 |
308
- | manhattan_precision | 0.6693 |
309
- | manhattan_recall | 0.9994 |
310
- | manhattan_ap | 0.5536 |
311
- | euclidean_accuracy | 0.6702 |
312
- | euclidean_accuracy_threshold | 34.8985 |
313
- | euclidean_f1 | 0.8022 |
314
- | euclidean_f1_threshold | 34.8985 |
315
- | euclidean_precision | 0.67 |
316
- | euclidean_recall | 0.9994 |
317
- | euclidean_ap | 0.5525 |
318
- | max_accuracy | 0.6702 |
319
- | max_accuracy_threshold | 753.6135 |
320
- | max_f1 | 0.8022 |
321
- | max_f1_threshold | 777.5819 |
322
- | max_precision | 0.67 |
323
- | max_recall | 0.9994 |
324
- | **max_ap** | **0.5536** |
325
-
326
- <!--
327
- ## Bias, Risks and Limitations
328
-
329
- *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
330
- -->
331
-
332
- <!--
333
  ### Recommendations
334
 
335
- *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
336
- -->
 
 
 
 
 
 
 
337
 
338
  ## Training Details
339
 
340
- ### Training Dataset
341
-
342
- #### csv
343
-
344
- * Dataset: csv
345
- * Size: 5,330 training samples
346
- * Columns: <code>text1</code>, <code>text2</code>, and <code>label</code>
347
- * Approximate statistics based on the first 1000 samples:
348
- | | text1 | text2 | label |
349
- |:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:------------------------------------------------|
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- | type | string | string | int |
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- | details | <ul><li>min: 7 tokens</li><li>mean: 36.14 tokens</li><li>max: 112 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 22.89 tokens</li><li>max: 77 tokens</li></ul> | <ul><li>0: ~31.80%</li><li>1: ~68.20%</li></ul> |
352
- * Samples:
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- | text1 | text2 | label |
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- |:---------------------------------------------------------|:--------------------------------------------|:---------------|
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- | <code>傘 を 持った 人 が 横断 歩道 を 使用 して 通り を 横断 して い ます 。</code> | <code>傘 を 持った 人 が ジェイ ウォーク して い ます 。</code> | <code>1</code> |
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- | <code>犬 は 、 上 を 見て 木 に 腰掛けて い ます 。</code> | <code>ペット は 車 に 乗ろう と して �� ます 。</code> | <code>1</code> |
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- | <code>飛行 しよう 女性 。</code> | <code>女性 立って ます 。</code> | <code>0</code> |
358
- * Loss: [<code>SoftmaxLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#softmaxloss)
359
-
360
- ### Evaluation Dataset
361
-
362
- #### csv
363
-
364
- * Dataset: csv
365
- * Size: 5,330 evaluation samples
366
- * Columns: <code>text1</code>, <code>text2</code>, and <code>label</code>
367
- * Approximate statistics based on the first 1000 samples:
368
- | | text1 | text2 | label |
369
- |:--------|:------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:------------------------------------------------|
370
- | type | string | string | int |
371
- | details | <ul><li>min: 12 tokens</li><li>mean: 36.38 tokens</li><li>max: 203 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 22.77 tokens</li><li>max: 76 tokens</li></ul> | <ul><li>0: ~31.40%</li><li>1: ~68.60%</li></ul> |
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- * Samples:
373
- | text1 | text2 | label |
374
- |:-----------------------------------------------------------------------|:--------------------------------------------|:---------------|
375
- | <code>白い シャツ と バスケットボール の ショート パンツ を 着た 男 が トレッドミル で 走って い ます 。</code> | <code>トレッドミル で 走って いる 人 。</code> | <code>0</code> |
376
- | <code>コスチューム に 身 を 包んだ 男女 が カメラ に 向かって ポーズ を とる 。</code> | <code>セット に は カメラ は 許可 さ れて い ませ ん 。</code> | <code>1</code> |
377
- | <code>オレンジ色 の ドレス を 着た 小さな 女の子 が ぼろ 人形 を ゲート に 押し付け ます 。</code> | <code>女の子 が 人形 を 共有 し たい 。</code> | <code>1</code> |
378
- * Loss: [<code>SoftmaxLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#softmaxloss)
379
-
380
- ### Training Hyperparameters
381
- #### Non-Default Hyperparameters
382
-
383
- - `eval_strategy`: epoch
384
- - `learning_rate`: 2e-05
385
- - `num_train_epochs`: 1
386
- - `warmup_ratio`: 0.4
387
- - `fp16`: True
388
- - `batch_sampler`: no_duplicates
389
-
390
- #### All Hyperparameters
391
- <details><summary>Click to expand</summary>
392
-
393
- - `overwrite_output_dir`: False
394
- - `do_predict`: False
395
- - `eval_strategy`: epoch
396
- - `prediction_loss_only`: True
397
- - `per_device_train_batch_size`: 8
398
- - `per_device_eval_batch_size`: 8
399
- - `per_gpu_train_batch_size`: None
400
- - `per_gpu_eval_batch_size`: None
401
- - `gradient_accumulation_steps`: 1
402
- - `eval_accumulation_steps`: None
403
- - `torch_empty_cache_steps`: None
404
- - `learning_rate`: 2e-05
405
- - `weight_decay`: 0.0
406
- - `adam_beta1`: 0.9
407
- - `adam_beta2`: 0.999
408
- - `adam_epsilon`: 1e-08
409
- - `max_grad_norm`: 1.0
410
- - `num_train_epochs`: 1
411
- - `max_steps`: -1
412
- - `lr_scheduler_type`: linear
413
- - `lr_scheduler_kwargs`: {}
414
- - `warmup_ratio`: 0.4
415
- - `warmup_steps`: 0
416
- - `log_level`: passive
417
- - `log_level_replica`: warning
418
- - `log_on_each_node`: True
419
- - `logging_nan_inf_filter`: True
420
- - `save_safetensors`: True
421
- - `save_on_each_node`: False
422
- - `save_only_model`: False
423
- - `restore_callback_states_from_checkpoint`: False
424
- - `no_cuda`: False
425
- - `use_cpu`: False
426
- - `use_mps_device`: False
427
- - `seed`: 42
428
- - `data_seed`: None
429
- - `jit_mode_eval`: False
430
- - `use_ipex`: False
431
- - `bf16`: False
432
- - `fp16`: True
433
- - `fp16_opt_level`: O1
434
- - `half_precision_backend`: auto
435
- - `bf16_full_eval`: False
436
- - `fp16_full_eval`: False
437
- - `tf32`: None
438
- - `local_rank`: 0
439
- - `ddp_backend`: None
440
- - `tpu_num_cores`: None
441
- - `tpu_metrics_debug`: False
442
- - `debug`: []
443
- - `dataloader_drop_last`: False
444
- - `dataloader_num_workers`: 0
445
- - `dataloader_prefetch_factor`: None
446
- - `past_index`: -1
447
- - `disable_tqdm`: False
448
- - `remove_unused_columns`: True
449
- - `label_names`: None
450
- - `load_best_model_at_end`: False
451
- - `ignore_data_skip`: False
452
- - `fsdp`: []
453
- - `fsdp_min_num_params`: 0
454
- - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
455
- - `fsdp_transformer_layer_cls_to_wrap`: None
456
- - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
457
- - `deepspeed`: None
458
- - `label_smoothing_factor`: 0.0
459
- - `optim`: adamw_torch
460
- - `optim_args`: None
461
- - `adafactor`: False
462
- - `group_by_length`: False
463
- - `length_column_name`: length
464
- - `ddp_find_unused_parameters`: None
465
- - `ddp_bucket_cap_mb`: None
466
- - `ddp_broadcast_buffers`: False
467
- - `dataloader_pin_memory`: True
468
- - `dataloader_persistent_workers`: False
469
- - `skip_memory_metrics`: True
470
- - `use_legacy_prediction_loop`: False
471
- - `push_to_hub`: False
472
- - `resume_from_checkpoint`: None
473
- - `hub_model_id`: None
474
- - `hub_strategy`: every_save
475
- - `hub_private_repo`: False
476
- - `hub_always_push`: False
477
- - `gradient_checkpointing`: False
478
- - `gradient_checkpointing_kwargs`: None
479
- - `include_inputs_for_metrics`: False
480
- - `eval_do_concat_batches`: True
481
- - `fp16_backend`: auto
482
- - `push_to_hub_model_id`: None
483
- - `push_to_hub_organization`: None
484
- - `mp_parameters`:
485
- - `auto_find_batch_size`: False
486
- - `full_determinism`: False
487
- - `torchdynamo`: None
488
- - `ray_scope`: last
489
- - `ddp_timeout`: 1800
490
- - `torch_compile`: False
491
- - `torch_compile_backend`: None
492
- - `torch_compile_mode`: None
493
- - `dispatch_batches`: None
494
- - `split_batches`: None
495
- - `include_tokens_per_second`: False
496
- - `include_num_input_tokens_seen`: False
497
- - `neftune_noise_alpha`: None
498
- - `optim_target_modules`: None
499
- - `batch_eval_metrics`: False
500
- - `eval_on_start`: False
501
- - `eval_use_gather_object`: False
502
- - `batch_sampler`: no_duplicates
503
- - `multi_dataset_batch_sampler`: proportional
504
-
505
- </details>
506
-
507
- ### Training Logs
508
- | Epoch | Step | Training Loss | loss | custom-arc-semantics-data-jp_max_ap |
509
- |:-----:|:----:|:-------------:|:------:|:-----------------------------------:|
510
- | 1.0 | 334 | 0.5612 | 0.4010 | 0.5536 |
511
-
512
-
513
- ### Framework Versions
514
- - Python: 3.10.14
515
- - Sentence Transformers: 3.1.0
516
- - Transformers: 4.44.2
517
- - PyTorch: 2.4.1+cu121
518
- - Accelerate: 0.34.2
519
- - Datasets: 2.20.0
520
- - Tokenizers: 0.19.1
521
-
522
- ## Citation
523
-
524
- ### BibTeX
525
-
526
- #### Sentence Transformers and SoftmaxLoss
527
- ```bibtex
528
- @inproceedings{reimers-2019-sentence-bert,
529
- title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
530
- author = "Reimers, Nils and Gurevych, Iryna",
531
- booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
532
- month = "11",
533
- year = "2019",
534
- publisher = "Association for Computational Linguistics",
535
- url = "https://arxiv.org/abs/1908.10084",
536
- }
537
- ```
538
-
539
- <!--
540
- ## Glossary
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-
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- *Clearly define terms in order to be accessible across audiences.*
543
- -->
544
-
545
- <!--
546
- ## Model Card Authors
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-
548
- *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
549
- -->
550
-
551
- <!--
552
  ## Model Card Contact
553
 
554
- *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
555
- -->
 
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. -->
 
 
 
 
 
 
 
 
 
 
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+ [More Information Needed]
 
 
 
 
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+ ### Downstream Use [optional]
 
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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 -->
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52
+ [More Information Needed]
 
53
 
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+ ### 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. -->
 
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+ [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
+
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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.
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
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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
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193
+ [More Information Needed]
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195
+ ## Model Card Authors [optional]
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197
+ [More Information Needed]
198
+
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
199
  ## Model Card Contact
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+ [More Information Needed]