LeoChiuu commited on
Commit
eda1a1e
1 Parent(s): 035945f

Upload README.md with huggingface_hub

Browse files
Files changed (1) hide show
  1. README.md +164 -540
README.md CHANGED
@@ -1,577 +1,201 @@
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:5330
47
- - loss:CoSENTLoss
48
- widget:
49
- - source_sentence: 家族 は 日陰 の 公園 で 午後 を 楽しんで い ます 。
50
- sentences:
51
- - 家族 は 公園 で ピクニック を して い ます 。
52
- - 人 が 会衆 に 説教 して い ます 。
53
- - 青い シャツ を 着た 子供 は フリスビー を 投げ ます 。
54
- - source_sentence: 雪 が 溶けて 、 小さな 男の子 が 裏庭 で 犬 と 遊んで い ます 。
55
- sentences:
56
- - 男性 は スポーツ を 見て い ます 。
57
- - 女性 は ビーチ サンダル を 運んで い ます 。
58
- - 吹雪 の 雲 の 後ろ から 太陽 が 出た 後 、 小さな 男の子 と 彼 の 犬 が 外 で 遊ぶ 。
59
- - source_sentence: 日焼け した パンツ を 着た 男性 と 、 膝 ブレース を 着た 女性 。
60
- sentences:
61
- - 二 人 が 踊って い ます 。
62
- - バック グラウンド に 空 の フィールド が あり ます 。
63
- - 3 人 の 若い 女の子 が そこ に 向かって いる 噴水 が あり ます 。
64
- - source_sentence: 3 人 の 子供 が 映画 を 見て い ます 。
65
- sentences:
66
- - で歩いて いる 女性 を 見て 食品 ベンダー
67
- - スケート ボード に 乗った 男 は 、 歓声 を 上げる 観客 に 手 の 込んだ ジャンプ を し ます 。
68
- - 素晴らしい 映画 を 見て いる 人間 。
69
- - source_sentence: 2 人 の 消防 士 が 消防 車 に 向かって 歩いて い ます 。
70
- sentences:
71
- - 2 人 の 消防 士 が トラック に 行く
72
- - 男 は 彼 の ne の 盗難 を 非難 し ます 。
73
- - 岩 だらけ の 海岸 を 歩いて いる と 、 波 を 見て いる 男性 が い ます 。
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: 0.7196998123827392
86
- name: Cosine Accuracy
87
- - type: cosine_accuracy_threshold
88
- value: 0.955563485622406
89
- name: Cosine Accuracy Threshold
90
- - type: cosine_f1
91
- value: 0.8188111721174504
92
- name: Cosine F1
93
- - type: cosine_f1_threshold
94
- value: 0.9209792017936707
95
- name: Cosine F1 Threshold
96
- - type: cosine_precision
97
- value: 0.7104391052195526
98
- name: Cosine Precision
99
- - type: cosine_recall
100
- value: 0.9661971830985916
101
- name: Cosine Recall
102
- - type: cosine_ap
103
- value: 0.7547086304231496
104
- name: Cosine Ap
105
- - type: dot_accuracy
106
- value: 0.7204502814258912
107
- name: Dot Accuracy
108
- - type: dot_accuracy_threshold
109
- value: 611.2391357421875
110
- name: Dot Accuracy Threshold
111
- - type: dot_f1
112
- value: 0.818549346016647
113
- name: Dot F1
114
- - type: dot_f1_threshold
115
- value: 588.0654296875
116
- name: Dot F1 Threshold
117
- - type: dot_precision
118
- value: 0.7082304526748971
119
- name: Dot Precision
120
- - type: dot_recall
121
- value: 0.9695774647887324
122
- name: Dot Recall
123
- - type: dot_ap
124
- value: 0.7795651338156695
125
- name: Dot Ap
126
- - type: manhattan_accuracy
127
- value: 0.7193245778611632
128
- name: Manhattan Accuracy
129
- - type: manhattan_accuracy_threshold
130
- value: 177.08966064453125
131
- name: Manhattan Accuracy Threshold
132
- - type: manhattan_f1
133
- value: 0.8182469548602818
134
- name: Manhattan F1
135
- - type: manhattan_f1_threshold
136
- value: 223.81216430664062
137
- name: Manhattan F1 Threshold
138
- - type: manhattan_precision
139
- value: 0.7101990049751243
140
- name: Manhattan Precision
141
- - type: manhattan_recall
142
- value: 0.9650704225352112
143
- name: Manhattan Recall
144
- - type: manhattan_ap
145
- value: 0.7546997956004545
146
- name: Manhattan Ap
147
- - type: euclidean_accuracy
148
- value: 0.7196998123827392
149
- name: Euclidean Accuracy
150
- - type: euclidean_accuracy_threshold
151
- value: 7.550699710845947
152
- name: Euclidean Accuracy Threshold
153
- - type: euclidean_f1
154
- value: 0.8188111721174504
155
- name: Euclidean F1
156
- - type: euclidean_f1_threshold
157
- value: 10.068297386169434
158
- name: Euclidean F1 Threshold
159
- - type: euclidean_precision
160
- value: 0.7104391052195526
161
- name: Euclidean Precision
162
- - type: euclidean_recall
163
- value: 0.9661971830985916
164
- name: Euclidean Recall
165
- - type: euclidean_ap
166
- value: 0.7545919416671392
167
- name: Euclidean Ap
168
- - type: max_accuracy
169
- value: 0.7204502814258912
170
- name: Max Accuracy
171
- - type: max_accuracy_threshold
172
- value: 611.2391357421875
173
- name: Max Accuracy Threshold
174
- - type: max_f1
175
- value: 0.8188111721174504
176
- name: Max F1
177
- - type: max_f1_threshold
178
- value: 588.0654296875
179
- name: Max F1 Threshold
180
- - type: max_precision
181
- value: 0.7104391052195526
182
- name: Max Precision
183
- - type: max_recall
184
- value: 0.9695774647887324
185
- name: Max Recall
186
- - type: max_ap
187
- value: 0.7795651338156695
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
- '2 人 の 消防 士 が 消防 車 に 向かって 歩いて い ます 。',
242
- '2 人 の 消防 士 が トラック に 行く',
243
- '岩 だらけ の 海岸 を 歩いて いる と 、 波 を 見て いる 男性 が い ます 。',
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 | 0.7197 |
290
- | cosine_accuracy_threshold | 0.9556 |
291
- | cosine_f1 | 0.8188 |
292
- | cosine_f1_threshold | 0.921 |
293
- | cosine_precision | 0.7104 |
294
- | cosine_recall | 0.9662 |
295
- | cosine_ap | 0.7547 |
296
- | dot_accuracy | 0.7205 |
297
- | dot_accuracy_threshold | 611.2391 |
298
- | dot_f1 | 0.8185 |
299
- | dot_f1_threshold | 588.0654 |
300
- | dot_precision | 0.7082 |
301
- | dot_recall | 0.9696 |
302
- | dot_ap | 0.7796 |
303
- | manhattan_accuracy | 0.7193 |
304
- | manhattan_accuracy_threshold | 177.0897 |
305
- | manhattan_f1 | 0.8182 |
306
- | manhattan_f1_threshold | 223.8122 |
307
- | manhattan_precision | 0.7102 |
308
- | manhattan_recall | 0.9651 |
309
- | manhattan_ap | 0.7547 |
310
- | euclidean_accuracy | 0.7197 |
311
- | euclidean_accuracy_threshold | 7.5507 |
312
- | euclidean_f1 | 0.8188 |
313
- | euclidean_f1_threshold | 10.0683 |
314
- | euclidean_precision | 0.7104 |
315
- | euclidean_recall | 0.9662 |
316
- | euclidean_ap | 0.7546 |
317
- | max_accuracy | 0.7205 |
318
- | max_accuracy_threshold | 611.2391 |
319
- | max_f1 | 0.8188 |
320
- | max_f1_threshold | 588.0654 |
321
- | max_precision | 0.7104 |
322
- | max_recall | 0.9696 |
323
- | **max_ap** | **0.7796** |
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: 5,330 training samples
345
- * Columns: <code>text1</code>, <code>text2</code>, and <code>label</code>
346
- * Approximate statistics based on the first 1000 samples:
347
- | | text1 | text2 | label |
348
- |:--------|:------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:------------------------------------------------|
349
- | type | string | string | int |
350
- | details | <ul><li>min: 12 tokens</li><li>mean: 35.93 tokens</li><li>max: 102 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 22.72 tokens</li><li>max: 66 tokens</li></ul> | <ul><li>0: ~32.50%</li><li>1: ~67.50%</li></ul> |
351
- * Samples:
352
- | text1 | text2 | label |
353
- |:------------------------------------------------------------------------------|:---------------------------------------------------------------|:---------------|
354
- | <code>明るい 茶色 の 犬 は 、 フェンス で 囲ま れた エリア で 赤 茶色 の 犬 と ボール を プレー して い ます 。</code> | <code>犬 は フェンス で 囲ま れた エリア で ボール を プレー し ます</code> | <code>0</code> |
355
- | <code>青い 服 を 着た 人 が バイク に 乗って い ます 。</code> | <code>その 人 は 、 地元 の 大学 で 授業 を 受ける ため に バイク に 乗って い ます 。</code> | <code>1</code> |
356
- | <code>真っ白な 女性 ピンク バナー 買い物 持って 立って ます 。</code> | <code>女性 は フットボール の 試合 で すべて 赤 を 着て い ます 。</code> | <code>1</code> |
357
- * Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
358
- ```json
359
- {
360
- "scale": 20.0,
361
- "similarity_fct": "pairwise_cos_sim"
362
- }
363
- ```
364
-
365
- ### Evaluation Dataset
366
-
367
- #### csv
368
-
369
- * Dataset: csv
370
- * Size: 5,330 evaluation samples
371
- * Columns: <code>text1</code>, <code>text2</code>, and <code>label</code>
372
- * Approximate statistics based on the first 1000 samples:
373
- | | text1 | text2 | label |
374
- |:--------|:------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:------------------------------------------------|
375
- | type | string | string | int |
376
- | details | <ul><li>min: 11 tokens</li><li>mean: 35.77 tokens</li><li>max: 109 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 22.73 tokens</li><li>max: 67 tokens</li></ul> | <ul><li>0: ~32.60%</li><li>1: ~67.40%</li></ul> |
377
- * Samples:
378
- | text1 | text2 | label |
379
- |:----------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------|:---------------|
380
- | <code>眼鏡 を かけた 男 は 、 「 FREE WORD 」 と いう 標識 を 持って い ます 。</code> | <code>人 は 眼鏡 を 外して い ます 。</code> | <code>1</code> |
381
- | <code>麦わら 帽子 を かぶった 2 人 の 男 、 青い シャツ 、 日焼け した 帽子 の 男 、 赤い 格子 縞 の シャツ 、 両方 と も 前 に バスケット を 持って 、 未 舗装 の 道路 の 脇 に 座って い ます 。</code> | <code>帽子 を かぶった 2 人 の 男 が 道路 の 脇 に 座って いる</code> | <code>0</code> |
382
- | <code>傘 を 持って 歩く 女性 。</code> | <code>女性 が 歩いて い ます 。</code> | <code>0</code> |
383
- * Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
384
- ```json
385
- {
386
- "scale": 20.0,
387
- "similarity_fct": "pairwise_cos_sim"
388
- }
389
- ```
390
-
391
- ### Training Hyperparameters
392
- #### Non-Default Hyperparameters
393
-
394
- - `eval_strategy`: epoch
395
- - `learning_rate`: 2e-05
396
- - `num_train_epochs`: 1
397
- - `warmup_ratio`: 0.4
398
- - `fp16`: True
399
- - `batch_sampler`: no_duplicates
400
-
401
- #### All Hyperparameters
402
- <details><summary>Click to expand</summary>
403
-
404
- - `overwrite_output_dir`: False
405
- - `do_predict`: False
406
- - `eval_strategy`: epoch
407
- - `prediction_loss_only`: True
408
- - `per_device_train_batch_size`: 8
409
- - `per_device_eval_batch_size`: 8
410
- - `per_gpu_train_batch_size`: None
411
- - `per_gpu_eval_batch_size`: None
412
- - `gradient_accumulation_steps`: 1
413
- - `eval_accumulation_steps`: None
414
- - `torch_empty_cache_steps`: None
415
- - `learning_rate`: 2e-05
416
- - `weight_decay`: 0.0
417
- - `adam_beta1`: 0.9
418
- - `adam_beta2`: 0.999
419
- - `adam_epsilon`: 1e-08
420
- - `max_grad_norm`: 1.0
421
- - `num_train_epochs`: 1
422
- - `max_steps`: -1
423
- - `lr_scheduler_type`: linear
424
- - `lr_scheduler_kwargs`: {}
425
- - `warmup_ratio`: 0.4
426
- - `warmup_steps`: 0
427
- - `log_level`: passive
428
- - `log_level_replica`: warning
429
- - `log_on_each_node`: True
430
- - `logging_nan_inf_filter`: True
431
- - `save_safetensors`: True
432
- - `save_on_each_node`: False
433
- - `save_only_model`: False
434
- - `restore_callback_states_from_checkpoint`: False
435
- - `no_cuda`: False
436
- - `use_cpu`: False
437
- - `use_mps_device`: False
438
- - `seed`: 42
439
- - `data_seed`: None
440
- - `jit_mode_eval`: False
441
- - `use_ipex`: False
442
- - `bf16`: False
443
- - `fp16`: True
444
- - `fp16_opt_level`: O1
445
- - `half_precision_backend`: auto
446
- - `bf16_full_eval`: False
447
- - `fp16_full_eval`: False
448
- - `tf32`: None
449
- - `local_rank`: 0
450
- - `ddp_backend`: None
451
- - `tpu_num_cores`: None
452
- - `tpu_metrics_debug`: False
453
- - `debug`: []
454
- - `dataloader_drop_last`: False
455
- - `dataloader_num_workers`: 0
456
- - `dataloader_prefetch_factor`: None
457
- - `past_index`: -1
458
- - `disable_tqdm`: False
459
- - `remove_unused_columns`: True
460
- - `label_names`: None
461
- - `load_best_model_at_end`: False
462
- - `ignore_data_skip`: False
463
- - `fsdp`: []
464
- - `fsdp_min_num_params`: 0
465
- - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
466
- - `fsdp_transformer_layer_cls_to_wrap`: None
467
- - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
468
- - `deepspeed`: None
469
- - `label_smoothing_factor`: 0.0
470
- - `optim`: adamw_torch
471
- - `optim_args`: None
472
- - `adafactor`: False
473
- - `group_by_length`: False
474
- - `length_column_name`: length
475
- - `ddp_find_unused_parameters`: None
476
- - `ddp_bucket_cap_mb`: None
477
- - `ddp_broadcast_buffers`: False
478
- - `dataloader_pin_memory`: True
479
- - `dataloader_persistent_workers`: False
480
- - `skip_memory_metrics`: True
481
- - `use_legacy_prediction_loop`: False
482
- - `push_to_hub`: False
483
- - `resume_from_checkpoint`: None
484
- - `hub_model_id`: None
485
- - `hub_strategy`: every_save
486
- - `hub_private_repo`: False
487
- - `hub_always_push`: False
488
- - `gradient_checkpointing`: False
489
- - `gradient_checkpointing_kwargs`: None
490
- - `include_inputs_for_metrics`: False
491
- - `eval_do_concat_batches`: True
492
- - `fp16_backend`: auto
493
- - `push_to_hub_model_id`: None
494
- - `push_to_hub_organization`: None
495
- - `mp_parameters`:
496
- - `auto_find_batch_size`: False
497
- - `full_determinism`: False
498
- - `torchdynamo`: None
499
- - `ray_scope`: last
500
- - `ddp_timeout`: 1800
501
- - `torch_compile`: False
502
- - `torch_compile_backend`: None
503
- - `torch_compile_mode`: None
504
- - `dispatch_batches`: None
505
- - `split_batches`: None
506
- - `include_tokens_per_second`: False
507
- - `include_num_input_tokens_seen`: False
508
- - `neftune_noise_alpha`: None
509
- - `optim_target_modules`: None
510
- - `batch_eval_metrics`: False
511
- - `eval_on_start`: False
512
- - `eval_use_gather_object`: False
513
- - `batch_sampler`: no_duplicates
514
- - `multi_dataset_batch_sampler`: proportional
515
-
516
- </details>
517
-
518
- ### Training Logs
519
- | Epoch | Step | Training Loss | loss | custom-arc-semantics-data-jp_max_ap |
520
- |:-----:|:----:|:-------------:|:------:|:-----------------------------------:|
521
- | 1.0 | 334 | 3.415 | 2.3727 | 0.7796 |
522
-
523
-
524
- ### Framework Versions
525
- - Python: 3.10.14
526
- - Sentence Transformers: 3.1.0
527
- - Transformers: 4.44.2
528
- - PyTorch: 2.4.1+cu121
529
- - Accelerate: 0.34.2
530
- - Datasets: 2.20.0
531
- - Tokenizers: 0.19.1
532
-
533
- ## Citation
534
-
535
- ### BibTeX
536
-
537
- #### Sentence Transformers
538
- ```bibtex
539
- @inproceedings{reimers-2019-sentence-bert,
540
- title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
541
- author = "Reimers, Nils and Gurevych, Iryna",
542
- booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
543
- month = "11",
544
- year = "2019",
545
- publisher = "Association for Computational Linguistics",
546
- url = "https://arxiv.org/abs/1908.10084",
547
- }
548
- ```
549
-
550
- #### CoSENTLoss
551
- ```bibtex
552
- @online{kexuefm-8847,
553
- title={CoSENT: A more efficient sentence vector scheme than Sentence-BERT},
554
- author={Su Jianlin},
555
- year={2022},
556
- month={Jan},
557
- url={https://kexue.fm/archives/8847},
558
- }
559
- ```
560
-
561
- <!--
562
- ## Glossary
563
-
564
- *Clearly define terms in order to be accessible across audiences.*
565
- -->
566
-
567
- <!--
568
- ## Model Card Authors
569
-
570
- *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
571
- -->
572
-
573
- <!--
574
  ## Model Card Contact
575
 
576
- *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
577
- -->
 
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
 
48
+ ### Downstream Use [optional]
 
49
 
50
+ <!-- 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
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
66
  ### 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
200
 
201
+ [More Information Needed]