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1
  ---
2
  base_model: sentence-transformers/all-MiniLM-L6-v2
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:593
47
- - loss:OnlineContrastiveLoss
48
- widget:
49
- - source_sentence: What city
50
- sentences:
51
- - What magic do other villagers use?
52
- - What does between the gods mean?
53
- - what about the city
54
- - source_sentence: What's your name?
55
- sentences:
56
- - what mystery?
57
- - Is this the flower
58
- - A globe.
59
- - source_sentence: I think we'll find dragons.
60
- sentences:
61
- - Do you know a mage who changes shape of material?
62
- - I don't think we'll find dragons.
63
- - The curtain is moving in the breeze
64
- - source_sentence: What happened to her?
65
- sentences:
66
- - Is this the flower
67
- - Do you have a second bucket?
68
- - There was a red stain on the dish
69
- - source_sentence: I don't see tomato on the shelf
70
- sentences:
71
- - What magic do other villagers use?
72
- - Yes please
73
- - Because the pot smelled spicy
74
- model-index:
75
- - name: SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2
76
- results:
77
- - task:
78
- type: binary-classification
79
- name: Binary Classification
80
- dataset:
81
- name: custom arc semantics data en
82
- type: custom-arc-semantics-data-en
83
- metrics:
84
- - type: cosine_accuracy
85
- value: 0.9495798319327731
86
- name: Cosine Accuracy
87
- - type: cosine_accuracy_threshold
88
- value: 0.6676459908485413
89
- name: Cosine Accuracy Threshold
90
- - type: cosine_f1
91
- value: 0.9
92
- name: Cosine F1
93
- - type: cosine_f1_threshold
94
- value: 0.6361173391342163
95
- name: Cosine F1 Threshold
96
- - type: cosine_precision
97
- value: 0.9
98
- name: Cosine Precision
99
- - type: cosine_recall
100
- value: 0.9
101
- name: Cosine Recall
102
- - type: cosine_ap
103
- value: 0.8400025542161988
104
- name: Cosine Ap
105
- - type: dot_accuracy
106
- value: 0.9495798319327731
107
- name: Dot Accuracy
108
- - type: dot_accuracy_threshold
109
- value: 0.6676459908485413
110
- name: Dot Accuracy Threshold
111
- - type: dot_f1
112
- value: 0.9
113
- name: Dot F1
114
- - type: dot_f1_threshold
115
- value: 0.6361173391342163
116
- name: Dot F1 Threshold
117
- - type: dot_precision
118
- value: 0.9
119
- name: Dot Precision
120
- - type: dot_recall
121
- value: 0.9
122
- name: Dot Recall
123
- - type: dot_ap
124
- value: 0.8400025542161988
125
- name: Dot Ap
126
- - type: manhattan_accuracy
127
- value: 0.9495798319327731
128
- name: Manhattan Accuracy
129
- - type: manhattan_accuracy_threshold
130
- value: 12.677780151367188
131
- name: Manhattan Accuracy Threshold
132
- - type: manhattan_f1
133
- value: 0.896551724137931
134
- name: Manhattan F1
135
- - type: manhattan_f1_threshold
136
- value: 12.677780151367188
137
- name: Manhattan F1 Threshold
138
- - type: manhattan_precision
139
- value: 0.9285714285714286
140
- name: Manhattan Precision
141
- - type: manhattan_recall
142
- value: 0.8666666666666667
143
- name: Manhattan Recall
144
- - type: manhattan_ap
145
- value: 0.8387174899512584
146
- name: Manhattan Ap
147
- - type: euclidean_accuracy
148
- value: 0.9495798319327731
149
- name: Euclidean Accuracy
150
- - type: euclidean_accuracy_threshold
151
- value: 0.8152118921279907
152
- name: Euclidean Accuracy Threshold
153
- - type: euclidean_f1
154
- value: 0.9
155
- name: Euclidean F1
156
- - type: euclidean_f1_threshold
157
- value: 0.8530915379524231
158
- name: Euclidean F1 Threshold
159
- - type: euclidean_precision
160
- value: 0.9
161
- name: Euclidean Precision
162
- - type: euclidean_recall
163
- value: 0.9
164
- name: Euclidean Recall
165
- - type: euclidean_ap
166
- value: 0.8400025542161988
167
- name: Euclidean Ap
168
- - type: max_accuracy
169
- value: 0.9495798319327731
170
- name: Max Accuracy
171
- - type: max_accuracy_threshold
172
- value: 12.677780151367188
173
- name: Max Accuracy Threshold
174
- - type: max_f1
175
- value: 0.9
176
- name: Max F1
177
- - type: max_f1_threshold
178
- value: 12.677780151367188
179
- name: Max F1 Threshold
180
- - type: max_precision
181
- value: 0.9285714285714286
182
- name: Max Precision
183
- - type: max_recall
184
- value: 0.9
185
- name: Max Recall
186
- - type: max_ap
187
- value: 0.8400025542161988
188
- name: Max Ap
189
  ---
190
 
191
- # SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2
 
 
 
192
 
193
- This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) on the csv dataset. It maps sentences & paragraphs to a 384-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:** [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) <!-- at revision 8b3219a92973c328a8e22fadcfa821b5dc75636a -->
200
- - **Maximum Sequence Length:** 256 tokens
201
- - **Output Dimensionality:** 384 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': 256, 'do_lower_case': False}) with Transformer model: BertModel
219
- (1): Pooling({'word_embedding_dimension': 384, '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
- (2): Normalize()
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("LeoChiuu/all-MiniLM-L6-v2-arc")
240
- # Run inference
241
- sentences = [
242
- "I don't see tomato on the shelf",
243
- 'Because the pot smelled spicy',
244
- 'What magic do other villagers use?',
245
- ]
246
- embeddings = model.encode(sentences)
247
- print(embeddings.shape)
248
- # [3, 384]
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-en`
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 | 0.9496 |
291
- | cosine_accuracy_threshold | 0.6676 |
292
- | cosine_f1 | 0.9 |
293
- | cosine_f1_threshold | 0.6361 |
294
- | cosine_precision | 0.9 |
295
- | cosine_recall | 0.9 |
296
- | cosine_ap | 0.84 |
297
- | dot_accuracy | 0.9496 |
298
- | dot_accuracy_threshold | 0.6676 |
299
- | dot_f1 | 0.9 |
300
- | dot_f1_threshold | 0.6361 |
301
- | dot_precision | 0.9 |
302
- | dot_recall | 0.9 |
303
- | dot_ap | 0.84 |
304
- | manhattan_accuracy | 0.9496 |
305
- | manhattan_accuracy_threshold | 12.6778 |
306
- | manhattan_f1 | 0.8966 |
307
- | manhattan_f1_threshold | 12.6778 |
308
- | manhattan_precision | 0.9286 |
309
- | manhattan_recall | 0.8667 |
310
- | manhattan_ap | 0.8387 |
311
- | euclidean_accuracy | 0.9496 |
312
- | euclidean_accuracy_threshold | 0.8152 |
313
- | euclidean_f1 | 0.9 |
314
- | euclidean_f1_threshold | 0.8531 |
315
- | euclidean_precision | 0.9 |
316
- | euclidean_recall | 0.9 |
317
- | euclidean_ap | 0.84 |
318
- | max_accuracy | 0.9496 |
319
- | max_accuracy_threshold | 12.6778 |
320
- | max_f1 | 0.9 |
321
- | max_f1_threshold | 12.6778 |
322
- | max_precision | 0.9286 |
323
- | max_recall | 0.9 |
324
- | **max_ap** | **0.84** |
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: 593 training samples
346
- * Columns: <code>text1</code>, <code>text2</code>, and <code>label</code>
347
- * Approximate statistics based on the first 593 samples:
348
- | | text1 | text2 | label |
349
- |:--------|:--------------------------------------------------------------------------------|:--------------------------------------------------------------------------------|:------------------------------------------------|
350
- | type | string | string | int |
351
- | details | <ul><li>min: 3 tokens</li><li>mean: 7.2 tokens</li><li>max: 18 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 6.8 tokens</li><li>max: 18 tokens</li></ul> | <ul><li>0: ~68.14%</li><li>1: ~31.86%</li></ul> |
352
- * Samples:
353
- | text1 | text2 | label |
354
- |:--------------------------------------|:-------------------------------|:---------------|
355
- | <code>Something is different</code> | <code>What did you say?</code> | <code>0</code> |
356
- | <code>what are the properties?</code> | <code>what about Jack?</code> | <code>0</code> |
357
- | <code>hint</code> | <code>hints</code> | <code>1</code> |
358
- * Loss: [<code>OnlineContrastiveLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#onlinecontrastiveloss)
359
-
360
- ### Evaluation Dataset
361
-
362
- #### csv
363
-
364
- * Dataset: csv
365
- * Size: 593 evaluation samples
366
- * Columns: <code>text1</code>, <code>text2</code>, and <code>label</code>
367
- * Approximate statistics based on the first 593 samples:
368
- | | text1 | text2 | label |
369
- |:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:------------------------------------------------|
370
- | type | string | string | int |
371
- | details | <ul><li>min: 3 tokens</li><li>mean: 7.26 tokens</li><li>max: 13 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 7.13 tokens</li><li>max: 13 tokens</li></ul> | <ul><li>0: ~74.79%</li><li>1: ~25.21%</li></ul> |
372
- * Samples:
373
- | text1 | text2 | label |
374
- |:---------------------------------------------|:----------------------------------------|:---------------|
375
- | <code>To have an adventure with us</code> | <code>Its name is Oblivion.</code> | <code>0</code> |
376
- | <code>Is the scarf on the nightstand?</code> | <code>Are you using my slippers?</code> | <code>0</code> |
377
- | <code>To test Unravel Spell</code> | <code>Tell me about Lily</code> | <code>0</code> |
378
- * Loss: [<code>OnlineContrastiveLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#onlinecontrastiveloss)
379
-
380
- ### Training Hyperparameters
381
- #### Non-Default Hyperparameters
382
-
383
- - `eval_strategy`: epoch
384
- - `learning_rate`: 2e-05
385
- - `num_train_epochs`: 13
386
- - `warmup_ratio`: 0.1
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`: 13
411
- - `max_steps`: -1
412
- - `lr_scheduler_type`: linear
413
- - `lr_scheduler_kwargs`: {}
414
- - `warmup_ratio`: 0.1
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-en_max_ap |
509
- |:-----:|:----:|:-------------:|:------:|:-----------------------------------:|
510
- | None | 0 | - | - | 0.7634 |
511
- | 1.0 | 60 | 0.3053 | 0.1297 | 0.7825 |
512
- | 2.0 | 120 | 0.1478 | 0.1071 | 0.8071 |
513
- | 3.0 | 180 | 0.0357 | 0.0904 | 0.8387 |
514
- | 4.0 | 240 | 0.0139 | 0.0829 | 0.8412 |
515
- | 5.0 | 300 | 0.017 | 0.0704 | 0.8429 |
516
- | 6.0 | 360 | 0.0132 | 0.0779 | 0.8411 |
517
- | 7.0 | 420 | 0.0 | 0.0700 | 0.8433 |
518
- | 8.0 | 480 | 0.0079 | 0.0808 | 0.8403 |
519
- | 9.0 | 540 | 0.0098 | 0.0808 | 0.8404 |
520
- | 10.0 | 600 | 0.0039 | 0.0804 | 0.8387 |
521
- | 11.0 | 660 | 0.0001 | 0.0815 | 0.8398 |
522
- | 12.0 | 720 | 0.0039 | 0.0816 | 0.8397 |
523
- | 13.0 | 780 | 0.0034 | 0.0814 | 0.8400 |
524
-
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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
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- ```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",
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- month = "11",
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- year = "2019",
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- publisher = "Association for Computational Linguistics",
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- url = "https://arxiv.org/abs/1908.10084",
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- }
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- ```
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-
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- <!--
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- ## Glossary
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  ---
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  base_model: sentence-transformers/all-MiniLM-L6-v2
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+ language: en
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+ license: apache-2.0
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+ model_name: LeoChiuu/all-MiniLM-L6-v2-arc
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ---
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+ # Model Card for LeoChiuu/all-MiniLM-L6-v2-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):** en
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+ - **License:** apache-2.0
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+ - **Finetuned from model [optional]:** sentence-transformers/all-MiniLM-L6-v2
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+ ### Model Sources [optional]
 
 
 
 
 
 
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+ - **Repository:** [More Information Needed]
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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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+ ## 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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+
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+ #### Preprocessing [optional]
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+ [More Information Needed]
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+
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+ #### Training Hyperparameters
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+
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+ - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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+
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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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+ ### Testing Data, Factors & Metrics
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+ #### Factors
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+ <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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+ [More Information Needed]
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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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+ [More Information Needed]
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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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+
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+ - **Hardware Type:** [More Information Needed]
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+ - **Cloud Provider:** [More Information Needed]
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+
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+ ## Technical Specifications [optional]
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+
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+ ### Model Architecture and Objective
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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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