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Add new SentenceTransformer model

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+ ---
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+ tags:
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+ - sentence-transformers
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+ - sentence-similarity
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+ - feature-extraction
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+ - generated_from_trainer
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+ - dataset_size:4322286
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+ - loss:MatryoshkaLoss
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+ - loss:MultipleNegativesRankingLoss
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+ widget:
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+ - source_sentence: how to sign legal documents as power of attorney?
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+ sentences:
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+ - 'After the principal''s name, write “by” and then sign your own name. Under or
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+ after the signature line, indicate your status as POA by including any of the
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+ following identifiers: as POA, as Agent, as Attorney in Fact or as Power of Attorney.'
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+ - '[''From the Home screen, swipe left to Apps.'', ''Tap Transfer my Data.'', ''Tap
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+ Menu (...).'', ''Tap Export to SD card.'']'
18
+ - Ginger Dank Nugs (Grape) - 350mg. Feast your eyes on these unique and striking
19
+ gourmet chocolates; Coco Nugs created by Ginger Dank. Crafted to resemble perfect
20
+ nugs of cannabis, each of the 10 buds contains 35mg of THC. ... This is a perfect
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+ product for both cannabis and chocolate lovers, who appreciate a little twist.
22
+ - source_sentence: how to delete vdom in fortigate?
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+ sentences:
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+ - Go to System -> VDOM -> VDOM2 and select 'Delete'. This VDOM is now successfully
25
+ removed from the configuration.
26
+ - 'Both combination birth control pills and progestin-only pills may cause headaches
27
+ as a side effect. Additional side effects of birth control pills may include:
28
+ breast tenderness. nausea.'
29
+ - White cheese tends to show imperfections more readily and as consumers got more
30
+ used to yellow-orange cheese, it became an expected option. Today, many cheddars
31
+ are yellow. While most cheesemakers use annatto, some use an artificial coloring
32
+ agent instead, according to Sachs.
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+ - source_sentence: where are earthquakes most likely to occur on earth?
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+ sentences:
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+ - Zelle in the Bank of the America app is a fast, safe, and easy way to send and
36
+ receive money with family and friends who have a bank account in the U.S., all
37
+ with no fees. Money moves in minutes directly between accounts that are already
38
+ enrolled with Zelle.
39
+ - It takes about 3 days for a spacecraft to reach the Moon. During that time a spacecraft
40
+ travels at least 240,000 miles (386,400 kilometers) which is the distance between
41
+ Earth and the Moon.
42
+ - Most earthquakes occur along the edge of the oceanic and continental plates. The
43
+ earth's crust (the outer layer of the planet) is made up of several pieces, called
44
+ plates. The plates under the oceans are called oceanic plates and the rest are
45
+ continental plates.
46
+ - source_sentence: fix iphone is disabled connect to itunes without itunes?
47
+ sentences:
48
+ - To fix a disabled iPhone or iPad without iTunes, you have to erase your device.
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+ Click on the "Erase iPhone" option and confirm your selection. Wait for a while
50
+ as the "Find My iPhone" feature will remotely erase your iOS device. Needless
51
+ to say, it will also disable its lock.
52
+ - How Māui brought fire to the world. One evening, after eating a hearty meal, Māui
53
+ lay beside his fire staring into the flames. ... In the middle of the night, while
54
+ everyone was sleeping, Māui went from village to village and extinguished all
55
+ the fires until not a single fire burned in the world.
56
+ - Angry Orchard makes a variety of year-round craft cider styles, including Angry
57
+ Orchard Crisp Apple, a fruit-forward hard cider that balances the sweetness of
58
+ culinary apples with dryness and bright acidity of bittersweet apples for a complex,
59
+ refreshing taste.
60
+ - source_sentence: how to reverse a video on tiktok that's not yours?
61
+ sentences:
62
+ - '[''Tap "Effects" at the bottom of your screen — it\''s an icon that looks like
63
+ a clock. Open the Effects menu. ... '', ''At the end of the new list that appears,
64
+ tap "Time." Select "Time" at the end. ... '', ''Select "Reverse" — you\''ll then
65
+ see a preview of your new, reversed video appear on the screen.'']'
66
+ - Franchise Facts Poke Bar has a franchise fee of up to $30,000, with a total initial
67
+ investment range of $157,800 to $438,000. The initial cost of a franchise includes
68
+ several fees -- Unlock this franchise to better understand the costs such as training
69
+ and territory fees.
70
+ - Relative age is the age of a rock layer (or the fossils it contains) compared
71
+ to other layers. It can be determined by looking at the position of rock layers.
72
+ Absolute age is the numeric age of a layer of rocks or fossils. Absolute age can
73
+ be determined by using radiometric dating.
74
+ pipeline_tag: sentence-similarity
75
+ library_name: sentence-transformers
76
+ ---
77
+
78
+ # SentenceTransformer
79
+
80
+ This is a [sentence-transformers](https://www.SBERT.net) model trained. It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
81
+
82
+ ## Model Details
83
+
84
+ ### Model Description
85
+ - **Model Type:** Sentence Transformer
86
+ <!-- - **Base model:** [Unknown](https://huggingface.co/unknown) -->
87
+ - **Maximum Sequence Length:** inf tokens
88
+ - **Output Dimensionality:** 1024 dimensions
89
+ - **Similarity Function:** Cosine Similarity
90
+ <!-- - **Training Dataset:** Unknown -->
91
+ <!-- - **Language:** Unknown -->
92
+ <!-- - **License:** Unknown -->
93
+
94
+ ### Model Sources
95
+
96
+ - **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
97
+ - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
98
+ - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
99
+
100
+ ### Full Model Architecture
101
+
102
+ ```
103
+ SentenceTransformer(
104
+ (0): StaticEmbedding(
105
+ (embedding): EmbeddingBag(256000, 1024, mode='mean')
106
+ )
107
+ )
108
+ ```
109
+
110
+ ## Usage
111
+
112
+ ### Direct Usage (Sentence Transformers)
113
+
114
+ First install the Sentence Transformers library:
115
+
116
+ ```bash
117
+ pip install -U sentence-transformers
118
+ ```
119
+
120
+ Then you can load this model and run inference.
121
+ ```python
122
+ from sentence_transformers import SentenceTransformer
123
+
124
+ # Download from the 🤗 Hub
125
+ model = SentenceTransformer("NickyNicky/StaticEmbedding-MatryoshkaLoss-gemma-2-2b-en-es")
126
+ # Run inference
127
+ sentences = [
128
+ "how to reverse a video on tiktok that's not yours?",
129
+ '[\'Tap "Effects" at the bottom of your screen — it\\\'s an icon that looks like a clock. Open the Effects menu. ... \', \'At the end of the new list that appears, tap "Time." Select "Time" at the end. ... \', \'Select "Reverse" — you\\\'ll then see a preview of your new, reversed video appear on the screen.\']',
130
+ 'Relative age is the age of a rock layer (or the fossils it contains) compared to other layers. It can be determined by looking at the position of rock layers. Absolute age is the numeric age of a layer of rocks or fossils. Absolute age can be determined by using radiometric dating.',
131
+ ]
132
+ embeddings = model.encode(sentences)
133
+ print(embeddings.shape)
134
+ # [3, 1024]
135
+
136
+ # Get the similarity scores for the embeddings
137
+ similarities = model.similarity(embeddings, embeddings)
138
+ print(similarities.shape)
139
+ # [3, 3]
140
+ ```
141
+
142
+ <!--
143
+ ### Direct Usage (Transformers)
144
+
145
+ <details><summary>Click to see the direct usage in Transformers</summary>
146
+
147
+ </details>
148
+ -->
149
+
150
+ <!--
151
+ ### Downstream Usage (Sentence Transformers)
152
+
153
+ You can finetune this model on your own dataset.
154
+
155
+ <details><summary>Click to expand</summary>
156
+
157
+ </details>
158
+ -->
159
+
160
+ <!--
161
+ ### Out-of-Scope Use
162
+
163
+ *List how the model may foreseeably be misused and address what users ought not to do with the model.*
164
+ -->
165
+
166
+ <!--
167
+ ## Bias, Risks and Limitations
168
+
169
+ *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
170
+ -->
171
+
172
+ <!--
173
+ ### Recommendations
174
+
175
+ *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
176
+ -->
177
+
178
+ ## Training Details
179
+
180
+ ### Training Dataset
181
+
182
+ #### Unnamed Dataset
183
+
184
+
185
+ * Size: 4,322,286 training samples
186
+ * Columns: <code>question</code> and <code>answer</code>
187
+ * Approximate statistics based on the first 1000 samples:
188
+ | | question | answer |
189
+ |:--------|:-----------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------|
190
+ | type | string | string |
191
+ | details | <ul><li>min: 18 characters</li><li>mean: 43.23 characters</li><li>max: 96 characters</li></ul> | <ul><li>min: 55 characters</li><li>mean: 253.36 characters</li><li>max: 371 characters</li></ul> |
192
+ * Samples:
193
+ | question | answer |
194
+ |:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
195
+ | <code>what is the difference between broilers and layers?</code> | <code>An egg laying poultry is called egger or layer whereas broilers are reared for obtaining meat. So a layer should be able to produce more number of large sized eggs, without growing too much. On the other hand, a broiler should yield more meat and hence should be able to grow well.</code> |
196
+ | <code>what is the difference between chronological order and spatial order?</code> | <code>As a writer, you should always remember that unlike chronological order and the other organizational methods for data, spatial order does not take into account the time. Spatial order is primarily focused on the location. All it does is take into account the location of objects and not the time.</code> |
197
+ | <code>is kamagra same as viagra?</code> | <code>Kamagra is thought to contain the same active ingredient as Viagra, sildenafil citrate. In theory, it should work in much the same way as Viagra, taking about 45 minutes to take effect, and lasting for around 4-6 hours. However, this will vary from person to person.</code> |
198
+ * Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
199
+ ```json
200
+ {
201
+ "loss": "MultipleNegativesRankingLoss",
202
+ "matryoshka_dims": [
203
+ 1024,
204
+ 768,
205
+ 512,
206
+ 256,
207
+ 128,
208
+ 64,
209
+ 32
210
+ ],
211
+ "matryoshka_weights": [
212
+ 1,
213
+ 1,
214
+ 1,
215
+ 1,
216
+ 1,
217
+ 1,
218
+ 1
219
+ ],
220
+ "n_dims_per_step": -1
221
+ }
222
+ ```
223
+
224
+ ### Evaluation Dataset
225
+
226
+ #### Unnamed Dataset
227
+
228
+
229
+ * Size: 10,005 evaluation samples
230
+ * Columns: <code>question</code> and <code>answer</code>
231
+ * Approximate statistics based on the first 1000 samples:
232
+ | | question | answer |
233
+ |:--------|:-----------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------|
234
+ | type | string | string |
235
+ | details | <ul><li>min: 18 characters</li><li>mean: 43.17 characters</li><li>max: 98 characters</li></ul> | <ul><li>min: 51 characters</li><li>mean: 254.12 characters</li><li>max: 360 characters</li></ul> |
236
+ * Samples:
237
+ | question | answer |
238
+ |:-----------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
239
+ | <code>how do i program my directv remote with my tv?</code> | <code>['Press MENU on your remote.', 'Select Settings & Help > Settings > Remote Control > Program Remote.', 'Choose the device (TV, audio, DVD) you wish to program. ... ', 'Follow the on-screen prompts to complete programming.']</code> |
240
+ | <code>are rodrigues fruit bats nocturnal?</code> | <code>Before its numbers were threatened by habitat destruction, storms, and hunting, some of those groups could number 500 or more members. Sunrise, sunset. Rodrigues fruit bats are most active at dawn, at dusk, and at night.</code> |
241
+ | <code>why does your heart rate increase during exercise bbc bitesize?</code> | <code>During exercise there is an increase in physical activity and muscle cells respire more than they do when the body is at rest. The heart rate increases during exercise. The rate and depth of breathing increases - this makes sure that more oxygen is absorbed into the blood, and more carbon dioxide is removed from it.</code> |
242
+ * Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
243
+ ```json
244
+ {
245
+ "loss": "MultipleNegativesRankingLoss",
246
+ "matryoshka_dims": [
247
+ 1024,
248
+ 768,
249
+ 512,
250
+ 256,
251
+ 128,
252
+ 64,
253
+ 32
254
+ ],
255
+ "matryoshka_weights": [
256
+ 1,
257
+ 1,
258
+ 1,
259
+ 1,
260
+ 1,
261
+ 1,
262
+ 1
263
+ ],
264
+ "n_dims_per_step": -1
265
+ }
266
+ ```
267
+
268
+ ### Training Hyperparameters
269
+ #### Non-Default Hyperparameters
270
+
271
+ - `eval_strategy`: steps
272
+ - `per_device_train_batch_size`: 2048
273
+ - `per_device_eval_batch_size`: 2048
274
+ - `learning_rate`: 0.2
275
+ - `warmup_ratio`: 0.1
276
+ - `bf16`: True
277
+ - `batch_sampler`: no_duplicates
278
+
279
+ #### All Hyperparameters
280
+ <details><summary>Click to expand</summary>
281
+
282
+ - `overwrite_output_dir`: False
283
+ - `do_predict`: False
284
+ - `eval_strategy`: steps
285
+ - `prediction_loss_only`: True
286
+ - `per_device_train_batch_size`: 2048
287
+ - `per_device_eval_batch_size`: 2048
288
+ - `per_gpu_train_batch_size`: None
289
+ - `per_gpu_eval_batch_size`: None
290
+ - `gradient_accumulation_steps`: 1
291
+ - `eval_accumulation_steps`: None
292
+ - `torch_empty_cache_steps`: None
293
+ - `learning_rate`: 0.2
294
+ - `weight_decay`: 0.0
295
+ - `adam_beta1`: 0.9
296
+ - `adam_beta2`: 0.999
297
+ - `adam_epsilon`: 1e-08
298
+ - `max_grad_norm`: 1.0
299
+ - `num_train_epochs`: 3
300
+ - `max_steps`: -1
301
+ - `lr_scheduler_type`: linear
302
+ - `lr_scheduler_kwargs`: {}
303
+ - `warmup_ratio`: 0.1
304
+ - `warmup_steps`: 0
305
+ - `log_level`: passive
306
+ - `log_level_replica`: warning
307
+ - `log_on_each_node`: True
308
+ - `logging_nan_inf_filter`: True
309
+ - `save_safetensors`: True
310
+ - `save_on_each_node`: False
311
+ - `save_only_model`: False
312
+ - `restore_callback_states_from_checkpoint`: False
313
+ - `no_cuda`: False
314
+ - `use_cpu`: False
315
+ - `use_mps_device`: False
316
+ - `seed`: 42
317
+ - `data_seed`: None
318
+ - `jit_mode_eval`: False
319
+ - `use_ipex`: False
320
+ - `bf16`: True
321
+ - `fp16`: False
322
+ - `fp16_opt_level`: O1
323
+ - `half_precision_backend`: auto
324
+ - `bf16_full_eval`: False
325
+ - `fp16_full_eval`: False
326
+ - `tf32`: None
327
+ - `local_rank`: 0
328
+ - `ddp_backend`: None
329
+ - `tpu_num_cores`: None
330
+ - `tpu_metrics_debug`: False
331
+ - `debug`: []
332
+ - `dataloader_drop_last`: False
333
+ - `dataloader_num_workers`: 0
334
+ - `dataloader_prefetch_factor`: None
335
+ - `past_index`: -1
336
+ - `disable_tqdm`: False
337
+ - `remove_unused_columns`: True
338
+ - `label_names`: None
339
+ - `load_best_model_at_end`: False
340
+ - `ignore_data_skip`: False
341
+ - `fsdp`: []
342
+ - `fsdp_min_num_params`: 0
343
+ - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
344
+ - `fsdp_transformer_layer_cls_to_wrap`: None
345
+ - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
346
+ - `deepspeed`: None
347
+ - `label_smoothing_factor`: 0.0
348
+ - `optim`: adamw_torch
349
+ - `optim_args`: None
350
+ - `adafactor`: False
351
+ - `group_by_length`: False
352
+ - `length_column_name`: length
353
+ - `ddp_find_unused_parameters`: None
354
+ - `ddp_bucket_cap_mb`: None
355
+ - `ddp_broadcast_buffers`: False
356
+ - `dataloader_pin_memory`: True
357
+ - `dataloader_persistent_workers`: False
358
+ - `skip_memory_metrics`: True
359
+ - `use_legacy_prediction_loop`: False
360
+ - `push_to_hub`: False
361
+ - `resume_from_checkpoint`: None
362
+ - `hub_model_id`: None
363
+ - `hub_strategy`: every_save
364
+ - `hub_private_repo`: None
365
+ - `hub_always_push`: False
366
+ - `gradient_checkpointing`: False
367
+ - `gradient_checkpointing_kwargs`: None
368
+ - `include_inputs_for_metrics`: False
369
+ - `include_for_metrics`: []
370
+ - `eval_do_concat_batches`: True
371
+ - `fp16_backend`: auto
372
+ - `push_to_hub_model_id`: None
373
+ - `push_to_hub_organization`: None
374
+ - `mp_parameters`:
375
+ - `auto_find_batch_size`: False
376
+ - `full_determinism`: False
377
+ - `torchdynamo`: None
378
+ - `ray_scope`: last
379
+ - `ddp_timeout`: 1800
380
+ - `torch_compile`: False
381
+ - `torch_compile_backend`: None
382
+ - `torch_compile_mode`: None
383
+ - `dispatch_batches`: None
384
+ - `split_batches`: None
385
+ - `include_tokens_per_second`: False
386
+ - `include_num_input_tokens_seen`: False
387
+ - `neftune_noise_alpha`: None
388
+ - `optim_target_modules`: None
389
+ - `batch_eval_metrics`: False
390
+ - `eval_on_start`: False
391
+ - `use_liger_kernel`: False
392
+ - `eval_use_gather_object`: False
393
+ - `average_tokens_across_devices`: False
394
+ - `prompts`: None
395
+ - `batch_sampler`: no_duplicates
396
+ - `multi_dataset_batch_sampler`: proportional
397
+
398
+ </details>
399
+
400
+ ### Training Logs
401
+ | Epoch | Step | Training Loss | Validation Loss |
402
+ |:------:|:----:|:-------------:|:---------------:|
403
+ | 0.0005 | 1 | 49.8746 | - |
404
+ | 0.0474 | 100 | 35.8567 | 7.1776 |
405
+ | 0.0947 | 200 | 13.988 | 3.2848 |
406
+ | 0.1421 | 300 | 8.0009 | 2.3610 |
407
+ | 0.1895 | 400 | 6.3293 | 2.0293 |
408
+ | 0.2369 | 500 | 5.6296 | 1.8849 |
409
+ | 0.2842 | 600 | 5.238 | 1.7495 |
410
+ | 0.3316 | 700 | 4.9115 | 1.6694 |
411
+ | 0.3790 | 800 | 4.5779 | 1.5583 |
412
+ | 0.4263 | 900 | 4.2608 | 1.4784 |
413
+ | 0.4737 | 1000 | 4.0893 | 1.4020 |
414
+ | 0.5211 | 1100 | 3.8669 | 1.3426 |
415
+ | 0.5685 | 1200 | 3.7505 | 1.3160 |
416
+ | 0.6158 | 1300 | 3.6529 | 1.2822 |
417
+ | 0.6632 | 1400 | 3.5203 | 1.2612 |
418
+ | 0.7106 | 1500 | 5.1906 | 1.4469 |
419
+ | 0.7579 | 1600 | 4.0273 | 1.6219 |
420
+ | 0.8053 | 1700 | 4.8308 | 3.1338 |
421
+ | 0.8527 | 1800 | 0.5336 | 3.2854 |
422
+ | 0.9000 | 1900 | 0.3 | 3.3757 |
423
+ | 0.9474 | 2000 | 0.0886 | 3.3620 |
424
+ | 0.9948 | 2100 | 0.0817 | 3.3510 |
425
+ | 1.0417 | 2200 | 4.0692 | 1.3638 |
426
+
427
+
428
+ ### Framework Versions
429
+ - Python: 3.10.12
430
+ - Sentence Transformers: 3.3.1
431
+ - Transformers: 4.47.1
432
+ - PyTorch: 2.5.1+cu121
433
+ - Accelerate: 1.2.1
434
+ - Datasets: 3.2.0
435
+ - Tokenizers: 0.21.0
436
+
437
+ ## Citation
438
+
439
+ ### BibTeX
440
+
441
+ #### Sentence Transformers
442
+ ```bibtex
443
+ @inproceedings{reimers-2019-sentence-bert,
444
+ title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
445
+ author = "Reimers, Nils and Gurevych, Iryna",
446
+ booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
447
+ month = "11",
448
+ year = "2019",
449
+ publisher = "Association for Computational Linguistics",
450
+ url = "https://arxiv.org/abs/1908.10084",
451
+ }
452
+ ```
453
+
454
+ #### MatryoshkaLoss
455
+ ```bibtex
456
+ @misc{kusupati2024matryoshka,
457
+ title={Matryoshka Representation Learning},
458
+ author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
459
+ year={2024},
460
+ eprint={2205.13147},
461
+ archivePrefix={arXiv},
462
+ primaryClass={cs.LG}
463
+ }
464
+ ```
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+
466
+ #### MultipleNegativesRankingLoss
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+ ```bibtex
468
+ @misc{henderson2017efficient,
469
+ title={Efficient Natural Language Response Suggestion for Smart Reply},
470
+ author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
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+ year={2017},
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+ eprint={1705.00652},
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+ archivePrefix={arXiv},
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+ primaryClass={cs.CL}
475
+ }
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+ ```
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+
478
+ <!--
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+ ## Glossary
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+
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+ *Clearly define terms in order to be accessible across audiences.*
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+ -->
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+
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+ <!--
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+ ## Model Card Authors
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+
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+ *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
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+ -->
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+
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+ <!--
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+ ## Model Card Contact
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+
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+ *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
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+ -->
config_sentence_transformers.json ADDED
@@ -0,0 +1,10 @@
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "__version__": {
3
+ "sentence_transformers": "3.3.1",
4
+ "transformers": "4.47.1",
5
+ "pytorch": "2.5.1+cu121"
6
+ },
7
+ "prompts": {},
8
+ "default_prompt_name": null,
9
+ "similarity_fn_name": "cosine"
10
+ }
modules.json ADDED
@@ -0,0 +1,8 @@
 
 
 
 
 
 
 
 
 
1
+ [
2
+ {
3
+ "idx": 0,
4
+ "name": "0",
5
+ "path": "0_StaticEmbedding",
6
+ "type": "sentence_transformers.models.StaticEmbedding"
7
+ }
8
+ ]