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lv12
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esci-nomic-embed-text-v1_5_4

Sentence Similarity
sentence-transformers
Safetensors
nomic_bert
feature-extraction
Generated from Trainer
dataset_size:1600000
loss:TripletLoss
custom_code
Eval Results
Inference Endpoints
text-embeddings-inference
Model card Files Community
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Commit
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1 Parent(s): 0c56232

multi stage tuning

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  1. README.md +406 -567
  2. config.json +1 -1
  3. model.safetensors +1 -1
README.md CHANGED
@@ -6,10 +6,8 @@ tags:
6
  - sentence-similarity
7
  - feature-extraction
8
  - generated_from_trainer
9
- - dataset_size:1182198
10
- - loss:CachedMultipleNegativesRankingLoss
11
- - loss:AnglELoss
12
- base_model: nomic-ai/nomic-embed-text-v1.5
13
  datasets: []
14
  metrics:
15
  - cosine_accuracy
@@ -27,58 +25,68 @@ metrics:
27
  - spearman_dot
28
  - pearson_max
29
  - spearman_max
 
 
 
 
 
 
 
 
 
 
 
 
30
  widget:
31
- - source_sentence: dog instrument toy
32
  sentences:
33
- - VATOS 25-in-1 Mars Rover Building Kit Outer Space Explorer Educational Construction
34
- Toy for Kids 556 Pieces Solar Powered STEM Science Building Blocks Set, VATOS,
35
- White
36
- - Prefer Green 7 PCS Portion Control Containers Kit (with COMPLETE GUIDE & 21 DAY
37
- DAILY TRACKER & 21 DAY MEAL PLANNER & RECIPES PDFs),Label-Coded,Multi-Color-Coded
38
- System,Perfect Size for Lose Weight, Prefer Green, 7 PCS
39
- - Coolibar UPF 50+ Men's Women's Gannett UV Gloves - Sun Protective (Medium- Light
40
- Blue), Coolibar, Light Blue
41
- - source_sentence: flame decal stickers
42
  sentences:
43
- - Tribal Flames Splash Pair - Vinyl Decal Sticker - 12" x 5" - Blue Flames, Sticker
44
- Pimp, Blue Flames
45
- - PC Gaming Headset Headphone Hook Holder Hanger Mount, Headphones Stand with Adjustable
46
- & Rotating Arm Clamp , Under Desk Design , Universal Fit , Built in Cable Clip
47
- Organizer EURPMASK, EURPMASK Choose the color of europe, Black
48
- - Quick Charge 3.0 Wall Charger, 4-Pack 18W QC 3.0 USB Charger Adapter Fast Charging
49
- Block Compatible Wireless Charger Compatible with Samsung Galaxy S10 S9 S8 Plus
50
- S7 S6 Edge Note 9, LG, Kindle, Tablet, HONOT, Black
51
- - source_sentence: 'search_query: softies women''s ultra soft marshmallow hooded lounger'
52
  sentences:
53
- - 'search_document: Red-A Placemats for Dining Table Set of 6 Heat-Resistant Wipeable
54
- Table Mats for Kitchen Table Decoration Waterproof Vinyl Placemats Easy to Clean,Black
55
- w/Brown, Red-A, Black'
56
- - 'search_document: Softies Women''s Ultra Soft Marshmallow Hooded Lounger, Platinum,
57
- L/XL, Softies, Platinum'
58
- - 'search_document: Ekouaer Women''s Sleepwear Robe with Pockets Plus Size Maxi
59
- Lounger Zipper Short Sleeve Bathrobe Housecoat (Black,L), Ekouaer, Black'
60
- - source_sentence: 'search_query: wine glasses without stem'
 
61
  sentences:
62
- - 'search_document: STAUBER Best Bulb Changer with PowerLatch Extension Pole (Large
63
- Suction, 4 Feet), STAUBER, Large Suction'
64
- - 'search_document: Hand Blown Italian Style Crystal Burgundy Wine Glasses - Lead-Free
65
- Premium Crystal Clear Glass - Set of 2 - 21 Ounce - Gift-Box for any Occasion,
66
- JBHO, Burgundy'
67
- - 'search_document: MyGift Modern Copper Stemless Wine Glasses, Set of 4, MyGift,
68
- Copper'
69
- - source_sentence: 'search_query: weighted blanket without glass beads'
 
 
 
70
  sentences:
71
- - 'search_document: Eigso Women Men Spike Punk Rock Black Leather Cuff Rivet Bracelet
72
- Bangle Adjustable Snap Button, Eigso, Black'
73
- - 'search_document: Quility Weighted Blanket with Soft Cover - 20 lbs Full/Queen
74
- Size Heavy Blanket for Adults - Heating & Cooling, Machine Washable - (60" X 80")
75
- (Navy), Quility, Navy Cover + Grey Cotton Blanket'
76
- - 'search_document: Bedsure Queen Weighted Blanket 15 Pounds - Adult Weighted Blanket
77
- 60x80 - Soft Heavy Blanket with Breathable TPE Insert No Glass Beads, Bedsure,
78
- Navy'
79
  pipeline_tag: sentence-similarity
80
  model-index:
81
- - name: SentenceTransformer based on nomic-ai/nomic-embed-text-v1.5
82
  results:
83
  - task:
84
  type: triplet
@@ -88,19 +96,19 @@ model-index:
88
  type: unknown
89
  metrics:
90
  - type: cosine_accuracy
91
- value: 0.7236
92
  name: Cosine Accuracy
93
  - type: dot_accuracy
94
- value: 0.282
95
  name: Dot Accuracy
96
  - type: manhattan_accuracy
97
- value: 0.7231
98
  name: Manhattan Accuracy
99
  - type: euclidean_accuracy
100
- value: 0.7227
101
  name: Euclidean Accuracy
102
  - type: max_accuracy
103
- value: 0.7236
104
  name: Max Accuracy
105
  - task:
106
  type: semantic-similarity
@@ -110,52 +118,94 @@ model-index:
110
  type: unknown
111
  metrics:
112
  - type: pearson_cosine
113
- value: 0.4912162846043421
114
  name: Pearson Cosine
115
  - type: spearman_cosine
116
- value: 0.4658522123059972
117
  name: Spearman Cosine
118
  - type: pearson_manhattan
119
- value: 0.4599741171303018
120
  name: Pearson Manhattan
121
  - type: spearman_manhattan
122
- value: 0.4428141949345816
123
  name: Spearman Manhattan
124
  - type: pearson_euclidean
125
- value: 0.46194545823984606
126
  name: Pearson Euclidean
127
  - type: spearman_euclidean
128
- value: 0.44478471500226807
129
  name: Spearman Euclidean
130
  - type: pearson_dot
131
- value: 0.45451995456560107
132
  name: Pearson Dot
133
  - type: spearman_dot
134
- value: 0.43844636325741904
135
  name: Spearman Dot
136
  - type: pearson_max
137
- value: 0.4912162846043421
138
  name: Pearson Max
139
  - type: spearman_max
140
- value: 0.4658522123059972
141
  name: Spearman Max
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
142
  ---
143
 
144
- # SentenceTransformer based on nomic-ai/nomic-embed-text-v1.5
145
 
146
- This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [nomic-ai/nomic-embed-text-v1.5](https://huggingface.co/nomic-ai/nomic-embed-text-v1.5) on the triplets and pairs datasets. 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.
147
 
148
  ## Model Details
149
 
150
  ### Model Description
151
  - **Model Type:** Sentence Transformer
152
- - **Base model:** [nomic-ai/nomic-embed-text-v1.5](https://huggingface.co/nomic-ai/nomic-embed-text-v1.5) <!-- at revision b0753ae76394dd36bcfb912a46018088bca48be0 -->
153
  - **Maximum Sequence Length:** 8192 tokens
154
  - **Output Dimensionality:** 768 tokens
155
  - **Similarity Function:** Cosine Similarity
156
- - **Training Datasets:**
157
  - triplets
158
- - pairs
159
  <!-- - **Language:** Unknown -->
160
  <!-- - **License:** Unknown -->
161
 
@@ -192,9 +242,9 @@ from sentence_transformers import SentenceTransformer
192
  model = SentenceTransformer("lv12/esci-nomic-embed-text-v1_5_4")
193
  # Run inference
194
  sentences = [
195
- 'search_query: weighted blanket without glass beads',
196
- 'search_document: Bedsure Queen Weighted Blanket 15 Pounds - Adult Weighted Blanket 60x80 - Soft Heavy Blanket with Breathable TPE Insert No Glass Beads, Bedsure, Navy',
197
- 'search_document: Quility Weighted Blanket with Soft Cover - 20 lbs Full/Queen Size Heavy Blanket for Adults - Heating & Cooling, Machine Washable - (60" X 80") (Navy), Quility, Navy Cover + Grey Cotton Blanket',
198
  ]
199
  embeddings = model.encode(sentences)
200
  print(embeddings.shape)
@@ -240,11 +290,11 @@ You can finetune this model on your own dataset.
240
 
241
  | Metric | Value |
242
  |:--------------------|:-----------|
243
- | **cosine_accuracy** | **0.7236** |
244
- | dot_accuracy | 0.282 |
245
- | manhattan_accuracy | 0.7231 |
246
- | euclidean_accuracy | 0.7227 |
247
- | max_accuracy | 0.7236 |
248
 
249
  #### Semantic Similarity
250
 
@@ -252,16 +302,35 @@ You can finetune this model on your own dataset.
252
 
253
  | Metric | Value |
254
  |:--------------------|:-----------|
255
- | pearson_cosine | 0.4912 |
256
- | **spearman_cosine** | **0.4659** |
257
- | pearson_manhattan | 0.46 |
258
- | spearman_manhattan | 0.4428 |
259
- | pearson_euclidean | 0.4619 |
260
- | spearman_euclidean | 0.4448 |
261
- | pearson_dot | 0.4545 |
262
- | spearman_dot | 0.4384 |
263
- | pearson_max | 0.4912 |
264
- | spearman_max | 0.4659 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
265
 
266
  <!--
267
  ## Bias, Risks and Limitations
@@ -277,121 +346,73 @@ You can finetune this model on your own dataset.
277
 
278
  ## Training Details
279
 
280
- ### Training Datasets
281
 
282
  #### triplets
283
 
284
  * Dataset: triplets
285
- * Size: 684,084 training samples
286
  * Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
287
  * Approximate statistics based on the first 1000 samples:
288
- | | anchor | positive | negative |
289
- |:--------|:---------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
290
- | type | string | string | string |
291
- | details | <ul><li>min: 7 tokens</li><li>mean: 11.1 tokens</li><li>max: 22 tokens</li></ul> | <ul><li>min: 17 tokens</li><li>mean: 42.75 tokens</li><li>max: 95 tokens</li></ul> | <ul><li>min: 15 tokens</li><li>mean: 43.8 tokens</li><li>max: 127 tokens</li></ul> |
292
- * Samples:
293
- | anchor | positive | negative |
294
- |:----------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
295
- | <code>search_query: tarps heavy duty waterproof 8x10</code> | <code>search_document: 8' x 10' Super Heavy Duty 16 Mil Brown Poly Tarp Cover - Thick Waterproof, UV Resistant, Rip and Tear Proof Tarpaulin with Grommets and Reinforced Edges - by Xpose Safety, Xpose Safety, Brown</code> | <code>search_document: Grillkid 6'X8' 4.5 Mil Thick General Purpose Waterproof Poly Tarp, Grillkid, All Purpose</code> |
296
- | <code>search_query: wireless keyboard without number pad</code> | <code>search_document: Macally 2.4G Small Wireless Keyboard - Ergonomic & Comfortable Computer Keyboard - Compact Keyboard for Laptop or Windows PC Desktop, Tablet, Smart TV - Plug & Play Mini Keyboard with 12 Hot Keys, Macally, Black</code> | <code>search_document: Wireless Keyboard - iClever GKA22S Rechargeable Keyboard with Number Pad, Full-Size Stainless Steel Ultra Slim Keyboard, 2.4G Stable Connection Wireless Keyboard for iMac, Mackbook, PC, Laptop, iClever, Silver</code> |
297
- | <code>search_query: geometry earrings</code> | <code>search_document: Simple Stud Earrings for Women, Geometric Minimalist Stud Earring Set Tiny Circle Triangle Square Bar Stud Earrings Mini Cartilage Tragus Earrings, choice of all, B:Circle Sliver</code> | <code>search_document: BONALUNA Bohemian Wood And Marble Effect Oblong Shaped Drop Statement Earrings (VIVID TURQUOISE), BONALUNA, VIVID TURQUOISE</code> |
298
- * Loss: [<code>CachedMultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedmultiplenegativesrankingloss) with these parameters:
299
- ```json
300
- {
301
- "scale": 20.0,
302
- "similarity_fct": "cos_sim"
303
- }
304
- ```
305
-
306
- #### pairs
307
-
308
- * Dataset: pairs
309
- * Size: 498,114 training samples
310
- * Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
311
- * Approximate statistics based on the first 1000 samples:
312
- | | sentence1 | sentence2 | score |
313
- |:--------|:---------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:---------------------------------------------------------------|
314
- | type | string | string | float |
315
- | details | <ul><li>min: 3 tokens</li><li>mean: 6.73 tokens</li><li>max: 33 tokens</li></ul> | <ul><li>min: 10 tokens</li><li>mean: 40.14 tokens</li><li>max: 98 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.81</li><li>max: 1.0</li></ul> |
316
  * Samples:
317
- | sentence1 | sentence2 | score |
318
- |:-------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------|
319
- | <code>I would choose a medium weight waterproof fabric, hip length jacket or longer, long sleeves, zip front, with a hood and deep pockets with zips</code> | <code>ZSHOW Men's Winter Hooded Packable Down Jacket(Blue, XX-Large), ZSHOW, Blue</code> | <code>1.0</code> |
320
- | <code>sequin dance costume girls</code> | <code>Yeahdor Big Girls' Lyrical Latin Ballet Dance Costumes Dresses Halter Sequins Irregular Tutu Skirted Leotard Dancewear Pink 12-14, Yeahdor, Pink</code> | <code>1.0</code> |
321
- | <code>paint easel bulk</code> | <code>Artecho Artist Easel Display Easel Stand, 2 Pack Metal Tripod Stand Easel for Painting, Hold Canvas from 21" to 66", Floor and Tabletop Displaying, Painting with Portable Bag, Artecho, Black</code> | <code>1.0</code> |
322
- * Loss: [<code>AnglELoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#angleloss) with these parameters:
323
  ```json
324
  {
325
- "scale": 20.0,
326
- "similarity_fct": "pairwise_angle_sim"
327
  }
328
  ```
329
 
330
- ### Evaluation Datasets
331
 
332
  #### triplets
333
 
334
  * Dataset: triplets
335
- * Size: 10,000 evaluation samples
336
  * Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
337
  * Approximate statistics based on the first 1000 samples:
338
- | | anchor | positive | negative |
339
- |:--------|:----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
340
- | type | string | string | string |
341
- | details | <ul><li>min: 7 tokens</li><li>mean: 11.13 tokens</li><li>max: 23 tokens</li></ul> | <ul><li>min: 15 tokens</li><li>mean: 43.11 tokens</li><li>max: 107 tokens</li></ul> | <ul><li>min: 15 tokens</li><li>mean: 43.56 tokens</li><li>max: 99 tokens</li></ul> |
342
- * Samples:
343
- | anchor | positive | negative |
344
- |:-------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
345
- | <code>search_query: hitch fifth wheel</code> | <code>search_document: ENIXWILL 5th Wheel Trailer Hitch Lifting Device Bracket Pin Fit for Hitch Companion and Patriot Series Hitch, ENIXWILL, Black</code> | <code>search_document: ECOTRIC Fifth 5th Wheel Trailer Hitch Mount Rails and Installation Kits for Full-Size Trucks, ECOTRIC, black</code> |
346
- | <code>search_query: dek pro</code> | <code>search_document: Cubiker Computer Desk 47 inch Home Office Writing Study Desk, Modern Simple Style Laptop Table with Storage Bag, Brown, Cubiker, Brown</code> | <code>search_document: FEZIBO Dual Motor L Shaped Electric Standing Desk, 48 Inches Stand Up Corner Desk, Home Office Sit Stand Desk with Rustic Brown Top and Black Frame, FEZIBO, Rustic Brown</code> |
347
- | <code>search_query: 1 year baby mouth without teeth cleaner</code> | <code>search_document: Baby Toothbrush,Infant Toothbrush,Baby Tongue Cleaner,Infant Toothbrush,Baby Tongue Cleaner Newborn,Toothbrush Tongue Cleaner Dental Care for 0-36 Month Baby,36 Pcs + Free 4 Pcs, Babycolor, Blue</code> | <code>search_document: Slotic Baby Toothbrush for 0-2 Years, Safe and Sturdy, Toddler Oral Care Teether Brush, Extra Soft Bristle for Baby Teeth and Infant Gums, Dentist Recommended (4-Pack), Slotic, 4 Pack</code> |
348
- * Loss: [<code>CachedMultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedmultiplenegativesrankingloss) with these parameters:
349
- ```json
350
- {
351
- "scale": 20.0,
352
- "similarity_fct": "cos_sim"
353
- }
354
- ```
355
-
356
- #### pairs
357
-
358
- * Dataset: pairs
359
- * Size: 10,000 evaluation samples
360
- * Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
361
- * Approximate statistics based on the first 1000 samples:
362
- | | sentence1 | sentence2 | score |
363
- |:--------|:--------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------|
364
- | type | string | string | float |
365
- | details | <ul><li>min: 3 tokens</li><li>mean: 6.8 tokens</li><li>max: 34 tokens</li></ul> | <ul><li>min: 9 tokens</li><li>mean: 39.7 tokens</li><li>max: 101 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.77</li><li>max: 1.0</li></ul> |
366
  * Samples:
367
- | sentence1 | sentence2 | score |
368
- |:------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------|
369
- | <code>outdoor ceiling fans without light</code> | <code>44" Plaza Industrial Indoor Outdoor Ceiling Fan with Remote Control Oil Rubbed Bronze Damp Rated for Patio Porch - Casa Vieja, Casa Vieja, No Light Kit - Bronze</code> | <code>1.0</code> |
370
- | <code>bathroom cabinet</code> | <code>Homfa Bathroom Floor Cabinet Free Standing with Single Door Multifunctional Bathroom Storage Organizer Toiletries(Ivory White), Homfa, White</code> | <code>1.0</code> |
371
- | <code>fitbit charge 3</code> | <code>TreasureMax Compatible with Fitbit Charge 2 Bands for Women/Men,Silicone Fadeless Pattern Printed Replacement Floral Bands for Fitbit Charge 2 HR Wristbands, TreasureMax, Paw 2</code> | <code>0.4</code> |
372
- * Loss: [<code>AnglELoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#angleloss) with these parameters:
373
  ```json
374
  {
375
- "scale": 20.0,
376
- "similarity_fct": "pairwise_angle_sim"
377
  }
378
  ```
379
 
380
  ### Training Hyperparameters
381
  #### Non-Default Hyperparameters
382
 
383
- - `per_device_train_batch_size`: 16
384
- - `per_device_eval_batch_size`: 4
385
  - `gradient_accumulation_steps`: 2
386
- - `learning_rate`: 1e-06
387
- - `lr_scheduler_type`: cosine_with_restarts
388
- - `lr_scheduler_kwargs`: {'num_cycles': 1}
389
- - `warmup_ratio`: 0.01
390
- - `dataloader_drop_last`: True
391
  - `dataloader_num_workers`: 4
392
  - `dataloader_prefetch_factor`: 4
393
  - `load_best_model_at_end`: True
394
  - `gradient_checkpointing`: True
 
395
  - `batch_sampler`: no_duplicates
396
 
397
  #### All Hyperparameters
@@ -400,13 +421,13 @@ You can finetune this model on your own dataset.
400
  - `overwrite_output_dir`: False
401
  - `do_predict`: False
402
  - `prediction_loss_only`: True
403
- - `per_device_train_batch_size`: 16
404
- - `per_device_eval_batch_size`: 4
405
  - `per_gpu_train_batch_size`: None
406
  - `per_gpu_eval_batch_size`: None
407
  - `gradient_accumulation_steps`: 2
408
  - `eval_accumulation_steps`: None
409
- - `learning_rate`: 1e-06
410
  - `weight_decay`: 0.0
411
  - `adam_beta1`: 0.9
412
  - `adam_beta2`: 0.999
@@ -414,9 +435,9 @@ You can finetune this model on your own dataset.
414
  - `max_grad_norm`: 1.0
415
  - `num_train_epochs`: 3
416
  - `max_steps`: -1
417
- - `lr_scheduler_type`: cosine_with_restarts
418
- - `lr_scheduler_kwargs`: {'num_cycles': 1}
419
- - `warmup_ratio`: 0.01
420
  - `warmup_steps`: 0
421
  - `log_level`: passive
422
  - `log_level_replica`: warning
@@ -485,7 +506,7 @@ You can finetune this model on your own dataset.
485
  - `push_to_hub_model_id`: None
486
  - `push_to_hub_organization`: None
487
  - `mp_parameters`:
488
- - `auto_find_batch_size`: False
489
  - `full_determinism`: False
490
  - `torchdynamo`: None
491
  - `ray_scope`: last
@@ -506,378 +527,208 @@ You can finetune this model on your own dataset.
506
  ### Training Logs
507
  <details><summary>Click to expand</summary>
508
 
509
- | Epoch | Step | Training Loss | pairs loss | triplets loss | cosine_accuracy | spearman_cosine |
510
- |:------:|:-----:|:-------------:|:----------:|:-------------:|:---------------:|:---------------:|
511
- | 0.0027 | 100 | 2.4909 | - | - | - | - |
512
- | 0.0054 | 200 | 2.6666 | - | - | - | - |
513
- | 0.0081 | 300 | 2.76 | - | - | - | - |
514
- | 0.0108 | 400 | 2.6945 | - | - | - | - |
515
- | 0.0135 | 500 | 2.9113 | - | - | - | - |
516
- | 0.0162 | 600 | 2.3476 | - | - | - | - |
517
- | 0.0189 | 700 | 2.2818 | - | - | - | - |
518
- | 0.0217 | 800 | 2.4241 | - | - | - | - |
519
- | 0.0244 | 900 | 2.5126 | - | - | - | - |
520
- | 0.0271 | 1000 | 2.4106 | 4.7376 | 0.8087 | 0.6993 | 0.3844 |
521
- | 0.0298 | 1100 | 2.2369 | - | - | - | - |
522
- | 0.0325 | 1200 | 2.0614 | - | - | - | - |
523
- | 0.0352 | 1300 | 2.2178 | - | - | - | - |
524
- | 0.0379 | 1400 | 1.974 | - | - | - | - |
525
- | 0.0406 | 1500 | 1.9364 | - | - | - | - |
526
- | 0.0433 | 1600 | 2.0906 | - | - | - | - |
527
- | 0.0460 | 1700 | 1.8783 | - | - | - | - |
528
- | 0.0487 | 1800 | 2.1149 | - | - | - | - |
529
- | 0.0514 | 1900 | 1.7162 | - | - | - | - |
530
- | 0.0541 | 2000 | 1.6761 | 3.8862 | 0.7490 | 0.7097 | 0.4082 |
531
- | 0.0568 | 2100 | 2.1701 | - | - | - | - |
532
- | 0.0596 | 2200 | 2.1306 | - | - | - | - |
533
- | 0.0623 | 2300 | 1.6543 | - | - | - | - |
534
- | 0.0650 | 2400 | 1.8157 | - | - | - | - |
535
- | 0.0677 | 2500 | 1.7779 | - | - | - | - |
536
- | 0.0704 | 2600 | 1.9434 | - | - | - | - |
537
- | 0.0731 | 2700 | 1.7776 | - | - | - | - |
538
- | 0.0758 | 2800 | 1.8197 | - | - | - | - |
539
- | 0.0785 | 2900 | 1.9886 | - | - | - | - |
540
- | 0.0812 | 3000 | 2.0699 | 3.8031 | 0.7298 | 0.7147 | 0.4282 |
541
- | 0.0839 | 3100 | 1.9496 | - | - | - | - |
542
- | 0.0866 | 3200 | 1.8349 | - | - | - | - |
543
- | 0.0893 | 3300 | 2.111 | - | - | - | - |
544
- | 0.0920 | 3400 | 1.9956 | - | - | - | - |
545
- | 0.0947 | 3500 | 2.0379 | - | - | - | - |
546
- | 0.0974 | 3600 | 1.8975 | - | - | - | - |
547
- | 0.1002 | 3700 | 1.8552 | - | - | - | - |
548
- | 0.1029 | 3800 | 1.9566 | - | - | - | - |
549
- | 0.1056 | 3900 | 2.011 | - | - | - | - |
550
- | 0.1083 | 4000 | 2.1263 | 3.7799 | 0.7221 | 0.7176 | 0.4393 |
551
- | 0.1110 | 4100 | 1.8217 | - | - | - | - |
552
- | 0.1137 | 4200 | 1.8638 | - | - | - | - |
553
- | 0.1164 | 4300 | 1.7699 | - | - | - | - |
554
- | 0.1191 | 4400 | 1.8248 | - | - | - | - |
555
- | 0.1218 | 4500 | 1.835 | - | - | - | - |
556
- | 0.1245 | 4600 | 1.9294 | - | - | - | - |
557
- | 0.1272 | 4700 | 1.9817 | - | - | - | - |
558
- | 0.1299 | 4800 | 1.877 | - | - | - | - |
559
- | 0.1326 | 4900 | 1.5824 | - | - | - | - |
560
- | 0.1353 | 5000 | 1.7429 | 3.7728 | 0.7163 | 0.7196 | 0.4496 |
561
- | 0.1380 | 5100 | 1.8552 | - | - | - | - |
562
- | 0.1408 | 5200 | 1.6888 | - | - | - | - |
563
- | 0.1435 | 5300 | 1.9409 | - | - | - | - |
564
- | 0.1462 | 5400 | 1.9389 | - | - | - | - |
565
- | 0.1489 | 5500 | 1.82 | - | - | - | - |
566
- | 0.1516 | 5600 | 1.9763 | - | - | - | - |
567
- | 0.1543 | 5700 | 1.8122 | - | - | - | - |
568
- | 0.1570 | 5800 | 1.7204 | - | - | - | - |
569
- | 0.1597 | 5900 | 1.6901 | - | - | - | - |
570
- | 0.1624 | 6000 | 1.7785 | 3.7514 | 0.7124 | 0.7195 | 0.4516 |
571
- | 0.1651 | 6100 | 1.8559 | - | - | - | - |
572
- | 0.1678 | 6200 | 1.7646 | - | - | - | - |
573
- | 0.1705 | 6300 | 1.9068 | - | - | - | - |
574
- | 0.1732 | 6400 | 1.8848 | - | - | - | - |
575
- | 0.1759 | 6500 | 1.9384 | - | - | - | - |
576
- | 0.1787 | 6600 | 1.7692 | - | - | - | - |
577
- | 0.1814 | 6700 | 1.7093 | - | - | - | - |
578
- | 0.1841 | 6800 | 1.8759 | - | - | - | - |
579
- | 0.1868 | 6900 | 1.7319 | - | - | - | - |
580
- | 0.1895 | 7000 | 1.9428 | 3.7487 | 0.7076 | 0.7256 | 0.4496 |
581
- | 0.1922 | 7100 | 1.5733 | - | - | - | - |
582
- | 0.1949 | 7200 | 1.8487 | - | - | - | - |
583
- | 0.1976 | 7300 | 1.8361 | - | - | - | - |
584
- | 0.2003 | 7400 | 1.9911 | - | - | - | - |
585
- | 0.2030 | 7500 | 1.784 | - | - | - | - |
586
- | 0.2057 | 7600 | 1.8518 | - | - | - | - |
587
- | 0.2084 | 7700 | 1.6232 | - | - | - | - |
588
- | 0.2111 | 7800 | 1.6239 | - | - | - | - |
589
- | 0.2138 | 7900 | 1.7589 | - | - | - | - |
590
- | 0.2165 | 8000 | 1.8644 | 3.7387 | 0.7040 | 0.7241 | 0.4552 |
591
- | 0.2193 | 8100 | 1.7903 | - | - | - | - |
592
- | 0.2220 | 8200 | 1.7197 | - | - | - | - |
593
- | 0.2247 | 8300 | 1.9099 | - | - | - | - |
594
- | 0.2274 | 8400 | 1.6778 | - | - | - | - |
595
- | 0.2301 | 8500 | 1.9249 | - | - | - | - |
596
- | 0.2328 | 8600 | 1.8483 | - | - | - | - |
597
- | 0.2355 | 8700 | 1.6849 | - | - | - | - |
598
- | 0.2382 | 8800 | 1.8647 | - | - | - | - |
599
- | 0.2409 | 8900 | 1.8826 | - | - | - | - |
600
- | 0.2436 | 9000 | 1.7632 | 3.7403 | 0.7033 | 0.7225 | 0.4545 |
601
- | 0.2463 | 9100 | 1.8142 | - | - | - | - |
602
- | 0.2490 | 9200 | 1.7374 | - | - | - | - |
603
- | 0.2517 | 9300 | 1.8646 | - | - | - | - |
604
- | 0.2544 | 9400 | 1.7623 | - | - | - | - |
605
- | 0.2571 | 9500 | 1.7802 | - | - | - | - |
606
- | 0.2599 | 9600 | 1.843 | - | - | - | - |
607
- | 0.2626 | 9700 | 1.9797 | - | - | - | - |
608
- | 0.2653 | 9800 | 1.7748 | - | - | - | - |
609
- | 0.2680 | 9900 | 1.7031 | - | - | - | - |
610
- | 0.2707 | 10000 | 1.5536 | 3.7613 | 0.7016 | 0.7259 | 0.4548 |
611
- | 0.2734 | 10100 | 1.7663 | - | - | - | - |
612
- | 0.2761 | 10200 | 1.8218 | - | - | - | - |
613
- | 0.2788 | 10300 | 1.6327 | - | - | - | - |
614
- | 0.2815 | 10400 | 1.8802 | - | - | - | - |
615
- | 0.2842 | 10500 | 1.6294 | - | - | - | - |
616
- | 0.2869 | 10600 | 1.9001 | - | - | - | - |
617
- | 0.2896 | 10700 | 1.7873 | - | - | - | - |
618
- | 0.2923 | 10800 | 1.8121 | - | - | - | - |
619
- | 0.2950 | 10900 | 2.0197 | - | - | - | - |
620
- | 0.2978 | 11000 | 1.7006 | 3.7559 | 0.7004 | 0.727 | 0.4613 |
621
- | 0.3005 | 11100 | 1.6404 | - | - | - | - |
622
- | 0.3032 | 11200 | 1.9422 | - | - | - | - |
623
- | 0.3059 | 11300 | 1.5917 | - | - | - | - |
624
- | 0.3086 | 11400 | 1.7236 | - | - | - | - |
625
- | 0.3113 | 11500 | 1.8977 | - | - | - | - |
626
- | 0.3140 | 11600 | 1.7686 | - | - | - | - |
627
- | 0.3167 | 11700 | 1.4493 | - | - | - | - |
628
- | 0.3194 | 11800 | 1.7447 | - | - | - | - |
629
- | 0.3221 | 11900 | 1.9412 | - | - | - | - |
630
- | 0.3248 | 12000 | 1.8 | 3.7308 | 0.6997 | 0.7241 | 0.4618 |
631
- | 0.3275 | 12100 | 1.8855 | - | - | - | - |
632
- | 0.3302 | 12200 | 1.5133 | - | - | - | - |
633
- | 0.3329 | 12300 | 1.7893 | - | - | - | - |
634
- | 0.3356 | 12400 | 1.7861 | - | - | - | - |
635
- | 0.3384 | 12500 | 1.7733 | - | - | - | - |
636
- | 0.3411 | 12600 | 1.5877 | - | - | - | - |
637
- | 0.3438 | 12700 | 2.03 | - | - | - | - |
638
- | 0.3465 | 12800 | 1.7071 | - | - | - | - |
639
- | 0.3492 | 12900 | 1.7848 | - | - | - | - |
640
- | 0.3519 | 13000 | 1.7508 | 3.7326 | 0.7006 | 0.7247 | 0.4583 |
641
- | 0.3546 | 13100 | 1.7667 | - | - | - | - |
642
- | 0.3573 | 13200 | 1.6415 | - | - | - | - |
643
- | 0.3600 | 13300 | 1.7501 | - | - | - | - |
644
- | 0.3627 | 13400 | 1.8451 | - | - | - | - |
645
- | 0.3654 | 13500 | 1.7146 | - | - | - | - |
646
- | 0.3681 | 13600 | 1.6837 | - | - | - | - |
647
- | 0.3708 | 13700 | 1.92 | - | - | - | - |
648
- | 0.3735 | 13800 | 1.6925 | - | - | - | - |
649
- | 0.3763 | 13900 | 1.7799 | - | - | - | - |
650
- | 0.3790 | 14000 | 1.527 | 3.7260 | 0.6989 | 0.727 | 0.4510 |
651
- | 0.3817 | 14100 | 1.7222 | - | - | - | - |
652
- | 0.3844 | 14200 | 1.8278 | - | - | - | - |
653
- | 0.3871 | 14300 | 1.7669 | - | - | - | - |
654
- | 0.3898 | 14400 | 1.5856 | - | - | - | - |
655
- | 0.3925 | 14500 | 1.8234 | - | - | - | - |
656
- | 0.3952 | 14600 | 1.7151 | - | - | - | - |
657
- | 0.3979 | 14700 | 1.6432 | - | - | - | - |
658
- | 0.4006 | 14800 | 1.9005 | - | - | - | - |
659
- | 0.4033 | 14900 | 1.6946 | - | - | - | - |
660
- | 0.4060 | 15000 | 1.5543 | 3.7222 | 0.6969 | 0.7275 | 0.4634 |
661
- | 0.4087 | 15100 | 1.6736 | - | - | - | - |
662
- | 0.4114 | 15200 | 1.8898 | - | - | - | - |
663
- | 0.4141 | 15300 | 1.7224 | - | - | - | - |
664
- | 0.4169 | 15400 | 1.7909 | - | - | - | - |
665
- | 0.4196 | 15500 | 1.6555 | - | - | - | - |
666
- | 0.4223 | 15600 | 1.523 | - | - | - | - |
667
- | 0.4250 | 15700 | 1.7539 | - | - | - | - |
668
- | 0.4277 | 15800 | 1.5763 | - | - | - | - |
669
- | 0.4304 | 15900 | 1.7247 | - | - | - | - |
670
- | 0.4331 | 16000 | 1.876 | 3.7105 | 0.6977 | 0.7263 | 0.4636 |
671
- | 0.4358 | 16100 | 1.772 | - | - | - | - |
672
- | 0.4385 | 16200 | 1.6774 | - | - | - | - |
673
- | 0.4412 | 16300 | 1.7602 | - | - | - | - |
674
- | 0.4439 | 16400 | 1.705 | - | - | - | - |
675
- | 0.4466 | 16500 | 1.7893 | - | - | - | - |
676
- | 0.4493 | 16600 | 1.653 | - | - | - | - |
677
- | 0.4520 | 16700 | 1.8326 | - | - | - | - |
678
- | 0.4547 | 16800 | 1.5326 | - | - | - | - |
679
- | 0.4575 | 16900 | 1.8251 | - | - | - | - |
680
- | 0.4602 | 17000 | 1.766 | 3.7193 | 0.6973 | 0.7257 | 0.4655 |
681
- | 0.4629 | 17100 | 1.7162 | - | - | - | - |
682
- | 0.4656 | 17200 | 1.6969 | - | - | - | - |
683
- | 0.4683 | 17300 | 1.5172 | - | - | - | - |
684
- | 0.4710 | 17400 | 1.7102 | - | - | - | - |
685
- | 0.4737 | 17500 | 1.8369 | - | - | - | - |
686
- | 0.4764 | 17600 | 1.8069 | - | - | - | - |
687
- | 0.4791 | 17700 | 1.6299 | - | - | - | - |
688
- | 0.4818 | 17800 | 1.8474 | - | - | - | - |
689
- | 0.4845 | 17900 | 1.5864 | - | - | - | - |
690
- | 0.4872 | 18000 | 1.7455 | 3.7087 | 0.6986 | 0.7249 | 0.4626 |
691
- | 0.4899 | 18100 | 1.8263 | - | - | - | - |
692
- | 0.4926 | 18200 | 1.8548 | - | - | - | - |
693
- | 0.4954 | 18300 | 1.6442 | - | - | - | - |
694
- | 0.4981 | 18400 | 1.7467 | - | - | - | - |
695
- | 0.5008 | 18500 | 1.6174 | - | - | - | - |
696
- | 0.5035 | 18600 | 1.4465 | - | - | - | - |
697
- | 0.5062 | 18700 | 1.8866 | - | - | - | - |
698
- | 0.5089 | 18800 | 1.72 | - | - | - | - |
699
- | 0.5116 | 18900 | 1.7466 | - | - | - | - |
700
- | 0.5143 | 19000 | 1.9124 | 3.7247 | 0.6979 | 0.725 | 0.4602 |
701
- | 0.5170 | 19100 | 1.5687 | - | - | - | - |
702
- | 0.5197 | 19200 | 1.6391 | - | - | - | - |
703
- | 0.5224 | 19300 | 1.8248 | - | - | - | - |
704
- | 0.5251 | 19400 | 1.6231 | - | - | - | - |
705
- | 0.5278 | 19500 | 1.6152 | - | - | - | - |
706
- | 0.5305 | 19600 | 1.639 | - | - | - | - |
707
- | 0.5332 | 19700 | 1.6098 | - | - | - | - |
708
- | 0.5360 | 19800 | 1.6619 | - | - | - | - |
709
- | 0.5387 | 19900 | 1.6997 | - | - | - | - |
710
- | 0.5414 | 20000 | 1.718 | 3.7259 | 0.6989 | 0.7264 | 0.4660 |
711
- | 0.5441 | 20100 | 1.634 | - | - | - | - |
712
- | 0.5468 | 20200 | 1.7865 | - | - | - | - |
713
- | 0.5495 | 20300 | 1.8573 | - | - | - | - |
714
- | 0.5522 | 20400 | 1.5575 | - | - | - | - |
715
- | 0.5549 | 20500 | 1.6594 | - | - | - | - |
716
- | 0.5576 | 20600 | 1.8793 | - | - | - | - |
717
- | 0.5603 | 20700 | 1.7643 | - | - | - | - |
718
- | 0.5630 | 20800 | 1.538 | - | - | - | - |
719
- | 0.5657 | 20900 | 1.8634 | - | - | - | - |
720
- | 0.5684 | 21000 | 1.916 | 3.7223 | 0.6982 | 0.7258 | 0.4650 |
721
- | 0.5711 | 21100 | 1.5947 | - | - | - | - |
722
- | 0.5738 | 21200 | 1.5321 | - | - | - | - |
723
- | 0.5766 | 21300 | 1.7004 | - | - | - | - |
724
- | 0.5793 | 21400 | 1.6947 | - | - | - | - |
725
- | 0.5820 | 21500 | 1.5228 | - | - | - | - |
726
- | 0.5847 | 21600 | 1.7152 | - | - | - | - |
727
- | 0.5874 | 21700 | 1.6883 | - | - | - | - |
728
- | 0.5901 | 21800 | 1.6779 | - | - | - | - |
729
- | 0.5928 | 21900 | 1.7323 | - | - | - | - |
730
- | 0.5955 | 22000 | 1.9633 | 3.7266 | 0.6996 | 0.7288 | 0.4635 |
731
- | 0.5982 | 22100 | 1.7498 | - | - | - | - |
732
- | 0.6009 | 22200 | 1.7513 | - | - | - | - |
733
- | 0.6036 | 22300 | 1.7078 | - | - | - | - |
734
- | 0.6063 | 22400 | 1.6438 | - | - | - | - |
735
- | 0.6090 | 22500 | 1.6743 | - | - | - | - |
736
- | 0.6117 | 22600 | 1.6701 | - | - | - | - |
737
- | 0.6145 | 22700 | 1.7871 | - | - | - | - |
738
- | 0.6172 | 22800 | 1.6247 | - | - | - | - |
739
- | 0.6199 | 22900 | 1.7817 | - | - | - | - |
740
- | 0.6226 | 23000 | 1.6606 | 3.7321 | 0.6993 | 0.7286 | 0.4614 |
741
- | 0.6253 | 23100 | 1.8987 | - | - | - | - |
742
- | 0.6280 | 23200 | 1.6494 | - | - | - | - |
743
- | 0.6307 | 23300 | 1.6776 | - | - | - | - |
744
- | 0.6334 | 23400 | 1.75 | - | - | - | - |
745
- | 0.6361 | 23500 | 1.5131 | - | - | - | - |
746
- | 0.6388 | 23600 | 1.7946 | - | - | - | - |
747
- | 0.6415 | 23700 | 1.665 | - | - | - | - |
748
- | 0.6442 | 23800 | 1.6681 | - | - | - | - |
749
- | 0.6469 | 23900 | 1.8255 | - | - | - | - |
750
- | 0.6496 | 24000 | 1.6759 | 3.7227 | 0.7017 | 0.7281 | 0.4625 |
751
- | 0.6523 | 24100 | 1.554 | - | - | - | - |
752
- | 0.6551 | 24200 | 1.6435 | - | - | - | - |
753
- | 0.6578 | 24300 | 1.8224 | - | - | - | - |
754
- | 0.6605 | 24400 | 1.6186 | - | - | - | - |
755
- | 0.6632 | 24500 | 1.7156 | - | - | - | - |
756
- | 0.6659 | 24600 | 1.5247 | - | - | - | - |
757
- | 0.6686 | 24700 | 1.6264 | - | - | - | - |
758
- | 0.6713 | 24800 | 1.7673 | - | - | - | - |
759
- | 0.6740 | 24900 | 1.8072 | - | - | - | - |
760
- | 0.6767 | 25000 | 1.765 | 3.7407 | 0.7026 | 0.7283 | 0.4589 |
761
- | 0.6794 | 25100 | 1.6422 | - | - | - | - |
762
- | 0.6821 | 25200 | 1.7846 | - | - | - | - |
763
- | 0.6848 | 25300 | 1.7366 | - | - | - | - |
764
- | 0.6875 | 25400 | 1.7839 | - | - | - | - |
765
- | 0.6902 | 25500 | 1.441 | - | - | - | - |
766
- | 0.6930 | 25600 | 1.5533 | - | - | - | - |
767
- | 0.6957 | 25700 | 1.6922 | - | - | - | - |
768
- | 0.6984 | 25800 | 1.5544 | - | - | - | - |
769
- | 0.7011 | 25900 | 1.456 | - | - | - | - |
770
- | 0.7038 | 26000 | 1.6494 | 3.7274 | 0.7059 | 0.7268 | 0.4661 |
771
- | 0.7065 | 26100 | 1.6963 | - | - | - | - |
772
- | 0.7092 | 26200 | 1.7892 | - | - | - | - |
773
- | 0.7119 | 26300 | 1.6669 | - | - | - | - |
774
- | 0.7146 | 26400 | 1.6758 | - | - | - | - |
775
- | 0.7173 | 26500 | 1.6322 | - | - | - | - |
776
- | 0.7200 | 26600 | 1.5416 | - | - | - | - |
777
- | 0.7227 | 26700 | 1.681 | - | - | - | - |
778
- | 0.7254 | 26800 | 1.5159 | - | - | - | - |
779
- | 0.7281 | 26900 | 1.715 | - | - | - | - |
780
- | 0.7308 | 27000 | 1.6164 | 3.7456 | 0.7061 | 0.7257 | 0.4570 |
781
- | 0.7336 | 27100 | 1.6784 | - | - | - | - |
782
- | 0.7363 | 27200 | 1.5886 | - | - | - | - |
783
- | 0.7390 | 27300 | 1.6736 | - | - | - | - |
784
- | 0.7417 | 27400 | 1.5659 | - | - | - | - |
785
- | 0.7444 | 27500 | 1.6552 | - | - | - | - |
786
- | 0.7471 | 27600 | 1.5672 | - | - | - | - |
787
- | 0.7498 | 27700 | 1.5873 | - | - | - | - |
788
- | 0.7525 | 27800 | 1.6746 | - | - | - | - |
789
- | 0.7552 | 27900 | 1.7503 | - | - | - | - |
790
- | 0.7579 | 28000 | 1.7287 | 3.7390 | 0.7076 | 0.7244 | 0.4636 |
791
- | 0.7606 | 28100 | 1.6216 | - | - | - | - |
792
- | 0.7633 | 28200 | 1.6101 | - | - | - | - |
793
- | 0.7660 | 28300 | 1.5651 | - | - | - | - |
794
- | 0.7687 | 28400 | 1.5659 | - | - | - | - |
795
- | 0.7714 | 28500 | 1.5248 | - | - | - | - |
796
- | 0.7742 | 28600 | 1.3725 | - | - | - | - |
797
- | 0.7769 | 28700 | 1.7881 | - | - | - | - |
798
- | 0.7796 | 28800 | 1.739 | - | - | - | - |
799
- | 0.7823 | 28900 | 1.6464 | - | - | - | - |
800
- | 0.7850 | 29000 | 1.6841 | 3.7212 | 0.7073 | 0.7247 | 0.4626 |
801
- | 0.7877 | 29100 | 1.6254 | - | - | - | - |
802
- | 0.7904 | 29200 | 1.6728 | - | - | - | - |
803
- | 0.7931 | 29300 | 1.5605 | - | - | - | - |
804
- | 0.7958 | 29400 | 1.687 | - | - | - | - |
805
- | 0.7985 | 29500 | 1.7799 | - | - | - | - |
806
- | 0.8012 | 29600 | 1.6792 | - | - | - | - |
807
- | 0.8039 | 29700 | 1.5241 | - | - | - | - |
808
- | 0.8066 | 29800 | 1.6341 | - | - | - | - |
809
- | 0.8093 | 29900 | 1.5571 | - | - | - | - |
810
- | 0.8121 | 30000 | 1.5228 | 3.7397 | 0.7105 | 0.7234 | 0.4682 |
811
- | 0.8148 | 30100 | 1.5988 | - | - | - | - |
812
- | 0.8175 | 30200 | 1.4222 | - | - | - | - |
813
- | 0.8202 | 30300 | 1.4629 | - | - | - | - |
814
- | 0.8229 | 30400 | 1.6381 | - | - | - | - |
815
- | 0.8256 | 30500 | 1.4585 | - | - | - | - |
816
- | 0.8283 | 30600 | 1.6774 | - | - | - | - |
817
- | 0.8310 | 30700 | 1.811 | - | - | - | - |
818
- | 0.8337 | 30800 | 1.5872 | - | - | - | - |
819
- | 0.8364 | 30900 | 1.4762 | - | - | - | - |
820
- | 0.8391 | 31000 | 1.7079 | 3.7256 | 0.7128 | 0.7215 | 0.4645 |
821
- | 0.8418 | 31100 | 1.4948 | - | - | - | - |
822
- | 0.8445 | 31200 | 1.4556 | - | - | - | - |
823
- | 0.8472 | 31300 | 1.5191 | - | - | - | - |
824
- | 0.8499 | 31400 | 1.598 | - | - | - | - |
825
- | 0.8527 | 31500 | 1.6586 | - | - | - | - |
826
- | 0.8554 | 31600 | 1.6893 | - | - | - | - |
827
- | 0.8581 | 31700 | 1.7764 | - | - | - | - |
828
- | 0.8608 | 31800 | 1.3632 | - | - | - | - |
829
- | 0.8635 | 31900 | 1.6681 | - | - | - | - |
830
- | 0.8662 | 32000 | 1.6232 | 3.7358 | 0.7161 | 0.7232 | 0.4651 |
831
- | 0.8689 | 32100 | 1.4556 | - | - | - | - |
832
- | 0.8716 | 32200 | 1.8698 | - | - | - | - |
833
- | 0.8743 | 32300 | 1.7566 | - | - | - | - |
834
- | 0.8770 | 32400 | 1.6082 | - | - | - | - |
835
- | 0.8797 | 32500 | 1.6465 | - | - | - | - |
836
- | 0.8824 | 32600 | 1.5018 | - | - | - | - |
837
- | 0.8851 | 32700 | 1.8482 | - | - | - | - |
838
- | 0.8878 | 32800 | 1.5147 | - | - | - | - |
839
- | 0.8905 | 32900 | 1.699 | - | - | - | - |
840
- | 0.8933 | 33000 | 1.5738 | 3.7323 | 0.7176 | 0.7246 | 0.4657 |
841
- | 0.8960 | 33100 | 1.635 | - | - | - | - |
842
- | 0.8987 | 33200 | 1.7069 | - | - | - | - |
843
- | 0.9014 | 33300 | 1.6272 | - | - | - | - |
844
- | 0.9041 | 33400 | 1.7648 | - | - | - | - |
845
- | 0.9068 | 33500 | 1.6683 | - | - | - | - |
846
- | 0.9095 | 33600 | 1.4867 | - | - | - | - |
847
- | 0.9122 | 33700 | 1.6677 | - | - | - | - |
848
- | 0.9149 | 33800 | 1.5527 | - | - | - | - |
849
- | 0.9176 | 33900 | 1.6804 | - | - | - | - |
850
- | 0.9203 | 34000 | 1.425 | 3.7477 | 0.7172 | 0.7231 | 0.4596 |
851
- | 0.9230 | 34100 | 1.771 | - | - | - | - |
852
- | 0.9257 | 34200 | 1.5767 | - | - | - | - |
853
- | 0.9284 | 34300 | 1.5424 | - | - | - | - |
854
- | 0.9312 | 34400 | 1.5985 | - | - | - | - |
855
- | 0.9339 | 34500 | 1.6763 | - | - | - | - |
856
- | 0.9366 | 34600 | 1.6608 | - | - | - | - |
857
- | 0.9393 | 34700 | 1.7736 | - | - | - | - |
858
- | 0.9420 | 34800 | 1.8955 | - | - | - | - |
859
- | 0.9447 | 34900 | 1.5688 | - | - | - | - |
860
- | 0.9474 | 35000 | 1.6123 | 3.7410 | 0.7196 | 0.7226 | 0.4671 |
861
- | 0.9501 | 35100 | 1.7264 | - | - | - | - |
862
- | 0.9528 | 35200 | 1.5511 | - | - | - | - |
863
- | 0.9555 | 35300 | 1.6409 | - | - | - | - |
864
- | 0.9582 | 35400 | 1.47 | - | - | - | - |
865
- | 0.9609 | 35500 | 1.8675 | - | - | - | - |
866
- | 0.9636 | 35600 | 1.6868 | - | - | - | - |
867
- | 0.9663 | 35700 | 1.744 | - | - | - | - |
868
- | 0.9690 | 35800 | 1.6734 | - | - | - | - |
869
- | 0.9718 | 35900 | 1.4154 | - | - | - | - |
870
- | 0.9745 | 36000 | 1.4793 | 3.7393 | 0.7190 | 0.7223 | 0.4677 |
871
- | 0.9772 | 36100 | 1.7126 | - | - | - | - |
872
- | 0.9799 | 36200 | 1.7037 | - | - | - | - |
873
- | 0.9826 | 36300 | 1.6306 | - | - | - | - |
874
- | 0.9853 | 36400 | 1.7783 | - | - | - | - |
875
- | 0.9880 | 36500 | 1.5751 | - | - | - | - |
876
- | 0.9907 | 36600 | 1.6079 | - | - | - | - |
877
- | 0.9934 | 36700 | 1.7162 | - | - | - | - |
878
- | 0.9961 | 36800 | 1.447 | - | - | - | - |
879
- | 0.9988 | 36900 | 1.6155 | - | - | - | - |
880
- | 1.0015 | 37000 | 1.7294 | 3.7512 | 0.7177 | 0.7236 | 0.4659 |
881
 
882
  </details>
883
 
@@ -907,27 +758,15 @@ You can finetune this model on your own dataset.
907
  }
908
  ```
909
 
910
- #### CachedMultipleNegativesRankingLoss
911
- ```bibtex
912
- @misc{gao2021scaling,
913
- title={Scaling Deep Contrastive Learning Batch Size under Memory Limited Setup},
914
- author={Luyu Gao and Yunyi Zhang and Jiawei Han and Jamie Callan},
915
- year={2021},
916
- eprint={2101.06983},
917
- archivePrefix={arXiv},
918
- primaryClass={cs.LG}
919
- }
920
- ```
921
-
922
- #### AnglELoss
923
  ```bibtex
924
- @misc{li2023angleoptimized,
925
- title={AnglE-optimized Text Embeddings},
926
- author={Xianming Li and Jing Li},
927
- year={2023},
928
- eprint={2309.12871},
929
  archivePrefix={arXiv},
930
- primaryClass={cs.CL}
931
  }
932
  ```
933
 
 
6
  - sentence-similarity
7
  - feature-extraction
8
  - generated_from_trainer
9
+ - dataset_size:1500000
10
+ - loss:TripletLoss
 
 
11
  datasets: []
12
  metrics:
13
  - cosine_accuracy
 
25
  - spearman_dot
26
  - pearson_max
27
  - spearman_max
28
+ - cosine_accuracy@10
29
+ - cosine_precision@10
30
+ - cosine_recall@10
31
+ - cosine_ndcg@10
32
+ - cosine_mrr@10
33
+ - cosine_map@10
34
+ - dot_accuracy@10
35
+ - dot_precision@10
36
+ - dot_recall@10
37
+ - dot_ndcg@10
38
+ - dot_mrr@10
39
+ - dot_map@10
40
  widget:
41
+ - source_sentence: 'search_query: speeder man slippers for boys size 12'
42
  sentences:
43
+ - 'search_document: Marvel Boys Spider-Man Bootie Slippers (11-12 M US Little Kid),
44
+ BBC International, Red/Blue'
45
+ - 'search_document: MIXIN Little Kids Boys Spring Cozy Comfort Comfy Slippers Size
46
+ Dark Blue 13 13.5 M, MIXIN, Blue'
47
+ - 'search_document: Third Eye Blind, , '
48
+ - source_sentence: 'search_query: water filter refrigerator whirlpool'
 
 
 
49
  sentences:
50
+ - 'search_document: Assorted Tootsie Frooties - 2 lbs of Delicious Assorted Bulk
51
+ Wrapped Candy with Refrigerator Magnet, Emporium Candy, '
52
+ - 'search_document: Whirlpool WHER25 Reverse Osmosis (RO) Filtration System With
53
+ Chrome Faucet | Extra Long Life | Easy To Replace UltraEase Filter Cartridges,
54
+ White, Whirlpool, White'
55
+ - 'search_document: Everydrop by Whirlpool Ice and Water Refrigerator Filter 3,
56
+ EDR3RXD1, Single-Pack, White, EveryDrop by Whirlpool, White'
57
+ - source_sentence: 'search_query: t towels kitchen'
 
58
  sentences:
59
+ - 'search_document: Utopia Kitchen Flour Sack Tea Towels 24 Pack, 28" x 28" Ring
60
+ Spun 100% Cotton Dish Cloths - Machine Washable - for Cleaning & Drying - White,
61
+ Utopia Kitchen, White'
62
+ - 'search_document: Rael Certified Organic Cotton Panty Liners (Regular, 44 Count)
63
+ & Extra Long Overnight Pads (12 Count) Bundle, Rael, '
64
+ - 'search_document: LSK Bath Hand Towel Holder Standing,T-Shape Towel Rack Countertop
65
+ with Round Base for Kitchen Bathroom Toliet ,Rustproof Stainless Steel (Black),
66
+ LSK, Black'
67
+ - source_sentence: 'search_query: cozy lights bedroom'
68
  sentences:
69
+ - 'search_document: Fluorescent Light Covers | Fluorescent Light Covers for Ceiling
70
+ Lights, Classroom, Office, or Blue Light Covers Fluorescent Filter- Eliminates
71
+ Flicker & Glare - 48" by 24" (4 Pack, Sky Blue Panel), Everyday Educate, Blue'
72
+ - 'search_document: 43ft Led Globe String Lights, 100LEDs Outdoor Indoor String
73
+ Lights Plug in, 8 Modes Twinkle Lights, Warm White, Fairy String Lights Christmas
74
+ Decoration, Yard, Party, Bedroom, Yinuo Candle, White'
75
+ - 'search_document: PARMIDA 6 inch Dimmable LED Square Recessed Retrofit Lighting,
76
+ Easy Downlight Installation, 12W (100W Eqv.), 950lm, Ceiling Can Lights, Energy
77
+ Star & ETL-Listed, 5 Year Warranty, 3000K - 4 Pack, Parmida LED Technologies,
78
+ 3000k (Soft White)'
79
+ - source_sentence: 'search_query: punxhing bag'
80
  sentences:
81
+ - 'search_document: Power Core Bag (EA), Everlast, Black/White'
82
+ - 'search_document: Nifeida High Mount 3rd Brake Light for 2004-2008 Ford F150,
83
+ 2007-2010 Ford Explorer, 2006-2008 Lincoln Mark LT 22LEDs Waterproof Wedge Center
84
+ Tail Light Heavy Duty Third Stop Light Cargo Lamp, nifeida, '
85
+ - 'search_document: Everlast 70-Pound MMA Poly Canvas Heavy Bag (Black), Everlast,
86
+ Black'
 
 
87
  pipeline_tag: sentence-similarity
88
  model-index:
89
+ - name: SentenceTransformer
90
  results:
91
  - task:
92
  type: triplet
 
96
  type: unknown
97
  metrics:
98
  - type: cosine_accuracy
99
+ value: 0.7297333333333333
100
  name: Cosine Accuracy
101
  - type: dot_accuracy
102
+ value: 0.2886
103
  name: Dot Accuracy
104
  - type: manhattan_accuracy
105
+ value: 0.7276666666666667
106
  name: Manhattan Accuracy
107
  - type: euclidean_accuracy
108
+ value: 0.7302
109
  name: Euclidean Accuracy
110
  - type: max_accuracy
111
+ value: 0.7302
112
  name: Max Accuracy
113
  - task:
114
  type: semantic-similarity
 
118
  type: unknown
119
  metrics:
120
  - type: pearson_cosine
121
+ value: 0.4148970258272059
122
  name: Pearson Cosine
123
  - type: spearman_cosine
124
+ value: 0.398627797786331
125
  name: Spearman Cosine
126
  - type: pearson_manhattan
127
+ value: 0.37912086775286635
128
  name: Pearson Manhattan
129
  - type: spearman_manhattan
130
+ value: 0.3706070496698137
131
  name: Spearman Manhattan
132
  - type: pearson_euclidean
133
+ value: 0.3795867481634979
134
  name: Pearson Euclidean
135
  - type: spearman_euclidean
136
+ value: 0.3712739507251931
137
  name: Spearman Euclidean
138
  - type: pearson_dot
139
+ value: 0.37803301496759
140
  name: Pearson Dot
141
  - type: spearman_dot
142
+ value: 0.37716678508954316
143
  name: Spearman Dot
144
  - type: pearson_max
145
+ value: 0.4148970258272059
146
  name: Pearson Max
147
  - type: spearman_max
148
+ value: 0.398627797786331
149
  name: Spearman Max
150
+ - task:
151
+ type: information-retrieval
152
+ name: Information Retrieval
153
+ dataset:
154
+ name: Unknown
155
+ type: unknown
156
+ metrics:
157
+ - type: cosine_accuracy@10
158
+ value: 0.97
159
+ name: Cosine Accuracy@10
160
+ - type: cosine_precision@10
161
+ value: 0.6959
162
+ name: Cosine Precision@10
163
+ - type: cosine_recall@10
164
+ value: 0.6232158089273498
165
+ name: Cosine Recall@10
166
+ - type: cosine_ndcg@10
167
+ value: 0.8306950477475292
168
+ name: Cosine Ndcg@10
169
+ - type: cosine_mrr@10
170
+ value: 0.91050753968254
171
+ name: Cosine Mrr@10
172
+ - type: cosine_map@10
173
+ value: 0.7765982865646258
174
+ name: Cosine Map@10
175
+ - type: dot_accuracy@10
176
+ value: 0.945
177
+ name: Dot Accuracy@10
178
+ - type: dot_precision@10
179
+ value: 0.6282000000000001
180
+ name: Dot Precision@10
181
+ - type: dot_recall@10
182
+ value: 0.5617500930766831
183
+ name: Dot Recall@10
184
+ - type: dot_ndcg@10
185
+ value: 0.7560665190218724
186
+ name: Dot Ndcg@10
187
+ - type: dot_mrr@10
188
+ value: 0.8675638888888889
189
+ name: Dot Mrr@10
190
+ - type: dot_map@10
191
+ value: 0.682285591931217
192
+ name: Dot Map@10
193
  ---
194
 
195
+ # SentenceTransformer
196
 
197
+ This is a [sentence-transformers](https://www.SBERT.net) model trained on the triplets 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.
198
 
199
  ## Model Details
200
 
201
  ### Model Description
202
  - **Model Type:** Sentence Transformer
203
+ <!-- - **Base model:** [Unknown](https://huggingface.co/unknown) -->
204
  - **Maximum Sequence Length:** 8192 tokens
205
  - **Output Dimensionality:** 768 tokens
206
  - **Similarity Function:** Cosine Similarity
207
+ - **Training Dataset:**
208
  - triplets
 
209
  <!-- - **Language:** Unknown -->
210
  <!-- - **License:** Unknown -->
211
 
 
242
  model = SentenceTransformer("lv12/esci-nomic-embed-text-v1_5_4")
243
  # Run inference
244
  sentences = [
245
+ 'search_query: punxhing bag',
246
+ 'search_document: Everlast 70-Pound MMA Poly Canvas Heavy Bag (Black), Everlast, Black',
247
+ 'search_document: Power Core Bag (EA), Everlast, Black/White',
248
  ]
249
  embeddings = model.encode(sentences)
250
  print(embeddings.shape)
 
290
 
291
  | Metric | Value |
292
  |:--------------------|:-----------|
293
+ | **cosine_accuracy** | **0.7297** |
294
+ | dot_accuracy | 0.2886 |
295
+ | manhattan_accuracy | 0.7277 |
296
+ | euclidean_accuracy | 0.7302 |
297
+ | max_accuracy | 0.7302 |
298
 
299
  #### Semantic Similarity
300
 
 
302
 
303
  | Metric | Value |
304
  |:--------------------|:-----------|
305
+ | pearson_cosine | 0.4149 |
306
+ | **spearman_cosine** | **0.3986** |
307
+ | pearson_manhattan | 0.3791 |
308
+ | spearman_manhattan | 0.3706 |
309
+ | pearson_euclidean | 0.3796 |
310
+ | spearman_euclidean | 0.3713 |
311
+ | pearson_dot | 0.378 |
312
+ | spearman_dot | 0.3772 |
313
+ | pearson_max | 0.4149 |
314
+ | spearman_max | 0.3986 |
315
+
316
+ #### Information Retrieval
317
+
318
+ * Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
319
+
320
+ | Metric | Value |
321
+ |:--------------------|:-----------|
322
+ | cosine_accuracy@10 | 0.97 |
323
+ | cosine_precision@10 | 0.6959 |
324
+ | cosine_recall@10 | 0.6232 |
325
+ | cosine_ndcg@10 | 0.8307 |
326
+ | cosine_mrr@10 | 0.9105 |
327
+ | **cosine_map@10** | **0.7766** |
328
+ | dot_accuracy@10 | 0.945 |
329
+ | dot_precision@10 | 0.6282 |
330
+ | dot_recall@10 | 0.5618 |
331
+ | dot_ndcg@10 | 0.7561 |
332
+ | dot_mrr@10 | 0.8676 |
333
+ | dot_map@10 | 0.6823 |
334
 
335
  <!--
336
  ## Bias, Risks and Limitations
 
346
 
347
  ## Training Details
348
 
349
+ ### Training Dataset
350
 
351
  #### triplets
352
 
353
  * Dataset: triplets
354
+ * Size: 1,500,000 training samples
355
  * Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
356
  * Approximate statistics based on the first 1000 samples:
357
+ | | anchor | positive | negative |
358
+ |:--------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
359
+ | type | string | string | string |
360
+ | details | <ul><li>min: 7 tokens</li><li>mean: 11.0 tokens</li><li>max: 27 tokens</li></ul> | <ul><li>min: 9 tokens</li><li>mean: 38.87 tokens</li><li>max: 91 tokens</li></ul> | <ul><li>min: 9 tokens</li><li>mean: 39.17 tokens</li><li>max: 96 tokens</li></ul> |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
361
  * Samples:
362
+ | anchor | positive | negative |
363
+ |:--------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
364
+ | <code>search_query: chiffon ivory fabric</code> | <code>search_document: DJBM 59’’ Solid Color Sheer Chiffon Fabric Yards Continuous All Colors for DIY Decoration Valance Ivory/1 Yard, DJBM, Ivory</code> | <code>search_document: 58" Blush Solid Color Sheer Chiffon Fabric by The Bolt - 25 Yards, Stylish FABRIC, </code> |
365
+ | <code>search_query: tummy control lingerie for women for sex</code> | <code>search_document: Women's Plus Size Chemise Floral Lace Lingerie Sexy Bodysuit Mesh Babydoll Sleepwear (R007,White, XXXX-Large), Chic Lover, White</code> | <code>search_document: Avidlove Lingerie for Women Teddy One Piece Lace Babydoll Bodysuit Black Medium, Avidlove, Black</code> |
366
+ | <code>search_query: photo metal prints</code> | <code>search_document: Smile Art Design Personalized Photo Print Desktop Photo Panel Print with Kickstand Upload Your Tabletop Berlin Shape Frame Custom Photo Print Personalized Gift for Man Woman 5" x7", Smile Art Design, Berlin 5" X 7"</code> | <code>search_document: They Whispered to Her You Cannot Withstand The Storm - Positive Motivational Uplifting Encouragement Gifts for Women Teens - Inspirational Quote Wall Art - Boho Decoration Print - Dragonfly Wall Decor, Yellowbird Art & Design, </code> |
367
+ * Loss: [<code>TripletLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#tripletloss) with these parameters:
368
  ```json
369
  {
370
+ "distance_metric": "TripletDistanceMetric.COSINE",
371
+ "triplet_margin": 0.7
372
  }
373
  ```
374
 
375
+ ### Evaluation Dataset
376
 
377
  #### triplets
378
 
379
  * Dataset: triplets
380
+ * Size: 15,000 evaluation samples
381
  * Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
382
  * Approximate statistics based on the first 1000 samples:
383
+ | | anchor | positive | negative |
384
+ |:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
385
+ | type | string | string | string |
386
+ | details | <ul><li>min: 7 tokens</li><li>mean: 11.14 tokens</li><li>max: 31 tokens</li></ul> | <ul><li>min: 9 tokens</li><li>mean: 38.83 tokens</li><li>max: 105 tokens</li></ul> | <ul><li>min: 9 tokens</li><li>mean: 39.12 tokens</li><li>max: 113 tokens</li></ul> |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
387
  * Samples:
388
+ | anchor | positive | negative |
389
+ |:------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
390
+ | <code>search_query: a cold dark place</code> | <code>search_document: A Cold Dark Place (An Emily Kenyon Thriller Book 1), , </code> | <code>search_document: The Great Alone, , </code> |
391
+ | <code>search_query: quay sunglasses</code> | <code>search_document: Quay Women's Hindsight Sunglasses, Matte Black/Rainbow Mirror, Quay, Multicolor</code> | <code>search_document: SORVINO Aviator Sunglasses for Women Classic Oversized Sun Glasses UV400 Protection (A-Black Frame/Faded Lens, 60), SORVINO, Black Frame/Faded Lens</code> |
392
+ | <code>search_query: baby girl jean fress</code> | <code>search_document: Toddler Baby Girl Play Wear Floral Princess Summer Dresses with Bow Ruffle Denim Skirt Set for Summer 9-12months Light Blue, NZRVAWS, </code> | <code>search_document: Slowera Baby Toddler Girls Denim Ruffled Bodysuit Blue Soft One-Piece Romper (Blue, 6-12 Months), Slowera, Blue</code> |
393
+ * Loss: [<code>TripletLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#tripletloss) with these parameters:
394
  ```json
395
  {
396
+ "distance_metric": "TripletDistanceMetric.COSINE",
397
+ "triplet_margin": 0.7
398
  }
399
  ```
400
 
401
  ### Training Hyperparameters
402
  #### Non-Default Hyperparameters
403
 
404
+ - `per_device_train_batch_size`: 32
405
+ - `per_device_eval_batch_size`: 16
406
  - `gradient_accumulation_steps`: 2
407
+ - `learning_rate`: 1e-07
408
+ - `lr_scheduler_type`: polynomial
409
+ - `lr_scheduler_kwargs`: {'lr_end': 1e-08, 'power': 2.0}
410
+ - `warmup_ratio`: 0.05
 
411
  - `dataloader_num_workers`: 4
412
  - `dataloader_prefetch_factor`: 4
413
  - `load_best_model_at_end`: True
414
  - `gradient_checkpointing`: True
415
+ - `auto_find_batch_size`: True
416
  - `batch_sampler`: no_duplicates
417
 
418
  #### All Hyperparameters
 
421
  - `overwrite_output_dir`: False
422
  - `do_predict`: False
423
  - `prediction_loss_only`: True
424
+ - `per_device_train_batch_size`: 32
425
+ - `per_device_eval_batch_size`: 16
426
  - `per_gpu_train_batch_size`: None
427
  - `per_gpu_eval_batch_size`: None
428
  - `gradient_accumulation_steps`: 2
429
  - `eval_accumulation_steps`: None
430
+ - `learning_rate`: 1e-07
431
  - `weight_decay`: 0.0
432
  - `adam_beta1`: 0.9
433
  - `adam_beta2`: 0.999
 
435
  - `max_grad_norm`: 1.0
436
  - `num_train_epochs`: 3
437
  - `max_steps`: -1
438
+ - `lr_scheduler_type`: polynomial
439
+ - `lr_scheduler_kwargs`: {'lr_end': 1e-08, 'power': 2.0}
440
+ - `warmup_ratio`: 0.05
441
  - `warmup_steps`: 0
442
  - `log_level`: passive
443
  - `log_level_replica`: warning
 
506
  - `push_to_hub_model_id`: None
507
  - `push_to_hub_organization`: None
508
  - `mp_parameters`:
509
+ - `auto_find_batch_size`: True
510
  - `full_determinism`: False
511
  - `torchdynamo`: None
512
  - `ray_scope`: last
 
527
  ### Training Logs
528
  <details><summary>Click to expand</summary>
529
 
530
+ | Epoch | Step | Training Loss | triplets loss | cosine_accuracy | cosine_map@10 | spearman_cosine |
531
+ |:------:|:----:|:-------------:|:-------------:|:---------------:|:-------------:|:---------------:|
532
+ | 0.0004 | 10 | 0.6484 | - | - | - | - |
533
+ | 0.0009 | 20 | 0.6538 | - | - | - | - |
534
+ | 0.0013 | 30 | 0.651 | - | - | - | - |
535
+ | 0.0017 | 40 | 0.6491 | - | - | - | - |
536
+ | 0.0021 | 50 | 0.6433 | - | - | - | - |
537
+ | 0.0026 | 60 | 0.6545 | - | - | - | - |
538
+ | 0.0030 | 70 | 0.6459 | - | - | - | - |
539
+ | 0.0034 | 80 | 0.6517 | - | - | - | - |
540
+ | 0.0038 | 90 | 0.6505 | - | - | - | - |
541
+ | 0.0043 | 100 | 0.6477 | 0.6560 | 0.7255 | 0.7684 | 0.3931 |
542
+ | 0.0047 | 110 | 0.6493 | - | - | - | - |
543
+ | 0.0051 | 120 | 0.6514 | - | - | - | - |
544
+ | 0.0055 | 130 | 0.6482 | - | - | - | - |
545
+ | 0.0060 | 140 | 0.6566 | - | - | - | - |
546
+ | 0.0064 | 150 | 0.6476 | - | - | - | - |
547
+ | 0.0068 | 160 | 0.6523 | - | - | - | - |
548
+ | 0.0073 | 170 | 0.6503 | - | - | - | - |
549
+ | 0.0077 | 180 | 0.6568 | - | - | - | - |
550
+ | 0.0081 | 190 | 0.6496 | - | - | - | - |
551
+ | 0.0085 | 200 | 0.6512 | 0.6558 | 0.725 | 0.7687 | 0.3932 |
552
+ | 0.0090 | 210 | 0.642 | - | - | - | - |
553
+ | 0.0094 | 220 | 0.644 | - | - | - | - |
554
+ | 0.0098 | 230 | 0.6512 | - | - | - | - |
555
+ | 0.0102 | 240 | 0.6512 | - | - | - | - |
556
+ | 0.0107 | 250 | 0.6477 | - | - | - | - |
557
+ | 0.0111 | 260 | 0.6521 | - | - | - | - |
558
+ | 0.0115 | 270 | 0.6487 | - | - | - | - |
559
+ | 0.0119 | 280 | 0.6541 | - | - | - | - |
560
+ | 0.0124 | 290 | 0.6481 | - | - | - | - |
561
+ | 0.0128 | 300 | 0.6495 | 0.6555 | 0.7247 | 0.7690 | 0.3933 |
562
+ | 0.0132 | 310 | 0.6536 | - | - | - | - |
563
+ | 0.0137 | 320 | 0.6515 | - | - | - | - |
564
+ | 0.0141 | 330 | 0.6526 | - | - | - | - |
565
+ | 0.0145 | 340 | 0.6509 | - | - | - | - |
566
+ | 0.0149 | 350 | 0.6506 | - | - | - | - |
567
+ | 0.0154 | 360 | 0.6451 | - | - | - | - |
568
+ | 0.0158 | 370 | 0.6505 | - | - | - | - |
569
+ | 0.0162 | 380 | 0.6529 | - | - | - | - |
570
+ | 0.0166 | 390 | 0.6553 | - | - | - | - |
571
+ | 0.0171 | 400 | 0.6512 | 0.6550 | 0.7245 | 0.7693 | 0.3934 |
572
+ | 0.0175 | 410 | 0.6478 | - | - | - | - |
573
+ | 0.0179 | 420 | 0.6497 | - | - | - | - |
574
+ | 0.0183 | 430 | 0.6498 | - | - | - | - |
575
+ | 0.0188 | 440 | 0.6499 | - | - | - | - |
576
+ | 0.0192 | 450 | 0.6462 | - | - | - | - |
577
+ | 0.0196 | 460 | 0.6497 | - | - | - | - |
578
+ | 0.0201 | 470 | 0.6408 | - | - | - | - |
579
+ | 0.0205 | 480 | 0.6507 | - | - | - | - |
580
+ | 0.0209 | 490 | 0.6505 | - | - | - | - |
581
+ | 0.0213 | 500 | 0.6481 | 0.6545 | 0.7249 | 0.7694 | 0.3936 |
582
+ | 0.0218 | 510 | 0.6436 | - | - | - | - |
583
+ | 0.0222 | 520 | 0.653 | - | - | - | - |
584
+ | 0.0226 | 530 | 0.652 | - | - | - | - |
585
+ | 0.0230 | 540 | 0.6509 | - | - | - | - |
586
+ | 0.0235 | 550 | 0.6469 | - | - | - | - |
587
+ | 0.0239 | 560 | 0.6515 | - | - | - | - |
588
+ | 0.0243 | 570 | 0.6485 | - | - | - | - |
589
+ | 0.0247 | 580 | 0.6499 | - | - | - | - |
590
+ | 0.0252 | 590 | 0.6472 | - | - | - | - |
591
+ | 0.0256 | 600 | 0.6569 | 0.6538 | 0.7255 | 0.7702 | 0.3939 |
592
+ | 0.0260 | 610 | 0.6457 | - | - | - | - |
593
+ | 0.0265 | 620 | 0.6485 | - | - | - | - |
594
+ | 0.0269 | 630 | 0.6467 | - | - | - | - |
595
+ | 0.0273 | 640 | 0.6494 | - | - | - | - |
596
+ | 0.0277 | 650 | 0.6492 | - | - | - | - |
597
+ | 0.0282 | 660 | 0.6439 | - | - | - | - |
598
+ | 0.0286 | 670 | 0.6468 | - | - | - | - |
599
+ | 0.0290 | 680 | 0.6493 | - | - | - | - |
600
+ | 0.0294 | 690 | 0.6483 | - | - | - | - |
601
+ | 0.0299 | 700 | 0.6474 | 0.6530 | 0.7259 | 0.7704 | 0.3942 |
602
+ | 0.0303 | 710 | 0.6512 | - | - | - | - |
603
+ | 0.0307 | 720 | 0.6431 | - | - | - | - |
604
+ | 0.0311 | 730 | 0.6484 | - | - | - | - |
605
+ | 0.0316 | 740 | 0.6488 | - | - | - | - |
606
+ | 0.032 | 750 | 0.6525 | - | - | - | - |
607
+ | 0.0324 | 760 | 0.6464 | - | - | - | - |
608
+ | 0.0329 | 770 | 0.6453 | - | - | - | - |
609
+ | 0.0333 | 780 | 0.6471 | - | - | - | - |
610
+ | 0.0337 | 790 | 0.6478 | - | - | - | - |
611
+ | 0.0341 | 800 | 0.6456 | 0.6520 | 0.7262 | 0.7704 | 0.3945 |
612
+ | 0.0346 | 810 | 0.6494 | - | - | - | - |
613
+ | 0.0350 | 820 | 0.6374 | - | - | - | - |
614
+ | 0.0354 | 830 | 0.6441 | - | - | - | - |
615
+ | 0.0358 | 840 | 0.643 | - | - | - | - |
616
+ | 0.0363 | 850 | 0.6471 | - | - | - | - |
617
+ | 0.0367 | 860 | 0.6401 | - | - | - | - |
618
+ | 0.0371 | 870 | 0.6381 | - | - | - | - |
619
+ | 0.0375 | 880 | 0.6491 | - | - | - | - |
620
+ | 0.0380 | 890 | 0.6428 | - | - | - | - |
621
+ | 0.0384 | 900 | 0.6393 | 0.6509 | 0.7262 | 0.7712 | 0.3948 |
622
+ | 0.0388 | 910 | 0.6453 | - | - | - | - |
623
+ | 0.0393 | 920 | 0.6426 | - | - | - | - |
624
+ | 0.0397 | 930 | 0.644 | - | - | - | - |
625
+ | 0.0401 | 940 | 0.6469 | - | - | - | - |
626
+ | 0.0405 | 950 | 0.6372 | - | - | - | - |
627
+ | 0.0410 | 960 | 0.6491 | - | - | - | - |
628
+ | 0.0414 | 970 | 0.6441 | - | - | - | - |
629
+ | 0.0418 | 980 | 0.6394 | - | - | - | - |
630
+ | 0.0422 | 990 | 0.6416 | - | - | - | - |
631
+ | 0.0427 | 1000 | 0.6465 | 0.6497 | 0.7263 | 0.7719 | 0.3951 |
632
+ | 0.0431 | 1010 | 0.6485 | - | - | - | - |
633
+ | 0.0435 | 1020 | 0.6391 | - | - | - | - |
634
+ | 0.0439 | 1030 | 0.6489 | - | - | - | - |
635
+ | 0.0444 | 1040 | 0.6417 | - | - | - | - |
636
+ | 0.0448 | 1050 | 0.6355 | - | - | - | - |
637
+ | 0.0452 | 1060 | 0.6445 | - | - | - | - |
638
+ | 0.0457 | 1070 | 0.6427 | - | - | - | - |
639
+ | 0.0461 | 1080 | 0.6443 | - | - | - | - |
640
+ | 0.0465 | 1090 | 0.6422 | - | - | - | - |
641
+ | 0.0469 | 1100 | 0.6406 | 0.6483 | 0.7265 | 0.7721 | 0.3955 |
642
+ | 0.0474 | 1110 | 0.6415 | - | - | - | - |
643
+ | 0.0478 | 1120 | 0.6389 | - | - | - | - |
644
+ | 0.0482 | 1130 | 0.6437 | - | - | - | - |
645
+ | 0.0486 | 1140 | 0.6412 | - | - | - | - |
646
+ | 0.0491 | 1150 | 0.6457 | - | - | - | - |
647
+ | 0.0495 | 1160 | 0.6364 | - | - | - | - |
648
+ | 0.0499 | 1170 | 0.6389 | - | - | - | - |
649
+ | 0.0503 | 1180 | 0.6394 | - | - | - | - |
650
+ | 0.0508 | 1190 | 0.6465 | - | - | - | - |
651
+ | 0.0512 | 1200 | 0.6453 | 0.6468 | 0.7269 | 0.7734 | 0.3959 |
652
+ | 0.0516 | 1210 | 0.6465 | - | - | - | - |
653
+ | 0.0521 | 1220 | 0.6389 | - | - | - | - |
654
+ | 0.0525 | 1230 | 0.6448 | - | - | - | - |
655
+ | 0.0529 | 1240 | 0.6325 | - | - | - | - |
656
+ | 0.0533 | 1250 | 0.6347 | - | - | - | - |
657
+ | 0.0538 | 1260 | 0.6363 | - | - | - | - |
658
+ | 0.0542 | 1270 | 0.6387 | - | - | - | - |
659
+ | 0.0546 | 1280 | 0.641 | - | - | - | - |
660
+ | 0.0550 | 1290 | 0.6381 | - | - | - | - |
661
+ | 0.0555 | 1300 | 0.6412 | 0.6452 | 0.7273 | 0.7747 | 0.3962 |
662
+ | 0.0559 | 1310 | 0.6384 | - | - | - | - |
663
+ | 0.0563 | 1320 | 0.6391 | - | - | - | - |
664
+ | 0.0567 | 1330 | 0.6327 | - | - | - | - |
665
+ | 0.0572 | 1340 | 0.6388 | - | - | - | - |
666
+ | 0.0576 | 1350 | 0.6328 | - | - | - | - |
667
+ | 0.0580 | 1360 | 0.6327 | - | - | - | - |
668
+ | 0.0585 | 1370 | 0.6333 | - | - | - | - |
669
+ | 0.0589 | 1380 | 0.6388 | - | - | - | - |
670
+ | 0.0593 | 1390 | 0.6373 | - | - | - | - |
671
+ | 0.0597 | 1400 | 0.636 | 0.6434 | 0.7278 | 0.7749 | 0.3966 |
672
+ | 0.0602 | 1410 | 0.6357 | - | - | - | - |
673
+ | 0.0606 | 1420 | 0.6394 | - | - | - | - |
674
+ | 0.0610 | 1430 | 0.6339 | - | - | - | - |
675
+ | 0.0614 | 1440 | 0.6385 | - | - | - | - |
676
+ | 0.0619 | 1450 | 0.6288 | - | - | - | - |
677
+ | 0.0623 | 1460 | 0.6393 | - | - | - | - |
678
+ | 0.0627 | 1470 | 0.638 | - | - | - | - |
679
+ | 0.0631 | 1480 | 0.6353 | - | - | - | - |
680
+ | 0.0636 | 1490 | 0.6335 | - | - | - | - |
681
+ | 0.064 | 1500 | 0.6329 | 0.6414 | 0.7281 | 0.7749 | 0.3970 |
682
+ | 0.0644 | 1510 | 0.6363 | - | - | - | - |
683
+ | 0.0649 | 1520 | 0.6311 | - | - | - | - |
684
+ | 0.0653 | 1530 | 0.6367 | - | - | - | - |
685
+ | 0.0657 | 1540 | 0.636 | - | - | - | - |
686
+ | 0.0661 | 1550 | 0.6351 | - | - | - | - |
687
+ | 0.0666 | 1560 | 0.637 | - | - | - | - |
688
+ | 0.0670 | 1570 | 0.6352 | - | - | - | - |
689
+ | 0.0674 | 1580 | 0.6285 | - | - | - | - |
690
+ | 0.0678 | 1590 | 0.6311 | - | - | - | - |
691
+ | 0.0683 | 1600 | 0.6347 | 0.6393 | 0.7283 | 0.7752 | 0.3974 |
692
+ | 0.0687 | 1610 | 0.6393 | - | - | - | - |
693
+ | 0.0691 | 1620 | 0.6368 | - | - | - | - |
694
+ | 0.0695 | 1630 | 0.6354 | - | - | - | - |
695
+ | 0.0700 | 1640 | 0.6283 | - | - | - | - |
696
+ | 0.0704 | 1650 | 0.6289 | - | - | - | - |
697
+ | 0.0708 | 1660 | 0.6291 | - | - | - | - |
698
+ | 0.0713 | 1670 | 0.6274 | - | - | - | - |
699
+ | 0.0717 | 1680 | 0.6217 | - | - | - | - |
700
+ | 0.0721 | 1690 | 0.6281 | - | - | - | - |
701
+ | 0.0725 | 1700 | 0.6341 | 0.6371 | 0.7289 | 0.7757 | 0.3978 |
702
+ | 0.0730 | 1710 | 0.6354 | - | - | - | - |
703
+ | 0.0734 | 1720 | 0.6265 | - | - | - | - |
704
+ | 0.0738 | 1730 | 0.6214 | - | - | - | - |
705
+ | 0.0742 | 1740 | 0.6301 | - | - | - | - |
706
+ | 0.0747 | 1750 | 0.621 | - | - | - | - |
707
+ | 0.0751 | 1760 | 0.6259 | - | - | - | - |
708
+ | 0.0755 | 1770 | 0.6261 | - | - | - | - |
709
+ | 0.0759 | 1780 | 0.6273 | - | - | - | - |
710
+ | 0.0764 | 1790 | 0.6311 | - | - | - | - |
711
+ | 0.0768 | 1800 | 0.6232 | 0.6347 | 0.7292 | 0.7755 | 0.3981 |
712
+ | 0.0772 | 1810 | 0.6293 | - | - | - | - |
713
+ | 0.0777 | 1820 | 0.617 | - | - | - | - |
714
+ | 0.0781 | 1830 | 0.6303 | - | - | - | - |
715
+ | 0.0785 | 1840 | 0.6225 | - | - | - | - |
716
+ | 0.0789 | 1850 | 0.6313 | - | - | - | - |
717
+ | 0.0794 | 1860 | 0.6229 | - | - | - | - |
718
+ | 0.0798 | 1870 | 0.6236 | - | - | - | - |
719
+ | 0.0802 | 1880 | 0.6265 | - | - | - | - |
720
+ | 0.0806 | 1890 | 0.6179 | - | - | - | - |
721
+ | 0.0811 | 1900 | 0.6277 | 0.6321 | 0.7292 | 0.7760 | 0.3984 |
722
+ | 0.0815 | 1910 | 0.6266 | - | - | - | - |
723
+ | 0.0819 | 1920 | 0.6209 | - | - | - | - |
724
+ | 0.0823 | 1930 | 0.6258 | - | - | - | - |
725
+ | 0.0828 | 1940 | 0.6143 | - | - | - | - |
726
+ | 0.0832 | 1950 | 0.6176 | - | - | - | - |
727
+ | 0.0836 | 1960 | 0.628 | - | - | - | - |
728
+ | 0.0841 | 1970 | 0.6147 | - | - | - | - |
729
+ | 0.0845 | 1980 | 0.6175 | - | - | - | - |
730
+ | 0.0849 | 1990 | 0.6135 | - | - | - | - |
731
+ | 0.0853 | 2000 | 0.6214 | 0.6293 | 0.7297 | 0.7766 | 0.3986 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
732
 
733
  </details>
734
 
 
758
  }
759
  ```
760
 
761
+ #### TripletLoss
 
 
 
 
 
 
 
 
 
 
 
 
762
  ```bibtex
763
+ @misc{hermans2017defense,
764
+ title={In Defense of the Triplet Loss for Person Re-Identification},
765
+ author={Alexander Hermans and Lucas Beyer and Bastian Leibe},
766
+ year={2017},
767
+ eprint={1703.07737},
768
  archivePrefix={arXiv},
769
+ primaryClass={cs.CV}
770
  }
771
  ```
772
 
config.json CHANGED
@@ -1,5 +1,5 @@
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  {
2
- "_name_or_path": "models/nomic-embed-text-esci/checkpoint-37000",
3
  "activation_function": "swiglu",
4
  "architectures": [
5
  "NomicBertModel"
 
1
  {
2
+ "_name_or_path": "models/nomic-embed-text-train-esci/checkpoint-2000",
3
  "activation_function": "swiglu",
4
  "architectures": [
5
  "NomicBertModel"
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  size 546938168
 
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