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youssefkhalil320
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all-MiniLM-L6-v3-pairscore

Sentence Similarity
sentence-transformers
Safetensors
English
bert
feature-extraction
Generated from Trainer
dataset_size:8004920
loss:CoSENTLoss
text-embeddings-inference
Inference Endpoints
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  1. README.md +1038 -111
  2. config.json +1 -1
  3. config_sentence_transformers.json +2 -2
  4. model.safetensors +1 -1
README.md CHANGED
@@ -4,17 +4,6 @@ language:
4
  - en
5
  library_name: sentence-transformers
6
  license: apache-2.0
7
- metrics:
8
- - pearson_cosine
9
- - spearman_cosine
10
- - pearson_manhattan
11
- - spearman_manhattan
12
- - pearson_euclidean
13
- - spearman_euclidean
14
- - pearson_dot
15
- - spearman_dot
16
- - pearson_max
17
- - spearman_max
18
  pipeline_tag: sentence-similarity
19
  tags:
20
  - sentence-transformers
@@ -24,71 +13,31 @@ tags:
24
  - dataset_size:8004920
25
  - loss:CoSENTLoss
26
  widget:
27
- - source_sentence: Fast dry hijab
28
  sentences:
29
- - Old navy/white color combination
30
- - Foam slides
31
- - reversible scarf
32
- - source_sentence: shiny hair Shampoo
33
  sentences:
34
- - comfort shirt
35
- - treat dry hair Mask
36
- - jeans trowsers
37
- - source_sentence: Mustard purse
38
  sentences:
39
- - matcha lemonade trowsers
40
- - shadow stalker trowsers
41
- - petroleum satchel
42
- - source_sentence: naan cutting knife
43
  sentences:
44
- - gibna omelet
45
- - lemon exfoliator
46
- - blue shark bundle
47
- - source_sentence: deep macarona plates
48
  sentences:
49
- - tea cup
50
- - handmade Fanny backpack
51
- - back pocket
52
- model-index:
53
- - name: all-MiniLM-L6-v3-pair_score
54
- results:
55
- - task:
56
- type: semantic-similarity
57
- name: Semantic Similarity
58
- dataset:
59
- name: sts dev
60
- type: sts-dev
61
- metrics:
62
- - type: pearson_cosine
63
- value: 0.49121212197149733
64
- name: Pearson Cosine
65
- - type: spearman_cosine
66
- value: 0.4987790688691961
67
- name: Spearman Cosine
68
- - type: pearson_manhattan
69
- value: 0.4739753233013834
70
- name: Pearson Manhattan
71
- - type: spearman_manhattan
72
- value: 0.5210618252307972
73
- name: Spearman Manhattan
74
- - type: pearson_euclidean
75
- value: 0.460118456049799
76
- name: Pearson Euclidean
77
- - type: spearman_euclidean
78
- value: 0.4987790688691961
79
- name: Spearman Euclidean
80
- - type: pearson_dot
81
- value: 0.49121219031215435
82
- name: Pearson Dot
83
- - type: spearman_dot
84
- value: 0.4987790688691961
85
- name: Spearman Dot
86
- - type: pearson_max
87
- value: 0.49121219031215435
88
- name: Pearson Max
89
- - type: spearman_max
90
- value: 0.5210618252307972
91
- name: Spearman Max
92
  ---
93
 
94
  # all-MiniLM-L6-v3-pair_score
@@ -141,9 +90,9 @@ from sentence_transformers import SentenceTransformer
141
  model = SentenceTransformer("sentence_transformers_model_id")
142
  # Run inference
143
  sentences = [
144
- 'deep macarona plates',
145
- 'tea cup',
146
- 'back pocket',
147
  ]
148
  embeddings = model.encode(sentences)
149
  print(embeddings.shape)
@@ -179,27 +128,6 @@ You can finetune this model on your own dataset.
179
  *List how the model may foreseeably be misused and address what users ought not to do with the model.*
180
  -->
181
 
182
- ## Evaluation
183
-
184
- ### Metrics
185
-
186
- #### Semantic Similarity
187
- * Dataset: `sts-dev`
188
- * Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
189
-
190
- | Metric | Value |
191
- |:--------------------|:-----------|
192
- | pearson_cosine | 0.4912 |
193
- | **spearman_cosine** | **0.4988** |
194
- | pearson_manhattan | 0.474 |
195
- | spearman_manhattan | 0.5211 |
196
- | pearson_euclidean | 0.4601 |
197
- | spearman_euclidean | 0.4988 |
198
- | pearson_dot | 0.4912 |
199
- | spearman_dot | 0.4988 |
200
- | pearson_max | 0.4912 |
201
- | spearman_max | 0.5211 |
202
-
203
  <!--
204
  ## Bias, Risks and Limitations
205
 
@@ -218,11 +146,12 @@ You can finetune this model on your own dataset.
218
  #### Non-Default Hyperparameters
219
 
220
  - `eval_strategy`: steps
221
- - `per_device_train_batch_size`: 1
222
- - `per_device_eval_batch_size`: 1
223
  - `learning_rate`: 2e-05
224
  - `num_train_epochs`: 4
225
  - `warmup_ratio`: 0.1
 
226
 
227
  #### All Hyperparameters
228
  <details><summary>Click to expand</summary>
@@ -231,8 +160,8 @@ You can finetune this model on your own dataset.
231
  - `do_predict`: False
232
  - `eval_strategy`: steps
233
  - `prediction_loss_only`: True
234
- - `per_device_train_batch_size`: 1
235
- - `per_device_eval_batch_size`: 1
236
  - `per_gpu_train_batch_size`: None
237
  - `per_gpu_eval_batch_size`: None
238
  - `gradient_accumulation_steps`: 1
@@ -266,7 +195,7 @@ You can finetune this model on your own dataset.
266
  - `jit_mode_eval`: False
267
  - `use_ipex`: False
268
  - `bf16`: False
269
- - `fp16`: False
270
  - `fp16_opt_level`: O1
271
  - `half_precision_backend`: auto
272
  - `bf16_full_eval`: False
@@ -343,23 +272,1021 @@ You can finetune this model on your own dataset.
343
  </details>
344
 
345
  ### Training Logs
346
- | Epoch | Step | Training Loss | sts-dev_spearman_cosine |
347
- |:-----:|:----:|:-------------:|:-----------------------:|
348
- | 0 | 0 | - | 0.4988 |
349
- | 1.0 | 100 | 0.0 | - |
350
- | 2.0 | 200 | 0.0 | - |
351
- | 3.0 | 300 | 0.0 | - |
352
- | 4.0 | 400 | 0.0 | - |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
353
 
 
354
 
355
  ### Framework Versions
356
  - Python: 3.8.10
357
  - Sentence Transformers: 3.1.1
358
- - Transformers: 4.45.1
359
- - PyTorch: 2.4.1+cu121
360
- - Accelerate: 0.34.2
361
  - Datasets: 3.0.1
362
- - Tokenizers: 0.20.0
363
 
364
  ## Citation
365
 
 
4
  - en
5
  library_name: sentence-transformers
6
  license: apache-2.0
 
 
 
 
 
 
 
 
 
 
 
7
  pipeline_tag: sentence-similarity
8
  tags:
9
  - sentence-transformers
 
13
  - dataset_size:8004920
14
  - loss:CoSENTLoss
15
  widget:
16
+ - source_sentence: ulker marie biscuits
17
  sentences:
18
+ - mozzarella cracker
19
+ - four pockets slacks
20
+ - luminous sun cream primer
21
+ - source_sentence: Owl Diaries 4 Eva and the New book
22
  sentences:
23
+ - gibna Tortilla
24
+ - Advanced swimmer tankini
25
+ - flat waistband swim stretch pants
26
+ - source_sentence: compartment for footwear racket luggage
27
  sentences:
28
+ - hair ties
29
+ - designer shoulder satchel
30
+ - Dishwasher safe Platter
31
+ - source_sentence: pearch purse
32
  sentences:
33
+ - riding boots
34
+ - Creme lipstick
35
+ - dior tote
36
+ - source_sentence: momax q led desk lamp charging base
37
  sentences:
38
+ - JUTE tote
39
+ - walking trousers
40
+ - sephora selftanning body mist
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
41
  ---
42
 
43
  # all-MiniLM-L6-v3-pair_score
 
90
  model = SentenceTransformer("sentence_transformers_model_id")
91
  # Run inference
92
  sentences = [
93
+ 'momax q led desk lamp charging base',
94
+ 'JUTE tote',
95
+ 'sephora selftanning body mist',
96
  ]
97
  embeddings = model.encode(sentences)
98
  print(embeddings.shape)
 
128
  *List how the model may foreseeably be misused and address what users ought not to do with the model.*
129
  -->
130
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
131
  <!--
132
  ## Bias, Risks and Limitations
133
 
 
146
  #### Non-Default Hyperparameters
147
 
148
  - `eval_strategy`: steps
149
+ - `per_device_train_batch_size`: 64
150
+ - `per_device_eval_batch_size`: 64
151
  - `learning_rate`: 2e-05
152
  - `num_train_epochs`: 4
153
  - `warmup_ratio`: 0.1
154
+ - `fp16`: True
155
 
156
  #### All Hyperparameters
157
  <details><summary>Click to expand</summary>
 
160
  - `do_predict`: False
161
  - `eval_strategy`: steps
162
  - `prediction_loss_only`: True
163
+ - `per_device_train_batch_size`: 64
164
+ - `per_device_eval_batch_size`: 64
165
  - `per_gpu_train_batch_size`: None
166
  - `per_gpu_eval_batch_size`: None
167
  - `gradient_accumulation_steps`: 1
 
195
  - `jit_mode_eval`: False
196
  - `use_ipex`: False
197
  - `bf16`: False
198
+ - `fp16`: True
199
  - `fp16_opt_level`: O1
200
  - `half_precision_backend`: auto
201
  - `bf16_full_eval`: False
 
272
  </details>
273
 
274
  ### Training Logs
275
+ <details><summary>Click to expand</summary>
276
+
277
+ | Epoch | Step | Training Loss | loss |
278
+ |:------:|:------:|:-------------:|:------:|
279
+ | 0.0040 | 500 | 11.0937 | - |
280
+ | 0.0080 | 1000 | 10.4099 | - |
281
+ | 0.0120 | 1500 | 9.5203 | - |
282
+ | 0.0160 | 2000 | 8.5096 | - |
283
+ | 0.0200 | 2500 | 7.7136 | - |
284
+ | 0.0240 | 3000 | 7.0622 | - |
285
+ | 0.0280 | 3500 | 6.656 | - |
286
+ | 0.0320 | 4000 | 6.4973 | - |
287
+ | 0.0360 | 4500 | 6.4192 | - |
288
+ | 0.0400 | 5000 | 6.3691 | - |
289
+ | 0.0440 | 5500 | 6.3185 | - |
290
+ | 0.0480 | 6000 | 6.3008 | - |
291
+ | 0.0520 | 6500 | 6.2712 | - |
292
+ | 0.0560 | 7000 | 6.1812 | - |
293
+ | 0.0600 | 7500 | 6.1804 | - |
294
+ | 0.0640 | 8000 | 6.2158 | - |
295
+ | 0.0680 | 8500 | 6.1458 | - |
296
+ | 0.0720 | 9000 | 6.1272 | - |
297
+ | 0.0760 | 9500 | 6.1191 | - |
298
+ | 0.0800 | 10000 | 6.1326 | - |
299
+ | 0.0839 | 10500 | 6.1149 | - |
300
+ | 0.0879 | 11000 | 6.113 | - |
301
+ | 0.0919 | 11500 | 6.0837 | - |
302
+ | 0.0959 | 12000 | 6.0694 | - |
303
+ | 0.0999 | 12500 | 6.0823 | - |
304
+ | 0.1039 | 13000 | 6.0237 | - |
305
+ | 0.1079 | 13500 | 6.0599 | - |
306
+ | 0.1119 | 14000 | 6.0395 | - |
307
+ | 0.1159 | 14500 | 6.0278 | - |
308
+ | 0.1199 | 15000 | 6.0407 | - |
309
+ | 0.1239 | 15500 | 5.9927 | - |
310
+ | 0.1279 | 16000 | 6.007 | - |
311
+ | 0.1319 | 16500 | 5.9956 | - |
312
+ | 0.1359 | 17000 | 5.9771 | - |
313
+ | 0.1399 | 17500 | 5.9549 | - |
314
+ | 0.1439 | 18000 | 6.0142 | - |
315
+ | 0.1479 | 18500 | 5.9834 | - |
316
+ | 0.1519 | 19000 | 5.9187 | - |
317
+ | 0.1559 | 19500 | 5.9599 | - |
318
+ | 0.1599 | 20000 | 5.9338 | - |
319
+ | 0.1639 | 20500 | 5.9025 | - |
320
+ | 0.1679 | 21000 | 5.9289 | - |
321
+ | 0.1719 | 21500 | 5.9003 | - |
322
+ | 0.1759 | 22000 | 5.9284 | - |
323
+ | 0.1799 | 22500 | 5.9084 | - |
324
+ | 0.1839 | 23000 | 5.9171 | - |
325
+ | 0.1879 | 23500 | 5.9341 | - |
326
+ | 0.1919 | 24000 | 5.9336 | - |
327
+ | 0.1959 | 24500 | 5.8839 | - |
328
+ | 0.1999 | 25000 | 5.9089 | - |
329
+ | 0.2039 | 25500 | 5.8855 | - |
330
+ | 0.2079 | 26000 | 5.8739 | - |
331
+ | 0.2119 | 26500 | 5.8558 | - |
332
+ | 0.2159 | 27000 | 5.8715 | - |
333
+ | 0.2199 | 27500 | 5.8257 | - |
334
+ | 0.2239 | 28000 | 5.8951 | - |
335
+ | 0.2279 | 28500 | 5.8489 | - |
336
+ | 0.2319 | 29000 | 5.853 | - |
337
+ | 0.2359 | 29500 | 5.865 | - |
338
+ | 0.2399 | 30000 | 5.8399 | - |
339
+ | 0.2438 | 30500 | 5.8356 | - |
340
+ | 0.2478 | 31000 | 5.828 | - |
341
+ | 0.2518 | 31500 | 5.8467 | - |
342
+ | 0.2558 | 32000 | 5.856 | - |
343
+ | 0.2598 | 32500 | 5.8059 | - |
344
+ | 0.2638 | 33000 | 5.8476 | - |
345
+ | 0.2678 | 33500 | 5.7928 | - |
346
+ | 0.2718 | 34000 | 5.8001 | - |
347
+ | 0.2758 | 34500 | 5.8136 | - |
348
+ | 0.2798 | 35000 | 5.8163 | - |
349
+ | 0.2838 | 35500 | 5.7882 | - |
350
+ | 0.2878 | 36000 | 5.7785 | - |
351
+ | 0.2918 | 36500 | 5.7778 | - |
352
+ | 0.2958 | 37000 | 5.7418 | - |
353
+ | 0.2998 | 37500 | 5.8014 | - |
354
+ | 0.3038 | 38000 | 5.8088 | - |
355
+ | 0.3078 | 38500 | 5.7458 | - |
356
+ | 0.3118 | 39000 | 5.7981 | - |
357
+ | 0.3158 | 39500 | 5.7693 | - |
358
+ | 0.3198 | 40000 | 5.7612 | - |
359
+ | 0.3238 | 40500 | 5.7782 | - |
360
+ | 0.3278 | 41000 | 5.7576 | - |
361
+ | 0.3318 | 41500 | 5.7524 | - |
362
+ | 0.3358 | 42000 | 5.73 | - |
363
+ | 0.3398 | 42500 | 5.7805 | - |
364
+ | 0.3438 | 43000 | 5.7296 | - |
365
+ | 0.3478 | 43500 | 5.7579 | - |
366
+ | 0.3518 | 44000 | 5.7592 | - |
367
+ | 0.3558 | 44500 | 5.7467 | - |
368
+ | 0.3598 | 45000 | 5.7158 | - |
369
+ | 0.3638 | 45500 | 5.731 | - |
370
+ | 0.3678 | 46000 | 5.7482 | - |
371
+ | 0.3718 | 46500 | 5.7176 | - |
372
+ | 0.3758 | 47000 | 5.7273 | - |
373
+ | 0.3798 | 47500 | 5.6963 | - |
374
+ | 0.3838 | 48000 | 5.6946 | - |
375
+ | 0.3878 | 48500 | 5.7576 | - |
376
+ | 0.3918 | 49000 | 5.6921 | - |
377
+ | 0.3958 | 49500 | 5.6965 | - |
378
+ | 0.3998 | 50000 | 5.7335 | - |
379
+ | 0.4038 | 50500 | 5.7064 | - |
380
+ | 0.4077 | 51000 | 5.6945 | - |
381
+ | 0.4117 | 51500 | 5.7319 | - |
382
+ | 0.4157 | 52000 | 5.735 | - |
383
+ | 0.4197 | 52500 | 5.6942 | - |
384
+ | 0.4237 | 53000 | 5.7037 | - |
385
+ | 0.4277 | 53500 | 5.6724 | - |
386
+ | 0.4317 | 54000 | 5.6971 | - |
387
+ | 0.4357 | 54500 | 5.7163 | - |
388
+ | 0.4397 | 55000 | 5.6842 | - |
389
+ | 0.4437 | 55500 | 5.6924 | - |
390
+ | 0.4477 | 56000 | 5.6814 | - |
391
+ | 0.4517 | 56500 | 5.671 | - |
392
+ | 0.4557 | 57000 | 5.6563 | - |
393
+ | 0.4597 | 57500 | 5.6385 | - |
394
+ | 0.4637 | 58000 | 5.6595 | - |
395
+ | 0.4677 | 58500 | 5.6744 | - |
396
+ | 0.4717 | 59000 | 5.6285 | - |
397
+ | 0.4757 | 59500 | 5.6202 | - |
398
+ | 0.4797 | 60000 | 5.6484 | - |
399
+ | 0.4837 | 60500 | 5.647 | - |
400
+ | 0.4877 | 61000 | 5.6641 | - |
401
+ | 0.4917 | 61500 | 5.6681 | - |
402
+ | 0.4957 | 62000 | 5.6344 | - |
403
+ | 0.4997 | 62500 | 5.6253 | - |
404
+ | 0.5037 | 63000 | 5.6258 | - |
405
+ | 0.5077 | 63500 | 5.6525 | - |
406
+ | 0.5117 | 64000 | 5.5764 | - |
407
+ | 0.5157 | 64500 | 5.6265 | - |
408
+ | 0.5197 | 65000 | 5.6201 | - |
409
+ | 0.5237 | 65500 | 5.6297 | - |
410
+ | 0.5277 | 66000 | 5.6133 | - |
411
+ | 0.5317 | 66500 | 5.5981 | - |
412
+ | 0.5357 | 67000 | 5.6085 | - |
413
+ | 0.5397 | 67500 | 5.6128 | - |
414
+ | 0.5437 | 68000 | 5.6237 | - |
415
+ | 0.5477 | 68500 | 5.6005 | - |
416
+ | 0.5517 | 69000 | 5.6156 | - |
417
+ | 0.5557 | 69500 | 5.5723 | - |
418
+ | 0.5597 | 70000 | 5.5817 | - |
419
+ | 0.5637 | 70500 | 5.6186 | - |
420
+ | 0.5677 | 71000 | 5.588 | - |
421
+ | 0.5716 | 71500 | 5.5219 | - |
422
+ | 0.5756 | 72000 | 5.5718 | - |
423
+ | 0.5796 | 72500 | 5.5878 | - |
424
+ | 0.5836 | 73000 | 5.5702 | - |
425
+ | 0.5876 | 73500 | 5.5274 | - |
426
+ | 0.5916 | 74000 | 5.5608 | - |
427
+ | 0.5956 | 74500 | 5.5328 | - |
428
+ | 0.5996 | 75000 | 5.5848 | - |
429
+ | 0.6036 | 75500 | 5.5713 | - |
430
+ | 0.6076 | 76000 | 5.5529 | - |
431
+ | 0.6116 | 76500 | 5.5224 | - |
432
+ | 0.6156 | 77000 | 5.57 | - |
433
+ | 0.6196 | 77500 | 5.5874 | - |
434
+ | 0.6236 | 78000 | 5.5441 | - |
435
+ | 0.6276 | 78500 | 5.5127 | - |
436
+ | 0.6316 | 79000 | 5.5557 | - |
437
+ | 0.6356 | 79500 | 5.5101 | - |
438
+ | 0.6396 | 80000 | 5.5153 | - |
439
+ | 0.6436 | 80500 | 5.4958 | - |
440
+ | 0.6476 | 81000 | 5.518 | - |
441
+ | 0.6516 | 81500 | 5.5022 | - |
442
+ | 0.6556 | 82000 | 5.5013 | - |
443
+ | 0.6596 | 82500 | 5.4832 | - |
444
+ | 0.6636 | 83000 | 5.5174 | - |
445
+ | 0.6676 | 83500 | 5.5052 | - |
446
+ | 0.6716 | 84000 | 5.5315 | - |
447
+ | 0.6756 | 84500 | 5.533 | - |
448
+ | 0.6796 | 85000 | 5.4937 | - |
449
+ | 0.6836 | 85500 | 5.4697 | - |
450
+ | 0.6876 | 86000 | 5.5085 | - |
451
+ | 0.6916 | 86500 | 5.4901 | - |
452
+ | 0.6956 | 87000 | 5.4667 | - |
453
+ | 0.6996 | 87500 | 5.5047 | - |
454
+ | 0.7036 | 88000 | 5.495 | - |
455
+ | 0.7076 | 88500 | 5.4677 | - |
456
+ | 0.7116 | 89000 | 5.4779 | - |
457
+ | 0.7156 | 89500 | 5.4467 | - |
458
+ | 0.7196 | 90000 | 5.4772 | - |
459
+ | 0.7236 | 90500 | 5.4988 | - |
460
+ | 0.7276 | 91000 | 5.4832 | - |
461
+ | 0.7315 | 91500 | 5.4669 | - |
462
+ | 0.7355 | 92000 | 5.447 | - |
463
+ | 0.7395 | 92500 | 5.4725 | - |
464
+ | 0.7435 | 93000 | 5.458 | - |
465
+ | 0.7475 | 93500 | 5.4872 | - |
466
+ | 0.7515 | 94000 | 5.4491 | - |
467
+ | 0.7555 | 94500 | 5.4729 | - |
468
+ | 0.7595 | 95000 | 5.4506 | - |
469
+ | 0.7635 | 95500 | 5.4585 | - |
470
+ | 0.7675 | 96000 | 5.4173 | - |
471
+ | 0.7715 | 96500 | 5.4371 | - |
472
+ | 0.7755 | 97000 | 5.4433 | - |
473
+ | 0.7795 | 97500 | 5.4664 | - |
474
+ | 0.7835 | 98000 | 5.4302 | - |
475
+ | 0.7875 | 98500 | 5.4389 | - |
476
+ | 0.7915 | 99000 | 5.4451 | - |
477
+ | 0.7955 | 99500 | 5.4432 | - |
478
+ | 0.7995 | 100000 | 5.4322 | - |
479
+ | 0.8035 | 100500 | 5.4166 | - |
480
+ | 0.8075 | 101000 | 5.4405 | - |
481
+ | 0.8115 | 101500 | 5.4114 | - |
482
+ | 0.8155 | 102000 | 5.4646 | - |
483
+ | 0.8195 | 102500 | 5.442 | - |
484
+ | 0.8235 | 103000 | 5.4145 | - |
485
+ | 0.8275 | 103500 | 5.432 | - |
486
+ | 0.8315 | 104000 | 5.4458 | - |
487
+ | 0.8355 | 104500 | 5.4044 | - |
488
+ | 0.8395 | 105000 | 5.4376 | - |
489
+ | 0.8435 | 105500 | 5.432 | - |
490
+ | 0.8475 | 106000 | 5.4196 | - |
491
+ | 0.8515 | 106500 | 5.4193 | - |
492
+ | 0.8555 | 107000 | 5.4272 | - |
493
+ | 0.8595 | 107500 | 5.4235 | - |
494
+ | 0.8635 | 108000 | 5.4332 | - |
495
+ | 0.8675 | 108500 | 5.4434 | - |
496
+ | 0.8715 | 109000 | 5.3986 | - |
497
+ | 0.8755 | 109500 | 5.3906 | - |
498
+ | 0.8795 | 110000 | 5.3775 | - |
499
+ | 0.8835 | 110500 | 5.3805 | - |
500
+ | 0.8875 | 111000 | 5.404 | - |
501
+ | 0.8915 | 111500 | 5.3914 | - |
502
+ | 0.8954 | 112000 | 5.4238 | - |
503
+ | 0.8994 | 112500 | 5.4133 | - |
504
+ | 0.9034 | 113000 | 5.3882 | - |
505
+ | 0.9074 | 113500 | 5.4108 | - |
506
+ | 0.9114 | 114000 | 5.4203 | - |
507
+ | 0.9154 | 114500 | 5.3607 | - |
508
+ | 0.9194 | 115000 | 5.3691 | - |
509
+ | 0.9234 | 115500 | 5.3354 | - |
510
+ | 0.9274 | 116000 | 5.3859 | - |
511
+ | 0.9314 | 116500 | 5.3877 | - |
512
+ | 0.9354 | 117000 | 5.3874 | - |
513
+ | 0.9394 | 117500 | 5.3595 | - |
514
+ | 0.9434 | 118000 | 5.3769 | - |
515
+ | 0.9474 | 118500 | 5.3635 | - |
516
+ | 0.9514 | 119000 | 5.3546 | - |
517
+ | 0.9554 | 119500 | 5.3652 | - |
518
+ | 0.9594 | 120000 | 5.3204 | - |
519
+ | 0.9634 | 120500 | 5.3674 | - |
520
+ | 0.9674 | 121000 | 5.3512 | - |
521
+ | 0.9714 | 121500 | 5.3539 | - |
522
+ | 0.9754 | 122000 | 5.3259 | - |
523
+ | 0.9794 | 122500 | 5.3189 | - |
524
+ | 0.9834 | 123000 | 5.342 | - |
525
+ | 0.9874 | 123500 | 5.3491 | - |
526
+ | 0.9914 | 124000 | 5.3455 | - |
527
+ | 0.9954 | 124500 | 5.3106 | - |
528
+ | 0.9994 | 125000 | 5.2932 | - |
529
+ | 1.0034 | 125500 | 5.3176 | - |
530
+ | 1.0074 | 126000 | 5.3073 | - |
531
+ | 1.0114 | 126500 | 5.3194 | - |
532
+ | 1.0154 | 127000 | 5.2482 | - |
533
+ | 1.0194 | 127500 | 5.2502 | - |
534
+ | 1.0234 | 128000 | 5.3199 | - |
535
+ | 1.0274 | 128500 | 5.2374 | - |
536
+ | 1.0314 | 129000 | 5.2641 | - |
537
+ | 1.0354 | 129500 | 5.2663 | - |
538
+ | 1.0394 | 130000 | 5.2887 | - |
539
+ | 1.0434 | 130500 | 5.2731 | - |
540
+ | 1.0474 | 131000 | 5.2297 | - |
541
+ | 1.0514 | 131500 | 5.2704 | - |
542
+ | 1.0553 | 132000 | 5.2723 | - |
543
+ | 1.0593 | 132500 | 5.2846 | - |
544
+ | 1.0633 | 133000 | 5.2857 | - |
545
+ | 1.0673 | 133500 | 5.2984 | - |
546
+ | 1.0713 | 134000 | 5.2492 | - |
547
+ | 1.0753 | 134500 | 5.2847 | - |
548
+ | 1.0793 | 135000 | 5.2376 | - |
549
+ | 1.0833 | 135500 | 5.2299 | - |
550
+ | 1.0873 | 136000 | 5.2214 | - |
551
+ | 1.0913 | 136500 | 5.2429 | - |
552
+ | 1.0953 | 137000 | 5.2232 | - |
553
+ | 1.0993 | 137500 | 5.261 | - |
554
+ | 1.1033 | 138000 | 5.2394 | - |
555
+ | 1.1073 | 138500 | 5.2877 | - |
556
+ | 1.1113 | 139000 | 5.1936 | - |
557
+ | 1.1153 | 139500 | 5.2483 | - |
558
+ | 1.1193 | 140000 | 5.2412 | - |
559
+ | 1.1233 | 140500 | 5.1841 | - |
560
+ | 1.1273 | 141000 | 5.2741 | - |
561
+ | 1.1313 | 141500 | 5.1711 | - |
562
+ | 1.1353 | 142000 | 5.2154 | - |
563
+ | 1.1393 | 142500 | 5.2667 | - |
564
+ | 1.1433 | 143000 | 5.217 | - |
565
+ | 1.1473 | 143500 | 5.261 | - |
566
+ | 1.1513 | 144000 | 5.2169 | - |
567
+ | 1.1553 | 144500 | 5.2471 | - |
568
+ | 1.1593 | 145000 | 5.2486 | - |
569
+ | 1.1633 | 145500 | 5.2252 | - |
570
+ | 1.1673 | 146000 | 5.2488 | - |
571
+ | 1.1713 | 146500 | 5.184 | - |
572
+ | 1.1753 | 147000 | 5.2547 | - |
573
+ | 1.1793 | 147500 | 5.207 | - |
574
+ | 1.1833 | 148000 | 5.2087 | - |
575
+ | 1.1873 | 148500 | 5.2478 | - |
576
+ | 1.1913 | 149000 | 5.2409 | - |
577
+ | 1.1953 | 149500 | 5.1968 | - |
578
+ | 1.1993 | 150000 | 5.182 | - |
579
+ | 1.2033 | 150500 | 5.1807 | - |
580
+ | 1.2073 | 151000 | 5.1927 | - |
581
+ | 1.2113 | 151500 | 5.1859 | - |
582
+ | 1.2153 | 152000 | 5.1874 | - |
583
+ | 1.2192 | 152500 | 5.2234 | - |
584
+ | 1.2232 | 153000 | 5.1858 | - |
585
+ | 1.2272 | 153500 | 5.2104 | - |
586
+ | 1.2312 | 154000 | 5.2259 | - |
587
+ | 1.2352 | 154500 | 5.2022 | - |
588
+ | 1.2392 | 155000 | 5.2162 | - |
589
+ | 1.2432 | 155500 | 5.1691 | - |
590
+ | 1.2472 | 156000 | 5.1845 | - |
591
+ | 1.2512 | 156500 | 5.1577 | - |
592
+ | 1.2552 | 157000 | 5.1921 | - |
593
+ | 1.2592 | 157500 | 5.2277 | - |
594
+ | 1.2632 | 158000 | 5.2049 | - |
595
+ | 1.2672 | 158500 | 5.1672 | - |
596
+ | 1.2712 | 159000 | 5.2376 | - |
597
+ | 1.2752 | 159500 | 5.1943 | - |
598
+ | 1.2792 | 160000 | 5.1384 | - |
599
+ | 1.2832 | 160500 | 5.1651 | - |
600
+ | 1.2872 | 161000 | 5.1992 | - |
601
+ | 1.2912 | 161500 | 5.1707 | - |
602
+ | 1.2952 | 162000 | 5.1796 | - |
603
+ | 1.2992 | 162500 | 5.0851 | - |
604
+ | 1.3032 | 163000 | 5.202 | - |
605
+ | 1.3072 | 163500 | 5.1546 | - |
606
+ | 1.3112 | 164000 | 5.1962 | - |
607
+ | 1.3152 | 164500 | 5.1498 | - |
608
+ | 1.3192 | 165000 | 5.1587 | - |
609
+ | 1.3232 | 165500 | 5.1674 | - |
610
+ | 1.3272 | 166000 | 5.1375 | - |
611
+ | 1.3312 | 166500 | 5.1558 | - |
612
+ | 1.3352 | 167000 | 5.1298 | - |
613
+ | 1.3392 | 167500 | 5.1469 | - |
614
+ | 1.3432 | 168000 | 5.0647 | - |
615
+ | 1.3472 | 168500 | 5.1455 | - |
616
+ | 1.3512 | 169000 | 5.1312 | - |
617
+ | 1.3552 | 169500 | 5.1067 | - |
618
+ | 1.3592 | 170000 | 5.109 | - |
619
+ | 1.3632 | 170500 | 5.1282 | - |
620
+ | 1.3672 | 171000 | 5.1348 | - |
621
+ | 1.3712 | 171500 | 5.1415 | - |
622
+ | 1.3752 | 172000 | 5.0964 | - |
623
+ | 1.3792 | 172500 | 5.1503 | - |
624
+ | 1.3831 | 173000 | 5.1629 | - |
625
+ | 1.3871 | 173500 | 5.105 | - |
626
+ | 1.3911 | 174000 | 5.0606 | - |
627
+ | 1.3951 | 174500 | 5.151 | - |
628
+ | 1.3991 | 175000 | 5.1262 | - |
629
+ | 1.4031 | 175500 | 5.1856 | - |
630
+ | 1.4071 | 176000 | 5.1216 | - |
631
+ | 1.4111 | 176500 | 5.1419 | - |
632
+ | 1.4151 | 177000 | 5.121 | - |
633
+ | 1.4191 | 177500 | 5.1393 | - |
634
+ | 1.4231 | 178000 | 5.1029 | - |
635
+ | 1.4271 | 178500 | 5.0734 | - |
636
+ | 1.4311 | 179000 | 5.1087 | - |
637
+ | 1.4351 | 179500 | 5.1404 | - |
638
+ | 1.4391 | 180000 | 5.1152 | - |
639
+ | 1.4431 | 180500 | 5.1041 | - |
640
+ | 1.4471 | 181000 | 5.0889 | - |
641
+ | 1.4511 | 181500 | 5.1602 | - |
642
+ | 1.4551 | 182000 | 5.1193 | - |
643
+ | 1.4591 | 182500 | 5.092 | - |
644
+ | 1.4631 | 183000 | 5.0901 | - |
645
+ | 1.4671 | 183500 | 5.0899 | - |
646
+ | 1.4711 | 184000 | 5.1426 | - |
647
+ | 1.4751 | 184500 | 5.0812 | - |
648
+ | 1.4791 | 185000 | 5.0964 | - |
649
+ | 1.4831 | 185500 | 5.0828 | - |
650
+ | 1.4871 | 186000 | 5.116 | - |
651
+ | 1.4911 | 186500 | 5.1069 | - |
652
+ | 1.4951 | 187000 | 5.0598 | - |
653
+ | 1.4991 | 187500 | 5.0734 | - |
654
+ | 1.5031 | 188000 | 5.0516 | - |
655
+ | 1.5071 | 188500 | 5.1049 | - |
656
+ | 1.5111 | 189000 | 5.0636 | - |
657
+ | 1.5151 | 189500 | 5.0715 | - |
658
+ | 1.5191 | 190000 | 5.0757 | - |
659
+ | 1.5231 | 190500 | 5.0947 | - |
660
+ | 1.5271 | 191000 | 5.0433 | - |
661
+ | 1.5311 | 191500 | 5.1079 | - |
662
+ | 1.5351 | 192000 | 5.0872 | - |
663
+ | 1.5391 | 192500 | 5.016 | - |
664
+ | 1.5430 | 193000 | 5.0627 | - |
665
+ | 1.5470 | 193500 | 5.0841 | - |
666
+ | 1.5510 | 194000 | 5.1012 | - |
667
+ | 1.5550 | 194500 | 5.0415 | - |
668
+ | 1.5590 | 195000 | 5.0871 | - |
669
+ | 1.5630 | 195500 | 5.0678 | - |
670
+ | 1.5670 | 196000 | 5.0399 | - |
671
+ | 1.5710 | 196500 | 5.0794 | - |
672
+ | 1.5750 | 197000 | 5.0639 | - |
673
+ | 1.5790 | 197500 | 5.0335 | - |
674
+ | 1.5830 | 198000 | 5.0606 | - |
675
+ | 1.5870 | 198500 | 5.1059 | - |
676
+ | 1.5910 | 199000 | 5.0426 | - |
677
+ | 1.5950 | 199500 | 5.0185 | - |
678
+ | 1.5990 | 200000 | 5.0194 | - |
679
+ | 1.6030 | 200500 | 5.0887 | - |
680
+ | 1.6070 | 201000 | 5.004 | - |
681
+ | 1.6110 | 201500 | 5.0834 | - |
682
+ | 1.6150 | 202000 | 5.0363 | - |
683
+ | 1.6190 | 202500 | 5.0819 | - |
684
+ | 1.6230 | 203000 | 5.0344 | - |
685
+ | 1.6270 | 203500 | 5.107 | - |
686
+ | 1.6310 | 204000 | 5.0201 | - |
687
+ | 1.6350 | 204500 | 5.0305 | - |
688
+ | 1.6390 | 205000 | 5.0074 | - |
689
+ | 1.6430 | 205500 | 5.0507 | - |
690
+ | 1.6470 | 206000 | 5.0419 | - |
691
+ | 1.6510 | 206500 | 5.0099 | - |
692
+ | 1.6550 | 207000 | 5.0673 | - |
693
+ | 1.6590 | 207500 | 5.0449 | - |
694
+ | 1.6630 | 208000 | 4.9631 | - |
695
+ | 1.6670 | 208500 | 5.0013 | - |
696
+ | 1.6710 | 209000 | 5.02 | - |
697
+ | 1.6750 | 209500 | 5.032 | - |
698
+ | 1.6790 | 210000 | 4.9984 | - |
699
+ | 1.6830 | 210500 | 4.987 | - |
700
+ | 1.6870 | 211000 | 5.0095 | - |
701
+ | 1.6910 | 211500 | 5.0801 | - |
702
+ | 1.6950 | 212000 | 5.0061 | - |
703
+ | 1.6990 | 212500 | 5.0193 | - |
704
+ | 1.7030 | 213000 | 5.0453 | - |
705
+ | 1.7069 | 213500 | 4.991 | - |
706
+ | 1.7109 | 214000 | 5.0149 | - |
707
+ | 1.7149 | 214500 | 5.0181 | - |
708
+ | 1.7189 | 215000 | 5.0341 | - |
709
+ | 1.7229 | 215500 | 4.9987 | - |
710
+ | 1.7269 | 216000 | 4.9864 | - |
711
+ | 1.7309 | 216500 | 4.993 | - |
712
+ | 1.7349 | 217000 | 4.9888 | - |
713
+ | 1.7389 | 217500 | 5.0125 | - |
714
+ | 1.7429 | 218000 | 5.0023 | - |
715
+ | 1.7469 | 218500 | 5.0205 | - |
716
+ | 1.7509 | 219000 | 5.0141 | - |
717
+ | 1.7549 | 219500 | 5.0071 | - |
718
+ | 1.7589 | 220000 | 4.9684 | - |
719
+ | 1.7629 | 220500 | 4.9898 | - |
720
+ | 1.7669 | 221000 | 4.9889 | - |
721
+ | 1.7709 | 221500 | 4.9894 | - |
722
+ | 1.7749 | 222000 | 4.989 | - |
723
+ | 1.7789 | 222500 | 4.9179 | - |
724
+ | 1.7829 | 223000 | 4.9818 | - |
725
+ | 1.7869 | 223500 | 5.0056 | - |
726
+ | 1.7909 | 224000 | 4.9475 | - |
727
+ | 1.7949 | 224500 | 5.0104 | - |
728
+ | 1.7989 | 225000 | 4.9827 | - |
729
+ | 1.8029 | 225500 | 4.9716 | - |
730
+ | 1.8069 | 226000 | 4.9924 | - |
731
+ | 1.8109 | 226500 | 5.0384 | - |
732
+ | 1.8149 | 227000 | 4.9853 | - |
733
+ | 1.8189 | 227500 | 4.9858 | - |
734
+ | 1.8229 | 228000 | 4.9423 | - |
735
+ | 1.8269 | 228500 | 4.9476 | - |
736
+ | 1.8309 | 229000 | 4.9631 | - |
737
+ | 1.8349 | 229500 | 4.9819 | - |
738
+ | 1.8389 | 230000 | 4.9464 | - |
739
+ | 1.8429 | 230500 | 4.9688 | - |
740
+ | 1.8469 | 231000 | 4.9569 | - |
741
+ | 1.8509 | 231500 | 4.9515 | - |
742
+ | 1.8549 | 232000 | 4.9447 | - |
743
+ | 1.8589 | 232500 | 4.9845 | - |
744
+ | 1.8629 | 233000 | 4.9834 | - |
745
+ | 1.8669 | 233500 | 4.9545 | - |
746
+ | 1.8708 | 234000 | 4.9452 | - |
747
+ | 1.8748 | 234500 | 4.9623 | - |
748
+ | 1.8788 | 235000 | 4.9869 | - |
749
+ | 1.8828 | 235500 | 4.9603 | - |
750
+ | 1.8868 | 236000 | 4.9504 | - |
751
+ | 1.8908 | 236500 | 4.9537 | - |
752
+ | 1.8948 | 237000 | 5.014 | - |
753
+ | 1.8988 | 237500 | 4.9226 | - |
754
+ | 1.9028 | 238000 | 4.9528 | - |
755
+ | 1.9068 | 238500 | 4.9213 | - |
756
+ | 1.9108 | 239000 | 4.8836 | - |
757
+ | 1.9148 | 239500 | 4.9354 | - |
758
+ | 1.9188 | 240000 | 4.8704 | - |
759
+ | 1.9228 | 240500 | 4.9336 | - |
760
+ | 1.9268 | 241000 | 4.8654 | - |
761
+ | 1.9308 | 241500 | 4.9376 | - |
762
+ | 1.9348 | 242000 | 4.9164 | - |
763
+ | 1.9388 | 242500 | 4.9584 | - |
764
+ | 1.9428 | 243000 | 4.8759 | - |
765
+ | 1.9468 | 243500 | 4.9324 | - |
766
+ | 1.9508 | 244000 | 4.8457 | - |
767
+ | 1.9548 | 244500 | 4.9048 | - |
768
+ | 1.9588 | 245000 | 4.9234 | - |
769
+ | 1.9628 | 245500 | 4.8536 | - |
770
+ | 1.9668 | 246000 | 4.9681 | - |
771
+ | 1.9708 | 246500 | 4.9161 | - |
772
+ | 1.9748 | 247000 | 4.9786 | - |
773
+ | 1.9788 | 247500 | 4.8784 | - |
774
+ | 1.9828 | 248000 | 4.9222 | - |
775
+ | 1.9868 | 248500 | 4.9061 | - |
776
+ | 1.9908 | 249000 | 4.9281 | - |
777
+ | 1.9948 | 249500 | 4.9007 | - |
778
+ | 1.9988 | 250000 | 4.9466 | - |
779
+ | 2.0028 | 250500 | 4.8483 | - |
780
+ | 2.0068 | 251000 | 4.8103 | - |
781
+ | 2.0108 | 251500 | 4.8288 | - |
782
+ | 2.0148 | 252000 | 4.8127 | - |
783
+ | 2.0188 | 252500 | 4.8619 | - |
784
+ | 2.0228 | 253000 | 4.7683 | - |
785
+ | 2.0268 | 253500 | 4.8246 | - |
786
+ | 2.0307 | 254000 | 4.7784 | - |
787
+ | 2.0347 | 254500 | 4.8416 | - |
788
+ | 2.0387 | 255000 | 4.7759 | - |
789
+ | 2.0427 | 255500 | 4.8185 | - |
790
+ | 2.0467 | 256000 | 4.7993 | - |
791
+ | 2.0507 | 256500 | 4.8505 | - |
792
+ | 2.0547 | 257000 | 4.7538 | - |
793
+ | 2.0587 | 257500 | 4.8128 | - |
794
+ | 2.0627 | 258000 | 4.7945 | - |
795
+ | 2.0667 | 258500 | 4.8436 | - |
796
+ | 2.0707 | 259000 | 4.8455 | - |
797
+ | 2.0747 | 259500 | 4.8235 | - |
798
+ | 2.0787 | 260000 | 4.7401 | - |
799
+ | 2.0827 | 260500 | 4.8347 | - |
800
+ | 2.0867 | 261000 | 4.7746 | - |
801
+ | 2.0907 | 261500 | 4.8129 | - |
802
+ | 2.0947 | 262000 | 4.8213 | - |
803
+ | 2.0987 | 262500 | 4.7762 | - |
804
+ | 2.1027 | 263000 | 4.8996 | - |
805
+ | 2.1067 | 263500 | 4.7671 | - |
806
+ | 2.1107 | 264000 | 4.7543 | - |
807
+ | 2.1147 | 264500 | 4.8171 | - |
808
+ | 2.1187 | 265000 | 4.8018 | - |
809
+ | 2.1227 | 265500 | 4.8622 | - |
810
+ | 2.1267 | 266000 | 4.7957 | - |
811
+ | 2.1307 | 266500 | 4.8256 | - |
812
+ | 2.1347 | 267000 | 4.7887 | - |
813
+ | 2.1387 | 267500 | 4.8366 | - |
814
+ | 2.1427 | 268000 | 4.7425 | - |
815
+ | 2.1467 | 268500 | 4.7731 | - |
816
+ | 2.1507 | 269000 | 4.7837 | - |
817
+ | 2.1547 | 269500 | 4.8199 | - |
818
+ | 2.1587 | 270000 | 4.8294 | - |
819
+ | 2.1627 | 270500 | 4.7913 | - |
820
+ | 2.1667 | 271000 | 4.7904 | - |
821
+ | 2.1707 | 271500 | 4.8006 | - |
822
+ | 2.1747 | 272000 | 4.7291 | - |
823
+ | 2.1787 | 272500 | 4.7825 | - |
824
+ | 2.1827 | 273000 | 4.7188 | - |
825
+ | 2.1867 | 273500 | 4.8076 | - |
826
+ | 2.1907 | 274000 | 4.8095 | - |
827
+ | 2.1946 | 274500 | 4.8016 | - |
828
+ | 2.1986 | 275000 | 4.7654 | - |
829
+ | 2.2026 | 275500 | 4.8253 | - |
830
+ | 2.2066 | 276000 | 4.7669 | - |
831
+ | 2.2106 | 276500 | 4.7752 | - |
832
+ | 2.2146 | 277000 | 4.8365 | - |
833
+ | 2.2186 | 277500 | 4.736 | - |
834
+ | 2.2226 | 278000 | 4.7251 | - |
835
+ | 2.2266 | 278500 | 4.7384 | - |
836
+ | 2.2306 | 279000 | 4.7001 | - |
837
+ | 2.2346 | 279500 | 4.8238 | - |
838
+ | 2.2386 | 280000 | 4.7455 | - |
839
+ | 2.2426 | 280500 | 4.7353 | - |
840
+ | 2.2466 | 281000 | 4.7557 | - |
841
+ | 2.2506 | 281500 | 4.7732 | - |
842
+ | 2.2546 | 282000 | 4.7795 | - |
843
+ | 2.2586 | 282500 | 4.8051 | - |
844
+ | 2.2626 | 283000 | 4.7726 | - |
845
+ | 2.2666 | 283500 | 4.8173 | - |
846
+ | 2.2706 | 284000 | 4.7607 | - |
847
+ | 2.2746 | 284500 | 4.7855 | - |
848
+ | 2.2786 | 285000 | 4.8191 | - |
849
+ | 2.2826 | 285500 | 4.7365 | - |
850
+ | 2.2866 | 286000 | 4.7416 | - |
851
+ | 2.2906 | 286500 | 4.7495 | - |
852
+ | 2.2946 | 287000 | 4.7597 | - |
853
+ | 2.2986 | 287500 | 4.7924 | - |
854
+ | 2.3026 | 288000 | 4.7603 | - |
855
+ | 2.3066 | 288500 | 4.7443 | - |
856
+ | 2.3106 | 289000 | 4.7826 | - |
857
+ | 2.3146 | 289500 | 4.7053 | - |
858
+ | 2.3186 | 290000 | 4.7274 | - |
859
+ | 2.3226 | 290500 | 4.7756 | - |
860
+ | 2.3266 | 291000 | 4.7881 | - |
861
+ | 2.3306 | 291500 | 4.7875 | - |
862
+ | 2.3346 | 292000 | 4.7254 | - |
863
+ | 2.3386 | 292500 | 4.7193 | - |
864
+ | 2.3426 | 293000 | 4.7639 | - |
865
+ | 2.3466 | 293500 | 4.7244 | - |
866
+ | 2.3506 | 294000 | 4.7996 | - |
867
+ | 2.3545 | 294500 | 4.7699 | - |
868
+ | 2.3585 | 295000 | 4.7504 | - |
869
+ | 2.3625 | 295500 | 4.6969 | - |
870
+ | 2.3665 | 296000 | 4.7523 | - |
871
+ | 2.3705 | 296500 | 4.6984 | - |
872
+ | 2.3745 | 297000 | 4.7297 | - |
873
+ | 2.3785 | 297500 | 4.7366 | - |
874
+ | 2.3825 | 298000 | 4.7177 | - |
875
+ | 2.3865 | 298500 | 4.7276 | - |
876
+ | 2.3905 | 299000 | 4.7916 | - |
877
+ | 2.3945 | 299500 | 4.7726 | - |
878
+ | 2.3985 | 300000 | 4.6847 | 4.6182 |
879
+ | 2.4025 | 300500 | 4.7426 | - |
880
+ | 2.4065 | 301000 | 4.7426 | - |
881
+ | 2.4105 | 301500 | 4.7588 | - |
882
+ | 2.4145 | 302000 | 4.7516 | - |
883
+ | 2.4185 | 302500 | 4.7318 | - |
884
+ | 2.4225 | 303000 | 4.7366 | - |
885
+ | 2.4265 | 303500 | 4.7328 | - |
886
+ | 2.4305 | 304000 | 4.687 | - |
887
+ | 2.4345 | 304500 | 4.6934 | - |
888
+ | 2.4385 | 305000 | 4.8018 | - |
889
+ | 2.4425 | 305500 | 4.709 | - |
890
+ | 2.4465 | 306000 | 4.6837 | - |
891
+ | 2.4505 | 306500 | 4.7268 | - |
892
+ | 2.4545 | 307000 | 4.7508 | - |
893
+ | 2.4585 | 307500 | 4.6809 | - |
894
+ | 2.4625 | 308000 | 4.7127 | - |
895
+ | 2.4665 | 308500 | 4.7319 | - |
896
+ | 2.4705 | 309000 | 4.6616 | - |
897
+ | 2.4745 | 309500 | 4.6795 | - |
898
+ | 2.4785 | 310000 | 4.6834 | - |
899
+ | 2.4825 | 310500 | 4.7565 | - |
900
+ | 2.4865 | 311000 | 4.71 | - |
901
+ | 2.4905 | 311500 | 4.7456 | - |
902
+ | 2.4945 | 312000 | 4.7009 | - |
903
+ | 2.4985 | 312500 | 4.7716 | - |
904
+ | 2.5025 | 313000 | 4.6919 | - |
905
+ | 2.5065 | 313500 | 4.7159 | - |
906
+ | 2.5105 | 314000 | 4.7297 | - |
907
+ | 2.5145 | 314500 | 4.7487 | - |
908
+ | 2.5184 | 315000 | 4.7104 | - |
909
+ | 2.5224 | 315500 | 4.6836 | - |
910
+ | 2.5264 | 316000 | 4.6765 | - |
911
+ | 2.5304 | 316500 | 4.7597 | - |
912
+ | 2.5344 | 317000 | 4.6589 | - |
913
+ | 2.5384 | 317500 | 4.6776 | - |
914
+ | 2.5424 | 318000 | 4.757 | - |
915
+ | 2.5464 | 318500 | 4.7112 | - |
916
+ | 2.5504 | 319000 | 4.7098 | - |
917
+ | 2.5544 | 319500 | 4.7395 | - |
918
+ | 2.5584 | 320000 | 4.7164 | - |
919
+ | 2.5624 | 320500 | 4.759 | - |
920
+ | 2.5664 | 321000 | 4.7274 | - |
921
+ | 2.5704 | 321500 | 4.6674 | - |
922
+ | 2.5744 | 322000 | 4.6751 | - |
923
+ | 2.5784 | 322500 | 4.613 | - |
924
+ | 2.5824 | 323000 | 4.6354 | - |
925
+ | 2.5864 | 323500 | 4.6394 | - |
926
+ | 2.5904 | 324000 | 4.6935 | - |
927
+ | 2.5944 | 324500 | 4.7022 | - |
928
+ | 2.5984 | 325000 | 4.6831 | - |
929
+ | 2.6024 | 325500 | 4.7033 | - |
930
+ | 2.6064 | 326000 | 4.7113 | - |
931
+ | 2.6104 | 326500 | 4.6515 | - |
932
+ | 2.6144 | 327000 | 4.5857 | - |
933
+ | 2.6184 | 327500 | 4.6675 | - |
934
+ | 2.6224 | 328000 | 4.7054 | - |
935
+ | 2.6264 | 328500 | 4.6774 | - |
936
+ | 2.6304 | 329000 | 4.6585 | - |
937
+ | 2.6344 | 329500 | 4.7232 | - |
938
+ | 2.6384 | 330000 | 4.6977 | - |
939
+ | 2.6424 | 330500 | 4.6576 | - |
940
+ | 2.6464 | 331000 | 4.6725 | - |
941
+ | 2.6504 | 331500 | 4.6805 | - |
942
+ | 2.6544 | 332000 | 4.6577 | - |
943
+ | 2.6584 | 332500 | 4.7071 | - |
944
+ | 2.6624 | 333000 | 4.6369 | - |
945
+ | 2.6664 | 333500 | 4.6759 | - |
946
+ | 2.6704 | 334000 | 4.6337 | - |
947
+ | 2.6744 | 334500 | 4.6379 | - |
948
+ | 2.6784 | 335000 | 4.6706 | - |
949
+ | 2.6823 | 335500 | 4.6853 | - |
950
+ | 2.6863 | 336000 | 4.7038 | - |
951
+ | 2.6903 | 336500 | 4.6554 | - |
952
+ | 2.6943 | 337000 | 4.6431 | - |
953
+ | 2.6983 | 337500 | 4.6991 | - |
954
+ | 2.7023 | 338000 | 4.6217 | - |
955
+ | 2.7063 | 338500 | 4.668 | - |
956
+ | 2.7103 | 339000 | 4.6611 | - |
957
+ | 2.7143 | 339500 | 4.6793 | - |
958
+ | 2.7183 | 340000 | 4.6465 | - |
959
+ | 2.7223 | 340500 | 4.6846 | - |
960
+ | 2.7263 | 341000 | 4.6407 | - |
961
+ | 2.7303 | 341500 | 4.7138 | - |
962
+ | 2.7343 | 342000 | 4.659 | - |
963
+ | 2.7383 | 342500 | 4.6315 | - |
964
+ | 2.7423 | 343000 | 4.6272 | - |
965
+ | 2.7463 | 343500 | 4.6833 | - |
966
+ | 2.7503 | 344000 | 4.6754 | - |
967
+ | 2.7543 | 344500 | 4.653 | - |
968
+ | 2.7583 | 345000 | 4.6996 | - |
969
+ | 2.7623 | 345500 | 4.679 | - |
970
+ | 2.7663 | 346000 | 4.6452 | - |
971
+ | 2.7703 | 346500 | 4.6275 | - |
972
+ | 2.7743 | 347000 | 4.6215 | - |
973
+ | 2.7783 | 347500 | 4.654 | - |
974
+ | 2.7823 | 348000 | 4.5852 | - |
975
+ | 2.7863 | 348500 | 4.5764 | - |
976
+ | 2.7903 | 349000 | 4.641 | - |
977
+ | 2.7943 | 349500 | 4.6139 | - |
978
+ | 2.7983 | 350000 | 4.6775 | - |
979
+ | 2.8023 | 350500 | 4.6022 | - |
980
+ | 2.8063 | 351000 | 4.6272 | - |
981
+ | 2.8103 | 351500 | 4.6111 | - |
982
+ | 2.8143 | 352000 | 4.58 | - |
983
+ | 2.8183 | 352500 | 4.6763 | - |
984
+ | 2.8223 | 353000 | 4.6233 | - |
985
+ | 2.8263 | 353500 | 4.5973 | - |
986
+ | 2.8303 | 354000 | 4.6608 | - |
987
+ | 2.8343 | 354500 | 4.592 | - |
988
+ | 2.8383 | 355000 | 4.6801 | - |
989
+ | 2.8422 | 355500 | 4.6838 | - |
990
+ | 2.8462 | 356000 | 4.596 | - |
991
+ | 2.8502 | 356500 | 4.59 | - |
992
+ | 2.8542 | 357000 | 4.5696 | - |
993
+ | 2.8582 | 357500 | 4.5852 | - |
994
+ | 2.8622 | 358000 | 4.6176 | - |
995
+ | 2.8662 | 358500 | 4.7878 | - |
996
+ | 2.8702 | 359000 | 4.5917 | - |
997
+ | 2.8742 | 359500 | 4.659 | - |
998
+ | 2.8782 | 360000 | 4.6217 | - |
999
+ | 2.8822 | 360500 | 4.5605 | - |
1000
+ | 2.8862 | 361000 | 4.5948 | - |
1001
+ | 2.8902 | 361500 | 4.6097 | - |
1002
+ | 2.8942 | 362000 | 4.6381 | - |
1003
+ | 2.8982 | 362500 | 4.5962 | - |
1004
+ | 2.9022 | 363000 | 4.6115 | - |
1005
+ | 2.9062 | 363500 | 4.6171 | - |
1006
+ | 2.9102 | 364000 | 4.6593 | - |
1007
+ | 2.9142 | 364500 | 4.6264 | - |
1008
+ | 2.9182 | 365000 | 4.6625 | - |
1009
+ | 2.9222 | 365500 | 4.6538 | - |
1010
+ | 2.9262 | 366000 | 4.6148 | - |
1011
+ | 2.9302 | 366500 | 4.6189 | - |
1012
+ | 2.9342 | 367000 | 4.6181 | - |
1013
+ | 2.9382 | 367500 | 4.6206 | - |
1014
+ | 2.9422 | 368000 | 4.6196 | - |
1015
+ | 2.9462 | 368500 | 4.6006 | - |
1016
+ | 2.9502 | 369000 | 4.6136 | - |
1017
+ | 2.9542 | 369500 | 4.6126 | - |
1018
+ | 2.9582 | 370000 | 4.5722 | - |
1019
+ | 2.9622 | 370500 | 4.6372 | - |
1020
+ | 2.9662 | 371000 | 4.5645 | - |
1021
+ | 2.9702 | 371500 | 4.6004 | - |
1022
+ | 2.9742 | 372000 | 4.6302 | - |
1023
+ | 2.9782 | 372500 | 4.596 | - |
1024
+ | 2.9822 | 373000 | 4.5669 | - |
1025
+ | 2.9862 | 373500 | 4.602 | - |
1026
+ | 2.9902 | 374000 | 4.5663 | - |
1027
+ | 2.9942 | 374500 | 4.642 | - |
1028
+ | 2.9982 | 375000 | 4.6571 | - |
1029
+ | 3.0022 | 375500 | 4.5416 | - |
1030
+ | 3.0061 | 376000 | 4.401 | - |
1031
+ | 3.0101 | 376500 | 4.5286 | - |
1032
+ | 3.0141 | 377000 | 4.5782 | - |
1033
+ | 3.0181 | 377500 | 4.4813 | - |
1034
+ | 3.0221 | 378000 | 4.6178 | - |
1035
+ | 3.0261 | 378500 | 4.465 | - |
1036
+ | 3.0301 | 379000 | 4.54 | - |
1037
+ | 3.0341 | 379500 | 4.4754 | - |
1038
+ | 3.0381 | 380000 | 4.4952 | - |
1039
+ | 3.0421 | 380500 | 4.4559 | - |
1040
+ | 3.0461 | 381000 | 4.4628 | - |
1041
+ | 3.0501 | 381500 | 4.4921 | - |
1042
+ | 3.0541 | 382000 | 4.5431 | - |
1043
+ | 3.0581 | 382500 | 4.458 | - |
1044
+ | 3.0621 | 383000 | 4.4635 | - |
1045
+ | 3.0661 | 383500 | 4.5708 | - |
1046
+ | 3.0701 | 384000 | 4.527 | - |
1047
+ | 3.0741 | 384500 | 4.4934 | - |
1048
+ | 3.0781 | 385000 | 4.5327 | - |
1049
+ | 3.0821 | 385500 | 4.4403 | - |
1050
+ | 3.0861 | 386000 | 4.5091 | - |
1051
+ | 3.0901 | 386500 | 4.5706 | - |
1052
+ | 3.0941 | 387000 | 4.5111 | - |
1053
+ | 3.0981 | 387500 | 4.4952 | - |
1054
+ | 3.1021 | 388000 | 4.5416 | - |
1055
+ | 3.1061 | 388500 | 4.4591 | - |
1056
+ | 3.1101 | 389000 | 4.4738 | - |
1057
+ | 3.1141 | 389500 | 4.514 | - |
1058
+ | 3.1181 | 390000 | 4.5517 | - |
1059
+ | 3.1221 | 390500 | 4.5412 | - |
1060
+ | 3.1261 | 391000 | 4.4587 | - |
1061
+ | 3.1301 | 391500 | 4.4099 | - |
1062
+ | 3.1341 | 392000 | 4.5022 | - |
1063
+ | 3.1381 | 392500 | 4.4698 | - |
1064
+ | 3.1421 | 393000 | 4.4923 | - |
1065
+ | 3.1461 | 393500 | 4.4601 | - |
1066
+ | 3.1501 | 394000 | 4.5446 | - |
1067
+ | 3.1541 | 394500 | 4.4247 | - |
1068
+ | 3.1581 | 395000 | 4.4242 | - |
1069
+ | 3.1621 | 395500 | 4.4761 | - |
1070
+ | 3.1660 | 396000 | 4.4489 | - |
1071
+ | 3.1700 | 396500 | 4.4729 | - |
1072
+ | 3.1740 | 397000 | 4.4916 | - |
1073
+ | 3.1780 | 397500 | 4.4595 | - |
1074
+ | 3.1820 | 398000 | 4.4726 | - |
1075
+ | 3.1860 | 398500 | 4.4582 | - |
1076
+ | 3.1900 | 399000 | 4.4528 | - |
1077
+ | 3.1940 | 399500 | 4.4559 | - |
1078
+ | 3.1980 | 400000 | 4.4422 | - |
1079
+ | 3.2020 | 400500 | 4.4876 | - |
1080
+ | 3.2060 | 401000 | 4.4733 | - |
1081
+ | 3.2100 | 401500 | 4.4214 | - |
1082
+ | 3.2140 | 402000 | 4.4644 | - |
1083
+ | 3.2180 | 402500 | 4.4732 | - |
1084
+ | 3.2220 | 403000 | 4.4603 | - |
1085
+ | 3.2260 | 403500 | 4.4993 | - |
1086
+ | 3.2300 | 404000 | 4.4994 | - |
1087
+ | 3.2340 | 404500 | 4.4778 | - |
1088
+ | 3.2380 | 405000 | 4.5121 | - |
1089
+ | 3.2420 | 405500 | 4.4108 | - |
1090
+ | 3.2460 | 406000 | 4.3834 | - |
1091
+ | 3.2500 | 406500 | 4.4434 | - |
1092
+ | 3.2540 | 407000 | 4.4464 | - |
1093
+ | 3.2580 | 407500 | 4.4645 | - |
1094
+ | 3.2620 | 408000 | 4.5341 | - |
1095
+ | 3.2660 | 408500 | 4.5013 | - |
1096
+ | 3.2700 | 409000 | 4.4671 | - |
1097
+ | 3.2740 | 409500 | 4.4962 | - |
1098
+ | 3.2780 | 410000 | 4.444 | - |
1099
+ | 3.2820 | 410500 | 4.5596 | - |
1100
+ | 3.2860 | 411000 | 4.5458 | - |
1101
+ | 3.2900 | 411500 | 4.4768 | - |
1102
+ | 3.2940 | 412000 | 4.5219 | - |
1103
+ | 3.2980 | 412500 | 4.4747 | - |
1104
+ | 3.3020 | 413000 | 4.5522 | - |
1105
+ | 3.3060 | 413500 | 4.4709 | - |
1106
+ | 3.3100 | 414000 | 4.4982 | - |
1107
+ | 3.3140 | 414500 | 4.4459 | - |
1108
+ | 3.3180 | 415000 | 4.4523 | - |
1109
+ | 3.3220 | 415500 | 4.4214 | - |
1110
+ | 3.3260 | 416000 | 4.3863 | - |
1111
+ | 3.3299 | 416500 | 4.4348 | - |
1112
+ | 3.3339 | 417000 | 4.4873 | - |
1113
+ | 3.3379 | 417500 | 4.5004 | - |
1114
+ | 3.3419 | 418000 | 4.5359 | - |
1115
+ | 3.3459 | 418500 | 4.458 | - |
1116
+ | 3.3499 | 419000 | 4.4721 | - |
1117
+ | 3.3539 | 419500 | 4.5148 | - |
1118
+ | 3.3579 | 420000 | 4.4239 | - |
1119
+ | 3.3619 | 420500 | 4.423 | - |
1120
+ | 3.3659 | 421000 | 4.4774 | - |
1121
+ | 3.3699 | 421500 | 4.4258 | - |
1122
+ | 3.3739 | 422000 | 4.5019 | - |
1123
+ | 3.3779 | 422500 | 4.4487 | - |
1124
+ | 3.3819 | 423000 | 4.4691 | - |
1125
+ | 3.3859 | 423500 | 4.5267 | - |
1126
+ | 3.3899 | 424000 | 4.4422 | - |
1127
+ | 3.3939 | 424500 | 4.4965 | - |
1128
+ | 3.3979 | 425000 | 4.407 | - |
1129
+ | 3.4019 | 425500 | 4.4443 | - |
1130
+ | 3.4059 | 426000 | 4.5078 | - |
1131
+ | 3.4099 | 426500 | 4.4561 | - |
1132
+ | 3.4139 | 427000 | 4.4057 | - |
1133
+ | 3.4179 | 427500 | 4.4829 | - |
1134
+ | 3.4219 | 428000 | 4.4281 | - |
1135
+ | 3.4259 | 428500 | 4.4486 | - |
1136
+ | 3.4299 | 429000 | 4.4626 | - |
1137
+ | 3.4339 | 429500 | 4.4792 | - |
1138
+ | 3.4379 | 430000 | 4.4109 | - |
1139
+ | 3.4419 | 430500 | 4.531 | - |
1140
+ | 3.4459 | 431000 | 4.4599 | - |
1141
+ | 3.4499 | 431500 | 4.376 | - |
1142
+ | 3.4539 | 432000 | 4.4899 | - |
1143
+ | 3.4579 | 432500 | 4.4339 | - |
1144
+ | 3.4619 | 433000 | 4.3908 | - |
1145
+ | 3.4659 | 433500 | 4.3601 | - |
1146
+ | 3.4699 | 434000 | 4.4492 | - |
1147
+ | 3.4739 | 434500 | 4.4114 | - |
1148
+ | 3.4779 | 435000 | 4.3885 | - |
1149
+ | 3.4819 | 435500 | 4.4452 | - |
1150
+ | 3.4859 | 436000 | 4.4125 | - |
1151
+ | 3.4899 | 436500 | 4.4369 | - |
1152
+ | 3.4938 | 437000 | 4.4511 | - |
1153
+ | 3.4978 | 437500 | 4.4088 | - |
1154
+ | 3.5018 | 438000 | 4.4583 | - |
1155
+ | 3.5058 | 438500 | 4.4259 | - |
1156
+ | 3.5098 | 439000 | 4.4397 | - |
1157
+ | 3.5138 | 439500 | 4.3635 | - |
1158
+ | 3.5178 | 440000 | 4.4461 | - |
1159
+ | 3.5218 | 440500 | 4.4595 | - |
1160
+ | 3.5258 | 441000 | 4.5471 | - |
1161
+ | 3.5298 | 441500 | 4.4739 | - |
1162
+ | 3.5338 | 442000 | 4.4534 | - |
1163
+ | 3.5378 | 442500 | 4.369 | - |
1164
+ | 3.5418 | 443000 | 4.4104 | - |
1165
+ | 3.5458 | 443500 | 4.4219 | - |
1166
+ | 3.5498 | 444000 | 4.3791 | - |
1167
+ | 3.5538 | 444500 | 4.4981 | - |
1168
+ | 3.5578 | 445000 | 4.3899 | - |
1169
+ | 3.5618 | 445500 | 4.4454 | - |
1170
+ | 3.5658 | 446000 | 4.3621 | - |
1171
+ | 3.5698 | 446500 | 4.4437 | - |
1172
+ | 3.5738 | 447000 | 4.4827 | - |
1173
+ | 3.5778 | 447500 | 4.4761 | - |
1174
+ | 3.5818 | 448000 | 4.4698 | - |
1175
+ | 3.5858 | 448500 | 4.4386 | - |
1176
+ | 3.5898 | 449000 | 4.4147 | - |
1177
+ | 3.5938 | 449500 | 4.4644 | - |
1178
+ | 3.5978 | 450000 | 4.3913 | - |
1179
+ | 3.6018 | 450500 | 4.4642 | - |
1180
+ | 3.6058 | 451000 | 4.4529 | - |
1181
+ | 3.6098 | 451500 | 4.4912 | - |
1182
+ | 3.6138 | 452000 | 4.3711 | - |
1183
+ | 3.6178 | 452500 | 4.5121 | - |
1184
+ | 3.6218 | 453000 | 4.4718 | - |
1185
+ | 3.6258 | 453500 | 4.4593 | - |
1186
+ | 3.6298 | 454000 | 4.3954 | - |
1187
+ | 3.6338 | 454500 | 4.3387 | - |
1188
+ | 3.6378 | 455000 | 4.3933 | - |
1189
+ | 3.6418 | 455500 | 4.4689 | - |
1190
+ | 3.6458 | 456000 | 4.4221 | - |
1191
+ | 3.6498 | 456500 | 4.4561 | - |
1192
+ | 3.6537 | 457000 | 4.3731 | - |
1193
+ | 3.6577 | 457500 | 4.4143 | - |
1194
+ | 3.6617 | 458000 | 4.4378 | - |
1195
+ | 3.6657 | 458500 | 4.3778 | - |
1196
+ | 3.6697 | 459000 | 4.4467 | - |
1197
+ | 3.6737 | 459500 | 4.4633 | - |
1198
+ | 3.6777 | 460000 | 4.438 | - |
1199
+ | 3.6817 | 460500 | 4.4633 | - |
1200
+ | 3.6857 | 461000 | 4.4209 | - |
1201
+ | 3.6897 | 461500 | 4.4838 | - |
1202
+ | 3.6937 | 462000 | 4.3811 | - |
1203
+ | 3.6977 | 462500 | 4.4388 | - |
1204
+ | 3.7017 | 463000 | 4.462 | - |
1205
+ | 3.7057 | 463500 | 4.3969 | - |
1206
+ | 3.7097 | 464000 | 4.4324 | - |
1207
+ | 3.7137 | 464500 | 4.3826 | - |
1208
+ | 3.7177 | 465000 | 4.4277 | - |
1209
+ | 3.7217 | 465500 | 4.4819 | - |
1210
+ | 3.7257 | 466000 | 4.4314 | - |
1211
+ | 3.7297 | 466500 | 4.4364 | - |
1212
+ | 3.7337 | 467000 | 4.4119 | - |
1213
+ | 3.7377 | 467500 | 4.4413 | - |
1214
+ | 3.7417 | 468000 | 4.434 | - |
1215
+ | 3.7457 | 468500 | 4.4263 | - |
1216
+ | 3.7497 | 469000 | 4.4241 | - |
1217
+ | 3.7537 | 469500 | 4.4055 | - |
1218
+ | 3.7577 | 470000 | 4.4456 | - |
1219
+ | 3.7617 | 470500 | 4.4056 | - |
1220
+ | 3.7657 | 471000 | 4.4655 | - |
1221
+ | 3.7697 | 471500 | 4.4211 | - |
1222
+ | 3.7737 | 472000 | 4.4314 | - |
1223
+ | 3.7777 | 472500 | 4.4651 | - |
1224
+ | 3.7817 | 473000 | 4.3825 | - |
1225
+ | 3.7857 | 473500 | 4.4665 | - |
1226
+ | 3.7897 | 474000 | 4.4425 | - |
1227
+ | 3.7937 | 474500 | 4.3956 | - |
1228
+ | 3.7977 | 475000 | 4.4302 | - |
1229
+ | 3.8017 | 475500 | 4.4057 | - |
1230
+ | 3.8057 | 476000 | 4.337 | - |
1231
+ | 3.8097 | 476500 | 4.4776 | - |
1232
+ | 3.8137 | 477000 | 4.4289 | - |
1233
+ | 3.8176 | 477500 | 4.4508 | - |
1234
+ | 3.8216 | 478000 | 4.4438 | - |
1235
+ | 3.8256 | 478500 | 4.3681 | - |
1236
+ | 3.8296 | 479000 | 4.4492 | - |
1237
+ | 3.8336 | 479500 | 4.3427 | - |
1238
+ | 3.8376 | 480000 | 4.4412 | - |
1239
+ | 3.8416 | 480500 | 4.385 | - |
1240
+ | 3.8456 | 481000 | 4.401 | - |
1241
+ | 3.8496 | 481500 | 4.3606 | - |
1242
+ | 3.8536 | 482000 | 4.3404 | - |
1243
+ | 3.8576 | 482500 | 4.4591 | - |
1244
+ | 3.8616 | 483000 | 4.3984 | - |
1245
+ | 3.8656 | 483500 | 4.4195 | - |
1246
+ | 3.8696 | 484000 | 4.4414 | - |
1247
+ | 3.8736 | 484500 | 4.4028 | - |
1248
+ | 3.8776 | 485000 | 4.3685 | - |
1249
+ | 3.8816 | 485500 | 4.3658 | - |
1250
+ | 3.8856 | 486000 | 4.3967 | - |
1251
+ | 3.8896 | 486500 | 4.5247 | - |
1252
+ | 3.8936 | 487000 | 4.4359 | - |
1253
+ | 3.8976 | 487500 | 4.4493 | - |
1254
+ | 3.9016 | 488000 | 4.4604 | - |
1255
+ | 3.9056 | 488500 | 4.3784 | - |
1256
+ | 3.9096 | 489000 | 4.4631 | - |
1257
+ | 3.9136 | 489500 | 4.4359 | - |
1258
+ | 3.9176 | 490000 | 4.3923 | - |
1259
+ | 3.9216 | 490500 | 4.3844 | - |
1260
+ | 3.9256 | 491000 | 4.41 | - |
1261
+ | 3.9296 | 491500 | 4.4547 | - |
1262
+ | 3.9336 | 492000 | 4.3895 | - |
1263
+ | 3.9376 | 492500 | 4.3827 | - |
1264
+ | 3.9416 | 493000 | 4.3615 | - |
1265
+ | 3.9456 | 493500 | 4.4237 | - |
1266
+ | 3.9496 | 494000 | 4.4513 | - |
1267
+ | 3.9536 | 494500 | 4.4038 | - |
1268
+ | 3.9576 | 495000 | 4.4093 | - |
1269
+ | 3.9616 | 495500 | 4.3791 | - |
1270
+ | 3.9656 | 496000 | 4.2971 | - |
1271
+ | 3.9696 | 496500 | 4.4166 | - |
1272
+ | 3.9736 | 497000 | 4.454 | - |
1273
+ | 3.9775 | 497500 | 4.4146 | - |
1274
+ | 3.9815 | 498000 | 4.3621 | - |
1275
+ | 3.9855 | 498500 | 4.3588 | - |
1276
+ | 3.9895 | 499000 | 4.4758 | - |
1277
+ | 3.9935 | 499500 | 4.4936 | - |
1278
+ | 3.9975 | 500000 | 4.4108 | - |
1279
 
1280
+ </details>
1281
 
1282
  ### Framework Versions
1283
  - Python: 3.8.10
1284
  - Sentence Transformers: 3.1.1
1285
+ - Transformers: 4.45.2
1286
+ - PyTorch: 2.4.1+cu118
1287
+ - Accelerate: 1.0.1
1288
  - Datasets: 3.0.1
1289
+ - Tokenizers: 0.20.3
1290
 
1291
  ## Citation
1292
 
config.json CHANGED
@@ -19,7 +19,7 @@
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21
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22
- "transformers_version": "4.45.1",
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  "type_vocab_size": 2,
24
  "use_cache": true,
25
  "vocab_size": 30522
 
19
  "pad_token_id": 0,
20
  "position_embedding_type": "absolute",
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  "torch_dtype": "float32",
22
+ "transformers_version": "4.45.2",
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  "type_vocab_size": 2,
24
  "use_cache": true,
25
  "vocab_size": 30522
config_sentence_transformers.json CHANGED
@@ -1,8 +1,8 @@
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- "transformers": "4.45.1",
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4
+ "transformers": "4.45.2",
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+ "pytorch": "2.4.1+cu118"
6
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