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omarelshehy
/
Arabic-Retrieval-v1.0

Sentence Similarity
sentence-transformers
Safetensors
Arabic
bert
feature-extraction
Generated from Trainer
loss:MultipleNegativesRankingLoss
retrieval
mteb
Eval Results
text-embeddings-inference
Inference Endpoints
Model card Files Community
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  1. .ipynb_checkpoints/README-checkpoint.md +1312 -0
  2. README.md +0 -0
  3. config.json +1 -1
  4. model.safetensors +1 -1
  5. optimizer.pt +1 -1
  6. rng_state.pth +1 -1
  7. scheduler.pt +1 -1
  8. sentence_bert_config.json +1 -1
  9. tokenizer.json +1 -1
  10. tokenizer_config.json +8 -1
  11. trainer_state.json +0 -0
  12. training_args.bin +1 -1
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1
+ ---
2
+ tags:
3
+ - sentence-transformers
4
+ - sentence-similarity
5
+ - feature-extraction
6
+ - generated_from_trainer
7
+ - dataset_size:753920
8
+ - loss:MultipleNegativesRankingLoss
9
+ base_model: egyllm/pretrained-arabert
10
+ widget:
11
+ - source_sentence: '<query>: استجابة الحادث بعد حادث كشف عن أوجه القصور في الشركة'
12
+ sentences:
13
+ - '<query>: لقد وقعت حادثة مؤسفة في الشركة'
14
+ - '<query>: الحادثة التي حدثت في الشركة لم تكن غلطتهم'
15
+ - '<query>: من غير الواضح بالنسبة لي ان كانوا قد اعط كل المعلومات قبل النطق بالحكم
16
+ .'
17
+ - source_sentence: '<query>: ما معنى اختصار rq؟'
18
+ sentences:
19
+ - '<passage>: تم نشر غلوبال هوك عسكريًا لدعم العمليات الاستثنائية منذ نوفمبر 2001.
20
+ في اسم RQ-4، يشير R إلى التعيين الذي تستخدمه وزارة الدفاع للتعقب، وQ يعني نظام
21
+ طيران بدون طيار. يشير الرقم 4 إلى سلسلة من أنظمة الطيران المأهولة عن بعد.'
22
+ - '<passage>: برنامج تسجيل الشاشة Camtasia. Camtasia هو برنامج يستخدم لتسجيل الأنشطة
23
+ على الشاشة، والصوت، والفيديو من الكاميرا، وتقديم العروض التقديمية من PowerPoint.
24
+ من خلال Camtasia، يمكنك تسجيل وتحرير وإنتاج ومشاركة محتوى الدروس. تشمل ميزات التحرير
25
+ إشارات التوضيح، والتحولات، والتقريب والتحريك، وتعزيزات الصوت، وغيرها. أنتج ملف
26
+ الفيديو النهائي الذي يشاهده الطلاب حسب ملاءمةهم، ويمكنك تضمين جدول المحتويات للمساعدة
27
+ في التنقل.'
28
+ - '<passage>: تعريف أعلى. RQ. اختصار لـ ''Rage Quit''، وهو ما يحدث عندما يغادر اللاعب/المستخدم
29
+ اللعبة بسبب الغضب، عادة عندما يقتل. يستخدم في الألعاب متعددة اللاعبين عبر الإنترنت
30
+ أو LAN، أو الدردشة. على سبيل المثال، WC3(DOTA).'
31
+ - source_sentence: '<query>: فتاة تلعب في ساحة لعب'
32
+ sentences:
33
+ - '<query>: فتاة تلعب في الفناء الأمامي لمنزلهم.'
34
+ - '<query>: واذا لم يكمل المتعاقد عمله في الوقت المناسب , فانه قد يتعين عليه تعويض
35
+ الحكومة .'
36
+ - '<query>: فتاة في ساحة لعب'
37
+ - source_sentence: '<query>: ما هو كاسكوس'
38
+ sentences:
39
+ - '<passage>: تستخدم تفاعل بوليميراز سلسلة متعدد الأطراف الكمي (qPCR) لتحديد عدد
40
+ نسخ الحمض النووي المحدد في عينة، مقارنةً بمقياس. في PCR في الوقت الحقيقي، يمكن
41
+ تحديد عدد نسخ الحمض النووي بعد كل دورة من عمليات التضاعيف.'
42
+ - '<passage>: كاسكوس هو تحالف متوسط قائم على كرات التداول البيضاء والبنية.'
43
+ - '<passage>: تُظهر الرسوم البيانية أعلاه نشاط حالة الخدمة لكاسكوس.كو.ايد خلال آخر
44
+ 10 فحوصات أوتوماتيكية. يُظهر الشريط الأزرق وقت الاستجابة، وهو أفضل عندما يكون
45
+ أصغر. إذا لم يُعرض أي شريط لوقت معين، فهذا يعني أن الخدمة كانت غير متاحة وكان
46
+ الموقع غير متصل بالإنترنت.'
47
+ - source_sentence: '<query>: يبدو أن الفتاة ذات الوشاح الأخضر والكلب الأبيض يلعبان.'
48
+ sentences:
49
+ - '<query>: يبدو أن الفتاة والكلب يلعبان.'
50
+ - '<query>: الفتاة والكلب لا يتفاعلان'
51
+ - '<passage>: فيلكيتونوريا هي اضطراب وراثي يزيد من مستويات الفينيلalanine في الدم.'
52
+ pipeline_tag: sentence-similarity
53
+ library_name: sentence-transformers
54
+ metrics:
55
+ - cosine_accuracy@1
56
+ - cosine_accuracy@3
57
+ - cosine_accuracy@5
58
+ - cosine_accuracy@10
59
+ - cosine_precision@1
60
+ - cosine_precision@3
61
+ - cosine_precision@5
62
+ - cosine_precision@10
63
+ - cosine_recall@1
64
+ - cosine_recall@3
65
+ - cosine_recall@5
66
+ - cosine_recall@10
67
+ - cosine_ndcg@10
68
+ - cosine_mrr@10
69
+ - cosine_map@100
70
+ - pearson_cosine
71
+ - spearman_cosine
72
+ - pearson_manhattan
73
+ - spearman_manhattan
74
+ - pearson_euclidean
75
+ - spearman_euclidean
76
+ - pearson_dot
77
+ - spearman_dot
78
+ - pearson_max
79
+ - spearman_max
80
+ model-index:
81
+ - name: SentenceTransformer based on egyllm/pretrained-arabert
82
+ results:
83
+ - task:
84
+ type: information-retrieval
85
+ name: Information Retrieval
86
+ dataset:
87
+ name: Unknown
88
+ type: unknown
89
+ metrics:
90
+ - type: cosine_accuracy@1
91
+ value: 0.7175
92
+ name: Cosine Accuracy@1
93
+ - type: cosine_accuracy@3
94
+ value: 0.841
95
+ name: Cosine Accuracy@3
96
+ - type: cosine_accuracy@5
97
+ value: 0.878
98
+ name: Cosine Accuracy@5
99
+ - type: cosine_accuracy@10
100
+ value: 0.9155
101
+ name: Cosine Accuracy@10
102
+ - type: cosine_precision@1
103
+ value: 0.7175
104
+ name: Cosine Precision@1
105
+ - type: cosine_precision@3
106
+ value: 0.28033333333333327
107
+ name: Cosine Precision@3
108
+ - type: cosine_precision@5
109
+ value: 0.17560000000000003
110
+ name: Cosine Precision@5
111
+ - type: cosine_precision@10
112
+ value: 0.09155
113
+ name: Cosine Precision@10
114
+ - type: cosine_recall@1
115
+ value: 0.7175
116
+ name: Cosine Recall@1
117
+ - type: cosine_recall@3
118
+ value: 0.841
119
+ name: Cosine Recall@3
120
+ - type: cosine_recall@5
121
+ value: 0.878
122
+ name: Cosine Recall@5
123
+ - type: cosine_recall@10
124
+ value: 0.9155
125
+ name: Cosine Recall@10
126
+ - type: cosine_ndcg@10
127
+ value: 0.8172358824512647
128
+ name: Cosine Ndcg@10
129
+ - type: cosine_mrr@10
130
+ value: 0.7856547619047611
131
+ name: Cosine Mrr@10
132
+ - type: cosine_map@100
133
+ value: 0.7890154491139222
134
+ name: Cosine Map@100
135
+ - task:
136
+ type: semantic-similarity
137
+ name: Semantic Similarity
138
+ dataset:
139
+ name: sts dev
140
+ type: sts-dev
141
+ metrics:
142
+ - type: pearson_cosine
143
+ value: 0.8015277726105404
144
+ name: Pearson Cosine
145
+ - type: spearman_cosine
146
+ value: 0.8038248041571585
147
+ name: Spearman Cosine
148
+ - type: pearson_manhattan
149
+ value: 0.7895258398435966
150
+ name: Pearson Manhattan
151
+ - type: spearman_manhattan
152
+ value: 0.8012166855619245
153
+ name: Spearman Manhattan
154
+ - type: pearson_euclidean
155
+ value: 0.7893816883662468
156
+ name: Pearson Euclidean
157
+ - type: spearman_euclidean
158
+ value: 0.8029392819509334
159
+ name: Spearman Euclidean
160
+ - type: pearson_dot
161
+ value: 0.7952010752539163
162
+ name: Pearson Dot
163
+ - type: spearman_dot
164
+ value: 0.7982104142453529
165
+ name: Spearman Dot
166
+ - type: pearson_max
167
+ value: 0.8015277726105404
168
+ name: Pearson Max
169
+ - type: spearman_max
170
+ value: 0.8038248041571585
171
+ name: Spearman Max
172
+ ---
173
+
174
+ # SentenceTransformer based on egyllm/pretrained-arabert
175
+
176
+ This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [egyllm/pretrained-arabert](https://huggingface.co/egyllm/pretrained-arabert). 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.
177
+
178
+ ## Model Details
179
+
180
+ ### Model Description
181
+ - **Model Type:** Sentence Transformer
182
+ - **Base model:** [egyllm/pretrained-arabert](https://huggingface.co/egyllm/pretrained-arabert) <!-- at revision dae389d2f3006f50f1b6ec8ec4caf67804b19822 -->
183
+ - **Maximum Sequence Length:** 256 tokens
184
+ - **Output Dimensionality:** 768 tokens
185
+ - **Similarity Function:** Cosine Similarity
186
+ <!-- - **Training Dataset:** Unknown -->
187
+ <!-- - **Language:** Unknown -->
188
+ <!-- - **License:** Unknown -->
189
+
190
+ ### Model Sources
191
+
192
+ - **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
193
+ - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
194
+ - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
195
+
196
+ ### Full Model Architecture
197
+
198
+ ```
199
+ SentenceTransformer(
200
+ (0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: BertModel
201
+ (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
202
+ )
203
+ ```
204
+
205
+ ## Usage
206
+
207
+ ### Direct Usage (Sentence Transformers)
208
+
209
+ First install the Sentence Transformers library:
210
+
211
+ ```bash
212
+ pip install -U sentence-transformers
213
+ ```
214
+
215
+ Then you can load this model and run inference.
216
+ ```python
217
+ from sentence_transformers import SentenceTransformer
218
+
219
+ # Download from the 🤗 Hub
220
+ model = SentenceTransformer("sentence_transformers_model_id")
221
+ # Run inference
222
+ sentences = [
223
+ '<query>: يبدو أن الفتاة ذات الوشاح الأخضر والكلب الأبيض يلعبان.',
224
+ '<query>: يبدو أن الفتاة والكلب يلعبان.',
225
+ '<query>: الفتاة والكلب لا يتفاعلان',
226
+ ]
227
+ embeddings = model.encode(sentences)
228
+ print(embeddings.shape)
229
+ # [3, 768]
230
+
231
+ # Get the similarity scores for the embeddings
232
+ similarities = model.similarity(embeddings, embeddings)
233
+ print(similarities.shape)
234
+ # [3, 3]
235
+ ```
236
+
237
+ <!--
238
+ ### Direct Usage (Transformers)
239
+
240
+ <details><summary>Click to see the direct usage in Transformers</summary>
241
+
242
+ </details>
243
+ -->
244
+
245
+ <!--
246
+ ### Downstream Usage (Sentence Transformers)
247
+
248
+ You can finetune this model on your own dataset.
249
+
250
+ <details><summary>Click to expand</summary>
251
+
252
+ </details>
253
+ -->
254
+
255
+ <!--
256
+ ### Out-of-Scope Use
257
+
258
+ *List how the model may foreseeably be misused and address what users ought not to do with the model.*
259
+ -->
260
+
261
+ ## Evaluation
262
+
263
+ ### Metrics
264
+
265
+ #### Information Retrieval
266
+
267
+ * Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
268
+
269
+ | Metric | Value |
270
+ |:--------------------|:----------|
271
+ | cosine_accuracy@1 | 0.7175 |
272
+ | cosine_accuracy@3 | 0.841 |
273
+ | cosine_accuracy@5 | 0.878 |
274
+ | cosine_accuracy@10 | 0.9155 |
275
+ | cosine_precision@1 | 0.7175 |
276
+ | cosine_precision@3 | 0.2803 |
277
+ | cosine_precision@5 | 0.1756 |
278
+ | cosine_precision@10 | 0.0916 |
279
+ | cosine_recall@1 | 0.7175 |
280
+ | cosine_recall@3 | 0.841 |
281
+ | cosine_recall@5 | 0.878 |
282
+ | cosine_recall@10 | 0.9155 |
283
+ | cosine_ndcg@10 | 0.8172 |
284
+ | cosine_mrr@10 | 0.7857 |
285
+ | **cosine_map@100** | **0.789** |
286
+
287
+ #### Semantic Similarity
288
+ * Dataset: `sts-dev`
289
+ * Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
290
+
291
+ | Metric | Value |
292
+ |:--------------------|:-----------|
293
+ | pearson_cosine | 0.8015 |
294
+ | **spearman_cosine** | **0.8038** |
295
+ | pearson_manhattan | 0.7895 |
296
+ | spearman_manhattan | 0.8012 |
297
+ | pearson_euclidean | 0.7894 |
298
+ | spearman_euclidean | 0.8029 |
299
+ | pearson_dot | 0.7952 |
300
+ | spearman_dot | 0.7982 |
301
+ | pearson_max | 0.8015 |
302
+ | spearman_max | 0.8038 |
303
+
304
+ <!--
305
+ ## Bias, Risks and Limitations
306
+
307
+ *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
308
+ -->
309
+
310
+ <!--
311
+ ### Recommendations
312
+
313
+ *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
314
+ -->
315
+
316
+ ## Training Details
317
+
318
+ ### Training Hyperparameters
319
+ #### Non-Default Hyperparameters
320
+
321
+ - `eval_strategy`: steps
322
+ - `per_device_train_batch_size`: 64
323
+ - `per_device_eval_batch_size`: 64
324
+ - `learning_rate`: 1e-05
325
+ - `num_train_epochs`: 1
326
+ - `lr_scheduler_type`: cosine
327
+ - `warmup_ratio`: 0.1
328
+ - `fp16`: True
329
+ - `batch_sampler`: no_duplicates
330
+
331
+ #### All Hyperparameters
332
+ <details><summary>Click to expand</summary>
333
+
334
+ - `overwrite_output_dir`: False
335
+ - `do_predict`: False
336
+ - `eval_strategy`: steps
337
+ - `prediction_loss_only`: True
338
+ - `per_device_train_batch_size`: 64
339
+ - `per_device_eval_batch_size`: 64
340
+ - `per_gpu_train_batch_size`: None
341
+ - `per_gpu_eval_batch_size`: None
342
+ - `gradient_accumulation_steps`: 1
343
+ - `eval_accumulation_steps`: None
344
+ - `torch_empty_cache_steps`: None
345
+ - `learning_rate`: 1e-05
346
+ - `weight_decay`: 0.0
347
+ - `adam_beta1`: 0.9
348
+ - `adam_beta2`: 0.999
349
+ - `adam_epsilon`: 1e-08
350
+ - `max_grad_norm`: 1.0
351
+ - `num_train_epochs`: 1
352
+ - `max_steps`: -1
353
+ - `lr_scheduler_type`: cosine
354
+ - `lr_scheduler_kwargs`: {}
355
+ - `warmup_ratio`: 0.1
356
+ - `warmup_steps`: 0
357
+ - `log_level`: passive
358
+ - `log_level_replica`: warning
359
+ - `log_on_each_node`: True
360
+ - `logging_nan_inf_filter`: True
361
+ - `save_safetensors`: True
362
+ - `save_on_each_node`: False
363
+ - `save_only_model`: False
364
+ - `restore_callback_states_from_checkpoint`: False
365
+ - `no_cuda`: False
366
+ - `use_cpu`: False
367
+ - `use_mps_device`: False
368
+ - `seed`: 42
369
+ - `data_seed`: None
370
+ - `jit_mode_eval`: False
371
+ - `use_ipex`: False
372
+ - `bf16`: False
373
+ - `fp16`: True
374
+ - `fp16_opt_level`: O1
375
+ - `half_precision_backend`: auto
376
+ - `bf16_full_eval`: False
377
+ - `fp16_full_eval`: False
378
+ - `tf32`: None
379
+ - `local_rank`: 0
380
+ - `ddp_backend`: None
381
+ - `tpu_num_cores`: None
382
+ - `tpu_metrics_debug`: False
383
+ - `debug`: []
384
+ - `dataloader_drop_last`: True
385
+ - `dataloader_num_workers`: 0
386
+ - `dataloader_prefetch_factor`: None
387
+ - `past_index`: -1
388
+ - `disable_tqdm`: False
389
+ - `remove_unused_columns`: True
390
+ - `label_names`: None
391
+ - `load_best_model_at_end`: False
392
+ - `ignore_data_skip`: False
393
+ - `fsdp`: []
394
+ - `fsdp_min_num_params`: 0
395
+ - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
396
+ - `fsdp_transformer_layer_cls_to_wrap`: None
397
+ - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
398
+ - `deepspeed`: None
399
+ - `label_smoothing_factor`: 0.0
400
+ - `optim`: adamw_torch
401
+ - `optim_args`: None
402
+ - `adafactor`: False
403
+ - `group_by_length`: False
404
+ - `length_column_name`: length
405
+ - `ddp_find_unused_parameters`: None
406
+ - `ddp_bucket_cap_mb`: None
407
+ - `ddp_broadcast_buffers`: False
408
+ - `dataloader_pin_memory`: True
409
+ - `dataloader_persistent_workers`: False
410
+ - `skip_memory_metrics`: True
411
+ - `use_legacy_prediction_loop`: False
412
+ - `push_to_hub`: False
413
+ - `resume_from_checkpoint`: None
414
+ - `hub_model_id`: None
415
+ - `hub_strategy`: every_save
416
+ - `hub_private_repo`: False
417
+ - `hub_always_push`: False
418
+ - `gradient_checkpointing`: False
419
+ - `gradient_checkpointing_kwargs`: None
420
+ - `include_inputs_for_metrics`: False
421
+ - `eval_do_concat_batches`: True
422
+ - `fp16_backend`: auto
423
+ - `push_to_hub_model_id`: None
424
+ - `push_to_hub_organization`: None
425
+ - `mp_parameters`:
426
+ - `auto_find_batch_size`: False
427
+ - `full_determinism`: False
428
+ - `torchdynamo`: None
429
+ - `ray_scope`: last
430
+ - `ddp_timeout`: 1800
431
+ - `torch_compile`: False
432
+ - `torch_compile_backend`: None
433
+ - `torch_compile_mode`: None
434
+ - `dispatch_batches`: None
435
+ - `split_batches`: None
436
+ - `include_tokens_per_second`: False
437
+ - `include_num_input_tokens_seen`: False
438
+ - `neftune_noise_alpha`: None
439
+ - `optim_target_modules`: None
440
+ - `batch_eval_metrics`: False
441
+ - `eval_on_start`: False
442
+ - `use_liger_kernel`: False
443
+ - `eval_use_gather_object`: False
444
+ - `batch_sampler`: no_duplicates
445
+ - `multi_dataset_batch_sampler`: proportional
446
+
447
+ </details>
448
+
449
+ ### Training Logs
450
+ <details><summary>Click to expand</summary>
451
+
452
+ | Epoch | Step | Training Loss | Validation Loss | cosine_map@100 | sts-dev_spearman_cosine |
453
+ |:------:|:----:|:-------------:|:---------------:|:--------------:|:-----------------------:|
454
+ | 0 | 0 | - | - | 0.6380 | 0.6561 |
455
+ | 0.0008 | 10 | 3.8114 | - | - | - |
456
+ | 0.0017 | 20 | 3.8901 | - | - | - |
457
+ | 0.0025 | 30 | 3.598 | - | - | - |
458
+ | 0.0034 | 40 | 3.8369 | - | - | - |
459
+ | 0.0042 | 50 | 3.4766 | - | - | - |
460
+ | 0.0051 | 60 | 3.5983 | - | - | - |
461
+ | 0.0059 | 70 | 3.3285 | - | - | - |
462
+ | 0.0068 | 80 | 3.1135 | - | - | - |
463
+ | 0.0076 | 90 | 2.9757 | - | - | - |
464
+ | 0.0085 | 100 | 3.3373 | - | - | - |
465
+ | 0.0093 | 110 | 3.1236 | - | - | - |
466
+ | 0.0102 | 120 | 2.7132 | - | - | - |
467
+ | 0.0110 | 130 | 2.8783 | - | - | - |
468
+ | 0.0119 | 140 | 2.3779 | - | - | - |
469
+ | 0.0127 | 150 | 2.6556 | - | - | - |
470
+ | 0.0136 | 160 | 2.2028 | - | - | - |
471
+ | 0.0144 | 170 | 2.2236 | - | - | - |
472
+ | 0.0153 | 180 | 2.7309 | - | - | - |
473
+ | 0.0161 | 190 | 2.4107 | - | - | - |
474
+ | 0.0170 | 200 | 2.3434 | - | - | - |
475
+ | 0.0178 | 210 | 1.9811 | - | - | - |
476
+ | 0.0187 | 220 | 2.6514 | - | - | - |
477
+ | 0.0195 | 230 | 2.6114 | - | - | - |
478
+ | 0.0204 | 240 | 2.5214 | - | - | - |
479
+ | 0.0212 | 250 | 2.01 | - | - | - |
480
+ | 0.0221 | 260 | 1.7568 | - | - | - |
481
+ | 0.0229 | 270 | 2.356 | - | - | - |
482
+ | 0.0238 | 280 | 2.5519 | - | - | - |
483
+ | 0.0246 | 290 | 2.0232 | - | - | - |
484
+ | 0.0255 | 300 | 1.6215 | - | - | - |
485
+ | 0.0263 | 310 | 2.6331 | - | - | - |
486
+ | 0.0272 | 320 | 2.0053 | - | - | - |
487
+ | 0.0280 | 330 | 2.3054 | - | - | - |
488
+ | 0.0289 | 340 | 1.9774 | - | - | - |
489
+ | 0.0297 | 350 | 1.8434 | - | - | - |
490
+ | 0.0306 | 360 | 1.3065 | - | - | - |
491
+ | 0.0314 | 370 | 2.5697 | - | - | - |
492
+ | 0.0323 | 380 | 2.3131 | - | - | - |
493
+ | 0.0331 | 390 | 2.0535 | - | - | - |
494
+ | 0.0340 | 400 | 1.5674 | - | - | - |
495
+ | 0.0348 | 410 | 2.45 | - | - | - |
496
+ | 0.0357 | 420 | 1.9994 | - | - | - |
497
+ | 0.0365 | 430 | 2.6629 | - | - | - |
498
+ | 0.0374 | 440 | 2.0677 | - | - | - |
499
+ | 0.0382 | 450 | 1.7282 | - | - | - |
500
+ | 0.0391 | 460 | 2.1117 | - | - | - |
501
+ | 0.0399 | 470 | 2.374 | - | - | - |
502
+ | 0.0408 | 480 | 1.7799 | - | - | - |
503
+ | 0.0416 | 490 | 1.6734 | - | - | - |
504
+ | 0.0425 | 500 | 1.4893 | - | - | - |
505
+ | 0.0433 | 510 | 2.031 | - | - | - |
506
+ | 0.0442 | 520 | 2.4175 | - | - | - |
507
+ | 0.0450 | 530 | 2.2505 | - | - | - |
508
+ | 0.0459 | 540 | 2.3695 | - | - | - |
509
+ | 0.0467 | 550 | 2.1952 | - | - | - |
510
+ | 0.0476 | 560 | 2.582 | - | - | - |
511
+ | 0.0484 | 570 | 1.7935 | - | - | - |
512
+ | 0.0493 | 580 | 2.156 | - | - | - |
513
+ | 0.0501 | 590 | 1.5579 | - | - | - |
514
+ | 0.0510 | 600 | 2.572 | - | - | - |
515
+ | 0.0518 | 610 | 1.8751 | - | - | - |
516
+ | 0.0527 | 620 | 2.1146 | - | - | - |
517
+ | 0.0535 | 630 | 1.739 | - | - | - |
518
+ | 0.0544 | 640 | 1.7652 | - | - | - |
519
+ | 0.0552 | 650 | 2.3194 | - | - | - |
520
+ | 0.0561 | 660 | 1.8637 | - | - | - |
521
+ | 0.0569 | 670 | 1.9794 | - | - | - |
522
+ | 0.0578 | 680 | 1.6374 | - | - | - |
523
+ | 0.0586 | 690 | 1.4355 | - | - | - |
524
+ | 0.0595 | 700 | 1.3763 | - | - | - |
525
+ | 0.0603 | 710 | 2.2797 | - | - | - |
526
+ | 0.0612 | 720 | 1.6895 | - | - | - |
527
+ | 0.0620 | 730 | 1.6998 | - | - | - |
528
+ | 0.0629 | 740 | 2.0926 | - | - | - |
529
+ | 0.0637 | 750 | 2.2495 | - | - | - |
530
+ | 0.0646 | 760 | 1.8361 | - | - | - |
531
+ | 0.0654 | 770 | 2.0814 | - | - | - |
532
+ | 0.0663 | 780 | 1.9751 | - | - | - |
533
+ | 0.0671 | 790 | 1.5877 | - | - | - |
534
+ | 0.0680 | 800 | 2.9411 | - | - | - |
535
+ | 0.0688 | 810 | 2.466 | - | - | - |
536
+ | 0.0697 | 820 | 1.8303 | - | - | - |
537
+ | 0.0705 | 830 | 1.3468 | - | - | - |
538
+ | 0.0714 | 840 | 1.5485 | - | - | - |
539
+ | 0.0722 | 850 | 2.0856 | - | - | - |
540
+ | 0.0731 | 860 | 1.9067 | - | - | - |
541
+ | 0.0739 | 870 | 1.5406 | - | - | - |
542
+ | 0.0748 | 880 | 2.0842 | - | - | - |
543
+ | 0.0756 | 890 | 1.3399 | - | - | - |
544
+ | 0.0765 | 900 | 1.8138 | - | - | - |
545
+ | 0.0773 | 910 | 1.8355 | - | - | - |
546
+ | 0.0782 | 920 | 2.2083 | - | - | - |
547
+ | 0.0790 | 930 | 1.849 | - | - | - |
548
+ | 0.0799 | 940 | 1.9105 | - | - | - |
549
+ | 0.0807 | 950 | 1.5099 | - | - | - |
550
+ | 0.0816 | 960 | 1.2589 | - | - | - |
551
+ | 0.0824 | 970 | 1.5917 | - | - | - |
552
+ | 0.0833 | 980 | 1.5236 | - | - | - |
553
+ | 0.0841 | 990 | 1.9194 | - | - | - |
554
+ | 0.0850 | 1000 | 1.6147 | 1.7406 | 0.7580 | 0.8109 |
555
+ | 0.0858 | 1010 | 1.8092 | - | - | - |
556
+ | 0.0867 | 1020 | 2.2912 | - | - | - |
557
+ | 0.0875 | 1030 | 1.8473 | - | - | - |
558
+ | 0.0884 | 1040 | 1.3879 | - | - | - |
559
+ | 0.0892 | 1050 | 2.5645 | - | - | - |
560
+ | 0.0901 | 1060 | 1.9847 | - | - | - |
561
+ | 0.0909 | 1070 | 1.7767 | - | - | - |
562
+ | 0.0918 | 1080 | 1.8132 | - | - | - |
563
+ | 0.0926 | 1090 | 2.356 | - | - | - |
564
+ | 0.0935 | 1100 | 1.8806 | - | - | - |
565
+ | 0.0943 | 1110 | 1.7226 | - | - | - |
566
+ | 0.0952 | 1120 | 1.6482 | - | - | - |
567
+ | 0.0960 | 1130 | 2.5 | - | - | - |
568
+ | 0.0969 | 1140 | 1.5931 | - | - | - |
569
+ | 0.0977 | 1150 | 1.3899 | - | - | - |
570
+ | 0.0986 | 1160 | 1.5451 | - | - | - |
571
+ | 0.0994 | 1170 | 1.59 | - | - | - |
572
+ | 0.1003 | 1180 | 1.8115 | - | - | - |
573
+ | 0.1011 | 1190 | 2.062 | - | - | - |
574
+ | 0.1020 | 1200 | 1.9508 | - | - | - |
575
+ | 0.1028 | 1210 | 2.4069 | - | - | - |
576
+ | 0.1037 | 1220 | 2.0273 | - | - | - |
577
+ | 0.1045 | 1230 | 1.6278 | - | - | - |
578
+ | 0.1054 | 1240 | 2.5481 | - | - | - |
579
+ | 0.1062 | 1250 | 1.9195 | - | - | - |
580
+ | 0.1071 | 1260 | 1.3667 | - | - | - |
581
+ | 0.1079 | 1270 | 2.4832 | - | - | - |
582
+ | 0.1088 | 1280 | 2.0343 | - | - | - |
583
+ | 0.1096 | 1290 | 2.0113 | - | - | - |
584
+ | 0.1105 | 1300 | 1.5492 | - | - | - |
585
+ | 0.1113 | 1310 | 1.6053 | - | - | - |
586
+ | 0.1122 | 1320 | 1.7595 | - | - | - |
587
+ | 0.1130 | 1330 | 1.356 | - | - | - |
588
+ | 0.1139 | 1340 | 1.5716 | - | - | - |
589
+ | 0.1147 | 1350 | 2.1764 | - | - | - |
590
+ | 0.1156 | 1360 | 1.9217 | - | - | - |
591
+ | 0.1164 | 1370 | 2.1936 | - | - | - |
592
+ | 0.1173 | 1380 | 1.3914 | - | - | - |
593
+ | 0.1181 | 1390 | 1.9944 | - | - | - |
594
+ | 0.1190 | 1400 | 2.1162 | - | - | - |
595
+ | 0.1198 | 1410 | 1.7333 | - | - | - |
596
+ | 0.1207 | 1420 | 2.1856 | - | - | - |
597
+ | 0.1215 | 1430 | 2.1026 | - | - | - |
598
+ | 0.1224 | 1440 | 1.2478 | - | - | - |
599
+ | 0.1232 | 1450 | 2.1637 | - | - | - |
600
+ | 0.1241 | 1460 | 1.8734 | - | - | - |
601
+ | 0.1249 | 1470 | 1.8867 | - | - | - |
602
+ | 0.1258 | 1480 | 2.2377 | - | - | - |
603
+ | 0.1266 | 1490 | 1.6174 | - | - | - |
604
+ | 0.1275 | 1500 | 1.356 | - | - | - |
605
+ | 0.1283 | 1510 | 2.0684 | - | - | - |
606
+ | 0.1292 | 1520 | 1.4745 | - | - | - |
607
+ | 0.1300 | 1530 | 2.0965 | - | - | - |
608
+ | 0.1309 | 1540 | 1.8437 | - | - | - |
609
+ | 0.1317 | 1550 | 1.4531 | - | - | - |
610
+ | 0.1326 | 1560 | 2.4221 | - | - | - |
611
+ | 0.1334 | 1570 | 1.5201 | - | - | - |
612
+ | 0.1343 | 1580 | 1.5904 | - | - | - |
613
+ | 0.1351 | 1590 | 1.5357 | - | - | - |
614
+ | 0.1360 | 1600 | 2.2998 | - | - | - |
615
+ | 0.1368 | 1610 | 1.2875 | - | - | - |
616
+ | 0.1377 | 1620 | 1.089 | - | - | - |
617
+ | 0.1385 | 1630 | 2.0749 | - | - | - |
618
+ | 0.1394 | 1640 | 2.2554 | - | - | - |
619
+ | 0.1402 | 1650 | 1.969 | - | - | - |
620
+ | 0.1411 | 1660 | 2.6012 | - | - | - |
621
+ | 0.1419 | 1670 | 2.4911 | - | - | - |
622
+ | 0.1428 | 1680 | 2.5227 | - | - | - |
623
+ | 0.1436 | 1690 | 1.4801 | - | - | - |
624
+ | 0.1445 | 1700 | 1.8368 | - | - | - |
625
+ | 0.1453 | 1710 | 1.3036 | - | - | - |
626
+ | 0.1462 | 1720 | 1.0037 | - | - | - |
627
+ | 0.1470 | 1730 | 1.9339 | - | - | - |
628
+ | 0.1479 | 1740 | 1.3418 | - | - | - |
629
+ | 0.1487 | 1750 | 1.6051 | - | - | - |
630
+ | 0.1496 | 1760 | 1.519 | - | - | - |
631
+ | 0.1504 | 1770 | 1.7575 | - | - | - |
632
+ | 0.1513 | 1780 | 2.4666 | - | - | - |
633
+ | 0.1521 | 1790 | 1.6071 | - | - | - |
634
+ | 0.1530 | 1800 | 1.5381 | - | - | - |
635
+ | 0.1538 | 1810 | 2.0542 | - | - | - |
636
+ | 0.1547 | 1820 | 1.489 | - | - | - |
637
+ | 0.1555 | 1830 | 1.6377 | - | - | - |
638
+ | 0.1564 | 1840 | 1.8472 | - | - | - |
639
+ | 0.1572 | 1850 | 1.1818 | - | - | - |
640
+ | 0.1581 | 1860 | 1.3088 | - | - | - |
641
+ | 0.1589 | 1870 | 1.7981 | - | - | - |
642
+ | 0.1598 | 1880 | 1.6091 | - | - | - |
643
+ | 0.1606 | 1890 | 1.9716 | - | - | - |
644
+ | 0.1615 | 1900 | 1.9483 | - | - | - |
645
+ | 0.1623 | 1910 | 2.0124 | - | - | - |
646
+ | 0.1632 | 1920 | 1.6491 | - | - | - |
647
+ | 0.1640 | 1930 | 1.7327 | - | - | - |
648
+ | 0.1649 | 1940 | 2.1865 | - | - | - |
649
+ | 0.1657 | 1950 | 2.169 | - | - | - |
650
+ | 0.1666 | 1960 | 1.1178 | - | - | - |
651
+ | 0.1674 | 1970 | 1.8374 | - | - | - |
652
+ | 0.1683 | 1980 | 1.493 | - | - | - |
653
+ | 0.1691 | 1990 | 1.4554 | - | - | - |
654
+ | 0.1700 | 2000 | 1.5359 | 1.6272 | 0.7663 | 0.8068 |
655
+ | 0.1708 | 2010 | 1.5926 | - | - | - |
656
+ | 0.1717 | 2020 | 1.5631 | - | - | - |
657
+ | 0.1725 | 2030 | 2.054 | - | - | - |
658
+ | 0.1734 | 2040 | 1.7155 | - | - | - |
659
+ | 0.1742 | 2050 | 2.2145 | - | - | - |
660
+ | 0.1751 | 2060 | 1.9712 | - | - | - |
661
+ | 0.1759 | 2070 | 1.2845 | - | - | - |
662
+ | 0.1768 | 2080 | 1.5927 | - | - | - |
663
+ | 0.1776 | 2090 | 2.0479 | - | - | - |
664
+ | 0.1785 | 2100 | 1.6388 | - | - | - |
665
+ | 0.1793 | 2110 | 1.4514 | - | - | - |
666
+ | 0.1801 | 2120 | 1.5075 | - | - | - |
667
+ | 0.1810 | 2130 | 1.3573 | - | - | - |
668
+ | 0.1818 | 2140 | 1.6252 | - | - | - |
669
+ | 0.1827 | 2150 | 1.73 | - | - | - |
670
+ | 0.1835 | 2160 | 1.6867 | - | - | - |
671
+ | 0.1844 | 2170 | 1.4409 | - | - | - |
672
+ | 0.1852 | 2180 | 1.0126 | - | - | - |
673
+ | 0.1861 | 2190 | 1.5874 | - | - | - |
674
+ | 0.1869 | 2200 | 1.5113 | - | - | - |
675
+ | 0.1878 | 2210 | 2.129 | - | - | - |
676
+ | 0.1886 | 2220 | 1.2366 | - | - | - |
677
+ | 0.1895 | 2230 | 2.0757 | - | - | - |
678
+ | 0.1903 | 2240 | 1.8596 | - | - | - |
679
+ | 0.1912 | 2250 | 2.1074 | - | - | - |
680
+ | 0.1920 | 2260 | 1.5711 | - | - | - |
681
+ | 0.1929 | 2270 | 1.3869 | - | - | - |
682
+ | 0.1937 | 2280 | 1.7303 | - | - | - |
683
+ | 0.1946 | 2290 | 1.8375 | - | - | - |
684
+ | 0.1954 | 2300 | 1.6658 | - | - | - |
685
+ | 0.1963 | 2310 | 2.4472 | - | - | - |
686
+ | 0.1971 | 2320 | 1.1964 | - | - | - |
687
+ | 0.1980 | 2330 | 2.1802 | - | - | - |
688
+ | 0.1988 | 2340 | 2.2913 | - | - | - |
689
+ | 0.1997 | 2350 | 1.7305 | - | - | - |
690
+ | 0.2005 | 2360 | 1.2718 | - | - | - |
691
+ | 0.2014 | 2370 | 2.1567 | - | - | - |
692
+ | 0.2022 | 2380 | 1.4862 | - | - | - |
693
+ | 0.2031 | 2390 | 1.8498 | - | - | - |
694
+ | 0.2039 | 2400 | 2.0407 | - | - | - |
695
+ | 0.2048 | 2410 | 1.9914 | - | - | - |
696
+ | 0.2056 | 2420 | 1.7447 | - | - | - |
697
+ | 0.2065 | 2430 | 1.944 | - | - | - |
698
+ | 0.2073 | 2440 | 1.7682 | - | - | - |
699
+ | 0.2082 | 2450 | 2.0332 | - | - | - |
700
+ | 0.2090 | 2460 | 2.4602 | - | - | - |
701
+ | 0.2099 | 2470 | 1.6737 | - | - | - |
702
+ | 0.2107 | 2480 | 1.2002 | - | - | - |
703
+ | 0.2116 | 2490 | 2.0536 | - | - | - |
704
+ | 0.2124 | 2500 | 1.2564 | - | - | - |
705
+ | 0.2133 | 2510 | 1.7968 | - | - | - |
706
+ | 0.2141 | 2520 | 1.7934 | - | - | - |
707
+ | 0.2150 | 2530 | 1.3855 | - | - | - |
708
+ | 0.2158 | 2540 | 1.5086 | - | - | - |
709
+ | 0.2167 | 2550 | 2.3278 | - | - | - |
710
+ | 0.2175 | 2560 | 1.62 | - | - | - |
711
+ | 0.2184 | 2570 | 2.0118 | - | - | - |
712
+ | 0.2192 | 2580 | 1.7665 | - | - | - |
713
+ | 0.2201 | 2590 | 1.4106 | - | - | - |
714
+ | 0.2209 | 2600 | 2.0529 | - | - | - |
715
+ | 0.2218 | 2610 | 1.5266 | - | - | - |
716
+ | 0.2226 | 2620 | 2.2004 | - | - | - |
717
+ | 0.2235 | 2630 | 1.2109 | - | - | - |
718
+ | 0.2243 | 2640 | 1.4509 | - | - | - |
719
+ | 0.2252 | 2650 | 1.494 | - | - | - |
720
+ | 0.2260 | 2660 | 1.5459 | - | - | - |
721
+ | 0.2269 | 2670 | 2.0089 | - | - | - |
722
+ | 0.2277 | 2680 | 1.9762 | - | - | - |
723
+ | 0.2286 | 2690 | 1.3596 | - | - | - |
724
+ | 0.2294 | 2700 | 1.5094 | - | - | - |
725
+ | 0.2303 | 2710 | 1.7427 | - | - | - |
726
+ | 0.2311 | 2720 | 1.354 | - | - | - |
727
+ | 0.2320 | 2730 | 1.9882 | - | - | - |
728
+ | 0.2328 | 2740 | 1.3848 | - | - | - |
729
+ | 0.2337 | 2750 | 1.6313 | - | - | - |
730
+ | 0.2345 | 2760 | 1.7722 | - | - | - |
731
+ | 0.2354 | 2770 | 1.2339 | - | - | - |
732
+ | 0.2362 | 2780 | 1.3144 | - | - | - |
733
+ | 0.2371 | 2790 | 1.7124 | - | - | - |
734
+ | 0.2379 | 2800 | 1.8489 | - | - | - |
735
+ | 0.2388 | 2810 | 1.4535 | - | - | - |
736
+ | 0.2396 | 2820 | 1.6224 | - | - | - |
737
+ | 0.2405 | 2830 | 1.6815 | - | - | - |
738
+ | 0.2413 | 2840 | 1.2336 | - | - | - |
739
+ | 0.2422 | 2850 | 1.4843 | - | - | - |
740
+ | 0.2430 | 2860 | 1.295 | - | - | - |
741
+ | 0.2439 | 2870 | 1.6095 | - | - | - |
742
+ | 0.2447 | 2880 | 1.7894 | - | - | - |
743
+ | 0.2456 | 2890 | 1.6503 | - | - | - |
744
+ | 0.2464 | 2900 | 1.6089 | - | - | - |
745
+ | 0.2473 | 2910 | 1.8407 | - | - | - |
746
+ | 0.2481 | 2920 | 1.5631 | - | - | - |
747
+ | 0.2490 | 2930 | 1.4495 | - | - | - |
748
+ | 0.2498 | 2940 | 2.0262 | - | - | - |
749
+ | 0.2507 | 2950 | 1.7444 | - | - | - |
750
+ | 0.2515 | 2960 | 1.1065 | - | - | - |
751
+ | 0.2524 | 2970 | 2.1085 | - | - | - |
752
+ | 0.2532 | 2980 | 1.8828 | - | - | - |
753
+ | 0.2541 | 2990 | 1.9617 | - | - | - |
754
+ | 0.2549 | 3000 | 2.1222 | 1.5225 | 0.7716 | 0.7985 |
755
+ | 0.2558 | 3010 | 1.8215 | - | - | - |
756
+ | 0.2566 | 3020 | 2.3271 | - | - | - |
757
+ | 0.2575 | 3030 | 1.3244 | - | - | - |
758
+ | 0.2583 | 3040 | 1.5012 | - | - | - |
759
+ | 0.2592 | 3050 | 1.7094 | - | - | - |
760
+ | 0.2600 | 3060 | 1.7635 | - | - | - |
761
+ | 0.2609 | 3070 | 1.4024 | - | - | - |
762
+ | 0.2617 | 3080 | 1.8977 | - | - | - |
763
+ | 0.2626 | 3090 | 1.4965 | - | - | - |
764
+ | 0.2634 | 3100 | 1.986 | - | - | - |
765
+ | 0.2643 | 3110 | 1.6921 | - | - | - |
766
+ | 0.2651 | 3120 | 1.1191 | - | - | - |
767
+ | 0.2660 | 3130 | 1.5588 | - | - | - |
768
+ | 0.2668 | 3140 | 2.2996 | - | - | - |
769
+ | 0.2677 | 3150 | 1.3422 | - | - | - |
770
+ | 0.2685 | 3160 | 1.9579 | - | - | - |
771
+ | 0.2694 | 3170 | 1.0521 | - | - | - |
772
+ | 0.2702 | 3180 | 1.8859 | - | - | - |
773
+ | 0.2711 | 3190 | 1.6077 | - | - | - |
774
+ | 0.2719 | 3200 | 1.0576 | - | - | - |
775
+ | 0.2728 | 3210 | 1.527 | - | - | - |
776
+ | 0.2736 | 3220 | 1.2154 | - | - | - |
777
+ | 0.2745 | 3230 | 1.6487 | - | - | - |
778
+ | 0.2753 | 3240 | 1.918 | - | - | - |
779
+ | 0.2762 | 3250 | 1.8735 | - | - | - |
780
+ | 0.2770 | 3260 | 2.508 | - | - | - |
781
+ | 0.2779 | 3270 | 1.5813 | - | - | - |
782
+ | 0.2787 | 3280 | 1.3501 | - | - | - |
783
+ | 0.2796 | 3290 | 1.364 | - | - | - |
784
+ | 0.2804 | 3300 | 1.5669 | - | - | - |
785
+ | 0.2813 | 3310 | 1.2687 | - | - | - |
786
+ | 0.2821 | 3320 | 1.9495 | - | - | - |
787
+ | 0.2830 | 3330 | 1.1315 | - | - | - |
788
+ | 0.2838 | 3340 | 0.9636 | - | - | - |
789
+ | 0.2847 | 3350 | 1.3071 | - | - | - |
790
+ | 0.2855 | 3360 | 1.3237 | - | - | - |
791
+ | 0.2864 | 3370 | 2.1571 | - | - | - |
792
+ | 0.2872 | 3380 | 1.5394 | - | - | - |
793
+ | 0.2881 | 3390 | 1.493 | - | - | - |
794
+ | 0.2889 | 3400 | 1.8023 | - | - | - |
795
+ | 0.2898 | 3410 | 1.9951 | - | - | - |
796
+ | 0.2906 | 3420 | 1.4618 | - | - | - |
797
+ | 0.2915 | 3430 | 1.5207 | - | - | - |
798
+ | 0.2923 | 3440 | 1.8013 | - | - | - |
799
+ | 0.2932 | 3450 | 1.4841 | - | - | - |
800
+ | 0.2940 | 3460 | 2.1567 | - | - | - |
801
+ | 0.2949 | 3470 | 1.7638 | - | - | - |
802
+ | 0.2957 | 3480 | 1.4507 | - | - | - |
803
+ | 0.2966 | 3490 | 2.1364 | - | - | - |
804
+ | 0.2974 | 3500 | 1.3655 | - | - | - |
805
+ | 0.2983 | 3510 | 1.147 | - | - | - |
806
+ | 0.2991 | 3520 | 1.8986 | - | - | - |
807
+ | 0.3000 | 3530 | 1.6014 | - | - | - |
808
+ | 0.3008 | 3540 | 1.2619 | - | - | - |
809
+ | 0.3017 | 3550 | 1.3716 | - | - | - |
810
+ | 0.3025 | 3560 | 1.5904 | - | - | - |
811
+ | 0.3034 | 3570 | 1.726 | - | - | - |
812
+ | 0.3042 | 3580 | 1.6235 | - | - | - |
813
+ | 0.3051 | 3590 | 1.7598 | - | - | - |
814
+ | 0.3059 | 3600 | 1.8795 | - | - | - |
815
+ | 0.3068 | 3610 | 1.6107 | - | - | - |
816
+ | 0.3076 | 3620 | 1.3525 | - | - | - |
817
+ | 0.3085 | 3630 | 1.8275 | - | - | - |
818
+ | 0.3093 | 3640 | 1.333 | - | - | - |
819
+ | 0.3102 | 3650 | 1.6917 | - | - | - |
820
+ | 0.3110 | 3660 | 1.6108 | - | - | - |
821
+ | 0.3119 | 3670 | 1.6899 | - | - | - |
822
+ | 0.3127 | 3680 | 1.2133 | - | - | - |
823
+ | 0.3136 | 3690 | 1.4407 | - | - | - |
824
+ | 0.3144 | 3700 | 1.8746 | - | - | - |
825
+ | 0.3153 | 3710 | 1.6211 | - | - | - |
826
+ | 0.3161 | 3720 | 1.5504 | - | - | - |
827
+ | 0.3170 | 3730 | 1.8787 | - | - | - |
828
+ | 0.3178 | 3740 | 2.0654 | - | - | - |
829
+ | 0.3187 | 3750 | 1.4762 | - | - | - |
830
+ | 0.3195 | 3760 | 1.7039 | - | - | - |
831
+ | 0.3204 | 3770 | 1.8382 | - | - | - |
832
+ | 0.3212 | 3780 | 1.684 | - | - | - |
833
+ | 0.3221 | 3790 | 1.5044 | - | - | - |
834
+ | 0.3229 | 3800 | 1.9366 | - | - | - |
835
+ | 0.3238 | 3810 | 1.3692 | - | - | - |
836
+ | 0.3246 | 3820 | 1.9425 | - | - | - |
837
+ | 0.3255 | 3830 | 1.9457 | - | - | - |
838
+ | 0.3263 | 3840 | 2.0349 | - | - | - |
839
+ | 0.3272 | 3850 | 2.2629 | - | - | - |
840
+ | 0.3280 | 3860 | 1.782 | - | - | - |
841
+ | 0.3289 | 3870 | 1.1131 | - | - | - |
842
+ | 0.3297 | 3880 | 1.6522 | - | - | - |
843
+ | 0.3306 | 3890 | 1.4468 | - | - | - |
844
+ | 0.3314 | 3900 | 1.2263 | - | - | - |
845
+ | 0.3323 | 3910 | 1.4744 | - | - | - |
846
+ | 0.3331 | 3920 | 1.346 | - | - | - |
847
+ | 0.3340 | 3930 | 1.6235 | - | - | - |
848
+ | 0.3348 | 3940 | 1.5373 | - | - | - |
849
+ | 0.3357 | 3950 | 1.9912 | - | - | - |
850
+ | 0.3365 | 3960 | 1.5235 | - | - | - |
851
+ | 0.3374 | 3970 | 1.2973 | - | - | - |
852
+ | 0.3382 | 3980 | 1.8943 | - | - | - |
853
+ | 0.3391 | 3990 | 1.796 | - | - | - |
854
+ | 0.3399 | 4000 | 1.4485 | 1.4988 | 0.7767 | 0.8003 |
855
+ | 0.3408 | 4010 | 1.4139 | - | - | - |
856
+ | 0.3416 | 4020 | 1.5104 | - | - | - |
857
+ | 0.3425 | 4030 | 1.4306 | - | - | - |
858
+ | 0.3433 | 4040 | 2.0212 | - | - | - |
859
+ | 0.3442 | 4050 | 1.4815 | - | - | - |
860
+ | 0.3450 | 4060 | 1.0738 | - | - | - |
861
+ | 0.3459 | 4070 | 0.9565 | - | - | - |
862
+ | 0.3467 | 4080 | 1.0451 | - | - | - |
863
+ | 0.3476 | 4090 | 1.5975 | - | - | - |
864
+ | 0.3484 | 4100 | 1.8642 | - | - | - |
865
+ | 0.3493 | 4110 | 1.8995 | - | - | - |
866
+ | 0.3501 | 4120 | 1.8488 | - | - | - |
867
+ | 0.3510 | 4130 | 1.1606 | - | - | - |
868
+ | 0.3518 | 4140 | 1.8689 | - | - | - |
869
+ | 0.3527 | 4150 | 1.2646 | - | - | - |
870
+ | 0.3535 | 4160 | 0.8987 | - | - | - |
871
+ | 0.3544 | 4170 | 1.4526 | - | - | - |
872
+ | 0.3552 | 4180 | 1.8155 | - | - | - |
873
+ | 0.3561 | 4190 | 1.4764 | - | - | - |
874
+ | 0.3569 | 4200 | 1.2846 | - | - | - |
875
+ | 0.3577 | 4210 | 1.7014 | - | - | - |
876
+ | 0.3586 | 4220 | 1.2782 | - | - | - |
877
+ | 0.3594 | 4230 | 1.4259 | - | - | - |
878
+ | 0.3603 | 4240 | 1.6493 | - | - | - |
879
+ | 0.3611 | 4250 | 2.1898 | - | - | - |
880
+ | 0.3620 | 4260 | 2.011 | - | - | - |
881
+ | 0.3628 | 4270 | 1.4618 | - | - | - |
882
+ | 0.3637 | 4280 | 1.4918 | - | - | - |
883
+ | 0.3645 | 4290 | 1.203 | - | - | - |
884
+ | 0.3654 | 4300 | 2.0598 | - | - | - |
885
+ | 0.3662 | 4310 | 1.2831 | - | - | - |
886
+ | 0.3671 | 4320 | 1.6989 | - | - | - |
887
+ | 0.3679 | 4330 | 1.5319 | - | - | - |
888
+ | 0.3688 | 4340 | 1.7994 | - | - | - |
889
+ | 0.3696 | 4350 | 1.9254 | - | - | - |
890
+ | 0.3705 | 4360 | 1.373 | - | - | - |
891
+ | 0.3713 | 4370 | 1.7809 | - | - | - |
892
+ | 0.3722 | 4380 | 1.5119 | - | - | - |
893
+ | 0.3730 | 4390 | 0.9275 | - | - | - |
894
+ | 0.3739 | 4400 | 1.9906 | - | - | - |
895
+ | 0.3747 | 4410 | 1.6756 | - | - | - |
896
+ | 0.3756 | 4420 | 1.8964 | - | - | - |
897
+ | 0.3764 | 4430 | 1.3878 | - | - | - |
898
+ | 0.3773 | 4440 | 2.1686 | - | - | - |
899
+ | 0.3781 | 4450 | 1.7287 | - | - | - |
900
+ | 0.3790 | 4460 | 1.4491 | - | - | - |
901
+ | 0.3798 | 4470 | 1.2374 | - | - | - |
902
+ | 0.3807 | 4480 | 1.7013 | - | - | - |
903
+ | 0.3815 | 4490 | 1.511 | - | - | - |
904
+ | 0.3824 | 4500 | 1.7912 | - | - | - |
905
+ | 0.3832 | 4510 | 1.3491 | - | - | - |
906
+ | 0.3841 | 4520 | 1.1391 | - | - | - |
907
+ | 0.3849 | 4530 | 2.2409 | - | - | - |
908
+ | 0.3858 | 4540 | 1.1876 | - | - | - |
909
+ | 0.3866 | 4550 | 1.6563 | - | - | - |
910
+ | 0.3875 | 4560 | 1.4501 | - | - | - |
911
+ | 0.3883 | 4570 | 1.4546 | - | - | - |
912
+ | 0.3892 | 4580 | 1.8082 | - | - | - |
913
+ | 0.3900 | 4590 | 1.6279 | - | - | - |
914
+ | 0.3909 | 4600 | 1.6263 | - | - | - |
915
+ | 0.3917 | 4610 | 1.3064 | - | - | - |
916
+ | 0.3926 | 4620 | 1.3364 | - | - | - |
917
+ | 0.3934 | 4630 | 1.3731 | - | - | - |
918
+ | 0.3943 | 4640 | 1.6393 | - | - | - |
919
+ | 0.3951 | 4650 | 1.5386 | - | - | - |
920
+ | 0.3960 | 4660 | 1.3492 | - | - | - |
921
+ | 0.3968 | 4670 | 1.3999 | - | - | - |
922
+ | 0.3977 | 4680 | 1.6538 | - | - | - |
923
+ | 0.3985 | 4690 | 1.1034 | - | - | - |
924
+ | 0.3994 | 4700 | 1.2209 | - | - | - |
925
+ | 0.4002 | 4710 | 1.2475 | - | - | - |
926
+ | 0.4011 | 4720 | 1.4437 | - | - | - |
927
+ | 0.4019 | 4730 | 1.3123 | - | - | - |
928
+ | 0.4028 | 4740 | 1.3572 | - | - | - |
929
+ | 0.4036 | 4750 | 1.7064 | - | - | - |
930
+ | 0.4045 | 4760 | 1.1078 | - | - | - |
931
+ | 0.4053 | 4770 | 1.5242 | - | - | - |
932
+ | 0.4062 | 4780 | 1.9819 | - | - | - |
933
+ | 0.4070 | 4790 | 1.2159 | - | - | - |
934
+ | 0.4079 | 4800 | 0.9277 | - | - | - |
935
+ | 0.4087 | 4810 | 1.7686 | - | - | - |
936
+ | 0.4096 | 4820 | 1.2682 | - | - | - |
937
+ | 0.4104 | 4830 | 1.4559 | - | - | - |
938
+ | 0.4113 | 4840 | 1.6704 | - | - | - |
939
+ | 0.4121 | 4850 | 1.8827 | - | - | - |
940
+ | 0.4130 | 4860 | 1.8031 | - | - | - |
941
+ | 0.4138 | 4870 | 1.5041 | - | - | - |
942
+ | 0.4147 | 4880 | 1.7433 | - | - | - |
943
+ | 0.4155 | 4890 | 1.1801 | - | - | - |
944
+ | 0.4164 | 4900 | 1.7493 | - | - | - |
945
+ | 0.4172 | 4910 | 1.3221 | - | - | - |
946
+ | 0.4181 | 4920 | 1.5274 | - | - | - |
947
+ | 0.4189 | 4930 | 1.2865 | - | - | - |
948
+ | 0.4198 | 4940 | 1.1829 | - | - | - |
949
+ | 0.4206 | 4950 | 1.6341 | - | - | - |
950
+ | 0.4215 | 4960 | 1.7116 | - | - | - |
951
+ | 0.4223 | 4970 | 2.116 | - | - | - |
952
+ | 0.4232 | 4980 | 1.0212 | - | - | - |
953
+ | 0.4240 | 4990 | 1.6326 | - | - | - |
954
+ | 0.4249 | 5000 | 1.5782 | 1.4283 | 0.7817 | 0.8030 |
955
+ | 0.4257 | 5010 | 1.1953 | - | - | - |
956
+ | 0.4266 | 5020 | 1.2725 | - | - | - |
957
+ | 0.4274 | 5030 | 1.1633 | - | - | - |
958
+ | 0.4283 | 5040 | 1.4567 | - | - | - |
959
+ | 0.4291 | 5050 | 1.5835 | - | - | - |
960
+ | 0.4300 | 5060 | 1.7031 | - | - | - |
961
+ | 0.4308 | 5070 | 1.8205 | - | - | - |
962
+ | 0.4317 | 5080 | 1.7956 | - | - | - |
963
+ | 0.4325 | 5090 | 1.4548 | - | - | - |
964
+ | 0.4334 | 5100 | 1.3128 | - | - | - |
965
+ | 0.4342 | 5110 | 1.4953 | - | - | - |
966
+ | 0.4351 | 5120 | 1.2878 | - | - | - |
967
+ | 0.4359 | 5130 | 1.2808 | - | - | - |
968
+ | 0.4368 | 5140 | 1.6998 | - | - | - |
969
+ | 0.4376 | 5150 | 1.5072 | - | - | - |
970
+ | 0.4385 | 5160 | 2.1685 | - | - | - |
971
+ | 0.4393 | 5170 | 1.5449 | - | - | - |
972
+ | 0.4402 | 5180 | 1.5365 | - | - | - |
973
+ | 0.4410 | 5190 | 2.8665 | - | - | - |
974
+ | 0.4419 | 5200 | 1.3293 | - | - | - |
975
+ | 0.4427 | 5210 | 1.9454 | - | - | - |
976
+ | 0.4436 | 5220 | 2.1613 | - | - | - |
977
+ | 0.4444 | 5230 | 1.8404 | - | - | - |
978
+ | 0.4453 | 5240 | 1.7808 | - | - | - |
979
+ | 0.4461 | 5250 | 1.2141 | - | - | - |
980
+ | 0.4470 | 5260 | 1.3211 | - | - | - |
981
+ | 0.4478 | 5270 | 2.0617 | - | - | - |
982
+ | 0.4487 | 5280 | 2.0629 | - | - | - |
983
+ | 0.4495 | 5290 | 1.2651 | - | - | - |
984
+ | 0.4504 | 5300 | 1.9326 | - | - | - |
985
+ | 0.4512 | 5310 | 1.455 | - | - | - |
986
+ | 0.4521 | 5320 | 2.0163 | - | - | - |
987
+ | 0.4529 | 5330 | 1.3844 | - | - | - |
988
+ | 0.4538 | 5340 | 2.1358 | - | - | - |
989
+ | 0.4546 | 5350 | 1.6149 | - | - | - |
990
+ | 0.4555 | 5360 | 1.5739 | - | - | - |
991
+ | 0.4563 | 5370 | 1.365 | - | - | - |
992
+ | 0.4572 | 5380 | 1.4386 | - | - | - |
993
+ | 0.4580 | 5390 | 1.8719 | - | - | - |
994
+ | 0.4589 | 5400 | 1.357 | - | - | - |
995
+ | 0.4597 | 5410 | 1.5401 | - | - | - |
996
+ | 0.4606 | 5420 | 1.6023 | - | - | - |
997
+ | 0.4614 | 5430 | 1.277 | - | - | - |
998
+ | 0.4623 | 5440 | 1.5706 | - | - | - |
999
+ | 0.4631 | 5450 | 1.7458 | - | - | - |
1000
+ | 0.4640 | 5460 | 1.2394 | - | - | - |
1001
+ | 0.4648 | 5470 | 1.1898 | - | - | - |
1002
+ | 0.4657 | 5480 | 1.6555 | - | - | - |
1003
+ | 0.4665 | 5490 | 2.1313 | - | - | - |
1004
+ | 0.4674 | 5500 | 1.5389 | - | - | - |
1005
+ | 0.4682 | 5510 | 1.8014 | - | - | - |
1006
+ | 0.4691 | 5520 | 0.8131 | - | - | - |
1007
+ | 0.4699 | 5530 | 1.9825 | - | - | - |
1008
+ | 0.4708 | 5540 | 1.1446 | - | - | - |
1009
+ | 0.4716 | 5550 | 1.6029 | - | - | - |
1010
+ | 0.4725 | 5560 | 0.8073 | - | - | - |
1011
+ | 0.4733 | 5570 | 1.4648 | - | - | - |
1012
+ | 0.4742 | 5580 | 1.4102 | - | - | - |
1013
+ | 0.4750 | 5590 | 1.3797 | - | - | - |
1014
+ | 0.4759 | 5600 | 1.5279 | - | - | - |
1015
+ | 0.4767 | 5610 | 1.5366 | - | - | - |
1016
+ | 0.4776 | 5620 | 1.7663 | - | - | - |
1017
+ | 0.4784 | 5630 | 1.4334 | - | - | - |
1018
+ | 0.4793 | 5640 | 1.7049 | - | - | - |
1019
+ | 0.4801 | 5650 | 1.9447 | - | - | - |
1020
+ | 0.4810 | 5660 | 1.3648 | - | - | - |
1021
+ | 0.4818 | 5670 | 1.7867 | - | - | - |
1022
+ | 0.4827 | 5680 | 1.6188 | - | - | - |
1023
+ | 0.4835 | 5690 | 1.7816 | - | - | - |
1024
+ | 0.4844 | 5700 | 1.4414 | - | - | - |
1025
+ | 0.4852 | 5710 | 1.1949 | - | - | - |
1026
+ | 0.4861 | 5720 | 1.9432 | - | - | - |
1027
+ | 0.4869 | 5730 | 1.6184 | - | - | - |
1028
+ | 0.4878 | 5740 | 1.5613 | - | - | - |
1029
+ | 0.4886 | 5750 | 1.7348 | - | - | - |
1030
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1078
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1079
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1080
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1081
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1082
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1083
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1084
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1085
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1086
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1087
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1088
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1089
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1090
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1091
+ | 0.5413 | 6370 | 0.9979 | - | - | - |
1092
+ | 0.5421 | 6380 | 1.1718 | - | - | - |
1093
+ | 0.5430 | 6390 | 1.9122 | - | - | - |
1094
+ | 0.5438 | 6400 | 1.7934 | - | - | - |
1095
+ | 0.5447 | 6410 | 1.6539 | - | - | - |
1096
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1097
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1098
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1099
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1100
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1101
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1102
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1103
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1104
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1105
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1106
+ | 0.5540 | 6520 | 1.2956 | - | - | - |
1107
+ | 0.5549 | 6530 | 1.4456 | - | - | - |
1108
+ | 0.5557 | 6540 | 1.1975 | - | - | - |
1109
+ | 0.5566 | 6550 | 2.0751 | - | - | - |
1110
+ | 0.5574 | 6560 | 1.5858 | - | - | - |
1111
+ | 0.5583 | 6570 | 1.8451 | - | - | - |
1112
+ | 0.5591 | 6580 | 0.9895 | - | - | - |
1113
+ | 0.5600 | 6590 | 1.5388 | - | - | - |
1114
+ | 0.5608 | 6600 | 1.443 | - | - | - |
1115
+ | 0.5617 | 6610 | 1.4455 | - | - | - |
1116
+ | 0.5625 | 6620 | 1.5491 | - | - | - |
1117
+ | 0.5634 | 6630 | 1.2772 | - | - | - |
1118
+ | 0.5642 | 6640 | 1.566 | - | - | - |
1119
+ | 0.5651 | 6650 | 1.1092 | - | - | - |
1120
+ | 0.5659 | 6660 | 1.4266 | - | - | - |
1121
+ | 0.5668 | 6670 | 1.9267 | - | - | - |
1122
+ | 0.5676 | 6680 | 1.4297 | - | - | - |
1123
+ | 0.5685 | 6690 | 1.4397 | - | - | - |
1124
+ | 0.5693 | 6700 | 1.4476 | - | - | - |
1125
+ | 0.5702 | 6710 | 1.6113 | - | - | - |
1126
+ | 0.5710 | 6720 | 0.8579 | - | - | - |
1127
+ | 0.5719 | 6730 | 2.1762 | - | - | - |
1128
+ | 0.5727 | 6740 | 1.7159 | - | - | - |
1129
+ | 0.5736 | 6750 | 1.247 | - | - | - |
1130
+ | 0.5744 | 6760 | 1.4467 | - | - | - |
1131
+ | 0.5753 | 6770 | 1.8219 | - | - | - |
1132
+ | 0.5761 | 6780 | 1.729 | - | - | - |
1133
+ | 0.5770 | 6790 | 1.58 | - | - | - |
1134
+ | 0.5778 | 6800 | 1.5089 | - | - | - |
1135
+ | 0.5787 | 6810 | 1.2977 | - | - | - |
1136
+ | 0.5795 | 6820 | 1.6302 | - | - | - |
1137
+ | 0.5804 | 6830 | 1.7185 | - | - | - |
1138
+ | 0.5812 | 6840 | 1.1584 | - | - | - |
1139
+ | 0.5821 | 6850 | 1.6683 | - | - | - |
1140
+ | 0.5829 | 6860 | 1.1037 | - | - | - |
1141
+ | 0.5838 | 6870 | 1.7633 | - | - | - |
1142
+ | 0.5846 | 6880 | 1.4152 | - | - | - |
1143
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1144
+ | 0.5863 | 6900 | 1.6294 | - | - | - |
1145
+ | 0.5872 | 6910 | 1.2872 | - | - | - |
1146
+ | 0.5880 | 6920 | 1.3789 | - | - | - |
1147
+ | 0.5889 | 6930 | 1.6389 | - | - | - |
1148
+ | 0.5897 | 6940 | 2.172 | - | - | - |
1149
+ | 0.5906 | 6950 | 1.2677 | - | - | - |
1150
+ | 0.5914 | 6960 | 1.5623 | - | - | - |
1151
+ | 0.5923 | 6970 | 1.993 | - | - | - |
1152
+ | 0.5931 | 6980 | 0.9549 | - | - | - |
1153
+ | 0.5940 | 6990 | 1.3705 | - | - | - |
1154
+ | 0.5948 | 7000 | 1.0568 | 1.3680 | 0.7842 | 0.8020 |
1155
+ | 0.5957 | 7010 | 1.2301 | - | - | - |
1156
+ | 0.5965 | 7020 | 1.7126 | - | - | - |
1157
+ | 0.5974 | 7030 | 1.5412 | - | - | - |
1158
+ | 0.5982 | 7040 | 1.1385 | - | - | - |
1159
+ | 0.5991 | 7050 | 1.2436 | - | - | - |
1160
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1161
+ | 0.6008 | 7070 | 1.4247 | - | - | - |
1162
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1163
+ | 0.6025 | 7090 | 1.4213 | - | - | - |
1164
+ | 0.6033 | 7100 | 0.9983 | - | - | - |
1165
+ | 0.6042 | 7110 | 1.5862 | - | - | - |
1166
+ | 0.6050 | 7120 | 1.118 | - | - | - |
1167
+ | 0.6059 | 7130 | 1.6444 | - | - | - |
1168
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1169
+ | 0.6076 | 7150 | 1.8345 | - | - | - |
1170
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1171
+ | 0.6093 | 7170 | 1.0519 | - | - | - |
1172
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1173
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1174
+ | 0.6118 | 7200 | 1.7157 | - | - | - |
1175
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1176
+ | 0.6135 | 7220 | 1.5365 | - | - | - |
1177
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1178
+ | 0.6152 | 7240 | 1.5818 | - | - | - |
1179
+ | 0.6161 | 7250 | 1.3997 | - | - | - |
1180
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1181
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1182
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1183
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1184
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1185
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1186
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1187
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1188
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1189
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1190
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1191
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1192
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1193
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1194
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1195
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1196
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1197
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1198
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1199
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1200
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1201
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1202
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1203
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1204
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1205
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1206
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1207
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1208
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1209
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1210
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1211
+ | 0.6433 | 7570 | 1.2858 | - | - | - |
1212
+ | 0.6441 | 7580 | 1.5097 | - | - | - |
1213
+ | 0.6450 | 7590 | 1.1406 | - | - | - |
1214
+ | 0.6458 | 7600 | 1.6047 | - | - | - |
1215
+ | 0.6467 | 7610 | 1.2911 | - | - | - |
1216
+ | 0.6475 | 7620 | 1.4758 | - | - | - |
1217
+ | 0.6484 | 7630 | 1.4608 | - | - | - |
1218
+ | 0.6492 | 7640 | 1.4307 | - | - | - |
1219
+ | 0.6501 | 7650 | 1.1705 | - | - | - |
1220
+ | 0.6509 | 7660 | 1.1394 | - | - | - |
1221
+ | 0.6518 | 7670 | 1.133 | - | - | - |
1222
+ | 0.6526 | 7680 | 1.8461 | - | - | - |
1223
+ | 0.6535 | 7690 | 1.6305 | - | - | - |
1224
+ | 0.6543 | 7700 | 1.3304 | - | - | - |
1225
+ | 0.6552 | 7710 | 0.9695 | - | - | - |
1226
+ | 0.6560 | 7720 | 1.3937 | - | - | - |
1227
+ | 0.6569 | 7730 | 1.4486 | - | - | - |
1228
+ | 0.6577 | 7740 | 1.3141 | - | - | - |
1229
+ | 0.6586 | 7750 | 1.1174 | - | - | - |
1230
+ | 0.6594 | 7760 | 1.0358 | - | - | - |
1231
+ | 0.6603 | 7770 | 1.4542 | - | - | - |
1232
+ | 0.6611 | 7780 | 1.3459 | - | - | - |
1233
+ | 0.6620 | 7790 | 1.3809 | - | - | - |
1234
+ | 0.6628 | 7800 | 1.1335 | - | - | - |
1235
+ | 0.6637 | 7810 | 2.2354 | - | - | - |
1236
+ | 0.6645 | 7820 | 1.9021 | - | - | - |
1237
+ | 0.6654 | 7830 | 1.4453 | - | - | - |
1238
+ | 0.6662 | 7840 | 1.621 | - | - | - |
1239
+ | 0.6671 | 7850 | 1.3936 | - | - | - |
1240
+ | 0.6679 | 7860 | 1.5465 | - | - | - |
1241
+ | 0.6688 | 7870 | 1.4917 | - | - | - |
1242
+ | 0.6696 | 7880 | 1.9427 | - | - | - |
1243
+ | 0.6705 | 7890 | 1.2764 | - | - | - |
1244
+ | 0.6713 | 7900 | 1.8721 | - | - | - |
1245
+ | 0.6722 | 7910 | 1.6532 | - | - | - |
1246
+ | 0.6730 | 7920 | 0.9971 | - | - | - |
1247
+ | 0.6739 | 7930 | 1.4542 | - | - | - |
1248
+ | 0.6747 | 7940 | 1.5839 | - | - | - |
1249
+ | 0.6756 | 7950 | 1.6431 | - | - | - |
1250
+ | 0.6764 | 7960 | 1.8941 | - | - | - |
1251
+ | 0.6773 | 7970 | 1.0336 | - | - | - |
1252
+ | 0.6781 | 7980 | 1.7703 | - | - | - |
1253
+ | 0.6790 | 7990 | 1.1059 | - | - | - |
1254
+ | 0.6798 | 8000 | 1.7855 | 1.3473 | 0.7890 | 0.8038 |
1255
+
1256
+ </details>
1257
+
1258
+ ### Framework Versions
1259
+ - Python: 3.10.12
1260
+ - Sentence Transformers: 3.2.1
1261
+ - Transformers: 4.45.2
1262
+ - PyTorch: 2.1.0+cu118
1263
+ - Accelerate: 1.0.1
1264
+ - Datasets: 3.0.2
1265
+ - Tokenizers: 0.20.3
1266
+
1267
+ ## Citation
1268
+
1269
+ ### BibTeX
1270
+
1271
+ #### Sentence Transformers
1272
+ ```bibtex
1273
+ @inproceedings{reimers-2019-sentence-bert,
1274
+ title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
1275
+ author = "Reimers, Nils and Gurevych, Iryna",
1276
+ booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
1277
+ month = "11",
1278
+ year = "2019",
1279
+ publisher = "Association for Computational Linguistics",
1280
+ url = "https://arxiv.org/abs/1908.10084",
1281
+ }
1282
+ ```
1283
+
1284
+ #### MultipleNegativesRankingLoss
1285
+ ```bibtex
1286
+ @misc{henderson2017efficient,
1287
+ title={Efficient Natural Language Response Suggestion for Smart Reply},
1288
+ author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
1289
+ year={2017},
1290
+ eprint={1705.00652},
1291
+ archivePrefix={arXiv},
1292
+ primaryClass={cs.CL}
1293
+ }
1294
+ ```
1295
+
1296
+ <!--
1297
+ ## Glossary
1298
+
1299
+ *Clearly define terms in order to be accessible across audiences.*
1300
+ -->
1301
+
1302
+ <!--
1303
+ ## Model Card Authors
1304
+
1305
+ *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
1306
+ -->
1307
+
1308
+ <!--
1309
+ ## Model Card Contact
1310
+
1311
+ *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
1312
+ -->
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