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WeihaoLi
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full_icd

Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
dense
Generated from Trainer
dataset_size:828486
loss:SymmetricLoss
text-embeddings-inference
Model card Files
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Upload final model from ../experiments/HiT-biobert-v1.1-full_icd-temp/final

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Files changed (11) hide show
  1. 1_Pooling/config.json +10 -0
  2. README.md +1685 -0
  3. config.json +25 -0
  4. config_sentence_transformers.json +14 -0
  5. model.safetensors +3 -0
  6. modules.json +14 -0
  7. sentence_bert_config.json +4 -0
  8. special_tokens_map.json +37 -0
  9. tokenizer.json +0 -0
  10. tokenizer_config.json +65 -0
  11. vocab.txt +0 -0
1_Pooling/config.json ADDED
@@ -0,0 +1,10 @@
 
 
 
 
 
 
 
 
 
 
 
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+ {
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+ "word_embedding_dimension": 768,
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+ "pooling_mode_cls_token": false,
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+ "pooling_mode_mean_tokens": true,
5
+ "pooling_mode_max_tokens": false,
6
+ "pooling_mode_mean_sqrt_len_tokens": false,
7
+ "pooling_mode_weightedmean_tokens": false,
8
+ "pooling_mode_lasttoken": false,
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+ "include_prompt": true
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+ }
README.md ADDED
@@ -0,0 +1,1685 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ tags:
3
+ - sentence-transformers
4
+ - sentence-similarity
5
+ - feature-extraction
6
+ - dense
7
+ - generated_from_trainer
8
+ - dataset_size:828486
9
+ - loss:SymmetricLoss
10
+ widget:
11
+ - source_sentence: Diseases of the genitourinary system → Diseases of male genital
12
+ organs
13
+ sentences:
14
+ - Diseases of the nervous system and sense organs → Paralysis
15
+ - Diseases of the genitourinary system
16
+ - Diseases of the skin and subcutaneous tissue
17
+ - source_sentence: Complications of pregnancy; childbirth; and the puerperium → Abortion-related
18
+ disorders
19
+ sentences:
20
+ - Diseases of the digestive system → Disorders of teeth and jaw
21
+ - Complications of pregnancy; childbirth; and the puerperium
22
+ - Endocrine; nutritional; and metabolic diseases and immunity disorders
23
+ - source_sentence: Diseases of the musculoskeletal system and connective tissue →
24
+ Infective arthritis and osteomyelitis (except that caused by TB or STD)
25
+ sentences:
26
+ - Mental illness
27
+ - Diseases of the circulatory system → Diseases of veins and lymphatics
28
+ - Diseases of the musculoskeletal system and connective tissue
29
+ - source_sentence: Diseases of the musculoskeletal system and connective tissue →
30
+ Other connective tissue disease
31
+ sentences:
32
+ - Diseases of the skin and subcutaneous tissue
33
+ - Diseases of the respiratory system → Other upper respiratory disease
34
+ - Diseases of the musculoskeletal system and connective tissue
35
+ - source_sentence: Congenital anomalies → Nervous system congenital anomalies
36
+ sentences:
37
+ - Congenital anomalies
38
+ - Diseases of the nervous system and sense organs → Central nervous system infection
39
+ - Diseases of the nervous system and sense organs
40
+ pipeline_tag: sentence-similarity
41
+ library_name: sentence-transformers
42
+ ---
43
+
44
+ # HierarchyTransformerCompat
45
+
46
+ This is a [sentence-transformers](https://www.SBERT.net) model trained on the csv dataset. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
47
+
48
+ ## Model Details
49
+
50
+ ### Model Description
51
+ - **Model Type:** Sentence Transformer
52
+ <!-- - **Base model:** [Unknown](https://huggingface.co/unknown) -->
53
+ - **Maximum Sequence Length:** 256 tokens
54
+ - **Output Dimensionality:** 768 dimensions
55
+ - **Similarity Function:** Cosine Similarity
56
+ - **Training Dataset:**
57
+ - csv
58
+ <!-- - **Language:** Unknown -->
59
+ <!-- - **License:** Unknown -->
60
+
61
+ ### Model Sources
62
+
63
+ - **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
64
+ - **Repository:** [Sentence Transformers on GitHub](https://github.com/huggingface/sentence-transformers)
65
+ - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
66
+
67
+ ### Full Model Architecture
68
+
69
+ ```
70
+ HierarchyTransformerCompat(
71
+ (0): Transformer({'max_seq_length': 256, 'do_lower_case': False, 'architecture': 'BertModel'})
72
+ (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})
73
+ )
74
+ ```
75
+
76
+ ## Usage
77
+
78
+ ### Direct Usage (Sentence Transformers)
79
+
80
+ First install the Sentence Transformers library:
81
+
82
+ ```bash
83
+ pip install -U sentence-transformers
84
+ ```
85
+
86
+ Then you can load this model and run inference.
87
+ ```python
88
+ from sentence_transformers import SentenceTransformer
89
+
90
+ # Download from the 🤗 Hub
91
+ model = SentenceTransformer("sentence_transformers_model_id")
92
+ # Run inference
93
+ sentences = [
94
+ 'Congenital anomalies → Nervous system congenital anomalies',
95
+ 'Congenital anomalies',
96
+ 'Diseases of the nervous system and sense organs → Central nervous system infection',
97
+ ]
98
+ embeddings = model.encode(sentences)
99
+ print(embeddings.shape)
100
+ # [3, 768]
101
+
102
+ # Get the similarity scores for the embeddings
103
+ similarities = model.similarity(embeddings, embeddings)
104
+ print(similarities)
105
+ # tensor([[1.0000, 0.8669, 0.6612],
106
+ # [0.8669, 1.0000, 0.5137],
107
+ # [0.6612, 0.5137, 1.0000]])
108
+ ```
109
+
110
+ <!--
111
+ ### Direct Usage (Transformers)
112
+
113
+ <details><summary>Click to see the direct usage in Transformers</summary>
114
+
115
+ </details>
116
+ -->
117
+
118
+ <!--
119
+ ### Downstream Usage (Sentence Transformers)
120
+
121
+ You can finetune this model on your own dataset.
122
+
123
+ <details><summary>Click to expand</summary>
124
+
125
+ </details>
126
+ -->
127
+
128
+ <!--
129
+ ### Out-of-Scope Use
130
+
131
+ *List how the model may foreseeably be misused and address what users ought not to do with the model.*
132
+ -->
133
+
134
+ <!--
135
+ ## Bias, Risks and Limitations
136
+
137
+ *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
138
+ -->
139
+
140
+ <!--
141
+ ### Recommendations
142
+
143
+ *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
144
+ -->
145
+
146
+ ## Training Details
147
+
148
+ ### Training Dataset
149
+
150
+ #### csv
151
+
152
+ * Dataset: csv
153
+ * Size: 828,486 training samples
154
+ * Columns: <code>child</code>, <code>parent</code>, <code>child_negative</code>, and <code>parent_negative</code>
155
+ * Approximate statistics based on the first 1000 samples:
156
+ | | child | parent | child_negative | parent_negative |
157
+ |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
158
+ | type | string | string | string | string |
159
+ | details | <ul><li>min: 8 tokens</li><li>mean: 25.09 tokens</li><li>max: 65 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 16.19 tokens</li><li>max: 41 tokens</li></ul> | <ul><li>min: 11 tokens</li><li>mean: 23.47 tokens</li><li>max: 65 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 16.95 tokens</li><li>max: 34 tokens</li></ul> |
160
+ * Samples:
161
+ | child | parent | child_negative | parent_negative |
162
+ |:---------------------------------------------------------------------|:-----------------------------------------------|:----------------------------------------------------------------------------------------------------|:----------------------------|
163
+ | <code>Infectious and parasitic diseases → Bacterial infection</code> | <code>Infectious and parasitic diseases</code> | <code>Diseases of the nervous system and sense organs → Central nervous system infection</code> | <code>Mental illness</code> |
164
+ | <code>Infectious and parasitic diseases → Bacterial infection</code> | <code>Infectious and parasitic diseases</code> | <code>Diseases of the digestive system → Intestinal infection</code> | <code>Mental illness</code> |
165
+ | <code>Infectious and parasitic diseases → Bacterial infection</code> | <code>Infectious and parasitic diseases</code> | <code>Diseases of the skin and subcutaneous tissue → Skin and subcutaneous tissue infections</code> | <code>Mental illness</code> |
166
+ * Loss: <code>hierarchy_transformers.losses.symmetric_loss.SymmetricLoss</code> with these parameters:
167
+ ```json
168
+ {
169
+ "distance_metric": "PoincareBall(c=0.0013021096820011735).dist and dist0",
170
+ "HyperbolicChildTriplet": {
171
+ "weight": 1.0,
172
+ "distance_metric": "PoincareBall(c=0.0013021096820011735).dist",
173
+ "margin": 3.0
174
+ },
175
+ "HyperbolicParentTriplet": {
176
+ "weight": 1.0,
177
+ "distance_metric": "PoincareBall(c=0.0013021096820011735).dist",
178
+ "margin": 3.0
179
+ }
180
+ }
181
+ ```
182
+
183
+ ### Training Hyperparameters
184
+ #### Non-Default Hyperparameters
185
+
186
+ - `eval_strategy`: epoch
187
+ - `per_device_train_batch_size`: 64
188
+ - `per_device_eval_batch_size`: 128
189
+ - `learning_rate`: 1e-05
190
+ - `num_train_epochs`: 10
191
+ - `warmup_steps`: 500
192
+ - `bf16`: True
193
+ - `load_best_model_at_end`: True
194
+
195
+ #### All Hyperparameters
196
+ <details><summary>Click to expand</summary>
197
+
198
+ - `overwrite_output_dir`: False
199
+ - `do_predict`: False
200
+ - `eval_strategy`: epoch
201
+ - `prediction_loss_only`: True
202
+ - `per_device_train_batch_size`: 64
203
+ - `per_device_eval_batch_size`: 128
204
+ - `per_gpu_train_batch_size`: None
205
+ - `per_gpu_eval_batch_size`: None
206
+ - `gradient_accumulation_steps`: 1
207
+ - `eval_accumulation_steps`: None
208
+ - `torch_empty_cache_steps`: None
209
+ - `learning_rate`: 1e-05
210
+ - `weight_decay`: 0.0
211
+ - `adam_beta1`: 0.9
212
+ - `adam_beta2`: 0.999
213
+ - `adam_epsilon`: 1e-08
214
+ - `max_grad_norm`: 1.0
215
+ - `num_train_epochs`: 10
216
+ - `max_steps`: -1
217
+ - `lr_scheduler_type`: linear
218
+ - `lr_scheduler_kwargs`: {}
219
+ - `warmup_ratio`: 0.0
220
+ - `warmup_steps`: 500
221
+ - `log_level`: passive
222
+ - `log_level_replica`: warning
223
+ - `log_on_each_node`: True
224
+ - `logging_nan_inf_filter`: True
225
+ - `save_safetensors`: True
226
+ - `save_on_each_node`: False
227
+ - `save_only_model`: False
228
+ - `restore_callback_states_from_checkpoint`: False
229
+ - `no_cuda`: False
230
+ - `use_cpu`: False
231
+ - `use_mps_device`: False
232
+ - `seed`: 42
233
+ - `data_seed`: None
234
+ - `jit_mode_eval`: False
235
+ - `bf16`: True
236
+ - `fp16`: False
237
+ - `fp16_opt_level`: O1
238
+ - `half_precision_backend`: auto
239
+ - `bf16_full_eval`: False
240
+ - `fp16_full_eval`: False
241
+ - `tf32`: None
242
+ - `local_rank`: 0
243
+ - `ddp_backend`: None
244
+ - `tpu_num_cores`: None
245
+ - `tpu_metrics_debug`: False
246
+ - `debug`: []
247
+ - `dataloader_drop_last`: False
248
+ - `dataloader_num_workers`: 0
249
+ - `dataloader_prefetch_factor`: None
250
+ - `past_index`: -1
251
+ - `disable_tqdm`: False
252
+ - `remove_unused_columns`: True
253
+ - `label_names`: None
254
+ - `load_best_model_at_end`: True
255
+ - `ignore_data_skip`: False
256
+ - `fsdp`: []
257
+ - `fsdp_min_num_params`: 0
258
+ - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
259
+ - `fsdp_transformer_layer_cls_to_wrap`: None
260
+ - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
261
+ - `parallelism_config`: None
262
+ - `deepspeed`: None
263
+ - `label_smoothing_factor`: 0.0
264
+ - `optim`: adamw_torch_fused
265
+ - `optim_args`: None
266
+ - `adafactor`: False
267
+ - `group_by_length`: False
268
+ - `length_column_name`: length
269
+ - `project`: huggingface
270
+ - `trackio_space_id`: trackio
271
+ - `ddp_find_unused_parameters`: None
272
+ - `ddp_bucket_cap_mb`: None
273
+ - `ddp_broadcast_buffers`: False
274
+ - `dataloader_pin_memory`: True
275
+ - `dataloader_persistent_workers`: False
276
+ - `skip_memory_metrics`: True
277
+ - `use_legacy_prediction_loop`: False
278
+ - `push_to_hub`: False
279
+ - `resume_from_checkpoint`: None
280
+ - `hub_model_id`: None
281
+ - `hub_strategy`: every_save
282
+ - `hub_private_repo`: None
283
+ - `hub_always_push`: False
284
+ - `hub_revision`: None
285
+ - `gradient_checkpointing`: False
286
+ - `gradient_checkpointing_kwargs`: None
287
+ - `include_inputs_for_metrics`: False
288
+ - `include_for_metrics`: []
289
+ - `eval_do_concat_batches`: True
290
+ - `fp16_backend`: auto
291
+ - `push_to_hub_model_id`: None
292
+ - `push_to_hub_organization`: None
293
+ - `mp_parameters`:
294
+ - `auto_find_batch_size`: False
295
+ - `full_determinism`: False
296
+ - `torchdynamo`: None
297
+ - `ray_scope`: last
298
+ - `ddp_timeout`: 1800
299
+ - `torch_compile`: False
300
+ - `torch_compile_backend`: None
301
+ - `torch_compile_mode`: None
302
+ - `include_tokens_per_second`: False
303
+ - `include_num_input_tokens_seen`: no
304
+ - `neftune_noise_alpha`: None
305
+ - `optim_target_modules`: None
306
+ - `batch_eval_metrics`: False
307
+ - `eval_on_start`: False
308
+ - `use_liger_kernel`: False
309
+ - `liger_kernel_config`: None
310
+ - `eval_use_gather_object`: False
311
+ - `average_tokens_across_devices`: True
312
+ - `prompts`: None
313
+ - `batch_sampler`: batch_sampler
314
+ - `multi_dataset_batch_sampler`: proportional
315
+ - `router_mapping`: {}
316
+ - `learning_rate_mapping`: {}
317
+
318
+ </details>
319
+
320
+ ### Training Logs
321
+ <details><summary>Click to expand</summary>
322
+
323
+ | Epoch | Step | Training Loss |
324
+ |:--------:|:----------:|:-------------:|
325
+ | 0.0077 | 100 | 3.6276 |
326
+ | 0.0154 | 200 | 3.4842 |
327
+ | 0.0232 | 300 | 3.5613 |
328
+ | 0.0309 | 400 | 3.5928 |
329
+ | 0.0386 | 500 | 3.6165 |
330
+ | 0.0463 | 600 | 3.6449 |
331
+ | 0.0541 | 700 | 3.6473 |
332
+ | 0.0618 | 800 | 3.6231 |
333
+ | 0.0695 | 900 | 3.6427 |
334
+ | 0.0772 | 1000 | 3.652 |
335
+ | 0.0850 | 1100 | 3.6026 |
336
+ | 0.0927 | 1200 | 3.5859 |
337
+ | 0.1004 | 1300 | 3.6434 |
338
+ | 0.1081 | 1400 | 3.4939 |
339
+ | 0.1159 | 1500 | 3.5408 |
340
+ | 0.1236 | 1600 | 3.6617 |
341
+ | 0.1313 | 1700 | 3.6957 |
342
+ | 0.1390 | 1800 | 3.5437 |
343
+ | 0.1468 | 1900 | 3.5704 |
344
+ | 0.1545 | 2000 | 3.6258 |
345
+ | 0.1622 | 2100 | 3.5813 |
346
+ | 0.1699 | 2200 | 3.594 |
347
+ | 0.1777 | 2300 | 3.6185 |
348
+ | 0.1854 | 2400 | 3.5176 |
349
+ | 0.1931 | 2500 | 3.5923 |
350
+ | 0.2008 | 2600 | 3.6309 |
351
+ | 0.2086 | 2700 | 3.5815 |
352
+ | 0.2163 | 2800 | 3.6397 |
353
+ | 0.2240 | 2900 | 3.6417 |
354
+ | 0.2317 | 3000 | 3.5435 |
355
+ | 0.2395 | 3100 | 3.6306 |
356
+ | 0.2472 | 3200 | 3.6114 |
357
+ | 0.2549 | 3300 | 3.6267 |
358
+ | 0.2626 | 3400 | 3.6287 |
359
+ | 0.2704 | 3500 | 3.5758 |
360
+ | 0.2781 | 3600 | 3.5548 |
361
+ | 0.2858 | 3700 | 3.6129 |
362
+ | 0.2935 | 3800 | 3.6204 |
363
+ | 0.3013 | 3900 | 3.5385 |
364
+ | 0.3090 | 4000 | 3.5946 |
365
+ | 0.3167 | 4100 | 3.5989 |
366
+ | 0.3244 | 4200 | 3.5837 |
367
+ | 0.3321 | 4300 | 3.6612 |
368
+ | 0.3399 | 4400 | 3.6166 |
369
+ | 0.3476 | 4500 | 3.6462 |
370
+ | 0.3553 | 4600 | 3.5894 |
371
+ | 0.3630 | 4700 | 3.6166 |
372
+ | 0.3708 | 4800 | 3.5795 |
373
+ | 0.3785 | 4900 | 3.5852 |
374
+ | 0.3862 | 5000 | 3.6338 |
375
+ | 0.3939 | 5100 | 3.7029 |
376
+ | 0.4017 | 5200 | 3.5662 |
377
+ | 0.4094 | 5300 | 3.5772 |
378
+ | 0.4171 | 5400 | 3.6157 |
379
+ | 0.4248 | 5500 | 3.7262 |
380
+ | 0.4326 | 5600 | 3.6279 |
381
+ | 0.4403 | 5700 | 3.5867 |
382
+ | 0.4480 | 5800 | 3.5776 |
383
+ | 0.4557 | 5900 | 3.6355 |
384
+ | 0.4635 | 6000 | 3.5871 |
385
+ | 0.4712 | 6100 | 3.5771 |
386
+ | 0.4789 | 6200 | 3.6358 |
387
+ | 0.4866 | 6300 | 3.5656 |
388
+ | 0.4944 | 6400 | 3.5761 |
389
+ | 0.5021 | 6500 | 3.5821 |
390
+ | 0.5098 | 6600 | 3.6595 |
391
+ | 0.5175 | 6700 | 3.5476 |
392
+ | 0.5253 | 6800 | 3.6195 |
393
+ | 0.5330 | 6900 | 3.577 |
394
+ | 0.5407 | 7000 | 3.6238 |
395
+ | 0.5484 | 7100 | 3.5597 |
396
+ | 0.5562 | 7200 | 3.5693 |
397
+ | 0.5639 | 7300 | 3.571 |
398
+ | 0.5716 | 7400 | 3.6079 |
399
+ | 0.5793 | 7500 | 3.6155 |
400
+ | 0.5871 | 7600 | 3.6764 |
401
+ | 0.5948 | 7700 | 3.5932 |
402
+ | 0.6025 | 7800 | 3.6165 |
403
+ | 0.6102 | 7900 | 3.6194 |
404
+ | 0.6180 | 8000 | 3.6389 |
405
+ | 0.6257 | 8100 | 3.6233 |
406
+ | 0.6334 | 8200 | 3.6425 |
407
+ | 0.6411 | 8300 | 3.5846 |
408
+ | 0.6488 | 8400 | 3.5645 |
409
+ | 0.6566 | 8500 | 3.644 |
410
+ | 0.6643 | 8600 | 3.6221 |
411
+ | 0.6720 | 8700 | 3.6055 |
412
+ | 0.6797 | 8800 | 3.6684 |
413
+ | 0.6875 | 8900 | 3.6414 |
414
+ | 0.6952 | 9000 | 3.6554 |
415
+ | 0.7029 | 9100 | 3.6288 |
416
+ | 0.7106 | 9200 | 3.6622 |
417
+ | 0.7184 | 9300 | 3.6268 |
418
+ | 0.7261 | 9400 | 3.5981 |
419
+ | 0.7338 | 9500 | 3.5467 |
420
+ | 0.7415 | 9600 | 3.5467 |
421
+ | 0.7493 | 9700 | 3.5983 |
422
+ | 0.7570 | 9800 | 3.6417 |
423
+ | 0.7647 | 9900 | 3.6063 |
424
+ | 0.7724 | 10000 | 3.6323 |
425
+ | 0.7802 | 10100 | 3.6415 |
426
+ | 0.7879 | 10200 | 3.5821 |
427
+ | 0.7956 | 10300 | 3.6245 |
428
+ | 0.8033 | 10400 | 3.6186 |
429
+ | 0.8111 | 10500 | 3.5869 |
430
+ | 0.8188 | 10600 | 3.6328 |
431
+ | 0.8265 | 10700 | 3.5869 |
432
+ | 0.8342 | 10800 | 3.6522 |
433
+ | 0.8420 | 10900 | 3.5991 |
434
+ | 0.8497 | 11000 | 3.5371 |
435
+ | 0.8574 | 11100 | 3.6127 |
436
+ | 0.8651 | 11200 | 3.6358 |
437
+ | 0.8729 | 11300 | 3.5633 |
438
+ | 0.8806 | 11400 | 3.614 |
439
+ | 0.8883 | 11500 | 3.5756 |
440
+ | 0.8960 | 11600 | 3.641 |
441
+ | 0.9038 | 11700 | 3.671 |
442
+ | 0.9115 | 11800 | 3.6016 |
443
+ | 0.9192 | 11900 | 3.5704 |
444
+ | 0.9269 | 12000 | 3.583 |
445
+ | 0.9347 | 12100 | 3.5559 |
446
+ | 0.9424 | 12200 | 3.5673 |
447
+ | 0.9501 | 12300 | 3.6007 |
448
+ | 0.9578 | 12400 | 3.6176 |
449
+ | 0.9655 | 12500 | 3.6646 |
450
+ | 0.9733 | 12600 | 3.6553 |
451
+ | 0.9810 | 12700 | 3.6347 |
452
+ | 0.9887 | 12800 | 3.5682 |
453
+ | 0.9964 | 12900 | 3.5742 |
454
+ | 1.0 | 12946 | - |
455
+ | 1.0042 | 13000 | 3.5657 |
456
+ | 1.0119 | 13100 | 3.5603 |
457
+ | 1.0196 | 13200 | 3.6119 |
458
+ | 1.0273 | 13300 | 3.5359 |
459
+ | 1.0351 | 13400 | 3.5613 |
460
+ | 1.0428 | 13500 | 3.629 |
461
+ | 1.0505 | 13600 | 3.6154 |
462
+ | 1.0582 | 13700 | 3.6195 |
463
+ | 1.0660 | 13800 | 3.54 |
464
+ | 1.0737 | 13900 | 3.6009 |
465
+ | 1.0814 | 14000 | 3.5876 |
466
+ | 1.0891 | 14100 | 3.6353 |
467
+ | 1.0969 | 14200 | 3.5528 |
468
+ | 1.1046 | 14300 | 3.588 |
469
+ | 1.1123 | 14400 | 3.585 |
470
+ | 1.1200 | 14500 | 3.6188 |
471
+ | 1.1278 | 14600 | 3.558 |
472
+ | 1.1355 | 14700 | 3.6953 |
473
+ | 1.1432 | 14800 | 3.6209 |
474
+ | 1.1509 | 14900 | 3.6157 |
475
+ | 1.1587 | 15000 | 3.5562 |
476
+ | 1.1664 | 15100 | 3.6143 |
477
+ | 1.1741 | 15200 | 3.6799 |
478
+ | 1.1818 | 15300 | 3.6357 |
479
+ | 1.1896 | 15400 | 3.5259 |
480
+ | 1.1973 | 15500 | 3.641 |
481
+ | 1.2050 | 15600 | 3.6252 |
482
+ | 1.2127 | 15700 | 3.6125 |
483
+ | 1.2205 | 15800 | 3.5837 |
484
+ | 1.2282 | 15900 | 3.6463 |
485
+ | 1.2359 | 16000 | 3.6657 |
486
+ | 1.2436 | 16100 | 3.6027 |
487
+ | 1.2514 | 16200 | 3.6861 |
488
+ | 1.2591 | 16300 | 3.5984 |
489
+ | 1.2668 | 16400 | 3.5957 |
490
+ | 1.2745 | 16500 | 3.5385 |
491
+ | 1.2822 | 16600 | 3.6543 |
492
+ | 1.2900 | 16700 | 3.6226 |
493
+ | 1.2977 | 16800 | 3.6053 |
494
+ | 1.3054 | 16900 | 3.591 |
495
+ | 1.3131 | 17000 | 3.5578 |
496
+ | 1.3209 | 17100 | 3.6423 |
497
+ | 1.3286 | 17200 | 3.5829 |
498
+ | 1.3363 | 17300 | 3.6219 |
499
+ | 1.3440 | 17400 | 3.5736 |
500
+ | 1.3518 | 17500 | 3.5584 |
501
+ | 1.3595 | 17600 | 3.5737 |
502
+ | 1.3672 | 17700 | 3.6468 |
503
+ | 1.3749 | 17800 | 3.6286 |
504
+ | 1.3827 | 17900 | 3.6283 |
505
+ | 1.3904 | 18000 | 3.6238 |
506
+ | 1.3981 | 18100 | 3.5336 |
507
+ | 1.4058 | 18200 | 3.6249 |
508
+ | 1.4136 | 18300 | 3.5865 |
509
+ | 1.4213 | 18400 | 3.5356 |
510
+ | 1.4290 | 18500 | 3.5957 |
511
+ | 1.4367 | 18600 | 3.5985 |
512
+ | 1.4445 | 18700 | 3.5243 |
513
+ | 1.4522 | 18800 | 3.6062 |
514
+ | 1.4599 | 18900 | 3.6297 |
515
+ | 1.4676 | 19000 | 3.6293 |
516
+ | 1.4754 | 19100 | 3.5973 |
517
+ | 1.4831 | 19200 | 3.5546 |
518
+ | 1.4908 | 19300 | 3.5521 |
519
+ | 1.4985 | 19400 | 3.5857 |
520
+ | 1.5063 | 19500 | 3.5288 |
521
+ | 1.5140 | 19600 | 3.6396 |
522
+ | 1.5217 | 19700 | 3.5916 |
523
+ | 1.5294 | 19800 | 3.5872 |
524
+ | 1.5372 | 19900 | 3.5978 |
525
+ | 1.5449 | 20000 | 3.6291 |
526
+ | 1.5526 | 20100 | 3.6172 |
527
+ | 1.5603 | 20200 | 3.5698 |
528
+ | 1.5681 | 20300 | 3.5951 |
529
+ | 1.5758 | 20400 | 3.5977 |
530
+ | 1.5835 | 20500 | 3.5327 |
531
+ | 1.5912 | 20600 | 3.6882 |
532
+ | 1.5989 | 20700 | 3.5978 |
533
+ | 1.6067 | 20800 | 3.5933 |
534
+ | 1.6144 | 20900 | 3.6507 |
535
+ | 1.6221 | 21000 | 3.5715 |
536
+ | 1.6298 | 21100 | 3.554 |
537
+ | 1.6376 | 21200 | 3.6916 |
538
+ | 1.6453 | 21300 | 3.6557 |
539
+ | 1.6530 | 21400 | 3.579 |
540
+ | 1.6607 | 21500 | 3.5537 |
541
+ | 1.6685 | 21600 | 3.6193 |
542
+ | 1.6762 | 21700 | 3.6505 |
543
+ | 1.6839 | 21800 | 3.5629 |
544
+ | 1.6916 | 21900 | 3.5513 |
545
+ | 1.6994 | 22000 | 3.6284 |
546
+ | 1.7071 | 22100 | 3.5721 |
547
+ | 1.7148 | 22200 | 3.5727 |
548
+ | 1.7225 | 22300 | 3.6115 |
549
+ | 1.7303 | 22400 | 3.5507 |
550
+ | 1.7380 | 22500 | 3.5274 |
551
+ | 1.7457 | 22600 | 3.5467 |
552
+ | 1.7534 | 22700 | 3.5935 |
553
+ | 1.7612 | 22800 | 3.6604 |
554
+ | 1.7689 | 22900 | 3.5761 |
555
+ | 1.7766 | 23000 | 3.5724 |
556
+ | 1.7843 | 23100 | 3.6254 |
557
+ | 1.7921 | 23200 | 3.5631 |
558
+ | 1.7998 | 23300 | 3.5304 |
559
+ | 1.8075 | 23400 | 3.6048 |
560
+ | 1.8152 | 23500 | 3.6152 |
561
+ | 1.8230 | 23600 | 3.5525 |
562
+ | 1.8307 | 23700 | 3.6373 |
563
+ | 1.8384 | 23800 | 3.5769 |
564
+ | 1.8461 | 23900 | 3.5917 |
565
+ | 1.8539 | 24000 | 3.597 |
566
+ | 1.8616 | 24100 | 3.532 |
567
+ | 1.8693 | 24200 | 3.6267 |
568
+ | 1.8770 | 24300 | 3.6104 |
569
+ | 1.8848 | 24400 | 3.6096 |
570
+ | 1.8925 | 24500 | 3.5788 |
571
+ | 1.9002 | 24600 | 3.6394 |
572
+ | 1.9079 | 24700 | 3.5561 |
573
+ | 1.9156 | 24800 | 3.6027 |
574
+ | 1.9234 | 24900 | 3.5929 |
575
+ | 1.9311 | 25000 | 3.5861 |
576
+ | 1.9388 | 25100 | 3.6458 |
577
+ | 1.9465 | 25200 | 3.6135 |
578
+ | 1.9543 | 25300 | 3.6685 |
579
+ | 1.9620 | 25400 | 3.5868 |
580
+ | 1.9697 | 25500 | 3.6063 |
581
+ | 1.9774 | 25600 | 3.5569 |
582
+ | 1.9852 | 25700 | 3.6159 |
583
+ | 1.9929 | 25800 | 3.5561 |
584
+ | 2.0 | 25892 | - |
585
+ | 2.0006 | 25900 | 3.6137 |
586
+ | 2.0083 | 26000 | 3.5875 |
587
+ | 2.0161 | 26100 | 3.5944 |
588
+ | 2.0238 | 26200 | 3.6448 |
589
+ | 2.0315 | 26300 | 3.5724 |
590
+ | 2.0392 | 26400 | 3.5393 |
591
+ | 2.0470 | 26500 | 3.5749 |
592
+ | 2.0547 | 26600 | 3.6046 |
593
+ | 2.0624 | 26700 | 3.6086 |
594
+ | 2.0701 | 26800 | 3.6123 |
595
+ | 2.0779 | 26900 | 3.5889 |
596
+ | 2.0856 | 27000 | 3.5522 |
597
+ | 2.0933 | 27100 | 3.6304 |
598
+ | 2.1010 | 27200 | 3.6193 |
599
+ | 2.1088 | 27300 | 3.618 |
600
+ | 2.1165 | 27400 | 3.5772 |
601
+ | 2.1242 | 27500 | 3.6359 |
602
+ | 2.1319 | 27600 | 3.6261 |
603
+ | 2.1397 | 27700 | 3.5946 |
604
+ | 2.1474 | 27800 | 3.609 |
605
+ | 2.1551 | 27900 | 3.6127 |
606
+ | 2.1628 | 28000 | 3.6093 |
607
+ | 2.1706 | 28100 | 3.6276 |
608
+ | 2.1783 | 28200 | 3.5725 |
609
+ | 2.1860 | 28300 | 3.5564 |
610
+ | 2.1937 | 28400 | 3.5631 |
611
+ | 2.2015 | 28500 | 3.5285 |
612
+ | 2.2092 | 28600 | 3.5518 |
613
+ | 2.2169 | 28700 | 3.6052 |
614
+ | 2.2246 | 28800 | 3.5858 |
615
+ | 2.2323 | 28900 | 3.5721 |
616
+ | 2.2401 | 29000 | 3.6102 |
617
+ | 2.2478 | 29100 | 3.6299 |
618
+ | 2.2555 | 29200 | 3.5618 |
619
+ | 2.2632 | 29300 | 3.6312 |
620
+ | 2.2710 | 29400 | 3.651 |
621
+ | 2.2787 | 29500 | 3.5446 |
622
+ | 2.2864 | 29600 | 3.5835 |
623
+ | 2.2941 | 29700 | 3.5625 |
624
+ | 2.3019 | 29800 | 3.5689 |
625
+ | 2.3096 | 29900 | 3.6338 |
626
+ | 2.3173 | 30000 | 3.5742 |
627
+ | 2.3250 | 30100 | 3.6116 |
628
+ | 2.3328 | 30200 | 3.5703 |
629
+ | 2.3405 | 30300 | 3.6307 |
630
+ | 2.3482 | 30400 | 3.6073 |
631
+ | 2.3559 | 30500 | 3.5629 |
632
+ | 2.3637 | 30600 | 3.5832 |
633
+ | 2.3714 | 30700 | 3.6242 |
634
+ | 2.3791 | 30800 | 3.5511 |
635
+ | 2.3868 | 30900 | 3.5845 |
636
+ | 2.3946 | 31000 | 3.5915 |
637
+ | 2.4023 | 31100 | 3.6094 |
638
+ | 2.4100 | 31200 | 3.5599 |
639
+ | 2.4177 | 31300 | 3.5573 |
640
+ | 2.4255 | 31400 | 3.6302 |
641
+ | 2.4332 | 31500 | 3.5535 |
642
+ | 2.4409 | 31600 | 3.5844 |
643
+ | 2.4486 | 31700 | 3.6236 |
644
+ | 2.4564 | 31800 | 3.6475 |
645
+ | 2.4641 | 31900 | 3.601 |
646
+ | 2.4718 | 32000 | 3.6491 |
647
+ | 2.4795 | 32100 | 3.6121 |
648
+ | 2.4873 | 32200 | 3.5585 |
649
+ | 2.4950 | 32300 | 3.5564 |
650
+ | 2.5027 | 32400 | 3.6702 |
651
+ | 2.5104 | 32500 | 3.5262 |
652
+ | 2.5182 | 32600 | 3.5695 |
653
+ | 2.5259 | 32700 | 3.6821 |
654
+ | 2.5336 | 32800 | 3.5373 |
655
+ | 2.5413 | 32900 | 3.5276 |
656
+ | 2.5490 | 33000 | 3.544 |
657
+ | 2.5568 | 33100 | 3.6296 |
658
+ | 2.5645 | 33200 | 3.581 |
659
+ | 2.5722 | 33300 | 3.6394 |
660
+ | 2.5799 | 33400 | 3.583 |
661
+ | 2.5877 | 33500 | 3.5602 |
662
+ | 2.5954 | 33600 | 3.6008 |
663
+ | 2.6031 | 33700 | 3.5382 |
664
+ | 2.6108 | 33800 | 3.6576 |
665
+ | 2.6186 | 33900 | 3.5506 |
666
+ | 2.6263 | 34000 | 3.5915 |
667
+ | 2.6340 | 34100 | 3.5242 |
668
+ | 2.6417 | 34200 | 3.6099 |
669
+ | 2.6495 | 34300 | 3.6125 |
670
+ | 2.6572 | 34400 | 3.5802 |
671
+ | 2.6649 | 34500 | 3.6058 |
672
+ | 2.6726 | 34600 | 3.5031 |
673
+ | 2.6804 | 34700 | 3.6191 |
674
+ | 2.6881 | 34800 | 3.6056 |
675
+ | 2.6958 | 34900 | 3.6464 |
676
+ | 2.7035 | 35000 | 3.6208 |
677
+ | 2.7113 | 35100 | 3.6437 |
678
+ | 2.7190 | 35200 | 3.5581 |
679
+ | 2.7267 | 35300 | 3.604 |
680
+ | 2.7344 | 35400 | 3.603 |
681
+ | 2.7422 | 35500 | 3.533 |
682
+ | 2.7499 | 35600 | 3.5641 |
683
+ | 2.7576 | 35700 | 3.6319 |
684
+ | 2.7653 | 35800 | 3.5671 |
685
+ | 2.7731 | 35900 | 3.5897 |
686
+ | 2.7808 | 36000 | 3.5365 |
687
+ | 2.7885 | 36100 | 3.634 |
688
+ | 2.7962 | 36200 | 3.6243 |
689
+ | 2.8040 | 36300 | 3.5238 |
690
+ | 2.8117 | 36400 | 3.6204 |
691
+ | 2.8194 | 36500 | 3.5868 |
692
+ | 2.8271 | 36600 | 3.5714 |
693
+ | 2.8349 | 36700 | 3.5368 |
694
+ | 2.8426 | 36800 | 3.6029 |
695
+ | 2.8503 | 36900 | 3.583 |
696
+ | 2.8580 | 37000 | 3.6072 |
697
+ | 2.8658 | 37100 | 3.592 |
698
+ | 2.8735 | 37200 | 3.6164 |
699
+ | 2.8812 | 37300 | 3.5636 |
700
+ | 2.8889 | 37400 | 3.6543 |
701
+ | 2.8966 | 37500 | 3.5595 |
702
+ | 2.9044 | 37600 | 3.5979 |
703
+ | 2.9121 | 37700 | 3.5075 |
704
+ | 2.9198 | 37800 | 3.5977 |
705
+ | 2.9275 | 37900 | 3.6485 |
706
+ | 2.9353 | 38000 | 3.5062 |
707
+ | 2.9430 | 38100 | 3.5459 |
708
+ | 2.9507 | 38200 | 3.567 |
709
+ | 2.9584 | 38300 | 3.6045 |
710
+ | 2.9662 | 38400 | 3.5975 |
711
+ | 2.9739 | 38500 | 3.5999 |
712
+ | 2.9816 | 38600 | 3.555 |
713
+ | 2.9893 | 38700 | 3.566 |
714
+ | 2.9971 | 38800 | 3.6291 |
715
+ | 3.0 | 38838 | - |
716
+ | 3.0048 | 38900 | 3.5615 |
717
+ | 3.0125 | 39000 | 3.5605 |
718
+ | 3.0202 | 39100 | 3.5934 |
719
+ | 3.0280 | 39200 | 3.5844 |
720
+ | 3.0357 | 39300 | 3.5816 |
721
+ | 3.0434 | 39400 | 3.5161 |
722
+ | 3.0511 | 39500 | 3.5476 |
723
+ | 3.0589 | 39600 | 3.6755 |
724
+ | 3.0666 | 39700 | 3.5579 |
725
+ | 3.0743 | 39800 | 3.5442 |
726
+ | 3.0820 | 39900 | 3.5241 |
727
+ | 3.0898 | 40000 | 3.6378 |
728
+ | 3.0975 | 40100 | 3.5823 |
729
+ | 3.1052 | 40200 | 3.5353 |
730
+ | 3.1129 | 40300 | 3.5906 |
731
+ | 3.1207 | 40400 | 3.6024 |
732
+ | 3.1284 | 40500 | 3.5905 |
733
+ | 3.1361 | 40600 | 3.5412 |
734
+ | 3.1438 | 40700 | 3.5378 |
735
+ | 3.1516 | 40800 | 3.5241 |
736
+ | 3.1593 | 40900 | 3.6623 |
737
+ | 3.1670 | 41000 | 3.5981 |
738
+ | 3.1747 | 41100 | 3.5108 |
739
+ | 3.1825 | 41200 | 3.5962 |
740
+ | 3.1902 | 41300 | 3.5345 |
741
+ | 3.1979 | 41400 | 3.5472 |
742
+ | 3.2056 | 41500 | 3.6412 |
743
+ | 3.2133 | 41600 | 3.6571 |
744
+ | 3.2211 | 41700 | 3.6009 |
745
+ | 3.2288 | 41800 | 3.5897 |
746
+ | 3.2365 | 41900 | 3.6351 |
747
+ | 3.2442 | 42000 | 3.5725 |
748
+ | 3.2520 | 42100 | 3.653 |
749
+ | 3.2597 | 42200 | 3.601 |
750
+ | 3.2674 | 42300 | 3.6031 |
751
+ | 3.2751 | 42400 | 3.5387 |
752
+ | 3.2829 | 42500 | 3.6151 |
753
+ | 3.2906 | 42600 | 3.5731 |
754
+ | 3.2983 | 42700 | 3.524 |
755
+ | 3.3060 | 42800 | 3.6096 |
756
+ | 3.3138 | 42900 | 3.5254 |
757
+ | 3.3215 | 43000 | 3.5775 |
758
+ | 3.3292 | 43100 | 3.5583 |
759
+ | 3.3369 | 43200 | 3.5383 |
760
+ | 3.3447 | 43300 | 3.5147 |
761
+ | 3.3524 | 43400 | 3.6309 |
762
+ | 3.3601 | 43500 | 3.5682 |
763
+ | 3.3678 | 43600 | 3.6368 |
764
+ | 3.3756 | 43700 | 3.6024 |
765
+ | 3.3833 | 43800 | 3.6037 |
766
+ | 3.3910 | 43900 | 3.597 |
767
+ | 3.3987 | 44000 | 3.6357 |
768
+ | 3.4065 | 44100 | 3.6082 |
769
+ | 3.4142 | 44200 | 3.5489 |
770
+ | 3.4219 | 44300 | 3.5475 |
771
+ | 3.4296 | 44400 | 3.5442 |
772
+ | 3.4374 | 44500 | 3.6057 |
773
+ | 3.4451 | 44600 | 3.586 |
774
+ | 3.4528 | 44700 | 3.531 |
775
+ | 3.4605 | 44800 | 3.4946 |
776
+ | 3.4683 | 44900 | 3.6103 |
777
+ | 3.4760 | 45000 | 3.5329 |
778
+ | 3.4837 | 45100 | 3.532 |
779
+ | 3.4914 | 45200 | 3.6199 |
780
+ | 3.4992 | 45300 | 3.5643 |
781
+ | 3.5069 | 45400 | 3.5838 |
782
+ | 3.5146 | 45500 | 3.6262 |
783
+ | 3.5223 | 45600 | 3.5967 |
784
+ | 3.5300 | 45700 | 3.5427 |
785
+ | 3.5378 | 45800 | 3.6189 |
786
+ | 3.5455 | 45900 | 3.5664 |
787
+ | 3.5532 | 46000 | 3.6675 |
788
+ | 3.5609 | 46100 | 3.6173 |
789
+ | 3.5687 | 46200 | 3.5813 |
790
+ | 3.5764 | 46300 | 3.5255 |
791
+ | 3.5841 | 46400 | 3.5625 |
792
+ | 3.5918 | 46500 | 3.6215 |
793
+ | 3.5996 | 46600 | 3.6194 |
794
+ | 3.6073 | 46700 | 3.5973 |
795
+ | 3.6150 | 46800 | 3.5775 |
796
+ | 3.6227 | 46900 | 3.5332 |
797
+ | 3.6305 | 47000 | 3.5614 |
798
+ | 3.6382 | 47100 | 3.5791 |
799
+ | 3.6459 | 47200 | 3.605 |
800
+ | 3.6536 | 47300 | 3.5324 |
801
+ | 3.6614 | 47400 | 3.5165 |
802
+ | 3.6691 | 47500 | 3.5099 |
803
+ | 3.6768 | 47600 | 3.5905 |
804
+ | 3.6845 | 47700 | 3.5627 |
805
+ | 3.6923 | 47800 | 3.5292 |
806
+ | 3.7000 | 47900 | 3.6211 |
807
+ | 3.7077 | 48000 | 3.5929 |
808
+ | 3.7154 | 48100 | 3.5743 |
809
+ | 3.7232 | 48200 | 3.6056 |
810
+ | 3.7309 | 48300 | 3.5969 |
811
+ | 3.7386 | 48400 | 3.5685 |
812
+ | 3.7463 | 48500 | 3.54 |
813
+ | 3.7541 | 48600 | 3.5534 |
814
+ | 3.7618 | 48700 | 3.5718 |
815
+ | 3.7695 | 48800 | 3.6468 |
816
+ | 3.7772 | 48900 | 3.6451 |
817
+ | 3.7850 | 49000 | 3.6543 |
818
+ | 3.7927 | 49100 | 3.5599 |
819
+ | 3.8004 | 49200 | 3.6708 |
820
+ | 3.8081 | 49300 | 3.5965 |
821
+ | 3.8159 | 49400 | 3.5303 |
822
+ | 3.8236 | 49500 | 3.6126 |
823
+ | 3.8313 | 49600 | 3.5486 |
824
+ | 3.8390 | 49700 | 3.5529 |
825
+ | 3.8467 | 49800 | 3.5878 |
826
+ | 3.8545 | 49900 | 3.5643 |
827
+ | 3.8622 | 50000 | 3.6026 |
828
+ | 3.8699 | 50100 | 3.5819 |
829
+ | 3.8776 | 50200 | 3.5566 |
830
+ | 3.8854 | 50300 | 3.5706 |
831
+ | 3.8931 | 50400 | 3.5167 |
832
+ | 3.9008 | 50500 | 3.6793 |
833
+ | 3.9085 | 50600 | 3.5539 |
834
+ | 3.9163 | 50700 | 3.558 |
835
+ | 3.9240 | 50800 | 3.6148 |
836
+ | 3.9317 | 50900 | 3.5697 |
837
+ | 3.9394 | 51000 | 3.5901 |
838
+ | 3.9472 | 51100 | 3.5933 |
839
+ | 3.9549 | 51200 | 3.6098 |
840
+ | 3.9626 | 51300 | 3.5678 |
841
+ | 3.9703 | 51400 | 3.5298 |
842
+ | 3.9781 | 51500 | 3.5915 |
843
+ | 3.9858 | 51600 | 3.6285 |
844
+ | 3.9935 | 51700 | 3.6121 |
845
+ | 4.0 | 51784 | - |
846
+ | 4.0012 | 51800 | 3.5377 |
847
+ | 4.0090 | 51900 | 3.6035 |
848
+ | 4.0167 | 52000 | 3.5444 |
849
+ | 4.0244 | 52100 | 3.6043 |
850
+ | 4.0321 | 52200 | 3.584 |
851
+ | 4.0399 | 52300 | 3.6264 |
852
+ | 4.0476 | 52400 | 3.6143 |
853
+ | 4.0553 | 52500 | 3.5572 |
854
+ | 4.0630 | 52600 | 3.6155 |
855
+ | 4.0708 | 52700 | 3.5455 |
856
+ | 4.0785 | 52800 | 3.5482 |
857
+ | 4.0862 | 52900 | 3.618 |
858
+ | 4.0939 | 53000 | 3.5418 |
859
+ | 4.1017 | 53100 | 3.6525 |
860
+ | 4.1094 | 53200 | 3.5874 |
861
+ | 4.1171 | 53300 | 3.5112 |
862
+ | 4.1248 | 53400 | 3.5517 |
863
+ | 4.1326 | 53500 | 3.6333 |
864
+ | 4.1403 | 53600 | 3.5984 |
865
+ | 4.1480 | 53700 | 3.5923 |
866
+ | 4.1557 | 53800 | 3.4877 |
867
+ | 4.1634 | 53900 | 3.6383 |
868
+ | 4.1712 | 54000 | 3.5288 |
869
+ | 4.1789 | 54100 | 3.5499 |
870
+ | 4.1866 | 54200 | 3.5549 |
871
+ | 4.1943 | 54300 | 3.5762 |
872
+ | 4.2021 | 54400 | 3.5145 |
873
+ | 4.2098 | 54500 | 3.6302 |
874
+ | 4.2175 | 54600 | 3.5701 |
875
+ | 4.2252 | 54700 | 3.6079 |
876
+ | 4.2330 | 54800 | 3.5595 |
877
+ | 4.2407 | 54900 | 3.5299 |
878
+ | 4.2484 | 55000 | 3.5616 |
879
+ | 4.2561 | 55100 | 3.4761 |
880
+ | 4.2639 | 55200 | 3.649 |
881
+ | 4.2716 | 55300 | 3.578 |
882
+ | 4.2793 | 55400 | 3.6605 |
883
+ | 4.2870 | 55500 | 3.6203 |
884
+ | 4.2948 | 55600 | 3.6097 |
885
+ | 4.3025 | 55700 | 3.6298 |
886
+ | 4.3102 | 55800 | 3.5684 |
887
+ | 4.3179 | 55900 | 3.5793 |
888
+ | 4.3257 | 56000 | 3.5016 |
889
+ | 4.3334 | 56100 | 3.5862 |
890
+ | 4.3411 | 56200 | 3.5878 |
891
+ | 4.3488 | 56300 | 3.5383 |
892
+ | 4.3566 | 56400 | 3.5733 |
893
+ | 4.3643 | 56500 | 3.5529 |
894
+ | 4.3720 | 56600 | 3.5443 |
895
+ | 4.3797 | 56700 | 3.5587 |
896
+ | 4.3875 | 56800 | 3.6017 |
897
+ | 4.3952 | 56900 | 3.6768 |
898
+ | 4.4029 | 57000 | 3.5793 |
899
+ | 4.4106 | 57100 | 3.6074 |
900
+ | 4.4184 | 57200 | 3.5632 |
901
+ | 4.4261 | 57300 | 3.6009 |
902
+ | 4.4338 | 57400 | 3.5993 |
903
+ | 4.4415 | 57500 | 3.5648 |
904
+ | 4.4493 | 57600 | 3.6307 |
905
+ | 4.4570 | 57700 | 3.5573 |
906
+ | 4.4647 | 57800 | 3.5309 |
907
+ | 4.4724 | 57900 | 3.6046 |
908
+ | 4.4801 | 58000 | 3.5909 |
909
+ | 4.4879 | 58100 | 3.569 |
910
+ | 4.4956 | 58200 | 3.5425 |
911
+ | 4.5033 | 58300 | 3.5399 |
912
+ | 4.5110 | 58400 | 3.5885 |
913
+ | 4.5188 | 58500 | 3.5727 |
914
+ | 4.5265 | 58600 | 3.4748 |
915
+ | 4.5342 | 58700 | 3.566 |
916
+ | 4.5419 | 58800 | 3.6219 |
917
+ | 4.5497 | 58900 | 3.5604 |
918
+ | 4.5574 | 59000 | 3.6545 |
919
+ | 4.5651 | 59100 | 3.5184 |
920
+ | 4.5728 | 59200 | 3.5701 |
921
+ | 4.5806 | 59300 | 3.5326 |
922
+ | 4.5883 | 59400 | 3.5853 |
923
+ | 4.5960 | 59500 | 3.5779 |
924
+ | 4.6037 | 59600 | 3.5325 |
925
+ | 4.6115 | 59700 | 3.5257 |
926
+ | 4.6192 | 59800 | 3.5705 |
927
+ | 4.6269 | 59900 | 3.5037 |
928
+ | 4.6346 | 60000 | 3.5824 |
929
+ | 4.6424 | 60100 | 3.5474 |
930
+ | 4.6501 | 60200 | 3.6237 |
931
+ | 4.6578 | 60300 | 3.5426 |
932
+ | 4.6655 | 60400 | 3.5276 |
933
+ | 4.6733 | 60500 | 3.5681 |
934
+ | 4.6810 | 60600 | 3.5633 |
935
+ | 4.6887 | 60700 | 3.5537 |
936
+ | 4.6964 | 60800 | 3.6371 |
937
+ | 4.7042 | 60900 | 3.5319 |
938
+ | 4.7119 | 61000 | 3.595 |
939
+ | 4.7196 | 61100 | 3.6041 |
940
+ | 4.7273 | 61200 | 3.5813 |
941
+ | 4.7351 | 61300 | 3.6182 |
942
+ | 4.7428 | 61400 | 3.4867 |
943
+ | 4.7505 | 61500 | 3.5809 |
944
+ | 4.7582 | 61600 | 3.5502 |
945
+ | 4.7660 | 61700 | 3.587 |
946
+ | 4.7737 | 61800 | 3.6642 |
947
+ | 4.7814 | 61900 | 3.5558 |
948
+ | 4.7891 | 62000 | 3.6016 |
949
+ | 4.7968 | 62100 | 3.6195 |
950
+ | 4.8046 | 62200 | 3.6058 |
951
+ | 4.8123 | 62300 | 3.5651 |
952
+ | 4.8200 | 62400 | 3.6315 |
953
+ | 4.8277 | 62500 | 3.6014 |
954
+ | 4.8355 | 62600 | 3.5607 |
955
+ | 4.8432 | 62700 | 3.5842 |
956
+ | 4.8509 | 62800 | 3.6358 |
957
+ | 4.8586 | 62900 | 3.5416 |
958
+ | 4.8664 | 63000 | 3.589 |
959
+ | 4.8741 | 63100 | 3.6284 |
960
+ | 4.8818 | 63200 | 3.6548 |
961
+ | 4.8895 | 63300 | 3.6196 |
962
+ | 4.8973 | 63400 | 3.5262 |
963
+ | 4.9050 | 63500 | 3.5628 |
964
+ | 4.9127 | 63600 | 3.5908 |
965
+ | 4.9204 | 63700 | 3.5904 |
966
+ | 4.9282 | 63800 | 3.465 |
967
+ | 4.9359 | 63900 | 3.5462 |
968
+ | 4.9436 | 64000 | 3.5941 |
969
+ | 4.9513 | 64100 | 3.533 |
970
+ | 4.9591 | 64200 | 3.5616 |
971
+ | 4.9668 | 64300 | 3.5611 |
972
+ | 4.9745 | 64400 | 3.5855 |
973
+ | 4.9822 | 64500 | 3.564 |
974
+ | 4.9900 | 64600 | 3.5492 |
975
+ | 4.9977 | 64700 | 3.5962 |
976
+ | 5.0 | 64730 | - |
977
+ | 5.0054 | 64800 | 3.6 |
978
+ | 5.0131 | 64900 | 3.5732 |
979
+ | 5.0209 | 65000 | 3.58 |
980
+ | 5.0286 | 65100 | 3.5575 |
981
+ | 5.0363 | 65200 | 3.539 |
982
+ | 5.0440 | 65300 | 3.6133 |
983
+ | 5.0518 | 65400 | 3.5455 |
984
+ | 5.0595 | 65500 | 3.5785 |
985
+ | 5.0672 | 65600 | 3.5327 |
986
+ | 5.0749 | 65700 | 3.5178 |
987
+ | 5.0827 | 65800 | 3.596 |
988
+ | 5.0904 | 65900 | 3.5061 |
989
+ | 5.0981 | 66000 | 3.529 |
990
+ | 5.1058 | 66100 | 3.574 |
991
+ | 5.1135 | 66200 | 3.5834 |
992
+ | 5.1213 | 66300 | 3.5305 |
993
+ | 5.1290 | 66400 | 3.5732 |
994
+ | 5.1367 | 66500 | 3.5264 |
995
+ | 5.1444 | 66600 | 3.5814 |
996
+ | 5.1522 | 66700 | 3.5624 |
997
+ | 5.1599 | 66800 | 3.6005 |
998
+ | 5.1676 | 66900 | 3.5552 |
999
+ | 5.1753 | 67000 | 3.6337 |
1000
+ | 5.1831 | 67100 | 3.5887 |
1001
+ | 5.1908 | 67200 | 3.6187 |
1002
+ | 5.1985 | 67300 | 3.5848 |
1003
+ | 5.2062 | 67400 | 3.6265 |
1004
+ | 5.2140 | 67500 | 3.4996 |
1005
+ | 5.2217 | 67600 | 3.5364 |
1006
+ | 5.2294 | 67700 | 3.5231 |
1007
+ | 5.2371 | 67800 | 3.5737 |
1008
+ | 5.2449 | 67900 | 3.555 |
1009
+ | 5.2526 | 68000 | 3.6009 |
1010
+ | 5.2603 | 68100 | 3.6319 |
1011
+ | 5.2680 | 68200 | 3.5326 |
1012
+ | 5.2758 | 68300 | 3.5836 |
1013
+ | 5.2835 | 68400 | 3.532 |
1014
+ | 5.2912 | 68500 | 3.6527 |
1015
+ | 5.2989 | 68600 | 3.5875 |
1016
+ | 5.3067 | 68700 | 3.6051 |
1017
+ | 5.3144 | 68800 | 3.6242 |
1018
+ | 5.3221 | 68900 | 3.6027 |
1019
+ | 5.3298 | 69000 | 3.5581 |
1020
+ | 5.3376 | 69100 | 3.6552 |
1021
+ | 5.3453 | 69200 | 3.582 |
1022
+ | 5.3530 | 69300 | 3.5467 |
1023
+ | 5.3607 | 69400 | 3.5864 |
1024
+ | 5.3685 | 69500 | 3.5881 |
1025
+ | 5.3762 | 69600 | 3.6014 |
1026
+ | 5.3839 | 69700 | 3.5559 |
1027
+ | 5.3916 | 69800 | 3.5263 |
1028
+ | 5.3994 | 69900 | 3.608 |
1029
+ | 5.4071 | 70000 | 3.5951 |
1030
+ | 5.4148 | 70100 | 3.5871 |
1031
+ | 5.4225 | 70200 | 3.5642 |
1032
+ | 5.4302 | 70300 | 3.6349 |
1033
+ | 5.4380 | 70400 | 3.5389 |
1034
+ | 5.4457 | 70500 | 3.5653 |
1035
+ | 5.4534 | 70600 | 3.5484 |
1036
+ | 5.4611 | 70700 | 3.4994 |
1037
+ | 5.4689 | 70800 | 3.5436 |
1038
+ | 5.4766 | 70900 | 3.5602 |
1039
+ | 5.4843 | 71000 | 3.5965 |
1040
+ | 5.4920 | 71100 | 3.545 |
1041
+ | 5.4998 | 71200 | 3.6109 |
1042
+ | 5.5075 | 71300 | 3.6022 |
1043
+ | 5.5152 | 71400 | 3.5536 |
1044
+ | 5.5229 | 71500 | 3.6007 |
1045
+ | 5.5307 | 71600 | 3.5169 |
1046
+ | 5.5384 | 71700 | 3.5321 |
1047
+ | 5.5461 | 71800 | 3.5673 |
1048
+ | 5.5538 | 71900 | 3.5333 |
1049
+ | 5.5616 | 72000 | 3.5473 |
1050
+ | 5.5693 | 72100 | 3.5231 |
1051
+ | 5.5770 | 72200 | 3.5349 |
1052
+ | 5.5847 | 72300 | 3.5344 |
1053
+ | 5.5925 | 72400 | 3.5842 |
1054
+ | 5.6002 | 72500 | 3.547 |
1055
+ | 5.6079 | 72600 | 3.5192 |
1056
+ | 5.6156 | 72700 | 3.5959 |
1057
+ | 5.6234 | 72800 | 3.5267 |
1058
+ | 5.6311 | 72900 | 3.564 |
1059
+ | 5.6388 | 73000 | 3.5558 |
1060
+ | 5.6465 | 73100 | 3.4982 |
1061
+ | 5.6543 | 73200 | 3.5688 |
1062
+ | 5.6620 | 73300 | 3.5888 |
1063
+ | 5.6697 | 73400 | 3.5306 |
1064
+ | 5.6774 | 73500 | 3.6029 |
1065
+ | 5.6852 | 73600 | 3.5912 |
1066
+ | 5.6929 | 73700 | 3.4638 |
1067
+ | 5.7006 | 73800 | 3.6581 |
1068
+ | 5.7083 | 73900 | 3.5281 |
1069
+ | 5.7161 | 74000 | 3.5718 |
1070
+ | 5.7238 | 74100 | 3.6627 |
1071
+ | 5.7315 | 74200 | 3.5567 |
1072
+ | 5.7392 | 74300 | 3.6252 |
1073
+ | 5.7469 | 74400 | 3.5895 |
1074
+ | 5.7547 | 74500 | 3.5305 |
1075
+ | 5.7624 | 74600 | 3.5747 |
1076
+ | 5.7701 | 74700 | 3.5803 |
1077
+ | 5.7778 | 74800 | 3.5624 |
1078
+ | 5.7856 | 74900 | 3.5675 |
1079
+ | 5.7933 | 75000 | 3.5036 |
1080
+ | 5.8010 | 75100 | 3.543 |
1081
+ | 5.8087 | 75200 | 3.5818 |
1082
+ | 5.8165 | 75300 | 3.5355 |
1083
+ | 5.8242 | 75400 | 3.6583 |
1084
+ | 5.8319 | 75500 | 3.5694 |
1085
+ | 5.8396 | 75600 | 3.5175 |
1086
+ | 5.8474 | 75700 | 3.5812 |
1087
+ | 5.8551 | 75800 | 3.5877 |
1088
+ | 5.8628 | 75900 | 3.553 |
1089
+ | 5.8705 | 76000 | 3.5981 |
1090
+ | 5.8783 | 76100 | 3.5747 |
1091
+ | 5.8860 | 76200 | 3.5469 |
1092
+ | 5.8937 | 76300 | 3.5701 |
1093
+ | 5.9014 | 76400 | 3.5776 |
1094
+ | 5.9092 | 76500 | 3.5699 |
1095
+ | 5.9169 | 76600 | 3.5803 |
1096
+ | 5.9246 | 76700 | 3.6343 |
1097
+ | 5.9323 | 76800 | 3.5711 |
1098
+ | 5.9401 | 76900 | 3.4578 |
1099
+ | 5.9478 | 77000 | 3.5494 |
1100
+ | 5.9555 | 77100 | 3.6247 |
1101
+ | 5.9632 | 77200 | 3.6089 |
1102
+ | 5.9710 | 77300 | 3.5784 |
1103
+ | 5.9787 | 77400 | 3.6039 |
1104
+ | 5.9864 | 77500 | 3.5489 |
1105
+ | 5.9941 | 77600 | 3.599 |
1106
+ | 6.0 | 77676 | - |
1107
+ | 6.0019 | 77700 | 3.5749 |
1108
+ | 6.0096 | 77800 | 3.6291 |
1109
+ | 6.0173 | 77900 | 3.5352 |
1110
+ | 6.0250 | 78000 | 3.5792 |
1111
+ | 6.0328 | 78100 | 3.5634 |
1112
+ | 6.0405 | 78200 | 3.6688 |
1113
+ | 6.0482 | 78300 | 3.5931 |
1114
+ | 6.0559 | 78400 | 3.5747 |
1115
+ | 6.0636 | 78500 | 3.5496 |
1116
+ | 6.0714 | 78600 | 3.6033 |
1117
+ | 6.0791 | 78700 | 3.547 |
1118
+ | 6.0868 | 78800 | 3.6 |
1119
+ | 6.0945 | 78900 | 3.585 |
1120
+ | 6.1023 | 79000 | 3.5342 |
1121
+ | 6.1100 | 79100 | 3.5318 |
1122
+ | 6.1177 | 79200 | 3.6161 |
1123
+ | 6.1254 | 79300 | 3.6281 |
1124
+ | 6.1332 | 79400 | 3.5966 |
1125
+ | 6.1409 | 79500 | 3.5425 |
1126
+ | 6.1486 | 79600 | 3.5749 |
1127
+ | 6.1563 | 79700 | 3.5161 |
1128
+ | 6.1641 | 79800 | 3.5676 |
1129
+ | 6.1718 | 79900 | 3.4982 |
1130
+ | 6.1795 | 80000 | 3.5371 |
1131
+ | 6.1872 | 80100 | 3.5988 |
1132
+ | 6.1950 | 80200 | 3.5774 |
1133
+ | 6.2027 | 80300 | 3.5693 |
1134
+ | 6.2104 | 80400 | 3.6693 |
1135
+ | 6.2181 | 80500 | 3.5836 |
1136
+ | 6.2259 | 80600 | 3.563 |
1137
+ | 6.2336 | 80700 | 3.5738 |
1138
+ | 6.2413 | 80800 | 3.5334 |
1139
+ | 6.2490 | 80900 | 3.5563 |
1140
+ | 6.2568 | 81000 | 3.5627 |
1141
+ | 6.2645 | 81100 | 3.6318 |
1142
+ | 6.2722 | 81200 | 3.5134 |
1143
+ | 6.2799 | 81300 | 3.5576 |
1144
+ | 6.2877 | 81400 | 3.4738 |
1145
+ | 6.2954 | 81500 | 3.5917 |
1146
+ | 6.3031 | 81600 | 3.6035 |
1147
+ | 6.3108 | 81700 | 3.5532 |
1148
+ | 6.3186 | 81800 | 3.511 |
1149
+ | 6.3263 | 81900 | 3.6258 |
1150
+ | 6.3340 | 82000 | 3.6249 |
1151
+ | 6.3417 | 82100 | 3.5882 |
1152
+ | 6.3495 | 82200 | 3.5695 |
1153
+ | 6.3572 | 82300 | 3.6258 |
1154
+ | 6.3649 | 82400 | 3.5056 |
1155
+ | 6.3726 | 82500 | 3.6583 |
1156
+ | 6.3803 | 82600 | 3.5679 |
1157
+ | 6.3881 | 82700 | 3.5265 |
1158
+ | 6.3958 | 82800 | 3.6324 |
1159
+ | 6.4035 | 82900 | 3.5068 |
1160
+ | 6.4112 | 83000 | 3.5669 |
1161
+ | 6.4190 | 83100 | 3.6119 |
1162
+ | 6.4267 | 83200 | 3.5873 |
1163
+ | 6.4344 | 83300 | 3.604 |
1164
+ | 6.4421 | 83400 | 3.5581 |
1165
+ | 6.4499 | 83500 | 3.6205 |
1166
+ | 6.4576 | 83600 | 3.5556 |
1167
+ | 6.4653 | 83700 | 3.5283 |
1168
+ | 6.4730 | 83800 | 3.5559 |
1169
+ | 6.4808 | 83900 | 3.5482 |
1170
+ | 6.4885 | 84000 | 3.6079 |
1171
+ | 6.4962 | 84100 | 3.5762 |
1172
+ | 6.5039 | 84200 | 3.5181 |
1173
+ | 6.5117 | 84300 | 3.649 |
1174
+ | 6.5194 | 84400 | 3.5575 |
1175
+ | 6.5271 | 84500 | 3.5785 |
1176
+ | 6.5348 | 84600 | 3.5318 |
1177
+ | 6.5426 | 84700 | 3.5016 |
1178
+ | 6.5503 | 84800 | 3.552 |
1179
+ | 6.5580 | 84900 | 3.6086 |
1180
+ | 6.5657 | 85000 | 3.535 |
1181
+ | 6.5735 | 85100 | 3.5552 |
1182
+ | 6.5812 | 85200 | 3.567 |
1183
+ | 6.5889 | 85300 | 3.5454 |
1184
+ | 6.5966 | 85400 | 3.6012 |
1185
+ | 6.6044 | 85500 | 3.626 |
1186
+ | 6.6121 | 85600 | 3.5381 |
1187
+ | 6.6198 | 85700 | 3.613 |
1188
+ | 6.6275 | 85800 | 3.6178 |
1189
+ | 6.6353 | 85900 | 3.4871 |
1190
+ | 6.6430 | 86000 | 3.5689 |
1191
+ | 6.6507 | 86100 | 3.5123 |
1192
+ | 6.6584 | 86200 | 3.5702 |
1193
+ | 6.6662 | 86300 | 3.545 |
1194
+ | 6.6739 | 86400 | 3.5592 |
1195
+ | 6.6816 | 86500 | 3.5938 |
1196
+ | 6.6893 | 86600 | 3.5182 |
1197
+ | 6.6970 | 86700 | 3.5748 |
1198
+ | 6.7048 | 86800 | 3.5845 |
1199
+ | 6.7125 | 86900 | 3.5532 |
1200
+ | 6.7202 | 87000 | 3.555 |
1201
+ | 6.7279 | 87100 | 3.526 |
1202
+ | 6.7357 | 87200 | 3.5788 |
1203
+ | 6.7434 | 87300 | 3.6399 |
1204
+ | 6.7511 | 87400 | 3.6043 |
1205
+ | 6.7588 | 87500 | 3.5552 |
1206
+ | 6.7666 | 87600 | 3.5933 |
1207
+ | 6.7743 | 87700 | 3.5264 |
1208
+ | 6.7820 | 87800 | 3.5977 |
1209
+ | 6.7897 | 87900 | 3.5955 |
1210
+ | 6.7975 | 88000 | 3.5836 |
1211
+ | 6.8052 | 88100 | 3.5809 |
1212
+ | 6.8129 | 88200 | 3.5769 |
1213
+ | 6.8206 | 88300 | 3.5817 |
1214
+ | 6.8284 | 88400 | 3.5671 |
1215
+ | 6.8361 | 88500 | 3.5038 |
1216
+ | 6.8438 | 88600 | 3.6053 |
1217
+ | 6.8515 | 88700 | 3.5688 |
1218
+ | 6.8593 | 88800 | 3.5702 |
1219
+ | 6.8670 | 88900 | 3.5403 |
1220
+ | 6.8747 | 89000 | 3.5881 |
1221
+ | 6.8824 | 89100 | 3.5979 |
1222
+ | 6.8902 | 89200 | 3.5471 |
1223
+ | 6.8979 | 89300 | 3.6459 |
1224
+ | 6.9056 | 89400 | 3.4989 |
1225
+ | 6.9133 | 89500 | 3.5155 |
1226
+ | 6.9211 | 89600 | 3.5071 |
1227
+ | 6.9288 | 89700 | 3.5497 |
1228
+ | 6.9365 | 89800 | 3.5597 |
1229
+ | 6.9442 | 89900 | 3.5699 |
1230
+ | 6.9520 | 90000 | 3.5338 |
1231
+ | 6.9597 | 90100 | 3.6545 |
1232
+ | 6.9674 | 90200 | 3.5855 |
1233
+ | 6.9751 | 90300 | 3.5965 |
1234
+ | 6.9829 | 90400 | 3.4925 |
1235
+ | 6.9906 | 90500 | 3.4906 |
1236
+ | 6.9983 | 90600 | 3.4989 |
1237
+ | 7.0 | 90622 | - |
1238
+ | 7.0060 | 90700 | 3.5088 |
1239
+ | 7.0137 | 90800 | 3.5534 |
1240
+ | 7.0215 | 90900 | 3.5651 |
1241
+ | 7.0292 | 91000 | 3.5625 |
1242
+ | 7.0369 | 91100 | 3.6184 |
1243
+ | 7.0446 | 91200 | 3.5775 |
1244
+ | 7.0524 | 91300 | 3.6276 |
1245
+ | 7.0601 | 91400 | 3.6014 |
1246
+ | 7.0678 | 91500 | 3.5633 |
1247
+ | 7.0755 | 91600 | 3.5745 |
1248
+ | 7.0833 | 91700 | 3.5475 |
1249
+ | 7.0910 | 91800 | 3.5259 |
1250
+ | 7.0987 | 91900 | 3.62 |
1251
+ | 7.1064 | 92000 | 3.4788 |
1252
+ | 7.1142 | 92100 | 3.6074 |
1253
+ | 7.1219 | 92200 | 3.5754 |
1254
+ | 7.1296 | 92300 | 3.5663 |
1255
+ | 7.1373 | 92400 | 3.6712 |
1256
+ | 7.1451 | 92500 | 3.5545 |
1257
+ | 7.1528 | 92600 | 3.5414 |
1258
+ | 7.1605 | 92700 | 3.5747 |
1259
+ | 7.1682 | 92800 | 3.5833 |
1260
+ | 7.1760 | 92900 | 3.5288 |
1261
+ | 7.1837 | 93000 | 3.5036 |
1262
+ | 7.1914 | 93100 | 3.5741 |
1263
+ | 7.1991 | 93200 | 3.5475 |
1264
+ | 7.2069 | 93300 | 3.5761 |
1265
+ | 7.2146 | 93400 | 3.5747 |
1266
+ | 7.2223 | 93500 | 3.605 |
1267
+ | 7.2300 | 93600 | 3.6304 |
1268
+ | 7.2378 | 93700 | 3.5715 |
1269
+ | 7.2455 | 93800 | 3.5437 |
1270
+ | 7.2532 | 93900 | 3.5902 |
1271
+ | 7.2609 | 94000 | 3.5903 |
1272
+ | 7.2687 | 94100 | 3.6003 |
1273
+ | 7.2764 | 94200 | 3.5907 |
1274
+ | 7.2841 | 94300 | 3.6081 |
1275
+ | 7.2918 | 94400 | 3.557 |
1276
+ | 7.2996 | 94500 | 3.518 |
1277
+ | 7.3073 | 94600 | 3.53 |
1278
+ | 7.3150 | 94700 | 3.6208 |
1279
+ | 7.3227 | 94800 | 3.5272 |
1280
+ | 7.3304 | 94900 | 3.6043 |
1281
+ | 7.3382 | 95000 | 3.5509 |
1282
+ | 7.3459 | 95100 | 3.5207 |
1283
+ | 7.3536 | 95200 | 3.5672 |
1284
+ | 7.3613 | 95300 | 3.5473 |
1285
+ | 7.3691 | 95400 | 3.563 |
1286
+ | 7.3768 | 95500 | 3.546 |
1287
+ | 7.3845 | 95600 | 3.5267 |
1288
+ | 7.3922 | 95700 | 3.6104 |
1289
+ | 7.4000 | 95800 | 3.6281 |
1290
+ | 7.4077 | 95900 | 3.5319 |
1291
+ | 7.4154 | 96000 | 3.542 |
1292
+ | 7.4231 | 96100 | 3.6156 |
1293
+ | 7.4309 | 96200 | 3.5497 |
1294
+ | 7.4386 | 96300 | 3.5937 |
1295
+ | 7.4463 | 96400 | 3.5305 |
1296
+ | 7.4540 | 96500 | 3.5817 |
1297
+ | 7.4618 | 96600 | 3.585 |
1298
+ | 7.4695 | 96700 | 3.5224 |
1299
+ | 7.4772 | 96800 | 3.4943 |
1300
+ | 7.4849 | 96900 | 3.6074 |
1301
+ | 7.4927 | 97000 | 3.5525 |
1302
+ | 7.5004 | 97100 | 3.4805 |
1303
+ | 7.5081 | 97200 | 3.5752 |
1304
+ | 7.5158 | 97300 | 3.5226 |
1305
+ | 7.5236 | 97400 | 3.6341 |
1306
+ | 7.5313 | 97500 | 3.5572 |
1307
+ | 7.5390 | 97600 | 3.4738 |
1308
+ | 7.5467 | 97700 | 3.5147 |
1309
+ | 7.5545 | 97800 | 3.497 |
1310
+ | 7.5622 | 97900 | 3.6335 |
1311
+ | 7.5699 | 98000 | 3.6071 |
1312
+ | 7.5776 | 98100 | 3.6028 |
1313
+ | 7.5854 | 98200 | 3.5391 |
1314
+ | 7.5931 | 98300 | 3.5055 |
1315
+ | 7.6008 | 98400 | 3.5914 |
1316
+ | 7.6085 | 98500 | 3.5495 |
1317
+ | 7.6163 | 98600 | 3.5862 |
1318
+ | 7.6240 | 98700 | 3.5574 |
1319
+ | 7.6317 | 98800 | 3.5495 |
1320
+ | 7.6394 | 98900 | 3.4793 |
1321
+ | 7.6471 | 99000 | 3.5794 |
1322
+ | 7.6549 | 99100 | 3.5252 |
1323
+ | 7.6626 | 99200 | 3.5643 |
1324
+ | 7.6703 | 99300 | 3.5654 |
1325
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1326
+ | 7.6858 | 99500 | 3.5745 |
1327
+ | 7.6935 | 99600 | 3.5173 |
1328
+ | 7.7012 | 99700 | 3.5547 |
1329
+ | 7.7089 | 99800 | 3.5822 |
1330
+ | 7.7167 | 99900 | 3.5805 |
1331
+ | 7.7244 | 100000 | 3.5914 |
1332
+ | 7.7321 | 100100 | 3.5405 |
1333
+ | 7.7398 | 100200 | 3.5911 |
1334
+ | 7.7476 | 100300 | 3.505 |
1335
+ | 7.7553 | 100400 | 3.5082 |
1336
+ | 7.7630 | 100500 | 3.5239 |
1337
+ | 7.7707 | 100600 | 3.5434 |
1338
+ | 7.7785 | 100700 | 3.617 |
1339
+ | 7.7862 | 100800 | 3.5752 |
1340
+ | 7.7939 | 100900 | 3.5354 |
1341
+ | 7.8016 | 101000 | 3.6349 |
1342
+ | 7.8094 | 101100 | 3.5262 |
1343
+ | 7.8171 | 101200 | 3.5888 |
1344
+ | 7.8248 | 101300 | 3.6494 |
1345
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1346
+ | 7.8403 | 101500 | 3.4964 |
1347
+ | 7.8480 | 101600 | 3.5527 |
1348
+ | 7.8557 | 101700 | 3.5171 |
1349
+ | 7.8634 | 101800 | 3.5282 |
1350
+ | 7.8712 | 101900 | 3.6184 |
1351
+ | 7.8789 | 102000 | 3.5619 |
1352
+ | 7.8866 | 102100 | 3.5511 |
1353
+ | 7.8943 | 102200 | 3.5678 |
1354
+ | 7.9021 | 102300 | 3.6199 |
1355
+ | 7.9098 | 102400 | 3.6236 |
1356
+ | 7.9175 | 102500 | 3.6352 |
1357
+ | 7.9252 | 102600 | 3.5636 |
1358
+ | 7.9330 | 102700 | 3.5376 |
1359
+ | 7.9407 | 102800 | 3.523 |
1360
+ | 7.9484 | 102900 | 3.571 |
1361
+ | 7.9561 | 103000 | 3.5827 |
1362
+ | 7.9638 | 103100 | 3.5902 |
1363
+ | 7.9716 | 103200 | 3.5697 |
1364
+ | 7.9793 | 103300 | 3.5945 |
1365
+ | 7.9870 | 103400 | 3.5112 |
1366
+ | 7.9947 | 103500 | 3.5166 |
1367
+ | 8.0 | 103568 | - |
1368
+ | 8.0025 | 103600 | 3.5336 |
1369
+ | 8.0102 | 103700 | 3.5099 |
1370
+ | 8.0179 | 103800 | 3.6205 |
1371
+ | 8.0256 | 103900 | 3.585 |
1372
+ | 8.0334 | 104000 | 3.5917 |
1373
+ | 8.0411 | 104100 | 3.6217 |
1374
+ | 8.0488 | 104200 | 3.5836 |
1375
+ | 8.0565 | 104300 | 3.5957 |
1376
+ | 8.0643 | 104400 | 3.5954 |
1377
+ | 8.0720 | 104500 | 3.6093 |
1378
+ | 8.0797 | 104600 | 3.5077 |
1379
+ | 8.0874 | 104700 | 3.5992 |
1380
+ | 8.0952 | 104800 | 3.4937 |
1381
+ | 8.1029 | 104900 | 3.5773 |
1382
+ | 8.1106 | 105000 | 3.4919 |
1383
+ | 8.1183 | 105100 | 3.6044 |
1384
+ | 8.1261 | 105200 | 3.6254 |
1385
+ | 8.1338 | 105300 | 3.5467 |
1386
+ | 8.1415 | 105400 | 3.56 |
1387
+ | 8.1492 | 105500 | 3.4964 |
1388
+ | 8.1570 | 105600 | 3.571 |
1389
+ | 8.1647 | 105700 | 3.5262 |
1390
+ | 8.1724 | 105800 | 3.5674 |
1391
+ | 8.1801 | 105900 | 3.514 |
1392
+ | 8.1879 | 106000 | 3.5334 |
1393
+ | 8.1956 | 106100 | 3.5309 |
1394
+ | 8.2033 | 106200 | 3.5433 |
1395
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1396
+ | 8.2188 | 106400 | 3.5983 |
1397
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1398
+ | 8.2342 | 106600 | 3.5025 |
1399
+ | 8.2419 | 106700 | 3.5801 |
1400
+ | 8.2497 | 106800 | 3.576 |
1401
+ | 8.2574 | 106900 | 3.5255 |
1402
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1403
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1404
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1405
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1406
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1407
+ | 8.3037 | 107500 | 3.5054 |
1408
+ | 8.3114 | 107600 | 3.5978 |
1409
+ | 8.3192 | 107700 | 3.5679 |
1410
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1411
+ | 8.3346 | 107900 | 3.5653 |
1412
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1413
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1414
+ | 8.3578 | 108200 | 3.5375 |
1415
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1416
+ | 8.3732 | 108400 | 3.5482 |
1417
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1418
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1419
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1420
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1421
+ | 8.4119 | 108900 | 3.5684 |
1422
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1423
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1424
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1425
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1426
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1427
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1428
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1429
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1430
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1431
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1432
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1433
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1434
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1435
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1436
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1437
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1438
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1439
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1440
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1441
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1442
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1443
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1444
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1445
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1446
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1447
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1448
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1449
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1450
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1451
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1452
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1453
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1454
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1455
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1456
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1457
+ | 8.6899 | 112500 | 3.5344 |
1458
+ | 8.6977 | 112600 | 3.5097 |
1459
+ | 8.7054 | 112700 | 3.5624 |
1460
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1461
+ | 8.7208 | 112900 | 3.578 |
1462
+ | 8.7286 | 113000 | 3.5686 |
1463
+ | 8.7363 | 113100 | 3.5652 |
1464
+ | 8.7440 | 113200 | 3.4779 |
1465
+ | 8.7517 | 113300 | 3.5267 |
1466
+ | 8.7595 | 113400 | 3.5998 |
1467
+ | 8.7672 | 113500 | 3.6133 |
1468
+ | 8.7749 | 113600 | 3.5117 |
1469
+ | 8.7826 | 113700 | 3.5831 |
1470
+ | 8.7904 | 113800 | 3.5942 |
1471
+ | 8.7981 | 113900 | 3.5676 |
1472
+ | 8.8058 | 114000 | 3.5774 |
1473
+ | 8.8135 | 114100 | 3.5826 |
1474
+ | 8.8213 | 114200 | 3.5104 |
1475
+ | 8.8290 | 114300 | 3.5837 |
1476
+ | 8.8367 | 114400 | 3.5164 |
1477
+ | 8.8444 | 114500 | 3.5797 |
1478
+ | 8.8522 | 114600 | 3.5548 |
1479
+ | 8.8599 | 114700 | 3.5446 |
1480
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1481
+ | 8.8753 | 114900 | 3.534 |
1482
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1483
+ | 8.8908 | 115100 | 3.5606 |
1484
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1485
+ | 8.9062 | 115300 | 3.5134 |
1486
+ | 8.9140 | 115400 | 3.5133 |
1487
+ | 8.9217 | 115500 | 3.5392 |
1488
+ | 8.9294 | 115600 | 3.6146 |
1489
+ | 8.9371 | 115700 | 3.6073 |
1490
+ | 8.9448 | 115800 | 3.6348 |
1491
+ | 8.9526 | 115900 | 3.5017 |
1492
+ | 8.9603 | 116000 | 3.5393 |
1493
+ | 8.9680 | 116100 | 3.5289 |
1494
+ | 8.9757 | 116200 | 3.574 |
1495
+ | 8.9835 | 116300 | 3.6075 |
1496
+ | 8.9912 | 116400 | 3.5764 |
1497
+ | 8.9989 | 116500 | 3.5556 |
1498
+ | 9.0 | 116514 | - |
1499
+ | 9.0066 | 116600 | 3.5693 |
1500
+ | 9.0144 | 116700 | 3.5447 |
1501
+ | 9.0221 | 116800 | 3.547 |
1502
+ | 9.0298 | 116900 | 3.5535 |
1503
+ | 9.0375 | 117000 | 3.6399 |
1504
+ | 9.0453 | 117100 | 3.6004 |
1505
+ | 9.0530 | 117200 | 3.5478 |
1506
+ | 9.0607 | 117300 | 3.5591 |
1507
+ | 9.0684 | 117400 | 3.5547 |
1508
+ | 9.0762 | 117500 | 3.5562 |
1509
+ | 9.0839 | 117600 | 3.5528 |
1510
+ | 9.0916 | 117700 | 3.4959 |
1511
+ | 9.0993 | 117800 | 3.6083 |
1512
+ | 9.1071 | 117900 | 3.5458 |
1513
+ | 9.1148 | 118000 | 3.5683 |
1514
+ | 9.1225 | 118100 | 3.5616 |
1515
+ | 9.1302 | 118200 | 3.5477 |
1516
+ | 9.1380 | 118300 | 3.6412 |
1517
+ | 9.1457 | 118400 | 3.606 |
1518
+ | 9.1534 | 118500 | 3.5239 |
1519
+ | 9.1611 | 118600 | 3.648 |
1520
+ | 9.1689 | 118700 | 3.5421 |
1521
+ | 9.1766 | 118800 | 3.5405 |
1522
+ | 9.1843 | 118900 | 3.5311 |
1523
+ | 9.1920 | 119000 | 3.459 |
1524
+ | 9.1998 | 119100 | 3.5588 |
1525
+ | 9.2075 | 119200 | 3.5825 |
1526
+ | 9.2152 | 119300 | 3.6303 |
1527
+ | 9.2229 | 119400 | 3.5738 |
1528
+ | 9.2307 | 119500 | 3.5932 |
1529
+ | 9.2384 | 119600 | 3.5872 |
1530
+ | 9.2461 | 119700 | 3.5188 |
1531
+ | 9.2538 | 119800 | 3.5609 |
1532
+ | 9.2615 | 119900 | 3.5675 |
1533
+ | 9.2693 | 120000 | 3.5693 |
1534
+ | 9.2770 | 120100 | 3.556 |
1535
+ | 9.2847 | 120200 | 3.5249 |
1536
+ | 9.2924 | 120300 | 3.5187 |
1537
+ | 9.3002 | 120400 | 3.5571 |
1538
+ | 9.3079 | 120500 | 3.5878 |
1539
+ | 9.3156 | 120600 | 3.5475 |
1540
+ | 9.3233 | 120700 | 3.5304 |
1541
+ | 9.3311 | 120800 | 3.6067 |
1542
+ | 9.3388 | 120900 | 3.5687 |
1543
+ | 9.3465 | 121000 | 3.5218 |
1544
+ | 9.3542 | 121100 | 3.5415 |
1545
+ | 9.3620 | 121200 | 3.5858 |
1546
+ | 9.3697 | 121300 | 3.5889 |
1547
+ | 9.3774 | 121400 | 3.5353 |
1548
+ | 9.3851 | 121500 | 3.5139 |
1549
+ | 9.3929 | 121600 | 3.5563 |
1550
+ | 9.4006 | 121700 | 3.5495 |
1551
+ | 9.4083 | 121800 | 3.5183 |
1552
+ | 9.4160 | 121900 | 3.5616 |
1553
+ | 9.4238 | 122000 | 3.5899 |
1554
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1555
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1556
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1557
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1558
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1560
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1561
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1562
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1563
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1564
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1565
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1566
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1567
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1568
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1569
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1570
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1571
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1572
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1573
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1574
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1575
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1576
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1578
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1584
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1585
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1586
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1587
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1588
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1589
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1590
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1591
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1592
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1593
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1594
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1595
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1597
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1599
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1600
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1601
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1602
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1603
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1604
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1605
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1606
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1607
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1608
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1609
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1610
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1611
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1612
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1613
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1614
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1615
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1616
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1617
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1618
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1619
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1620
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1621
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1622
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1623
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1624
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1625
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1626
+ | 9.9876 | 129300 | 3.5341 |
1627
+ | 9.9954 | 129400 | 3.5907 |
1628
+ | **10.0** | **129460** | **-** |
1629
+
1630
+ * The bold row denotes the saved checkpoint.
1631
+ </details>
1632
+
1633
+ ### Framework Versions
1634
+ - Python: 3.10.13
1635
+ - Sentence Transformers: 5.1.2
1636
+ - Transformers: 4.57.1
1637
+ - PyTorch: 2.9.0+cu128
1638
+ - Accelerate: 1.11.0
1639
+ - Datasets: 4.3.0
1640
+ - Tokenizers: 0.22.1
1641
+
1642
+ ## Citation
1643
+
1644
+ ### BibTeX
1645
+
1646
+ #### Sentence Transformers
1647
+ ```bibtex
1648
+ @inproceedings{reimers-2019-sentence-bert,
1649
+ title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
1650
+ author = "Reimers, Nils and Gurevych, Iryna",
1651
+ booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
1652
+ month = "11",
1653
+ year = "2019",
1654
+ publisher = "Association for Computational Linguistics",
1655
+ url = "https://arxiv.org/abs/1908.10084",
1656
+ }
1657
+ ```
1658
+
1659
+ #### SymmetricLoss
1660
+ ```bibtex
1661
+ @article{he2024language,
1662
+ title={Language models as hierarchy encoders},
1663
+ author={He, Yuan and Yuan, Zhangdie and Chen, Jiaoyan and Horrocks, Ian},
1664
+ journal={arXiv preprint arXiv:2401.11374},
1665
+ year={2024}
1666
+ }
1667
+ ```
1668
+
1669
+ <!--
1670
+ ## Glossary
1671
+
1672
+ *Clearly define terms in order to be accessible across audiences.*
1673
+ -->
1674
+
1675
+ <!--
1676
+ ## Model Card Authors
1677
+
1678
+ *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
1679
+ -->
1680
+
1681
+ <!--
1682
+ ## Model Card Contact
1683
+
1684
+ *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
1685
+ -->
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