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youssefkhalil320
/
all-MiniLM-L6-v8-pair_score

Sentence Similarity
sentence-transformers
Safetensors
English
bert
feature-extraction
Generated from Trainer
dataset_size:80000003
loss:CoSENTLoss
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  1. checkpoint-119000/1_Pooling/config.json +10 -0
  2. checkpoint-119000/README.md +1579 -0
  3. checkpoint-119000/config.json +26 -0
  4. checkpoint-119000/config_sentence_transformers.json +10 -0
  5. checkpoint-119000/model.safetensors +3 -0
  6. checkpoint-119000/modules.json +20 -0
  7. checkpoint-119000/optimizer.pt +3 -0
  8. checkpoint-119000/rng_state.pth +3 -0
  9. checkpoint-119000/scheduler.pt +3 -0
  10. checkpoint-119000/sentence_bert_config.json +4 -0
  11. checkpoint-119000/special_tokens_map.json +37 -0
  12. checkpoint-119000/tokenizer.json +0 -0
  13. checkpoint-119000/tokenizer_config.json +64 -0
  14. checkpoint-119000/trainer_state.json +0 -0
  15. checkpoint-119000/training_args.bin +3 -0
  16. checkpoint-119000/vocab.txt +0 -0
checkpoint-119000/1_Pooling/config.json ADDED
@@ -0,0 +1,10 @@
 
 
 
 
 
 
 
 
 
 
 
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+ {
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+ "word_embedding_dimension": 384,
3
+ "pooling_mode_cls_token": false,
4
+ "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,
9
+ "include_prompt": true
10
+ }
checkpoint-119000/README.md ADDED
@@ -0,0 +1,1579 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ datasets:
3
+ - youssefkhalil320/pairs_three_scores_v5
4
+ language:
5
+ - en
6
+ library_name: sentence-transformers
7
+ license: apache-2.0
8
+ pipeline_tag: sentence-similarity
9
+ tags:
10
+ - sentence-transformers
11
+ - sentence-similarity
12
+ - feature-extraction
13
+ - generated_from_trainer
14
+ - dataset_size:80000003
15
+ - loss:CoSENTLoss
16
+ widget:
17
+ - source_sentence: durable pvc swim ring
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+ sentences:
19
+ - flaky croissant
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+ - urban shoes
21
+ - warm drinks mug
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+ - source_sentence: iso mak retard capsules
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+ sentences:
24
+ - savory baguette
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+ - shea butter body cream
26
+ - softwheeled cruiser
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+ - source_sentence: love sandra potty
28
+ sentences:
29
+ - utensil holder
30
+ - olive pants
31
+ - headwear
32
+ - source_sentence: dusky hair brush
33
+ sentences:
34
+ - back compartment laptop
35
+ - rubber feet platter
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+ - honed blade knife
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+ - source_sentence: nkd skn
38
+ sentences:
39
+ - fruit fragrances nail polish remover
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+ - panini salmon
41
+ - hand drawing bag
42
+ ---
43
+
44
+ # all-MiniLM-L6-v8-pair_score
45
+
46
+ This is a [sentence-transformers](https://www.SBERT.net) model trained on the [pairs_three_scores_v5](https://huggingface.co/datasets/youssefkhalil320/pairs_three_scores_v5) dataset. It maps sentences & paragraphs to a 384-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:** 384 tokens
55
+ - **Similarity Function:** Cosine Similarity
56
+ - **Training Dataset:**
57
+ - [pairs_three_scores_v5](https://huggingface.co/datasets/youssefkhalil320/pairs_three_scores_v5)
58
+ - **Language:** en
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+ - **License:** apache-2.0
60
+
61
+ ### Model Sources
62
+
63
+ - **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
64
+ - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/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
+ SentenceTransformer(
71
+ (0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: BertModel
72
+ (1): Pooling({'word_embedding_dimension': 384, '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
+ (2): Normalize()
74
+ )
75
+ ```
76
+
77
+ ## Usage
78
+
79
+ ### Direct Usage (Sentence Transformers)
80
+
81
+ First install the Sentence Transformers library:
82
+
83
+ ```bash
84
+ pip install -U sentence-transformers
85
+ ```
86
+
87
+ Then you can load this model and run inference.
88
+ ```python
89
+ from sentence_transformers import SentenceTransformer
90
+
91
+ # Download from the 🤗 Hub
92
+ model = SentenceTransformer("sentence_transformers_model_id")
93
+ # Run inference
94
+ sentences = [
95
+ 'nkd skn',
96
+ 'hand drawing bag',
97
+ 'panini salmon',
98
+ ]
99
+ embeddings = model.encode(sentences)
100
+ print(embeddings.shape)
101
+ # [3, 384]
102
+
103
+ # Get the similarity scores for the embeddings
104
+ similarities = model.similarity(embeddings, embeddings)
105
+ print(similarities.shape)
106
+ # [3, 3]
107
+ ```
108
+
109
+ <!--
110
+ ### Direct Usage (Transformers)
111
+
112
+ <details><summary>Click to see the direct usage in Transformers</summary>
113
+
114
+ </details>
115
+ -->
116
+
117
+ <!--
118
+ ### Downstream Usage (Sentence Transformers)
119
+
120
+ You can finetune this model on your own dataset.
121
+
122
+ <details><summary>Click to expand</summary>
123
+
124
+ </details>
125
+ -->
126
+
127
+ <!--
128
+ ### Out-of-Scope Use
129
+
130
+ *List how the model may foreseeably be misused and address what users ought not to do with the model.*
131
+ -->
132
+
133
+ <!--
134
+ ## Bias, Risks and Limitations
135
+
136
+ *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
137
+ -->
138
+
139
+ <!--
140
+ ### Recommendations
141
+
142
+ *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
143
+ -->
144
+
145
+ ## Training Details
146
+
147
+ ### Training Dataset
148
+
149
+ #### pairs_three_scores_v5
150
+
151
+ * Dataset: [pairs_three_scores_v5](https://huggingface.co/datasets/youssefkhalil320/pairs_three_scores_v5) at [3d8c457](https://huggingface.co/datasets/youssefkhalil320/pairs_three_scores_v5/tree/3d8c45703846bd2adfaaf422abafbc389b283de1)
152
+ * Size: 80,000,003 training samples
153
+ * Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
154
+ * Approximate statistics based on the first 1000 samples:
155
+ | | sentence1 | sentence2 | score |
156
+ |:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------|
157
+ | type | string | string | float |
158
+ | details | <ul><li>min: 3 tokens</li><li>mean: 6.06 tokens</li><li>max: 12 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 5.71 tokens</li><li>max: 13 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.11</li><li>max: 1.0</li></ul> |
159
+ * Samples:
160
+ | sentence1 | sentence2 | score |
161
+ |:-------------------------------------|:---------------------------------------|:-----------------|
162
+ | <code>vanilla hair cream</code> | <code>free of paraben hair mask</code> | <code>0.5</code> |
163
+ | <code>nourishing shampoo</code> | <code>cumin lemon tea</code> | <code>0.0</code> |
164
+ | <code>safe materials pacifier</code> | <code>facial serum</code> | <code>0.5</code> |
165
+ * Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
166
+ ```json
167
+ {
168
+ "scale": 20.0,
169
+ "similarity_fct": "pairwise_cos_sim"
170
+ }
171
+ ```
172
+
173
+ ### Evaluation Dataset
174
+
175
+ #### pairs_three_scores_v5
176
+
177
+ * Dataset: [pairs_three_scores_v5](https://huggingface.co/datasets/youssefkhalil320/pairs_three_scores_v5) at [3d8c457](https://huggingface.co/datasets/youssefkhalil320/pairs_three_scores_v5/tree/3d8c45703846bd2adfaaf422abafbc389b283de1)
178
+ * Size: 20,000,001 evaluation samples
179
+ * Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
180
+ * Approximate statistics based on the first 1000 samples:
181
+ | | sentence1 | sentence2 | score |
182
+ |:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------|
183
+ | type | string | string | float |
184
+ | details | <ul><li>min: 3 tokens</li><li>mean: 6.21 tokens</li><li>max: 12 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 5.75 tokens</li><li>max: 12 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.11</li><li>max: 1.0</li></ul> |
185
+ * Samples:
186
+ | sentence1 | sentence2 | score |
187
+ |:----------------------------------------|:-----------------------------------|:-----------------|
188
+ | <code>teddy bear toy</code> | <code>long lasting cat food</code> | <code>0.0</code> |
189
+ | <code>eva hair treatment</code> | <code>fresh pineapple</code> | <code>0.0</code> |
190
+ | <code>soft wave hair conditioner</code> | <code>hybrid seat bike</code> | <code>0.0</code> |
191
+ * Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
192
+ ```json
193
+ {
194
+ "scale": 20.0,
195
+ "similarity_fct": "pairwise_cos_sim"
196
+ }
197
+ ```
198
+
199
+ ### Training Hyperparameters
200
+ #### Non-Default Hyperparameters
201
+
202
+ - `eval_strategy`: steps
203
+ - `per_device_train_batch_size`: 128
204
+ - `per_device_eval_batch_size`: 128
205
+ - `learning_rate`: 2e-05
206
+ - `num_train_epochs`: 1
207
+ - `warmup_ratio`: 0.1
208
+ - `fp16`: True
209
+
210
+ #### All Hyperparameters
211
+ <details><summary>Click to expand</summary>
212
+
213
+ - `overwrite_output_dir`: False
214
+ - `do_predict`: False
215
+ - `eval_strategy`: steps
216
+ - `prediction_loss_only`: True
217
+ - `per_device_train_batch_size`: 128
218
+ - `per_device_eval_batch_size`: 128
219
+ - `per_gpu_train_batch_size`: None
220
+ - `per_gpu_eval_batch_size`: None
221
+ - `gradient_accumulation_steps`: 1
222
+ - `eval_accumulation_steps`: None
223
+ - `torch_empty_cache_steps`: None
224
+ - `learning_rate`: 2e-05
225
+ - `weight_decay`: 0.0
226
+ - `adam_beta1`: 0.9
227
+ - `adam_beta2`: 0.999
228
+ - `adam_epsilon`: 1e-08
229
+ - `max_grad_norm`: 1.0
230
+ - `num_train_epochs`: 1
231
+ - `max_steps`: -1
232
+ - `lr_scheduler_type`: linear
233
+ - `lr_scheduler_kwargs`: {}
234
+ - `warmup_ratio`: 0.1
235
+ - `warmup_steps`: 0
236
+ - `log_level`: passive
237
+ - `log_level_replica`: warning
238
+ - `log_on_each_node`: True
239
+ - `logging_nan_inf_filter`: True
240
+ - `save_safetensors`: True
241
+ - `save_on_each_node`: False
242
+ - `save_only_model`: False
243
+ - `restore_callback_states_from_checkpoint`: False
244
+ - `no_cuda`: False
245
+ - `use_cpu`: False
246
+ - `use_mps_device`: False
247
+ - `seed`: 42
248
+ - `data_seed`: None
249
+ - `jit_mode_eval`: False
250
+ - `use_ipex`: False
251
+ - `bf16`: False
252
+ - `fp16`: True
253
+ - `fp16_opt_level`: O1
254
+ - `half_precision_backend`: auto
255
+ - `bf16_full_eval`: False
256
+ - `fp16_full_eval`: False
257
+ - `tf32`: None
258
+ - `local_rank`: 0
259
+ - `ddp_backend`: None
260
+ - `tpu_num_cores`: None
261
+ - `tpu_metrics_debug`: False
262
+ - `debug`: []
263
+ - `dataloader_drop_last`: False
264
+ - `dataloader_num_workers`: 0
265
+ - `dataloader_prefetch_factor`: None
266
+ - `past_index`: -1
267
+ - `disable_tqdm`: False
268
+ - `remove_unused_columns`: True
269
+ - `label_names`: None
270
+ - `load_best_model_at_end`: False
271
+ - `ignore_data_skip`: False
272
+ - `fsdp`: []
273
+ - `fsdp_min_num_params`: 0
274
+ - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
275
+ - `fsdp_transformer_layer_cls_to_wrap`: None
276
+ - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
277
+ - `deepspeed`: None
278
+ - `label_smoothing_factor`: 0.0
279
+ - `optim`: adamw_torch
280
+ - `optim_args`: None
281
+ - `adafactor`: False
282
+ - `group_by_length`: False
283
+ - `length_column_name`: length
284
+ - `ddp_find_unused_parameters`: None
285
+ - `ddp_bucket_cap_mb`: None
286
+ - `ddp_broadcast_buffers`: False
287
+ - `dataloader_pin_memory`: True
288
+ - `dataloader_persistent_workers`: False
289
+ - `skip_memory_metrics`: True
290
+ - `use_legacy_prediction_loop`: False
291
+ - `push_to_hub`: False
292
+ - `resume_from_checkpoint`: None
293
+ - `hub_model_id`: None
294
+ - `hub_strategy`: every_save
295
+ - `hub_private_repo`: False
296
+ - `hub_always_push`: False
297
+ - `gradient_checkpointing`: False
298
+ - `gradient_checkpointing_kwargs`: None
299
+ - `include_inputs_for_metrics`: False
300
+ - `eval_do_concat_batches`: True
301
+ - `fp16_backend`: auto
302
+ - `push_to_hub_model_id`: None
303
+ - `push_to_hub_organization`: None
304
+ - `mp_parameters`:
305
+ - `auto_find_batch_size`: False
306
+ - `full_determinism`: False
307
+ - `torchdynamo`: None
308
+ - `ray_scope`: last
309
+ - `ddp_timeout`: 1800
310
+ - `torch_compile`: False
311
+ - `torch_compile_backend`: None
312
+ - `torch_compile_mode`: None
313
+ - `dispatch_batches`: None
314
+ - `split_batches`: None
315
+ - `include_tokens_per_second`: False
316
+ - `include_num_input_tokens_seen`: False
317
+ - `neftune_noise_alpha`: None
318
+ - `optim_target_modules`: None
319
+ - `batch_eval_metrics`: False
320
+ - `eval_on_start`: False
321
+ - `use_liger_kernel`: False
322
+ - `eval_use_gather_object`: False
323
+ - `batch_sampler`: batch_sampler
324
+ - `multi_dataset_batch_sampler`: proportional
325
+
326
+ </details>
327
+
328
+ ### Training Logs
329
+ <details><summary>Click to expand</summary>
330
+
331
+ | Epoch | Step | Training Loss |
332
+ |:------:|:------:|:-------------:|
333
+ | 0.0002 | 100 | 1.0055 |
334
+ | 0.0003 | 200 | 1.4548 |
335
+ | 0.0005 | 300 | 1.3606 |
336
+ | 0.0006 | 400 | 1.8727 |
337
+ | 0.0008 | 500 | 1.2193 |
338
+ | 0.0010 | 600 | 0.8869 |
339
+ | 0.0011 | 700 | 0.946 |
340
+ | 0.0013 | 800 | 1.1679 |
341
+ | 0.0014 | 900 | 1.4445 |
342
+ | 0.0016 | 1000 | 1.1232 |
343
+ | 0.0018 | 1100 | 1.3656 |
344
+ | 0.0019 | 1200 | 0.9544 |
345
+ | 0.0021 | 1300 | 1.1602 |
346
+ | 0.0022 | 1400 | 1.1699 |
347
+ | 0.0024 | 1500 | 1.1922 |
348
+ | 0.0026 | 1600 | 1.2176 |
349
+ | 0.0027 | 1700 | 0.902 |
350
+ | 0.0029 | 1800 | 1.2558 |
351
+ | 0.0030 | 1900 | 0.8901 |
352
+ | 0.0032 | 2000 | 0.8455 |
353
+ | 0.0034 | 2100 | 1.2258 |
354
+ | 0.0035 | 2200 | 1.4426 |
355
+ | 0.0037 | 2300 | 1.002 |
356
+ | 0.0038 | 2400 | 1.2667 |
357
+ | 0.0040 | 2500 | 1.3221 |
358
+ | 0.0042 | 2600 | 1.0549 |
359
+ | 0.0043 | 2700 | 1.065 |
360
+ | 0.0045 | 2800 | 1.0281 |
361
+ | 0.0046 | 2900 | 1.1742 |
362
+ | 0.0048 | 3000 | 1.1371 |
363
+ | 0.0050 | 3100 | 0.7274 |
364
+ | 0.0051 | 3200 | 1.0354 |
365
+ | 0.0053 | 3300 | 1.1904 |
366
+ | 0.0054 | 3400 | 0.7227 |
367
+ | 0.0056 | 3500 | 0.7959 |
368
+ | 0.0058 | 3600 | 0.9314 |
369
+ | 0.0059 | 3700 | 1.3199 |
370
+ | 0.0061 | 3800 | 0.8648 |
371
+ | 0.0062 | 3900 | 1.0403 |
372
+ | 0.0064 | 4000 | 1.2671 |
373
+ | 0.0066 | 4100 | 0.9584 |
374
+ | 0.0067 | 4200 | 0.7968 |
375
+ | 0.0069 | 4300 | 1.0191 |
376
+ | 0.0070 | 4400 | 0.8385 |
377
+ | 0.0072 | 4500 | 0.8621 |
378
+ | 0.0074 | 4600 | 1.1347 |
379
+ | 0.0075 | 4700 | 1.0102 |
380
+ | 0.0077 | 4800 | 1.1672 |
381
+ | 0.0078 | 4900 | 1.1201 |
382
+ | 0.0080 | 5000 | 0.9398 |
383
+ | 0.0082 | 5100 | 0.7871 |
384
+ | 0.0083 | 5200 | 1.0549 |
385
+ | 0.0085 | 5300 | 1.1945 |
386
+ | 0.0086 | 5400 | 0.6957 |
387
+ | 0.0088 | 5500 | 1.2681 |
388
+ | 0.0090 | 5600 | 1.2085 |
389
+ | 0.0091 | 5700 | 1.2695 |
390
+ | 0.0093 | 5800 | 1.1804 |
391
+ | 0.0094 | 5900 | 0.9978 |
392
+ | 0.0096 | 6000 | 0.6592 |
393
+ | 0.0098 | 6100 | 1.0246 |
394
+ | 0.0099 | 6200 | 1.2401 |
395
+ | 0.0101 | 6300 | 0.9192 |
396
+ | 0.0102 | 6400 | 0.8097 |
397
+ | 0.0104 | 6500 | 1.1171 |
398
+ | 0.0106 | 6600 | 0.8701 |
399
+ | 0.0107 | 6700 | 0.7356 |
400
+ | 0.0109 | 6800 | 1.1336 |
401
+ | 0.0110 | 6900 | 0.7735 |
402
+ | 0.0112 | 7000 | 0.8299 |
403
+ | 0.0114 | 7100 | 0.7268 |
404
+ | 0.0115 | 7200 | 1.2443 |
405
+ | 0.0117 | 7300 | 1.063 |
406
+ | 0.0118 | 7400 | 1.5062 |
407
+ | 0.0120 | 7500 | 0.9161 |
408
+ | 0.0122 | 7600 | 1.1862 |
409
+ | 0.0123 | 7700 | 0.6727 |
410
+ | 0.0125 | 7800 | 0.9152 |
411
+ | 0.0126 | 7900 | 0.8266 |
412
+ | 0.0128 | 8000 | 0.9861 |
413
+ | 0.0130 | 8100 | 1.3754 |
414
+ | 0.0131 | 8200 | 1.0408 |
415
+ | 0.0133 | 8300 | 1.083 |
416
+ | 0.0134 | 8400 | 1.5943 |
417
+ | 0.0136 | 8500 | 0.902 |
418
+ | 0.0138 | 8600 | 1.142 |
419
+ | 0.0139 | 8700 | 0.8885 |
420
+ | 0.0141 | 8800 | 1.3122 |
421
+ | 0.0142 | 8900 | 1.2599 |
422
+ | 0.0144 | 9000 | 1.249 |
423
+ | 0.0146 | 9100 | 0.9218 |
424
+ | 0.0147 | 9200 | 1.1997 |
425
+ | 0.0149 | 9300 | 0.9838 |
426
+ | 0.0150 | 9400 | 0.8914 |
427
+ | 0.0152 | 9500 | 0.8091 |
428
+ | 0.0154 | 9600 | 0.9083 |
429
+ | 0.0155 | 9700 | 1.17 |
430
+ | 0.0157 | 9800 | 0.9922 |
431
+ | 0.0158 | 9900 | 1.0668 |
432
+ | 0.0160 | 10000 | 0.8559 |
433
+ | 0.0162 | 10100 | 0.9612 |
434
+ | 0.0163 | 10200 | 0.937 |
435
+ | 0.0165 | 10300 | 0.6887 |
436
+ | 0.0166 | 10400 | 1.3222 |
437
+ | 0.0168 | 10500 | 0.8616 |
438
+ | 0.0170 | 10600 | 0.756 |
439
+ | 0.0171 | 10700 | 0.8091 |
440
+ | 0.0173 | 10800 | 0.9924 |
441
+ | 0.0174 | 10900 | 0.7771 |
442
+ | 0.0176 | 11000 | 1.1859 |
443
+ | 0.0178 | 11100 | 1.1057 |
444
+ | 0.0179 | 11200 | 1.0206 |
445
+ | 0.0181 | 11300 | 1.1066 |
446
+ | 0.0182 | 11400 | 0.8359 |
447
+ | 0.0184 | 11500 | 1.0751 |
448
+ | 0.0186 | 11600 | 1.193 |
449
+ | 0.0187 | 11700 | 0.9008 |
450
+ | 0.0189 | 11800 | 1.0155 |
451
+ | 0.0190 | 11900 | 1.3188 |
452
+ | 0.0192 | 12000 | 0.8217 |
453
+ | 0.0194 | 12100 | 1.0203 |
454
+ | 0.0195 | 12200 | 1.0958 |
455
+ | 0.0197 | 12300 | 0.5594 |
456
+ | 0.0198 | 12400 | 0.9775 |
457
+ | 0.0200 | 12500 | 1.1247 |
458
+ | 0.0202 | 12600 | 0.9579 |
459
+ | 0.0203 | 12700 | 1.2574 |
460
+ | 0.0205 | 12800 | 1.1222 |
461
+ | 0.0206 | 12900 | 1.2031 |
462
+ | 0.0208 | 13000 | 1.0072 |
463
+ | 0.0210 | 13100 | 1.3833 |
464
+ | 0.0211 | 13200 | 1.2336 |
465
+ | 0.0213 | 13300 | 1.3666 |
466
+ | 0.0214 | 13400 | 1.0923 |
467
+ | 0.0216 | 13500 | 1.378 |
468
+ | 0.0218 | 13600 | 1.3529 |
469
+ | 0.0219 | 13700 | 0.9503 |
470
+ | 0.0221 | 13800 | 0.9235 |
471
+ | 0.0222 | 13900 | 0.9167 |
472
+ | 0.0224 | 14000 | 1.0486 |
473
+ | 0.0226 | 14100 | 0.8933 |
474
+ | 0.0227 | 14200 | 0.7918 |
475
+ | 0.0229 | 14300 | 0.8914 |
476
+ | 0.0230 | 14400 | 1.0752 |
477
+ | 0.0232 | 14500 | 1.1496 |
478
+ | 0.0234 | 14600 | 1.1857 |
479
+ | 0.0235 | 14700 | 1.3716 |
480
+ | 0.0237 | 14800 | 1.1283 |
481
+ | 0.0238 | 14900 | 0.7685 |
482
+ | 0.0240 | 15000 | 1.0773 |
483
+ | 0.0242 | 15100 | 0.8706 |
484
+ | 0.0243 | 15200 | 0.9911 |
485
+ | 0.0245 | 15300 | 0.8004 |
486
+ | 0.0246 | 15400 | 0.761 |
487
+ | 0.0248 | 15500 | 0.8822 |
488
+ | 0.0250 | 15600 | 0.9188 |
489
+ | 0.0251 | 15700 | 1.0375 |
490
+ | 0.0253 | 15800 | 1.0479 |
491
+ | 0.0254 | 15900 | 1.051 |
492
+ | 0.0256 | 16000 | 0.9797 |
493
+ | 0.0258 | 16100 | 1.0863 |
494
+ | 0.0259 | 16200 | 0.8768 |
495
+ | 0.0261 | 16300 | 1.49 |
496
+ | 0.0262 | 16400 | 0.7819 |
497
+ | 0.0264 | 16500 | 0.8337 |
498
+ | 0.0266 | 16600 | 0.9316 |
499
+ | 0.0267 | 16700 | 1.0621 |
500
+ | 0.0269 | 16800 | 0.903 |
501
+ | 0.0270 | 16900 | 1.3651 |
502
+ | 0.0272 | 17000 | 0.9713 |
503
+ | 0.0274 | 17100 | 0.9579 |
504
+ | 0.0275 | 17200 | 1.1495 |
505
+ | 0.0277 | 17300 | 1.1833 |
506
+ | 0.0278 | 17400 | 1.3523 |
507
+ | 0.0280 | 17500 | 1.2028 |
508
+ | 0.0282 | 17600 | 0.8778 |
509
+ | 0.0283 | 17700 | 0.7266 |
510
+ | 0.0285 | 17800 | 0.8763 |
511
+ | 0.0286 | 17900 | 1.4955 |
512
+ | 0.0288 | 18000 | 0.6863 |
513
+ | 0.0290 | 18100 | 1.0725 |
514
+ | 0.0291 | 18200 | 1.2501 |
515
+ | 0.0293 | 18300 | 0.6407 |
516
+ | 0.0294 | 18400 | 0.6379 |
517
+ | 0.0296 | 18500 | 1.0366 |
518
+ | 0.0298 | 18600 | 0.7725 |
519
+ | 0.0299 | 18700 | 0.8563 |
520
+ | 0.0301 | 18800 | 1.1865 |
521
+ | 0.0302 | 18900 | 0.8072 |
522
+ | 0.0304 | 19000 | 1.2811 |
523
+ | 0.0306 | 19100 | 0.6197 |
524
+ | 0.0307 | 19200 | 0.6532 |
525
+ | 0.0309 | 19300 | 0.7419 |
526
+ | 0.0310 | 19400 | 1.2057 |
527
+ | 0.0312 | 19500 | 0.9907 |
528
+ | 0.0314 | 19600 | 1.0071 |
529
+ | 0.0315 | 19700 | 0.7657 |
530
+ | 0.0317 | 19800 | 0.8365 |
531
+ | 0.0318 | 19900 | 1.1842 |
532
+ | 0.0320 | 20000 | 0.7767 |
533
+ | 0.0322 | 20100 | 1.0015 |
534
+ | 0.0323 | 20200 | 1.0913 |
535
+ | 0.0325 | 20300 | 1.2954 |
536
+ | 0.0326 | 20400 | 0.9872 |
537
+ | 0.0328 | 20500 | 1.093 |
538
+ | 0.0330 | 20600 | 0.9084 |
539
+ | 0.0331 | 20700 | 0.7178 |
540
+ | 0.0333 | 20800 | 1.2123 |
541
+ | 0.0334 | 20900 | 1.1359 |
542
+ | 0.0336 | 21000 | 0.8423 |
543
+ | 0.0338 | 21100 | 0.7461 |
544
+ | 0.0339 | 21200 | 0.8495 |
545
+ | 0.0341 | 21300 | 1.3753 |
546
+ | 0.0342 | 21400 | 0.7703 |
547
+ | 0.0344 | 21500 | 1.1249 |
548
+ | 0.0346 | 21600 | 0.8316 |
549
+ | 0.0347 | 21700 | 0.7906 |
550
+ | 0.0349 | 21800 | 0.6205 |
551
+ | 0.0350 | 21900 | 1.5219 |
552
+ | 0.0352 | 22000 | 0.8956 |
553
+ | 0.0354 | 22100 | 0.6276 |
554
+ | 0.0355 | 22200 | 1.0288 |
555
+ | 0.0357 | 22300 | 0.9522 |
556
+ | 0.0358 | 22400 | 0.9521 |
557
+ | 0.0360 | 22500 | 0.8796 |
558
+ | 0.0362 | 22600 | 0.9758 |
559
+ | 0.0363 | 22700 | 0.7527 |
560
+ | 0.0365 | 22800 | 0.9528 |
561
+ | 0.0366 | 22900 | 1.0652 |
562
+ | 0.0368 | 23000 | 1.2811 |
563
+ | 0.0370 | 23100 | 1.7616 |
564
+ | 0.0371 | 23200 | 0.8629 |
565
+ | 0.0373 | 23300 | 1.3002 |
566
+ | 0.0374 | 23400 | 0.7786 |
567
+ | 0.0376 | 23500 | 0.7828 |
568
+ | 0.0378 | 23600 | 0.9787 |
569
+ | 0.0379 | 23700 | 1.04 |
570
+ | 0.0381 | 23800 | 0.7141 |
571
+ | 0.0382 | 23900 | 1.1718 |
572
+ | 0.0384 | 24000 | 0.9633 |
573
+ | 0.0386 | 24100 | 0.7282 |
574
+ | 0.0387 | 24200 | 0.9484 |
575
+ | 0.0389 | 24300 | 0.995 |
576
+ | 0.0390 | 24400 | 0.7906 |
577
+ | 0.0392 | 24500 | 0.8972 |
578
+ | 0.0394 | 24600 | 0.5984 |
579
+ | 0.0395 | 24700 | 1.101 |
580
+ | 0.0397 | 24800 | 0.9908 |
581
+ | 0.0398 | 24900 | 0.9852 |
582
+ | 0.0400 | 25000 | 0.9014 |
583
+ | 0.0402 | 25100 | 1.065 |
584
+ | 0.0403 | 25200 | 1.1163 |
585
+ | 0.0405 | 25300 | 1.3019 |
586
+ | 0.0406 | 25400 | 0.6841 |
587
+ | 0.0408 | 25500 | 0.8652 |
588
+ | 0.0410 | 25600 | 0.9686 |
589
+ | 0.0411 | 25700 | 1.2666 |
590
+ | 0.0413 | 25800 | 0.6733 |
591
+ | 0.0414 | 25900 | 1.1346 |
592
+ | 0.0416 | 26000 | 1.033 |
593
+ | 0.0418 | 26100 | 0.9257 |
594
+ | 0.0419 | 26200 | 0.9916 |
595
+ | 0.0421 | 26300 | 1.1998 |
596
+ | 0.0422 | 26400 | 0.8283 |
597
+ | 0.0424 | 26500 | 0.9418 |
598
+ | 0.0426 | 26600 | 0.6662 |
599
+ | 0.0427 | 26700 | 1.203 |
600
+ | 0.0429 | 26800 | 0.7963 |
601
+ | 0.0430 | 26900 | 1.1371 |
602
+ | 0.0432 | 27000 | 1.2205 |
603
+ | 0.0434 | 27100 | 0.8822 |
604
+ | 0.0435 | 27200 | 0.568 |
605
+ | 0.0437 | 27300 | 1.0166 |
606
+ | 0.0438 | 27400 | 1.2316 |
607
+ | 0.0440 | 27500 | 1.1823 |
608
+ | 0.0442 | 27600 | 1.0475 |
609
+ | 0.0443 | 27700 | 1.3679 |
610
+ | 0.0445 | 27800 | 0.7292 |
611
+ | 0.0446 | 27900 | 1.0197 |
612
+ | 0.0448 | 28000 | 1.1642 |
613
+ | 0.0450 | 28100 | 0.86 |
614
+ | 0.0451 | 28200 | 1.0193 |
615
+ | 0.0453 | 28300 | 0.8424 |
616
+ | 0.0454 | 28400 | 1.1718 |
617
+ | 0.0456 | 28500 | 0.8821 |
618
+ | 0.0458 | 28600 | 0.6682 |
619
+ | 0.0459 | 28700 | 1.0162 |
620
+ | 0.0461 | 28800 | 0.6683 |
621
+ | 0.0462 | 28900 | 1.0394 |
622
+ | 0.0464 | 29000 | 1.1651 |
623
+ | 0.0466 | 29100 | 1.2507 |
624
+ | 0.0467 | 29200 | 0.9884 |
625
+ | 0.0469 | 29300 | 1.2907 |
626
+ | 0.0470 | 29400 | 0.722 |
627
+ | 0.0472 | 29500 | 1.0291 |
628
+ | 0.0474 | 29600 | 0.8465 |
629
+ | 0.0475 | 29700 | 1.2208 |
630
+ | 0.0477 | 29800 | 1.1156 |
631
+ | 0.0478 | 29900 | 1.2081 |
632
+ | 0.0480 | 30000 | 0.7713 |
633
+ | 0.0482 | 30100 | 0.7625 |
634
+ | 0.0483 | 30200 | 1.0527 |
635
+ | 0.0485 | 30300 | 1.0007 |
636
+ | 0.0486 | 30400 | 1.1602 |
637
+ | 0.0488 | 30500 | 1.0247 |
638
+ | 0.0490 | 30600 | 0.8904 |
639
+ | 0.0491 | 30700 | 1.2021 |
640
+ | 0.0493 | 30800 | 1.2234 |
641
+ | 0.0494 | 30900 | 0.5206 |
642
+ | 0.0496 | 31000 | 0.6348 |
643
+ | 0.0498 | 31100 | 1.4066 |
644
+ | 0.0499 | 31200 | 0.8977 |
645
+ | 0.0501 | 31300 | 0.9366 |
646
+ | 0.0502 | 31400 | 0.8298 |
647
+ | 0.0504 | 31500 | 1.2081 |
648
+ | 0.0506 | 31600 | 0.7719 |
649
+ | 0.0507 | 31700 | 0.8549 |
650
+ | 0.0509 | 31800 | 0.8314 |
651
+ | 0.0510 | 31900 | 0.9677 |
652
+ | 0.0512 | 32000 | 1.076 |
653
+ | 0.0514 | 32100 | 0.8621 |
654
+ | 0.0515 | 32200 | 0.7732 |
655
+ | 0.0517 | 32300 | 1.2712 |
656
+ | 0.0518 | 32400 | 0.7716 |
657
+ | 0.0520 | 32500 | 1.2117 |
658
+ | 0.0522 | 32600 | 0.5914 |
659
+ | 0.0523 | 32700 | 0.9641 |
660
+ | 0.0525 | 32800 | 0.8344 |
661
+ | 0.0526 | 32900 | 1.1114 |
662
+ | 0.0528 | 33000 | 0.5916 |
663
+ | 0.0530 | 33100 | 1.1659 |
664
+ | 0.0531 | 33200 | 1.2546 |
665
+ | 0.0533 | 33300 | 1.1775 |
666
+ | 0.0534 | 33400 | 0.8546 |
667
+ | 0.0536 | 33500 | 0.965 |
668
+ | 0.0538 | 33600 | 1.0205 |
669
+ | 0.0539 | 33700 | 0.9915 |
670
+ | 0.0541 | 33800 | 0.6512 |
671
+ | 0.0542 | 33900 | 1.5724 |
672
+ | 0.0544 | 34000 | 0.585 |
673
+ | 0.0546 | 34100 | 0.5739 |
674
+ | 0.0547 | 34200 | 1.1366 |
675
+ | 0.0549 | 34300 | 0.8275 |
676
+ | 0.0550 | 34400 | 0.9622 |
677
+ | 0.0552 | 34500 | 0.9501 |
678
+ | 0.0554 | 34600 | 0.9669 |
679
+ | 0.0555 | 34700 | 0.6698 |
680
+ | 0.0557 | 34800 | 0.8761 |
681
+ | 0.0558 | 34900 | 1.164 |
682
+ | 0.0560 | 35000 | 1.1078 |
683
+ | 0.0562 | 35100 | 0.6677 |
684
+ | 0.0563 | 35200 | 0.9063 |
685
+ | 0.0565 | 35300 | 0.8596 |
686
+ | 0.0566 | 35400 | 0.6038 |
687
+ | 0.0568 | 35500 | 0.9595 |
688
+ | 0.0570 | 35600 | 0.7065 |
689
+ | 0.0571 | 35700 | 1.0015 |
690
+ | 0.0573 | 35800 | 1.1689 |
691
+ | 0.0574 | 35900 | 0.7457 |
692
+ | 0.0576 | 36000 | 1.0503 |
693
+ | 0.0578 | 36100 | 0.9348 |
694
+ | 0.0579 | 36200 | 1.1477 |
695
+ | 0.0581 | 36300 | 1.5041 |
696
+ | 0.0582 | 36400 | 1.1587 |
697
+ | 0.0584 | 36500 | 1.6659 |
698
+ | 0.0586 | 36600 | 0.8894 |
699
+ | 0.0587 | 36700 | 0.9926 |
700
+ | 0.0589 | 36800 | 0.823 |
701
+ | 0.0590 | 36900 | 1.0827 |
702
+ | 0.0592 | 37000 | 0.8602 |
703
+ | 0.0594 | 37100 | 0.9764 |
704
+ | 0.0595 | 37200 | 1.107 |
705
+ | 0.0597 | 37300 | 1.1195 |
706
+ | 0.0598 | 37400 | 1.0136 |
707
+ | 0.0600 | 37500 | 1.0256 |
708
+ | 0.0602 | 37600 | 0.6159 |
709
+ | 0.0603 | 37700 | 0.8101 |
710
+ | 0.0605 | 37800 | 1.205 |
711
+ | 0.0606 | 37900 | 0.9412 |
712
+ | 0.0608 | 38000 | 0.5807 |
713
+ | 0.0610 | 38100 | 0.9288 |
714
+ | 0.0611 | 38200 | 0.8912 |
715
+ | 0.0613 | 38300 | 0.7465 |
716
+ | 0.0614 | 38400 | 0.838 |
717
+ | 0.0616 | 38500 | 1.0469 |
718
+ | 0.0618 | 38600 | 1.1268 |
719
+ | 0.0619 | 38700 | 0.8408 |
720
+ | 0.0621 | 38800 | 0.7585 |
721
+ | 0.0622 | 38900 | 1.5577 |
722
+ | 0.0624 | 39000 | 1.0238 |
723
+ | 0.0626 | 39100 | 0.7825 |
724
+ | 0.0627 | 39200 | 1.0145 |
725
+ | 0.0629 | 39300 | 1.0283 |
726
+ | 0.0630 | 39400 | 0.8745 |
727
+ | 0.0632 | 39500 | 0.9517 |
728
+ | 0.0634 | 39600 | 0.7688 |
729
+ | 0.0635 | 39700 | 1.2549 |
730
+ | 0.0637 | 39800 | 1.019 |
731
+ | 0.0638 | 39900 | 1.174 |
732
+ | 0.0640 | 40000 | 1.0786 |
733
+ | 0.0642 | 40100 | 1.0131 |
734
+ | 0.0643 | 40200 | 1.3418 |
735
+ | 0.0645 | 40300 | 1.343 |
736
+ | 0.0646 | 40400 | 0.7159 |
737
+ | 0.0648 | 40500 | 0.8846 |
738
+ | 0.0650 | 40600 | 1.2983 |
739
+ | 0.0651 | 40700 | 1.1324 |
740
+ | 0.0653 | 40800 | 1.1314 |
741
+ | 0.0654 | 40900 | 0.8385 |
742
+ | 0.0656 | 41000 | 1.2278 |
743
+ | 0.0658 | 41100 | 0.958 |
744
+ | 0.0659 | 41200 | 0.9532 |
745
+ | 0.0661 | 41300 | 1.2848 |
746
+ | 0.0662 | 41400 | 1.1034 |
747
+ | 0.0664 | 41500 | 0.7877 |
748
+ | 0.0666 | 41600 | 1.3461 |
749
+ | 0.0667 | 41700 | 0.8843 |
750
+ | 0.0669 | 41800 | 1.1037 |
751
+ | 0.0670 | 41900 | 0.9413 |
752
+ | 0.0672 | 42000 | 0.9834 |
753
+ | 0.0674 | 42100 | 1.1334 |
754
+ | 0.0675 | 42200 | 1.5354 |
755
+ | 0.0677 | 42300 | 1.0558 |
756
+ | 0.0678 | 42400 | 0.8658 |
757
+ | 0.0680 | 42500 | 1.0295 |
758
+ | 0.0682 | 42600 | 0.8486 |
759
+ | 0.0683 | 42700 | 0.9365 |
760
+ | 0.0685 | 42800 | 1.3354 |
761
+ | 0.0686 | 42900 | 1.0064 |
762
+ | 0.0688 | 43000 | 0.8501 |
763
+ | 0.0690 | 43100 | 0.9073 |
764
+ | 0.0691 | 43200 | 0.9001 |
765
+ | 0.0693 | 43300 | 0.9947 |
766
+ | 0.0694 | 43400 | 1.1538 |
767
+ | 0.0696 | 43500 | 0.9939 |
768
+ | 0.0698 | 43600 | 0.8462 |
769
+ | 0.0699 | 43700 | 0.6063 |
770
+ | 0.0701 | 43800 | 0.6154 |
771
+ | 0.0702 | 43900 | 0.9854 |
772
+ | 0.0704 | 44000 | 1.8853 |
773
+ | 0.0706 | 44100 | 1.2866 |
774
+ | 0.0707 | 44200 | 1.0157 |
775
+ | 0.0709 | 44300 | 0.9644 |
776
+ | 0.0710 | 44400 | 1.0259 |
777
+ | 0.0712 | 44500 | 1.5043 |
778
+ | 0.0714 | 44600 | 1.0966 |
779
+ | 0.0715 | 44700 | 0.8624 |
780
+ | 0.0717 | 44800 | 1.0841 |
781
+ | 0.0718 | 44900 | 0.6193 |
782
+ | 0.0720 | 45000 | 0.998 |
783
+ | 0.0722 | 45100 | 0.7944 |
784
+ | 0.0723 | 45200 | 1.2486 |
785
+ | 0.0725 | 45300 | 1.1488 |
786
+ | 0.0726 | 45400 | 0.7305 |
787
+ | 0.0728 | 45500 | 0.79 |
788
+ | 0.0730 | 45600 | 0.8706 |
789
+ | 0.0731 | 45700 | 1.3494 |
790
+ | 0.0733 | 45800 | 1.0641 |
791
+ | 0.0734 | 45900 | 0.7652 |
792
+ | 0.0736 | 46000 | 0.9833 |
793
+ | 0.0738 | 46100 | 0.6565 |
794
+ | 0.0739 | 46200 | 0.6975 |
795
+ | 0.0741 | 46300 | 1.1075 |
796
+ | 0.0742 | 46400 | 1.4451 |
797
+ | 0.0744 | 46500 | 1.167 |
798
+ | 0.0746 | 46600 | 0.8649 |
799
+ | 0.0747 | 46700 | 0.8355 |
800
+ | 0.0749 | 46800 | 0.9473 |
801
+ | 0.0750 | 46900 | 1.234 |
802
+ | 0.0752 | 47000 | 1.085 |
803
+ | 0.0754 | 47100 | 1.0164 |
804
+ | 0.0755 | 47200 | 1.1595 |
805
+ | 0.0757 | 47300 | 0.8753 |
806
+ | 0.0758 | 47400 | 0.6639 |
807
+ | 0.0760 | 47500 | 0.7695 |
808
+ | 0.0762 | 47600 | 1.1087 |
809
+ | 0.0763 | 47700 | 1.2592 |
810
+ | 0.0765 | 47800 | 1.1147 |
811
+ | 0.0766 | 47900 | 0.8526 |
812
+ | 0.0768 | 48000 | 0.7581 |
813
+ | 0.0770 | 48100 | 0.8721 |
814
+ | 0.0771 | 48200 | 0.8537 |
815
+ | 0.0773 | 48300 | 1.1973 |
816
+ | 0.0774 | 48400 | 1.0531 |
817
+ | 0.0776 | 48500 | 0.7506 |
818
+ | 0.0778 | 48600 | 1.1752 |
819
+ | 0.0779 | 48700 | 0.8971 |
820
+ | 0.0781 | 48800 | 0.8309 |
821
+ | 0.0782 | 48900 | 1.0782 |
822
+ | 0.0784 | 49000 | 1.361 |
823
+ | 0.0786 | 49100 | 0.8282 |
824
+ | 0.0787 | 49200 | 1.4737 |
825
+ | 0.0789 | 49300 | 0.9203 |
826
+ | 0.0790 | 49400 | 1.3218 |
827
+ | 0.0792 | 49500 | 1.1755 |
828
+ | 0.0794 | 49600 | 1.1629 |
829
+ | 0.0795 | 49700 | 1.0052 |
830
+ | 0.0797 | 49800 | 1.1751 |
831
+ | 0.0798 | 49900 | 0.9488 |
832
+ | 0.0800 | 50000 | 0.9379 |
833
+ | 0.0802 | 50100 | 1.0444 |
834
+ | 0.0803 | 50200 | 1.0576 |
835
+ | 0.0805 | 50300 | 0.9335 |
836
+ | 0.0806 | 50400 | 1.0101 |
837
+ | 0.0808 | 50500 | 1.4957 |
838
+ | 0.0810 | 50600 | 0.7922 |
839
+ | 0.0811 | 50700 | 1.2119 |
840
+ | 0.0813 | 50800 | 1.5268 |
841
+ | 0.0814 | 50900 | 1.2212 |
842
+ | 0.0816 | 51000 | 0.9496 |
843
+ | 0.0818 | 51100 | 1.1323 |
844
+ | 0.0819 | 51200 | 0.8317 |
845
+ | 0.0821 | 51300 | 0.7998 |
846
+ | 0.0822 | 51400 | 1.039 |
847
+ | 0.0824 | 51500 | 0.9261 |
848
+ | 0.0826 | 51600 | 0.9348 |
849
+ | 0.0827 | 51700 | 0.8244 |
850
+ | 0.0829 | 51800 | 0.6638 |
851
+ | 0.0830 | 51900 | 0.7766 |
852
+ | 0.0832 | 52000 | 0.9662 |
853
+ | 0.0834 | 52100 | 0.9644 |
854
+ | 0.0835 | 52200 | 0.7819 |
855
+ | 0.0837 | 52300 | 1.2733 |
856
+ | 0.0838 | 52400 | 0.8968 |
857
+ | 0.0840 | 52500 | 1.0587 |
858
+ | 0.0842 | 52600 | 1.0327 |
859
+ | 0.0843 | 52700 | 0.843 |
860
+ | 0.0845 | 52800 | 0.8563 |
861
+ | 0.0846 | 52900 | 0.9833 |
862
+ | 0.0848 | 53000 | 0.7342 |
863
+ | 0.0850 | 53100 | 0.836 |
864
+ | 0.0851 | 53200 | 1.1273 |
865
+ | 0.0853 | 53300 | 1.2048 |
866
+ | 0.0854 | 53400 | 1.2806 |
867
+ | 0.0856 | 53500 | 1.0142 |
868
+ | 0.0858 | 53600 | 0.9115 |
869
+ | 0.0859 | 53700 | 0.9163 |
870
+ | 0.0861 | 53800 | 0.6333 |
871
+ | 0.0862 | 53900 | 0.6333 |
872
+ | 0.0864 | 54000 | 1.0598 |
873
+ | 0.0866 | 54100 | 0.8303 |
874
+ | 0.0867 | 54200 | 1.0918 |
875
+ | 0.0869 | 54300 | 1.2201 |
876
+ | 0.0870 | 54400 | 0.9549 |
877
+ | 0.0872 | 54500 | 1.0094 |
878
+ | 0.0874 | 54600 | 1.178 |
879
+ | 0.0875 | 54700 | 0.8639 |
880
+ | 0.0877 | 54800 | 0.8104 |
881
+ | 0.0878 | 54900 | 0.736 |
882
+ | 0.0880 | 55000 | 0.9126 |
883
+ | 0.0882 | 55100 | 0.8342 |
884
+ | 0.0883 | 55200 | 0.6095 |
885
+ | 0.0885 | 55300 | 1.3398 |
886
+ | 0.0886 | 55400 | 0.9715 |
887
+ | 0.0888 | 55500 | 1.0402 |
888
+ | 0.0890 | 55600 | 0.9575 |
889
+ | 0.0891 | 55700 | 0.6911 |
890
+ | 0.0893 | 55800 | 1.3068 |
891
+ | 0.0894 | 55900 | 1.1445 |
892
+ | 0.0896 | 56000 | 1.0125 |
893
+ | 0.0898 | 56100 | 1.0411 |
894
+ | 0.0899 | 56200 | 0.7514 |
895
+ | 0.0901 | 56300 | 1.1912 |
896
+ | 0.0902 | 56400 | 0.8718 |
897
+ | 0.0904 | 56500 | 1.0592 |
898
+ | 0.0906 | 56600 | 0.9106 |
899
+ | 0.0907 | 56700 | 0.7074 |
900
+ | 0.0909 | 56800 | 1.3677 |
901
+ | 0.0910 | 56900 | 0.5115 |
902
+ | 0.0912 | 57000 | 1.275 |
903
+ | 0.0914 | 57100 | 0.8179 |
904
+ | 0.0915 | 57200 | 1.0204 |
905
+ | 0.0917 | 57300 | 0.5723 |
906
+ | 0.0918 | 57400 | 0.9797 |
907
+ | 0.0920 | 57500 | 1.4259 |
908
+ | 0.0922 | 57600 | 1.1392 |
909
+ | 0.0923 | 57700 | 1.0604 |
910
+ | 0.0925 | 57800 | 1.1851 |
911
+ | 0.0926 | 57900 | 1.0228 |
912
+ | 0.0928 | 58000 | 0.8434 |
913
+ | 0.0930 | 58100 | 1.2932 |
914
+ | 0.0931 | 58200 | 1.0418 |
915
+ | 0.0933 | 58300 | 1.39 |
916
+ | 0.0934 | 58400 | 1.2831 |
917
+ | 0.0936 | 58500 | 0.9678 |
918
+ | 0.0938 | 58600 | 0.8928 |
919
+ | 0.0939 | 58700 | 0.7011 |
920
+ | 0.0941 | 58800 | 1.5027 |
921
+ | 0.0942 | 58900 | 1.1131 |
922
+ | 0.0944 | 59000 | 1.1221 |
923
+ | 0.0946 | 59100 | 1.0595 |
924
+ | 0.0947 | 59200 | 1.3894 |
925
+ | 0.0949 | 59300 | 1.3597 |
926
+ | 0.0950 | 59400 | 1.059 |
927
+ | 0.0952 | 59500 | 0.9638 |
928
+ | 0.0954 | 59600 | 1.5768 |
929
+ | 0.0955 | 59700 | 1.3683 |
930
+ | 0.0957 | 59800 | 1.1326 |
931
+ | 0.0958 | 59900 | 0.7561 |
932
+ | 0.0960 | 60000 | 0.8902 |
933
+ | 0.0962 | 60100 | 0.8149 |
934
+ | 0.0963 | 60200 | 0.8194 |
935
+ | 0.0965 | 60300 | 1.2256 |
936
+ | 0.0966 | 60400 | 0.7583 |
937
+ | 0.0968 | 60500 | 0.544 |
938
+ | 0.0970 | 60600 | 0.7481 |
939
+ | 0.0971 | 60700 | 0.9471 |
940
+ | 0.0973 | 60800 | 1.1132 |
941
+ | 0.0974 | 60900 | 0.9176 |
942
+ | 0.0976 | 61000 | 0.9302 |
943
+ | 0.0978 | 61100 | 0.9768 |
944
+ | 0.0979 | 61200 | 0.989 |
945
+ | 0.0981 | 61300 | 1.2852 |
946
+ | 0.0982 | 61400 | 0.9333 |
947
+ | 0.0984 | 61500 | 1.0405 |
948
+ | 0.0986 | 61600 | 0.9677 |
949
+ | 0.0987 | 61700 | 0.9543 |
950
+ | 0.0989 | 61800 | 0.7171 |
951
+ | 0.0990 | 61900 | 0.8934 |
952
+ | 0.0992 | 62000 | 0.8682 |
953
+ | 0.0994 | 62100 | 0.9693 |
954
+ | 0.0995 | 62200 | 0.6721 |
955
+ | 0.0997 | 62300 | 1.1378 |
956
+ | 0.0998 | 62400 | 0.8788 |
957
+ | 0.1000 | 62500 | 1.1977 |
958
+ | 0.1002 | 62600 | 0.6824 |
959
+ | 0.1003 | 62700 | 0.9298 |
960
+ | 0.1005 | 62800 | 0.7153 |
961
+ | 0.1006 | 62900 | 0.8993 |
962
+ | 0.1008 | 63000 | 1.2881 |
963
+ | 0.1010 | 63100 | 1.009 |
964
+ | 0.1011 | 63200 | 0.9879 |
965
+ | 0.1013 | 63300 | 0.981 |
966
+ | 0.1014 | 63400 | 1.1676 |
967
+ | 0.1016 | 63500 | 1.5251 |
968
+ | 0.1018 | 63600 | 0.6786 |
969
+ | 0.1019 | 63700 | 1.1555 |
970
+ | 0.1021 | 63800 | 1.1603 |
971
+ | 0.1022 | 63900 | 1.0195 |
972
+ | 0.1024 | 64000 | 1.349 |
973
+ | 0.1026 | 64100 | 1.2512 |
974
+ | 0.1027 | 64200 | 0.9326 |
975
+ | 0.1029 | 64300 | 0.826 |
976
+ | 0.1030 | 64400 | 0.7685 |
977
+ | 0.1032 | 64500 | 0.6922 |
978
+ | 0.1034 | 64600 | 1.1789 |
979
+ | 0.1035 | 64700 | 1.0798 |
980
+ | 0.1037 | 64800 | 0.9082 |
981
+ | 0.1038 | 64900 | 1.039 |
982
+ | 0.1040 | 65000 | 1.1412 |
983
+ | 0.1042 | 65100 | 0.7442 |
984
+ | 0.1043 | 65200 | 1.0123 |
985
+ | 0.1045 | 65300 | 0.4859 |
986
+ | 0.1046 | 65400 | 0.9406 |
987
+ | 0.1048 | 65500 | 0.8441 |
988
+ | 0.1050 | 65600 | 1.0119 |
989
+ | 0.1051 | 65700 | 1.0438 |
990
+ | 0.1053 | 65800 | 1.0605 |
991
+ | 0.1054 | 65900 | 0.9334 |
992
+ | 0.1056 | 66000 | 0.6697 |
993
+ | 0.1058 | 66100 | 0.8317 |
994
+ | 0.1059 | 66200 | 1.029 |
995
+ | 0.1061 | 66300 | 1.2021 |
996
+ | 0.1062 | 66400 | 0.9772 |
997
+ | 0.1064 | 66500 | 1.2378 |
998
+ | 0.1066 | 66600 | 0.8794 |
999
+ | 0.1067 | 66700 | 1.1039 |
1000
+ | 0.1069 | 66800 | 1.8308 |
1001
+ | 0.1070 | 66900 | 1.0144 |
1002
+ | 0.1072 | 67000 | 0.8842 |
1003
+ | 0.1074 | 67100 | 0.9512 |
1004
+ | 0.1075 | 67200 | 0.8279 |
1005
+ | 0.1077 | 67300 | 0.6618 |
1006
+ | 0.1078 | 67400 | 0.8978 |
1007
+ | 0.1080 | 67500 | 1.0159 |
1008
+ | 0.1082 | 67600 | 1.0393 |
1009
+ | 0.1083 | 67700 | 1.0982 |
1010
+ | 0.1085 | 67800 | 1.0854 |
1011
+ | 0.1086 | 67900 | 0.9571 |
1012
+ | 0.1088 | 68000 | 0.8593 |
1013
+ | 0.1090 | 68100 | 0.7391 |
1014
+ | 0.1091 | 68200 | 0.8797 |
1015
+ | 0.1093 | 68300 | 0.5395 |
1016
+ | 0.1094 | 68400 | 1.114 |
1017
+ | 0.1096 | 68500 | 0.8078 |
1018
+ | 0.1098 | 68600 | 0.6807 |
1019
+ | 0.1099 | 68700 | 0.7836 |
1020
+ | 0.1101 | 68800 | 1.0234 |
1021
+ | 0.1102 | 68900 | 1.0903 |
1022
+ | 0.1104 | 69000 | 0.9431 |
1023
+ | 0.1106 | 69100 | 0.8331 |
1024
+ | 0.1107 | 69200 | 0.8313 |
1025
+ | 0.1109 | 69300 | 0.8974 |
1026
+ | 0.1110 | 69400 | 0.999 |
1027
+ | 0.1112 | 69500 | 1.1252 |
1028
+ | 0.1114 | 69600 | 1.0037 |
1029
+ | 0.1115 | 69700 | 1.1471 |
1030
+ | 0.1117 | 69800 | 0.4359 |
1031
+ | 0.1118 | 69900 | 0.9489 |
1032
+ | 0.1120 | 70000 | 1.0918 |
1033
+ | 0.1122 | 70100 | 1.0105 |
1034
+ | 0.1123 | 70200 | 0.9658 |
1035
+ | 0.1125 | 70300 | 1.0924 |
1036
+ | 0.1126 | 70400 | 0.8999 |
1037
+ | 0.1128 | 70500 | 0.8329 |
1038
+ | 0.1130 | 70600 | 1.3656 |
1039
+ | 0.1131 | 70700 | 0.8539 |
1040
+ | 0.1133 | 70800 | 1.0306 |
1041
+ | 0.1134 | 70900 | 0.9843 |
1042
+ | 0.1136 | 71000 | 1.095 |
1043
+ | 0.1138 | 71100 | 0.9461 |
1044
+ | 0.1139 | 71200 | 1.1666 |
1045
+ | 0.1141 | 71300 | 0.872 |
1046
+ | 0.1142 | 71400 | 0.9566 |
1047
+ | 0.1144 | 71500 | 0.7816 |
1048
+ | 0.1146 | 71600 | 0.9807 |
1049
+ | 0.1147 | 71700 | 0.887 |
1050
+ | 0.1149 | 71800 | 1.1062 |
1051
+ | 0.1150 | 71900 | 0.8014 |
1052
+ | 0.1152 | 72000 | 1.0652 |
1053
+ | 0.1154 | 72100 | 1.1422 |
1054
+ | 0.1155 | 72200 | 0.8654 |
1055
+ | 0.1157 | 72300 | 0.7491 |
1056
+ | 0.1158 | 72400 | 0.8395 |
1057
+ | 0.1160 | 72500 | 0.7764 |
1058
+ | 0.1162 | 72600 | 1.124 |
1059
+ | 0.1163 | 72700 | 1.084 |
1060
+ | 0.1165 | 72800 | 0.8545 |
1061
+ | 0.1166 | 72900 | 0.6059 |
1062
+ | 0.1168 | 73000 | 1.0053 |
1063
+ | 0.1170 | 73100 | 1.012 |
1064
+ | 0.1171 | 73200 | 1.3014 |
1065
+ | 0.1173 | 73300 | 0.6559 |
1066
+ | 0.1174 | 73400 | 0.9681 |
1067
+ | 0.1176 | 73500 | 0.8436 |
1068
+ | 0.1178 | 73600 | 0.8916 |
1069
+ | 0.1179 | 73700 | 1.1995 |
1070
+ | 0.1181 | 73800 | 0.9891 |
1071
+ | 0.1182 | 73900 | 1.382 |
1072
+ | 0.1184 | 74000 | 0.7432 |
1073
+ | 0.1186 | 74100 | 0.5479 |
1074
+ | 0.1187 | 74200 | 0.8149 |
1075
+ | 0.1189 | 74300 | 0.7375 |
1076
+ | 0.1190 | 74400 | 0.8813 |
1077
+ | 0.1192 | 74500 | 0.8745 |
1078
+ | 0.1194 | 74600 | 1.0679 |
1079
+ | 0.1195 | 74700 | 1.1579 |
1080
+ | 0.1197 | 74800 | 0.7131 |
1081
+ | 0.1198 | 74900 | 0.6479 |
1082
+ | 0.1200 | 75000 | 1.1209 |
1083
+ | 0.1202 | 75100 | 0.7519 |
1084
+ | 0.1203 | 75200 | 0.9916 |
1085
+ | 0.1205 | 75300 | 0.9934 |
1086
+ | 0.1206 | 75400 | 0.892 |
1087
+ | 0.1208 | 75500 | 0.8444 |
1088
+ | 0.1210 | 75600 | 0.6853 |
1089
+ | 0.1211 | 75700 | 0.8024 |
1090
+ | 0.1213 | 75800 | 1.0165 |
1091
+ | 0.1214 | 75900 | 0.669 |
1092
+ | 0.1216 | 76000 | 0.7899 |
1093
+ | 0.1218 | 76100 | 0.9388 |
1094
+ | 0.1219 | 76200 | 0.905 |
1095
+ | 0.1221 | 76300 | 0.8953 |
1096
+ | 0.1222 | 76400 | 1.3296 |
1097
+ | 0.1224 | 76500 | 0.9959 |
1098
+ | 0.1226 | 76600 | 1.0196 |
1099
+ | 0.1227 | 76700 | 1.1041 |
1100
+ | 0.1229 | 76800 | 1.2212 |
1101
+ | 0.1230 | 76900 | 0.7915 |
1102
+ | 0.1232 | 77000 | 0.9688 |
1103
+ | 0.1234 | 77100 | 0.929 |
1104
+ | 0.1235 | 77200 | 0.8934 |
1105
+ | 0.1237 | 77300 | 0.8938 |
1106
+ | 0.1238 | 77400 | 0.8811 |
1107
+ | 0.1240 | 77500 | 0.9009 |
1108
+ | 0.1242 | 77600 | 0.73 |
1109
+ | 0.1243 | 77700 | 1.1269 |
1110
+ | 0.1245 | 77800 | 0.7162 |
1111
+ | 0.1246 | 77900 | 1.2201 |
1112
+ | 0.1248 | 78000 | 0.7263 |
1113
+ | 0.1250 | 78100 | 0.6865 |
1114
+ | 0.1251 | 78200 | 1.1775 |
1115
+ | 0.1253 | 78300 | 0.6926 |
1116
+ | 0.1254 | 78400 | 0.9815 |
1117
+ | 0.1256 | 78500 | 1.0121 |
1118
+ | 0.1258 | 78600 | 0.7952 |
1119
+ | 0.1259 | 78700 | 0.8145 |
1120
+ | 0.1261 | 78800 | 0.6613 |
1121
+ | 0.1262 | 78900 | 0.8341 |
1122
+ | 0.1264 | 79000 | 1.2011 |
1123
+ | 0.1266 | 79100 | 1.0006 |
1124
+ | 0.1267 | 79200 | 0.9488 |
1125
+ | 0.1269 | 79300 | 0.672 |
1126
+ | 0.1270 | 79400 | 0.987 |
1127
+ | 0.1272 | 79500 | 0.7719 |
1128
+ | 0.1274 | 79600 | 1.1624 |
1129
+ | 0.1275 | 79700 | 0.7907 |
1130
+ | 0.1277 | 79800 | 0.7254 |
1131
+ | 0.1278 | 79900 | 1.1489 |
1132
+ | 0.1280 | 80000 | 1.0205 |
1133
+ | 0.1282 | 80100 | 1.2573 |
1134
+ | 0.1283 | 80200 | 1.0857 |
1135
+ | 0.1285 | 80300 | 0.6454 |
1136
+ | 0.1286 | 80400 | 0.7855 |
1137
+ | 0.1288 | 80500 | 0.8202 |
1138
+ | 0.1290 | 80600 | 1.0329 |
1139
+ | 0.1291 | 80700 | 0.6666 |
1140
+ | 0.1293 | 80800 | 0.3453 |
1141
+ | 0.1294 | 80900 | 0.9624 |
1142
+ | 0.1296 | 81000 | 0.8213 |
1143
+ | 0.1298 | 81100 | 0.9486 |
1144
+ | 0.1299 | 81200 | 0.7437 |
1145
+ | 0.1301 | 81300 | 0.8703 |
1146
+ | 0.1302 | 81400 | 1.3842 |
1147
+ | 0.1304 | 81500 | 0.7337 |
1148
+ | 0.1306 | 81600 | 0.9107 |
1149
+ | 0.1307 | 81700 | 0.8235 |
1150
+ | 0.1309 | 81800 | 0.7732 |
1151
+ | 0.1310 | 81900 | 1.4958 |
1152
+ | 0.1312 | 82000 | 0.7968 |
1153
+ | 0.1314 | 82100 | 0.5104 |
1154
+ | 0.1315 | 82200 | 1.0408 |
1155
+ | 0.1317 | 82300 | 0.885 |
1156
+ | 0.1318 | 82400 | 0.7941 |
1157
+ | 0.1320 | 82500 | 0.9131 |
1158
+ | 0.1322 | 82600 | 1.0114 |
1159
+ | 0.1323 | 82700 | 0.8596 |
1160
+ | 0.1325 | 82800 | 0.789 |
1161
+ | 0.1326 | 82900 | 0.7642 |
1162
+ | 0.1328 | 83000 | 0.8418 |
1163
+ | 0.1330 | 83100 | 0.901 |
1164
+ | 0.1331 | 83200 | 1.1646 |
1165
+ | 0.1333 | 83300 | 0.5562 |
1166
+ | 0.1334 | 83400 | 1.1399 |
1167
+ | 0.1336 | 83500 | 0.6294 |
1168
+ | 0.1338 | 83600 | 0.8082 |
1169
+ | 0.1339 | 83700 | 1.041 |
1170
+ | 0.1341 | 83800 | 1.2033 |
1171
+ | 0.1342 | 83900 | 0.7803 |
1172
+ | 0.1344 | 84000 | 0.6999 |
1173
+ | 0.1346 | 84100 | 0.7163 |
1174
+ | 0.1347 | 84200 | 0.7636 |
1175
+ | 0.1349 | 84300 | 0.9021 |
1176
+ | 0.1350 | 84400 | 0.7589 |
1177
+ | 0.1352 | 84500 | 1.6093 |
1178
+ | 0.1354 | 84600 | 0.5688 |
1179
+ | 0.1355 | 84700 | 0.8353 |
1180
+ | 0.1357 | 84800 | 0.7335 |
1181
+ | 0.1358 | 84900 | 0.7659 |
1182
+ | 0.1360 | 85000 | 0.8073 |
1183
+ | 0.1362 | 85100 | 1.3349 |
1184
+ | 0.1363 | 85200 | 1.0155 |
1185
+ | 0.1365 | 85300 | 1.0584 |
1186
+ | 0.1366 | 85400 | 0.9786 |
1187
+ | 0.1368 | 85500 | 0.7077 |
1188
+ | 0.1370 | 85600 | 0.918 |
1189
+ | 0.1371 | 85700 | 0.9776 |
1190
+ | 0.1373 | 85800 | 0.9961 |
1191
+ | 0.1374 | 85900 | 0.8974 |
1192
+ | 0.1376 | 86000 | 0.7558 |
1193
+ | 0.1378 | 86100 | 0.8644 |
1194
+ | 0.1379 | 86200 | 0.7654 |
1195
+ | 0.1381 | 86300 | 1.0623 |
1196
+ | 0.1382 | 86400 | 1.186 |
1197
+ | 0.1384 | 86500 | 0.6479 |
1198
+ | 0.1386 | 86600 | 0.7403 |
1199
+ | 0.1387 | 86700 | 1.0903 |
1200
+ | 0.1389 | 86800 | 1.251 |
1201
+ | 0.1390 | 86900 | 1.3176 |
1202
+ | 0.1392 | 87000 | 0.8293 |
1203
+ | 0.1394 | 87100 | 1.0111 |
1204
+ | 0.1395 | 87200 | 0.9344 |
1205
+ | 0.1397 | 87300 | 0.8197 |
1206
+ | 0.1398 | 87400 | 0.8103 |
1207
+ | 0.1400 | 87500 | 0.9249 |
1208
+ | 0.1402 | 87600 | 0.7484 |
1209
+ | 0.1403 | 87700 | 1.1759 |
1210
+ | 0.1405 | 87800 | 0.6712 |
1211
+ | 0.1406 | 87900 | 0.9891 |
1212
+ | 0.1408 | 88000 | 1.2745 |
1213
+ | 0.1410 | 88100 | 0.9468 |
1214
+ | 0.1411 | 88200 | 1.0717 |
1215
+ | 0.1413 | 88300 | 0.7655 |
1216
+ | 0.1414 | 88400 | 0.6614 |
1217
+ | 0.1416 | 88500 | 0.9985 |
1218
+ | 0.1418 | 88600 | 1.0301 |
1219
+ | 0.1419 | 88700 | 0.77 |
1220
+ | 0.1421 | 88800 | 1.0269 |
1221
+ | 0.1422 | 88900 | 1.0318 |
1222
+ | 0.1424 | 89000 | 0.8761 |
1223
+ | 0.1426 | 89100 | 0.8752 |
1224
+ | 0.1427 | 89200 | 0.7722 |
1225
+ | 0.1429 | 89300 | 0.7436 |
1226
+ | 0.1430 | 89400 | 0.8016 |
1227
+ | 0.1432 | 89500 | 1.3292 |
1228
+ | 0.1434 | 89600 | 0.7852 |
1229
+ | 0.1435 | 89700 | 0.9436 |
1230
+ | 0.1437 | 89800 | 1.3337 |
1231
+ | 0.1438 | 89900 | 0.955 |
1232
+ | 0.1440 | 90000 | 0.746 |
1233
+ | 0.1442 | 90100 | 0.8023 |
1234
+ | 0.1443 | 90200 | 0.8846 |
1235
+ | 0.1445 | 90300 | 1.0445 |
1236
+ | 0.1446 | 90400 | 0.8104 |
1237
+ | 0.1448 | 90500 | 0.684 |
1238
+ | 0.1450 | 90600 | 0.6247 |
1239
+ | 0.1451 | 90700 | 0.8092 |
1240
+ | 0.1453 | 90800 | 0.9294 |
1241
+ | 0.1454 | 90900 | 0.4233 |
1242
+ | 0.1456 | 91000 | 0.8631 |
1243
+ | 0.1458 | 91100 | 0.8486 |
1244
+ | 0.1459 | 91200 | 0.9475 |
1245
+ | 0.1461 | 91300 | 0.9199 |
1246
+ | 0.1462 | 91400 | 0.9874 |
1247
+ | 0.1464 | 91500 | 0.6451 |
1248
+ | 0.1466 | 91600 | 0.9525 |
1249
+ | 0.1467 | 91700 | 0.8998 |
1250
+ | 0.1469 | 91800 | 1.0077 |
1251
+ | 0.1470 | 91900 | 1.2134 |
1252
+ | 0.1472 | 92000 | 0.962 |
1253
+ | 0.1474 | 92100 | 0.5146 |
1254
+ | 0.1475 | 92200 | 1.071 |
1255
+ | 0.1477 | 92300 | 0.6658 |
1256
+ | 0.1478 | 92400 | 0.8342 |
1257
+ | 0.1480 | 92500 | 0.7611 |
1258
+ | 0.1482 | 92600 | 1.2874 |
1259
+ | 0.1483 | 92700 | 1.0789 |
1260
+ | 0.1485 | 92800 | 0.6855 |
1261
+ | 0.1486 | 92900 | 1.4517 |
1262
+ | 0.1488 | 93000 | 0.9887 |
1263
+ | 0.1490 | 93100 | 1.0415 |
1264
+ | 0.1491 | 93200 | 1.1893 |
1265
+ | 0.1493 | 93300 | 1.3397 |
1266
+ | 0.1494 | 93400 | 0.7581 |
1267
+ | 0.1496 | 93500 | 0.9914 |
1268
+ | 0.1498 | 93600 | 0.6435 |
1269
+ | 0.1499 | 93700 | 1.1258 |
1270
+ | 0.1501 | 93800 | 0.6477 |
1271
+ | 0.1502 | 93900 | 0.8336 |
1272
+ | 0.1504 | 94000 | 0.9054 |
1273
+ | 0.1506 | 94100 | 1.1708 |
1274
+ | 0.1507 | 94200 | 0.8689 |
1275
+ | 0.1509 | 94300 | 1.252 |
1276
+ | 0.1510 | 94400 | 0.7444 |
1277
+ | 0.1512 | 94500 | 1.0548 |
1278
+ | 0.1514 | 94600 | 0.7673 |
1279
+ | 0.1515 | 94700 | 0.6656 |
1280
+ | 0.1517 | 94800 | 0.6603 |
1281
+ | 0.1518 | 94900 | 0.7993 |
1282
+ | 0.1520 | 95000 | 0.7296 |
1283
+ | 0.1522 | 95100 | 0.4561 |
1284
+ | 0.1523 | 95200 | 0.6552 |
1285
+ | 0.1525 | 95300 | 0.8182 |
1286
+ | 0.1526 | 95400 | 0.9183 |
1287
+ | 0.1528 | 95500 | 0.8321 |
1288
+ | 0.1530 | 95600 | 0.9209 |
1289
+ | 0.1531 | 95700 | 0.5904 |
1290
+ | 0.1533 | 95800 | 0.8455 |
1291
+ | 0.1534 | 95900 | 0.7415 |
1292
+ | 0.1536 | 96000 | 0.9702 |
1293
+ | 0.1538 | 96100 | 1.2053 |
1294
+ | 0.1539 | 96200 | 1.0772 |
1295
+ | 0.1541 | 96300 | 0.7618 |
1296
+ | 0.1542 | 96400 | 0.7445 |
1297
+ | 0.1544 | 96500 | 1.187 |
1298
+ | 0.1546 | 96600 | 0.8339 |
1299
+ | 0.1547 | 96700 | 1.0538 |
1300
+ | 0.1549 | 96800 | 1.0187 |
1301
+ | 0.1550 | 96900 | 0.5244 |
1302
+ | 0.1552 | 97000 | 0.9148 |
1303
+ | 0.1554 | 97100 | 1.0144 |
1304
+ | 0.1555 | 97200 | 0.9064 |
1305
+ | 0.1557 | 97300 | 0.9753 |
1306
+ | 0.1558 | 97400 | 0.4476 |
1307
+ | 0.1560 | 97500 | 0.8081 |
1308
+ | 0.1562 | 97600 | 0.6826 |
1309
+ | 0.1563 | 97700 | 0.6585 |
1310
+ | 0.1565 | 97800 | 0.8211 |
1311
+ | 0.1566 | 97900 | 1.295 |
1312
+ | 0.1568 | 98000 | 0.9736 |
1313
+ | 0.1570 | 98100 | 0.5973 |
1314
+ | 0.1571 | 98200 | 1.0313 |
1315
+ | 0.1573 | 98300 | 0.9406 |
1316
+ | 0.1574 | 98400 | 0.8189 |
1317
+ | 0.1576 | 98500 | 1.1088 |
1318
+ | 0.1578 | 98600 | 1.093 |
1319
+ | 0.1579 | 98700 | 1.0456 |
1320
+ | 0.1581 | 98800 | 1.0274 |
1321
+ | 0.1582 | 98900 | 0.864 |
1322
+ | 0.1584 | 99000 | 0.5974 |
1323
+ | 0.1586 | 99100 | 0.6967 |
1324
+ | 0.1587 | 99200 | 0.765 |
1325
+ | 0.1589 | 99300 | 1.0606 |
1326
+ | 0.1590 | 99400 | 1.0649 |
1327
+ | 0.1592 | 99500 | 0.9324 |
1328
+ | 0.1594 | 99600 | 0.8013 |
1329
+ | 0.1595 | 99700 | 0.774 |
1330
+ | 0.1597 | 99800 | 0.8554 |
1331
+ | 0.1598 | 99900 | 0.7356 |
1332
+ | 0.1600 | 100000 | 0.8227 |
1333
+ | 0.1602 | 100100 | 0.9887 |
1334
+ | 0.1603 | 100200 | 1.0289 |
1335
+ | 0.1605 | 100300 | 0.9152 |
1336
+ | 0.1606 | 100400 | 0.7427 |
1337
+ | 0.1608 | 100500 | 0.8354 |
1338
+ | 0.1610 | 100600 | 0.9719 |
1339
+ | 0.1611 | 100700 | 0.5524 |
1340
+ | 0.1613 | 100800 | 0.8127 |
1341
+ | 0.1614 | 100900 | 0.8138 |
1342
+ | 0.1616 | 101000 | 1.0482 |
1343
+ | 0.1618 | 101100 | 0.771 |
1344
+ | 0.1619 | 101200 | 0.9133 |
1345
+ | 0.1621 | 101300 | 1.0731 |
1346
+ | 0.1622 | 101400 | 1.47 |
1347
+ | 0.1624 | 101500 | 1.1688 |
1348
+ | 0.1626 | 101600 | 1.1179 |
1349
+ | 0.1627 | 101700 | 1.0963 |
1350
+ | 0.1629 | 101800 | 0.8282 |
1351
+ | 0.1630 | 101900 | 0.8117 |
1352
+ | 0.1632 | 102000 | 1.1391 |
1353
+ | 0.1634 | 102100 | 0.6215 |
1354
+ | 0.1635 | 102200 | 0.9664 |
1355
+ | 0.1637 | 102300 | 0.7186 |
1356
+ | 0.1638 | 102400 | 0.6447 |
1357
+ | 0.1640 | 102500 | 0.8139 |
1358
+ | 0.1642 | 102600 | 0.9408 |
1359
+ | 0.1643 | 102700 | 0.635 |
1360
+ | 0.1645 | 102800 | 1.014 |
1361
+ | 0.1646 | 102900 | 0.9391 |
1362
+ | 0.1648 | 103000 | 0.6588 |
1363
+ | 0.1650 | 103100 | 0.7857 |
1364
+ | 0.1651 | 103200 | 1.0643 |
1365
+ | 0.1653 | 103300 | 0.702 |
1366
+ | 0.1654 | 103400 | 1.3246 |
1367
+ | 0.1656 | 103500 | 1.0838 |
1368
+ | 0.1658 | 103600 | 0.8192 |
1369
+ | 0.1659 | 103700 | 0.8916 |
1370
+ | 0.1661 | 103800 | 0.5586 |
1371
+ | 0.1662 | 103900 | 0.6155 |
1372
+ | 0.1664 | 104000 | 0.8256 |
1373
+ | 0.1666 | 104100 | 1.3053 |
1374
+ | 0.1667 | 104200 | 0.6879 |
1375
+ | 0.1669 | 104300 | 0.9848 |
1376
+ | 0.1670 | 104400 | 0.7975 |
1377
+ | 0.1672 | 104500 | 0.6087 |
1378
+ | 0.1674 | 104600 | 1.1333 |
1379
+ | 0.1675 | 104700 | 0.7272 |
1380
+ | 0.1677 | 104800 | 0.8406 |
1381
+ | 0.1678 | 104900 | 0.7186 |
1382
+ | 0.1680 | 105000 | 0.965 |
1383
+ | 0.1682 | 105100 | 0.8089 |
1384
+ | 0.1683 | 105200 | 0.8435 |
1385
+ | 0.1685 | 105300 | 1.0715 |
1386
+ | 0.1686 | 105400 | 0.9627 |
1387
+ | 0.1688 | 105500 | 1.0776 |
1388
+ | 0.1690 | 105600 | 0.9316 |
1389
+ | 0.1691 | 105700 | 1.1259 |
1390
+ | 0.1693 | 105800 | 0.524 |
1391
+ | 0.1694 | 105900 | 0.8877 |
1392
+ | 0.1696 | 106000 | 1.1526 |
1393
+ | 0.1698 | 106100 | 0.7227 |
1394
+ | 0.1699 | 106200 | 0.8288 |
1395
+ | 0.1701 | 106300 | 1.2059 |
1396
+ | 0.1702 | 106400 | 1.0657 |
1397
+ | 0.1704 | 106500 | 0.5597 |
1398
+ | 0.1706 | 106600 | 0.7996 |
1399
+ | 0.1707 | 106700 | 0.7595 |
1400
+ | 0.1709 | 106800 | 0.9071 |
1401
+ | 0.1710 | 106900 | 0.9006 |
1402
+ | 0.1712 | 107000 | 1.099 |
1403
+ | 0.1714 | 107100 | 0.777 |
1404
+ | 0.1715 | 107200 | 1.0155 |
1405
+ | 0.1717 | 107300 | 0.4465 |
1406
+ | 0.1718 | 107400 | 1.2987 |
1407
+ | 0.1720 | 107500 | 1.0398 |
1408
+ | 0.1722 | 107600 | 1.0649 |
1409
+ | 0.1723 | 107700 | 0.7101 |
1410
+ | 0.1725 | 107800 | 0.5245 |
1411
+ | 0.1726 | 107900 | 0.8404 |
1412
+ | 0.1728 | 108000 | 0.8327 |
1413
+ | 0.1730 | 108100 | 0.942 |
1414
+ | 0.1731 | 108200 | 0.619 |
1415
+ | 0.1733 | 108300 | 0.9479 |
1416
+ | 0.1734 | 108400 | 0.7932 |
1417
+ | 0.1736 | 108500 | 0.9345 |
1418
+ | 0.1738 | 108600 | 0.666 |
1419
+ | 0.1739 | 108700 | 1.0121 |
1420
+ | 0.1741 | 108800 | 0.8663 |
1421
+ | 0.1742 | 108900 | 1.0017 |
1422
+ | 0.1744 | 109000 | 0.8348 |
1423
+ | 0.1746 | 109100 | 0.9073 |
1424
+ | 0.1747 | 109200 | 1.4063 |
1425
+ | 0.1749 | 109300 | 0.6282 |
1426
+ | 0.1750 | 109400 | 1.1002 |
1427
+ | 0.1752 | 109500 | 0.8512 |
1428
+ | 0.1754 | 109600 | 1.0361 |
1429
+ | 0.1755 | 109700 | 0.5101 |
1430
+ | 0.1757 | 109800 | 0.8852 |
1431
+ | 0.1758 | 109900 | 0.8135 |
1432
+ | 0.1760 | 110000 | 0.9248 |
1433
+ | 0.1762 | 110100 | 0.5741 |
1434
+ | 0.1763 | 110200 | 0.6977 |
1435
+ | 0.1765 | 110300 | 1.1059 |
1436
+ | 0.1766 | 110400 | 0.8288 |
1437
+ | 0.1768 | 110500 | 1.1198 |
1438
+ | 0.1770 | 110600 | 1.0859 |
1439
+ | 0.1771 | 110700 | 0.7555 |
1440
+ | 0.1773 | 110800 | 0.6248 |
1441
+ | 0.1774 | 110900 | 0.8378 |
1442
+ | 0.1776 | 111000 | 0.8421 |
1443
+ | 0.1778 | 111100 | 0.5569 |
1444
+ | 0.1779 | 111200 | 0.8503 |
1445
+ | 0.1781 | 111300 | 0.7912 |
1446
+ | 0.1782 | 111400 | 0.7345 |
1447
+ | 0.1784 | 111500 | 1.0769 |
1448
+ | 0.1786 | 111600 | 1.05 |
1449
+ | 0.1787 | 111700 | 0.9701 |
1450
+ | 0.1789 | 111800 | 0.8786 |
1451
+ | 0.1790 | 111900 | 0.8323 |
1452
+ | 0.1792 | 112000 | 0.8527 |
1453
+ | 0.1794 | 112100 | 0.8292 |
1454
+ | 0.1795 | 112200 | 0.7884 |
1455
+ | 0.1797 | 112300 | 0.9813 |
1456
+ | 0.1798 | 112400 | 0.904 |
1457
+ | 0.1800 | 112500 | 0.5651 |
1458
+ | 0.1802 | 112600 | 0.9604 |
1459
+ | 0.1803 | 112700 | 1.0793 |
1460
+ | 0.1805 | 112800 | 0.713 |
1461
+ | 0.1806 | 112900 | 0.7267 |
1462
+ | 0.1808 | 113000 | 1.0512 |
1463
+ | 0.1810 | 113100 | 0.6308 |
1464
+ | 0.1811 | 113200 | 0.8194 |
1465
+ | 0.1813 | 113300 | 1.0049 |
1466
+ | 0.1814 | 113400 | 0.5987 |
1467
+ | 0.1816 | 113500 | 0.5518 |
1468
+ | 0.1818 | 113600 | 0.9958 |
1469
+ | 0.1819 | 113700 | 0.8461 |
1470
+ | 0.1821 | 113800 | 0.9465 |
1471
+ | 0.1822 | 113900 | 0.9536 |
1472
+ | 0.1824 | 114000 | 0.8151 |
1473
+ | 0.1826 | 114100 | 0.932 |
1474
+ | 0.1827 | 114200 | 0.6843 |
1475
+ | 0.1829 | 114300 | 0.7734 |
1476
+ | 0.1830 | 114400 | 0.6753 |
1477
+ | 0.1832 | 114500 | 0.8436 |
1478
+ | 0.1834 | 114600 | 1.0587 |
1479
+ | 0.1835 | 114700 | 0.8789 |
1480
+ | 0.1837 | 114800 | 1.0258 |
1481
+ | 0.1838 | 114900 | 1.2134 |
1482
+ | 0.1840 | 115000 | 0.7992 |
1483
+ | 0.1842 | 115100 | 0.7856 |
1484
+ | 0.1843 | 115200 | 0.7826 |
1485
+ | 0.1845 | 115300 | 0.6631 |
1486
+ | 0.1846 | 115400 | 0.6298 |
1487
+ | 0.1848 | 115500 | 0.8271 |
1488
+ | 0.1850 | 115600 | 1.3478 |
1489
+ | 0.1851 | 115700 | 0.8497 |
1490
+ | 0.1853 | 115800 | 0.8856 |
1491
+ | 0.1854 | 115900 | 0.6793 |
1492
+ | 0.1856 | 116000 | 0.9473 |
1493
+ | 0.1858 | 116100 | 1.4317 |
1494
+ | 0.1859 | 116200 | 1.2314 |
1495
+ | 0.1861 | 116300 | 1.0326 |
1496
+ | 0.1862 | 116400 | 0.6336 |
1497
+ | 0.1864 | 116500 | 1.1677 |
1498
+ | 0.1866 | 116600 | 1.3599 |
1499
+ | 0.1867 | 116700 | 0.9534 |
1500
+ | 0.1869 | 116800 | 0.9814 |
1501
+ | 0.1870 | 116900 | 1.1306 |
1502
+ | 0.1872 | 117000 | 1.0571 |
1503
+ | 0.1874 | 117100 | 1.245 |
1504
+ | 0.1875 | 117200 | 0.6443 |
1505
+ | 0.1877 | 117300 | 0.9152 |
1506
+ | 0.1878 | 117400 | 1.1344 |
1507
+ | 0.1880 | 117500 | 1.0237 |
1508
+ | 0.1882 | 117600 | 1.3814 |
1509
+ | 0.1883 | 117700 | 1.5149 |
1510
+ | 0.1885 | 117800 | 0.9121 |
1511
+ | 0.1886 | 117900 | 0.7316 |
1512
+ | 0.1888 | 118000 | 1.4285 |
1513
+ | 0.1890 | 118100 | 0.7335 |
1514
+ | 0.1891 | 118200 | 1.0581 |
1515
+ | 0.1893 | 118300 | 0.7865 |
1516
+ | 0.1894 | 118400 | 0.9308 |
1517
+ | 0.1896 | 118500 | 0.792 |
1518
+ | 0.1898 | 118600 | 1.0158 |
1519
+ | 0.1899 | 118700 | 1.3033 |
1520
+ | 0.1901 | 118800 | 1.0238 |
1521
+ | 0.1902 | 118900 | 1.0894 |
1522
+ | 0.1904 | 119000 | 0.7931 |
1523
+
1524
+ </details>
1525
+
1526
+ ### Framework Versions
1527
+ - Python: 3.8.10
1528
+ - Sentence Transformers: 3.1.1
1529
+ - Transformers: 4.45.2
1530
+ - PyTorch: 2.4.1+cu118
1531
+ - Accelerate: 1.0.1
1532
+ - Datasets: 3.0.1
1533
+ - Tokenizers: 0.20.3
1534
+
1535
+ ## Citation
1536
+
1537
+ ### BibTeX
1538
+
1539
+ #### Sentence Transformers
1540
+ ```bibtex
1541
+ @inproceedings{reimers-2019-sentence-bert,
1542
+ title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
1543
+ author = "Reimers, Nils and Gurevych, Iryna",
1544
+ booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
1545
+ month = "11",
1546
+ year = "2019",
1547
+ publisher = "Association for Computational Linguistics",
1548
+ url = "https://arxiv.org/abs/1908.10084",
1549
+ }
1550
+ ```
1551
+
1552
+ #### CoSENTLoss
1553
+ ```bibtex
1554
+ @online{kexuefm-8847,
1555
+ title={CoSENT: A more efficient sentence vector scheme than Sentence-BERT},
1556
+ author={Su Jianlin},
1557
+ year={2022},
1558
+ month={Jan},
1559
+ url={https://kexue.fm/archives/8847},
1560
+ }
1561
+ ```
1562
+
1563
+ <!--
1564
+ ## Glossary
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+
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+ *Clearly define terms in order to be accessible across audiences.*
1567
+ -->
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+
1569
+ <!--
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+ ## Model Card Authors
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+
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+ *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
1573
+ -->
1574
+
1575
+ <!--
1576
+ ## Model Card Contact
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+
1578
+ *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
1579
+ -->
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