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IconicAI
/
all-mpnet-base-v2-anteater

Sentence Similarity
sentence-transformers
Safetensors
mpnet
feature-extraction
Generated from Trainer
dataset_size:645861
loss:ContrastiveLoss
Eval Results
Inference Endpoints
Model card Files Community
anokas commited on
Commit
77cbd9a
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1 Parent(s): a79340d

Add new SentenceTransformer model.

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Files changed (11) hide show
  1. 1_Pooling/config.json +10 -0
  2. README.md +1592 -0
  3. config.json +24 -0
  4. config_sentence_transformers.json +10 -0
  5. model.safetensors +3 -0
  6. modules.json +20 -0
  7. sentence_bert_config.json +4 -0
  8. special_tokens_map.json +51 -0
  9. tokenizer.json +0 -0
  10. tokenizer_config.json +72 -0
  11. vocab.txt +0 -0
1_Pooling/config.json ADDED
@@ -0,0 +1,10 @@
 
 
 
 
 
 
 
 
 
 
 
1
+ {
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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,
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+ "pooling_mode_mean_sqrt_len_tokens": false,
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+ "pooling_mode_weightedmean_tokens": false,
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+ "pooling_mode_lasttoken": false,
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+ "include_prompt": true
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+ }
README.md ADDED
@@ -0,0 +1,1592 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ base_model: sentence-transformers/all-mpnet-base-v2
3
+ datasets: []
4
+ language: []
5
+ library_name: sentence-transformers
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+ metrics:
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+ - cosine_accuracy
8
+ - cosine_accuracy_threshold
9
+ - cosine_f1
10
+ - cosine_f1_threshold
11
+ - cosine_precision
12
+ - cosine_recall
13
+ - cosine_ap
14
+ - dot_accuracy
15
+ - dot_accuracy_threshold
16
+ - dot_f1
17
+ - dot_f1_threshold
18
+ - dot_precision
19
+ - dot_recall
20
+ - dot_ap
21
+ - manhattan_accuracy
22
+ - manhattan_accuracy_threshold
23
+ - manhattan_f1
24
+ - manhattan_f1_threshold
25
+ - manhattan_precision
26
+ - manhattan_recall
27
+ - manhattan_ap
28
+ - euclidean_accuracy
29
+ - euclidean_accuracy_threshold
30
+ - euclidean_f1
31
+ - euclidean_f1_threshold
32
+ - euclidean_precision
33
+ - euclidean_recall
34
+ - euclidean_ap
35
+ - max_accuracy
36
+ - max_accuracy_threshold
37
+ - max_f1
38
+ - max_f1_threshold
39
+ - max_precision
40
+ - max_recall
41
+ - max_ap
42
+ pipeline_tag: sentence-similarity
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+ tags:
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+ - sentence-transformers
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+ - sentence-similarity
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+ - feature-extraction
47
+ - generated_from_trainer
48
+ - dataset_size:645861
49
+ - loss:ContrastiveLoss
50
+ widget:
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+ - source_sentence: There was an Eye OS alert.
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+ sentences:
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+ - i see lots of tubes
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+ - On the door is lima mike zero twenty three north exit
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+ - EyeOS, that’s some kind of tech, right
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+ - source_sentence: how to use
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+ sentences:
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+ - how do i use it
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+ - This fallen panel might lead to the control room.
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+ - The rings appear to be completely unmoving now.
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+ - source_sentence: I'm unsure about this room's name how do I find out?
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+ sentences:
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+ - How do I identify the room I'm in without any obvious signs?
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+ - The door shows l m zero twenty three north exit
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+ - it reads Cryochamber Medical Support Systems
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+ - source_sentence: i see Cryochamber Atmospheric Sealing
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+ sentences:
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+ - Can you guide me on how to identify this room?
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+ - it's Laboratory Chemical Storage
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+ - it reads Cryochamber Atmospheric Sealing
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+ - source_sentence: floating up
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+ sentences:
73
+ - All indicators are blue.
74
+ - i can see an interface
75
+ - Found a narrow corridor leading somewhere.
76
+ model-index:
77
+ - name: SentenceTransformer based on sentence-transformers/all-mpnet-base-v2
78
+ results:
79
+ - task:
80
+ type: binary-classification
81
+ name: Binary Classification
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+ dataset:
83
+ name: sts dev
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+ type: sts-dev
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+ metrics:
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+ - type: cosine_accuracy
87
+ value: 0.9002097965885251
88
+ name: Cosine Accuracy
89
+ - type: cosine_accuracy_threshold
90
+ value: 0.4494956135749817
91
+ name: Cosine Accuracy Threshold
92
+ - type: cosine_f1
93
+ value: 0.8908462575859745
94
+ name: Cosine F1
95
+ - type: cosine_f1_threshold
96
+ value: 0.41577932238578796
97
+ name: Cosine F1 Threshold
98
+ - type: cosine_precision
99
+ value: 0.8739044154126013
100
+ name: Cosine Precision
101
+ - type: cosine_recall
102
+ value: 0.908457968024755
103
+ name: Cosine Recall
104
+ - type: cosine_ap
105
+ value: 0.9618224590785398
106
+ name: Cosine Ap
107
+ - type: dot_accuracy
108
+ value: 0.9002097965885251
109
+ name: Dot Accuracy
110
+ - type: dot_accuracy_threshold
111
+ value: 0.4494956135749817
112
+ name: Dot Accuracy Threshold
113
+ - type: dot_f1
114
+ value: 0.8908462575859745
115
+ name: Dot F1
116
+ - type: dot_f1_threshold
117
+ value: 0.4157792925834656
118
+ name: Dot F1 Threshold
119
+ - type: dot_precision
120
+ value: 0.8739044154126013
121
+ name: Dot Precision
122
+ - type: dot_recall
123
+ value: 0.908457968024755
124
+ name: Dot Recall
125
+ - type: dot_ap
126
+ value: 0.961822458350164
127
+ name: Dot Ap
128
+ - type: manhattan_accuracy
129
+ value: 0.8989979280958028
130
+ name: Manhattan Accuracy
131
+ - type: manhattan_accuracy_threshold
132
+ value: 22.644113540649414
133
+ name: Manhattan Accuracy Threshold
134
+ - type: manhattan_f1
135
+ value: 0.8901100449479366
136
+ name: Manhattan F1
137
+ - type: manhattan_f1_threshold
138
+ value: 23.330610275268555
139
+ name: Manhattan F1 Threshold
140
+ - type: manhattan_precision
141
+ value: 0.8757104438714686
142
+ name: Manhattan Precision
143
+ - type: manhattan_recall
144
+ value: 0.9049911179875079
145
+ name: Manhattan Recall
146
+ - type: manhattan_ap
147
+ value: 0.9615309074220045
148
+ name: Manhattan Ap
149
+ - type: euclidean_accuracy
150
+ value: 0.9002097965885251
151
+ name: Euclidean Accuracy
152
+ - type: euclidean_accuracy_threshold
153
+ value: 1.0492897033691406
154
+ name: Euclidean Accuracy Threshold
155
+ - type: euclidean_f1
156
+ value: 0.8908462575859745
157
+ name: Euclidean F1
158
+ - type: euclidean_f1_threshold
159
+ value: 1.080944538116455
160
+ name: Euclidean F1 Threshold
161
+ - type: euclidean_precision
162
+ value: 0.8739044154126013
163
+ name: Euclidean Precision
164
+ - type: euclidean_recall
165
+ value: 0.908457968024755
166
+ name: Euclidean Recall
167
+ - type: euclidean_ap
168
+ value: 0.9618224553002042
169
+ name: Euclidean Ap
170
+ - type: max_accuracy
171
+ value: 0.9002097965885251
172
+ name: Max Accuracy
173
+ - type: max_accuracy_threshold
174
+ value: 22.644113540649414
175
+ name: Max Accuracy Threshold
176
+ - type: max_f1
177
+ value: 0.8908462575859745
178
+ name: Max F1
179
+ - type: max_f1_threshold
180
+ value: 23.330610275268555
181
+ name: Max F1 Threshold
182
+ - type: max_precision
183
+ value: 0.8757104438714686
184
+ name: Max Precision
185
+ - type: max_recall
186
+ value: 0.908457968024755
187
+ name: Max Recall
188
+ - type: max_ap
189
+ value: 0.9618224590785398
190
+ name: Max Ap
191
+ ---
192
+
193
+ # SentenceTransformer based on sentence-transformers/all-mpnet-base-v2
194
+
195
+ This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2). 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.
196
+
197
+ ## Model Details
198
+
199
+ ### Model Description
200
+ - **Model Type:** Sentence Transformer
201
+ - **Base model:** [sentence-transformers/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) <!-- at revision 84f2bcc00d77236f9e89c8a360a00fb1139bf47d -->
202
+ - **Maximum Sequence Length:** 384 tokens
203
+ - **Output Dimensionality:** 768 tokens
204
+ - **Similarity Function:** Cosine Similarity
205
+ <!-- - **Training Dataset:** Unknown -->
206
+ <!-- - **Language:** Unknown -->
207
+ <!-- - **License:** Unknown -->
208
+
209
+ ### Model Sources
210
+
211
+ - **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
212
+ - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
213
+ - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
214
+
215
+ ### Full Model Architecture
216
+
217
+ ```
218
+ SentenceTransformer(
219
+ (0): Transformer({'max_seq_length': 384, 'do_lower_case': False}) with Transformer model: MPNetModel
220
+ (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})
221
+ (2): Normalize()
222
+ )
223
+ ```
224
+
225
+ ## Usage
226
+
227
+ ### Direct Usage (Sentence Transformers)
228
+
229
+ First install the Sentence Transformers library:
230
+
231
+ ```bash
232
+ pip install -U sentence-transformers
233
+ ```
234
+
235
+ Then you can load this model and run inference.
236
+ ```python
237
+ from sentence_transformers import SentenceTransformer
238
+
239
+ # Download from the 🤗 Hub
240
+ model = SentenceTransformer("IconicAI/all-mpnet-base-v2-anteater")
241
+ # Run inference
242
+ sentences = [
243
+ 'floating up',
244
+ 'i can see an interface',
245
+ 'All indicators are blue.',
246
+ ]
247
+ embeddings = model.encode(sentences)
248
+ print(embeddings.shape)
249
+ # [3, 768]
250
+
251
+ # Get the similarity scores for the embeddings
252
+ similarities = model.similarity(embeddings, embeddings)
253
+ print(similarities.shape)
254
+ # [3, 3]
255
+ ```
256
+
257
+ <!--
258
+ ### Direct Usage (Transformers)
259
+
260
+ <details><summary>Click to see the direct usage in Transformers</summary>
261
+
262
+ </details>
263
+ -->
264
+
265
+ <!--
266
+ ### Downstream Usage (Sentence Transformers)
267
+
268
+ You can finetune this model on your own dataset.
269
+
270
+ <details><summary>Click to expand</summary>
271
+
272
+ </details>
273
+ -->
274
+
275
+ <!--
276
+ ### Out-of-Scope Use
277
+
278
+ *List how the model may foreseeably be misused and address what users ought not to do with the model.*
279
+ -->
280
+
281
+ ## Evaluation
282
+
283
+ ### Metrics
284
+
285
+ #### Binary Classification
286
+ * Dataset: `sts-dev`
287
+ * Evaluated with [<code>BinaryClassificationEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator)
288
+
289
+ | Metric | Value |
290
+ |:-----------------------------|:-----------|
291
+ | cosine_accuracy | 0.9002 |
292
+ | cosine_accuracy_threshold | 0.4495 |
293
+ | cosine_f1 | 0.8908 |
294
+ | cosine_f1_threshold | 0.4158 |
295
+ | cosine_precision | 0.8739 |
296
+ | cosine_recall | 0.9085 |
297
+ | cosine_ap | 0.9618 |
298
+ | dot_accuracy | 0.9002 |
299
+ | dot_accuracy_threshold | 0.4495 |
300
+ | dot_f1 | 0.8908 |
301
+ | dot_f1_threshold | 0.4158 |
302
+ | dot_precision | 0.8739 |
303
+ | dot_recall | 0.9085 |
304
+ | dot_ap | 0.9618 |
305
+ | manhattan_accuracy | 0.899 |
306
+ | manhattan_accuracy_threshold | 22.6441 |
307
+ | manhattan_f1 | 0.8901 |
308
+ | manhattan_f1_threshold | 23.3306 |
309
+ | manhattan_precision | 0.8757 |
310
+ | manhattan_recall | 0.905 |
311
+ | manhattan_ap | 0.9615 |
312
+ | euclidean_accuracy | 0.9002 |
313
+ | euclidean_accuracy_threshold | 1.0493 |
314
+ | euclidean_f1 | 0.8908 |
315
+ | euclidean_f1_threshold | 1.0809 |
316
+ | euclidean_precision | 0.8739 |
317
+ | euclidean_recall | 0.9085 |
318
+ | euclidean_ap | 0.9618 |
319
+ | max_accuracy | 0.9002 |
320
+ | max_accuracy_threshold | 22.6441 |
321
+ | max_f1 | 0.8908 |
322
+ | max_f1_threshold | 23.3306 |
323
+ | max_precision | 0.8757 |
324
+ | max_recall | 0.9085 |
325
+ | **max_ap** | **0.9618** |
326
+
327
+ <!--
328
+ ## Bias, Risks and Limitations
329
+
330
+ *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
331
+ -->
332
+
333
+ <!--
334
+ ### Recommendations
335
+
336
+ *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
337
+ -->
338
+
339
+ ## Training Details
340
+
341
+ ### Training Dataset
342
+
343
+ #### Unnamed Dataset
344
+
345
+
346
+ * Size: 645,861 training samples
347
+ * Columns: <code>example1</code>, <code>example2</code>, and <code>label</code>
348
+ * Approximate statistics based on the first 1000 samples:
349
+ | | example1 | example2 | label |
350
+ |:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:-----------------------------|
351
+ | type | string | string | int |
352
+ | details | <ul><li>min: 3 tokens</li><li>mean: 9.02 tokens</li><li>max: 25 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 9.19 tokens</li><li>max: 23 tokens</li></ul> | <ul><li>1: 100.00%</li></ul> |
353
+ * Samples:
354
+ | example1 | example2 | label |
355
+ |:---------------------------------------------------------------------------------|:--------------------------------------------------------------|:---------------|
356
+ | <code>Drones are present all around here.</code> | <code>What are those drones doing buzzing around here?</code> | <code>1</code> |
357
+ | <code>am i the only one</code> | <code>am i the only one alive on this ship</code> | <code>1</code> |
358
+ | <code>I’m in a room with a door in front of me and a terminal on the wall</code> | <code>mechanics room</code> | <code>1</code> |
359
+ * Loss: [<code>ContrastiveLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#contrastiveloss) with these parameters:
360
+ ```json
361
+ {
362
+ "distance_metric": "SiameseDistanceMetric.COSINE_DISTANCE",
363
+ "margin": 1.0,
364
+ "size_average": true
365
+ }
366
+ ```
367
+
368
+ ### Evaluation Dataset
369
+
370
+ #### Unnamed Dataset
371
+
372
+
373
+ * Size: 76,741 evaluation samples
374
+ * Columns: <code>example1</code>, <code>example2</code>, and <code>label</code>
375
+ * Approximate statistics based on the first 1000 samples:
376
+ | | example1 | example2 | label |
377
+ |:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:-----------------------------|
378
+ | type | string | string | int |
379
+ | details | <ul><li>min: 3 tokens</li><li>mean: 9.25 tokens</li><li>max: 21 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 9.15 tokens</li><li>max: 19 tokens</li></ul> | <ul><li>1: 100.00%</li></ul> |
380
+ * Samples:
381
+ | example1 | example2 | label |
382
+ |:----------------------------------------------|:----------------------------------------------------------|:---------------|
383
+ | <code>Not much, how about you?</code> | <code>Nothing, you?</code> | <code>1</code> |
384
+ | <code>Rings stopped moving.</code> | <code>I notice the rings are not spinning anymore.</code> | <code>1</code> |
385
+ | <code>it's Laboratory Chemical Storage</code> | <code>the switch is Laboratory Chemical Storage</code> | <code>1</code> |
386
+ * Loss: [<code>ContrastiveLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#contrastiveloss) with these parameters:
387
+ ```json
388
+ {
389
+ "distance_metric": "SiameseDistanceMetric.COSINE_DISTANCE",
390
+ "margin": 1.0,
391
+ "size_average": true
392
+ }
393
+ ```
394
+
395
+ ### Training Hyperparameters
396
+ #### Non-Default Hyperparameters
397
+
398
+ - `eval_strategy`: steps
399
+ - `per_device_train_batch_size`: 256
400
+ - `per_device_eval_batch_size`: 256
401
+ - `learning_rate`: 1e-07
402
+ - `weight_decay`: 0.01
403
+ - `max_grad_norm`: 0.02
404
+ - `num_train_epochs`: 5
405
+ - `warmup_steps`: 100
406
+ - `bf16`: True
407
+ - `eval_on_start`: True
408
+
409
+ #### All Hyperparameters
410
+ <details><summary>Click to expand</summary>
411
+
412
+ - `overwrite_output_dir`: False
413
+ - `do_predict`: False
414
+ - `eval_strategy`: steps
415
+ - `prediction_loss_only`: True
416
+ - `per_device_train_batch_size`: 256
417
+ - `per_device_eval_batch_size`: 256
418
+ - `per_gpu_train_batch_size`: None
419
+ - `per_gpu_eval_batch_size`: None
420
+ - `gradient_accumulation_steps`: 1
421
+ - `eval_accumulation_steps`: None
422
+ - `torch_empty_cache_steps`: None
423
+ - `learning_rate`: 1e-07
424
+ - `weight_decay`: 0.01
425
+ - `adam_beta1`: 0.9
426
+ - `adam_beta2`: 0.999
427
+ - `adam_epsilon`: 1e-08
428
+ - `max_grad_norm`: 0.02
429
+ - `num_train_epochs`: 5
430
+ - `max_steps`: -1
431
+ - `lr_scheduler_type`: linear
432
+ - `lr_scheduler_kwargs`: {}
433
+ - `warmup_ratio`: 0.0
434
+ - `warmup_steps`: 100
435
+ - `log_level`: passive
436
+ - `log_level_replica`: warning
437
+ - `log_on_each_node`: True
438
+ - `logging_nan_inf_filter`: True
439
+ - `save_safetensors`: True
440
+ - `save_on_each_node`: False
441
+ - `save_only_model`: False
442
+ - `restore_callback_states_from_checkpoint`: False
443
+ - `no_cuda`: False
444
+ - `use_cpu`: False
445
+ - `use_mps_device`: False
446
+ - `seed`: 42
447
+ - `data_seed`: None
448
+ - `jit_mode_eval`: False
449
+ - `use_ipex`: False
450
+ - `bf16`: True
451
+ - `fp16`: False
452
+ - `fp16_opt_level`: O1
453
+ - `half_precision_backend`: auto
454
+ - `bf16_full_eval`: False
455
+ - `fp16_full_eval`: False
456
+ - `tf32`: None
457
+ - `local_rank`: 0
458
+ - `ddp_backend`: None
459
+ - `tpu_num_cores`: None
460
+ - `tpu_metrics_debug`: False
461
+ - `debug`: []
462
+ - `dataloader_drop_last`: False
463
+ - `dataloader_num_workers`: 0
464
+ - `dataloader_prefetch_factor`: None
465
+ - `past_index`: -1
466
+ - `disable_tqdm`: False
467
+ - `remove_unused_columns`: True
468
+ - `label_names`: None
469
+ - `load_best_model_at_end`: False
470
+ - `ignore_data_skip`: False
471
+ - `fsdp`: []
472
+ - `fsdp_min_num_params`: 0
473
+ - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
474
+ - `fsdp_transformer_layer_cls_to_wrap`: None
475
+ - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
476
+ - `deepspeed`: None
477
+ - `label_smoothing_factor`: 0.0
478
+ - `optim`: adamw_torch
479
+ - `optim_args`: None
480
+ - `adafactor`: False
481
+ - `group_by_length`: False
482
+ - `length_column_name`: length
483
+ - `ddp_find_unused_parameters`: None
484
+ - `ddp_bucket_cap_mb`: None
485
+ - `ddp_broadcast_buffers`: False
486
+ - `dataloader_pin_memory`: True
487
+ - `dataloader_persistent_workers`: False
488
+ - `skip_memory_metrics`: True
489
+ - `use_legacy_prediction_loop`: False
490
+ - `push_to_hub`: False
491
+ - `resume_from_checkpoint`: None
492
+ - `hub_model_id`: None
493
+ - `hub_strategy`: every_save
494
+ - `hub_private_repo`: False
495
+ - `hub_always_push`: False
496
+ - `gradient_checkpointing`: False
497
+ - `gradient_checkpointing_kwargs`: None
498
+ - `include_inputs_for_metrics`: False
499
+ - `eval_do_concat_batches`: True
500
+ - `fp16_backend`: auto
501
+ - `push_to_hub_model_id`: None
502
+ - `push_to_hub_organization`: None
503
+ - `mp_parameters`:
504
+ - `auto_find_batch_size`: False
505
+ - `full_determinism`: False
506
+ - `torchdynamo`: None
507
+ - `ray_scope`: last
508
+ - `ddp_timeout`: 1800
509
+ - `torch_compile`: False
510
+ - `torch_compile_backend`: None
511
+ - `torch_compile_mode`: None
512
+ - `dispatch_batches`: None
513
+ - `split_batches`: None
514
+ - `include_tokens_per_second`: False
515
+ - `include_num_input_tokens_seen`: False
516
+ - `neftune_noise_alpha`: None
517
+ - `optim_target_modules`: None
518
+ - `batch_eval_metrics`: False
519
+ - `eval_on_start`: True
520
+ - `use_liger_kernel`: False
521
+ - `eval_use_gather_object`: False
522
+ - `batch_sampler`: batch_sampler
523
+ - `multi_dataset_batch_sampler`: proportional
524
+
525
+ </details>
526
+
527
+ ### Training Logs
528
+ <details><summary>Click to expand</summary>
529
+
530
+ | Epoch | Step | Training Loss | loss | sts-dev_max_ap |
531
+ |:------:|:-----:|:-------------:|:------:|:--------------:|
532
+ | 0 | 0 | - | 0.0764 | 0.9175 |
533
+ | 0.0040 | 10 | 0.0772 | - | - |
534
+ | 0.0079 | 20 | 0.0783 | - | - |
535
+ | 0.0119 | 30 | 0.0775 | - | - |
536
+ | 0.0159 | 40 | 0.0756 | - | - |
537
+ | 0.0198 | 50 | 0.075 | - | - |
538
+ | 0.0238 | 60 | 0.0777 | - | - |
539
+ | 0.0277 | 70 | 0.0784 | - | - |
540
+ | 0.0317 | 80 | 0.0721 | - | - |
541
+ | 0.0357 | 90 | 0.0755 | - | - |
542
+ | 0.0396 | 100 | 0.0778 | - | - |
543
+ | 0.0436 | 110 | 0.0735 | - | - |
544
+ | 0.0476 | 120 | 0.0753 | - | - |
545
+ | 0.0515 | 130 | 0.0741 | - | - |
546
+ | 0.0555 | 140 | 0.0791 | - | - |
547
+ | 0.0595 | 150 | 0.0753 | - | - |
548
+ | 0.0634 | 160 | 0.0748 | - | - |
549
+ | 0.0674 | 170 | 0.0709 | - | - |
550
+ | 0.0713 | 180 | 0.0738 | - | - |
551
+ | 0.0753 | 190 | 0.0759 | - | - |
552
+ | 0.0793 | 200 | 0.0703 | - | - |
553
+ | 0.0832 | 210 | 0.0724 | - | - |
554
+ | 0.0872 | 220 | 0.0726 | - | - |
555
+ | 0.0912 | 230 | 0.0734 | - | - |
556
+ | 0.0951 | 240 | 0.0718 | - | - |
557
+ | 0.0991 | 250 | 0.0776 | - | - |
558
+ | 0.1031 | 260 | 0.0757 | - | - |
559
+ | 0.1070 | 270 | 0.0722 | - | - |
560
+ | 0.1110 | 280 | 0.0746 | - | - |
561
+ | 0.1149 | 290 | 0.0718 | - | - |
562
+ | 0.1189 | 300 | 0.0733 | - | - |
563
+ | 0.1229 | 310 | 0.0725 | - | - |
564
+ | 0.1268 | 320 | 0.0724 | - | - |
565
+ | 0.1308 | 330 | 0.0681 | - | - |
566
+ | 0.1348 | 340 | 0.0735 | - | - |
567
+ | 0.1387 | 350 | 0.0716 | - | - |
568
+ | 0.1427 | 360 | 0.0698 | - | - |
569
+ | 0.1467 | 370 | 0.072 | - | - |
570
+ | 0.1506 | 380 | 0.071 | - | - |
571
+ | 0.1546 | 390 | 0.0713 | - | - |
572
+ | 0.1585 | 400 | 0.073 | - | - |
573
+ | 0.1625 | 410 | 0.077 | - | - |
574
+ | 0.1665 | 420 | 0.072 | - | - |
575
+ | 0.1704 | 430 | 0.0689 | - | - |
576
+ | 0.1744 | 440 | 0.0708 | - | - |
577
+ | 0.1784 | 450 | 0.0687 | - | - |
578
+ | 0.1823 | 460 | 0.0692 | - | - |
579
+ | 0.1863 | 470 | 0.0715 | - | - |
580
+ | 0.1902 | 480 | 0.0707 | - | - |
581
+ | 0.1942 | 490 | 0.0671 | - | - |
582
+ | 0.1982 | 500 | 0.0741 | 0.0703 | 0.9245 |
583
+ | 0.2021 | 510 | 0.0681 | - | - |
584
+ | 0.2061 | 520 | 0.0749 | - | - |
585
+ | 0.2101 | 530 | 0.0718 | - | - |
586
+ | 0.2140 | 540 | 0.0689 | - | - |
587
+ | 0.2180 | 550 | 0.0733 | - | - |
588
+ | 0.2220 | 560 | 0.067 | - | - |
589
+ | 0.2259 | 570 | 0.0685 | - | - |
590
+ | 0.2299 | 580 | 0.07 | - | - |
591
+ | 0.2338 | 590 | 0.0683 | - | - |
592
+ | 0.2378 | 600 | 0.0693 | - | - |
593
+ | 0.2418 | 610 | 0.0705 | - | - |
594
+ | 0.2457 | 620 | 0.0707 | - | - |
595
+ | 0.2497 | 630 | 0.0703 | - | - |
596
+ | 0.2537 | 640 | 0.068 | - | - |
597
+ | 0.2576 | 650 | 0.0682 | - | - |
598
+ | 0.2616 | 660 | 0.0654 | - | - |
599
+ | 0.2656 | 670 | 0.0682 | - | - |
600
+ | 0.2695 | 680 | 0.0698 | - | - |
601
+ | 0.2735 | 690 | 0.0701 | - | - |
602
+ | 0.2774 | 700 | 0.0674 | - | - |
603
+ | 0.2814 | 710 | 0.0669 | - | - |
604
+ | 0.2854 | 720 | 0.0677 | - | - |
605
+ | 0.2893 | 730 | 0.0674 | - | - |
606
+ | 0.2933 | 740 | 0.0682 | - | - |
607
+ | 0.2973 | 750 | 0.0677 | - | - |
608
+ | 0.3012 | 760 | 0.0661 | - | - |
609
+ | 0.3052 | 770 | 0.0634 | - | - |
610
+ | 0.3092 | 780 | 0.0658 | - | - |
611
+ | 0.3131 | 790 | 0.0687 | - | - |
612
+ | 0.3171 | 800 | 0.069 | - | - |
613
+ | 0.3210 | 810 | 0.0665 | - | - |
614
+ | 0.3250 | 820 | 0.0648 | - | - |
615
+ | 0.3290 | 830 | 0.0656 | - | - |
616
+ | 0.3329 | 840 | 0.0672 | - | - |
617
+ | 0.3369 | 850 | 0.0663 | - | - |
618
+ | 0.3409 | 860 | 0.0666 | - | - |
619
+ | 0.3448 | 870 | 0.0644 | - | - |
620
+ | 0.3488 | 880 | 0.065 | - | - |
621
+ | 0.3528 | 890 | 0.0666 | - | - |
622
+ | 0.3567 | 900 | 0.0657 | - | - |
623
+ | 0.3607 | 910 | 0.0636 | - | - |
624
+ | 0.3646 | 920 | 0.0681 | - | - |
625
+ | 0.3686 | 930 | 0.0671 | - | - |
626
+ | 0.3726 | 940 | 0.0653 | - | - |
627
+ | 0.3765 | 950 | 0.0643 | - | - |
628
+ | 0.3805 | 960 | 0.0637 | - | - |
629
+ | 0.3845 | 970 | 0.066 | - | - |
630
+ | 0.3884 | 980 | 0.0645 | - | - |
631
+ | 0.3924 | 990 | 0.0628 | - | - |
632
+ | 0.3964 | 1000 | 0.0627 | 0.0653 | 0.9325 |
633
+ | 0.4003 | 1010 | 0.0647 | - | - |
634
+ | 0.4043 | 1020 | 0.0649 | - | - |
635
+ | 0.4082 | 1030 | 0.0637 | - | - |
636
+ | 0.4122 | 1040 | 0.0648 | - | - |
637
+ | 0.4162 | 1050 | 0.0647 | - | - |
638
+ | 0.4201 | 1060 | 0.0646 | - | - |
639
+ | 0.4241 | 1070 | 0.0659 | - | - |
640
+ | 0.4281 | 1080 | 0.0641 | - | - |
641
+ | 0.4320 | 1090 | 0.0609 | - | - |
642
+ | 0.4360 | 1100 | 0.0642 | - | - |
643
+ | 0.4400 | 1110 | 0.0614 | - | - |
644
+ | 0.4439 | 1120 | 0.0603 | - | - |
645
+ | 0.4479 | 1130 | 0.0613 | - | - |
646
+ | 0.4518 | 1140 | 0.0646 | - | - |
647
+ | 0.4558 | 1150 | 0.0619 | - | - |
648
+ | 0.4598 | 1160 | 0.0611 | - | - |
649
+ | 0.4637 | 1170 | 0.0638 | - | - |
650
+ | 0.4677 | 1180 | 0.0636 | - | - |
651
+ | 0.4717 | 1190 | 0.0647 | - | - |
652
+ | 0.4756 | 1200 | 0.0622 | - | - |
653
+ | 0.4796 | 1210 | 0.0642 | - | - |
654
+ | 0.4836 | 1220 | 0.0607 | - | - |
655
+ | 0.4875 | 1230 | 0.0623 | - | - |
656
+ | 0.4915 | 1240 | 0.0614 | - | - |
657
+ | 0.4954 | 1250 | 0.0643 | - | - |
658
+ | 0.4994 | 1260 | 0.0614 | - | - |
659
+ | 0.5034 | 1270 | 0.0599 | - | - |
660
+ | 0.5073 | 1280 | 0.0615 | - | - |
661
+ | 0.5113 | 1290 | 0.0595 | - | - |
662
+ | 0.5153 | 1300 | 0.061 | - | - |
663
+ | 0.5192 | 1310 | 0.0623 | - | - |
664
+ | 0.5232 | 1320 | 0.0646 | - | - |
665
+ | 0.5272 | 1330 | 0.0621 | - | - |
666
+ | 0.5311 | 1340 | 0.0606 | - | - |
667
+ | 0.5351 | 1350 | 0.0597 | - | - |
668
+ | 0.5390 | 1360 | 0.0621 | - | - |
669
+ | 0.5430 | 1370 | 0.0586 | - | - |
670
+ | 0.5470 | 1380 | 0.0618 | - | - |
671
+ | 0.5509 | 1390 | 0.0601 | - | - |
672
+ | 0.5549 | 1400 | 0.0578 | - | - |
673
+ | 0.5589 | 1410 | 0.0628 | - | - |
674
+ | 0.5628 | 1420 | 0.0595 | - | - |
675
+ | 0.5668 | 1430 | 0.0576 | - | - |
676
+ | 0.5707 | 1440 | 0.0606 | - | - |
677
+ | 0.5747 | 1450 | 0.0618 | - | - |
678
+ | 0.5787 | 1460 | 0.0591 | - | - |
679
+ | 0.5826 | 1470 | 0.0598 | - | - |
680
+ | 0.5866 | 1480 | 0.0611 | - | - |
681
+ | 0.5906 | 1490 | 0.0594 | - | - |
682
+ | 0.5945 | 1500 | 0.0616 | 0.0619 | 0.9393 |
683
+ | 0.5985 | 1510 | 0.0592 | - | - |
684
+ | 0.6025 | 1520 | 0.0597 | - | - |
685
+ | 0.6064 | 1530 | 0.0619 | - | - |
686
+ | 0.6104 | 1540 | 0.0595 | - | - |
687
+ | 0.6143 | 1550 | 0.0598 | - | - |
688
+ | 0.6183 | 1560 | 0.0609 | - | - |
689
+ | 0.6223 | 1570 | 0.059 | - | - |
690
+ | 0.6262 | 1580 | 0.0601 | - | - |
691
+ | 0.6302 | 1590 | 0.0595 | - | - |
692
+ | 0.6342 | 1600 | 0.059 | - | - |
693
+ | 0.6381 | 1610 | 0.0606 | - | - |
694
+ | 0.6421 | 1620 | 0.0591 | - | - |
695
+ | 0.6461 | 1630 | 0.0617 | - | - |
696
+ | 0.6500 | 1640 | 0.0592 | - | - |
697
+ | 0.6540 | 1650 | 0.0588 | - | - |
698
+ | 0.6579 | 1660 | 0.0587 | - | - |
699
+ | 0.6619 | 1670 | 0.0585 | - | - |
700
+ | 0.6659 | 1680 | 0.0558 | - | - |
701
+ | 0.6698 | 1690 | 0.057 | - | - |
702
+ | 0.6738 | 1700 | 0.0598 | - | - |
703
+ | 0.6778 | 1710 | 0.0567 | - | - |
704
+ | 0.6817 | 1720 | 0.0555 | - | - |
705
+ | 0.6857 | 1730 | 0.0604 | - | - |
706
+ | 0.6897 | 1740 | 0.0558 | - | - |
707
+ | 0.6936 | 1750 | 0.0572 | - | - |
708
+ | 0.6976 | 1760 | 0.0577 | - | - |
709
+ | 0.7015 | 1770 | 0.0587 | - | - |
710
+ | 0.7055 | 1780 | 0.0589 | - | - |
711
+ | 0.7095 | 1790 | 0.0598 | - | - |
712
+ | 0.7134 | 1800 | 0.0583 | - | - |
713
+ | 0.7174 | 1810 | 0.058 | - | - |
714
+ | 0.7214 | 1820 | 0.0564 | - | - |
715
+ | 0.7253 | 1830 | 0.0589 | - | - |
716
+ | 0.7293 | 1840 | 0.0557 | - | - |
717
+ | 0.7333 | 1850 | 0.0586 | - | - |
718
+ | 0.7372 | 1860 | 0.0601 | - | - |
719
+ | 0.7412 | 1870 | 0.0556 | - | - |
720
+ | 0.7451 | 1880 | 0.0572 | - | - |
721
+ | 0.7491 | 1890 | 0.0574 | - | - |
722
+ | 0.7531 | 1900 | 0.0583 | - | - |
723
+ | 0.7570 | 1910 | 0.0573 | - | - |
724
+ | 0.7610 | 1920 | 0.0555 | - | - |
725
+ | 0.7650 | 1930 | 0.0561 | - | - |
726
+ | 0.7689 | 1940 | 0.0579 | - | - |
727
+ | 0.7729 | 1950 | 0.0557 | - | - |
728
+ | 0.7769 | 1960 | 0.0558 | - | - |
729
+ | 0.7808 | 1970 | 0.0589 | - | - |
730
+ | 0.7848 | 1980 | 0.0572 | - | - |
731
+ | 0.7887 | 1990 | 0.0572 | - | - |
732
+ | 0.7927 | 2000 | 0.0549 | 0.0592 | 0.9444 |
733
+ | 0.7967 | 2010 | 0.0548 | - | - |
734
+ | 0.8006 | 2020 | 0.0569 | - | - |
735
+ | 0.8046 | 2030 | 0.058 | - | - |
736
+ | 0.8086 | 2040 | 0.0581 | - | - |
737
+ | 0.8125 | 2050 | 0.0585 | - | - |
738
+ | 0.8165 | 2060 | 0.0542 | - | - |
739
+ | 0.8205 | 2070 | 0.0558 | - | - |
740
+ | 0.8244 | 2080 | 0.0569 | - | - |
741
+ | 0.8284 | 2090 | 0.0564 | - | - |
742
+ | 0.8323 | 2100 | 0.0552 | - | - |
743
+ | 0.8363 | 2110 | 0.0559 | - | - |
744
+ | 0.8403 | 2120 | 0.0534 | - | - |
745
+ | 0.8442 | 2130 | 0.0543 | - | - |
746
+ | 0.8482 | 2140 | 0.0573 | - | - |
747
+ | 0.8522 | 2150 | 0.0546 | - | - |
748
+ | 0.8561 | 2160 | 0.0554 | - | - |
749
+ | 0.8601 | 2170 | 0.0568 | - | - |
750
+ | 0.8641 | 2180 | 0.0544 | - | - |
751
+ | 0.8680 | 2190 | 0.0547 | - | - |
752
+ | 0.8720 | 2200 | 0.0549 | - | - |
753
+ | 0.8759 | 2210 | 0.0544 | - | - |
754
+ | 0.8799 | 2220 | 0.058 | - | - |
755
+ | 0.8839 | 2230 | 0.0557 | - | - |
756
+ | 0.8878 | 2240 | 0.0551 | - | - |
757
+ | 0.8918 | 2250 | 0.0558 | - | - |
758
+ | 0.8958 | 2260 | 0.0554 | - | - |
759
+ | 0.8997 | 2270 | 0.053 | - | - |
760
+ | 0.9037 | 2280 | 0.0552 | - | - |
761
+ | 0.9076 | 2290 | 0.0549 | - | - |
762
+ | 0.9116 | 2300 | 0.0533 | - | - |
763
+ | 0.9156 | 2310 | 0.0543 | - | - |
764
+ | 0.9195 | 2320 | 0.0531 | - | - |
765
+ | 0.9235 | 2330 | 0.0553 | - | - |
766
+ | 0.9275 | 2340 | 0.0542 | - | - |
767
+ | 0.9314 | 2350 | 0.0537 | - | - |
768
+ | 0.9354 | 2360 | 0.0536 | - | - |
769
+ | 0.9394 | 2370 | 0.055 | - | - |
770
+ | 0.9433 | 2380 | 0.0551 | - | - |
771
+ | 0.9473 | 2390 | 0.0532 | - | - |
772
+ | 0.9512 | 2400 | 0.0556 | - | - |
773
+ | 0.9552 | 2410 | 0.0548 | - | - |
774
+ | 0.9592 | 2420 | 0.0533 | - | - |
775
+ | 0.9631 | 2430 | 0.0536 | - | - |
776
+ | 0.9671 | 2440 | 0.0549 | - | - |
777
+ | 0.9711 | 2450 | 0.0548 | - | - |
778
+ | 0.9750 | 2460 | 0.0557 | - | - |
779
+ | 0.9790 | 2470 | 0.055 | - | - |
780
+ | 0.9830 | 2480 | 0.0535 | - | - |
781
+ | 0.9869 | 2490 | 0.0564 | - | - |
782
+ | 0.9909 | 2500 | 0.0526 | 0.0572 | 0.9482 |
783
+ | 0.9948 | 2510 | 0.0547 | - | - |
784
+ | 0.9988 | 2520 | 0.054 | - | - |
785
+ | 1.0028 | 2530 | 0.0527 | - | - |
786
+ | 1.0067 | 2540 | 0.0522 | - | - |
787
+ | 1.0107 | 2550 | 0.0535 | - | - |
788
+ | 1.0147 | 2560 | 0.0557 | - | - |
789
+ | 1.0186 | 2570 | 0.052 | - | - |
790
+ | 1.0226 | 2580 | 0.055 | - | - |
791
+ | 1.0266 | 2590 | 0.0542 | - | - |
792
+ | 1.0305 | 2600 | 0.0539 | - | - |
793
+ | 1.0345 | 2610 | 0.0523 | - | - |
794
+ | 1.0384 | 2620 | 0.0507 | - | - |
795
+ | 1.0424 | 2630 | 0.0517 | - | - |
796
+ | 1.0464 | 2640 | 0.0543 | - | - |
797
+ | 1.0503 | 2650 | 0.0543 | - | - |
798
+ | 1.0543 | 2660 | 0.054 | - | - |
799
+ | 1.0583 | 2670 | 0.0536 | - | - |
800
+ | 1.0622 | 2680 | 0.0531 | - | - |
801
+ | 1.0662 | 2690 | 0.0537 | - | - |
802
+ | 1.0702 | 2700 | 0.0521 | - | - |
803
+ | 1.0741 | 2710 | 0.054 | - | - |
804
+ | 1.0781 | 2720 | 0.0513 | - | - |
805
+ | 1.0820 | 2730 | 0.0496 | - | - |
806
+ | 1.0860 | 2740 | 0.0519 | - | - |
807
+ | 1.0900 | 2750 | 0.0529 | - | - |
808
+ | 1.0939 | 2760 | 0.0542 | - | - |
809
+ | 1.0979 | 2770 | 0.0526 | - | - |
810
+ | 1.1019 | 2780 | 0.051 | - | - |
811
+ | 1.1058 | 2790 | 0.0531 | - | - |
812
+ | 1.1098 | 2800 | 0.0539 | - | - |
813
+ | 1.1138 | 2810 | 0.0521 | - | - |
814
+ | 1.1177 | 2820 | 0.0539 | - | - |
815
+ | 1.1217 | 2830 | 0.0505 | - | - |
816
+ | 1.1256 | 2840 | 0.0513 | - | - |
817
+ | 1.1296 | 2850 | 0.0521 | - | - |
818
+ | 1.1336 | 2860 | 0.0537 | - | - |
819
+ | 1.1375 | 2870 | 0.0514 | - | - |
820
+ | 1.1415 | 2880 | 0.0511 | - | - |
821
+ | 1.1455 | 2890 | 0.0495 | - | - |
822
+ | 1.1494 | 2900 | 0.0505 | - | - |
823
+ | 1.1534 | 2910 | 0.0517 | - | - |
824
+ | 1.1574 | 2920 | 0.0509 | - | - |
825
+ | 1.1613 | 2930 | 0.0507 | - | - |
826
+ | 1.1653 | 2940 | 0.0535 | - | - |
827
+ | 1.1692 | 2950 | 0.0511 | - | - |
828
+ | 1.1732 | 2960 | 0.0507 | - | - |
829
+ | 1.1772 | 2970 | 0.052 | - | - |
830
+ | 1.1811 | 2980 | 0.0494 | - | - |
831
+ | 1.1851 | 2990 | 0.0524 | - | - |
832
+ | 1.1891 | 3000 | 0.052 | 0.0555 | 0.9512 |
833
+ | 1.1930 | 3010 | 0.0536 | - | - |
834
+ | 1.1970 | 3020 | 0.0502 | - | - |
835
+ | 1.2010 | 3030 | 0.0504 | - | - |
836
+ | 1.2049 | 3040 | 0.0532 | - | - |
837
+ | 1.2089 | 3050 | 0.0529 | - | - |
838
+ | 1.2128 | 3060 | 0.0514 | - | - |
839
+ | 1.2168 | 3070 | 0.0504 | - | - |
840
+ | 1.2208 | 3080 | 0.0501 | - | - |
841
+ | 1.2247 | 3090 | 0.0493 | - | - |
842
+ | 1.2287 | 3100 | 0.0507 | - | - |
843
+ | 1.2327 | 3110 | 0.0501 | - | - |
844
+ | 1.2366 | 3120 | 0.0502 | - | - |
845
+ | 1.2406 | 3130 | 0.0491 | - | - |
846
+ | 1.2446 | 3140 | 0.0495 | - | - |
847
+ | 1.2485 | 3150 | 0.051 | - | - |
848
+ | 1.2525 | 3160 | 0.0495 | - | - |
849
+ | 1.2564 | 3170 | 0.0534 | - | - |
850
+ | 1.2604 | 3180 | 0.0483 | - | - |
851
+ | 1.2644 | 3190 | 0.049 | - | - |
852
+ | 1.2683 | 3200 | 0.0532 | - | - |
853
+ | 1.2723 | 3210 | 0.0481 | - | - |
854
+ | 1.2763 | 3220 | 0.0496 | - | - |
855
+ | 1.2802 | 3230 | 0.0504 | - | - |
856
+ | 1.2842 | 3240 | 0.0477 | - | - |
857
+ | 1.2881 | 3250 | 0.0483 | - | - |
858
+ | 1.2921 | 3260 | 0.0493 | - | - |
859
+ | 1.2961 | 3270 | 0.0491 | - | - |
860
+ | 1.3000 | 3280 | 0.0489 | - | - |
861
+ | 1.3040 | 3290 | 0.0493 | - | - |
862
+ | 1.3080 | 3300 | 0.0507 | - | - |
863
+ | 1.3119 | 3310 | 0.0482 | - | - |
864
+ | 1.3159 | 3320 | 0.0506 | - | - |
865
+ | 1.3199 | 3330 | 0.0486 | - | - |
866
+ | 1.3238 | 3340 | 0.0487 | - | - |
867
+ | 1.3278 | 3350 | 0.0482 | - | - |
868
+ | 1.3317 | 3360 | 0.0492 | - | - |
869
+ | 1.3357 | 3370 | 0.049 | - | - |
870
+ | 1.3397 | 3380 | 0.0485 | - | - |
871
+ | 1.3436 | 3390 | 0.0501 | - | - |
872
+ | 1.3476 | 3400 | 0.0505 | - | - |
873
+ | 1.3516 | 3410 | 0.0508 | - | - |
874
+ | 1.3555 | 3420 | 0.0481 | - | - |
875
+ | 1.3595 | 3430 | 0.049 | - | - |
876
+ | 1.3635 | 3440 | 0.0495 | - | - |
877
+ | 1.3674 | 3450 | 0.0507 | - | - |
878
+ | 1.3714 | 3460 | 0.0478 | - | - |
879
+ | 1.3753 | 3470 | 0.0522 | - | - |
880
+ | 1.3793 | 3480 | 0.0505 | - | - |
881
+ | 1.3833 | 3490 | 0.0489 | - | - |
882
+ | 1.3872 | 3500 | 0.0504 | 0.0541 | 0.9537 |
883
+ | 1.3912 | 3510 | 0.0492 | - | - |
884
+ | 1.3952 | 3520 | 0.0469 | - | - |
885
+ | 1.3991 | 3530 | 0.0495 | - | - |
886
+ | 1.4031 | 3540 | 0.0486 | - | - |
887
+ | 1.4071 | 3550 | 0.0506 | - | - |
888
+ | 1.4110 | 3560 | 0.0506 | - | - |
889
+ | 1.4150 | 3570 | 0.0475 | - | - |
890
+ | 1.4189 | 3580 | 0.0483 | - | - |
891
+ | 1.4229 | 3590 | 0.0471 | - | - |
892
+ | 1.4269 | 3600 | 0.0477 | - | - |
893
+ | 1.4308 | 3610 | 0.0494 | - | - |
894
+ | 1.4348 | 3620 | 0.0481 | - | - |
895
+ | 1.4388 | 3630 | 0.0484 | - | - |
896
+ | 1.4427 | 3640 | 0.0505 | - | - |
897
+ | 1.4467 | 3650 | 0.0498 | - | - |
898
+ | 1.4507 | 3660 | 0.0482 | - | - |
899
+ | 1.4546 | 3670 | 0.0488 | - | - |
900
+ | 1.4586 | 3680 | 0.0458 | - | - |
901
+ | 1.4625 | 3690 | 0.0479 | - | - |
902
+ | 1.4665 | 3700 | 0.0474 | - | - |
903
+ | 1.4705 | 3710 | 0.0471 | - | - |
904
+ | 1.4744 | 3720 | 0.0498 | - | - |
905
+ | 1.4784 | 3730 | 0.0495 | - | - |
906
+ | 1.4824 | 3740 | 0.0505 | - | - |
907
+ | 1.4863 | 3750 | 0.0487 | - | - |
908
+ | 1.4903 | 3760 | 0.0485 | - | - |
909
+ | 1.4943 | 3770 | 0.0479 | - | - |
910
+ | 1.4982 | 3780 | 0.0475 | - | - |
911
+ | 1.5022 | 3790 | 0.0462 | - | - |
912
+ | 1.5061 | 3800 | 0.0487 | - | - |
913
+ | 1.5101 | 3810 | 0.0476 | - | - |
914
+ | 1.5141 | 3820 | 0.0485 | - | - |
915
+ | 1.5180 | 3830 | 0.0489 | - | - |
916
+ | 1.5220 | 3840 | 0.0475 | - | - |
917
+ | 1.5260 | 3850 | 0.0484 | - | - |
918
+ | 1.5299 | 3860 | 0.0465 | - | - |
919
+ | 1.5339 | 3870 | 0.0491 | - | - |
920
+ | 1.5379 | 3880 | 0.0477 | - | - |
921
+ | 1.5418 | 3890 | 0.0475 | - | - |
922
+ | 1.5458 | 3900 | 0.0489 | - | - |
923
+ | 1.5497 | 3910 | 0.0459 | - | - |
924
+ | 1.5537 | 3920 | 0.0488 | - | - |
925
+ | 1.5577 | 3930 | 0.0475 | - | - |
926
+ | 1.5616 | 3940 | 0.049 | - | - |
927
+ | 1.5656 | 3950 | 0.0469 | - | - |
928
+ | 1.5696 | 3960 | 0.0493 | - | - |
929
+ | 1.5735 | 3970 | 0.0481 | - | - |
930
+ | 1.5775 | 3980 | 0.0478 | - | - |
931
+ | 1.5815 | 3990 | 0.0456 | - | - |
932
+ | 1.5854 | 4000 | 0.047 | 0.0528 | 0.9556 |
933
+ | 1.5894 | 4010 | 0.0481 | - | - |
934
+ | 1.5933 | 4020 | 0.0468 | - | - |
935
+ | 1.5973 | 4030 | 0.0467 | - | - |
936
+ | 1.6013 | 4040 | 0.0448 | - | - |
937
+ | 1.6052 | 4050 | 0.0491 | - | - |
938
+ | 1.6092 | 4060 | 0.0476 | - | - |
939
+ | 1.6132 | 4070 | 0.0459 | - | - |
940
+ | 1.6171 | 4080 | 0.0456 | - | - |
941
+ | 1.6211 | 4090 | 0.0476 | - | - |
942
+ | 1.6250 | 4100 | 0.0443 | - | - |
943
+ | 1.6290 | 4110 | 0.0477 | - | - |
944
+ | 1.6330 | 4120 | 0.0476 | - | - |
945
+ | 1.6369 | 4130 | 0.0466 | - | - |
946
+ | 1.6409 | 4140 | 0.0457 | - | - |
947
+ | 1.6449 | 4150 | 0.0468 | - | - |
948
+ | 1.6488 | 4160 | 0.0462 | - | - |
949
+ | 1.6528 | 4170 | 0.0476 | - | - |
950
+ | 1.6568 | 4180 | 0.0464 | - | - |
951
+ | 1.6607 | 4190 | 0.0467 | - | - |
952
+ | 1.6647 | 4200 | 0.0455 | - | - |
953
+ | 1.6686 | 4210 | 0.0455 | - | - |
954
+ | 1.6726 | 4220 | 0.0474 | - | - |
955
+ | 1.6766 | 4230 | 0.0469 | - | - |
956
+ | 1.6805 | 4240 | 0.0453 | - | - |
957
+ | 1.6845 | 4250 | 0.0464 | - | - |
958
+ | 1.6885 | 4260 | 0.0448 | - | - |
959
+ | 1.6924 | 4270 | 0.0448 | - | - |
960
+ | 1.6964 | 4280 | 0.0461 | - | - |
961
+ | 1.7004 | 4290 | 0.0444 | - | - |
962
+ | 1.7043 | 4300 | 0.045 | - | - |
963
+ | 1.7083 | 4310 | 0.047 | - | - |
964
+ | 1.7122 | 4320 | 0.0473 | - | - |
965
+ | 1.7162 | 4330 | 0.0453 | - | - |
966
+ | 1.7202 | 4340 | 0.0461 | - | - |
967
+ | 1.7241 | 4350 | 0.0464 | - | - |
968
+ | 1.7281 | 4360 | 0.0474 | - | - |
969
+ | 1.7321 | 4370 | 0.0444 | - | - |
970
+ | 1.7360 | 4380 | 0.0465 | - | - |
971
+ | 1.7400 | 4390 | 0.0454 | - | - |
972
+ | 1.7440 | 4400 | 0.045 | - | - |
973
+ | 1.7479 | 4410 | 0.0444 | - | - |
974
+ | 1.7519 | 4420 | 0.0451 | - | - |
975
+ | 1.7558 | 4430 | 0.0454 | - | - |
976
+ | 1.7598 | 4440 | 0.0471 | - | - |
977
+ | 1.7638 | 4450 | 0.0467 | - | - |
978
+ | 1.7677 | 4460 | 0.0466 | - | - |
979
+ | 1.7717 | 4470 | 0.0452 | - | - |
980
+ | 1.7757 | 4480 | 0.0466 | - | - |
981
+ | 1.7796 | 4490 | 0.046 | - | - |
982
+ | 1.7836 | 4500 | 0.0462 | 0.0518 | 0.9570 |
983
+ | 1.7876 | 4510 | 0.0459 | - | - |
984
+ | 1.7915 | 4520 | 0.0455 | - | - |
985
+ | 1.7955 | 4530 | 0.0456 | - | - |
986
+ | 1.7994 | 4540 | 0.0476 | - | - |
987
+ | 1.8034 | 4550 | 0.0465 | - | - |
988
+ | 1.8074 | 4560 | 0.0447 | - | - |
989
+ | 1.8113 | 4570 | 0.0438 | - | - |
990
+ | 1.8153 | 4580 | 0.0463 | - | - |
991
+ | 1.8193 | 4590 | 0.0452 | - | - |
992
+ | 1.8232 | 4600 | 0.0454 | - | - |
993
+ | 1.8272 | 4610 | 0.0459 | - | - |
994
+ | 1.8312 | 4620 | 0.044 | - | - |
995
+ | 1.8351 | 4630 | 0.0445 | - | - |
996
+ | 1.8391 | 4640 | 0.0435 | - | - |
997
+ | 1.8430 | 4650 | 0.0435 | - | - |
998
+ | 1.8470 | 4660 | 0.0442 | - | - |
999
+ | 1.8510 | 4670 | 0.0424 | - | - |
1000
+ | 1.8549 | 4680 | 0.0438 | - | - |
1001
+ | 1.8589 | 4690 | 0.0451 | - | - |
1002
+ | 1.8629 | 4700 | 0.0451 | - | - |
1003
+ | 1.8668 | 4710 | 0.0455 | - | - |
1004
+ | 1.8708 | 4720 | 0.0441 | - | - |
1005
+ | 1.8748 | 4730 | 0.0432 | - | - |
1006
+ | 1.8787 | 4740 | 0.0445 | - | - |
1007
+ | 1.8827 | 4750 | 0.0482 | - | - |
1008
+ | 1.8866 | 4760 | 0.045 | - | - |
1009
+ | 1.8906 | 4770 | 0.0443 | - | - |
1010
+ | 1.8946 | 4780 | 0.0451 | - | - |
1011
+ | 1.8985 | 4790 | 0.0446 | - | - |
1012
+ | 1.9025 | 4800 | 0.0432 | - | - |
1013
+ | 1.9065 | 4810 | 0.0432 | - | - |
1014
+ | 1.9104 | 4820 | 0.0465 | - | - |
1015
+ | 1.9144 | 4830 | 0.0462 | - | - |
1016
+ | 1.9184 | 4840 | 0.0443 | - | - |
1017
+ | 1.9223 | 4850 | 0.0447 | - | - |
1018
+ | 1.9263 | 4860 | 0.0459 | - | - |
1019
+ | 1.9302 | 4870 | 0.043 | - | - |
1020
+ | 1.9342 | 4880 | 0.0456 | - | - |
1021
+ | 1.9382 | 4890 | 0.0444 | - | - |
1022
+ | 1.9421 | 4900 | 0.0455 | - | - |
1023
+ | 1.9461 | 4910 | 0.0427 | - | - |
1024
+ | 1.9501 | 4920 | 0.0461 | - | - |
1025
+ | 1.9540 | 4930 | 0.0454 | - | - |
1026
+ | 1.9580 | 4940 | 0.0447 | - | - |
1027
+ | 1.9620 | 4950 | 0.0434 | - | - |
1028
+ | 1.9659 | 4960 | 0.0444 | - | - |
1029
+ | 1.9699 | 4970 | 0.0451 | - | - |
1030
+ | 1.9738 | 4980 | 0.044 | - | - |
1031
+ | 1.9778 | 4990 | 0.0444 | - | - |
1032
+ | 1.9818 | 5000 | 0.0439 | 0.0508 | 0.9581 |
1033
+ | 1.9857 | 5010 | 0.0427 | - | - |
1034
+ | 1.9897 | 5020 | 0.0439 | - | - |
1035
+ | 1.9937 | 5030 | 0.0427 | - | - |
1036
+ | 1.9976 | 5040 | 0.0435 | - | - |
1037
+ | 2.0016 | 5050 | 0.0445 | - | - |
1038
+ | 2.0055 | 5060 | 0.0433 | - | - |
1039
+ | 2.0095 | 5070 | 0.0433 | - | - |
1040
+ | 2.0135 | 5080 | 0.0435 | - | - |
1041
+ | 2.0174 | 5090 | 0.0438 | - | - |
1042
+ | 2.0214 | 5100 | 0.0431 | - | - |
1043
+ | 2.0254 | 5110 | 0.0422 | - | - |
1044
+ | 2.0293 | 5120 | 0.0436 | - | - |
1045
+ | 2.0333 | 5130 | 0.0455 | - | - |
1046
+ | 2.0373 | 5140 | 0.044 | - | - |
1047
+ | 2.0412 | 5150 | 0.0423 | - | - |
1048
+ | 2.0452 | 5160 | 0.045 | - | - |
1049
+ | 2.0491 | 5170 | 0.0422 | - | - |
1050
+ | 2.0531 | 5180 | 0.0435 | - | - |
1051
+ | 2.0571 | 5190 | 0.0419 | - | - |
1052
+ | 2.0610 | 5200 | 0.0427 | - | - |
1053
+ | 2.0650 | 5210 | 0.0447 | - | - |
1054
+ | 2.0690 | 5220 | 0.0443 | - | - |
1055
+ | 2.0729 | 5230 | 0.0429 | - | - |
1056
+ | 2.0769 | 5240 | 0.0436 | - | - |
1057
+ | 2.0809 | 5250 | 0.0436 | - | - |
1058
+ | 2.0848 | 5260 | 0.0439 | - | - |
1059
+ | 2.0888 | 5270 | 0.0433 | - | - |
1060
+ | 2.0927 | 5280 | 0.0434 | - | - |
1061
+ | 2.0967 | 5290 | 0.0428 | - | - |
1062
+ | 2.1007 | 5300 | 0.0431 | - | - |
1063
+ | 2.1046 | 5310 | 0.0441 | - | - |
1064
+ | 2.1086 | 5320 | 0.0443 | - | - |
1065
+ | 2.1126 | 5330 | 0.0442 | - | - |
1066
+ | 2.1165 | 5340 | 0.044 | - | - |
1067
+ | 2.1205 | 5350 | 0.0431 | - | - |
1068
+ | 2.1245 | 5360 | 0.0432 | - | - |
1069
+ | 2.1284 | 5370 | 0.0421 | - | - |
1070
+ | 2.1324 | 5380 | 0.0439 | - | - |
1071
+ | 2.1363 | 5390 | 0.0436 | - | - |
1072
+ | 2.1403 | 5400 | 0.0428 | - | - |
1073
+ | 2.1443 | 5410 | 0.044 | - | - |
1074
+ | 2.1482 | 5420 | 0.0428 | - | - |
1075
+ | 2.1522 | 5430 | 0.0428 | - | - |
1076
+ | 2.1562 | 5440 | 0.0418 | - | - |
1077
+ | 2.1601 | 5450 | 0.0439 | - | - |
1078
+ | 2.1641 | 5460 | 0.0415 | - | - |
1079
+ | 2.1681 | 5470 | 0.0415 | - | - |
1080
+ | 2.1720 | 5480 | 0.0418 | - | - |
1081
+ | 2.1760 | 5490 | 0.042 | - | - |
1082
+ | 2.1799 | 5500 | 0.0418 | 0.0500 | 0.9591 |
1083
+ | 2.1839 | 5510 | 0.0434 | - | - |
1084
+ | 2.1879 | 5520 | 0.0424 | - | - |
1085
+ | 2.1918 | 5530 | 0.0425 | - | - |
1086
+ | 2.1958 | 5540 | 0.0427 | - | - |
1087
+ | 2.1998 | 5550 | 0.0418 | - | - |
1088
+ | 2.2037 | 5560 | 0.04 | - | - |
1089
+ | 2.2077 | 5570 | 0.0426 | - | - |
1090
+ | 2.2117 | 5580 | 0.0413 | - | - |
1091
+ | 2.2156 | 5590 | 0.0429 | - | - |
1092
+ | 2.2196 | 5600 | 0.0428 | - | - |
1093
+ | 2.2235 | 5610 | 0.044 | - | - |
1094
+ | 2.2275 | 5620 | 0.0423 | - | - |
1095
+ | 2.2315 | 5630 | 0.0398 | - | - |
1096
+ | 2.2354 | 5640 | 0.0427 | - | - |
1097
+ | 2.2394 | 5650 | 0.0419 | - | - |
1098
+ | 2.2434 | 5660 | 0.0424 | - | - |
1099
+ | 2.2473 | 5670 | 0.0422 | - | - |
1100
+ | 2.2513 | 5680 | 0.0426 | - | - |
1101
+ | 2.2553 | 5690 | 0.0434 | - | - |
1102
+ | 2.2592 | 5700 | 0.044 | - | - |
1103
+ | 2.2632 | 5710 | 0.0427 | - | - |
1104
+ | 2.2671 | 5720 | 0.0431 | - | - |
1105
+ | 2.2711 | 5730 | 0.0416 | - | - |
1106
+ | 2.2751 | 5740 | 0.0428 | - | - |
1107
+ | 2.2790 | 5750 | 0.0418 | - | - |
1108
+ | 2.2830 | 5760 | 0.0418 | - | - |
1109
+ | 2.2870 | 5770 | 0.0421 | - | - |
1110
+ | 2.2909 | 5780 | 0.041 | - | - |
1111
+ | 2.2949 | 5790 | 0.0419 | - | - |
1112
+ | 2.2989 | 5800 | 0.0422 | - | - |
1113
+ | 2.3028 | 5810 | 0.0428 | - | - |
1114
+ | 2.3068 | 5820 | 0.0432 | - | - |
1115
+ | 2.3107 | 5830 | 0.043 | - | - |
1116
+ | 2.3147 | 5840 | 0.0424 | - | - |
1117
+ | 2.3187 | 5850 | 0.0396 | - | - |
1118
+ | 2.3226 | 5860 | 0.0433 | - | - |
1119
+ | 2.3266 | 5870 | 0.0413 | - | - |
1120
+ | 2.3306 | 5880 | 0.0436 | - | - |
1121
+ | 2.3345 | 5890 | 0.0399 | - | - |
1122
+ | 2.3385 | 5900 | 0.0426 | - | - |
1123
+ | 2.3424 | 5910 | 0.0405 | - | - |
1124
+ | 2.3464 | 5920 | 0.0423 | - | - |
1125
+ | 2.3504 | 5930 | 0.0409 | - | - |
1126
+ | 2.3543 | 5940 | 0.0412 | - | - |
1127
+ | 2.3583 | 5950 | 0.0401 | - | - |
1128
+ | 2.3623 | 5960 | 0.042 | - | - |
1129
+ | 2.3662 | 5970 | 0.0397 | - | - |
1130
+ | 2.3702 | 5980 | 0.0422 | - | - |
1131
+ | 2.3742 | 5990 | 0.0416 | - | - |
1132
+ | 2.3781 | 6000 | 0.0422 | 0.0493 | 0.9599 |
1133
+ | 2.3821 | 6010 | 0.041 | - | - |
1134
+ | 2.3860 | 6020 | 0.0404 | - | - |
1135
+ | 2.3900 | 6030 | 0.0404 | - | - |
1136
+ | 2.3940 | 6040 | 0.0412 | - | - |
1137
+ | 2.3979 | 6050 | 0.0424 | - | - |
1138
+ | 2.4019 | 6060 | 0.043 | - | - |
1139
+ | 2.4059 | 6070 | 0.0416 | - | - |
1140
+ | 2.4098 | 6080 | 0.0405 | - | - |
1141
+ | 2.4138 | 6090 | 0.0408 | - | - |
1142
+ | 2.4178 | 6100 | 0.0413 | - | - |
1143
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1144
+ | 2.4257 | 6120 | 0.0407 | - | - |
1145
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1146
+ | 2.4336 | 6140 | 0.0387 | - | - |
1147
+ | 2.4376 | 6150 | 0.0408 | - | - |
1148
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1149
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1150
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1151
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1152
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1153
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1154
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1155
+ | 2.4693 | 6230 | 0.0412 | - | - |
1156
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1157
+ | 2.4772 | 6250 | 0.0423 | - | - |
1158
+ | 2.4812 | 6260 | 0.0423 | - | - |
1159
+ | 2.4851 | 6270 | 0.0406 | - | - |
1160
+ | 2.4891 | 6280 | 0.0402 | - | - |
1161
+ | 2.4931 | 6290 | 0.0428 | - | - |
1162
+ | 2.4970 | 6300 | 0.0422 | - | - |
1163
+ | 2.5010 | 6310 | 0.0399 | - | - |
1164
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1165
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1166
+ | 2.5129 | 6340 | 0.0403 | - | - |
1167
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1168
+ | 2.5208 | 6360 | 0.0412 | - | - |
1169
+ | 2.5248 | 6370 | 0.0424 | - | - |
1170
+ | 2.5287 | 6380 | 0.0409 | - | - |
1171
+ | 2.5327 | 6390 | 0.0409 | - | - |
1172
+ | 2.5367 | 6400 | 0.0418 | - | - |
1173
+ | 2.5406 | 6410 | 0.0403 | - | - |
1174
+ | 2.5446 | 6420 | 0.0413 | - | - |
1175
+ | 2.5486 | 6430 | 0.038 | - | - |
1176
+ | 2.5525 | 6440 | 0.0414 | - | - |
1177
+ | 2.5565 | 6450 | 0.0409 | - | - |
1178
+ | 2.5604 | 6460 | 0.0407 | - | - |
1179
+ | 2.5644 | 6470 | 0.0406 | - | - |
1180
+ | 2.5684 | 6480 | 0.0392 | - | - |
1181
+ | 2.5723 | 6490 | 0.0417 | - | - |
1182
+ | 2.5763 | 6500 | 0.0391 | 0.0487 | 0.9605 |
1183
+ | 2.5803 | 6510 | 0.039 | - | - |
1184
+ | 2.5842 | 6520 | 0.0414 | - | - |
1185
+ | 2.5882 | 6530 | 0.0411 | - | - |
1186
+ | 2.5922 | 6540 | 0.0395 | - | - |
1187
+ | 2.5961 | 6550 | 0.0405 | - | - |
1188
+ | 2.6001 | 6560 | 0.0392 | - | - |
1189
+ | 2.6040 | 6570 | 0.041 | - | - |
1190
+ | 2.6080 | 6580 | 0.0387 | - | - |
1191
+ | 2.6120 | 6590 | 0.0409 | - | - |
1192
+ | 2.6159 | 6600 | 0.0416 | - | - |
1193
+ | 2.6199 | 6610 | 0.0399 | - | - |
1194
+ | 2.6239 | 6620 | 0.0395 | - | - |
1195
+ | 2.6278 | 6630 | 0.0416 | - | - |
1196
+ | 2.6318 | 6640 | 0.0397 | - | - |
1197
+ | 2.6358 | 6650 | 0.041 | - | - |
1198
+ | 2.6397 | 6660 | 0.0422 | - | - |
1199
+ | 2.6437 | 6670 | 0.0404 | - | - |
1200
+ | 2.6476 | 6680 | 0.0405 | - | - |
1201
+ | 2.6516 | 6690 | 0.0413 | - | - |
1202
+ | 2.6556 | 6700 | 0.0405 | - | - |
1203
+ | 2.6595 | 6710 | 0.04 | - | - |
1204
+ | 2.6635 | 6720 | 0.0383 | - | - |
1205
+ | 2.6675 | 6730 | 0.0412 | - | - |
1206
+ | 2.6714 | 6740 | 0.0416 | - | - |
1207
+ | 2.6754 | 6750 | 0.0405 | - | - |
1208
+ | 2.6793 | 6760 | 0.0423 | - | - |
1209
+ | 2.6833 | 6770 | 0.0419 | - | - |
1210
+ | 2.6873 | 6780 | 0.0405 | - | - |
1211
+ | 2.6912 | 6790 | 0.0409 | - | - |
1212
+ | 2.6952 | 6800 | 0.04 | - | - |
1213
+ | 2.6992 | 6810 | 0.0397 | - | - |
1214
+ | 2.7031 | 6820 | 0.039 | - | - |
1215
+ | 2.7071 | 6830 | 0.0393 | - | - |
1216
+ | 2.7111 | 6840 | 0.0413 | - | - |
1217
+ | 2.7150 | 6850 | 0.039 | - | - |
1218
+ | 2.7190 | 6860 | 0.04 | - | - |
1219
+ | 2.7229 | 6870 | 0.0409 | - | - |
1220
+ | 2.7269 | 6880 | 0.0403 | - | - |
1221
+ | 2.7309 | 6890 | 0.0397 | - | - |
1222
+ | 2.7348 | 6900 | 0.0404 | - | - |
1223
+ | 2.7388 | 6910 | 0.0396 | - | - |
1224
+ | 2.7428 | 6920 | 0.04 | - | - |
1225
+ | 2.7467 | 6930 | 0.0397 | - | - |
1226
+ | 2.7507 | 6940 | 0.0393 | - | - |
1227
+ | 2.7547 | 6950 | 0.037 | - | - |
1228
+ | 2.7586 | 6960 | 0.0383 | - | - |
1229
+ | 2.7626 | 6970 | 0.04 | - | - |
1230
+ | 2.7665 | 6980 | 0.0406 | - | - |
1231
+ | 2.7705 | 6990 | 0.0394 | - | - |
1232
+ | 2.7745 | 7000 | 0.0385 | 0.0482 | 0.9609 |
1233
+ | 2.7784 | 7010 | 0.0383 | - | - |
1234
+ | 2.7824 | 7020 | 0.0403 | - | - |
1235
+ | 2.7864 | 7030 | 0.04 | - | - |
1236
+ | 2.7903 | 7040 | 0.0395 | - | - |
1237
+ | 2.7943 | 7050 | 0.039 | - | - |
1238
+ | 2.7983 | 7060 | 0.0398 | - | - |
1239
+ | 2.8022 | 7070 | 0.0401 | - | - |
1240
+ | 2.8062 | 7080 | 0.0401 | - | - |
1241
+ | 2.8101 | 7090 | 0.0395 | - | - |
1242
+ | 2.8141 | 7100 | 0.0396 | - | - |
1243
+ | 2.8181 | 7110 | 0.0395 | - | - |
1244
+ | 2.8220 | 7120 | 0.0411 | - | - |
1245
+ | 2.8260 | 7130 | 0.0386 | - | - |
1246
+ | 2.8300 | 7140 | 0.0382 | - | - |
1247
+ | 2.8339 | 7150 | 0.0386 | - | - |
1248
+ | 2.8379 | 7160 | 0.0389 | - | - |
1249
+ | 2.8419 | 7170 | 0.0396 | - | - |
1250
+ | 2.8458 | 7180 | 0.0394 | - | - |
1251
+ | 2.8498 | 7190 | 0.04 | - | - |
1252
+ | 2.8537 | 7200 | 0.0401 | - | - |
1253
+ | 2.8577 | 7210 | 0.0412 | - | - |
1254
+ | 2.8617 | 7220 | 0.0383 | - | - |
1255
+ | 2.8656 | 7230 | 0.0392 | - | - |
1256
+ | 2.8696 | 7240 | 0.0394 | - | - |
1257
+ | 2.8736 | 7250 | 0.0399 | - | - |
1258
+ | 2.8775 | 7260 | 0.0403 | - | - |
1259
+ | 2.8815 | 7270 | 0.0384 | - | - |
1260
+ | 2.8855 | 7280 | 0.0397 | - | - |
1261
+ | 2.8894 | 7290 | 0.0407 | - | - |
1262
+ | 2.8934 | 7300 | 0.0386 | - | - |
1263
+ | 2.8973 | 7310 | 0.0385 | - | - |
1264
+ | 2.9013 | 7320 | 0.0405 | - | - |
1265
+ | 2.9053 | 7330 | 0.0389 | - | - |
1266
+ | 2.9092 | 7340 | 0.0362 | - | - |
1267
+ | 2.9132 | 7350 | 0.0397 | - | - |
1268
+ | 2.9172 | 7360 | 0.0393 | - | - |
1269
+ | 2.9211 | 7370 | 0.0397 | - | - |
1270
+ | 2.9251 | 7380 | 0.0386 | - | - |
1271
+ | 2.9291 | 7390 | 0.0388 | - | - |
1272
+ | 2.9330 | 7400 | 0.0366 | - | - |
1273
+ | 2.9370 | 7410 | 0.0394 | - | - |
1274
+ | 2.9409 | 7420 | 0.0396 | - | - |
1275
+ | 2.9449 | 7430 | 0.0393 | - | - |
1276
+ | 2.9489 | 7440 | 0.0401 | - | - |
1277
+ | 2.9528 | 7450 | 0.0391 | - | - |
1278
+ | 2.9568 | 7460 | 0.0388 | - | - |
1279
+ | 2.9608 | 7470 | 0.0386 | - | - |
1280
+ | 2.9647 | 7480 | 0.0391 | - | - |
1281
+ | 2.9687 | 7490 | 0.037 | - | - |
1282
+ | 2.9727 | 7500 | 0.0386 | 0.0477 | 0.9613 |
1283
+ | 2.9766 | 7510 | 0.0392 | - | - |
1284
+ | 2.9806 | 7520 | 0.0399 | - | - |
1285
+ | 2.9845 | 7530 | 0.0385 | - | - |
1286
+ | 2.9885 | 7540 | 0.0381 | - | - |
1287
+ | 2.9925 | 7550 | 0.0392 | - | - |
1288
+ | 2.9964 | 7560 | 0.0386 | - | - |
1289
+ | 3.0004 | 7570 | 0.0394 | - | - |
1290
+ | 3.0044 | 7580 | 0.0401 | - | - |
1291
+ | 3.0083 | 7590 | 0.0404 | - | - |
1292
+ | 3.0123 | 7600 | 0.0384 | - | - |
1293
+ | 3.0163 | 7610 | 0.0381 | - | - |
1294
+ | 3.0202 | 7620 | 0.0383 | - | - |
1295
+ | 3.0242 | 7630 | 0.0389 | - | - |
1296
+ | 3.0281 | 7640 | 0.0364 | - | - |
1297
+ | 3.0321 | 7650 | 0.0399 | - | - |
1298
+ | 3.0361 | 7660 | 0.0383 | - | - |
1299
+ | 3.0400 | 7670 | 0.0401 | - | - |
1300
+ | 3.0440 | 7680 | 0.0388 | - | - |
1301
+ | 3.0480 | 7690 | 0.0389 | - | - |
1302
+ | 3.0519 | 7700 | 0.036 | - | - |
1303
+ | 3.0559 | 7710 | 0.0403 | - | - |
1304
+ | 3.0598 | 7720 | 0.0376 | - | - |
1305
+ | 3.0638 | 7730 | 0.0387 | - | - |
1306
+ | 3.0678 | 7740 | 0.0405 | - | - |
1307
+ | 3.0717 | 7750 | 0.0399 | - | - |
1308
+ | 3.0757 | 7760 | 0.0382 | - | - |
1309
+ | 3.0797 | 7770 | 0.0376 | - | - |
1310
+ | 3.0836 | 7780 | 0.0393 | - | - |
1311
+ | 3.0876 | 7790 | 0.0388 | - | - |
1312
+ | 3.0916 | 7800 | 0.0395 | - | - |
1313
+ | 3.0955 | 7810 | 0.0391 | - | - |
1314
+ | 3.0995 | 7820 | 0.0392 | - | - |
1315
+ | 3.1034 | 7830 | 0.0371 | - | - |
1316
+ | 3.1074 | 7840 | 0.039 | - | - |
1317
+ | 3.1114 | 7850 | 0.0395 | - | - |
1318
+ | 3.1153 | 7860 | 0.0385 | - | - |
1319
+ | 3.1193 | 7870 | 0.0362 | - | - |
1320
+ | 3.1233 | 7880 | 0.0375 | - | - |
1321
+ | 3.1272 | 7890 | 0.0376 | - | - |
1322
+ | 3.1312 | 7900 | 0.0384 | - | - |
1323
+ | 3.1352 | 7910 | 0.0378 | - | - |
1324
+ | 3.1391 | 7920 | 0.0393 | - | - |
1325
+ | 3.1431 | 7930 | 0.0378 | - | - |
1326
+ | 3.1470 | 7940 | 0.0404 | - | - |
1327
+ | 3.1510 | 7950 | 0.0361 | - | - |
1328
+ | 3.1550 | 7960 | 0.0369 | - | - |
1329
+ | 3.1589 | 7970 | 0.0396 | - | - |
1330
+ | 3.1629 | 7980 | 0.0404 | - | - |
1331
+ | 3.1669 | 7990 | 0.0386 | - | - |
1332
+ | 3.1708 | 8000 | 0.038 | 0.0473 | 0.9616 |
1333
+ | 3.1748 | 8010 | 0.0372 | - | - |
1334
+ | 3.1788 | 8020 | 0.0373 | - | - |
1335
+ | 3.1827 | 8030 | 0.0369 | - | - |
1336
+ | 3.1867 | 8040 | 0.0371 | - | - |
1337
+ | 3.1906 | 8050 | 0.0386 | - | - |
1338
+ | 3.1946 | 8060 | 0.038 | - | - |
1339
+ | 3.1986 | 8070 | 0.0366 | - | - |
1340
+ | 3.2025 | 8080 | 0.0378 | - | - |
1341
+ | 3.2065 | 8090 | 0.0379 | - | - |
1342
+ | 3.2105 | 8100 | 0.038 | - | - |
1343
+ | 3.2144 | 8110 | 0.0374 | - | - |
1344
+ | 3.2184 | 8120 | 0.0388 | - | - |
1345
+ | 3.2224 | 8130 | 0.038 | - | - |
1346
+ | 3.2263 | 8140 | 0.0363 | - | - |
1347
+ | 3.2303 | 8150 | 0.0369 | - | - |
1348
+ | 3.2342 | 8160 | 0.0371 | - | - |
1349
+ | 3.2382 | 8170 | 0.0377 | - | - |
1350
+ | 3.2422 | 8180 | 0.0364 | - | - |
1351
+ | 3.2461 | 8190 | 0.0372 | - | - |
1352
+ | 3.2501 | 8200 | 0.0403 | - | - |
1353
+ | 3.2541 | 8210 | 0.0385 | - | - |
1354
+ | 3.2580 | 8220 | 0.0385 | - | - |
1355
+ | 3.2620 | 8230 | 0.0386 | - | - |
1356
+ | 3.2660 | 8240 | 0.0369 | - | - |
1357
+ | 3.2699 | 8250 | 0.039 | - | - |
1358
+ | 3.2739 | 8260 | 0.0365 | - | - |
1359
+ | 3.2778 | 8270 | 0.0382 | - | - |
1360
+ | 3.2818 | 8280 | 0.0354 | - | - |
1361
+ | 3.2858 | 8290 | 0.0393 | - | - |
1362
+ | 3.2897 | 8300 | 0.0387 | - | - |
1363
+ | 3.2937 | 8310 | 0.0366 | - | - |
1364
+ | 3.2977 | 8320 | 0.0391 | - | - |
1365
+ | 3.3016 | 8330 | 0.0382 | - | - |
1366
+ | 3.3056 | 8340 | 0.0377 | - | - |
1367
+ | 3.3096 | 8350 | 0.0369 | - | - |
1368
+ | 3.3135 | 8360 | 0.0384 | - | - |
1369
+ | 3.3175 | 8370 | 0.0379 | - | - |
1370
+ | 3.3214 | 8380 | 0.0372 | - | - |
1371
+ | 3.3254 | 8390 | 0.0391 | - | - |
1372
+ | 3.3294 | 8400 | 0.0378 | - | - |
1373
+ | 3.3333 | 8410 | 0.0393 | - | - |
1374
+ | 3.3373 | 8420 | 0.0373 | - | - |
1375
+ | 3.3413 | 8430 | 0.0394 | - | - |
1376
+ | 3.3452 | 8440 | 0.0367 | - | - |
1377
+ | 3.3492 | 8450 | 0.0373 | - | - |
1378
+ | 3.3532 | 8460 | 0.0362 | - | - |
1379
+ | 3.3571 | 8470 | 0.0372 | - | - |
1380
+ | 3.3611 | 8480 | 0.0396 | - | - |
1381
+ | 3.3650 | 8490 | 0.0392 | - | - |
1382
+ | 3.3690 | 8500 | 0.0374 | 0.0470 | 0.9616 |
1383
+ | 3.3730 | 8510 | 0.0378 | - | - |
1384
+ | 3.3769 | 8520 | 0.0385 | - | - |
1385
+ | 3.3809 | 8530 | 0.0375 | - | - |
1386
+ | 3.3849 | 8540 | 0.0392 | - | - |
1387
+ | 3.3888 | 8550 | 0.0378 | - | - |
1388
+ | 3.3928 | 8560 | 0.0366 | - | - |
1389
+ | 3.3967 | 8570 | 0.0383 | - | - |
1390
+ | 3.4007 | 8580 | 0.0372 | - | - |
1391
+ | 3.4047 | 8590 | 0.038 | - | - |
1392
+ | 3.4086 | 8600 | 0.0384 | - | - |
1393
+ | 3.4126 | 8610 | 0.0359 | - | - |
1394
+ | 3.4166 | 8620 | 0.0377 | - | - |
1395
+ | 3.4205 | 8630 | 0.0387 | - | - |
1396
+ | 3.4245 | 8640 | 0.0365 | - | - |
1397
+ | 3.4285 | 8650 | 0.0359 | - | - |
1398
+ | 3.4324 | 8660 | 0.0358 | - | - |
1399
+ | 3.4364 | 8670 | 0.0366 | - | - |
1400
+ | 3.4403 | 8680 | 0.0369 | - | - |
1401
+ | 3.4443 | 8690 | 0.0365 | - | - |
1402
+ | 3.4483 | 8700 | 0.0366 | - | - |
1403
+ | 3.4522 | 8710 | 0.0357 | - | - |
1404
+ | 3.4562 | 8720 | 0.036 | - | - |
1405
+ | 3.4602 | 8730 | 0.0365 | - | - |
1406
+ | 3.4641 | 8740 | 0.0381 | - | - |
1407
+ | 3.4681 | 8750 | 0.0399 | - | - |
1408
+ | 3.4721 | 8760 | 0.0388 | - | - |
1409
+ | 3.4760 | 8770 | 0.0366 | - | - |
1410
+ | 3.4800 | 8780 | 0.0346 | - | - |
1411
+ | 3.4839 | 8790 | 0.0371 | - | - |
1412
+ | 3.4879 | 8800 | 0.0376 | - | - |
1413
+ | 3.4919 | 8810 | 0.0374 | - | - |
1414
+ | 3.4958 | 8820 | 0.0354 | - | - |
1415
+ | 3.4998 | 8830 | 0.0363 | - | - |
1416
+ | 3.5038 | 8840 | 0.0374 | - | - |
1417
+ | 3.5077 | 8850 | 0.0373 | - | - |
1418
+ | 3.5117 | 8860 | 0.0347 | - | - |
1419
+ | 3.5157 | 8870 | 0.0374 | - | - |
1420
+ | 3.5196 | 8880 | 0.0349 | - | - |
1421
+ | 3.5236 | 8890 | 0.0376 | - | - |
1422
+ | 3.5275 | 8900 | 0.0363 | - | - |
1423
+ | 3.5315 | 8910 | 0.036 | - | - |
1424
+ | 3.5355 | 8920 | 0.0378 | - | - |
1425
+ | 3.5394 | 8930 | 0.0376 | - | - |
1426
+ | 3.5434 | 8940 | 0.039 | - | - |
1427
+ | 3.5474 | 8950 | 0.0373 | - | - |
1428
+ | 3.5513 | 8960 | 0.0361 | - | - |
1429
+ | 3.5553 | 8970 | 0.0356 | - | - |
1430
+ | 3.5593 | 8980 | 0.0357 | - | - |
1431
+ | 3.5632 | 8990 | 0.0371 | - | - |
1432
+ | 3.5672 | 9000 | 0.0374 | 0.0468 | 0.9617 |
1433
+ | 3.5711 | 9010 | 0.0372 | - | - |
1434
+ | 3.5751 | 9020 | 0.0369 | - | - |
1435
+ | 3.5791 | 9030 | 0.0362 | - | - |
1436
+ | 3.5830 | 9040 | 0.0367 | - | - |
1437
+ | 3.5870 | 9050 | 0.0388 | - | - |
1438
+ | 3.5910 | 9060 | 0.0369 | - | - |
1439
+ | 3.5949 | 9070 | 0.0375 | - | - |
1440
+ | 3.5989 | 9080 | 0.0374 | - | - |
1441
+ | 3.6029 | 9090 | 0.0365 | - | - |
1442
+ | 3.6068 | 9100 | 0.0363 | - | - |
1443
+ | 3.6108 | 9110 | 0.0396 | - | - |
1444
+ | 3.6147 | 9120 | 0.0372 | - | - |
1445
+ | 3.6187 | 9130 | 0.0363 | - | - |
1446
+ | 3.6227 | 9140 | 0.0363 | - | - |
1447
+ | 3.6266 | 9150 | 0.0366 | - | - |
1448
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1449
+ | 3.6346 | 9170 | 0.038 | - | - |
1450
+ | 3.6385 | 9180 | 0.0359 | - | - |
1451
+ | 3.6425 | 9190 | 0.0374 | - | - |
1452
+ | 3.6465 | 9200 | 0.0363 | - | - |
1453
+ | 3.6504 | 9210 | 0.0356 | - | - |
1454
+ | 3.6544 | 9220 | 0.0354 | - | - |
1455
+ | 3.6583 | 9230 | 0.0377 | - | - |
1456
+ | 3.6623 | 9240 | 0.0361 | - | - |
1457
+ | 3.6663 | 9250 | 0.0374 | - | - |
1458
+ | 3.6702 | 9260 | 0.0373 | - | - |
1459
+ | 3.6742 | 9270 | 0.0357 | - | - |
1460
+ | 3.6782 | 9280 | 0.0359 | - | - |
1461
+ | 3.6821 | 9290 | 0.037 | - | - |
1462
+ | 3.6861 | 9300 | 0.0366 | - | - |
1463
+ | 3.6901 | 9310 | 0.0374 | - | - |
1464
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1465
+ | 3.6980 | 9330 | 0.0373 | - | - |
1466
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1467
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1468
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1469
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1470
+ | 3.7178 | 9380 | 0.0377 | - | - |
1471
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1472
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1473
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1474
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1475
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1476
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1477
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1478
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1479
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1480
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1481
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1482
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1483
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1484
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1485
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1486
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1487
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1488
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1489
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1490
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1491
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1492
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1493
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1494
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1495
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1496
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1497
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1498
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1499
+ | 3.8327 | 9670 | 0.0363 | - | - |
1500
+ | 3.8367 | 9680 | 0.0374 | - | - |
1501
+ | 3.8407 | 9690 | 0.0372 | - | - |
1502
+ | 3.8446 | 9700 | 0.0361 | - | - |
1503
+ | 3.8486 | 9710 | 0.0381 | - | - |
1504
+ | 3.8526 | 9720 | 0.0342 | - | - |
1505
+ | 3.8565 | 9730 | 0.0348 | - | - |
1506
+ | 3.8605 | 9740 | 0.0372 | - | - |
1507
+ | 3.8644 | 9750 | 0.0377 | - | - |
1508
+ | 3.8684 | 9760 | 0.0356 | - | - |
1509
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1510
+ | 3.8763 | 9780 | 0.0368 | - | - |
1511
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1512
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1513
+ | 3.8882 | 9810 | 0.0353 | - | - |
1514
+ | 3.8922 | 9820 | 0.0377 | - | - |
1515
+ | 3.8962 | 9830 | 0.0364 | - | - |
1516
+ | 3.9001 | 9840 | 0.0362 | - | - |
1517
+ | 3.9041 | 9850 | 0.0351 | - | - |
1518
+ | 3.9080 | 9860 | 0.0381 | - | - |
1519
+ | 3.9120 | 9870 | 0.0368 | - | - |
1520
+ | 3.9160 | 9880 | 0.0361 | - | - |
1521
+ | 3.9199 | 9890 | 0.0356 | - | - |
1522
+ | 3.9239 | 9900 | 0.035 | - | - |
1523
+ | 3.9279 | 9910 | 0.0345 | - | - |
1524
+ | 3.9318 | 9920 | 0.0378 | - | - |
1525
+ | 3.9358 | 9930 | 0.036 | - | - |
1526
+ | 3.9398 | 9940 | 0.0367 | - | - |
1527
+ | 3.9437 | 9950 | 0.0356 | - | - |
1528
+ | 3.9477 | 9960 | 0.034 | - | - |
1529
+ | 3.9516 | 9970 | 0.0377 | - | - |
1530
+ | 3.9556 | 9980 | 0.0379 | - | - |
1531
+ | 3.9596 | 9990 | 0.0388 | - | - |
1532
+ | 3.9635 | 10000 | 0.0362 | 0.0463 | 0.9618 |
1533
+
1534
+ </details>
1535
+
1536
+ ### Framework Versions
1537
+ - Python: 3.10.10
1538
+ - Sentence Transformers: 3.0.1
1539
+ - Transformers: 4.45.0.dev0
1540
+ - PyTorch: 2.2.1+cu121
1541
+ - Accelerate: 0.34.2
1542
+ - Datasets: 2.21.0
1543
+ - Tokenizers: 0.19.1
1544
+
1545
+ ## Citation
1546
+
1547
+ ### BibTeX
1548
+
1549
+ #### Sentence Transformers
1550
+ ```bibtex
1551
+ @inproceedings{reimers-2019-sentence-bert,
1552
+ title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
1553
+ author = "Reimers, Nils and Gurevych, Iryna",
1554
+ booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
1555
+ month = "11",
1556
+ year = "2019",
1557
+ publisher = "Association for Computational Linguistics",
1558
+ url = "https://arxiv.org/abs/1908.10084",
1559
+ }
1560
+ ```
1561
+
1562
+ #### ContrastiveLoss
1563
+ ```bibtex
1564
+ @inproceedings{hadsell2006dimensionality,
1565
+ author={Hadsell, R. and Chopra, S. and LeCun, Y.},
1566
+ booktitle={2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06)},
1567
+ title={Dimensionality Reduction by Learning an Invariant Mapping},
1568
+ year={2006},
1569
+ volume={2},
1570
+ number={},
1571
+ pages={1735-1742},
1572
+ doi={10.1109/CVPR.2006.100}
1573
+ }
1574
+ ```
1575
+
1576
+ <!--
1577
+ ## Glossary
1578
+
1579
+ *Clearly define terms in order to be accessible across audiences.*
1580
+ -->
1581
+
1582
+ <!--
1583
+ ## Model Card Authors
1584
+
1585
+ *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
1586
+ -->
1587
+
1588
+ <!--
1589
+ ## Model Card Contact
1590
+
1591
+ *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
1592
+ -->
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