Hugging Face's logo

naufalso
/
setfit-ctc-bge-base-en-v1.5

Text Classification
setfit
Safetensors
sentence-transformers
bert
generated_from_setfit_trainer
Model card Files
xet
Community
naufalso commited on
Commit
d66c8f8
·
verified ·
1 Parent(s): 8a63380

Push model using huggingface_hub.

Browse files
Files changed (13) hide show
  1. 1_Pooling/config.json +10 -0
  2. README.md +1517 -0
  3. config.json +32 -0
  4. config_sentence_transformers.json +10 -0
  5. config_setfit.json +7 -0
  6. model.safetensors +3 -0
  7. model_head.pkl +3 -0
  8. modules.json +20 -0
  9. sentence_bert_config.json +4 -0
  10. special_tokens_map.json +37 -0
  11. tokenizer.json +0 -0
  12. tokenizer_config.json +58 -0
  13. vocab.txt +0 -0
1_Pooling/config.json ADDED
@@ -0,0 +1,10 @@
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "word_embedding_dimension": 768,
3
+ "pooling_mode_cls_token": true,
4
+ "pooling_mode_mean_tokens": false,
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
+ }
README.md ADDED
@@ -0,0 +1,1517 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ tags:
3
+ - setfit
4
+ - sentence-transformers
5
+ - text-classification
6
+ - generated_from_setfit_trainer
7
+ widget:
8
+ - text: Now let us conceive a particular volition, namely, the mode of thinking whereby
9
+ the mind affirms, that the three interior angles of a triangle are equal to two
10
+ right angles.
11
+ - text: If we know beforehand what this state of affairs is, our desire is conscious;
12
+ if not, unconscious.
13
+ - text: 'The salvation of the soul in plain English: the world revolves around me.'
14
+ - text: Masculine myths find their most seductive incarnation in the hetaera; more
15
+ than any other woman, she is flesh and consciousness, idol, inspiration, muse;
16
+ painters and sculptors want her as their model; she will nourish poets' dreams;
17
+ it is in her that the intellectual will explore the treasures of feminine 'intuition';
18
+ she is more readily intelligent than the matron, because she is less set in hypocrisy.
19
+ - text: " Since 2004, the Mandiant name has represented unparalleled security expertise,\
20
+ \ earning the trust of cyber security professionals and company executives across\
21
+ \ the world. By joining this unparalleled frontline experience with our industry\
22
+ \ leading, nation-state grade threat intelligence and innovative technology, we\
23
+ \ have ensured that FireEye knows more about current advanced threats than anyone.\
24
+ \ Today the world looks a lot different than it did in 2004. The cyber security\
25
+ \ industry has expanded (some might say exploded), but through all this change,\
26
+ \ one thing has remained the same: there is no substitute for world-class expertise\
27
+ \ and intelligence. With that in mind, we’ve continued to push the boundaries\
28
+ \ of innovation by expanding our expertise- and intelligence-backed solutions\
29
+ \ to stay ahead of market needs. Each is considered the gold standard in its respective\
30
+ \ space. These solutions include Mandiant Consulting, Mandiant Managed Defense,\
31
+ \ FireEye Threat Intelligence, FireEye Expertise On Demand, and Verodin Security\
32
+ \ Validation. Now, to streamline options and simplify the process of identifying\
33
+ \ solutions our customers need to proactively combat cyber threats, we are renaming\
34
+ \ our expertise- and intelligence-backed solutions to Mandiant, under the collective\
35
+ \ term Mandiant Solutions. The renaming of our solutions does not change pricing,\
36
+ \ content, or delivery today. Current subscribers of these services will continue\
37
+ \ to receive the same unparalleled frontline expertise they have come to rely\
38
+ \ on. As we move forward, the goal of Mandiant Solutions is to deliver synergies\
39
+ \ between these solutions to help customers improve security effectiveness by\
40
+ \ automating the security operations center and augmenting their security teams\
41
+ \ with Mandiant expertise and intelligence, regardless of the SIEM and security\
42
+ \ technology they have deployed.  Our Mandiant Solutions portfolio will include:\
43
+ \ Each of these offerings combines our technologies, intelligence and expertise,\
44
+ \ helping organizations meet evolving security challenges. Customers can be confident\
45
+ \ that Mandiant Solutions are backed by the industry’s best expertise and informed\
46
+ \ by the best threat intelligence available today.   For example, following the\
47
+ \ acquisition of Verodin last year, we’ve been actively integrating our market-leading\
48
+ \ threat intelligence with the industry’s most comprehensive security validation\
49
+ \ platform, now known as Mandiant Security Validation. This represents a significant\
50
+ \ benefit to our customers who can test and validate their organization’s readiness\
51
+ \ against the very latest techniques employed by today’s threat actors. Of course,\
52
+ \ our suite of enterprise solutions (FireEye Helix, Endpoint, Network, and Email\
53
+ \ Security) also benefits from and enhances this wealth of frontline expertise\
54
+ \ through our unique Innovation Cycle. It ensures that our products and services\
55
+ \ are able to learn and adapt to new threats faster and better than anyone.  As\
56
+ \ we look to the future, our vision is to continue to integrate these capabilities\
57
+ \ through a seamless, modern platform that accelerates our customers’ ability\
58
+ \ to measurably improve the people, processes, and technology they need to protect\
59
+ \ their critical assets.  Stay tuned for more updates as we rollout our renaming!\t\
60
+ \tSince 2004, the Mandiant name has represented unparalleled security expertise,\
61
+ \ earning the trust of cyber security professionals and company executives across\
62
+ \ the world. By joining this unparalleled frontline experience with our industry\
63
+ \ leading, nation-state grade threat intelligence and innovative technology, we\
64
+ \ have ensured that FireEye knows more about current advanced threats than anyone.Today\
65
+ \ the world looks a lot different than it did in 2004. The cyber security industry\
66
+ \ has expanded (some might say exploded), but through all this change, one thing\
67
+ \ has remained the same: there is no substitute for world-class expertise and\
68
+ \ intelligence.With that in mind, we’ve continued to push the boundaries of innovation\
69
+ \ by expanding our expertise- and intelligence-backed solutions to stay ahead\
70
+ \ of market needs. Each is considered the gold standard in its respective space.\
71
+ \ These solutions include Mandiant Consulting, Mandiant Managed Defense, FireEye\
72
+ \ Threat Intelligence, FireEye Expertise On Demand, and Verodin Security Validation.Now,\
73
+ \ to streamline options and simplify the process of identifying solutions our\
74
+ \ customers need to proactively combat cyber threats, we are renaming our expertise-\
75
+ \ and intelligence-backed solutions to Mandiant, under the collective term Mandiant\
76
+ \ Solutions.The renaming of our solutions does not change pricing, content, or\
77
+ \ delivery today. Current subscribers of these services will continue to receive\
78
+ \ the same unparalleled frontline expertise they have come to rely on.As we move\
79
+ \ forward, the goal of Mandiant Solutions is to deliver synergies between these\
80
+ \ solutions to help customers improve security effectiveness by automating the\
81
+ \ security operations center and augmenting their security teams with Mandiant\
82
+ \ expertise and intelligence, regardless of the SIEM and security technology they\
83
+ \ have deployed. Our Mandiant Solutions portfolio will include:Mandiant ConsultingMandiant\
84
+ \ Managed DefenseMandiant Threat IntelligenceMandiant Expertise On DemandMandiant\
85
+ \ Security Validation (formerly Verodin)Each of these offerings combines our technologies,\
86
+ \ intelligence and expertise, helping organizations meet evolving security challenges.\
87
+ \ Customers can be confident that Mandiant Solutions are backed by the industry’s\
88
+ \ best expertise and informed by the best threat intelligence available today.  For\
89
+ \ example, following the acquisition of Verodin last year, we’ve been actively\
90
+ \ integrating our market-leading threat intelligence with the industry’s most\
91
+ \ comprehensive security validation platform, now known as Mandiant Security Validation.\
92
+ \ This represents a significant benefit to our customers who can test and validate\
93
+ \ their organization’s readiness against the very latest techniques employed by\
94
+ \ today’s threat actors.Of course, our suite of enterprise solutions (FireEye\
95
+ \ Helix, Endpoint, Network, and Email Security) also benefits from and enhances\
96
+ \ this wealth of frontline expertise through our unique Innovation Cycle. It ensures\
97
+ \ that our products and services are able to learn and adapt to new threats faster\
98
+ \ and better than anyone. As we look to the future, our vision is to continue\
99
+ \ to integrate these capabilities through a seamless, modern platform that accelerates\
100
+ \ our customers’ ability to measurably improve the people, processes, and technology\
101
+ \ they need to protect their critical assets. Stay tuned for more updates as we\
102
+ \ rollout our renaming!"
103
+ metrics:
104
+ - accuracy
105
+ pipeline_tag: text-classification
106
+ library_name: setfit
107
+ inference: true
108
+ base_model: BAAI/bge-base-en-v1.5
109
+ ---
110
+
111
+ # SetFit with BAAI/bge-base-en-v1.5
112
+
113
+ This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) as the Sentence Transformer embedding model. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance is used for classification.
114
+
115
+ The model has been trained using an efficient few-shot learning technique that involves:
116
+
117
+ 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
118
+ 2. Training a classification head with features from the fine-tuned Sentence Transformer.
119
+
120
+ ## Model Details
121
+
122
+ ### Model Description
123
+ - **Model Type:** SetFit
124
+ - **Sentence Transformer body:** [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5)
125
+ - **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
126
+ - **Maximum Sequence Length:** 512 tokens
127
+ - **Number of Classes:** 2 classes
128
+ <!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
129
+ <!-- - **Language:** Unknown -->
130
+ <!-- - **License:** Unknown -->
131
+
132
+ ### Model Sources
133
+
134
+ - **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit)
135
+ - **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)
136
+ - **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)
137
+
138
+ ### Model Labels
139
+ | Label | Examples |
140
+ |:-------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
141
+ | cybersec | <ul><li>"cracking this password?. http://postimg.org/image/mi3xit477/\nit's Gargoyle Router Management Utility\ni'm a pre-beginner in cracking, i setted this up in my router, but i don't want to press the reset button, it took me a few weeks to do it, so i don't wanna re-install the firmware, but i forgot the password.....\ni have unlimited times of enter times, it's a 192.168.2.1\nhow can i crack it? i don't think it's encrypted though..."</li><li>'How can someone prevent a sybil attack when connecting through TOR?. <p>As I understand it, running sybil BTC nodes through an anonymous network like TOR is much less expensive than in clearnet. This makes it possible that one could be connected to a majority of nodes controlled by the same entity, right?</p>\n\n<p>Is there any way to limit exposure to this when connection through TOR?</p>\n\n<p>( I am asking for a friend :P )</p>\n'</li><li>'Added gigabit qos switch at workstation to work around 10/100 pass though in Cisco IP phone. Widows says the LAN connection is 1Gbps, but there is a cat5, not 5e going to the machine, am I really getting gigabit?. Windows 7.\nLong story short, the network connection to our PCs was running through our Cisco IP phones, which only supported 10/100. Per my IT guy, everything else on our network, the switches etc. can support gigabit, the phone is the choke point. To workaround, I got a 5 port gigabit switch, and put the phone on the high priority qos port. Under the LAN connection in control panel, it went from 100Mbps to 1Gbps.\nThe reason I am skeptical is that the ethernet cable from the switch to the PC is cat5, not 5e. My understanding is it needs to be 5e. Since there are 3 cables (wall to switch, switch to phone, switch to pc) per machine, I would rather not replace every cable on 17 machines.\nSo, if Windows says gigabit, is that all there is to it? Or should I run some type of diagnostic?\nLonger question, we have 20ish IP phones, and a server, sharing modestly sized documents, and some server-centric ERP type software. Do I even need the Gigabit speed? Some users I have switched are noticing some improvement, but we are not transferring huge files across the network regularly, so it may just seem anecdotally faster to them. How can I tell if I really need the extra bandwidth, and what I am using?\n\nI feel like a total idiot here, be gentle...\n\nThanks!'</li></ul> |
142
+ | non-cybersec | <ul><li>'Tex-shell in AUCTeX. <p>Whenever I compile a file in AUCTeX (e.g. <code>C-c</code> <code>C-c</code> and then choosing an option) , it creates a buffer <code>tex-shell</code> where I can see the output of the compilation command. Once the compilation finishes this shell buffer stays open. What is the right way to close it? </p>\n\n<p>Besides showing me the compilation output, what else can I use it for?</p>\n'</li><li>'Inserting a Creative Commons Licence into a LaTeX document. <p>I\'d like to insert a CC license on a manuscript (a book or report). I\'ve seen the page for downloading the <a href="http://creativecommons.org/about/downloads/" rel="noreferrer">CC icons</a>, and also some questions asked in the forum <a href="https://tex.stackexchange.com/questions/20308/creative-commons-logo">CC logo</a> and <a href="https://tex.stackexchange.com/questions/1725/how-do-i-generate-creative-commons-license-information">Generate CC information</a>. </p>\n\n<p>However, I do not get how to create the actual thing!</p>\n\n<p><strong>Q:</strong> Can you please provide an example of a license info page (<em>MWE</em>)? That would be really helpful!</p>\n'</li><li>"Hey Reddit! We're Tritonal, and we just released our new U&Me album. Ask us anything!!. Yooo! What's up!? It's Dave & Chad of Tritonal, and we've just released our newest album, U&ME, available everywhere now! We're here to answer all of YOUR questions. Let's get this thing started!\n\nASK US ANYTHING! <3\n\nOur new album U&Me - https://enhanced.ffm.to/umealbum\nOur tour dates - http://tritonalmusic.com/shows\n\nProof: https://i.imgur.com/6cxJ9eU.jpg"</li></ul> |
143
+
144
+ ## Uses
145
+
146
+ ### Direct Use for Inference
147
+
148
+ First install the SetFit library:
149
+
150
+ ```bash
151
+ pip install setfit
152
+ ```
153
+
154
+ Then you can load this model and run inference.
155
+
156
+ ```python
157
+ from setfit import SetFitModel
158
+
159
+ # Download from the 🤗 Hub
160
+ model = SetFitModel.from_pretrained("naufalso/setfit-ctc-bge-base-en-v1.5")
161
+ # Run inference
162
+ preds = model("The salvation of the soul in plain English: the world revolves around me.")
163
+ ```
164
+
165
+ <!--
166
+ ### Downstream Use
167
+
168
+ *List how someone could finetune this model on their own dataset.*
169
+ -->
170
+
171
+ <!--
172
+ ### Out-of-Scope Use
173
+
174
+ *List how the model may foreseeably be misused and address what users ought not to do with the model.*
175
+ -->
176
+
177
+ <!--
178
+ ## Bias, Risks and Limitations
179
+
180
+ *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
181
+ -->
182
+
183
+ <!--
184
+ ### Recommendations
185
+
186
+ *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
187
+ -->
188
+
189
+ ## Training Details
190
+
191
+ ### Training Set Metrics
192
+ | Training set | Min | Median | Max |
193
+ |:-------------|:----|:--------|:------|
194
+ | Word count | 2 | 309.552 | 20280 |
195
+
196
+ | Label | Training Sample Count |
197
+ |:-------------|:----------------------|
198
+ | non-cybersec | 1000 |
199
+ | cybersec | 1000 |
200
+
201
+ ### Training Hyperparameters
202
+ - batch_size: (32, 32)
203
+ - num_epochs: (1, 1)
204
+ - max_steps: -1
205
+ - sampling_strategy: oversampling
206
+ - body_learning_rate: (2e-05, 1e-05)
207
+ - head_learning_rate: 0.01
208
+ - loss: CosineSimilarityLoss
209
+ - distance_metric: cosine_distance
210
+ - margin: 0.25
211
+ - end_to_end: False
212
+ - use_amp: False
213
+ - warmup_proportion: 0.1
214
+ - l2_weight: 0.01
215
+ - seed: 42
216
+ - eval_max_steps: -1
217
+ - load_best_model_at_end: False
218
+
219
+ ### Training Results
220
+ | Epoch | Step | Training Loss | Validation Loss |
221
+ |:------:|:-----:|:-------------:|:---------------:|
222
+ | 0.0000 | 1 | 0.2527 | - |
223
+ | 0.0008 | 50 | 0.2398 | - |
224
+ | 0.0016 | 100 | 0.2476 | - |
225
+ | 0.0024 | 150 | 0.2407 | - |
226
+ | 0.0032 | 200 | 0.2448 | - |
227
+ | 0.0040 | 250 | 0.241 | - |
228
+ | 0.0048 | 300 | 0.2381 | - |
229
+ | 0.0056 | 350 | 0.2345 | - |
230
+ | 0.0064 | 400 | 0.2344 | - |
231
+ | 0.0072 | 450 | 0.2284 | - |
232
+ | 0.0080 | 500 | 0.2232 | - |
233
+ | 0.0088 | 550 | 0.2167 | - |
234
+ | 0.0096 | 600 | 0.2082 | - |
235
+ | 0.0104 | 650 | 0.193 | - |
236
+ | 0.0112 | 700 | 0.163 | - |
237
+ | 0.0120 | 750 | 0.138 | - |
238
+ | 0.0128 | 800 | 0.1136 | - |
239
+ | 0.0136 | 850 | 0.0934 | - |
240
+ | 0.0144 | 900 | 0.0743 | - |
241
+ | 0.0152 | 950 | 0.0619 | - |
242
+ | 0.0160 | 1000 | 0.0455 | - |
243
+ | 0.0168 | 1050 | 0.0415 | - |
244
+ | 0.0176 | 1100 | 0.027 | - |
245
+ | 0.0184 | 1150 | 0.0276 | - |
246
+ | 0.0192 | 1200 | 0.0235 | - |
247
+ | 0.0200 | 1250 | 0.0183 | - |
248
+ | 0.0208 | 1300 | 0.0193 | - |
249
+ | 0.0216 | 1350 | 0.0161 | - |
250
+ | 0.0224 | 1400 | 0.0143 | - |
251
+ | 0.0232 | 1450 | 0.0134 | - |
252
+ | 0.0240 | 1500 | 0.0146 | - |
253
+ | 0.0248 | 1550 | 0.0152 | - |
254
+ | 0.0256 | 1600 | 0.0157 | - |
255
+ | 0.0264 | 1650 | 0.0138 | - |
256
+ | 0.0272 | 1700 | 0.0101 | - |
257
+ | 0.0280 | 1750 | 0.0089 | - |
258
+ | 0.0288 | 1800 | 0.0109 | - |
259
+ | 0.0296 | 1850 | 0.0122 | - |
260
+ | 0.0304 | 1900 | 0.0056 | - |
261
+ | 0.0312 | 1950 | 0.0094 | - |
262
+ | 0.0320 | 2000 | 0.0105 | - |
263
+ | 0.0328 | 2050 | 0.0101 | - |
264
+ | 0.0336 | 2100 | 0.0087 | - |
265
+ | 0.0344 | 2150 | 0.0089 | - |
266
+ | 0.0352 | 2200 | 0.0079 | - |
267
+ | 0.0360 | 2250 | 0.0091 | - |
268
+ | 0.0368 | 2300 | 0.0063 | - |
269
+ | 0.0376 | 2350 | 0.005 | - |
270
+ | 0.0384 | 2400 | 0.0083 | - |
271
+ | 0.0392 | 2450 | 0.0066 | - |
272
+ | 0.0400 | 2500 | 0.007 | - |
273
+ | 0.0408 | 2550 | 0.0049 | - |
274
+ | 0.0416 | 2600 | 0.0037 | - |
275
+ | 0.0424 | 2650 | 0.006 | - |
276
+ | 0.0432 | 2700 | 0.0063 | - |
277
+ | 0.0440 | 2750 | 0.0047 | - |
278
+ | 0.0448 | 2800 | 0.0062 | - |
279
+ | 0.0456 | 2850 | 0.0029 | - |
280
+ | 0.0464 | 2900 | 0.0038 | - |
281
+ | 0.0472 | 2950 | 0.0025 | - |
282
+ | 0.0480 | 3000 | 0.0021 | - |
283
+ | 0.0488 | 3050 | 0.0017 | - |
284
+ | 0.0496 | 3100 | 0.0041 | - |
285
+ | 0.0503 | 3150 | 0.0015 | - |
286
+ | 0.0511 | 3200 | 0.004 | - |
287
+ | 0.0519 | 3250 | 0.0019 | - |
288
+ | 0.0527 | 3300 | 0.005 | - |
289
+ | 0.0535 | 3350 | 0.0016 | - |
290
+ | 0.0543 | 3400 | 0.0037 | - |
291
+ | 0.0551 | 3450 | 0.0031 | - |
292
+ | 0.0559 | 3500 | 0.0024 | - |
293
+ | 0.0567 | 3550 | 0.0019 | - |
294
+ | 0.0575 | 3600 | 0.0036 | - |
295
+ | 0.0583 | 3650 | 0.0058 | - |
296
+ | 0.0591 | 3700 | 0.0024 | - |
297
+ | 0.0599 | 3750 | 0.0021 | - |
298
+ | 0.0607 | 3800 | 0.0015 | - |
299
+ | 0.0615 | 3850 | 0.0015 | - |
300
+ | 0.0623 | 3900 | 0.0016 | - |
301
+ | 0.0631 | 3950 | 0.0009 | - |
302
+ | 0.0639 | 4000 | 0.0014 | - |
303
+ | 0.0647 | 4050 | 0.0014 | - |
304
+ | 0.0655 | 4100 | 0.0021 | - |
305
+ | 0.0663 | 4150 | 0.0008 | - |
306
+ | 0.0671 | 4200 | 0.0031 | - |
307
+ | 0.0679 | 4250 | 0.0008 | - |
308
+ | 0.0687 | 4300 | 0.0025 | - |
309
+ | 0.0695 | 4350 | 0.0028 | - |
310
+ | 0.0703 | 4400 | 0.0025 | - |
311
+ | 0.0711 | 4450 | 0.0007 | - |
312
+ | 0.0719 | 4500 | 0.0018 | - |
313
+ | 0.0727 | 4550 | 0.0012 | - |
314
+ | 0.0735 | 4600 | 0.0012 | - |
315
+ | 0.0743 | 4650 | 0.0006 | - |
316
+ | 0.0751 | 4700 | 0.0006 | - |
317
+ | 0.0759 | 4750 | 0.0031 | - |
318
+ | 0.0767 | 4800 | 0.0017 | - |
319
+ | 0.0775 | 4850 | 0.0007 | - |
320
+ | 0.0783 | 4900 | 0.0011 | - |
321
+ | 0.0791 | 4950 | 0.0006 | - |
322
+ | 0.0799 | 5000 | 0.0006 | - |
323
+ | 0.0807 | 5050 | 0.0005 | - |
324
+ | 0.0815 | 5100 | 0.0005 | - |
325
+ | 0.0823 | 5150 | 0.0005 | - |
326
+ | 0.0831 | 5200 | 0.0005 | - |
327
+ | 0.0839 | 5250 | 0.0005 | - |
328
+ | 0.0847 | 5300 | 0.0005 | - |
329
+ | 0.0855 | 5350 | 0.0005 | - |
330
+ | 0.0863 | 5400 | 0.0005 | - |
331
+ | 0.0871 | 5450 | 0.0005 | - |
332
+ | 0.0879 | 5500 | 0.0004 | - |
333
+ | 0.0887 | 5550 | 0.0005 | - |
334
+ | 0.0895 | 5600 | 0.0004 | - |
335
+ | 0.0903 | 5650 | 0.0004 | - |
336
+ | 0.0911 | 5700 | 0.0004 | - |
337
+ | 0.0919 | 5750 | 0.0004 | - |
338
+ | 0.0927 | 5800 | 0.0004 | - |
339
+ | 0.0935 | 5850 | 0.0035 | - |
340
+ | 0.0943 | 5900 | 0.0112 | - |
341
+ | 0.0951 | 5950 | 0.0054 | - |
342
+ | 0.0959 | 6000 | 0.0058 | - |
343
+ | 0.0967 | 6050 | 0.0027 | - |
344
+ | 0.0975 | 6100 | 0.0051 | - |
345
+ | 0.0983 | 6150 | 0.0038 | - |
346
+ | 0.0991 | 6200 | 0.0031 | - |
347
+ | 0.0999 | 6250 | 0.0038 | - |
348
+ | 0.1007 | 6300 | 0.0021 | - |
349
+ | 0.1015 | 6350 | 0.0029 | - |
350
+ | 0.1023 | 6400 | 0.0018 | - |
351
+ | 0.1031 | 6450 | 0.0035 | - |
352
+ | 0.1039 | 6500 | 0.0017 | - |
353
+ | 0.1047 | 6550 | 0.0026 | - |
354
+ | 0.1055 | 6600 | 0.0016 | - |
355
+ | 0.1063 | 6650 | 0.0016 | - |
356
+ | 0.1071 | 6700 | 0.0004 | - |
357
+ | 0.1079 | 6750 | 0.001 | - |
358
+ | 0.1087 | 6800 | 0.0028 | - |
359
+ | 0.1095 | 6850 | 0.001 | - |
360
+ | 0.1103 | 6900 | 0.0003 | - |
361
+ | 0.1111 | 6950 | 0.001 | - |
362
+ | 0.1119 | 7000 | 0.0016 | - |
363
+ | 0.1127 | 7050 | 0.0003 | - |
364
+ | 0.1135 | 7100 | 0.0022 | - |
365
+ | 0.1143 | 7150 | 0.0022 | - |
366
+ | 0.1151 | 7200 | 0.0016 | - |
367
+ | 0.1159 | 7250 | 0.0007 | - |
368
+ | 0.1167 | 7300 | 0.0003 | - |
369
+ | 0.1175 | 7350 | 0.0006 | - |
370
+ | 0.1183 | 7400 | 0.0026 | - |
371
+ | 0.1191 | 7450 | 0.0004 | - |
372
+ | 0.1199 | 7500 | 0.0008 | - |
373
+ | 0.1207 | 7550 | 0.0004 | - |
374
+ | 0.1215 | 7600 | 0.0003 | - |
375
+ | 0.1223 | 7650 | 0.0004 | - |
376
+ | 0.1231 | 7700 | 0.0023 | - |
377
+ | 0.1239 | 7750 | 0.0004 | - |
378
+ | 0.1247 | 7800 | 0.0005 | - |
379
+ | 0.1255 | 7850 | 0.0005 | - |
380
+ | 0.1263 | 7900 | 0.0016 | - |
381
+ | 0.1271 | 7950 | 0.0005 | - |
382
+ | 0.1279 | 8000 | 0.0004 | - |
383
+ | 0.1287 | 8050 | 0.0003 | - |
384
+ | 0.1295 | 8100 | 0.0014 | - |
385
+ | 0.1303 | 8150 | 0.0052 | - |
386
+ | 0.1311 | 8200 | 0.005 | - |
387
+ | 0.1319 | 8250 | 0.0051 | - |
388
+ | 0.1327 | 8300 | 0.0009 | - |
389
+ | 0.1335 | 8350 | 0.0003 | - |
390
+ | 0.1343 | 8400 | 0.0004 | - |
391
+ | 0.1351 | 8450 | 0.0003 | - |
392
+ | 0.1359 | 8500 | 0.0003 | - |
393
+ | 0.1367 | 8550 | 0.0009 | - |
394
+ | 0.1375 | 8600 | 0.0003 | - |
395
+ | 0.1383 | 8650 | 0.0003 | - |
396
+ | 0.1391 | 8700 | 0.0003 | - |
397
+ | 0.1399 | 8750 | 0.0009 | - |
398
+ | 0.1407 | 8800 | 0.0012 | - |
399
+ | 0.1415 | 8850 | 0.0009 | - |
400
+ | 0.1423 | 8900 | 0.0003 | - |
401
+ | 0.1431 | 8950 | 0.0002 | - |
402
+ | 0.1439 | 9000 | 0.0002 | - |
403
+ | 0.1447 | 9050 | 0.0002 | - |
404
+ | 0.1455 | 9100 | 0.0002 | - |
405
+ | 0.1463 | 9150 | 0.0002 | - |
406
+ | 0.1471 | 9200 | 0.0002 | - |
407
+ | 0.1479 | 9250 | 0.0003 | - |
408
+ | 0.1487 | 9300 | 0.0002 | - |
409
+ | 0.1494 | 9350 | 0.0002 | - |
410
+ | 0.1502 | 9400 | 0.0002 | - |
411
+ | 0.1510 | 9450 | 0.0002 | - |
412
+ | 0.1518 | 9500 | 0.0002 | - |
413
+ | 0.1526 | 9550 | 0.0002 | - |
414
+ | 0.1534 | 9600 | 0.0002 | - |
415
+ | 0.1542 | 9650 | 0.0002 | - |
416
+ | 0.1550 | 9700 | 0.0002 | - |
417
+ | 0.1558 | 9750 | 0.0002 | - |
418
+ | 0.1566 | 9800 | 0.0002 | - |
419
+ | 0.1574 | 9850 | 0.0002 | - |
420
+ | 0.1582 | 9900 | 0.0002 | - |
421
+ | 0.1590 | 9950 | 0.0002 | - |
422
+ | 0.1598 | 10000 | 0.0002 | - |
423
+ | 0.1606 | 10050 | 0.0002 | - |
424
+ | 0.1614 | 10100 | 0.0002 | - |
425
+ | 0.1622 | 10150 | 0.0002 | - |
426
+ | 0.1630 | 10200 | 0.0002 | - |
427
+ | 0.1638 | 10250 | 0.0002 | - |
428
+ | 0.1646 | 10300 | 0.0002 | - |
429
+ | 0.1654 | 10350 | 0.0002 | - |
430
+ | 0.1662 | 10400 | 0.0002 | - |
431
+ | 0.1670 | 10450 | 0.0002 | - |
432
+ | 0.1678 | 10500 | 0.0002 | - |
433
+ | 0.1686 | 10550 | 0.0002 | - |
434
+ | 0.1694 | 10600 | 0.0002 | - |
435
+ | 0.1702 | 10650 | 0.0002 | - |
436
+ | 0.1710 | 10700 | 0.0002 | - |
437
+ | 0.1718 | 10750 | 0.0002 | - |
438
+ | 0.1726 | 10800 | 0.0002 | - |
439
+ | 0.1734 | 10850 | 0.0002 | - |
440
+ | 0.1742 | 10900 | 0.0002 | - |
441
+ | 0.1750 | 10950 | 0.0002 | - |
442
+ | 0.1758 | 11000 | 0.0002 | - |
443
+ | 0.1766 | 11050 | 0.0002 | - |
444
+ | 0.1774 | 11100 | 0.0002 | - |
445
+ | 0.1782 | 11150 | 0.0002 | - |
446
+ | 0.1790 | 11200 | 0.0002 | - |
447
+ | 0.1798 | 11250 | 0.0002 | - |
448
+ | 0.1806 | 11300 | 0.0002 | - |
449
+ | 0.1814 | 11350 | 0.0002 | - |
450
+ | 0.1822 | 11400 | 0.0002 | - |
451
+ | 0.1830 | 11450 | 0.0002 | - |
452
+ | 0.1838 | 11500 | 0.0002 | - |
453
+ | 0.1846 | 11550 | 0.0002 | - |
454
+ | 0.1854 | 11600 | 0.0002 | - |
455
+ | 0.1862 | 11650 | 0.0002 | - |
456
+ | 0.1870 | 11700 | 0.0002 | - |
457
+ | 0.1878 | 11750 | 0.0002 | - |
458
+ | 0.1886 | 11800 | 0.0001 | - |
459
+ | 0.1894 | 11850 | 0.0002 | - |
460
+ | 0.1902 | 11900 | 0.0002 | - |
461
+ | 0.1910 | 11950 | 0.0001 | - |
462
+ | 0.1918 | 12000 | 0.0001 | - |
463
+ | 0.1926 | 12050 | 0.0001 | - |
464
+ | 0.1934 | 12100 | 0.0001 | - |
465
+ | 0.1942 | 12150 | 0.0001 | - |
466
+ | 0.1950 | 12200 | 0.0001 | - |
467
+ | 0.1958 | 12250 | 0.0001 | - |
468
+ | 0.1966 | 12300 | 0.0001 | - |
469
+ | 0.1974 | 12350 | 0.0001 | - |
470
+ | 0.1982 | 12400 | 0.0001 | - |
471
+ | 0.1990 | 12450 | 0.0001 | - |
472
+ | 0.1998 | 12500 | 0.0001 | - |
473
+ | 0.2006 | 12550 | 0.0001 | - |
474
+ | 0.2014 | 12600 | 0.0001 | - |
475
+ | 0.2022 | 12650 | 0.0001 | - |
476
+ | 0.2030 | 12700 | 0.0001 | - |
477
+ | 0.2038 | 12750 | 0.0001 | - |
478
+ | 0.2046 | 12800 | 0.0001 | - |
479
+ | 0.2054 | 12850 | 0.0001 | - |
480
+ | 0.2062 | 12900 | 0.0001 | - |
481
+ | 0.2070 | 12950 | 0.0001 | - |
482
+ | 0.2078 | 13000 | 0.0001 | - |
483
+ | 0.2086 | 13050 | 0.0001 | - |
484
+ | 0.2094 | 13100 | 0.0001 | - |
485
+ | 0.2102 | 13150 | 0.0001 | - |
486
+ | 0.2110 | 13200 | 0.0001 | - |
487
+ | 0.2118 | 13250 | 0.0001 | - |
488
+ | 0.2126 | 13300 | 0.0001 | - |
489
+ | 0.2134 | 13350 | 0.0001 | - |
490
+ | 0.2142 | 13400 | 0.0001 | - |
491
+ | 0.2150 | 13450 | 0.0001 | - |
492
+ | 0.2158 | 13500 | 0.0001 | - |
493
+ | 0.2166 | 13550 | 0.0001 | - |
494
+ | 0.2174 | 13600 | 0.0001 | - |
495
+ | 0.2182 | 13650 | 0.0001 | - |
496
+ | 0.2190 | 13700 | 0.0001 | - |
497
+ | 0.2198 | 13750 | 0.0001 | - |
498
+ | 0.2206 | 13800 | 0.0001 | - |
499
+ | 0.2214 | 13850 | 0.0001 | - |
500
+ | 0.2222 | 13900 | 0.0001 | - |
501
+ | 0.2230 | 13950 | 0.0001 | - |
502
+ | 0.2238 | 14000 | 0.0001 | - |
503
+ | 0.2246 | 14050 | 0.0001 | - |
504
+ | 0.2254 | 14100 | 0.0001 | - |
505
+ | 0.2262 | 14150 | 0.0001 | - |
506
+ | 0.2270 | 14200 | 0.0001 | - |
507
+ | 0.2278 | 14250 | 0.0001 | - |
508
+ | 0.2286 | 14300 | 0.0001 | - |
509
+ | 0.2294 | 14350 | 0.0001 | - |
510
+ | 0.2302 | 14400 | 0.0001 | - |
511
+ | 0.2310 | 14450 | 0.0001 | - |
512
+ | 0.2318 | 14500 | 0.0001 | - |
513
+ | 0.2326 | 14550 | 0.0001 | - |
514
+ | 0.2334 | 14600 | 0.0001 | - |
515
+ | 0.2342 | 14650 | 0.0001 | - |
516
+ | 0.2350 | 14700 | 0.0001 | - |
517
+ | 0.2358 | 14750 | 0.0001 | - |
518
+ | 0.2366 | 14800 | 0.0001 | - |
519
+ | 0.2374 | 14850 | 0.0001 | - |
520
+ | 0.2382 | 14900 | 0.0001 | - |
521
+ | 0.2390 | 14950 | 0.0001 | - |
522
+ | 0.2398 | 15000 | 0.0001 | - |
523
+ | 0.2406 | 15050 | 0.0001 | - |
524
+ | 0.2414 | 15100 | 0.0001 | - |
525
+ | 0.2422 | 15150 | 0.0001 | - |
526
+ | 0.2430 | 15200 | 0.0001 | - |
527
+ | 0.2438 | 15250 | 0.0001 | - |
528
+ | 0.2446 | 15300 | 0.0001 | - |
529
+ | 0.2454 | 15350 | 0.0001 | - |
530
+ | 0.2462 | 15400 | 0.0001 | - |
531
+ | 0.2470 | 15450 | 0.0001 | - |
532
+ | 0.2478 | 15500 | 0.0001 | - |
533
+ | 0.2485 | 15550 | 0.0001 | - |
534
+ | 0.2493 | 15600 | 0.0001 | - |
535
+ | 0.2501 | 15650 | 0.0001 | - |
536
+ | 0.2509 | 15700 | 0.0001 | - |
537
+ | 0.2517 | 15750 | 0.0001 | - |
538
+ | 0.2525 | 15800 | 0.0001 | - |
539
+ | 0.2533 | 15850 | 0.0001 | - |
540
+ | 0.2541 | 15900 | 0.0001 | - |
541
+ | 0.2549 | 15950 | 0.0001 | - |
542
+ | 0.2557 | 16000 | 0.0001 | - |
543
+ | 0.2565 | 16050 | 0.0001 | - |
544
+ | 0.2573 | 16100 | 0.0001 | - |
545
+ | 0.2581 | 16150 | 0.0001 | - |
546
+ | 0.2589 | 16200 | 0.0001 | - |
547
+ | 0.2597 | 16250 | 0.0001 | - |
548
+ | 0.2605 | 16300 | 0.0001 | - |
549
+ | 0.2613 | 16350 | 0.0001 | - |
550
+ | 0.2621 | 16400 | 0.0001 | - |
551
+ | 0.2629 | 16450 | 0.0011 | - |
552
+ | 0.2637 | 16500 | 0.0011 | - |
553
+ | 0.2645 | 16550 | 0.0022 | - |
554
+ | 0.2653 | 16600 | 0.0055 | - |
555
+ | 0.2661 | 16650 | 0.0012 | - |
556
+ | 0.2669 | 16700 | 0.0023 | - |
557
+ | 0.2677 | 16750 | 0.0016 | - |
558
+ | 0.2685 | 16800 | 0.0001 | - |
559
+ | 0.2693 | 16850 | 0.0001 | - |
560
+ | 0.2701 | 16900 | 0.0001 | - |
561
+ | 0.2709 | 16950 | 0.0001 | - |
562
+ | 0.2717 | 17000 | 0.0001 | - |
563
+ | 0.2725 | 17050 | 0.0001 | - |
564
+ | 0.2733 | 17100 | 0.0001 | - |
565
+ | 0.2741 | 17150 | 0.0001 | - |
566
+ | 0.2749 | 17200 | 0.0001 | - |
567
+ | 0.2757 | 17250 | 0.0001 | - |
568
+ | 0.2765 | 17300 | 0.0001 | - |
569
+ | 0.2773 | 17350 | 0.0001 | - |
570
+ | 0.2781 | 17400 | 0.0001 | - |
571
+ | 0.2789 | 17450 | 0.0001 | - |
572
+ | 0.2797 | 17500 | 0.0001 | - |
573
+ | 0.2805 | 17550 | 0.0001 | - |
574
+ | 0.2813 | 17600 | 0.0001 | - |
575
+ | 0.2821 | 17650 | 0.0001 | - |
576
+ | 0.2829 | 17700 | 0.0001 | - |
577
+ | 0.2837 | 17750 | 0.0001 | - |
578
+ | 0.2845 | 17800 | 0.0003 | - |
579
+ | 0.2853 | 17850 | 0.0001 | - |
580
+ | 0.2861 | 17900 | 0.0001 | - |
581
+ | 0.2869 | 17950 | 0.0001 | - |
582
+ | 0.2877 | 18000 | 0.0001 | - |
583
+ | 0.2885 | 18050 | 0.0001 | - |
584
+ | 0.2893 | 18100 | 0.0001 | - |
585
+ | 0.2901 | 18150 | 0.0001 | - |
586
+ | 0.2909 | 18200 | 0.0001 | - |
587
+ | 0.2917 | 18250 | 0.0001 | - |
588
+ | 0.2925 | 18300 | 0.0001 | - |
589
+ | 0.2933 | 18350 | 0.0001 | - |
590
+ | 0.2941 | 18400 | 0.0001 | - |
591
+ | 0.2949 | 18450 | 0.0001 | - |
592
+ | 0.2957 | 18500 | 0.0001 | - |
593
+ | 0.2965 | 18550 | 0.0001 | - |
594
+ | 0.2973 | 18600 | 0.0001 | - |
595
+ | 0.2981 | 18650 | 0.0001 | - |
596
+ | 0.2989 | 18700 | 0.0001 | - |
597
+ | 0.2997 | 18750 | 0.0001 | - |
598
+ | 0.3005 | 18800 | 0.0001 | - |
599
+ | 0.3013 | 18850 | 0.0001 | - |
600
+ | 0.3021 | 18900 | 0.0001 | - |
601
+ | 0.3029 | 18950 | 0.0001 | - |
602
+ | 0.3037 | 19000 | 0.0001 | - |
603
+ | 0.3045 | 19050 | 0.0001 | - |
604
+ | 0.3053 | 19100 | 0.0001 | - |
605
+ | 0.3061 | 19150 | 0.0001 | - |
606
+ | 0.3069 | 19200 | 0.0001 | - |
607
+ | 0.3077 | 19250 | 0.0001 | - |
608
+ | 0.3085 | 19300 | 0.0001 | - |
609
+ | 0.3093 | 19350 | 0.0001 | - |
610
+ | 0.3101 | 19400 | 0.0001 | - |
611
+ | 0.3109 | 19450 | 0.0001 | - |
612
+ | 0.3117 | 19500 | 0.0001 | - |
613
+ | 0.3125 | 19550 | 0.0001 | - |
614
+ | 0.3133 | 19600 | 0.0001 | - |
615
+ | 0.3141 | 19650 | 0.0001 | - |
616
+ | 0.3149 | 19700 | 0.0001 | - |
617
+ | 0.3157 | 19750 | 0.0001 | - |
618
+ | 0.3165 | 19800 | 0.0 | - |
619
+ | 0.3173 | 19850 | 0.0001 | - |
620
+ | 0.3181 | 19900 | 0.0001 | - |
621
+ | 0.3189 | 19950 | 0.0001 | - |
622
+ | 0.3197 | 20000 | 0.0001 | - |
623
+ | 0.3205 | 20050 | 0.0001 | - |
624
+ | 0.3213 | 20100 | 0.0001 | - |
625
+ | 0.3221 | 20150 | 0.0001 | - |
626
+ | 0.3229 | 20200 | 0.0 | - |
627
+ | 0.3237 | 20250 | 0.0001 | - |
628
+ | 0.3245 | 20300 | 0.0 | - |
629
+ | 0.3253 | 20350 | 0.0001 | - |
630
+ | 0.3261 | 20400 | 0.0 | - |
631
+ | 0.3269 | 20450 | 0.0 | - |
632
+ | 0.3277 | 20500 | 0.0 | - |
633
+ | 0.3285 | 20550 | 0.0001 | - |
634
+ | 0.3293 | 20600 | 0.0 | - |
635
+ | 0.3301 | 20650 | 0.0 | - |
636
+ | 0.3309 | 20700 | 0.0 | - |
637
+ | 0.3317 | 20750 | 0.0 | - |
638
+ | 0.3325 | 20800 | 0.0 | - |
639
+ | 0.3333 | 20850 | 0.0 | - |
640
+ | 0.3341 | 20900 | 0.0 | - |
641
+ | 0.3349 | 20950 | 0.0 | - |
642
+ | 0.3357 | 21000 | 0.0 | - |
643
+ | 0.3365 | 21050 | 0.0 | - |
644
+ | 0.3373 | 21100 | 0.0 | - |
645
+ | 0.3381 | 21150 | 0.0 | - |
646
+ | 0.3389 | 21200 | 0.0 | - |
647
+ | 0.3397 | 21250 | 0.0 | - |
648
+ | 0.3405 | 21300 | 0.0 | - |
649
+ | 0.3413 | 21350 | 0.0 | - |
650
+ | 0.3421 | 21400 | 0.0 | - |
651
+ | 0.3429 | 21450 | 0.0 | - |
652
+ | 0.3437 | 21500 | 0.0 | - |
653
+ | 0.3445 | 21550 | 0.0 | - |
654
+ | 0.3453 | 21600 | 0.0 | - |
655
+ | 0.3461 | 21650 | 0.0 | - |
656
+ | 0.3469 | 21700 | 0.0 | - |
657
+ | 0.3476 | 21750 | 0.0 | - |
658
+ | 0.3484 | 21800 | 0.0 | - |
659
+ | 0.3492 | 21850 | 0.0 | - |
660
+ | 0.3500 | 21900 | 0.0 | - |
661
+ | 0.3508 | 21950 | 0.0 | - |
662
+ | 0.3516 | 22000 | 0.0 | - |
663
+ | 0.3524 | 22050 | 0.0 | - |
664
+ | 0.3532 | 22100 | 0.0 | - |
665
+ | 0.3540 | 22150 | 0.0 | - |
666
+ | 0.3548 | 22200 | 0.0 | - |
667
+ | 0.3556 | 22250 | 0.0 | - |
668
+ | 0.3564 | 22300 | 0.0 | - |
669
+ | 0.3572 | 22350 | 0.0 | - |
670
+ | 0.3580 | 22400 | 0.0 | - |
671
+ | 0.3588 | 22450 | 0.0 | - |
672
+ | 0.3596 | 22500 | 0.0 | - |
673
+ | 0.3604 | 22550 | 0.0 | - |
674
+ | 0.3612 | 22600 | 0.0 | - |
675
+ | 0.3620 | 22650 | 0.0 | - |
676
+ | 0.3628 | 22700 | 0.0 | - |
677
+ | 0.3636 | 22750 | 0.0 | - |
678
+ | 0.3644 | 22800 | 0.0 | - |
679
+ | 0.3652 | 22850 | 0.0 | - |
680
+ | 0.3660 | 22900 | 0.0 | - |
681
+ | 0.3668 | 22950 | 0.0 | - |
682
+ | 0.3676 | 23000 | 0.0 | - |
683
+ | 0.3684 | 23050 | 0.0 | - |
684
+ | 0.3692 | 23100 | 0.0 | - |
685
+ | 0.3700 | 23150 | 0.0 | - |
686
+ | 0.3708 | 23200 | 0.0 | - |
687
+ | 0.3716 | 23250 | 0.0 | - |
688
+ | 0.3724 | 23300 | 0.0 | - |
689
+ | 0.3732 | 23350 | 0.0 | - |
690
+ | 0.3740 | 23400 | 0.0 | - |
691
+ | 0.3748 | 23450 | 0.0 | - |
692
+ | 0.3756 | 23500 | 0.0 | - |
693
+ | 0.3764 | 23550 | 0.0 | - |
694
+ | 0.3772 | 23600 | 0.0 | - |
695
+ | 0.3780 | 23650 | 0.0 | - |
696
+ | 0.3788 | 23700 | 0.0 | - |
697
+ | 0.3796 | 23750 | 0.0 | - |
698
+ | 0.3804 | 23800 | 0.0 | - |
699
+ | 0.3812 | 23850 | 0.0 | - |
700
+ | 0.3820 | 23900 | 0.0 | - |
701
+ | 0.3828 | 23950 | 0.0 | - |
702
+ | 0.3836 | 24000 | 0.0 | - |
703
+ | 0.3844 | 24050 | 0.0 | - |
704
+ | 0.3852 | 24100 | 0.0 | - |
705
+ | 0.3860 | 24150 | 0.0 | - |
706
+ | 0.3868 | 24200 | 0.0 | - |
707
+ | 0.3876 | 24250 | 0.0 | - |
708
+ | 0.3884 | 24300 | 0.0 | - |
709
+ | 0.3892 | 24350 | 0.0 | - |
710
+ | 0.3900 | 24400 | 0.0 | - |
711
+ | 0.3908 | 24450 | 0.0 | - |
712
+ | 0.3916 | 24500 | 0.0 | - |
713
+ | 0.3924 | 24550 | 0.0 | - |
714
+ | 0.3932 | 24600 | 0.0 | - |
715
+ | 0.3940 | 24650 | 0.0 | - |
716
+ | 0.3948 | 24700 | 0.0 | - |
717
+ | 0.3956 | 24750 | 0.0 | - |
718
+ | 0.3964 | 24800 | 0.0 | - |
719
+ | 0.3972 | 24850 | 0.0 | - |
720
+ | 0.3980 | 24900 | 0.0 | - |
721
+ | 0.3988 | 24950 | 0.0 | - |
722
+ | 0.3996 | 25000 | 0.0 | - |
723
+ | 0.4004 | 25050 | 0.0 | - |
724
+ | 0.4012 | 25100 | 0.0 | - |
725
+ | 0.4020 | 25150 | 0.0 | - |
726
+ | 0.4028 | 25200 | 0.0 | - |
727
+ | 0.4036 | 25250 | 0.0 | - |
728
+ | 0.4044 | 25300 | 0.0 | - |
729
+ | 0.4052 | 25350 | 0.0 | - |
730
+ | 0.4060 | 25400 | 0.0 | - |
731
+ | 0.4068 | 25450 | 0.0 | - |
732
+ | 0.4076 | 25500 | 0.0 | - |
733
+ | 0.4084 | 25550 | 0.0 | - |
734
+ | 0.4092 | 25600 | 0.0 | - |
735
+ | 0.4100 | 25650 | 0.0 | - |
736
+ | 0.4108 | 25700 | 0.0 | - |
737
+ | 0.4116 | 25750 | 0.0 | - |
738
+ | 0.4124 | 25800 | 0.0 | - |
739
+ | 0.4132 | 25850 | 0.0 | - |
740
+ | 0.4140 | 25900 | 0.0 | - |
741
+ | 0.4148 | 25950 | 0.0 | - |
742
+ | 0.4156 | 26000 | 0.0 | - |
743
+ | 0.4164 | 26050 | 0.0 | - |
744
+ | 0.4172 | 26100 | 0.0 | - |
745
+ | 0.4180 | 26150 | 0.0 | - |
746
+ | 0.4188 | 26200 | 0.0 | - |
747
+ | 0.4196 | 26250 | 0.0 | - |
748
+ | 0.4204 | 26300 | 0.0 | - |
749
+ | 0.4212 | 26350 | 0.0 | - |
750
+ | 0.4220 | 26400 | 0.0 | - |
751
+ | 0.4228 | 26450 | 0.0 | - |
752
+ | 0.4236 | 26500 | 0.0 | - |
753
+ | 0.4244 | 26550 | 0.0 | - |
754
+ | 0.4252 | 26600 | 0.0 | - |
755
+ | 0.4260 | 26650 | 0.0 | - |
756
+ | 0.4268 | 26700 | 0.0 | - |
757
+ | 0.4276 | 26750 | 0.0 | - |
758
+ | 0.4284 | 26800 | 0.0 | - |
759
+ | 0.4292 | 26850 | 0.0 | - |
760
+ | 0.4300 | 26900 | 0.0 | - |
761
+ | 0.4308 | 26950 | 0.0 | - |
762
+ | 0.4316 | 27000 | 0.0 | - |
763
+ | 0.4324 | 27050 | 0.0 | - |
764
+ | 0.4332 | 27100 | 0.0 | - |
765
+ | 0.4340 | 27150 | 0.0 | - |
766
+ | 0.4348 | 27200 | 0.0 | - |
767
+ | 0.4356 | 27250 | 0.0 | - |
768
+ | 0.4364 | 27300 | 0.0 | - |
769
+ | 0.4372 | 27350 | 0.0 | - |
770
+ | 0.4380 | 27400 | 0.0 | - |
771
+ | 0.4388 | 27450 | 0.0 | - |
772
+ | 0.4396 | 27500 | 0.0 | - |
773
+ | 0.4404 | 27550 | 0.0 | - |
774
+ | 0.4412 | 27600 | 0.0 | - |
775
+ | 0.4420 | 27650 | 0.0 | - |
776
+ | 0.4428 | 27700 | 0.0 | - |
777
+ | 0.4436 | 27750 | 0.0 | - |
778
+ | 0.4444 | 27800 | 0.0 | - |
779
+ | 0.4452 | 27850 | 0.0 | - |
780
+ | 0.4460 | 27900 | 0.0 | - |
781
+ | 0.4467 | 27950 | 0.0 | - |
782
+ | 0.4475 | 28000 | 0.0 | - |
783
+ | 0.4483 | 28050 | 0.0 | - |
784
+ | 0.4491 | 28100 | 0.0 | - |
785
+ | 0.4499 | 28150 | 0.0 | - |
786
+ | 0.4507 | 28200 | 0.0 | - |
787
+ | 0.4515 | 28250 | 0.0 | - |
788
+ | 0.4523 | 28300 | 0.0 | - |
789
+ | 0.4531 | 28350 | 0.0 | - |
790
+ | 0.4539 | 28400 | 0.0 | - |
791
+ | 0.4547 | 28450 | 0.0 | - |
792
+ | 0.4555 | 28500 | 0.0 | - |
793
+ | 0.4563 | 28550 | 0.0 | - |
794
+ | 0.4571 | 28600 | 0.0 | - |
795
+ | 0.4579 | 28650 | 0.0 | - |
796
+ | 0.4587 | 28700 | 0.0 | - |
797
+ | 0.4595 | 28750 | 0.0 | - |
798
+ | 0.4603 | 28800 | 0.0 | - |
799
+ | 0.4611 | 28850 | 0.0 | - |
800
+ | 0.4619 | 28900 | 0.0 | - |
801
+ | 0.4627 | 28950 | 0.0 | - |
802
+ | 0.4635 | 29000 | 0.0 | - |
803
+ | 0.4643 | 29050 | 0.0 | - |
804
+ | 0.4651 | 29100 | 0.0 | - |
805
+ | 0.4659 | 29150 | 0.0 | - |
806
+ | 0.4667 | 29200 | 0.0 | - |
807
+ | 0.4675 | 29250 | 0.0 | - |
808
+ | 0.4683 | 29300 | 0.0 | - |
809
+ | 0.4691 | 29350 | 0.0003 | - |
810
+ | 0.4699 | 29400 | 0.0 | - |
811
+ | 0.4707 | 29450 | 0.0005 | - |
812
+ | 0.4715 | 29500 | 0.0 | - |
813
+ | 0.4723 | 29550 | 0.0 | - |
814
+ | 0.4731 | 29600 | 0.0 | - |
815
+ | 0.4739 | 29650 | 0.0001 | - |
816
+ | 0.4747 | 29700 | 0.0 | - |
817
+ | 0.4755 | 29750 | 0.0 | - |
818
+ | 0.4763 | 29800 | 0.0 | - |
819
+ | 0.4771 | 29850 | 0.0 | - |
820
+ | 0.4779 | 29900 | 0.0 | - |
821
+ | 0.4787 | 29950 | 0.0 | - |
822
+ | 0.4795 | 30000 | 0.0 | - |
823
+ | 0.4803 | 30050 | 0.0 | - |
824
+ | 0.4811 | 30100 | 0.0 | - |
825
+ | 0.4819 | 30150 | 0.0 | - |
826
+ | 0.4827 | 30200 | 0.0 | - |
827
+ | 0.4835 | 30250 | 0.0 | - |
828
+ | 0.4843 | 30300 | 0.0 | - |
829
+ | 0.4851 | 30350 | 0.0 | - |
830
+ | 0.4859 | 30400 | 0.0 | - |
831
+ | 0.4867 | 30450 | 0.0 | - |
832
+ | 0.4875 | 30500 | 0.0 | - |
833
+ | 0.4883 | 30550 | 0.0 | - |
834
+ | 0.4891 | 30600 | 0.0 | - |
835
+ | 0.4899 | 30650 | 0.0 | - |
836
+ | 0.4907 | 30700 | 0.0 | - |
837
+ | 0.4915 | 30750 | 0.0 | - |
838
+ | 0.4923 | 30800 | 0.0 | - |
839
+ | 0.4931 | 30850 | 0.0 | - |
840
+ | 0.4939 | 30900 | 0.0 | - |
841
+ | 0.4947 | 30950 | 0.0 | - |
842
+ | 0.4955 | 31000 | 0.0 | - |
843
+ | 0.4963 | 31050 | 0.0 | - |
844
+ | 0.4971 | 31100 | 0.0 | - |
845
+ | 0.4979 | 31150 | 0.0 | - |
846
+ | 0.4987 | 31200 | 0.0 | - |
847
+ | 0.4995 | 31250 | 0.0 | - |
848
+ | 0.5003 | 31300 | 0.0 | - |
849
+ | 0.5011 | 31350 | 0.0 | - |
850
+ | 0.5019 | 31400 | 0.0 | - |
851
+ | 0.5027 | 31450 | 0.0 | - |
852
+ | 0.5035 | 31500 | 0.0 | - |
853
+ | 0.5043 | 31550 | 0.0043 | - |
854
+ | 0.5051 | 31600 | 0.0008 | - |
855
+ | 0.5059 | 31650 | 0.0 | - |
856
+ | 0.5067 | 31700 | 0.0 | - |
857
+ | 0.5075 | 31750 | 0.0 | - |
858
+ | 0.5083 | 31800 | 0.0 | - |
859
+ | 0.5091 | 31850 | 0.0 | - |
860
+ | 0.5099 | 31900 | 0.0 | - |
861
+ | 0.5107 | 31950 | 0.0 | - |
862
+ | 0.5115 | 32000 | 0.0 | - |
863
+ | 0.5123 | 32050 | 0.0 | - |
864
+ | 0.5131 | 32100 | 0.0 | - |
865
+ | 0.5139 | 32150 | 0.0 | - |
866
+ | 0.5147 | 32200 | 0.0 | - |
867
+ | 0.5155 | 32250 | 0.0 | - |
868
+ | 0.5163 | 32300 | 0.0 | - |
869
+ | 0.5171 | 32350 | 0.0 | - |
870
+ | 0.5179 | 32400 | 0.0 | - |
871
+ | 0.5187 | 32450 | 0.0 | - |
872
+ | 0.5195 | 32500 | 0.0 | - |
873
+ | 0.5203 | 32550 | 0.0 | - |
874
+ | 0.5211 | 32600 | 0.0 | - |
875
+ | 0.5219 | 32650 | 0.0 | - |
876
+ | 0.5227 | 32700 | 0.0 | - |
877
+ | 0.5235 | 32750 | 0.0 | - |
878
+ | 0.5243 | 32800 | 0.0 | - |
879
+ | 0.5251 | 32850 | 0.0 | - |
880
+ | 0.5259 | 32900 | 0.0 | - |
881
+ | 0.5267 | 32950 | 0.0 | - |
882
+ | 0.5275 | 33000 | 0.0 | - |
883
+ | 0.5283 | 33050 | 0.0 | - |
884
+ | 0.5291 | 33100 | 0.0 | - |
885
+ | 0.5299 | 33150 | 0.0 | - |
886
+ | 0.5307 | 33200 | 0.0 | - |
887
+ | 0.5315 | 33250 | 0.0 | - |
888
+ | 0.5323 | 33300 | 0.0 | - |
889
+ | 0.5331 | 33350 | 0.0 | - |
890
+ | 0.5339 | 33400 | 0.0 | - |
891
+ | 0.5347 | 33450 | 0.0 | - |
892
+ | 0.5355 | 33500 | 0.0 | - |
893
+ | 0.5363 | 33550 | 0.0 | - |
894
+ | 0.5371 | 33600 | 0.0 | - |
895
+ | 0.5379 | 33650 | 0.0 | - |
896
+ | 0.5387 | 33700 | 0.0 | - |
897
+ | 0.5395 | 33750 | 0.0 | - |
898
+ | 0.5403 | 33800 | 0.0 | - |
899
+ | 0.5411 | 33850 | 0.0 | - |
900
+ | 0.5419 | 33900 | 0.0 | - |
901
+ | 0.5427 | 33950 | 0.0 | - |
902
+ | 0.5435 | 34000 | 0.0 | - |
903
+ | 0.5443 | 34050 | 0.0 | - |
904
+ | 0.5451 | 34100 | 0.0 | - |
905
+ | 0.5458 | 34150 | 0.0 | - |
906
+ | 0.5466 | 34200 | 0.0 | - |
907
+ | 0.5474 | 34250 | 0.0 | - |
908
+ | 0.5482 | 34300 | 0.0 | - |
909
+ | 0.5490 | 34350 | 0.0 | - |
910
+ | 0.5498 | 34400 | 0.0 | - |
911
+ | 0.5506 | 34450 | 0.0 | - |
912
+ | 0.5514 | 34500 | 0.0 | - |
913
+ | 0.5522 | 34550 | 0.0 | - |
914
+ | 0.5530 | 34600 | 0.0 | - |
915
+ | 0.5538 | 34650 | 0.0 | - |
916
+ | 0.5546 | 34700 | 0.0 | - |
917
+ | 0.5554 | 34750 | 0.0 | - |
918
+ | 0.5562 | 34800 | 0.0 | - |
919
+ | 0.5570 | 34850 | 0.0 | - |
920
+ | 0.5578 | 34900 | 0.0 | - |
921
+ | 0.5586 | 34950 | 0.0 | - |
922
+ | 0.5594 | 35000 | 0.0 | - |
923
+ | 0.5602 | 35050 | 0.0 | - |
924
+ | 0.5610 | 35100 | 0.0 | - |
925
+ | 0.5618 | 35150 | 0.0 | - |
926
+ | 0.5626 | 35200 | 0.0 | - |
927
+ | 0.5634 | 35250 | 0.0 | - |
928
+ | 0.5642 | 35300 | 0.0 | - |
929
+ | 0.5650 | 35350 | 0.0 | - |
930
+ | 0.5658 | 35400 | 0.0 | - |
931
+ | 0.5666 | 35450 | 0.0 | - |
932
+ | 0.5674 | 35500 | 0.0 | - |
933
+ | 0.5682 | 35550 | 0.0 | - |
934
+ | 0.5690 | 35600 | 0.0 | - |
935
+ | 0.5698 | 35650 | 0.0 | - |
936
+ | 0.5706 | 35700 | 0.0 | - |
937
+ | 0.5714 | 35750 | 0.0 | - |
938
+ | 0.5722 | 35800 | 0.0 | - |
939
+ | 0.5730 | 35850 | 0.0 | - |
940
+ | 0.5738 | 35900 | 0.0 | - |
941
+ | 0.5746 | 35950 | 0.0 | - |
942
+ | 0.5754 | 36000 | 0.0 | - |
943
+ | 0.5762 | 36050 | 0.0 | - |
944
+ | 0.5770 | 36100 | 0.0 | - |
945
+ | 0.5778 | 36150 | 0.0 | - |
946
+ | 0.5786 | 36200 | 0.0 | - |
947
+ | 0.5794 | 36250 | 0.0 | - |
948
+ | 0.5802 | 36300 | 0.0 | - |
949
+ | 0.5810 | 36350 | 0.0 | - |
950
+ | 0.5818 | 36400 | 0.0 | - |
951
+ | 0.5826 | 36450 | 0.0 | - |
952
+ | 0.5834 | 36500 | 0.0 | - |
953
+ | 0.5842 | 36550 | 0.0 | - |
954
+ | 0.5850 | 36600 | 0.0 | - |
955
+ | 0.5858 | 36650 | 0.0 | - |
956
+ | 0.5866 | 36700 | 0.0 | - |
957
+ | 0.5874 | 36750 | 0.0 | - |
958
+ | 0.5882 | 36800 | 0.0 | - |
959
+ | 0.5890 | 36850 | 0.0 | - |
960
+ | 0.5898 | 36900 | 0.0 | - |
961
+ | 0.5906 | 36950 | 0.0 | - |
962
+ | 0.5914 | 37000 | 0.0 | - |
963
+ | 0.5922 | 37050 | 0.0 | - |
964
+ | 0.5930 | 37100 | 0.0 | - |
965
+ | 0.5938 | 37150 | 0.0 | - |
966
+ | 0.5946 | 37200 | 0.0 | - |
967
+ | 0.5954 | 37250 | 0.0 | - |
968
+ | 0.5962 | 37300 | 0.0 | - |
969
+ | 0.5970 | 37350 | 0.0 | - |
970
+ | 0.5978 | 37400 | 0.0 | - |
971
+ | 0.5986 | 37450 | 0.0 | - |
972
+ | 0.5994 | 37500 | 0.0 | - |
973
+ | 0.6002 | 37550 | 0.0 | - |
974
+ | 0.6010 | 37600 | 0.0 | - |
975
+ | 0.6018 | 37650 | 0.0 | - |
976
+ | 0.6026 | 37700 | 0.0 | - |
977
+ | 0.6034 | 37750 | 0.0 | - |
978
+ | 0.6042 | 37800 | 0.0 | - |
979
+ | 0.6050 | 37850 | 0.0 | - |
980
+ | 0.6058 | 37900 | 0.0 | - |
981
+ | 0.6066 | 37950 | 0.0 | - |
982
+ | 0.6074 | 38000 | 0.0 | - |
983
+ | 0.6082 | 38050 | 0.0 | - |
984
+ | 0.6090 | 38100 | 0.0 | - |
985
+ | 0.6098 | 38150 | 0.0 | - |
986
+ | 0.6106 | 38200 | 0.0 | - |
987
+ | 0.6114 | 38250 | 0.0 | - |
988
+ | 0.6122 | 38300 | 0.0 | - |
989
+ | 0.6130 | 38350 | 0.0 | - |
990
+ | 0.6138 | 38400 | 0.0 | - |
991
+ | 0.6146 | 38450 | 0.0 | - |
992
+ | 0.6154 | 38500 | 0.0 | - |
993
+ | 0.6162 | 38550 | 0.0 | - |
994
+ | 0.6170 | 38600 | 0.0 | - |
995
+ | 0.6178 | 38650 | 0.0 | - |
996
+ | 0.6186 | 38700 | 0.0 | - |
997
+ | 0.6194 | 38750 | 0.0 | - |
998
+ | 0.6202 | 38800 | 0.0 | - |
999
+ | 0.6210 | 38850 | 0.0 | - |
1000
+ | 0.6218 | 38900 | 0.0 | - |
1001
+ | 0.6226 | 38950 | 0.0 | - |
1002
+ | 0.6234 | 39000 | 0.0 | - |
1003
+ | 0.6242 | 39050 | 0.0 | - |
1004
+ | 0.6250 | 39100 | 0.0 | - |
1005
+ | 0.6258 | 39150 | 0.0 | - |
1006
+ | 0.6266 | 39200 | 0.0 | - |
1007
+ | 0.6274 | 39250 | 0.0006 | - |
1008
+ | 0.6282 | 39300 | 0.0 | - |
1009
+ | 0.6290 | 39350 | 0.0022 | - |
1010
+ | 0.6298 | 39400 | 0.0 | - |
1011
+ | 0.6306 | 39450 | 0.0 | - |
1012
+ | 0.6314 | 39500 | 0.0 | - |
1013
+ | 0.6322 | 39550 | 0.0 | - |
1014
+ | 0.6330 | 39600 | 0.0 | - |
1015
+ | 0.6338 | 39650 | 0.0 | - |
1016
+ | 0.6346 | 39700 | 0.0 | - |
1017
+ | 0.6354 | 39750 | 0.0 | - |
1018
+ | 0.6362 | 39800 | 0.0 | - |
1019
+ | 0.6370 | 39850 | 0.0 | - |
1020
+ | 0.6378 | 39900 | 0.0 | - |
1021
+ | 0.6386 | 39950 | 0.0 | - |
1022
+ | 0.6394 | 40000 | 0.0 | - |
1023
+ | 0.6402 | 40050 | 0.0 | - |
1024
+ | 0.6410 | 40100 | 0.0 | - |
1025
+ | 0.6418 | 40150 | 0.0 | - |
1026
+ | 0.6426 | 40200 | 0.0 | - |
1027
+ | 0.6434 | 40250 | 0.0 | - |
1028
+ | 0.6442 | 40300 | 0.0 | - |
1029
+ | 0.6449 | 40350 | 0.0 | - |
1030
+ | 0.6457 | 40400 | 0.0 | - |
1031
+ | 0.6465 | 40450 | 0.0 | - |
1032
+ | 0.6473 | 40500 | 0.0 | - |
1033
+ | 0.6481 | 40550 | 0.0 | - |
1034
+ | 0.6489 | 40600 | 0.0 | - |
1035
+ | 0.6497 | 40650 | 0.0 | - |
1036
+ | 0.6505 | 40700 | 0.0 | - |
1037
+ | 0.6513 | 40750 | 0.0 | - |
1038
+ | 0.6521 | 40800 | 0.0 | - |
1039
+ | 0.6529 | 40850 | 0.0 | - |
1040
+ | 0.6537 | 40900 | 0.0 | - |
1041
+ | 0.6545 | 40950 | 0.0 | - |
1042
+ | 0.6553 | 41000 | 0.0 | - |
1043
+ | 0.6561 | 41050 | 0.0 | - |
1044
+ | 0.6569 | 41100 | 0.0 | - |
1045
+ | 0.6577 | 41150 | 0.0 | - |
1046
+ | 0.6585 | 41200 | 0.0 | - |
1047
+ | 0.6593 | 41250 | 0.0 | - |
1048
+ | 0.6601 | 41300 | 0.0 | - |
1049
+ | 0.6609 | 41350 | 0.0 | - |
1050
+ | 0.6617 | 41400 | 0.0 | - |
1051
+ | 0.6625 | 41450 | 0.0 | - |
1052
+ | 0.6633 | 41500 | 0.0 | - |
1053
+ | 0.6641 | 41550 | 0.0 | - |
1054
+ | 0.6649 | 41600 | 0.0 | - |
1055
+ | 0.6657 | 41650 | 0.0 | - |
1056
+ | 0.6665 | 41700 | 0.0 | - |
1057
+ | 0.6673 | 41750 | 0.0 | - |
1058
+ | 0.6681 | 41800 | 0.0 | - |
1059
+ | 0.6689 | 41850 | 0.0 | - |
1060
+ | 0.6697 | 41900 | 0.0 | - |
1061
+ | 0.6705 | 41950 | 0.0 | - |
1062
+ | 0.6713 | 42000 | 0.0 | - |
1063
+ | 0.6721 | 42050 | 0.0 | - |
1064
+ | 0.6729 | 42100 | 0.0 | - |
1065
+ | 0.6737 | 42150 | 0.0 | - |
1066
+ | 0.6745 | 42200 | 0.0 | - |
1067
+ | 0.6753 | 42250 | 0.0 | - |
1068
+ | 0.6761 | 42300 | 0.0 | - |
1069
+ | 0.6769 | 42350 | 0.0 | - |
1070
+ | 0.6777 | 42400 | 0.0 | - |
1071
+ | 0.6785 | 42450 | 0.0 | - |
1072
+ | 0.6793 | 42500 | 0.0 | - |
1073
+ | 0.6801 | 42550 | 0.0 | - |
1074
+ | 0.6809 | 42600 | 0.0 | - |
1075
+ | 0.6817 | 42650 | 0.0 | - |
1076
+ | 0.6825 | 42700 | 0.0 | - |
1077
+ | 0.6833 | 42750 | 0.0 | - |
1078
+ | 0.6841 | 42800 | 0.0 | - |
1079
+ | 0.6849 | 42850 | 0.0 | - |
1080
+ | 0.6857 | 42900 | 0.0 | - |
1081
+ | 0.6865 | 42950 | 0.0 | - |
1082
+ | 0.6873 | 43000 | 0.0 | - |
1083
+ | 0.6881 | 43050 | 0.0 | - |
1084
+ | 0.6889 | 43100 | 0.0 | - |
1085
+ | 0.6897 | 43150 | 0.0 | - |
1086
+ | 0.6905 | 43200 | 0.0 | - |
1087
+ | 0.6913 | 43250 | 0.0 | - |
1088
+ | 0.6921 | 43300 | 0.0 | - |
1089
+ | 0.6929 | 43350 | 0.0 | - |
1090
+ | 0.6937 | 43400 | 0.0 | - |
1091
+ | 0.6945 | 43450 | 0.0 | - |
1092
+ | 0.6953 | 43500 | 0.0 | - |
1093
+ | 0.6961 | 43550 | 0.0 | - |
1094
+ | 0.6969 | 43600 | 0.0 | - |
1095
+ | 0.6977 | 43650 | 0.0 | - |
1096
+ | 0.6985 | 43700 | 0.0 | - |
1097
+ | 0.6993 | 43750 | 0.0 | - |
1098
+ | 0.7001 | 43800 | 0.0 | - |
1099
+ | 0.7009 | 43850 | 0.0 | - |
1100
+ | 0.7017 | 43900 | 0.0 | - |
1101
+ | 0.7025 | 43950 | 0.0 | - |
1102
+ | 0.7033 | 44000 | 0.0 | - |
1103
+ | 0.7041 | 44050 | 0.0 | - |
1104
+ | 0.7049 | 44100 | 0.0 | - |
1105
+ | 0.7057 | 44150 | 0.0 | - |
1106
+ | 0.7065 | 44200 | 0.0 | - |
1107
+ | 0.7073 | 44250 | 0.0 | - |
1108
+ | 0.7081 | 44300 | 0.0 | - |
1109
+ | 0.7089 | 44350 | 0.0 | - |
1110
+ | 0.7097 | 44400 | 0.0 | - |
1111
+ | 0.7105 | 44450 | 0.0 | - |
1112
+ | 0.7113 | 44500 | 0.0 | - |
1113
+ | 0.7121 | 44550 | 0.0 | - |
1114
+ | 0.7129 | 44600 | 0.0 | - |
1115
+ | 0.7137 | 44650 | 0.0 | - |
1116
+ | 0.7145 | 44700 | 0.0 | - |
1117
+ | 0.7153 | 44750 | 0.0 | - |
1118
+ | 0.7161 | 44800 | 0.0 | - |
1119
+ | 0.7169 | 44850 | 0.0 | - |
1120
+ | 0.7177 | 44900 | 0.0 | - |
1121
+ | 0.7185 | 44950 | 0.0 | - |
1122
+ | 0.7193 | 45000 | 0.0 | - |
1123
+ | 0.7201 | 45050 | 0.0 | - |
1124
+ | 0.7209 | 45100 | 0.0 | - |
1125
+ | 0.7217 | 45150 | 0.0 | - |
1126
+ | 0.7225 | 45200 | 0.0 | - |
1127
+ | 0.7233 | 45250 | 0.0 | - |
1128
+ | 0.7241 | 45300 | 0.0 | - |
1129
+ | 0.7249 | 45350 | 0.0 | - |
1130
+ | 0.7257 | 45400 | 0.0 | - |
1131
+ | 0.7265 | 45450 | 0.0 | - |
1132
+ | 0.7273 | 45500 | 0.0 | - |
1133
+ | 0.7281 | 45550 | 0.0 | - |
1134
+ | 0.7289 | 45600 | 0.0 | - |
1135
+ | 0.7297 | 45650 | 0.0001 | - |
1136
+ | 0.7305 | 45700 | 0.0 | - |
1137
+ | 0.7313 | 45750 | 0.0 | - |
1138
+ | 0.7321 | 45800 | 0.0 | - |
1139
+ | 0.7329 | 45850 | 0.0 | - |
1140
+ | 0.7337 | 45900 | 0.0 | - |
1141
+ | 0.7345 | 45950 | 0.0 | - |
1142
+ | 0.7353 | 46000 | 0.0 | - |
1143
+ | 0.7361 | 46050 | 0.0 | - |
1144
+ | 0.7369 | 46100 | 0.0 | - |
1145
+ | 0.7377 | 46150 | 0.0 | - |
1146
+ | 0.7385 | 46200 | 0.0 | - |
1147
+ | 0.7393 | 46250 | 0.0 | - |
1148
+ | 0.7401 | 46300 | 0.0 | - |
1149
+ | 0.7409 | 46350 | 0.0 | - |
1150
+ | 0.7417 | 46400 | 0.0 | - |
1151
+ | 0.7425 | 46450 | 0.0 | - |
1152
+ | 0.7433 | 46500 | 0.0 | - |
1153
+ | 0.7440 | 46550 | 0.0 | - |
1154
+ | 0.7448 | 46600 | 0.0 | - |
1155
+ | 0.7456 | 46650 | 0.0 | - |
1156
+ | 0.7464 | 46700 | 0.0 | - |
1157
+ | 0.7472 | 46750 | 0.0 | - |
1158
+ | 0.7480 | 46800 | 0.0 | - |
1159
+ | 0.7488 | 46850 | 0.0 | - |
1160
+ | 0.7496 | 46900 | 0.0 | - |
1161
+ | 0.7504 | 46950 | 0.0 | - |
1162
+ | 0.7512 | 47000 | 0.0 | - |
1163
+ | 0.7520 | 47050 | 0.0 | - |
1164
+ | 0.7528 | 47100 | 0.0 | - |
1165
+ | 0.7536 | 47150 | 0.0 | - |
1166
+ | 0.7544 | 47200 | 0.0 | - |
1167
+ | 0.7552 | 47250 | 0.0 | - |
1168
+ | 0.7560 | 47300 | 0.0 | - |
1169
+ | 0.7568 | 47350 | 0.0 | - |
1170
+ | 0.7576 | 47400 | 0.0 | - |
1171
+ | 0.7584 | 47450 | 0.0 | - |
1172
+ | 0.7592 | 47500 | 0.0 | - |
1173
+ | 0.7600 | 47550 | 0.0 | - |
1174
+ | 0.7608 | 47600 | 0.0 | - |
1175
+ | 0.7616 | 47650 | 0.0 | - |
1176
+ | 0.7624 | 47700 | 0.0 | - |
1177
+ | 0.7632 | 47750 | 0.0 | - |
1178
+ | 0.7640 | 47800 | 0.0 | - |
1179
+ | 0.7648 | 47850 | 0.0 | - |
1180
+ | 0.7656 | 47900 | 0.0 | - |
1181
+ | 0.7664 | 47950 | 0.0 | - |
1182
+ | 0.7672 | 48000 | 0.0 | - |
1183
+ | 0.7680 | 48050 | 0.0 | - |
1184
+ | 0.7688 | 48100 | 0.0 | - |
1185
+ | 0.7696 | 48150 | 0.0 | - |
1186
+ | 0.7704 | 48200 | 0.0 | - |
1187
+ | 0.7712 | 48250 | 0.0 | - |
1188
+ | 0.7720 | 48300 | 0.0 | - |
1189
+ | 0.7728 | 48350 | 0.0 | - |
1190
+ | 0.7736 | 48400 | 0.0 | - |
1191
+ | 0.7744 | 48450 | 0.0 | - |
1192
+ | 0.7752 | 48500 | 0.0 | - |
1193
+ | 0.7760 | 48550 | 0.0 | - |
1194
+ | 0.7768 | 48600 | 0.0 | - |
1195
+ | 0.7776 | 48650 | 0.0 | - |
1196
+ | 0.7784 | 48700 | 0.0 | - |
1197
+ | 0.7792 | 48750 | 0.0 | - |
1198
+ | 0.7800 | 48800 | 0.0 | - |
1199
+ | 0.7808 | 48850 | 0.0 | - |
1200
+ | 0.7816 | 48900 | 0.0 | - |
1201
+ | 0.7824 | 48950 | 0.0 | - |
1202
+ | 0.7832 | 49000 | 0.0 | - |
1203
+ | 0.7840 | 49050 | 0.0 | - |
1204
+ | 0.7848 | 49100 | 0.0 | - |
1205
+ | 0.7856 | 49150 | 0.0 | - |
1206
+ | 0.7864 | 49200 | 0.0 | - |
1207
+ | 0.7872 | 49250 | 0.0 | - |
1208
+ | 0.7880 | 49300 | 0.0 | - |
1209
+ | 0.7888 | 49350 | 0.0 | - |
1210
+ | 0.7896 | 49400 | 0.0 | - |
1211
+ | 0.7904 | 49450 | 0.0 | - |
1212
+ | 0.7912 | 49500 | 0.0 | - |
1213
+ | 0.7920 | 49550 | 0.0 | - |
1214
+ | 0.7928 | 49600 | 0.0 | - |
1215
+ | 0.7936 | 49650 | 0.0 | - |
1216
+ | 0.7944 | 49700 | 0.0 | - |
1217
+ | 0.7952 | 49750 | 0.0 | - |
1218
+ | 0.7960 | 49800 | 0.0 | - |
1219
+ | 0.7968 | 49850 | 0.0 | - |
1220
+ | 0.7976 | 49900 | 0.0 | - |
1221
+ | 0.7984 | 49950 | 0.0 | - |
1222
+ | 0.7992 | 50000 | 0.0 | - |
1223
+ | 0.8000 | 50050 | 0.0 | - |
1224
+ | 0.8008 | 50100 | 0.0 | - |
1225
+ | 0.8016 | 50150 | 0.0 | - |
1226
+ | 0.8024 | 50200 | 0.0 | - |
1227
+ | 0.8032 | 50250 | 0.0 | - |
1228
+ | 0.8040 | 50300 | 0.0 | - |
1229
+ | 0.8048 | 50350 | 0.0 | - |
1230
+ | 0.8056 | 50400 | 0.0 | - |
1231
+ | 0.8064 | 50450 | 0.0 | - |
1232
+ | 0.8072 | 50500 | 0.0 | - |
1233
+ | 0.8080 | 50550 | 0.0 | - |
1234
+ | 0.8088 | 50600 | 0.0 | - |
1235
+ | 0.8096 | 50650 | 0.0 | - |
1236
+ | 0.8104 | 50700 | 0.0 | - |
1237
+ | 0.8112 | 50750 | 0.0 | - |
1238
+ | 0.8120 | 50800 | 0.0 | - |
1239
+ | 0.8128 | 50850 | 0.0 | - |
1240
+ | 0.8136 | 50900 | 0.0 | - |
1241
+ | 0.8144 | 50950 | 0.0 | - |
1242
+ | 0.8152 | 51000 | 0.0 | - |
1243
+ | 0.8160 | 51050 | 0.0 | - |
1244
+ | 0.8168 | 51100 | 0.0 | - |
1245
+ | 0.8176 | 51150 | 0.0 | - |
1246
+ | 0.8184 | 51200 | 0.0 | - |
1247
+ | 0.8192 | 51250 | 0.0 | - |
1248
+ | 0.8200 | 51300 | 0.0 | - |
1249
+ | 0.8208 | 51350 | 0.0 | - |
1250
+ | 0.8216 | 51400 | 0.0 | - |
1251
+ | 0.8224 | 51450 | 0.0 | - |
1252
+ | 0.8232 | 51500 | 0.0 | - |
1253
+ | 0.8240 | 51550 | 0.0 | - |
1254
+ | 0.8248 | 51600 | 0.0 | - |
1255
+ | 0.8256 | 51650 | 0.0 | - |
1256
+ | 0.8264 | 51700 | 0.0 | - |
1257
+ | 0.8272 | 51750 | 0.0 | - |
1258
+ | 0.8280 | 51800 | 0.0 | - |
1259
+ | 0.8288 | 51850 | 0.0 | - |
1260
+ | 0.8296 | 51900 | 0.0 | - |
1261
+ | 0.8304 | 51950 | 0.0 | - |
1262
+ | 0.8312 | 52000 | 0.0 | - |
1263
+ | 0.8320 | 52050 | 0.0 | - |
1264
+ | 0.8328 | 52100 | 0.0 | - |
1265
+ | 0.8336 | 52150 | 0.0 | - |
1266
+ | 0.8344 | 52200 | 0.0 | - |
1267
+ | 0.8352 | 52250 | 0.0 | - |
1268
+ | 0.8360 | 52300 | 0.0 | - |
1269
+ | 0.8368 | 52350 | 0.0 | - |
1270
+ | 0.8376 | 52400 | 0.0 | - |
1271
+ | 0.8384 | 52450 | 0.0 | - |
1272
+ | 0.8392 | 52500 | 0.0 | - |
1273
+ | 0.8400 | 52550 | 0.0 | - |
1274
+ | 0.8408 | 52600 | 0.0 | - |
1275
+ | 0.8416 | 52650 | 0.0 | - |
1276
+ | 0.8424 | 52700 | 0.0 | - |
1277
+ | 0.8432 | 52750 | 0.0 | - |
1278
+ | 0.8439 | 52800 | 0.0 | - |
1279
+ | 0.8447 | 52850 | 0.0 | - |
1280
+ | 0.8455 | 52900 | 0.0 | - |
1281
+ | 0.8463 | 52950 | 0.0 | - |
1282
+ | 0.8471 | 53000 | 0.0 | - |
1283
+ | 0.8479 | 53050 | 0.0 | - |
1284
+ | 0.8487 | 53100 | 0.0 | - |
1285
+ | 0.8495 | 53150 | 0.0 | - |
1286
+ | 0.8503 | 53200 | 0.0 | - |
1287
+ | 0.8511 | 53250 | 0.0 | - |
1288
+ | 0.8519 | 53300 | 0.0 | - |
1289
+ | 0.8527 | 53350 | 0.0 | - |
1290
+ | 0.8535 | 53400 | 0.0 | - |
1291
+ | 0.8543 | 53450 | 0.0 | - |
1292
+ | 0.8551 | 53500 | 0.0 | - |
1293
+ | 0.8559 | 53550 | 0.0 | - |
1294
+ | 0.8567 | 53600 | 0.0 | - |
1295
+ | 0.8575 | 53650 | 0.0 | - |
1296
+ | 0.8583 | 53700 | 0.0 | - |
1297
+ | 0.8591 | 53750 | 0.0 | - |
1298
+ | 0.8599 | 53800 | 0.0 | - |
1299
+ | 0.8607 | 53850 | 0.0 | - |
1300
+ | 0.8615 | 53900 | 0.0 | - |
1301
+ | 0.8623 | 53950 | 0.0 | - |
1302
+ | 0.8631 | 54000 | 0.0 | - |
1303
+ | 0.8639 | 54050 | 0.0 | - |
1304
+ | 0.8647 | 54100 | 0.0 | - |
1305
+ | 0.8655 | 54150 | 0.0 | - |
1306
+ | 0.8663 | 54200 | 0.0 | - |
1307
+ | 0.8671 | 54250 | 0.0 | - |
1308
+ | 0.8679 | 54300 | 0.0 | - |
1309
+ | 0.8687 | 54350 | 0.0 | - |
1310
+ | 0.8695 | 54400 | 0.0 | - |
1311
+ | 0.8703 | 54450 | 0.0 | - |
1312
+ | 0.8711 | 54500 | 0.0 | - |
1313
+ | 0.8719 | 54550 | 0.0 | - |
1314
+ | 0.8727 | 54600 | 0.0 | - |
1315
+ | 0.8735 | 54650 | 0.0 | - |
1316
+ | 0.8743 | 54700 | 0.0 | - |
1317
+ | 0.8751 | 54750 | 0.0 | - |
1318
+ | 0.8759 | 54800 | 0.0 | - |
1319
+ | 0.8767 | 54850 | 0.0 | - |
1320
+ | 0.8775 | 54900 | 0.0 | - |
1321
+ | 0.8783 | 54950 | 0.0 | - |
1322
+ | 0.8791 | 55000 | 0.0 | - |
1323
+ | 0.8799 | 55050 | 0.0 | - |
1324
+ | 0.8807 | 55100 | 0.0 | - |
1325
+ | 0.8815 | 55150 | 0.0 | - |
1326
+ | 0.8823 | 55200 | 0.0 | - |
1327
+ | 0.8831 | 55250 | 0.0 | - |
1328
+ | 0.8839 | 55300 | 0.0 | - |
1329
+ | 0.8847 | 55350 | 0.0 | - |
1330
+ | 0.8855 | 55400 | 0.0 | - |
1331
+ | 0.8863 | 55450 | 0.0 | - |
1332
+ | 0.8871 | 55500 | 0.0 | - |
1333
+ | 0.8879 | 55550 | 0.0 | - |
1334
+ | 0.8887 | 55600 | 0.0004 | - |
1335
+ | 0.8895 | 55650 | 0.0 | - |
1336
+ | 0.8903 | 55700 | 0.0 | - |
1337
+ | 0.8911 | 55750 | 0.0 | - |
1338
+ | 0.8919 | 55800 | 0.0 | - |
1339
+ | 0.8927 | 55850 | 0.0 | - |
1340
+ | 0.8935 | 55900 | 0.0 | - |
1341
+ | 0.8943 | 55950 | 0.0 | - |
1342
+ | 0.8951 | 56000 | 0.0 | - |
1343
+ | 0.8959 | 56050 | 0.0 | - |
1344
+ | 0.8967 | 56100 | 0.0 | - |
1345
+ | 0.8975 | 56150 | 0.0 | - |
1346
+ | 0.8983 | 56200 | 0.0 | - |
1347
+ | 0.8991 | 56250 | 0.0 | - |
1348
+ | 0.8999 | 56300 | 0.0 | - |
1349
+ | 0.9007 | 56350 | 0.0 | - |
1350
+ | 0.9015 | 56400 | 0.0 | - |
1351
+ | 0.9023 | 56450 | 0.0 | - |
1352
+ | 0.9031 | 56500 | 0.0 | - |
1353
+ | 0.9039 | 56550 | 0.0 | - |
1354
+ | 0.9047 | 56600 | 0.0 | - |
1355
+ | 0.9055 | 56650 | 0.0 | - |
1356
+ | 0.9063 | 56700 | 0.0 | - |
1357
+ | 0.9071 | 56750 | 0.0 | - |
1358
+ | 0.9079 | 56800 | 0.0 | - |
1359
+ | 0.9087 | 56850 | 0.0 | - |
1360
+ | 0.9095 | 56900 | 0.0 | - |
1361
+ | 0.9103 | 56950 | 0.0 | - |
1362
+ | 0.9111 | 57000 | 0.0 | - |
1363
+ | 0.9119 | 57050 | 0.0 | - |
1364
+ | 0.9127 | 57100 | 0.0 | - |
1365
+ | 0.9135 | 57150 | 0.0 | - |
1366
+ | 0.9143 | 57200 | 0.0 | - |
1367
+ | 0.9151 | 57250 | 0.0 | - |
1368
+ | 0.9159 | 57300 | 0.0 | - |
1369
+ | 0.9167 | 57350 | 0.0 | - |
1370
+ | 0.9175 | 57400 | 0.0 | - |
1371
+ | 0.9183 | 57450 | 0.0 | - |
1372
+ | 0.9191 | 57500 | 0.0 | - |
1373
+ | 0.9199 | 57550 | 0.0 | - |
1374
+ | 0.9207 | 57600 | 0.0 | - |
1375
+ | 0.9215 | 57650 | 0.0 | - |
1376
+ | 0.9223 | 57700 | 0.0 | - |
1377
+ | 0.9231 | 57750 | 0.0 | - |
1378
+ | 0.9239 | 57800 | 0.0 | - |
1379
+ | 0.9247 | 57850 | 0.0 | - |
1380
+ | 0.9255 | 57900 | 0.0 | - |
1381
+ | 0.9263 | 57950 | 0.0 | - |
1382
+ | 0.9271 | 58000 | 0.0 | - |
1383
+ | 0.9279 | 58050 | 0.0 | - |
1384
+ | 0.9287 | 58100 | 0.0 | - |
1385
+ | 0.9295 | 58150 | 0.0 | - |
1386
+ | 0.9303 | 58200 | 0.0 | - |
1387
+ | 0.9311 | 58250 | 0.0 | - |
1388
+ | 0.9319 | 58300 | 0.0 | - |
1389
+ | 0.9327 | 58350 | 0.0 | - |
1390
+ | 0.9335 | 58400 | 0.0 | - |
1391
+ | 0.9343 | 58450 | 0.0 | - |
1392
+ | 0.9351 | 58500 | 0.0 | - |
1393
+ | 0.9359 | 58550 | 0.0 | - |
1394
+ | 0.9367 | 58600 | 0.0 | - |
1395
+ | 0.9375 | 58650 | 0.0 | - |
1396
+ | 0.9383 | 58700 | 0.0 | - |
1397
+ | 0.9391 | 58750 | 0.0 | - |
1398
+ | 0.9399 | 58800 | 0.0 | - |
1399
+ | 0.9407 | 58850 | 0.0 | - |
1400
+ | 0.9415 | 58900 | 0.0 | - |
1401
+ | 0.9423 | 58950 | 0.0 | - |
1402
+ | 0.9430 | 59000 | 0.0 | - |
1403
+ | 0.9438 | 59050 | 0.0 | - |
1404
+ | 0.9446 | 59100 | 0.0 | - |
1405
+ | 0.9454 | 59150 | 0.0 | - |
1406
+ | 0.9462 | 59200 | 0.0 | - |
1407
+ | 0.9470 | 59250 | 0.0 | - |
1408
+ | 0.9478 | 59300 | 0.0 | - |
1409
+ | 0.9486 | 59350 | 0.0 | - |
1410
+ | 0.9494 | 59400 | 0.0 | - |
1411
+ | 0.9502 | 59450 | 0.0 | - |
1412
+ | 0.9510 | 59500 | 0.0 | - |
1413
+ | 0.9518 | 59550 | 0.0 | - |
1414
+ | 0.9526 | 59600 | 0.0 | - |
1415
+ | 0.9534 | 59650 | 0.0 | - |
1416
+ | 0.9542 | 59700 | 0.0 | - |
1417
+ | 0.9550 | 59750 | 0.0 | - |
1418
+ | 0.9558 | 59800 | 0.0 | - |
1419
+ | 0.9566 | 59850 | 0.0 | - |
1420
+ | 0.9574 | 59900 | 0.0 | - |
1421
+ | 0.9582 | 59950 | 0.0 | - |
1422
+ | 0.9590 | 60000 | 0.0 | - |
1423
+ | 0.9598 | 60050 | 0.0 | - |
1424
+ | 0.9606 | 60100 | 0.0 | - |
1425
+ | 0.9614 | 60150 | 0.0 | - |
1426
+ | 0.9622 | 60200 | 0.0 | - |
1427
+ | 0.9630 | 60250 | 0.0 | - |
1428
+ | 0.9638 | 60300 | 0.0 | - |
1429
+ | 0.9646 | 60350 | 0.0 | - |
1430
+ | 0.9654 | 60400 | 0.0 | - |
1431
+ | 0.9662 | 60450 | 0.0 | - |
1432
+ | 0.9670 | 60500 | 0.0 | - |
1433
+ | 0.9678 | 60550 | 0.0 | - |
1434
+ | 0.9686 | 60600 | 0.0 | - |
1435
+ | 0.9694 | 60650 | 0.0 | - |
1436
+ | 0.9702 | 60700 | 0.0 | - |
1437
+ | 0.9710 | 60750 | 0.0 | - |
1438
+ | 0.9718 | 60800 | 0.0 | - |
1439
+ | 0.9726 | 60850 | 0.0 | - |
1440
+ | 0.9734 | 60900 | 0.0 | - |
1441
+ | 0.9742 | 60950 | 0.0 | - |
1442
+ | 0.9750 | 61000 | 0.0 | - |
1443
+ | 0.9758 | 61050 | 0.0 | - |
1444
+ | 0.9766 | 61100 | 0.0 | - |
1445
+ | 0.9774 | 61150 | 0.0 | - |
1446
+ | 0.9782 | 61200 | 0.0 | - |
1447
+ | 0.9790 | 61250 | 0.0 | - |
1448
+ | 0.9798 | 61300 | 0.0 | - |
1449
+ | 0.9806 | 61350 | 0.0 | - |
1450
+ | 0.9814 | 61400 | 0.0 | - |
1451
+ | 0.9822 | 61450 | 0.0 | - |
1452
+ | 0.9830 | 61500 | 0.0 | - |
1453
+ | 0.9838 | 61550 | 0.0 | - |
1454
+ | 0.9846 | 61600 | 0.0 | - |
1455
+ | 0.9854 | 61650 | 0.0 | - |
1456
+ | 0.9862 | 61700 | 0.0 | - |
1457
+ | 0.9870 | 61750 | 0.0 | - |
1458
+ | 0.9878 | 61800 | 0.0 | - |
1459
+ | 0.9886 | 61850 | 0.0 | - |
1460
+ | 0.9894 | 61900 | 0.0 | - |
1461
+ | 0.9902 | 61950 | 0.0 | - |
1462
+ | 0.9910 | 62000 | 0.0 | - |
1463
+ | 0.9918 | 62050 | 0.0 | - |
1464
+ | 0.9926 | 62100 | 0.0 | - |
1465
+ | 0.9934 | 62150 | 0.0 | - |
1466
+ | 0.9942 | 62200 | 0.0 | - |
1467
+ | 0.9950 | 62250 | 0.0 | - |
1468
+ | 0.9958 | 62300 | 0.0 | - |
1469
+ | 0.9966 | 62350 | 0.0 | - |
1470
+ | 0.9974 | 62400 | 0.0 | - |
1471
+ | 0.9982 | 62450 | 0.0 | - |
1472
+ | 0.9990 | 62500 | 0.0 | - |
1473
+ | 0.9998 | 62550 | 0.0 | - |
1474
+ | 1.0 | 62563 | - | 0.0913 |
1475
+
1476
+ ### Framework Versions
1477
+ - Python: 3.12.7
1478
+ - SetFit: 1.1.0
1479
+ - Sentence Transformers: 3.3.1
1480
+ - Transformers: 4.47.0
1481
+ - PyTorch: 2.5.1+cu124
1482
+ - Datasets: 3.1.0
1483
+ - Tokenizers: 0.21.0
1484
+
1485
+ ## Citation
1486
+
1487
+ ### BibTeX
1488
+ ```bibtex
1489
+ @article{https://doi.org/10.48550/arxiv.2209.11055,
1490
+ doi = {10.48550/ARXIV.2209.11055},
1491
+ url = {https://arxiv.org/abs/2209.11055},
1492
+ author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
1493
+ keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
1494
+ title = {Efficient Few-Shot Learning Without Prompts},
1495
+ publisher = {arXiv},
1496
+ year = {2022},
1497
+ copyright = {Creative Commons Attribution 4.0 International}
1498
+ }
1499
+ ```
1500
+
1501
+ <!--
1502
+ ## Glossary
1503
+
1504
+ *Clearly define terms in order to be accessible across audiences.*
1505
+ -->
1506
+
1507
+ <!--
1508
+ ## Model Card Authors
1509
+
1510
+ *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
1511
+ -->
1512
+
1513
+ <!--
1514
+ ## Model Card Contact
1515
+
1516
+ *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
1517
+ -->
config.json ADDED
@@ -0,0 +1,32 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "_name_or_path": "BAAI/bge-base-en-v1.5",
3
+ "architectures": [
4
+ "BertModel"
5
+ ],
6
+ "attention_probs_dropout_prob": 0.1,
7
+ "classifier_dropout": null,
8
+ "gradient_checkpointing": false,
9
+ "hidden_act": "gelu",
10
+ "hidden_dropout_prob": 0.1,
11
+ "hidden_size": 768,
12
+ "id2label": {
13
+ "0": "LABEL_0"
14
+ },
15
+ "initializer_range": 0.02,
16
+ "intermediate_size": 3072,
17
+ "label2id": {
18
+ "LABEL_0": 0
19
+ },
20
+ "layer_norm_eps": 1e-12,
21
+ "max_position_embeddings": 512,
22
+ "model_type": "bert",
23
+ "num_attention_heads": 12,
24
+ "num_hidden_layers": 12,
25
+ "pad_token_id": 0,
26
+ "position_embedding_type": "absolute",
27
+ "torch_dtype": "float32",
28
+ "transformers_version": "4.47.0",
29
+ "type_vocab_size": 2,
30
+ "use_cache": true,
31
+ "vocab_size": 30522
32
+ }
config_sentence_transformers.json ADDED
@@ -0,0 +1,10 @@
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "__version__": {
3
+ "sentence_transformers": "3.3.1",
4
+ "transformers": "4.47.0",
5
+ "pytorch": "2.5.1+cu124"
6
+ },
7
+ "prompts": {},
8
+ "default_prompt_name": null,
9
+ "similarity_fn_name": "cosine"
10
+ }
config_setfit.json ADDED
@@ -0,0 +1,7 @@
 
 
 
 
 
 
 
 
1
+ {
2
+ "labels": [
3
+ "non-cybersec",
4
+ "cybersec"
5
+ ],
6
+ "normalize_embeddings": false
7
+ }
model.safetensors ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:32d07ec72c35c443f846cb155ff840b267043c8aee361a43ccc8906766057e25
3
+ size 437951328
model_head.pkl ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:e075c4eb2c968eebd043984022acc324398ab49cbb38d4fca1a04b386536fc22
3
+ size 6991
modules.json ADDED
@@ -0,0 +1,20 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ [
2
+ {
3
+ "idx": 0,
4
+ "name": "0",
5
+ "path": "",
6
+ "type": "sentence_transformers.models.Transformer"
7
+ },
8
+ {
9
+ "idx": 1,
10
+ "name": "1",
11
+ "path": "1_Pooling",
12
+ "type": "sentence_transformers.models.Pooling"
13
+ },
14
+ {
15
+ "idx": 2,
16
+ "name": "2",
17
+ "path": "2_Normalize",
18
+ "type": "sentence_transformers.models.Normalize"
19
+ }
20
+ ]
sentence_bert_config.json ADDED
@@ -0,0 +1,4 @@
 
 
 
 
 
1
+ {
2
+ "max_seq_length": 512,
3
+ "do_lower_case": true
4
+ }
special_tokens_map.json ADDED
@@ -0,0 +1,37 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "cls_token": {
3
+ "content": "[CLS]",
4
+ "lstrip": false,
5
+ "normalized": false,
6
+ "rstrip": false,
7
+ "single_word": false
8
+ },
9
+ "mask_token": {
10
+ "content": "[MASK]",
11
+ "lstrip": false,
12
+ "normalized": false,
13
+ "rstrip": false,
14
+ "single_word": false
15
+ },
16
+ "pad_token": {
17
+ "content": "[PAD]",
18
+ "lstrip": false,
19
+ "normalized": false,
20
+ "rstrip": false,
21
+ "single_word": false
22
+ },
23
+ "sep_token": {
24
+ "content": "[SEP]",
25
+ "lstrip": false,
26
+ "normalized": false,
27
+ "rstrip": false,
28
+ "single_word": false
29
+ },
30
+ "unk_token": {
31
+ "content": "[UNK]",
32
+ "lstrip": false,
33
+ "normalized": false,
34
+ "rstrip": false,
35
+ "single_word": false
36
+ }
37
+ }
tokenizer.json ADDED
The diff for this file is too large to render. See raw diff
 
tokenizer_config.json ADDED
@@ -0,0 +1,58 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "added_tokens_decoder": {
3
+ "0": {
4
+ "content": "[PAD]",
5
+ "lstrip": false,
6
+ "normalized": false,
7
+ "rstrip": false,
8
+ "single_word": false,
9
+ "special": true
10
+ },
11
+ "100": {
12
+ "content": "[UNK]",
13
+ "lstrip": false,
14
+ "normalized": false,
15
+ "rstrip": false,
16
+ "single_word": false,
17
+ "special": true
18
+ },
19
+ "101": {
20
+ "content": "[CLS]",
21
+ "lstrip": false,
22
+ "normalized": false,
23
+ "rstrip": false,
24
+ "single_word": false,
25
+ "special": true
26
+ },
27
+ "102": {
28
+ "content": "[SEP]",
29
+ "lstrip": false,
30
+ "normalized": false,
31
+ "rstrip": false,
32
+ "single_word": false,
33
+ "special": true
34
+ },
35
+ "103": {
36
+ "content": "[MASK]",
37
+ "lstrip": false,
38
+ "normalized": false,
39
+ "rstrip": false,
40
+ "single_word": false,
41
+ "special": true
42
+ }
43
+ },
44
+ "clean_up_tokenization_spaces": true,
45
+ "cls_token": "[CLS]",
46
+ "do_basic_tokenize": true,
47
+ "do_lower_case": true,
48
+ "extra_special_tokens": {},
49
+ "mask_token": "[MASK]",
50
+ "model_max_length": 512,
51
+ "never_split": null,
52
+ "pad_token": "[PAD]",
53
+ "sep_token": "[SEP]",
54
+ "strip_accents": null,
55
+ "tokenize_chinese_chars": true,
56
+ "tokenizer_class": "BertTokenizer",
57
+ "unk_token": "[UNK]"
58
+ }
vocab.txt ADDED
The diff for this file is too large to render. See raw diff