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billingsmoore
/
minilm-bo

Sentence Similarity
sentence-transformers
Safetensors
Tibetan
English
bert
feature-extraction
Generated from Trainer
dataset_size:878004
loss:MSELoss
Eval Results (legacy)
text-embeddings-inference
Model card Files
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billingsmoore commited on
Commit
0b52743
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verified ·
1 Parent(s): b00eb33

Add new SentenceTransformer model

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Files changed (11) hide show
  1. 1_Pooling/config.json +10 -0
  2. README.md +1400 -0
  3. config.json +26 -0
  4. config_sentence_transformers.json +10 -0
  5. model.safetensors +3 -0
  6. modules.json +14 -0
  7. sentence_bert_config.json +4 -0
  8. special_tokens_map.json +37 -0
  9. tokenizer.json +0 -0
  10. tokenizer_config.json +58 -0
  11. vocab.txt +0 -0
1_Pooling/config.json ADDED
@@ -0,0 +1,10 @@
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "word_embedding_dimension": 384,
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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 +1,1400 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ tags:
3
+ - sentence-transformers
4
+ - sentence-similarity
5
+ - feature-extraction
6
+ - generated_from_trainer
7
+ - dataset_size:878004
8
+ - loss:MSELoss
9
+ widget:
10
+ - source_sentence: Finally all melt into light and dissolve into me
11
+ sentences:
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+ - '- - གཡུང་དྲུང་འཇིགས་མེད།'
13
+ - མཐར་ནི་འོད་ཞུ་རང་ལ་ཐིམ།།
14
+ - དེ་ཤེས་རབ་ཀྱི་ཕ་རོལ་ཏུ་ཕྱིན་པ་ལ་སྤྱོད་པའི་ཚེ། རྣམ་པ་ཐམས་ཅད་མཁྱེན་པ་ཉིད་དང་ལྡན་པའི་ཡིད་ལ་བྱ་བ་མེད་པར།
15
+ གཟུགས་འདུས་བྱས་སྟོང་པ་ཞེས་བྱ་བར་ཡིད་ལ་བྱེད་དེ། དམིགས་པའི་ཚུལ་གྱིས་འདུས་བྱས་སྟོང་པ་ཉིད་ཀྱང་དམིགས་ལ།
16
+ སྟོང་པ་ཉིད་ཀྱིས་ཀྱང་རློམ་སེམས་སུ་བྱེད་དོ། །
17
+ - source_sentence: The pain I feel when betrayed is still so much larger than life.
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+ sentences:
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+ - ༢༠༡༠ ཟླ་བ་ ༡༠ ཚེས ༠༢ བོད་ཀྱི་བང་ཆེན། Comments Off on རྟའུ་བློ་བཟང་དཔལ་ལྡན་བཀའ་ཁྲིའི་འོས་མི་ནས་ཕྱིར་འཐེན།
20
+ - ༣ ས་པར་ གས་ ས་ ད་པར་ཤ་ཚ་ད ས་པ་ལས་ཧ་ཅང་ག ས་པར་བྱེད་ ་ ང་།
21
+ - ཅེས་གསུངས་པ་འདི་ནི། ཕྱི་ལོ་ ༢༠༡༡ ཟླ་ ༥ ཚེས་ ༡༨ ཉིན་ཤེས་རིག་
22
+ - source_sentence: I am confident in my own self.
23
+ sentences:
24
+ - རྗེས་ སུ་ བདག་ བསྒྲུབ་ ཀྱིས༔
25
+ - '"ཁྱི་སྐྱག ཡར་ལོངས། "'
26
+ - ང་ཡིད་ཆེས་ཀྱི་བརྟས་སོང རང་ས་རང་གིས་སྲུང་བཞིན
27
+ - source_sentence: God it isn't easy.
28
+ sentences:
29
+ - 7:6 ནོ་ཨ་ལོ་ ༦༠༠ ལོན་སྐབས་ས་གཞིར་ཆུ་ལོག་བྱུང་ངོ་།
30
+ - ༤ དངུལ་ཆུ་འདུལ་ཚུལ།
31
+ - དཀོན་མཆོག࿒ གསུམ࿒ ག་རེ࿒ ག་རེ࿒ རེད།
32
+ - source_sentence: He could do it, so he did.
33
+ sentences:
34
+ - རེས་བྱེད་ཐུབ་པ་དེ་རེད། འོན་ཀྱང་། ཁོ་མོས་
35
+ - ཕྱི་སྟོང་པ་ཉིད་ཡོངས་སུ་དག་པ། ཕྱི་སྟོང་པ་ཉིད་ཡོངས་སུ་དག་པས། ཤེས་པ་པོ་ཡོངས་སུ་དག་པ་སྟེ།
36
+ དེ་ལྟར་ན་ཤེས་པ་པོ་ཡོངས་སུ་དག་པ་དང་། ཕྱི་སྟོང་པ་ཉིད་ཡོངས་སུ་དག་པ་འདི་ལ་གཉིས་སུ་མྱེད་དེ་གཉིས་སུ་བྱར་མྱེད་སོ་སོ་མ་ཡིན་ཐ་མྱི་དད་དོ།
37
+ །ཤེས་པ་པོ་ཡོངས་སུ་དག་པས།
38
+ - འད་ི བསྐྱར་གསོ་བདྱེ ་དགོས་འདུག ཅེས་
39
+ pipeline_tag: sentence-similarity
40
+ library_name: sentence-transformers
41
+ metrics:
42
+ - negative_mse
43
+ model-index:
44
+ - name: SentenceTransformer
45
+ results:
46
+ - task:
47
+ type: knowledge-distillation
48
+ name: Knowledge Distillation
49
+ dataset:
50
+ name: stsb dev
51
+ type: stsb-dev
52
+ metrics:
53
+ - type: negative_mse
54
+ value: -0.17373771965503693
55
+ name: Negative Mse
56
+ ---
57
+
58
+ # SentenceTransformer
59
+
60
+ This is a [sentence-transformers](https://www.SBERT.net) model trained on the aggregated-bo-en dataset. It maps sentences & paragraphs to a 384-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
61
+
62
+ ## Model Details
63
+
64
+ ### Model Description
65
+ - **Model Type:** Sentence Transformer
66
+ <!-- - **Base model:** [Unknown](https://huggingface.co/unknown) -->
67
+ - **Maximum Sequence Length:** 512 tokens
68
+ - **Output Dimensionality:** 384 dimensions
69
+ - **Similarity Function:** Cosine Similarity
70
+ - **Training Dataset:**
71
+ - aggregated-bo-en
72
+ <!-- - **Language:** Unknown -->
73
+ <!-- - **License:** Unknown -->
74
+
75
+ ### Model Sources
76
+
77
+ - **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
78
+ - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
79
+ - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
80
+
81
+ ### Full Model Architecture
82
+
83
+ ```
84
+ SentenceTransformer(
85
+ (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
86
+ (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
87
+ )
88
+ ```
89
+
90
+ ## Usage
91
+
92
+ ### Direct Usage (Sentence Transformers)
93
+
94
+ First install the Sentence Transformers library:
95
+
96
+ ```bash
97
+ pip install -U sentence-transformers
98
+ ```
99
+
100
+ Then you can load this model and run inference.
101
+ ```python
102
+ from sentence_transformers import SentenceTransformer
103
+
104
+ # Download from the 🤗 Hub
105
+ model = SentenceTransformer("billingsmoore/minilm-bo")
106
+ # Run inference
107
+ sentences = [
108
+ 'He could do it, so he did.',
109
+ 'རེས་བྱེད་ཐུབ་པ་དེ་རེད། འོན་ཀྱང་། ཁོ་མོས་',
110
+ 'ཕྱི་སྟོང་པ་ཉིད་ཡོངས་སུ་དག་པ། ཕྱི་སྟོང་པ་ཉིད་ཡོངས་སུ་དག་པས། ཤེས་པ་པོ་ཡོངས་སུ་དག་པ་སྟེ། དེ་ལྟར་ན་ཤེས་པ་པོ་ཡོངས་སུ་དག་པ་དང་། ཕྱི་སྟོང་པ་ཉིད་ཡོངས་སུ་དག་པ་འདི་ལ་གཉིས་སུ་མྱེད་དེ་གཉིས་སུ་བྱར་མྱེད་སོ་སོ་མ་ཡིན་ཐ་མྱི་དད་དོ། །ཤེས་པ་པོ་ཡོངས་སུ་དག་པས།',
111
+ ]
112
+ embeddings = model.encode(sentences)
113
+ print(embeddings.shape)
114
+ # [3, 384]
115
+
116
+ # Get the similarity scores for the embeddings
117
+ similarities = model.similarity(embeddings, embeddings)
118
+ print(similarities.shape)
119
+ # [3, 3]
120
+ ```
121
+
122
+ <!--
123
+ ### Direct Usage (Transformers)
124
+
125
+ <details><summary>Click to see the direct usage in Transformers</summary>
126
+
127
+ </details>
128
+ -->
129
+
130
+ <!--
131
+ ### Downstream Usage (Sentence Transformers)
132
+
133
+ You can finetune this model on your own dataset.
134
+
135
+ <details><summary>Click to expand</summary>
136
+
137
+ </details>
138
+ -->
139
+
140
+ <!--
141
+ ### Out-of-Scope Use
142
+
143
+ *List how the model may foreseeably be misused and address what users ought not to do with the model.*
144
+ -->
145
+
146
+ ## Evaluation
147
+
148
+ ### Metrics
149
+
150
+ #### Knowledge Distillation
151
+
152
+ * Dataset: `stsb-dev`
153
+ * Evaluated with [<code>MSEEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.MSEEvaluator)
154
+
155
+ | Metric | Value |
156
+ |:-----------------|:------------|
157
+ | **negative_mse** | **-0.1737** |
158
+
159
+ <!--
160
+ ## Bias, Risks and Limitations
161
+
162
+ *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
163
+ -->
164
+
165
+ <!--
166
+ ### Recommendations
167
+
168
+ *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
169
+ -->
170
+
171
+ ## Training Details
172
+
173
+ ### Training Dataset
174
+
175
+ #### aggregated-bo-en
176
+
177
+ * Dataset: aggregated-bo-en
178
+ * Size: 878,004 training samples
179
+ * Columns: <code>tibetan</code> and <code>label</code>
180
+ * Approximate statistics based on the first 1000 samples:
181
+ | | tibetan | label |
182
+ |:--------|:-----------------------------------------------------------------------------------|:-------------------------------------|
183
+ | type | string | list |
184
+ | details | <ul><li>min: 4 tokens</li><li>mean: 29.06 tokens</li><li>max: 373 tokens</li></ul> | <ul><li>size: 384 elements</li></ul> |
185
+ * Samples:
186
+ | tibetan | label |
187
+ |:------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------|
188
+ | <code>ཀི་ལོ་མི་ཊར་ ༤༧.༣༩</code> | <code>[-0.026894396170973778, 0.07161899656057358, -0.06451261788606644, 0.004668479785323143, -0.13893075287342072, ...]</code> |
189
+ | <code>ཅ། ཁྱོད་དང་ང་།</code> | <code>[-0.03711550310254097, 0.04723873734474182, 0.027722617611289024, 0.03208618983626366, 0.0021679026540368795, ...]</code> |
190
+ | <code>མཚོན་རྨ་གསོ་བ། དེ་བས་མང་། >></code> | <code>[0.016887372359633446, -0.004544022027403116, -0.000849854841362685, -0.046510301530361176, -0.05679721385240555, ...]</code> |
191
+ * Loss: [<code>MSELoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#mseloss)
192
+
193
+ ### Evaluation Dataset
194
+
195
+ #### aggregated-bo-en
196
+
197
+ * Dataset: aggregated-bo-en
198
+ * Size: 878,004 evaluation samples
199
+ * Columns: <code>english</code>, <code>tibetan</code>, and <code>label</code>
200
+ * Approximate statistics based on the first 1000 samples:
201
+ | | english | tibetan | label |
202
+ |:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-------------------------------------|
203
+ | type | string | string | list |
204
+ | details | <ul><li>min: 3 tokens</li><li>mean: 22.2 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 32.42 tokens</li><li>max: 487 tokens</li></ul> | <ul><li>size: 384 elements</li></ul> |
205
+ * Samples:
206
+ | english | tibetan | label |
207
+ |:-----------------------------------------------------------------------|:------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------|
208
+ | <code>East TN Children's Hospital.</code> | <code>ཤར་གངས་ཕྲུག་གི་གསས་ཁང་།</code> | <code>[-0.05563941225409508, 0.09337888658046722, 0.01915512979030609, 0.02351493015885353, -0.09008331596851349, ...]</code> |
209
+ | <code>In this prayer, often called the "high priestly prayer of</code> | <code>སྡེ་ཚན་འདིའི་ནང་དུ་མང་། " མཁན་ཆེན་ཞི་བ་འཚོ། ཇོ་བོ་རྗེ་དཔལ་ལྡན་ཨ་ཏི་ཤ "</code> | <code>[0.033027056604623795, 0.013109864667057991, -0.051157161593437195, -0.07704736292362213, -0.04368748143315315, ...]</code> |
210
+ | <code>Spoilers: Oh, I don't know.</code> | <code>ལ་མེད། ཤེས་ཀྱི་མེད། 아니오, 모르겠습니다.</code> | <code>[0.008215248584747314, -0.02530045434832573, -0.029446149244904518, 0.04361790046095848, 0.05075978860259056, ...]</code> |
211
+ * Loss: [<code>MSELoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#mseloss)
212
+
213
+ ### Training Hyperparameters
214
+ #### Non-Default Hyperparameters
215
+
216
+ - `eval_strategy`: epoch
217
+ - `learning_rate`: 2e-05
218
+ - `num_train_epochs`: 25
219
+ - `warmup_ratio`: 0.1
220
+ - `save_safetensors`: False
221
+ - `auto_find_batch_size`: True
222
+
223
+ #### All Hyperparameters
224
+ <details><summary>Click to expand</summary>
225
+
226
+ - `overwrite_output_dir`: False
227
+ - `do_predict`: False
228
+ - `eval_strategy`: epoch
229
+ - `prediction_loss_only`: True
230
+ - `per_device_train_batch_size`: 8
231
+ - `per_device_eval_batch_size`: 8
232
+ - `per_gpu_train_batch_size`: None
233
+ - `per_gpu_eval_batch_size`: None
234
+ - `gradient_accumulation_steps`: 1
235
+ - `eval_accumulation_steps`: None
236
+ - `torch_empty_cache_steps`: None
237
+ - `learning_rate`: 2e-05
238
+ - `weight_decay`: 0.0
239
+ - `adam_beta1`: 0.9
240
+ - `adam_beta2`: 0.999
241
+ - `adam_epsilon`: 1e-08
242
+ - `max_grad_norm`: 1.0
243
+ - `num_train_epochs`: 25
244
+ - `max_steps`: -1
245
+ - `lr_scheduler_type`: linear
246
+ - `lr_scheduler_kwargs`: {}
247
+ - `warmup_ratio`: 0.1
248
+ - `warmup_steps`: 0
249
+ - `log_level`: passive
250
+ - `log_level_replica`: warning
251
+ - `log_on_each_node`: True
252
+ - `logging_nan_inf_filter`: True
253
+ - `save_safetensors`: False
254
+ - `save_on_each_node`: False
255
+ - `save_only_model`: False
256
+ - `restore_callback_states_from_checkpoint`: False
257
+ - `no_cuda`: False
258
+ - `use_cpu`: False
259
+ - `use_mps_device`: False
260
+ - `seed`: 42
261
+ - `data_seed`: None
262
+ - `jit_mode_eval`: False
263
+ - `use_ipex`: False
264
+ - `bf16`: False
265
+ - `fp16`: False
266
+ - `fp16_opt_level`: O1
267
+ - `half_precision_backend`: auto
268
+ - `bf16_full_eval`: False
269
+ - `fp16_full_eval`: False
270
+ - `tf32`: None
271
+ - `local_rank`: 0
272
+ - `ddp_backend`: None
273
+ - `tpu_num_cores`: None
274
+ - `tpu_metrics_debug`: False
275
+ - `debug`: []
276
+ - `dataloader_drop_last`: False
277
+ - `dataloader_num_workers`: 0
278
+ - `dataloader_prefetch_factor`: None
279
+ - `past_index`: -1
280
+ - `disable_tqdm`: False
281
+ - `remove_unused_columns`: True
282
+ - `label_names`: None
283
+ - `load_best_model_at_end`: False
284
+ - `ignore_data_skip`: False
285
+ - `fsdp`: []
286
+ - `fsdp_min_num_params`: 0
287
+ - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
288
+ - `fsdp_transformer_layer_cls_to_wrap`: None
289
+ - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
290
+ - `deepspeed`: None
291
+ - `label_smoothing_factor`: 0.0
292
+ - `optim`: adamw_torch
293
+ - `optim_args`: None
294
+ - `adafactor`: False
295
+ - `group_by_length`: False
296
+ - `length_column_name`: length
297
+ - `ddp_find_unused_parameters`: None
298
+ - `ddp_bucket_cap_mb`: None
299
+ - `ddp_broadcast_buffers`: False
300
+ - `dataloader_pin_memory`: True
301
+ - `dataloader_persistent_workers`: False
302
+ - `skip_memory_metrics`: True
303
+ - `use_legacy_prediction_loop`: False
304
+ - `push_to_hub`: False
305
+ - `resume_from_checkpoint`: None
306
+ - `hub_model_id`: None
307
+ - `hub_strategy`: every_save
308
+ - `hub_private_repo`: None
309
+ - `hub_always_push`: False
310
+ - `gradient_checkpointing`: False
311
+ - `gradient_checkpointing_kwargs`: None
312
+ - `include_inputs_for_metrics`: False
313
+ - `include_for_metrics`: []
314
+ - `eval_do_concat_batches`: True
315
+ - `fp16_backend`: auto
316
+ - `push_to_hub_model_id`: None
317
+ - `push_to_hub_organization`: None
318
+ - `mp_parameters`:
319
+ - `auto_find_batch_size`: True
320
+ - `full_determinism`: False
321
+ - `torchdynamo`: None
322
+ - `ray_scope`: last
323
+ - `ddp_timeout`: 1800
324
+ - `torch_compile`: False
325
+ - `torch_compile_backend`: None
326
+ - `torch_compile_mode`: None
327
+ - `dispatch_batches`: None
328
+ - `split_batches`: None
329
+ - `include_tokens_per_second`: False
330
+ - `include_num_input_tokens_seen`: False
331
+ - `neftune_noise_alpha`: None
332
+ - `optim_target_modules`: None
333
+ - `batch_eval_metrics`: False
334
+ - `eval_on_start`: False
335
+ - `use_liger_kernel`: False
336
+ - `eval_use_gather_object`: False
337
+ - `average_tokens_across_devices`: False
338
+ - `prompts`: None
339
+ - `batch_sampler`: batch_sampler
340
+ - `multi_dataset_batch_sampler`: proportional
341
+
342
+ </details>
343
+
344
+ ### Training Logs
345
+ <details><summary>Click to expand</summary>
346
+
347
+ | Epoch | Step | Training Loss | Validation Loss | stsb-dev_negative_mse |
348
+ |:------:|:-----:|:-------------:|:---------------:|:---------------------:|
349
+ | 0 | 0 | - | - | -7.179603 |
350
+ | 0.0051 | 500 | 0.0546 | - | - |
351
+ | 0.0101 | 1000 | 0.0348 | - | - |
352
+ | 0.0152 | 1500 | 0.0169 | - | - |
353
+ | 0.0202 | 2000 | 0.0087 | - | - |
354
+ | 0.0253 | 2500 | 0.0055 | - | - |
355
+ | 0.0304 | 3000 | 0.0041 | - | - |
356
+ | 0.0354 | 3500 | 0.0036 | - | - |
357
+ | 0.0405 | 4000 | 0.0033 | - | - |
358
+ | 0.0456 | 4500 | 0.003 | - | - |
359
+ | 0.0506 | 5000 | 0.0029 | - | - |
360
+ | 0.0557 | 5500 | 0.0028 | - | - |
361
+ | 0.0607 | 6000 | 0.0027 | - | - |
362
+ | 0.0658 | 6500 | 0.0027 | - | - |
363
+ | 0.0709 | 7000 | 0.0026 | - | - |
364
+ | 0.0759 | 7500 | 0.0025 | - | - |
365
+ | 0.0810 | 8000 | 0.0025 | - | - |
366
+ | 0.0861 | 8500 | 0.0025 | - | - |
367
+ | 0.0911 | 9000 | 0.0025 | - | - |
368
+ | 0.0962 | 9500 | 0.0025 | - | - |
369
+ | 0.1012 | 10000 | 0.0024 | - | - |
370
+ | 0.1063 | 10500 | 0.0024 | - | - |
371
+ | 0.1114 | 11000 | 0.0024 | - | - |
372
+ | 0.1164 | 11500 | 0.0024 | - | - |
373
+ | 0.1215 | 12000 | 0.0024 | - | - |
374
+ | 0.1265 | 12500 | 0.0024 | - | - |
375
+ | 0.1316 | 13000 | 0.0024 | - | - |
376
+ | 0.1367 | 13500 | 0.0024 | - | - |
377
+ | 0.1417 | 14000 | 0.0024 | - | - |
378
+ | 0.1468 | 14500 | 0.0024 | - | - |
379
+ | 0.1519 | 15000 | 0.0024 | - | - |
380
+ | 0.1569 | 15500 | 0.0024 | - | - |
381
+ | 0.1620 | 16000 | 0.0024 | - | - |
382
+ | 0.1670 | 16500 | 0.0024 | - | - |
383
+ | 0.1721 | 17000 | 0.0024 | - | - |
384
+ | 0.1772 | 17500 | 0.0024 | - | - |
385
+ | 0.1822 | 18000 | 0.0024 | - | - |
386
+ | 0.1873 | 18500 | 0.0024 | - | - |
387
+ | 0.1924 | 19000 | 0.0024 | - | - |
388
+ | 0.1974 | 19500 | 0.0024 | - | - |
389
+ | 0.2025 | 20000 | 0.0024 | - | - |
390
+ | 0.2075 | 20500 | 0.0024 | - | - |
391
+ | 0.2126 | 21000 | 0.0024 | - | - |
392
+ | 0.2177 | 21500 | 0.0024 | - | - |
393
+ | 0.2227 | 22000 | 0.0024 | - | - |
394
+ | 0.2278 | 22500 | 0.0024 | - | - |
395
+ | 0.2329 | 23000 | 0.0024 | - | - |
396
+ | 0.2379 | 23500 | 0.0024 | - | - |
397
+ | 0.2430 | 24000 | 0.0023 | - | - |
398
+ | 0.2480 | 24500 | 0.0024 | - | - |
399
+ | 0.2531 | 25000 | 0.0024 | - | - |
400
+ | 0.2582 | 25500 | 0.0023 | - | - |
401
+ | 0.2632 | 26000 | 0.0024 | - | - |
402
+ | 0.2683 | 26500 | 0.0024 | - | - |
403
+ | 0.2733 | 27000 | 0.0023 | - | - |
404
+ | 0.2784 | 27500 | 0.0023 | - | - |
405
+ | 0.2835 | 28000 | 0.0023 | - | - |
406
+ | 0.2885 | 28500 | 0.0023 | - | - |
407
+ | 0.2936 | 29000 | 0.0023 | - | - |
408
+ | 0.2987 | 29500 | 0.0023 | - | - |
409
+ | 0.3037 | 30000 | 0.0023 | - | - |
410
+ | 0.3088 | 30500 | 0.0023 | - | - |
411
+ | 0.3138 | 31000 | 0.0023 | - | - |
412
+ | 0.3189 | 31500 | 0.0023 | - | - |
413
+ | 0.3240 | 32000 | 0.0023 | - | - |
414
+ | 0.3290 | 32500 | 0.0023 | - | - |
415
+ | 0.3341 | 33000 | 0.0023 | - | - |
416
+ | 0.3392 | 33500 | 0.0023 | - | - |
417
+ | 0.3442 | 34000 | 0.0023 | - | - |
418
+ | 0.3493 | 34500 | 0.0023 | - | - |
419
+ | 0.3543 | 35000 | 0.0023 | - | - |
420
+ | 0.3594 | 35500 | 0.0023 | - | - |
421
+ | 0.3645 | 36000 | 0.0023 | - | - |
422
+ | 0.3695 | 36500 | 0.0023 | - | - |
423
+ | 0.3746 | 37000 | 0.0023 | - | - |
424
+ | 0.3796 | 37500 | 0.0023 | - | - |
425
+ | 0.3847 | 38000 | 0.0023 | - | - |
426
+ | 0.3898 | 38500 | 0.0023 | - | - |
427
+ | 0.3948 | 39000 | 0.0023 | - | - |
428
+ | 0.3999 | 39500 | 0.0023 | - | - |
429
+ | 0.4050 | 40000 | 0.0023 | - | - |
430
+ | 0.4100 | 40500 | 0.0023 | - | - |
431
+ | 0.4151 | 41000 | 0.0023 | - | - |
432
+ | 0.4201 | 41500 | 0.0023 | - | - |
433
+ | 0.4252 | 42000 | 0.0023 | - | - |
434
+ | 0.4303 | 42500 | 0.0023 | - | - |
435
+ | 0.4353 | 43000 | 0.0023 | - | - |
436
+ | 0.4404 | 43500 | 0.0023 | - | - |
437
+ | 0.4455 | 44000 | 0.0022 | - | - |
438
+ | 0.4505 | 44500 | 0.0023 | - | - |
439
+ | 0.4556 | 45000 | 0.0023 | - | - |
440
+ | 0.4606 | 45500 | 0.0022 | - | - |
441
+ | 0.4657 | 46000 | 0.0022 | - | - |
442
+ | 0.4708 | 46500 | 0.0022 | - | - |
443
+ | 0.4758 | 47000 | 0.0022 | - | - |
444
+ | 0.4809 | 47500 | 0.0022 | - | - |
445
+ | 0.4859 | 48000 | 0.0022 | - | - |
446
+ | 0.4910 | 48500 | 0.0022 | - | - |
447
+ | 0.4961 | 49000 | 0.0022 | - | - |
448
+ | 0.5011 | 49500 | 0.0022 | - | - |
449
+ | 0.5062 | 50000 | 0.0022 | - | - |
450
+ | 0.5113 | 50500 | 0.0022 | - | - |
451
+ | 0.5163 | 51000 | 0.0022 | - | - |
452
+ | 0.5214 | 51500 | 0.0022 | - | - |
453
+ | 0.5264 | 52000 | 0.0022 | - | - |
454
+ | 0.5315 | 52500 | 0.0022 | - | - |
455
+ | 0.5366 | 53000 | 0.0022 | - | - |
456
+ | 0.5416 | 53500 | 0.0022 | - | - |
457
+ | 0.5467 | 54000 | 0.0022 | - | - |
458
+ | 0.5518 | 54500 | 0.0022 | - | - |
459
+ | 0.5568 | 55000 | 0.0022 | - | - |
460
+ | 0.5619 | 55500 | 0.0022 | - | - |
461
+ | 0.5669 | 56000 | 0.0022 | - | - |
462
+ | 0.5720 | 56500 | 0.0022 | - | - |
463
+ | 0.5771 | 57000 | 0.0022 | - | - |
464
+ | 0.5821 | 57500 | 0.0022 | - | - |
465
+ | 0.5872 | 58000 | 0.0022 | - | - |
466
+ | 0.5922 | 58500 | 0.0022 | - | - |
467
+ | 0.5973 | 59000 | 0.0022 | - | - |
468
+ | 0.6024 | 59500 | 0.0022 | - | - |
469
+ | 0.6074 | 60000 | 0.0022 | - | - |
470
+ | 0.6125 | 60500 | 0.0022 | - | - |
471
+ | 0.6176 | 61000 | 0.0022 | - | - |
472
+ | 0.6226 | 61500 | 0.0022 | - | - |
473
+ | 0.6277 | 62000 | 0.0022 | - | - |
474
+ | 0.6327 | 62500 | 0.0022 | - | - |
475
+ | 0.6378 | 63000 | 0.0022 | - | - |
476
+ | 0.6429 | 63500 | 0.0022 | - | - |
477
+ | 0.6479 | 64000 | 0.0022 | - | - |
478
+ | 0.6530 | 64500 | 0.0022 | - | - |
479
+ | 0.6581 | 65000 | 0.0022 | - | - |
480
+ | 0.6631 | 65500 | 0.0022 | - | - |
481
+ | 0.6682 | 66000 | 0.0022 | - | - |
482
+ | 0.6732 | 66500 | 0.0021 | - | - |
483
+ | 0.6783 | 67000 | 0.0021 | - | - |
484
+ | 0.6834 | 67500 | 0.0021 | - | - |
485
+ | 0.6884 | 68000 | 0.0021 | - | - |
486
+ | 0.6935 | 68500 | 0.0021 | - | - |
487
+ | 0.6986 | 69000 | 0.0021 | - | - |
488
+ | 0.7036 | 69500 | 0.0021 | - | - |
489
+ | 0.7087 | 70000 | 0.0021 | - | - |
490
+ | 0.7137 | 70500 | 0.0021 | - | - |
491
+ | 0.7188 | 71000 | 0.0021 | - | - |
492
+ | 0.7239 | 71500 | 0.0021 | - | - |
493
+ | 0.7289 | 72000 | 0.0021 | - | - |
494
+ | 0.7340 | 72500 | 0.0021 | - | - |
495
+ | 0.7390 | 73000 | 0.0021 | - | - |
496
+ | 0.7441 | 73500 | 0.0021 | - | - |
497
+ | 0.7492 | 74000 | 0.0021 | - | - |
498
+ | 0.7542 | 74500 | 0.0021 | - | - |
499
+ | 0.7593 | 75000 | 0.0021 | - | - |
500
+ | 0.7644 | 75500 | 0.0021 | - | - |
501
+ | 0.7694 | 76000 | 0.0021 | - | - |
502
+ | 0.7745 | 76500 | 0.0021 | - | - |
503
+ | 0.7795 | 77000 | 0.0021 | - | - |
504
+ | 0.7846 | 77500 | 0.0021 | - | - |
505
+ | 0.7897 | 78000 | 0.0021 | - | - |
506
+ | 0.7947 | 78500 | 0.0021 | - | - |
507
+ | 0.7998 | 79000 | 0.0021 | - | - |
508
+ | 0.8049 | 79500 | 0.0021 | - | - |
509
+ | 0.8099 | 80000 | 0.0021 | - | - |
510
+ | 0.8150 | 80500 | 0.0021 | - | - |
511
+ | 0.8200 | 81000 | 0.0021 | - | - |
512
+ | 0.8251 | 81500 | 0.0021 | - | - |
513
+ | 0.8302 | 82000 | 0.0021 | - | - |
514
+ | 0.8352 | 82500 | 0.0021 | - | - |
515
+ | 0.8403 | 83000 | 0.0021 | - | - |
516
+ | 0.8453 | 83500 | 0.0021 | - | - |
517
+ | 0.8504 | 84000 | 0.0021 | - | - |
518
+ | 0.8555 | 84500 | 0.0021 | - | - |
519
+ | 0.8605 | 85000 | 0.0021 | - | - |
520
+ | 0.8656 | 85500 | 0.0021 | - | - |
521
+ | 0.8707 | 86000 | 0.0021 | - | - |
522
+ | 0.8757 | 86500 | 0.0021 | - | - |
523
+ | 0.8808 | 87000 | 0.0021 | - | - |
524
+ | 0.8858 | 87500 | 0.0021 | - | - |
525
+ | 0.8909 | 88000 | 0.0021 | - | - |
526
+ | 0.8960 | 88500 | 0.0021 | - | - |
527
+ | 0.9010 | 89000 | 0.0021 | - | - |
528
+ | 0.9061 | 89500 | 0.0021 | - | - |
529
+ | 0.9112 | 90000 | 0.0021 | - | - |
530
+ | 0.9162 | 90500 | 0.002 | - | - |
531
+ | 0.9213 | 91000 | 0.0021 | - | - |
532
+ | 0.9263 | 91500 | 0.0021 | - | - |
533
+ | 0.9314 | 92000 | 0.0021 | - | - |
534
+ | 0.9365 | 92500 | 0.0021 | - | - |
535
+ | 0.9415 | 93000 | 0.002 | - | - |
536
+ | 0.9466 | 93500 | 0.002 | - | - |
537
+ | 0.9516 | 94000 | 0.0021 | - | - |
538
+ | 0.9567 | 94500 | 0.002 | - | - |
539
+ | 0.9618 | 95000 | 0.002 | - | - |
540
+ | 0.9668 | 95500 | 0.002 | - | - |
541
+ | 0.9719 | 96000 | 0.002 | - | - |
542
+ | 0.9770 | 96500 | 0.002 | - | - |
543
+ | 0.9820 | 97000 | 0.002 | - | - |
544
+ | 0.9871 | 97500 | 0.002 | - | - |
545
+ | 0.9921 | 98000 | 0.002 | - | - |
546
+ | 0.9972 | 98500 | 0.002 | - | - |
547
+ | 1.0 | 98776 | - | 0.0022 | -0.1987867 |
548
+ | 1.0023 | 99000 | 0.002 | - | - |
549
+ | 0.0051 | 500 | 0.002 | - | - |
550
+ | 0.0101 | 1000 | 0.002 | - | - |
551
+ | 0.0152 | 1500 | 0.002 | - | - |
552
+ | 0.0202 | 2000 | 0.002 | - | - |
553
+ | 0.0253 | 2500 | 0.002 | - | - |
554
+ | 0.0304 | 3000 | 0.002 | - | - |
555
+ | 0.0354 | 3500 | 0.002 | - | - |
556
+ | 0.0405 | 4000 | 0.002 | - | - |
557
+ | 0.0456 | 4500 | 0.002 | - | - |
558
+ | 0.0506 | 5000 | 0.002 | - | - |
559
+ | 0.0557 | 5500 | 0.002 | - | - |
560
+ | 0.0607 | 6000 | 0.002 | - | - |
561
+ | 0.0658 | 6500 | 0.002 | - | - |
562
+ | 0.0709 | 7000 | 0.002 | - | - |
563
+ | 0.0759 | 7500 | 0.002 | - | - |
564
+ | 0.0810 | 8000 | 0.002 | - | - |
565
+ | 0.0861 | 8500 | 0.002 | - | - |
566
+ | 0.0911 | 9000 | 0.002 | - | - |
567
+ | 0.0962 | 9500 | 0.002 | - | - |
568
+ | 0.1012 | 10000 | 0.002 | - | - |
569
+ | 0.1063 | 10500 | 0.002 | - | - |
570
+ | 0.1114 | 11000 | 0.002 | - | - |
571
+ | 0.1164 | 11500 | 0.002 | - | - |
572
+ | 0.1215 | 12000 | 0.002 | - | - |
573
+ | 0.1265 | 12500 | 0.002 | - | - |
574
+ | 0.1316 | 13000 | 0.002 | - | - |
575
+ | 0.1367 | 13500 | 0.002 | - | - |
576
+ | 0.1417 | 14000 | 0.002 | - | - |
577
+ | 0.1468 | 14500 | 0.002 | - | - |
578
+ | 0.1519 | 15000 | 0.002 | - | - |
579
+ | 0.1569 | 15500 | 0.002 | - | - |
580
+ | 0.1620 | 16000 | 0.002 | - | - |
581
+ | 0.1670 | 16500 | 0.002 | - | - |
582
+ | 0.1721 | 17000 | 0.002 | - | - |
583
+ | 0.1772 | 17500 | 0.002 | - | - |
584
+ | 0.1822 | 18000 | 0.002 | - | - |
585
+ | 0.1873 | 18500 | 0.002 | - | - |
586
+ | 0.1924 | 19000 | 0.002 | - | - |
587
+ | 0.1974 | 19500 | 0.002 | - | - |
588
+ | 0.2025 | 20000 | 0.002 | - | - |
589
+ | 0.2075 | 20500 | 0.002 | - | - |
590
+ | 0.2126 | 21000 | 0.002 | - | - |
591
+ | 0.2177 | 21500 | 0.002 | - | - |
592
+ | 0.2227 | 22000 | 0.002 | - | - |
593
+ | 0.2278 | 22500 | 0.002 | - | - |
594
+ | 0.2329 | 23000 | 0.002 | - | - |
595
+ | 0.2379 | 23500 | 0.002 | - | - |
596
+ | 0.2430 | 24000 | 0.002 | - | - |
597
+ | 0.2480 | 24500 | 0.002 | - | - |
598
+ | 0.2531 | 25000 | 0.002 | - | - |
599
+ | 0.2582 | 25500 | 0.002 | - | - |
600
+ | 0.2632 | 26000 | 0.002 | - | - |
601
+ | 0.2683 | 26500 | 0.002 | - | - |
602
+ | 0.2733 | 27000 | 0.002 | - | - |
603
+ | 0.2784 | 27500 | 0.002 | - | - |
604
+ | 0.2835 | 28000 | 0.002 | - | - |
605
+ | 0.2885 | 28500 | 0.002 | - | - |
606
+ | 0.2936 | 29000 | 0.002 | - | - |
607
+ | 0.2987 | 29500 | 0.002 | - | - |
608
+ | 0.3037 | 30000 | 0.002 | - | - |
609
+ | 0.3088 | 30500 | 0.002 | - | - |
610
+ | 0.3138 | 31000 | 0.002 | - | - |
611
+ | 0.3189 | 31500 | 0.002 | - | - |
612
+ | 0.3240 | 32000 | 0.002 | - | - |
613
+ | 0.3290 | 32500 | 0.002 | - | - |
614
+ | 0.3341 | 33000 | 0.002 | - | - |
615
+ | 0.3392 | 33500 | 0.002 | - | - |
616
+ | 0.3442 | 34000 | 0.002 | - | - |
617
+ | 0.3493 | 34500 | 0.002 | - | - |
618
+ | 0.3543 | 35000 | 0.002 | - | - |
619
+ | 0.3594 | 35500 | 0.002 | - | - |
620
+ | 0.3645 | 36000 | 0.002 | - | - |
621
+ | 0.3695 | 36500 | 0.002 | - | - |
622
+ | 0.3746 | 37000 | 0.002 | - | - |
623
+ | 0.3796 | 37500 | 0.002 | - | - |
624
+ | 0.3847 | 38000 | 0.002 | - | - |
625
+ | 0.3898 | 38500 | 0.002 | - | - |
626
+ | 0.3948 | 39000 | 0.002 | - | - |
627
+ | 0.3999 | 39500 | 0.002 | - | - |
628
+ | 0.4050 | 40000 | 0.002 | - | - |
629
+ | 0.4100 | 40500 | 0.002 | - | - |
630
+ | 0.4151 | 41000 | 0.002 | - | - |
631
+ | 0.4201 | 41500 | 0.002 | - | - |
632
+ | 0.4252 | 42000 | 0.002 | - | - |
633
+ | 0.4303 | 42500 | 0.002 | - | - |
634
+ | 0.4353 | 43000 | 0.002 | - | - |
635
+ | 0.4404 | 43500 | 0.002 | - | - |
636
+ | 0.4455 | 44000 | 0.002 | - | - |
637
+ | 0.4505 | 44500 | 0.002 | - | - |
638
+ | 0.4556 | 45000 | 0.002 | - | - |
639
+ | 0.4606 | 45500 | 0.002 | - | - |
640
+ | 0.4657 | 46000 | 0.002 | - | - |
641
+ | 0.4708 | 46500 | 0.002 | - | - |
642
+ | 0.4758 | 47000 | 0.002 | - | - |
643
+ | 0.4809 | 47500 | 0.002 | - | - |
644
+ | 0.4859 | 48000 | 0.002 | - | - |
645
+ | 0.4910 | 48500 | 0.002 | - | - |
646
+ | 0.4961 | 49000 | 0.002 | - | - |
647
+ | 0.5011 | 49500 | 0.002 | - | - |
648
+ | 0.5062 | 50000 | 0.002 | - | - |
649
+ | 0.5113 | 50500 | 0.002 | - | - |
650
+ | 0.5163 | 51000 | 0.002 | - | - |
651
+ | 0.5214 | 51500 | 0.002 | - | - |
652
+ | 0.5264 | 52000 | 0.002 | - | - |
653
+ | 0.5315 | 52500 | 0.002 | - | - |
654
+ | 0.5366 | 53000 | 0.002 | - | - |
655
+ | 0.5416 | 53500 | 0.002 | - | - |
656
+ | 0.5467 | 54000 | 0.002 | - | - |
657
+ | 0.5518 | 54500 | 0.002 | - | - |
658
+ | 0.5568 | 55000 | 0.002 | - | - |
659
+ | 0.5619 | 55500 | 0.002 | - | - |
660
+ | 0.5669 | 56000 | 0.002 | - | - |
661
+ | 0.5720 | 56500 | 0.002 | - | - |
662
+ | 0.5771 | 57000 | 0.002 | - | - |
663
+ | 0.5821 | 57500 | 0.002 | - | - |
664
+ | 0.5872 | 58000 | 0.002 | - | - |
665
+ | 0.5922 | 58500 | 0.002 | - | - |
666
+ | 0.5973 | 59000 | 0.002 | - | - |
667
+ | 0.6024 | 59500 | 0.002 | - | - |
668
+ | 0.6074 | 60000 | 0.002 | - | - |
669
+ | 0.6125 | 60500 | 0.0019 | - | - |
670
+ | 0.6176 | 61000 | 0.002 | - | - |
671
+ | 0.6226 | 61500 | 0.002 | - | - |
672
+ | 0.6277 | 62000 | 0.002 | - | - |
673
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674
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675
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676
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677
+ | 0.6530 | 64500 | 0.0019 | - | - |
678
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679
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680
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681
+ | 0.6732 | 66500 | 0.0019 | - | - |
682
+ | 0.6783 | 67000 | 0.0019 | - | - |
683
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684
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685
+ | 0.6935 | 68500 | 0.0019 | - | - |
686
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687
+ | 0.7036 | 69500 | 0.0019 | - | - |
688
+ | 0.7087 | 70000 | 0.0019 | - | - |
689
+ | 0.7137 | 70500 | 0.0019 | - | - |
690
+ | 0.7188 | 71000 | 0.0019 | - | - |
691
+ | 0.7239 | 71500 | 0.0019 | - | - |
692
+ | 0.7289 | 72000 | 0.0019 | - | - |
693
+ | 0.7340 | 72500 | 0.0019 | - | - |
694
+ | 0.7390 | 73000 | 0.0019 | - | - |
695
+ | 0.7441 | 73500 | 0.0019 | - | - |
696
+ | 0.7492 | 74000 | 0.0019 | - | - |
697
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698
+ | 0.7593 | 75000 | 0.0019 | - | - |
699
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700
+ | 0.7694 | 76000 | 0.0019 | - | - |
701
+ | 0.7745 | 76500 | 0.0019 | - | - |
702
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703
+ | 0.7846 | 77500 | 0.0019 | - | - |
704
+ | 0.7897 | 78000 | 0.0019 | - | - |
705
+ | 0.7947 | 78500 | 0.0019 | - | - |
706
+ | 0.7998 | 79000 | 0.0019 | - | - |
707
+ | 0.8049 | 79500 | 0.0019 | - | - |
708
+ | 0.8099 | 80000 | 0.0019 | - | - |
709
+ | 0.8150 | 80500 | 0.0019 | - | - |
710
+ | 0.8200 | 81000 | 0.0019 | - | - |
711
+ | 0.8251 | 81500 | 0.0019 | - | - |
712
+ | 0.8302 | 82000 | 0.0019 | - | - |
713
+ | 0.8352 | 82500 | 0.0019 | - | - |
714
+ | 0.8403 | 83000 | 0.0019 | - | - |
715
+ | 0.8453 | 83500 | 0.0019 | - | - |
716
+ | 0.8504 | 84000 | 0.0019 | - | - |
717
+ | 0.8555 | 84500 | 0.0019 | - | - |
718
+ | 0.8605 | 85000 | 0.0019 | - | - |
719
+ | 0.8656 | 85500 | 0.0019 | - | - |
720
+ | 0.8707 | 86000 | 0.0019 | - | - |
721
+ | 0.8757 | 86500 | 0.0019 | - | - |
722
+ | 0.8808 | 87000 | 0.0019 | - | - |
723
+ | 0.8858 | 87500 | 0.0019 | - | - |
724
+ | 0.8909 | 88000 | 0.0019 | - | - |
725
+ | 0.8960 | 88500 | 0.0019 | - | - |
726
+ | 0.9010 | 89000 | 0.0019 | - | - |
727
+ | 0.9061 | 89500 | 0.0019 | - | - |
728
+ | 0.9112 | 90000 | 0.0019 | - | - |
729
+ | 0.9162 | 90500 | 0.0019 | - | - |
730
+ | 0.9213 | 91000 | 0.0019 | - | - |
731
+ | 0.9263 | 91500 | 0.0019 | - | - |
732
+ | 0.9314 | 92000 | 0.0019 | - | - |
733
+ | 0.9365 | 92500 | 0.0019 | - | - |
734
+ | 0.9415 | 93000 | 0.0019 | - | - |
735
+ | 0.9466 | 93500 | 0.0019 | - | - |
736
+ | 0.9516 | 94000 | 0.0019 | - | - |
737
+ | 0.9567 | 94500 | 0.0019 | - | - |
738
+ | 0.9618 | 95000 | 0.0019 | - | - |
739
+ | 0.9668 | 95500 | 0.0019 | - | - |
740
+ | 0.9719 | 96000 | 0.0019 | - | - |
741
+ | 0.9770 | 96500 | 0.0019 | - | - |
742
+ | 0.9820 | 97000 | 0.0019 | - | - |
743
+ | 0.9871 | 97500 | 0.0019 | - | - |
744
+ | 0.9921 | 98000 | 0.0019 | - | - |
745
+ | 0.9972 | 98500 | 0.0019 | - | - |
746
+ | 1.0 | 98776 | - | 0.0021 | -0.18616606 |
747
+ | 1.0023 | 99000 | 0.0019 | - | - |
748
+ | 0.0051 | 500 | 0.0019 | - | - |
749
+ | 0.0101 | 1000 | 0.0019 | - | - |
750
+ | 0.0152 | 1500 | 0.0019 | - | - |
751
+ | 0.0202 | 2000 | 0.0019 | - | - |
752
+ | 0.0253 | 2500 | 0.0019 | - | - |
753
+ | 0.0304 | 3000 | 0.0019 | - | - |
754
+ | 0.0354 | 3500 | 0.0019 | - | - |
755
+ | 0.0405 | 4000 | 0.0019 | - | - |
756
+ | 0.0456 | 4500 | 0.0019 | - | - |
757
+ | 0.0506 | 5000 | 0.0019 | - | - |
758
+ | 0.0557 | 5500 | 0.0019 | - | - |
759
+ | 0.0607 | 6000 | 0.0019 | - | - |
760
+ | 0.0658 | 6500 | 0.0019 | - | - |
761
+ | 0.0709 | 7000 | 0.0019 | - | - |
762
+ | 0.0759 | 7500 | 0.0019 | - | - |
763
+ | 0.0810 | 8000 | 0.0019 | - | - |
764
+ | 0.0861 | 8500 | 0.0019 | - | - |
765
+ | 0.0911 | 9000 | 0.0019 | - | - |
766
+ | 0.0962 | 9500 | 0.0019 | - | - |
767
+ | 0.1012 | 10000 | 0.0019 | - | - |
768
+ | 0.1063 | 10500 | 0.0019 | - | - |
769
+ | 0.1114 | 11000 | 0.0019 | - | - |
770
+ | 0.1164 | 11500 | 0.0019 | - | - |
771
+ | 0.1215 | 12000 | 0.0019 | - | - |
772
+ | 0.1265 | 12500 | 0.0019 | - | - |
773
+ | 0.1316 | 13000 | 0.0019 | - | - |
774
+ | 0.1367 | 13500 | 0.0019 | - | - |
775
+ | 0.1417 | 14000 | 0.0019 | - | - |
776
+ | 0.1468 | 14500 | 0.0019 | - | - |
777
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778
+ | 0.1569 | 15500 | 0.0019 | - | - |
779
+ | 0.1620 | 16000 | 0.0019 | - | - |
780
+ | 0.1670 | 16500 | 0.0019 | - | - |
781
+ | 0.1721 | 17000 | 0.0019 | - | - |
782
+ | 0.1772 | 17500 | 0.0019 | - | - |
783
+ | 0.1822 | 18000 | 0.0019 | - | - |
784
+ | 0.1873 | 18500 | 0.0019 | - | - |
785
+ | 0.1924 | 19000 | 0.0019 | - | - |
786
+ | 0.1974 | 19500 | 0.0019 | - | - |
787
+ | 0.2025 | 20000 | 0.0019 | - | - |
788
+ | 0.2075 | 20500 | 0.0019 | - | - |
789
+ | 0.2126 | 21000 | 0.0019 | - | - |
790
+ | 0.2177 | 21500 | 0.0019 | - | - |
791
+ | 0.2227 | 22000 | 0.0019 | - | - |
792
+ | 0.2278 | 22500 | 0.0019 | - | - |
793
+ | 0.2329 | 23000 | 0.0019 | - | - |
794
+ | 0.2379 | 23500 | 0.0019 | - | - |
795
+ | 0.2430 | 24000 | 0.0019 | - | - |
796
+ | 0.2480 | 24500 | 0.0019 | - | - |
797
+ | 0.2531 | 25000 | 0.0019 | - | - |
798
+ | 0.2582 | 25500 | 0.0019 | - | - |
799
+ | 0.2632 | 26000 | 0.0019 | - | - |
800
+ | 0.2683 | 26500 | 0.0019 | - | - |
801
+ | 0.2733 | 27000 | 0.0019 | - | - |
802
+ | 0.2784 | 27500 | 0.0019 | - | - |
803
+ | 0.2835 | 28000 | 0.0019 | - | - |
804
+ | 0.2885 | 28500 | 0.0019 | - | - |
805
+ | 0.2936 | 29000 | 0.0019 | - | - |
806
+ | 0.2987 | 29500 | 0.0019 | - | - |
807
+ | 0.3037 | 30000 | 0.0019 | - | - |
808
+ | 0.3088 | 30500 | 0.0019 | - | - |
809
+ | 0.3138 | 31000 | 0.0019 | - | - |
810
+ | 0.3189 | 31500 | 0.0019 | - | - |
811
+ | 0.3240 | 32000 | 0.0019 | - | - |
812
+ | 0.3290 | 32500 | 0.0019 | - | - |
813
+ | 0.3341 | 33000 | 0.0019 | - | - |
814
+ | 0.3392 | 33500 | 0.0019 | - | - |
815
+ | 0.3442 | 34000 | 0.0019 | - | - |
816
+ | 0.3493 | 34500 | 0.0019 | - | - |
817
+ | 0.3543 | 35000 | 0.0019 | - | - |
818
+ | 0.3594 | 35500 | 0.0019 | - | - |
819
+ | 0.3645 | 36000 | 0.0019 | - | - |
820
+ | 0.3695 | 36500 | 0.0019 | - | - |
821
+ | 0.3746 | 37000 | 0.0019 | - | - |
822
+ | 0.3796 | 37500 | 0.0019 | - | - |
823
+ | 0.3847 | 38000 | 0.0019 | - | - |
824
+ | 0.3898 | 38500 | 0.0019 | - | - |
825
+ | 0.3948 | 39000 | 0.0019 | - | - |
826
+ | 0.3999 | 39500 | 0.0019 | - | - |
827
+ | 0.4050 | 40000 | 0.0019 | - | - |
828
+ | 0.4100 | 40500 | 0.0019 | - | - |
829
+ | 0.4151 | 41000 | 0.0019 | - | - |
830
+ | 0.4201 | 41500 | 0.0019 | - | - |
831
+ | 0.4252 | 42000 | 0.0019 | - | - |
832
+ | 0.4303 | 42500 | 0.0019 | - | - |
833
+ | 0.4353 | 43000 | 0.0019 | - | - |
834
+ | 0.4404 | 43500 | 0.0019 | - | - |
835
+ | 0.4455 | 44000 | 0.0019 | - | - |
836
+ | 0.4505 | 44500 | 0.0019 | - | - |
837
+ | 0.4556 | 45000 | 0.0019 | - | - |
838
+ | 0.4606 | 45500 | 0.0019 | - | - |
839
+ | 0.4657 | 46000 | 0.0019 | - | - |
840
+ | 0.4708 | 46500 | 0.0019 | - | - |
841
+ | 0.4758 | 47000 | 0.0019 | - | - |
842
+ | 0.4809 | 47500 | 0.0019 | - | - |
843
+ | 0.4859 | 48000 | 0.0019 | - | - |
844
+ | 0.4910 | 48500 | 0.0019 | - | - |
845
+ | 0.4961 | 49000 | 0.0019 | - | - |
846
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847
+ | 0.5062 | 50000 | 0.0019 | - | - |
848
+ | 0.5113 | 50500 | 0.0019 | - | - |
849
+ | 0.5163 | 51000 | 0.0019 | - | - |
850
+ | 0.5214 | 51500 | 0.0018 | - | - |
851
+ | 0.5264 | 52000 | 0.0019 | - | - |
852
+ | 0.5315 | 52500 | 0.0019 | - | - |
853
+ | 0.5366 | 53000 | 0.0019 | - | - |
854
+ | 0.5416 | 53500 | 0.0019 | - | - |
855
+ | 0.5467 | 54000 | 0.0019 | - | - |
856
+ | 0.5518 | 54500 | 0.0019 | - | - |
857
+ | 0.5568 | 55000 | 0.0019 | - | - |
858
+ | 0.5619 | 55500 | 0.0018 | - | - |
859
+ | 0.5669 | 56000 | 0.0019 | - | - |
860
+ | 0.5720 | 56500 | 0.0019 | - | - |
861
+ | 0.5771 | 57000 | 0.0018 | - | - |
862
+ | 0.5821 | 57500 | 0.0018 | - | - |
863
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864
+ | 0.5922 | 58500 | 0.0019 | - | - |
865
+ | 0.5973 | 59000 | 0.0019 | - | - |
866
+ | 0.6024 | 59500 | 0.0019 | - | - |
867
+ | 0.6074 | 60000 | 0.0018 | - | - |
868
+ | 0.6125 | 60500 | 0.0018 | - | - |
869
+ | 0.6176 | 61000 | 0.0019 | - | - |
870
+ | 0.6226 | 61500 | 0.0018 | - | - |
871
+ | 0.6277 | 62000 | 0.0019 | - | - |
872
+ | 0.6327 | 62500 | 0.0019 | - | - |
873
+ | 0.6378 | 63000 | 0.0019 | - | - |
874
+ | 0.6429 | 63500 | 0.0019 | - | - |
875
+ | 0.6479 | 64000 | 0.0018 | - | - |
876
+ | 0.6530 | 64500 | 0.0018 | - | - |
877
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878
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879
+ | 0.6682 | 66000 | 0.0019 | - | - |
880
+ | 0.6732 | 66500 | 0.0018 | - | - |
881
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882
+ | 0.6834 | 67500 | 0.0018 | - | - |
883
+ | 0.6884 | 68000 | 0.0019 | - | - |
884
+ | 0.6935 | 68500 | 0.0018 | - | - |
885
+ | 0.6986 | 69000 | 0.0019 | - | - |
886
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887
+ | 0.7087 | 70000 | 0.0018 | - | - |
888
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889
+ | 0.7188 | 71000 | 0.0018 | - | - |
890
+ | 0.7239 | 71500 | 0.0018 | - | - |
891
+ | 0.7289 | 72000 | 0.0018 | - | - |
892
+ | 0.7340 | 72500 | 0.0018 | - | - |
893
+ | 0.7390 | 73000 | 0.0018 | - | - |
894
+ | 0.7441 | 73500 | 0.0018 | - | - |
895
+ | 0.7492 | 74000 | 0.0018 | - | - |
896
+ | 0.7542 | 74500 | 0.0018 | - | - |
897
+ | 0.7593 | 75000 | 0.0018 | - | - |
898
+ | 0.7644 | 75500 | 0.0018 | - | - |
899
+ | 0.7694 | 76000 | 0.0018 | - | - |
900
+ | 0.7745 | 76500 | 0.0018 | - | - |
901
+ | 0.7795 | 77000 | 0.0018 | - | - |
902
+ | 0.7846 | 77500 | 0.0018 | - | - |
903
+ | 0.7897 | 78000 | 0.0018 | - | - |
904
+ | 0.7947 | 78500 | 0.0018 | - | - |
905
+ | 0.7998 | 79000 | 0.0018 | - | - |
906
+ | 0.8049 | 79500 | 0.0018 | - | - |
907
+ | 0.8099 | 80000 | 0.0018 | - | - |
908
+ | 0.8150 | 80500 | 0.0018 | - | - |
909
+ | 0.8200 | 81000 | 0.0018 | - | - |
910
+ | 0.8251 | 81500 | 0.0018 | - | - |
911
+ | 0.8302 | 82000 | 0.0018 | - | - |
912
+ | 0.8352 | 82500 | 0.0019 | - | - |
913
+ | 0.8403 | 83000 | 0.0018 | - | - |
914
+ | 0.8453 | 83500 | 0.0018 | - | - |
915
+ | 0.8504 | 84000 | 0.0018 | - | - |
916
+ | 0.8555 | 84500 | 0.0018 | - | - |
917
+ | 0.8605 | 85000 | 0.0018 | - | - |
918
+ | 0.8656 | 85500 | 0.0018 | - | - |
919
+ | 0.8707 | 86000 | 0.0018 | - | - |
920
+ | 0.8757 | 86500 | 0.0018 | - | - |
921
+ | 0.8808 | 87000 | 0.0018 | - | - |
922
+ | 0.8858 | 87500 | 0.0018 | - | - |
923
+ | 0.8909 | 88000 | 0.0018 | - | - |
924
+ | 0.8960 | 88500 | 0.0018 | - | - |
925
+ | 0.9010 | 89000 | 0.0018 | - | - |
926
+ | 0.9061 | 89500 | 0.0018 | - | - |
927
+ | 0.9112 | 90000 | 0.0018 | - | - |
928
+ | 0.9162 | 90500 | 0.0018 | - | - |
929
+ | 0.9213 | 91000 | 0.0018 | - | - |
930
+ | 0.9263 | 91500 | 0.0018 | - | - |
931
+ | 0.9314 | 92000 | 0.0018 | - | - |
932
+ | 0.9365 | 92500 | 0.0018 | - | - |
933
+ | 0.9415 | 93000 | 0.0018 | - | - |
934
+ | 0.9466 | 93500 | 0.0018 | - | - |
935
+ | 0.9516 | 94000 | 0.0018 | - | - |
936
+ | 0.9567 | 94500 | 0.0018 | - | - |
937
+ | 0.9618 | 95000 | 0.0018 | - | - |
938
+ | 0.9668 | 95500 | 0.0018 | - | - |
939
+ | 0.9719 | 96000 | 0.0018 | - | - |
940
+ | 0.9770 | 96500 | 0.0018 | - | - |
941
+ | 0.9820 | 97000 | 0.0018 | - | - |
942
+ | 0.9871 | 97500 | 0.0018 | - | - |
943
+ | 0.9921 | 98000 | 0.0018 | - | - |
944
+ | 0.9972 | 98500 | 0.0018 | - | - |
945
+ | 1.0 | 98776 | - | 0.0021 | -0.17975432 |
946
+ | 0.0051 | 500 | 0.0018 | - | - |
947
+ | 0.0101 | 1000 | 0.0018 | - | - |
948
+ | 0.0152 | 1500 | 0.0018 | - | - |
949
+ | 0.0202 | 2000 | 0.0018 | - | - |
950
+ | 0.0253 | 2500 | 0.0018 | - | - |
951
+ | 0.0304 | 3000 | 0.0018 | - | - |
952
+ | 0.0354 | 3500 | 0.0018 | - | - |
953
+ | 0.0405 | 4000 | 0.0018 | - | - |
954
+ | 0.0456 | 4500 | 0.0018 | - | - |
955
+ | 0.0506 | 5000 | 0.0018 | - | - |
956
+ | 0.0557 | 5500 | 0.0018 | - | - |
957
+ | 0.0607 | 6000 | 0.0018 | - | - |
958
+ | 0.0658 | 6500 | 0.0018 | - | - |
959
+ | 0.0709 | 7000 | 0.0018 | - | - |
960
+ | 0.0759 | 7500 | 0.0018 | - | - |
961
+ | 0.0810 | 8000 | 0.0018 | - | - |
962
+ | 0.0861 | 8500 | 0.0018 | - | - |
963
+ | 0.0911 | 9000 | 0.0018 | - | - |
964
+ | 0.0962 | 9500 | 0.0018 | - | - |
965
+ | 0.1012 | 10000 | 0.0018 | - | - |
966
+ | 0.1063 | 10500 | 0.0018 | - | - |
967
+ | 0.1114 | 11000 | 0.0018 | - | - |
968
+ | 0.1164 | 11500 | 0.0018 | - | - |
969
+ | 0.1215 | 12000 | 0.0018 | - | - |
970
+ | 0.1265 | 12500 | 0.0018 | - | - |
971
+ | 0.1316 | 13000 | 0.0018 | - | - |
972
+ | 0.1367 | 13500 | 0.0018 | - | - |
973
+ | 0.1417 | 14000 | 0.0018 | - | - |
974
+ | 0.1468 | 14500 | 0.0018 | - | - |
975
+ | 0.1519 | 15000 | 0.0018 | - | - |
976
+ | 0.1569 | 15500 | 0.0018 | - | - |
977
+ | 0.1620 | 16000 | 0.0018 | - | - |
978
+ | 0.1670 | 16500 | 0.0018 | - | - |
979
+ | 0.1721 | 17000 | 0.0018 | - | - |
980
+ | 0.1772 | 17500 | 0.0018 | - | - |
981
+ | 0.1822 | 18000 | 0.0018 | - | - |
982
+ | 0.1873 | 18500 | 0.0018 | - | - |
983
+ | 0.1924 | 19000 | 0.0018 | - | - |
984
+ | 0.1974 | 19500 | 0.0018 | - | - |
985
+ | 0.2025 | 20000 | 0.0018 | - | - |
986
+ | 0.2075 | 20500 | 0.0018 | - | - |
987
+ | 0.2126 | 21000 | 0.0018 | - | - |
988
+ | 0.2177 | 21500 | 0.0018 | - | - |
989
+ | 0.2227 | 22000 | 0.0018 | - | - |
990
+ | 0.2278 | 22500 | 0.0018 | - | - |
991
+ | 0.2329 | 23000 | 0.0018 | - | - |
992
+ | 0.2379 | 23500 | 0.0018 | - | - |
993
+ | 0.2430 | 24000 | 0.0018 | - | - |
994
+ | 0.2480 | 24500 | 0.0018 | - | - |
995
+ | 0.2531 | 25000 | 0.0018 | - | - |
996
+ | 0.2582 | 25500 | 0.0018 | - | - |
997
+ | 0.2632 | 26000 | 0.0018 | - | - |
998
+ | 0.2683 | 26500 | 0.0018 | - | - |
999
+ | 0.2733 | 27000 | 0.0018 | - | - |
1000
+ | 0.2784 | 27500 | 0.0018 | - | - |
1001
+ | 0.2835 | 28000 | 0.0018 | - | - |
1002
+ | 0.2885 | 28500 | 0.0018 | - | - |
1003
+ | 0.2936 | 29000 | 0.0018 | - | - |
1004
+ | 0.2987 | 29500 | 0.0018 | - | - |
1005
+ | 0.3037 | 30000 | 0.0018 | - | - |
1006
+ | 0.3088 | 30500 | 0.0018 | - | - |
1007
+ | 0.3138 | 31000 | 0.0018 | - | - |
1008
+ | 0.3189 | 31500 | 0.0018 | - | - |
1009
+ | 0.3240 | 32000 | 0.0018 | - | - |
1010
+ | 0.3290 | 32500 | 0.0018 | - | - |
1011
+ | 0.3341 | 33000 | 0.0018 | - | - |
1012
+ | 0.3392 | 33500 | 0.0018 | - | - |
1013
+ | 0.3442 | 34000 | 0.0018 | - | - |
1014
+ | 0.3493 | 34500 | 0.0018 | - | - |
1015
+ | 0.3543 | 35000 | 0.0018 | - | - |
1016
+ | 0.3594 | 35500 | 0.0018 | - | - |
1017
+ | 0.3645 | 36000 | 0.0018 | - | - |
1018
+ | 0.3695 | 36500 | 0.0018 | - | - |
1019
+ | 0.3746 | 37000 | 0.0018 | - | - |
1020
+ | 0.3796 | 37500 | 0.0018 | - | - |
1021
+ | 0.3847 | 38000 | 0.0018 | - | - |
1022
+ | 0.3898 | 38500 | 0.0018 | - | - |
1023
+ | 0.3948 | 39000 | 0.0018 | - | - |
1024
+ | 0.3999 | 39500 | 0.0018 | - | - |
1025
+ | 0.4050 | 40000 | 0.0018 | - | - |
1026
+ | 0.4100 | 40500 | 0.0018 | - | - |
1027
+ | 0.4151 | 41000 | 0.0018 | - | - |
1028
+ | 0.4201 | 41500 | 0.0018 | - | - |
1029
+ | 0.4252 | 42000 | 0.0018 | - | - |
1030
+ | 0.4303 | 42500 | 0.0018 | - | - |
1031
+ | 0.4353 | 43000 | 0.0018 | - | - |
1032
+ | 0.4404 | 43500 | 0.0018 | - | - |
1033
+ | 0.4455 | 44000 | 0.0018 | - | - |
1034
+ | 0.4505 | 44500 | 0.0018 | - | - |
1035
+ | 0.4556 | 45000 | 0.0018 | - | - |
1036
+ | 0.4606 | 45500 | 0.0018 | - | - |
1037
+ | 0.4657 | 46000 | 0.0018 | - | - |
1038
+ | 0.4708 | 46500 | 0.0018 | - | - |
1039
+ | 0.4758 | 47000 | 0.0018 | - | - |
1040
+ | 0.4809 | 47500 | 0.0018 | - | - |
1041
+ | 0.4859 | 48000 | 0.0018 | - | - |
1042
+ | 0.4910 | 48500 | 0.0018 | - | - |
1043
+ | 0.4961 | 49000 | 0.0018 | - | - |
1044
+ | 0.5011 | 49500 | 0.0018 | - | - |
1045
+ | 0.5062 | 50000 | 0.0018 | - | - |
1046
+ | 0.5113 | 50500 | 0.0018 | - | - |
1047
+ | 0.5163 | 51000 | 0.0018 | - | - |
1048
+ | 0.5214 | 51500 | 0.0018 | - | - |
1049
+ | 0.5264 | 52000 | 0.0018 | - | - |
1050
+ | 0.5315 | 52500 | 0.0018 | - | - |
1051
+ | 0.5366 | 53000 | 0.0018 | - | - |
1052
+ | 0.5416 | 53500 | 0.0018 | - | - |
1053
+ | 0.5467 | 54000 | 0.0018 | - | - |
1054
+ | 0.5518 | 54500 | 0.0018 | - | - |
1055
+ | 0.5568 | 55000 | 0.0018 | - | - |
1056
+ | 0.5619 | 55500 | 0.0018 | - | - |
1057
+ | 0.5669 | 56000 | 0.0018 | - | - |
1058
+ | 0.5720 | 56500 | 0.0018 | - | - |
1059
+ | 0.5771 | 57000 | 0.0018 | - | - |
1060
+ | 0.5821 | 57500 | 0.0018 | - | - |
1061
+ | 0.5872 | 58000 | 0.0018 | - | - |
1062
+ | 0.5922 | 58500 | 0.0018 | - | - |
1063
+ | 0.5973 | 59000 | 0.0018 | - | - |
1064
+ | 0.6024 | 59500 | 0.0018 | - | - |
1065
+ | 0.6074 | 60000 | 0.0018 | - | - |
1066
+ | 0.6125 | 60500 | 0.0018 | - | - |
1067
+ | 0.6176 | 61000 | 0.0018 | - | - |
1068
+ | 0.6226 | 61500 | 0.0018 | - | - |
1069
+ | 0.6277 | 62000 | 0.0018 | - | - |
1070
+ | 0.6327 | 62500 | 0.0018 | - | - |
1071
+ | 0.6378 | 63000 | 0.0018 | - | - |
1072
+ | 0.6429 | 63500 | 0.0018 | - | - |
1073
+ | 0.6479 | 64000 | 0.0018 | - | - |
1074
+ | 0.6530 | 64500 | 0.0018 | - | - |
1075
+ | 0.6581 | 65000 | 0.0018 | - | - |
1076
+ | 0.6631 | 65500 | 0.0018 | - | - |
1077
+ | 0.6682 | 66000 | 0.0018 | - | - |
1078
+ | 0.6732 | 66500 | 0.0018 | - | - |
1079
+ | 0.6783 | 67000 | 0.0018 | - | - |
1080
+ | 0.6834 | 67500 | 0.0018 | - | - |
1081
+ | 0.6884 | 68000 | 0.0018 | - | - |
1082
+ | 0.6935 | 68500 | 0.0018 | - | - |
1083
+ | 0.6986 | 69000 | 0.0018 | - | - |
1084
+ | 0.7036 | 69500 | 0.0018 | - | - |
1085
+ | 0.7087 | 70000 | 0.0018 | - | - |
1086
+ | 0.7137 | 70500 | 0.0018 | - | - |
1087
+ | 0.7188 | 71000 | 0.0018 | - | - |
1088
+ | 0.7239 | 71500 | 0.0018 | - | - |
1089
+ | 0.7289 | 72000 | 0.0018 | - | - |
1090
+ | 0.7340 | 72500 | 0.0018 | - | - |
1091
+ | 0.7390 | 73000 | 0.0018 | - | - |
1092
+ | 0.7441 | 73500 | 0.0018 | - | - |
1093
+ | 0.7492 | 74000 | 0.0018 | - | - |
1094
+ | 0.7542 | 74500 | 0.0018 | - | - |
1095
+ | 0.7593 | 75000 | 0.0018 | - | - |
1096
+ | 0.7644 | 75500 | 0.0018 | - | - |
1097
+ | 0.7694 | 76000 | 0.0018 | - | - |
1098
+ | 0.7745 | 76500 | 0.0018 | - | - |
1099
+ | 0.7795 | 77000 | 0.0018 | - | - |
1100
+ | 0.7846 | 77500 | 0.0018 | - | - |
1101
+ | 0.7897 | 78000 | 0.0018 | - | - |
1102
+ | 0.7947 | 78500 | 0.0018 | - | - |
1103
+ | 0.7998 | 79000 | 0.0018 | - | - |
1104
+ | 0.8049 | 79500 | 0.0018 | - | - |
1105
+ | 0.8099 | 80000 | 0.0018 | - | - |
1106
+ | 0.8150 | 80500 | 0.0018 | - | - |
1107
+ | 0.8200 | 81000 | 0.0018 | - | - |
1108
+ | 0.8251 | 81500 | 0.0018 | - | - |
1109
+ | 0.8302 | 82000 | 0.0018 | - | - |
1110
+ | 0.8352 | 82500 | 0.0018 | - | - |
1111
+ | 0.8403 | 83000 | 0.0018 | - | - |
1112
+ | 0.8453 | 83500 | 0.0018 | - | - |
1113
+ | 0.8504 | 84000 | 0.0018 | - | - |
1114
+ | 0.8555 | 84500 | 0.0018 | - | - |
1115
+ | 0.8605 | 85000 | 0.0018 | - | - |
1116
+ | 0.8656 | 85500 | 0.0018 | - | - |
1117
+ | 0.8707 | 86000 | 0.0018 | - | - |
1118
+ | 0.8757 | 86500 | 0.0018 | - | - |
1119
+ | 0.8808 | 87000 | 0.0018 | - | - |
1120
+ | 0.8858 | 87500 | 0.0018 | - | - |
1121
+ | 0.8909 | 88000 | 0.0018 | - | - |
1122
+ | 0.8960 | 88500 | 0.0018 | - | - |
1123
+ | 0.9010 | 89000 | 0.0018 | - | - |
1124
+ | 0.9061 | 89500 | 0.0018 | - | - |
1125
+ | 0.9112 | 90000 | 0.0018 | - | - |
1126
+ | 0.9162 | 90500 | 0.0018 | - | - |
1127
+ | 0.9213 | 91000 | 0.0018 | - | - |
1128
+ | 0.9263 | 91500 | 0.0018 | - | - |
1129
+ | 0.9314 | 92000 | 0.0018 | - | - |
1130
+ | 0.9365 | 92500 | 0.0018 | - | - |
1131
+ | 0.9415 | 93000 | 0.0018 | - | - |
1132
+ | 0.9466 | 93500 | 0.0018 | - | - |
1133
+ | 0.9516 | 94000 | 0.0018 | - | - |
1134
+ | 0.9567 | 94500 | 0.0018 | - | - |
1135
+ | 0.9618 | 95000 | 0.0017 | - | - |
1136
+ | 0.9668 | 95500 | 0.0018 | - | - |
1137
+ | 0.9719 | 96000 | 0.0018 | - | - |
1138
+ | 0.9770 | 96500 | 0.0018 | - | - |
1139
+ | 0.9820 | 97000 | 0.0018 | - | - |
1140
+ | 0.9871 | 97500 | 0.0018 | - | - |
1141
+ | 0.9921 | 98000 | 0.0018 | - | - |
1142
+ | 0.9972 | 98500 | 0.0018 | - | - |
1143
+ | 1.0 | 98776 | - | 0.0021 | -0.17605598 |
1144
+ | 0.0051 | 500 | 0.0018 | - | - |
1145
+ | 0.0101 | 1000 | 0.0018 | - | - |
1146
+ | 0.0152 | 1500 | 0.0018 | - | - |
1147
+ | 0.0202 | 2000 | 0.0018 | - | - |
1148
+ | 0.0253 | 2500 | 0.0018 | - | - |
1149
+ | 0.0304 | 3000 | 0.0018 | - | - |
1150
+ | 0.0354 | 3500 | 0.0018 | - | - |
1151
+ | 0.0405 | 4000 | 0.0018 | - | - |
1152
+ | 0.0456 | 4500 | 0.0018 | - | - |
1153
+ | 0.0506 | 5000 | 0.0018 | - | - |
1154
+ | 0.0557 | 5500 | 0.0018 | - | - |
1155
+ | 0.0607 | 6000 | 0.0018 | - | - |
1156
+ | 0.0658 | 6500 | 0.0018 | - | - |
1157
+ | 0.0709 | 7000 | 0.0018 | - | - |
1158
+ | 0.0759 | 7500 | 0.0018 | - | - |
1159
+ | 0.0810 | 8000 | 0.0018 | - | - |
1160
+ | 0.0861 | 8500 | 0.0018 | - | - |
1161
+ | 0.0911 | 9000 | 0.0018 | - | - |
1162
+ | 0.0962 | 9500 | 0.0018 | - | - |
1163
+ | 0.1012 | 10000 | 0.0018 | - | - |
1164
+ | 0.1063 | 10500 | 0.0018 | - | - |
1165
+ | 0.1114 | 11000 | 0.0018 | - | - |
1166
+ | 0.1164 | 11500 | 0.0018 | - | - |
1167
+ | 0.1215 | 12000 | 0.0018 | - | - |
1168
+ | 0.1265 | 12500 | 0.0018 | - | - |
1169
+ | 0.1316 | 13000 | 0.0018 | - | - |
1170
+ | 0.1367 | 13500 | 0.0018 | - | - |
1171
+ | 0.1417 | 14000 | 0.0018 | - | - |
1172
+ | 0.1468 | 14500 | 0.0018 | - | - |
1173
+ | 0.1519 | 15000 | 0.0018 | - | - |
1174
+ | 0.1569 | 15500 | 0.0018 | - | - |
1175
+ | 0.1620 | 16000 | 0.0018 | - | - |
1176
+ | 0.1670 | 16500 | 0.0018 | - | - |
1177
+ | 0.1721 | 17000 | 0.0018 | - | - |
1178
+ | 0.1772 | 17500 | 0.0018 | - | - |
1179
+ | 0.1822 | 18000 | 0.0018 | - | - |
1180
+ | 0.1873 | 18500 | 0.0018 | - | - |
1181
+ | 0.1924 | 19000 | 0.0018 | - | - |
1182
+ | 0.1974 | 19500 | 0.0018 | - | - |
1183
+ | 0.2025 | 20000 | 0.0018 | - | - |
1184
+ | 0.2075 | 20500 | 0.0018 | - | - |
1185
+ | 0.2126 | 21000 | 0.0018 | - | - |
1186
+ | 0.2177 | 21500 | 0.0018 | - | - |
1187
+ | 0.2227 | 22000 | 0.0018 | - | - |
1188
+ | 0.2278 | 22500 | 0.0017 | - | - |
1189
+ | 0.2329 | 23000 | 0.0018 | - | - |
1190
+ | 0.2379 | 23500 | 0.0018 | - | - |
1191
+ | 0.2430 | 24000 | 0.0018 | - | - |
1192
+ | 0.2480 | 24500 | 0.0018 | - | - |
1193
+ | 0.2531 | 25000 | 0.0018 | - | - |
1194
+ | 0.2582 | 25500 | 0.0018 | - | - |
1195
+ | 0.2632 | 26000 | 0.0018 | - | - |
1196
+ | 0.2683 | 26500 | 0.0018 | - | - |
1197
+ | 0.2733 | 27000 | 0.0018 | - | - |
1198
+ | 0.2784 | 27500 | 0.0018 | - | - |
1199
+ | 0.2835 | 28000 | 0.0018 | - | - |
1200
+ | 0.2885 | 28500 | 0.0018 | - | - |
1201
+ | 0.2936 | 29000 | 0.0018 | - | - |
1202
+ | 0.2987 | 29500 | 0.0018 | - | - |
1203
+ | 0.3037 | 30000 | 0.0018 | - | - |
1204
+ | 0.3088 | 30500 | 0.0018 | - | - |
1205
+ | 0.3138 | 31000 | 0.0018 | - | - |
1206
+ | 0.3189 | 31500 | 0.0018 | - | - |
1207
+ | 0.3240 | 32000 | 0.0018 | - | - |
1208
+ | 0.3290 | 32500 | 0.0018 | - | - |
1209
+ | 0.3341 | 33000 | 0.0018 | - | - |
1210
+ | 0.3392 | 33500 | 0.0018 | - | - |
1211
+ | 0.3442 | 34000 | 0.0018 | - | - |
1212
+ | 0.3493 | 34500 | 0.0018 | - | - |
1213
+ | 0.3543 | 35000 | 0.0018 | - | - |
1214
+ | 0.3594 | 35500 | 0.0018 | - | - |
1215
+ | 0.3645 | 36000 | 0.0018 | - | - |
1216
+ | 0.3695 | 36500 | 0.0018 | - | - |
1217
+ | 0.3746 | 37000 | 0.0018 | - | - |
1218
+ | 0.3796 | 37500 | 0.0018 | - | - |
1219
+ | 0.3847 | 38000 | 0.0018 | - | - |
1220
+ | 0.3898 | 38500 | 0.0018 | - | - |
1221
+ | 0.3948 | 39000 | 0.0018 | - | - |
1222
+ | 0.3999 | 39500 | 0.0018 | - | - |
1223
+ | 0.4050 | 40000 | 0.0018 | - | - |
1224
+ | 0.4100 | 40500 | 0.0018 | - | - |
1225
+ | 0.4151 | 41000 | 0.0018 | - | - |
1226
+ | 0.4201 | 41500 | 0.0018 | - | - |
1227
+ | 0.4252 | 42000 | 0.0018 | - | - |
1228
+ | 0.4303 | 42500 | 0.0018 | - | - |
1229
+ | 0.4353 | 43000 | 0.0018 | - | - |
1230
+ | 0.4404 | 43500 | 0.0018 | - | - |
1231
+ | 0.4455 | 44000 | 0.0018 | - | - |
1232
+ | 0.4505 | 44500 | 0.0018 | - | - |
1233
+ | 0.4556 | 45000 | 0.0018 | - | - |
1234
+ | 0.4606 | 45500 | 0.0018 | - | - |
1235
+ | 0.4657 | 46000 | 0.0018 | - | - |
1236
+ | 0.4708 | 46500 | 0.0018 | - | - |
1237
+ | 0.4758 | 47000 | 0.0018 | - | - |
1238
+ | 0.4809 | 47500 | 0.0018 | - | - |
1239
+ | 0.4859 | 48000 | 0.0018 | - | - |
1240
+ | 0.4910 | 48500 | 0.0018 | - | - |
1241
+ | 0.4961 | 49000 | 0.0018 | - | - |
1242
+ | 0.5011 | 49500 | 0.0018 | - | - |
1243
+ | 0.5062 | 50000 | 0.0018 | - | - |
1244
+ | 0.5113 | 50500 | 0.0018 | - | - |
1245
+ | 0.5163 | 51000 | 0.0018 | - | - |
1246
+ | 0.5214 | 51500 | 0.0017 | - | - |
1247
+ | 0.5264 | 52000 | 0.0018 | - | - |
1248
+ | 0.5315 | 52500 | 0.0018 | - | - |
1249
+ | 0.5366 | 53000 | 0.0018 | - | - |
1250
+ | 0.5416 | 53500 | 0.0018 | - | - |
1251
+ | 0.5467 | 54000 | 0.0018 | - | - |
1252
+ | 0.5518 | 54500 | 0.0018 | - | - |
1253
+ | 0.5568 | 55000 | 0.0017 | - | - |
1254
+ | 0.5619 | 55500 | 0.0017 | - | - |
1255
+ | 0.5669 | 56000 | 0.0018 | - | - |
1256
+ | 0.5720 | 56500 | 0.0017 | - | - |
1257
+ | 0.5771 | 57000 | 0.0017 | - | - |
1258
+ | 0.5821 | 57500 | 0.0017 | - | - |
1259
+ | 0.5872 | 58000 | 0.0018 | - | - |
1260
+ | 0.5922 | 58500 | 0.0017 | - | - |
1261
+ | 0.5973 | 59000 | 0.0018 | - | - |
1262
+ | 0.6024 | 59500 | 0.0018 | - | - |
1263
+ | 0.6074 | 60000 | 0.0017 | - | - |
1264
+ | 0.6125 | 60500 | 0.0017 | - | - |
1265
+ | 0.6176 | 61000 | 0.0018 | - | - |
1266
+ | 0.6226 | 61500 | 0.0017 | - | - |
1267
+ | 0.6277 | 62000 | 0.0018 | - | - |
1268
+ | 0.6327 | 62500 | 0.0018 | - | - |
1269
+ | 0.6378 | 63000 | 0.0018 | - | - |
1270
+ | 0.6429 | 63500 | 0.0018 | - | - |
1271
+ | 0.6479 | 64000 | 0.0017 | - | - |
1272
+ | 0.6530 | 64500 | 0.0017 | - | - |
1273
+ | 0.6581 | 65000 | 0.0017 | - | - |
1274
+ | 0.6631 | 65500 | 0.0017 | - | - |
1275
+ | 0.6682 | 66000 | 0.0018 | - | - |
1276
+ | 0.6732 | 66500 | 0.0017 | - | - |
1277
+ | 0.6783 | 67000 | 0.0017 | - | - |
1278
+ | 0.6834 | 67500 | 0.0017 | - | - |
1279
+ | 0.6884 | 68000 | 0.0018 | - | - |
1280
+ | 0.6935 | 68500 | 0.0017 | - | - |
1281
+ | 0.6986 | 69000 | 0.0018 | - | - |
1282
+ | 0.7036 | 69500 | 0.0017 | - | - |
1283
+ | 0.7087 | 70000 | 0.0017 | - | - |
1284
+ | 0.7137 | 70500 | 0.0017 | - | - |
1285
+ | 0.7188 | 71000 | 0.0017 | - | - |
1286
+ | 0.7239 | 71500 | 0.0017 | - | - |
1287
+ | 0.7289 | 72000 | 0.0017 | - | - |
1288
+ | 0.7340 | 72500 | 0.0017 | - | - |
1289
+ | 0.7390 | 73000 | 0.0017 | - | - |
1290
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1291
+ | 0.7492 | 74000 | 0.0018 | - | - |
1292
+ | 0.7542 | 74500 | 0.0017 | - | - |
1293
+ | 0.7593 | 75000 | 0.0017 | - | - |
1294
+ | 0.7644 | 75500 | 0.0017 | - | - |
1295
+ | 0.7694 | 76000 | 0.0017 | - | - |
1296
+ | 0.7745 | 76500 | 0.0017 | - | - |
1297
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1298
+ | 0.7846 | 77500 | 0.0017 | - | - |
1299
+ | 0.7897 | 78000 | 0.0017 | - | - |
1300
+ | 0.7947 | 78500 | 0.0017 | - | - |
1301
+ | 0.7998 | 79000 | 0.0017 | - | - |
1302
+ | 0.8049 | 79500 | 0.0017 | - | - |
1303
+ | 0.8099 | 80000 | 0.0017 | - | - |
1304
+ | 0.8150 | 80500 | 0.0017 | - | - |
1305
+ | 0.8200 | 81000 | 0.0017 | - | - |
1306
+ | 0.8251 | 81500 | 0.0017 | - | - |
1307
+ | 0.8302 | 82000 | 0.0017 | - | - |
1308
+ | 0.8352 | 82500 | 0.0018 | - | - |
1309
+ | 0.8403 | 83000 | 0.0017 | - | - |
1310
+ | 0.8453 | 83500 | 0.0017 | - | - |
1311
+ | 0.8504 | 84000 | 0.0017 | - | - |
1312
+ | 0.8555 | 84500 | 0.0017 | - | - |
1313
+ | 0.8605 | 85000 | 0.0017 | - | - |
1314
+ | 0.8656 | 85500 | 0.0017 | - | - |
1315
+ | 0.8707 | 86000 | 0.0017 | - | - |
1316
+ | 0.8757 | 86500 | 0.0017 | - | - |
1317
+ | 0.8808 | 87000 | 0.0017 | - | - |
1318
+ | 0.8858 | 87500 | 0.0017 | - | - |
1319
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1320
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1321
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1322
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1323
+ | 0.9112 | 90000 | 0.0017 | - | - |
1324
+ | 0.9162 | 90500 | 0.0017 | - | - |
1325
+ | 0.9213 | 91000 | 0.0017 | - | - |
1326
+ | 0.9263 | 91500 | 0.0017 | - | - |
1327
+ | 0.9314 | 92000 | 0.0017 | - | - |
1328
+ | 0.9365 | 92500 | 0.0017 | - | - |
1329
+ | 0.9415 | 93000 | 0.0017 | - | - |
1330
+ | 0.9466 | 93500 | 0.0017 | - | - |
1331
+ | 0.9516 | 94000 | 0.0017 | - | - |
1332
+ | 0.9567 | 94500 | 0.0017 | - | - |
1333
+ | 0.9618 | 95000 | 0.0017 | - | - |
1334
+ | 0.9668 | 95500 | 0.0017 | - | - |
1335
+ | 0.9719 | 96000 | 0.0017 | - | - |
1336
+ | 0.9770 | 96500 | 0.0017 | - | - |
1337
+ | 0.9820 | 97000 | 0.0017 | - | - |
1338
+ | 0.9871 | 97500 | 0.0017 | - | - |
1339
+ | 0.9921 | 98000 | 0.0017 | - | - |
1340
+ | 0.9972 | 98500 | 0.0017 | - | - |
1341
+ | 1.0 | 98776 | - | 0.0021 | -0.17373772 |
1342
+
1343
+ </details>
1344
+
1345
+ ### Framework Versions
1346
+ - Python: 3.12.3
1347
+ - Sentence Transformers: 3.3.1
1348
+ - Transformers: 4.48.1
1349
+ - PyTorch: 2.5.1+cu124
1350
+ - Accelerate: 1.2.0
1351
+ - Datasets: 3.1.0
1352
+ - Tokenizers: 0.21.0
1353
+
1354
+ ## Citation
1355
+
1356
+ ### BibTeX
1357
+
1358
+ #### Sentence Transformers
1359
+ ```bibtex
1360
+ @inproceedings{reimers-2019-sentence-bert,
1361
+ title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
1362
+ author = "Reimers, Nils and Gurevych, Iryna",
1363
+ booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
1364
+ month = "11",
1365
+ year = "2019",
1366
+ publisher = "Association for Computational Linguistics",
1367
+ url = "https://arxiv.org/abs/1908.10084",
1368
+ }
1369
+ ```
1370
+
1371
+ #### MSELoss
1372
+ ```bibtex
1373
+ @inproceedings{reimers-2020-multilingual-sentence-bert,
1374
+ title = "Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation",
1375
+ author = "Reimers, Nils and Gurevych, Iryna",
1376
+ booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing",
1377
+ month = "11",
1378
+ year = "2020",
1379
+ publisher = "Association for Computational Linguistics",
1380
+ url = "https://arxiv.org/abs/2004.09813",
1381
+ }
1382
+ ```
1383
+
1384
+ <!--
1385
+ ## Glossary
1386
+
1387
+ *Clearly define terms in order to be accessible across audiences.*
1388
+ -->
1389
+
1390
+ <!--
1391
+ ## Model Card Authors
1392
+
1393
+ *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
1394
+ -->
1395
+
1396
+ <!--
1397
+ ## Model Card Contact
1398
+
1399
+ *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
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+ -->
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