Hugging Face's logo

nazhan
/
bge-small-en-v1.5-brahmaputra-iter-10-2nd

Text Classification
setfit
Safetensors
sentence-transformers
bert
generated_from_setfit_trainer
Eval Results
Model card Files Community
nazhan commited on
Commit
604b35e
·
verified ·
1 Parent(s): e4cf711

Add SetFit model

Browse files
Files changed (13) hide show
  1. 1_Pooling/config.json +10 -0
  2. README.md +918 -0
  3. config.json +31 -0
  4. config_sentence_transformers.json +9 -0
  5. config_setfit.json +12 -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 +64 -0
  13. vocab.txt +0 -0
1_Pooling/config.json ADDED
@@ -0,0 +1,10 @@
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "word_embedding_dimension": 384,
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 +1,918 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ base_model: BAAI/bge-small-en-v1.5
3
+ library_name: setfit
4
+ metrics:
5
+ - accuracy
6
+ pipeline_tag: text-classification
7
+ tags:
8
+ - setfit
9
+ - sentence-transformers
10
+ - text-classification
11
+ - generated_from_setfit_trainer
12
+ widget:
13
+ - text: Show me data_asset_kpi_cf group by quarter.
14
+ - text: i want to get trend analysis and group by product
15
+ - text: Can I have data_asset_kpi_bs details.
16
+ - text: I don't want to produce that specific data.
17
+ - text: What are the details of the orders placed before December 31st, 2023?
18
+ inference: true
19
+ model-index:
20
+ - name: SetFit with BAAI/bge-small-en-v1.5
21
+ results:
22
+ - task:
23
+ type: text-classification
24
+ name: Text Classification
25
+ dataset:
26
+ name: Unknown
27
+ type: unknown
28
+ split: test
29
+ metrics:
30
+ - type: accuracy
31
+ value: 0.9915254237288136
32
+ name: Accuracy
33
+ ---
34
+
35
+ # SetFit with BAAI/bge-small-en-v1.5
36
+
37
+ This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-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.
38
+
39
+ The model has been trained using an efficient few-shot learning technique that involves:
40
+
41
+ 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
42
+ 2. Training a classification head with features from the fine-tuned Sentence Transformer.
43
+
44
+ ## Model Details
45
+
46
+ ### Model Description
47
+ - **Model Type:** SetFit
48
+ - **Sentence Transformer body:** [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5)
49
+ - **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
50
+ - **Maximum Sequence Length:** 512 tokens
51
+ - **Number of Classes:** 7 classes
52
+ <!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
53
+ <!-- - **Language:** Unknown -->
54
+ <!-- - **License:** Unknown -->
55
+
56
+ ### Model Sources
57
+
58
+ - **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit)
59
+ - **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)
60
+ - **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)
61
+
62
+ ### Model Labels
63
+ | Label | Examples |
64
+ |:-------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
65
+ | Lookup | <ul><li>"Show me the products with 'Tablet' in the name and filter by price above 200."</li><li>'Can you get me the products with a price above 100?'</li><li>'Filter by employees with a salary above 60,000 and show me their first names.'</li></ul> |
66
+ | Aggregation | <ul><li>'What’s the total revenue generated by each employee in 2023?'</li><li>'Get me data_asset_001_pcc group by category.'</li><li>'Show me max revenue'</li></ul> |
67
+ | Tablejoin | <ul><li>'Show me a merge of key performance metrics and cash flow.'</li><li>'How can I integrate the Customers and Orders tables to identify customers with multiple recent orders?'</li><li>'Can you integrate data from the Products and Orders tables to determine the revenue generated by each product?'</li></ul> |
68
+ | Viewtables | <ul><li>'How can I view all of the tables stored within the starhub_data_asset database?'</li><li>'What are the tables that I can access in the starhub_data_asset database?'</li><li>'What are the available tables that are relevant to pricing strategies within starhub_data_asset database?'</li></ul> |
69
+ | Lookup_1 | <ul><li>'Display data_asset_kpi_cf.'</li><li>'Get me data_asset_001_ta trend history.'</li><li>'Show me data_asset_kpi_cf details.'</li></ul> |
70
+ | Rejection | <ul><li>"I don't want to apply any filters now."</li><li>"I don't want to apply any filters to this."</li><li>"I'd prefer not to apply any filters."</li></ul> |
71
+ | Generalreply | <ul><li>"What's your favorite TV show of all time?"</li><li>"i'll start dinner at 6:00."</li><li>"Oh, that's a tough one! There are so many good memories to choose from. But if I had to pick just one, I think it would be spending summers at my grandparent's house. We would play board games, make homemade ice cream, and have big family dinners. It was always so much fun!"</li></ul> |
72
+
73
+ ## Evaluation
74
+
75
+ ### Metrics
76
+ | Label | Accuracy |
77
+ |:--------|:---------|
78
+ | **all** | 0.9915 |
79
+
80
+ ## Uses
81
+
82
+ ### Direct Use for Inference
83
+
84
+ First install the SetFit library:
85
+
86
+ ```bash
87
+ pip install setfit
88
+ ```
89
+
90
+ Then you can load this model and run inference.
91
+
92
+ ```python
93
+ from setfit import SetFitModel
94
+
95
+ # Download from the 🤗 Hub
96
+ model = SetFitModel.from_pretrained("nazhan/bge-small-en-v1.5-brahmaputra-iter-10-2nd")
97
+ # Run inference
98
+ preds = model("Can I have data_asset_kpi_bs details.")
99
+ ```
100
+
101
+ <!--
102
+ ### Downstream Use
103
+
104
+ *List how someone could finetune this model on their own dataset.*
105
+ -->
106
+
107
+ <!--
108
+ ### Out-of-Scope Use
109
+
110
+ *List how the model may foreseeably be misused and address what users ought not to do with the model.*
111
+ -->
112
+
113
+ <!--
114
+ ## Bias, Risks and Limitations
115
+
116
+ *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
117
+ -->
118
+
119
+ <!--
120
+ ### Recommendations
121
+
122
+ *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
123
+ -->
124
+
125
+ ## Training Details
126
+
127
+ ### Training Set Metrics
128
+ | Training set | Min | Median | Max |
129
+ |:-------------|:----|:-------|:----|
130
+ | Word count | 1 | 8.8375 | 62 |
131
+
132
+ | Label | Training Sample Count |
133
+ |:-------------|:----------------------|
134
+ | Tablejoin | 122 |
135
+ | Rejection | 69 |
136
+ | Aggregation | 287 |
137
+ | Lookup | 59 |
138
+ | Generalreply | 71 |
139
+ | Viewtables | 79 |
140
+ | Lookup_1 | 156 |
141
+
142
+ ### Training Hyperparameters
143
+ - batch_size: (16, 16)
144
+ - num_epochs: (1, 1)
145
+ - max_steps: -1
146
+ - sampling_strategy: oversampling
147
+ - body_learning_rate: (2e-05, 1e-05)
148
+ - head_learning_rate: 0.01
149
+ - loss: CosineSimilarityLoss
150
+ - distance_metric: cosine_distance
151
+ - margin: 0.25
152
+ - end_to_end: False
153
+ - use_amp: False
154
+ - warmup_proportion: 0.1
155
+ - seed: 42
156
+ - eval_max_steps: -1
157
+ - load_best_model_at_end: True
158
+
159
+ ### Training Results
160
+ | Epoch | Step | Training Loss | Validation Loss |
161
+ |:-------:|:---------:|:-------------:|:---------------:|
162
+ | 0.0000 | 1 | 0.2355 | - |
163
+ | 0.0014 | 50 | 0.2202 | - |
164
+ | 0.0028 | 100 | 0.1664 | - |
165
+ | 0.0042 | 150 | 0.216 | - |
166
+ | 0.0056 | 200 | 0.2341 | - |
167
+ | 0.0070 | 250 | 0.2279 | - |
168
+ | 0.0084 | 300 | 0.1786 | - |
169
+ | 0.0098 | 350 | 0.1603 | - |
170
+ | 0.0112 | 400 | 0.0821 | - |
171
+ | 0.0126 | 450 | 0.1498 | - |
172
+ | 0.0140 | 500 | 0.0942 | - |
173
+ | 0.0155 | 550 | 0.0999 | - |
174
+ | 0.0169 | 600 | 0.0895 | - |
175
+ | 0.0183 | 650 | 0.0841 | - |
176
+ | 0.0197 | 700 | 0.1433 | - |
177
+ | 0.0211 | 750 | 0.0808 | - |
178
+ | 0.0225 | 800 | 0.0346 | - |
179
+ | 0.0239 | 850 | 0.0556 | - |
180
+ | 0.0253 | 900 | 0.0755 | - |
181
+ | 0.0267 | 950 | 0.0346 | - |
182
+ | 0.0281 | 1000 | 0.0486 | - |
183
+ | 0.0295 | 1050 | 0.0207 | - |
184
+ | 0.0309 | 1100 | 0.0126 | - |
185
+ | 0.0323 | 1150 | 0.0113 | - |
186
+ | 0.0337 | 1200 | 0.0076 | - |
187
+ | 0.0351 | 1250 | 0.0082 | - |
188
+ | 0.0365 | 1300 | 0.0142 | - |
189
+ | 0.0379 | 1350 | 0.011 | - |
190
+ | 0.0393 | 1400 | 0.0034 | - |
191
+ | 0.0407 | 1450 | 0.0123 | - |
192
+ | 0.0421 | 1500 | 0.0062 | - |
193
+ | 0.0435 | 1550 | 0.0021 | - |
194
+ | 0.0449 | 1600 | 0.005 | - |
195
+ | 0.0464 | 1650 | 0.0124 | - |
196
+ | 0.0478 | 1700 | 0.0026 | - |
197
+ | 0.0492 | 1750 | 0.0029 | - |
198
+ | 0.0506 | 1800 | 0.0023 | - |
199
+ | 0.0520 | 1850 | 0.0017 | - |
200
+ | 0.0534 | 1900 | 0.0027 | - |
201
+ | 0.0548 | 1950 | 0.0017 | - |
202
+ | 0.0562 | 2000 | 0.0043 | - |
203
+ | 0.0576 | 2050 | 0.0018 | - |
204
+ | 0.0590 | 2100 | 0.0032 | - |
205
+ | 0.0604 | 2150 | 0.0022 | - |
206
+ | 0.0618 | 2200 | 0.0052 | - |
207
+ | 0.0632 | 2250 | 0.0025 | - |
208
+ | 0.0646 | 2300 | 0.0018 | - |
209
+ | 0.0660 | 2350 | 0.0016 | - |
210
+ | 0.0674 | 2400 | 0.0016 | - |
211
+ | 0.0688 | 2450 | 0.001 | - |
212
+ | 0.0702 | 2500 | 0.0015 | - |
213
+ | 0.0716 | 2550 | 0.0013 | - |
214
+ | 0.0730 | 2600 | 0.0012 | - |
215
+ | 0.0744 | 2650 | 0.0012 | - |
216
+ | 0.0759 | 2700 | 0.0017 | - |
217
+ | 0.0773 | 2750 | 0.0016 | - |
218
+ | 0.0787 | 2800 | 0.0018 | - |
219
+ | 0.0801 | 2850 | 0.0007 | - |
220
+ | 0.0815 | 2900 | 0.0008 | - |
221
+ | 0.0829 | 2950 | 0.0016 | - |
222
+ | 0.0843 | 3000 | 0.0008 | - |
223
+ | 0.0857 | 3050 | 0.0011 | - |
224
+ | 0.0871 | 3100 | 0.0013 | - |
225
+ | 0.0885 | 3150 | 0.0012 | - |
226
+ | 0.0899 | 3200 | 0.0006 | - |
227
+ | 0.0913 | 3250 | 0.0012 | - |
228
+ | 0.0927 | 3300 | 0.0009 | - |
229
+ | 0.0941 | 3350 | 0.0007 | - |
230
+ | 0.0955 | 3400 | 0.0006 | - |
231
+ | 0.0969 | 3450 | 0.0011 | - |
232
+ | 0.0983 | 3500 | 0.0012 | - |
233
+ | 0.0997 | 3550 | 0.0008 | - |
234
+ | 0.1011 | 3600 | 0.0009 | - |
235
+ | 0.1025 | 3650 | 0.0007 | - |
236
+ | 0.1039 | 3700 | 0.001 | - |
237
+ | 0.1053 | 3750 | 0.0006 | - |
238
+ | 0.1068 | 3800 | 0.0008 | - |
239
+ | 0.1082 | 3850 | 0.0007 | - |
240
+ | 0.1096 | 3900 | 0.0008 | - |
241
+ | 0.1110 | 3950 | 0.0006 | - |
242
+ | 0.1124 | 4000 | 0.0004 | - |
243
+ | 0.1138 | 4050 | 0.001 | - |
244
+ | 0.1152 | 4100 | 0.001 | - |
245
+ | 0.1166 | 4150 | 0.0007 | - |
246
+ | 0.1180 | 4200 | 0.0006 | - |
247
+ | 0.1194 | 4250 | 0.0006 | - |
248
+ | 0.1208 | 4300 | 0.0004 | - |
249
+ | 0.1222 | 4350 | 0.0008 | - |
250
+ | 0.1236 | 4400 | 0.0005 | - |
251
+ | 0.1250 | 4450 | 0.0007 | - |
252
+ | 0.1264 | 4500 | 0.0007 | - |
253
+ | 0.1278 | 4550 | 0.001 | - |
254
+ | 0.1292 | 4600 | 0.0007 | - |
255
+ | 0.1306 | 4650 | 0.0005 | - |
256
+ | 0.1320 | 4700 | 0.0006 | - |
257
+ | 0.1334 | 4750 | 0.0007 | - |
258
+ | 0.1348 | 4800 | 0.0003 | - |
259
+ | 0.1363 | 4850 | 0.0009 | - |
260
+ | 0.1377 | 4900 | 0.0008 | - |
261
+ | 0.1391 | 4950 | 0.0005 | - |
262
+ | 0.1405 | 5000 | 0.0005 | - |
263
+ | 0.1419 | 5050 | 0.0005 | - |
264
+ | 0.1433 | 5100 | 0.0005 | - |
265
+ | 0.1447 | 5150 | 0.0004 | - |
266
+ | 0.1461 | 5200 | 0.0005 | - |
267
+ | 0.1475 | 5250 | 0.0006 | - |
268
+ | 0.1489 | 5300 | 0.0007 | - |
269
+ | 0.1503 | 5350 | 0.0004 | - |
270
+ | 0.1517 | 5400 | 0.0007 | - |
271
+ | 0.1531 | 5450 | 0.0006 | - |
272
+ | 0.1545 | 5500 | 0.0006 | - |
273
+ | 0.1559 | 5550 | 0.0005 | - |
274
+ | 0.1573 | 5600 | 0.0005 | - |
275
+ | 0.1587 | 5650 | 0.0005 | - |
276
+ | 0.1601 | 5700 | 0.0007 | - |
277
+ | 0.1615 | 5750 | 0.0007 | - |
278
+ | 0.1629 | 5800 | 0.0004 | - |
279
+ | 0.1643 | 5850 | 0.0007 | - |
280
+ | 0.1657 | 5900 | 0.0006 | - |
281
+ | 0.1672 | 5950 | 0.0005 | - |
282
+ | 0.1686 | 6000 | 0.0005 | - |
283
+ | 0.1700 | 6050 | 0.0004 | - |
284
+ | 0.1714 | 6100 | 0.0005 | - |
285
+ | 0.1728 | 6150 | 0.0005 | - |
286
+ | 0.1742 | 6200 | 0.0004 | - |
287
+ | 0.1756 | 6250 | 0.0006 | - |
288
+ | 0.1770 | 6300 | 0.0004 | - |
289
+ | 0.1784 | 6350 | 0.0004 | - |
290
+ | 0.1798 | 6400 | 0.0004 | - |
291
+ | 0.1812 | 6450 | 0.0005 | - |
292
+ | 0.1826 | 6500 | 0.0005 | - |
293
+ | 0.1840 | 6550 | 0.0004 | - |
294
+ | 0.1854 | 6600 | 0.0003 | - |
295
+ | 0.1868 | 6650 | 0.0004 | - |
296
+ | 0.1882 | 6700 | 0.0004 | - |
297
+ | 0.1896 | 6750 | 0.0004 | - |
298
+ | 0.1910 | 6800 | 0.0006 | - |
299
+ | 0.1924 | 6850 | 0.0004 | - |
300
+ | 0.1938 | 6900 | 0.0004 | - |
301
+ | 0.1952 | 6950 | 0.0003 | - |
302
+ | 0.1967 | 7000 | 0.0004 | - |
303
+ | 0.1981 | 7050 | 0.0004 | - |
304
+ | 0.1995 | 7100 | 0.0003 | - |
305
+ | 0.2009 | 7150 | 0.0006 | - |
306
+ | 0.2023 | 7200 | 0.0005 | - |
307
+ | 0.2037 | 7250 | 0.0005 | - |
308
+ | 0.2051 | 7300 | 0.0003 | - |
309
+ | 0.2065 | 7350 | 0.0003 | - |
310
+ | 0.2079 | 7400 | 0.0004 | - |
311
+ | 0.2093 | 7450 | 0.0006 | - |
312
+ | 0.2107 | 7500 | 0.0004 | - |
313
+ | 0.2121 | 7550 | 0.0003 | - |
314
+ | 0.2135 | 7600 | 0.0005 | - |
315
+ | 0.2149 | 7650 | 0.0005 | - |
316
+ | 0.2163 | 7700 | 0.0005 | - |
317
+ | 0.2177 | 7750 | 0.0003 | - |
318
+ | 0.2191 | 7800 | 0.0004 | - |
319
+ | 0.2205 | 7850 | 0.0003 | - |
320
+ | 0.2219 | 7900 | 0.0004 | - |
321
+ | 0.2233 | 7950 | 0.0003 | - |
322
+ | 0.2247 | 8000 | 0.0003 | - |
323
+ | 0.2261 | 8050 | 0.0008 | - |
324
+ | 0.2276 | 8100 | 0.0003 | - |
325
+ | 0.2290 | 8150 | 0.0003 | - |
326
+ | 0.2304 | 8200 | 0.0003 | - |
327
+ | 0.2318 | 8250 | 0.0003 | - |
328
+ | 0.2332 | 8300 | 0.0004 | - |
329
+ | 0.2346 | 8350 | 0.0003 | - |
330
+ | 0.2360 | 8400 | 0.0002 | - |
331
+ | 0.2374 | 8450 | 0.0005 | - |
332
+ | 0.2388 | 8500 | 0.0003 | - |
333
+ | 0.2402 | 8550 | 0.0002 | - |
334
+ | 0.2416 | 8600 | 0.0005 | - |
335
+ | 0.2430 | 8650 | 0.0005 | - |
336
+ | 0.2444 | 8700 | 0.0005 | - |
337
+ | 0.2458 | 8750 | 0.0002 | - |
338
+ | 0.2472 | 8800 | 0.0004 | - |
339
+ | 0.2486 | 8850 | 0.0003 | - |
340
+ | 0.2500 | 8900 | 0.0002 | - |
341
+ | 0.2514 | 8950 | 0.0003 | - |
342
+ | 0.2528 | 9000 | 0.0003 | - |
343
+ | 0.2542 | 9050 | 0.0002 | - |
344
+ | 0.2556 | 9100 | 0.0003 | - |
345
+ | 0.2571 | 9150 | 0.0003 | - |
346
+ | 0.2585 | 9200 | 0.0005 | - |
347
+ | 0.2599 | 9250 | 0.0004 | - |
348
+ | 0.2613 | 9300 | 0.0002 | - |
349
+ | 0.2627 | 9350 | 0.0002 | - |
350
+ | 0.2641 | 9400 | 0.0003 | - |
351
+ | 0.2655 | 9450 | 0.0003 | - |
352
+ | 0.2669 | 9500 | 0.0003 | - |
353
+ | 0.2683 | 9550 | 0.0002 | - |
354
+ | 0.2697 | 9600 | 0.0003 | - |
355
+ | 0.2711 | 9650 | 0.0003 | - |
356
+ | 0.2725 | 9700 | 0.0003 | - |
357
+ | 0.2739 | 9750 | 0.0006 | - |
358
+ | 0.2753 | 9800 | 0.0003 | - |
359
+ | 0.2767 | 9850 | 0.0002 | - |
360
+ | 0.2781 | 9900 | 0.0003 | - |
361
+ | 0.2795 | 9950 | 0.0004 | - |
362
+ | 0.2809 | 10000 | 0.0005 | - |
363
+ | 0.2823 | 10050 | 0.0003 | - |
364
+ | 0.2837 | 10100 | 0.0003 | - |
365
+ | 0.2851 | 10150 | 0.0003 | - |
366
+ | 0.2865 | 10200 | 0.0004 | - |
367
+ | 0.2880 | 10250 | 0.0004 | - |
368
+ | 0.2894 | 10300 | 0.0003 | - |
369
+ | 0.2908 | 10350 | 0.0003 | - |
370
+ | 0.2922 | 10400 | 0.0003 | - |
371
+ | 0.2936 | 10450 | 0.0002 | - |
372
+ | 0.2950 | 10500 | 0.0003 | - |
373
+ | 0.2964 | 10550 | 0.0002 | - |
374
+ | 0.2978 | 10600 | 0.0003 | - |
375
+ | 0.2992 | 10650 | 0.0003 | - |
376
+ | 0.3006 | 10700 | 0.0003 | - |
377
+ | 0.3020 | 10750 | 0.0003 | - |
378
+ | 0.3034 | 10800 | 0.0003 | - |
379
+ | 0.3048 | 10850 | 0.0004 | - |
380
+ | 0.3062 | 10900 | 0.0003 | - |
381
+ | 0.3076 | 10950 | 0.0002 | - |
382
+ | 0.3090 | 11000 | 0.0003 | - |
383
+ | 0.3104 | 11050 | 0.0002 | - |
384
+ | 0.3118 | 11100 | 0.0003 | - |
385
+ | 0.3132 | 11150 | 0.0002 | - |
386
+ | 0.3146 | 11200 | 0.0003 | - |
387
+ | 0.3160 | 11250 | 0.0004 | - |
388
+ | 0.3175 | 11300 | 0.0003 | - |
389
+ | 0.3189 | 11350 | 0.0003 | - |
390
+ | 0.3203 | 11400 | 0.0003 | - |
391
+ | 0.3217 | 11450 | 0.0001 | - |
392
+ | 0.3231 | 11500 | 0.0002 | - |
393
+ | 0.3245 | 11550 | 0.0003 | - |
394
+ | 0.3259 | 11600 | 0.0003 | - |
395
+ | 0.3273 | 11650 | 0.0002 | - |
396
+ | 0.3287 | 11700 | 0.0004 | - |
397
+ | 0.3301 | 11750 | 0.0003 | - |
398
+ | 0.3315 | 11800 | 0.0002 | - |
399
+ | 0.3329 | 11850 | 0.0003 | - |
400
+ | 0.3343 | 11900 | 0.0003 | - |
401
+ | 0.3357 | 11950 | 0.0003 | - |
402
+ | 0.3371 | 12000 | 0.0003 | - |
403
+ | 0.3385 | 12050 | 0.0002 | - |
404
+ | 0.3399 | 12100 | 0.0002 | - |
405
+ | 0.3413 | 12150 | 0.0002 | - |
406
+ | 0.3427 | 12200 | 0.0002 | - |
407
+ | 0.3441 | 12250 | 0.0003 | - |
408
+ | 0.3455 | 12300 | 0.0003 | - |
409
+ | 0.3469 | 12350 | 0.0003 | - |
410
+ | 0.3484 | 12400 | 0.0003 | - |
411
+ | 0.3498 | 12450 | 0.0002 | - |
412
+ | 0.3512 | 12500 | 0.0003 | - |
413
+ | 0.3526 | 12550 | 0.0002 | - |
414
+ | 0.3540 | 12600 | 0.0004 | - |
415
+ | 0.3554 | 12650 | 0.0003 | - |
416
+ | 0.3568 | 12700 | 0.0003 | - |
417
+ | 0.3582 | 12750 | 0.0003 | - |
418
+ | 0.3596 | 12800 | 0.0002 | - |
419
+ | 0.3610 | 12850 | 0.0002 | - |
420
+ | 0.3624 | 12900 | 0.0003 | - |
421
+ | 0.3638 | 12950 | 0.0002 | - |
422
+ | 0.3652 | 13000 | 0.0003 | - |
423
+ | 0.3666 | 13050 | 0.0002 | - |
424
+ | 0.3680 | 13100 | 0.0003 | - |
425
+ | 0.3694 | 13150 | 0.0003 | - |
426
+ | 0.3708 | 13200 | 0.0003 | - |
427
+ | 0.3722 | 13250 | 0.0002 | - |
428
+ | 0.3736 | 13300 | 0.0002 | - |
429
+ | 0.3750 | 13350 | 0.0003 | - |
430
+ | 0.3764 | 13400 | 0.0002 | - |
431
+ | 0.3779 | 13450 | 0.0004 | - |
432
+ | 0.3793 | 13500 | 0.0003 | - |
433
+ | 0.3807 | 13550 | 0.0002 | - |
434
+ | 0.3821 | 13600 | 0.0003 | - |
435
+ | 0.3835 | 13650 | 0.0002 | - |
436
+ | 0.3849 | 13700 | 0.0003 | - |
437
+ | 0.3863 | 13750 | 0.0003 | - |
438
+ | 0.3877 | 13800 | 0.0003 | - |
439
+ | 0.3891 | 13850 | 0.0002 | - |
440
+ | 0.3905 | 13900 | 0.0003 | - |
441
+ | 0.3919 | 13950 | 0.0002 | - |
442
+ | 0.3933 | 14000 | 0.0003 | - |
443
+ | 0.3947 | 14050 | 0.0004 | - |
444
+ | 0.3961 | 14100 | 0.0003 | - |
445
+ | 0.3975 | 14150 | 0.0003 | - |
446
+ | 0.3989 | 14200 | 0.0003 | - |
447
+ | 0.4003 | 14250 | 0.0002 | - |
448
+ | 0.4017 | 14300 | 0.0003 | - |
449
+ | 0.4031 | 14350 | 0.0002 | - |
450
+ | 0.4045 | 14400 | 0.0003 | - |
451
+ | 0.4059 | 14450 | 0.0002 | - |
452
+ | 0.4073 | 14500 | 0.0002 | - |
453
+ | 0.4088 | 14550 | 0.0002 | - |
454
+ | 0.4102 | 14600 | 0.0002 | - |
455
+ | 0.4116 | 14650 | 0.0002 | - |
456
+ | 0.4130 | 14700 | 0.0002 | - |
457
+ | 0.4144 | 14750 | 0.0004 | - |
458
+ | 0.4158 | 14800 | 0.0002 | - |
459
+ | 0.4172 | 14850 | 0.0002 | - |
460
+ | 0.4186 | 14900 | 0.0002 | - |
461
+ | 0.4200 | 14950 | 0.0002 | - |
462
+ | 0.4214 | 15000 | 0.0003 | - |
463
+ | 0.4228 | 15050 | 0.0002 | - |
464
+ | 0.4242 | 15100 | 0.0003 | - |
465
+ | 0.4256 | 15150 | 0.0002 | - |
466
+ | 0.4270 | 15200 | 0.0003 | - |
467
+ | 0.4284 | 15250 | 0.0003 | - |
468
+ | 0.4298 | 15300 | 0.0003 | - |
469
+ | 0.4312 | 15350 | 0.0013 | - |
470
+ | 0.4326 | 15400 | 0.0002 | - |
471
+ | 0.4340 | 15450 | 0.0002 | - |
472
+ | 0.4354 | 15500 | 0.0003 | - |
473
+ | 0.4368 | 15550 | 0.0003 | - |
474
+ | 0.4383 | 15600 | 0.0002 | - |
475
+ | 0.4397 | 15650 | 0.0002 | - |
476
+ | 0.4411 | 15700 | 0.0002 | - |
477
+ | 0.4425 | 15750 | 0.0002 | - |
478
+ | 0.4439 | 15800 | 0.0003 | - |
479
+ | 0.4453 | 15850 | 0.0001 | - |
480
+ | 0.4467 | 15900 | 0.0003 | - |
481
+ | 0.4481 | 15950 | 0.0002 | - |
482
+ | 0.4495 | 16000 | 0.0001 | - |
483
+ | 0.4509 | 16050 | 0.0003 | - |
484
+ | 0.4523 | 16100 | 0.0003 | - |
485
+ | 0.4537 | 16150 | 0.0003 | - |
486
+ | 0.4551 | 16200 | 0.0002 | - |
487
+ | 0.4565 | 16250 | 0.0001 | - |
488
+ | 0.4579 | 16300 | 0.0001 | - |
489
+ | 0.4593 | 16350 | 0.0001 | - |
490
+ | 0.4607 | 16400 | 0.0003 | - |
491
+ | 0.4621 | 16450 | 0.0002 | - |
492
+ | 0.4635 | 16500 | 0.0002 | - |
493
+ | 0.4649 | 16550 | 0.0002 | - |
494
+ | 0.4663 | 16600 | 0.0003 | - |
495
+ | 0.4677 | 16650 | 0.0002 | - |
496
+ | 0.4692 | 16700 | 0.0003 | - |
497
+ | 0.4706 | 16750 | 0.0002 | - |
498
+ | 0.4720 | 16800 | 0.0002 | - |
499
+ | 0.4734 | 16850 | 0.0002 | - |
500
+ | 0.4748 | 16900 | 0.0002 | - |
501
+ | 0.4762 | 16950 | 0.0003 | - |
502
+ | 0.4776 | 17000 | 0.0002 | - |
503
+ | 0.4790 | 17050 | 0.0002 | - |
504
+ | 0.4804 | 17100 | 0.0003 | - |
505
+ | 0.4818 | 17150 | 0.0001 | - |
506
+ | 0.4832 | 17200 | 0.0002 | - |
507
+ | 0.4846 | 17250 | 0.0002 | - |
508
+ | 0.4860 | 17300 | 0.0002 | - |
509
+ | 0.4874 | 17350 | 0.0001 | - |
510
+ | 0.4888 | 17400 | 0.0002 | - |
511
+ | 0.4902 | 17450 | 0.0002 | - |
512
+ | 0.4916 | 17500 | 0.0002 | - |
513
+ | 0.4930 | 17550 | 0.0002 | - |
514
+ | 0.4944 | 17600 | 0.0002 | - |
515
+ | 0.4958 | 17650 | 0.0003 | - |
516
+ | 0.4972 | 17700 | 0.0003 | - |
517
+ | 0.4987 | 17750 | 0.0002 | - |
518
+ | 0.5001 | 17800 | 0.0001 | - |
519
+ | 0.5015 | 17850 | 0.0002 | - |
520
+ | 0.5029 | 17900 | 0.0003 | - |
521
+ | 0.5043 | 17950 | 0.0002 | - |
522
+ | 0.5057 | 18000 | 0.0001 | - |
523
+ | 0.5071 | 18050 | 0.0003 | - |
524
+ | 0.5085 | 18100 | 0.0004 | - |
525
+ | 0.5099 | 18150 | 0.0002 | - |
526
+ | 0.5113 | 18200 | 0.0002 | - |
527
+ | 0.5127 | 18250 | 0.0002 | - |
528
+ | 0.5141 | 18300 | 0.0002 | - |
529
+ | 0.5155 | 18350 | 0.0002 | - |
530
+ | 0.5169 | 18400 | 0.0001 | - |
531
+ | 0.5183 | 18450 | 0.0001 | - |
532
+ | 0.5197 | 18500 | 0.0002 | - |
533
+ | 0.5211 | 18550 | 0.0002 | - |
534
+ | 0.5225 | 18600 | 0.0618 | - |
535
+ | 0.5239 | 18650 | 0.0003 | - |
536
+ | 0.5253 | 18700 | 0.0003 | - |
537
+ | 0.5267 | 18750 | 0.0002 | - |
538
+ | 0.5281 | 18800 | 0.0002 | - |
539
+ | 0.5296 | 18850 | 0.0002 | - |
540
+ | 0.5310 | 18900 | 0.0001 | - |
541
+ | 0.5324 | 18950 | 0.0002 | - |
542
+ | 0.5338 | 19000 | 0.0002 | - |
543
+ | 0.5352 | 19050 | 0.0003 | - |
544
+ | 0.5366 | 19100 | 0.0002 | - |
545
+ | 0.5380 | 19150 | 0.0002 | - |
546
+ | 0.5394 | 19200 | 0.0001 | - |
547
+ | 0.5408 | 19250 | 0.0003 | - |
548
+ | 0.5422 | 19300 | 0.0003 | - |
549
+ | 0.5436 | 19350 | 0.0002 | - |
550
+ | 0.5450 | 19400 | 0.0002 | - |
551
+ | 0.5464 | 19450 | 0.0002 | - |
552
+ | 0.5478 | 19500 | 0.0002 | - |
553
+ | 0.5492 | 19550 | 0.0002 | - |
554
+ | 0.5506 | 19600 | 0.0001 | - |
555
+ | 0.5520 | 19650 | 0.0002 | - |
556
+ | 0.5534 | 19700 | 0.0003 | - |
557
+ | 0.5548 | 19750 | 0.0002 | - |
558
+ | 0.5562 | 19800 | 0.0003 | - |
559
+ | 0.5576 | 19850 | 0.0002 | - |
560
+ | 0.5591 | 19900 | 0.0001 | - |
561
+ | 0.5605 | 19950 | 0.0001 | - |
562
+ | 0.5619 | 20000 | 0.0001 | - |
563
+ | 0.5633 | 20050 | 0.0002 | - |
564
+ | 0.5647 | 20100 | 0.0002 | - |
565
+ | 0.5661 | 20150 | 0.0002 | - |
566
+ | 0.5675 | 20200 | 0.0002 | - |
567
+ | 0.5689 | 20250 | 0.0002 | - |
568
+ | 0.5703 | 20300 | 0.0002 | - |
569
+ | 0.5717 | 20350 | 0.0001 | - |
570
+ | 0.5731 | 20400 | 0.0001 | - |
571
+ | 0.5745 | 20450 | 0.0002 | - |
572
+ | 0.5759 | 20500 | 0.0002 | - |
573
+ | 0.5773 | 20550 | 0.0001 | - |
574
+ | 0.5787 | 20600 | 0.0001 | - |
575
+ | 0.5801 | 20650 | 0.0002 | - |
576
+ | 0.5815 | 20700 | 0.0001 | - |
577
+ | 0.5829 | 20750 | 0.0002 | - |
578
+ | 0.5843 | 20800 | 0.0001 | - |
579
+ | 0.5857 | 20850 | 0.0002 | - |
580
+ | 0.5871 | 20900 | 0.0002 | - |
581
+ | 0.5885 | 20950 | 0.0001 | - |
582
+ | 0.5900 | 21000 | 0.0001 | - |
583
+ | 0.5914 | 21050 | 0.0001 | - |
584
+ | 0.5928 | 21100 | 0.0002 | - |
585
+ | 0.5942 | 21150 | 0.0002 | - |
586
+ | 0.5956 | 21200 | 0.0001 | - |
587
+ | 0.5970 | 21250 | 0.0002 | - |
588
+ | 0.5984 | 21300 | 0.0001 | - |
589
+ | 0.5998 | 21350 | 0.0002 | - |
590
+ | 0.6012 | 21400 | 0.0002 | - |
591
+ | 0.6026 | 21450 | 0.0002 | - |
592
+ | 0.6040 | 21500 | 0.0003 | - |
593
+ | 0.6054 | 21550 | 0.0002 | - |
594
+ | 0.6068 | 21600 | 0.0002 | - |
595
+ | 0.6082 | 21650 | 0.0003 | - |
596
+ | 0.6096 | 21700 | 0.0002 | - |
597
+ | 0.6110 | 21750 | 0.0001 | - |
598
+ | 0.6124 | 21800 | 0.0003 | - |
599
+ | 0.6138 | 21850 | 0.0001 | - |
600
+ | 0.6152 | 21900 | 0.0002 | - |
601
+ | 0.6166 | 21950 | 0.0001 | - |
602
+ | 0.6180 | 22000 | 0.0002 | - |
603
+ | 0.6195 | 22050 | 0.0002 | - |
604
+ | 0.6209 | 22100 | 0.0001 | - |
605
+ | 0.6223 | 22150 | 0.0002 | - |
606
+ | 0.6237 | 22200 | 0.0001 | - |
607
+ | 0.6251 | 22250 | 0.0002 | - |
608
+ | 0.6265 | 22300 | 0.0002 | - |
609
+ | 0.6279 | 22350 | 0.0001 | - |
610
+ | 0.6293 | 22400 | 0.0002 | - |
611
+ | 0.6307 | 22450 | 0.0003 | - |
612
+ | 0.6321 | 22500 | 0.0001 | - |
613
+ | 0.6335 | 22550 | 0.0002 | - |
614
+ | 0.6349 | 22600 | 0.0001 | - |
615
+ | 0.6363 | 22650 | 0.0002 | - |
616
+ | 0.6377 | 22700 | 0.0002 | - |
617
+ | 0.6391 | 22750 | 0.0001 | - |
618
+ | 0.6405 | 22800 | 0.0002 | - |
619
+ | 0.6419 | 22850 | 0.0002 | - |
620
+ | 0.6433 | 22900 | 0.0002 | - |
621
+ | 0.6447 | 22950 | 0.0002 | - |
622
+ | 0.6461 | 23000 | 0.0003 | - |
623
+ | 0.6475 | 23050 | 0.0002 | - |
624
+ | 0.6489 | 23100 | 0.0001 | - |
625
+ | 0.6504 | 23150 | 0.0002 | - |
626
+ | 0.6518 | 23200 | 0.0001 | - |
627
+ | 0.6532 | 23250 | 0.0002 | - |
628
+ | 0.6546 | 23300 | 0.0001 | - |
629
+ | 0.6560 | 23350 | 0.0002 | - |
630
+ | 0.6574 | 23400 | 0.0003 | - |
631
+ | 0.6588 | 23450 | 0.0002 | - |
632
+ | 0.6602 | 23500 | 0.0002 | - |
633
+ | 0.6616 | 23550 | 0.0001 | - |
634
+ | 0.6630 | 23600 | 0.0003 | - |
635
+ | 0.6644 | 23650 | 0.0002 | - |
636
+ | 0.6658 | 23700 | 0.0001 | - |
637
+ | 0.6672 | 23750 | 0.0002 | - |
638
+ | 0.6686 | 23800 | 0.0001 | - |
639
+ | 0.6700 | 23850 | 0.0001 | - |
640
+ | 0.6714 | 23900 | 0.0002 | - |
641
+ | 0.6728 | 23950 | 0.0002 | - |
642
+ | 0.6742 | 24000 | 0.0002 | - |
643
+ | 0.6756 | 24050 | 0.0002 | - |
644
+ | 0.6770 | 24100 | 0.0001 | - |
645
+ | 0.6784 | 24150 | 0.0002 | - |
646
+ | 0.6799 | 24200 | 0.0002 | - |
647
+ | 0.6813 | 24250 | 0.0002 | - |
648
+ | 0.6827 | 24300 | 0.0001 | - |
649
+ | 0.6841 | 24350 | 0.0002 | - |
650
+ | 0.6855 | 24400 | 0.0002 | - |
651
+ | 0.6869 | 24450 | 0.0001 | - |
652
+ | 0.6883 | 24500 | 0.0001 | - |
653
+ | 0.6897 | 24550 | 0.0002 | - |
654
+ | 0.6911 | 24600 | 0.0001 | - |
655
+ | 0.6925 | 24650 | 0.0002 | - |
656
+ | 0.6939 | 24700 | 0.0001 | - |
657
+ | 0.6953 | 24750 | 0.0003 | - |
658
+ | 0.6967 | 24800 | 0.0001 | - |
659
+ | 0.6981 | 24850 | 0.0002 | - |
660
+ | 0.6995 | 24900 | 0.0001 | - |
661
+ | 0.7009 | 24950 | 0.0001 | - |
662
+ | 0.7023 | 25000 | 0.0002 | - |
663
+ | 0.7037 | 25050 | 0.0001 | - |
664
+ | 0.7051 | 25100 | 0.0002 | - |
665
+ | 0.7065 | 25150 | 0.0001 | - |
666
+ | 0.7079 | 25200 | 0.0002 | - |
667
+ | 0.7093 | 25250 | 0.0002 | - |
668
+ | 0.7108 | 25300 | 0.0001 | - |
669
+ | 0.7122 | 25350 | 0.0002 | - |
670
+ | 0.7136 | 25400 | 0.0001 | - |
671
+ | 0.7150 | 25450 | 0.0001 | - |
672
+ | 0.7164 | 25500 | 0.0001 | - |
673
+ | 0.7178 | 25550 | 0.0001 | - |
674
+ | 0.7192 | 25600 | 0.0002 | - |
675
+ | 0.7206 | 25650 | 0.0002 | - |
676
+ | 0.7220 | 25700 | 0.0001 | - |
677
+ | 0.7234 | 25750 | 0.0001 | - |
678
+ | 0.7248 | 25800 | 0.0001 | - |
679
+ | 0.7262 | 25850 | 0.0002 | - |
680
+ | 0.7276 | 25900 | 0.0002 | - |
681
+ | 0.7290 | 25950 | 0.0001 | - |
682
+ | 0.7304 | 26000 | 0.0001 | - |
683
+ | 0.7318 | 26050 | 0.0002 | - |
684
+ | 0.7332 | 26100 | 0.0001 | - |
685
+ | 0.7346 | 26150 | 0.0001 | - |
686
+ | 0.7360 | 26200 | 0.0001 | - |
687
+ | 0.7374 | 26250 | 0.0001 | - |
688
+ | 0.7388 | 26300 | 0.0001 | - |
689
+ | 0.7403 | 26350 | 0.0002 | - |
690
+ | 0.7417 | 26400 | 0.0002 | - |
691
+ | 0.7431 | 26450 | 0.0001 | - |
692
+ | 0.7445 | 26500 | 0.0002 | - |
693
+ | 0.7459 | 26550 | 0.0001 | - |
694
+ | 0.7473 | 26600 | 0.0001 | - |
695
+ | 0.7487 | 26650 | 0.0002 | - |
696
+ | 0.7501 | 26700 | 0.0001 | - |
697
+ | 0.7515 | 26750 | 0.0001 | - |
698
+ | 0.7529 | 26800 | 0.0001 | - |
699
+ | 0.7543 | 26850 | 0.0001 | - |
700
+ | 0.7557 | 26900 | 0.0001 | - |
701
+ | 0.7571 | 26950 | 0.0001 | - |
702
+ | 0.7585 | 27000 | 0.0002 | - |
703
+ | 0.7599 | 27050 | 0.0001 | - |
704
+ | 0.7613 | 27100 | 0.0002 | - |
705
+ | 0.7627 | 27150 | 0.0002 | - |
706
+ | 0.7641 | 27200 | 0.0001 | - |
707
+ | 0.7655 | 27250 | 0.0002 | - |
708
+ | 0.7669 | 27300 | 0.0001 | - |
709
+ | 0.7683 | 27350 | 0.0002 | - |
710
+ | 0.7697 | 27400 | 0.0001 | - |
711
+ | 0.7712 | 27450 | 0.0002 | - |
712
+ | 0.7726 | 27500 | 0.0001 | - |
713
+ | 0.7740 | 27550 | 0.0001 | - |
714
+ | 0.7754 | 27600 | 0.0001 | - |
715
+ | 0.7768 | 27650 | 0.0001 | - |
716
+ | 0.7782 | 27700 | 0.0001 | - |
717
+ | 0.7796 | 27750 | 0.0001 | - |
718
+ | 0.7810 | 27800 | 0.0001 | - |
719
+ | 0.7824 | 27850 | 0.0001 | - |
720
+ | 0.7838 | 27900 | 0.0001 | - |
721
+ | 0.7852 | 27950 | 0.0001 | - |
722
+ | 0.7866 | 28000 | 0.0001 | - |
723
+ | 0.7880 | 28050 | 0.0001 | - |
724
+ | 0.7894 | 28100 | 0.0001 | - |
725
+ | 0.7908 | 28150 | 0.0001 | - |
726
+ | 0.7922 | 28200 | 0.0001 | - |
727
+ | 0.7936 | 28250 | 0.0002 | - |
728
+ | 0.7950 | 28300 | 0.0002 | - |
729
+ | 0.7964 | 28350 | 0.0001 | - |
730
+ | 0.7978 | 28400 | 0.0002 | - |
731
+ | 0.7992 | 28450 | 0.0001 | - |
732
+ | 0.8007 | 28500 | 0.0001 | - |
733
+ | 0.8021 | 28550 | 0.0001 | - |
734
+ | 0.8035 | 28600 | 0.0001 | - |
735
+ | 0.8049 | 28650 | 0.0002 | - |
736
+ | 0.8063 | 28700 | 0.0001 | - |
737
+ | 0.8077 | 28750 | 0.0002 | - |
738
+ | 0.8091 | 28800 | 0.0001 | - |
739
+ | 0.8105 | 28850 | 0.0001 | - |
740
+ | 0.8119 | 28900 | 0.0001 | - |
741
+ | 0.8133 | 28950 | 0.0002 | - |
742
+ | 0.8147 | 29000 | 0.0001 | - |
743
+ | 0.8161 | 29050 | 0.0002 | - |
744
+ | 0.8175 | 29100 | 0.0002 | - |
745
+ | 0.8189 | 29150 | 0.0002 | - |
746
+ | 0.8203 | 29200 | 0.0001 | - |
747
+ | 0.8217 | 29250 | 0.0002 | - |
748
+ | 0.8231 | 29300 | 0.0001 | - |
749
+ | 0.8245 | 29350 | 0.0001 | - |
750
+ | 0.8259 | 29400 | 0.0001 | - |
751
+ | 0.8273 | 29450 | 0.0002 | - |
752
+ | 0.8287 | 29500 | 0.0001 | - |
753
+ | 0.8301 | 29550 | 0.0002 | - |
754
+ | 0.8316 | 29600 | 0.0001 | - |
755
+ | 0.8330 | 29650 | 0.0001 | - |
756
+ | 0.8344 | 29700 | 0.0001 | - |
757
+ | 0.8358 | 29750 | 0.0001 | - |
758
+ | 0.8372 | 29800 | 0.0001 | - |
759
+ | 0.8386 | 29850 | 0.0001 | - |
760
+ | 0.8400 | 29900 | 0.0001 | - |
761
+ | 0.8414 | 29950 | 0.0002 | - |
762
+ | 0.8428 | 30000 | 0.0002 | - |
763
+ | 0.8442 | 30050 | 0.0001 | - |
764
+ | 0.8456 | 30100 | 0.0001 | - |
765
+ | 0.8470 | 30150 | 0.0001 | - |
766
+ | 0.8484 | 30200 | 0.0001 | - |
767
+ | 0.8498 | 30250 | 0.0001 | - |
768
+ | 0.8512 | 30300 | 0.0001 | - |
769
+ | 0.8526 | 30350 | 0.0001 | - |
770
+ | 0.8540 | 30400 | 0.0001 | - |
771
+ | 0.8554 | 30450 | 0.0002 | - |
772
+ | 0.8568 | 30500 | 0.0001 | - |
773
+ | 0.8582 | 30550 | 0.0001 | - |
774
+ | 0.8596 | 30600 | 0.0 | - |
775
+ | 0.8611 | 30650 | 0.0001 | - |
776
+ | 0.8625 | 30700 | 0.0002 | - |
777
+ | 0.8639 | 30750 | 0.0002 | - |
778
+ | 0.8653 | 30800 | 0.0002 | - |
779
+ | 0.8667 | 30850 | 0.0001 | - |
780
+ | 0.8681 | 30900 | 0.0002 | - |
781
+ | 0.8695 | 30950 | 0.0001 | - |
782
+ | 0.8709 | 31000 | 0.0001 | - |
783
+ | 0.8723 | 31050 | 0.0001 | - |
784
+ | 0.8737 | 31100 | 0.0002 | - |
785
+ | 0.8751 | 31150 | 0.0002 | - |
786
+ | 0.8765 | 31200 | 0.0001 | - |
787
+ | 0.8779 | 31250 | 0.0001 | - |
788
+ | 0.8793 | 31300 | 0.0001 | - |
789
+ | 0.8807 | 31350 | 0.0001 | - |
790
+ | 0.8821 | 31400 | 0.0001 | - |
791
+ | 0.8835 | 31450 | 0.0001 | - |
792
+ | 0.8849 | 31500 | 0.0001 | - |
793
+ | 0.8863 | 31550 | 0.0002 | - |
794
+ | 0.8877 | 31600 | 0.0001 | - |
795
+ | 0.8891 | 31650 | 0.0001 | - |
796
+ | 0.8905 | 31700 | 0.0002 | - |
797
+ | 0.8920 | 31750 | 0.0001 | - |
798
+ | 0.8934 | 31800 | 0.0001 | - |
799
+ | 0.8948 | 31850 | 0.0001 | - |
800
+ | 0.8962 | 31900 | 0.0003 | - |
801
+ | 0.8976 | 31950 | 0.0002 | - |
802
+ | 0.8990 | 32000 | 0.0002 | - |
803
+ | 0.9004 | 32050 | 0.0001 | - |
804
+ | 0.9018 | 32100 | 0.0001 | - |
805
+ | 0.9032 | 32150 | 0.0002 | - |
806
+ | 0.9046 | 32200 | 0.0003 | - |
807
+ | 0.9060 | 32250 | 0.0001 | - |
808
+ | 0.9074 | 32300 | 0.0002 | - |
809
+ | 0.9088 | 32350 | 0.0001 | - |
810
+ | 0.9102 | 32400 | 0.0002 | - |
811
+ | 0.9116 | 32450 | 0.0002 | - |
812
+ | 0.9130 | 32500 | 0.0001 | - |
813
+ | 0.9144 | 32550 | 0.0001 | - |
814
+ | 0.9158 | 32600 | 0.0001 | - |
815
+ | 0.9172 | 32650 | 0.0001 | - |
816
+ | 0.9186 | 32700 | 0.0001 | - |
817
+ | 0.9200 | 32750 | 0.0001 | - |
818
+ | 0.9215 | 32800 | 0.0001 | - |
819
+ | 0.9229 | 32850 | 0.0001 | - |
820
+ | 0.9243 | 32900 | 0.0001 | - |
821
+ | 0.9257 | 32950 | 0.0001 | - |
822
+ | 0.9271 | 33000 | 0.0001 | - |
823
+ | 0.9285 | 33050 | 0.0002 | - |
824
+ | 0.9299 | 33100 | 0.0001 | - |
825
+ | 0.9313 | 33150 | 0.0002 | - |
826
+ | 0.9327 | 33200 | 0.0001 | - |
827
+ | 0.9341 | 33250 | 0.0001 | - |
828
+ | 0.9355 | 33300 | 0.0002 | - |
829
+ | 0.9369 | 33350 | 0.0001 | - |
830
+ | 0.9383 | 33400 | 0.0001 | - |
831
+ | 0.9397 | 33450 | 0.0001 | - |
832
+ | 0.9411 | 33500 | 0.0001 | - |
833
+ | 0.9425 | 33550 | 0.0001 | - |
834
+ | 0.9439 | 33600 | 0.0001 | - |
835
+ | 0.9453 | 33650 | 0.0001 | - |
836
+ | 0.9467 | 33700 | 0.0002 | - |
837
+ | 0.9481 | 33750 | 0.0001 | - |
838
+ | 0.9495 | 33800 | 0.0001 | - |
839
+ | 0.9509 | 33850 | 0.0002 | - |
840
+ | 0.9524 | 33900 | 0.0001 | - |
841
+ | 0.9538 | 33950 | 0.0001 | - |
842
+ | 0.9552 | 34000 | 0.0002 | - |
843
+ | 0.9566 | 34050 | 0.0001 | - |
844
+ | 0.9580 | 34100 | 0.0001 | - |
845
+ | 0.9594 | 34150 | 0.0001 | - |
846
+ | 0.9608 | 34200 | 0.0002 | - |
847
+ | 0.9622 | 34250 | 0.0001 | - |
848
+ | 0.9636 | 34300 | 0.0001 | - |
849
+ | 0.9650 | 34350 | 0.0001 | - |
850
+ | 0.9664 | 34400 | 0.0001 | - |
851
+ | 0.9678 | 34450 | 0.0003 | - |
852
+ | 0.9692 | 34500 | 0.0001 | - |
853
+ | 0.9706 | 34550 | 0.0001 | - |
854
+ | 0.9720 | 34600 | 0.0001 | - |
855
+ | 0.9734 | 34650 | 0.0001 | - |
856
+ | 0.9748 | 34700 | 0.0001 | - |
857
+ | 0.9762 | 34750 | 0.0001 | - |
858
+ | 0.9776 | 34800 | 0.0002 | - |
859
+ | 0.9790 | 34850 | 0.0001 | - |
860
+ | 0.9804 | 34900 | 0.0002 | - |
861
+ | 0.9819 | 34950 | 0.0001 | - |
862
+ | 0.9833 | 35000 | 0.0002 | - |
863
+ | 0.9847 | 35050 | 0.0001 | - |
864
+ | 0.9861 | 35100 | 0.0001 | - |
865
+ | 0.9875 | 35150 | 0.0001 | - |
866
+ | 0.9889 | 35200 | 0.0001 | - |
867
+ | 0.9903 | 35250 | 0.0001 | - |
868
+ | 0.9917 | 35300 | 0.0001 | - |
869
+ | 0.9931 | 35350 | 0.0001 | - |
870
+ | 0.9945 | 35400 | 0.0001 | - |
871
+ | 0.9959 | 35450 | 0.0001 | - |
872
+ | 0.9973 | 35500 | 0.0001 | - |
873
+ | 0.9987 | 35550 | 0.0001 | - |
874
+ | **1.0** | **35596** | **-** | **0.0121** |
875
+
876
+ * The bold row denotes the saved checkpoint.
877
+ ### Framework Versions
878
+ - Python: 3.11.9
879
+ - SetFit: 1.0.3
880
+ - Sentence Transformers: 2.7.0
881
+ - Transformers: 4.42.4
882
+ - PyTorch: 2.4.0+cu121
883
+ - Datasets: 2.21.0
884
+ - Tokenizers: 0.19.1
885
+
886
+ ## Citation
887
+
888
+ ### BibTeX
889
+ ```bibtex
890
+ @article{https://doi.org/10.48550/arxiv.2209.11055,
891
+ doi = {10.48550/ARXIV.2209.11055},
892
+ url = {https://arxiv.org/abs/2209.11055},
893
+ author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
894
+ keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
895
+ title = {Efficient Few-Shot Learning Without Prompts},
896
+ publisher = {arXiv},
897
+ year = {2022},
898
+ copyright = {Creative Commons Attribution 4.0 International}
899
+ }
900
+ ```
901
+
902
+ <!--
903
+ ## Glossary
904
+
905
+ *Clearly define terms in order to be accessible across audiences.*
906
+ -->
907
+
908
+ <!--
909
+ ## Model Card Authors
910
+
911
+ *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
912
+ -->
913
+
914
+ <!--
915
+ ## Model Card Contact
916
+
917
+ *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
918
+ -->
config.json ADDED
@@ -0,0 +1,31 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "_name_or_path": "checkpoints/step_35596",
3
+ "architectures": [
4
+ "BertModel"
5
+ ],
6
+ "attention_probs_dropout_prob": 0.1,
7
+ "classifier_dropout": null,
8
+ "hidden_act": "gelu",
9
+ "hidden_dropout_prob": 0.1,
10
+ "hidden_size": 384,
11
+ "id2label": {
12
+ "0": "LABEL_0"
13
+ },
14
+ "initializer_range": 0.02,
15
+ "intermediate_size": 1536,
16
+ "label2id": {
17
+ "LABEL_0": 0
18
+ },
19
+ "layer_norm_eps": 1e-12,
20
+ "max_position_embeddings": 512,
21
+ "model_type": "bert",
22
+ "num_attention_heads": 12,
23
+ "num_hidden_layers": 12,
24
+ "pad_token_id": 0,
25
+ "position_embedding_type": "absolute",
26
+ "torch_dtype": "float32",
27
+ "transformers_version": "4.42.4",
28
+ "type_vocab_size": 2,
29
+ "use_cache": true,
30
+ "vocab_size": 30522
31
+ }
config_sentence_transformers.json ADDED
@@ -0,0 +1,9 @@
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "__version__": {
3
+ "sentence_transformers": "2.2.2",
4
+ "transformers": "4.28.1",
5
+ "pytorch": "1.13.0+cu117"
6
+ },
7
+ "prompts": {},
8
+ "default_prompt_name": null
9
+ }
config_setfit.json ADDED
@@ -0,0 +1,12 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "normalize_embeddings": false,
3
+ "labels": [
4
+ "Tablejoin",
5
+ "Rejection",
6
+ "Aggregation",
7
+ "Lookup",
8
+ "Generalreply",
9
+ "Viewtables",
10
+ "Lookup_1"
11
+ ]
12
+ }
model.safetensors ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:963643c11d2bdf395924eb0db102d84655f26d2172ccf7c5340f14bbd42f86fd
3
+ size 133462128
model_head.pkl ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:49f6961abbc8c46be7c8558d37e1158b2511094ebae01b41a79a73dbddfa9469
3
+ size 22735
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 +1,64 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
+ "mask_token": "[MASK]",
49
+ "max_length": 512,
50
+ "model_max_length": 512,
51
+ "never_split": null,
52
+ "pad_to_multiple_of": null,
53
+ "pad_token": "[PAD]",
54
+ "pad_token_type_id": 0,
55
+ "padding_side": "right",
56
+ "sep_token": "[SEP]",
57
+ "stride": 0,
58
+ "strip_accents": null,
59
+ "tokenize_chinese_chars": true,
60
+ "tokenizer_class": "BertTokenizer",
61
+ "truncation_side": "right",
62
+ "truncation_strategy": "longest_first",
63
+ "unk_token": "[UNK]"
64
+ }
vocab.txt ADDED
The diff for this file is too large to render. See raw diff