Hugging Face's logo

nazhan
/
bge-large-en-v1.5-brahmaputra-iter-8-2nd-1-epoch

Text Classification
setfit
Safetensors
sentence-transformers
bert
generated_from_setfit_trainer
Eval Results
Model card Files Community
nazhan commited on
Commit
1951715
•
1 Parent(s): 688759e

Add SetFit model

Browse files
Files changed (13) hide show
  1. 1_Pooling/config.json +10 -0
  2. README.md +900 -0
  3. config.json +32 -0
  4. config_sentence_transformers.json +10 -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": 1024,
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 +1,900 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ base_model: BAAI/bge-large-en-v1.5
3
+ datasets:
4
+ - nazhan/brahmaputra-full-datasets-iter-8-2nd-fixed
5
+ library_name: setfit
6
+ metrics:
7
+ - accuracy
8
+ pipeline_tag: text-classification
9
+ tags:
10
+ - setfit
11
+ - sentence-transformers
12
+ - text-classification
13
+ - generated_from_setfit_trainer
14
+ widget:
15
+ - text: Can you filter by the 'Fashion' category and show me the products available?
16
+ - text: Get forecast by service type.
17
+ - text: How many orders were placed in each quarter?
18
+ - text: What are the details of customers with no phone number listed?
19
+ - text: I don't want to filter the database currently.
20
+ inference: true
21
+ model-index:
22
+ - name: SetFit with BAAI/bge-large-en-v1.5
23
+ results:
24
+ - task:
25
+ type: text-classification
26
+ name: Text Classification
27
+ dataset:
28
+ name: nazhan/brahmaputra-full-datasets-iter-8-2nd-fixed
29
+ type: nazhan/brahmaputra-full-datasets-iter-8-2nd-fixed
30
+ split: test
31
+ metrics:
32
+ - type: accuracy
33
+ value: 0.9739130434782609
34
+ name: Accuracy
35
+ ---
36
+
37
+ # SetFit with BAAI/bge-large-en-v1.5
38
+
39
+ This is a [SetFit](https://github.com/huggingface/setfit) model trained on the [nazhan/brahmaputra-full-datasets-iter-8-2nd-fixed](https://huggingface.co/datasets/nazhan/brahmaputra-full-datasets-iter-8-2nd-fixed) dataset that can be used for Text Classification. This SetFit model uses [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-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.
40
+
41
+ The model has been trained using an efficient few-shot learning technique that involves:
42
+
43
+ 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
44
+ 2. Training a classification head with features from the fine-tuned Sentence Transformer.
45
+
46
+ ## Model Details
47
+
48
+ ### Model Description
49
+ - **Model Type:** SetFit
50
+ - **Sentence Transformer body:** [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5)
51
+ - **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
52
+ - **Maximum Sequence Length:** 512 tokens
53
+ - **Number of Classes:** 7 classes
54
+ - **Training Dataset:** [nazhan/brahmaputra-full-datasets-iter-8-2nd-fixed](https://huggingface.co/datasets/nazhan/brahmaputra-full-datasets-iter-8-2nd-fixed)
55
+ <!-- - **Language:** Unknown -->
56
+ <!-- - **License:** Unknown -->
57
+
58
+ ### Model Sources
59
+
60
+ - **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit)
61
+ - **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)
62
+ - **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)
63
+
64
+ ### Model Labels
65
+ | Label | Examples |
66
+ |:-------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
67
+ | Aggregation | <ul><li>'How many unique customers made purchases last year?'</li><li>'Determine the minimum order amount for each customer.'</li><li>'Get me sum of total_revenue.'</li></ul> |
68
+ | Tablejoin | <ul><li>'Show me a join of cash flow and variance.'</li><li>'Join data_asset_001_forecast with data_asset_kpi_bs tables.'</li><li>'Join data_asset_kpi_ma_product with data_asset_001_variance.'</li></ul> |
69
+ | Lookup_1 | <ul><li>'Show me asset impairment by year.'</li><li>'Get me data_asset_001_pcc group by category.'</li><li>'Show me data_asset_001_variance group by category.'</li></ul> |
70
+ | Viewtables | <ul><li>'What are the table names within the starhub_data_asset database that enable data analysis of customer feedback?'</li><li>'How can I access the table directory for starhub_data_asset database to view all the available tables?'</li><li>'Please show me the tables that contain data related to customer transactions present in the starhub_data_asset database.'</li></ul> |
71
+ | Generalreply | <ul><li>"Oh my favorite food? That's a tough one. I love so many different kinds of food, but if I had to choose one it would probably be pizza. What about you? What's your favorite food?"</li><li>"Hmm, let me think... I'm actually pretty good at playing guitar! I've been playing for a few years now and it's always been one of my favorite hobbies. How about you, do you play any instruments or have any interesting hobbies?"</li><li>'What is your favorite color?'</li></ul> |
72
+ | Lookup | <ul><li>"Get me all the customers who haven't placed any orders."</li><li>'Get me the list of customers who have a phone number listed.'</li><li>'Can you filter by customers who registered without an email address?'</li></ul> |
73
+ | Rejection | <ul><li>"I'm not keen on producing any new data sets."</li><li>"Please don't generate any new data."</li><li>"I don't want to create any new data outputs."</li></ul> |
74
+
75
+ ## Evaluation
76
+
77
+ ### Metrics
78
+ | Label | Accuracy |
79
+ |:--------|:---------|
80
+ | **all** | 0.9739 |
81
+
82
+ ## Uses
83
+
84
+ ### Direct Use for Inference
85
+
86
+ First install the SetFit library:
87
+
88
+ ```bash
89
+ pip install setfit
90
+ ```
91
+
92
+ Then you can load this model and run inference.
93
+
94
+ ```python
95
+ from setfit import SetFitModel
96
+
97
+ # Download from the 🤗 Hub
98
+ model = SetFitModel.from_pretrained("nazhan/bge-large-en-v1.5-brahmaputra-iter-8-2nd-1-epoch")
99
+ # Run inference
100
+ preds = model("Get forecast by service type.")
101
+ ```
102
+
103
+ <!--
104
+ ### Downstream Use
105
+
106
+ *List how someone could finetune this model on their own dataset.*
107
+ -->
108
+
109
+ <!--
110
+ ### Out-of-Scope Use
111
+
112
+ *List how the model may foreseeably be misused and address what users ought not to do with the model.*
113
+ -->
114
+
115
+ <!--
116
+ ## Bias, Risks and Limitations
117
+
118
+ *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
119
+ -->
120
+
121
+ <!--
122
+ ### Recommendations
123
+
124
+ *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
125
+ -->
126
+
127
+ ## Training Details
128
+
129
+ ### Training Set Metrics
130
+ | Training set | Min | Median | Max |
131
+ |:-------------|:----|:-------|:----|
132
+ | Word count | 2 | 8.8252 | 62 |
133
+
134
+ | Label | Training Sample Count |
135
+ |:-------------|:----------------------|
136
+ | Tablejoin | 129 |
137
+ | Rejection | 74 |
138
+ | Aggregation | 210 |
139
+ | Lookup | 60 |
140
+ | Generalreply | 59 |
141
+ | Viewtables | 75 |
142
+ | Lookup_1 | 217 |
143
+
144
+ ### Training Hyperparameters
145
+ - batch_size: (16, 16)
146
+ - num_epochs: (1, 1)
147
+ - max_steps: -1
148
+ - sampling_strategy: oversampling
149
+ - body_learning_rate: (2e-05, 1e-05)
150
+ - head_learning_rate: 0.01
151
+ - loss: CosineSimilarityLoss
152
+ - distance_metric: cosine_distance
153
+ - margin: 0.25
154
+ - end_to_end: False
155
+ - use_amp: False
156
+ - warmup_proportion: 0.1
157
+ - seed: 42
158
+ - eval_max_steps: -1
159
+ - load_best_model_at_end: True
160
+
161
+ ### Training Results
162
+ | Epoch | Step | Training Loss | Validation Loss |
163
+ |:-------:|:---------:|:-------------:|:---------------:|
164
+ | 0.0000 | 1 | 0.1706 | - |
165
+ | 0.0014 | 50 | 0.1976 | - |
166
+ | 0.0029 | 100 | 0.2045 | - |
167
+ | 0.0043 | 150 | 0.1846 | - |
168
+ | 0.0058 | 200 | 0.1608 | - |
169
+ | 0.0072 | 250 | 0.105 | - |
170
+ | 0.0087 | 300 | 0.1618 | - |
171
+ | 0.0101 | 350 | 0.1282 | - |
172
+ | 0.0116 | 400 | 0.0382 | - |
173
+ | 0.0130 | 450 | 0.0328 | - |
174
+ | 0.0145 | 500 | 0.0483 | - |
175
+ | 0.0159 | 550 | 0.0245 | - |
176
+ | 0.0174 | 600 | 0.0093 | - |
177
+ | 0.0188 | 650 | 0.0084 | - |
178
+ | 0.0203 | 700 | 0.0042 | - |
179
+ | 0.0217 | 750 | 0.0044 | - |
180
+ | 0.0231 | 800 | 0.0035 | - |
181
+ | 0.0246 | 850 | 0.0065 | - |
182
+ | 0.0260 | 900 | 0.0036 | - |
183
+ | 0.0275 | 950 | 0.0039 | - |
184
+ | 0.0289 | 1000 | 0.0037 | - |
185
+ | 0.0304 | 1050 | 0.005 | - |
186
+ | 0.0318 | 1100 | 0.0024 | - |
187
+ | 0.0333 | 1150 | 0.0023 | - |
188
+ | 0.0347 | 1200 | 0.0023 | - |
189
+ | 0.0362 | 1250 | 0.0019 | - |
190
+ | 0.0376 | 1300 | 0.0015 | - |
191
+ | 0.0391 | 1350 | 0.0023 | - |
192
+ | 0.0405 | 1400 | 0.0011 | - |
193
+ | 0.0420 | 1450 | 0.0017 | - |
194
+ | 0.0434 | 1500 | 0.0015 | - |
195
+ | 0.0448 | 1550 | 0.0014 | - |
196
+ | 0.0463 | 1600 | 0.0014 | - |
197
+ | 0.0477 | 1650 | 0.0013 | - |
198
+ | 0.0492 | 1700 | 0.0013 | - |
199
+ | 0.0506 | 1750 | 0.001 | - |
200
+ | 0.0521 | 1800 | 0.0013 | - |
201
+ | 0.0535 | 1850 | 0.0013 | - |
202
+ | 0.0550 | 1900 | 0.0011 | - |
203
+ | 0.0564 | 1950 | 0.0012 | - |
204
+ | 0.0579 | 2000 | 0.001 | - |
205
+ | 0.0593 | 2050 | 0.0012 | - |
206
+ | 0.0608 | 2100 | 0.0008 | - |
207
+ | 0.0622 | 2150 | 0.0008 | - |
208
+ | 0.0637 | 2200 | 0.001 | - |
209
+ | 0.0651 | 2250 | 0.0007 | - |
210
+ | 0.0665 | 2300 | 0.0006 | - |
211
+ | 0.0680 | 2350 | 0.0007 | - |
212
+ | 0.0694 | 2400 | 0.0008 | - |
213
+ | 0.0709 | 2450 | 0.0008 | - |
214
+ | 0.0723 | 2500 | 0.0006 | - |
215
+ | 0.0738 | 2550 | 0.0006 | - |
216
+ | 0.0752 | 2600 | 0.0007 | - |
217
+ | 0.0767 | 2650 | 0.0008 | - |
218
+ | 0.0781 | 2700 | 0.0005 | - |
219
+ | 0.0796 | 2750 | 0.0008 | - |
220
+ | 0.0810 | 2800 | 0.0006 | - |
221
+ | 0.0825 | 2850 | 0.0007 | - |
222
+ | 0.0839 | 2900 | 0.0007 | - |
223
+ | 0.0854 | 2950 | 0.0005 | - |
224
+ | 0.0868 | 3000 | 0.0007 | - |
225
+ | 0.0882 | 3050 | 0.0005 | - |
226
+ | 0.0897 | 3100 | 0.0005 | - |
227
+ | 0.0911 | 3150 | 0.0007 | - |
228
+ | 0.0926 | 3200 | 0.0005 | - |
229
+ | 0.0940 | 3250 | 0.0005 | - |
230
+ | 0.0955 | 3300 | 0.0007 | - |
231
+ | 0.0969 | 3350 | 0.0004 | - |
232
+ | 0.0984 | 3400 | 0.0005 | - |
233
+ | 0.0998 | 3450 | 0.0004 | - |
234
+ | 0.1013 | 3500 | 0.0007 | - |
235
+ | 0.1027 | 3550 | 0.0004 | - |
236
+ | 0.1042 | 3600 | 0.0004 | - |
237
+ | 0.1056 | 3650 | 0.0006 | - |
238
+ | 0.1071 | 3700 | 0.0005 | - |
239
+ | 0.1085 | 3750 | 0.0004 | - |
240
+ | 0.1100 | 3800 | 0.0005 | - |
241
+ | 0.1114 | 3850 | 0.0004 | - |
242
+ | 0.1128 | 3900 | 0.0004 | - |
243
+ | 0.1143 | 3950 | 0.0003 | - |
244
+ | 0.1157 | 4000 | 0.0004 | - |
245
+ | 0.1172 | 4050 | 0.0004 | - |
246
+ | 0.1186 | 4100 | 0.0004 | - |
247
+ | 0.1201 | 4150 | 0.0004 | - |
248
+ | 0.1215 | 4200 | 0.0004 | - |
249
+ | 0.1230 | 4250 | 0.0004 | - |
250
+ | 0.1244 | 4300 | 0.0003 | - |
251
+ | 0.1259 | 4350 | 0.0004 | - |
252
+ | 0.1273 | 4400 | 0.0003 | - |
253
+ | 0.1288 | 4450 | 0.0003 | - |
254
+ | 0.1302 | 4500 | 0.0003 | - |
255
+ | 0.1317 | 4550 | 0.0002 | - |
256
+ | 0.1331 | 4600 | 0.0003 | - |
257
+ | 0.1345 | 4650 | 0.0004 | - |
258
+ | 0.1360 | 4700 | 0.0003 | - |
259
+ | 0.1374 | 4750 | 0.0003 | - |
260
+ | 0.1389 | 4800 | 0.0002 | - |
261
+ | 0.1403 | 4850 | 0.0003 | - |
262
+ | 0.1418 | 4900 | 0.0003 | - |
263
+ | 0.1432 | 4950 | 0.0003 | - |
264
+ | 0.1447 | 5000 | 0.0002 | - |
265
+ | 0.1461 | 5050 | 0.0002 | - |
266
+ | 0.1476 | 5100 | 0.0003 | - |
267
+ | 0.1490 | 5150 | 0.0002 | - |
268
+ | 0.1505 | 5200 | 0.0004 | - |
269
+ | 0.1519 | 5250 | 0.0003 | - |
270
+ | 0.1534 | 5300 | 0.0003 | - |
271
+ | 0.1548 | 5350 | 0.0002 | - |
272
+ | 0.1562 | 5400 | 0.0003 | - |
273
+ | 0.1577 | 5450 | 0.0002 | - |
274
+ | 0.1591 | 5500 | 0.0002 | - |
275
+ | 0.1606 | 5550 | 0.0002 | - |
276
+ | 0.1620 | 5600 | 0.0002 | - |
277
+ | 0.1635 | 5650 | 0.0002 | - |
278
+ | 0.1649 | 5700 | 0.0003 | - |
279
+ | 0.1664 | 5750 | 0.0002 | - |
280
+ | 0.1678 | 5800 | 0.0003 | - |
281
+ | 0.1693 | 5850 | 0.0003 | - |
282
+ | 0.1707 | 5900 | 0.0002 | - |
283
+ | 0.1722 | 5950 | 0.0007 | - |
284
+ | 0.1736 | 6000 | 0.0003 | - |
285
+ | 0.1751 | 6050 | 0.0002 | - |
286
+ | 0.1765 | 6100 | 0.0002 | - |
287
+ | 0.1779 | 6150 | 0.0003 | - |
288
+ | 0.1794 | 6200 | 0.0002 | - |
289
+ | 0.1808 | 6250 | 0.0002 | - |
290
+ | 0.1823 | 6300 | 0.0002 | - |
291
+ | 0.1837 | 6350 | 0.0003 | - |
292
+ | 0.1852 | 6400 | 0.0002 | - |
293
+ | 0.1866 | 6450 | 0.0003 | - |
294
+ | 0.1881 | 6500 | 0.0002 | - |
295
+ | 0.1895 | 6550 | 0.0003 | - |
296
+ | 0.1910 | 6600 | 0.0002 | - |
297
+ | 0.1924 | 6650 | 0.0003 | - |
298
+ | 0.1939 | 6700 | 0.0002 | - |
299
+ | 0.1953 | 6750 | 0.0002 | - |
300
+ | 0.1968 | 6800 | 0.0002 | - |
301
+ | 0.1982 | 6850 | 0.0003 | - |
302
+ | 0.1996 | 6900 | 0.0003 | - |
303
+ | 0.2011 | 6950 | 0.0002 | - |
304
+ | 0.2025 | 7000 | 0.0002 | - |
305
+ | 0.2040 | 7050 | 0.0001 | - |
306
+ | 0.2054 | 7100 | 0.0002 | - |
307
+ | 0.2069 | 7150 | 0.0002 | - |
308
+ | 0.2083 | 7200 | 0.0002 | - |
309
+ | 0.2098 | 7250 | 0.0002 | - |
310
+ | 0.2112 | 7300 | 0.0002 | - |
311
+ | 0.2127 | 7350 | 0.0002 | - |
312
+ | 0.2141 | 7400 | 0.0002 | - |
313
+ | 0.2156 | 7450 | 0.0004 | - |
314
+ | 0.2170 | 7500 | 0.0002 | - |
315
+ | 0.2185 | 7550 | 0.0002 | - |
316
+ | 0.2199 | 7600 | 0.0003 | - |
317
+ | 0.2213 | 7650 | 0.0002 | - |
318
+ | 0.2228 | 7700 | 0.0003 | - |
319
+ | 0.2242 | 7750 | 0.0002 | - |
320
+ | 0.2257 | 7800 | 0.0001 | - |
321
+ | 0.2271 | 7850 | 0.0001 | - |
322
+ | 0.2286 | 7900 | 0.0002 | - |
323
+ | 0.2300 | 7950 | 0.0002 | - |
324
+ | 0.2315 | 8000 | 0.0001 | - |
325
+ | 0.2329 | 8050 | 0.0002 | - |
326
+ | 0.2344 | 8100 | 0.0002 | - |
327
+ | 0.2358 | 8150 | 0.0002 | - |
328
+ | 0.2373 | 8200 | 0.0002 | - |
329
+ | 0.2387 | 8250 | 0.0002 | - |
330
+ | 0.2402 | 8300 | 0.0001 | - |
331
+ | 0.2416 | 8350 | 0.0005 | - |
332
+ | 0.2430 | 8400 | 0.002 | - |
333
+ | 0.2445 | 8450 | 0.0037 | - |
334
+ | 0.2459 | 8500 | 0.0516 | - |
335
+ | 0.2474 | 8550 | 0.0028 | - |
336
+ | 0.2488 | 8600 | 0.0013 | - |
337
+ | 0.2503 | 8650 | 0.0017 | - |
338
+ | 0.2517 | 8700 | 0.0012 | - |
339
+ | 0.2532 | 8750 | 0.0513 | - |
340
+ | 0.2546 | 8800 | 0.001 | - |
341
+ | 0.2561 | 8850 | 0.035 | - |
342
+ | 0.2575 | 8900 | 0.0005 | - |
343
+ | 0.2590 | 8950 | 0.0076 | - |
344
+ | 0.2604 | 9000 | 0.0113 | - |
345
+ | 0.2619 | 9050 | 0.0006 | - |
346
+ | 0.2633 | 9100 | 0.0006 | - |
347
+ | 0.2647 | 9150 | 0.0018 | - |
348
+ | 0.2662 | 9200 | 0.0025 | - |
349
+ | 0.2676 | 9250 | 0.0011 | - |
350
+ | 0.2691 | 9300 | 0.001 | - |
351
+ | 0.2705 | 9350 | 0.0011 | - |
352
+ | 0.2720 | 9400 | 0.0004 | - |
353
+ | 0.2734 | 9450 | 0.0012 | - |
354
+ | 0.2749 | 9500 | 0.0011 | - |
355
+ | 0.2763 | 9550 | 0.0009 | - |
356
+ | 0.2778 | 9600 | 0.0003 | - |
357
+ | 0.2792 | 9650 | 0.0005 | - |
358
+ | 0.2807 | 9700 | 0.0006 | - |
359
+ | 0.2821 | 9750 | 0.0004 | - |
360
+ | 0.2836 | 9800 | 0.0004 | - |
361
+ | 0.2850 | 9850 | 0.0009 | - |
362
+ | 0.2865 | 9900 | 0.0014 | - |
363
+ | 0.2879 | 9950 | 0.0007 | - |
364
+ | 0.2893 | 10000 | 0.0014 | - |
365
+ | 0.2908 | 10050 | 0.0007 | - |
366
+ | 0.2922 | 10100 | 0.0003 | - |
367
+ | 0.2937 | 10150 | 0.0015 | - |
368
+ | 0.2951 | 10200 | 0.0003 | - |
369
+ | 0.2966 | 10250 | 0.0006 | - |
370
+ | 0.2980 | 10300 | 0.0003 | - |
371
+ | 0.2995 | 10350 | 0.0003 | - |
372
+ | 0.3009 | 10400 | 0.0004 | - |
373
+ | 0.3024 | 10450 | 0.0003 | - |
374
+ | 0.3038 | 10500 | 0.0008 | - |
375
+ | 0.3053 | 10550 | 0.0002 | - |
376
+ | 0.3067 | 10600 | 0.0005 | - |
377
+ | 0.3082 | 10650 | 0.0004 | - |
378
+ | 0.3096 | 10700 | 0.0006 | - |
379
+ | 0.3110 | 10750 | 0.0002 | - |
380
+ | 0.3125 | 10800 | 0.0008 | - |
381
+ | 0.3139 | 10850 | 0.0005 | - |
382
+ | 0.3154 | 10900 | 0.0004 | - |
383
+ | 0.3168 | 10950 | 0.0002 | - |
384
+ | 0.3183 | 11000 | 0.0002 | - |
385
+ | 0.3197 | 11050 | 0.0002 | - |
386
+ | 0.3212 | 11100 | 0.0006 | - |
387
+ | 0.3226 | 11150 | 0.0003 | - |
388
+ | 0.3241 | 11200 | 0.0002 | - |
389
+ | 0.3255 | 11250 | 0.0002 | - |
390
+ | 0.3270 | 11300 | 0.0003 | - |
391
+ | 0.3284 | 11350 | 0.0001 | - |
392
+ | 0.3299 | 11400 | 0.0002 | - |
393
+ | 0.3313 | 11450 | 0.0004 | - |
394
+ | 0.3327 | 11500 | 0.0006 | - |
395
+ | 0.3342 | 11550 | 0.0003 | - |
396
+ | 0.3356 | 11600 | 0.0003 | - |
397
+ | 0.3371 | 11650 | 0.0002 | - |
398
+ | 0.3385 | 11700 | 0.0002 | - |
399
+ | 0.3400 | 11750 | 0.0005 | - |
400
+ | 0.3414 | 11800 | 0.0003 | - |
401
+ | 0.3429 | 11850 | 0.0004 | - |
402
+ | 0.3443 | 11900 | 0.0004 | - |
403
+ | 0.3458 | 11950 | 0.0002 | - |
404
+ | 0.3472 | 12000 | 0.0004 | - |
405
+ | 0.3487 | 12050 | 0.0002 | - |
406
+ | 0.3501 | 12100 | 0.0002 | - |
407
+ | 0.3516 | 12150 | 0.0002 | - |
408
+ | 0.3530 | 12200 | 0.0002 | - |
409
+ | 0.3544 | 12250 | 0.0002 | - |
410
+ | 0.3559 | 12300 | 0.0002 | - |
411
+ | 0.3573 | 12350 | 0.0003 | - |
412
+ | 0.3588 | 12400 | 0.0002 | - |
413
+ | 0.3602 | 12450 | 0.0002 | - |
414
+ | 0.3617 | 12500 | 0.0002 | - |
415
+ | 0.3631 | 12550 | 0.0005 | - |
416
+ | 0.3646 | 12600 | 0.0003 | - |
417
+ | 0.3660 | 12650 | 0.0003 | - |
418
+ | 0.3675 | 12700 | 0.0002 | - |
419
+ | 0.3689 | 12750 | 0.0004 | - |
420
+ | 0.3704 | 12800 | 0.0003 | - |
421
+ | 0.3718 | 12850 | 0.0003 | - |
422
+ | 0.3733 | 12900 | 0.0001 | - |
423
+ | 0.3747 | 12950 | 0.0002 | - |
424
+ | 0.3761 | 13000 | 0.0001 | - |
425
+ | 0.3776 | 13050 | 0.0002 | - |
426
+ | 0.3790 | 13100 | 0.0001 | - |
427
+ | 0.3805 | 13150 | 0.0001 | - |
428
+ | 0.3819 | 13200 | 0.0002 | - |
429
+ | 0.3834 | 13250 | 0.0003 | - |
430
+ | 0.3848 | 13300 | 0.0001 | - |
431
+ | 0.3863 | 13350 | 0.0003 | - |
432
+ | 0.3877 | 13400 | 0.0002 | - |
433
+ | 0.3892 | 13450 | 0.0001 | - |
434
+ | 0.3906 | 13500 | 0.0003 | - |
435
+ | 0.3921 | 13550 | 0.0002 | - |
436
+ | 0.3935 | 13600 | 0.0002 | - |
437
+ | 0.3950 | 13650 | 0.0001 | - |
438
+ | 0.3964 | 13700 | 0.0004 | - |
439
+ | 0.3978 | 13750 | 0.0002 | - |
440
+ | 0.3993 | 13800 | 0.0002 | - |
441
+ | 0.4007 | 13850 | 0.0003 | - |
442
+ | 0.4022 | 13900 | 0.0002 | - |
443
+ | 0.4036 | 13950 | 0.0003 | - |
444
+ | 0.4051 | 14000 | 0.0003 | - |
445
+ | 0.4065 | 14050 | 0.0003 | - |
446
+ | 0.4080 | 14100 | 0.0002 | - |
447
+ | 0.4094 | 14150 | 0.0002 | - |
448
+ | 0.4109 | 14200 | 0.0002 | - |
449
+ | 0.4123 | 14250 | 0.0002 | - |
450
+ | 0.4138 | 14300 | 0.0002 | - |
451
+ | 0.4152 | 14350 | 0.0002 | - |
452
+ | 0.4167 | 14400 | 0.0002 | - |
453
+ | 0.4181 | 14450 | 0.0003 | - |
454
+ | 0.4195 | 14500 | 0.0002 | - |
455
+ | 0.4210 | 14550 | 0.0002 | - |
456
+ | 0.4224 | 14600 | 0.0001 | - |
457
+ | 0.4239 | 14650 | 0.0003 | - |
458
+ | 0.4253 | 14700 | 0.0002 | - |
459
+ | 0.4268 | 14750 | 0.0002 | - |
460
+ | 0.4282 | 14800 | 0.0002 | - |
461
+ | 0.4297 | 14850 | 0.0002 | - |
462
+ | 0.4311 | 14900 | 0.0002 | - |
463
+ | 0.4326 | 14950 | 0.0003 | - |
464
+ | 0.4340 | 15000 | 0.0002 | - |
465
+ | 0.4355 | 15050 | 0.0002 | - |
466
+ | 0.4369 | 15100 | 0.0002 | - |
467
+ | 0.4384 | 15150 | 0.0002 | - |
468
+ | 0.4398 | 15200 | 0.0002 | - |
469
+ | 0.4412 | 15250 | 0.0001 | - |
470
+ | 0.4427 | 15300 | 0.0002 | - |
471
+ | 0.4441 | 15350 | 0.0003 | - |
472
+ | 0.4456 | 15400 | 0.0003 | - |
473
+ | 0.4470 | 15450 | 0.0003 | - |
474
+ | 0.4485 | 15500 | 0.0002 | - |
475
+ | 0.4499 | 15550 | 0.0001 | - |
476
+ | 0.4514 | 15600 | 0.0001 | - |
477
+ | 0.4528 | 15650 | 0.0001 | - |
478
+ | 0.4543 | 15700 | 0.0001 | - |
479
+ | 0.4557 | 15750 | 0.0002 | - |
480
+ | 0.4572 | 15800 | 0.0001 | - |
481
+ | 0.4586 | 15850 | 0.0002 | - |
482
+ | 0.4601 | 15900 | 0.0003 | - |
483
+ | 0.4615 | 15950 | 0.0002 | - |
484
+ | 0.4629 | 16000 | 0.0002 | - |
485
+ | 0.4644 | 16050 | 0.0002 | - |
486
+ | 0.4658 | 16100 | 0.0001 | - |
487
+ | 0.4673 | 16150 | 0.0001 | - |
488
+ | 0.4687 | 16200 | 0.0001 | - |
489
+ | 0.4702 | 16250 | 0.0002 | - |
490
+ | 0.4716 | 16300 | 0.0003 | - |
491
+ | 0.4731 | 16350 | 0.0001 | - |
492
+ | 0.4745 | 16400 | 0.0001 | - |
493
+ | 0.4760 | 16450 | 0.0001 | - |
494
+ | 0.4774 | 16500 | 0.0002 | - |
495
+ | 0.4789 | 16550 | 0.0006 | - |
496
+ | 0.4803 | 16600 | 0.0002 | - |
497
+ | 0.4818 | 16650 | 0.0001 | - |
498
+ | 0.4832 | 16700 | 0.0002 | - |
499
+ | 0.4847 | 16750 | 0.0001 | - |
500
+ | 0.4861 | 16800 | 0.0003 | - |
501
+ | 0.4875 | 16850 | 0.0001 | - |
502
+ | 0.4890 | 16900 | 0.0002 | - |
503
+ | 0.4904 | 16950 | 0.0002 | - |
504
+ | 0.4919 | 17000 | 0.0001 | - |
505
+ | 0.4933 | 17050 | 0.0002 | - |
506
+ | 0.4948 | 17100 | 0.0001 | - |
507
+ | 0.4962 | 17150 | 0.0002 | - |
508
+ | 0.4977 | 17200 | 0.0002 | - |
509
+ | 0.4991 | 17250 | 0.0001 | - |
510
+ | 0.5006 | 17300 | 0.0002 | - |
511
+ | 0.5020 | 17350 | 0.0002 | - |
512
+ | 0.5035 | 17400 | 0.0001 | - |
513
+ | 0.5049 | 17450 | 0.0002 | - |
514
+ | 0.5064 | 17500 | 0.0003 | - |
515
+ | 0.5078 | 17550 | 0.0001 | - |
516
+ | 0.5092 | 17600 | 0.0002 | - |
517
+ | 0.5107 | 17650 | 0.0001 | - |
518
+ | 0.5121 | 17700 | 0.0002 | - |
519
+ | 0.5136 | 17750 | 0.0002 | - |
520
+ | 0.5150 | 17800 | 0.0003 | - |
521
+ | 0.5165 | 17850 | 0.0002 | - |
522
+ | 0.5179 | 17900 | 0.0002 | - |
523
+ | 0.5194 | 17950 | 0.0001 | - |
524
+ | 0.5208 | 18000 | 0.0002 | - |
525
+ | 0.5223 | 18050 | 0.0001 | - |
526
+ | 0.5237 | 18100 | 0.0001 | - |
527
+ | 0.5252 | 18150 | 0.0001 | - |
528
+ | 0.5266 | 18200 | 0.0003 | - |
529
+ | 0.5281 | 18250 | 0.0001 | - |
530
+ | 0.5295 | 18300 | 0.0001 | - |
531
+ | 0.5309 | 18350 | 0.0001 | - |
532
+ | 0.5324 | 18400 | 0.0001 | - |
533
+ | 0.5338 | 18450 | 0.0002 | - |
534
+ | 0.5353 | 18500 | 0.0008 | - |
535
+ | 0.5367 | 18550 | 0.0002 | - |
536
+ | 0.5382 | 18600 | 0.0001 | - |
537
+ | 0.5396 | 18650 | 0.0002 | - |
538
+ | 0.5411 | 18700 | 0.0002 | - |
539
+ | 0.5425 | 18750 | 0.0001 | - |
540
+ | 0.5440 | 18800 | 0.0001 | - |
541
+ | 0.5454 | 18850 | 0.0001 | - |
542
+ | 0.5469 | 18900 | 0.0002 | - |
543
+ | 0.5483 | 18950 | 0.0001 | - |
544
+ | 0.5498 | 19000 | 0.0001 | - |
545
+ | 0.5512 | 19050 | 0.0001 | - |
546
+ | 0.5526 | 19100 | 0.0002 | - |
547
+ | 0.5541 | 19150 | 0.0001 | - |
548
+ | 0.5555 | 19200 | 0.0001 | - |
549
+ | 0.5570 | 19250 | 0.0002 | - |
550
+ | 0.5584 | 19300 | 0.0001 | - |
551
+ | 0.5599 | 19350 | 0.0002 | - |
552
+ | 0.5613 | 19400 | 0.0001 | - |
553
+ | 0.5628 | 19450 | 0.0002 | - |
554
+ | 0.5642 | 19500 | 0.0001 | - |
555
+ | 0.5657 | 19550 | 0.0002 | - |
556
+ | 0.5671 | 19600 | 0.0002 | - |
557
+ | 0.5686 | 19650 | 0.0002 | - |
558
+ | 0.5700 | 19700 | 0.0001 | - |
559
+ | 0.5715 | 19750 | 0.0001 | - |
560
+ | 0.5729 | 19800 | 0.0003 | - |
561
+ | 0.5743 | 19850 | 0.0001 | - |
562
+ | 0.5758 | 19900 | 0.0001 | - |
563
+ | 0.5772 | 19950 | 0.0001 | - |
564
+ | 0.5787 | 20000 | 0.0001 | - |
565
+ | 0.5801 | 20050 | 0.0001 | - |
566
+ | 0.5816 | 20100 | 0.0001 | - |
567
+ | 0.5830 | 20150 | 0.0001 | - |
568
+ | 0.5845 | 20200 | 0.0001 | - |
569
+ | 0.5859 | 20250 | 0.0001 | - |
570
+ | 0.5874 | 20300 | 0.0002 | - |
571
+ | 0.5888 | 20350 | 0.0002 | - |
572
+ | 0.5903 | 20400 | 0.0001 | - |
573
+ | 0.5917 | 20450 | 0.0002 | - |
574
+ | 0.5932 | 20500 | 0.0001 | - |
575
+ | 0.5946 | 20550 | 0.0001 | - |
576
+ | 0.5960 | 20600 | 0.0001 | - |
577
+ | 0.5975 | 20650 | 0.0002 | - |
578
+ | 0.5989 | 20700 | 0.0002 | - |
579
+ | 0.6004 | 20750 | 0.0001 | - |
580
+ | 0.6018 | 20800 | 0.0001 | - |
581
+ | 0.6033 | 20850 | 0.0002 | - |
582
+ | 0.6047 | 20900 | 0.0001 | - |
583
+ | 0.6062 | 20950 | 0.0002 | - |
584
+ | 0.6076 | 21000 | 0.0001 | - |
585
+ | 0.6091 | 21050 | 0.0001 | - |
586
+ | 0.6105 | 21100 | 0.0001 | - |
587
+ | 0.6120 | 21150 | 0.0002 | - |
588
+ | 0.6134 | 21200 | 0.0001 | - |
589
+ | 0.6149 | 21250 | 0.0001 | - |
590
+ | 0.6163 | 21300 | 0.0001 | - |
591
+ | 0.6177 | 21350 | 0.0001 | - |
592
+ | 0.6192 | 21400 | 0.0002 | - |
593
+ | 0.6206 | 21450 | 0.0001 | - |
594
+ | 0.6221 | 21500 | 0.0002 | - |
595
+ | 0.6235 | 21550 | 0.0003 | - |
596
+ | 0.6250 | 21600 | 0.0001 | - |
597
+ | 0.6264 | 21650 | 0.0001 | - |
598
+ | 0.6279 | 21700 | 0.0001 | - |
599
+ | 0.6293 | 21750 | 0.0001 | - |
600
+ | 0.6308 | 21800 | 0.0002 | - |
601
+ | 0.6322 | 21850 | 0.0001 | - |
602
+ | 0.6337 | 21900 | 0.0001 | - |
603
+ | 0.6351 | 21950 | 0.0001 | - |
604
+ | 0.6366 | 22000 | 0.0002 | - |
605
+ | 0.6380 | 22050 | 0.0001 | - |
606
+ | 0.6394 | 22100 | 0.0001 | - |
607
+ | 0.6409 | 22150 | 0.0002 | - |
608
+ | 0.6423 | 22200 | 0.0002 | - |
609
+ | 0.6438 | 22250 | 0.0003 | - |
610
+ | 0.6452 | 22300 | 0.0001 | - |
611
+ | 0.6467 | 22350 | 0.0001 | - |
612
+ | 0.6481 | 22400 | 0.0001 | - |
613
+ | 0.6496 | 22450 | 0.0002 | - |
614
+ | 0.6510 | 22500 | 0.0001 | - |
615
+ | 0.6525 | 22550 | 0.0001 | - |
616
+ | 0.6539 | 22600 | 0.0001 | - |
617
+ | 0.6554 | 22650 | 0.0001 | - |
618
+ | 0.6568 | 22700 | 0.0002 | - |
619
+ | 0.6583 | 22750 | 0.0001 | - |
620
+ | 0.6597 | 22800 | 0.0001 | - |
621
+ | 0.6611 | 22850 | 0.0001 | - |
622
+ | 0.6626 | 22900 | 0.0001 | - |
623
+ | 0.6640 | 22950 | 0.0001 | - |
624
+ | 0.6655 | 23000 | 0.0001 | - |
625
+ | 0.6669 | 23050 | 0.0002 | - |
626
+ | 0.6684 | 23100 | 0.0001 | - |
627
+ | 0.6698 | 23150 | 0.0001 | - |
628
+ | 0.6713 | 23200 | 0.0001 | - |
629
+ | 0.6727 | 23250 | 0.0001 | - |
630
+ | 0.6742 | 23300 | 0.0002 | - |
631
+ | 0.6756 | 23350 | 0.0002 | - |
632
+ | 0.6771 | 23400 | 0.0001 | - |
633
+ | 0.6785 | 23450 | 0.0001 | - |
634
+ | 0.6800 | 23500 | 0.0001 | - |
635
+ | 0.6814 | 23550 | 0.0001 | - |
636
+ | 0.6829 | 23600 | 0.0002 | - |
637
+ | 0.6843 | 23650 | 0.0001 | - |
638
+ | 0.6857 | 23700 | 0.0001 | - |
639
+ | 0.6872 | 23750 | 0.0001 | - |
640
+ | 0.6886 | 23800 | 0.0001 | - |
641
+ | 0.6901 | 23850 | 0.0002 | - |
642
+ | 0.6915 | 23900 | 0.0001 | - |
643
+ | 0.6930 | 23950 | 0.0001 | - |
644
+ | 0.6944 | 24000 | 0.0002 | - |
645
+ | 0.6959 | 24050 | 0.0001 | - |
646
+ | 0.6973 | 24100 | 0.0001 | - |
647
+ | 0.6988 | 24150 | 0.0001 | - |
648
+ | 0.7002 | 24200 | 0.0001 | - |
649
+ | 0.7017 | 24250 | 0.0001 | - |
650
+ | 0.7031 | 24300 | 0.0001 | - |
651
+ | 0.7046 | 24350 | 0.0001 | - |
652
+ | 0.7060 | 24400 | 0.0001 | - |
653
+ | 0.7074 | 24450 | 0.0002 | - |
654
+ | 0.7089 | 24500 | 0.0001 | - |
655
+ | 0.7103 | 24550 | 0.0002 | - |
656
+ | 0.7118 | 24600 | 0.0001 | - |
657
+ | 0.7132 | 24650 | 0.0001 | - |
658
+ | 0.7147 | 24700 | 0.0001 | - |
659
+ | 0.7161 | 24750 | 0.0001 | - |
660
+ | 0.7176 | 24800 | 0.0001 | - |
661
+ | 0.7190 | 24850 | 0.0001 | - |
662
+ | 0.7205 | 24900 | 0.0001 | - |
663
+ | 0.7219 | 24950 | 0.0001 | - |
664
+ | 0.7234 | 25000 | 0.0001 | - |
665
+ | 0.7248 | 25050 | 0.0002 | - |
666
+ | 0.7263 | 25100 | 0.0001 | - |
667
+ | 0.7277 | 25150 | 0.0001 | - |
668
+ | 0.7291 | 25200 | 0.0001 | - |
669
+ | 0.7306 | 25250 | 0.0001 | - |
670
+ | 0.7320 | 25300 | 0.0001 | - |
671
+ | 0.7335 | 25350 | 0.0001 | - |
672
+ | 0.7349 | 25400 | 0.0 | - |
673
+ | 0.7364 | 25450 | 0.0001 | - |
674
+ | 0.7378 | 25500 | 0.0001 | - |
675
+ | 0.7393 | 25550 | 0.0001 | - |
676
+ | 0.7407 | 25600 | 0.0001 | - |
677
+ | 0.7422 | 25650 | 0.0001 | - |
678
+ | 0.7436 | 25700 | 0.0001 | - |
679
+ | 0.7451 | 25750 | 0.0001 | - |
680
+ | 0.7465 | 25800 | 0.0 | - |
681
+ | 0.7480 | 25850 | 0.0001 | - |
682
+ | 0.7494 | 25900 | 0.0001 | - |
683
+ | 0.7508 | 25950 | 0.0001 | - |
684
+ | 0.7523 | 26000 | 0.0001 | - |
685
+ | 0.7537 | 26050 | 0.0001 | - |
686
+ | 0.7552 | 26100 | 0.0001 | - |
687
+ | 0.7566 | 26150 | 0.0001 | - |
688
+ | 0.7581 | 26200 | 0.0001 | - |
689
+ | 0.7595 | 26250 | 0.0001 | - |
690
+ | 0.7610 | 26300 | 0.0001 | - |
691
+ | 0.7624 | 26350 | 0.0001 | - |
692
+ | 0.7639 | 26400 | 0.0002 | - |
693
+ | 0.7653 | 26450 | 0.0001 | - |
694
+ | 0.7668 | 26500 | 0.0001 | - |
695
+ | 0.7682 | 26550 | 0.0001 | - |
696
+ | 0.7697 | 26600 | 0.0001 | - |
697
+ | 0.7711 | 26650 | 0.0002 | - |
698
+ | 0.7725 | 26700 | 0.0001 | - |
699
+ | 0.7740 | 26750 | 0.0001 | - |
700
+ | 0.7754 | 26800 | 0.0001 | - |
701
+ | 0.7769 | 26850 | 0.0001 | - |
702
+ | 0.7783 | 26900 | 0.0001 | - |
703
+ | 0.7798 | 26950 | 0.0001 | - |
704
+ | 0.7812 | 27000 | 0.0001 | - |
705
+ | 0.7827 | 27050 | 0.0001 | - |
706
+ | 0.7841 | 27100 | 0.0001 | - |
707
+ | 0.7856 | 27150 | 0.0001 | - |
708
+ | 0.7870 | 27200 | 0.0001 | - |
709
+ | 0.7885 | 27250 | 0.0001 | - |
710
+ | 0.7899 | 27300 | 0.0001 | - |
711
+ | 0.7914 | 27350 | 0.0001 | - |
712
+ | 0.7928 | 27400 | 0.0001 | - |
713
+ | 0.7942 | 27450 | 0.0001 | - |
714
+ | 0.7957 | 27500 | 0.0001 | - |
715
+ | 0.7971 | 27550 | 0.0001 | - |
716
+ | 0.7986 | 27600 | 0.0001 | - |
717
+ | 0.8000 | 27650 | 0.0001 | - |
718
+ | 0.8015 | 27700 | 0.0001 | - |
719
+ | 0.8029 | 27750 | 0.0001 | - |
720
+ | 0.8044 | 27800 | 0.0 | - |
721
+ | 0.8058 | 27850 | 0.0001 | - |
722
+ | 0.8073 | 27900 | 0.0001 | - |
723
+ | 0.8087 | 27950 | 0.0001 | - |
724
+ | 0.8102 | 28000 | 0.0001 | - |
725
+ | 0.8116 | 28050 | 0.0 | - |
726
+ | 0.8131 | 28100 | 0.0 | - |
727
+ | 0.8145 | 28150 | 0.0001 | - |
728
+ | 0.8159 | 28200 | 0.0001 | - |
729
+ | 0.8174 | 28250 | 0.0001 | - |
730
+ | 0.8188 | 28300 | 0.0001 | - |
731
+ | 0.8203 | 28350 | 0.0001 | - |
732
+ | 0.8217 | 28400 | 0.0001 | - |
733
+ | 0.8232 | 28450 | 0.0001 | - |
734
+ | 0.8246 | 28500 | 0.0001 | - |
735
+ | 0.8261 | 28550 | 0.0001 | - |
736
+ | 0.8275 | 28600 | 0.0001 | - |
737
+ | 0.8290 | 28650 | 0.0001 | - |
738
+ | 0.8304 | 28700 | 0.0001 | - |
739
+ | 0.8319 | 28750 | 0.0001 | - |
740
+ | 0.8333 | 28800 | 0.0001 | - |
741
+ | 0.8348 | 28850 | 0.0002 | - |
742
+ | 0.8362 | 28900 | 0.0001 | - |
743
+ | 0.8376 | 28950 | 0.0001 | - |
744
+ | 0.8391 | 29000 | 0.0001 | - |
745
+ | 0.8405 | 29050 | 0.0001 | - |
746
+ | 0.8420 | 29100 | 0.0001 | - |
747
+ | 0.8434 | 29150 | 0.0001 | - |
748
+ | 0.8449 | 29200 | 0.0 | - |
749
+ | 0.8463 | 29250 | 0.0001 | - |
750
+ | 0.8478 | 29300 | 0.0001 | - |
751
+ | 0.8492 | 29350 | 0.0001 | - |
752
+ | 0.8507 | 29400 | 0.0001 | - |
753
+ | 0.8521 | 29450 | 0.0001 | - |
754
+ | 0.8536 | 29500 | 0.0001 | - |
755
+ | 0.8550 | 29550 | 0.0001 | - |
756
+ | 0.8565 | 29600 | 0.0002 | - |
757
+ | 0.8579 | 29650 | 0.0 | - |
758
+ | 0.8594 | 29700 | 0.0001 | - |
759
+ | 0.8608 | 29750 | 0.0001 | - |
760
+ | 0.8622 | 29800 | 0.0001 | - |
761
+ | 0.8637 | 29850 | 0.0001 | - |
762
+ | 0.8651 | 29900 | 0.0 | - |
763
+ | 0.8666 | 29950 | 0.0001 | - |
764
+ | 0.8680 | 30000 | 0.0001 | - |
765
+ | 0.8695 | 30050 | 0.0001 | - |
766
+ | 0.8709 | 30100 | 0.0 | - |
767
+ | 0.8724 | 30150 | 0.0 | - |
768
+ | 0.8738 | 30200 | 0.0001 | - |
769
+ | 0.8753 | 30250 | 0.0001 | - |
770
+ | 0.8767 | 30300 | 0.0001 | - |
771
+ | 0.8782 | 30350 | 0.0001 | - |
772
+ | 0.8796 | 30400 | 0.0001 | - |
773
+ | 0.8811 | 30450 | 0.0001 | - |
774
+ | 0.8825 | 30500 | 0.0001 | - |
775
+ | 0.8839 | 30550 | 0.0001 | - |
776
+ | 0.8854 | 30600 | 0.0 | - |
777
+ | 0.8868 | 30650 | 0.0001 | - |
778
+ | 0.8883 | 30700 | 0.0001 | - |
779
+ | 0.8897 | 30750 | 0.0001 | - |
780
+ | 0.8912 | 30800 | 0.0001 | - |
781
+ | 0.8926 | 30850 | 0.0 | - |
782
+ | 0.8941 | 30900 | 0.0 | - |
783
+ | 0.8955 | 30950 | 0.0001 | - |
784
+ | 0.8970 | 31000 | 0.0001 | - |
785
+ | 0.8984 | 31050 | 0.0001 | - |
786
+ | 0.8999 | 31100 | 0.0001 | - |
787
+ | 0.9013 | 31150 | 0.0 | - |
788
+ | 0.9028 | 31200 | 0.0001 | - |
789
+ | 0.9042 | 31250 | 0.0001 | - |
790
+ | 0.9056 | 31300 | 0.0001 | - |
791
+ | 0.9071 | 31350 | 0.0001 | - |
792
+ | 0.9085 | 31400 | 0.0001 | - |
793
+ | 0.9100 | 31450 | 0.0002 | - |
794
+ | 0.9114 | 31500 | 0.0001 | - |
795
+ | 0.9129 | 31550 | 0.0001 | - |
796
+ | 0.9143 | 31600 | 0.0001 | - |
797
+ | 0.9158 | 31650 | 0.0001 | - |
798
+ | 0.9172 | 31700 | 0.0001 | - |
799
+ | 0.9187 | 31750 | 0.0001 | - |
800
+ | 0.9201 | 31800 | 0.0001 | - |
801
+ | 0.9216 | 31850 | 0.0001 | - |
802
+ | 0.9230 | 31900 | 0.0001 | - |
803
+ | 0.9245 | 31950 | 0.0001 | - |
804
+ | 0.9259 | 32000 | 0.0001 | - |
805
+ | 0.9273 | 32050 | 0.0 | - |
806
+ | 0.9288 | 32100 | 0.0002 | - |
807
+ | 0.9302 | 32150 | 0.0001 | - |
808
+ | 0.9317 | 32200 | 0.0001 | - |
809
+ | 0.9331 | 32250 | 0.0001 | - |
810
+ | 0.9346 | 32300 | 0.0002 | - |
811
+ | 0.9360 | 32350 | 0.0 | - |
812
+ | 0.9375 | 32400 | 0.0001 | - |
813
+ | 0.9389 | 32450 | 0.0001 | - |
814
+ | 0.9404 | 32500 | 0.0 | - |
815
+ | 0.9418 | 32550 | 0.0001 | - |
816
+ | 0.9433 | 32600 | 0.0001 | - |
817
+ | 0.9447 | 32650 | 0.0001 | - |
818
+ | 0.9462 | 32700 | 0.0001 | - |
819
+ | 0.9476 | 32750 | 0.0001 | - |
820
+ | 0.9490 | 32800 | 0.0001 | - |
821
+ | 0.9505 | 32850 | 0.0001 | - |
822
+ | 0.9519 | 32900 | 0.0 | - |
823
+ | 0.9534 | 32950 | 0.0001 | - |
824
+ | 0.9548 | 33000 | 0.0001 | - |
825
+ | 0.9563 | 33050 | 0.0001 | - |
826
+ | 0.9577 | 33100 | 0.0001 | - |
827
+ | 0.9592 | 33150 | 0.0001 | - |
828
+ | 0.9606 | 33200 | 0.0001 | - |
829
+ | 0.9621 | 33250 | 0.0001 | - |
830
+ | 0.9635 | 33300 | 0.0001 | - |
831
+ | 0.9650 | 33350 | 0.0 | - |
832
+ | 0.9664 | 33400 | 0.0001 | - |
833
+ | 0.9679 | 33450 | 0.0001 | - |
834
+ | 0.9693 | 33500 | 0.0 | - |
835
+ | 0.9707 | 33550 | 0.0001 | - |
836
+ | 0.9722 | 33600 | 0.0 | - |
837
+ | 0.9736 | 33650 | 0.0001 | - |
838
+ | 0.9751 | 33700 | 0.0001 | - |
839
+ | 0.9765 | 33750 | 0.0001 | - |
840
+ | 0.9780 | 33800 | 0.0 | - |
841
+ | 0.9794 | 33850 | 0.0001 | - |
842
+ | 0.9809 | 33900 | 0.0001 | - |
843
+ | 0.9823 | 33950 | 0.0001 | - |
844
+ | 0.9838 | 34000 | 0.0001 | - |
845
+ | 0.9852 | 34050 | 0.0 | - |
846
+ | 0.9867 | 34100 | 0.0001 | - |
847
+ | 0.9881 | 34150 | 0.0 | - |
848
+ | 0.9896 | 34200 | 0.0001 | - |
849
+ | 0.9910 | 34250 | 0.0 | - |
850
+ | 0.9924 | 34300 | 0.0001 | - |
851
+ | 0.9939 | 34350 | 0.0 | - |
852
+ | 0.9953 | 34400 | 0.0001 | - |
853
+ | 0.9968 | 34450 | 0.0 | - |
854
+ | 0.9982 | 34500 | 0.0 | - |
855
+ | 0.9997 | 34550 | 0.0001 | - |
856
+ | **1.0** | **34561** | **-** | **0.0036** |
857
+
858
+ * The bold row denotes the saved checkpoint.
859
+ ### Framework Versions
860
+ - Python: 3.11.9
861
+ - SetFit: 1.1.0.dev0
862
+ - Sentence Transformers: 3.0.1
863
+ - Transformers: 4.44.2
864
+ - PyTorch: 2.4.0+cu121
865
+ - Datasets: 2.21.0
866
+ - Tokenizers: 0.19.1
867
+
868
+ ## Citation
869
+
870
+ ### BibTeX
871
+ ```bibtex
872
+ @article{https://doi.org/10.48550/arxiv.2209.11055,
873
+ doi = {10.48550/ARXIV.2209.11055},
874
+ url = {https://arxiv.org/abs/2209.11055},
875
+ author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
876
+ keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
877
+ title = {Efficient Few-Shot Learning Without Prompts},
878
+ publisher = {arXiv},
879
+ year = {2022},
880
+ copyright = {Creative Commons Attribution 4.0 International}
881
+ }
882
+ ```
883
+
884
+ <!--
885
+ ## Glossary
886
+
887
+ *Clearly define terms in order to be accessible across audiences.*
888
+ -->
889
+
890
+ <!--
891
+ ## Model Card Authors
892
+
893
+ *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
894
+ -->
895
+
896
+ <!--
897
+ ## Model Card Contact
898
+
899
+ *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
900
+ -->
config.json ADDED
@@ -0,0 +1,32 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "_name_or_path": "bge-large-en-v1.5-brahmaputra-iter-8-2nd-1-epoch/step_34561",
3
+ "architectures": [
4
+ "BertModel"
5
+ ],
6
+ "attention_probs_dropout_prob": 0.1,
7
+ "classifier_dropout": null,
8
+ "gradient_checkpointing": false,
9
+ "hidden_act": "gelu",
10
+ "hidden_dropout_prob": 0.1,
11
+ "hidden_size": 1024,
12
+ "id2label": {
13
+ "0": "LABEL_0"
14
+ },
15
+ "initializer_range": 0.02,
16
+ "intermediate_size": 4096,
17
+ "label2id": {
18
+ "LABEL_0": 0
19
+ },
20
+ "layer_norm_eps": 1e-12,
21
+ "max_position_embeddings": 512,
22
+ "model_type": "bert",
23
+ "num_attention_heads": 16,
24
+ "num_hidden_layers": 24,
25
+ "pad_token_id": 0,
26
+ "position_embedding_type": "absolute",
27
+ "torch_dtype": "float32",
28
+ "transformers_version": "4.44.2",
29
+ "type_vocab_size": 2,
30
+ "use_cache": true,
31
+ "vocab_size": 30522
32
+ }
config_sentence_transformers.json ADDED
@@ -0,0 +1,10 @@
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "__version__": {
3
+ "sentence_transformers": "3.0.1",
4
+ "transformers": "4.44.2",
5
+ "pytorch": "2.4.0+cu121"
6
+ },
7
+ "prompts": {},
8
+ "default_prompt_name": null,
9
+ "similarity_fn_name": null
10
+ }
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 sha256:3e986b412bf3f073661b51827c19071c3df22b9c74a4dcecd74c48eeff045337
3
+ size 1340612432
model_head.pkl ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:db2a03407a1eac2a1009a34322a84afeee04992e61dd11e75696a84ba9c35059
3
+ size 58575
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