Hugging Face's logo

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

Text Classification
setfit
Safetensors
sentence-transformers
bert
generated_from_setfit_trainer
Eval Results
Model card Files Community
nazhan commited on
Commit
6116755
•
1 Parent(s): 5205b97

Add SetFit model

Browse files
Files changed (13) hide show
  1. 1_Pooling/config.json +10 -0
  2. README.md +779 -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 +1,779 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ base_model: BAAI/bge-large-en-v1.5
3
+ datasets:
4
+ - nazhan/brahmaputra-full-datasets-iter-8
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: I'm not interested in filtering the results.
16
+ - text: Please don't filter the data at this point.
17
+ - text: How's your day going?
18
+ - text: What’s the best way to merge the Products and Orders tables to identify products
19
+ with the highest sales growth?
20
+ - text: When is your birthday?
21
+ inference: true
22
+ model-index:
23
+ - name: SetFit with BAAI/bge-large-en-v1.5
24
+ results:
25
+ - task:
26
+ type: text-classification
27
+ name: Text Classification
28
+ dataset:
29
+ name: nazhan/brahmaputra-full-datasets-iter-8
30
+ type: nazhan/brahmaputra-full-datasets-iter-8
31
+ split: test
32
+ metrics:
33
+ - type: accuracy
34
+ value: 1.0
35
+ name: Accuracy
36
+ ---
37
+
38
+ # SetFit with BAAI/bge-large-en-v1.5
39
+
40
+ This is a [SetFit](https://github.com/huggingface/setfit) model trained on the [nazhan/brahmaputra-full-datasets-iter-8](https://huggingface.co/datasets/nazhan/brahmaputra-full-datasets-iter-8) 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.
41
+
42
+ The model has been trained using an efficient few-shot learning technique that involves:
43
+
44
+ 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
45
+ 2. Training a classification head with features from the fine-tuned Sentence Transformer.
46
+
47
+ ## Model Details
48
+
49
+ ### Model Description
50
+ - **Model Type:** SetFit
51
+ - **Sentence Transformer body:** [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5)
52
+ - **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
53
+ - **Maximum Sequence Length:** 512 tokens
54
+ - **Number of Classes:** 7 classes
55
+ - **Training Dataset:** [nazhan/brahmaputra-full-datasets-iter-8](https://huggingface.co/datasets/nazhan/brahmaputra-full-datasets-iter-8)
56
+ <!-- - **Language:** Unknown -->
57
+ <!-- - **License:** Unknown -->
58
+
59
+ ### Model Sources
60
+
61
+ - **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit)
62
+ - **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)
63
+ - **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)
64
+
65
+ ### Model Labels
66
+ | Label | Examples |
67
+ |:-------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
68
+ | Lookup_1 | <ul><li>'Analyze product category revenue impact.'</li><li>'Show me monthly EBIT by product.'</li><li>'Visualize M&A deal size distribution.'</li></ul> |
69
+ | Tablejoin | <ul><li>'Could you link the Orders and Employees tables to find out which departments are processing the most orders?'</li><li>'Is it possible to combine the Employees and Orders tables to see which employees are assigned to specific order types?'</li><li>'Join data_asset_kpi_cf with data_asset_001_kpm tables.'</li></ul> |
70
+ | Lookup | <ul><li>"Show me the details of employees with the last name 'Smith'."</li><li>"Filter by customers with the first name 'Emily' and show me their email addresses."</li><li>"Show me the products with 'Tablet' in the name and filter by price above 200."</li></ul> |
71
+ | Rejection | <ul><li>"Let's not worry about generating additional data."</li><li>"I'd prefer not to apply any filters."</li><li>"I don't want to sort or filter right now."</li></ul> |
72
+ | Viewtables | <ul><li>'What is the inventory of tables held in the starhub_data_asset database?'</li><li>'What tables are available in the starhub_data_asset database for performing basic data explorations?'</li><li>'What is the complete list of all the tables stored in the starhub_data_asset database that require a join operation for data analysis?'</li></ul> |
73
+ | Generalreply | <ul><li>"Oh, I enjoy spending my free time doing a few different things! Sometimes I like to read, other times I might go for a walk or watch a movie. It really just depends on what I'm in the mood for. What about you, how do you like to spend your free time?"</li><li>'What is your favorite color?'</li><li>"that's not good."</li></ul> |
74
+ | Aggregation | <ul><li>'What’s the total number of products sold in the Electronics category?'</li><li>'Determine the total number of orders placed during promotional periods.'</li><li>'What’s the total sales amount recorded in the Orders table?'</li></ul> |
75
+
76
+ ## Evaluation
77
+
78
+ ### Metrics
79
+ | Label | Accuracy |
80
+ |:--------|:---------|
81
+ | **all** | 1.0 |
82
+
83
+ ## Uses
84
+
85
+ ### Direct Use for Inference
86
+
87
+ First install the SetFit library:
88
+
89
+ ```bash
90
+ pip install setfit
91
+ ```
92
+
93
+ Then you can load this model and run inference.
94
+
95
+ ```python
96
+ from setfit import SetFitModel
97
+
98
+ # Download from the 🤗 Hub
99
+ model = SetFitModel.from_pretrained("nazhan/bge-large-en-v1.5-brahmaputra-iter-8-2-epoch")
100
+ # Run inference
101
+ preds = model("How's your day going?")
102
+ ```
103
+
104
+ <!--
105
+ ### Downstream Use
106
+
107
+ *List how someone could finetune this model on their own dataset.*
108
+ -->
109
+
110
+ <!--
111
+ ### Out-of-Scope Use
112
+
113
+ *List how the model may foreseeably be misused and address what users ought not to do with the model.*
114
+ -->
115
+
116
+ <!--
117
+ ## Bias, Risks and Limitations
118
+
119
+ *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
120
+ -->
121
+
122
+ <!--
123
+ ### Recommendations
124
+
125
+ *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
126
+ -->
127
+
128
+ ## Training Details
129
+
130
+ ### Training Set Metrics
131
+ | Training set | Min | Median | Max |
132
+ |:-------------|:----|:--------|:----|
133
+ | Word count | 3 | 11.0696 | 62 |
134
+
135
+ | Label | Training Sample Count |
136
+ |:-------------|:----------------------|
137
+ | Tablejoin | 112 |
138
+ | Rejection | 67 |
139
+ | Aggregation | 71 |
140
+ | Lookup | 56 |
141
+ | Generalreply | 69 |
142
+ | Viewtables | 73 |
143
+ | Lookup_1 | 69 |
144
+
145
+ ### Training Hyperparameters
146
+ - batch_size: (16, 16)
147
+ - num_epochs: (2, 2)
148
+ - max_steps: -1
149
+ - sampling_strategy: oversampling
150
+ - body_learning_rate: (2e-05, 1e-05)
151
+ - head_learning_rate: 0.01
152
+ - loss: CosineSimilarityLoss
153
+ - distance_metric: cosine_distance
154
+ - margin: 0.25
155
+ - end_to_end: False
156
+ - use_amp: False
157
+ - warmup_proportion: 0.1
158
+ - seed: 42
159
+ - eval_max_steps: -1
160
+ - load_best_model_at_end: True
161
+
162
+ ### Training Results
163
+ | Epoch | Step | Training Loss | Validation Loss |
164
+ |:-------:|:---------:|:-------------:|:---------------:|
165
+ | 0.0001 | 1 | 0.1865 | - |
166
+ | 0.0035 | 50 | 0.1599 | - |
167
+ | 0.0070 | 100 | 0.1933 | - |
168
+ | 0.0106 | 150 | 0.1595 | - |
169
+ | 0.0141 | 200 | 0.0899 | - |
170
+ | 0.0176 | 250 | 0.1334 | - |
171
+ | 0.0211 | 300 | 0.0722 | - |
172
+ | 0.0246 | 350 | 0.0411 | - |
173
+ | 0.0282 | 400 | 0.0171 | - |
174
+ | 0.0317 | 450 | 0.0293 | - |
175
+ | 0.0352 | 500 | 0.0218 | - |
176
+ | 0.0387 | 550 | 0.0057 | - |
177
+ | 0.0422 | 600 | 0.0065 | - |
178
+ | 0.0458 | 650 | 0.0047 | - |
179
+ | 0.0493 | 700 | 0.0045 | - |
180
+ | 0.0528 | 750 | 0.0048 | - |
181
+ | 0.0563 | 800 | 0.0032 | - |
182
+ | 0.0599 | 850 | 0.0038 | - |
183
+ | 0.0634 | 900 | 0.0033 | - |
184
+ | 0.0669 | 950 | 0.0027 | - |
185
+ | 0.0704 | 1000 | 0.0025 | - |
186
+ | 0.0739 | 1050 | 0.0024 | - |
187
+ | 0.0775 | 1100 | 0.0021 | - |
188
+ | 0.0810 | 1150 | 0.0025 | - |
189
+ | 0.0845 | 1200 | 0.0016 | - |
190
+ | 0.0880 | 1250 | 0.0019 | - |
191
+ | 0.0915 | 1300 | 0.0017 | - |
192
+ | 0.0951 | 1350 | 0.0016 | - |
193
+ | 0.0986 | 1400 | 0.0025 | - |
194
+ | 0.1021 | 1450 | 0.0016 | - |
195
+ | 0.1056 | 1500 | 0.0015 | - |
196
+ | 0.1091 | 1550 | 0.0012 | - |
197
+ | 0.1127 | 1600 | 0.001 | - |
198
+ | 0.1162 | 1650 | 0.0012 | - |
199
+ | 0.1197 | 1700 | 0.0012 | - |
200
+ | 0.1232 | 1750 | 0.0013 | - |
201
+ | 0.1267 | 1800 | 0.0012 | - |
202
+ | 0.1303 | 1850 | 0.0009 | - |
203
+ | 0.1338 | 1900 | 0.0011 | - |
204
+ | 0.1373 | 1950 | 0.001 | - |
205
+ | 0.1408 | 2000 | 0.0009 | - |
206
+ | 0.1443 | 2050 | 0.0009 | - |
207
+ | 0.1479 | 2100 | 0.0008 | - |
208
+ | 0.1514 | 2150 | 0.0007 | - |
209
+ | 0.1549 | 2200 | 0.0008 | - |
210
+ | 0.1584 | 2250 | 0.0008 | - |
211
+ | 0.1619 | 2300 | 0.0008 | - |
212
+ | 0.1655 | 2350 | 0.0007 | - |
213
+ | 0.1690 | 2400 | 0.0008 | - |
214
+ | 0.1725 | 2450 | 0.0006 | - |
215
+ | 0.1760 | 2500 | 0.0005 | - |
216
+ | 0.1796 | 2550 | 0.0006 | - |
217
+ | 0.1831 | 2600 | 0.0005 | - |
218
+ | 0.1866 | 2650 | 0.0006 | - |
219
+ | 0.1901 | 2700 | 0.0005 | - |
220
+ | 0.1936 | 2750 | 0.0007 | - |
221
+ | 0.1972 | 2800 | 0.0006 | - |
222
+ | 0.2007 | 2850 | 0.0005 | - |
223
+ | 0.2042 | 2900 | 0.0006 | - |
224
+ | 0.2077 | 2950 | 0.0007 | - |
225
+ | 0.2112 | 3000 | 0.0006 | - |
226
+ | 0.2148 | 3050 | 0.0005 | - |
227
+ | 0.2183 | 3100 | 0.0005 | - |
228
+ | 0.2218 | 3150 | 0.0005 | - |
229
+ | 0.2253 | 3200 | 0.0006 | - |
230
+ | 0.2288 | 3250 | 0.0005 | - |
231
+ | 0.2324 | 3300 | 0.0006 | - |
232
+ | 0.2359 | 3350 | 0.0004 | - |
233
+ | 0.2394 | 3400 | 0.0005 | - |
234
+ | 0.2429 | 3450 | 0.0005 | - |
235
+ | 0.2464 | 3500 | 0.0004 | - |
236
+ | 0.2500 | 3550 | 0.0006 | - |
237
+ | 0.2535 | 3600 | 0.0004 | - |
238
+ | 0.2570 | 3650 | 0.0004 | - |
239
+ | 0.2605 | 3700 | 0.0004 | - |
240
+ | 0.2640 | 3750 | 0.0004 | - |
241
+ | 0.2676 | 3800 | 0.0003 | - |
242
+ | 0.2711 | 3850 | 0.0004 | - |
243
+ | 0.2746 | 3900 | 0.0005 | - |
244
+ | 0.2781 | 3950 | 0.0004 | - |
245
+ | 0.2817 | 4000 | 0.0004 | - |
246
+ | 0.2852 | 4050 | 0.0003 | - |
247
+ | 0.2887 | 4100 | 0.0004 | - |
248
+ | 0.2922 | 4150 | 0.0004 | - |
249
+ | 0.2957 | 4200 | 0.0004 | - |
250
+ | 0.2993 | 4250 | 0.0005 | - |
251
+ | 0.3028 | 4300 | 0.0004 | - |
252
+ | 0.3063 | 4350 | 0.0004 | - |
253
+ | 0.3098 | 4400 | 0.0003 | - |
254
+ | 0.3133 | 4450 | 0.0004 | - |
255
+ | 0.3169 | 4500 | 0.0004 | - |
256
+ | 0.3204 | 4550 | 0.0003 | - |
257
+ | 0.3239 | 4600 | 0.0003 | - |
258
+ | 0.3274 | 4650 | 0.0004 | - |
259
+ | 0.3309 | 4700 | 0.0003 | - |
260
+ | 0.3345 | 4750 | 0.0003 | - |
261
+ | 0.3380 | 4800 | 0.0003 | - |
262
+ | 0.3415 | 4850 | 0.0003 | - |
263
+ | 0.3450 | 4900 | 0.0004 | - |
264
+ | 0.3485 | 4950 | 0.0003 | - |
265
+ | 0.3521 | 5000 | 0.0003 | - |
266
+ | 0.3556 | 5050 | 0.0003 | - |
267
+ | 0.3591 | 5100 | 0.0003 | - |
268
+ | 0.3626 | 5150 | 0.0004 | - |
269
+ | 0.3661 | 5200 | 0.0002 | - |
270
+ | 0.3697 | 5250 | 0.0004 | - |
271
+ | 0.3732 | 5300 | 0.0003 | - |
272
+ | 0.3767 | 5350 | 0.0003 | - |
273
+ | 0.3802 | 5400 | 0.0002 | - |
274
+ | 0.3837 | 5450 | 0.0003 | - |
275
+ | 0.3873 | 5500 | 0.0003 | - |
276
+ | 0.3908 | 5550 | 0.0003 | - |
277
+ | 0.3943 | 5600 | 0.0002 | - |
278
+ | 0.3978 | 5650 | 0.0003 | - |
279
+ | 0.4014 | 5700 | 0.0003 | - |
280
+ | 0.4049 | 5750 | 0.0002 | - |
281
+ | 0.4084 | 5800 | 0.0003 | - |
282
+ | 0.4119 | 5850 | 0.0003 | - |
283
+ | 0.4154 | 5900 | 0.0003 | - |
284
+ | 0.4190 | 5950 | 0.0002 | - |
285
+ | 0.4225 | 6000 | 0.0002 | - |
286
+ | 0.4260 | 6050 | 0.0002 | - |
287
+ | 0.4295 | 6100 | 0.0003 | - |
288
+ | 0.4330 | 6150 | 0.0003 | - |
289
+ | 0.4366 | 6200 | 0.0002 | - |
290
+ | 0.4401 | 6250 | 0.0003 | - |
291
+ | 0.4436 | 6300 | 0.0003 | - |
292
+ | 0.4471 | 6350 | 0.0002 | - |
293
+ | 0.4506 | 6400 | 0.0002 | - |
294
+ | 0.4542 | 6450 | 0.0002 | - |
295
+ | 0.4577 | 6500 | 0.0002 | - |
296
+ | 0.4612 | 6550 | 0.0002 | - |
297
+ | 0.4647 | 6600 | 0.0002 | - |
298
+ | 0.4682 | 6650 | 0.0002 | - |
299
+ | 0.4718 | 6700 | 0.0002 | - |
300
+ | 0.4753 | 6750 | 0.0003 | - |
301
+ | 0.4788 | 6800 | 0.0003 | - |
302
+ | 0.4823 | 6850 | 0.0002 | - |
303
+ | 0.4858 | 6900 | 0.0003 | - |
304
+ | 0.4894 | 6950 | 0.0002 | - |
305
+ | 0.4929 | 7000 | 0.0003 | - |
306
+ | 0.4964 | 7050 | 0.0002 | - |
307
+ | 0.4999 | 7100 | 0.0002 | - |
308
+ | 0.5035 | 7150 | 0.0002 | - |
309
+ | 0.5070 | 7200 | 0.0003 | - |
310
+ | 0.5105 | 7250 | 0.0002 | - |
311
+ | 0.5140 | 7300 | 0.0003 | - |
312
+ | 0.5175 | 7350 | 0.0004 | - |
313
+ | 0.5211 | 7400 | 0.0002 | - |
314
+ | 0.5246 | 7450 | 0.0002 | - |
315
+ | 0.5281 | 7500 | 0.0002 | - |
316
+ | 0.5316 | 7550 | 0.0002 | - |
317
+ | 0.5351 | 7600 | 0.0002 | - |
318
+ | 0.5387 | 7650 | 0.0002 | - |
319
+ | 0.5422 | 7700 | 0.0002 | - |
320
+ | 0.5457 | 7750 | 0.0002 | - |
321
+ | 0.5492 | 7800 | 0.0003 | - |
322
+ | 0.5527 | 7850 | 0.0002 | - |
323
+ | 0.5563 | 7900 | 0.0002 | - |
324
+ | 0.5598 | 7950 | 0.0002 | - |
325
+ | 0.5633 | 8000 | 0.0002 | - |
326
+ | 0.5668 | 8050 | 0.0002 | - |
327
+ | 0.5703 | 8100 | 0.0002 | - |
328
+ | 0.5739 | 8150 | 0.0002 | - |
329
+ | 0.5774 | 8200 | 0.0003 | - |
330
+ | 0.5809 | 8250 | 0.0002 | - |
331
+ | 0.5844 | 8300 | 0.0002 | - |
332
+ | 0.5879 | 8350 | 0.0002 | - |
333
+ | 0.5915 | 8400 | 0.0002 | - |
334
+ | 0.5950 | 8450 | 0.0001 | - |
335
+ | 0.5985 | 8500 | 0.0001 | - |
336
+ | 0.6020 | 8550 | 0.0001 | - |
337
+ | 0.6055 | 8600 | 0.0001 | - |
338
+ | 0.6091 | 8650 | 0.0002 | - |
339
+ | 0.6126 | 8700 | 0.0002 | - |
340
+ | 0.6161 | 8750 | 0.0002 | - |
341
+ | 0.6196 | 8800 | 0.0002 | - |
342
+ | 0.6232 | 8850 | 0.0002 | - |
343
+ | 0.6267 | 8900 | 0.0001 | - |
344
+ | 0.6302 | 8950 | 0.0001 | - |
345
+ | 0.6337 | 9000 | 0.0002 | - |
346
+ | 0.6372 | 9050 | 0.0002 | - |
347
+ | 0.6408 | 9100 | 0.0002 | - |
348
+ | 0.6443 | 9150 | 0.0001 | - |
349
+ | 0.6478 | 9200 | 0.0002 | - |
350
+ | 0.6513 | 9250 | 0.0003 | - |
351
+ | 0.6548 | 9300 | 0.0002 | - |
352
+ | 0.6584 | 9350 | 0.0003 | - |
353
+ | 0.6619 | 9400 | 0.0001 | - |
354
+ | 0.6654 | 9450 | 0.0001 | - |
355
+ | 0.6689 | 9500 | 0.0001 | - |
356
+ | 0.6724 | 9550 | 0.0001 | - |
357
+ | 0.6760 | 9600 | 0.0001 | - |
358
+ | 0.6795 | 9650 | 0.0002 | - |
359
+ | 0.6830 | 9700 | 0.0002 | - |
360
+ | 0.6865 | 9750 | 0.0002 | - |
361
+ | 0.6900 | 9800 | 0.0001 | - |
362
+ | 0.6936 | 9850 | 0.0001 | - |
363
+ | 0.6971 | 9900 | 0.0002 | - |
364
+ | 0.7006 | 9950 | 0.0001 | - |
365
+ | 0.7041 | 10000 | 0.0001 | - |
366
+ | 0.7076 | 10050 | 0.0001 | - |
367
+ | 0.7112 | 10100 | 0.0002 | - |
368
+ | 0.7147 | 10150 | 0.0001 | - |
369
+ | 0.7182 | 10200 | 0.0002 | - |
370
+ | 0.7217 | 10250 | 0.0002 | - |
371
+ | 0.7252 | 10300 | 0.0001 | - |
372
+ | 0.7288 | 10350 | 0.0001 | - |
373
+ | 0.7323 | 10400 | 0.0001 | - |
374
+ | 0.7358 | 10450 | 0.0001 | - |
375
+ | 0.7393 | 10500 | 0.0002 | - |
376
+ | 0.7429 | 10550 | 0.0001 | - |
377
+ | 0.7464 | 10600 | 0.0002 | - |
378
+ | 0.7499 | 10650 | 0.0001 | - |
379
+ | 0.7534 | 10700 | 0.0001 | - |
380
+ | 0.7569 | 10750 | 0.0002 | - |
381
+ | 0.7605 | 10800 | 0.0002 | - |
382
+ | 0.7640 | 10850 | 0.0001 | - |
383
+ | 0.7675 | 10900 | 0.0001 | - |
384
+ | 0.7710 | 10950 | 0.0001 | - |
385
+ | 0.7745 | 11000 | 0.0001 | - |
386
+ | 0.7781 | 11050 | 0.0001 | - |
387
+ | 0.7816 | 11100 | 0.0001 | - |
388
+ | 0.7851 | 11150 | 0.0001 | - |
389
+ | 0.7886 | 11200 | 0.0001 | - |
390
+ | 0.7921 | 11250 | 0.0001 | - |
391
+ | 0.7957 | 11300 | 0.0001 | - |
392
+ | 0.7992 | 11350 | 0.0001 | - |
393
+ | 0.8027 | 11400 | 0.0002 | - |
394
+ | 0.8062 | 11450 | 0.0001 | - |
395
+ | 0.8097 | 11500 | 0.0001 | - |
396
+ | 0.8133 | 11550 | 0.0001 | - |
397
+ | 0.8168 | 11600 | 0.0001 | - |
398
+ | 0.8203 | 11650 | 0.0001 | - |
399
+ | 0.8238 | 11700 | 0.0001 | - |
400
+ | 0.8273 | 11750 | 0.0001 | - |
401
+ | 0.8309 | 11800 | 0.0001 | - |
402
+ | 0.8344 | 11850 | 0.0001 | - |
403
+ | 0.8379 | 11900 | 0.0001 | - |
404
+ | 0.8414 | 11950 | 0.0001 | - |
405
+ | 0.8450 | 12000 | 0.0001 | - |
406
+ | 0.8485 | 12050 | 0.0001 | - |
407
+ | 0.8520 | 12100 | 0.0001 | - |
408
+ | 0.8555 | 12150 | 0.0001 | - |
409
+ | 0.8590 | 12200 | 0.0001 | - |
410
+ | 0.8626 | 12250 | 0.0002 | - |
411
+ | 0.8661 | 12300 | 0.0002 | - |
412
+ | 0.8696 | 12350 | 0.0002 | - |
413
+ | 0.8731 | 12400 | 0.0002 | - |
414
+ | 0.8766 | 12450 | 0.0001 | - |
415
+ | 0.8802 | 12500 | 0.0001 | - |
416
+ | 0.8837 | 12550 | 0.0001 | - |
417
+ | 0.8872 | 12600 | 0.0001 | - |
418
+ | 0.8907 | 12650 | 0.0002 | - |
419
+ | 0.8942 | 12700 | 0.0001 | - |
420
+ | 0.8978 | 12750 | 0.0001 | - |
421
+ | 0.9013 | 12800 | 0.0001 | - |
422
+ | 0.9048 | 12850 | 0.0001 | - |
423
+ | 0.9083 | 12900 | 0.0001 | - |
424
+ | 0.9118 | 12950 | 0.0001 | - |
425
+ | 0.9154 | 13000 | 0.0001 | - |
426
+ | 0.9189 | 13050 | 0.0001 | - |
427
+ | 0.9224 | 13100 | 0.0001 | - |
428
+ | 0.9259 | 13150 | 0.0001 | - |
429
+ | 0.9294 | 13200 | 0.0001 | - |
430
+ | 0.9330 | 13250 | 0.0001 | - |
431
+ | 0.9365 | 13300 | 0.0001 | - |
432
+ | 0.9400 | 13350 | 0.0001 | - |
433
+ | 0.9435 | 13400 | 0.0001 | - |
434
+ | 0.9470 | 13450 | 0.0001 | - |
435
+ | 0.9506 | 13500 | 0.0001 | - |
436
+ | 0.9541 | 13550 | 0.0001 | - |
437
+ | 0.9576 | 13600 | 0.0001 | - |
438
+ | 0.9611 | 13650 | 0.0001 | - |
439
+ | 0.9647 | 13700 | 0.0001 | - |
440
+ | 0.9682 | 13750 | 0.0001 | - |
441
+ | 0.9717 | 13800 | 0.0001 | - |
442
+ | 0.9752 | 13850 | 0.0001 | - |
443
+ | 0.9787 | 13900 | 0.0001 | - |
444
+ | 0.9823 | 13950 | 0.0001 | - |
445
+ | 0.9858 | 14000 | 0.0001 | - |
446
+ | 0.9893 | 14050 | 0.0001 | - |
447
+ | 0.9928 | 14100 | 0.0001 | - |
448
+ | 0.9963 | 14150 | 0.0002 | - |
449
+ | 0.9999 | 14200 | 0.0001 | - |
450
+ | 1.0 | 14202 | - | 0.0082 |
451
+ | 1.0034 | 14250 | 0.0001 | - |
452
+ | 1.0069 | 14300 | 0.0001 | - |
453
+ | 1.0104 | 14350 | 0.0001 | - |
454
+ | 1.0139 | 14400 | 0.0001 | - |
455
+ | 1.0175 | 14450 | 0.0001 | - |
456
+ | 1.0210 | 14500 | 0.0001 | - |
457
+ | 1.0245 | 14550 | 0.0001 | - |
458
+ | 1.0280 | 14600 | 0.0001 | - |
459
+ | 1.0315 | 14650 | 0.0001 | - |
460
+ | 1.0351 | 14700 | 0.0001 | - |
461
+ | 1.0386 | 14750 | 0.0001 | - |
462
+ | 1.0421 | 14800 | 0.0001 | - |
463
+ | 1.0456 | 14850 | 0.0001 | - |
464
+ | 1.0491 | 14900 | 0.0001 | - |
465
+ | 1.0527 | 14950 | 0.0001 | - |
466
+ | 1.0562 | 15000 | 0.0001 | - |
467
+ | 1.0597 | 15050 | 0.0001 | - |
468
+ | 1.0632 | 15100 | 0.0001 | - |
469
+ | 1.0668 | 15150 | 0.0001 | - |
470
+ | 1.0703 | 15200 | 0.0001 | - |
471
+ | 1.0738 | 15250 | 0.0001 | - |
472
+ | 1.0773 | 15300 | 0.0001 | - |
473
+ | 1.0808 | 15350 | 0.0001 | - |
474
+ | 1.0844 | 15400 | 0.0001 | - |
475
+ | 1.0879 | 15450 | 0.0001 | - |
476
+ | 1.0914 | 15500 | 0.0001 | - |
477
+ | 1.0949 | 15550 | 0.0001 | - |
478
+ | 1.0984 | 15600 | 0.0001 | - |
479
+ | 1.1020 | 15650 | 0.0001 | - |
480
+ | 1.1055 | 15700 | 0.0001 | - |
481
+ | 1.1090 | 15750 | 0.0001 | - |
482
+ | 1.1125 | 15800 | 0.0001 | - |
483
+ | 1.1160 | 15850 | 0.0001 | - |
484
+ | 1.1196 | 15900 | 0.0001 | - |
485
+ | 1.1231 | 15950 | 0.0001 | - |
486
+ | 1.1266 | 16000 | 0.0001 | - |
487
+ | 1.1301 | 16050 | 0.0001 | - |
488
+ | 1.1336 | 16100 | 0.0001 | - |
489
+ | 1.1372 | 16150 | 0.0001 | - |
490
+ | 1.1407 | 16200 | 0.0001 | - |
491
+ | 1.1442 | 16250 | 0.0001 | - |
492
+ | 1.1477 | 16300 | 0.0001 | - |
493
+ | 1.1512 | 16350 | 0.0001 | - |
494
+ | 1.1548 | 16400 | 0.0001 | - |
495
+ | 1.1583 | 16450 | 0.0001 | - |
496
+ | 1.1618 | 16500 | 0.0001 | - |
497
+ | 1.1653 | 16550 | 0.0001 | - |
498
+ | 1.1688 | 16600 | 0.0001 | - |
499
+ | 1.1724 | 16650 | 0.0001 | - |
500
+ | 1.1759 | 16700 | 0.0001 | - |
501
+ | 1.1794 | 16750 | 0.0001 | - |
502
+ | 1.1829 | 16800 | 0.0001 | - |
503
+ | 1.1865 | 16850 | 0.0001 | - |
504
+ | 1.1900 | 16900 | 0.0001 | - |
505
+ | 1.1935 | 16950 | 0.0001 | - |
506
+ | 1.1970 | 17000 | 0.0001 | - |
507
+ | 1.2005 | 17050 | 0.0001 | - |
508
+ | 1.2041 | 17100 | 0.0001 | - |
509
+ | 1.2076 | 17150 | 0.0001 | - |
510
+ | 1.2111 | 17200 | 0.0001 | - |
511
+ | 1.2146 | 17250 | 0.0001 | - |
512
+ | 1.2181 | 17300 | 0.0001 | - |
513
+ | 1.2217 | 17350 | 0.0001 | - |
514
+ | 1.2252 | 17400 | 0.0001 | - |
515
+ | 1.2287 | 17450 | 0.0001 | - |
516
+ | 1.2322 | 17500 | 0.0001 | - |
517
+ | 1.2357 | 17550 | 0.0001 | - |
518
+ | 1.2393 | 17600 | 0.0001 | - |
519
+ | 1.2428 | 17650 | 0.0001 | - |
520
+ | 1.2463 | 17700 | 0.0001 | - |
521
+ | 1.2498 | 17750 | 0.0001 | - |
522
+ | 1.2533 | 17800 | 0.0001 | - |
523
+ | 1.2569 | 17850 | 0.0001 | - |
524
+ | 1.2604 | 17900 | 0.0001 | - |
525
+ | 1.2639 | 17950 | 0.0001 | - |
526
+ | 1.2674 | 18000 | 0.0001 | - |
527
+ | 1.2709 | 18050 | 0.0001 | - |
528
+ | 1.2745 | 18100 | 0.0001 | - |
529
+ | 1.2780 | 18150 | 0.0001 | - |
530
+ | 1.2815 | 18200 | 0.0001 | - |
531
+ | 1.2850 | 18250 | 0.0001 | - |
532
+ | 1.2886 | 18300 | 0.0001 | - |
533
+ | 1.2921 | 18350 | 0.0001 | - |
534
+ | 1.2956 | 18400 | 0.0001 | - |
535
+ | 1.2991 | 18450 | 0.0001 | - |
536
+ | 1.3026 | 18500 | 0.0001 | - |
537
+ | 1.3062 | 18550 | 0.0001 | - |
538
+ | 1.3097 | 18600 | 0.0001 | - |
539
+ | 1.3132 | 18650 | 0.0001 | - |
540
+ | 1.3167 | 18700 | 0.0001 | - |
541
+ | 1.3202 | 18750 | 0.0001 | - |
542
+ | 1.3238 | 18800 | 0.0001 | - |
543
+ | 1.3273 | 18850 | 0.0001 | - |
544
+ | 1.3308 | 18900 | 0.0001 | - |
545
+ | 1.3343 | 18950 | 0.0001 | - |
546
+ | 1.3378 | 19000 | 0.0001 | - |
547
+ | 1.3414 | 19050 | 0.0001 | - |
548
+ | 1.3449 | 19100 | 0.0001 | - |
549
+ | 1.3484 | 19150 | 0.0001 | - |
550
+ | 1.3519 | 19200 | 0.0001 | - |
551
+ | 1.3554 | 19250 | 0.0001 | - |
552
+ | 1.3590 | 19300 | 0.0001 | - |
553
+ | 1.3625 | 19350 | 0.0001 | - |
554
+ | 1.3660 | 19400 | 0.0001 | - |
555
+ | 1.3695 | 19450 | 0.0001 | - |
556
+ | 1.3730 | 19500 | 0.0001 | - |
557
+ | 1.3766 | 19550 | 0.0001 | - |
558
+ | 1.3801 | 19600 | 0.0001 | - |
559
+ | 1.3836 | 19650 | 0.0001 | - |
560
+ | 1.3871 | 19700 | 0.0001 | - |
561
+ | 1.3906 | 19750 | 0.0001 | - |
562
+ | 1.3942 | 19800 | 0.0001 | - |
563
+ | 1.3977 | 19850 | 0.0001 | - |
564
+ | 1.4012 | 19900 | 0.0001 | - |
565
+ | 1.4047 | 19950 | 0.0001 | - |
566
+ | 1.4083 | 20000 | 0.0001 | - |
567
+ | 1.4118 | 20050 | 0.0001 | - |
568
+ | 1.4153 | 20100 | 0.0001 | - |
569
+ | 1.4188 | 20150 | 0.0001 | - |
570
+ | 1.4223 | 20200 | 0.0001 | - |
571
+ | 1.4259 | 20250 | 0.0001 | - |
572
+ | 1.4294 | 20300 | 0.0001 | - |
573
+ | 1.4329 | 20350 | 0.0001 | - |
574
+ | 1.4364 | 20400 | 0.0 | - |
575
+ | 1.4399 | 20450 | 0.0001 | - |
576
+ | 1.4435 | 20500 | 0.0001 | - |
577
+ | 1.4470 | 20550 | 0.0001 | - |
578
+ | 1.4505 | 20600 | 0.0001 | - |
579
+ | 1.4540 | 20650 | 0.0001 | - |
580
+ | 1.4575 | 20700 | 0.0001 | - |
581
+ | 1.4611 | 20750 | 0.0001 | - |
582
+ | 1.4646 | 20800 | 0.0001 | - |
583
+ | 1.4681 | 20850 | 0.0 | - |
584
+ | 1.4716 | 20900 | 0.0001 | - |
585
+ | 1.4751 | 20950 | 0.0001 | - |
586
+ | 1.4787 | 21000 | 0.0 | - |
587
+ | 1.4822 | 21050 | 0.0001 | - |
588
+ | 1.4857 | 21100 | 0.0001 | - |
589
+ | 1.4892 | 21150 | 0.0001 | - |
590
+ | 1.4927 | 21200 | 0.0001 | - |
591
+ | 1.4963 | 21250 | 0.0001 | - |
592
+ | 1.4998 | 21300 | 0.0001 | - |
593
+ | 1.5033 | 21350 | 0.0 | - |
594
+ | 1.5068 | 21400 | 0.0001 | - |
595
+ | 1.5104 | 21450 | 0.0001 | - |
596
+ | 1.5139 | 21500 | 0.0001 | - |
597
+ | 1.5174 | 21550 | 0.0 | - |
598
+ | 1.5209 | 21600 | 0.0001 | - |
599
+ | 1.5244 | 21650 | 0.0001 | - |
600
+ | 1.5280 | 21700 | 0.0001 | - |
601
+ | 1.5315 | 21750 | 0.0001 | - |
602
+ | 1.5350 | 21800 | 0.0001 | - |
603
+ | 1.5385 | 21850 | 0.0001 | - |
604
+ | 1.5420 | 21900 | 0.0 | - |
605
+ | 1.5456 | 21950 | 0.0001 | - |
606
+ | 1.5491 | 22000 | 0.0001 | - |
607
+ | 1.5526 | 22050 | 0.0001 | - |
608
+ | 1.5561 | 22100 | 0.0001 | - |
609
+ | 1.5596 | 22150 | 0.0001 | - |
610
+ | 1.5632 | 22200 | 0.0001 | - |
611
+ | 1.5667 | 22250 | 0.0 | - |
612
+ | 1.5702 | 22300 | 0.0 | - |
613
+ | 1.5737 | 22350 | 0.0001 | - |
614
+ | 1.5772 | 22400 | 0.0001 | - |
615
+ | 1.5808 | 22450 | 0.0001 | - |
616
+ | 1.5843 | 22500 | 0.0001 | - |
617
+ | 1.5878 | 22550 | 0.0001 | - |
618
+ | 1.5913 | 22600 | 0.0001 | - |
619
+ | 1.5948 | 22650 | 0.0001 | - |
620
+ | 1.5984 | 22700 | 0.0 | - |
621
+ | 1.6019 | 22750 | 0.0001 | - |
622
+ | 1.6054 | 22800 | 0.0001 | - |
623
+ | 1.6089 | 22850 | 0.0001 | - |
624
+ | 1.6124 | 22900 | 0.0001 | - |
625
+ | 1.6160 | 22950 | 0.0001 | - |
626
+ | 1.6195 | 23000 | 0.0001 | - |
627
+ | 1.6230 | 23050 | 0.0001 | - |
628
+ | 1.6265 | 23100 | 0.0001 | - |
629
+ | 1.6301 | 23150 | 0.0 | - |
630
+ | 1.6336 | 23200 | 0.0001 | - |
631
+ | 1.6371 | 23250 | 0.0001 | - |
632
+ | 1.6406 | 23300 | 0.0 | - |
633
+ | 1.6441 | 23350 | 0.0001 | - |
634
+ | 1.6477 | 23400 | 0.0 | - |
635
+ | 1.6512 | 23450 | 0.0001 | - |
636
+ | 1.6547 | 23500 | 0.0 | - |
637
+ | 1.6582 | 23550 | 0.0001 | - |
638
+ | 1.6617 | 23600 | 0.0001 | - |
639
+ | 1.6653 | 23650 | 0.0 | - |
640
+ | 1.6688 | 23700 | 0.0 | - |
641
+ | 1.6723 | 23750 | 0.0001 | - |
642
+ | 1.6758 | 23800 | 0.0001 | - |
643
+ | 1.6793 | 23850 | 0.0 | - |
644
+ | 1.6829 | 23900 | 0.0001 | - |
645
+ | 1.6864 | 23950 | 0.0 | - |
646
+ | 1.6899 | 24000 | 0.0 | - |
647
+ | 1.6934 | 24050 | 0.0 | - |
648
+ | 1.6969 | 24100 | 0.0001 | - |
649
+ | 1.7005 | 24150 | 0.0001 | - |
650
+ | 1.7040 | 24200 | 0.0001 | - |
651
+ | 1.7075 | 24250 | 0.0001 | - |
652
+ | 1.7110 | 24300 | 0.0001 | - |
653
+ | 1.7145 | 24350 | 0.0001 | - |
654
+ | 1.7181 | 24400 | 0.0001 | - |
655
+ | 1.7216 | 24450 | 0.0 | - |
656
+ | 1.7251 | 24500 | 0.0001 | - |
657
+ | 1.7286 | 24550 | 0.0 | - |
658
+ | 1.7322 | 24600 | 0.0001 | - |
659
+ | 1.7357 | 24650 | 0.0001 | - |
660
+ | 1.7392 | 24700 | 0.0 | - |
661
+ | 1.7427 | 24750 | 0.0001 | - |
662
+ | 1.7462 | 24800 | 0.0001 | - |
663
+ | 1.7498 | 24850 | 0.0001 | - |
664
+ | 1.7533 | 24900 | 0.0 | - |
665
+ | 1.7568 | 24950 | 0.0 | - |
666
+ | 1.7603 | 25000 | 0.0001 | - |
667
+ | 1.7638 | 25050 | 0.0001 | - |
668
+ | 1.7674 | 25100 | 0.0001 | - |
669
+ | 1.7709 | 25150 | 0.0001 | - |
670
+ | 1.7744 | 25200 | 0.0 | - |
671
+ | 1.7779 | 25250 | 0.0001 | - |
672
+ | 1.7814 | 25300 | 0.0 | - |
673
+ | 1.7850 | 25350 | 0.0 | - |
674
+ | 1.7885 | 25400 | 0.0 | - |
675
+ | 1.7920 | 25450 | 0.0 | - |
676
+ | 1.7955 | 25500 | 0.0 | - |
677
+ | 1.7990 | 25550 | 0.0 | - |
678
+ | 1.8026 | 25600 | 0.0001 | - |
679
+ | 1.8061 | 25650 | 0.0 | - |
680
+ | 1.8096 | 25700 | 0.0001 | - |
681
+ | 1.8131 | 25750 | 0.0001 | - |
682
+ | 1.8166 | 25800 | 0.0 | - |
683
+ | 1.8202 | 25850 | 0.0 | - |
684
+ | 1.8237 | 25900 | 0.0 | - |
685
+ | 1.8272 | 25950 | 0.0 | - |
686
+ | 1.8307 | 26000 | 0.0001 | - |
687
+ | 1.8342 | 26050 | 0.0 | - |
688
+ | 1.8378 | 26100 | 0.0 | - |
689
+ | 1.8413 | 26150 | 0.0 | - |
690
+ | 1.8448 | 26200 | 0.0 | - |
691
+ | 1.8483 | 26250 | 0.0 | - |
692
+ | 1.8519 | 26300 | 0.0 | - |
693
+ | 1.8554 | 26350 | 0.0001 | - |
694
+ | 1.8589 | 26400 | 0.0 | - |
695
+ | 1.8624 | 26450 | 0.0 | - |
696
+ | 1.8659 | 26500 | 0.0 | - |
697
+ | 1.8695 | 26550 | 0.0 | - |
698
+ | 1.8730 | 26600 | 0.0 | - |
699
+ | 1.8765 | 26650 | 0.0 | - |
700
+ | 1.8800 | 26700 | 0.0 | - |
701
+ | 1.8835 | 26750 | 0.0001 | - |
702
+ | 1.8871 | 26800 | 0.0 | - |
703
+ | 1.8906 | 26850 | 0.0 | - |
704
+ | 1.8941 | 26900 | 0.0 | - |
705
+ | 1.8976 | 26950 | 0.0 | - |
706
+ | 1.9011 | 27000 | 0.0001 | - |
707
+ | 1.9047 | 27050 | 0.0 | - |
708
+ | 1.9082 | 27100 | 0.0 | - |
709
+ | 1.9117 | 27150 | 0.0 | - |
710
+ | 1.9152 | 27200 | 0.0001 | - |
711
+ | 1.9187 | 27250 | 0.0 | - |
712
+ | 1.9223 | 27300 | 0.0001 | - |
713
+ | 1.9258 | 27350 | 0.0 | - |
714
+ | 1.9293 | 27400 | 0.0 | - |
715
+ | 1.9328 | 27450 | 0.0 | - |
716
+ | 1.9363 | 27500 | 0.0 | - |
717
+ | 1.9399 | 27550 | 0.0 | - |
718
+ | 1.9434 | 27600 | 0.0 | - |
719
+ | 1.9469 | 27650 | 0.0 | - |
720
+ | 1.9504 | 27700 | 0.0 | - |
721
+ | 1.9540 | 27750 | 0.0001 | - |
722
+ | 1.9575 | 27800 | 0.0 | - |
723
+ | 1.9610 | 27850 | 0.0 | - |
724
+ | 1.9645 | 27900 | 0.0 | - |
725
+ | 1.9680 | 27950 | 0.0001 | - |
726
+ | 1.9716 | 28000 | 0.0 | - |
727
+ | 1.9751 | 28050 | 0.0 | - |
728
+ | 1.9786 | 28100 | 0.0001 | - |
729
+ | 1.9821 | 28150 | 0.0 | - |
730
+ | 1.9856 | 28200 | 0.0 | - |
731
+ | 1.9892 | 28250 | 0.0 | - |
732
+ | 1.9927 | 28300 | 0.0 | - |
733
+ | 1.9962 | 28350 | 0.0 | - |
734
+ | 1.9997 | 28400 | 0.0001 | - |
735
+ | **2.0** | **28404** | **-** | **0.0076** |
736
+
737
+ * The bold row denotes the saved checkpoint.
738
+ ### Framework Versions
739
+ - Python: 3.11.9
740
+ - SetFit: 1.1.0.dev0
741
+ - Sentence Transformers: 3.0.1
742
+ - Transformers: 4.44.2
743
+ - PyTorch: 2.4.0+cu121
744
+ - Datasets: 2.21.0
745
+ - Tokenizers: 0.19.1
746
+
747
+ ## Citation
748
+
749
+ ### BibTeX
750
+ ```bibtex
751
+ @article{https://doi.org/10.48550/arxiv.2209.11055,
752
+ doi = {10.48550/ARXIV.2209.11055},
753
+ url = {https://arxiv.org/abs/2209.11055},
754
+ author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
755
+ keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
756
+ title = {Efficient Few-Shot Learning Without Prompts},
757
+ publisher = {arXiv},
758
+ year = {2022},
759
+ copyright = {Creative Commons Attribution 4.0 International}
760
+ }
761
+ ```
762
+
763
+ <!--
764
+ ## Glossary
765
+
766
+ *Clearly define terms in order to be accessible across audiences.*
767
+ -->
768
+
769
+ <!--
770
+ ## Model Card Authors
771
+
772
+ *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
773
+ -->
774
+
775
+ <!--
776
+ ## Model Card Contact
777
+
778
+ *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
779
+ -->
config.json ADDED
@@ -0,0 +1,32 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "_name_or_path": "bge-large-en-v1.5-brahmaputra-iter-8-2-epoch/step_28404",
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:c7896502d148fc80505ee74565902e6e586458af3c3dd1f071c47aee6d8fb545
3
+ size 1340612432
model_head.pkl ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:2bdf1aa3e5c8690e9e906c7d840b07c61e9c575b43a49297257607eb32b49efb
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