Hugging Face's logo

mann2107
/
BCMPIIRAB_MiniLM_ALLNewV2

Text Classification
setfit
Safetensors
sentence-transformers
bert
generated_from_setfit_trainer
text-embeddings-inference
Model card Files
xet
Community
mann2107 commited on
Commit
0898960
·
verified ·
1 Parent(s): 468c935

Push model using huggingface_hub.

Browse files
Files changed (13) hide show
  1. 1_Pooling/config.json +10 -0
  2. README.md +784 -0
  3. config.json +26 -0
  4. config_sentence_transformers.json +10 -0
  5. config_setfit.json +4 -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": false,
4
+ "pooling_mode_mean_tokens": true,
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 +1,784 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ base_model: sentence-transformers/all-MiniLM-L6-v2
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: Thank you for your email. Please go ahead and issue. Please invoice in KES
14
+ - text: Hi, We are missing some invoices, can you please provide it. 02 - 12 - 2020
15
+ AGENT FEE 8900784339018 $21.00 02 - 19 - 2020 AGENT FEE 0017417554160 $22.00 02
16
+ - 19 - 2020 AGENT FEE 0017417554143 $22.00 02 - 19 - 2020 AGENT FEE 8900783383420
17
+ $21.00
18
+ - text: We need your assistance with the payment for the recent office supplies order.
19
+ Let us know once it's done.
20
+ - text: I have reported this in November and not only was the trip supposed to be
21
+ cancelled and credited I was double billed and the billing has not been corrected.
22
+ The total credit should be $667.20. Please confirm this will be done.
23
+ - text: The invoice for the travel arrangements needs to be settled. Kindly provide
24
+ payment confirmation.
25
+ inference: true
26
+ ---
27
+
28
+ # SetFit with sentence-transformers/all-MiniLM-L6-v2
29
+
30
+ This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) 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.
31
+
32
+ The model has been trained using an efficient few-shot learning technique that involves:
33
+
34
+ 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
35
+ 2. Training a classification head with features from the fine-tuned Sentence Transformer.
36
+
37
+ ## Model Details
38
+
39
+ ### Model Description
40
+ - **Model Type:** SetFit
41
+ - **Sentence Transformer body:** [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2)
42
+ - **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
43
+ - **Maximum Sequence Length:** 256 tokens
44
+ - **Number of Classes:** 14 classes
45
+ <!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
46
+ <!-- - **Language:** Unknown -->
47
+ <!-- - **License:** Unknown -->
48
+
49
+ ### Model Sources
50
+
51
+ - **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit)
52
+ - **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)
53
+ - **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)
54
+
55
+ ### Model Labels
56
+ | Label | Examples |
57
+ |:------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
58
+ | 0 | <ul><li>'Please send me quotation for a flight for Lindelani Mkhize - East London/ Durban 31 August @ 12:00'</li><li>"I need to go to Fort Smith AR via XNA for PD days. I'd like to take AA 4064 at 10:00 am arriving 11:58 am on Monday, May 11 returning on AA 4064 at 12:26 pm arriving 2:16 pm on Saturday May 16. I will need a Hertz rental. I d like to stay at the Courtyard Marriott in Fort Smith on Monday through Thursday nights checking out on Friday morning."</li><li>'Can you please send me flight quotations for Mr Mthetho Sovara for travel to Bologna, Italy as per details below: 7 Oct: JHB to Bologna, Italy 14 Oct: Bologna, Italy to JHB'</li></ul> |
59
+ | 1 | <ul><li>'I need to cancel my flight booking from London Heathrow to JFK, New York, scheduled for August 15th, 2024. The booking reference is XJ12345.'</li><li>'Please cancel my flight for late March to Chicago and DC. Meetings have been cancelled. I am not available by phone.'</li><li>'I need to cancel the below trip due to illness in family. Could you please assist with this?'</li></ul> |
60
+ | 2 | <ul><li>'I need to change the departure time for my one-way flight from SFO to LAX on October 15th. Could you please reschedule it to a later flight around 6:00 PM on the same day?'</li><li>'Can you please extend my hotel reservation at the Marriott in Denver from November 19th to November 23rd, 2024? Originally, I was scheduled to check out on the 19th.'</li><li>"Lerato I checked Selbourne B/B, its not a nice place. Your colleague Stella booked Lindelani Mkhize in Hempston it's a beautiful place next to Garden Court, please change the accommodation from Selbourne to Hempston. This Selbourne is on the outskirt and my colleagues are not familiar with East London"</li></ul> |
61
+ | 3 | <ul><li>'Please add the below employee to our Concur system. In addition, make sure the Ghost Card is added into their profile. Lindsay Griffin lgriffin@arlingtonroe.com'</li><li>"Good afternoon - CAEP has 4 new staff members that we'd like to set - up new user profiles for. Please see the below information and let me know should anything additional be required. Last First Middle Travel Class Email Gender DOB Graham Rose - Helen Xiuqing Staff rose - helen.graham@caepnet.org Female 6/14/1995 Gumbs Mary - Frances Akua Staff mary.gumbs@caepnet.org Female 10/18/1995 Lee Elizabeth Andie Staff liz.lee@caepnet.org Female 4/23/1991 Gilchrist Gabriel Jake Staff gabriel.gilchrist@caepnet.org Male"</li><li>'Good Morning, Please create a profile for Amelia West: Name: Amelia Jean - Danielle West DOB: 05/21/1987 PH: 202 - 997 - 6592 Email: asuermann@facs.org'</li></ul> |
62
+ | 4 | <ul><li>'Hi, My name is Lucia De Las Heras property accountant at Trion Properties. I am missing a few receipts to allocate the following charges. Would you please be able to provide a detailed invoice? 10/10/2019 FROSCH/GANT TRAVEL MBLOOMINGTON IN - 21'</li><li>'I would like to request an invoice/s for the above-mentioned employee who stayed at your establishment.'</li><li>"Hello, Looking for an invoice for the below charge to Ryan Schulke's card - could you please assist? Vendor: United Airlines Transaction Date: 02/04/2020 Amount: $2,132.07 Ticket Number: 0167515692834"</li></ul> |
63
+ | 5 | <ul><li>'This is the second email with this trip, but I still need an itinerary for trip scheduled for January 27. Derek'</li><li>'Please send us all the flights used by G4S Kenya in the year 2022. Sorry for the short notice but we need the information by 12:00 noon today.'</li><li>'Jen Holt Can you please send me the itinerary for Jen Holt for this trip this week to Jackson Mississippi?'</li></ul> |
64
+ | 6 | <ul><li>"I've had to call off my vacation. What are my options for getting refunded?"</li><li>"Looks like I won't be traveling due to some health issues. Is getting a refund for my booking possible?"</li><li>"I've fallen ill and can't travel as planned. Can you process a refund for me?"</li></ul> |
65
+ | 7 | <ul><li>'The arrangements as stated are acceptable. Please go ahead and confirm all bookings accordingly.'</li><li>"I've reviewed the details and everything seems in order. Please proceed with the booking."</li><li>'This travel plan is satisfactory. Please secure the necessary reservations.'</li></ul> |
66
+ | 8 | <ul><li>'I need some clarification on charges for a rebooked flight. It seems higher than anticipated. Who can provide more details?'</li><li>'Wishing you and your family a very Merry Christmas and a Happy and Healthy New Year. I have one unidentified item this month, hope you can help, and as always thanks in advance. Very limited information on this. 11/21/2019 #N/A #N/A #N/A 142.45 Rail Europe North Amer'</li><li>"We've identified a mismatch between our booking records and credit card statement. Who can assist with this issue?"</li></ul> |
67
+ | 9 | <ul><li>'I booked a hotel in Berlin for next month, but the confirmation email I received has the wrong dates. Can you please correct this and resend the confirmation?'</li><li>"I need to arrange a shuttle for our team from the airport to the conference venue, but I haven't received any confirmation yet. Can someone check on this for me?"</li><li>"When trying to book a flight for our CEO, the system shows an error stating 'payment not processed.' Can you assist in resolving this issue quickly?"</li></ul> |
68
+ | 10 | <ul><li>'Please assist with payment for the conference room booking at Hilton last week.'</li><li>'Kindly process the invoice for the catering services provided during the annual company meeting.'</li><li>"Supplier, please find a statement with all invoices listed due for the IT maintenance services. If you've already paid, please forward proof and date of payment. Thank you for your support."</li></ul> |
69
+ | 11 | <ul><li>"Congratulations! You've been selected to win a brand new iPhone 14. Click here to claim your prize now!"</li><li>'Get rich quick! Invest in our exclusive cryptocurrency and watch your money grow 10x in just a month. Limited time offer!'</li><li>'Your PayPal account has been compromised. Please click here to verify your information and secure your account.'</li></ul> |
70
+ | 12 | <ul><li>'Your flight booking has been confirmed. Flight details: Flight #BA283 from LHR to LAX on November 10th, departure at 12:30 PM.'</li><li>'We regret to inform you that your hotel reservation at The Plaza, New York, was unsuccessful due to unavailability. Please try booking another date.'</li><li>'Your car rental reservation with Hertz has been confirmed. Pickup location: JFK Airport, Date: October 20th, Time: 10:00 AM.'</li></ul> |
71
+ | 13 | <ul><li>'We have received a request to charge the attached invoice to the corporate credit card on file for Jane Doe. Please confirm the payment details at your earliest convenience.'</li><li>'Dear Travel Agency, we regret to inform you that the room booked for Mr. John Smith is unavailable due to overbooking. We have arranged an alternative accommodation at a nearby hotel. Please advise if this is acceptable.'</li><li>'Regarding the recent stay of Mr. Alan Harper, we noticed a discrepancy in the billing. The minibar charges were not included in the initial invoice. Kindly review the attached revised bill.'</li></ul> |
72
+
73
+ ## Uses
74
+
75
+ ### Direct Use for Inference
76
+
77
+ First install the SetFit library:
78
+
79
+ ```bash
80
+ pip install setfit
81
+ ```
82
+
83
+ Then you can load this model and run inference.
84
+
85
+ ```python
86
+ from setfit import SetFitModel
87
+
88
+ # Download from the 🤗 Hub
89
+ model = SetFitModel.from_pretrained("mann2107/BCMPIIRAB_MiniLM_ALLNewV2")
90
+ # Run inference
91
+ preds = model("Thank you for your email. Please go ahead and issue. Please invoice in KES")
92
+ ```
93
+
94
+ <!--
95
+ ### Downstream Use
96
+
97
+ *List how someone could finetune this model on their own dataset.*
98
+ -->
99
+
100
+ <!--
101
+ ### Out-of-Scope Use
102
+
103
+ *List how the model may foreseeably be misused and address what users ought not to do with the model.*
104
+ -->
105
+
106
+ <!--
107
+ ## Bias, Risks and Limitations
108
+
109
+ *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
110
+ -->
111
+
112
+ <!--
113
+ ### Recommendations
114
+
115
+ *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
116
+ -->
117
+
118
+ ## Training Details
119
+
120
+ ### Training Set Metrics
121
+ | Training set | Min | Median | Max |
122
+ |:-------------|:----|:--------|:----|
123
+ | Word count | 1 | 25.6577 | 136 |
124
+
125
+ | Label | Training Sample Count |
126
+ |:------|:----------------------|
127
+ | 0 | 24 |
128
+ | 1 | 24 |
129
+ | 2 | 24 |
130
+ | 3 | 24 |
131
+ | 4 | 24 |
132
+ | 5 | 24 |
133
+ | 6 | 24 |
134
+ | 7 | 24 |
135
+ | 8 | 24 |
136
+ | 9 | 24 |
137
+ | 10 | 24 |
138
+ | 11 | 24 |
139
+ | 12 | 24 |
140
+ | 13 | 24 |
141
+
142
+ ### Training Hyperparameters
143
+ - batch_size: (8, 8)
144
+ - num_epochs: (5, 5)
145
+ - max_steps: -1
146
+ - sampling_strategy: oversampling
147
+ - num_iterations: 68
148
+ - body_learning_rate: (1.44030579311381e-05, 1.44030579311381e-05)
149
+ - head_learning_rate: 0.01
150
+ - loss: CosineSimilarityLoss
151
+ - distance_metric: cosine_distance
152
+ - margin: 0.25
153
+ - end_to_end: False
154
+ - use_amp: False
155
+ - warmup_proportion: 0.1
156
+ - max_length: 512
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
+ | 0.0002 | 1 | 0.2917 | - |
165
+ | 0.0088 | 50 | 0.2434 | - |
166
+ | 0.0175 | 100 | 0.2053 | - |
167
+ | 0.0263 | 150 | 0.1789 | - |
168
+ | 0.0350 | 200 | 0.2249 | - |
169
+ | 0.0438 | 250 | 0.1773 | - |
170
+ | 0.0525 | 300 | 0.1648 | - |
171
+ | 0.0613 | 350 | 0.2617 | - |
172
+ | 0.0700 | 400 | 0.1342 | - |
173
+ | 0.0788 | 450 | 0.1064 | - |
174
+ | 0.0875 | 500 | 0.1273 | - |
175
+ | 0.0963 | 550 | 0.1248 | - |
176
+ | 0.1050 | 600 | 0.2013 | - |
177
+ | 0.1138 | 650 | 0.1979 | - |
178
+ | 0.1225 | 700 | 0.1631 | - |
179
+ | 0.1313 | 750 | 0.1079 | - |
180
+ | 0.1401 | 800 | 0.0858 | - |
181
+ | 0.1488 | 850 | 0.0999 | - |
182
+ | 0.1576 | 900 | 0.0638 | - |
183
+ | 0.1663 | 950 | 0.1287 | - |
184
+ | 0.1751 | 1000 | 0.1408 | - |
185
+ | 0.1838 | 1050 | 0.1902 | - |
186
+ | 0.1926 | 1100 | 0.0648 | - |
187
+ | 0.2013 | 1150 | 0.1383 | - |
188
+ | 0.2101 | 1200 | 0.0609 | - |
189
+ | 0.2188 | 1250 | 0.0865 | - |
190
+ | 0.2276 | 1300 | 0.1069 | - |
191
+ | 0.2363 | 1350 | 0.051 | - |
192
+ | 0.2451 | 1400 | 0.0692 | - |
193
+ | 0.2539 | 1450 | 0.123 | - |
194
+ | 0.2626 | 1500 | 0.0758 | - |
195
+ | 0.2714 | 1550 | 0.0835 | - |
196
+ | 0.2801 | 1600 | 0.0523 | - |
197
+ | 0.2889 | 1650 | 0.0946 | - |
198
+ | 0.2976 | 1700 | 0.0445 | - |
199
+ | 0.3064 | 1750 | 0.0248 | - |
200
+ | 0.3151 | 1800 | 0.0373 | - |
201
+ | 0.3239 | 1850 | 0.0248 | - |
202
+ | 0.3326 | 1900 | 0.0446 | - |
203
+ | 0.3414 | 1950 | 0.0142 | - |
204
+ | 0.3501 | 2000 | 0.023 | - |
205
+ | 0.3589 | 2050 | 0.0119 | - |
206
+ | 0.3676 | 2100 | 0.0383 | - |
207
+ | 0.3764 | 2150 | 0.0188 | - |
208
+ | 0.3852 | 2200 | 0.0204 | - |
209
+ | 0.3939 | 2250 | 0.0109 | - |
210
+ | 0.4027 | 2300 | 0.0273 | - |
211
+ | 0.4114 | 2350 | 0.0216 | - |
212
+ | 0.4202 | 2400 | 0.0073 | - |
213
+ | 0.4289 | 2450 | 0.0338 | - |
214
+ | 0.4377 | 2500 | 0.0047 | - |
215
+ | 0.4464 | 2550 | 0.0096 | - |
216
+ | 0.4552 | 2600 | 0.0069 | - |
217
+ | 0.4639 | 2650 | 0.0078 | - |
218
+ | 0.4727 | 2700 | 0.0122 | - |
219
+ | 0.4814 | 2750 | 0.0578 | - |
220
+ | 0.4902 | 2800 | 0.0074 | - |
221
+ | 0.4989 | 2850 | 0.0103 | - |
222
+ | 0.5077 | 2900 | 0.0092 | - |
223
+ | 0.5165 | 2950 | 0.004 | - |
224
+ | 0.5252 | 3000 | 0.0061 | - |
225
+ | 0.5340 | 3050 | 0.0214 | - |
226
+ | 0.5427 | 3100 | 0.0048 | - |
227
+ | 0.5515 | 3150 | 0.0036 | - |
228
+ | 0.5602 | 3200 | 0.0041 | - |
229
+ | 0.5690 | 3250 | 0.0151 | - |
230
+ | 0.5777 | 3300 | 0.0042 | - |
231
+ | 0.5865 | 3350 | 0.0029 | - |
232
+ | 0.5952 | 3400 | 0.0021 | - |
233
+ | 0.6040 | 3450 | 0.0018 | - |
234
+ | 0.6127 | 3500 | 0.0058 | - |
235
+ | 0.6215 | 3550 | 0.0011 | - |
236
+ | 0.6303 | 3600 | 0.0078 | - |
237
+ | 0.6390 | 3650 | 0.0011 | - |
238
+ | 0.6478 | 3700 | 0.0017 | - |
239
+ | 0.6565 | 3750 | 0.0022 | - |
240
+ | 0.6653 | 3800 | 0.0016 | - |
241
+ | 0.6740 | 3850 | 0.002 | - |
242
+ | 0.6828 | 3900 | 0.0023 | - |
243
+ | 0.6915 | 3950 | 0.0011 | - |
244
+ | 0.7003 | 4000 | 0.0012 | - |
245
+ | 0.7090 | 4050 | 0.0007 | - |
246
+ | 0.7178 | 4100 | 0.0021 | - |
247
+ | 0.7265 | 4150 | 0.0019 | - |
248
+ | 0.7353 | 4200 | 0.002 | - |
249
+ | 0.7440 | 4250 | 0.0018 | - |
250
+ | 0.7528 | 4300 | 0.0029 | - |
251
+ | 0.7616 | 4350 | 0.0015 | - |
252
+ | 0.7703 | 4400 | 0.0022 | - |
253
+ | 0.7791 | 4450 | 0.0012 | - |
254
+ | 0.7878 | 4500 | 0.0007 | - |
255
+ | 0.7966 | 4550 | 0.0015 | - |
256
+ | 0.8053 | 4600 | 0.0011 | - |
257
+ | 0.8141 | 4650 | 0.0016 | - |
258
+ | 0.8228 | 4700 | 0.0009 | - |
259
+ | 0.8316 | 4750 | 0.0007 | - |
260
+ | 0.8403 | 4800 | 0.0011 | - |
261
+ | 0.8491 | 4850 | 0.001 | - |
262
+ | 0.8578 | 4900 | 0.0008 | - |
263
+ | 0.8666 | 4950 | 0.0014 | - |
264
+ | 0.8754 | 5000 | 0.0022 | - |
265
+ | 0.8841 | 5050 | 0.0012 | - |
266
+ | 0.8929 | 5100 | 0.0007 | - |
267
+ | 0.9016 | 5150 | 0.0014 | - |
268
+ | 0.9104 | 5200 | 0.0007 | - |
269
+ | 0.9191 | 5250 | 0.0012 | - |
270
+ | 0.9279 | 5300 | 0.0011 | - |
271
+ | 0.9366 | 5350 | 0.0012 | - |
272
+ | 0.9454 | 5400 | 0.0029 | - |
273
+ | 0.9541 | 5450 | 0.001 | - |
274
+ | 0.9629 | 5500 | 0.0011 | - |
275
+ | 0.9716 | 5550 | 0.0004 | - |
276
+ | 0.9804 | 5600 | 0.0009 | - |
277
+ | 0.9891 | 5650 | 0.0004 | - |
278
+ | 0.9979 | 5700 | 0.003 | - |
279
+ | 1.0 | 5712 | - | 0.0459 |
280
+ | 1.0067 | 5750 | 0.0014 | - |
281
+ | 1.0154 | 5800 | 0.0008 | - |
282
+ | 1.0242 | 5850 | 0.0009 | - |
283
+ | 1.0329 | 5900 | 0.0007 | - |
284
+ | 1.0417 | 5950 | 0.0007 | - |
285
+ | 1.0504 | 6000 | 0.0006 | - |
286
+ | 1.0592 | 6050 | 0.0008 | - |
287
+ | 1.0679 | 6100 | 0.0006 | - |
288
+ | 1.0767 | 6150 | 0.0006 | - |
289
+ | 1.0854 | 6200 | 0.0007 | - |
290
+ | 1.0942 | 6250 | 0.0025 | - |
291
+ | 1.1029 | 6300 | 0.0006 | - |
292
+ | 1.1117 | 6350 | 0.0009 | - |
293
+ | 1.1204 | 6400 | 0.0009 | - |
294
+ | 1.1292 | 6450 | 0.0009 | - |
295
+ | 1.1380 | 6500 | 0.0006 | - |
296
+ | 1.1467 | 6550 | 0.0004 | - |
297
+ | 1.1555 | 6600 | 0.0014 | - |
298
+ | 1.1642 | 6650 | 0.0029 | - |
299
+ | 1.1730 | 6700 | 0.0004 | - |
300
+ | 1.1817 | 6750 | 0.0027 | - |
301
+ | 1.1905 | 6800 | 0.0003 | - |
302
+ | 1.1992 | 6850 | 0.0003 | - |
303
+ | 1.2080 | 6900 | 0.0006 | - |
304
+ | 1.2167 | 6950 | 0.0015 | - |
305
+ | 1.2255 | 7000 | 0.0005 | - |
306
+ | 1.2342 | 7050 | 0.0005 | - |
307
+ | 1.2430 | 7100 | 0.0016 | - |
308
+ | 1.2518 | 7150 | 0.0005 | - |
309
+ | 1.2605 | 7200 | 0.0003 | - |
310
+ | 1.2693 | 7250 | 0.0006 | - |
311
+ | 1.2780 | 7300 | 0.0007 | - |
312
+ | 1.2868 | 7350 | 0.0004 | - |
313
+ | 1.2955 | 7400 | 0.0007 | - |
314
+ | 1.3043 | 7450 | 0.0007 | - |
315
+ | 1.3130 | 7500 | 0.0007 | - |
316
+ | 1.3218 | 7550 | 0.0003 | - |
317
+ | 1.3305 | 7600 | 0.0002 | - |
318
+ | 1.3393 | 7650 | 0.0002 | - |
319
+ | 1.3480 | 7700 | 0.0005 | - |
320
+ | 1.3568 | 7750 | 0.0014 | - |
321
+ | 1.3655 | 7800 | 0.0012 | - |
322
+ | 1.3743 | 7850 | 0.0002 | - |
323
+ | 1.3831 | 7900 | 0.0002 | - |
324
+ | 1.3918 | 7950 | 0.0003 | - |
325
+ | 1.4006 | 8000 | 0.0005 | - |
326
+ | 1.4093 | 8050 | 0.0006 | - |
327
+ | 1.4181 | 8100 | 0.0003 | - |
328
+ | 1.4268 | 8150 | 0.0009 | - |
329
+ | 1.4356 | 8200 | 0.0004 | - |
330
+ | 1.4443 | 8250 | 0.0002 | - |
331
+ | 1.4531 | 8300 | 0.0004 | - |
332
+ | 1.4618 | 8350 | 0.0008 | - |
333
+ | 1.4706 | 8400 | 0.0002 | - |
334
+ | 1.4793 | 8450 | 0.0004 | - |
335
+ | 1.4881 | 8500 | 0.0006 | - |
336
+ | 1.4968 | 8550 | 0.0011 | - |
337
+ | 1.5056 | 8600 | 0.0003 | - |
338
+ | 1.5144 | 8650 | 0.0003 | - |
339
+ | 1.5231 | 8700 | 0.0004 | - |
340
+ | 1.5319 | 8750 | 0.0004 | - |
341
+ | 1.5406 | 8800 | 0.0002 | - |
342
+ | 1.5494 | 8850 | 0.0007 | - |
343
+ | 1.5581 | 8900 | 0.0003 | - |
344
+ | 1.5669 | 8950 | 0.0002 | - |
345
+ | 1.5756 | 9000 | 0.0007 | - |
346
+ | 1.5844 | 9050 | 0.0005 | - |
347
+ | 1.5931 | 9100 | 0.0005 | - |
348
+ | 1.6019 | 9150 | 0.0011 | - |
349
+ | 1.6106 | 9200 | 0.0004 | - |
350
+ | 1.6194 | 9250 | 0.0004 | - |
351
+ | 1.6282 | 9300 | 0.0003 | - |
352
+ | 1.6369 | 9350 | 0.0002 | - |
353
+ | 1.6457 | 9400 | 0.0003 | - |
354
+ | 1.6544 | 9450 | 0.0006 | - |
355
+ | 1.6632 | 9500 | 0.0004 | - |
356
+ | 1.6719 | 9550 | 0.0004 | - |
357
+ | 1.6807 | 9600 | 0.0006 | - |
358
+ | 1.6894 | 9650 | 0.0001 | - |
359
+ | 1.6982 | 9700 | 0.0002 | - |
360
+ | 1.7069 | 9750 | 0.0004 | - |
361
+ | 1.7157 | 9800 | 0.0004 | - |
362
+ | 1.7244 | 9850 | 0.0001 | - |
363
+ | 1.7332 | 9900 | 0.0004 | - |
364
+ | 1.7419 | 9950 | 0.0004 | - |
365
+ | 1.7507 | 10000 | 0.0006 | - |
366
+ | 1.7595 | 10050 | 0.0003 | - |
367
+ | 1.7682 | 10100 | 0.0002 | - |
368
+ | 1.7770 | 10150 | 0.0004 | - |
369
+ | 1.7857 | 10200 | 0.0004 | - |
370
+ | 1.7945 | 10250 | 0.0002 | - |
371
+ | 1.8032 | 10300 | 0.0008 | - |
372
+ | 1.8120 | 10350 | 0.0004 | - |
373
+ | 1.8207 | 10400 | 0.0005 | - |
374
+ | 1.8295 | 10450 | 0.0004 | - |
375
+ | 1.8382 | 10500 | 0.0001 | - |
376
+ | 1.8470 | 10550 | 0.0003 | - |
377
+ | 1.8557 | 10600 | 0.0003 | - |
378
+ | 1.8645 | 10650 | 0.0005 | - |
379
+ | 1.8732 | 10700 | 0.0005 | - |
380
+ | 1.8820 | 10750 | 0.0003 | - |
381
+ | 1.8908 | 10800 | 0.0001 | - |
382
+ | 1.8995 | 10850 | 0.0002 | - |
383
+ | 1.9083 | 10900 | 0.0001 | - |
384
+ | 1.9170 | 10950 | 0.0003 | - |
385
+ | 1.9258 | 11000 | 0.0005 | - |
386
+ | 1.9345 | 11050 | 0.0003 | - |
387
+ | 1.9433 | 11100 | 0.0004 | - |
388
+ | 1.9520 | 11150 | 0.0007 | - |
389
+ | 1.9608 | 11200 | 0.0002 | - |
390
+ | 1.9695 | 11250 | 0.0003 | - |
391
+ | 1.9783 | 11300 | 0.0001 | - |
392
+ | 1.9870 | 11350 | 0.0001 | - |
393
+ | 1.9958 | 11400 | 0.0002 | - |
394
+ | 2.0 | 11424 | - | 0.042 |
395
+ | 2.0046 | 11450 | 0.0003 | - |
396
+ | 2.0133 | 11500 | 0.0002 | - |
397
+ | 2.0221 | 11550 | 0.0002 | - |
398
+ | 2.0308 | 11600 | 0.0002 | - |
399
+ | 2.0396 | 11650 | 0.0003 | - |
400
+ | 2.0483 | 11700 | 0.0003 | - |
401
+ | 2.0571 | 11750 | 0.0002 | - |
402
+ | 2.0658 | 11800 | 0.0002 | - |
403
+ | 2.0746 | 11850 | 0.0002 | - |
404
+ | 2.0833 | 11900 | 0.0002 | - |
405
+ | 2.0921 | 11950 | 0.0001 | - |
406
+ | 2.1008 | 12000 | 0.0003 | - |
407
+ | 2.1096 | 12050 | 0.0005 | - |
408
+ | 2.1183 | 12100 | 0.0002 | - |
409
+ | 2.1271 | 12150 | 0.0003 | - |
410
+ | 2.1359 | 12200 | 0.0002 | - |
411
+ | 2.1446 | 12250 | 0.0003 | - |
412
+ | 2.1534 | 12300 | 0.0003 | - |
413
+ | 2.1621 | 12350 | 0.0001 | - |
414
+ | 2.1709 | 12400 | 0.0002 | - |
415
+ | 2.1796 | 12450 | 0.0002 | - |
416
+ | 2.1884 | 12500 | 0.0002 | - |
417
+ | 2.1971 | 12550 | 0.0002 | - |
418
+ | 2.2059 | 12600 | 0.0001 | - |
419
+ | 2.2146 | 12650 | 0.0002 | - |
420
+ | 2.2234 | 12700 | 0.0003 | - |
421
+ | 2.2321 | 12750 | 0.0003 | - |
422
+ | 2.2409 | 12800 | 0.0004 | - |
423
+ | 2.2496 | 12850 | 0.0002 | - |
424
+ | 2.2584 | 12900 | 0.0002 | - |
425
+ | 2.2672 | 12950 | 0.0003 | - |
426
+ | 2.2759 | 13000 | 0.0002 | - |
427
+ | 2.2847 | 13050 | 0.0002 | - |
428
+ | 2.2934 | 13100 | 0.0002 | - |
429
+ | 2.3022 | 13150 | 0.0001 | - |
430
+ | 2.3109 | 13200 | 0.0002 | - |
431
+ | 2.3197 | 13250 | 0.0001 | - |
432
+ | 2.3284 | 13300 | 0.0002 | - |
433
+ | 2.3372 | 13350 | 0.0003 | - |
434
+ | 2.3459 | 13400 | 0.0002 | - |
435
+ | 2.3547 | 13450 | 0.0001 | - |
436
+ | 2.3634 | 13500 | 0.0002 | - |
437
+ | 2.3722 | 13550 | 0.0001 | - |
438
+ | 2.3810 | 13600 | 0.0006 | - |
439
+ | 2.3897 | 13650 | 0.0001 | - |
440
+ | 2.3985 | 13700 | 0.0002 | - |
441
+ | 2.4072 | 13750 | 0.0002 | - |
442
+ | 2.4160 | 13800 | 0.0004 | - |
443
+ | 2.4247 | 13850 | 0.0001 | - |
444
+ | 2.4335 | 13900 | 0.0003 | - |
445
+ | 2.4422 | 13950 | 0.0001 | - |
446
+ | 2.4510 | 14000 | 0.0001 | - |
447
+ | 2.4597 | 14050 | 0.0001 | - |
448
+ | 2.4685 | 14100 | 0.0005 | - |
449
+ | 2.4772 | 14150 | 0.0002 | - |
450
+ | 2.4860 | 14200 | 0.0001 | - |
451
+ | 2.4947 | 14250 | 0.0003 | - |
452
+ | 2.5035 | 14300 | 0.0005 | - |
453
+ | 2.5123 | 14350 | 0.0002 | - |
454
+ | 2.5210 | 14400 | 0.0002 | - |
455
+ | 2.5298 | 14450 | 0.0003 | - |
456
+ | 2.5385 | 14500 | 0.0001 | - |
457
+ | 2.5473 | 14550 | 0.0001 | - |
458
+ | 2.5560 | 14600 | 0.0002 | - |
459
+ | 2.5648 | 14650 | 0.0002 | - |
460
+ | 2.5735 | 14700 | 0.0001 | - |
461
+ | 2.5823 | 14750 | 0.0001 | - |
462
+ | 2.5910 | 14800 | 0.0001 | - |
463
+ | 2.5998 | 14850 | 0.0003 | - |
464
+ | 2.6085 | 14900 | 0.0002 | - |
465
+ | 2.6173 | 14950 | 0.0001 | - |
466
+ | 2.6261 | 15000 | 0.0001 | - |
467
+ | 2.6348 | 15050 | 0.0001 | - |
468
+ | 2.6436 | 15100 | 0.0001 | - |
469
+ | 2.6523 | 15150 | 0.0002 | - |
470
+ | 2.6611 | 15200 | 0.0001 | - |
471
+ | 2.6698 | 15250 | 0.0002 | - |
472
+ | 2.6786 | 15300 | 0.0002 | - |
473
+ | 2.6873 | 15350 | 0.0002 | - |
474
+ | 2.6961 | 15400 | 0.0002 | - |
475
+ | 2.7048 | 15450 | 0.0002 | - |
476
+ | 2.7136 | 15500 | 0.0001 | - |
477
+ | 2.7223 | 15550 | 0.0002 | - |
478
+ | 2.7311 | 15600 | 0.0002 | - |
479
+ | 2.7398 | 15650 | 0.0003 | - |
480
+ | 2.7486 | 15700 | 0.0002 | - |
481
+ | 2.7574 | 15750 | 0.0001 | - |
482
+ | 2.7661 | 15800 | 0.0002 | - |
483
+ | 2.7749 | 15850 | 0.0002 | - |
484
+ | 2.7836 | 15900 | 0.0003 | - |
485
+ | 2.7924 | 15950 | 0.0004 | - |
486
+ | 2.8011 | 16000 | 0.0007 | - |
487
+ | 2.8099 | 16050 | 0.0001 | - |
488
+ | 2.8186 | 16100 | 0.0001 | - |
489
+ | 2.8274 | 16150 | 0.0002 | - |
490
+ | 2.8361 | 16200 | 0.0002 | - |
491
+ | 2.8449 | 16250 | 0.0001 | - |
492
+ | 2.8536 | 16300 | 0.0001 | - |
493
+ | 2.8624 | 16350 | 0.0002 | - |
494
+ | 2.8711 | 16400 | 0.0002 | - |
495
+ | 2.8799 | 16450 | 0.0001 | - |
496
+ | 2.8887 | 16500 | 0.0002 | - |
497
+ | 2.8974 | 16550 | 0.0002 | - |
498
+ | 2.9062 | 16600 | 0.0001 | - |
499
+ | 2.9149 | 16650 | 0.0001 | - |
500
+ | 2.9237 | 16700 | 0.0001 | - |
501
+ | 2.9324 | 16750 | 0.0003 | - |
502
+ | 2.9412 | 16800 | 0.0002 | - |
503
+ | 2.9499 | 16850 | 0.0003 | - |
504
+ | 2.9587 | 16900 | 0.0001 | - |
505
+ | 2.9674 | 16950 | 0.0002 | - |
506
+ | 2.9762 | 17000 | 0.0001 | - |
507
+ | 2.9849 | 17050 | 0.0001 | - |
508
+ | 2.9937 | 17100 | 0.0001 | - |
509
+ | **3.0** | **17136** | **-** | **0.0419** |
510
+ | 3.0025 | 17150 | 0.0002 | - |
511
+ | 3.0112 | 17200 | 0.0002 | - |
512
+ | 3.0200 | 17250 | 0.0003 | - |
513
+ | 3.0287 | 17300 | 0.0001 | - |
514
+ | 3.0375 | 17350 | 0.0002 | - |
515
+ | 3.0462 | 17400 | 0.0001 | - |
516
+ | 3.0550 | 17450 | 0.0002 | - |
517
+ | 3.0637 | 17500 | 0.0002 | - |
518
+ | 3.0725 | 17550 | 0.0002 | - |
519
+ | 3.0812 | 17600 | 0.0001 | - |
520
+ | 3.0900 | 17650 | 0.0001 | - |
521
+ | 3.0987 | 17700 | 0.0001 | - |
522
+ | 3.1075 | 17750 | 0.0001 | - |
523
+ | 3.1162 | 17800 | 0.0001 | - |
524
+ | 3.125 | 17850 | 0.0001 | - |
525
+ | 3.1338 | 17900 | 0.0002 | - |
526
+ | 3.1425 | 17950 | 0.0001 | - |
527
+ | 3.1513 | 18000 | 0.0003 | - |
528
+ | 3.1600 | 18050 | 0.0001 | - |
529
+ | 3.1688 | 18100 | 0.0001 | - |
530
+ | 3.1775 | 18150 | 0.0001 | - |
531
+ | 3.1863 | 18200 | 0.0002 | - |
532
+ | 3.1950 | 18250 | 0.0002 | - |
533
+ | 3.2038 | 18300 | 0.0001 | - |
534
+ | 3.2125 | 18350 | 0.0001 | - |
535
+ | 3.2213 | 18400 | 0.0001 | - |
536
+ | 3.2300 | 18450 | 0.0002 | - |
537
+ | 3.2388 | 18500 | 0.0001 | - |
538
+ | 3.2475 | 18550 | 0.0002 | - |
539
+ | 3.2563 | 18600 | 0.0001 | - |
540
+ | 3.2651 | 18650 | 0.0002 | - |
541
+ | 3.2738 | 18700 | 0.0001 | - |
542
+ | 3.2826 | 18750 | 0.0001 | - |
543
+ | 3.2913 | 18800 | 0.0001 | - |
544
+ | 3.3001 | 18850 | 0.0001 | - |
545
+ | 3.3088 | 18900 | 0.0003 | - |
546
+ | 3.3176 | 18950 | 0.0002 | - |
547
+ | 3.3263 | 19000 | 0.0001 | - |
548
+ | 3.3351 | 19050 | 0.0003 | - |
549
+ | 3.3438 | 19100 | 0.0001 | - |
550
+ | 3.3526 | 19150 | 0.0001 | - |
551
+ | 3.3613 | 19200 | 0.0001 | - |
552
+ | 3.3701 | 19250 | 0.0001 | - |
553
+ | 3.3789 | 19300 | 0.0001 | - |
554
+ | 3.3876 | 19350 | 0.0002 | - |
555
+ | 3.3964 | 19400 | 0.0001 | - |
556
+ | 3.4051 | 19450 | 0.0001 | - |
557
+ | 3.4139 | 19500 | 0.0001 | - |
558
+ | 3.4226 | 19550 | 0.0001 | - |
559
+ | 3.4314 | 19600 | 0.0001 | - |
560
+ | 3.4401 | 19650 | 0.0001 | - |
561
+ | 3.4489 | 19700 | 0.0002 | - |
562
+ | 3.4576 | 19750 | 0.0001 | - |
563
+ | 3.4664 | 19800 | 0.0001 | - |
564
+ | 3.4751 | 19850 | 0.0001 | - |
565
+ | 3.4839 | 19900 | 0.0001 | - |
566
+ | 3.4926 | 19950 | 0.0001 | - |
567
+ | 3.5014 | 20000 | 0.0001 | - |
568
+ | 3.5102 | 20050 | 0.0002 | - |
569
+ | 3.5189 | 20100 | 0.0003 | - |
570
+ | 3.5277 | 20150 | 0.0001 | - |
571
+ | 3.5364 | 20200 | 0.0002 | - |
572
+ | 3.5452 | 20250 | 0.0001 | - |
573
+ | 3.5539 | 20300 | 0.0001 | - |
574
+ | 3.5627 | 20350 | 0.0001 | - |
575
+ | 3.5714 | 20400 | 0.0004 | - |
576
+ | 3.5802 | 20450 | 0.0001 | - |
577
+ | 3.5889 | 20500 | 0.0001 | - |
578
+ | 3.5977 | 20550 | 0.0001 | - |
579
+ | 3.6064 | 20600 | 0.0002 | - |
580
+ | 3.6152 | 20650 | 0.0001 | - |
581
+ | 3.6239 | 20700 | 0.0001 | - |
582
+ | 3.6327 | 20750 | 0.0 | - |
583
+ | 3.6415 | 20800 | 0.0002 | - |
584
+ | 3.6502 | 20850 | 0.0001 | - |
585
+ | 3.6590 | 20900 | 0.0001 | - |
586
+ | 3.6677 | 20950 | 0.0002 | - |
587
+ | 3.6765 | 21000 | 0.0001 | - |
588
+ | 3.6852 | 21050 | 0.0001 | - |
589
+ | 3.6940 | 21100 | 0.0001 | - |
590
+ | 3.7027 | 21150 | 0.0002 | - |
591
+ | 3.7115 | 21200 | 0.0004 | - |
592
+ | 3.7202 | 21250 | 0.0001 | - |
593
+ | 3.7290 | 21300 | 0.0002 | - |
594
+ | 3.7377 | 21350 | 0.0001 | - |
595
+ | 3.7465 | 21400 | 0.0004 | - |
596
+ | 3.7553 | 21450 | 0.0002 | - |
597
+ | 3.7640 | 21500 | 0.0001 | - |
598
+ | 3.7728 | 21550 | 0.0001 | - |
599
+ | 3.7815 | 21600 | 0.0001 | - |
600
+ | 3.7903 | 21650 | 0.0001 | - |
601
+ | 3.7990 | 21700 | 0.0001 | - |
602
+ | 3.8078 | 21750 | 0.0001 | - |
603
+ | 3.8165 | 21800 | 0.0 | - |
604
+ | 3.8253 | 21850 | 0.0 | - |
605
+ | 3.8340 | 21900 | 0.0001 | - |
606
+ | 3.8428 | 21950 | 0.0003 | - |
607
+ | 3.8515 | 22000 | 0.0001 | - |
608
+ | 3.8603 | 22050 | 0.0001 | - |
609
+ | 3.8690 | 22100 | 0.0002 | - |
610
+ | 3.8778 | 22150 | 0.0001 | - |
611
+ | 3.8866 | 22200 | 0.0003 | - |
612
+ | 3.8953 | 22250 | 0.0001 | - |
613
+ | 3.9041 | 22300 | 0.0 | - |
614
+ | 3.9128 | 22350 | 0.0001 | - |
615
+ | 3.9216 | 22400 | 0.0002 | - |
616
+ | 3.9303 | 22450 | 0.0001 | - |
617
+ | 3.9391 | 22500 | 0.0001 | - |
618
+ | 3.9478 | 22550 | 0.0 | - |
619
+ | 3.9566 | 22600 | 0.0003 | - |
620
+ | 3.9653 | 22650 | 0.0001 | - |
621
+ | 3.9741 | 22700 | 0.0001 | - |
622
+ | 3.9828 | 22750 | 0.0001 | - |
623
+ | 3.9916 | 22800 | 0.0002 | - |
624
+ | 4.0 | 22848 | - | 0.0419 |
625
+ | 4.0004 | 22850 | 0.0 | - |
626
+ | 4.0091 | 22900 | 0.0001 | - |
627
+ | 4.0179 | 22950 | 0.0001 | - |
628
+ | 4.0266 | 23000 | 0.0001 | - |
629
+ | 4.0354 | 23050 | 0.0001 | - |
630
+ | 4.0441 | 23100 | 0.0002 | - |
631
+ | 4.0529 | 23150 | 0.0001 | - |
632
+ | 4.0616 | 23200 | 0.0001 | - |
633
+ | 4.0704 | 23250 | 0.0002 | - |
634
+ | 4.0791 | 23300 | 0.0 | - |
635
+ | 4.0879 | 23350 | 0.0001 | - |
636
+ | 4.0966 | 23400 | 0.0001 | - |
637
+ | 4.1054 | 23450 | 0.0001 | - |
638
+ | 4.1141 | 23500 | 0.0001 | - |
639
+ | 4.1229 | 23550 | 0.0002 | - |
640
+ | 4.1317 | 23600 | 0.0001 | - |
641
+ | 4.1404 | 23650 | 0.0001 | - |
642
+ | 4.1492 | 23700 | 0.0001 | - |
643
+ | 4.1579 | 23750 | 0.0002 | - |
644
+ | 4.1667 | 23800 | 0.0002 | - |
645
+ | 4.1754 | 23850 | 0.0001 | - |
646
+ | 4.1842 | 23900 | 0.0001 | - |
647
+ | 4.1929 | 23950 | 0.0001 | - |
648
+ | 4.2017 | 24000 | 0.0001 | - |
649
+ | 4.2104 | 24050 | 0.0001 | - |
650
+ | 4.2192 | 24100 | 0.0001 | - |
651
+ | 4.2279 | 24150 | 0.0 | - |
652
+ | 4.2367 | 24200 | 0.0001 | - |
653
+ | 4.2454 | 24250 | 0.0001 | - |
654
+ | 4.2542 | 24300 | 0.0003 | - |
655
+ | 4.2630 | 24350 | 0.0 | - |
656
+ | 4.2717 | 24400 | 0.0001 | - |
657
+ | 4.2805 | 24450 | 0.0 | - |
658
+ | 4.2892 | 24500 | 0.0001 | - |
659
+ | 4.2980 | 24550 | 0.0001 | - |
660
+ | 4.3067 | 24600 | 0.0002 | - |
661
+ | 4.3155 | 24650 | 0.0 | - |
662
+ | 4.3242 | 24700 | 0.0001 | - |
663
+ | 4.3330 | 24750 | 0.0001 | - |
664
+ | 4.3417 | 24800 | 0.0001 | - |
665
+ | 4.3505 | 24850 | 0.0001 | - |
666
+ | 4.3592 | 24900 | 0.0001 | - |
667
+ | 4.3680 | 24950 | 0.0 | - |
668
+ | 4.3768 | 25000 | 0.0002 | - |
669
+ | 4.3855 | 25050 | 0.0001 | - |
670
+ | 4.3943 | 25100 | 0.0001 | - |
671
+ | 4.4030 | 25150 | 0.0001 | - |
672
+ | 4.4118 | 25200 | 0.0001 | - |
673
+ | 4.4205 | 25250 | 0.0001 | - |
674
+ | 4.4293 | 25300 | 0.0002 | - |
675
+ | 4.4380 | 25350 | 0.0002 | - |
676
+ | 4.4468 | 25400 | 0.0001 | - |
677
+ | 4.4555 | 25450 | 0.0001 | - |
678
+ | 4.4643 | 25500 | 0.0001 | - |
679
+ | 4.4730 | 25550 | 0.0001 | - |
680
+ | 4.4818 | 25600 | 0.0001 | - |
681
+ | 4.4905 | 25650 | 0.0001 | - |
682
+ | 4.4993 | 25700 | 0.0001 | - |
683
+ | 4.5081 | 25750 | 0.0001 | - |
684
+ | 4.5168 | 25800 | 0.0001 | - |
685
+ | 4.5256 | 25850 | 0.0001 | - |
686
+ | 4.5343 | 25900 | 0.0001 | - |
687
+ | 4.5431 | 25950 | 0.0001 | - |
688
+ | 4.5518 | 26000 | 0.0 | - |
689
+ | 4.5606 | 26050 | 0.0 | - |
690
+ | 4.5693 | 26100 | 0.0001 | - |
691
+ | 4.5781 | 26150 | 0.0001 | - |
692
+ | 4.5868 | 26200 | 0.0001 | - |
693
+ | 4.5956 | 26250 | 0.0001 | - |
694
+ | 4.6043 | 26300 | 0.0001 | - |
695
+ | 4.6131 | 26350 | 0.0001 | - |
696
+ | 4.6218 | 26400 | 0.0002 | - |
697
+ | 4.6306 | 26450 | 0.0001 | - |
698
+ | 4.6394 | 26500 | 0.0001 | - |
699
+ | 4.6481 | 26550 | 0.0001 | - |
700
+ | 4.6569 | 26600 | 0.0001 | - |
701
+ | 4.6656 | 26650 | 0.0 | - |
702
+ | 4.6744 | 26700 | 0.0002 | - |
703
+ | 4.6831 | 26750 | 0.0 | - |
704
+ | 4.6919 | 26800 | 0.0001 | - |
705
+ | 4.7006 | 26850 | 0.0002 | - |
706
+ | 4.7094 | 26900 | 0.0002 | - |
707
+ | 4.7181 | 26950 | 0.0001 | - |
708
+ | 4.7269 | 27000 | 0.0001 | - |
709
+ | 4.7356 | 27050 | 0.0001 | - |
710
+ | 4.7444 | 27100 | 0.0 | - |
711
+ | 4.7532 | 27150 | 0.0001 | - |
712
+ | 4.7619 | 27200 | 0.0001 | - |
713
+ | 4.7707 | 27250 | 0.0001 | - |
714
+ | 4.7794 | 27300 | 0.0 | - |
715
+ | 4.7882 | 27350 | 0.0001 | - |
716
+ | 4.7969 | 27400 | 0.0001 | - |
717
+ | 4.8057 | 27450 | 0.0002 | - |
718
+ | 4.8144 | 27500 | 0.0 | - |
719
+ | 4.8232 | 27550 | 0.0001 | - |
720
+ | 4.8319 | 27600 | 0.0001 | - |
721
+ | 4.8407 | 27650 | 0.0001 | - |
722
+ | 4.8494 | 27700 | 0.0 | - |
723
+ | 4.8582 | 27750 | 0.0001 | - |
724
+ | 4.8669 | 27800 | 0.0001 | - |
725
+ | 4.8757 | 27850 | 0.0001 | - |
726
+ | 4.8845 | 27900 | 0.0001 | - |
727
+ | 4.8932 | 27950 | 0.0001 | - |
728
+ | 4.9020 | 28000 | 0.0001 | - |
729
+ | 4.9107 | 28050 | 0.0001 | - |
730
+ | 4.9195 | 28100 | 0.0 | - |
731
+ | 4.9282 | 28150 | 0.0001 | - |
732
+ | 4.9370 | 28200 | 0.0001 | - |
733
+ | 4.9457 | 28250 | 0.0001 | - |
734
+ | 4.9545 | 28300 | 0.0001 | - |
735
+ | 4.9632 | 28350 | 0.0001 | - |
736
+ | 4.9720 | 28400 | 0.0001 | - |
737
+ | 4.9807 | 28450 | 0.0001 | - |
738
+ | 4.9895 | 28500 | 0.0002 | - |
739
+ | 4.9982 | 28550 | 0.0 | - |
740
+ | 5.0 | 28560 | - | 0.0425 |
741
+
742
+ * The bold row denotes the saved checkpoint.
743
+ ### Framework Versions
744
+ - Python: 3.10.12
745
+ - SetFit: 1.1.0.dev0
746
+ - Sentence Transformers: 3.0.1
747
+ - Transformers: 4.42.4
748
+ - PyTorch: 2.3.1+cu121
749
+ - Datasets: 2.20.0
750
+ - Tokenizers: 0.19.1
751
+
752
+ ## Citation
753
+
754
+ ### BibTeX
755
+ ```bibtex
756
+ @article{https://doi.org/10.48550/arxiv.2209.11055,
757
+ doi = {10.48550/ARXIV.2209.11055},
758
+ url = {https://arxiv.org/abs/2209.11055},
759
+ author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
760
+ keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
761
+ title = {Efficient Few-Shot Learning Without Prompts},
762
+ publisher = {arXiv},
763
+ year = {2022},
764
+ copyright = {Creative Commons Attribution 4.0 International}
765
+ }
766
+ ```
767
+
768
+ <!--
769
+ ## Glossary
770
+
771
+ *Clearly define terms in order to be accessible across audiences.*
772
+ -->
773
+
774
+ <!--
775
+ ## Model Card Authors
776
+
777
+ *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
778
+ -->
779
+
780
+ <!--
781
+ ## Model Card Contact
782
+
783
+ *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
784
+ -->
config.json ADDED
@@ -0,0 +1,26 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "_name_or_path": "checkpoints/step_17136",
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": 384,
12
+ "initializer_range": 0.02,
13
+ "intermediate_size": 1536,
14
+ "layer_norm_eps": 1e-12,
15
+ "max_position_embeddings": 512,
16
+ "model_type": "bert",
17
+ "num_attention_heads": 12,
18
+ "num_hidden_layers": 6,
19
+ "pad_token_id": 0,
20
+ "position_embedding_type": "absolute",
21
+ "torch_dtype": "float32",
22
+ "transformers_version": "4.42.4",
23
+ "type_vocab_size": 2,
24
+ "use_cache": true,
25
+ "vocab_size": 30522
26
+ }
config_sentence_transformers.json ADDED
@@ -0,0 +1,10 @@
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "__version__": {
3
+ "sentence_transformers": "3.0.1",
4
+ "transformers": "4.42.4",
5
+ "pytorch": "2.3.1+cu121"
6
+ },
7
+ "prompts": {},
8
+ "default_prompt_name": null,
9
+ "similarity_fn_name": null
10
+ }
config_setfit.json ADDED
@@ -0,0 +1,4 @@
 
 
 
 
 
1
+ {
2
+ "normalize_embeddings": false,
3
+ "labels": null
4
+ }
model.safetensors ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:a17a5814df85340f6d8fef442c03234e55a5dfef82fb6a84ed112e0815b73f41
3
+ size 90864192
model_head.pkl ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:241904c9052b42781ad7e5b97edb407c8d43ce4bbbcbbe6084a9a3e88f0f1bf5
3
+ size 44071
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": 256,
3
+ "do_lower_case": false
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": 128,
50
+ "model_max_length": 256,
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