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rkoh
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setfit-bert

Text Classification
setfit
Safetensors
sentence-transformers
bert
generated_from_setfit_trainer
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metadata
library_name: setfit
metrics:
  - accuracy
pipeline_tag: text-classification
tags:
  - setfit
  - sentence-transformers
  - text-classification
  - generated_from_setfit_trainer
widget:
  - text: >-
      If the Probable Cause Committee determines that charges should be filed,
      the respondent is notified of the specific nature of the charges and the
      Board's proposed settlement of the issues. Said notice shall be sent by
      certified mail, return receipt requested, to the respondent's last known
      address. If a hearing is to be scheduled, the notice shall be sent by
      certified mail, return receipt requested, to the respondent's last known
      address not less than ten (10) days before the date of the scheduled
      hearing. The Board will conduct the hearing with the assistance of a
      hearing officer, who will hear all competent and relevant evidence in
      support of the charges. The hearing will be conducted in accordance with
      the Alabama Administrative Procedures Act, Section 41-22-13, Code of Ala.
      1975. Upon conclusion of the hearing, the members of the Board (excluding
      the Probable Cause Committee Board member) will determine the appropriate
      action to be taken, and shall notify, or cause to be notified, the
      respondent of such action. If the Board suspends or revokes a
      registration, or issues a reprimand or fine against the respondent, he or
      she may appeal to the Circuit Court of Montgomery County, Alabama.
  - text: >-
      Definitions governing the construction of this subchapter can be found in
      Chapter 1, Section 790 of this subdivision.
  - text: >-
      Any decision to deny, restrict or limit an inmate of any right, service,
      item or article, guaranteed an inmate by the provisions of this Part,
      shall be done in accordance with section 7075.5 of this Title.
  - text: >-
      After a port drayage motor carrier has been placed on the public list, the
      Labor Commissioner shall remove the motor carrier from the list within 15
      business days upon the following: (a) The Labor Commissioner's Office
      determines after review of submitted documents specified in subsections
      (1), (2), and (3) that there has been full payment of an unsatisfied
      judgment or any other final liability for all violations identified in
      Labor Code sections 2810.4(b)(1)(A)-(B) or that the port drayage motor
      carrier has entered into an approved settlement dispensing of the judgment
      or liability; or, in the case of a subsequent liability against a prior
      offender, the prior offender prevailed in an appeal. (1) A port drayage
      motor carrier shall present such proof by submitting a written statement
      under penalty of perjury stating the basis for removal of the listing,
      along with the accompanying documentation specified in subsections (2) and
      (3), as applicable, by mail to the Labor Commissioner's Office, Attn: SB
      1402 Proof of Payment or Settlement, 1500 Hughes Way, Suite C-202, Long
      Beach, CA 90810, or electronically in pdf format via email to:
      [email protected]. (2) For purposes of sufficiently documenting the
      payment or satisfaction of a judgment, tax assessment, or tax lien or a
      citation or ODA, the port drayage motor carrier shall identify and provide
      the documentation required under Section 13878, as applicable. (3) For
      purposes of sufficiently documenting a disposition regarding a port
      drayage motor carrier who is a prior offender who prevailed on appeal from
      a subsequent non-final judgment or ruling or final citation or ODA, the
      motor carrier shall identify and provide a conformed copy of the final
      judgment, ruling, citation, tax assessment, tax, order, decision, or award
      which indicates the final disposition on the appeal. (4) The port drayage
      motor carrier shall also provide documentation to show that violations of
      any labor or employment law or regulation subject to a final judgment or
      final citation or ODA have been sufficiently abated. This documentation
      shall include: a statement under penalty of perjury that the port drayage
      motor carrier does not currently engage in the labor practices identified
      as unlawful in the final judgment, final citation or ODA, and a
      description of the steps the motor carrier took to abate the violation(s).
      Subject to the Labor Commissioner's request, the agency may determine
      whether an applicable violation was abated by reviewing any documents the
      motor carrier is required to maintain under the Labor Code, wage orders,
      or any other applicable law. (b) The Labor Commissioner's Office will
      inform the port drayage motor carrier by letter of the agency's
      determination of whether the motor carrier has presented sufficient proof
      to merit removal from the public list. (c) If a port drayage motor carrier
      on the public list has multiple liability determinations posted on the
      public list, a separate request for removal must be provided for each
      determination. Each removal request will be considered individually and
      only the liability determination that is the subject of that removal
      request may be removed.
  - text: '(Repealed). Author: Michael E. Mason, CPA'
inference: true

SetFit

This is a SetFit model that can be used for Text Classification. A LogisticRegression instance is used for classification.

The model has been trained using an efficient few-shot learning technique that involves:

  1. Fine-tuning a Sentence Transformer with contrastive learning.
  2. Training a classification head with features from the fine-tuned Sentence Transformer.

Model Details

Model Description

  • Model Type: SetFit
  • Classification head: a LogisticRegression instance
  • Maximum Sequence Length: 512 tokens
  • Number of Classes: 5000 classes

Model Sources

Uses

Direct Use for Inference

First install the SetFit library:

pip install setfit

Then you can load this model and run inference.

from setfit import SetFitModel

# Download from the 🤗 Hub
model = SetFitModel.from_pretrained("rkoh/setfit-bert")
# Run inference
preds = model("(Repealed). Author: Michael E. Mason, CPA")

Training Details

Training Set Metrics

Training set Min Median Max
Word count tensor(1) tensor(370.1842) tensor(52538)
Label Training Sample Count
Purpose - Regulatory Objective 0
Scope and Applicability 0
Authority and Legal Basis 0
Administrative Details 0
Non-Purpose 0

Training Hyperparameters

  • batch_size: (32, 32)
  • num_epochs: (1, 1)
  • max_steps: -1
  • sampling_strategy: oversampling
  • num_iterations: 20
  • body_learning_rate: (2e-05, 1e-05)
  • head_learning_rate: 0.01
  • loss: CosineSimilarityLoss
  • distance_metric: cosine_distance
  • margin: 0.25
  • end_to_end: False
  • use_amp: False
  • warmup_proportion: 0.1
  • l2_weight: 0.01
  • seed: 42
  • eval_max_steps: -1
  • load_best_model_at_end: True

Training Results

Epoch Step Training Loss Validation Loss
0.0002 1 0.1006 -
0.0016 10 0.0759 -
0.0032 20 0.0767 -
0.0048 30 0.0852 -
0.0064 40 0.0765 -
0.008 50 0.078 -
0.0096 60 0.0734 -
0.0112 70 0.0687 -
0.0128 80 0.0566 -
0.0144 90 0.065 -
0.016 100 0.0583 -
0.0176 110 0.0584 -
0.0192 120 0.0466 -
0.0208 130 0.0661 -
0.0224 140 0.0583 -
0.024 150 0.0494 -
0.0256 160 0.0451 -
0.0272 170 0.0443 -
0.0288 180 0.0409 -
0.0304 190 0.0513 -
0.032 200 0.0415 -
0.0336 210 0.0413 -
0.0352 220 0.0478 -
0.0368 230 0.0319 -
0.0384 240 0.0273 -
0.04 250 0.0418 -
0.0416 260 0.0415 -
0.0432 270 0.0454 -
0.0448 280 0.0333 -
0.0464 290 0.0341 -
0.048 300 0.0504 -
0.0496 310 0.0296 -
0.0512 320 0.0293 -
0.0528 330 0.0263 -
0.0544 340 0.0292 -
0.056 350 0.0394 -
0.0576 360 0.0246 -
0.0592 370 0.0419 -
0.0608 380 0.0251 -
0.0624 390 0.02 -
0.064 400 0.0397 -
0.0656 410 0.0151 -
0.0672 420 0.0312 -
0.0688 430 0.0336 -
0.0704 440 0.0194 -
0.072 450 0.0251 -
0.0736 460 0.0167 -
0.0752 470 0.0203 -
0.0768 480 0.0158 -
0.0784 490 0.0165 -
0.08 500 0.0181 -
0.0816 510 0.0153 -
0.0832 520 0.0301 -
0.0848 530 0.0243 -
0.0864 540 0.0271 -
0.088 550 0.0185 -
0.0896 560 0.0221 -
0.0912 570 0.0171 -
0.0928 580 0.0284 -
0.0944 590 0.0335 -
0.096 600 0.0163 -
0.0976 610 0.0199 -
0.0992 620 0.0212 -
0.1008 630 0.0253 -
0.1024 640 0.0173 -
0.104 650 0.0376 -
0.1056 660 0.0135 -
0.1072 670 0.0216 -
0.1088 680 0.0279 -
0.1104 690 0.0126 -
0.112 700 0.0144 -
0.1136 710 0.0149 -
0.1152 720 0.0186 -
0.1168 730 0.0084 -
0.1184 740 0.0231 -
0.12 750 0.0152 -
0.1216 760 0.0174 -
0.1232 770 0.0235 -
0.1248 780 0.0144 -
0.1264 790 0.0081 -
0.128 800 0.0209 -
0.1296 810 0.014 -
0.1312 820 0.0208 -
0.1328 830 0.0146 -
0.1344 840 0.0159 -
0.136 850 0.0119 -
0.1376 860 0.0251 -
0.1392 870 0.0153 -
0.1408 880 0.0077 -
0.1424 890 0.0136 -
0.144 900 0.0131 -
0.1456 910 0.0058 -
0.1472 920 0.0146 -
0.1488 930 0.0186 -
0.1504 940 0.014 -
0.152 950 0.0127 -
0.1536 960 0.0074 -
0.1552 970 0.0246 -
0.1568 980 0.0137 -
0.1584 990 0.0061 -
0.16 1000 0.0067 -
0.1616 1010 0.0125 -
0.1632 1020 0.01 -
0.1648 1030 0.0116 -
0.1664 1040 0.0098 -
0.168 1050 0.0116 -
0.1696 1060 0.0051 -
0.1712 1070 0.0014 -
0.1728 1080 0.0056 -
0.1744 1090 0.0009 -
0.176 1100 0.0074 -
0.1776 1110 0.0019 -
0.1792 1120 0.0022 -
0.1808 1130 0.0063 -
0.1824 1140 0.0059 -
0.184 1150 0.0065 -
0.1856 1160 0.0151 -
0.1872 1170 0.0034 -
0.1888 1180 0.0033 -
0.1904 1190 0.0085 -
0.192 1200 0.0041 -
0.1936 1210 0.0084 -
0.1952 1220 0.004 -
0.1968 1230 0.0148 -
0.1984 1240 0.0111 -
0.2 1250 0.0125 -
0.2016 1260 0.0086 -
0.2032 1270 0.0042 -
0.2048 1280 0.0041 -
0.2064 1290 0.0078 -
0.208 1300 0.0042 -
0.2096 1310 0.0078 -
0.2112 1320 0.0065 -
0.2128 1330 0.0079 -
0.2144 1340 0.0157 -
0.216 1350 0.0086 -
0.2176 1360 0.0057 -
0.2192 1370 0.0025 -
0.2208 1380 0.0057 -
0.2224 1390 0.0051 -
0.224 1400 0.0054 -
0.2256 1410 0.0048 -
0.2272 1420 0.01 -
0.2288 1430 0.0087 -
0.2304 1440 0.0053 -
0.232 1450 0.0046 -
0.2336 1460 0.004 -
0.2352 1470 0.0062 -
0.2368 1480 0.0088 -
0.2384 1490 0.0093 -
0.24 1500 0.0005 -
0.2416 1510 0.0074 -
0.2432 1520 0.0042 -
0.2448 1530 0.0072 -
0.2464 1540 0.0007 -
0.248 1550 0.005 -
0.2496 1560 0.002 -
0.2512 1570 0.001 -
0.2528 1580 0.0062 -
0.2544 1590 0.0004 -
0.256 1600 0.0009 -
0.2576 1610 0.0041 -
0.2592 1620 0.0119 -
0.2608 1630 0.0011 -
0.2624 1640 0.0104 -
0.264 1650 0.0037 -
0.2656 1660 0.0005 -
0.2672 1670 0.004 -
0.2688 1680 0.0036 -
0.2704 1690 0.0037 -
0.272 1700 0.0013 -
0.2736 1710 0.0004 -
0.2752 1720 0.0006 -
0.2768 1730 0.0065 -
0.2784 1740 0.0033 -
0.28 1750 0.0009 -
0.2816 1760 0.0117 -
0.2832 1770 0.0033 -
0.2848 1780 0.0032 -
0.2864 1790 0.0037 -
0.288 1800 0.0022 -
0.2896 1810 0.0011 -
0.2912 1820 0.0006 -
0.2928 1830 0.0007 -
0.2944 1840 0.0054 -
0.296 1850 0.0007 -
0.2976 1860 0.0035 -
0.2992 1870 0.0038 -
0.3008 1880 0.0075 -
0.3024 1890 0.0017 -
0.304 1900 0.0005 -
0.3056 1910 0.0002 -
0.3072 1920 0.0002 -
0.3088 1930 0.0002 -
0.3104 1940 0.0033 -
0.312 1950 0.0085 -
0.3136 1960 0.0004 -
0.3152 1970 0.0005 -
0.3168 1980 0.0002 -
0.3184 1990 0.003 -
0.32 2000 0.0007 -
0.3216 2010 0.0009 -
0.3232 2020 0.0003 -
0.3248 2030 0.0012 -
0.3264 2040 0.0086 -
0.328 2050 0.001 -
0.3296 2060 0.0009 -
0.3312 2070 0.0029 -
0.3328 2080 0.0033 -
0.3344 2090 0.0005 -
0.336 2100 0.0003 -
0.3376 2110 0.0033 -
0.3392 2120 0.0029 -
0.3408 2130 0.0001 -
0.3424 2140 0.0057 -
0.344 2150 0.0001 -
0.3456 2160 0.0002 -
0.3472 2170 0.004 -
0.3488 2180 0.002 -
0.3504 2190 0.0073 -
0.352 2200 0.0004 -
0.3536 2210 0.0006 -
0.3552 2220 0.0004 -
0.3568 2230 0.0032 -
0.3584 2240 0.007 -
0.36 2250 0.0096 -
0.3616 2260 0.0032 -
0.3632 2270 0.0006 -
0.3648 2280 0.0002 -
0.3664 2290 0.0032 -
0.368 2300 0.0002 -
0.3696 2310 0.0025 -
0.3712 2320 0.0002 -
0.3728 2330 0.0053 -
0.3744 2340 0.0017 -
0.376 2350 0.0013 -
0.3776 2360 0.0001 -
0.3792 2370 0.0032 -
0.3808 2380 0.0002 -
0.3824 2390 0.0019 -
0.384 2400 0.0015 -
0.3856 2410 0.0009 -
0.3872 2420 0.0006 -
0.3888 2430 0.0032 -
0.3904 2440 0.0033 -
0.392 2450 0.0003 -
0.3936 2460 0.0003 -
0.3952 2470 0.0016 -
0.3968 2480 0.0065 -
0.3984 2490 0.0011 -
0.4 2500 0.0032 -
0.4016 2510 0.0045 -
0.4032 2520 0.0001 -
0.4048 2530 0.0004 -
0.4064 2540 0.0001 -
0.408 2550 0.0027 -
0.4096 2560 0.0032 -
0.4112 2570 0.0034 -
0.4128 2580 0.0057 -
0.4144 2590 0.0029 -
0.416 2600 0.0008 -
0.4176 2610 0.0002 -
0.4192 2620 0.0033 -
0.4208 2630 0.0004 -
0.4224 2640 0.0057 -
0.424 2650 0.0001 -
0.4256 2660 0.0048 -
0.4272 2670 0.0043 -
0.4288 2680 0.0011 -
0.4304 2690 0.0053 -
0.432 2700 0.0001 -
0.4336 2710 0.0045 -
0.4352 2720 0.0032 -
0.4368 2730 0.0034 -
0.4384 2740 0.0031 -
0.44 2750 0.0065 -
0.4416 2760 0.0013 -
0.4432 2770 0.0027 -
0.4448 2780 0.0014 -
0.4464 2790 0.0036 -
0.448 2800 0.0009 -
0.4496 2810 0.0053 -
0.4512 2820 0.0001 -
0.4528 2830 0.0005 -
0.4544 2840 0.0006 -
0.456 2850 0.0015 -
0.4576 2860 0.0028 -
0.4592 2870 0.0006 -
0.4608 2880 0.0001 -
0.4624 2890 0.0024 -
0.464 2900 0.0012 -
0.4656 2910 0.0003 -
0.4672 2920 0.0028 -
0.4688 2930 0.0022 -
0.4704 2940 0.0002 -
0.472 2950 0.0006 -
0.4736 2960 0.0002 -
0.4752 2970 0.0034 -
0.4768 2980 0.0032 -
0.4784 2990 0.0001 -
0.48 3000 0.0001 -
0.4816 3010 0.0003 -
0.4832 3020 0.0001 -
0.4848 3030 0.0011 -
0.4864 3040 0.0001 -
0.488 3050 0.0003 -
0.4896 3060 0.0031 -
0.4912 3070 0.0032 -
0.4928 3080 0.0028 -
0.4944 3090 0.0032 -
0.496 3100 0.0002 -
0.4976 3110 0.0001 -
0.4992 3120 0.0008 -
0.5008 3130 0.0028 -
0.5024 3140 0.0001 -
0.504 3150 0.0001 -
0.5056 3160 0.0001 -
0.5072 3170 0.0007 -
0.5088 3180 0.0054 -
0.5104 3190 0.0001 -
0.512 3200 0.0001 -
0.5136 3210 0.0001 -
0.5152 3220 0.0001 -
0.5168 3230 0.0027 -
0.5184 3240 0.0001 -
0.52 3250 0.0028 -
0.5216 3260 0.0001 -
0.5232 3270 0.0001 -
0.5248 3280 0.0007 -
0.5264 3290 0.0001 -
0.528 3300 0.0001 -
0.5296 3310 0.0001 -
0.5312 3320 0.0001 -
0.5328 3330 0.004 -
0.5344 3340 0.0001 -
0.536 3350 0.0049 -
0.5376 3360 0.0034 -
0.5392 3370 0.0004 -
0.5408 3380 0.0001 -
0.5424 3390 0.001 -
0.544 3400 0.0023 -
0.5456 3410 0.0019 -
0.5472 3420 0.0001 -
0.5488 3430 0.0027 -
0.5504 3440 0.0002 -
0.552 3450 0.0016 -
0.5536 3460 0.0001 -
0.5552 3470 0.0001 -
0.5568 3480 0.0005 -
0.5584 3490 0.0 -
0.56 3500 0.0001 -
0.5616 3510 0.0001 -
0.5632 3520 0.0001 -
0.5648 3530 0.0001 -
0.5664 3540 0.003 -
0.568 3550 0.0001 -
0.5696 3560 0.0002 -
0.5712 3570 0.0001 -
0.5728 3580 0.0001 -
0.5744 3590 0.0002 -
0.576 3600 0.0 -
0.5776 3610 0.0001 -
0.5792 3620 0.0034 -
0.5808 3630 0.0001 -
0.5824 3640 0.0001 -
0.584 3650 0.0001 -
0.5856 3660 0.0001 -
0.5872 3670 0.0003 -
0.5888 3680 0.0031 -
0.5904 3690 0.0001 -
0.592 3700 0.0001 -
0.5936 3710 0.003 -
0.5952 3720 0.0002 -
0.5968 3730 0.0031 -
0.5984 3740 0.0001 -
0.6 3750 0.0035 -
0.6016 3760 0.0001 -
0.6032 3770 0.003 -
0.6048 3780 0.0033 -
0.6064 3790 0.0026 -
0.608 3800 0.0024 -
0.6096 3810 0.0002 -
0.6112 3820 0.0001 -
0.6128 3830 0.0001 -
0.6144 3840 0.0001 -
0.616 3850 0.0001 -
0.6176 3860 0.0022 -
0.6192 3870 0.0001 -
0.6208 3880 0.0004 -
0.6224 3890 0.0066 -
0.624 3900 0.0033 -
0.6256 3910 0.0001 -
0.6272 3920 0.0001 -
0.6288 3930 0.0001 -
0.6304 3940 0.0032 -
0.632 3950 0.0003 -
0.6336 3960 0.0031 -
0.6352 3970 0.0001 -
0.6368 3980 0.0001 -
0.6384 3990 0.0001 -
0.64 4000 0.0001 -
0.6416 4010 0.0003 -
0.6432 4020 0.0001 -
0.6448 4030 0.0029 -
0.6464 4040 0.0001 -
0.648 4050 0.0001 -
0.6496 4060 0.0029 -
0.6512 4070 0.0001 -
0.6528 4080 0.0001 -
0.6544 4090 0.0001 -
0.656 4100 0.0001 -
0.6576 4110 0.0001 -
0.6592 4120 0.0001 -
0.6608 4130 0.0001 -
0.6624 4140 0.0001 -
0.664 4150 0.0001 -
0.6656 4160 0.0023 -
0.6672 4170 0.0002 -
0.6688 4180 0.0002 -
0.6704 4190 0.0014 -
0.672 4200 0.0004 -
0.6736 4210 0.0035 -
0.6752 4220 0.0001 -
0.6768 4230 0.0005 -
0.6784 4240 0.0001 -
0.68 4250 0.0029 -
0.6816 4260 0.0001 -
0.6832 4270 0.0001 -
0.6848 4280 0.0001 -
0.6864 4290 0.0001 -
0.688 4300 0.0003 -
0.6896 4310 0.0002 -
0.6912 4320 0.0001 -
0.6928 4330 0.0 -
0.6944 4340 0.0 -
0.696 4350 0.0 -
0.6976 4360 0.0001 -
0.6992 4370 0.0 -
0.7008 4380 0.0 -
0.7024 4390 0.0 -
0.704 4400 0.0 -
0.7056 4410 0.0 -
0.7072 4420 0.0 -
0.7088 4430 0.0 -
0.7104 4440 0.0001 -
0.712 4450 0.0001 -
0.7136 4460 0.0 -
0.7152 4470 0.0 -
0.7168 4480 0.0001 -
0.7184 4490 0.0 -
0.72 4500 0.0 -
0.7216 4510 0.0 -
0.7232 4520 0.0 -
0.7248 4530 0.0 -
0.7264 4540 0.0001 -
0.728 4550 0.0058 -
0.7296 4560 0.0001 -
0.7312 4570 0.0002 -
0.7328 4580 0.0001 -
0.7344 4590 0.0 -
0.736 4600 0.0001 -
0.7376 4610 0.0001 -
0.7392 4620 0.0 -
0.7408 4630 0.0002 -
0.7424 4640 0.0 -
0.744 4650 0.0 -
0.7456 4660 0.0004 -
0.7472 4670 0.0 -
0.7488 4680 0.0001 -
0.7504 4690 0.0 -
0.752 4700 0.0 -
0.7536 4710 0.0001 -
0.7552 4720 0.0001 -
0.7568 4730 0.0 -
0.7584 4740 0.0037 -
0.76 4750 0.0001 -
0.7616 4760 0.0032 -
0.7632 4770 0.0 -
0.7648 4780 0.0 -
0.7664 4790 0.0001 -
0.768 4800 0.0031 -
0.7696 4810 0.0001 -
0.7712 4820 0.0002 -
0.7728 4830 0.0 -
0.7744 4840 0.0001 -
0.776 4850 0.0001 -
0.7776 4860 0.0002 -
0.7792 4870 0.0 -
0.7808 4880 0.0 -
0.7824 4890 0.0001 -
0.784 4900 0.0 -
0.7856 4910 0.0 -
0.7872 4920 0.0001 -
0.7888 4930 0.0 -
0.7904 4940 0.0 -
0.792 4950 0.0001 -
0.7936 4960 0.0 -
0.7952 4970 0.0001 -
0.7968 4980 0.0 -
0.7984 4990 0.0029 -
0.8 5000 0.0001 -
0.8016 5010 0.0 -
0.8032 5020 0.0001 -
0.8048 5030 0.0005 -
0.8064 5040 0.0 -
0.808 5050 0.0 -
0.8096 5060 0.0014 -
0.8112 5070 0.0031 -
0.8128 5080 0.0 -
0.8144 5090 0.0001 -
0.816 5100 0.0 -
0.8176 5110 0.0001 -
0.8192 5120 0.0001 -
0.8208 5130 0.0 -
0.8224 5140 0.0 -
0.824 5150 0.0001 -
0.8256 5160 0.0 -
0.8272 5170 0.0 -
0.8288 5180 0.0 -
0.8304 5190 0.0006 -
0.832 5200 0.006 -
0.8336 5210 0.0032 -
0.8352 5220 0.0001 -
0.8368 5230 0.0 -
0.8384 5240 0.0 -
0.84 5250 0.0 -
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0.912 5700 0.0 -
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0.9152 5720 0.0032 -
0.9168 5730 0.0001 -
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0.9232 5770 0.0017 -
0.9248 5780 0.0 -
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1.0 6250 0.0 0.0479

Framework Versions

  • Python: 3.10.12
  • SetFit: 1.1.0
  • Sentence Transformers: 3.2.0
  • Transformers: 4.44.2
  • PyTorch: 2.4.1+cu121
  • Datasets: 3.0.1
  • Tokenizers: 0.19.1

Citation

BibTeX 

@article{https://doi.org/10.48550/arxiv.2209.11055,
    doi = {10.48550/ARXIV.2209.11055},
    url = {https://arxiv.org/abs/2209.11055},
    author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
    keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
    title = {Efficient Few-Shot Learning Without Prompts},
    publisher = {arXiv},
    year = {2022},
    copyright = {Creative Commons Attribution 4.0 International}
}