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rkoh
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setfit-bert

Text Classification
setfit
Safetensors
sentence-transformers
bert
generated_from_setfit_trainer
Model card Files Community
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Add SetFit model

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Files changed (13) hide show
  1. 1_Pooling/config.json +10 -0
  2. README.md +1598 -0
  3. config.json +43 -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 +14 -0
  9. sentence_bert_config.json +4 -0
  10. special_tokens_map.json +37 -0
  11. tokenizer.json +0 -0
  12. tokenizer_config.json +57 -0
  13. vocab.txt +0 -0
1_Pooling/config.json ADDED
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+ {
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+ "word_embedding_dimension": 768,
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+ "pooling_mode_cls_token": false,
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+ "pooling_mode_mean_tokens": true,
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+ "pooling_mode_max_tokens": false,
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+ "pooling_mode_mean_sqrt_len_tokens": false,
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+ "pooling_mode_weightedmean_tokens": false,
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+ "pooling_mode_lasttoken": false,
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+ "include_prompt": true
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+ }
README.md ADDED
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+ ---
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+ library_name: setfit
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+ metrics:
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+ - accuracy
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+ pipeline_tag: text-classification
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+ tags:
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+ - setfit
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+ - sentence-transformers
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+ - text-classification
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+ - generated_from_setfit_trainer
11
+ widget:
12
+ - text: If the Probable Cause Committee determines that charges should be filed, the
13
+ respondent is notified of the specific nature of the charges and the Board's proposed
14
+ settlement of the issues. Said notice shall be sent by certified mail, return
15
+ receipt requested, to the respondent's last known address. If a hearing is to
16
+ be scheduled, the notice shall be sent by certified mail, return receipt requested,
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+ to the respondent's last known address not less than ten (10) days before the
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+ date of the scheduled hearing. The Board will conduct the hearing with the assistance
19
+ of a hearing officer, who will hear all competent and relevant evidence in support
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+ of the charges. The hearing will be conducted in accordance with the Alabama Administrative
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+ Procedures Act, Section 41-22-13, Code of Ala. 1975. Upon conclusion of the hearing,
22
+ the members of the Board (excluding the Probable Cause Committee Board member)
23
+ will determine the appropriate action to be taken, and shall notify, or cause
24
+ to be notified, the respondent of such action. If the Board suspends or revokes
25
+ a registration, or issues a reprimand or fine against the respondent, he or she
26
+ may appeal to the Circuit Court of Montgomery County, Alabama.
27
+ - text: Definitions governing the construction of this subchapter can be found in
28
+ Chapter 1, Section 790 of this subdivision.
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+ - text: Any decision to deny, restrict or limit an inmate of any right, service, item
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+ or article, guaranteed an inmate by the provisions of this Part, shall be done
31
+ in accordance with section 7075.5 of this Title.
32
+ - text: 'After a port drayage motor carrier has been placed on the public list, the
33
+ Labor Commissioner shall remove the motor carrier from the list within 15 business
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+ days upon the following: (a) The Labor Commissioner''s Office determines after
35
+ review of submitted documents specified in subsections (1), (2), and (3) that
36
+ there has been full payment of an unsatisfied judgment or any other final liability
37
+ for all violations identified in Labor Code sections 2810.4(b)(1)(A)-(B) or that
38
+ the port drayage motor carrier has entered into an approved settlement dispensing
39
+ of the judgment or liability; or, in the case of a subsequent liability against
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+ a prior offender, the prior offender prevailed in an appeal. (1) A port drayage
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+ motor carrier shall present such proof by submitting a written statement under
42
+ penalty of perjury stating the basis for removal of the listing, along with the
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+ accompanying documentation specified in subsections (2) and (3), as applicable,
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+ by mail to the Labor Commissioner''s Office, Attn: SB 1402 Proof of Payment or
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+ Settlement, 1500 Hughes Way, Suite C-202, Long Beach, CA 90810, or electronically
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+ in pdf format via email to: [email protected]. (2) For purposes of sufficiently
47
+ documenting the payment or satisfaction of a judgment, tax assessment, or tax
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+ lien or a citation or ODA, the port drayage motor carrier shall identify and provide
49
+ the documentation required under Section 13878, as applicable. (3) For purposes
50
+ of sufficiently documenting a disposition regarding a port drayage motor carrier
51
+ who is a prior offender who prevailed on appeal from a subsequent non-final judgment
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+ or ruling or final citation or ODA, the motor carrier shall identify and provide
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+ a conformed copy of the final judgment, ruling, citation, tax assessment, tax,
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+ order, decision, or award which indicates the final disposition on the appeal.
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+ (4) The port drayage motor carrier shall also provide documentation to show that
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+ violations of any labor or employment law or regulation subject to a final judgment
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+ or final citation or ODA have been sufficiently abated. This documentation shall
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+ include: a statement under penalty of perjury that the port drayage motor carrier
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+ does not currently engage in the labor practices identified as unlawful in the
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+ final judgment, final citation or ODA, and a description of the steps the motor
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+ carrier took to abate the violation(s). Subject to the Labor Commissioner''s request,
62
+ the agency may determine whether an applicable violation was abated by reviewing
63
+ any documents the motor carrier is required to maintain under the Labor Code,
64
+ wage orders, or any other applicable law. (b) The Labor Commissioner''s Office
65
+ will inform the port drayage motor carrier by letter of the agency''s determination
66
+ of whether the motor carrier has presented sufficient proof to merit removal from
67
+ the public list. (c) If a port drayage motor carrier on the public list has multiple
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+ liability determinations posted on the public list, a separate request for removal
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+ must be provided for each determination. Each removal request will be considered
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+ individually and only the liability determination that is the subject of that
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+ removal request may be removed.'
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+ - text: '(Repealed). Author: Michael E. Mason, CPA'
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+ inference: true
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+ ---
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+
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+ # SetFit
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+
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+ This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance is used for classification.
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+
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+ The model has been trained using an efficient few-shot learning technique that involves:
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+
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+ 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
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+ 2. Training a classification head with features from the fine-tuned Sentence Transformer.
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+
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+ ## Model Details
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+
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+ ### Model Description
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+ - **Model Type:** SetFit
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+ <!-- - **Sentence Transformer:** [Unknown](https://huggingface.co/unknown) -->
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+ - **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
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+ - **Maximum Sequence Length:** 512 tokens
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+ - **Number of Classes:** 1000 classes
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+ <!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
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+ <!-- - **Language:** Unknown -->
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+ <!-- - **License:** Unknown -->
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+
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+ ### Model Sources
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+
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+ - **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit)
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+ - **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)
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+ - **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)
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+
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+ ## Uses
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+
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+ ### Direct Use for Inference
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+
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+ First install the SetFit library:
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+
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+ ```bash
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+ pip install setfit
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+ ```
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+
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+ Then you can load this model and run inference.
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+
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+ ```python
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+ from setfit import SetFitModel
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+
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+ # Download from the 🤗 Hub
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+ model = SetFitModel.from_pretrained("rkoh/setfit-bert")
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+ # Run inference
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+ preds = model("(Repealed). Author: Michael E. Mason, CPA")
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+ ```
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+
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+ <!--
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+ ### Downstream Use
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+
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+ *List how someone could finetune this model on their own dataset.*
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+ -->
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+
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+ <!--
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+ ### Out-of-Scope Use
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+
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+ *List how the model may foreseeably be misused and address what users ought not to do with the model.*
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+ -->
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+
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+ <!--
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+ ## Bias, Risks and Limitations
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+
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+ *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
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+ -->
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+
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+ <!--
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+ ### Recommendations
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+
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+ *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
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+ -->
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+
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+ ## Training Details
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+
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+ ### Training Set Metrics
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+ | Training set | Min | Median | Max |
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+ |:-------------|:----------|:-----------------|:--------------|
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+ | Word count | tensor(1) | tensor(350.0160) | tensor(29510) |
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+
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+ | Label | Training Sample Count |
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+ |:----------|:----------------------|
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+ | tensor(0) | 1 |
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+ | tensor(0) | 1 |
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+ | tensor(3) | 1 |
979
+ | tensor(3) | 1 |
980
+ | tensor(3) | 1 |
981
+ | tensor(3) | 1 |
982
+ | tensor(3) | 1 |
983
+ | tensor(3) | 1 |
984
+ | tensor(3) | 1 |
985
+ | tensor(3) | 1 |
986
+ | tensor(3) | 1 |
987
+ | tensor(3) | 1 |
988
+ | tensor(3) | 1 |
989
+ | tensor(3) | 1 |
990
+ | tensor(3) | 1 |
991
+ | tensor(3) | 1 |
992
+ | tensor(3) | 1 |
993
+ | tensor(3) | 1 |
994
+ | tensor(3) | 1 |
995
+ | tensor(3) | 1 |
996
+ | tensor(3) | 1 |
997
+ | tensor(3) | 1 |
998
+ | tensor(3) | 1 |
999
+ | tensor(3) | 1 |
1000
+ | tensor(3) | 1 |
1001
+ | tensor(3) | 1 |
1002
+ | tensor(3) | 1 |
1003
+ | tensor(3) | 1 |
1004
+ | tensor(3) | 1 |
1005
+ | tensor(3) | 1 |
1006
+ | tensor(3) | 1 |
1007
+ | tensor(3) | 1 |
1008
+ | tensor(3) | 1 |
1009
+ | tensor(3) | 1 |
1010
+ | tensor(3) | 1 |
1011
+ | tensor(3) | 1 |
1012
+ | tensor(3) | 1 |
1013
+ | tensor(3) | 1 |
1014
+ | tensor(3) | 1 |
1015
+ | tensor(3) | 1 |
1016
+ | tensor(3) | 1 |
1017
+ | tensor(3) | 1 |
1018
+ | tensor(3) | 1 |
1019
+ | tensor(3) | 1 |
1020
+ | tensor(3) | 1 |
1021
+ | tensor(3) | 1 |
1022
+ | tensor(3) | 1 |
1023
+ | tensor(3) | 1 |
1024
+ | tensor(3) | 1 |
1025
+ | tensor(3) | 1 |
1026
+ | tensor(3) | 1 |
1027
+ | tensor(3) | 1 |
1028
+ | tensor(3) | 1 |
1029
+ | tensor(3) | 1 |
1030
+ | tensor(3) | 1 |
1031
+ | tensor(3) | 1 |
1032
+ | tensor(3) | 1 |
1033
+ | tensor(3) | 1 |
1034
+ | tensor(3) | 1 |
1035
+ | tensor(3) | 1 |
1036
+ | tensor(3) | 1 |
1037
+ | tensor(3) | 1 |
1038
+ | tensor(3) | 1 |
1039
+ | tensor(3) | 1 |
1040
+ | tensor(3) | 1 |
1041
+ | tensor(3) | 1 |
1042
+ | tensor(3) | 1 |
1043
+ | tensor(3) | 1 |
1044
+ | tensor(3) | 1 |
1045
+ | tensor(3) | 1 |
1046
+ | tensor(3) | 1 |
1047
+ | tensor(3) | 1 |
1048
+ | tensor(3) | 1 |
1049
+ | tensor(3) | 1 |
1050
+ | tensor(3) | 1 |
1051
+ | tensor(3) | 1 |
1052
+ | tensor(3) | 1 |
1053
+ | tensor(3) | 1 |
1054
+ | tensor(3) | 1 |
1055
+ | tensor(3) | 1 |
1056
+ | tensor(3) | 1 |
1057
+ | tensor(3) | 1 |
1058
+ | tensor(3) | 1 |
1059
+ | tensor(3) | 1 |
1060
+ | tensor(3) | 1 |
1061
+ | tensor(3) | 1 |
1062
+ | tensor(3) | 1 |
1063
+ | tensor(3) | 1 |
1064
+ | tensor(3) | 1 |
1065
+ | tensor(3) | 1 |
1066
+ | tensor(3) | 1 |
1067
+ | tensor(3) | 1 |
1068
+ | tensor(3) | 1 |
1069
+ | tensor(3) | 1 |
1070
+ | tensor(3) | 1 |
1071
+ | tensor(3) | 1 |
1072
+ | tensor(3) | 1 |
1073
+ | tensor(3) | 1 |
1074
+ | tensor(3) | 1 |
1075
+ | tensor(3) | 1 |
1076
+ | tensor(3) | 1 |
1077
+ | tensor(3) | 1 |
1078
+ | tensor(3) | 1 |
1079
+ | tensor(3) | 1 |
1080
+ | tensor(3) | 1 |
1081
+ | tensor(3) | 1 |
1082
+ | tensor(3) | 1 |
1083
+ | tensor(3) | 1 |
1084
+ | tensor(3) | 1 |
1085
+ | tensor(3) | 1 |
1086
+ | tensor(3) | 1 |
1087
+ | tensor(3) | 1 |
1088
+ | tensor(3) | 1 |
1089
+ | tensor(3) | 1 |
1090
+ | tensor(3) | 1 |
1091
+ | tensor(3) | 1 |
1092
+ | tensor(3) | 1 |
1093
+ | tensor(3) | 1 |
1094
+ | tensor(3) | 1 |
1095
+ | tensor(3) | 1 |
1096
+ | tensor(3) | 1 |
1097
+ | tensor(3) | 1 |
1098
+ | tensor(3) | 1 |
1099
+ | tensor(3) | 1 |
1100
+ | tensor(3) | 1 |
1101
+ | tensor(3) | 1 |
1102
+ | tensor(3) | 1 |
1103
+ | tensor(3) | 1 |
1104
+ | tensor(3) | 1 |
1105
+ | tensor(3) | 1 |
1106
+ | tensor(3) | 1 |
1107
+ | tensor(3) | 1 |
1108
+ | tensor(3) | 1 |
1109
+ | tensor(3) | 1 |
1110
+ | tensor(3) | 1 |
1111
+ | tensor(3) | 1 |
1112
+ | tensor(4) | 1 |
1113
+ | tensor(4) | 1 |
1114
+ | tensor(4) | 1 |
1115
+ | tensor(4) | 1 |
1116
+ | tensor(4) | 1 |
1117
+ | tensor(4) | 1 |
1118
+ | tensor(4) | 1 |
1119
+ | tensor(4) | 1 |
1120
+ | tensor(4) | 1 |
1121
+ | tensor(4) | 1 |
1122
+ | tensor(4) | 1 |
1123
+ | tensor(4) | 1 |
1124
+ | tensor(4) | 1 |
1125
+ | tensor(4) | 1 |
1126
+ | tensor(4) | 1 |
1127
+ | tensor(4) | 1 |
1128
+ | tensor(4) | 1 |
1129
+ | tensor(4) | 1 |
1130
+ | tensor(4) | 1 |
1131
+ | tensor(4) | 1 |
1132
+ | tensor(4) | 1 |
1133
+ | tensor(4) | 1 |
1134
+ | tensor(4) | 1 |
1135
+ | tensor(4) | 1 |
1136
+ | tensor(4) | 1 |
1137
+ | tensor(4) | 1 |
1138
+ | tensor(4) | 1 |
1139
+ | tensor(4) | 1 |
1140
+ | tensor(4) | 1 |
1141
+ | tensor(4) | 1 |
1142
+ | tensor(4) | 1 |
1143
+ | tensor(4) | 1 |
1144
+ | tensor(4) | 1 |
1145
+ | tensor(4) | 1 |
1146
+ | tensor(4) | 1 |
1147
+ | tensor(4) | 1 |
1148
+ | tensor(4) | 1 |
1149
+ | tensor(4) | 1 |
1150
+ | tensor(4) | 1 |
1151
+ | tensor(4) | 1 |
1152
+ | tensor(4) | 1 |
1153
+ | tensor(4) | 1 |
1154
+ | tensor(4) | 1 |
1155
+ | tensor(4) | 1 |
1156
+ | tensor(4) | 1 |
1157
+
1158
+ ### Training Hyperparameters
1159
+ - batch_size: (32, 32)
1160
+ - num_epochs: (3, 3)
1161
+ - max_steps: -1
1162
+ - sampling_strategy: oversampling
1163
+ - num_iterations: 20
1164
+ - body_learning_rate: (2e-05, 2e-05)
1165
+ - head_learning_rate: 0.01
1166
+ - loss: CosineSimilarityLoss
1167
+ - distance_metric: cosine_distance
1168
+ - margin: 0.25
1169
+ - end_to_end: False
1170
+ - use_amp: False
1171
+ - warmup_proportion: 0.1
1172
+ - l2_weight: 0.01
1173
+ - seed: 42
1174
+ - eval_max_steps: -1
1175
+ - load_best_model_at_end: True
1176
+
1177
+ ### Training Results
1178
+ | Epoch | Step | Training Loss | Validation Loss |
1179
+ |:------:|:----:|:-------------:|:---------------:|
1180
+ | 0.0008 | 1 | 0.0641 | - |
1181
+ | 0.008 | 10 | 0.0831 | - |
1182
+ | 0.016 | 20 | 0.0955 | - |
1183
+ | 0.024 | 30 | 0.08 | - |
1184
+ | 0.032 | 40 | 0.0729 | - |
1185
+ | 0.04 | 50 | 0.063 | - |
1186
+ | 0.048 | 60 | 0.0581 | - |
1187
+ | 0.056 | 70 | 0.0622 | - |
1188
+ | 0.064 | 80 | 0.0432 | - |
1189
+ | 0.072 | 90 | 0.032 | - |
1190
+ | 0.08 | 100 | 0.0381 | - |
1191
+ | 0.088 | 110 | 0.0423 | - |
1192
+ | 0.096 | 120 | 0.0312 | - |
1193
+ | 0.104 | 130 | 0.0289 | - |
1194
+ | 0.112 | 140 | 0.0487 | - |
1195
+ | 0.12 | 150 | 0.0346 | - |
1196
+ | 0.128 | 160 | 0.0185 | - |
1197
+ | 0.136 | 170 | 0.0319 | - |
1198
+ | 0.144 | 180 | 0.0168 | - |
1199
+ | 0.152 | 190 | 0.0223 | - |
1200
+ | 0.16 | 200 | 0.0144 | - |
1201
+ | 0.168 | 210 | 0.0089 | - |
1202
+ | 0.176 | 220 | 0.0161 | - |
1203
+ | 0.184 | 230 | 0.0047 | - |
1204
+ | 0.192 | 240 | 0.0066 | - |
1205
+ | 0.2 | 250 | 0.0202 | - |
1206
+ | 0.208 | 260 | 0.0062 | - |
1207
+ | 0.216 | 270 | 0.0031 | - |
1208
+ | 0.224 | 280 | 0.012 | - |
1209
+ | 0.232 | 290 | 0.0023 | - |
1210
+ | 0.24 | 300 | 0.0016 | - |
1211
+ | 0.248 | 310 | 0.0195 | - |
1212
+ | 0.256 | 320 | 0.0045 | - |
1213
+ | 0.264 | 330 | 0.0092 | - |
1214
+ | 0.272 | 340 | 0.0055 | - |
1215
+ | 0.28 | 350 | 0.0025 | - |
1216
+ | 0.288 | 360 | 0.001 | - |
1217
+ | 0.296 | 370 | 0.0044 | - |
1218
+ | 0.304 | 380 | 0.0073 | - |
1219
+ | 0.312 | 390 | 0.0054 | - |
1220
+ | 0.32 | 400 | 0.0009 | - |
1221
+ | 0.328 | 410 | 0.0006 | - |
1222
+ | 0.336 | 420 | 0.0037 | - |
1223
+ | 0.344 | 430 | 0.001 | - |
1224
+ | 0.352 | 440 | 0.0009 | - |
1225
+ | 0.36 | 450 | 0.005 | - |
1226
+ | 0.368 | 460 | 0.0036 | - |
1227
+ | 0.376 | 470 | 0.0042 | - |
1228
+ | 0.384 | 480 | 0.0006 | - |
1229
+ | 0.392 | 490 | 0.0017 | - |
1230
+ | 0.4 | 500 | 0.0079 | - |
1231
+ | 0.408 | 510 | 0.0015 | - |
1232
+ | 0.416 | 520 | 0.001 | - |
1233
+ | 0.424 | 530 | 0.0005 | - |
1234
+ | 0.432 | 540 | 0.0009 | - |
1235
+ | 0.44 | 550 | 0.0002 | - |
1236
+ | 0.448 | 560 | 0.0034 | - |
1237
+ | 0.456 | 570 | 0.0002 | - |
1238
+ | 0.464 | 580 | 0.0035 | - |
1239
+ | 0.472 | 590 | 0.0002 | - |
1240
+ | 0.48 | 600 | 0.0003 | - |
1241
+ | 0.488 | 610 | 0.0003 | - |
1242
+ | 0.496 | 620 | 0.0002 | - |
1243
+ | 0.504 | 630 | 0.0034 | - |
1244
+ | 0.512 | 640 | 0.0033 | - |
1245
+ | 0.52 | 650 | 0.0029 | - |
1246
+ | 0.528 | 660 | 0.0002 | - |
1247
+ | 0.536 | 670 | 0.0002 | - |
1248
+ | 0.544 | 680 | 0.0051 | - |
1249
+ | 0.552 | 690 | 0.0028 | - |
1250
+ | 0.56 | 700 | 0.0023 | - |
1251
+ | 0.568 | 710 | 0.0003 | - |
1252
+ | 0.576 | 720 | 0.0003 | - |
1253
+ | 0.584 | 730 | 0.0004 | - |
1254
+ | 0.592 | 740 | 0.0001 | - |
1255
+ | 0.6 | 750 | 0.0018 | - |
1256
+ | 0.608 | 760 | 0.0007 | - |
1257
+ | 0.616 | 770 | 0.0006 | - |
1258
+ | 0.624 | 780 | 0.0025 | - |
1259
+ | 0.632 | 790 | 0.0006 | - |
1260
+ | 0.64 | 800 | 0.0002 | - |
1261
+ | 0.648 | 810 | 0.0001 | - |
1262
+ | 0.656 | 820 | 0.0002 | - |
1263
+ | 0.664 | 830 | 0.0033 | - |
1264
+ | 0.672 | 840 | 0.0003 | - |
1265
+ | 0.68 | 850 | 0.0002 | - |
1266
+ | 0.688 | 860 | 0.0001 | - |
1267
+ | 0.696 | 870 | 0.0029 | - |
1268
+ | 0.704 | 880 | 0.0001 | - |
1269
+ | 0.712 | 890 | 0.0002 | - |
1270
+ | 0.72 | 900 | 0.0001 | - |
1271
+ | 0.728 | 910 | 0.0001 | - |
1272
+ | 0.736 | 920 | 0.0002 | - |
1273
+ | 0.744 | 930 | 0.0038 | - |
1274
+ | 0.752 | 940 | 0.0032 | - |
1275
+ | 0.76 | 950 | 0.0007 | - |
1276
+ | 0.768 | 960 | 0.0002 | - |
1277
+ | 0.776 | 970 | 0.0021 | - |
1278
+ | 0.784 | 980 | 0.0008 | - |
1279
+ | 0.792 | 990 | 0.0003 | - |
1280
+ | 0.8 | 1000 | 0.0002 | - |
1281
+ | 0.808 | 1010 | 0.0001 | - |
1282
+ | 0.816 | 1020 | 0.0001 | - |
1283
+ | 0.824 | 1030 | 0.0002 | - |
1284
+ | 0.832 | 1040 | 0.0002 | - |
1285
+ | 0.84 | 1050 | 0.0002 | - |
1286
+ | 0.848 | 1060 | 0.0026 | - |
1287
+ | 0.856 | 1070 | 0.0001 | - |
1288
+ | 0.864 | 1080 | 0.0001 | - |
1289
+ | 0.872 | 1090 | 0.0001 | - |
1290
+ | 0.88 | 1100 | 0.0001 | - |
1291
+ | 0.888 | 1110 | 0.0001 | - |
1292
+ | 0.896 | 1120 | 0.0001 | - |
1293
+ | 0.904 | 1130 | 0.0001 | - |
1294
+ | 0.912 | 1140 | 0.0001 | - |
1295
+ | 0.92 | 1150 | 0.0001 | - |
1296
+ | 0.928 | 1160 | 0.0001 | - |
1297
+ | 0.936 | 1170 | 0.0001 | - |
1298
+ | 0.944 | 1180 | 0.0001 | - |
1299
+ | 0.952 | 1190 | 0.0001 | - |
1300
+ | 0.96 | 1200 | 0.0001 | - |
1301
+ | 0.968 | 1210 | 0.0001 | - |
1302
+ | 0.976 | 1220 | 0.0001 | - |
1303
+ | 0.984 | 1230 | 0.0029 | - |
1304
+ | 0.992 | 1240 | 0.0001 | - |
1305
+ | 1.0 | 1250 | 0.0001 | 0.0290 |
1306
+ | 1.008 | 1260 | 0.0001 | - |
1307
+ | 1.016 | 1270 | 0.0001 | - |
1308
+ | 1.024 | 1280 | 0.0033 | - |
1309
+ | 1.032 | 1290 | 0.0001 | - |
1310
+ | 1.04 | 1300 | 0.0002 | - |
1311
+ | 1.048 | 1310 | 0.0001 | - |
1312
+ | 1.056 | 1320 | 0.0001 | - |
1313
+ | 1.064 | 1330 | 0.0001 | - |
1314
+ | 1.072 | 1340 | 0.0001 | - |
1315
+ | 1.08 | 1350 | 0.0001 | - |
1316
+ | 1.088 | 1360 | 0.0001 | - |
1317
+ | 1.096 | 1370 | 0.0001 | - |
1318
+ | 1.104 | 1380 | 0.0001 | - |
1319
+ | 1.112 | 1390 | 0.0001 | - |
1320
+ | 1.12 | 1400 | 0.0019 | - |
1321
+ | 1.1280 | 1410 | 0.0002 | - |
1322
+ | 1.1360 | 1420 | 0.0001 | - |
1323
+ | 1.144 | 1430 | 0.0007 | - |
1324
+ | 1.152 | 1440 | 0.0001 | - |
1325
+ | 1.16 | 1450 | 0.0001 | - |
1326
+ | 1.168 | 1460 | 0.0002 | - |
1327
+ | 1.176 | 1470 | 0.0001 | - |
1328
+ | 1.184 | 1480 | 0.0001 | - |
1329
+ | 1.192 | 1490 | 0.0001 | - |
1330
+ | 1.2 | 1500 | 0.0001 | - |
1331
+ | 1.208 | 1510 | 0.0001 | - |
1332
+ | 1.216 | 1520 | 0.0001 | - |
1333
+ | 1.224 | 1530 | 0.0001 | - |
1334
+ | 1.232 | 1540 | 0.0001 | - |
1335
+ | 1.24 | 1550 | 0.0 | - |
1336
+ | 1.248 | 1560 | 0.0001 | - |
1337
+ | 1.256 | 1570 | 0.0 | - |
1338
+ | 1.264 | 1580 | 0.0001 | - |
1339
+ | 1.272 | 1590 | 0.0001 | - |
1340
+ | 1.28 | 1600 | 0.0001 | - |
1341
+ | 1.288 | 1610 | 0.0001 | - |
1342
+ | 1.296 | 1620 | 0.0001 | - |
1343
+ | 1.304 | 1630 | 0.0 | - |
1344
+ | 1.312 | 1640 | 0.0001 | - |
1345
+ | 1.32 | 1650 | 0.0001 | - |
1346
+ | 1.328 | 1660 | 0.0 | - |
1347
+ | 1.336 | 1670 | 0.0001 | - |
1348
+ | 1.3440 | 1680 | 0.0001 | - |
1349
+ | 1.3520 | 1690 | 0.0 | - |
1350
+ | 1.3600 | 1700 | 0.0001 | - |
1351
+ | 1.3680 | 1710 | 0.0 | - |
1352
+ | 1.376 | 1720 | 0.0 | - |
1353
+ | 1.384 | 1730 | 0.0001 | - |
1354
+ | 1.392 | 1740 | 0.0 | - |
1355
+ | 1.4 | 1750 | 0.0001 | - |
1356
+ | 1.408 | 1760 | 0.0001 | - |
1357
+ | 1.416 | 1770 | 0.0 | - |
1358
+ | 1.424 | 1780 | 0.0001 | - |
1359
+ | 1.432 | 1790 | 0.0001 | - |
1360
+ | 1.44 | 1800 | 0.0001 | - |
1361
+ | 1.448 | 1810 | 0.0001 | - |
1362
+ | 1.456 | 1820 | 0.0001 | - |
1363
+ | 1.464 | 1830 | 0.0001 | - |
1364
+ | 1.472 | 1840 | 0.0 | - |
1365
+ | 1.48 | 1850 | 0.0 | - |
1366
+ | 1.488 | 1860 | 0.0001 | - |
1367
+ | 1.496 | 1870 | 0.0001 | - |
1368
+ | 1.504 | 1880 | 0.0 | - |
1369
+ | 1.512 | 1890 | 0.0 | - |
1370
+ | 1.52 | 1900 | 0.0001 | - |
1371
+ | 1.528 | 1910 | 0.0001 | - |
1372
+ | 1.536 | 1920 | 0.0001 | - |
1373
+ | 1.544 | 1930 | 0.0 | - |
1374
+ | 1.552 | 1940 | 0.0 | - |
1375
+ | 1.56 | 1950 | 0.0001 | - |
1376
+ | 1.568 | 1960 | 0.0001 | - |
1377
+ | 1.576 | 1970 | 0.0001 | - |
1378
+ | 1.584 | 1980 | 0.0 | - |
1379
+ | 1.592 | 1990 | 0.0 | - |
1380
+ | 1.6 | 2000 | 0.0 | - |
1381
+ | 1.608 | 2010 | 0.0 | - |
1382
+ | 1.616 | 2020 | 0.0 | - |
1383
+ | 1.624 | 2030 | 0.0 | - |
1384
+ | 1.6320 | 2040 | 0.0001 | - |
1385
+ | 1.6400 | 2050 | 0.0 | - |
1386
+ | 1.6480 | 2060 | 0.0 | - |
1387
+ | 1.6560 | 2070 | 0.0 | - |
1388
+ | 1.6640 | 2080 | 0.0 | - |
1389
+ | 1.6720 | 2090 | 0.0 | - |
1390
+ | 1.6800 | 2100 | 0.0 | - |
1391
+ | 1.688 | 2110 | 0.0 | - |
1392
+ | 1.696 | 2120 | 0.0 | - |
1393
+ | 1.704 | 2130 | 0.0001 | - |
1394
+ | 1.712 | 2140 | 0.0 | - |
1395
+ | 1.72 | 2150 | 0.0 | - |
1396
+ | 1.728 | 2160 | 0.0 | - |
1397
+ | 1.736 | 2170 | 0.0 | - |
1398
+ | 1.744 | 2180 | 0.0 | - |
1399
+ | 1.752 | 2190 | 0.0 | - |
1400
+ | 1.76 | 2200 | 0.0001 | - |
1401
+ | 1.768 | 2210 | 0.0 | - |
1402
+ | 1.776 | 2220 | 0.0 | - |
1403
+ | 1.784 | 2230 | 0.0 | - |
1404
+ | 1.792 | 2240 | 0.0 | - |
1405
+ | 1.8 | 2250 | 0.0 | - |
1406
+ | 1.808 | 2260 | 0.0 | - |
1407
+ | 1.8160 | 2270 | 0.0 | - |
1408
+ | 1.8240 | 2280 | 0.0 | - |
1409
+ | 1.8320 | 2290 | 0.0 | - |
1410
+ | 1.8400 | 2300 | 0.0 | - |
1411
+ | 1.8480 | 2310 | 0.0 | - |
1412
+ | 1.8560 | 2320 | 0.0 | - |
1413
+ | 1.8640 | 2330 | 0.0 | - |
1414
+ | 1.8720 | 2340 | 0.0 | - |
1415
+ | 1.88 | 2350 | 0.0 | - |
1416
+ | 1.888 | 2360 | 0.0 | - |
1417
+ | 1.896 | 2370 | 0.0 | - |
1418
+ | 1.904 | 2380 | 0.0 | - |
1419
+ | 1.912 | 2390 | 0.0 | - |
1420
+ | 1.92 | 2400 | 0.0 | - |
1421
+ | 1.928 | 2410 | 0.0 | - |
1422
+ | 1.936 | 2420 | 0.0 | - |
1423
+ | 1.944 | 2430 | 0.0 | - |
1424
+ | 1.952 | 2440 | 0.0 | - |
1425
+ | 1.96 | 2450 | 0.0 | - |
1426
+ | 1.968 | 2460 | 0.0 | - |
1427
+ | 1.976 | 2470 | 0.0 | - |
1428
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1523
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1530
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1556
+
1557
+ ### Framework Versions
1558
+ - Python: 3.10.12
1559
+ - SetFit: 1.1.0
1560
+ - Sentence Transformers: 3.2.0
1561
+ - Transformers: 4.44.2
1562
+ - PyTorch: 2.4.1+cu121
1563
+ - Datasets: 3.0.1
1564
+ - Tokenizers: 0.19.1
1565
+
1566
+ ## Citation
1567
+
1568
+ ### BibTeX
1569
+ ```bibtex
1570
+ @article{https://doi.org/10.48550/arxiv.2209.11055,
1571
+ doi = {10.48550/ARXIV.2209.11055},
1572
+ url = {https://arxiv.org/abs/2209.11055},
1573
+ author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
1574
+ keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
1575
+ title = {Efficient Few-Shot Learning Without Prompts},
1576
+ publisher = {arXiv},
1577
+ year = {2022},
1578
+ copyright = {Creative Commons Attribution 4.0 International}
1579
+ }
1580
+ ```
1581
+
1582
+ <!--
1583
+ ## Glossary
1584
+
1585
+ *Clearly define terms in order to be accessible across audiences.*
1586
+ -->
1587
+
1588
+ <!--
1589
+ ## Model Card Authors
1590
+
1591
+ *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
1592
+ -->
1593
+
1594
+ <!--
1595
+ ## Model Card Contact
1596
+
1597
+ *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
1598
+ -->
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