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metadata
base_model: sentence-transformers/paraphrase-mpnet-base-v2
library_name: setfit
metrics:
  - accuracy
pipeline_tag: text-classification
tags:
  - setfit
  - sentence-transformers
  - text-classification
  - generated_from_setfit_trainer
widget:
  - text: one piece
  - text: tube
  - text: heavy weight
  - text: track
  - text: unitard
inference: true
model-index:
  - name: SetFit with sentence-transformers/paraphrase-mpnet-base-v2
    results:
      - task:
          type: text-classification
          name: Text Classification
        dataset:
          name: Unknown
          type: unknown
          split: test
        metrics:
          - type: accuracy
            value: 0.5493273542600897
            name: Accuracy

SetFit with sentence-transformers/paraphrase-mpnet-base-v2

This is a SetFit model that can be used for Text Classification. This SetFit model uses sentence-transformers/paraphrase-mpnet-base-v2 as the Sentence Transformer embedding model. 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 Sources

Model Labels

Label Examples
79
  • 'peony middle notes'
  • 'lemon middle notes'
  • 'coconut middle notes'
86
  • 'no print/no pattern'
  • 'two tone'
  • 'diagonal stripe'
37
  • 'eel skin leather'
  • 'metal'
  • 'raffia'
82
  • 'collarless'
  • 'peaked lapel'
  • 'front keyhole'
95
  • 'standard toe'
  • 'wide toe'
  • 'extra wide toe'
83
  • 'indoor'
  • 'hike'
  • 'beach'
107
  • 'surplice'
  • 'messenger bag'
  • 'camera bag'
19
  • 'mary jane'
  • 'zip around wallet'
  • 'tongue buckle'
102
  • 'slits at knee'
  • 'slits above hips'
  • 'front slit at hem'
35
  • 'tie'
  • 'gem embellishment'
  • 'caged'
18
  • 'rolo chain'
  • 'cord bracelet'
  • 'figaro'
65
  • 'wheat protein'
  • 'rosemary ingredient'
  • 'pea protein'
68
  • 'bath towel'
  • 'art print'
  • 'reusable bottle'
40
  • 'polyfill'
  • 'silk fill'
  • 'feather fill'
50
  • 'palm grip'
  • 'carpenter hook'
  • 'storm flap'
113
  • 'wide waistband'
  • 'elastic inset'
  • 'belt loops'
75
  • 'glass'
  • 'acrylic'
  • 'opal'
11
  • 'foam cups'
  • 'wire'
  • 'molded cups'
38
  • 'dual layer fabric'
  • '2 way stretch'
  • '4 way stretch'
63
  • 'light support'
  • 'medium supprt'
  • 'high support'
44
  • 'face'
  • 'hand'
  • 'neck/dècolletage'
115
  • 'soy wax'
  • 'paraffin wax'
42
  • 'regular'
  • 'tailored'
  • 'fitted'
97
  • 'king'
  • 'euro'
  • 'standard'
70
  • 'wrist length'
  • 'above thigh'
  • 'below bust'
34
  • 'feminine'
  • 'religious'
  • 'boho'
10
  • 'slim'
  • 'regular'
15
  • '6-10 oz'
  • '11-20 oz'
77
  • 'rose gold metal'
  • 'gold plated'
  • 'alloy'
43
  • 'contrast inner lining'
  • 'simple seaming'
  • 'princess seams'
7
  • 'neroli base notes'
  • 'amber base notes'
  • 'musk base notes'
17
  • 'spot clean'
  • 'dry clean'
  • 'microwave safe'
8
  • 'nourishing'
  • 'firming'
  • 'soothing/healing'
103
  • 'lugged soles'
  • 'non marking soles'
26
  • 'wall control'
  • 'switch control'
99
  • 'fitted sleeves'
  • 'fitted sleeve'
  • 'structured sleeves'
33
  • 'rim'
  • 'feet'
  • '5 panel construction'
64
  • 'mineral oil free'
  • 'propylene glycol free'
  • 'paraffin free'
96
  • 'double strap'
  • 'spaghetti straps'
  • 'thin straps'
1
  • 'shoulder back'
  • 'full coverage'
  • 'low back'
62
  • 'rustic'
  • 'coastal'
  • 'scandinavian'
39
  • 'metallic'
  • 'swiss dot'
  • 'base layer'
60
  • 'halloween'
  • 'christmas holiday'
92
  • 'seamless'
  • 'mid rise waist seam'
  • 'flat seam'
114
  • 'ultra high rise'
  • 'mid rise'
  • 'high waisted'
105
  • 'top handle'
  • 'detachable straps'
  • 'chain strap'
90
  • 'floral'
  • 'psychedelic print'
  • 'paisley'
91
  • 'night'
  • 'day'
45
  • 'serum formulation'
  • 'cream/creme'
  • 'solid'
59
  • 'strong hold'
  • 'flexible hold'
46
  • 'leather'
  • 'fresh aquatic'
  • 'green aromatic'
21
  • 'matte'
  • 'metallic'
  • 'olive'
69
  • 'cinnamon key notes'
  • 'violet key notes'
  • 'pepper key notes'
101
  • 'dropped shoulder'
  • 'puff shoulder'
  • 'flutter sleeve'
61
  • 'summer'
  • 'everyday'
  • 'indoor'
104
  • 'wedding guest'
  • 'bridal'
  • 'halloween'
32
  • 'indigo wash'
  • 'acid wash'
  • 'stonewash'
51
  • 'still life graphic'
  • 'sports graphic'
  • 'star wars'
48
  • 'beige'
  • 'black'
  • 'rose gold frame'
87
  • 'medium pile'
  • 'low pile'
22
  • 'bright'
  • 'pastel'
  • 'light'
41
  • 'matte finish'
  • 'shiny finish'
93
  • 'no buckle'
  • 'geometric shape'
  • 'straight silhouette'
71
  • 'polarized'
  • 'color tinted'
  • 'mirrored'
2
  • 'split back'
  • 'racer back'
  • 'open back'
89
  • 'round stitch pocket'
  • 'seam pocket'
  • 'kangaroo pocket'
20
  • 'removable hoodie'
  • 'packable hood collar'
  • 'hooded'
52
  • 'thick'
  • 'medium thick'
55
  • 'amber head notes'
  • 'lime head notes'
  • 'musk head notes'
58
  • 'back curved hem'
  • 'twist hem'
  • 'ribbed hem'
118
  • 'light wood'
  • 'medium wood'
25
  • 'gifts for him'
  • 'apres ski'
  • 'cozy'
109
  • 'closed toe'
  • 'square toe'
  • 'round toe'
30
  • 'extended cuffs'
  • 'storm cuffs'
  • 'elastic cuff'
24
  • 'ingrown hairs'
  • 'frizz'
  • 'redness'
9
  • 'high cut'
  • 'string bikini'
94
  • 'opaque'
  • 'sheer'
16
  • '2 card slot'
  • 'card slots'
78
  • 'gothcore'
  • 'vanilla girl'
  • 'dyed out'
4
  • 'layered'
  • 'bangle'
  • 'cuff'
23
  • 'parfum'
  • 'eau de toilette'
111
  • 'delicate'
  • 'statement'
12
  • 'flat brim'
  • 'curved brim'
  • 'fold over brim'
98
  • 'dry'
  • 'acne prone'
  • 'mature'
57
  • 'stacked heel'
  • 'kitten heel'
  • 'cone heel'
67
  • 'id slot'
  • 'interior pocket'
  • 'interior zipper pocket'
31
  • 'light wash'
  • 'medium wash'
  • 'colored'
85
  • 'detailed stitching pant'
  • 'simple seaming'
116
  • 'knotted'
  • 'percale'
  • 'waffle weave'
88
  • 'shag'
  • 'cut pile'
74
  • 'study hall'
  • 'y2k'
  • 'enchanted'
72
  • 'fur'
  • 'fleece'
  • 'mesh'
108
  • 'animal'
  • 'love'
73
  • 'unlined'
  • 'fully lined'
  • 'partially lined'
13
  • 'wide brim'
  • 'medium brim'
76
  • 'bpa free material'
  • 'scratch resistant material'
54
  • 'straight handle'
  • 'curved handle'
100
  • 'rolled up sleeves'
  • '3/4 sleeve'
  • 'bracelet length'
84
  • 'manual open'
  • 'auto open'
14
  • 'wide'
  • 'medium'
27
  • 'superhero'
  • 'disney'
49
  • 'half rim'
  • 'full rim'
29
  • 'tall crown'
  • 'short crown'
106
  • 'low stretch'
  • 'non stretch'
112
  • 'mid vamp'
  • 'high vamp'
66
  • 'large interior'
  • 'medium interior'
  • 'small interior'
53
  • 'all hair types'
  • 'damaged/dry hair'
117
  • 'light weight'
  • 'mid weight'
81
  • 'low cut'
  • 'mid chest neckline'
  • 'open front'
5
  • 'thin band'
  • 'soft band elastic'
  • 'elastic band'
28
  • 'flat top crown'
  • 'round crown'
  • 'no crown'
56
  • 'ultra high heel'
  • 'mid heel'
  • 'high heel'
110
  • 'relaxed'
  • 'tailored'
47
  • 'uplifting'
  • 'bold'
3
  • 'changing pad'
  • 'bottle pocket'
0
  • 'squeeze dispenser'
  • 'dropper'
80
  • 'wall mount'
  • 'ceiling mount'
6
  • 'medium'
  • 'wide'
36
  • 'exterior pocket'
  • 'exterior snap pocket'

Evaluation

Metrics

Label Accuracy
all 0.5493

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("kaustubhgap/kaustubh_setfit_1iteration")
# Run inference
preds = model("tube")

Training Details

Training Set Metrics

Training set Min Median Max
Word count 1 1.7047 6
Label Training Sample Count
0 2
1 5
2 12
3 2
4 6
5 3
6 2
7 12
8 16
9 2
10 2
11 11
12 4
13 2
14 2
15 2
16 2
17 6
18 9
19 63
20 8
21 31
22 6
23 2
24 13
25 5
26 2
27 2
28 3
29 2
30 13
31 3
32 7
33 22
34 12
35 102
36 2
37 119
38 34
39 32
40 6
41 2
42 13
43 17
44 5
45 10
46 6
47 2
48 10
49 2
50 91
51 13
52 2
53 2
54 2
55 12
56 4
57 7
58 17
59 2
60 2
61 7
62 9
63 3
64 14
65 53
66 3
67 6
68 41
69 41
70 33
71 5
72 5
73 4
74 7
75 49
76 2
77 23
78 11
79 12
80 2
81 5
82 33
83 33
84 2
85 2
86 17
87 2
88 2
89 10
90 29
91 2
92 8
93 21
94 2
95 3
96 5
97 10
98 5
99 6
100 6
101 12
102 13
103 2
104 10
105 28
106 2
107 321
108 2
109 10
110 2
111 2
112 2
113 15
114 4
115 2
116 5
117 2
118 2

Training Hyperparameters

  • batch_size: (16, 16)
  • num_epochs: (1, 1)
  • max_steps: -1
  • sampling_strategy: oversampling
  • num_iterations: 10
  • 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
  • seed: 42
  • eval_max_steps: -1
  • load_best_model_at_end: False

Training Results

Epoch Step Training Loss Validation Loss
0.0004 1 0.2895 -
0.0225 50 0.2059 -
0.0449 100 0.1794 -
0.0674 150 0.1994 -
0.0898 200 0.2708 -
0.1123 250 0.1355 -
0.1347 300 0.0695 -
0.1572 350 0.117 -
0.1796 400 0.0601 -
0.2021 450 0.0873 -
0.2245 500 0.07 -
0.2470 550 0.0805 -
0.2694 600 0.0204 -
0.2919 650 0.1059 -
0.3143 700 0.1178 -
0.3368 750 0.1804 -
0.3592 800 0.0979 -
0.3817 850 0.1597 -
0.4041 900 0.1215 -
0.4266 950 0.0188 -
0.4490 1000 0.0738 -
0.4715 1050 0.0635 -
0.4939 1100 0.1439 -
0.5164 1150 0.0684 -
0.5388 1200 0.0732 -
0.5613 1250 0.0401 -
0.5837 1300 0.1223 -
0.6062 1350 0.1044 -
0.6286 1400 0.0717 -
0.6511 1450 0.0413 -
0.6736 1500 0.0544 -
0.6960 1550 0.1419 -
0.7185 1600 0.0284 -
0.7409 1650 0.0484 -
0.7634 1700 0.0049 -
0.7858 1750 0.0229 -
0.8083 1800 0.0739 -
0.8307 1850 0.0371 -
0.8532 1900 0.0213 -
0.8756 1950 0.0753 -
0.8981 2000 0.0359 -
0.9205 2050 0.0232 -
0.9430 2100 0.0507 -
0.9654 2150 0.0258 -
0.9879 2200 0.0606 -
1.0 2227 - 0.2105

Framework Versions

  • Python: 3.10.12
  • SetFit: 1.0.3
  • Sentence Transformers: 3.0.1
  • Transformers: 4.36.1
  • PyTorch: 2.0.1+cu118
  • Datasets: 2.20.0
  • Tokenizers: 0.15.0

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}
}