setfit safetensors mpnet sentence-transformers text-classification generated_from_setfit_trainer arxiv:2209.11055 base_model:sentence-transformers/paraphrase-mpnet-base-v2 base_model:finetune:sentence-transformers/paraphrase-mpnet-base-v2 model-index region:us
---
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](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [sentence-transformers/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-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.

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

1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.

## Model Details

### Model Description
- **Model Type:** SetFit
- **Sentence Transformer body:** [sentence-transformers/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2)
- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
- **Maximum Sequence Length:** 512 tokens
- **Number of Classes:** 119 classes
<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->

### Model Sources

- **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit)
- **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)
- **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)

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

## Evaluation

### Metrics
| Label   | Accuracy |
|:--------|:---------|
| **all** | 0.5493   |

## Uses

### Direct Use for Inference

First install the SetFit library:

```bash
pip install setfit
```

Then you can load this model and run inference.

```python
from setfit import SetFitModel

# Download from the 🤗 Hub
model = SetFitModel.from_pretrained("kaustubhgap/kaustubh_setfit_1iteration")
# Run inference
preds = model("tube")
```

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## 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
```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}
}
```

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