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kaustubh_setfit

Text Classification
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sentence-transformers
mpnet
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---
library_name: setfit
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
metrics:
- accuracy
widget:
- text: one piece
- text: tube
- text: heavy weight
- text: track
- text: unitard
pipeline_tag: text-classification
inference: true
base_model: sentence-transformers/paraphrase-mpnet-base-v2
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: 0.5762331838565022
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** | 0.5762 |
## 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")
# Run inference
preds = model("tube")
```
<!--
### Downstream Use
*List how someone could finetune this model on their own dataset.*
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## 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: (5, 5)
- 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
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: False
### Training Results
| Epoch | Step | Training Loss | Validation Loss |
|:------:|:-----:|:-------------:|:---------------:|
| 0.0002 | 1 | 0.2895 | - |
| 0.0112 | 50 | 0.2531 | - |
| 0.0225 | 100 | 0.2622 | - |
| 0.0337 | 150 | 0.2535 | - |
| 0.0449 | 200 | 0.2144 | - |
| 0.0561 | 250 | 0.206 | - |
| 0.0674 | 300 | 0.1583 | - |
| 0.0786 | 350 | 0.1384 | - |
| 0.0898 | 400 | 0.1778 | - |
| 0.1011 | 450 | 0.2111 | - |
| 0.1123 | 500 | 0.1791 | - |
| 0.1235 | 550 | 0.2198 | - |
| 0.1347 | 600 | 0.0918 | - |
| 0.1460 | 650 | 0.1027 | - |
| 0.1572 | 700 | 0.1837 | - |
| 0.1684 | 750 | 0.1762 | - |
| 0.1797 | 800 | 0.1552 | - |
| 0.1909 | 850 | 0.2045 | - |
| 0.2021 | 900 | 0.1338 | - |
| 0.2133 | 950 | 0.0495 | - |
| 0.2246 | 1000 | 0.1136 | - |
| 0.2358 | 1050 | 0.0878 | - |
| 0.2470 | 1100 | 0.1671 | - |
| 0.2583 | 1150 | 0.0791 | - |
| 0.2695 | 1200 | 0.1332 | - |
| 0.2807 | 1250 | 0.0712 | - |
| 0.2919 | 1300 | 0.1853 | - |
| 0.3032 | 1350 | 0.134 | - |
| 0.3144 | 1400 | 0.1123 | - |
| 0.3256 | 1450 | 0.0525 | - |
| 0.3369 | 1500 | 0.0901 | - |
| 0.3481 | 1550 | 0.1554 | - |
| 0.3593 | 1600 | 0.0417 | - |
| 0.3705 | 1650 | 0.0762 | - |
| 0.3818 | 1700 | 0.0155 | - |
| 0.3930 | 1750 | 0.0115 | - |
| 0.4042 | 1800 | 0.0665 | - |
| 0.4155 | 1850 | 0.0578 | - |
| 0.4267 | 1900 | 0.0271 | - |
| 0.4379 | 1950 | 0.1374 | - |
| 0.4491 | 2000 | 0.1125 | - |
| 0.4604 | 2050 | 0.0304 | - |
| 0.4716 | 2100 | 0.0636 | - |
| 0.4828 | 2150 | 0.0668 | - |
| 0.4940 | 2200 | 0.1055 | - |
| 0.5053 | 2250 | 0.1147 | - |
| 0.5165 | 2300 | 0.0358 | - |
| 0.5277 | 2350 | 0.1516 | - |
| 0.5390 | 2400 | 0.008 | - |
| 0.5502 | 2450 | 0.082 | - |
| 0.5614 | 2500 | 0.0937 | - |
| 0.5726 | 2550 | 0.1382 | - |
| 0.5839 | 2600 | 0.0527 | - |
| 0.5951 | 2650 | 0.1091 | - |
| 0.6063 | 2700 | 0.0031 | - |
| 0.6176 | 2750 | 0.0181 | - |
| 0.6288 | 2800 | 0.1366 | - |
| 0.6400 | 2850 | 0.0178 | - |
| 0.6512 | 2900 | 0.0571 | - |
| 0.6625 | 2950 | 0.0271 | - |
| 0.6737 | 3000 | 0.0368 | - |
| 0.6849 | 3050 | 0.0652 | - |
| 0.6962 | 3100 | 0.0858 | - |
| 0.7074 | 3150 | 0.016 | - |
| 0.7186 | 3200 | 0.0318 | - |
| 0.7298 | 3250 | 0.0119 | - |
| 0.7411 | 3300 | 0.0314 | - |
| 0.7523 | 3350 | 0.008 | - |
| 0.7635 | 3400 | 0.0192 | - |
| 0.7748 | 3450 | 0.0363 | - |
| 0.7860 | 3500 | 0.0474 | - |
| 0.7972 | 3550 | 0.0172 | - |
| 0.8084 | 3600 | 0.0308 | - |
| 0.8197 | 3650 | 0.1168 | - |
| 0.8309 | 3700 | 0.0367 | - |
| 0.8421 | 3750 | 0.1572 | - |
| 0.8534 | 3800 | 0.0865 | - |
| 0.8646 | 3850 | 0.0124 | - |
| 0.8758 | 3900 | 0.0674 | - |
| 0.8870 | 3950 | 0.0534 | - |
| 0.8983 | 4000 | 0.0042 | - |
| 0.9095 | 4050 | 0.0503 | - |
| 0.9207 | 4100 | 0.0753 | - |
| 0.9320 | 4150 | 0.0079 | - |
| 0.9432 | 4200 | 0.1386 | - |
| 0.9544 | 4250 | 0.0693 | - |
| 0.9656 | 4300 | 0.0505 | - |
| 0.9769 | 4350 | 0.0153 | - |
| 0.9881 | 4400 | 0.0456 | - |
| 0.9993 | 4450 | 0.077 | - |
| 1.0 | 4453 | - | 0.1885 |
| 1.0106 | 4500 | 0.0107 | - |
| 1.0218 | 4550 | 0.0533 | - |
| 1.0330 | 4600 | 0.0069 | - |
| 1.0442 | 4650 | 0.0073 | - |
| 1.0555 | 4700 | 0.0521 | - |
| 1.0667 | 4750 | 0.0084 | - |
| 1.0779 | 4800 | 0.0443 | - |
| 1.0892 | 4850 | 0.0504 | - |
| 1.1004 | 4900 | 0.0445 | - |
| 1.1116 | 4950 | 0.0169 | - |
| 1.1228 | 5000 | 0.016 | - |
| 1.1341 | 5050 | 0.0046 | - |
| 1.1453 | 5100 | 0.0103 | - |
| 1.1565 | 5150 | 0.0404 | - |
| 1.1678 | 5200 | 0.0117 | - |
| 1.1790 | 5250 | 0.0399 | - |
| 1.1902 | 5300 | 0.0598 | - |
| 1.2014 | 5350 | 0.015 | - |
| 1.2127 | 5400 | 0.0048 | - |
| 1.2239 | 5450 | 0.0047 | - |
| 1.2351 | 5500 | 0.0042 | - |
| 1.2464 | 5550 | 0.0106 | - |
| 1.2576 | 5600 | 0.0041 | - |
| 1.2688 | 5650 | 0.1593 | - |
| 1.2800 | 5700 | 0.0386 | - |
| 1.2913 | 5750 | 0.0059 | - |
| 1.3025 | 5800 | 0.0043 | - |
| 1.3137 | 5850 | 0.0039 | - |
| 1.3249 | 5900 | 0.0101 | - |
| 1.3362 | 5950 | 0.0043 | - |
| 1.3474 | 6000 | 0.0056 | - |
| 1.3586 | 6050 | 0.002 | - |
| 1.3699 | 6100 | 0.0064 | - |
| 1.3811 | 6150 | 0.0106 | - |
| 1.3923 | 6200 | 0.03 | - |
| 1.4035 | 6250 | 0.0945 | - |
| 1.4148 | 6300 | 0.0025 | - |
| 1.4260 | 6350 | 0.0631 | - |
| 1.4372 | 6400 | 0.0068 | - |
| 1.4485 | 6450 | 0.0583 | - |
| 1.4597 | 6500 | 0.0015 | - |
| 1.4709 | 6550 | 0.0042 | - |
| 1.4821 | 6600 | 0.0093 | - |
| 1.4934 | 6650 | 0.0046 | - |
| 1.5046 | 6700 | 0.009 | - |
| 1.5158 | 6750 | 0.0279 | - |
| 1.5271 | 6800 | 0.0357 | - |
| 1.5383 | 6850 | 0.0282 | - |
| 1.5495 | 6900 | 0.0188 | - |
| 1.5607 | 6950 | 0.0405 | - |
| 1.5720 | 7000 | 0.0645 | - |
| 1.5832 | 7050 | 0.0066 | - |
| 1.5944 | 7100 | 0.0205 | - |
| 1.6057 | 7150 | 0.0038 | - |
| 1.6169 | 7200 | 0.0696 | - |
| 1.6281 | 7250 | 0.0055 | - |
| 1.6393 | 7300 | 0.0034 | - |
| 1.6506 | 7350 | 0.006 | - |
| 1.6618 | 7400 | 0.015 | - |
| 1.6730 | 7450 | 0.0023 | - |
| 1.6843 | 7500 | 0.0173 | - |
| 1.6955 | 7550 | 0.0601 | - |
| 1.7067 | 7600 | 0.0039 | - |
| 1.7179 | 7650 | 0.0201 | - |
| 1.7292 | 7700 | 0.0206 | - |
| 1.7404 | 7750 | 0.0042 | - |
| 1.7516 | 7800 | 0.0156 | - |
| 1.7629 | 7850 | 0.002 | - |
| 1.7741 | 7900 | 0.0059 | - |
| 1.7853 | 7950 | 0.0327 | - |
| 1.7965 | 8000 | 0.0206 | - |
| 1.8078 | 8050 | 0.0698 | - |
| 1.8190 | 8100 | 0.0217 | - |
| 1.8302 | 8150 | 0.0309 | - |
| 1.8415 | 8200 | 0.0136 | - |
| 1.8527 | 8250 | 0.0455 | - |
| 1.8639 | 8300 | 0.0645 | - |
| 1.8751 | 8350 | 0.0127 | - |
| 1.8864 | 8400 | 0.0056 | - |
| 1.8976 | 8450 | 0.0127 | - |
| 1.9088 | 8500 | 0.0024 | - |
| 1.9201 | 8550 | 0.0117 | - |
| 1.9313 | 8600 | 0.0626 | - |
| 1.9425 | 8650 | 0.0357 | - |
| 1.9537 | 8700 | 0.056 | - |
| 1.9650 | 8750 | 0.0311 | - |
| 1.9762 | 8800 | 0.0123 | - |
| 1.9874 | 8850 | 0.0638 | - |
| 1.9987 | 8900 | 0.0328 | - |
| 2.0 | 8906 | - | 0.2196 |
| 2.0099 | 8950 | 0.0015 | - |
| 2.0211 | 9000 | 0.0178 | - |
| 2.0323 | 9050 | 0.08 | - |
| 2.0436 | 9100 | 0.0983 | - |
| 2.0548 | 9150 | 0.0049 | - |
| 2.0660 | 9200 | 0.0092 | - |
| 2.0773 | 9250 | 0.0619 | - |
| 2.0885 | 9300 | 0.0159 | - |
| 2.0997 | 9350 | 0.0598 | - |
| 2.1109 | 9400 | 0.0343 | - |
| 2.1222 | 9450 | 0.0092 | - |
| 2.1334 | 9500 | 0.0013 | - |
| 2.1446 | 9550 | 0.0042 | - |
| 2.1558 | 9600 | 0.0059 | - |
| 2.1671 | 9650 | 0.0076 | - |
| 2.1783 | 9700 | 0.0027 | - |
| 2.1895 | 9750 | 0.0174 | - |
| 2.2008 | 9800 | 0.0044 | - |
| 2.2120 | 9850 | 0.0164 | - |
| 2.2232 | 9900 | 0.0015 | - |
| 2.2344 | 9950 | 0.0026 | - |
| 2.2457 | 10000 | 0.0118 | - |
| 2.2569 | 10050 | 0.0054 | - |
| 2.2681 | 10100 | 0.0016 | - |
| 2.2794 | 10150 | 0.0095 | - |
| 2.2906 | 10200 | 0.0157 | - |
| 2.3018 | 10250 | 0.0465 | - |
| 2.3130 | 10300 | 0.0024 | - |
| 2.3243 | 10350 | 0.0009 | - |
| 2.3355 | 10400 | 0.0101 | - |
| 2.3467 | 10450 | 0.0266 | - |
| 2.3580 | 10500 | 0.0022 | - |
| 2.3692 | 10550 | 0.0016 | - |
| 2.3804 | 10600 | 0.0096 | - |
| 2.3916 | 10650 | 0.0052 | - |
| 2.4029 | 10700 | 0.0656 | - |
| 2.4141 | 10750 | 0.0481 | - |
| 2.4253 | 10800 | 0.0148 | - |
| 2.4366 | 10850 | 0.0024 | - |
| 2.4478 | 10900 | 0.0039 | - |
| 2.4590 | 10950 | 0.0011 | - |
| 2.4702 | 11000 | 0.0142 | - |
| 2.4815 | 11050 | 0.0617 | - |
| 2.4927 | 11100 | 0.0069 | - |
| 2.5039 | 11150 | 0.0063 | - |
| 2.5152 | 11200 | 0.0218 | - |
| 2.5264 | 11250 | 0.0018 | - |
| 2.5376 | 11300 | 0.0017 | - |
| 2.5488 | 11350 | 0.0105 | - |
| 2.5601 | 11400 | 0.0019 | - |
| 2.5713 | 11450 | 0.0027 | - |
| 2.5825 | 11500 | 0.0616 | - |
| 2.5938 | 11550 | 0.0704 | - |
| 2.6050 | 11600 | 0.0047 | - |
| 2.6162 | 11650 | 0.0106 | - |
| 2.6274 | 11700 | 0.0067 | - |
| 2.6387 | 11750 | 0.0272 | - |
| 2.6499 | 11800 | 0.0476 | - |
| 2.6611 | 11850 | 0.0401 | - |
| 2.6724 | 11900 | 0.0017 | - |
| 2.6836 | 11950 | 0.0247 | - |
| 2.6948 | 12000 | 0.0173 | - |
| 2.7060 | 12050 | 0.0129 | - |
| 2.7173 | 12100 | 0.0041 | - |
| 2.7285 | 12150 | 0.0017 | - |
| 2.7397 | 12200 | 0.0137 | - |
| 2.7510 | 12250 | 0.0629 | - |
| 2.7622 | 12300 | 0.034 | - |
| 2.7734 | 12350 | 0.0533 | - |
| 2.7846 | 12400 | 0.057 | - |
| 2.7959 | 12450 | 0.0153 | - |
| 2.8071 | 12500 | 0.0023 | - |
| 2.8183 | 12550 | 0.0013 | - |
| 2.8296 | 12600 | 0.0014 | - |
| 2.8408 | 12650 | 0.0023 | - |
| 2.8520 | 12700 | 0.0026 | - |
| 2.8632 | 12750 | 0.0027 | - |
| 2.8745 | 12800 | 0.0064 | - |
| 2.8857 | 12850 | 0.0174 | - |
| 2.8969 | 12900 | 0.0017 | - |
| 2.9082 | 12950 | 0.0242 | - |
| 2.9194 | 13000 | 0.0487 | - |
| 2.9306 | 13050 | 0.0022 | - |
| 2.9418 | 13100 | 0.0108 | - |
| 2.9531 | 13150 | 0.0079 | - |
| 2.9643 | 13200 | 0.0108 | - |
| 2.9755 | 13250 | 0.0027 | - |
| 2.9868 | 13300 | 0.0053 | - |
| 2.9980 | 13350 | 0.0039 | - |
| 3.0 | 13359 | - | 0.2038 |
| 3.0092 | 13400 | 0.0089 | - |
| 3.0204 | 13450 | 0.0369 | - |
| 3.0317 | 13500 | 0.0107 | - |
| 3.0429 | 13550 | 0.0187 | - |
| 3.0541 | 13600 | 0.0038 | - |
| 3.0653 | 13650 | 0.0072 | - |
| 3.0766 | 13700 | 0.005 | - |
| 3.0878 | 13750 | 0.0192 | - |
| 3.0990 | 13800 | 0.0084 | - |
| 3.1103 | 13850 | 0.002 | - |
| 3.1215 | 13900 | 0.0011 | - |
| 3.1327 | 13950 | 0.0037 | - |
| 3.1439 | 14000 | 0.0087 | - |
| 3.1552 | 14050 | 0.0014 | - |
| 3.1664 | 14100 | 0.0029 | - |
| 3.1776 | 14150 | 0.0176 | - |
| 3.1889 | 14200 | 0.0028 | - |
| 3.2001 | 14250 | 0.012 | - |
| 3.2113 | 14300 | 0.0933 | - |
| 3.2225 | 14350 | 0.002 | - |
| 3.2338 | 14400 | 0.053 | - |
| 3.2450 | 14450 | 0.0117 | - |
| 3.2562 | 14500 | 0.0227 | - |
| 3.2675 | 14550 | 0.0055 | - |
| 3.2787 | 14600 | 0.008 | - |
| 3.2899 | 14650 | 0.0512 | - |
| 3.3011 | 14700 | 0.0025 | - |
| 3.3124 | 14750 | 0.0432 | - |
| 3.3236 | 14800 | 0.002 | - |
| 3.3348 | 14850 | 0.013 | - |
| 3.3461 | 14900 | 0.0026 | - |
| 3.3573 | 14950 | 0.0022 | - |
| 3.3685 | 15000 | 0.0225 | - |
| 3.3797 | 15050 | 0.0611 | - |
| 3.3910 | 15100 | 0.0261 | - |
| 3.4022 | 15150 | 0.0026 | - |
| 3.4134 | 15200 | 0.004 | - |
| 3.4247 | 15250 | 0.0054 | - |
| 3.4359 | 15300 | 0.0132 | - |
| 3.4471 | 15350 | 0.0017 | - |
| 3.4583 | 15400 | 0.0213 | - |
| 3.4696 | 15450 | 0.007 | - |
| 3.4808 | 15500 | 0.0507 | - |
| 3.4920 | 15550 | 0.0039 | - |
| 3.5033 | 15600 | 0.0059 | - |
| 3.5145 | 15650 | 0.0357 | - |
| 3.5257 | 15700 | 0.0009 | - |
| 3.5369 | 15750 | 0.0014 | - |
| 3.5482 | 15800 | 0.0011 | - |
| 3.5594 | 15850 | 0.0082 | - |
| 3.5706 | 15900 | 0.001 | - |
| 3.5819 | 15950 | 0.0045 | - |
| 3.5931 | 16000 | 0.0205 | - |
| 3.6043 | 16050 | 0.0096 | - |
| 3.6155 | 16100 | 0.0286 | - |
| 3.6268 | 16150 | 0.0043 | - |
| 3.6380 | 16200 | 0.0029 | - |
| 3.6492 | 16250 | 0.0079 | - |
| 3.6605 | 16300 | 0.0036 | - |
| 3.6717 | 16350 | 0.0013 | - |
| 3.6829 | 16400 | 0.0086 | - |
| 3.6941 | 16450 | 0.0049 | - |
| 3.7054 | 16500 | 0.0006 | - |
| 3.7166 | 16550 | 0.0467 | - |
| 3.7278 | 16600 | 0.002 | - |
| 3.7391 | 16650 | 0.0229 | - |
| 3.7503 | 16700 | 0.0532 | - |
| 3.7615 | 16750 | 0.001 | - |
| 3.7727 | 16800 | 0.0034 | - |
| 3.7840 | 16850 | 0.0117 | - |
| 3.7952 | 16900 | 0.0424 | - |
| 3.8064 | 16950 | 0.0032 | - |
| 3.8177 | 17000 | 0.0024 | - |
| 3.8289 | 17050 | 0.0011 | - |
| 3.8401 | 17100 | 0.0024 | - |
| 3.8513 | 17150 | 0.0059 | - |
| 3.8626 | 17200 | 0.0005 | - |
| 3.8738 | 17250 | 0.0074 | - |
| 3.8850 | 17300 | 0.0517 | - |
| 3.8962 | 17350 | 0.0081 | - |
| 3.9075 | 17400 | 0.0131 | - |
| 3.9187 | 17450 | 0.051 | - |
| 3.9299 | 17500 | 0.0114 | - |
| 3.9412 | 17550 | 0.0008 | - |
| 3.9524 | 17600 | 0.0094 | - |
| 3.9636 | 17650 | 0.001 | - |
| 3.9748 | 17700 | 0.0069 | - |
| 3.9861 | 17750 | 0.002 | - |
| 3.9973 | 17800 | 0.003 | - |
| 4.0 | 17812 | - | 0.2278 |
| 4.0085 | 17850 | 0.0309 | - |
| 4.0198 | 17900 | 0.005 | - |
| 4.0310 | 17950 | 0.0028 | - |
| 4.0422 | 18000 | 0.0069 | - |
| 4.0534 | 18050 | 0.002 | - |
| 4.0647 | 18100 | 0.0384 | - |
| 4.0759 | 18150 | 0.0123 | - |
| 4.0871 | 18200 | 0.0657 | - |
| 4.0984 | 18250 | 0.0042 | - |
| 4.1096 | 18300 | 0.0043 | - |
| 4.1208 | 18350 | 0.0035 | - |
| 4.1320 | 18400 | 0.0389 | - |
| 4.1433 | 18450 | 0.0303 | - |
| 4.1545 | 18500 | 0.002 | - |
| 4.1657 | 18550 | 0.0009 | - |
| 4.1770 | 18600 | 0.0025 | - |
| 4.1882 | 18650 | 0.1035 | - |
| 4.1994 | 18700 | 0.0033 | - |
| 4.2106 | 18750 | 0.0038 | - |
| 4.2219 | 18800 | 0.0161 | - |
| 4.2331 | 18850 | 0.0415 | - |
| 4.2443 | 18900 | 0.003 | - |
| 4.2556 | 18950 | 0.0055 | - |
| 4.2668 | 19000 | 0.0064 | - |
| 4.2780 | 19050 | 0.0656 | - |
| 4.2892 | 19100 | 0.0011 | - |
| 4.3005 | 19150 | 0.0252 | - |
| 4.3117 | 19200 | 0.0076 | - |
| 4.3229 | 19250 | 0.0051 | - |
| 4.3342 | 19300 | 0.0042 | - |
| 4.3454 | 19350 | 0.0043 | - |
| 4.3566 | 19400 | 0.014 | - |
| 4.3678 | 19450 | 0.0047 | - |
| 4.3791 | 19500 | 0.0043 | - |
| 4.3903 | 19550 | 0.0014 | - |
| 4.4015 | 19600 | 0.0017 | - |
| 4.4128 | 19650 | 0.0811 | - |
| 4.4240 | 19700 | 0.0013 | - |
| 4.4352 | 19750 | 0.0332 | - |
| 4.4464 | 19800 | 0.0636 | - |
| 4.4577 | 19850 | 0.0068 | - |
| 4.4689 | 19900 | 0.0076 | - |
| 4.4801 | 19950 | 0.0217 | - |
| 4.4914 | 20000 | 0.0387 | - |
| 4.5026 | 20050 | 0.0077 | - |
| 4.5138 | 20100 | 0.0778 | - |
| 4.5250 | 20150 | 0.0523 | - |
| 4.5363 | 20200 | 0.0597 | - |
| 4.5475 | 20250 | 0.0092 | - |
| 4.5587 | 20300 | 0.0684 | - |
| 4.5700 | 20350 | 0.0151 | - |
| 4.5812 | 20400 | 0.0007 | - |
| 4.5924 | 20450 | 0.0018 | - |
| 4.6036 | 20500 | 0.0003 | - |
| 4.6149 | 20550 | 0.0051 | - |
| 4.6261 | 20600 | 0.0144 | - |
| 4.6373 | 20650 | 0.011 | - |
| 4.6486 | 20700 | 0.0061 | - |
| 4.6598 | 20750 | 0.0066 | - |
| 4.6710 | 20800 | 0.0046 | - |
| 4.6822 | 20850 | 0.0511 | - |
| 4.6935 | 20900 | 0.0198 | - |
| 4.7047 | 20950 | 0.001 | - |
| 4.7159 | 21000 | 0.0022 | - |
| 4.7272 | 21050 | 0.053 | - |
| 4.7384 | 21100 | 0.0025 | - |
| 4.7496 | 21150 | 0.034 | - |
| 4.7608 | 21200 | 0.0147 | - |
| 4.7721 | 21250 | 0.0684 | - |
| 4.7833 | 21300 | 0.0012 | - |
| 4.7945 | 21350 | 0.0029 | - |
| 4.8057 | 21400 | 0.0014 | - |
| 4.8170 | 21450 | 0.0522 | - |
| 4.8282 | 21500 | 0.0766 | - |
| 4.8394 | 21550 | 0.0031 | - |
| 4.8507 | 21600 | 0.0012 | - |
| 4.8619 | 21650 | 0.0011 | - |
| 4.8731 | 21700 | 0.0235 | - |
| 4.8843 | 21750 | 0.001 | - |
| 4.8956 | 21800 | 0.0178 | - |
| 4.9068 | 21850 | 0.0006 | - |
| 4.9180 | 21900 | 0.0092 | - |
| 4.9293 | 21950 | 0.025 | - |
| 4.9405 | 22000 | 0.017 | - |
| 4.9517 | 22050 | 0.0052 | - |
| 4.9629 | 22100 | 0.0437 | - |
| 4.9742 | 22150 | 0.0019 | - |
| 4.9854 | 22200 | 0.0039 | - |
| 4.9966 | 22250 | 0.0015 | - |
| 5.0 | 22265 | - | 0.2357 |
### Framework Versions
- Python: 3.10.12
- SetFit: 1.0.3
- Sentence Transformers: 2.2.2
- Transformers: 4.36.1
- PyTorch: 2.0.1+cu118
- Datasets: 2.15.0
- 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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