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:
- Fine-tuning a Sentence Transformer with contrastive learning.
- Training a classification head with features from the fine-tuned Sentence Transformer.
Model Details
Model Description
Model Sources
Model Labels
Label |
Examples |
general_faq |
- 'What makes Banarasi silk sarees unique compared to other types of sarees, and what are their main varieties?'
- 'How to identify mashru silk'
- 'How can I verify the authenticity of Real Zari in a saree'
|
product discoverability |
- 'bakery boxes with custom designs'
- 'What are the different fabric options available for sarees?'
- 'show me some trending sneakers under 25k'
|
product faq |
- 'Is the Wmns Dunk Low Harvest Moon available in size 7?'
- 'What type of color is the Pure Katan silk Kadhwa Bootidaar Banarasi Saree?'
- 'What type of color is the Pure Katan Silk Pastel Orange Kadhwa Satin Tanchoi Banarasi Saree?'
|
product policy |
- 'What is the policy for returning a product that was part of a special sale celebration?'
- 'Can I return an item if it was damaged during delivery preparation?'
- 'Do you offer express shipping for sneakers?'
|
order tracking |
- 'I ordered the Cupcake Cases 3 days ago with order no 34567 how long will it take to deliver?'
- 'Do you provide shipping insurance for high-value orders?'
- 'My order has been shipped 1 day ago but still not out for delivery. Can you tell how long will it take to deliver?'
|
Evaluation
Metrics
Label |
Accuracy |
all |
0.9245 |
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
model = SetFitModel.from_pretrained("Shankhdhar/classifier_woog_hkv")
preds = model("cookie boxes with inserts")
Training Details
Training Set Metrics
Training set |
Min |
Median |
Max |
Word count |
4 |
11.9441 |
24 |
Label |
Training Sample Count |
general_faq |
4 |
order tracking |
28 |
product discoverability |
40 |
product faq |
40 |
product policy |
31 |
Training Hyperparameters
- batch_size: (16, 16)
- num_epochs: (2, 2)
- max_steps: -1
- sampling_strategy: oversampling
- 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: True
Training Results
Epoch |
Step |
Training Loss |
Validation Loss |
0.0010 |
1 |
0.3031 |
- |
0.0517 |
50 |
0.1396 |
- |
0.1033 |
100 |
0.0959 |
- |
0.1550 |
150 |
0.0036 |
- |
0.2066 |
200 |
0.0009 |
- |
0.2583 |
250 |
0.0008 |
- |
0.3099 |
300 |
0.0011 |
- |
0.3616 |
350 |
0.0005 |
- |
0.4132 |
400 |
0.0004 |
- |
0.4649 |
450 |
0.0003 |
- |
0.5165 |
500 |
0.0003 |
- |
0.5682 |
550 |
0.0003 |
- |
0.6198 |
600 |
0.0003 |
- |
0.6715 |
650 |
0.0001 |
- |
0.7231 |
700 |
0.0002 |
- |
0.7748 |
750 |
0.0001 |
- |
0.8264 |
800 |
0.0002 |
- |
0.8781 |
850 |
0.0002 |
- |
0.9298 |
900 |
0.0001 |
- |
0.0010 |
1 |
0.0002 |
- |
0.0517 |
50 |
0.0002 |
- |
0.1033 |
100 |
0.0007 |
- |
0.1550 |
150 |
0.0001 |
- |
0.2066 |
200 |
0.0002 |
- |
0.2583 |
250 |
0.0002 |
- |
0.3099 |
300 |
0.0001 |
- |
0.3616 |
350 |
0.0502 |
- |
0.4132 |
400 |
0.0001 |
- |
0.4649 |
450 |
0.0001 |
- |
0.5165 |
500 |
0.0001 |
- |
0.5682 |
550 |
0.0001 |
- |
0.6198 |
600 |
0.0 |
- |
0.6715 |
650 |
0.0 |
- |
0.7231 |
700 |
0.0001 |
- |
0.7748 |
750 |
0.0 |
- |
0.8264 |
800 |
0.0001 |
- |
0.8781 |
850 |
0.0001 |
- |
0.9298 |
900 |
0.0001 |
- |
0.9814 |
950 |
0.0001 |
- |
1.0331 |
1000 |
0.0001 |
- |
1.0847 |
1050 |
0.0001 |
- |
1.1364 |
1100 |
0.0 |
- |
1.1880 |
1150 |
0.0 |
- |
1.2397 |
1200 |
0.0 |
- |
1.2913 |
1250 |
0.0 |
- |
1.3430 |
1300 |
0.0001 |
- |
1.3946 |
1350 |
0.0 |
- |
1.4463 |
1400 |
0.0 |
- |
1.4979 |
1450 |
0.0 |
- |
1.5496 |
1500 |
0.0 |
- |
1.6012 |
1550 |
0.0 |
- |
1.6529 |
1600 |
0.0 |
- |
1.7045 |
1650 |
0.0 |
- |
1.7562 |
1700 |
0.0001 |
- |
1.8079 |
1750 |
0.0 |
- |
1.8595 |
1800 |
0.0 |
- |
1.9112 |
1850 |
0.0 |
- |
1.9628 |
1900 |
0.0 |
- |
0.0010 |
1 |
0.0 |
- |
0.0517 |
50 |
0.0 |
- |
0.1033 |
100 |
0.0001 |
- |
0.1550 |
150 |
0.0 |
- |
0.2066 |
200 |
0.0001 |
- |
0.2583 |
250 |
0.0001 |
- |
0.3099 |
300 |
0.0 |
- |
0.3616 |
350 |
0.0402 |
- |
0.4132 |
400 |
0.0001 |
- |
0.4649 |
450 |
0.0 |
- |
0.5165 |
500 |
0.0 |
- |
0.5682 |
550 |
0.0 |
- |
0.6198 |
600 |
0.0 |
- |
0.6715 |
650 |
0.0 |
- |
0.7231 |
700 |
0.0 |
- |
0.7748 |
750 |
0.0 |
- |
0.8264 |
800 |
0.0 |
- |
0.8781 |
850 |
0.0 |
- |
0.9298 |
900 |
0.0 |
- |
0.9814 |
950 |
0.0 |
- |
1.0331 |
1000 |
0.0 |
- |
1.0847 |
1050 |
0.0 |
- |
1.1364 |
1100 |
0.0 |
- |
1.1880 |
1150 |
0.0 |
- |
1.2397 |
1200 |
0.0 |
- |
1.2913 |
1250 |
0.0 |
- |
1.3430 |
1300 |
0.0 |
- |
1.3946 |
1350 |
0.0 |
- |
1.4463 |
1400 |
0.0 |
- |
1.4979 |
1450 |
0.0 |
- |
1.5496 |
1500 |
0.0 |
- |
1.6012 |
1550 |
0.0 |
- |
1.6529 |
1600 |
0.0 |
- |
1.7045 |
1650 |
0.0 |
- |
1.7562 |
1700 |
0.0 |
- |
1.8079 |
1750 |
0.0 |
- |
1.8595 |
1800 |
0.0 |
- |
1.9112 |
1850 |
0.0 |
- |
1.9628 |
1900 |
0.0 |
- |
Framework Versions
- Python: 3.10.12
- SetFit: 1.0.3
- Sentence Transformers: 3.0.1
- Transformers: 4.39.0
- PyTorch: 2.2.2+cu121
- Datasets: 2.20.0
- Tokenizers: 0.15.2
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}
}