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 |
product faq |
- 'What are the different sizes available for the Love is in the Air Proposal Ring, and do they come at different price points?'
- 'What is the material of the Open Pear Cut Ring and are there different sizes available?'
- 'What is the material used for making the Golden Spin Hoop Earring, and does it come with any kind of warranty or guarantee?'
|
product discoveribility |
- 'What are the latest choker styles available for a wedding occasion?'
- "I'm interested in sustainable jewelry; do you have any eco-friendly necklaces?"
- 'Could you recommend some necklaces with a vintage vibe to them?'
|
order tracking |
- 'I recently purchased the Seher Pearl Choker Set and I would like to know the current status of my order delivery.'
- "I placed an order for the Tiara Silver Ring, but I haven't received any shipping updates yet. Can you provide me with the current status of my order?"
- 'I recently ordered the Toes Of Love Pendant but have not received any shipping confirmation. Could you please provide me with the tracking details?'
|
product policy |
- 'Are there any restocking fees for bracelet returns?'
- "Can I exchange a ring if it doesn't fit properly?"
- 'Are there any care instructions included with the purchase of a ring?'
|
Evaluation
Metrics
Label |
Accuracy |
all |
0.8025 |
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("setfit_model_id")
preds = model("I recently purchased the Three Crystal Proposal Ring, but I'm disappointed to find that one of the crystals is loose. Can you assist me with this issue?")
Training Details
Training Set Metrics
Training set |
Min |
Median |
Max |
Word count |
6 |
16.4474 |
30 |
Label |
Training Sample Count |
negative |
0 |
positive |
0 |
Training Hyperparameters
- batch_size: (16, 16)
- num_epochs: (4, 4)
- 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 |
0.0016 |
1 |
0.1464 |
- |
0.0822 |
50 |
0.0907 |
- |
0.1645 |
100 |
0.0059 |
- |
0.2467 |
150 |
0.0013 |
- |
0.3289 |
200 |
0.0009 |
- |
0.4112 |
250 |
0.0007 |
- |
0.4934 |
300 |
0.0004 |
- |
0.5757 |
350 |
0.0003 |
- |
0.6579 |
400 |
0.0001 |
- |
0.7401 |
450 |
0.0002 |
- |
0.8224 |
500 |
0.0002 |
- |
0.9046 |
550 |
0.0002 |
- |
0.9868 |
600 |
0.0001 |
- |
1.0 |
608 |
- |
0.2272 |
1.0691 |
650 |
0.0001 |
- |
1.1513 |
700 |
0.0001 |
- |
1.2336 |
750 |
0.0001 |
- |
1.3158 |
800 |
0.0001 |
- |
1.3980 |
850 |
0.0001 |
- |
1.4803 |
900 |
0.0001 |
- |
1.5625 |
950 |
0.0001 |
- |
1.6447 |
1000 |
0.0001 |
- |
1.7270 |
1050 |
0.0001 |
- |
1.8092 |
1100 |
0.0 |
- |
1.8914 |
1150 |
0.0001 |
- |
1.9737 |
1200 |
0.0001 |
- |
2.0 |
1216 |
- |
0.2807 |
2.0559 |
1250 |
0.0001 |
- |
2.1382 |
1300 |
0.0001 |
- |
2.2204 |
1350 |
0.0001 |
- |
2.3026 |
1400 |
0.0 |
- |
2.3849 |
1450 |
0.0001 |
- |
2.4671 |
1500 |
0.0001 |
- |
2.5493 |
1550 |
0.0 |
- |
2.6316 |
1600 |
0.0001 |
- |
2.7138 |
1650 |
0.0 |
- |
2.7961 |
1700 |
0.0001 |
- |
2.8783 |
1750 |
0.0 |
- |
2.9605 |
1800 |
0.0 |
- |
3.0 |
1824 |
- |
0.3011 |
3.0428 |
1850 |
0.0 |
- |
3.125 |
1900 |
0.0001 |
- |
3.2072 |
1950 |
0.0001 |
- |
3.2895 |
2000 |
0.0 |
- |
3.3717 |
2050 |
0.0001 |
- |
3.4539 |
2100 |
0.0001 |
- |
3.5362 |
2150 |
0.0 |
- |
3.6184 |
2200 |
0.0001 |
- |
3.7007 |
2250 |
0.0001 |
- |
3.7829 |
2300 |
0.0 |
- |
3.8651 |
2350 |
0.0 |
- |
3.9474 |
2400 |
0.0001 |
- |
4.0 |
2432 |
- |
0.311 |
- The bold row denotes the saved checkpoint.
Framework Versions
- Python: 3.9.16
- SetFit: 1.0.3
- Sentence Transformers: 2.2.2
- Transformers: 4.35.2
- PyTorch: 2.1.1
- Datasets: 2.15.0
- 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}
}