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 |
store neighbourhood analysis |
- 'How does the area compare in an analysis for this store?'
- 'How do the local beauty trends influence product demand at Sephora?'
- 'What is the socio-economic status of the neighborhood for this store?'
|
exploratory |
- 'Which are the stores with the largest parking lots?'
- 'Which stores are located in commercial zones?'
- 'Which are the largest stores by square footage?'
|
site recommendations |
- 'How would you analyze potential sites for recommendations?'
- 'Which neighborhoods offer potential for opening new Michaels craft stores to meet the demand for art supplies and creative outlets?'
- 'Can you provide an analysis to recommend sites?'
|
baseline compare |
- "What is the variance in revenue generation between this Best Buy outlet and the brand's expected benchmarks?"
- "What is the disparity in sales performance between this Home Depot store and the brand's standard expectations?"
- 'What are the variations in customer loyalty scores between this Target store and the brand average?'
|
store competition |
- 'How does the service quality at Safeway compare to other grocery stores in the neighborhood?'
- "How does Target's product selection compare to that of neighboring retail stores?"
- 'What competitive strategies has Staples employed to stand out among other office supply retailers?'
|
Evaluation
Metrics
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("a-n-a-n-y-a-123/setfit-model-intent")
preds = model("Analyze the sites and provide recommendations.")
Training Details
Training Set Metrics
Training set |
Min |
Median |
Max |
Word count |
6 |
11.7467 |
20 |
Label |
Training Sample Count |
baseline compare |
15 |
exploratory |
15 |
site recommendations |
15 |
store competition |
15 |
store neighbourhood analysis |
15 |
Training Hyperparameters
- batch_size: (16, 16)
- num_epochs: (1, 1)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 20
- body_learning_rate: (2e-05, 2e-05)
- head_learning_rate: 2e-05
- 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 |
0.0053 |
1 |
0.2398 |
- |
0.2660 |
50 |
0.0757 |
- |
0.5319 |
100 |
0.0021 |
- |
0.7979 |
150 |
0.0013 |
- |
1.0638 |
200 |
0.0005 |
- |
1.3298 |
250 |
0.0006 |
- |
1.5957 |
300 |
0.0006 |
- |
1.8617 |
350 |
0.0004 |
- |
2.1277 |
400 |
0.0006 |
- |
2.3936 |
450 |
0.0004 |
- |
2.6596 |
500 |
0.0003 |
- |
2.9255 |
550 |
0.0003 |
- |
0.0053 |
1 |
0.0002 |
- |
0.2660 |
50 |
0.0003 |
- |
0.5319 |
100 |
0.0004 |
- |
0.7979 |
150 |
0.0001 |
- |
Framework Versions
- Python: 3.10.12
- SetFit: 1.0.3
- Sentence Transformers: 3.0.0
- Transformers: 4.39.0
- PyTorch: 2.3.0+cu121
- Datasets: 2.19.2
- 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}
}