SetFit with intfloat/multilingual-e5-large
This is a SetFit model that can be used for Text Classification. This SetFit model uses intfloat/multilingual-e5-large 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 |
2 |
- 'Which brand has the highest change in lift for NATURAL JUICES category in 2022?'
- 'What are the main reasons for Lift decline for ULTRASTORE in 2023 compared to 2022?'
- 'Why has the overall Lift declined in 2023 in BREEZEFIZZ vs 2022?'
|
5 |
- 'How will the introduction of a 20% discount promotion for Rice Krispies in August affect incremental volume and ROI?'
- 'If I raise the discount by 20% on Brand BREEZEFIZZ, what will be the incremental roi?'
- 'How will increasing the discount by 50 percent on Brand BREEZEFIZZ affect the incremental volume lift?'
|
1 |
- 'How do the performance metrics of brands in the FIZZY DRINKS category compare to those in HYDRA and NATURAL JUICES concerning ROI change between 2021 to 2022?'
- 'Were there any sku-specific promotions that may have influenced their ROI and contributed to the overall decline?'
- 'Which category has contributed the most to ROI change between 2021 to 2022?'
|
0 |
- 'How is the promotion efficacy in 2022 compared to 2021 for CHEDRAUI channel?'
- 'Which subcategory have the highest ROI in 2022?'
- 'Which channel has the max ROI and Vol Lift when we run the Promotion for FIZZY DRINKS category?'
|
3 |
- 'Which promotion types are better for high discounts in Hydra category for 2022?'
- 'Which promotion types are preferable for high discounts in FIZZY DRINKS for CORN POPS?'
- 'Which promotion strategies in FIZZY DRINKS allow for offering substantial discounts while maintaining profitability?'
|
4 |
- 'Which promotions have scope for higher investment to drive more ROIs in Hydra ?'
- 'How can Hydra category investors diversify their investment portfolio to improve ROI?'
- 'For FIZZY DRINKS what would be a better investment strategy to gain ROI'
|
Evaluation
Metrics
Label |
Accuracy |
all |
0.9714 |
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("vgarg/promo_prescriptive_05_04_2024")
preds = model("Which promotion types are better for low discounts for Zucaritas ?")
Training Details
Training Set Metrics
Training set |
Min |
Median |
Max |
Word count |
8 |
15.1667 |
27 |
Label |
Training Sample Count |
0 |
10 |
1 |
10 |
2 |
10 |
3 |
10 |
4 |
10 |
5 |
10 |
Training Hyperparameters
- batch_size: (16, 16)
- num_epochs: (3, 3)
- 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 |
0.0067 |
1 |
0.3577 |
- |
0.3333 |
50 |
0.04 |
- |
0.6667 |
100 |
0.002 |
- |
1.0 |
150 |
0.0013 |
- |
1.3333 |
200 |
0.0009 |
- |
1.6667 |
250 |
0.0006 |
- |
2.0 |
300 |
0.0006 |
- |
2.3333 |
350 |
0.0004 |
- |
2.6667 |
400 |
0.0006 |
- |
3.0 |
450 |
0.0004 |
- |
Framework Versions
- Python: 3.10.12
- SetFit: 1.0.3
- Sentence Transformers: 2.6.1
- Transformers: 4.38.2
- PyTorch: 2.2.1+cu121
- Datasets: 2.18.0
- 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}
}