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 |
6 |
- 'What kind of promotions generally lead to higher cannibalization?'
- 'Which Skus has higher Canninibalization in Natural Juices for 2023?'
- 'Which two Product can have simultaneous Promotions?'
|
2 |
- 'Which Promotions contributred the most lift Change between 2022 and 2023?'
- 'Which category x brand has seen major decline in Volume Lift for 2023?'
- 'What actions were taken to increase the volume lift for MEGAMART in 2023?'
|
3 |
- 'What types of promotions within the FIZZY DRINKS category are best suited for offering high discounts?'
- 'Which promotion types are better for high discounts in Hydra category for 2022?'
- 'Which promotion types in are better for low discounts in FIZZY DRINKS category?'
|
5 |
- 'How will increasing the discount by 50 percent on Brand BREEZEFIZZ affect the incremental volume lift?'
- '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?'
|
0 |
- 'For which category MULTISAVING type of promotions worked best for WorldMart in 2022?'
- 'What type of promotions worked best for WorldMart in 2022?'
- 'Which subcategory have the highest ROI in 2022?'
|
4 |
- 'Suggest a better investment strategy to gain better ROI in 2023 for FIZZY DRINKS'
- 'Which promotions have scope for higher investment to drive more ROIs in UrbanHub ?'
- 'What promotions in FIZZY DRINKS have shown declining effectiveneHydra and can be discontinued?'
|
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?'
- 'Can you identify the specific factors or challenges that contributed to the decline in ROI within ULTRASTORE in 2022 compared to 2021?'
- 'What are the main reasons for ROI decline in 2022 compared to 2021?'
|
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("vgarg/promo_prescriptive_gpt_30_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 |
7 |
14.6667 |
27 |
Label |
Training Sample Count |
0 |
10 |
1 |
10 |
2 |
10 |
3 |
10 |
4 |
10 |
5 |
10 |
6 |
9 |
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 |
0.0058 |
1 |
0.3528 |
- |
0.2890 |
50 |
0.0485 |
- |
0.5780 |
100 |
0.0052 |
- |
0.8671 |
150 |
0.0014 |
- |
1.1561 |
200 |
0.0006 |
- |
1.4451 |
250 |
0.0004 |
- |
1.7341 |
300 |
0.0005 |
- |
2.0231 |
350 |
0.0004 |
- |
2.3121 |
400 |
0.0004 |
- |
2.6012 |
450 |
0.0005 |
- |
2.8902 |
500 |
0.0004 |
- |
Framework Versions
- Python: 3.10.12
- SetFit: 1.0.3
- Sentence Transformers: 2.7.0
- Transformers: 4.40.0
- PyTorch: 2.2.1+cu121
- Datasets: 2.19.0
- Tokenizers: 0.19.1
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}
}