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 |
- 'Can you identify the category that demonstrates a higher sensitivity to internal cannibalization?'
- 'What kind of promotions generally lead to higher cannibalization for HYPER for year 2022?'
- "Which two sku's can have simultaneous Promotions for subcategory CHIPS & SNACKS?"
|
3 |
- 'Which promotion strategies in RTEC allow for offering substantial discounts while maintaining profitability?'
- 'Which promotion types are better for high discounts in Alsuper for Pringles?'
- 'Are there specific promotional tactics in the RTEC category that are particularly effective for implementing high discount offers?'
|
4 |
- 'Which promotions have scope for higher investment to drive more ROIs in WALMART ?'
- 'Are there any promotional strategies in RTEC that have consistently underperformed and should be considered for discontinuation?'
- 'Suggest a better investment strategy to gain better ROI for SS?'
|
0 |
- 'Which subcategory have the highest ROI in 2022?'
- 'Which sku have the highest ROI in 2022? '
- 'Which channel has the max ROI and Vol Lift when we run the Promotion for RTEC category?'
|
1 |
- 'What role do promotional strategies play in the Lift decline for Zucaritas in 2023, and how does this compare to promotional strategies employed by other brands like Pringles or Frutela?'
- 'Is there a particular sku that stand out as major driver behind the decrease in ROI during 2022?'
- 'Are there plans to enhance promotional activities specific to the HYPER to mitigate the ROI decline in 2023?'
|
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_15_03_2024")
preds = model("Which two Categories can have simultaneous Promotions?")
Training Details
Training Set Metrics
Training set |
Min |
Median |
Max |
Word count |
8 |
14.9796 |
30 |
Label |
Training Sample Count |
0 |
10 |
1 |
10 |
2 |
10 |
3 |
9 |
4 |
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 |
0.0081 |
1 |
0.3585 |
- |
0.4065 |
50 |
0.0558 |
- |
0.8130 |
100 |
0.0011 |
- |
1.2195 |
150 |
0.0007 |
- |
1.6260 |
200 |
0.0006 |
- |
2.0325 |
250 |
0.0003 |
- |
2.4390 |
300 |
0.0005 |
- |
2.8455 |
350 |
0.0003 |
- |
Framework Versions
- Python: 3.10.12
- SetFit: 1.0.3
- Sentence Transformers: 2.5.1
- 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}
}