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metadata
library_name: setfit
tags:
  - setfit
  - sentence-transformers
  - text-classification
  - generated_from_setfit_trainer
metrics:
  - accuracy
widget:
  - text: >-
      What promotions in RTEC have shown declining effectiveness and can be
      discontinued?
  - text: What are my priority brands in RTEC to get positive Lift Change in 2022?
  - text: >-
      What would be the expected incremental volume lift if the discount on
      Brand Zucaritas is raised by 5%?
  - text: Which promotion types are better for low discounts for Zucaritas ?
  - text: Which Promotions contributred the most ROI Change between 2022 and 2023?
pipeline_tag: text-classification
inference: true
base_model: intfloat/multilingual-e5-large
model-index:
  - name: SetFit with intfloat/multilingual-e5-large
    results:
      - task:
          type: text-classification
          name: Text Classification
        dataset:
          name: Unknown
          type: unknown
          split: test
        metrics:
          - type: accuracy
            value: 0.9714285714285714
            name: Accuracy

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:

  1. Fine-tuning a Sentence Transformer with contrastive learning.
  2. 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

# Download from the 🤗 Hub
model = SetFitModel.from_pretrained("vgarg/promo_prescriptive_05_04_2024")
# Run inference
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}
}