metadata

library_name: setfit
tags:
  - setfit
  - sentence-transformers
  - text-classification
  - generated_from_setfit_trainer
metrics:
  - accuracy
widget:
  - text: >-
      What promotional strategies within RTEC offer the greatest potential for
      increased ROI with higher investment?
  - text: Which brands are being cannibalized the most by SS between 2020 to 2022?
  - text: Which two Categories can have simultaneous Promotions?
  - text: >-
      How do the ROI contributions of various categories compare when examining
      the shift from 2021 to 2022?
  - text: Which promotion types are better for high discounts for Zucaritas ?
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: 1
            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:

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 Type: SetFit
Sentence Transformer body: intfloat/multilingual-e5-large
Classification head: a LogisticRegression instance
Maximum Sequence Length: 512 tokens
Number of Classes: 5 classes

Model Sources

Repository: SetFit on GitHub
Paper: Efficient Few-Shot Learning Without Prompts
Blogpost: SetFit: Efficient Few-Shot Learning Without Prompts

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

Label	Accuracy
all	1.0

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_15_03_2024")
# Run inference
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}
}