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: 3 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	'Are there particular factors or trends contributing to the high level of cannibalization for certain brands in the SS category?' 'How does the degree of cannibalization vary among different SKUs in the RTEC ?' 'Which Sku cannibalizes higher margin Skus the most?'
1	'Are there plans to enhance promotional activities specific to the MT to mitigate the ROI decline in 2023?' 'What are the main reasons for ROI decline in 2022 in MT compared to 2021?' 'Are there changes in consumer preferences or trends that have impacted the Lift of Zucaritas, and how does this compare to other brands like Pringles or Frutela?'
0	'What type of promotions worked best for MT Walmart in 2022?' 'Which channel has the max ROI and Vol Lift when we run the Promotion for RTEC category?' 'Which sub_catg_nm have the highest ROI in 2022?'

Evaluation

Metrics

Label	Accuracy
all	0.9130

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_harshal_trail_27_02_2024")
# Run inference
preds = model("Which Sku cannibalizes higher margin Skus the most for CHEDRAUI channel_name?")

Training Details

Training Set Metrics

Training set	Min	Median	Max
Word count	7	15.8333	30

Label	Training Sample Count
0	10
1	10
2	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
0.0133	1	0.3582	-
0.6667	50	0.0024	-
1.3333	100	0.0005	-
2.0	150	0.0004	-
2.6667	200	0.0002	-

Framework Versions

Python: 3.10.12
SetFit: 1.0.3
Sentence Transformers: 2.4.0
Transformers: 4.37.2
PyTorch: 2.1.0+cu121
Datasets: 2.17.1
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}
}

vgarg
/

promo_prescriptive_harshal_trail_27_02_2024