Edit model card

SetFit with sentence-transformers/paraphrase-mpnet-base-v2

This is a SetFit model that can be used for Text Classification. This SetFit model uses sentence-transformers/paraphrase-mpnet-base-v2 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
store neighbourhood analysis
  • 'How does the area compare in an analysis for this store?'
  • 'How do the local beauty trends influence product demand at Sephora?'
  • 'What is the socio-economic status of the neighborhood for this store?'
exploratory
  • 'Which are the stores with the largest parking lots?'
  • 'Which stores are located in commercial zones?'
  • 'Which are the largest stores by square footage?'
site recommendations
  • 'How would you analyze potential sites for recommendations?'
  • 'Which neighborhoods offer potential for opening new Michaels craft stores to meet the demand for art supplies and creative outlets?'
  • 'Can you provide an analysis to recommend sites?'
baseline compare
  • "What is the variance in revenue generation between this Best Buy outlet and the brand's expected benchmarks?"
  • "What is the disparity in sales performance between this Home Depot store and the brand's standard expectations?"
  • 'What are the variations in customer loyalty scores between this Target store and the brand average?'
store competition
  • 'How does the service quality at Safeway compare to other grocery stores in the neighborhood?'
  • "How does Target's product selection compare to that of neighboring retail stores?"
  • 'What competitive strategies has Staples employed to stand out among other office supply retailers?'

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("a-n-a-n-y-a-123/setfit-model-intent")
# Run inference
preds = model("Analyze the sites and provide recommendations.")

Training Details

Training Set Metrics

Training set Min Median Max
Word count 6 11.7467 20
Label Training Sample Count
baseline compare 15
exploratory 15
site recommendations 15
store competition 15
store neighbourhood analysis 15

Training Hyperparameters

  • batch_size: (16, 16)
  • num_epochs: (1, 1)
  • 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
0.0053 1 0.2398 -
0.2660 50 0.0757 -
0.5319 100 0.0021 -
0.7979 150 0.0013 -
1.0638 200 0.0005 -
1.3298 250 0.0006 -
1.5957 300 0.0006 -
1.8617 350 0.0004 -
2.1277 400 0.0006 -
2.3936 450 0.0004 -
2.6596 500 0.0003 -
2.9255 550 0.0003 -
0.0053 1 0.0002 -
0.2660 50 0.0003 -
0.5319 100 0.0004 -
0.7979 150 0.0001 -

Framework Versions

  • Python: 3.10.12
  • SetFit: 1.0.3
  • Sentence Transformers: 3.0.0
  • Transformers: 4.39.0
  • PyTorch: 2.3.0+cu121
  • Datasets: 2.19.2
  • 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}
}
Downloads last month
37
Safetensors
Model size
109M params
Tensor type
F32
·
Inference API
This model can be loaded on Inference API (serverless).

Finetuned from

Evaluation results