classifier_woog / README.md
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metadata
library_name: setfit
tags:
  - setfit
  - sentence-transformers
  - text-classification
  - generated_from_setfit_trainer
base_model: sentence-transformers/paraphrase-mpnet-base-v2
metrics:
  - accuracy
widget:
  - text: cookie boxes for gifting under $20
  - text: Are there any restrictions on returning candle supplies?
  - text: special features for bakery boxes
  - text: >-
      I need to confirm the shipping date for my recent purchase. Can you help
      me with that?
  - text: different types of bakery boxes available
pipeline_tag: text-classification
inference: true
model-index:
  - name: SetFit with sentence-transformers/paraphrase-mpnet-base-v2
    results:
      - task:
          type: text-classification
          name: Text Classification
        dataset:
          name: Unknown
          type: unknown
          split: test
        metrics:
          - type: accuracy
            value: 0.8380952380952381
            name: Accuracy

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
product discoverability
  • 'Do you have Adidas Superstar shoes?'
  • 'Do you have any running shoes in pink color?'
  • 'Do you have black Yeezy sneakers in size 9?'
order tracking
  • "I'm concerned about the delay in the delivery of my order. Can you please provide me with the status?"
  • 'What is the estimated delivery time for orders within the same city?'
  • "I placed an order last week and it still hasn't arrived. Can you check the status for me?"
product policy
  • 'Are there any exceptions to the return policy for items that were purchased with a student discount?'
  • 'Do you offer a try-and-buy option for sneakers?'
  • 'Do you offer a price adjustment for sneakers if the price drops after purchase?'
product faq
  • 'Do you have any limited edition sneakers available?'
  • 'Are the Adidas Yeezy Foam Runner available in size 7?'
  • "Are the Nike Air Force 1 sneakers available in women's sizes?"

Evaluation

Metrics

Label Accuracy
all 0.8381

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("setfit_model_id")
# Run inference
preds = model("special features for bakery boxes")

Training Details

Training Set Metrics

Training set Min Median Max
Word count 3 11.6415 24
Label Training Sample Count
order tracking 30
product discoverability 30
product faq 16
product policy 30

Training Hyperparameters

  • batch_size: (16, 16)
  • num_epochs: (2, 2)
  • max_steps: -1
  • sampling_strategy: oversampling
  • body_learning_rate: (2e-05, 1e-05)
  • head_learning_rate: 0.01
  • 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: True

Training Results

Epoch Step Training Loss Validation Loss
0.0019 1 0.1782 -
0.0965 50 0.0628 -
0.1931 100 0.0036 -
0.2896 150 0.0013 -
0.3861 200 0.0012 -
0.4826 250 0.0003 -
0.5792 300 0.0002 -
0.6757 350 0.0003 -
0.7722 400 0.0002 -
0.8687 450 0.0005 -
0.9653 500 0.0003 -
1.0618 550 0.0001 -
1.1583 600 0.0002 -
1.2548 650 0.0002 -
1.3514 700 0.0002 -
1.4479 750 0.0001 -
1.5444 800 0.0001 -
1.6409 850 0.0001 -
1.7375 900 0.0002 -
1.8340 950 0.0001 -
1.9305 1000 0.0001 -

Framework Versions

  • Python: 3.9.16
  • SetFit: 1.0.3
  • Sentence Transformers: 2.7.0
  • Transformers: 4.40.2
  • PyTorch: 2.3.0
  • Datasets: 2.19.1
  • Tokenizers: 0.19.1

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}
}