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Shankhdhar
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classifier_woog_firstbud

Text Classification
setfit
Safetensors
sentence-transformers
mpnet
generated_from_setfit_trainer
Eval Results
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classifier_woog_firstbud / 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: What is the price of the organic honey?
  - text: Variety of cookie boxes
  - text: Is the Popcorn Box available in a pack of 50?
  - text: >-
      What is the price range for the sugarfree chocolate heart sugarfree
      chocolate box pack of 5?
  - text: >-
      Do you have the Off-White x Air Jordan 2 Retro Low SP Black Varsity Royal
      in size 10?
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: 0.8533333333333334
            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 faq
  • 'Does the Meenakari jal jangla -Rani saree have meenakari?'
  • 'Is the Nike Dunk Low Premium Bacon available in size 7?'
  • 'What is the best way to recycle the packaging boxes for wholesale orders for wholesale orders?'
order tracking
  • 'I ordered the Cake Boards 7 days ago with order no 43210 how long will it take to deliver?'
  • 'I want to deliver bags to Pune, how many days will it take to deliver?'
  • 'I want to deliver packaging to Surat, how many days will it take to deliver?'
product policy
  • 'What is the procedure for returning a product that was part of a special promotion occasion?'
  • 'Can I return an item if it was damaged during delivery preparation?'
  • 'What is the procedure for returning a product that was part of a special occasion promotion?'
general faq
  • 'What are the key factors to consider when developing a personalized diet plan for weight loss?'
  • 'What are some tips for maximizing the antioxidant content when brewing green tea?'
  • 'Can you explain why Mashru silk is considered more comfortable to wear compared to pure silk sarees?'
product discoverability
  • 'Can you show me sarees in bright colors suitable for weddings?'
  • 'Do you have adidas Superstar shoes?'
  • 'Do you have any bestseller teas available?'

Evaluation

Metrics

Label Accuracy
all 0.8533

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("Shankhdhar/classifier_woog_firstbud")
# Run inference
preds = model("Variety of cookie boxes")

Training Details

Training Set Metrics

Training set Min Median Max
Word count 4 12.1961 28
Label Training Sample Count
general faq 24
order tracking 32
product discoverability 50
product faq 50
product policy 48

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
0.0005 1 0.2265 -
0.0244 50 0.1831 -
0.0489 100 0.1876 -
0.0733 150 0.1221 -
0.0978 200 0.0228 -
0.1222 250 0.0072 -
0.1467 300 0.0282 -
0.1711 350 0.0015 -
0.1956 400 0.0005 -
0.2200 450 0.0008 -
0.2445 500 0.0004 -
0.2689 550 0.0003 -
0.2934 600 0.0003 -
0.3178 650 0.0002 -
0.3423 700 0.0002 -
0.3667 750 0.0002 -
0.3912 800 0.0003 -
0.4156 850 0.0002 -
0.4401 900 0.0002 -
0.4645 950 0.0001 -
0.4890 1000 0.0001 -
0.5134 1050 0.0001 -
0.5379 1100 0.0001 -
0.5623 1150 0.0002 -
0.5868 1200 0.0002 -
0.6112 1250 0.0001 -
0.6357 1300 0.0001 -
0.6601 1350 0.0001 -
0.6846 1400 0.0001 -
0.7090 1450 0.0001 -
0.7335 1500 0.0001 -
0.7579 1550 0.0001 -
0.7824 1600 0.0001 -
0.8068 1650 0.0001 -
0.8313 1700 0.0001 -
0.8557 1750 0.0011 -
0.8802 1800 0.0002 -
0.9046 1850 0.0001 -
0.9291 1900 0.0001 -
0.9535 1950 0.0002 -
0.9780 2000 0.0001 -
1.0024 2050 0.0001 -
1.0269 2100 0.0002 -
1.0513 2150 0.0001 -
1.0758 2200 0.0001 -
1.1002 2250 0.0001 -
1.1247 2300 0.0001 -
1.1491 2350 0.0001 -
1.1736 2400 0.0001 -
1.1980 2450 0.0001 -
1.2225 2500 0.0001 -
1.2469 2550 0.0001 -
1.2714 2600 0.0001 -
1.2958 2650 0.0001 -
1.3203 2700 0.0001 -
1.3447 2750 0.0001 -
1.3692 2800 0.0001 -
1.3936 2850 0.0001 -
1.4181 2900 0.0001 -
1.4425 2950 0.0001 -
1.4670 3000 0.0001 -
1.4914 3050 0.0001 -
1.5159 3100 0.0001 -
1.5403 3150 0.0001 -
1.5648 3200 0.0001 -
1.5892 3250 0.0001 -
1.6137 3300 0.0001 -
1.6381 3350 0.0001 -
1.6626 3400 0.0001 -
1.6870 3450 0.0001 -
1.7115 3500 0.0001 -
1.7359 3550 0.0 -
1.7604 3600 0.0001 -
1.7848 3650 0.0001 -
1.8093 3700 0.0001 -
1.8337 3750 0.0 -
1.8582 3800 0.0001 -
1.8826 3850 0.0001 -
1.9071 3900 0.0001 -
1.9315 3950 0.0 -
1.9560 4000 0.0 -
1.9804 4050 0.0001 -

Framework Versions

  • Python: 3.10.13
  • SetFit: 1.0.3
  • Sentence Transformers: 3.0.1
  • Transformers: 4.39.0
  • PyTorch: 2.2.2+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}
}