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
Disclaimer
  • '"Terms of Use API restrictions?"'
  • '"Terms of Use age restrictions?"'
  • '"Disclaimer update alerts?"'
IB Loan against Sovereign Gold Bond
  • '"Sovereign Jewel Bond loan margin requirements?"'
  • '"Sovereign Gold Bond joint holder rules?"'
  • '"Sovereign Jewel Bond nomination process?"'
Ind Advantage (Reward Program)
  • '"Advantage Rewards international redemption fees?"'
  • '"Blackout dates for reward travel bookings?"'
  • '"Advantage Program customer support channels?"'
Amalgamation
  • '"Merger documentation checklist for branches?"'
  • '"Banking Amalgamation customer notification process?"'
  • '"Amalgamation loan portfolio transfer details?"'
Loan / OD against NSC / KVP / Relief bonds of RBI / LIC policies
  • '"Relief Bonds OD interest payment frequency?"'
  • '"KVP valuation for overdraft approval criteria?"'
  • '"NSC loan documentation checklist?"'
Chief General Managers
  • '"Chief General Managers' office working hours?"'
  • '"How to contact Chief General Managers for escalations?"'
  • '"Senior General Managers' regional jurisdiction list?"'
Point of Sale (PoS)
  • '"Offline PoS transaction capabilities?"'
  • '"PoS transaction audit trails?"'
  • '"PoS batch settlement timing?"'
Featured Products / Services / Schemes
  • '"Highlighted Products insurance coverage details?"'
  • '"Highlighted Products loan-to-value ratio?"'
  • '"Featured schemes disbursement timeline?"'
e-Allahabad Bank Journey
  • '"e-Allahabad Experience customer support channels?"'
  • '"Allahabad Online Journey QR code payments?"'
  • '"Allahabad Online Journey statement download process?"'
Centralized Pension Processing Centre
  • '"Processing time for pension applications?"'
  • '"QR code payments at Payment Office?"'
  • '"Central Pension Management Centre contact details?"'

Evaluation

Metrics

Label Accuracy
all 0.975

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("kneau007/my-classifier")
# Run inference
preds = model("\"Relief Bonds redemption during OD tenure?\"")

Training Details

Training Set Metrics

Training set Min Median Max
Word count 3 5.2062 8
Label Training Sample Count
Amalgamation 14
Chief General Managers 16
Disclaimer 11
Featured Products / Services / Schemes 18
IB Loan against Sovereign Gold Bond 18
Ind Advantage (Reward Program) 19
Loan / OD against NSC / KVP / Relief bonds of RBI / LIC policies 16
Point of Sale (PoS) 16
e-Allahabad Bank Journey 15
Centralized Pension Processing Centre 17

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
  • l2_weight: 0.01
  • seed: 42
  • eval_max_steps: -1
  • load_best_model_at_end: False

Training Results

Epoch Step Training Loss Validation Loss
0.0025 1 0.172 -
0.125 50 0.1198 -
0.25 100 0.0251 -
0.375 150 0.0068 -
0.5 200 0.003 -
0.625 250 0.0018 -
0.75 300 0.0015 -
0.875 350 0.0013 -
1.0 400 0.0013 -

Framework Versions

  • Python: 3.11.11
  • SetFit: 1.1.1
  • Sentence Transformers: 3.4.1
  • Transformers: 4.48.3
  • PyTorch: 2.5.1+cu124
  • Datasets: 3.3.2
  • Tokenizers: 0.21.0

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}
}
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