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Add SetFit model
c221563
metadata
library_name: setfit
tags:
  - setfit
  - sentence-transformers
  - text-classification
  - generated_from_setfit_trainer
metrics:
  - accuracy
widget:
  - text: >-
      receive upi mandate collect request marg techno project private limit inr
      15000.00. log google pay app authorize - axis bank
  - text: >-
      sep-23 statement credit card x6343 total due : inr 5575.55 min due : inr
      4811.55 due date : 08-oct-23 . pay www.kotak.com/rd/ccpymt - kotak bank
  - text: '< # > use otp : 8233 login turtlemintpro zck+rfoaqnm'
  - text: >-
      arrive today : please use otp-550041 carefully read instructions secure
      amazon package ( id : sptp719784310 )
  - text: >-
      a/c xxx51941 credit rs 132.00 12-08-2023 - fd1186130010001148int:132.00
      tax:0.00. a/c balance rs 67022.91 .please call 18002082121 query . ujjivan
      sfb
pipeline_tag: text-classification
inference: true
base_model: sentence-transformers/all-mpnet-base-v2
model-index:
  - name: SetFit with sentence-transformers/all-mpnet-base-v2
    results:
      - task:
          type: text-classification
          name: Text Classification
        dataset:
          name: Unknown
          type: unknown
          split: test
        metrics:
          - type: accuracy
            value: 0.9722222222222222
            name: Accuracy

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

This is a SetFit model that can be used for Text Classification. This SetFit model uses sentence-transformers/all-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
2
  • 'validity airtel xstream fiber id 20001896982 expire 04-sep-23 . please recharge rs 589 enjoy uninterrupted service . recharge , click www.airtel.in/5/c_summary ? n=021710937343_dsl . please ignore already pay .'
  • 'initiate process add a/c . xxxx59 upi app - axis bank'
  • 'google-pay registration initiate icici bank . do , report bank . card details/otp/cvv secret . disclose anyone .'
0
  • 'rs 260.00 debit a/c xxxxxx7783 credit krjngm @ oksbi upi ref:325154274303. ? call 18005700 -bob'
  • 'send rs.400.00 kotak bank ac x4524 7800600122 @ ybl 15-10-23.upi ref 328855774953. , kotak.com/fraud'
  • 'send rs.400.00 kotak bank ac x4524 7800600122 @ ybl 15-10-23.upi ref 328855774953. , kotak.com/fraud'
1
  • 'dear bob upi user , account credit inr 50.00 date 2023-08-27 11:41:09 upi ref 360562629741 - bob'
  • 'receive rs.10000.00 kotak bank ac x4524 mahimagyamlani08 @ okaxis 21-08-23.bal:197,838.14.upi ref:323334598750'
  • 'update ! inr5.66 credit federal bank account xxxx9374 jupiter app . happy bank !'

Evaluation

Metrics

Label Accuracy
all 0.9722

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("vipinbansal179/SetFit_sms_Analyzer5c95292")
# Run inference
preds = model("< # > use otp : 8233 login turtlemintpro zck+rfoaqnm")

Training Details

Training Set Metrics

Training set Min Median Max
Word count 5 20.5357 35
Label Training Sample Count
0 31
1 28
2 81

Training Hyperparameters

  • batch_size: (16, 16)
  • num_epochs: (4, 4)
  • 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
0.0014 1 0.2939 -
0.0708 50 0.1698 -
0.1416 100 0.0557 -
0.2125 150 0.0614 -
0.2833 200 0.0099 -
0.3541 250 0.0005 -
0.4249 300 0.0002 -
0.4958 350 0.0001 -
0.5666 400 0.0001 -
0.6374 450 0.0001 -
0.7082 500 0.0001 -
0.7790 550 0.0001 -
0.8499 600 0.0002 -
0.9207 650 0.0001 -
0.9915 700 0.0001 -
1.0 706 - 0.0312
1.0623 750 0.0001 -
1.1331 800 0.0001 -
1.2040 850 0.0001 -
1.2748 900 0.0 -
1.3456 950 0.0001 -
1.4164 1000 0.0 -
1.4873 1050 0.0 -
1.5581 1100 0.0 -
1.6289 1150 0.0 -
1.6997 1200 0.0 -
1.7705 1250 0.0 -
1.8414 1300 0.0001 -
1.9122 1350 0.0 -
1.9830 1400 0.0001 -
2.0 1412 - 0.0366
2.0538 1450 0.0 -
2.1246 1500 0.0001 -
2.1955 1550 0.0 -
2.2663 1600 0.0 -
2.3371 1650 0.0 -
2.4079 1700 0.0 -
2.4788 1750 0.0 -
2.5496 1800 0.0 -
2.6204 1850 0.0 -
2.6912 1900 0.0 -
2.7620 1950 0.0 -
2.8329 2000 0.0 -
2.9037 2050 0.0 -
2.9745 2100 0.0 -
3.0 2118 - 0.0414
3.0453 2150 0.0 -
3.1161 2200 0.0 -
3.1870 2250 0.0 -
3.2578 2300 0.0 -
3.3286 2350 0.0 -
3.3994 2400 0.0 -
3.4703 2450 0.0 -
3.5411 2500 0.0 -
3.6119 2550 0.0 -
3.6827 2600 0.0 -
3.7535 2650 0.0 -
3.8244 2700 0.0 -
3.8952 2750 0.0 -
3.9660 2800 0.0 -
4.0 2824 - 0.0366
  • The bold row denotes the saved checkpoint.

Framework Versions

  • Python: 3.10.12
  • SetFit: 1.0.1
  • Sentence Transformers: 2.2.2
  • Transformers: 4.35.2
  • PyTorch: 2.1.0+cu121
  • Datasets: 2.16.0
  • Tokenizers: 0.15.0

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}
}