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job_level_model

Text Classification
setfit
Safetensors
sentence-transformers
mpnet
generated_from_setfit_trainer
Eval Results
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job_level_model / README.md
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metadata
base_model: sentence-transformers/paraphrase-mpnet-base-v2
library_name: setfit
metrics:
  - accuracy
pipeline_tag: text-classification
tags:
  - setfit
  - sentence-transformers
  - text-classification
  - generated_from_setfit_trainer
widget:
  - text: Proof Reader
  - text: product owner
  - text: chief community officer
  - text: planner
  - text: information technology administrator
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: 1
            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
3
  • 'academic head'
  • 'admin director'
  • 'admin head'
4
  • 'account director'
  • 'area vice president'
  • 'assistant chief executive officer'
2
  • 'account manager'
  • 'admin'
  • 'admin officer'
1
  • 'accountant'
  • 'administrator'
  • 'adviser'

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("setfit_model_id")
# Run inference
preds = model("planner")

Training Details

Training Set Metrics

Training set Min Median Max
Word count 1 2.1124 6
Label Training Sample Count
1 380
2 107
3 67
4 193

Training Hyperparameters

  • batch_size: (16, 16)
  • num_epochs: (3, 3)
  • 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
0.0005 1 0.2621 -
0.0268 50 0.2631 -
0.0535 100 0.2043 -
0.0803 150 0.1561 -
0.1071 200 0.203 -
0.1338 250 0.1823 -
0.1606 300 0.1082 -
0.1874 350 0.0702 -
0.2141 400 0.1159 -
0.2409 450 0.0532 -
0.2677 500 0.0767 -
0.2944 550 0.0965 -
0.3212 600 0.0479 -
0.3480 650 0.0353 -
0.3747 700 0.0235 -
0.4015 750 0.0028 -
0.4283 800 0.004 -
0.4550 850 0.0908 -
0.4818 900 0.0078 -
0.5086 950 0.0149 -
0.5353 1000 0.0841 -
0.5621 1050 0.0141 -
0.5889 1100 0.0328 -
0.6156 1150 0.0031 -
0.6424 1200 0.0027 -
0.6692 1250 0.0205 -
0.6959 1300 0.0584 -
0.7227 1350 0.002 -
0.7495 1400 0.0009 -
0.7762 1450 0.0018 -
0.8030 1500 0.001 -
0.8298 1550 0.0004 -
0.8565 1600 0.0008 -
0.8833 1650 0.0006 -
0.9101 1700 0.0021 -
0.9368 1750 0.009 -
0.9636 1800 0.0031 -
0.9904 1850 0.0024 -
1.0171 1900 0.0327 -
1.0439 1950 0.0257 -
1.0707 2000 0.0006 -
1.0974 2050 0.0009 -
1.1242 2100 0.0006 -
1.1510 2150 0.0004 -
1.1777 2200 0.0011 -
1.2045 2250 0.0004 -
1.2313 2300 0.0012 -
1.2580 2350 0.0005 -
1.2848 2400 0.0013 -
1.3116 2450 0.0007 -
1.3383 2500 0.0002 -
1.3651 2550 0.0005 -
1.3919 2600 0.0006 -
1.4186 2650 0.0006 -
1.4454 2700 0.0004 -
1.4722 2750 0.0004 -
1.4989 2800 0.0008 -
1.5257 2850 0.0003 -
1.5525 2900 0.0012 -
1.5792 2950 0.0006 -
1.6060 3000 0.0003 -
1.6328 3050 0.0002 -
1.6595 3100 0.0026 -
1.6863 3150 0.0003 -
1.7131 3200 0.0003 -
1.7398 3250 0.0003 -
1.7666 3300 0.0003 -
1.7934 3350 0.0003 -
1.8201 3400 0.0004 -
1.8469 3450 0.0003 -
1.8737 3500 0.0005 -
1.9004 3550 0.0003 -
1.9272 3600 0.0003 -
1.9540 3650 0.0002 -
1.9807 3700 0.0003 -
2.0075 3750 0.0003 -
2.0343 3800 0.0003 -
2.0610 3850 0.0002 -
2.0878 3900 0.0004 -
2.1146 3950 0.0003 -
2.1413 4000 0.0003 -
2.1681 4050 0.0002 -
2.1949 4100 0.0541 -
2.2216 4150 0.0002 -
2.2484 4200 0.0003 -
2.2752 4250 0.0582 -
2.3019 4300 0.0003 -
2.3287 4350 0.0002 -
2.3555 4400 0.0003 -
2.3822 4450 0.0005 -
2.4090 4500 0.0004 -
2.4358 4550 0.0003 -
2.4625 4600 0.0003 -
2.4893 4650 0.0002 -
2.5161 4700 0.0002 -
2.5428 4750 0.0003 -
2.5696 4800 0.0008 -
2.5964 4850 0.0002 -
2.6231 4900 0.0002 -
2.6499 4950 0.0005 -
2.6767 5000 0.0003 -
2.7034 5050 0.0002 -
2.7302 5100 0.0004 -
2.7570 5150 0.0002 -
2.7837 5200 0.0005 -
2.8105 5250 0.0004 -
2.8373 5300 0.0394 -
2.8640 5350 0.0002 -
2.8908 5400 0.0399 -
2.9176 5450 0.0002 -
2.9443 5500 0.0002 -
2.9711 5550 0.0002 -
2.9979 5600 0.0002 -

Framework Versions

  • Python: 3.10.12
  • SetFit: 1.0.3
  • Sentence Transformers: 3.0.1
  • Transformers: 4.39.0
  • PyTorch: 2.3.1+cu121
  • Datasets: 2.20.0
  • 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}
}