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setfit

Text Classification
setfit
Safetensors
sentence-transformers
bert
generated_from_setfit_trainer
Eval Results
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metadata
library_name: setfit
tags:
  - setfit
  - sentence-transformers
  - text-classification
  - generated_from_setfit_trainer
base_model: BAAI/bge-small-en-v1.5
metrics:
  - accuracy
widget:
  - text: sales affects ceo pay
  - text: time affects entrepreneurship intention
  - text: operations planning affects entrepreneurship intention
  - text: entrepreneurial self-efficacy affects entrepreneurship intention
  - text: empirical training affects entrepreneurship intention
pipeline_tag: text-classification
inference: true
model-index:
  - name: SetFit with BAAI/bge-small-en-v1.5
    results:
      - task:
          type: text-classification
          name: Text Classification
        dataset:
          name: Unknown
          type: unknown
          split: test
        metrics:
          - type: accuracy
            value: 0.9058823529411765
            name: Accuracy

SetFit with BAAI/bge-small-en-v1.5

This is a SetFit model that can be used for Text Classification. This SetFit model uses BAAI/bge-small-en-v1.5 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
1
  • 'board diversity affects ceo pay'
  • 'perceptions of formal learning affects entrepreneurship intention'
  • 'proactiveness affects entrepreneurship intention'
0
  • 'sales and takeovers affects entrepreneurship intention'
  • 'uk affects entrepreneurship intention'
  • 'economics affects entrepreneurship intention'

Evaluation

Metrics

Label Accuracy
all 0.9059

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("abehandlerorg/setfit")
# Run inference
preds = model("sales affects ceo pay")

Training Details

Training Set Metrics

Training set Min Median Max
Word count 4 5.4307 12
Label Training Sample Count
0 168
1 171

Training Hyperparameters

  • batch_size: (32, 32)
  • 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: False

Training Results

Epoch Step Training Loss Validation Loss
0.0006 1 0.3133 -
0.0277 50 0.289 -
0.0553 100 0.2506 -
0.0830 150 0.2243 -
0.1107 200 0.2388 -
0.1384 250 0.2084 -
0.1660 300 0.1316 -
0.1937 350 0.0142 -
0.2214 400 0.0065 -
0.2490 450 0.0037 -
0.2767 500 0.003 -
0.3044 550 0.002 -
0.3320 600 0.0018 -
0.3597 650 0.0026 -
0.3874 700 0.0013 -
0.4151 750 0.0012 -
0.4427 800 0.0284 -
0.4704 850 0.0145 -
0.4981 900 0.0053 -
0.5257 950 0.0075 -
0.5534 1000 0.005 -
0.5811 1050 0.0008 -
0.6087 1100 0.0008 -
0.6364 1150 0.0008 -
0.6641 1200 0.0007 -
0.6918 1250 0.0008 -
0.7194 1300 0.0009 -
0.7471 1350 0.0007 -
0.7748 1400 0.0008 -
0.8024 1450 0.0006 -
0.8301 1500 0.0006 -
0.8578 1550 0.0192 -
0.8854 1600 0.0005 -
0.9131 1650 0.002 -
0.9408 1700 0.0204 -
0.9685 1750 0.0039 -
0.9961 1800 0.0007 -
1.0238 1850 0.0005 -
1.0515 1900 0.0004 -
1.0791 1950 0.0005 -
1.1068 2000 0.0006 -
1.1345 2050 0.0004 -
1.1621 2100 0.0006 -
1.1898 2150 0.0004 -
1.2175 2200 0.0004 -
1.2452 2250 0.0018 -
1.2728 2300 0.0041 -
1.3005 2350 0.0004 -
1.3282 2400 0.0107 -
1.3558 2450 0.0005 -
1.3835 2500 0.0004 -
1.4112 2550 0.0004 -
1.4388 2600 0.0167 -
1.4665 2650 0.0068 -
1.4942 2700 0.0004 -
1.5219 2750 0.0064 -
1.5495 2800 0.0041 -
1.5772 2850 0.0004 -
1.6049 2900 0.0003 -
1.6325 2950 0.0004 -
1.6602 3000 0.0004 -
1.6879 3050 0.0003 -
1.7156 3100 0.0057 -
1.7432 3150 0.0044 -
1.7709 3200 0.0004 -
1.7986 3250 0.0166 -
1.8262 3300 0.0004 -
1.8539 3350 0.0032 -
1.8816 3400 0.0133 -
1.9092 3450 0.0003 -
1.9369 3500 0.0003 -
1.9646 3550 0.0052 -
1.9923 3600 0.0004 -
2.0199 3650 0.004 -
2.0476 3700 0.0003 -
2.0753 3750 0.0054 -
2.1029 3800 0.0057 -
2.1306 3850 0.0004 -
2.1583 3900 0.0272 -
2.1859 3950 0.0003 -
2.2136 4000 0.006 -
2.2413 4050 0.0044 -
2.2690 4100 0.0003 -
2.2966 4150 0.0167 -
2.3243 4200 0.0048 -
2.3520 4250 0.0086 -
2.3796 4300 0.0051 -
2.4073 4350 0.0003 -
2.4350 4400 0.0037 -
2.4626 4450 0.0003 -
2.4903 4500 0.0021 -
2.5180 4550 0.0003 -
2.5457 4600 0.004 -
2.5733 4650 0.0025 -
2.6010 4700 0.0003 -
2.6287 4750 0.0003 -
2.6563 4800 0.0003 -
2.6840 4850 0.0031 -
2.7117 4900 0.0168 -
2.7393 4950 0.0019 -
2.7670 5000 0.004 -
2.7947 5050 0.0003 -
2.8224 5100 0.0003 -
2.8500 5150 0.003 -
2.8777 5200 0.0003 -
2.9054 5250 0.0003 -
2.9330 5300 0.0171 -
2.9607 5350 0.0003 -
2.9884 5400 0.0162 -
3.0160 5450 0.0143 -
3.0437 5500 0.0134 -
3.0714 5550 0.0133 -
3.0991 5600 0.0003 -
3.1267 5650 0.0003 -
3.1544 5700 0.0093 -
3.1821 5750 0.0003 -
3.2097 5800 0.0003 -
3.2374 5850 0.0003 -
3.2651 5900 0.0003 -
3.2928 5950 0.0003 -
3.3204 6000 0.0029 -
3.3481 6050 0.0126 -
3.3758 6100 0.0003 -
3.4034 6150 0.0002 -
3.4311 6200 0.0003 -
3.4588 6250 0.0062 -
3.4864 6300 0.0002 -
3.5141 6350 0.0002 -
3.5418 6400 0.0003 -
3.5695 6450 0.0002 -
3.5971 6500 0.0041 -
3.6248 6550 0.0465 -
3.6525 6600 0.0148 -
3.6801 6650 0.0181 -
3.7078 6700 0.0037 -
3.7355 6750 0.0002 -
3.7631 6800 0.0003 -
3.7908 6850 0.0003 -
3.8185 6900 0.0034 -
3.8462 6950 0.0002 -
3.8738 7000 0.0148 -
3.9015 7050 0.0002 -
3.9292 7100 0.0003 -
3.9568 7150 0.0002 -
3.9845 7200 0.0003 -

Framework Versions

  • Python: 3.10.12
  • SetFit: 1.0.3
  • Sentence Transformers: 2.7.0
  • Transformers: 4.40.1
  • PyTorch: 2.2.1+cu121
  • 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}
}