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johnpaulbin
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toxicity-setfit-3-large

Text Classification
setfit
Safetensors
sentence-transformers
modernbert
generated_from_setfit_trainer
Eval Results (legacy)
text-embeddings-inference
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metadata 
tags:
  - setfit
  - sentence-transformers
  - text-classification
  - generated_from_setfit_trainer
metrics:
  - accuracy
pipeline_tag: text-classification
library_name: setfit
inference: true
base_model: jhu-clsp/mmBERT-base
model-index:
  - name: SetFit with jhu-clsp/mmBERT-base
    results:
      - task:
          type: text-classification
          name: Text Classification
        dataset:
          name: Unknown
          type: unknown
          split: test
        metrics:
          - type: accuracy
            value: 0.9878113407525172
            name: Accuracy

SetFit with jhu-clsp/mmBERT-base

This is a SetFit model that can be used for Text Classification. This SetFit model uses jhu-clsp/mmBERT-base 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

Evaluation

Metrics

Label Accuracy
all 0.9878

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("johnpaulbin/toxicity-setfit-3-large")
# Run inference
preds = model("just here")

Training Details

Training Set Metrics

Training set Min Median Max
Word count 1 4.8969 81
Label Training Sample Count
not toxic 9846
toxic 7132

Training Hyperparameters

  • batch_size: (128, 128)
  • num_epochs: (1, 1)
  • max_steps: -1
  • sampling_strategy: num_iterations
  • num_iterations: 8
  • 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: True
  • warmup_proportion: 0.1
  • l2_weight: 0.01
  • seed: 42
  • eval_max_steps: -1
  • load_best_model_at_end: True

Training Results

Epoch Step Training Loss Validation Loss
0.0005 1 0.3202 -
0.0236 50 0.2709 -
0.0471 100 0.1783 -
0.0707 150 0.0719 -
0.0942 200 0.0453 -
0.1178 250 0.0266 -
0.1413 300 0.0175 -
0.1649 350 0.0158 -
0.1884 400 0.0102 -
0.2120 450 0.0075 -
0.2355 500 0.0072 -
0.2591 550 0.0064 -
0.2826 600 0.0041 -
0.3062 650 0.0028 -
0.3297 700 0.003 -
0.3533 750 0.0029 -
0.3768 800 0.0026 -
0.4004 850 0.0028 -
0.4239 900 0.0026 -
0.4475 950 0.0023 -
0.4710 1000 0.002 -
0.4946 1050 0.0009 -
0.5181 1100 0.0018 -
0.5417 1150 0.0008 -
0.5652 1200 0.0011 -
0.5888 1250 0.0019 -
0.6123 1300 0.001 -
0.6359 1350 0.0005 -
0.6594 1400 0.0012 -
0.6830 1450 0.0013 -
0.7065 1500 0.0003 -
0.7301 1550 0.0008 -
0.7537 1600 0.001 -
0.7772 1650 0.0009 -
0.8008 1700 0.001 -
0.8243 1750 0.0007 -
0.8479 1800 0.0009 -
0.8714 1850 0.0005 -
0.8950 1900 0.0005 -
0.9185 1950 0.0004 -
0.9421 2000 0.0004 -
0.9656 2050 0.0004 -
0.9892 2100 0.0011 -
1.0 2123 - 0.0231

Framework Versions

  • Python: 3.12.12
  • SetFit: 1.2.0.dev0
  • Sentence Transformers: 5.2.0
  • Transformers: 4.57.3
  • PyTorch: 2.9.0+cu126
  • Datasets: 4.0.0
  • Tokenizers: 0.22.2

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}
}