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Zlovoblachko
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dimension1_setfit

Text Classification
setfit
Safetensors
sentence-transformers
bert
generated_from_setfit_trainer
Eval Results
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dimension1_setfit / README.md
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metadata
base_model: sentence-transformers/all-MiniLM-L6-v2
library_name: setfit
metrics:
  - f1
pipeline_tag: text-classification
tags:
  - setfit
  - sentence-transformers
  - text-classification
  - generated_from_setfit_trainer
widget: []
inference: true
model-index:
  - name: SetFit with sentence-transformers/all-MiniLM-L6-v2
    results:
      - task:
          type: text-classification
          name: Text Classification
        dataset:
          name: Unknown
          type: unknown
          split: test
        metrics:
          - type: f1
            value: 0.7727272727272727
            name: F1

SetFit with sentence-transformers/all-MiniLM-L6-v2

This is a SetFit model that can be used for Text Classification. This SetFit model uses sentence-transformers/all-MiniLM-L6-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

Evaluation

Metrics

Label F1
all 0.7727

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("Zlovoblachko/dimension1_setfit")
# Run inference
preds = model("I loved the spiderman movie!")

Training Details

Training Hyperparameters

  • batch_size: (16, 16)
  • num_epochs: (1, 1)
  • max_steps: -1
  • sampling_strategy: oversampling
  • body_learning_rate: (4.4226261631087265e-05, 4.4226261631087265e-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
  • l2_weight: 0.01
  • seed: 42
  • eval_max_steps: -1
  • load_best_model_at_end: False

Training Results

Epoch Step Training Loss Validation Loss
0.0006 1 0.2748 -
0.0280 50 0.2678 -
0.0559 100 0.2688 -
0.0839 150 0.2709 -
0.1119 200 0.2656 -
0.1398 250 0.259 -
0.1678 300 0.2565 -
0.1957 350 0.2655 -
0.2237 400 0.2737 -
0.2517 450 0.2501 -
0.2796 500 0.2512 -
0.3076 550 0.2381 -
0.3356 600 0.2568 -
0.3635 650 0.2642 -
0.3915 700 0.2743 -
0.4195 750 0.2635 -
0.4474 800 0.263 -
0.4754 850 0.2541 -
0.5034 900 0.2492 -
0.5313 950 0.26 -
0.5593 1000 0.257 -
0.5872 1050 0.2525 -
0.6152 1100 0.2594 -
0.6432 1150 0.2656 -
0.6711 1200 0.2737 -
0.6991 1250 0.2683 -
0.7271 1300 0.259 -
0.7550 1350 0.2617 -
0.7830 1400 0.294 -
0.8110 1450 0.2446 -
0.8389 1500 0.2618 -
0.8669 1550 0.2562 -
0.8949 1600 0.264 -
0.9228 1650 0.2534 -
0.9508 1700 0.2484 -
0.9787 1750 0.2666 -

Framework Versions

  • Python: 3.10.12
  • SetFit: 1.1.0
  • Sentence Transformers: 3.2.1
  • Transformers: 4.44.2
  • PyTorch: 2.5.0+cu121
  • Datasets: 3.0.2
  • 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}
}