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my-setfit-classifier

Text Classification
setfit
Safetensors
sentence-transformers
mpnet
generated_from_setfit_trainer
Eval Results
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my-setfit-classifier / README.md
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metadata
library_name: setfit
tags:
  - setfit
  - sentence-transformers
  - text-classification
  - generated_from_setfit_trainer
metrics:
  - accuracy
widget:
  - text: >-
      is completely right on this. carnildo’s comment is just a waste of space.
      176.12.107.140
  - text: >-
      "   please do not vandalize pages, as you did with this edit to bella
      swan. if you continue to do so, you will be blocked from editing.   (talk)
      "
  - text: >-
      ipv6   mirc doesn't natively supports ipv6 protocols. it could be enabled
      by adding a external dll plugin who will enable a special protocol for dns
      and connecting to ipv6 servers.
  - text: >-
      "   link   thanks for fixing that disambiguation link on usher's album )
      flash; "
  - text: >-
      |b-class-1= yes  |b-class-2= yes  |b-class-3= yes  |b-class-4= yes 
      |b-class-5= yes
pipeline_tag: text-classification
inference: false
base_model: sentence-transformers/paraphrase-mpnet-base-v2
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: 0.8041298489691044
            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 OneVsRestClassifier 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 0.8041

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("waterabbit114/my-setfit-classifier")
# Run inference
preds = model("\"   link   thanks for fixing that disambiguation link on usher's album ) flash; \"")

Training Details

Training Set Metrics

Training set Min Median Max
Word count 3 69.1481 898

Training Hyperparameters

  • batch_size: (1, 1)
  • num_epochs: (1, 1)
  • 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 0.2094 -
0.0231 50 0.033 -
0.0463 100 0.0439 -
0.0694 150 0.001 -
0.0926 200 0.0245 -
0.1157 250 0.0008 -
0.1389 300 0.0001 -
0.1620 350 0.0 -
0.1852 400 0.0012 -
0.2083 450 0.0 -
0.2315 500 0.0002 -
0.2546 550 0.0006 -
0.2778 600 0.002 -
0.3009 650 0.0044 -
0.3241 700 0.0015 -
0.3472 750 0.0007 -
0.3704 800 0.0001 -
0.3935 850 0.0001 -
0.4167 900 0.0001 -
0.4398 950 0.0004 -
0.4630 1000 0.0001 -
0.4861 1050 0.0001 -
0.5093 1100 0.0 -
0.5324 1150 0.0052 -
0.5556 1200 0.0002 -
0.5787 1250 0.0 -
0.6019 1300 0.0003 -
0.625 1350 0.0 -
0.6481 1400 0.0001 -
0.6713 1450 0.0 -
0.6944 1500 0.0 -
0.7176 1550 0.0 -
0.7407 1600 0.0002 -
0.7639 1650 0.0001 -
0.7870 1700 0.0011 -
0.8102 1750 0.0001 -
0.8333 1800 0.0 -
0.8565 1850 0.0001 -
0.8796 1900 0.0006 -
0.9028 1950 0.0002 -
0.9259 2000 0.0002 -
0.9491 2050 0.0 -
0.9722 2100 0.0001 -
0.9954 2150 0.0 -

Framework Versions

  • Python: 3.11.7
  • SetFit: 1.0.3
  • Sentence Transformers: 2.2.2
  • Transformers: 4.35.2
  • PyTorch: 2.1.1+cu121
  • Datasets: 2.14.5
  • Tokenizers: 0.15.1

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}
}