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my-setfit-classifier_obscene

Text Classification
setfit
Safetensors
sentence-transformers
mpnet
generated_from_setfit_trainer
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my-setfit-classifier_obscene / 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: true
base_model: sentence-transformers/paraphrase-mpnet-base-v2

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
0
  • ', metal unless it was 70s oldskool'
  • "do whichever you think would be best then: if you rename the account, just let me know, and i'll go over there to recreate it; but if you'd prefer to rename the account, recreate it, and send me the password which i can then change, that's fine with me."
  • '" no, it was a far-too-much-of-an-in-joke on the fact that principle→principal is usually one of the first things the fa regulars jump on (along with the dreaded spaced em dash)\xa0–\xa0scent "'
1
  • "oh, no! i just read the vile diatribe you left for me on my user page. no, you get no respect you ass little shit fuck you, you're an unhappy little dick puller!"
  • 'fuck you youfuckingidiot'
  • "hey , you are a chicken shit coward i told you that everytime you had one of your administrator buddies block me, i would quickly be back on with a new ip address editing your vandalism of this article. i meant it!!! why don't you stop masturbating to wikipedia and get a real life? i told you that you don't know who you're fuck with!!!"

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_obscene")
# 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 57.2 426
Label Training Sample Count
0 10
1 10

Training Hyperparameters

  • batch_size: (1, 1)
  • num_epochs: (10, 10)
  • 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
0.0013 1 0.1758 -
0.0625 50 0.0036 -
0.125 100 0.1383 -
0.1875 150 0.0148 -
0.25 200 0.0216 -
0.3125 250 0.0001 -
0.375 300 0.0021 -
0.4375 350 0.001 -
0.5 400 0.0015 -
0.5625 450 0.0004 -
0.625 500 0.0 -
0.6875 550 0.0003 -
0.75 600 0.0 -
0.8125 650 0.0 -
0.875 700 0.0 -
0.9375 750 0.0001 -
1.0 800 0.0 -
1.0625 850 0.0 -
1.125 900 0.0002 -
1.1875 950 0.0 -
1.25 1000 0.0008 -
1.3125 1050 0.0002 -
1.375 1100 0.0 -
1.4375 1150 0.0 -
1.5 1200 0.0 -
1.5625 1250 0.0001 -
1.625 1300 0.0 -
1.6875 1350 0.0 -
1.75 1400 0.0 -
1.8125 1450 0.0 -
1.875 1500 0.0 -
1.9375 1550 0.0 -
2.0 1600 0.0 -
2.0625 1650 0.0001 -
2.125 1700 0.0001 -
2.1875 1750 0.0 -
2.25 1800 0.0001 -
2.3125 1850 0.0001 -
2.375 1900 0.0002 -
2.4375 1950 0.0 -
2.5 2000 0.0001 -
2.5625 2050 0.0001 -
2.625 2100 0.0 -
2.6875 2150 0.0001 -
2.75 2200 0.0003 -
2.8125 2250 0.0001 -
2.875 2300 0.0 -
2.9375 2350 0.0 -
3.0 2400 0.0003 -
3.0625 2450 0.0 -
3.125 2500 0.0 -
3.1875 2550 0.0 -
3.25 2600 0.0 -
3.3125 2650 0.0 -
3.375 2700 0.0001 -
3.4375 2750 0.0 -
3.5 2800 0.0 -
3.5625 2850 0.0 -
3.625 2900 0.0001 -
3.6875 2950 0.0 -
3.75 3000 0.0001 -
3.8125 3050 0.0 -
3.875 3100 0.0 -
3.9375 3150 0.0 -
4.0 3200 0.0 -
4.0625 3250 0.0 -
4.125 3300 0.0 -
4.1875 3350 0.0 -
4.25 3400 0.0 -
4.3125 3450 0.0 -
4.375 3500 0.0001 -
4.4375 3550 0.0001 -
4.5 3600 0.0 -
4.5625 3650 0.0 -
4.625 3700 0.0 -
4.6875 3750 0.0 -
4.75 3800 0.0001 -
4.8125 3850 0.0 -
4.875 3900 0.0 -
4.9375 3950 0.0 -
5.0 4000 0.0 -
5.0625 4050 0.0 -
5.125 4100 0.0 -
5.1875 4150 0.0 -
5.25 4200 0.0 -
5.3125 4250 0.0 -
5.375 4300 0.0001 -
5.4375 4350 0.0 -
5.5 4400 0.0 -
5.5625 4450 0.0 -
5.625 4500 0.0 -
5.6875 4550 0.0 -
5.75 4600 0.0 -
5.8125 4650 0.0 -
5.875 4700 0.0 -
5.9375 4750 0.0 -
6.0 4800 0.0 -
6.0625 4850 0.0 -
6.125 4900 0.0 -
6.1875 4950 0.0 -
6.25 5000 0.0 -
6.3125 5050 0.0 -
6.375 5100 0.0 -
6.4375 5150 0.0001 -
6.5 5200 0.0 -
6.5625 5250 0.0 -
6.625 5300 0.0 -
6.6875 5350 0.0 -
6.75 5400 0.0 -
6.8125 5450 0.0 -
6.875 5500 0.0 -
6.9375 5550 0.0 -
7.0 5600 0.0001 -
7.0625 5650 0.0 -
7.125 5700 0.0 -
7.1875 5750 0.0 -
7.25 5800 0.0 -
7.3125 5850 0.0 -
7.375 5900 0.0001 -
7.4375 5950 0.0 -
7.5 6000 0.0 -
7.5625 6050 0.0 -
7.625 6100 0.0 -
7.6875 6150 0.0 -
7.75 6200 0.0 -
7.8125 6250 0.0 -
7.875 6300 0.0 -
7.9375 6350 0.0 -
8.0 6400 0.0 -
8.0625 6450 0.0 -
8.125 6500 0.0 -
8.1875 6550 0.0 -
8.25 6600 0.0 -
8.3125 6650 0.0 -
8.375 6700 0.0 -
8.4375 6750 0.0 -
8.5 6800 0.0 -
8.5625 6850 0.0 -
8.625 6900 0.0 -
8.6875 6950 0.0 -
8.75 7000 0.0 -
8.8125 7050 0.0 -
8.875 7100 0.0 -
8.9375 7150 0.0 -
9.0 7200 0.0 -
9.0625 7250 0.0 -
9.125 7300 0.0 -
9.1875 7350 0.0 -
9.25 7400 0.0 -
9.3125 7450 0.0 -
9.375 7500 0.0 -
9.4375 7550 0.0 -
9.5 7600 0.0 -
9.5625 7650 0.0 -
9.625 7700 0.0 -
9.6875 7750 0.0 -
9.75 7800 0.0 -
9.8125 7850 0.0 -
9.875 7900 0.0 -
9.9375 7950 0.0 -
10.0 8000 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}
}