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pEpOo
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catastrophy6

Text Classification
setfit
Safetensors
sentence-transformers
mpnet
generated_from_setfit_trainer
Eval Results
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SetFit with sentence-transformers/all-mpnet-base-v2

This is a SetFit model that can be used for Text Classification. This SetFit model uses sentence-transformers/all-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
1
  • 'Police officer wounded suspect dead after exchanging shots: RICHMOND Va. (AP) \x89ÛÓ A Richmond police officer wa... http://t.co/Y0qQS2L7bS'
  • "There's a weird siren going off here...I hope Hunterston isn't in the process of blowing itself to smithereens..."
  • 'Iranian warship points weapon at American helicopter... http://t.co/cgFZk8Ha1R'

Evaluation

Metrics

Label Accuracy
all 0.8058

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("pEpOo/catastrophy6")
# Run inference
preds = model("SHOUOUT TO @kasad1lla CAUSE HER VOCALS ARE BLAZING HOT LIKE THE WEATHER SHES IN")

Training Details

Training Set Metrics

Training set Min Median Max
Word count 1 14.7175 54
Label Training Sample Count
0 1335
1 948

Training Hyperparameters

  • batch_size: (16, 16)
  • 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
0.0094 1 0.0044 -
0.4717 50 0.005 -
0.9434 100 0.0007 -
0.0002 1 0.4675 -
0.0088 50 0.3358 -
0.0175 100 0.2516 -
0.0263 150 0.2158 -
0.0350 200 0.1924 -
0.0438 250 0.1907 -
0.0526 300 0.2166 -
0.0613 350 0.2243 -
0.0701 400 0.0644 -
0.0788 450 0.1924 -
0.0876 500 0.166 -
0.0964 550 0.2117 -
0.1051 600 0.0793 -
0.1139 650 0.0808 -
0.1226 700 0.1183 -
0.1314 750 0.0808 -
0.1402 800 0.0194 -
0.1489 850 0.0699 -
0.1577 900 0.0042 -
0.1664 950 0.0048 -
0.1752 1000 0.1886 -
0.1840 1050 0.0008 -
0.1927 1100 0.0033 -
0.2015 1150 0.0361 -
0.2102 1200 0.12 -
0.2190 1250 0.0035 -
0.2278 1300 0.0002 -
0.2365 1350 0.0479 -
0.2453 1400 0.0568 -
0.2540 1450 0.0004 -
0.2628 1500 0.0002 -
0.2715 1550 0.0013 -
0.2803 1600 0.0005 -
0.2891 1650 0.0014 -
0.2978 1700 0.0004 -
0.3066 1750 0.0008 -
0.3153 1800 0.0616 -
0.3241 1850 0.0003 -
0.3329 1900 0.001 -
0.3416 1950 0.0581 -
0.3504 2000 0.0657 -
0.3591 2050 0.0584 -
0.3679 2100 0.0339 -
0.3767 2150 0.0081 -
0.3854 2200 0.0001 -
0.3942 2250 0.0009 -
0.4029 2300 0.0018 -
0.4117 2350 0.0001 -
0.4205 2400 0.0012 -
0.4292 2450 0.0001 -
0.4380 2500 0.0003 -
0.4467 2550 0.0035 -
0.4555 2600 0.0172 -
0.4643 2650 0.0383 -
0.4730 2700 0.0222 -
0.4818 2750 0.0013 -
0.4905 2800 0.0007 -
0.4993 2850 0.0003 -
0.5081 2900 0.1247 -
0.5168 2950 0.023 -
0.5256 3000 0.0002 -
0.5343 3050 0.0002 -
0.5431 3100 0.0666 -
0.5519 3150 0.0002 -
0.5606 3200 0.0003 -
0.5694 3250 0.0012 -
0.5781 3300 0.0085 -
0.5869 3350 0.0003 -
0.5957 3400 0.0002 -
0.6044 3450 0.0004 -
0.6132 3500 0.013 -
0.6219 3550 0.0089 -
0.6307 3600 0.0001 -
0.6395 3650 0.0002 -
0.6482 3700 0.0039 -
0.6570 3750 0.0031 -
0.6657 3800 0.0009 -
0.6745 3850 0.0002 -
0.6833 3900 0.0002 -
0.6920 3950 0.0001 -
0.7008 4000 0.0 -
0.7095 4050 0.0212 -
0.7183 4100 0.0001 -
0.7270 4150 0.0586 -
0.7358 4200 0.0001 -
0.7446 4250 0.0003 -
0.7533 4300 0.0126 -
0.7621 4350 0.0001 -
0.7708 4400 0.0001 -
0.7796 4450 0.0001 -
0.7884 4500 0.0 -
0.7971 4550 0.0002 -
0.8059 4600 0.0002 -
0.8146 4650 0.0001 -
0.8234 4700 0.0035 -
0.8322 4750 0.0002 -
0.8409 4800 0.0002 -
0.8497 4850 0.0001 -
0.8584 4900 0.0001 -
0.8672 4950 0.0001 -
0.8760 5000 0.0003 -
0.8847 5050 0.0 -
0.8935 5100 0.0041 -
0.9022 5150 0.0001 -
0.9110 5200 0.0001 -
0.9198 5250 0.0001 -
0.9285 5300 0.0001 -
0.9373 5350 0.0001 -
0.9460 5400 0.0001 -
0.9548 5450 0.0001 -
0.9636 5500 0.0001 -
0.9723 5550 0.0001 -
0.9811 5600 0.0002 -
0.9898 5650 0.0271 -
0.9986 5700 0.0 -

Framework Versions

  • Python: 3.10.12
  • SetFit: 1.0.1
  • Sentence Transformers: 2.2.2
  • Transformers: 4.35.2
  • PyTorch: 2.1.0+cu121
  • Datasets: 2.15.0
  • Tokenizers: 0.15.0

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}
}
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