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catastrophy

Text Classification
setfit
Safetensors
sentence-transformers
mpnet
generated_from_setfit_trainer
Eval Results
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metadata
library_name: setfit
tags:
  - setfit
  - sentence-transformers
  - text-classification
  - generated_from_setfit_trainer
metrics:
  - accuracy
widget:
  - text: >-
      The best thing about this is it drowned out the call from the guy angry
      cause he hadn't gotten a tracking number... http://t.co/QYu8grOrQ1
  - text: >-
      http://t.co/a0v1ybySOD Its the best time of day!! åÊ @Siren_Voice is
      #liveonstreamate!
  - text: >-
      16yr old PKK suicide bomber who detonated bomb in Turkey Army trench
      released http://t.co/mMkLapX2ok
  - text: >-
      #hot  Reddit's new content policy goes into effect many horrible
      subreddits banned or quarantined http://t.co/HqdCZzdmbN #prebreak #best
  - text: >-
      Heat wave warning aa? Ayyo dei. Just when I plan to visit friends after a
      year.
pipeline_tag: text-classification
inference: true
base_model: sentence-transformers/all-mpnet-base-v2
model-index:
  - name: SetFit with sentence-transformers/all-mpnet-base-v2
    results:
      - task:
          type: text-classification
          name: Text Classification
        dataset:
          name: Unknown
          type: unknown
          split: test
        metrics:
          - type: accuracy
            value: 0.8098990736900318
            name: Accuracy

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
  • 'peanut butter cookie dough blizzard is ??????????????????????'
  • 'Free Ebay Sniping RT? http://t.co/B231Ul1O1K Lumbar Extender Back Stretcher Excellent Condition!! ?Please Favorite & Share'
  • "'13 M. Chapoutier Crozes Hermitage so much purple violets slate crushed gravel white pepper. Yum #france #wine #DC http://t.co/skvWN38HZ7"
1
  • 'DUST IN THE WIND: @82ndABNDIV paratroopers move to a loading zone during a dust storm in support of Operation Fury: http://t.co/uGesKLCn8M'
  • 'Delhi Government to Provide Free Treatment to Acid Attack Victims in Private Hospitals http://t.co/H6PM1W7elL'
  • 'National Briefing

Evaluation

Metrics

Label Accuracy
all 0.8099

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/catastrophy")
# Run inference
preds = model("Heat wave warning aa? Ayyo dei. Just when I plan to visit friends after a year.")

Training Details

Training Set Metrics

Training set Min Median Max
Word count 2 15.3737 31
Label Training Sample Count
0 222
1 158

Training Hyperparameters

  • batch_size: (8, 8)
  • 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 0.3038 -
0.0263 50 0.1867 -
0.0526 100 0.2578 -
0.0789 150 0.2298 -
0.1053 200 0.1253 -
0.1316 250 0.0446 -
0.1579 300 0.1624 -
0.1842 350 0.0028 -
0.2105 400 0.0059 -
0.2368 450 0.0006 -
0.2632 500 0.0287 -
0.2895 550 0.003 -
0.3158 600 0.0004 -
0.3421 650 0.0014 -
0.3684 700 0.0002 -
0.3947 750 0.0001 -
0.4211 800 0.0002 -
0.4474 850 0.0002 -
0.4737 900 0.0002 -
0.5 950 0.0826 -
0.5263 1000 0.0002 -
0.5526 1050 0.0001 -
0.5789 1100 0.0003 -
0.6053 1150 0.0303 -
0.6316 1200 0.0001 -
0.6579 1250 0.0 -
0.6842 1300 0.0001 -
0.7105 1350 0.0 -
0.7368 1400 0.0001 -
0.7632 1450 0.0002 -
0.7895 1500 0.0434 -
0.8158 1550 0.0001 -
0.8421 1600 0.0 -
0.8684 1650 0.0001 -
0.8947 1700 0.0001 -
0.9211 1750 0.0001 -
0.9474 1800 0.0001 -
0.9737 1850 0.0001 -
1.0 1900 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}
}