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catastrophy4

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: >-
      I wonder how times someone has wrecked trying to do the 'stare and drive'
      move from 2 Fast 2 Furious
  - text: >-
      Plains All American Pipeline company may have spilled 40% more crude oil
      than previously estimated #KSBYNews @lilitan http://t.co/PegibIqk2w
  - text: >-
      ThisIsFaz: Anti Collision Rear- #technology #cool http://t.co/KEfxTjTAKB
      Via Techesback #Tech
  - text: >-
      Official kinesiology tape of IRONMANå¨ long-lasting durability
      effectiveness on common injuries http://t.co/ejymkZPEEx
      http://t.co/0IYuntXDUv
  - text: >-
      Well as I was chaning an iPad screen it fucking exploded and glass went
      all over the place. Looks like my job is going to need a new one.
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: 0.8233459202101461
            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
  • 'FOOTBALL IS BACK THIS WEEKEND ITS JUST SUNK IN ??????'
  • 'Tried orange aftershock today. My life will never be the same'
  • "Attack on Titan game on PS Vita yay! Can't wait for 2016"
1

Evaluation

Metrics

Label Accuracy
all 0.8233

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/catastrophy4")
# Run inference
preds = model("ThisIsFaz: Anti Collision Rear- #technology #cool http://t.co/KEfxTjTAKB Via Techesback #Tech")

Training Details

Training Set Metrics

Training set Min Median Max
Word count 2 15.0486 30
Label Training Sample Count
0 836
1 686

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
0.0003 1 0.4126 -
0.0131 50 0.2779 -
0.0263 100 0.2507 -
0.0394 150 0.2475 -
0.0526 200 0.1045 -
0.0657 250 0.2595 -
0.0788 300 0.1541 -
0.0920 350 0.1761 -
0.1051 400 0.0456 -
0.1183 450 0.1091 -
0.1314 500 0.1335 -
0.1445 550 0.0956 -
0.1577 600 0.0583 -
0.1708 650 0.0067 -
0.1840 700 0.0021 -
0.1971 750 0.0057 -
0.2102 800 0.065 -
0.2234 850 0.0224 -
0.2365 900 0.0008 -
0.2497 950 0.1282 -
0.2628 1000 0.1045 -
0.2760 1050 0.001 -
0.2891 1100 0.0005 -
0.3022 1150 0.0013 -
0.3154 1200 0.0007 -
0.3285 1250 0.0015 -
0.3417 1300 0.0007 -
0.3548 1350 0.0027 -
0.3679 1400 0.0006 -
0.3811 1450 0.0001 -
0.3942 1500 0.0009 -
0.4074 1550 0.0002 -
0.4205 1600 0.0004 -
0.4336 1650 0.0003 -
0.4468 1700 0.0013 -
0.4599 1750 0.0004 -
0.4731 1800 0.0007 -
0.4862 1850 0.0001 -
0.4993 1900 0.0001 -
0.5125 1950 0.0476 -
0.5256 2000 0.0561 -
0.5388 2050 0.0009 -
0.5519 2100 0.0381 -
0.5650 2150 0.017 -
0.5782 2200 0.033 -
0.5913 2250 0.0001 -
0.6045 2300 0.0077 -
0.6176 2350 0.0002 -
0.6307 2400 0.0003 -
0.6439 2450 0.0001 -
0.6570 2500 0.0155 -
0.6702 2550 0.0002 -
0.6833 2600 0.0001 -
0.6965 2650 0.031 -
0.7096 2700 0.0215 -
0.7227 2750 0.0002 -
0.7359 2800 0.0002 -
0.7490 2850 0.0001 -
0.7622 2900 0.0001 -
0.7753 2950 0.0001 -
0.7884 3000 0.0001 -
0.8016 3050 0.0001 -
0.8147 3100 0.0001 -
0.8279 3150 0.0001 -
0.8410 3200 0.0001 -
0.8541 3250 0.0001 -
0.8673 3300 0.0001 -
0.8804 3350 0.0001 -
0.8936 3400 0.0 -
0.9067 3450 0.0156 -
0.9198 3500 0.0 -
0.9330 3550 0.0 -
0.9461 3600 0.0001 -
0.9593 3650 0.0208 -
0.9724 3700 0.0 -
0.9855 3750 0.0001 -
0.9987 3800 0.0001 -

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}
}