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PolyQual-3

Text Classification
setfit
Safetensors
sentence-transformers
mpnet
generated_from_setfit_trainer
text-embeddings-inference
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metadata 
tags:
  - setfit
  - sentence-transformers
  - text-classification
  - generated_from_setfit_trainer
widget:
  - text: 'Trump stated he wanted to stockpile 1% of all BTC. '
  - text: about what nigga
  - text: hehe panicked yield chaser exiting
  - text: >-
      Haarland should win the only reason he doesnt is because his international
      team is shit
  - text: Lol prove it
metrics:
  - name: Macro F1
    type: f1_macro
    value: 0.6928
  - name: Accuracy
    type: accuracy
    value: 0.7607
  - name: F1 NOISE
    type: f1_noise
    value: 0.8366
  - name: Precision NOISE
    type: precision_noise
    value: 0.8421
  - name: Recall NOISE
    type: recall_noise
    value: 0.8312
  - name: F1 META
    type: f1_meta
    value: 0.5238
  - name: Precision META
    type: precision_meta
    value: 0.5
  - name: Recall META
    type: recall_meta
    value: 0.55
  - name: F1 SUBSTANTIVE
    type: f1_substantive
    value: 0.7179
  - name: Precision SUBSTANTIVE
    type: precision_substantive
    value: 0.7368
  - name: Recall SUBSTANTIVE
    type: recall_substantive
    value: 0.7
pipeline_tag: text-classification
library_name: setfit
inference: true
base_model: sentence-transformers/all-mpnet-base-v2

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
  • 'maybe coffeezilla was right that Polymarket is just a tool for insiders to make money....'
  • 'Coping yes kids in chat '
  • 'The most common with ABOP'
1
2
  • 'Somewhere in GOP 1-64 is where I think it'll end up. Feel good about PA, GA, and NC. Normally Trump underpolls big time in Wisconsin, but recent statewide elections there don't look good for Trump. Michigan was a fluke in 2016, when Dems are focused that state is so hard to flip. AZ is full of McCain & Flake "cuck" Republicans so don't feel good there about Trump's chances, NV is a better flip opportunity in the Southwest imho.'
  • 'According to multiple sources, President Joe Biden signed a bill to avoid a government shutdown on December 20, 2024. This action was reported across various news outlets and social media: The Senate passed a stopgap funding bill shortly after the midnight funding deadline, and the House had passed it earlier that evening. President Biden was set to sign this legislation, which would extend government funding into March and include provisions for disaster relief and farm aid. Posts on X also confirmed that the bill was passed by Congress and sent to President Biden for signing, with the explicit mention that it averted a shutdown. These sources collectively indicate that the signing took place on December 20, 2024.'
  • "Looks like he's about to do it https://apnews.com/article/trump-deportees-el-salvador-contempt-boasberg-da282511ac6f5c8dd19af620995ca440"

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("gehaustein/PolyQual-3")
# Run inference
preds = model("Lol prove it")

Training Details

Training Set Metrics

Training set Min Median Max
Word count 1 22.3420 199
Label Training Sample Count
0 307
1 307
2 307

Training Hyperparameters

  • batch_size: (8, 8)
  • num_epochs: (1, 1)
  • max_steps: -1
  • sampling_strategy: oversampling
  • num_iterations: 20
  • body_learning_rate: (2e-05, 1e-05)
  • head_learning_rate: 0.01
  • loss: CosineSimilarityLoss
  • distance_metric: cosine_distance
  • margin: 0.25
  • end_to_end: False
  • use_amp: False
  • warmup_proportion: 0.1
  • l2_weight: 0.01
  • seed: 42
  • eval_max_steps: -1
  • load_best_model_at_end: True

Training Results

Epoch Step Training Loss Validation Loss
0.0002 1 0.6034 -
0.0109 50 0.3441 0.3746
0.0217 100 0.3198 0.3002
0.0326 150 0.2498 0.2823
0.0434 200 0.2468 0.2755
0.0543 250 0.2242 0.2678
0.0651 300 0.174 0.2492
0.0760 350 0.1182 0.2157
0.0869 400 0.0824 0.2100
0.0977 450 0.0433 0.2346
0.1086 500 0.0248 0.2168
0.1194 550 0.0183 0.2211

Framework Versions

  • Python: 3.12.13
  • SetFit: 1.1.3
  • Sentence Transformers: 5.3.0
  • Transformers: 4.49.0
  • PyTorch: 2.10.0+cu128
  • Datasets: 4.0.0
  • Tokenizers: 0.21.4

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}
}