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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: >-
      "larpas" is liquidating all his millions of YES Trump and NO Harris. He is
      still 1.2 million USD to go.
  - text: Lol prove it
metrics:
  - name: Macro F1
    type: f1_macro
    value: 0.8245
  - name: Accuracy
    type: accuracy
    value: 0.8376
  - name: F1 NOISE
    type: f1_noise
    value: 0.8725
  - name: Precision NOISE
    type: precision_noise
    value: 0.9028
  - name: Recall NOISE
    type: recall_noise
    value: 0.8442
  - name: F1 CONTRIBUTING
    type: f1_contributing
    value: 0.7765
  - name: Precision CONTRIBUTING
    type: precision_contributing
    value: 0.7333
  - name: Recall CONTRIBUTING
    type: recall_contributing
    value: 0.825
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:

Fine-tuning a Sentence Transformer with contrastive learning.
Training a classification head with features from the fine-tuned Sentence Transformer.

Model Details

Model Description

Model Type: SetFit
Sentence Transformer body: sentence-transformers/all-mpnet-base-v2
Classification head: a LogisticRegression instance
Maximum Sequence Length: 384 tokens
Number of Classes: 2 classes

Model Sources

Repository: SetFit on GitHub
Paper: Efficient Few-Shot Learning Without Prompts
Blogpost: SetFit: Efficient Few-Shot Learning Without Prompts

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	"Just dispute the market even though rules always mislead sometimes others can't understand English." 'I sold some of this' "Yes you're missing something, if the GOP wins all swing states they win by between 65-104. They have to also win virginia or minnesota to get more than that"

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/polymarket-comments-binary")
# Run inference
preds = model("Lol prove it")

Training Details

Training Set Metrics

Training set	Min	Median	Max
Word count	1	16.6287	117

Label	Training Sample Count
0	307
1	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
0.0003	1	0.4909	-
0.0163	50	0.3797	0.3548
0.0326	100	0.2726	0.2727
0.0489	150	0.2527	0.2652
0.0651	200	0.2342	0.2476
0.0814	250	0.1839	0.2088
0.0977	300	0.0915	0.2271
0.1140	350	0.0417	0.2716
0.1303	400	0.0197	0.3131

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}
}