metadata

library_name: setfit
tags:
  - setfit
  - sentence-transformers
  - text-classification
  - generated_from_setfit_trainer
base_model: sentence-transformers/paraphrase-mpnet-base-v2
datasets:
  - ethos
metrics:
  - accuracy
widget:
  - text: Why are niggers superior athletically to all other races?
  - text: >-
      An ugly man in a wig and dress, is still just an ugly man in a wig and
      dress... NOT A WOMAN!!!
  - text: >-
      Perfect place for Lana- China and Myanmar. No trouble there. They know how
      to handle those pigs.
  - text: >-
      No counting when Hispanics get probation if they do find them self in some
      trouble they get probation then get deported and if they come back boom
      there in violation of the probation we gave them but knowingly sent them
      back. Those rats are smart. Bunch of GOVERNMENT BULL SHIT
  - text: Most Americans are nazis or kkk members so they elected  Trump.
pipeline_tag: text-classification
inference: false
model-index:
  - name: SetFit with sentence-transformers/paraphrase-mpnet-base-v2
    results:
      - task:
          type: text-classification
          name: Text Classification
        dataset:
          name: ethos
          type: ethos
          split: test
        metrics:
          - type: accuracy
            value: 0.46524064171123
            name: Accuracy

SetFit with sentence-transformers/paraphrase-mpnet-base-v2

This is a SetFit model trained on the ethos dataset that can be used for Text Classification. This SetFit model uses sentence-transformers/paraphrase-mpnet-base-v2 as the Sentence Transformer embedding model. A OneVsRestClassifier 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/paraphrase-mpnet-base-v2
Classification head: a OneVsRestClassifier instance
Maximum Sequence Length: 512 tokens
Training Dataset: ethos

Model Sources

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

Evaluation

Metrics

Label	Accuracy
all	0.4652

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("Ankit15nov/setfit-ethos-multilabel-example")
# Run inference
preds = model("Why are niggers superior athletically to all other races?")

Training Details

Training Set Metrics

Training set	Min	Median	Max
Word count	3	27.0625	463

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
0.0063	1	0.2956	-
0.3125	50	0.1114	-
0.625	100	0.1177	-
0.9375	150	0.0695	-

Framework Versions

Python: 3.10.14
SetFit: 1.0.3
Sentence Transformers: 2.7.0
Transformers: 4.40.1
PyTorch: 2.1.0
Datasets: 2.3.2
Tokenizers: 0.19.1

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}
}