README.md

metadata

tags:
  - setfit
  - sentence-transformers
  - text-classification
  - generated_from_setfit_trainer
widget:
  - text: The system should be able to support x simultaneous users.
  - text: >-
      The TCS shall have an uninterrupted power supply for critical phases
      (landing and takeoff as a minimum) of mission execution.
  - text: >-
      Handheld mobile equipment shall be capable of withstanding the following
      levels of continuous sinusoidal vibration: (M)
  - text: The character set used shall support different languages.
  - text: >-
      It is the responsibility of each individual GSM-R operator to acquire a
      public domain NDC from their national telecommunications regulator. (I)
metrics:
  - accuracy
pipeline_tag: text-classification
library_name: setfit
inference: false
model-index:
  - name: SetFit
    results:
      - task:
          type: text-classification
          name: Text Classification
        dataset:
          name: Unknown
          type: unknown
          split: test
        metrics:
          - type: accuracy
            value: 0.7016574585635359
            name: Accuracy

SetFit

This is a SetFit model that can be used for Text Classification. A ClassifierChain 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 Type: SetFit
• Classification head: a ClassifierChain instance
• Maximum Sequence Length: 384 tokens

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	0.7017

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("Hulyyy/req-quality-setfit-32")
# Run inference
preds = model("The character set used shall support different languages.")

Training Details

Training Set Metrics

Training set	Min	Median	Max
Word count	4	17.9190	26

Framework Versions

• Python: 3.13.7
• SetFit: 1.1.3
• Sentence Transformers: 5.1.1
• Transformers: 4.57.0
• PyTorch: 2.8.0+cu129
• Datasets: 4.2.0
• Tokenizers: 0.22.1

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}
}