fairness_model / README.md
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metadata
library_name: setfit
tags:
  - setfit
  - sentence-transformers
  - text-classification
  - generated_from_setfit_trainer
metrics:
  - accuracy
widget:
  - text: >-
      first: We recommend self-help books on conflict resolution, available in
      our office library, as supplemental resources. second: Our company
      conducts regular surveys to identify and address recurring disputes.
  - text: >-
      first: Conflict Resolution Apps: We offer technology solutions for
      reporting and tracking conflicts. second: Employees can request a mediator
      to assist in resolving issues with their supervisor, ensuring fair dispute
      resolution.
  - text: >-
      first: Our organization encourages employees to participate in leadership
      development programs, enhancing their ability to interact with
      supervisors. second: Conflict Simulation Exercises: Role-playing helps
      employees practice resolving conflicts.
  - text: >-
      first: Mediation sessions are scheduled outside of regular working hours
      for convenience. second: Employee Conflict Coaches: Coaches work
      one-on-one with employees to resolve disputes.
  - text: >-
      first: We provide conflict resolution pamphlets in the breakroom, offering
      helpful tips. second: We provide resources for employees to seek external
      mediation or counseling services if disputes with supervisors persist.
pipeline_tag: text-classification
inference: true
base_model: sentence-transformers/all-MiniLM-L6-v2
model-index:
  - name: SetFit with sentence-transformers/all-MiniLM-L6-v2
    results:
      - task:
          type: text-classification
          name: Text Classification
        dataset:
          name: Unknown
          type: unknown
          split: test
        metrics:
          - type: accuracy
            value: 0.4090909090909091
            name: Accuracy

SetFit with sentence-transformers/all-MiniLM-L6-v2

This is a SetFit model that can be used for Text Classification. This SetFit model uses sentence-transformers/all-MiniLM-L6-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
  • 'first: Employee Support Groups: Peer-led support groups for employees facing similar issues. second: We offer conflict resolution workshops to provide employees with valuable skills.'
1
  • 'first: Conflict Resolution Peer Mentoring: Experienced employees mentor newcomers in conflict resolution. second: Diversity and Inclusion Training: Programs that promote understanding and reduce conflicts related to diversity.'

Evaluation

Metrics

Label Accuracy
all 0.4091

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("diegofiggie/fairness_model")
# Run inference
preds = model("first: Mediation sessions are scheduled outside of regular working hours for convenience. second: Employee Conflict Coaches: Coaches work one-on-one with employees to resolve disputes.")

Training Details

Training Set Metrics

Training set Min Median Max
Word count 24 25.5 27
Label Training Sample Count
0 1
1 1

Training Hyperparameters

  • batch_size: (4, 4)
  • num_epochs: (1, 1)
  • max_steps: -1
  • sampling_strategy: oversampling
  • num_iterations: 30
  • 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
0.0333 1 0.2322 -

Framework Versions

  • Python: 3.10.9
  • SetFit: 1.0.3
  • Sentence Transformers: 2.4.0
  • Transformers: 4.38.1
  • PyTorch: 2.2.1+cpu
  • Datasets: 2.17.1
  • Tokenizers: 0.15.2

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}
}