setfit safetensors bert sentence-transformers text-classification generated_from_setfit_trainer arxiv:2209.11055 region:us
---
library_name: setfit
metrics:
- accuracy
pipeline_tag: text-classification
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: '(a) The enterprise fund may be used to cover closure costs only for major
    waste tire facilities operated by government agencies. (b) The enterprise fund
    shall dedicate its revenue exclusively or with exclusive first priority to financing
    closure activities. (c) The enterprise fund shall be established and the documents
    shall be worded as specified by using form CalRecycle 144 "Enterprise Fund for
    Financial Assurances" (03/17), which is incorporated herein by reference. (See
    Appendix A.) The wording, however, may be modified to accommodate special circumstances
    on a case-by-case basis, as approved by the Board or its designee. (d) Revenue
    generated by an enterprise fund shall be deposited into a financial assurance
    mechanism which: (1) Provides equivalent protection to a trust fund as described
    in section 18474 of this Article; (2) Shall be funded within five years as described
    in Section 18474 of this Article; (3) Is used exclusively to finance closure activities
    and shall remain inviolate against all other claims, including any claims by the
    operator, the operator''s governing body, and the creditors of the operator and
    its governing body; (4) Authorizes the Board or its designee to direct the provider
    of financial assurance to pay closure costs if the Board or its designee determines
    that the operator has failed to perform closure activities covered by the mechanism;
    (5) Is maintained by a provider whose financial operations are regulated by a
    federal or state agency, or the provider is otherwise certain to maintain and
    disburse the assured funds properly; (6) Is maintained by a provider who has authority
    to invest revenue deposited into the mechanism. (7) Meets other requirements that
    the Board determines are necessary to ensure that the assured amount of funds
    shall be available for closure activities in a timely manner.'
- text: (a) Various laws provide for the issuance of certifications by the state board
    or regional boards. These regulations specify how the state board and the regional
    boards implement various certification programs and how the state board acts on
    petitions for reconsideration of certification actions or failures to act by the
    executive director, regional boards, and executive officers. (b) Within five years
    from the effective date of these regulations, the state board, in consultation
    with the Secretary for Environmental Protection, shall review the provisions of
    this Chapter to determine whether they should be retained, revised, or repealed.
- text: The Tax Reform Act of 1986, as amended, (the "act") establishes a Federal
    tax credit ("low- income housing credit," "LIHTC" or "credit") administered by
    state housing agencies for owners of housing for persons of low-income. The act
    authorizes the governor of each state to allocate the low-income housing credit
    ceiling among governmental units and other issuing authorities in the state. The
    act requires that the allocation of credit to owners of low-income housing be
    coordinated by a single state housing credit agency. The act further requires
    each agency allocating credits to adopt a qualified allocation plan (the "plan"
    or the "QAP") which sets forth the criteria and preferences by which credit will
    be allocated to projects. By Executive Order, the New York State Division of Housing
    and Community Renewal has been designated as the State Housing Credit Agency to
    allocate the credit in a manner which maximizes the public benefit by addressing
    the State's need for low-income housing and community revitalization incentives.
    In order to provide for the effective coordination of the State's low-income housing
    credit program with section 42 of the United States Internal Revenue Code (the
    "code"), this plan shall be construed and administered in a manner consistent
    with the code and regulations promulgated thereunder.
- text: (1) The purpose of these rules is to provide administrative procedures for
    fetal, infant, and maternal death reviews, and maternal and family interviews,
    or both. (2) The program brings together key members of the community to review
    cases of fetal, infant, and maternal deaths in order to identify the factors associated
    with those deaths, to determine if those deaths represent system issues that require
    change, to develop recommendations for change, and to assist in the implementation
    of change. (3) The program's goal is to enhance the health and well-being of women,
    infants, and families by improving the community resources and service delivery
    systems available to them. The programs are operated under the auspices of the
    Alabama Department of Public Health (ADPH), Bureau of Family Health Services,
    State Perinatal Program.
- text: The regulations contained in this article govern procedures affecting the
    appeal to the Board of orders to comply with the Surface Mining and Reclamation
    Act of 1975 (SMARA) issued by the supervisor of the Division of Mine Reclamation
    (DMR), or by the Board when acting in the capacity of lead agency pursuant to
    Public Resources Code Section 2774.4 or 2774.5.
inference: true
---

# SetFit

This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) 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](https://www.sbert.net) with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.

## Model Details

### Model Description
- **Model Type:** SetFit
<!-- - **Sentence Transformer:** [Unknown](https://huggingface.co/unknown) -->
- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
- **Maximum Sequence Length:** 512 tokens
- **Number of Classes:** 32 classes
<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->

### Model Sources

- **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit)
- **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)
- **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)

## Uses

### Direct Use for Inference

First install the SetFit library:

```bash
pip install setfit
```

Then you can load this model and run inference.

```python
from setfit import SetFitModel

# Download from the 🤗 Hub
model = SetFitModel.from_pretrained("rkoh/setfit-bert-a6-8per")
# Run inference
preds = model("The regulations contained in this article govern procedures affecting the appeal to the Board of orders to comply with the Surface Mining and Reclamation Act of 1975 (SMARA) issued by the supervisor of the Division of Mine Reclamation (DMR), or by the Board when acting in the capacity of lead agency pursuant to Public Resources Code Section 2774.4 or 2774.5.")
```

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## Training Details

### Training Set Metrics
| Training set | Min        | Median           | Max          |
|:-------------|:-----------|:-----------------|:-------------|
| Word count   | tensor(31) | tensor(329.9688) | tensor(4265) |

| Label                  | Training Sample Count |
|:-----------------------|:----------------------|
| non-purpose            | 0                     |
| purpose-administrative | 0                     |
| purpose-regulatory     | 0                     |
| purpose-with-authority | 0                     |
| purpose-with-scope     | 0                     |

### Training Hyperparameters
- batch_size: (32, 32)
- 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 |
|:-----:|:----:|:-------------:|:---------------:|
| 0.025 | 1    | 0.478         | -               |
| 0.25  | 10   | 0.3818        | -               |
| 0.5   | 20   | 0.3011        | -               |
| 0.75  | 30   | 0.2555        | -               |
| 1.0   | 40   | 0.1937        | 0.2208          |

### Framework Versions
- Python: 3.10.12
- SetFit: 1.1.0
- Sentence Transformers: 3.2.1
- Transformers: 4.44.2
- PyTorch: 2.5.0+cu121
- Datasets: 3.0.2
- Tokenizers: 0.19.1

## Citation

### BibTeX
```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}
}
```

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