metadata
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 model that can be used for Text Classification. 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
- Classification head: a LogisticRegression instance
- Maximum Sequence Length: 512 tokens
- Number of Classes: 32 classes
Model Sources
- Repository: SetFit on GitHub
- Paper: Efficient Few-Shot Learning Without Prompts
- Blogpost: SetFit: Efficient Few-Shot Learning Without Prompts
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("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.")
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
@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}
}