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rkoh/setfit-bert-a6
setfit-bert-a6 is a text classification model from rkoh. Use it when you need a label for a piece of text. It is set up for setfit.
This is a SetFit model that can be used for Text Classification. A LogisticRegression instance is used for classification.
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From the Hugging Face model README
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:
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")
# Run inference
preds = model("The purpose of this Part is to establish the new source review (NSR) preconstruction, construction and operation requirements for new and modified facilities in a manner which furthers the policy and objectives of article 19 of the Environmental Conservation Law, and meets the plan requirements for nonattainment areas (part D) and prevention of significant deterioration (PSD) of air quality (part C) of subchapter I of the act.")
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| Training set | Min | Median | Max |
|---|---|---|---|
| Word count | tensor(15) | tensor(242.2050) | tensor(4265) |
| Label | Training Sample Count |
|---|---|
| Purpose - Regulatory Objective | 0 |
| Scope and Applicability | 0 |
| Authority and Legal Basis | 0 |
| Administrative Details | 0 |
| Non-Purpose | 0 |
| Epoch | Step | Training Loss | Validation Loss |
|---|---|---|---|
| 0.004 | 1 | 0.3869 | - |
| 0.04 | 10 | 0.4354 | - |
| 0.08 | 20 | 0.3435 | - |
| 0.12 | 30 | 0.2742 | - |
| 0.16 | 40 | 0.2615 | - |
| 0.2 | 50 | 0.2462 | - |
| 0.24 | 60 | 0.2092 | - |
| 0.28 | 70 | 0.2323 | - |
| 0.32 | 80 | 0.1956 | - |
| 0.36 | 90 | 0.2324 | - |
| 0.4 | 100 | 0.2026 | - |
| 0.44 | 110 | 0.1941 | - |
| 0.48 | 120 | 0.1728 | - |
| 0.52 | 130 | 0.1674 | - |
| 0.56 | 140 | 0.1754 | - |
| 0.6 | 150 | 0.1746 | - |
| 0.64 | 160 | 0.1502 | - |
| 0.68 | 170 | 0.1704 | - |
| 0.72 | 180 | 0.1373 | - |
| 0.76 | 190 | 0.152 | - |
| 0.8 | 200 | 0.15 | - |
| 0.84 | 210 | 0.1397 | - |
| 0.88 | 220 | 0.135 | - |
| 0.92 | 230 | 0.137 | - |
| 0.96 | 240 | 0.106 | - |
| 1.0 | 250 | 0.1309 | 0.2323 |
@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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