CPU-Vulnerability Analysis
Collection
This collection of dataset, model and space focus on getting the insights regarding the 'Vulnerable Section' mention/focus in Policy Documents.
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4 items
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Updated
This is a SetFit model that can be used for Text Classification. This SetFit model uses sentence-transformers/all-mpnet-base-v2 as the Sentence Transformer embedding model. A SetFitHead 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("leavoigt/vulnerability_multilabel_updated")
# Run inference
preds = model("Workers in the formal sector. Formal sector workers also face economic risks. A number of them experience income instability due to contractualization, retrenchment, and firm closures. In 2014, contractual workers accounted for 22 percent of the total 4.5 million workers employed in establishments with 20 or more employees.")
Training set | Min | Median | Max |
---|---|---|---|
Word count | 21 | 72.6472 | 238 |
Epoch | Step | Training Loss | Validation Loss |
---|---|---|---|
0.0006 | 1 | 0.1906 | - |
0.0316 | 50 | 0.1275 | 0.1394 |
0.0631 | 100 | 0.0851 | 0.1247 |
0.0947 | 150 | 0.0959 | 0.1269 |
0.1263 | 200 | 0.1109 | 0.1179 |
0.1578 | 250 | 0.0923 | 0.1354 |
0.1894 | 300 | 0.063 | 0.1292 |
0.2210 | 350 | 0.0555 | 0.1326 |
0.2525 | 400 | 0.0362 | 0.1127 |
0.2841 | 450 | 0.0582 | 0.132 |
0.3157 | 500 | 0.0952 | 0.1339 |
0.3472 | 550 | 0.0793 | 0.1171 |
0.3788 | 600 | 0.059 | 0.1187 |
0.4104 | 650 | 0.0373 | 0.1131 |
0.4419 | 700 | 0.0593 | 0.1144 |
0.4735 | 750 | 0.0405 | 0.1174 |
0.5051 | 800 | 0.0284 | 0.1196 |
0.5366 | 850 | 0.0329 | 0.1116 |
0.5682 | 900 | 0.0895 | 0.1193 |
0.5997 | 950 | 0.0576 | 0.1159 |
0.6313 | 1000 | 0.0385 | 0.1203 |
0.6629 | 1050 | 0.0842 | 0.1195 |
0.6944 | 1100 | 0.0274 | 0.113 |
0.7260 | 1150 | 0.0226 | 0.1137 |
0.7576 | 1200 | 0.0276 | 0.1204 |
0.7891 | 1250 | 0.0355 | 0.1163 |
0.8207 | 1300 | 0.077 | 0.1161 |
0.8523 | 1350 | 0.0735 | 0.1135 |
0.8838 | 1400 | 0.0357 | 0.1175 |
0.9154 | 1450 | 0.0313 | 0.1207 |
0.9470 | 1500 | 0.0241 | 0.1159 |
0.9785 | 1550 | 0.0339 | 0.1161 |
@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}
}
Base model
sentence-transformers/all-mpnet-base-v2