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 OneVsRestClassifier instance is used for classification.
The model has been trained using an efficient few-shot learning technique that involves:
Label | Precision_Micro | Precision_Weighted | Precision_Samples | Recall_Micro | Recall_Weighted | Recall_Samples | F1-Score | Accuracy |
---|---|---|---|---|---|---|---|---|
all | 0.7762 | 0.7969 | 0.7762 | 0.7762 | 0.7762 | 0.7762 | 0.7762 | 0.7762 |
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_target")
# Run inference
preds = model("To support the traditional knowledge and adaptive capacity of indigenous peoples in the face of climate change, we aim to establish 50 community-based adaptation projects led by indigenous peoples by 2030, focusing on the sustainable management of natural resources and the preservation of cultural practices.")
Training set | Min | Median | Max |
---|---|---|---|
Word count | 15 | 70.8675 | 238 |
Epoch | Step | Training Loss | Validation Loss |
---|---|---|---|
0.0012 | 1 | 0.3493 | - |
0.0602 | 50 | 0.2285 | - |
0.1205 | 100 | 0.1092 | - |
0.1807 | 150 | 0.1348 | - |
0.2410 | 200 | 0.0365 | - |
0.3012 | 250 | 0.0052 | - |
0.3614 | 300 | 0.0012 | - |
0.4217 | 350 | 0.0031 | - |
0.4819 | 400 | 0.0001 | - |
0.5422 | 450 | 0.0011 | - |
0.6024 | 500 | 0.0001 | - |
0.6627 | 550 | 0.0001 | - |
0.7229 | 600 | 0.0001 | - |
0.7831 | 650 | 0.0002 | - |
0.8434 | 700 | 0.0001 | - |
0.9036 | 750 | 0.0001 | - |
0.9639 | 800 | 0.0001 | - |
@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