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faodl/model_cca_multilabel_MiniLM-L12-v01
model_cca_multilabel_MiniLM-L12-v01 is a text classification model from faodl. 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. This SetFit model uses sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 as the Sentence Transformer embedding model. A OneVsRestClassifier in…
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From the Hugging Face model README
This is a SetFit model that can be used for Text Classification. This SetFit model uses sentence-transformers/paraphrase-multilingual-MiniLM-L12-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:
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("faodl/model_cca_multilabel_MiniLM-L12-v01")
# Run inference
preds = model("To monitor market dynamics and inform policy responses, the government will track the retail value of ultra-processed foods and analyze shifts in consumption in relation to labeling and advertising reforms. Data from these analyses will feed annual dashboards that link labeling density, promotional intensity, and dietary outcomes to guide targeted interventions and budget planning.")
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| Training set | Min | Median | Max |
|---|---|---|---|
| Word count | 1 | 123.6200 | 951 |
| Epoch | Step | Training Loss | Validation Loss |
|---|---|---|---|
| 0.0011 | 1 | 0.1892 | - |
| 0.0566 | 50 | 0.192 | - |
| 0.1131 | 100 | 0.1681 | - |
| 0.1697 | 150 | 0.1518 | - |
| 0.2262 | 200 | 0.1361 | - |
| 0.2828 | 250 | 0.1389 | - |
| 0.3394 | 300 | 0.1321 | - |
| 0.3959 | 350 | 0.1297 | - |
| 0.4525 | 400 | 0.1236 | - |
| 0.5090 | 450 | 0.1116 | - |
| 0.5656 | 500 | 0.1194 | - |
| 0.6222 | 550 | 0.1105 | - |
| 0.6787 | 600 | 0.1047 | - |
| 0.7353 | 650 | 0.1124 | - |
| 0.7919 | 700 | 0.1069 | - |
| 0.8484 | 750 | 0.108 | - |
| 0.9050 | 800 | 0.1072 | - |
| 0.9615 | 850 | 0.1011 | - |
| 1.0181 | 900 | 0.098 | - |
| 1.0747 | 950 | 0.0893 | - |
| 1.1312 | 1000 | 0.0979 | - |
| 1.1878 | 1050 | 0.0967 | - |
| 1.2443 | 1100 | 0.0887 | - |
| 1.3009 | 1150 | 0.0908 | - |
| 1.3575 | 1200 | 0.0906 | - |
| 1.4140 | 1250 | 0.0869 | - |
| 1.4706 | 1300 | 0.0873 | - |
| 1.5271 | 1350 | 0.0943 | - |
| 1.5837 | 1400 | 0.0886 | - |
| 1.6403 | 1450 | 0.0911 | - |
| 1.6968 | 1500 | 0.0832 | - |
| 1.7534 | 1550 | 0.0859 | - |
| 1.8100 | 1600 | 0.0862 | - |
| 1.8665 | 1650 | 0.09 | - |
| 1.9231 | 1700 | 0.0836 | - |
| 1.9796 | 1750 | 0.0884 | - |
@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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