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ardi555/setfit_mpnet_reuters21578_reducedto15
setfit_mpnet_reuters21578_reducedto15 is a text classification model from ardi555. 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-mpnet-base-v2 as the Sentence Transformer embedding model. A OneVsRestClassifier instance is use…
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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-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 | Accuracy |
|---|---|
| all | 0.9161 |
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("ardi555/setfit_mpnet_reuters21578_reducedto15")
# Run inference
preds = model("The European Community Commission
confirmed it granted export licences for 59,000 tonnes of
current series white sugar at a maximum export rebate of 45.678
European Currency Units (ECUs) per 100 kilos.
Out of this, traders in West Germany received 34,750
tonnes, in the U.K. 13,000, in Denmark 7,250 tonnes and in
France 4,000 tonnes.
REUTER
")
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| Training set | Min | Median | Max |
|---|---|---|---|
| Word count | 20 | 204.4467 | 1075 |
| Epoch | Step | Training Loss | Validation Loss |
|---|---|---|---|
| 0.0013 | 1 | 0.2676 | - |
| 0.0667 | 50 | 0.1617 | - |
| 0.1333 | 100 | 0.0869 | - |
| 0.2 | 150 | 0.0583 | - |
| 0.2667 | 200 | 0.0766 | - |
| 0.3333 | 250 | 0.0578 | - |
| 0.4 | 300 | 0.0483 | - |
| 0.4667 | 350 | 0.0374 | - |
| 0.5333 | 400 | 0.0372 | - |
| 0.6 | 450 | 0.039 | - |
| 0.6667 | 500 | 0.0367 | - |
| 0.7333 | 550 | 0.0378 | - |
| 0.8 | 600 | 0.0299 | - |
| 0.8667 | 650 | 0.0317 | - |
| 0.9333 | 700 | 0.0308 | - |
| 1.0 | 750 | 0.0293 | - |
@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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