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Ankit15nov/setfit-ethos-multilabel-example
setfit-ethos-multilabel-example is a text classification model from Ankit15nov. 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 | 0.2754 |
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("Ankit15nov/setfit-ethos-multilabel-example")
# Run inference
preds = model("NiSource Inc. NYSE NI completes the issuance of a 19.9 indirect equity interest in NIPSCO to Blackstone Infrastructure Partners affiliate for 2.16 billion with an additional equity commitment of 250 million. The investment aims to strengthen NIPSCO's financial foundation support sustainable long term growth and fund ongoing capital requirements for energy transition and reindustrialization of the Midwest. ")
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| Training set | Min | Median | Max |
|---|---|---|---|
| Word count | 2 | 590.5 | 2491 |
| Epoch | Step | Training Loss | Validation Loss |
|---|---|---|---|
| 0.0018 | 1 | 0.4292 | - |
| 0.0893 | 50 | 0.0057 | - |
| 0.1786 | 100 | 0.2115 | - |
| 0.2679 | 150 | 0.0003 | - |
| 0.3571 | 200 | 0.0022 | - |
| 0.4464 | 250 | 0.0003 | - |
| 0.5357 | 300 | 0.0083 | - |
| 0.625 | 350 | 0.0043 | - |
| 0.7143 | 400 | 0.0038 | - |
| 0.8036 | 450 | 0.0014 | - |
| 0.8929 | 500 | 0.0031 | - |
| 0.9821 | 550 | 0.0014 | - |
@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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