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TheoLvs/wsl-prescreening-multi-v0.0
wsl-prescreening-multi-v0.0 is a text classification model from TheoLvs. 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 BAAI/bge-small-en-v1.5 as the Sentence Transformer embedding model. A MultiOutputClassifier instance is used for classification.
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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 BAAI/bge-small-en-v1.5 as the Sentence Transformer embedding model. A MultiOutputClassifier instance is used for classification.
The model has been trained using an efficient few-shot learning technique that involves:
| Label | Accuracy |
|---|---|
| all | 0.8485 |
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("TheoLvs/wsl-prescreening-multi-v0.0")
# Run inference
preds = model("Wetland carbon sequestration capacity shows non-linear response to restoration technique and hydrological regime This study examines carbon sequestration outcomes from 124 wetland restoration projects across North America, Europe, and Asia over a 15-year monitoring period. Using standardized carbon flux measurements and sediment coring, we quantified how restoration approach and hydrological management influence carbon accumulation rates. Results demonstrate that restoration technique explained 53% of variance in carbon sequestration outcomes, with significant interaction effects between technique and hydroperiod. Projects restoring natural hydrological fluctuations achieved 2.7 times higher carbon accumulation rates than those maintaining static water levels. Vegetation community composition emerged as a significant mediating variable, with diverse native assemblages sequestering 34% more carbon than simplified or non-native communities. Our findings indicate that wetland restoration prioritizing hydrological dynamism and diverse vegetation delivers superior climate mitigation benefits while simultaneously enhancing habitat value and water quality functions.")
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| Training set | Min | Median | Max |
|---|---|---|---|
| Word count | 90 | 191.8561 | 348 |
| Epoch | Step | Training Loss | Validation Loss |
|---|---|---|---|
| 0.0006 | 1 | 0.158 | - |
| 0.0288 | 50 | 0.2511 | - |
| 0.0575 | 100 | 0.215 | - |
| 0.0863 | 150 | 0.1883 | - |
| 0.1151 | 200 | 0.165 | - |
| 0.1438 | 250 | 0.1274 | - |
| 0.1726 | 300 | 0.0801 | - |
| 0.2014 | 350 | 0.0635 | - |
| 0.2301 | 400 | 0.0427 | - |
| 0.2589 | 450 | 0.0355 | - |
| 0.2877 | 500 | 0.0337 | - |
| 0.3165 | 550 | 0.0271 | - |
| 0.3452 | 600 | 0.0069 | - |
| 0.3740 | 650 | 0.0032 | - |
| 0.4028 | 700 | 0.0033 | - |
| 0.4315 | 750 | 0.0027 | - |
| 0.4603 | 800 | 0.0022 | - |
| 0.4891 | 850 | 0.002 | - |
| 0.5178 | 900 | 0.0019 | - |
| 0.5466 | 950 | 0.0017 | - |
| 0.5754 | 1000 | 0.0017 | - |
| 0.6041 | 1050 | 0.0015 | - |
| 0.6329 | 1100 | 0.0015 | - |
| 0.6617 | 1150 | 0.0013 | - |
| 0.6904 | 1200 | 0.0013 | - |
| 0.7192 | 1250 | 0.0014 | - |
| 0.7480 | 1300 | 0.0012 | - |
| 0.7768 | 1350 | 0.0012 | - |
| 0.8055 | 1400 | 0.0011 | - |
| 0.8343 | 1450 | 0.0012 | - |
| 0.8631 | 1500 | 0.0011 | - |
| 0.8918 | 1550 | 0.0011 | - |
| 0.9206 | 1600 | 0.0011 | - |
| 0.9494 | 1650 | 0.001 | - |
| 0.9781 | 1700 | 0.001 | - |
| 1.0069 | 1750 | 0.001 | - |
| 1.0357 | 1800 | 0.001 | - |
| 1.0644 | 1850 | 0.0009 | - |
| 1.0932 | 1900 | 0.0009 | - |
| 1.1220 | 1950 | 0.0009 | - |
| 1.1507 | 2000 | 0.0009 | - |
| 1.1795 | 2050 | 0.0009 | - |
| 1.2083 | 2100 | 0.0009 | - |
| 1.2371 | 2150 | 0.0008 | - |
| 1.2658 | 2200 | 0.0009 | - |
| 1.2946 | 2250 | 0.0008 | - |
| 1.3234 | 2300 | 0.0008 | - |
| 1.3521 | 2350 | 0.0008 | - |
| 1.3809 | 2400 | 0.0008 | - |
| 1.4097 | 2450 | 0.0008 | - |
| 1.4384 | 2500 | 0.0008 | - |
| 1.4672 | 2550 | 0.0007 | - |
| 1.4960 | 2600 | 0.0007 | - |
| 1.5247 | 2650 | 0.0007 | - |
| 1.5535 | 2700 | 0.0007 | - |
| 1.5823 | 2750 | 0.0007 | - |
| 1.6110 | 2800 | 0.0007 | - |
| 1.6398 | 2850 | 0.0007 | - |
| 1.6686 | 2900 | 0.0007 | - |
| 1.6974 | 2950 | 0.0007 | - |
| 1.7261 | 3000 | 0.0006 | - |
| 1.7549 | 3050 | 0.0007 | - |
| 1.7837 | 3100 | 0.0007 | - |
| 1.8124 | 3150 | 0.0007 | - |
| 1.8412 | 3200 | 0.0007 | - |
| 1.8700 | 3250 | 0.0007 | - |
| 1.8987 | 3300 | 0.0006 | - |
| 1.9275 | 3350 | 0.0006 | - |
| 1.9563 | 3400 | 0.0006 | - |
| 1.9850 | 3450 | 0.0006 | - |
| 2.0138 | 3500 | 0.0006 | - |
| 2.0426 | 3550 | 0.0006 | - |
| 2.0713 | 3600 | 0.0006 | - |
| 2.1001 | 3650 | 0.0006 | - |
| 2.1289 | 3700 | 0.0006 | - |
| 2.1577 | 3750 | 0.0006 | - |
| 2.1864 | 3800 | 0.0006 | - |
| 2.2152 | 3850 | 0.0006 | - |
| 2.2440 | 3900 | 0.0006 | - |
| 2.2727 | 3950 | 0.0006 | - |
| 2.3015 | 4000 | 0.0006 | - |
| 2.3303 | 4050 | 0.0006 | - |
| 2.3590 | 4100 | 0.0006 | - |
| 2.3878 | 4150 | 0.0006 | - |
| 2.4166 | 4200 | 0.0005 | - |
| 2.4453 | 4250 | 0.0006 | - |
| 2.4741 | 4300 | 0.0005 | - |
| 2.5029 | 4350 | 0.0006 | - |
| 2.5316 | 4400 | 0.0006 | - |
| 2.5604 | 4450 | 0.0005 | - |
| 2.5892 | 4500 | 0.0005 | - |
| 2.6180 | 4550 | 0.0005 | - |
| 2.6467 | 4600 | 0.0005 | - |
| 2.6755 | 4650 | 0.0005 | - |
| 2.7043 | 4700 | 0.0005 | - |
| 2.7330 | 4750 | 0.0005 | - |
| 2.7618 | 4800 | 0.0005 | - |
| 2.7906 | 4850 | 0.0005 | - |
| 2.8193 | 4900 | 0.0005 | - |
| 2.8481 | 4950 | 0.0005 | - |
| 2.8769 | 5000 | 0.0005 | - |
@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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