metadata
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: >-
Freshwater resource governance models demonstrate divergent outcomes in
scarcity adaptation across hydroclimatic regions
This study evaluates the effectiveness of five distinct freshwater
governance models across 34 river basins experiencing varying degrees of
water stress. Using comparative institutional analysis and quantitative
metrics from 2005-2022, we assessed how governance structures influence
water allocation efficiency, ecosystem protection, and adaptation capacity
under increasing scarcity conditions. Results demonstrate that polycentric
governance systems outperformed both centralized bureaucratic and
market-based models in 76% of ecological indicators and 64% of adaptation
metrics. Basin-level integrated management frameworks reduced
inter-sectoral water conflicts by 42% compared to fragmented governance
approaches. Water pricing mechanisms showed mixed effectiveness, with
progressive tariff structures achieving 38% higher conservation outcomes
than flat-rate systems while maintaining equity considerations.
Importantly, governance systems incorporating traditional ecological
knowledge alongside scientific monitoring demonstrated 57% better
ecological outcomes in seasonal flow maintenance. Statistical modeling
revealed that institutional flexibility and stakeholder participation were
stronger predictors of adaptive capacity than technical infrastructure or
financial resources. These findings challenge conventional water
governance approaches emphasizing centralized control or marketization,
suggesting that context-sensitive institutional design addressing both
biophysical constraints and social dynamics provides more sustainable
freshwater management under climate uncertainty.
- text: >-
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.
- text: >-
CONSERVATION OF URBAN WETLAND WITH POTENTIAL INTERNATIONAL SIGNIFICANCE: A
CASE STUDY ON NAJAFGARH JHEEL, DELHI, INDIA
Urban lakes, or jheels, are essential ecological elements that help
maintain ecosystem services such as groundwater, regional climate, and
biodiversity. The continuous urban sprawl and population growth in urban
areas are essential factors in the decline of freshwater bodies. However,
these ecosystems have functional advantages. The National Capital Region
of India has a population of 46 million and is situated on the Yamuna
watershed. The resilience plan for the city requires research on
hydrological sustainability. The present study focuses on the case study
of Najafgarh Jheel, a trans-boundary lake that has recently received the
status of a water body under the wetland rules of 2017 of India by the
National Green Tribunal after 215 years of existence and deterioration.
The primary data collection was through field visits of avifauana data,
and secondary data from eBird data, research articles, government reports,
and newspaper articles have been the main tools for analysis. The
baselines of international significance for Najafgrah Jheel were compared
to criteria laid out by the Important Bird and Biodiversity Area Programme
and the Ramsar Convention. The Najafgarh Jheel area could be a prospective
wetland of international significance for its ornithological significance.
The Jheel is facing several anthropogenic stressors with an urgent need
for protection and demarcation under the protected area network. © 2023
Universitatea "Alexandru Ioan Cuza" din Iasi. All rights reserved.
- text: >-
Educational experiences during adolescence predict midlife fulfillment
through skill development rather than credential attainment This study
investigates long-term effects of educational experiences on life outcomes
beyond economic returns. Using data from a 32-year longitudinal study
tracking 3,842 individuals from adolescence through midlife, we examined
how educational characteristics predicted fulfillment indicators. Results
demonstrate that educational quality metrics (student engagement, teacher
relationships, skill-building opportunities) predicted midlife flourishing
more strongly than years of education or credential attainment (β=0.48 vs.
β=0.27, p<0.001). The relationship was mediated by skill development in
three key domains: metacognitive skills (critical thinking, learning
strategies), social capabilities (communication, collaboration), and
emotional competencies (self-regulation, resilience). Notably, individuals
who experienced high-quality secondary education but terminated formal
education early showed better life outcomes than those completing advanced
degrees in low-engagement educational environments. Education quality
effects remained significant after controlling for family background,
cognitive ability, and subsequent earnings. These findings challenge the
credentialist paradigm dominating educational policy and suggest greater
emphasis on qualitative educational experiences rather than simply
maximizing credential attainment.
- text: >-
Climate adaptation funding reveals systematic biases against most
vulnerable communities This research examines the distribution of climate
adaptation resources across 174 implemented projects in 28 countries from
2010-2022. Using spatial analysis integrating climate vulnerability
indices, adaptation fund disbursement data, and field assessments, we
evaluated whether resources flow to populations with greatest need.
Results demonstrate an inverse relationship between community climate
vulnerability and adaptation funding received, with the most vulnerable
quintile receiving only 16% of resources while the least vulnerable
quintile received 31%. This distributional inequity persisted after
controlling for project implementation capacity, population size, and
accessibility. Governance analysis identified key mechanisms driving this
pattern, including proposal requirements favoring technically
sophisticated applicants, co-financing mandates, and risk-averse funder
behavior. Project-level analysis revealed that even within funded regions,
resources disproportionately benefited less vulnerable sub-populations
through elite capture dynamics. These findings document systemic
distributional injustice in climate adaptation financing and suggest
specific reforms to funding mechanisms necessary for more equitable
vulnerability reduction.
metrics:
- accuracy
pipeline_tag: text-classification
library_name: setfit
inference: false
base_model: BAAI/bge-small-en-v1.5
model-index:
- name: SetFit with BAAI/bge-small-en-v1.5
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: Unknown
type: unknown
split: test
metrics:
- type: accuracy
value: 0.8484848484848485
name: Accuracy
SetFit with BAAI/bge-small-en-v1.5
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:
- Fine-tuning a Sentence Transformer with contrastive learning.
- Training a classification head with features from the fine-tuned Sentence Transformer.
Model Details
Model Description
- Model Type: SetFit
- Sentence Transformer body: BAAI/bge-small-en-v1.5
- Classification head: a MultiOutputClassifier instance
- Maximum Sequence Length: 512 tokens
Model Sources
- Repository: SetFit on GitHub
- Paper: Efficient Few-Shot Learning Without Prompts
- Blogpost: SetFit: Efficient Few-Shot Learning Without Prompts
Evaluation
Metrics
| Label | Accuracy |
|---|---|
| all | 0.8485 |
Uses
Direct Use for Inference
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.")
Training Details
Training Set Metrics
| Training set | Min | Median | Max |
|---|---|---|---|
| Word count | 90 | 191.8561 | 348 |
Training Hyperparameters
- batch_size: (8, 8)
- num_epochs: (5, 5)
- max_steps: 5000
- sampling_strategy: oversampling
- body_learning_rate: (2e-05, 1e-05)
- head_learning_rate: 0.01
- loss: CosineSimilarityLoss
- distance_metric: cosine_distance
- margin: 0.25
- end_to_end: False
- use_amp: False
- warmup_proportion: 0.1
- l2_weight: 0.01
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: False
Training Results
| 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 | - |
Framework Versions
- Python: 3.11.12
- SetFit: 1.1.2
- Sentence Transformers: 4.1.0
- Transformers: 4.45.2
- PyTorch: 2.6.0+cu124
- Datasets: 3.6.0
- Tokenizers: 0.20.3
Citation
BibTeX
@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}
}