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TheoLvs
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wsl-prescreening-multi-v0.0

Text Classification
setfit
Safetensors
sentence-transformers
bert
generated_from_setfit_trainer
Eval Results
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wsl-prescreening-multi-v0.0 / README.md
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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](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [BAAI/bge-small-en-v1.5](https://huggingface.co/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:
1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. 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](https://huggingface.co/BAAI/bge-small-en-v1.5)
- **Classification head:** a MultiOutputClassifier instance
- **Maximum Sequence Length:** 512 tokens
<!-- - **Number of Classes:** Unknown -->
<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit)
- **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)
- **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)
## Evaluation
### Metrics
| Label | Accuracy |
|:--------|:---------|
| **all** | 0.8485 |
## Uses
### Direct Use for Inference
First install the SetFit library:
```bash
pip install setfit
```
Then you can load this model and run inference.
```python
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 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
```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}
}
```
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