Push model using huggingface_hub.

1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+  "word_embedding_dimension": 384,
+  "pooling_mode_cls_token": true,
+  "pooling_mode_mean_tokens": false,
+  "pooling_mode_max_tokens": false,
+  "pooling_mode_mean_sqrt_len_tokens": false,
+  "pooling_mode_weightedmean_tokens": false,
+  "pooling_mode_lasttoken": false,
+  "include_prompt": true
+}

README.md

@@ -0,0 +1,375 @@
+---
+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.")
+```
+
+<!--
+### Downstream Use
+
+*List how someone could finetune this model on their own dataset.*
+-->
+
+<!--
+### Out-of-Scope Use
+
+*List how the model may foreseeably be misused and address what users ought not to do with the model.*
+-->
+
+<!--
+## Bias, Risks and Limitations
+
+*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
+-->
+
+<!--
+### Recommendations
+
+*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
+-->
+
+## 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}
+}
+```
+
+<!--
+## Glossary
+
+*Clearly define terms in order to be accessible across audiences.*
+-->
+
+<!--
+## Model Card Authors
+
+*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
+-->
+
+<!--
+## Model Card Contact
+
+*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
+-->

config.json

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+{
+  "_name_or_path": "BAAI/bge-small-en-v1.5",
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 384,
+  "id2label": {
+    "0": "LABEL_0"
+  },
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+  "intermediate_size": 1536,
+  "label2id": {
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+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.45.2",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

config_sentence_transformers.json

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+{
+  "__version__": {
+    "sentence_transformers": "4.1.0",
+    "transformers": "4.45.2",
+    "pytorch": "2.6.0+cu124"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
+}

config_setfit.json

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+{
+  "labels": null,
+  "normalize_embeddings": false
+}

model.safetensors

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model_head.pkl

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modules.json

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+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  },
+  {
+    "idx": 2,
+    "name": "2",
+    "path": "2_Normalize",
+    "type": "sentence_transformers.models.Normalize"
+  }
+]

sentence_bert_config.json

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+{
+  "max_seq_length": 512,
+  "do_lower_case": true
+}

special_tokens_map.json

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+  "cls_token": {
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+    "lstrip": false,
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+}

tokenizer_config.json

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+}