Push model using huggingface_hub.

.gitattributes

@@ -33,3 +33,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text
+unigram.json filter=lfs diff=lfs merge=lfs -text

1_Pooling/config.json

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+{
+    "word_embedding_dimension": 384,
+    "pooling_mode_cls_token": false,
+    "pooling_mode_mean_tokens": true,
+    "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 +1,295 @@
+---
+tags:
+- setfit
+- sentence-transformers
+- text-classification
+- generated_from_setfit_trainer
+widget:
+- text: 4.3.3 Strategies for Comprehensive Sexuality Education and (CSE) Youth-friendly
+    Health Services 1. To promote volunteerism as a tool for fostering active participation
+    of young people in national development; 5. To promote volunteerism as a tool
+    for fostering active participation of young people in national development; 5.
+- text: 4) Mainstream appropriate food and nutrition issues in relevant sector policies
+    and strategies. 4) Mainstream appropriate food and nutrition issues in relevant
+    sector policies and strategies. ), these and many others have varying requirements
+    related to 3.5 Communication Support for Food and Nutrition Programmes and Interventions
+    National Food and Nutrition Strategic Plan 2011-2015 11 generation of demand by
+    the population.
+- text: incidence of stunting reduced from 39 to 35 percent, and population with calories
+    deficit from 35 to 31 percent) and public food distribution ( i.e from 20 thousand
+    MT to 39 thousand MT and food sales by 29 thousand MT). It states that “the main
+    objective of the food security plan is to make the life of the targeted people
+    healthy and productive by improving national food sovereignty and the food and
+    nutrition situation.” Accordingly, the TYIP set out and scaled up the quantities
+    targets in terms of per capita food production (i.e., from 280–289 kg per capita
+    annually), indicators of nutrition ( i.e. Food procurement policy should be made
+    as a vehicle of ensuring sufficient supply of essential food items and also a
+    means of containing prices.
+- text: 'UP-5978 “On additional measures to support the public, economic3 April 2020:
+    Presidential Decree No. Tax benefitsTax benefits The Decree 5969, the Decree 5978,
+    and the Decree 5986 (together the “Decrees”) have introduced the followingThe
+    Decree 5969, the Decree 5978, and the Decree 5986 (together the “Decrees”) have
+    introduced the following tax reductions (benefits) for businesses:tax reductions
+    (benefits) for businesses: for the period from 1 April 2020 to 1 October 2020:for
+    the period from 1 April 2020 to 1 October 2020: 02/06/2020 COVID-19: Uzbekistan
+    Government Financial Assistance Measures - Lexology https://www.lexology.com/library/detail.aspx?g=1d5e31b2-e7b1-44c9-8c9e-7d4bc5975bc2
+    3/5 the minimum amount of social tax for individual entrepreneurs is reduced to
+    the minimum amount of social tax for individual entrepreneurs is reduced to 50%50%
+    of the base of the base calculated amount (“BCA”) per month;calculated amount
+    (“BCA”) per month; the amount of mandatory payments for wholesalers of alcoholic
+    beverages is reduced from the amount of mandatory payments for wholesalers of
+    alcoholic beverages is reduced from 5 to5 to 3%3%; and; and fees for the right
+    to retail sale of alcoholic products by catering enterprises are reduced byfees
+    for the right to retail sale of alcoholic products by catering enterprises are
+    reduced by 25% 25% of of the amounts set under law.the amounts set under law.
+    These measures provide certain guarantees and protections, including deferred
+    tax payments, decrease of taxThese measures provide certain guarantees and protections,
+    including deferred tax payments, decrease of tax rates, tax related waivers and
+    exemptions, as well as liquidity support measures.rates, tax related waivers and
+    exemptions, as well as liquidity support measures.'
+- text: 'The composition and nutritional content of the food ration for each beneficiary
+    group are as follows: 19 While only the poorest families in the most food-insecure
+    districts will receive general food distributions, in the poorest districts supplementary
+    feeding will be targeted to all children 6-24 months, pregnant/lactating women
+    and all moderately- malnourished children. 10767.0 Results-Chain (Logic Model)
+    Performance Indicators Risks, Assumptions STRATEGIC OBJECTIVE 1 - Save Lives and
+    Protect Livelihoods in Emergencies Outcome 1.1: Reduced acute malnutrition in
+    children under 5 in targeted emergency-affected populations Outcome 1.3: Improved
+    food consumption over assistance period for targeted crisis-affected beneficiaries.
+    (b) The food and nutrition situation 9.'
+metrics:
+- accuracy
+pipeline_tag: text-classification
+library_name: setfit
+inference: false
+base_model: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
+---
+
+# SetFit with sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
+
+This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-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:
+
+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:** [sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2)
+- **Classification head:** a OneVsRestClassifier instance
+- **Maximum Sequence Length:** 128 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)
+
+## 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("faodl/20250908_model_g20_multilabel_MiniLM-L12-all-labels")
+# Run inference
+preds = model("4.3.3 Strategies for Comprehensive Sexuality Education and (CSE) Youth-friendly Health Services 1. To promote volunteerism as a tool for fostering active participation of young people in national development; 5. To promote volunteerism as a tool for fostering active participation of young people in national development; 5.")
+```
+
+<!--
+### 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   | 2   | 70.5122 | 1194 |
+
+### Training Hyperparameters
+- batch_size: (32, 32)
+- num_epochs: (2, 2)
+- max_steps: -1
+- sampling_strategy: oversampling
+- num_iterations: 10
+- body_learning_rate: (2e-05, 2e-05)
+- head_learning_rate: 2e-05
+- 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 |
+|:------:|:----:|:-------------:|:---------------:|
+| 0.0005 | 1    | 0.1435        | -               |
+| 0.0241 | 50   | 0.1438        | -               |
+| 0.0482 | 100  | 0.1239        | -               |
+| 0.0723 | 150  | 0.1073        | -               |
+| 0.0964 | 200  | 0.0992        | -               |
+| 0.1205 | 250  | 0.0883        | -               |
+| 0.1446 | 300  | 0.08          | -               |
+| 0.1687 | 350  | 0.0801        | -               |
+| 0.1928 | 400  | 0.073         | -               |
+| 0.2169 | 450  | 0.0647        | -               |
+| 0.2410 | 500  | 0.0549        | -               |
+| 0.2651 | 550  | 0.0575        | -               |
+| 0.2892 | 600  | 0.0544        | -               |
+| 0.3133 | 650  | 0.0523        | -               |
+| 0.3373 | 700  | 0.0506        | -               |
+| 0.3614 | 750  | 0.0467        | -               |
+| 0.3855 | 800  | 0.0443        | -               |
+| 0.4096 | 850  | 0.0385        | -               |
+| 0.4337 | 900  | 0.0425        | -               |
+| 0.4578 | 950  | 0.0412        | -               |
+| 0.4819 | 1000 | 0.036         | -               |
+| 0.5060 | 1050 | 0.0323        | -               |
+| 0.5301 | 1100 | 0.0352        | -               |
+| 0.5542 | 1150 | 0.0347        | -               |
+| 0.5783 | 1200 | 0.0319        | -               |
+| 0.6024 | 1250 | 0.0254        | -               |
+| 0.6265 | 1300 | 0.0291        | -               |
+| 0.6506 | 1350 | 0.0253        | -               |
+| 0.6747 | 1400 | 0.0283        | -               |
+| 0.6988 | 1450 | 0.0248        | -               |
+| 0.7229 | 1500 | 0.02          | -               |
+| 0.7470 | 1550 | 0.0249        | -               |
+| 0.7711 | 1600 | 0.0208        | -               |
+| 0.7952 | 1650 | 0.021         | -               |
+| 0.8193 | 1700 | 0.0238        | -               |
+| 0.8434 | 1750 | 0.0196        | -               |
+| 0.8675 | 1800 | 0.0213        | -               |
+| 0.8916 | 1850 | 0.0222        | -               |
+| 0.9157 | 1900 | 0.019         | -               |
+| 0.9398 | 1950 | 0.0226        | -               |
+| 0.9639 | 2000 | 0.0156        | -               |
+| 0.9880 | 2050 | 0.0193        | -               |
+| 1.0120 | 2100 | 0.016         | -               |
+| 1.0361 | 2150 | 0.019         | -               |
+| 1.0602 | 2200 | 0.0154        | -               |
+| 1.0843 | 2250 | 0.0136        | -               |
+| 1.1084 | 2300 | 0.014         | -               |
+| 1.1325 | 2350 | 0.0147        | -               |
+| 1.1566 | 2400 | 0.0126        | -               |
+| 1.1807 | 2450 | 0.0161        | -               |
+| 1.2048 | 2500 | 0.0123        | -               |
+| 1.2289 | 2550 | 0.0151        | -               |
+| 1.2530 | 2600 | 0.0123        | -               |
+| 1.2771 | 2650 | 0.0122        | -               |
+| 1.3012 | 2700 | 0.0084        | -               |
+| 1.3253 | 2750 | 0.0154        | -               |
+| 1.3494 | 2800 | 0.014         | -               |
+| 1.3735 | 2850 | 0.0124        | -               |
+| 1.3976 | 2900 | 0.0146        | -               |
+| 1.4217 | 2950 | 0.0103        | -               |
+| 1.4458 | 3000 | 0.0116        | -               |
+| 1.4699 | 3050 | 0.013         | -               |
+| 1.4940 | 3100 | 0.0104        | -               |
+| 1.5181 | 3150 | 0.0124        | -               |
+| 1.5422 | 3200 | 0.0127        | -               |
+| 1.5663 | 3250 | 0.0122        | -               |
+| 1.5904 | 3300 | 0.0092        | -               |
+| 1.6145 | 3350 | 0.0108        | -               |
+| 1.6386 | 3400 | 0.0121        | -               |
+| 1.6627 | 3450 | 0.0125        | -               |
+| 1.6867 | 3500 | 0.0162        | -               |
+| 1.7108 | 3550 | 0.0105        | -               |
+| 1.7349 | 3600 | 0.0133        | -               |
+| 1.7590 | 3650 | 0.0145        | -               |
+| 1.7831 | 3700 | 0.0113        | -               |
+| 1.8072 | 3750 | 0.009         | -               |
+| 1.8313 | 3800 | 0.0105        | -               |
+| 1.8554 | 3850 | 0.011         | -               |
+| 1.8795 | 3900 | 0.0087        | -               |
+| 1.9036 | 3950 | 0.0159        | -               |
+| 1.9277 | 4000 | 0.0101        | -               |
+| 1.9518 | 4050 | 0.0112        | -               |
+| 1.9759 | 4100 | 0.0111        | -               |
+| 2.0    | 4150 | 0.0124        | -               |
+
+### Framework Versions
+- Python: 3.12.11
+- SetFit: 1.1.3
+- Sentence Transformers: 5.1.0
+- Transformers: 4.56.0
+- PyTorch: 2.8.0+cu126
+- Datasets: 4.0.0
+- Tokenizers: 0.22.0
+
+## 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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+  "architectures": [
+    "BertModel"
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+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 250037
+}

config_sentence_transformers.json

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+  "__version__": {
+    "sentence_transformers": "5.1.0",
+    "transformers": "4.56.0",
+    "pytorch": "2.8.0+cu126"
+  },
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+  "prompts": {
+    "query": "",
+    "document": ""
+  },
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
+}

config_setfit.json

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sentence_bert_config.json

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special_tokens_map.json

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