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model_g20_multilabel_MiniLM-L12-v_final

Text Classification
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model_g20_multilabel_MiniLM-L12-v_final / README.md
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 ---
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/model_g20_multilabel_MiniLM-L12-v_final")
# 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.")
```
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## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:--------|:-----|
| Word count | 2 | 70.3945 | 1194 |
### Training Hyperparameters
- batch_size: (32, 32)
- num_epochs: (2, 2)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 15
- 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 |
|:------:|:----:|:-------------:|:---------------:|
| 0.0003 | 1 | 0.1541 | - |
| 0.0166 | 50 | 0.1451 | - |
| 0.0331 | 100 | 0.1302 | - |
| 0.0497 | 150 | 0.1155 | - |
| 0.0663 | 200 | 0.098 | - |
| 0.0828 | 250 | 0.0984 | - |
| 0.0994 | 300 | 0.0872 | - |
| 0.1160 | 350 | 0.0813 | - |
| 0.1325 | 400 | 0.0778 | - |
| 0.1491 | 450 | 0.0732 | - |
| 0.1657 | 500 | 0.0694 | - |
| 0.1822 | 550 | 0.0585 | - |
| 0.1988 | 600 | 0.0621 | - |
| 0.2154 | 650 | 0.0597 | - |
| 0.2319 | 700 | 0.0511 | - |
| 0.2485 | 750 | 0.0461 | - |
| 0.2651 | 800 | 0.0463 | - |
| 0.2816 | 850 | 0.0377 | - |
| 0.2982 | 900 | 0.0345 | - |
| 0.3148 | 950 | 0.0371 | - |
| 0.3313 | 1000 | 0.0392 | - |
| 0.3479 | 1050 | 0.0378 | - |
| 0.3645 | 1100 | 0.0317 | - |
| 0.3810 | 1150 | 0.0302 | - |
| 0.3976 | 1200 | 0.0352 | - |
| 0.4142 | 1250 | 0.0298 | - |
| 0.4307 | 1300 | 0.0266 | - |
| 0.4473 | 1350 | 0.0287 | - |
| 0.4639 | 1400 | 0.0269 | - |
| 0.4805 | 1450 | 0.0227 | - |
| 0.4970 | 1500 | 0.0254 | - |
| 0.5136 | 1550 | 0.0228 | - |
| 0.5302 | 1600 | 0.0197 | - |
| 0.5467 | 1650 | 0.021 | - |
| 0.5633 | 1700 | 0.0217 | - |
| 0.5799 | 1750 | 0.0219 | - |
| 0.5964 | 1800 | 0.0189 | - |
| 0.6130 | 1850 | 0.0172 | - |
| 0.6296 | 1900 | 0.0199 | - |
| 0.6461 | 1950 | 0.017 | - |
| 0.6627 | 2000 | 0.017 | - |
| 0.6793 | 2050 | 0.0165 | - |
| 0.6958 | 2100 | 0.0162 | - |
| 0.7124 | 2150 | 0.0186 | - |
| 0.7290 | 2200 | 0.0171 | - |
| 0.7455 | 2250 | 0.0165 | - |
| 0.7621 | 2300 | 0.0186 | - |
| 0.7787 | 2350 | 0.0161 | - |
| 0.7952 | 2400 | 0.0146 | - |
| 0.8118 | 2450 | 0.0142 | - |
| 0.8284 | 2500 | 0.0142 | - |
| 0.8449 | 2550 | 0.0183 | - |
| 0.8615 | 2600 | 0.0162 | - |
| 0.8781 | 2650 | 0.0152 | - |
| 0.8946 | 2700 | 0.0119 | - |
| 0.9112 | 2750 | 0.0137 | - |
| 0.9278 | 2800 | 0.0161 | - |
| 0.9443 | 2850 | 0.0126 | - |
| 0.9609 | 2900 | 0.014 | - |
| 0.9775 | 2950 | 0.0169 | - |
| 0.9940 | 3000 | 0.0135 | - |
| 1.0106 | 3050 | 0.0149 | - |
| 1.0272 | 3100 | 0.0134 | - |
| 1.0437 | 3150 | 0.0096 | - |
| 1.0603 | 3200 | 0.0117 | - |
| 1.0769 | 3250 | 0.0075 | - |
| 1.0934 | 3300 | 0.0119 | - |
| 1.1100 | 3350 | 0.0098 | - |
| 1.1266 | 3400 | 0.011 | - |
| 1.1431 | 3450 | 0.0088 | - |
| 1.1597 | 3500 | 0.0125 | - |
| 1.1763 | 3550 | 0.0132 | - |
| 1.1928 | 3600 | 0.0103 | - |
| 1.2094 | 3650 | 0.0111 | - |
| 1.2260 | 3700 | 0.0091 | - |
| 1.2425 | 3750 | 0.0081 | - |
| 1.2591 | 3800 | 0.0108 | - |
| 1.2757 | 3850 | 0.0127 | - |
| 1.2922 | 3900 | 0.0115 | - |
| 1.3088 | 3950 | 0.011 | - |
| 1.3254 | 4000 | 0.0065 | - |
| 1.3419 | 4050 | 0.0113 | - |
| 1.3585 | 4100 | 0.0092 | - |
| 1.3751 | 4150 | 0.0085 | - |
| 1.3917 | 4200 | 0.0078 | - |
| 1.4082 | 4250 | 0.0071 | - |
| 1.4248 | 4300 | 0.0094 | - |
| 1.4414 | 4350 | 0.0085 | - |
| 1.4579 | 4400 | 0.0126 | - |
| 1.4745 | 4450 | 0.0084 | - |
| 1.4911 | 4500 | 0.0106 | - |
| 1.5076 | 4550 | 0.0093 | - |
| 1.5242 | 4600 | 0.0085 | - |
| 1.5408 | 4650 | 0.0072 | - |
| 1.5573 | 4700 | 0.0079 | - |
| 1.5739 | 4750 | 0.0111 | - |
| 1.5905 | 4800 | 0.0082 | - |
| 1.6070 | 4850 | 0.0075 | - |
| 1.6236 | 4900 | 0.0111 | - |
| 1.6402 | 4950 | 0.0098 | - |
| 1.6567 | 5000 | 0.01 | - |
| 1.6733 | 5050 | 0.0086 | - |
| 1.6899 | 5100 | 0.0116 | - |
| 1.7064 | 5150 | 0.0095 | - |
| 1.7230 | 5200 | 0.0106 | - |
| 1.7396 | 5250 | 0.0087 | - |
| 1.7561 | 5300 | 0.0079 | - |
| 1.7727 | 5350 | 0.0092 | - |
| 1.7893 | 5400 | 0.0085 | - |
| 1.8058 | 5450 | 0.0095 | - |
| 1.8224 | 5500 | 0.0066 | - |
| 1.8390 | 5550 | 0.0085 | - |
| 1.8555 | 5600 | 0.0103 | - |
| 1.8721 | 5650 | 0.0059 | - |
| 1.8887 | 5700 | 0.0064 | - |
| 1.9052 | 5750 | 0.0069 | - |
| 1.9218 | 5800 | 0.0086 | - |
| 1.9384 | 5850 | 0.0069 | - |
| 1.9549 | 5900 | 0.0089 | - |
| 1.9715 | 5950 | 0.0091 | - |
| 1.9881 | 6000 | 0.0083 | - |
### Framework Versions
- Python: 3.11.13
- SetFit: 1.1.2
- Sentence Transformers: 4.1.0
- Transformers: 4.52.4
- PyTorch: 2.6.0+cu124
- Datasets: 3.6.0
- Tokenizers: 0.21.2
## 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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