metadata
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
model-index:
- name: SetFit with sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: Unknown
type: unknown
split: test
metrics:
- type: accuracy
value: 0.39185750636132316
name: Accuracy
SetFit with sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
This is a SetFit model that can be used for Text Classification. This SetFit model uses 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:
- 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: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
- Classification head: a OneVsRestClassifier instance
- Maximum Sequence Length: 128 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 | 0.3919 |
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("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.")
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
@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}
}