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---
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: To monitor market dynamics and inform policy responses, the government will
track the retail value of ultra-processed foods and analyze shifts in consumption
in relation to labeling and advertising reforms. Data from these analyses will
feed annual dashboards that link labeling density, promotional intensity, and
dietary outcomes to guide targeted interventions and budget planning.
- text: the national agricultural plan is a national sectoral plan of grenada of 2015-2030.
its main goal is to stimulate economic growth in the agriculture sector through
the development of a well-coordinated planning and implementation framework that
is interactive and effective, and involve the full participation of the stakeholders,
and which promotes food security, income generation and poverty alleviation. in
the area of food security, the document aims to reduce dependence on food imports
and imported staples in particular and increase availability of local fresh and
fresh processed products; increase economic access to food by vulnerable persons
and their capacity to address their food and nutrition needs; and to improve the
health status and wellbeing of the grenadians through the consumption of nutritious
and safe foods. the plan also seeks to make agriculture, forestry and fisheries
more productive and sustainable. specifically, it envisions to build climate resilience
to avoid, prevent, or minimize climate change impacts on agriculture (including
forestry and fisheries), the environment and biodiversity; improve preparedness
for climate change impacts and extreme events; enhance the country’s response
capacity in case of extremes; facilitate recovery from impacts and extremes; and
reduce the impact of land based agriculture on climate change and the environment;
and preserve and optimize resources (land, sea, genetic). moreover, the document
aims to reduce rural poverty. in particular, it provides for making additional
investments in economic infrastructure for increased contribution of the agricultural
sector to economic growth, poverty alleviation and environmental sustainability.
further, the plan targets to increase exports of traditional crops, fish, fruits,
vegetables, root crops, minor spices, and value added products to international
and regional markets; increase production of targeted fruits, vegetables, root
crops, herbs and minor spices for targeted domestic markets; make additional investments
in institutional and human resource capacity development in the agricultural sector
to improve governance and efficiency; achieve greater collaboration in regional
and international trade for agricultural products; create framework for donor
and development partner coordination in providing support for the agriculture
sector; leverage opportunities in the tourism sector to strengthen the linkage
between agriculture and tourism; and invest in upgrading agricultural research
and development capacity. institutional responsibility for the implementation
of the plan is with the ministry of agriculture, lands, forestry, fisheries and
the environment. the minister will be obligated to report to the cabinet and parliament
on progress in the implementation of the plan. it is expected that the plan will
be incorporated into the national sustainable development plan 2030 (nsdp2030).
the ministry through the permanent secretary will be expected to report to the
monitoring committee of the nsdp2030 on a monthly basis on progress in implementation.
the reports to the cabinet will be submitted biannually.
- text: 'the seven key objectives are: 1. improve coordination in the sector to successfully
implement the fruit and vegetable strategy 2. improve market intelligence, promotion
and dissemination across the whole value chain 3. build a supply sub sector that
can guarantee consistent quality and supply of fresh fruit and vegetables 4. build
a sector that is well trained and supported by a comprehensive and properly executed
capability plan 5. improve financial situation of sector farmers and enterprises
6. promote integrated management of resources to ensure sustainability of the
fruit and vegetable sector 7. strengthen samoa association for manufacturers and
exporters (same) to provide services that will increase returns and overall value
addition for sector'
- text: Trade facilitation should be aligned with nutrition security and rural development
by prioritizing critical food and input imports, harmonizing rules of origin with
neighboring economies, and strengthening transit corridors to support small producers.
Progress indicators include the ratio of food imports to merchandise imports and
the share of agricultural raw materials imports, alongside the incidence of firms
naming customs and trade regulations as top obstacles (6.6.3.3).
- text: 1. general objectives striving to be a developing country with modern industry
and high middle income by 2030; have a modern, competitive, effective and effective
management institution; the economy develops dynamically, quickly and sustainably,
independently and autonomously on the basis of science, technology and innovation
in association with improving efficiency in external activities and international
integration; arousing the aspiration to develop the country, promoting the creativity,
will and strength of the whole nation, building a prosperous, democratic, fair,
civilized, orderly, disciplined and safe society, ensuring a peaceful and happy
life of the people; constantly improve all aspects of people's lives; firmly protect
the fatherland, a peaceful and stable environment for national development; improve
vietnam's position and prestige in the international arena. striving to become
a developed and high-income country by 2045. 2. principal indicators a) regarding
the economy - the average growth rate of gross domestic product (gdp) is about
7%/year; gdp per capita at current prices by 2030 will reach about 7,500 usd3.
- the proportion of the processing and manufacturing industry will reach about
30% of gdp, and the digital economy will reach about 30% of gdp. - the urbanization
rate will reach over 50%. - the average total social investment will reach 33-35%
of gdp; public debt does not exceed 60% of gdp. - the contribution of total factor
productivity (tfp) to growth reached 50%. - the average growth rate of social
labor productivity will reach over 6.5%/year. - reduce energy consumption per
unit of gdp at 1-1.5%/year. b) regarding social - the human development index
(hdi) remained above 0.74. - the average life expectancy is 75 years, of which
the healthy life span is at least 68 years. - the percentage of trained workers
with degrees and certificates reaches 35-40%. - the proportion of agricultural
labor in the total social labor force will decrease to less than 20%. c) regarding
the environment - the forest cover rate is stable at 42%. - the rate of treatment
and reuse of wastewater into the river basin environment will reach over 70%.
- reduce greenhouse gas emissions by 9%5. - 100% of production and business establishments
meet environmental standards. - to increase the area of marine and coastal protected
areas to 3-5% of the natural area of national waters.
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: 0.2326797385620915
name: Accuracy
---
# 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)
## Evaluation
### Metrics
| Label | Accuracy |
|:--------|:---------|
| **all** | 0.2327 |
## 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_cca_multilabel_MiniLM-L12-v01")
# Run inference
preds = model("To monitor market dynamics and inform policy responses, the government will track the retail value of ultra-processed foods and analyze shifts in consumption in relation to labeling and advertising reforms. Data from these analyses will feed annual dashboards that link labeling density, promotional intensity, and dietary outcomes to guide targeted interventions and budget planning.")
```
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## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:---------|:----|
| Word count | 1 | 123.6200 | 951 |
### Training Hyperparameters
- batch_size: (32, 32)
- num_epochs: (2, 2)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 20
- 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 |
|:------:|:----:|:-------------:|:---------------:|
| 0.0011 | 1 | 0.1892 | - |
| 0.0566 | 50 | 0.192 | - |
| 0.1131 | 100 | 0.1681 | - |
| 0.1697 | 150 | 0.1518 | - |
| 0.2262 | 200 | 0.1361 | - |
| 0.2828 | 250 | 0.1389 | - |
| 0.3394 | 300 | 0.1321 | - |
| 0.3959 | 350 | 0.1297 | - |
| 0.4525 | 400 | 0.1236 | - |
| 0.5090 | 450 | 0.1116 | - |
| 0.5656 | 500 | 0.1194 | - |
| 0.6222 | 550 | 0.1105 | - |
| 0.6787 | 600 | 0.1047 | - |
| 0.7353 | 650 | 0.1124 | - |
| 0.7919 | 700 | 0.1069 | - |
| 0.8484 | 750 | 0.108 | - |
| 0.9050 | 800 | 0.1072 | - |
| 0.9615 | 850 | 0.1011 | - |
| 1.0181 | 900 | 0.098 | - |
| 1.0747 | 950 | 0.0893 | - |
| 1.1312 | 1000 | 0.0979 | - |
| 1.1878 | 1050 | 0.0967 | - |
| 1.2443 | 1100 | 0.0887 | - |
| 1.3009 | 1150 | 0.0908 | - |
| 1.3575 | 1200 | 0.0906 | - |
| 1.4140 | 1250 | 0.0869 | - |
| 1.4706 | 1300 | 0.0873 | - |
| 1.5271 | 1350 | 0.0943 | - |
| 1.5837 | 1400 | 0.0886 | - |
| 1.6403 | 1450 | 0.0911 | - |
| 1.6968 | 1500 | 0.0832 | - |
| 1.7534 | 1550 | 0.0859 | - |
| 1.8100 | 1600 | 0.0862 | - |
| 1.8665 | 1650 | 0.09 | - |
| 1.9231 | 1700 | 0.0836 | - |
| 1.9796 | 1750 | 0.0884 | - |
| 0.0006 | 1 | 0.0898 | - |
| 0.0283 | 50 | 0.09 | - |
| 0.0566 | 100 | 0.091 | - |
| 0.0849 | 150 | 0.0905 | - |
| 0.1132 | 200 | 0.085 | - |
| 0.1415 | 250 | 0.0862 | - |
| 0.1698 | 300 | 0.0915 | - |
| 0.1981 | 350 | 0.0865 | - |
| 0.2264 | 400 | 0.0873 | - |
| 0.2547 | 450 | 0.0897 | - |
| 0.2830 | 500 | 0.0906 | - |
| 0.3113 | 550 | 0.096 | - |
| 0.3396 | 600 | 0.0886 | - |
| 0.3679 | 650 | 0.0831 | - |
| 0.3962 | 700 | 0.0852 | - |
| 0.4244 | 750 | 0.0858 | - |
| 0.4527 | 800 | 0.0831 | - |
| 0.4810 | 850 | 0.0858 | - |
| 0.5093 | 900 | 0.0898 | - |
| 0.5376 | 950 | 0.0866 | - |
| 0.5659 | 1000 | 0.0836 | - |
| 0.5942 | 1050 | 0.0809 | - |
| 0.6225 | 1100 | 0.0838 | - |
| 0.6508 | 1150 | 0.0845 | - |
| 0.6791 | 1200 | 0.0803 | - |
| 0.7074 | 1250 | 0.0831 | - |
| 0.7357 | 1300 | 0.0799 | - |
| 0.7640 | 1350 | 0.0853 | - |
| 0.7923 | 1400 | 0.0786 | - |
| 0.8206 | 1450 | 0.0763 | - |
| 0.8489 | 1500 | 0.0795 | - |
| 0.8772 | 1550 | 0.08 | - |
| 0.9055 | 1600 | 0.0786 | - |
| 0.9338 | 1650 | 0.0759 | - |
| 0.9621 | 1700 | 0.0817 | - |
| 0.9904 | 1750 | 0.0712 | - |
| 1.0187 | 1800 | 0.0703 | - |
| 1.0470 | 1850 | 0.0702 | - |
| 1.0753 | 1900 | 0.0704 | - |
| 1.1036 | 1950 | 0.0759 | - |
| 1.1319 | 2000 | 0.0716 | - |
| 1.1602 | 2050 | 0.0714 | - |
| 1.1885 | 2100 | 0.0698 | - |
| 1.2168 | 2150 | 0.0734 | - |
| 1.2450 | 2200 | 0.0717 | - |
| 1.2733 | 2250 | 0.0671 | - |
| 1.3016 | 2300 | 0.0681 | - |
| 1.3299 | 2350 | 0.072 | - |
| 1.3582 | 2400 | 0.0685 | - |
| 1.3865 | 2450 | 0.0702 | - |
| 1.4148 | 2500 | 0.0673 | - |
| 1.4431 | 2550 | 0.0698 | - |
| 1.4714 | 2600 | 0.0667 | - |
| 1.4997 | 2650 | 0.0658 | - |
| 1.5280 | 2700 | 0.0759 | - |
| 1.5563 | 2750 | 0.067 | - |
| 1.5846 | 2800 | 0.0777 | - |
| 1.6129 | 2850 | 0.0699 | - |
| 1.6412 | 2900 | 0.0773 | - |
| 1.6695 | 2950 | 0.0704 | - |
| 1.6978 | 3000 | 0.0731 | - |
| 1.7261 | 3050 | 0.0682 | - |
| 1.7544 | 3100 | 0.0684 | - |
| 1.7827 | 3150 | 0.0628 | - |
| 1.8110 | 3200 | 0.0689 | - |
| 1.8393 | 3250 | 0.068 | - |
| 1.8676 | 3300 | 0.0652 | - |
| 1.8959 | 3350 | 0.0714 | - |
| 1.9242 | 3400 | 0.0714 | - |
| 1.9525 | 3450 | 0.0701 | - |
| 1.9808 | 3500 | 0.0644 | - |
### Framework Versions
- Python: 3.12.12
- SetFit: 1.1.3
- Sentence Transformers: 5.1.1
- Transformers: 4.57.1
- PyTorch: 2.8.0+cu126
- Datasets: 4.0.0
- Tokenizers: 0.22.1
## 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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