metadata
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 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 | 0.2327 |
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/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.")
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
@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}
}