metadata

library_name: setfit
tags:
  - setfit
  - sentence-transformers
  - text-classification
  - generated_from_setfit_trainer
metrics:
  - accuracy
widget:
  - text: >-
      Specific information applicable to Parties, including regional economic
      integration organizations and their member States, that have reached an
      agreement to act jointly under Article 4, paragraph 2, of the Paris
      Agreement, including the Parties that agreed to act jointly and the terms
      of the agreement, in accordance with Article 4, paragraphs 16–18, of the
      Paris Agreement. Not applicable. (c). How the Party’s preparation of its
      nationally determined contribution has been informed by the outcomes of
      the global stocktake, in accordance with Article 4, paragraph 9, of the
      Paris Agreement.
  - text: >-
      In the shipping and aviation sectors, emission reduction efforts will be
      focused on distributing eco-friendly ships and enhancing the operational
      efficiency of aircraft. Agriculture, livestock farming and fisheries: The
      Republic Korea is introducing various options to accelerate low-carbon
      farming, for instance, improving irrigation techniques in rice paddies and
      adopting low-input systems for nitrogen fertilizers.
  - text: >-
      As part of this commitment, Oman s upstream oil and gas industry is
      developing economically viable solutions to phase out routine flaring as
      quickly as possible and ahead of the World Bank s target date. IV. Climate
      Preparedness and Resilience. The Sultanate of Oman has stepped up its
      efforts in advancing its expertise and methodologies to better manage the
      climate change risks over the past five years. The adaptation efforts are
      underway, and the status of adaptation planning is still at a nascent
      stage.
  - text: >-
      Synergy and coherence 46 VII- Gender and youth 46 VIII- Education and
      employment 48 ANNEXES. 49 Annex No. 1: Details of mitigation measures,
      conditional and non-conditional, by sector 49 Annex No.2: List of
      adaptation actions proposed by sectors. 57 Annex No.3: GCF project
      portfolio. 63 CONTRIBUTION DENTERMINEE AT NATIONAL LEVEL CDN MAURITANIE
      LIST OF TABLES Table 1: Summary of funding needs for the CND 2021-2030
      updated. 12 Table 2: CND 2021-2030 mitigation measures updated by sector
      (cumulative cost and reduction potential for the period). 14 Table 3: CND
      2021-2030 adaptation measures updated by sector. Error!
  - text: >-
      In the transport sector, restructuing is planned through a number of large
      infrastructure initiatives aiming to revive the role of public transport
      and achieving a relevant share of fuel efficient vehicles. Under both the
      conditional and unconditional mitigation scenarios, Lebanon will achieve
      sizeable emission reductions. With regards to adaptation, Lebanon has
      planned comprehensive sectoral actions related to water,
      agriculture/forestry and biodiversity, for example related to irrigation,
      forest management, etc. It also continues developing adaptation strategies
      in the remaining sectors.
pipeline_tag: text-classification
inference: false
co2_eq_emissions:
  emissions: 25.8151164022705
  source: codecarbon
  training_type: fine-tuning
  on_cloud: false
  cpu_model: Intel(R) Xeon(R) CPU @ 2.00GHz
  ram_total_size: 12.674781799316406
  hours_used: 0.622
  hardware_used: 1 x Tesla T4
base_model: ppsingh/SECTOR-multilabel-mpnet_w

SetFit with ppsingh/SECTOR-multilabel-mpnet_w

This is a SetFit model that can be used for Text Classification. This SetFit model uses ppsingh/SECTOR-multilabel-mpnet_w as the Sentence Transformer embedding model. A SetFitHead 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: ppsingh/SECTOR-multilabel-mpnet_w
Classification head: a SetFitHead instance
Maximum Sequence Length: 512 tokens
Number of Classes: 4 classes

Model Sources

Repository: SetFit on GitHub
Paper: Efficient Few-Shot Learning Without Prompts
Blogpost: SetFit: Efficient Few-Shot Learning Without Prompts

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("ppsingh/iki_sector_setfit")
# Run inference
preds = model("In the shipping and aviation sectors, emission reduction efforts will be focused on distributing eco-friendly ships and enhancing the operational efficiency of aircraft. Agriculture, livestock farming and fisheries: The Republic Korea is introducing various options to accelerate low-carbon farming, for instance, improving irrigation techniques in rice paddies and adopting low-input systems for nitrogen fertilizers.")

Training Details

Training Set Metrics

Training set	Min	Median	Max
Word count	35	76.164	170

Training Dataset: 250

Class Positive Count of Class

Economy-wide 88

Energy 63

Other Sector 64

Transport 139
Validation Dataset: 42

Class Positive Count of Class

Economy-wide 15

Energy 11

Other Sector 11

Transport 24

Class	Positive Count of Class
Economy-wide	88
Energy	63
Other Sector	64
Transport	139

Class	Positive Count of Class
Economy-wide	15
Energy	11
Other Sector	11
Transport	24

Training Hyperparameters

batch_size: (16, 2)
num_epochs: (1, 10)
max_steps: -1
sampling_strategy: oversampling
body_learning_rate: (2e-05, 1e-05)
head_learning_rate: 0.01
loss: CosineSimilarityLoss
distance_metric: cosine_distance
margin: 0.25
end_to_end: False
use_amp: False
warmup_proportion: 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	0.2029	-
0.0993	200	0.0111	0.1124
0.1985	400	0.0063	0.111
0.2978	600	0.0183	0.1214
0.3970	800	0.0197	0.1248
0.4963	1000	0.0387	0.1339
0.5955	1200	0.0026	0.1181
0.6948	1400	0.0378	0.1208
0.7940	1600	0.0285	0.1267
0.8933	1800	0.0129	0.1254
0.9926	2000	0.0341	0.1271

Classifier Training Results

Epoch	Training F1-micro	Training F1-Samples	Training F1-weighted	Validation F1-micro	Validation F1-samples	Validation F1-weighted
0	0.954	0.972	0.945	0.824	0.819	0.813
1	0.994	0.996	0.994	0.850	0.832	0.852
2	0.981	0.989	0.979	0.850	0.843	0.852
3	0.995	0.997	0.995	0.852	0.843	0.858
4	0.994	0.996	0.994	0.852	0.843	0.858
5	0.995	0.997	0.995	0.859	0.848	0.863

label	precision	recall	f1-score	support
Economy-wide	0.857	0.800	0.827	15.0
Energy	1.00	0.818	0.900	11.0
Other Sector	0.615	0.727	0.667	11.0
Transport	0.958	0.958	0.958	24.0

Micro Avg: Precision = 0.866, Recall = 0.852, F1 = 0.859504
Samples Avg: Precision = 0.869, Recall = 0.861, F1 = 0.848

Environmental Impact

Carbon emissions were measured using CodeCarbon.

Carbon Emitted: 0.026 kg of CO2
Hours Used: 0.622 hours

Training Hardware

On Cloud: No
GPU Model: 1 x Tesla T4
CPU Model: Intel(R) Xeon(R) CPU @ 2.00GHz
RAM Size: 12.67 GB

Framework Versions

Python: 3.10.12
SetFit: 1.0.3
Sentence Transformers: 2.3.1
Transformers: 4.35.2
PyTorch: 2.1.0+cu121
Datasets: 2.3.0
Tokenizers: 0.15.1

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}
}