README.md

---
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](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [ppsingh/SECTOR-multilabel-mpnet_w](https://huggingface.co/ppsingh/SECTOR-multilabel-mpnet_w) as the Sentence Transformer embedding model. A [SetFitHead](huggingface.co/docs/setfit/reference/main#setfit.SetFitHead) 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:** [ppsingh/SECTOR-multilabel-mpnet_w](https://huggingface.co/ppsingh/SECTOR-multilabel-mpnet_w)
- **Classification head:** a [SetFitHead](huggingface.co/docs/setfit/reference/main#setfit.SetFitHead) instance
- **Maximum Sequence Length:** 512 tokens
- **Number of Classes:** 4 classes
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### 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("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.")
```

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## 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 |


### 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](https://github.com/mlco2/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
```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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