iki_sector_setfit

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:

1. Fine-tuning a Sentence Transformer 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
• Classification head: a SetFitHead instance
• Maximum Sequence Length: 512 tokens
• Number of Classes: 4 classes

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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.")

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Training Details

Training Set Metrics

• 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

Classifier Training Results

• 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}
}

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