JoshELambert/forced-labor

SetFit with sentence-transformers/paraphrase-mpnet-base-v2

This is a SetFit model that can be used for Text Classification. This SetFit model uses sentence-transformers/paraphrase-mpnet-base-v2 as the Sentence Transformer embedding model. A LogisticRegression instance is used for classification.

The model has been trained using an efficient few-shot learning technique that involves:

1. 1.Fine-tuning a Sentence Transformer with contrastive learning.
2. 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-mpnet-base-v2
• —Classification head: a LogisticRegression instance
• —Maximum Sequence Length: 512 tokens
• —Number of Classes: 2 classes <!-- - Training Dataset: Unknown --> <!-- - Language: Unknown --> <!-- - License: Unknown -->

Model Sources

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

Model Labels

Evaluation

Metrics

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("JoshELambert/forced-labor")
# Run inference
preds = model(" in their own villages by debt bondage or born ito slavery, work in construction, textiles, brick-making, mines, fish and prawn processing and hospitality.Russia490,000 - 540,000Migrant workers endure extortion and physical abuse; anecdotal evidence suggests that forced labour camps still operate in Siberia.China2,800,000 - 3,100,000Severe forced labour in brick kilns in the north; forced labour in modern industries including fashion and computer supply chains.Myanmar360,000 - 400,000Slavery includes reports of deceptive recruitment of women for sale as brides in China, forced labour of adults on plantations and in industry and forced labour of children in tea shops, home industries and as beggars.Thailand450,000 - 500,000An explosion in global demand for seafood has led to an increased need for cheap migrant labour, including on fishing boats. High numbers of children are exploited, particulary those from ethnic minorities and hill tribes.SOURCE: THE GLOBAL SLAVERY INDEX 2013")

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Training Details

Training Set Metrics

Training Hyperparameters

• —batch_size: (16, 16)
• —num_epochs: (4, 4)
• —max_steps: -1
• —sampling_strategy: oversampling
• —bodylearningrate: (2e-05, 1e-05)
• —headlearningrate: 0.01
• —loss: CosineSimilarityLoss
• —distancemetric: cosinedistance
• —margin: 0.25
• —endtoend: False
• —use_amp: False
• —warmup_proportion: 0.1
• —l2_weight: 0.01
• —seed: 42
• —evalmaxsteps: -1
• —loadbestmodelatend: True

Training Results

Framework Versions

• —Python: 3.10.12
• —SetFit: 1.1.0
• —Sentence Transformers: 3.3.1
• —Transformers: 4.42.2
• —PyTorch: 2.5.1+cu121
• —Datasets: 3.2.0
• —Tokenizers: 0.19.1

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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