sumtxt/paraphrase-MiniLM-L3-v2_immig

paraphrase-MiniLM-L3-v2_immig

This SetFit model was trained on 48 title-abstracts samples (24 per class) to differeniate between published studies related to immigration/migration research and those that are not.

• —Model Type: SetFit
• —Sentence Transformer body: sentence-transformers/paraphrase-MiniLM-L3-v2
• —Classification head: a LogisticRegression instance
• —Train data/script repository: SetFit on GitHub

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

model = SetFitModel.from_pretrained("mmarbach/paraphrase-MiniLM-L3-v2_immig")
preds = model("TITLE: ...  ABSTRACT: ....")

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: False

Training Results

Framework Versions

• —Python: 3.12.11
• —SetFit: 1.1.2
• —Sentence Transformers: 5.0.0
• —Transformers: 4.53.0
• —PyTorch: 2.7.1
• —Datasets: 3.6.0
• —Tokenizers: 0.21.2