SetFit with mini1013/master_item_top_ps_flat

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: mini1013/master_item_top_ps_flat
• Classification head: a LogisticRegression instance
• Maximum Sequence Length: 512 tokens
• Number of Classes: 124 classes

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

Training Details

Training Set Metrics

Training Hyperparameters

• batch_size: (64, 64)
• num_epochs: (100, 100)
• max_steps: -1
• sampling_strategy: oversampling
• num_iterations: 20
• body_learning_rate: (2e-05, 1e-05)
• head_learning_rate: 0.01
• loss: BatchAllTripletLoss
• distance_metric: cosine_distance
• margin: 0.25
• end_to_end: False
• use_amp: False
• warmup_proportion: 0.1
• l2_weight: 0.01
• seed: 42
• eval_max_steps: -1
• load_best_model_at_end: False

Training Results

Framework Versions

• Python: 3.10.12
• SetFit: 1.1.2
• Sentence Transformers: 4.1.0
• Transformers: 4.52.1
• PyTorch: 2.7.0+cu126
• Datasets: 3.6.0
• Tokenizers: 0.21.1

Citation

BibTeX