SetFit with mini1013/master_item_top_el_flat_test

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_el_flat_test
• Classification head: a LogisticRegression instance
• Maximum Sequence Length: 512 tokens
• Number of Classes: 256 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: (50, 50)
• 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: True
• 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: 3.11.11
• SetFit: 1.1.3
• Sentence Transformers: 5.0.0
• Transformers: 4.55.0
• PyTorch: 2.8.0.dev20250319+cu128
• Datasets: 3.1.0
• Tokenizers: 0.21.4

Citation

BibTeX