mini1013/master_item_top_bt_flat

SetFit with mini1013/master_domain

This is a SetFit model that can be used for Text Classification. This SetFit model uses mini1013/master_domain 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: mini1013/master_domain
• —Classification head: a LogisticRegression instance
• —Maximum Sequence Length: 512 tokens
• —Number of Classes: 121 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("mini1013/master_item_top_bt_flat")
# Run inference
preds = model("[태연픽]베네피트 WANDERful World 블러쉬 12종 택1 단델리온 홈>치크;(#M)홈>★특별 구성★ Naverstore > 화장품/미용 > 색조메이크업 > 블러셔")

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Training Details

Training Set Metrics

Training Hyperparameters

• —batch_size: (64, 64)
• —num_epochs: (30, 30)
• —max_steps: -1
• —sampling_strategy: oversampling
• —num_iterations: 100
• —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