gsjang/text2sql_encoder

SetFit with jhgan/ko-sroberta-multitask

This is a SetFit model that can be used for Text Classification. This SetFit model uses jhgan/ko-sroberta-multitask 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: jhgan/ko-sroberta-multitask
• —Classification head: a LogisticRegression instance
• —Maximum Sequence Length: 128 tokens
• —Number of Classes: 29 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("NTIS/text2sql_encoder")
# Run inference
preds = model("2020년부터 2022년까지 반도체에 해당하는 중점 기술 분류 코드가 'NAT0' 인 과제의 총 연구비 현물 금액을 조회해주세요.")

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Training Details

Training Set Metrics

Training Hyperparameters

• —batch_size: (128, 128)
• —num_epochs: (5, 5)
• —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
• —seed: 42
• —evalmaxsteps: -1
• —loadbestmodelatend: True

Training Results