hojzas/setfit-proj8-code

SetFit with flax-sentence-embeddings/st-codesearch-distilroberta-base

This is a SetFit model trained on the hojzas/proj8-label2 dataset that can be used for Text Classification. This SetFit model uses flax-sentence-embeddings/st-codesearch-distilroberta-base 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: flax-sentence-embeddings/st-codesearch-distilroberta-base
• —Classification head: a LogisticRegression instance
• —Maximum Sequence Length: 128 tokens
• —Number of Classes: 2 classes
• —Training Dataset: hojzas/proj8-label2 <!-- - 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

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("hojzas/setfit-proj8-code")
# Run inference
preds = model("def first_with_given_key(iterable, key=lambda x: x):\n    keys=[]\n    for i in iterable:\n        if key(i) not in keys:\n            yield i\n            keys.append(key(i))")

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Training Details

Training Set Metrics

Training Hyperparameters

• —batch_size: (16, 16)
• —num_epochs: (1, 1)
• —max_steps: -1
• —sampling_strategy: oversampling
• —num_iterations: 20
• —bodylearningrate: (2e-05, 2e-05)
• —headlearningrate: 2e-05
• —loss: CosineSimilarityLoss
• —distancemetric: cosinedistance
• —margin: 0.25
• —endtoend: False
• —use_amp: False
• —warmup_proportion: 0.1
• —seed: 42
• —evalmaxsteps: -1
• —loadbestmodelatend: False

Training Results

Environmental Impact

Carbon emissions were measured using CodeCarbon.

• —Carbon Emitted: 0.000 kg of CO2
• —Hours Used: 0.002 hours

Training Hardware

• —On Cloud: No
• —GPU Model: No GPU used
• —CPU Model: Intel(R) Xeon(R) Silver 4314 CPU @ 2.40GHz
• —RAM Size: 251.49 GB

Framework Versions

• —Python: 3.10.12
• —SetFit: 1.0.3
• —Sentence Transformers: 2.2.2
• —Transformers: 4.36.1
• —PyTorch: 2.1.2+cu121
• —Datasets: 2.14.7
• —Tokenizers: 0.15.1

Citation

BibTeX

@article{https://doi.org/10.48550/arxiv.2209.11055,
    doi = {10.48550/ARXIV.2209.11055},
    url = {https://arxiv.org/abs/2209.11055},
    author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
    keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
    title = {Efficient Few-Shot Learning Without Prompts},
    publisher = {arXiv},
    year = {2022},
    copyright = {Creative Commons Attribution 4.0 International}
}

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