Setfit All MiniLM L6 V2 Sst2 32 Shot

🚀 SetFit with sentence-transformers/all-MiniLM-L6-v2 on sst2

This is a SetFit model trained on the sst2 dataset for text classification, leveraging sentence-transformers/all-MiniLM-L6-v2 and LogisticRegression.

🚀 Quick Start

This is a SetFit model trained on the sst2 dataset that can be used for Text Classification. This SetFit model uses sentence-transformers/all-MiniLM-L6-v2 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. Fine-tuning a Sentence Transformer with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.

✨ Features

• Few-shot Learning: Trained with an efficient few-shot learning technique, reducing the need for large amounts of training data.
• Text Classification: Specifically designed for text classification tasks on the sst2 dataset.
• Sentence Transformer: Utilizes sentence-transformers/all-MiniLM-L6-v2 for embedding.
• Logistic Regression: Employs LogisticRegression for classification.

📦 Installation

First install the SetFit library:

pip install setfit

💻 Usage Examples

Basic Usage

from setfit import SetFitModel

# Download from 🤗 Hub
model = SetFitModel.from_pretrained("tomaarsen/setfit-all-MiniLM-L6-v2-sst2-8-shot")
# Run inference
preds = model("a fast , funny , highly enjoyable movie . ")

📚 Documentation

Model Details

Model Description

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

Training Details

Training Set Metrics

Training Hyperparameters

• batch_size: (16, 16)
• num_epochs: (3, 3)
• max_steps: -1
• sampling_strategy: oversampling
• body_learning_rate: (2e-05, 1e-05)
• head_learning_rate: 0.01
• loss: CosineSimilarityLoss
• distance_metric: cosine_distance
• margin: 0.25
• end_to_end: False
• use_amp: False
• warmup_proportion: 0.1
• seed: 42
• load_best_model_at_end: True

Training Results

• The bold row denotes the saved checkpoint.

Environmental Impact

Carbon emissions were measured using CodeCarbon.

• Carbon Emitted: 0.003 kg of CO2
• Hours Used: 0.072 hours

Training Hardware

• On Cloud: No
• GPU Model: 1 x NVIDIA GeForce RTX 3090
• CPU Model: 13th Gen Intel(R) Core(TM) i7-13700K
• RAM Size: 31.78 GB

Framework Versions

• Python: 3.9.16
• SetFit: 1.0.0.dev0
• Sentence Transformers: 2.2.2
• Transformers: 4.29.0
• PyTorch: 1.13.1+cu117
• Datasets: 2.15.0
• Tokenizers: 0.13.3

🔧 Technical Details

The model uses an efficient few-shot learning technique. First, it fine-tunes a Sentence Transformer with contrastive learning. Then, it trains a classification head with features from the fine-tuned Sentence Transformer. This approach allows the model to achieve good performance with a relatively small amount of training data.

📄 License

This model is licensed under the Apache 2.0 license.

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}
}