moshew/gte_tiny_setfit-sst2-english
This is a SetFit model that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves:
- Fine-tuning a Sentence Transformer ("TaylorAI/gte-tiny") with contrastive learning.
- Training a classification head with features from the fine-tuned Sentence Transformer.
Training code
from setfit import SetFitModel
from datasets import load_dataset
from setfit import SetFitModel, SetFitTrainer
# Load a dataset from the Hugging Face Hub
dataset = load_dataset("SetFit/sst2")
# Upload Train and Test data
num_classes = 2
test_ds = dataset["test"]
train_ds = dataset["train"]
model = SetFitModel.from_pretrained("TaylorAI/gte-tiny")
trainer = SetFitTrainer(model=model, train_dataset=train_ds, eval_dataset=test_ds)
# Train and evaluate
trainer.train()
trainer.evaluate()['accuracy']
Usage
To use this model for inference, first install the SetFit library:
python -m pip install setfit
You can then run inference as follows:
from setfit import SetFitModel
# Download from Hub and run inference
model = SetFitModel.from_pretrained("moshew/gte_tiny_setfit-sst2-english")
# Run inference
preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"])
Accuracy
On SST-2 dev set:
90.7% SetFit
85.5% (no Fine-Tuning)
BibTeX entry and citation info
@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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Inference API (serverless) does not yet support sentence-transformers models for this pipeline type.