README.md

---
license: apache-2.0
tags:
- setfit
- sentence-transformers
- text-classification
pipeline_tag: text-classification
---

# moshew/bge-small-en-v1.5_setfit-sst2-english

This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves:

1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) ("BAAI/bge-small-en-v1.5") with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.

## Training code

```python
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("BAAI/bge-small-en-v1.5") 
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:

```bash
python -m pip install setfit
```

You can then run inference as follows:

```python
from setfit import SetFitModel

# Download from Hub and run inference
model = SetFitModel.from_pretrained("moshew/bge-small-en-v1.5_setfit-sst2-english")
# Run inference
preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"])
```

## Accuracy
On SST-2 dev set:

91.4%  SetFit

88.4% (no Fine-Tuning) 

## BibTeX entry and citation info

```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}
}
```