SetFit with BAAI/bge-small-en-v1.5

This is a SetFit model that can be used for Text Classification. This SetFit model uses BAAI/bge-small-en-v1.5 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:

Fine-tuning a Sentence Transformer with contrastive learning.
Training a classification head with features from the fine-tuned Sentence Transformer.

Model Details

Model Description

Model Type: SetFit
Sentence Transformer body: BAAI/bge-small-en-v1.5
Classification head: a LogisticRegression instance
Maximum Sequence Length: 512 tokens
Number of Classes: 2 classes

Model Sources

Repository: SetFit on GitHub
Paper: Efficient Few-Shot Learning Without Prompts
Blogpost: SetFit: Efficient Few-Shot Learning Without Prompts

Model Labels

Label	Examples
positive	'some people march to the beat of a different drum , and if you ever wondered what kind of houses those people live in , this documentary takes a look at 5 alternative housing options .' "she 's all-powerful , a voice for a pop-cyber culture that feeds on her bjorkness ." "it 's a sharp movie about otherwise dull subjects ."
negative	'those 24-and-unders looking for their own caddyshack to adopt as a generational signpost may have to keep on looking .' "set in a 1986 harlem that does n't look much like anywhere in new york ." "the movie 's major and most devastating flaw is its reliance on formula , though , and it 's quite enough to lessen the overall impact the movie could have had ."

Evaluation

Metrics

Label	F1
all	0.8647

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("Mohamedsheded33/SetFit-few-shot-classification-sst2")
# Run inference
preds = model("... too dull to enjoy .")

Training Details

Training Set Metrics

Training set	Min	Median	Max
Word count	3	17.75	36

Label	Training Sample Count
negative	16
positive	16

Training Hyperparameters

batch_size: (16, 2)
num_epochs: (3, 3)
max_steps: -1
sampling_strategy: oversampling
num_iterations: 20
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
l2_weight: 0.01
seed: 42
eval_max_steps: -1
load_best_model_at_end: False

Training Results

Epoch	Step	Training Loss	Validation Loss
0.0125	1	0.21	-
0.625	50	0.1655	-
1.25	100	0.0076	-
1.875	150	0.0028	-
2.5	200	0.0022	-

Framework Versions

Python: 3.11.11
SetFit: 1.1.1
Sentence Transformers: 3.3.1
Transformers: 4.47.1
PyTorch: 2.5.1+cu121
Datasets: 3.2.0
Tokenizers: 0.21.0

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}
}

Mohamedsheded33
/

SetFit-few-shot-classification-sst2