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

This is a SetFit model trained on the SetFit/sst2 dataset that can be used for Text Classification. This SetFit model uses BAAI/bge-small-en-v1.5 as the Sentence Transformer embedding model. A SetFitHead 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 SetFitHead instance
Maximum Sequence Length: 512 tokens
Number of Classes: 2 classes
Training Dataset: SetFit/sst2

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
1	'a stirring , funny and finally transporting re-imagining of beauty and the beast and 1930s horror films' 'this is a visually stunning rumination on love , memory , history and the war between art and commerce .' "jonathan parker 's bartleby should have been the be-all-end-all of the modern-office anomie films ."
0	'apparently reassembled from the cutting-room floor of any given daytime soap .' "they presume their audience wo n't sit still for a sociology lesson , however entertainingly presented , so they trot out the conventional science-fiction elements of bug-eyed monsters and futuristic women in skimpy clothes ." 'a fan film that for the uninitiated plays better on video with the sound turned down .'

Evaluation

Metrics

Label	Accuracy
all	0.8842

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("dkorat/bge-small-en-v1.5_setfit-sst2-english")
# Run inference
preds = model("a noble failure .")

Training Details

Training Set Metrics

Training set	Min	Median	Max
Word count	2	19.591	46

Label	Training Sample Count
0	479
1	521

Training Hyperparameters

batch_size: (16, 2)
num_epochs: (1, 1)
max_steps: -1
sampling_strategy: oversampling
num_iterations: 1
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
eval_max_steps: -1
load_best_model_at_end: False

Training Results

Epoch	Step	Training Loss	Validation Loss
0.008	1	0.241	-
0.4	50	0.2525	-
0.8	100	0.0607	-

Framework Versions

Python: 3.10.13
SetFit: 1.0.3
Sentence Transformers: 2.3.0
Transformers: 4.37.2
PyTorch: 2.1.2+cu121
Datasets: 2.16.1
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}
}

danielkorat
/

bge-small-en-v1.5_setfit-sst2-english